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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 71	96.26 46	72.84 32	99.38 1	92.64 33	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 105	71.10 27	97.02 3	96.04 6	88.70 2	91.57 19	96.19 48	70.12 49	98.91 21	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 169	76.68 2	97.29 1	95.35 17	82.87 37	91.58 18	97.22 8	79.93 5	99.10 9	83.12 128	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 88	94.37 61	72.48 237	92.07 11	96.85 27	83.82 2	99.15 2	91.53 46	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 109	92.83 84	64.03 233	93.06 133	94.33 63	82.19 45	93.65 3	96.15 50	85.89 1	97.19 98	91.02 50	97.75 1	96.43 31
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
MGCNet		90.32 6	90.90 7	88.55 23	94.05 49	70.23 37	97.00 5	93.73 82	87.30 4	92.15 8	96.15 50	66.38 75	98.94 20	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 36	84.83 17	89.07 43	96.80 30	70.86 45	99.06 15	92.64 33	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 75	73.88 9	97.01 4	94.40 59	88.32 3	85.71 72	94.91 91	74.11 23	98.91 21	87.26 78	95.94 8	97.03 12
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
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 59	96.89 6	94.44 55	71.65 267	92.11 9	97.21 9	76.79 9	99.11 6	92.34 35	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 167	93.00 80	58.16 374	96.72 9	94.41 57	86.50 9	90.25 34	97.83 1	75.46 16	98.67 29	92.78 32	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 112	96.04 25	63.70 249	95.04 42	95.19 22	86.74 8	91.53 20	95.15 84	73.86 24	97.58 69	93.38 27	92.00 75	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 43	69.71 52	96.53 13	93.78 75	86.89 7	89.68 40	95.78 57	65.94 80	99.10 9	92.99 30	93.91 46	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 51	71.92 253	90.55 30	96.93 21	73.77 25	99.08 11	91.91 41	94.90 22	96.29 35
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
HPM-MVS++		89.37 14	89.95 13	87.64 35	95.10 31	68.23 100	95.24 34	94.49 53	82.43 42	88.90 45	96.35 41	71.89 42	98.63 30	88.76 64	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 23	89.81 6	93.66 58	75.15 5	90.61 271	93.43 97	84.06 24	86.20 66	90.17 225	72.42 37	96.98 115	93.09 29	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 74	96.38 15	94.64 46	84.42 21	86.74 61	96.20 47	66.56 74	98.76 27	89.03 63	94.56 34	95.92 49

MED-MVS		88.94 17	89.45 16	87.42 45	94.76 34	67.28 125	94.47 61	94.87 32	70.09 299	91.27 23	96.95 17	76.77 11	98.98 16	91.55 43	94.28 37	95.99 45

DPE-MVS		88.77 18	89.21 19	87.45 44	96.26 21	67.56 118	94.17 74	94.15 68	68.77 320	90.74 28	97.27 6	76.09 14	98.49 33	90.58 54	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		88.66 19	88.99 21	87.70 33	94.76 34	68.73 83	94.47 61	94.87 32	73.09 224	91.27 23	96.95 17	76.77 11	98.98 16	84.41 111	94.28 37	95.37 69

ME-MVS		88.25 20	88.55 27	87.33 50	96.33 18	67.28 125	93.93 90	94.81 37	70.09 299	88.91 44	96.95 17	70.12 49	98.73 28	91.55 43	94.28 37	95.99 45

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 156	88.15 230	61.94 299	95.65 25	89.70 295	85.54 12	92.07 11	97.33 5	67.51 66	97.27 93	96.23 5	92.07 74	95.35 73

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 151	89.29 180	61.41 316	92.97 138	88.36 350	86.96 6	91.49 21	97.49 3	69.48 54	97.46 76	97.00 1	89.88 111	95.89 50

SMA-MVS		88.14 22	88.29 31	87.67 34	93.21 72	68.72 85	93.85 96	94.03 71	74.18 198	91.74 15	96.67 33	65.61 85	98.42 37	89.24 60	96.08 7	95.88 51
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
PS-MVSNAJ		88.14 22	87.61 41	89.71 7	92.06 108	76.72 1	95.75 20	93.26 103	83.86 25	89.55 41	96.06 52	53.55 264	97.89 51	91.10 48	93.31 57	94.54 131

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 87	91.73 123	68.04 104	90.36 278	93.55 89	82.89 35	91.29 22	92.89 146	72.27 39	96.03 169	87.99 68	94.77 26	95.54 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 193	89.07 188	61.60 309	94.87 49	89.06 323	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 182

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 146	88.43 217	61.78 302	94.73 56	91.74 177	85.87 10	91.66 17	97.50 2	64.03 106	98.33 38	96.28 4	90.08 107	95.10 90

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 71	93.51 66	65.32 188	95.15 37	93.84 74	78.17 129	85.93 70	94.80 94	75.80 15	98.21 40	89.38 57	88.78 123	96.59 19

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 31	95.86 28	68.32 94	95.74 21	94.11 69	83.82 26	83.49 97	96.19 48	64.53 101	98.44 35	83.42 127	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 119	88.69 199	63.71 247	94.56 59	90.22 271	85.04 15	92.27 6	97.05 12	63.67 114	98.15 42	95.09 12	91.39 87	95.27 81

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 12	91.41 135	76.43 3	95.74 21	93.12 111	83.53 29	89.55 41	95.95 55	53.45 268	97.68 59	91.07 49	92.62 65	94.54 131

EPNet		87.84 32	88.38 29	86.23 99	93.30 69	66.05 167	95.26 33	94.84 35	87.09 5	88.06 48	94.53 100	66.79 71	97.34 86	83.89 118	91.68 81	95.29 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 33	87.77 38	87.63 39	89.24 185	71.18 24	96.57 12	92.90 121	82.70 39	87.13 56	95.27 77	64.99 91	95.80 184	89.34 58	91.80 79	95.93 48

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 139	87.05 265	63.55 256	93.69 106	91.08 220	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 108	94.26 150

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 136	86.92 275	62.63 282	95.02 44	90.28 266	84.95 16	90.27 33	96.86 25	65.36 87	97.52 74	94.93 15	90.03 108	95.76 54

APDe-MVS		87.54 35	87.84 37	86.65 74	96.07 24	66.30 162	94.84 51	93.78 75	69.35 309	88.39 47	96.34 42	67.74 64	97.66 64	90.62 53	93.44 55	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 205	86.89 277	60.04 350	95.05 40	92.17 156	84.80 18	92.27 6	96.37 39	64.62 98	96.54 141	94.43 19	91.86 77	94.94 99

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 130	86.95 270	64.37 220	94.30 71	88.45 348	80.51 70	92.70 4	96.86 25	69.98 51	97.15 103	95.83 7	88.08 131	94.65 124

SD-MVS		87.49 38	87.49 43	87.50 43	93.60 60	68.82 81	93.90 93	92.63 136	76.86 155	87.90 50	95.76 58	66.17 77	97.63 66	89.06 62	91.48 85	96.05 42
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_l_conf0.5_n_a		87.44 40	88.15 34	85.30 136	87.10 263	64.19 228	94.41 66	88.14 358	80.24 81	92.54 5	96.97 16	69.52 53	97.17 99	95.89 6	88.51 126	94.56 128

dcpmvs_2		87.37 41	87.55 42	86.85 62	95.04 33	68.20 101	90.36 278	90.66 246	79.37 104	81.20 121	93.67 130	74.73 18	96.55 140	90.88 51	92.00 75	95.82 52

alignmvs		87.28 42	86.97 49	88.24 27	91.30 137	71.14 26	95.61 26	93.56 88	79.30 105	87.07 58	95.25 79	68.43 56	96.93 123	87.87 69	84.33 182	96.65 17

train_agg		87.21 43	87.42 44	86.60 77	94.18 45	67.28 125	94.16 75	93.51 91	71.87 258	85.52 75	95.33 71	68.19 59	97.27 93	89.09 61	94.90 22	95.25 85

MG-MVS		87.11 44	86.27 62	89.62 8	97.79 1	76.27 4	94.96 46	94.49 53	78.74 120	83.87 93	92.94 144	64.34 102	96.94 121	75.19 205	94.09 42	95.66 57

SF-MVS		87.03 45	87.09 47	86.84 63	92.70 90	67.45 123	93.64 109	93.76 78	70.78 291	86.25 64	96.44 38	66.98 69	97.79 55	88.68 65	94.56 34	95.28 80

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 219	87.26 257	60.74 330	93.21 130	87.94 365	84.22 22	91.70 16	97.27 6	65.91 82	95.02 227	93.95 24	90.42 103	94.99 96

CSCG		86.87 47	86.26 63	88.72 17	95.05 32	70.79 29	93.83 101	95.33 18	68.48 324	77.63 180	94.35 109	73.04 30	98.45 34	84.92 103	93.71 51	96.92 14

sasdasda		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 180	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

canonicalmvs		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 180	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

UBG		86.83 50	86.70 55	87.20 52	93.07 78	69.81 47	93.43 122	95.56 13	81.52 52	81.50 116	92.12 167	73.58 28	96.28 153	84.37 112	85.20 169	95.51 63

PHI-MVS		86.83 50	86.85 54	86.78 67	93.47 67	65.55 183	95.39 31	95.10 25	71.77 263	85.69 73	96.52 35	62.07 143	98.77 26	86.06 91	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 80	90.42 154	66.38 159	96.09 17	93.87 73	77.73 138	84.01 92	95.66 60	63.39 121	97.94 47	87.40 76	93.55 54	95.42 65
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 191	86.15 293	61.48 313	94.69 57	91.16 207	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 143	93.11 200

PVSNet_Blended		86.73 54	86.86 53	86.31 98	93.76 54	67.53 120	96.33 16	93.61 86	82.34 44	81.00 127	93.08 140	63.19 125	97.29 89	87.08 82	91.38 88	94.13 159

testing11		86.71 55	86.44 60	87.55 41	93.54 64	71.35 21	93.65 108	95.58 11	81.36 59	80.69 132	92.21 165	72.30 38	96.46 146	85.18 99	83.43 196	94.82 109

test_fmvsmconf_n		86.58 56	87.17 46	84.82 158	85.28 311	62.55 283	94.26 73	89.78 286	83.81 27	87.78 52	96.33 43	65.33 88	96.98 115	94.40 20	87.55 137	94.95 98

BP-MVS1		86.54 57	86.68 57	86.13 102	87.80 245	67.18 132	92.97 138	95.62 10	79.92 86	82.84 104	94.14 118	74.95 17	96.46 146	82.91 132	88.96 122	94.74 114

jason		86.40 58	86.17 66	87.11 55	86.16 292	70.54 32	95.71 24	92.19 153	82.00 47	84.58 85	94.34 110	61.86 146	95.53 209	87.76 70	90.89 96	95.27 81
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 124	92.12 105	65.95 172	94.88 47	90.83 234	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	91.15 92	93.93 170

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 158	87.36 256	63.54 257	94.74 53	90.02 279	82.52 40	90.14 37	96.92 23	62.93 132	97.84 54	95.28 11	82.26 207	93.07 203

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 169	84.67 323	63.29 262	94.04 84	89.99 281	82.88 36	87.85 51	96.03 53	62.89 134	96.36 150	94.15 21	89.95 110	94.48 140

