High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

test0726					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 69

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 325	92.39 6				88.94 28	96.63 4	94.85 21

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 147	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 107	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 85
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 98	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 54

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 123
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 38

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

PC_three_1452									68.21 313	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

test_part2					95.06 8	72.65 32				91.80 16

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 38
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 153	91.30 18

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 114	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 234	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 98	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 246	67.30 174	89.50 101	90.98 154	76.25 101	90.56 22	94.75 29	68.38 117	94.24 135	90.80 7	92.32 89	94.19 71

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 106
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 207	87.08 260	65.21 225	89.09 123	90.21 183	79.67 19	89.98 24	95.02 24	73.17 42	91.71 269	91.30 3	91.60 99	92.34 173

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 47

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 101

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 102	79.94 17	89.74 27	94.86 26	68.63 114	94.20 136	90.83 5	91.39 104	94.38 61

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 190	87.12 259	66.01 199	88.56 148	89.43 210	75.59 116	89.32 28	94.32 44	72.89 46	91.21 296	90.11 11	92.33 87	93.16 133

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 112	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 151	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 148	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 133

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 198	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 156
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 169	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 144	86.70 271	65.83 205	88.77 136	89.78 195	75.46 120	88.35 36	93.73 74	69.19 104	93.06 210	91.30 3	88.44 159	94.02 81

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 151	86.26 280	67.40 170	89.18 115	89.31 219	72.50 203	88.31 37	93.86 70	69.66 93	91.96 257	89.81 13	91.05 109	93.38 119

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 286	69.93 92	88.65 144	90.78 163	69.97 269	88.27 38	93.98 66	71.39 67	91.54 279	88.49 35	90.45 120	93.91 86

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 181	86.17 284	65.00 233	86.96 210	87.28 289	74.35 156	88.25 39	94.23 50	61.82 204	92.60 228	89.85 12	88.09 168	93.84 92

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 185	87.32 248	65.13 228	88.86 130	91.63 133	75.41 121	88.23 40	93.45 81	68.56 115	92.47 236	89.52 18	92.78 79	93.20 131

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 100	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 77

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 64

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 148	85.42 303	68.81 116	88.49 150	87.26 294	68.08 314	88.03 44	93.49 77	72.04 57	91.77 265	88.90 29	89.14 146	92.24 180

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

fmvsm_s_conf0.1_n_2		83.80 106	83.79 106	83.83 179	85.62 297	64.94 238	87.03 207	86.62 312	74.32 157	87.97 47	94.33 43	60.67 228	92.60 228	89.72 14	87.79 175	93.96 83

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 142

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 370	69.39 107	89.65 95	90.29 181	73.31 188	87.77 49	94.15 55	71.72 61	93.23 195	90.31 9	90.67 117	93.89 89

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 316	69.51 100	89.62 98	90.58 167	73.42 184	87.75 50	94.02 61	72.85 48	93.24 194	90.37 8	90.75 115	93.96 83

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 239	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 102	87.73 52	91.46 144	70.32 80	93.78 158	81.51 104	88.95 147	94.63 44

MGCFI-Net		85.06 85	85.51 74	83.70 183	89.42 139	63.01 291	89.43 104	92.62 78	76.43 89	87.53 53	91.34 147	72.82 49	93.42 187	81.28 108	88.74 153	94.66 41

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 251	68.54 130	89.57 99	90.44 172	75.31 125	87.49 54	94.39 42	72.86 47	92.72 225	89.04 27	90.56 118	94.16 72

fmvsm_l_conf0.5_n_a		84.13 97	84.16 94	84.06 164	85.38 304	68.40 133	88.34 158	86.85 306	67.48 321	87.48 55	93.40 82	70.89 73	91.61 270	88.38 37	89.22 143	92.16 187

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 135	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 149	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

fmvsm_s_conf0.1_n_a		83.32 126	82.99 123	84.28 146	83.79 342	68.07 145	89.34 111	82.85 372	69.80 273	87.36 58	94.06 59	68.34 119	91.56 275	87.95 42	83.46 260	93.21 129

fmvsm_s_conf0.5_n_a		83.63 115	83.41 115	84.28 146	86.14 285	68.12 143	89.43 104	82.87 371	70.27 262	87.27 59	93.80 73	69.09 105	91.58 272	88.21 38	83.65 254	93.14 136

fmvsm_s_conf0.1_n		83.56 117	83.38 116	84.10 155	84.86 318	67.28 175	89.40 108	83.01 367	70.67 246	87.08 60	93.96 67	68.38 117	91.45 285	88.56 34	84.50 234	93.56 113

旧先验2							86.56 229		58.10 432	87.04 61			88.98 348	74.07 203

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 412	69.03 110	89.47 102	89.65 202	73.24 192	86.98 62	94.27 47	66.62 138	93.23 195	90.26 10	89.95 130	93.78 98

fmvsm_s_conf0.5_n		83.80 106	83.71 108	84.07 161	86.69 272	67.31 173	89.46 103	83.07 366	71.09 234	86.96 63	93.70 75	69.02 110	91.47 284	88.79 30	84.62 233	93.44 118

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 152	86.84 64	94.65 31	67.31 131	95.77 64	84.80 68	92.85 78	92.84 154

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 172	87.78 218	66.09 196	89.96 86	90.80 162	77.37 57	86.72 65	94.20 52	72.51 51	92.78 224	89.08 22	92.33 87	93.13 137

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 206	82.14 3	86.65 66	94.28 46	68.28 120	97.46 6	90.81 6	95.31 38	95.15 8

dcpmvs_2		85.63 70	86.15 60	84.06 164	91.71 84	64.94 238	86.47 232	91.87 121	73.63 176	86.60 67	93.02 93	76.57 18	91.87 263	83.36 84	92.15 90	95.35 3

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 134	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 180	85.94 69	94.51 35	65.80 154	95.61 67	83.04 89	92.51 83	93.53 116

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 228	92.02 111	79.45 22	85.88 70	94.80 27	68.07 122	96.21 50	86.69 52	95.34 36	93.23 126

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 289	76.41 90	85.80 71	90.22 186	74.15 35	95.37 85	81.82 103	91.88 94	92.65 160

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 76

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	65.00 162	95.56 68	82.75 94	91.87 95	92.50 166

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	63.87 170		82.75 94	91.87 95	92.50 166

testdata				79.97 302	90.90 98	64.21 258		84.71 337	59.27 420	85.40 75	92.91 94	62.02 201	89.08 346	68.95 262	91.37 105	86.63 380

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 229	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 66

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 204	85.22 78	91.90 122	69.47 95	96.42 44	83.28 86	95.94 23	94.35 63

patch_mono-2		83.65 113	84.54 89	80.99 275	90.06 120	65.83 205	84.21 304	88.74 253	71.60 222	85.01 79	92.44 105	74.51 29	83.50 412	82.15 101	92.15 90	93.64 108

TEST9					93.26 56	72.96 25	88.75 138	91.89 119	68.44 310	85.00 80	93.10 88	74.36 32	95.41 80

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 119	68.69 305	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 144

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 102

test_prior2							88.85 132		75.41 121	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

test_8					93.13 60	72.57 35	88.68 143	91.84 123	68.69 305	84.87 84	93.10 88	74.43 30	95.16 90

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 196	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 60

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 88

h-mvs33		83.15 129	82.19 140	86.02 76	90.56 105	70.85 79	88.15 166	89.16 229	76.02 105	84.67 87	91.39 146	61.54 209	95.50 73	82.71 96	75.48 367	91.72 200

hse-mvs2		81.72 155	80.94 160	84.07 161	88.72 175	67.68 160	85.87 254	87.26 294	76.02 105	84.67 87	88.22 247	61.54 209	93.48 182	82.71 96	73.44 395	91.06 219

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 111	96.65 34	84.53 72	94.90 45	94.00 82

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 201	84.64 90	91.71 130	71.85 58	96.03 55	84.77 69	94.45 60	94.49 56

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 312	84.61 91	93.48 78	72.32 52	96.15 53	79.00 140	95.43 34	94.28 68

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 160	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 234	90.88 114	93.07 139

CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 105	96.70 31	84.37 74	94.83 49	94.03 80

agg_prior					92.85 68	71.94 52		91.78 127		84.41 95			94.93 101

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 105	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	94.65 52	94.56 51

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 268	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	90.91 113	93.21 129

VDD-MVS		83.01 134	82.36 136	84.96 107	91.02 95	66.40 191	88.91 128	88.11 264	77.57 49	84.39 96	93.29 85	52.19 309	93.91 152	77.05 165	88.70 154	94.57 49

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 251	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MSLP-MVS++		85.43 75	85.76 69	84.45 132	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 133	92.94 215	80.36 119	94.35 63	90.16 258

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 123	69.04 109	95.43 77	83.93 81	93.77 69	93.01 145

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 315	69.32 98	95.38 82	80.82 113	91.37 105	92.72 155

VNet		82.21 145	82.41 134	81.62 255	90.82 100	60.93 330	84.47 293	89.78 195	76.36 96	84.07 104	91.88 123	64.71 163	90.26 322	70.68 241	88.89 148	93.66 102

baseline		84.93 86	84.98 83	84.80 117	87.30 249	65.39 218	87.30 200	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

BP-MVS1		84.32 91	83.71 108	86.17 68	87.84 213	67.85 154	89.38 109	89.64 203	77.73 45	83.98 106	92.12 118	56.89 266	95.43 77	84.03 80	91.75 98	95.24 7

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 131	84.12 334	69.37 108	88.15 166	87.96 271	70.01 267	83.95 107	93.23 86	68.80 112	91.51 282	88.61 32	89.96 129	92.57 161

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 117	83.86 108	94.42 40	67.87 126	96.64 35	82.70 98	94.57 56	93.66 102

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 104	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 66
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GDP-MVS		83.52 118	82.64 130	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 202	83.71 111	91.86 125	55.69 275	95.35 86	80.03 122	89.74 134	94.69 33

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 124	95.95 62	84.20 78	94.39 61	93.23 126

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

X-MVStestdata		80.37 199	77.83 239	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 492	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 268	93.44 32	78.70 34	83.63 115	89.03 219	74.57 27	95.71 66	80.26 121	94.04 67	93.66 102
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
E5new		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

E6new		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 120	91.20 153	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

E4		84.10 98	83.99 101	84.45 132	87.58 239	64.99 234	86.54 230	92.25 96	76.38 94	83.37 121	92.09 119	69.88 90	93.58 166	79.78 130	88.03 171	94.77 25

viewmacassd2359aftdt		83.76 109	83.66 110	84.07 161	86.59 275	64.56 247	86.88 215	91.82 124	75.72 111	83.34 122	92.15 117	68.24 121	92.88 218	79.05 136	89.15 145	94.77 25

E2		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

E3		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

LFMVS		81.82 154	81.23 154	83.57 188	91.89 82	63.43 283	89.84 87	81.85 383	77.04 70	83.21 123	93.10 88	52.26 308	93.43 186	71.98 229	89.95 130	93.85 90

VDDNet		81.52 164	80.67 164	84.05 167	90.44 108	64.13 260	89.73 93	85.91 323	71.11 233	83.18 126	93.48 78	50.54 340	93.49 179	73.40 210	88.25 165	94.54 53

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 167	83.16 127	91.07 158	75.94 21	95.19 89	79.94 124	94.38 62	93.55 114

viewmanbaseed2359cas		83.66 112	83.55 112	84.00 172	86.81 267	64.53 248	86.65 225	91.75 129	74.89 142	83.15 128	91.68 131	68.74 113	92.83 222	79.02 138	89.24 142	94.63 44

viewcassd2359sk11		83.89 103	83.74 107	84.34 140	87.76 221	64.91 241	86.30 241	92.22 100	75.47 119	83.04 129	91.52 140	70.15 83	93.53 174	79.26 135	87.96 172	94.57 49

nrg030		83.88 104	83.53 113	84.96 107	86.77 269	69.28 109	90.46 75	92.67 72	74.79 146	82.95 130	91.33 148	72.70 50	93.09 208	80.79 115	79.28 315	92.50 166

EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 93	88.18 194	67.85 154	87.66 182	89.73 200	80.05 15	82.95 130	89.59 204	70.74 76	94.82 108	80.66 118	84.72 231	93.28 125