SymmetryMVS		86.32 62	86.39 61	86.12 103	90.52 152	65.95 172	94.88 47	94.58 50	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	86.59 152	95.51 63

WTY-MVS		86.32 62	85.81 74	87.85 29	92.82 86	69.37 61	95.20 35	95.25 20	82.71 38	81.91 112	94.73 95	67.93 63	97.63 66	79.55 170	82.25 209	96.54 22

myMVS_eth3d28		86.31 64	86.15 67	86.78 67	93.56 62	70.49 33	92.94 141	95.28 19	82.47 41	78.70 170	92.07 169	72.45 36	95.41 211	82.11 141	85.78 162	94.44 142

MSLP-MVS++		86.27 65	85.91 73	87.35 48	92.01 112	68.97 77	95.04 42	92.70 127	79.04 115	81.50 116	96.50 37	58.98 191	96.78 131	83.49 126	93.93 45	96.29 35

VNet		86.20 66	85.65 78	87.84 30	93.92 51	69.99 39	95.73 23	95.94 7	78.43 125	86.00 69	93.07 141	58.22 201	97.00 111	85.22 97	84.33 182	96.52 23

MVS_111021_HR		86.19 67	85.80 75	87.37 47	93.17 74	69.79 48	93.99 87	93.76 78	79.08 112	78.88 166	93.99 124	62.25 141	98.15 42	85.93 92	91.15 92	94.15 158

SPE-MVS-test		86.14 68	87.01 48	83.52 220	92.63 92	59.36 362	95.49 28	91.92 166	80.09 82	85.46 77	95.53 66	61.82 148	95.77 188	86.77 86	93.37 56	95.41 66

ACMMP_NAP		86.05 69	85.80 75	86.80 66	91.58 127	67.53 120	91.79 207	93.49 94	74.93 188	84.61 84	95.30 73	59.42 181	97.92 48	86.13 89	94.92 20	94.94 99

testing99		86.01 70	85.47 80	87.63 39	93.62 59	71.25 23	93.47 120	95.23 21	80.42 73	80.60 134	91.95 176	71.73 43	96.50 144	80.02 167	82.22 210	95.13 88

ETV-MVS		86.01 70	86.11 68	85.70 120	90.21 159	67.02 139	93.43 122	91.92 166	81.21 61	84.13 91	94.07 123	60.93 157	95.63 198	89.28 59	89.81 112	94.46 141

testing91		85.93 72	85.31 84	87.78 32	93.59 61	71.47 19	93.50 117	95.08 28	80.26 78	80.53 137	91.93 177	70.43 47	96.51 143	80.32 165	82.13 212	95.37 69

APD-MVS		85.93 72	85.99 71	85.76 116	95.98 27	65.21 191	93.59 112	92.58 138	66.54 343	86.17 67	95.88 56	63.83 110	97.00 111	86.39 88	92.94 61	95.06 92
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 74	85.46 81	87.18 53	88.20 229	72.42 15	92.41 175	92.77 125	82.11 46	80.34 140	93.07 141	68.27 57	95.02 227	78.39 186	93.59 53	94.09 161

CS-MVS		85.80 75	86.65 59	83.27 232	92.00 113	58.92 366	95.31 32	91.86 171	79.97 83	84.82 83	95.40 69	62.26 140	95.51 210	86.11 90	92.08 73	95.37 69

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 167	85.73 304	63.58 254	93.79 102	89.32 306	81.42 57	90.21 35	96.91 24	62.41 139	97.67 61	94.48 18	80.56 235	92.90 209

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 177	80.83 373	62.33 288	93.84 99	88.81 335	83.50 30	87.00 59	96.01 54	63.36 122	96.93 123	94.04 23	87.29 140	94.61 126

CDPH-MVS		85.71 77	85.46 81	86.46 89	94.75 38	67.19 130	93.89 94	92.83 123	70.90 287	83.09 102	95.28 75	63.62 116	97.36 84	80.63 161	94.18 41	94.84 105

casdiffmvs_mvg		85.66 79	85.18 86	87.09 56	88.22 228	69.35 62	93.74 105	91.89 169	81.47 53	80.10 143	91.45 189	64.80 96	96.35 151	87.23 79	87.69 135	95.58 60
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 171	82.95 355	63.48 259	94.03 86	89.46 300	81.69 50	89.86 38	96.74 31	61.85 147	97.75 57	94.74 17	82.01 214	92.81 213

MGCFI-Net		85.59 81	85.73 77	85.17 143	91.41 135	62.44 284	92.87 146	91.31 197	79.65 93	86.99 60	95.14 85	62.90 133	96.12 161	87.13 81	84.13 188	96.96 13

GDP-MVS		85.54 82	85.32 83	86.18 100	87.64 248	67.95 108	92.91 144	92.36 143	77.81 135	83.69 94	94.31 112	72.84 32	96.41 148	80.39 164	85.95 159	94.19 154

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 94	88.80 197	66.64 153	92.15 185	93.68 84	81.07 63	76.91 192	93.64 131	62.59 136	98.44 35	85.50 93	92.84 63	94.03 165
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 84	84.87 92	86.84 63	88.25 226	69.07 71	93.04 135	91.76 176	81.27 60	80.84 130	92.07 169	64.23 104	96.06 167	84.98 102	87.43 139	95.39 67
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		85.33 85	85.08 88	86.06 104	93.09 77	65.65 179	93.89 94	93.41 99	73.75 209	79.94 145	94.68 97	60.61 162	98.03 45	82.63 136	93.72 50	94.52 133

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 305	84.52 328	60.10 348	93.35 125	90.35 259	83.41 31	86.54 63	96.27 45	60.50 163	90.02 389	94.84 16	90.38 104	92.61 217

MP-MVS-pluss		85.24 86	85.13 87	85.56 125	91.42 132	65.59 181	91.54 224	92.51 140	74.56 191	80.62 133	95.64 61	59.15 186	97.00 111	86.94 84	93.80 47	94.07 163
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 88	84.69 96	86.63 76	92.91 82	69.91 43	92.61 161	95.80 9	80.31 77	80.38 139	92.27 161	68.73 55	95.19 224	75.94 199	83.27 198	94.81 111

PAPR		85.15 89	84.47 97	87.18 53	96.02 26	68.29 95	91.85 205	93.00 117	76.59 166	79.03 162	95.00 86	61.59 149	97.61 68	78.16 187	89.00 121	95.63 58

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 226	86.92 275	60.53 337	94.41 66	87.31 373	83.30 32	88.72 46	96.72 32	54.28 256	97.75 57	94.07 22	84.68 179	92.04 240

MP-MVS		85.02 91	84.97 90	85.17 143	92.60 93	64.27 225	93.24 127	92.27 146	73.13 220	79.63 154	94.43 103	61.90 144	97.17 99	85.00 101	92.56 66	94.06 164
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 92	84.44 98	86.71 70	88.33 223	68.73 83	90.24 283	91.82 175	81.05 64	81.18 122	92.50 153	63.69 113	96.08 166	84.45 110	86.71 150	95.32 76

CHOSEN 1792x2688		84.98 93	83.45 118	89.57 11	89.94 164	75.14 6	92.07 191	92.32 144	81.87 48	75.68 201	88.27 259	60.18 167	98.60 31	80.46 163	90.27 106	94.96 97

MVSMamba_PlusPlus		84.97 94	83.65 111	88.93 14	90.17 160	74.04 8	87.84 341	92.69 130	62.18 384	81.47 118	87.64 273	71.47 44	96.28 153	84.69 105	94.74 31	96.47 28

E3new		84.94 95	84.36 100	86.69 73	89.06 189	69.31 63	92.68 158	91.29 202	80.72 67	81.03 125	92.14 166	61.89 145	95.91 173	84.59 107	85.85 161	94.86 101

viewmanbaseed2359cas		84.89 96	84.26 102	86.78 67	88.50 208	69.77 50	92.69 157	91.13 213	81.11 62	81.54 115	91.98 173	60.35 164	95.73 190	84.47 109	86.56 153	94.84 105

EIA-MVS		84.84 97	84.88 91	84.69 170	91.30 137	62.36 287	93.85 96	92.04 159	79.45 100	79.33 159	94.28 114	62.42 138	96.35 151	80.05 166	91.25 91	95.38 68

lecture		84.77 98	84.81 94	84.65 173	92.12 105	62.27 291	94.74 53	92.64 135	68.35 325	85.53 74	95.30 73	59.77 174	97.91 49	83.73 122	91.15 92	93.77 179

fmvsm_s_conf0.1_n_a		84.76 99	84.84 93	84.53 179	80.23 386	63.50 258	92.79 148	88.73 338	80.46 71	89.84 39	96.65 34	60.96 156	97.57 71	93.80 25	80.14 237	92.53 222

viewcassd2359sk11		84.74 100	84.11 103	86.64 75	88.57 202	69.20 69	92.61 161	91.23 204	80.58 68	80.85 129	91.96 174	61.39 151	95.89 175	84.28 113	85.49 166	94.82 109

HFP-MVS		84.73 101	84.40 99	85.72 118	93.75 56	65.01 197	93.50 117	93.19 107	72.19 247	79.22 160	94.93 89	59.04 189	97.67 61	81.55 150	92.21 69	94.49 139

MVS		84.66 102	82.86 140	90.06 2	90.93 144	74.56 7	87.91 339	95.54 14	68.55 322	72.35 258	94.71 96	59.78 173	98.90 23	81.29 156	94.69 32	96.74 16

GST-MVS		84.63 103	84.29 101	85.66 121	92.82 86	65.27 189	93.04 135	93.13 110	73.20 218	78.89 163	94.18 117	59.41 182	97.85 53	81.45 152	92.48 68	93.86 176

EC-MVSNet		84.53 104	85.04 89	83.01 238	89.34 176	61.37 317	94.42 65	91.09 217	77.91 133	83.24 98	94.20 116	58.37 199	95.40 212	85.35 94	91.41 86	92.27 234

E2		84.45 105	83.74 107	86.56 82	87.90 238	69.06 72	92.53 169	91.13 213	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

E3		84.45 105	83.74 107	86.56 82	87.90 238	69.06 72	92.53 169	91.13 213	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

fmvsm_s_conf0.1_n_2		84.40 107	84.78 95	83.27 232	85.25 312	60.41 340	94.13 78	85.69 398	83.05 34	87.99 49	96.37 39	52.75 273	97.68 59	93.75 26	84.05 189	91.71 248

ACMMPR		84.37 108	84.06 104	85.28 138	93.56 62	64.37 220	93.50 117	93.15 109	72.19 247	78.85 168	94.86 92	56.69 224	97.45 77	81.55 150	92.20 70	94.02 166

region2R		84.36 109	84.03 105	85.36 134	93.54 64	64.31 223	93.43 122	92.95 119	72.16 250	78.86 167	94.84 93	56.97 219	97.53 73	81.38 154	92.11 72	94.24 152

LFMVS		84.34 110	82.73 142	89.18 13	94.76 34	73.25 11	94.99 45	91.89 169	71.90 255	82.16 111	93.49 135	47.98 326	97.05 106	82.55 137	84.82 175	97.25 8

test_yl		84.28 111	83.16 131	87.64 35	94.52 41	69.24 67	95.78 18	95.09 26	69.19 312	81.09 123	92.88 147	57.00 217	97.44 78	81.11 158	81.76 218	96.23 38

DCV-MVSNet		84.28 111	83.16 131	87.64 35	94.52 41	69.24 67	95.78 18	95.09 26	69.19 312	81.09 123	92.88 147	57.00 217	97.44 78	81.11 158	81.76 218	96.23 38

diffmvs		84.28 111	83.83 106	85.61 123	87.40 254	68.02 105	90.88 256	89.24 309	80.54 69	81.64 114	92.52 152	59.83 172	94.52 256	87.32 77	85.11 170	94.29 149
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 111	83.36 124	87.02 59	92.22 100	67.74 113	84.65 370	94.50 52	79.15 109	82.23 110	87.93 268	66.88 70	96.94 121	80.53 162	82.20 211	96.39 33

ETVMVS		84.22 115	83.71 109	85.76 116	92.58 94	68.25 99	92.45 173	95.53 15	79.54 99	79.46 156	91.64 187	70.29 48	94.18 270	69.16 268	82.76 204	94.84 105

MAR-MVS		84.18 116	83.43 119	86.44 91	96.25 22	65.93 174	94.28 72	94.27 65	74.41 193	79.16 161	95.61 62	53.99 259	98.88 25	69.62 262	93.26 58	94.50 138
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
MVS_Test		84.16 117	83.20 128	87.05 58	91.56 128	69.82 46	89.99 292	92.05 158	77.77 137	82.84 104	86.57 290	63.93 109	96.09 163	74.91 210	89.18 118	95.25 85

CANet_DTU		84.09 118	83.52 112	85.81 113	90.30 157	66.82 147	91.87 203	89.01 326	85.27 13	86.09 68	93.74 128	47.71 332	96.98 115	77.90 189	89.78 114	93.65 183

viewdifsd2359ckpt13		84.08 119	83.21 127	86.70 71	88.49 212	69.55 55	92.25 179	91.14 211	79.71 91	79.73 151	91.72 183	58.83 192	95.89 175	82.06 142	84.99 171	94.66 123

viewmacassd2359aftdt		84.03 120	83.18 130	86.59 79	86.76 278	69.44 56	92.44 174	90.85 233	80.38 74	80.78 131	91.33 195	58.54 196	95.62 200	82.15 140	85.41 167	94.72 117

ET-MVSNet_ETH3D		84.01 121	83.15 133	86.58 80	90.78 149	70.89 28	94.74 53	94.62 47	81.44 56	58.19 402	93.64 131	73.64 27	92.35 344	82.66 135	78.66 257	96.50 27

E4		84.00 122	83.19 129	86.46 89	86.99 266	68.85 79	92.39 176	90.99 228	79.94 84	80.17 142	91.36 194	59.73 175	95.79 185	82.87 133	84.22 186	94.74 114

diffmvs_AUTHOR		83.97 123	83.49 115	85.39 130	86.09 294	67.83 110	90.76 261	89.05 324	79.94 84	81.43 119	92.23 164	59.53 178	94.42 259	87.18 80	85.22 168	93.92 172

PVSNet_Blended_VisFu		83.97 123	83.50 114	85.39 130	90.02 162	66.59 156	93.77 103	91.73 178	77.43 147	77.08 191	89.81 235	63.77 112	96.97 118	79.67 169	88.21 129	92.60 218

MTAPA		83.91 125	83.38 123	85.50 126	91.89 119	65.16 193	81.75 401	92.23 147	75.32 183	80.53 137	95.21 82	56.06 233	97.16 102	84.86 104	92.55 67	94.18 155

XVS		83.87 126	83.47 117	85.05 147	93.22 70	63.78 241	92.92 142	92.66 132	73.99 201	78.18 174	94.31 112	55.25 239	97.41 81	79.16 176	91.58 83	93.95 168

Effi-MVS+		83.82 127	82.76 141	86.99 60	89.56 172	69.40 57	91.35 236	86.12 392	72.59 234	83.22 101	92.81 150	59.60 177	96.01 171	81.76 149	87.80 134	95.56 61

test_fmvsmvis_n_1920		83.80 128	83.48 116	84.77 162	82.51 358	63.72 246	91.37 233	83.99 416	81.42 57	77.68 179	95.74 59	58.37 199	97.58 69	93.38 27	86.87 144	93.00 206

EI-MVSNet-Vis-set		83.77 129	83.67 110	84.06 196	92.79 89	63.56 255	91.76 212	94.81 37	79.65 93	77.87 177	94.09 121	63.35 123	97.90 50	79.35 174	79.36 247	90.74 269

MVSFormer		83.75 130	82.88 139	86.37 94	89.24 185	71.18 24	89.07 317	90.69 243	65.80 351	87.13 56	94.34 110	64.99 91	92.67 330	72.83 227	91.80 79	95.27 81

CP-MVS		83.71 131	83.40 122	84.65 173	93.14 75	63.84 239	94.59 58	92.28 145	71.03 285	77.41 184	94.92 90	55.21 242	96.19 158	81.32 155	90.70 98	93.91 173

test_fmvsmconf0.01_n		83.70 132	83.52 112	84.25 192	75.26 435	61.72 306	92.17 184	87.24 375	82.36 43	84.91 82	95.41 68	55.60 237	96.83 130	92.85 31	85.87 160	94.21 153

baseline2		83.68 133	83.42 121	84.48 182	87.37 255	66.00 169	90.06 287	95.93 8	79.71 91	69.08 296	90.39 213	77.92 6	96.28 153	78.91 181	81.38 222	91.16 262

E6new		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 216	90.95 230	79.60 97	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E6		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 216	90.95 230	79.60 97	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E5		83.62 134	82.65 144	86.55 84	86.98 267	69.28 66	91.69 216	90.96 229	79.61 95	79.80 147	91.25 197	58.04 204	95.84 178	81.83 148	83.66 194	94.52 133

viewdifsd2359ckpt09		83.52 137	82.57 148	86.37 94	88.02 235	68.47 90	91.78 209	89.63 296	79.61 95	78.56 172	92.00 172	59.28 184	95.96 172	81.94 144	82.35 205	94.69 118

reproduce-ours		83.51 138	83.33 125	84.06 196	92.18 103	60.49 338	90.74 263	92.04 159	64.35 361	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 147

our_new_method		83.51 138	83.33 125	84.06 196	92.18 103	60.49 338	90.74 263	92.04 159	64.35 361	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 147

thisisatest0515		83.41 140	82.49 150	86.16 101	89.46 175	68.26 97	93.54 114	94.70 43	74.31 196	75.75 199	90.92 203	72.62 34	96.52 142	69.64 260	81.50 221	93.71 180

PVSNet_BlendedMVS		83.38 141	83.43 119	83.22 234	93.76 54	67.53 120	94.06 80	93.61 86	79.13 110	81.00 127	85.14 308	63.19 125	97.29 89	87.08 82	73.91 295	84.83 374

test2506		83.29 142	82.92 138	84.37 186	88.39 220	63.18 268	92.01 194	91.35 196	77.66 140	78.49 173	91.42 190	64.58 100	95.09 226	73.19 223	89.23 116	94.85 102

PGM-MVS		83.25 143	82.70 143	84.92 151	92.81 88	64.07 232	90.44 273	92.20 151	71.28 279	77.23 188	94.43 103	55.17 243	97.31 88	79.33 175	91.38 88	93.37 190

HPM-MVS		83.25 143	82.95 137	84.17 194	92.25 99	62.88 277	90.91 253	91.86 171	70.30 296	77.12 189	93.96 125	56.75 222	96.28 153	82.04 143	91.34 90	93.34 191
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 145	82.96 135	83.73 211	92.02 109	59.74 354	90.37 277	92.08 157	63.70 368	82.86 103	95.48 67	58.62 194	97.17 99	83.06 129	88.42 127	94.26 150

EI-MVSNet-UG-set		83.14 146	82.96 135	83.67 216	92.28 98	63.19 267	91.38 232	94.68 44	79.22 107	76.60 194	93.75 127	62.64 135	97.76 56	78.07 188	78.01 260	90.05 278

testing3-2		83.11 147	83.15 133	82.98 239	91.92 116	64.01 234	94.39 69	95.37 16	78.32 126	75.53 206	90.06 231	73.18 29	93.18 309	74.34 215	75.27 284	91.77 247

VDD-MVS		83.06 148	81.81 161	86.81 65	90.86 147	67.70 114	95.40 30	91.50 191	75.46 178	81.78 113	92.34 160	40.09 379	97.13 104	86.85 85	82.04 213	95.60 59

h-mvs33		83.01 149	82.56 149	84.35 187	89.34 176	62.02 295	92.72 151	93.76 78	81.45 54	82.73 107	92.25 163	60.11 168	97.13 104	87.69 71	62.96 380	93.91 173

PAPM_NR		82.97 150	81.84 160	86.37 94	94.10 48	66.76 150	87.66 345	92.84 122	69.96 302	74.07 231	93.57 133	63.10 130	97.50 75	70.66 255	90.58 100	94.85 102

mPP-MVS		82.96 151	82.44 151	84.52 180	92.83 84	62.92 275	92.76 149	91.85 173	71.52 275	75.61 204	94.24 115	53.48 267	96.99 114	78.97 179	90.73 97	93.64 184

viewdifsd2359ckpt07		82.95 152	82.04 155	85.66 121	87.19 260	66.73 151	91.56 223	90.39 258	77.58 143	77.58 183	91.19 200	58.57 195	95.65 197	82.32 138	82.01 214	94.60 127

SR-MVS		82.81 153	82.58 147	83.50 223	93.35 68	61.16 320	92.23 182	91.28 203	64.48 360	81.27 120	95.28 75	53.71 263	95.86 177	82.87 133	88.77 124	93.49 188

DP-MVS Recon		82.73 154	81.65 162	85.98 106	97.31 4	67.06 135	95.15 37	91.99 163	69.08 317	76.50 196	93.89 126	54.48 252	98.20 41	70.76 253	85.66 164	92.69 214

CLD-MVS		82.73 154	82.35 153	83.86 204	87.90 238	67.65 116	95.45 29	92.18 154	85.06 14	72.58 249	92.27 161	52.46 276	95.78 186	84.18 114	79.06 252	88.16 306
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 156	82.38 152	83.73 211	89.25 182	59.58 357	92.24 181	94.89 31	77.96 131	79.86 146	92.38 158	56.70 223	97.05 106	77.26 192	80.86 230	94.55 129

3Dnovator	73.91 6	82.69 157	80.82 175	88.31 26	89.57 171	71.26 22	92.60 163	94.39 60	78.84 117	67.89 318	92.48 156	48.42 321	98.52 32	68.80 273	94.40 36	95.15 87

RRT-MVS		82.61 158	81.16 166	86.96 61	91.10 141	68.75 82	87.70 344	92.20 151	76.97 153	72.68 245	87.10 284	51.30 290	96.41 148	83.56 125	87.84 133	95.74 55

viewmambaseed2359dif		82.60 159	81.91 159	84.67 172	85.83 301	66.09 166	90.50 272	89.01 326	75.46 178	79.64 153	92.01 171	59.51 179	94.38 261	82.99 131	82.26 207	93.54 186