E3new		83.78 108	83.60 111	84.31 142	87.76 221	64.89 242	86.24 244	92.20 103	75.15 135	82.87 132	91.23 149	70.11 84	93.52 176	79.05 136	87.79 175	94.51 55

MVS_Test		83.15 129	83.06 121	83.41 194	86.86 264	63.21 287	86.11 248	92.00 113	74.31 158	82.87 132	89.44 212	70.03 87	93.21 197	77.39 161	88.50 158	93.81 94

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 204	93.04 46	69.80 273	82.85 134	91.22 152	73.06 44	96.02 57	76.72 174	94.63 54	91.46 210

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 135	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 106
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 100	76.87 74	82.81 136	94.25 49	66.44 142	96.24 49	82.88 92	94.28 64	93.38 119

test12				86.80 58	92.63 73	70.70 81		91.79 126		82.71 137		71.67 63	96.16 52		94.50 57	93.54 115

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 134	73.89 170	82.67 138	94.09 57	62.60 188	95.54 70	80.93 111	92.93 77	93.57 112

diffmvs_AUTHOR		82.38 143	82.27 139	82.73 233	83.26 356	63.80 267	83.89 311	89.76 197	73.35 187	82.37 139	90.84 166	66.25 145	90.79 312	82.77 93	87.93 173	93.59 111

viewdifsd2359ckpt07		82.83 137	82.78 129	82.99 214	86.51 277	62.58 299	85.09 277	90.83 161	75.22 128	82.28 140	91.63 135	69.43 96	92.03 253	77.71 156	86.32 202	94.34 64

Effi-MVS+		83.62 116	83.08 120	85.24 95	88.38 188	67.45 167	88.89 129	89.15 230	75.50 118	82.27 141	88.28 244	69.61 94	94.45 127	77.81 154	87.84 174	93.84 92

EI-MVSNet-UG-set		83.81 105	83.38 116	85.09 103	87.87 211	67.53 166	87.44 195	89.66 201	79.74 18	82.23 142	89.41 213	70.24 82	94.74 114	79.95 123	83.92 246	92.99 147

KinetiMVS		83.31 127	82.61 131	85.39 91	87.08 260	67.56 165	88.06 168	91.65 132	77.80 44	82.21 143	91.79 126	57.27 261	94.07 142	77.77 155	89.89 132	94.56 51

fmvsm_s_conf0.5_n_7		83.34 124	84.03 100	81.28 266	85.73 294	65.13 228	85.40 269	89.90 193	74.96 140	82.13 144	93.89 69	66.65 137	87.92 365	86.56 53	91.05 109	90.80 229

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 250	90.33 178	76.11 103	82.08 145	91.61 138	71.36 68	94.17 139	81.02 110	92.58 82	92.08 189

diffmvs		82.10 146	81.88 148	82.76 231	83.00 366	63.78 269	83.68 316	89.76 197	72.94 199	82.02 146	89.85 191	65.96 153	90.79 312	82.38 100	87.30 185	93.71 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
xiu_mvs_v1_base_debu		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 334	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 334	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base_debi		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 334	92.85 219	78.29 150	87.56 179	89.06 299

新几何1				83.42 192	93.13 60	70.71 80		85.48 329	57.43 438	81.80 150	91.98 120	63.28 174	92.27 246	64.60 300	92.99 76	87.27 360

test_yl		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

DCV-MVSNet		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

viewdifsd2359ckpt13		82.91 135	82.29 138	84.77 118	86.96 263	66.90 187	87.47 187	91.62 134	72.19 209	81.68 153	90.71 169	66.92 135	93.28 190	75.90 182	87.15 188	94.12 75

viewdifsd2359ckpt09		83.34 124	82.55 132	85.70 81	87.64 230	67.72 159	88.43 151	91.68 131	71.91 216	81.65 154	90.68 170	67.10 134	94.75 113	76.17 177	87.70 178	94.62 46

test_cas_vis1_n_1920		73.76 332	73.74 321	73.81 402	75.90 447	59.77 348	80.51 374	82.40 376	58.30 429	81.62 155	85.69 316	44.35 402	76.41 452	76.29 175	78.61 318	85.23 404

MG-MVS		83.41 121	83.45 114	83.28 197	92.74 71	62.28 308	88.17 164	89.50 208	75.22 128	81.49 156	92.74 103	66.75 136	95.11 94	72.85 216	91.58 101	92.45 170

LuminaMVS		80.68 186	79.62 195	83.83 179	85.07 315	68.01 148	86.99 209	88.83 244	70.36 257	81.38 157	87.99 255	50.11 345	92.51 235	79.02 138	86.89 194	90.97 224

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 158	91.43 145	70.34 79	97.23 17	84.26 75	93.36 74	94.37 62

MVSFormer		82.85 136	82.05 144	85.24 95	87.35 241	70.21 86	90.50 72	90.38 174	68.55 307	81.32 158	89.47 207	61.68 206	93.46 184	78.98 141	90.26 123	92.05 190

lupinMVS		81.39 167	80.27 175	84.76 119	87.35 241	70.21 86	85.55 264	86.41 314	62.85 387	81.32 158	88.61 234	61.68 206	92.24 248	78.41 148	90.26 123	91.83 193

xiu_mvs_v2_base		81.69 157	81.05 157	83.60 185	89.15 155	68.03 147	84.46 295	90.02 188	70.67 246	81.30 161	86.53 300	63.17 179	94.19 138	75.60 187	88.54 156	88.57 324

PS-MVSNAJ		81.69 157	81.02 158	83.70 183	89.51 134	68.21 142	84.28 303	90.09 187	70.79 243	81.26 162	85.62 320	63.15 180	94.29 129	75.62 186	88.87 149	88.59 323

原ACMM1				84.35 139	93.01 66	68.79 117		92.44 82	63.96 376	81.09 163	91.57 139	66.06 150	95.45 75	67.19 279	94.82 50	88.81 314

jason		81.39 167	80.29 174	84.70 121	86.63 274	69.90 94	85.95 251	86.77 307	63.24 380	81.07 164	89.47 207	61.08 222	92.15 250	78.33 149	90.07 128	92.05 190
jason: jason.
viewmambaseed2359dif		80.41 195	79.84 187	82.12 244	82.95 372	62.50 302	83.39 324	88.06 268	67.11 323	80.98 165	90.31 181	66.20 147	91.01 304	74.62 196	84.90 228	92.86 152

OPM-MVS		83.50 119	82.95 124	85.14 98	88.79 172	70.95 74	89.13 121	91.52 138	77.55 52	80.96 166	91.75 129	60.71 226	94.50 124	79.67 132	86.51 200	89.97 274
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt11		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 308	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 322	92.95 149

viewmsd2359difaftdt		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 308	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 322	92.95 149

Vis-MVSNet		83.46 120	82.80 127	85.43 90	90.25 112	68.74 121	90.30 80	90.13 186	76.33 97	80.87 169	92.89 95	61.00 223	94.20 136	72.45 226	90.97 111	93.35 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AstraMVS		80.81 178	80.14 179	82.80 225	86.05 289	63.96 262	86.46 233	85.90 324	73.71 174	80.85 170	90.56 175	54.06 292	91.57 274	79.72 131	83.97 245	92.86 152

guyue		81.13 171	80.64 165	82.60 236	86.52 276	63.92 265	86.69 224	87.73 279	73.97 166	80.83 171	89.69 198	56.70 267	91.33 290	78.26 153	85.40 224	92.54 163

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 172	93.82 72	64.33 166	96.29 46	82.67 99	90.69 116	93.23 126
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SSM_0404		81.91 151	80.84 162	85.13 101	89.24 151	68.26 137	87.84 179	89.25 224	71.06 236	80.62 173	90.39 179	59.57 239	94.65 119	72.45 226	87.19 187	92.47 169

Anonymous20240529		80.19 205	78.89 214	84.10 155	90.60 104	64.75 245	88.95 127	90.90 157	65.97 342	80.59 174	91.17 155	49.97 347	93.73 164	69.16 260	82.70 272	93.81 94

Elysia		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 374	94.82 108	76.85 167	89.57 136	93.80 96

StellarMVS		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 374	94.82 108	76.85 167	89.57 136	93.80 96

MVS_111021_LR		82.61 140	82.11 141	84.11 154	88.82 166	71.58 57	85.15 274	86.16 320	74.69 148	80.47 177	91.04 159	62.29 195	90.55 318	80.33 120	90.08 127	90.20 257

ECVR-MVS		79.61 212	79.26 205	80.67 283	90.08 116	54.69 416	87.89 176	77.44 431	74.88 143	80.27 178	92.79 100	48.96 363	92.45 237	68.55 266	92.50 84	94.86 19

VPA-MVSNet		80.60 190	80.55 167	80.76 281	88.07 202	60.80 333	86.86 216	91.58 137	75.67 115	80.24 179	89.45 211	63.34 173	90.25 323	70.51 243	79.22 316	91.23 214

test1111		79.43 219	79.18 208	80.15 297	89.99 121	53.31 429	87.33 199	77.05 435	75.04 136	80.23 180	92.77 102	48.97 362	92.33 245	68.87 263	92.40 86	94.81 22

test2506		77.30 278	76.49 275	79.74 311	90.08 116	52.02 435	87.86 178	63.10 478	74.88 143	80.16 181	92.79 100	38.29 440	92.35 243	68.74 265	92.50 84	94.86 19

Anonymous202405211		78.25 250	77.01 261	81.99 249	91.03 94	60.67 337	84.77 284	83.90 350	70.65 250	80.00 182	91.20 153	41.08 424	91.43 286	65.21 294	85.26 225	93.85 90

RRT-MVS		82.60 142	82.10 142	84.10 155	87.98 207	62.94 296	87.45 190	91.27 145	77.42 56	79.85 183	90.28 182	56.62 269	94.70 117	79.87 129	88.15 167	94.67 38

test222					91.50 86	68.26 137	84.16 307	83.20 364	54.63 450	79.74 184	91.63 135	58.97 244			91.42 103	86.77 375

OMC-MVS		82.69 138	81.97 147	84.85 114	88.75 174	67.42 168	87.98 170	90.87 159	74.92 141	79.72 185	91.65 133	62.19 198	93.96 144	75.26 192	86.42 201	93.16 133

FA-MVS(test-final)		80.96 174	79.91 184	84.10 155	88.30 191	65.01 232	84.55 292	90.01 189	73.25 191	79.61 186	87.57 264	58.35 250	94.72 115	71.29 235	86.25 205	92.56 162

CPTT-MVS		83.73 110	83.33 118	84.92 111	93.28 53	70.86 78	92.09 41	90.38 174	68.75 304	79.57 187	92.83 97	60.60 232	93.04 213	80.92 112	91.56 102	90.86 228

IS-MVSNet		83.15 129	82.81 126	84.18 153	89.94 123	63.30 285	91.59 51	88.46 261	79.04 30	79.49 188	92.16 115	65.10 159	94.28 130	67.71 272	91.86 97	94.95 12

mamba_0408		79.37 224	77.52 251	84.93 110	88.81 167	67.96 149	65.03 476	88.66 255	70.96 240	79.48 189	89.80 194	58.69 245	94.65 119	70.35 245	85.93 213	92.18 183

SSM_04072		77.67 271	77.52 251	78.12 345	88.81 167	67.96 149	65.03 476	88.66 255	70.96 240	79.48 189	89.80 194	58.69 245	74.23 468	70.35 245	85.93 213	92.18 183

SSM_0407		81.58 161	80.48 169	84.87 113	88.81 167	67.96 149	87.37 196	89.25 224	71.06 236	79.48 189	90.39 179	59.57 239	94.48 126	72.45 226	85.93 213	92.18 183

PS-MVSNAJss		82.07 148	81.31 152	84.34 140	86.51 277	67.27 176	89.27 112	91.51 139	71.75 217	79.37 192	90.22 186	63.15 180	94.27 131	77.69 157	82.36 275	91.49 207

EPP-MVSNet		83.40 122	83.02 122	84.57 123	90.13 114	64.47 253	92.32 35	90.73 164	74.45 155	79.35 193	91.10 156	69.05 108	95.12 92	72.78 217	87.22 186	94.13 74

test_vis1_n_1920		75.52 310	75.78 284	74.75 391	79.84 418	57.44 379	83.26 328	85.52 328	62.83 388	79.34 194	86.17 308	45.10 396	79.71 434	78.75 143	81.21 287	87.10 369