MVSTER		82.47 160	82.05 154	83.74 209	92.68 91	69.01 75	91.90 202	93.21 104	79.83 87	72.14 259	85.71 303	74.72 19	94.72 241	75.72 201	72.49 305	87.50 313

TESTMET0.1,1		82.41 161	81.98 158	83.72 213	88.08 231	63.74 243	92.70 153	93.77 77	79.30 105	77.61 181	87.57 275	58.19 202	94.08 275	73.91 217	86.68 151	93.33 193

CostFormer		82.33 162	81.15 167	85.86 111	89.01 192	68.46 91	82.39 398	93.01 115	75.59 176	80.25 141	81.57 354	72.03 41	94.96 231	79.06 178	77.48 268	94.16 157

API-MVS		82.28 163	80.53 184	87.54 42	96.13 23	70.59 31	93.63 110	91.04 226	65.72 353	75.45 207	92.83 149	56.11 232	98.89 24	64.10 326	89.75 115	93.15 198

IB-MVS	77.80 4	82.18 164	80.46 186	87.35 48	89.14 187	70.28 36	95.59 27	95.17 24	78.85 116	70.19 284	85.82 301	70.66 46	97.67 61	72.19 239	66.52 349	94.09 161
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
xiu_mvs_v1_base_debu		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 255	73.12 221	84.20 88	94.36 105	38.04 392	95.73 190	84.12 115	86.81 145	91.33 255

xiu_mvs_v1_base		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 255	73.12 221	84.20 88	94.36 105	38.04 392	95.73 190	84.12 115	86.81 145	91.33 255

xiu_mvs_v1_base_debi		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 255	73.12 221	84.20 88	94.36 105	38.04 392	95.73 190	84.12 115	86.81 145	91.33 255

3Dnovator+	73.60 7	82.10 168	80.60 182	86.60 77	90.89 146	66.80 149	95.20 35	93.44 96	74.05 200	67.42 325	92.49 155	49.46 311	97.65 65	70.80 252	91.68 81	95.33 74

MVS_111021_LR		82.02 169	81.52 163	83.51 222	88.42 218	62.88 277	89.77 295	88.93 331	76.78 158	75.55 205	93.10 138	50.31 300	95.38 214	83.82 119	87.02 142	92.26 235

PMMVS		81.98 170	82.04 155	81.78 275	89.76 168	56.17 395	91.13 249	90.69 243	77.96 131	80.09 144	93.57 133	46.33 348	94.99 230	81.41 153	87.46 138	94.17 156

baseline1		81.84 171	81.03 172	84.28 190	91.60 126	66.62 154	91.08 250	91.66 185	81.87 48	74.86 217	91.67 186	69.98 51	94.92 234	71.76 242	64.75 366	91.29 260

EPP-MVSNet		81.79 172	81.52 163	82.61 249	88.77 198	60.21 346	93.02 137	93.66 85	68.52 323	72.90 243	90.39 213	72.19 40	94.96 231	74.93 209	79.29 250	92.67 215

WBMVS		81.67 173	80.98 174	83.72 213	93.07 78	69.40 57	94.33 70	93.05 113	76.84 156	72.05 261	84.14 320	74.49 21	93.88 289	72.76 230	68.09 335	87.88 308

test_vis1_n_1920		81.66 174	82.01 157	80.64 308	82.24 360	55.09 404	94.76 52	86.87 379	81.67 51	84.40 87	94.63 98	38.17 389	94.67 247	91.98 40	83.34 197	92.16 238

APD-MVS_3200maxsize		81.64 175	81.32 165	82.59 251	92.36 96	58.74 368	91.39 230	91.01 227	63.35 372	79.72 152	94.62 99	51.82 279	96.14 160	79.71 168	87.93 132	92.89 210

mvsmamba		81.55 176	80.72 177	84.03 200	91.42 132	66.93 145	83.08 389	89.13 317	78.55 124	67.50 323	87.02 285	51.79 281	90.07 388	87.48 74	90.49 102	95.10 90

ACMMP		81.49 177	80.67 179	83.93 202	91.71 124	62.90 276	92.13 186	92.22 150	71.79 262	71.68 267	93.49 135	50.32 299	96.96 119	78.47 185	84.22 186	91.93 245
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
KinetiMVS		81.43 178	80.11 188	85.38 133	86.60 281	65.47 187	92.90 145	93.54 90	75.33 182	77.31 186	90.39 213	46.81 340	96.75 132	71.65 245	86.46 156	93.93 170

CDS-MVSNet		81.43 178	80.74 176	83.52 220	86.26 289	64.45 214	92.09 189	90.65 247	75.83 174	73.95 233	89.81 235	63.97 108	92.91 320	71.27 246	82.82 201	93.20 197
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 180	79.99 192	85.46 127	90.39 156	68.40 92	86.88 356	90.61 248	74.41 193	70.31 283	84.67 313	63.79 111	92.32 346	73.13 224	85.70 163	95.67 56

ECVR-MVS		81.29 181	80.38 187	84.01 201	88.39 220	61.96 297	92.56 168	86.79 381	77.66 140	76.63 193	91.42 190	46.34 347	95.24 223	74.36 214	89.23 116	94.85 102

guyue		81.23 182	80.57 183	83.21 236	86.64 279	61.85 300	92.52 171	92.78 124	78.69 121	74.92 216	89.42 239	50.07 303	95.35 215	80.79 160	79.31 249	92.42 224

IMVS_0403		81.19 183	79.88 194	85.13 145	88.54 203	64.75 202	88.84 322	90.80 237	76.73 161	75.21 210	90.18 219	54.22 257	96.21 157	73.47 219	80.95 225	94.43 143

thisisatest0530		81.15 184	80.07 189	84.39 185	88.26 225	65.63 180	91.40 228	94.62 47	71.27 280	70.93 274	89.18 244	72.47 35	96.04 168	65.62 313	76.89 275	91.49 251

Fast-Effi-MVS+		81.14 185	80.01 191	84.51 181	90.24 158	65.86 175	94.12 79	89.15 315	73.81 208	75.37 209	88.26 260	57.26 212	94.53 255	66.97 297	84.92 174	93.15 198

HQP-MVS		81.14 185	80.64 180	82.64 248	87.54 250	63.66 252	94.06 80	91.70 183	79.80 88	74.18 224	90.30 216	51.63 284	95.61 201	77.63 190	78.90 253	88.63 297

hse-mvs2		81.12 187	81.11 171	81.16 293	86.52 284	57.48 383	89.40 308	91.16 207	81.45 54	82.73 107	90.49 211	60.11 168	94.58 248	87.69 71	60.41 407	91.41 254

SR-MVS-dyc-post		81.06 188	80.70 178	82.15 266	92.02 109	58.56 371	90.90 254	90.45 251	62.76 379	78.89 163	94.46 101	51.26 291	95.61 201	78.77 183	86.77 148	92.28 231

HyFIR lowres test		81.03 189	79.56 201	85.43 128	87.81 244	68.11 103	90.18 284	90.01 280	70.65 293	72.95 242	86.06 297	63.61 117	94.50 257	75.01 208	79.75 241	93.67 181

nrg030		80.93 190	79.86 195	84.13 195	83.69 344	68.83 80	93.23 128	91.20 205	75.55 177	75.06 212	88.22 263	63.04 131	94.74 240	81.88 145	66.88 346	88.82 295

Vis-MVSNet		80.92 191	79.98 193	83.74 209	88.48 214	61.80 301	93.44 121	88.26 357	73.96 204	77.73 178	91.76 180	49.94 305	94.76 238	65.84 309	90.37 105	94.65 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 192	80.02 190	83.33 227	87.87 241	60.76 328	92.62 160	86.86 380	77.86 134	75.73 200	91.39 192	46.35 346	94.70 246	72.79 229	88.68 125	94.52 133

UWE-MVS		80.81 193	81.01 173	80.20 318	89.33 178	57.05 389	91.91 201	94.71 42	75.67 175	75.01 213	89.37 240	63.13 129	91.44 371	67.19 294	82.80 203	92.12 239

IMVS_0407		80.80 194	79.39 207	85.00 150	88.54 203	64.75 202	88.40 330	90.80 237	76.73 161	73.95 233	90.18 219	51.55 286	95.81 183	73.47 219	80.95 225	94.43 143

1314		80.70 195	78.95 215	85.94 108	87.77 247	67.56 118	87.91 339	92.55 139	72.17 249	67.44 324	93.09 139	50.27 301	97.04 109	71.68 244	87.64 136	93.23 195

AstraMVS		80.66 196	79.79 197	83.28 231	85.07 318	61.64 308	92.19 183	90.58 249	79.40 102	74.77 219	90.18 219	45.93 352	95.61 201	83.04 130	76.96 274	92.60 218

tpmrst		80.57 197	79.14 213	84.84 157	90.10 161	68.28 96	81.70 402	89.72 293	77.63 142	75.96 198	79.54 386	64.94 93	92.71 327	75.43 203	77.28 271	93.55 185

1112_ss		80.56 198	79.83 196	82.77 243	88.65 200	60.78 326	92.29 178	88.36 350	72.58 235	72.46 255	94.95 87	65.09 90	93.42 305	66.38 303	77.71 262	94.10 160

VDDNet		80.50 199	78.26 223	87.21 51	86.19 290	69.79 48	94.48 60	91.31 197	60.42 400	79.34 158	90.91 204	38.48 387	96.56 139	82.16 139	81.05 224	95.27 81

BH-w/o		80.49 200	79.30 209	84.05 199	90.83 148	64.36 222	93.60 111	89.42 303	74.35 195	69.09 295	90.15 227	55.23 241	95.61 201	64.61 322	86.43 157	92.17 237

test_cas_vis1_n_1920		80.45 201	80.61 181	79.97 327	78.25 413	57.01 391	94.04 84	88.33 352	79.06 114	82.81 106	93.70 129	38.65 384	91.63 362	90.82 52	79.81 239	91.27 261

icg_test_0407_2		80.38 202	79.22 211	83.88 203	88.54 203	64.75 202	86.79 357	90.80 237	76.73 161	73.95 233	90.18 219	51.55 286	92.45 339	73.47 219	80.95 225	94.43 143

TAMVS		80.37 203	79.45 204	83.13 237	85.14 315	63.37 260	91.23 243	90.76 242	74.81 190	72.65 247	88.49 253	60.63 161	92.95 315	69.41 264	81.95 216	93.08 202

HQP_MVS		80.34 204	79.75 198	82.12 268	86.94 271	62.42 285	93.13 131	91.31 197	78.81 118	72.53 250	89.14 246	50.66 296	95.55 207	76.74 193	78.53 258	88.39 303

SDMVSNet		80.26 205	78.88 216	84.40 184	89.25 182	67.63 117	85.35 366	93.02 114	76.77 159	70.84 275	87.12 282	47.95 329	96.09 163	85.04 100	74.55 286	89.48 288

HPM-MVS_fast		80.25 206	79.55 203	82.33 258	91.55 129	59.95 351	91.32 238	89.16 314	65.23 357	74.71 221	93.07 141	47.81 331	95.74 189	74.87 212	88.23 128	91.31 259

ab-mvs		80.18 207	78.31 222	85.80 114	88.44 216	65.49 186	83.00 392	92.67 131	71.82 261	77.36 185	85.01 309	54.50 249	96.59 136	76.35 198	75.63 282	95.32 76

IS-MVSNet		80.14 208	79.41 205	82.33 258	87.91 237	60.08 349	91.97 198	88.27 355	72.90 230	71.44 271	91.73 182	61.44 150	93.66 298	62.47 340	86.53 154	93.24 194

test-LLR		80.10 209	79.56 201	81.72 277	86.93 273	61.17 318	92.70 153	91.54 188	71.51 276	75.62 202	86.94 286	53.83 260	92.38 341	72.21 237	84.76 177	91.60 249

PVSNet	73.49 8	80.05 210	78.63 218	84.31 188	90.92 145	64.97 198	92.47 172	91.05 225	79.18 108	72.43 256	90.51 210	37.05 404	94.06 277	68.06 281	86.00 158	93.90 175

UA-Net		80.02 211	79.65 199	81.11 296	89.33 178	57.72 378	86.33 362	89.00 330	77.44 146	81.01 126	89.15 245	59.33 183	95.90 174	61.01 347	84.28 184	89.73 284

test-mter		79.96 212	79.38 208	81.72 277	86.93 273	61.17 318	92.70 153	91.54 188	73.85 206	75.62 202	86.94 286	49.84 307	92.38 341	72.21 237	84.76 177	91.60 249

QAPM		79.95 213	77.39 244	87.64 35	89.63 170	71.41 20	93.30 126	93.70 83	65.34 356	67.39 327	91.75 181	47.83 330	98.96 19	57.71 363	89.81 112	92.54 221

UGNet		79.87 214	78.68 217	83.45 225	89.96 163	61.51 311	92.13 186	90.79 241	76.83 157	78.85 168	86.33 294	38.16 390	96.17 159	67.93 284	87.17 141	92.67 215
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
tpm2		79.80 215	77.95 230	85.34 135	88.28 224	68.26 97	81.56 404	91.42 194	70.11 298	77.59 182	80.50 372	67.40 67	94.26 268	67.34 291	77.35 269	93.51 187

thres200		79.66 216	78.33 221	83.66 217	92.54 95	65.82 177	93.06 133	96.31 3	74.90 189	73.30 239	88.66 251	59.67 176	95.61 201	47.84 407	78.67 256	89.56 287

CPTT-MVS		79.59 217	79.16 212	80.89 306	91.54 130	59.80 353	92.10 188	88.54 347	60.42 400	72.96 241	93.28 137	48.27 322	92.80 324	78.89 182	86.50 155	90.06 277

Test_1112_low_res		79.56 218	78.60 219	82.43 253	88.24 227	60.39 342	92.09 189	87.99 362	72.10 251	71.84 263	87.42 277	64.62 98	93.04 311	65.80 310	77.30 270	93.85 177

tttt0517		79.50 219	78.53 220	82.41 256	87.22 259	61.43 315	89.75 296	94.76 39	69.29 310	67.91 316	88.06 267	72.92 31	95.63 198	62.91 336	73.90 296	90.16 276

reproduce_monomvs		79.49 220	79.11 214	80.64 308	92.91 82	61.47 314	91.17 248	93.28 102	83.09 33	64.04 357	82.38 340	66.19 76	94.57 250	81.19 157	57.71 415	85.88 357

FIs		79.47 221	79.41 205	79.67 335	85.95 297	59.40 359	91.68 219	93.94 72	78.06 130	68.96 301	88.28 258	66.61 73	91.77 358	66.20 306	74.99 285	87.82 309

SSM_0404		79.46 222	77.65 234	84.91 153	88.37 222	67.04 137	89.59 297	87.03 376	67.99 328	75.45 207	89.32 241	47.98 326	95.34 217	71.23 247	81.90 217	92.34 227

BH-RMVSNet		79.46 222	77.65 234	84.89 154	91.68 125	65.66 178	93.55 113	88.09 360	72.93 227	73.37 238	91.12 202	46.20 350	96.12 161	56.28 369	85.61 165	92.91 208

viewdifsd2359ckpt11		79.42 224	77.95 230	83.81 206	83.87 341	63.85 237	89.54 302	87.38 369	77.39 149	74.94 214	89.95 232	51.11 292	94.72 241	79.52 171	67.90 338	92.88 211

viewmsd2359difaftdt		79.42 224	77.96 229	83.81 206	83.88 340	63.85 237	89.54 302	87.38 369	77.39 149	74.94 214	89.95 232	51.11 292	94.72 241	79.52 171	67.90 338	92.88 211

PCF-MVS	73.15 9	79.29 226	77.63 236	84.29 189	86.06 295	65.96 171	87.03 352	91.10 216	69.86 304	69.79 291	90.64 206	57.54 211	96.59 136	64.37 325	82.29 206	90.32 274
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 227	79.57 200	78.24 356	88.46 215	52.29 415	90.41 275	89.12 318	74.24 197	69.13 294	91.91 178	65.77 83	90.09 387	59.00 359	88.09 130	92.33 228

114514_t		79.17 228	77.67 233	83.68 215	95.32 30	65.53 184	92.85 147	91.60 187	63.49 370	67.92 315	90.63 208	46.65 343	95.72 195	67.01 296	83.54 195	89.79 282

FA-MVS(test-final)		79.12 229	77.23 246	84.81 161	90.54 151	63.98 236	81.35 407	91.71 180	71.09 284	74.85 218	82.94 333	52.85 271	97.05 106	67.97 282	81.73 220	93.41 189

SSM_0407		79.09 230	77.21 247	84.75 165	88.50 208	66.98 141	89.21 313	87.03 376	67.99 328	74.12 228	89.32 241	47.98 326	95.29 222	71.23 247	79.52 242	91.98 242

VPA-MVSNet		79.03 231	78.00 227	82.11 271	85.95 297	64.48 213	93.22 129	94.66 45	75.05 187	74.04 232	84.95 310	52.17 278	93.52 300	74.90 211	67.04 345	88.32 305

OPM-MVS		79.00 232	78.09 225	81.73 276	83.52 347	63.83 240	91.64 221	90.30 264	76.36 170	71.97 262	89.93 234	46.30 349	95.17 225	75.10 206	77.70 263	86.19 345
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 233	78.22 224	81.25 290	85.33 308	62.73 280	89.53 305	93.21 104	72.39 242	72.14 259	90.13 228	60.99 154	94.72 241	67.73 286	72.49 305	86.29 342

AdaColmap		78.94 234	77.00 251	84.76 164	96.34 17	65.86 175	92.66 159	87.97 364	62.18 384	70.56 277	92.37 159	43.53 364	97.35 85	64.50 324	82.86 200	91.05 264

GeoE		78.90 235	77.43 240	83.29 230	88.95 193	62.02 295	92.31 177	86.23 388	70.24 297	71.34 272	89.27 243	54.43 253	94.04 280	63.31 332	80.81 232	93.81 178

miper_enhance_ethall		78.86 236	77.97 228	81.54 283	88.00 236	65.17 192	91.41 226	89.15 315	75.19 185	68.79 304	83.98 323	67.17 68	92.82 322	72.73 231	65.30 356	86.62 335

VPNet		78.82 237	77.53 239	82.70 246	84.52 328	66.44 158	93.93 90	92.23 147	80.46 71	72.60 248	88.38 257	49.18 315	93.13 310	72.47 235	63.97 376	88.55 300

EPNet_dtu		78.80 238	79.26 210	77.43 364	88.06 232	49.71 432	91.96 199	91.95 165	77.67 139	76.56 195	91.28 196	58.51 197	90.20 385	56.37 368	80.95 225	92.39 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 239	77.43 240	82.88 241	92.21 101	64.49 211	92.05 192	96.28 4	73.48 215	71.75 265	88.26 260	60.07 170	95.32 218	45.16 420	77.58 265	88.83 293

TR-MVS		78.77 240	77.37 245	82.95 240	90.49 153	60.88 324	93.67 107	90.07 275	70.08 301	74.51 222	91.37 193	45.69 353	95.70 196	60.12 353	80.32 236	92.29 230

thres400		78.68 241	77.43 240	82.43 253	92.21 101	64.49 211	92.05 192	96.28 4	73.48 215	71.75 265	88.26 260	60.07 170	95.32 218	45.16 420	77.58 265	87.48 314

BH-untuned		78.68 241	77.08 248	83.48 224	89.84 165	63.74 243	92.70 153	88.59 344	71.57 273	66.83 334	88.65 252	51.75 282	95.39 213	59.03 358	84.77 176	91.32 258

OMC-MVS		78.67 243	77.91 232	80.95 303	85.76 303	57.40 385	88.49 328	88.67 341	73.85 206	72.43 256	92.10 168	49.29 314	94.55 254	72.73 231	77.89 261	90.91 268

tpm		78.58 244	77.03 249	83.22 234	85.94 299	64.56 209	83.21 388	91.14 211	78.31 127	73.67 236	79.68 384	64.01 107	92.09 352	66.07 307	71.26 315	93.03 204

OpenMVS	70.45 11	78.54 245	75.92 269	86.41 93	85.93 300	71.68 18	92.74 150	92.51 140	66.49 344	64.56 351	91.96 174	43.88 363	98.10 44	54.61 374	90.65 99	89.44 290

EPMVS		78.49 246	75.98 268	86.02 105	91.21 139	69.68 53	80.23 416	91.20 205	75.25 184	72.48 254	78.11 395	54.65 248	93.69 297	57.66 364	83.04 199	94.69 118

AUN-MVS		78.37 247	77.43 240	81.17 292	86.60 281	57.45 384	89.46 307	91.16 207	74.11 199	74.40 223	90.49 211	55.52 238	94.57 250	74.73 213	60.43 406	91.48 252

thres100view900		78.37 247	77.01 250	82.46 252	91.89 119	63.21 266	91.19 247	96.33 1	72.28 245	70.45 280	87.89 269	60.31 165	95.32 218	45.16 420	77.58 265	88.83 293

GA-MVS		78.33 249	76.23 264	84.65 173	83.65 345	66.30 162	91.44 225	90.14 273	76.01 172	70.32 282	84.02 322	42.50 368	94.72 241	70.98 250	77.00 273	92.94 207

cascas		78.18 250	75.77 271	85.41 129	87.14 262	69.11 70	92.96 140	91.15 210	66.71 342	70.47 278	86.07 296	37.49 398	96.48 145	70.15 258	79.80 240	90.65 270

UniMVSNet_NR-MVSNet		78.15 251	77.55 238	79.98 325	84.46 331	60.26 344	92.25 179	93.20 106	77.50 145	68.88 302	86.61 289	66.10 78	92.13 350	66.38 303	62.55 384	87.54 312

LuminaMVS		78.14 252	76.66 255	82.60 250	80.82 374	64.64 208	89.33 309	90.45 251	68.25 326	74.73 220	85.51 305	41.15 374	94.14 271	78.96 180	80.69 234	89.04 291

IMVS_0404		78.11 253	76.29 263	83.59 218	88.54 203	64.75 202	84.63 371	90.80 237	76.73 161	61.16 380	90.18 219	40.17 378	91.58 364	73.47 219	80.95 225	94.43 143

thres600view7		78.00 254	76.66 255	82.03 273	91.93 115	63.69 250	91.30 239	96.33 1	72.43 240	70.46 279	87.89 269	60.31 165	94.92 234	42.64 432	76.64 276	87.48 314