DP-MVS Recon		83.11 132	82.09 143	86.15 70	94.44 23	70.92 76	88.79 135	92.20 103	70.53 251	79.17 195	91.03 161	64.12 168	96.03 55	68.39 269	90.14 125	91.50 206

ab-mvs		79.51 215	78.97 212	81.14 271	88.46 184	60.91 331	83.84 312	89.24 226	70.36 257	79.03 196	88.87 227	63.23 178	90.21 324	65.12 295	82.57 273	92.28 177

EIA-MVS		83.31 127	82.80 127	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 150	78.96 197	86.42 302	69.06 107	95.26 87	75.54 188	90.09 126	93.62 109

PVSNet_Blended_VisFu		82.62 139	81.83 149	84.96 107	90.80 101	69.76 97	88.74 140	91.70 130	69.39 282	78.96 197	88.46 239	65.47 156	94.87 107	74.42 199	88.57 155	90.24 256

HQP_MVS		83.64 114	83.14 119	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 199	91.00 163	60.42 234	95.38 82	78.71 144	86.32 202	91.33 211

plane_prior3						68.60 128			78.44 36	78.92 199

test_fmvs1_n		70.86 372	70.24 368	72.73 413	72.51 470	55.28 411	81.27 362	79.71 412	51.49 460	78.73 201	84.87 338	27.54 466	77.02 446	76.06 179	79.97 305	85.88 394

EI-MVSNet		80.52 194	79.98 182	82.12 244	84.28 330	63.19 289	86.41 234	88.95 241	74.18 163	78.69 202	87.54 267	66.62 138	92.43 238	72.57 220	80.57 297	90.74 234

MVSTER		79.01 232	77.88 238	82.38 240	83.07 363	64.80 244	84.08 310	88.95 241	69.01 298	78.69 202	87.17 278	54.70 285	92.43 238	74.69 195	80.57 297	89.89 277

API-MVS		81.99 150	81.23 154	84.26 150	90.94 97	70.18 91	91.10 63	89.32 218	71.51 224	78.66 204	88.28 244	65.26 157	95.10 97	64.74 299	91.23 107	87.51 350

GeoE		81.71 156	81.01 159	83.80 182	89.51 134	64.45 254	88.97 126	88.73 254	71.27 230	78.63 205	89.76 197	66.32 144	93.20 200	69.89 252	86.02 210	93.74 99

test_fmvs1		70.93 371	70.52 363	72.16 417	73.71 459	55.05 413	80.82 365	78.77 421	51.21 461	78.58 206	84.41 346	31.20 460	76.94 447	75.88 183	80.12 304	84.47 416

UniMVSNet (Re)		81.60 160	81.11 156	83.09 207	88.38 188	64.41 255	87.60 183	93.02 50	78.42 37	78.56 207	88.16 248	69.78 91	93.26 193	69.58 256	76.49 349	91.60 201

MAR-MVS		81.84 153	80.70 163	85.27 94	91.32 89	71.53 58	89.82 88	90.92 156	69.77 275	78.50 208	86.21 306	62.36 194	94.52 123	65.36 293	92.05 93	89.77 282
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
IMVS_0403		80.80 181	80.12 180	82.87 221	87.13 254	63.59 274	85.19 271	89.33 214	70.51 252	78.49 209	89.03 219	63.26 176	93.27 192	72.56 222	85.56 220	91.74 196

Fast-Effi-MVS+		80.81 178	79.92 183	83.47 189	88.85 163	64.51 250	85.53 266	89.39 212	70.79 243	78.49 209	85.06 335	67.54 128	93.58 166	67.03 282	86.58 198	92.32 175

FIs		82.07 148	82.42 133	81.04 274	88.80 171	58.34 361	88.26 161	93.49 31	76.93 72	78.47 211	91.04 159	69.92 89	92.34 244	69.87 253	84.97 227	92.44 171

UniMVSNet_NR-MVSNet		81.88 152	81.54 151	82.92 218	88.46 184	63.46 281	87.13 203	92.37 86	80.19 12	78.38 212	89.14 215	71.66 64	93.05 211	70.05 249	76.46 350	92.25 178

DU-MVS		81.12 172	80.52 168	82.90 219	87.80 215	63.46 281	87.02 208	91.87 121	79.01 31	78.38 212	89.07 217	65.02 160	93.05 211	70.05 249	76.46 350	92.20 181

CLD-MVS		82.31 144	81.65 150	84.29 145	88.47 183	67.73 158	85.81 258	92.35 87	75.78 110	78.33 214	86.58 297	64.01 169	94.35 128	76.05 180	87.48 182	90.79 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VPNet		78.69 241	78.66 217	78.76 330	88.31 190	55.72 405	84.45 296	86.63 311	76.79 76	78.26 215	90.55 176	59.30 242	89.70 334	66.63 283	77.05 340	90.88 227

V42		79.38 223	78.24 228	82.83 222	81.10 404	65.50 215	85.55 264	89.82 194	71.57 223	78.21 216	86.12 309	60.66 229	93.18 203	75.64 185	75.46 369	89.81 281

BH-RMVSNet		79.61 212	78.44 222	83.14 205	89.38 143	65.93 202	84.95 281	87.15 297	73.56 179	78.19 217	89.79 196	56.67 268	93.36 188	59.53 353	86.74 196	90.13 260

v2v482		80.23 203	79.29 204	83.05 211	83.62 348	64.14 259	87.04 206	89.97 190	73.61 177	78.18 218	87.22 275	61.10 221	93.82 156	76.11 178	76.78 346	91.18 215

PVSNet_BlendedMVS		80.60 190	80.02 181	82.36 241	88.85 163	65.40 216	86.16 247	92.00 113	69.34 284	78.11 219	86.09 310	66.02 151	94.27 131	71.52 231	82.06 278	87.39 352

PVSNet_Blended		80.98 173	80.34 172	82.90 219	88.85 163	65.40 216	84.43 298	92.00 113	67.62 318	78.11 219	85.05 336	66.02 151	94.27 131	71.52 231	89.50 138	89.01 304

v1144		80.03 207	79.03 210	83.01 213	83.78 343	64.51 250	87.11 205	90.57 169	71.96 215	78.08 221	86.20 307	61.41 213	93.94 147	74.93 194	77.23 337	90.60 240

FE-MVS		77.78 265	75.68 286	84.08 160	88.09 201	66.00 200	83.13 331	87.79 277	68.42 311	78.01 222	85.23 330	45.50 394	95.12 92	59.11 358	85.83 217	91.11 217

TranMVSNet+NR-MVSNet		80.84 176	80.31 173	82.42 239	87.85 212	62.33 306	87.74 181	91.33 144	80.55 9	77.99 223	89.86 190	65.23 158	92.62 226	67.05 281	75.24 377	92.30 176

Baseline_NR-MVSNet		78.15 255	78.33 226	77.61 357	85.79 292	56.21 399	86.78 220	85.76 326	73.60 178	77.93 224	87.57 264	65.02 160	88.99 347	67.14 280	75.33 374	87.63 344

icg_test_0407_2		78.92 236	78.93 213	78.90 328	87.13 254	63.59 274	76.58 423	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	81.38 427	72.56 222	85.56 220	91.74 196

IMVS_0407		80.61 188	79.90 185	82.75 232	87.13 254	63.59 274	85.33 270	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	92.91 216	72.56 222	85.56 220	91.74 196

TR-MVS		77.44 274	76.18 281	81.20 269	88.24 192	63.24 286	84.61 290	86.40 315	67.55 319	77.81 227	86.48 301	54.10 290	93.15 204	57.75 373	82.72 271	87.20 362

v1192		79.59 214	78.43 223	83.07 210	83.55 350	64.52 249	86.93 213	90.58 167	70.83 242	77.78 228	85.90 311	59.15 243	93.94 147	73.96 204	77.19 339	90.76 232

PCF-MVS	73.52 7	80.38 197	78.84 215	85.01 105	87.71 224	68.99 113	83.65 317	91.46 143	63.00 384	77.77 229	90.28 182	66.10 148	95.09 98	61.40 337	88.22 166	90.94 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WR-MVS		79.49 216	79.22 207	80.27 292	88.79 172	58.35 360	85.06 278	88.61 259	78.56 35	77.65 230	88.34 242	63.81 172	90.66 317	64.98 297	77.22 338	91.80 195

XVG-OURS		80.41 195	79.23 206	83.97 175	85.64 296	69.02 112	83.03 336	90.39 173	71.09 234	77.63 231	91.49 143	54.62 287	91.35 288	75.71 184	83.47 259	91.54 204

v144192		79.47 217	78.37 224	82.78 229	83.35 353	63.96 262	86.96 210	90.36 177	69.99 268	77.50 232	85.67 318	60.66 229	93.77 160	74.27 201	76.58 347	90.62 238

v1921920		79.22 226	78.03 232	82.80 225	83.30 355	63.94 264	86.80 218	90.33 178	69.91 271	77.48 233	85.53 322	58.44 249	93.75 162	73.60 206	76.85 344	90.71 236

thisisatest0530		79.40 221	77.76 244	84.31 142	87.69 228	65.10 231	87.36 197	84.26 346	70.04 265	77.42 234	88.26 246	49.94 348	94.79 112	70.20 247	84.70 232	93.03 143

FC-MVSNet-test		81.52 164	82.02 145	80.03 299	88.42 187	55.97 401	87.95 172	93.42 34	77.10 68	77.38 235	90.98 165	69.96 88	91.79 264	68.46 268	84.50 234	92.33 174

v1240		78.99 233	77.78 242	82.64 234	83.21 358	63.54 278	86.62 227	90.30 180	69.74 278	77.33 236	85.68 317	57.04 264	93.76 161	73.13 214	76.92 341	90.62 238

PAPM_NR		83.02 133	82.41 134	84.82 115	92.47 76	66.37 192	87.93 174	91.80 125	73.82 171	77.32 237	90.66 171	67.90 125	94.90 104	70.37 244	89.48 139	93.19 132

ACMM	73.20 8	80.78 185	79.84 187	83.58 187	89.31 147	68.37 134	89.99 84	91.60 136	70.28 261	77.25 238	89.66 200	53.37 299	93.53 174	74.24 202	82.85 268	88.85 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP4-MVS										77.24 239			95.11 94			91.03 221

AUN-MVS		79.21 227	77.60 249	84.05 167	88.71 176	67.61 162	85.84 256	87.26 294	69.08 294	77.23 240	88.14 252	53.20 301	93.47 183	75.50 189	73.45 394	91.06 219

HQP-NCC					89.33 144		89.17 116		76.41 90	77.23 240

ACMP_Plane					89.33 144		89.17 116		76.41 90	77.23 240

HQP-MVS		82.61 140	82.02 145	84.37 137	89.33 144	66.98 183	89.17 116	92.19 105	76.41 90	77.23 240	90.23 185	60.17 237	95.11 94	77.47 159	85.99 211	91.03 221

mmtdpeth		74.16 326	73.01 330	77.60 359	83.72 345	61.13 324	85.10 276	85.10 333	72.06 213	77.21 244	80.33 414	43.84 405	85.75 388	77.14 164	52.61 470	85.91 393

tt0805		78.73 239	77.83 239	81.43 260	85.17 309	60.30 343	89.41 107	90.90 157	71.21 231	77.17 245	88.73 229	46.38 380	93.21 197	72.57 220	78.96 317	90.79 230

TAPA-MVS	73.13 9	79.15 228	77.94 234	82.79 228	89.59 130	62.99 295	88.16 165	91.51 139	65.77 343	77.14 246	91.09 157	60.91 224	93.21 197	50.26 422	87.05 190	92.17 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPR		81.66 159	80.89 161	83.99 174	90.27 111	64.00 261	86.76 222	91.77 128	68.84 303	77.13 247	89.50 205	67.63 127	94.88 106	67.55 274	88.52 157	93.09 138

UniMVSNet_ETH3D		79.10 230	78.24 228	81.70 254	86.85 265	60.24 344	87.28 201	88.79 246	74.25 161	76.84 248	90.53 177	49.48 353	91.56 275	67.98 270	82.15 276	93.29 124

EPNet		83.72 111	82.92 125	86.14 72	84.22 332	69.48 101	91.05 64	85.27 330	81.30 6	76.83 249	91.65 133	66.09 149	95.56 68	76.00 181	93.85 68	93.38 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		76.98 283	76.75 271	77.66 355	88.13 198	55.66 406	85.12 275	81.89 381	73.04 197	76.79 250	88.90 225	62.43 193	87.78 368	63.30 309	71.18 411	89.55 288

tttt0517		79.40 221	77.91 235	83.90 178	88.10 200	63.84 266	88.37 157	84.05 348	71.45 225	76.78 251	89.12 216	49.93 350	94.89 105	70.18 248	83.18 265	92.96 148

TAMVS		78.89 237	77.51 253	83.03 212	87.80 215	67.79 157	84.72 285	85.05 335	67.63 317	76.75 252	87.70 260	62.25 196	90.82 311	58.53 365	87.13 189	90.49 245

XVG-OURS-SEG-HR		80.81 178	79.76 189	83.96 176	85.60 298	68.78 118	83.54 323	90.50 170	70.66 249	76.71 253	91.66 132	60.69 227	91.26 291	76.94 166	81.58 283	91.83 193

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 254	93.37 83	60.40 236	96.75 30	77.20 162	93.73 70	95.29 6

LPG-MVS_test		82.08 147	81.27 153	84.50 129	89.23 152	68.76 119	90.22 81	91.94 117	75.37 123	76.64 255	91.51 141	54.29 288	94.91 102	78.44 146	83.78 247	89.83 279

LGP-MVS_train				84.50 129	89.23 152	68.76 119		91.94 117	75.37 123	76.64 255	91.51 141	54.29 288	94.91 102	78.44 146	83.78 247	89.83 279

SDMVSNet		80.38 197	80.18 176	80.99 275	89.03 161	64.94 238	80.45 376	89.40 211	75.19 132	76.61 257	89.98 188	60.61 231	87.69 369	76.83 170	83.55 256	90.33 252

sd_testset		77.70 269	77.40 254	78.60 333	89.03 161	60.02 346	79.00 397	85.83 325	75.19 132	76.61 257	89.98 188	54.81 280	85.46 394	62.63 322	83.55 256	90.33 252

testing3-2		75.12 318	75.19 300	74.91 387	90.40 109	45.09 471	80.29 379	78.42 423	78.37 40	76.54 259	87.75 258	44.36 401	87.28 374	57.04 380	83.49 258	92.37 172

tfpn200view9		76.42 297	75.37 295	79.55 318	89.13 156	57.65 375	85.17 272	83.60 353	73.41 185	76.45 260	86.39 303	52.12 310	91.95 258	48.33 432	83.75 250	89.07 297

thres400		76.50 291	75.37 295	79.86 304	89.13 156	57.65 375	85.17 272	83.60 353	73.41 185	76.45 260	86.39 303	52.12 310	91.95 258	48.33 432	83.75 250	90.00 270

HyFIR lowres test		77.53 273	75.40 293	83.94 177	89.59 130	66.62 188	80.36 377	88.64 258	56.29 444	76.45 260	85.17 332	57.64 256	93.28 190	61.34 339	83.10 266	91.91 192

CDS-MVSNet		79.07 231	77.70 246	83.17 204	87.60 231	68.23 141	84.40 301	86.20 319	67.49 320	76.36 263	86.54 299	61.54 209	90.79 312	61.86 333	87.33 184	90.49 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres100view900		76.50 291	75.55 290	79.33 320	89.52 133	56.99 384	85.83 257	83.23 361	73.94 168	76.32 264	87.12 279	51.89 320	91.95 258	48.33 432	83.75 250	89.07 297

thres600view7		76.50 291	75.44 291	79.68 313	89.40 141	57.16 381	85.53 266	83.23 361	73.79 172	76.26 265	87.09 280	51.89 320	91.89 261	48.05 437	83.72 253	90.00 270

UGNet		80.83 177	79.59 196	84.54 124	88.04 203	68.09 144	89.42 106	88.16 263	76.95 71	76.22 266	89.46 209	49.30 357	93.94 147	68.48 267	90.31 121	91.60 201
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test_djsdf		80.30 202	79.32 203	83.27 198	83.98 338	65.37 219	90.50 72	90.38 174	68.55 307	76.19 267	88.70 230	56.44 270	93.46 184	78.98 141	80.14 303	90.97 224

v148		78.72 240	77.80 241	81.47 259	82.73 376	61.96 314	86.30 241	88.08 266	73.26 190	76.18 268	85.47 324	62.46 192	92.36 242	71.92 230	73.82 391	90.09 264

WTY-MVS		75.65 308	75.68 286	75.57 377	86.40 279	56.82 386	77.92 415	82.40 376	65.10 356	76.18 268	87.72 259	63.13 183	80.90 430	60.31 346	81.96 279	89.00 306

mvs_anonymous		79.42 220	79.11 209	80.34 290	84.45 329	57.97 367	82.59 338	87.62 281	67.40 322	76.17 270	88.56 237	68.47 116	89.59 335	70.65 242	86.05 209	93.47 117

Anonymous20231211		78.97 234	77.69 247	82.81 224	90.54 106	64.29 257	90.11 83	91.51 139	65.01 359	76.16 271	88.13 253	50.56 339	93.03 214	69.68 255	77.56 336	91.11 217

thisisatest0515		77.33 277	75.38 294	83.18 203	85.27 308	63.80 267	82.11 346	83.27 360	65.06 357	75.91 272	83.84 362	49.54 352	94.27 131	67.24 278	86.19 206	91.48 208

CANet_DTU		80.61 188	79.87 186	82.83 222	85.60 298	63.17 290	87.36 197	88.65 257	76.37 95	75.88 273	88.44 240	53.51 297	93.07 209	73.30 211	89.74 134	92.25 178

thres200		75.55 309	74.47 310	78.82 329	87.78 218	57.85 370	83.07 334	83.51 356	72.44 206	75.84 274	84.42 345	52.08 313	91.75 266	47.41 439	83.64 255	86.86 373

CHOSEN 1792x2688		77.63 272	75.69 285	83.44 191	89.98 122	68.58 129	78.70 402	87.50 284	56.38 443	75.80 275	86.84 283	58.67 247	91.40 287	61.58 336	85.75 218	90.34 251

AdaColmap		80.58 193	79.42 199	84.06 164	93.09 63	68.91 115	89.36 110	88.97 240	69.27 286	75.70 276	89.69 198	57.20 263	95.77 64	63.06 314	88.41 160	87.50 351

UWE-MVS		72.13 362	71.49 345	74.03 399	86.66 273	47.70 458	81.40 359	76.89 437	63.60 379	75.59 277	84.22 354	39.94 429	85.62 391	48.98 429	86.13 208	88.77 316

c3_l		78.75 238	77.91 235	81.26 267	82.89 373	61.56 319	84.09 309	89.13 232	69.97 269	75.56 278	84.29 350	66.36 143	92.09 252	73.47 209	75.48 367	90.12 261

miper_ehance_all_eth		78.59 244	77.76 244	81.08 273	82.66 378	61.56 319	83.65 317	89.15 230	68.87 302	75.55 279	83.79 364	66.49 141	92.03 253	73.25 212	76.39 352	89.64 285

miper_enhance_ethall		77.87 264	76.86 265	80.92 278	81.65 392	61.38 323	82.68 337	88.98 238	65.52 347	75.47 280	82.30 393	65.76 155	92.00 256	72.95 215	76.39 352	89.39 292

3Dnovator	76.31 5	83.38 123	82.31 137	86.59 61	87.94 208	72.94 28	90.64 68	92.14 110	77.21 63	75.47 280	92.83 97	58.56 248	94.72 115	73.24 213	92.71 81	92.13 188

jajsoiax		79.29 225	77.96 233	83.27 198	84.68 323	66.57 190	89.25 113	90.16 185	69.20 291	75.46 282	89.49 206	45.75 391	93.13 206	76.84 169	80.80 293	90.11 262

IterMVS-LS		80.06 206	79.38 200	82.11 246	85.89 290	63.20 288	86.79 219	89.34 213	74.19 162	75.45 283	86.72 287	66.62 138	92.39 240	72.58 219	76.86 343	90.75 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 217	78.60 218	82.05 247	89.19 154	65.91 203	86.07 249	88.52 260	72.18 210	75.42 284	87.69 261	61.15 220	93.54 173	60.38 345	86.83 195	86.70 377

mvs_tets		79.13 229	77.77 243	83.22 202	84.70 322	66.37 192	89.17 116	90.19 184	69.38 283	75.40 285	89.46 209	44.17 403	93.15 204	76.78 173	80.70 295	90.14 259

mvsmamba		80.60 190	79.38 200	84.27 148	89.74 128	67.24 178	87.47 187	86.95 302	70.02 266	75.38 286	88.93 224	51.24 331	92.56 231	75.47 190	89.22 143	93.00 146

HY-MVS	69.67 12	77.95 261	77.15 259	80.36 289	87.57 240	60.21 345	83.37 326	87.78 278	66.11 338	75.37 287	87.06 282	63.27 175	90.48 319	61.38 338	82.43 274	90.40 249

testing91		76.54 289	75.66 288	79.18 324	88.43 186	55.89 402	81.08 363	83.00 368	73.76 173	75.34 288	84.29 350	46.20 385	90.07 326	64.33 301	84.50 234	91.58 203

GBi-Net		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 281	91.11 297	62.72 318	79.57 307	90.09 264

test1		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 281	91.11 297	62.72 318	79.57 307	90.09 264

FMVSNet3		77.88 263	76.85 266	80.97 277	86.84 266	62.36 305	86.52 231	88.77 247	71.13 232	75.34 288	86.66 293	54.07 291	91.10 300	62.72 318	79.57 307	89.45 290

CostFormer		75.24 316	73.90 318	79.27 321	82.65 379	58.27 362	80.80 366	82.73 374	61.57 401	75.33 292	83.13 379	55.52 276	91.07 303	64.98 297	78.34 327	88.45 326

test_vis1_n		69.85 386	69.21 375	71.77 419	72.66 469	55.27 412	81.48 356	76.21 440	52.03 457	75.30 293	83.20 378	28.97 463	76.22 454	74.60 197	78.41 326	83.81 424

FMVSNet2		78.20 253	77.21 258	81.20 269	87.60 231	62.89 297	87.47 187	89.02 236	71.63 219	75.29 294	87.28 271	54.80 281	91.10 300	62.38 325	79.38 313	89.61 286

v8		79.97 209	79.02 211	82.80 225	84.09 335	64.50 252	87.96 171	90.29 181	74.13 165	75.24 295	86.81 284	62.88 187	93.89 155	74.39 200	75.40 372	90.00 270

testing99		76.09 303	75.12 302	79.00 325	88.16 195	55.50 408	80.79 367	81.40 388	73.30 189	75.17 296	84.27 353	44.48 400	90.02 327	64.28 302	84.22 243	91.48 208

anonymousdsp		78.60 243	77.15 259	82.98 216	80.51 410	67.08 181	87.24 202	89.53 207	65.66 345	75.16 297	87.19 277	52.52 303	92.25 247	77.17 163	79.34 314	89.61 286

QAPM		80.88 175	79.50 198	85.03 104	88.01 206	68.97 114	91.59 51	92.00 113	66.63 334	75.15 298	92.16 115	57.70 255	95.45 75	63.52 305	88.76 152	90.66 237

v10		79.74 211	78.67 216	82.97 217	84.06 336	64.95 235	87.88 177	90.62 166	73.11 195	75.11 299	86.56 298	61.46 212	94.05 143	73.68 205	75.55 365	89.90 276

Vis-MVSNet (Re-imp)		78.36 249	78.45 221	78.07 347	88.64 178	51.78 441	86.70 223	79.63 413	74.14 164	75.11 299	90.83 167	61.29 217	89.75 332	58.10 370	91.60 99	92.69 158

cl22		78.07 257	77.01 261	81.23 268	82.37 385	61.83 316	83.55 321	87.98 270	68.96 301	75.06 301	83.87 360	61.40 214	91.88 262	73.53 207	76.39 352	89.98 273

ACMP	74.13 6	81.51 166	80.57 166	84.36 138	89.42 139	68.69 126	89.97 85	91.50 142	74.46 154	75.04 302	90.41 178	53.82 294	94.54 121	77.56 158	82.91 267	89.86 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VortexMVS		78.57 245	77.89 237	80.59 284	85.89 290	62.76 298	85.61 259	89.62 204	72.06 213	74.99 303	85.38 326	55.94 274	90.77 315	74.99 193	76.58 347	88.23 332