FC-MVSNet-test		77.99 255	78.08 226	77.70 359	84.89 321	55.51 401	90.27 281	93.75 81	76.87 154	66.80 335	87.59 274	65.71 84	90.23 384	62.89 337	73.94 294	87.37 317

Anonymous202405211		77.96 256	75.33 277	85.87 110	93.73 57	64.52 210	94.85 50	85.36 401	62.52 382	76.11 197	90.18 219	29.43 437	97.29 89	68.51 275	77.24 272	95.81 53

cl22		77.94 257	76.78 253	81.42 285	87.57 249	64.93 200	90.67 266	88.86 334	72.45 239	67.63 322	82.68 337	64.07 105	92.91 320	71.79 240	65.30 356	86.44 337

XXY-MVS		77.94 257	76.44 258	82.43 253	82.60 357	64.44 215	92.01 194	91.83 174	73.59 214	70.00 287	85.82 301	54.43 253	94.76 238	69.63 261	68.02 337	88.10 307

MS-PatchMatch		77.90 259	76.50 257	82.12 268	85.99 296	69.95 42	91.75 214	92.70 127	73.97 203	62.58 374	84.44 317	41.11 375	95.78 186	63.76 329	92.17 71	80.62 422

FE-MVSNET3		77.89 260	76.39 261	82.40 257	81.92 365	67.01 140	91.94 200	93.00 117	77.01 152	68.44 311	84.15 319	54.78 247	93.25 307	65.76 311	70.53 318	86.94 326

FMVSNet3		77.73 261	76.04 267	82.80 242	91.20 140	68.99 76	91.87 203	91.99 163	73.35 217	67.04 330	83.19 332	56.62 225	92.14 349	59.80 355	69.34 323	87.28 320

VortexMVS		77.62 262	76.44 258	81.13 294	88.58 201	63.73 245	91.24 242	91.30 201	77.81 135	65.76 340	81.97 346	49.69 309	93.72 293	76.40 197	65.26 359	85.94 355

miper_ehance_all_eth		77.60 263	76.44 258	81.09 300	85.70 305	64.41 218	90.65 267	88.64 343	72.31 243	67.37 328	82.52 338	64.77 97	92.64 333	70.67 254	65.30 356	86.24 344

UniMVSNet (Re)		77.58 264	76.78 253	79.98 325	84.11 337	60.80 325	91.76 212	93.17 108	76.56 167	69.93 290	84.78 312	63.32 124	92.36 343	64.89 320	62.51 386	86.78 329

PatchmatchNet		77.46 265	74.63 284	85.96 107	89.55 173	70.35 35	79.97 421	89.55 298	72.23 246	70.94 273	76.91 408	57.03 215	92.79 325	54.27 376	81.17 223	94.74 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 266	75.65 273	82.73 244	80.38 382	67.13 134	91.85 205	90.23 269	75.09 186	69.37 292	83.39 329	53.79 262	94.44 258	71.77 241	65.00 363	86.63 334

CHOSEN 280x420		77.35 267	76.95 252	78.55 351	87.07 264	62.68 281	69.71 453	82.95 423	68.80 319	71.48 270	87.27 281	66.03 79	84.00 436	76.47 196	82.81 202	88.95 292

PS-MVSNAJss		77.26 268	76.31 262	80.13 320	80.64 378	59.16 364	90.63 270	91.06 222	72.80 231	68.58 308	84.57 315	53.55 264	93.96 285	72.97 225	71.96 309	87.27 321

gg-mvs-nofinetune		77.18 269	74.31 291	85.80 114	91.42 132	68.36 93	71.78 447	94.72 41	49.61 445	77.12 189	45.92 474	77.41 8	93.98 284	67.62 287	93.16 59	95.05 93

WB-MVSnew		77.14 270	76.18 266	80.01 324	86.18 291	63.24 264	91.26 240	94.11 69	71.72 265	73.52 237	87.29 280	45.14 358	93.00 313	56.98 366	79.42 245	83.80 383

MVP-Stereo		77.12 271	76.23 264	79.79 332	81.72 366	66.34 161	89.29 310	90.88 232	70.56 294	62.01 377	82.88 334	49.34 312	94.13 272	65.55 315	93.80 47	78.88 438
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 272	75.37 275	82.20 264	89.25 182	62.11 294	82.06 399	89.09 320	76.77 159	70.84 275	87.12 282	41.43 373	95.01 229	67.23 293	74.55 286	89.48 288

MonoMVSNet		76.99 273	75.08 280	82.73 244	83.32 349	63.24 264	86.47 361	86.37 384	79.08 112	66.31 338	79.30 388	49.80 308	91.72 359	79.37 173	65.70 354	93.23 195

dmvs_re		76.93 274	75.36 276	81.61 281	87.78 246	60.71 332	80.00 420	87.99 362	79.42 101	69.02 298	89.47 238	46.77 341	94.32 262	63.38 331	74.45 289	89.81 281

X-MVStestdata		76.86 275	74.13 297	85.05 147	93.22 70	63.78 241	92.92 142	92.66 132	73.99 201	78.18 174	10.19 489	55.25 239	97.41 81	79.16 176	91.58 83	93.95 168

DU-MVS		76.86 275	75.84 270	79.91 328	82.96 353	60.26 344	91.26 240	91.54 188	76.46 169	68.88 302	86.35 292	56.16 230	92.13 350	66.38 303	62.55 384	87.35 318

Anonymous20240529		76.84 277	74.15 296	84.88 155	91.02 142	64.95 199	93.84 99	91.09 217	53.57 433	73.00 240	87.42 277	35.91 408	97.32 87	69.14 269	72.41 307	92.36 226

UWE-MVS-28		76.83 278	77.60 237	74.51 394	84.58 327	50.34 428	88.22 333	94.60 49	74.46 192	66.66 336	88.98 250	62.53 137	85.50 428	57.55 365	80.80 233	87.69 311

c3_l		76.83 278	75.47 274	80.93 304	85.02 319	64.18 229	90.39 276	88.11 359	71.66 266	66.65 337	81.64 352	63.58 120	92.56 334	69.31 266	62.86 381	86.04 350

WR-MVS		76.76 280	75.74 272	79.82 331	84.60 325	62.27 291	92.60 163	92.51 140	76.06 171	67.87 319	85.34 306	56.76 221	90.24 383	62.20 341	63.69 378	86.94 326

v1144		76.73 281	74.88 281	82.27 260	80.23 386	66.60 155	91.68 219	90.21 272	73.69 211	69.06 297	81.89 347	52.73 274	94.40 260	69.21 267	65.23 360	85.80 358

IterMVS-LS		76.49 282	75.18 279	80.43 312	84.49 330	62.74 279	90.64 268	88.80 336	72.40 241	65.16 346	81.72 350	60.98 155	92.27 347	67.74 285	64.65 368	86.29 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 283	74.55 287	82.19 265	79.14 400	67.82 111	90.26 282	89.42 303	73.75 209	68.63 307	81.89 347	51.31 289	94.09 274	71.69 243	64.84 364	84.66 375

Elysia		76.45 284	74.17 294	83.30 228	80.43 380	64.12 230	89.58 298	90.83 234	61.78 392	72.53 250	85.92 299	34.30 415	94.81 236	68.10 279	84.01 190	90.97 265

StellarMVS		76.45 284	74.17 294	83.30 228	80.43 380	64.12 230	89.58 298	90.83 234	61.78 392	72.53 250	85.92 299	34.30 415	94.81 236	68.10 279	84.01 190	90.97 265

mamba_0408		76.22 286	73.37 308	84.77 162	88.50 208	66.98 141	58.80 474	86.18 390	69.12 315	74.12 228	89.01 248	47.50 333	95.35 215	67.57 288	79.52 242	91.98 242

v148		76.19 287	74.47 289	81.36 287	80.05 388	64.44 215	91.75 214	90.23 269	73.68 212	67.13 329	80.84 367	55.92 235	93.86 292	68.95 271	61.73 395	85.76 361

Effi-MVS+-dtu		76.14 288	75.28 278	78.72 350	83.22 350	55.17 403	89.87 293	87.78 366	75.42 180	67.98 314	81.43 356	45.08 359	92.52 336	75.08 207	71.63 310	88.48 301

cl____		76.07 289	74.67 282	80.28 315	85.15 314	61.76 304	90.12 285	88.73 338	71.16 281	65.43 343	81.57 354	61.15 152	92.95 315	66.54 300	62.17 388	86.13 348

DIV-MVS_self_test		76.07 289	74.67 282	80.28 315	85.14 315	61.75 305	90.12 285	88.73 338	71.16 281	65.42 344	81.60 353	61.15 152	92.94 319	66.54 300	62.16 390	86.14 346

FMVSNet2		76.07 289	74.01 299	82.26 262	88.85 194	67.66 115	91.33 237	91.61 186	70.84 288	65.98 339	82.25 342	48.03 323	92.00 354	58.46 360	68.73 331	87.10 323

v144192		76.05 292	74.03 298	82.12 268	79.50 394	66.55 157	91.39 230	89.71 294	72.30 244	68.17 312	81.33 359	51.75 282	94.03 282	67.94 283	64.19 371	85.77 359

NR-MVSNet		76.05 292	74.59 285	80.44 311	82.96 353	62.18 293	90.83 258	91.73 178	77.12 151	60.96 382	86.35 292	59.28 184	91.80 357	60.74 348	61.34 399	87.35 318

v1192		75.98 294	73.92 300	82.15 266	79.73 390	66.24 164	91.22 244	89.75 288	72.67 233	68.49 309	81.42 357	49.86 306	94.27 266	67.08 295	65.02 362	85.95 353

FE-MVS		75.97 295	73.02 314	84.82 158	89.78 166	65.56 182	77.44 432	91.07 221	64.55 359	72.66 246	79.85 382	46.05 351	96.69 134	54.97 373	80.82 231	92.21 236

eth_miper_zixun_eth		75.96 296	74.40 290	80.66 307	84.66 324	63.02 270	89.28 311	88.27 355	71.88 257	65.73 341	81.65 351	59.45 180	92.81 323	68.13 278	60.53 404	86.14 346

TranMVSNet+NR-MVSNet		75.86 297	74.52 288	79.89 329	82.44 359	60.64 335	91.37 233	91.37 195	76.63 165	67.65 321	86.21 295	52.37 277	91.55 365	61.84 343	60.81 402	87.48 314

SCA		75.82 298	72.76 318	85.01 149	86.63 280	70.08 38	81.06 409	89.19 312	71.60 272	70.01 286	77.09 406	45.53 354	90.25 380	60.43 350	73.27 298	94.68 120

LPG-MVS_test		75.82 298	74.58 286	79.56 339	84.31 334	59.37 360	90.44 273	89.73 291	69.49 307	64.86 347	88.42 255	38.65 384	94.30 264	72.56 233	72.76 302	85.01 372