Effi-MVS+-dtu		80.03 207	78.57 219	84.42 134	85.13 313	68.74 121	88.77 136	88.10 265	74.99 137	74.97 304	83.49 373	57.27 261	93.36 188	73.53 207	80.88 291	91.18 215

XXY-MVS		75.41 313	75.56 289	74.96 386	83.59 349	57.82 371	80.59 373	83.87 351	66.54 335	74.93 305	88.31 243	63.24 177	80.09 433	62.16 329	76.85 344	86.97 371

eth_miper_zixun_eth		77.92 262	76.69 272	81.61 257	83.00 366	61.98 313	83.15 330	89.20 228	69.52 281	74.86 306	84.35 349	61.76 205	92.56 231	71.50 233	72.89 399	90.28 255

GA-MVS		76.87 285	75.17 301	81.97 250	82.75 375	62.58 299	81.44 358	86.35 317	72.16 212	74.74 307	82.89 384	46.20 385	92.02 255	68.85 264	81.09 288	91.30 213

MonoMVSNet		76.49 294	75.80 283	78.58 334	81.55 395	58.45 359	86.36 239	86.22 318	74.87 145	74.73 308	83.73 366	51.79 323	88.73 353	70.78 238	72.15 404	88.55 325

sss		73.60 334	73.64 322	73.51 404	82.80 374	55.01 414	76.12 425	81.69 384	62.47 393	74.68 309	85.85 314	57.32 260	78.11 441	60.86 342	80.93 289	87.39 352

testing222		74.04 328	72.66 334	78.19 343	87.89 210	55.36 409	81.06 364	79.20 418	71.30 229	74.65 310	83.57 372	39.11 435	88.67 355	51.43 414	85.75 218	90.53 243

test_fmvs2		68.35 399	67.48 398	70.98 428	69.50 474	51.95 437	80.05 383	76.38 439	49.33 463	74.65 310	84.38 347	23.30 475	75.40 463	74.51 198	75.17 378	85.60 397

BH-w/o		78.21 252	77.33 257	80.84 279	88.81 167	65.13 228	84.87 282	87.85 276	69.75 276	74.52 312	84.74 342	61.34 215	93.11 207	58.24 369	85.84 216	84.27 417

WBMVS		73.43 336	72.81 332	75.28 383	87.91 209	50.99 448	78.59 405	81.31 390	65.51 349	74.47 313	84.83 339	46.39 379	86.68 378	58.41 366	77.86 330	88.17 335

FMVSNet1		77.44 274	76.12 282	81.40 262	86.81 267	63.01 291	88.39 154	89.28 220	70.49 256	74.39 314	87.28 271	49.06 361	91.11 297	60.91 341	78.52 320	90.09 264

cl____		77.72 267	76.76 269	80.58 285	82.49 382	60.48 340	83.09 332	87.87 274	69.22 289	74.38 315	85.22 331	62.10 199	91.53 280	71.09 236	75.41 371	89.73 284

DIV-MVS_self_test		77.72 267	76.76 269	80.58 285	82.48 383	60.48 340	83.09 332	87.86 275	69.22 289	74.38 315	85.24 329	62.10 199	91.53 280	71.09 236	75.40 372	89.74 283

114514_t		80.68 186	79.51 197	84.20 152	94.09 42	67.27 176	89.64 96	91.11 152	58.75 427	74.08 317	90.72 168	58.10 251	95.04 99	69.70 254	89.42 140	90.30 254

myMVS_eth3d28		73.62 333	73.53 323	73.90 401	88.20 193	47.41 461	78.06 412	79.37 415	74.29 160	73.98 318	84.29 350	44.67 397	83.54 411	51.47 412	87.39 183	90.74 234

WR-MVS_H		78.51 246	78.49 220	78.56 335	88.02 204	56.38 395	88.43 151	92.67 72	77.14 65	73.89 319	87.55 266	66.25 145	89.24 342	58.92 360	73.55 393	90.06 268

UBG		73.08 347	72.27 339	75.51 379	88.02 204	51.29 446	78.35 409	77.38 432	65.52 347	73.87 320	82.36 391	45.55 392	86.48 381	55.02 393	84.39 240	88.75 317

ETVMVS		72.25 360	71.05 355	75.84 373	87.77 220	51.91 438	79.39 390	74.98 444	69.26 287	73.71 321	82.95 382	40.82 426	86.14 384	46.17 445	84.43 239	89.47 289

SSC-MVS3.2		73.35 342	73.39 324	73.23 405	85.30 307	49.01 456	74.58 440	81.57 385	75.21 130	73.68 322	85.58 321	52.53 302	82.05 422	54.33 398	77.69 334	88.63 322

WB-MVSnew		71.96 364	71.65 344	72.89 411	84.67 326	51.88 439	82.29 343	77.57 428	62.31 394	73.67 323	83.00 381	53.49 298	81.10 429	45.75 448	82.13 277	85.70 396

tpm2		73.26 344	71.46 346	78.63 331	83.34 354	56.71 389	80.65 372	80.40 404	56.63 442	73.55 324	82.02 398	51.80 322	91.24 292	56.35 388	78.42 325	87.95 337

CP-MVSNet		78.22 251	78.34 225	77.84 351	87.83 214	54.54 418	87.94 173	91.17 149	77.65 46	73.48 325	88.49 238	62.24 197	88.43 359	62.19 328	74.07 386	90.55 242

pm-mvs1		77.25 279	76.68 273	78.93 327	84.22 332	58.62 358	86.41 234	88.36 262	71.37 226	73.31 326	88.01 254	61.22 219	89.15 345	64.24 303	73.01 398	89.03 303

PS-CasMVS		78.01 260	78.09 231	77.77 353	87.71 224	54.39 420	88.02 169	91.22 146	77.50 54	73.26 327	88.64 233	60.73 225	88.41 360	61.88 332	73.88 390	90.53 243

CVMVSNet		72.99 349	72.58 335	74.25 396	84.28 330	50.85 449	86.41 234	83.45 358	44.56 469	73.23 328	87.54 267	49.38 355	85.70 389	65.90 289	78.44 322	86.19 385

PEN-MVS		77.73 266	77.69 247	77.84 351	87.07 262	53.91 423	87.91 175	91.18 148	77.56 51	73.14 329	88.82 228	61.23 218	89.17 344	59.95 348	72.37 401	90.43 247

1112_ss		77.40 276	76.43 277	80.32 291	89.11 160	60.41 342	83.65 317	87.72 280	62.13 397	73.05 330	86.72 287	62.58 190	89.97 328	62.11 331	80.80 293	90.59 241

usedtu_dtu_shiyan1		76.43 295	75.32 297	79.76 309	83.00 366	60.72 334	81.74 350	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 320	62.39 323	79.40 311	88.31 329

FE-MVSNET3		76.43 295	75.32 297	79.76 309	83.00 366	60.72 334	81.74 350	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 320	62.39 323	79.40 311	88.31 329

mamv4		76.81 286	78.23 230	72.54 415	86.12 286	65.75 210	78.76 401	82.07 380	64.12 370	72.97 333	91.02 162	67.97 123	68.08 480	83.04 89	78.02 329	83.80 425

tpm		72.37 357	71.71 343	74.35 394	82.19 386	52.00 436	79.22 393	77.29 433	64.56 363	72.95 334	83.68 369	51.35 326	83.26 415	58.33 368	75.80 361	87.81 341

cascas		76.72 288	74.64 306	82.99 214	85.78 293	65.88 204	82.33 342	89.21 227	60.85 406	72.74 335	81.02 405	47.28 370	93.75 162	67.48 275	85.02 226	89.34 294

CR-MVSNet		73.37 339	71.27 351	79.67 314	81.32 402	65.19 226	75.92 427	80.30 405	59.92 414	72.73 336	81.19 402	52.50 304	86.69 377	59.84 349	77.71 332	87.11 367

RPMNet		73.51 335	70.49 364	82.58 237	81.32 402	65.19 226	75.92 427	92.27 93	57.60 436	72.73 336	76.45 447	52.30 307	95.43 77	48.14 436	77.71 332	87.11 367

testing11		75.14 317	74.01 315	78.53 337	88.16 195	56.38 395	80.74 370	80.42 403	70.67 246	72.69 338	83.72 367	43.61 407	89.86 329	62.29 327	83.76 249	89.36 293

DTE-MVSNet		76.99 282	76.80 267	77.54 360	86.24 281	53.06 433	87.52 185	90.66 165	77.08 69	72.50 339	88.67 232	60.48 233	89.52 336	57.33 377	70.74 413	90.05 269

Test_1112_low_res		76.40 298	75.44 291	79.27 321	89.28 149	58.09 363	81.69 353	87.07 300	59.53 418	72.48 340	86.67 292	61.30 216	89.33 339	60.81 343	80.15 302	90.41 248

v7n		78.97 234	77.58 250	83.14 205	83.45 352	65.51 214	88.32 159	91.21 147	73.69 175	72.41 341	86.32 305	57.93 252	93.81 157	69.18 259	75.65 363	90.11 262

SCA		74.22 325	72.33 338	79.91 303	84.05 337	62.17 309	79.96 385	79.29 417	66.30 337	72.38 342	80.13 417	51.95 316	88.60 356	59.25 356	77.67 335	88.96 308

CNLPA		78.08 256	76.79 268	81.97 250	90.40 109	71.07 70	87.59 184	84.55 340	66.03 341	72.38 342	89.64 201	57.56 257	86.04 386	59.61 352	83.35 261	88.79 315

reproduce_monomvs		75.40 314	74.38 312	78.46 340	83.92 340	57.80 372	83.78 313	86.94 303	73.47 183	72.25 344	84.47 344	38.74 436	89.27 341	75.32 191	70.53 414	88.31 329

NR-MVSNet		80.23 203	79.38 200	82.78 229	87.80 215	63.34 284	86.31 240	91.09 153	79.01 31	72.17 345	89.07 217	67.20 132	92.81 223	66.08 288	75.65 363	92.20 181

OpenMVS	72.83 10	79.77 210	78.33 226	84.09 159	85.17 309	69.91 93	90.57 69	90.97 155	66.70 328	72.17 345	91.91 121	54.70 285	93.96 144	61.81 334	90.95 112	88.41 328

MVS		78.19 254	76.99 263	81.78 252	85.66 295	66.99 182	84.66 287	90.47 171	55.08 449	72.02 347	85.27 328	63.83 171	94.11 141	66.10 287	89.80 133	84.24 418

XVG-ACMP-BASELINE		76.11 302	74.27 314	81.62 255	83.20 359	64.67 246	83.60 320	89.75 199	69.75 276	71.85 348	87.09 280	32.78 455	92.11 251	69.99 251	80.43 299	88.09 336

PatchmatchNet		73.12 346	71.33 349	78.49 339	83.18 360	60.85 332	79.63 387	78.57 422	64.13 369	71.73 349	79.81 422	51.20 332	85.97 387	57.40 376	76.36 357	88.66 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		72.39 355	72.13 340	73.18 409	80.54 409	49.91 453	79.91 386	79.08 419	63.11 382	71.69 350	79.95 419	55.32 277	82.77 418	65.66 292	73.89 389	86.87 372

mvs5depth		69.45 388	67.45 399	75.46 381	73.93 457	55.83 403	79.19 394	83.23 361	66.89 324	71.63 351	83.32 375	33.69 454	85.09 397	59.81 350	55.34 466	85.46 400

TransMVSNet (Re)		75.39 315	74.56 308	77.86 350	85.50 302	57.10 383	86.78 220	86.09 322	72.17 211	71.53 352	87.34 270	63.01 184	89.31 340	56.84 383	61.83 451	87.17 363

Fast-Effi-MVS+-dtu		78.02 259	76.49 275	82.62 235	83.16 362	66.96 185	86.94 212	87.45 286	72.45 204	71.49 353	84.17 357	54.79 284	91.58 272	67.61 273	80.31 300	89.30 295

sc_t1		72.19 361	69.51 372	80.23 294	84.81 319	61.09 326	84.68 286	80.22 407	60.70 407	71.27 354	83.58 371	36.59 446	89.24 342	60.41 344	63.31 446	90.37 250

PAPM		77.68 270	76.40 279	81.51 258	87.29 250	61.85 315	83.78 313	89.59 205	64.74 361	71.23 355	88.70 230	62.59 189	93.66 165	52.66 406	87.03 191	89.01 304

tfpnnormal		74.39 322	73.16 328	78.08 346	86.10 288	58.05 364	84.65 289	87.53 283	70.32 260	71.22 356	85.63 319	54.97 279	89.86 329	43.03 456	75.02 379	86.32 382

RPSCF		73.23 345	71.46 346	78.54 336	82.50 381	59.85 347	82.18 345	82.84 373	58.96 423	71.15 357	89.41 213	45.48 395	84.77 401	58.82 362	71.83 407	91.02 223

PatchT		68.46 398	67.85 389	70.29 430	80.70 407	43.93 474	72.47 446	74.88 445	60.15 412	70.55 358	76.57 446	49.94 348	81.59 424	50.58 416	74.83 381	85.34 402

CL-MVSNet_self_test		72.37 357	71.46 346	75.09 385	79.49 425	53.53 425	80.76 369	85.01 336	69.12 293	70.51 359	82.05 397	57.92 253	84.13 405	52.27 408	66.00 433	87.60 345

IterMVS-SCA-FT		75.43 312	73.87 319	80.11 298	82.69 377	64.85 243	81.57 355	83.47 357	69.16 292	70.49 360	84.15 358	51.95 316	88.15 362	69.23 258	72.14 405	87.34 357

miper_lstm_enhance		74.11 327	73.11 329	77.13 365	80.11 414	59.62 350	72.23 447	86.92 305	66.76 327	70.40 361	82.92 383	56.93 265	82.92 416	69.06 261	72.63 400	88.87 311

gg-mvs-nofinetune		69.95 384	67.96 387	75.94 372	83.07 363	54.51 419	77.23 420	70.29 459	63.11 382	70.32 362	62.33 473	43.62 406	88.69 354	53.88 400	87.76 177	84.62 415

DP-MVS		76.78 287	74.57 307	83.42 192	93.29 52	69.46 104	88.55 149	83.70 352	63.98 375	70.20 363	88.89 226	54.01 293	94.80 111	46.66 441	81.88 281	86.01 390

pmmvs6		74.69 320	73.39 324	78.61 332	81.38 399	57.48 378	86.64 226	87.95 272	64.99 360	70.18 364	86.61 294	50.43 341	89.52 336	62.12 330	70.18 416	88.83 313

PVSNet	64.34 18	72.08 363	70.87 359	75.69 375	86.21 282	56.44 393	74.37 441	80.73 395	62.06 398	70.17 365	82.23 395	42.86 411	83.31 414	54.77 395	84.45 238	87.32 358

1314		76.53 290	75.30 299	80.21 295	83.93 339	62.32 307	84.66 287	88.81 245	60.23 411	70.16 366	84.07 359	55.30 278	90.73 316	67.37 276	83.21 264	87.59 347

Patchmtry		70.74 373	69.16 376	75.49 380	80.72 406	54.07 422	74.94 438	80.30 405	58.34 428	70.01 367	81.19 402	52.50 304	86.54 379	53.37 403	71.09 412	85.87 395

EPMVS		69.02 391	68.16 383	71.59 420	79.61 423	49.80 455	77.40 418	66.93 469	62.82 389	70.01 367	79.05 427	45.79 389	77.86 443	56.58 386	75.26 376	87.13 366

IterMVS		74.29 323	72.94 331	78.35 341	81.53 396	63.49 280	81.58 354	82.49 375	68.06 315	69.99 369	83.69 368	51.66 325	85.54 392	65.85 290	71.64 408	86.01 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		72.94 350	72.43 336	74.48 392	81.35 400	58.04 365	78.38 406	77.46 429	66.66 329	69.95 370	79.00 429	48.06 366	79.24 435	66.13 285	84.83 229	86.15 386

test-mter		71.41 366	70.39 367	74.48 392	81.35 400	58.04 365	78.38 406	77.46 429	60.32 410	69.95 370	79.00 429	36.08 449	79.24 435	66.13 285	84.83 229	86.15 386

pmmvs4		74.03 330	71.91 341	80.39 288	81.96 388	68.32 135	81.45 357	82.14 378	59.32 419	69.87 372	85.13 333	52.40 306	88.13 363	60.21 347	74.74 382	84.73 414

PLC	70.83 11	78.05 258	76.37 280	83.08 209	91.88 83	67.80 156	88.19 163	89.46 209	64.33 368	69.87 372	88.38 241	53.66 295	93.58 166	58.86 361	82.73 270	87.86 340
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	69.57 13	76.25 300	74.54 309	81.41 261	88.60 179	64.38 256	79.24 392	89.12 233	70.76 245	69.79 374	87.86 257	49.09 360	93.20 200	56.21 389	80.16 301	86.65 379
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
LS3D		76.95 284	74.82 304	83.37 195	90.45 107	67.36 172	89.15 120	86.94 303	61.87 400	69.52 375	90.61 174	51.71 324	94.53 122	46.38 444	86.71 197	88.21 334

IB-MVS	68.01 15	75.85 306	73.36 326	83.31 196	84.76 321	66.03 197	83.38 325	85.06 334	70.21 264	69.40 376	81.05 404	45.76 390	94.66 118	65.10 296	75.49 366	89.25 296
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
PatchMatch-RL		72.38 356	70.90 358	76.80 368	88.60 179	67.38 171	79.53 388	76.17 441	62.75 390	69.36 377	82.00 399	45.51 393	84.89 400	53.62 401	80.58 296	78.12 459

MDTV_nov1_ep13			69.97 370		83.18 360	53.48 426	77.10 422	80.18 409	60.45 408	69.33 378	80.44 411	48.89 364	86.90 376	51.60 411	78.51 321

dmvs_re		71.14 368	70.58 362	72.80 412	81.96 388	59.68 349	75.60 431	79.34 416	68.55 307	69.27 379	80.72 410	49.42 354	76.54 449	52.56 407	77.79 331	82.19 442

testing3		68.56 396	67.67 395	71.22 426	87.33 246	42.87 476	83.06 335	71.54 456	70.36 257	69.08 380	84.38 347	30.33 462	85.69 390	37.50 469	75.45 370	85.09 409

D2MVS		74.82 319	73.21 327	79.64 315	79.81 419	62.56 301	80.34 378	87.35 288	64.37 367	68.86 381	82.66 388	46.37 381	90.10 325	67.91 271	81.24 286	86.25 383

PMMVS		69.34 389	68.67 378	71.35 424	75.67 450	62.03 312	75.17 433	73.46 451	50.00 462	68.68 382	79.05 427	52.07 314	78.13 440	61.16 340	82.77 269	73.90 466

Patchmatch-RL test		70.24 380	67.78 393	77.61 357	77.43 442	59.57 352	71.16 451	70.33 458	62.94 386	68.65 383	72.77 464	50.62 338	85.49 393	69.58 256	66.58 430	87.77 342

blended_shiyan8		73.38 337	71.17 353	80.02 300	78.36 433	61.51 321	82.43 340	87.28 289	65.40 351	68.61 384	77.53 442	51.91 319	91.00 307	63.28 310	65.76 434	87.53 349

MS-PatchMatch		73.83 331	72.67 333	77.30 363	83.87 341	66.02 198	81.82 348	84.66 338	61.37 404	68.61 384	82.82 386	47.29 369	88.21 361	59.27 355	84.32 241	77.68 460

blended_shiyan6		73.38 337	71.17 353	80.01 301	78.36 433	61.48 322	82.43 340	87.27 292	65.40 351	68.56 386	77.55 441	51.94 318	91.01 304	63.27 311	65.76 434	87.55 348

tpm cat1		70.57 375	68.31 381	77.35 362	82.41 384	57.95 368	78.08 411	80.22 407	52.04 456	68.54 387	77.66 440	52.00 315	87.84 367	51.77 409	72.07 406	86.25 383

SD_0403		74.65 321	74.77 305	74.29 395	86.20 283	47.42 460	83.71 315	85.12 332	69.30 285	68.50 388	87.95 256	59.40 241	86.05 385	49.38 426	83.35 261	89.40 291

mvsany_test1		62.30 427	61.26 431	65.41 449	69.52 473	54.86 415	66.86 468	49.78 489	46.65 466	68.50 388	83.21 377	49.15 359	66.28 481	56.93 382	60.77 454	75.11 465

blend_shiyan4		72.29 359	69.65 371	80.21 295	78.24 436	62.16 310	82.29 343	87.27 292	65.41 350	68.43 390	76.42 449	39.91 430	91.23 293	63.21 312	65.66 439	87.22 361

wanda-best-256-512		72.94 350	70.66 360	79.79 307	77.80 438	61.03 328	81.31 360	87.15 297	65.18 354	68.09 391	76.28 450	51.32 327	90.97 308	63.06 314	65.76 434	87.35 354

FE-blended-shiyan7		72.94 350	70.66 360	79.79 307	77.80 438	61.03 328	81.31 360	87.15 297	65.18 354	68.09 391	76.28 450	51.32 327	90.97 308	63.06 314	65.76 434	87.35 354

usedtu_blend_shiyan5		73.29 343	70.96 357	80.25 293	77.80 438	62.16 310	84.44 297	87.38 287	64.41 365	68.09 391	76.28 450	51.32 327	91.23 293	63.21 312	65.76 434	87.35 354

TESTMET0.1,1		69.89 385	69.00 377	72.55 414	79.27 428	56.85 385	78.38 406	74.71 448	57.64 435	68.09 391	77.19 444	37.75 442	76.70 448	63.92 304	84.09 244	84.10 421

MIMVSNet		70.69 374	69.30 373	74.88 388	84.52 327	56.35 397	75.87 429	79.42 414	64.59 362	67.76 395	82.41 390	41.10 423	81.54 425	46.64 443	81.34 284	86.75 376

ACMH+	68.96 14	76.01 304	74.01 315	82.03 248	88.60 179	65.31 224	88.86 130	87.55 282	70.25 263	67.75 396	87.47 269	41.27 422	93.19 202	58.37 367	75.94 360	87.60 345

LCM-MVSNet-Re		77.05 281	76.94 264	77.36 361	87.20 251	51.60 442	80.06 382	80.46 401	75.20 131	67.69 397	86.72 287	62.48 191	88.98 348	63.44 307	89.25 141	91.51 205

ITE_SJBPF				78.22 342	81.77 391	60.57 338		83.30 359	69.25 288	67.54 398	87.20 276	36.33 448	87.28 374	54.34 397	74.62 383	86.80 374

test_fmvs3		63.36 425	61.82 427	67.98 443	62.51 483	46.96 464	77.37 419	74.03 450	45.24 468	67.50 399	78.79 432	12.16 487	72.98 472	72.77 218	66.02 432	83.99 422

pmmvs5		71.55 365	70.20 369	75.61 376	77.83 437	56.39 394	81.74 350	80.89 392	57.76 434	67.46 400	84.49 343	49.26 358	85.32 396	57.08 379	75.29 375	85.11 408

MVP-Stereo		76.12 301	74.46 311	81.13 272	85.37 305	69.79 95	84.42 300	87.95 272	65.03 358	67.46 400	85.33 327	53.28 300	91.73 268	58.01 371	83.27 263	81.85 445
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tt0320		70.49 378	68.03 386	77.89 349	84.78 320	59.12 355	83.55 321	80.44 402	58.13 431	67.43 402	80.41 413	39.26 433	87.54 371	55.12 392	63.18 447	86.99 370

test_0402		72.79 354	70.44 365	79.84 305	88.13 198	65.99 201	85.93 252	84.29 344	65.57 346	67.40 403	85.49 323	46.92 373	92.61 227	35.88 471	74.38 385	80.94 450

GG-mvs-BLEND				75.38 382	81.59 394	55.80 404	79.32 391	69.63 461		67.19 404	73.67 462	43.24 408	88.90 352	50.41 417	84.50 234	81.45 447

tpmvs		71.09 369	69.29 374	76.49 369	82.04 387	56.04 400	78.92 399	81.37 389	64.05 373	67.18 405	78.28 435	49.74 351	89.77 331	49.67 425	72.37 401	83.67 426

tt0320-xc		70.11 382	67.45 399	78.07 347	85.33 306	59.51 353	83.28 327	78.96 420	58.77 425	67.10 406	80.28 415	36.73 445	87.42 372	56.83 384	59.77 458	87.29 359