GBi-Net		75.65 300	73.83 301	81.10 297	88.85 194	65.11 194	90.01 289	90.32 260	70.84 288	67.04 330	80.25 377	48.03 323	91.54 366	59.80 355	69.34 323	86.64 331

test1		75.65 300	73.83 301	81.10 297	88.85 194	65.11 194	90.01 289	90.32 260	70.84 288	67.04 330	80.25 377	48.03 323	91.54 366	59.80 355	69.34 323	86.64 331

v1921920		75.63 302	73.49 306	82.06 272	79.38 395	66.35 160	91.07 252	89.48 299	71.98 252	67.99 313	81.22 362	49.16 317	93.90 288	66.56 299	64.56 369	85.92 356

ACMP	71.68 10	75.58 303	74.23 293	79.62 337	84.97 320	59.64 355	90.80 259	89.07 322	70.39 295	62.95 370	87.30 279	38.28 388	93.87 290	72.89 226	71.45 313	85.36 368
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 304	73.26 312	81.61 281	80.67 377	66.82 147	89.54 302	89.27 308	71.65 267	63.30 365	80.30 376	54.99 245	94.06 277	67.33 292	62.33 387	83.94 381

tpm cat1		75.30 305	72.21 327	84.58 178	88.52 207	67.77 112	78.16 430	88.02 361	61.88 390	68.45 310	76.37 415	60.65 160	94.03 282	53.77 380	74.11 292	91.93 245

PLC	68.80 14	75.23 306	73.68 304	79.86 330	92.93 81	58.68 369	90.64 268	88.30 353	60.90 397	64.43 355	90.53 209	42.38 369	94.57 250	56.52 367	76.54 277	86.33 341
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 307	72.98 316	81.88 274	79.20 397	66.00 169	90.75 262	89.11 319	71.63 271	67.41 326	81.22 362	47.36 335	93.87 290	65.46 316	64.72 367	85.77 359

blend_shiyan4		75.18 308	73.00 315	81.69 279	75.62 432	64.75 202	91.78 209	91.06 222	65.89 350	61.35 379	77.39 400	62.16 142	93.71 294	68.18 276	63.60 379	86.61 336

Fast-Effi-MVS+-dtu		75.04 309	73.37 308	80.07 321	80.86 372	59.52 358	91.20 246	85.38 400	71.90 255	65.20 345	84.84 311	41.46 372	92.97 314	66.50 302	72.96 301	87.73 310

dp		75.01 310	72.09 328	83.76 208	89.28 181	66.22 165	79.96 422	89.75 288	71.16 281	67.80 320	77.19 405	51.81 280	92.54 335	50.39 390	71.44 314	92.51 223

TAPA-MVS	70.22 12	74.94 311	73.53 305	79.17 345	90.40 155	52.07 416	89.19 315	89.61 297	62.69 381	70.07 285	92.67 151	48.89 320	94.32 262	38.26 446	79.97 238	91.12 263
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 312	73.32 311	79.74 334	86.53 283	60.31 343	89.03 320	92.70 127	78.61 123	68.98 300	83.34 330	41.93 371	92.23 348	52.77 384	65.97 352	86.69 330

SSM_04072		74.86 313	73.37 308	79.35 342	88.50 208	66.98 141	58.80 474	86.18 390	69.12 315	74.12 228	89.01 248	47.50 333	79.09 458	67.57 288	79.52 242	91.98 242

v10		74.77 314	72.54 324	81.46 284	80.33 384	66.71 152	89.15 316	89.08 321	70.94 286	63.08 368	79.86 381	52.52 275	94.04 280	65.70 312	62.17 388	83.64 384

XVG-OURS-SEG-HR		74.70 315	73.08 313	79.57 338	78.25 413	57.33 386	80.49 412	87.32 371	63.22 374	68.76 305	90.12 230	44.89 360	91.59 363	70.55 256	74.09 293	89.79 282

ACMM	69.62 13	74.34 316	72.73 320	79.17 345	84.25 336	57.87 376	90.36 278	89.93 282	63.17 376	65.64 342	86.04 298	37.79 396	94.10 273	65.89 308	71.52 312	85.55 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 317	72.30 326	80.32 313	91.49 131	61.66 307	90.85 257	80.72 429	56.67 424	63.85 360	90.64 206	46.75 342	90.84 374	53.79 379	75.99 281	88.47 302

XVG-OURS		74.25 318	72.46 325	79.63 336	78.45 411	57.59 382	80.33 414	87.39 368	63.86 366	68.76 305	89.62 237	40.50 377	91.72 359	69.00 270	74.25 291	89.58 285

test_fmvs1		74.07 319	73.69 303	75.22 384	78.91 404	47.34 445	89.06 319	74.69 446	63.68 369	79.41 157	91.59 188	24.36 448	87.77 410	85.22 97	76.26 279	90.55 273

CVMVSNet		74.04 320	74.27 292	73.33 404	85.33 308	43.94 459	89.53 305	88.39 349	54.33 432	70.37 281	90.13 228	49.17 316	84.05 434	61.83 344	79.36 247	91.99 241

Baseline_NR-MVSNet		73.99 321	72.83 317	77.48 363	80.78 375	59.29 363	91.79 207	84.55 409	68.85 318	68.99 299	80.70 368	56.16 230	92.04 353	62.67 338	60.98 401	81.11 416

pmmvs4		73.92 322	71.81 332	80.25 317	79.17 398	65.24 190	87.43 348	87.26 374	67.64 335	63.46 363	83.91 324	48.96 319	91.53 369	62.94 335	65.49 355	83.96 380

D2MVS		73.80 323	72.02 329	79.15 347	79.15 399	62.97 271	88.58 327	90.07 275	72.94 226	59.22 395	78.30 392	42.31 370	92.70 329	65.59 314	72.00 308	81.79 411

SD_0403		73.79 324	73.48 307	74.69 391	85.33 308	45.56 455	83.80 378	85.57 399	76.55 168	62.96 369	88.45 254	50.62 298	87.59 414	48.80 400	79.28 251	90.92 267

CR-MVSNet		73.79 324	70.82 340	82.70 246	83.15 351	67.96 106	70.25 450	84.00 414	73.67 213	69.97 288	72.41 432	57.82 208	89.48 393	52.99 383	73.13 299	90.64 271

test_djsdf		73.76 326	72.56 323	77.39 365	77.00 425	53.93 409	89.07 317	90.69 243	65.80 351	63.92 358	82.03 345	43.14 367	92.67 330	72.83 227	68.53 332	85.57 363

pmmvs5		73.35 327	71.52 334	78.86 349	78.64 408	60.61 336	91.08 250	86.90 378	67.69 332	63.32 364	83.64 325	44.33 362	90.53 377	62.04 342	66.02 351	85.46 366

Anonymous20231211		73.08 328	70.39 344	81.13 294	90.62 150	63.33 261	91.40 228	90.06 277	51.84 438	64.46 354	80.67 370	36.49 406	94.07 276	63.83 328	64.17 372	85.98 352

tt0805		73.07 329	70.73 341	80.07 321	78.37 412	57.05 389	87.78 342	92.18 154	61.23 396	67.04 330	86.49 291	31.35 429	94.58 248	65.06 319	67.12 344	88.57 299

miper_lstm_enhance		73.05 330	71.73 333	77.03 370	83.80 342	58.32 373	81.76 400	88.88 332	69.80 305	61.01 381	78.23 394	57.19 213	87.51 416	65.34 317	59.53 409	85.27 371

jajsoiax		73.05 330	71.51 335	77.67 360	77.46 422	54.83 405	88.81 323	90.04 278	69.13 314	62.85 372	83.51 327	31.16 430	92.75 326	70.83 251	69.80 319	85.43 367

LCM-MVSNet-Re		72.93 332	71.84 331	76.18 379	88.49 212	48.02 440	80.07 419	70.17 461	73.96 204	52.25 429	80.09 380	49.98 304	88.24 404	67.35 290	84.23 185	92.28 231

pm-mvs1		72.89 333	71.09 337	78.26 355	79.10 401	57.62 380	90.80 259	89.30 307	67.66 333	62.91 371	81.78 349	49.11 318	92.95 315	60.29 352	58.89 412	84.22 379

tpmvs		72.88 334	69.76 350	82.22 263	90.98 143	67.05 136	78.22 429	88.30 353	63.10 377	64.35 356	74.98 422	55.09 244	94.27 266	43.25 426	69.57 322	85.34 369

test0.0.03 1		72.76 335	72.71 321	72.88 408	80.25 385	47.99 441	91.22 244	89.45 301	71.51 276	62.51 375	87.66 272	53.83 260	85.06 430	50.16 392	67.84 342	85.58 362

UniMVSNet_ETH3D		72.74 336	70.53 343	79.36 341	78.62 409	56.64 393	85.01 368	89.20 311	63.77 367	64.84 349	84.44 317	34.05 417	91.86 356	63.94 327	70.89 317	89.57 286

mvs_tets		72.71 337	71.11 336	77.52 361	77.41 423	54.52 407	88.45 329	89.76 287	68.76 321	62.70 373	83.26 331	29.49 436	92.71 327	70.51 257	69.62 321	85.34 369

FMVSNet1		72.71 337	69.91 348	81.10 297	83.60 346	65.11 194	90.01 289	90.32 260	63.92 365	63.56 362	80.25 377	36.35 407	91.54 366	54.46 375	66.75 347	86.64 331

test_fmvs1_n		72.69 339	71.92 330	74.99 389	71.15 449	47.08 447	87.34 350	75.67 441	63.48 371	78.08 176	91.17 201	20.16 462	87.87 407	84.65 106	75.57 283	90.01 279

IterMVS		72.65 340	70.83 338	78.09 357	82.17 361	62.96 272	87.64 346	86.28 386	71.56 274	60.44 387	78.85 390	45.42 356	86.66 420	63.30 333	61.83 392	84.65 376
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 341	72.74 319	72.10 416	87.87 241	49.45 434	88.07 335	89.01 326	72.91 228	63.11 366	88.10 264	63.63 115	85.54 425	32.73 462	69.23 326	81.32 414

FE-blended-shiyan7		72.42 342	69.43 352	81.37 286	75.39 433	64.24 227	91.58 222	91.09 217	66.36 345	60.64 384	76.86 409	47.20 337	93.47 302	64.80 321	50.98 435	86.40 338

blended_shiyan6		72.26 343	69.26 354	81.27 289	75.24 436	64.00 235	91.37 233	91.06 222	66.12 348	60.34 388	76.75 411	46.82 339	93.45 304	64.61 322	50.98 435	86.37 340

PatchMatch-RL		72.06 344	69.98 345	78.28 354	89.51 174	55.70 400	83.49 381	83.39 421	61.24 395	63.72 361	82.76 335	34.77 412	93.03 312	53.37 382	77.59 264	86.12 349

PVSNet_0	68.08 15	71.81 345	68.32 361	82.27 260	84.68 322	62.31 290	88.68 325	90.31 263	75.84 173	57.93 407	80.65 371	37.85 395	94.19 269	69.94 259	29.05 477	90.31 275

MIMVSNet		71.64 346	68.44 359	81.23 291	81.97 364	64.44 215	73.05 444	88.80 336	69.67 306	64.59 350	74.79 424	32.79 421	87.82 408	53.99 377	76.35 278	91.42 253

test_vis1_n		71.63 347	70.73 341	74.31 398	69.63 456	47.29 446	86.91 354	72.11 454	63.21 375	75.18 211	90.17 225	20.40 460	85.76 424	84.59 107	74.42 290	89.87 280