OurMVSNet-221017-0		74.26 324	72.42 337	79.80 306	83.76 344	59.59 351	85.92 253	86.64 310	66.39 336	66.96 407	87.58 263	39.46 431	91.60 271	65.76 291	69.27 419	88.22 333

baseline2		75.70 307	73.83 320	81.30 265	83.26 356	61.79 317	82.57 339	80.65 396	66.81 325	66.88 408	83.42 374	57.86 254	92.19 249	63.47 306	79.57 307	89.91 275

F-COLMAP		76.38 299	74.33 313	82.50 238	89.28 149	66.95 186	88.41 153	89.03 235	64.05 373	66.83 409	88.61 234	46.78 376	92.89 217	57.48 374	78.55 319	87.67 343

ACMH	67.68 16	75.89 305	73.93 317	81.77 253	88.71 176	66.61 189	88.62 145	89.01 237	69.81 272	66.78 410	86.70 291	41.95 419	91.51 282	55.64 390	78.14 328	87.17 363
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Syy-MVS		68.05 400	67.85 389	68.67 439	84.68 323	40.97 482	78.62 403	73.08 453	66.65 332	66.74 411	79.46 424	52.11 312	82.30 420	32.89 474	76.38 355	82.75 437

myMVS_eth3d		67.02 407	66.29 407	69.21 434	84.68 323	42.58 477	78.62 403	73.08 453	66.65 332	66.74 411	79.46 424	31.53 459	82.30 420	39.43 466	76.38 355	82.75 437

test0.0.03 1		68.00 401	67.69 394	68.90 436	77.55 441	47.43 459	75.70 430	72.95 455	66.66 329	66.56 413	82.29 394	48.06 366	75.87 458	44.97 452	74.51 384	83.41 428

MDTV_nov1_ep13_2view						37.79 485	75.16 434		55.10 448	66.53 414		49.34 356		53.98 399		87.94 338

KD-MVS_2432*1600		66.22 414	63.89 417	73.21 406	75.47 453	53.42 427	70.76 454	84.35 342	64.10 371	66.52 415	78.52 433	34.55 452	84.98 398	50.40 418	50.33 473	81.23 448

miper_refine_blended		66.22 414	63.89 417	73.21 406	75.47 453	53.42 427	70.76 454	84.35 342	64.10 371	66.52 415	78.52 433	34.55 452	84.98 398	50.40 418	50.33 473	81.23 448

ET-MVSNet_ETH3D		78.63 242	76.63 274	84.64 122	86.73 270	69.47 102	85.01 279	84.61 339	69.54 280	66.51 417	86.59 295	50.16 344	91.75 266	76.26 176	84.24 242	92.69 158

EU-MVSNet		68.53 397	67.61 396	71.31 425	78.51 432	47.01 463	84.47 293	84.27 345	42.27 472	66.44 418	84.79 341	40.44 427	83.76 407	58.76 363	68.54 424	83.17 430

EPNet_dtu		75.46 311	74.86 303	77.23 364	82.57 380	54.60 417	86.89 214	83.09 365	71.64 218	66.25 419	85.86 313	55.99 273	88.04 364	54.92 394	86.55 199	89.05 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IMVS_0404		77.16 280	76.42 278	79.37 319	87.13 254	63.59 274	77.12 421	89.33 214	70.51 252	66.22 420	89.03 219	50.36 342	82.78 417	72.56 222	85.56 220	91.74 196

Anonymous20231206		68.60 394	67.80 392	71.02 427	80.23 413	50.75 450	78.30 410	80.47 400	56.79 441	66.11 421	82.63 389	46.35 382	78.95 437	43.62 454	75.70 362	83.36 429

SixPastTwentyTwo		73.37 339	71.26 352	79.70 312	85.08 314	57.89 369	85.57 260	83.56 355	71.03 238	65.66 422	85.88 312	42.10 417	92.57 230	59.11 358	63.34 445	88.65 321

MSDG		73.36 341	70.99 356	80.49 287	84.51 328	65.80 207	80.71 371	86.13 321	65.70 344	65.46 423	83.74 365	44.60 398	90.91 310	51.13 415	76.89 342	84.74 413

OpenMVS_ROB	64.09 19	70.56 376	68.19 382	77.65 356	80.26 411	59.41 354	85.01 279	82.96 370	58.76 426	65.43 424	82.33 392	37.63 443	91.23 293	45.34 451	76.03 359	82.32 440

ppachtmachnet_test		70.04 383	67.34 401	78.14 344	79.80 420	61.13 324	79.19 394	80.59 397	59.16 421	65.27 425	79.29 426	46.75 377	87.29 373	49.33 427	66.72 428	86.00 392

ADS-MVSNet2		66.20 416	63.33 420	74.82 389	79.92 416	58.75 357	67.55 466	75.19 443	53.37 453	65.25 426	75.86 454	42.32 414	80.53 432	41.57 461	68.91 421	85.18 405

ADS-MVSNet		64.36 422	62.88 424	68.78 438	79.92 416	47.17 462	67.55 466	71.18 457	53.37 453	65.25 426	75.86 454	42.32 414	73.99 469	41.57 461	68.91 421	85.18 405

testgi		66.67 410	66.53 406	67.08 446	75.62 451	41.69 481	75.93 426	76.50 438	66.11 338	65.20 428	86.59 295	35.72 450	74.71 465	43.71 453	73.38 396	84.84 412

PM-MVS		66.41 412	64.14 415	73.20 408	73.92 458	56.45 392	78.97 398	64.96 475	63.88 377	64.72 429	80.24 416	19.84 479	83.44 413	66.24 284	64.52 443	79.71 456

FE-MVSNET2		72.88 353	71.28 350	77.67 354	78.30 435	57.78 373	84.43 298	88.92 243	69.56 279	64.61 430	81.67 400	46.73 378	88.54 358	59.33 354	67.99 425	86.69 378

JIA-IIPM		66.32 413	62.82 425	76.82 367	77.09 444	61.72 318	65.34 474	75.38 442	58.04 433	64.51 431	62.32 474	42.05 418	86.51 380	51.45 413	69.22 420	82.21 441

ambc				75.24 384	73.16 465	50.51 451	63.05 481	87.47 285		64.28 432	77.81 439	17.80 481	89.73 333	57.88 372	60.64 455	85.49 399

EG-PatchMatch MVS		74.04 328	71.82 342	80.71 282	84.92 317	67.42 168	85.86 255	88.08 266	66.04 340	64.22 433	83.85 361	35.10 451	92.56 231	57.44 375	80.83 292	82.16 443

UWE-MVS-28		65.32 417	64.93 411	66.49 447	78.70 430	38.55 484	77.86 416	64.39 476	62.00 399	64.13 434	83.60 370	41.44 420	76.00 456	31.39 476	80.89 290	84.92 410

dp		66.80 408	65.43 409	70.90 429	79.74 422	48.82 457	75.12 436	74.77 446	59.61 416	64.08 435	77.23 443	42.89 410	80.72 431	48.86 430	66.58 430	83.16 431

KD-MVS_self_test		68.81 392	67.59 397	72.46 416	74.29 456	45.45 466	77.93 414	87.00 301	63.12 381	63.99 436	78.99 431	42.32 414	84.77 401	56.55 387	64.09 444	87.16 365

pmmvs-eth3d		70.50 377	67.83 391	78.52 338	77.37 443	66.18 195	81.82 348	81.51 386	58.90 424	63.90 437	80.42 412	42.69 412	86.28 383	58.56 364	65.30 441	83.11 432

COLMAP_ROB	66.92 17	73.01 348	70.41 366	80.81 280	87.13 254	65.63 211	88.30 160	84.19 347	62.96 385	63.80 438	87.69 261	38.04 441	92.56 231	46.66 441	74.91 380	84.24 418
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		69.50 387	67.96 387	74.15 397	82.97 371	55.35 410	80.01 384	82.12 379	62.56 392	63.02 439	81.53 401	36.92 444	81.92 423	48.42 431	74.06 387	85.17 407

test20.03		67.45 403	66.95 404	68.94 435	75.48 452	44.84 472	77.50 417	77.67 427	66.66 329	63.01 440	83.80 363	47.02 372	78.40 439	42.53 460	68.86 423	83.58 427

K. test v3		71.19 367	68.51 379	79.21 323	83.04 365	57.78 373	84.35 302	76.91 436	72.90 200	62.99 441	82.86 385	39.27 432	91.09 302	61.65 335	52.66 469	88.75 317

our_test_3		69.14 390	67.00 403	75.57 377	79.80 420	58.80 356	77.96 413	77.81 426	59.55 417	62.90 442	78.25 436	47.43 368	83.97 406	51.71 410	67.58 427	83.93 423

CHOSEN 280x420		66.51 411	64.71 413	71.90 418	81.45 397	63.52 279	57.98 483	68.95 465	53.57 452	62.59 443	76.70 445	46.22 384	75.29 464	55.25 391	79.68 306	76.88 462

ttmdpeth		59.91 431	57.10 435	68.34 441	67.13 478	46.65 465	74.64 439	67.41 468	48.30 464	62.52 444	85.04 337	20.40 477	75.93 457	42.55 459	45.90 479	82.44 439

Anonymous20240521		68.80 393	67.22 402	73.55 403	74.33 455	54.11 421	83.18 329	85.61 327	58.15 430	61.68 445	80.94 407	30.71 461	81.27 428	57.00 381	73.34 397	85.28 403

USDC		70.33 379	68.37 380	76.21 371	80.60 408	56.23 398	79.19 394	86.49 313	60.89 405	61.29 446	85.47 324	31.78 458	89.47 338	53.37 403	76.21 358	82.94 436

lessismore_v0				78.97 326	81.01 405	57.15 382		65.99 471		61.16 447	82.82 386	39.12 434	91.34 289	59.67 351	46.92 476	88.43 327

UnsupCasMVSNet_eth		67.33 404	65.99 408	71.37 422	73.48 462	51.47 444	75.16 434	85.19 331	65.20 353	60.78 448	80.93 409	42.35 413	77.20 445	57.12 378	53.69 468	85.44 401

FE-MVSNET		67.25 406	65.33 410	73.02 410	75.86 448	52.54 434	80.26 381	80.56 398	63.80 378	60.39 449	79.70 423	41.41 421	84.66 403	43.34 455	62.62 449	81.86 444

dmvs_testset		62.63 426	64.11 416	58.19 457	78.55 431	24.76 495	75.28 432	65.94 472	67.91 316	60.34 450	76.01 453	53.56 296	73.94 470	31.79 475	67.65 426	75.88 464

AllTest		70.96 370	68.09 385	79.58 316	85.15 311	63.62 270	84.58 291	79.83 410	62.31 394	60.32 451	86.73 285	32.02 456	88.96 350	50.28 420	71.57 409	86.15 386

TestCases				79.58 316	85.15 311	63.62 270		79.83 410	62.31 394	60.32 451	86.73 285	32.02 456	88.96 350	50.28 420	71.57 409	86.15 386

Patchmatch-test		64.82 420	63.24 421	69.57 432	79.42 426	49.82 454	63.49 480	69.05 464	51.98 458	59.95 453	80.13 417	50.91 334	70.98 473	40.66 463	73.57 392	87.90 339

MIMVSNet1		68.58 395	66.78 405	73.98 400	80.07 415	51.82 440	80.77 368	84.37 341	64.40 366	59.75 454	82.16 396	36.47 447	83.63 409	42.73 457	70.33 415	86.48 381

test_vis1_rt		60.28 430	58.42 433	65.84 448	67.25 477	55.60 407	70.44 456	60.94 481	44.33 470	59.00 455	66.64 471	24.91 470	68.67 478	62.80 317	69.48 417	73.25 467

LF4IMVS		64.02 423	62.19 426	69.50 433	70.90 471	53.29 430	76.13 424	77.18 434	52.65 455	58.59 456	80.98 406	23.55 474	76.52 450	53.06 405	66.66 429	78.68 458

PVSNet_0	57.27 20	61.67 429	59.27 432	68.85 437	79.61 423	57.44 379	68.01 464	73.44 452	55.93 446	58.54 457	70.41 469	44.58 399	77.55 444	47.01 440	35.91 481	71.55 469

TDRefinement		67.49 402	64.34 414	76.92 366	73.47 463	61.07 327	84.86 283	82.98 369	59.77 415	58.30 458	85.13 333	26.06 467	87.89 366	47.92 438	60.59 456	81.81 446

mvsany_test3		53.99 438	51.45 443	61.61 454	55.51 488	44.74 473	63.52 479	45.41 493	43.69 471	58.11 459	76.45 447	17.99 480	63.76 484	54.77 395	47.59 475	76.34 463

UnsupCasMVSNet_bld		63.70 424	61.53 430	70.21 431	73.69 460	51.39 445	72.82 445	81.89 381	55.63 447	57.81 460	71.80 466	38.67 437	78.61 438	49.26 428	52.21 471	80.63 452

DSMNet-mixed		57.77 434	56.90 436	60.38 455	67.70 476	35.61 486	69.18 460	53.97 487	32.30 485	57.49 461	79.88 420	40.39 428	68.57 479	38.78 467	72.37 401	76.97 461

N_pmnet		52.79 442	53.26 440	51.40 467	78.99 429	7.68 501	69.52 458	3.89 500	51.63 459	57.01 462	74.98 458	40.83 425	65.96 482	37.78 468	64.67 442	80.56 454

new-patchmatchnet		61.73 428	61.73 428	61.70 453	72.74 468	24.50 496	69.16 461	78.03 425	61.40 402	56.72 463	75.53 457	38.42 438	76.48 451	45.95 447	57.67 459	84.13 420

CMPMVS	51.72 21	70.19 381	68.16 383	76.28 370	73.15 466	57.55 377	79.47 389	83.92 349	48.02 465	56.48 464	84.81 340	43.13 409	86.42 382	62.67 321	81.81 282	84.89 411
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
usedtu_dtu_shiyan2		64.75 421	61.63 429	74.10 398	70.64 472	53.18 432	82.10 347	81.27 391	56.22 445	56.39 465	74.67 459	27.94 465	83.56 410	42.71 458	62.73 448	85.57 398

TinyColmap		67.30 405	64.81 412	74.76 390	81.92 390	56.68 390	80.29 379	81.49 387	60.33 409	56.27 466	83.22 376	24.77 471	87.66 370	45.52 449	69.47 418	79.95 455

test_f		52.09 443	50.82 444	55.90 461	53.82 491	42.31 480	59.42 482	58.31 485	36.45 480	56.12 467	70.96 468	12.18 486	57.79 487	53.51 402	56.57 462	67.60 472

YYNet1		65.03 418	62.91 423	71.38 421	75.85 449	56.60 391	69.12 462	74.66 449	57.28 439	54.12 468	77.87 438	45.85 388	74.48 466	49.95 423	61.52 453	83.05 433

MDA-MVSNet_test_wron		65.03 418	62.92 422	71.37 422	75.93 446	56.73 387	69.09 463	74.73 447	57.28 439	54.03 469	77.89 437	45.88 387	74.39 467	49.89 424	61.55 452	82.99 435

pmmvs3		57.79 433	54.26 438	68.37 440	64.02 482	56.72 388	75.12 436	65.17 473	40.20 474	52.93 470	69.86 470	20.36 478	75.48 461	45.45 450	55.25 467	72.90 468

MVS-HIRNet		59.14 432	57.67 434	63.57 451	81.65 392	43.50 475	71.73 448	65.06 474	39.59 476	51.43 471	57.73 479	38.34 439	82.58 419	39.53 464	73.95 388	64.62 475

WB-MVS		54.94 436	54.72 437	55.60 463	73.50 461	20.90 497	74.27 442	61.19 480	59.16 421	50.61 472	74.15 460	47.19 371	75.78 459	17.31 488	35.07 482	70.12 470

MVStest1		56.63 435	52.76 441	68.25 442	61.67 484	53.25 431	71.67 449	68.90 466	38.59 477	50.59 473	83.05 380	25.08 469	70.66 474	36.76 470	38.56 480	80.83 451

MDA-MVSNet-bldmvs		66.68 409	63.66 419	75.75 374	79.28 427	60.56 339	73.92 443	78.35 424	64.43 364	50.13 474	79.87 421	44.02 404	83.67 408	46.10 446	56.86 460	83.03 434

dongtai		45.42 450	45.38 451	45.55 469	73.36 464	26.85 493	67.72 465	34.19 495	54.15 451	49.65 475	56.41 482	25.43 468	62.94 485	19.45 486	28.09 486	46.86 485

SSC-MVS		53.88 439	53.59 439	54.75 465	72.87 467	19.59 498	73.84 444	60.53 482	57.58 437	49.18 476	73.45 463	46.34 383	75.47 462	16.20 491	32.28 484	69.20 471

new_pmnet		50.91 445	50.29 445	52.78 466	68.58 475	34.94 488	63.71 478	56.63 486	39.73 475	44.95 477	65.47 472	21.93 476	58.48 486	34.98 472	56.62 461	64.92 474

test_vis3_rt		49.26 447	47.02 449	56.00 460	54.30 489	45.27 470	66.76 470	48.08 490	36.83 479	44.38 478	53.20 483	7.17 494	64.07 483	56.77 385	55.66 463	58.65 479

kuosan		39.70 454	40.40 455	37.58 472	64.52 481	26.98 491	65.62 473	33.02 496	46.12 467	42.79 479	48.99 485	24.10 473	46.56 493	12.16 494	26.30 487	39.20 486

FPMVS		53.68 440	51.64 442	59.81 456	65.08 480	51.03 447	69.48 459	69.58 462	41.46 473	40.67 480	72.32 465	16.46 483	70.00 477	24.24 484	65.42 440	58.40 480

APD_test1		53.31 441	49.93 446	63.42 452	65.68 479	50.13 452	71.59 450	66.90 470	34.43 482	40.58 481	71.56 467	8.65 492	76.27 453	34.64 473	55.36 465	63.86 476

LCM-MVSNet		54.25 437	49.68 447	67.97 444	53.73 492	45.28 469	66.85 469	80.78 394	35.96 481	39.45 482	62.23 475	8.70 491	78.06 442	48.24 435	51.20 472	80.57 453

PMMVS2		40.82 453	38.86 457	46.69 468	53.84 490	16.45 499	48.61 486	49.92 488	37.49 478	31.67 483	60.97 476	8.14 493	56.42 488	28.42 479	30.72 485	67.19 473

ANet_high		50.57 446	46.10 450	63.99 450	48.67 495	39.13 483	70.99 453	80.85 393	61.39 403	31.18 484	57.70 480	17.02 482	73.65 471	31.22 477	15.89 492	79.18 457

testf1		45.72 448	41.96 452	57.00 458	56.90 486	45.32 467	66.14 471	59.26 483	26.19 486	30.89 485	60.96 477	4.14 495	70.64 475	26.39 482	46.73 477	55.04 481

APD_test2		45.72 448	41.96 452	57.00 458	56.90 486	45.32 467	66.14 471	59.26 483	26.19 486	30.89 485	60.96 477	4.14 495	70.64 475	26.39 482	46.73 477	55.04 481

Gipuma		45.18 451	41.86 454	55.16 464	77.03 445	51.52 443	32.50 489	80.52 399	32.46 484	27.12 487	35.02 488	9.52 490	75.50 460	22.31 485	60.21 457	38.45 487
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 452	40.28 456	55.82 462	40.82 497	42.54 479	65.12 475	63.99 477	34.43 482	24.48 488	57.12 481	3.92 497	76.17 455	17.10 489	55.52 464	48.75 483
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 475	40.17 498	26.90 492		24.59 499	17.44 491	23.95 489	48.61 486	9.77 489	26.48 494	18.06 487	24.47 488	28.83 488

tmp_tt		18.61 460	21.40 463	10.23 477	4.82 500	10.11 500	34.70 488	30.74 498	1.48 494	23.91 490	26.07 491	28.42 464	13.41 496	27.12 480	15.35 493	7.17 491

test_method		31.52 456	29.28 460	38.23 471	27.03 499	6.50 502	20.94 491	62.21 479	4.05 493	22.35 491	52.50 484	13.33 484	47.58 491	27.04 481	34.04 483	60.62 477

MVE	26.22 23	30.37 458	25.89 462	43.81 470	44.55 496	35.46 487	28.87 490	39.07 494	18.20 490	18.58 492	40.18 487	2.68 498	47.37 492	17.07 490	23.78 489	48.60 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 455	30.64 458	35.15 473	52.87 493	27.67 490	57.09 484	47.86 491	24.64 488	16.40 493	33.05 489	11.23 488	54.90 489	14.46 492	18.15 490	22.87 489

EMVS		30.81 457	29.65 459	34.27 474	50.96 494	25.95 494	56.58 485	46.80 492	24.01 489	15.53 494	30.68 490	12.47 485	54.43 490	12.81 493	17.05 491	22.43 490

wuyk23d		16.82 461	15.94 464	19.46 476	58.74 485	31.45 489	39.22 487	3.74 501	6.84 492	6.04 495	2.70 495	1.27 499	24.29 495	10.54 495	14.40 494	2.63 492

EGC-MVSNET		52.07 444	47.05 448	67.14 445	83.51 351	60.71 336	80.50 375	67.75 467	0.07 495	0.43 496	75.85 456	24.26 472	81.54 425	28.82 478	62.25 450	59.16 478

testmvs		6.04 464	8.02 467	0.10 479	0.08 501	0.03 504	69.74 457	0.04 502	0.05 496	0.31 497	1.68 496	0.02 501	0.04 497	0.24 496	0.02 495	0.25 494

test123		6.12 463	8.11 466	0.14 478	0.06 502	0.09 503	71.05 452	0.03 503	0.04 497	0.25 498	1.30 497	0.05 500	0.03 498	0.21 497	0.01 496	0.29 493

mmdepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

monomultidepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

test_blank		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet_test		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

DCPMVS		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

cdsmvs_eth3d_5k		19.96 459	26.61 461	0.00 480	0.00 503	0.00 505	0.00 492	89.26 223	0.00 498	0.00 499	88.61 234	61.62 208	0.00 499	0.00 498	0.00 497	0.00 495

pcd_1.5k_mvsjas		5.26 465	7.02 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	63.15 180	0.00 499	0.00 498	0.00 497	0.00 495

sosnet-low-res		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uncertanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

Regformer		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

ab-mvs-re		7.23 462	9.64 465	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	86.72 287	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	0.00 498	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

TestfortrainingZip							93.28 12

WAC-MVS						42.58 477								39.46 465

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

eth-test2					0.00 503

eth-test					0.00 503

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

save fliter					93.80 44	72.35 44	90.47 74	91.17 149	74.31 158

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 70

GSMVS																88.96 308

sam_mvs1												51.32 327				88.96 308

sam_mvs												50.01 346

MTGPA								92.02 111

test_post1							78.90 400				5.43 494	48.81 365	85.44 395	59.25 356

test_post											5.46 493	50.36 342	84.24 404

patchmatchnet-post											74.00 461	51.12 333	88.60 356

MTMP							92.18 39	32.83 497

gm-plane-assit					81.40 398	53.83 424			62.72 391		80.94 407		92.39 240	63.40 308

test9_res														84.90 64	95.70 30	92.87 151

agg_prior2														82.91 91	95.45 33	92.70 156

test_prior4						72.60 34	89.01 125

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 86

新几何2							86.29 243

旧先验1					91.96 80	65.79 208		86.37 316			93.08 92	69.31 99			92.74 80	88.74 319

无先验							87.48 186	88.98 238	60.00 413				94.12 140	67.28 277		88.97 307

原ACMM2							86.86 216

testdata2													91.01 304	62.37 326

segment_acmp												73.08 43

testdata1							84.14 308		75.71 112

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 234

plane_prior5								92.44 82					95.38 82	78.71 144	86.32 202	91.33 211

plane_prior4											91.00 163

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 207

n2								0.00 504

nn								0.00 504

door-mid								69.98 460

test11								92.23 97

door								69.44 463

HQP5-MVS						66.98 183

BP-MVS														77.47 159

HQP3-MVS								92.19 105							85.99 211

HQP2-MVS												60.17 237

NP-MVS					89.62 129	68.32 135					90.24 184

ACMMP++_ref															81.95 280

ACMMP++															81.25 285

Test By Simon												64.33 166

High-resolution multi-view benchmark