IterMVS-SCA-FT		71.55 348	69.97 346	76.32 377	81.48 368	60.67 334	87.64 346	85.99 393	66.17 347	59.50 393	78.88 389	45.53 354	83.65 438	62.58 339	61.93 391	84.63 378

v7n		71.31 349	68.65 356	79.28 343	76.40 427	60.77 327	86.71 358	89.45 301	64.17 364	58.77 400	78.24 393	44.59 361	93.54 299	57.76 362	61.75 394	83.52 387

anonymousdsp		71.14 350	69.37 353	76.45 376	72.95 444	54.71 406	84.19 375	88.88 332	61.92 389	62.15 376	79.77 383	38.14 391	91.44 371	68.90 272	67.45 343	83.21 393

usedtu_blend_shiyan5		71.06 351	67.54 364	81.62 280	75.39 433	64.75 202	85.67 364	86.47 383	56.48 425	60.64 384	76.85 410	47.20 337	93.71 294	68.18 276	50.98 435	86.40 338

F-COLMAP		70.66 352	68.44 359	77.32 366	86.37 288	55.91 398	88.00 337	86.32 385	56.94 422	57.28 411	88.07 266	33.58 419	92.49 337	51.02 387	68.37 333	83.55 385

WR-MVS_H		70.59 353	69.94 347	72.53 410	81.03 371	51.43 420	87.35 349	92.03 162	67.38 336	60.23 390	80.70 368	55.84 236	83.45 441	46.33 415	58.58 414	82.72 400

CP-MVSNet		70.50 354	69.91 348	72.26 413	80.71 376	51.00 424	87.23 351	90.30 264	67.84 331	59.64 392	82.69 336	50.23 302	82.30 449	51.28 386	59.28 410	83.46 389

RPMNet		70.42 355	65.68 376	84.63 176	83.15 351	67.96 106	70.25 450	90.45 251	46.83 454	69.97 288	65.10 457	56.48 229	95.30 221	35.79 451	73.13 299	90.64 271

testing3		70.38 356	70.83 338	69.03 429	85.82 302	43.93 460	90.72 265	90.56 250	68.06 327	60.24 389	86.82 288	64.83 95	84.12 432	26.33 470	64.10 373	79.04 436

tfpnnormal		70.10 357	67.36 366	78.32 353	83.45 348	60.97 323	88.85 321	92.77 125	64.85 358	60.83 383	78.53 391	43.52 365	93.48 301	31.73 465	61.70 396	80.52 423

TransMVSNet (Re)		70.07 358	67.66 363	77.31 367	80.62 379	59.13 365	91.78 209	84.94 405	65.97 349	60.08 391	80.44 373	50.78 295	91.87 355	48.84 399	45.46 450	80.94 418

CL-MVSNet_self_test		69.92 359	68.09 362	75.41 382	73.25 443	55.90 399	90.05 288	89.90 283	69.96 302	61.96 378	76.54 412	51.05 294	87.64 411	49.51 396	50.59 439	82.70 402

DP-MVS		69.90 360	66.48 368	80.14 319	95.36 29	62.93 273	89.56 300	76.11 439	50.27 444	57.69 409	85.23 307	39.68 380	95.73 190	33.35 456	71.05 316	81.78 412

PS-CasMVS		69.86 361	69.13 355	72.07 417	80.35 383	50.57 427	87.02 353	89.75 288	67.27 337	59.19 396	82.28 341	46.58 344	82.24 450	50.69 389	59.02 411	83.39 391

Syy-MVS		69.65 362	69.52 351	70.03 425	87.87 241	43.21 461	88.07 335	89.01 326	72.91 228	63.11 366	88.10 264	45.28 357	85.54 425	22.07 475	69.23 326	81.32 414

MSDG		69.54 363	65.73 375	80.96 302	85.11 317	63.71 247	84.19 375	83.28 422	56.95 421	54.50 418	84.03 321	31.50 427	96.03 169	42.87 430	69.13 328	83.14 395

PEN-MVS		69.46 364	68.56 357	72.17 415	79.27 396	49.71 432	86.90 355	89.24 309	67.24 340	59.08 397	82.51 339	47.23 336	83.54 440	48.42 402	57.12 416	83.25 392

LS3D		69.17 365	66.40 370	77.50 362	91.92 116	56.12 396	85.12 367	80.37 431	46.96 452	56.50 413	87.51 276	37.25 399	93.71 294	32.52 464	79.40 246	82.68 403

PatchT		69.11 366	65.37 380	80.32 313	82.07 363	63.68 251	67.96 460	87.62 367	50.86 442	69.37 292	65.18 456	57.09 214	88.53 400	41.59 435	66.60 348	88.74 296

KD-MVS_2432*1600		69.03 367	66.37 371	77.01 371	85.56 306	61.06 321	81.44 405	90.25 267	67.27 337	58.00 405	76.53 413	54.49 250	87.63 412	48.04 404	35.77 468	82.34 406

miper_refine_blended		69.03 367	66.37 371	77.01 371	85.56 306	61.06 321	81.44 405	90.25 267	67.27 337	58.00 405	76.53 413	54.49 250	87.63 412	48.04 404	35.77 468	82.34 406

mvsany_test1		68.77 369	68.56 357	69.39 427	73.57 442	45.88 454	80.93 410	60.88 475	59.65 406	71.56 268	90.26 218	43.22 366	75.05 462	74.26 216	62.70 383	87.25 322

ACMH	63.93 17	68.62 370	64.81 382	80.03 323	85.22 313	63.25 263	87.72 343	84.66 407	60.83 398	51.57 433	79.43 387	27.29 443	94.96 231	41.76 433	64.84 364	81.88 410
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 371	65.41 379	77.96 358	78.69 407	62.93 273	89.86 294	89.17 313	60.55 399	50.27 439	77.73 399	22.60 456	94.06 277	47.18 411	72.65 304	76.88 450

ADS-MVSNet		68.54 372	64.38 389	81.03 301	88.06 232	66.90 146	68.01 458	84.02 413	57.57 415	64.48 352	69.87 444	38.68 382	89.21 395	40.87 437	67.89 340	86.97 324

DTE-MVSNet		68.46 373	67.33 367	71.87 419	77.94 417	49.00 438	86.16 363	88.58 345	66.36 345	58.19 402	82.21 343	46.36 345	83.87 437	44.97 423	55.17 423	82.73 399

mmtdpeth		68.33 374	66.37 371	74.21 399	82.81 356	51.73 417	84.34 373	80.42 430	67.01 341	71.56 268	68.58 448	30.52 434	92.35 344	75.89 200	36.21 466	78.56 443

our_test_3		68.29 375	64.69 384	79.11 348	78.92 402	64.85 201	88.40 330	85.06 403	60.32 402	52.68 427	76.12 417	40.81 376	89.80 392	44.25 425	55.65 421	82.67 404

Patchmatch-RL test		68.17 376	64.49 387	79.19 344	71.22 448	53.93 409	70.07 452	71.54 458	69.22 311	56.79 412	62.89 461	56.58 226	88.61 397	69.53 263	52.61 431	95.03 95

XVG-ACMP-BASELINE		68.04 377	65.53 378	75.56 381	74.06 441	52.37 414	78.43 426	85.88 394	62.03 387	58.91 399	81.21 364	20.38 461	91.15 373	60.69 349	68.18 334	83.16 394

FMVSNet5		68.04 377	65.66 377	75.18 386	84.43 332	57.89 375	83.54 380	86.26 387	61.83 391	53.64 424	73.30 427	37.15 402	85.08 429	48.99 398	61.77 393	82.56 405

ppachtmachnet_test		67.72 379	63.70 392	79.77 333	78.92 402	66.04 168	88.68 325	82.90 424	60.11 404	55.45 415	75.96 418	39.19 381	90.55 376	39.53 441	52.55 432	82.71 401

ACMH+	65.35 16	67.65 380	64.55 385	76.96 373	84.59 326	57.10 388	88.08 334	80.79 428	58.59 413	53.00 426	81.09 366	26.63 445	92.95 315	46.51 413	61.69 397	80.82 419

pmmvs6		67.57 381	64.76 383	76.00 380	72.82 446	53.37 411	88.71 324	86.78 382	53.19 434	57.58 410	78.03 396	35.33 411	92.41 340	55.56 371	54.88 425	82.21 408

Anonymous20231206		67.53 382	65.78 374	72.79 409	74.95 437	47.59 443	88.23 332	87.32 371	61.75 394	58.07 404	77.29 403	37.79 396	87.29 418	42.91 428	63.71 377	83.48 388

Patchmtry		67.53 382	63.93 391	78.34 352	82.12 362	64.38 219	68.72 455	84.00 414	48.23 451	59.24 394	72.41 432	57.82 208	89.27 394	46.10 416	56.68 420	81.36 413

USDC		67.43 384	64.51 386	76.19 378	77.94 417	55.29 402	78.38 427	85.00 404	73.17 219	48.36 447	80.37 374	21.23 458	92.48 338	52.15 385	64.02 375	80.81 420

ADS-MVSNet2		66.90 385	63.44 394	77.26 368	88.06 232	60.70 333	68.01 458	75.56 443	57.57 415	64.48 352	69.87 444	38.68 382	84.10 433	40.87 437	67.89 340	86.97 324

FE-MVSNET2		66.80 386	64.06 390	75.03 387	69.84 454	57.11 387	86.57 359	88.57 346	67.94 330	50.97 437	72.16 436	33.79 418	87.55 415	53.94 378	52.74 429	80.45 424

CMPMVS	48.56 21	66.77 387	64.41 388	73.84 401	70.65 452	50.31 429	77.79 431	85.73 397	45.54 457	44.76 458	82.14 344	35.40 410	90.14 386	63.18 334	74.54 288	81.07 417
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 388	62.92 397	76.80 375	76.51 426	57.77 377	89.22 312	83.41 420	55.48 429	53.86 422	77.84 397	26.28 446	93.95 286	34.90 453	68.76 330	78.68 441

LTVRE_ROB	59.60 19	66.27 389	63.54 393	74.45 395	84.00 339	51.55 419	67.08 462	83.53 418	58.78 411	54.94 417	80.31 375	34.54 413	93.23 308	40.64 439	68.03 336	78.58 442
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
JIA-IIPM		66.06 390	62.45 400	76.88 374	81.42 370	54.45 408	57.49 476	88.67 341	49.36 446	63.86 359	46.86 473	56.06 233	90.25 380	49.53 395	68.83 329	85.95 353

Patchmatch-test		65.86 391	60.94 406	80.62 310	83.75 343	58.83 367	58.91 473	75.26 445	44.50 460	50.95 438	77.09 406	58.81 193	87.90 406	35.13 452	64.03 374	95.12 89

UnsupCasMVSNet_eth		65.79 392	63.10 395	73.88 400	70.71 451	50.29 430	81.09 408	89.88 284	72.58 235	49.25 444	74.77 425	32.57 423	87.43 417	55.96 370	41.04 458	83.90 382

test_fmvs2		65.78 393	64.84 381	68.60 431	66.54 463	41.71 463	83.27 385	69.81 462	54.38 431	67.91 316	84.54 316	15.35 467	81.22 454	75.65 202	66.16 350	82.88 396

dmvs_testset		65.55 394	66.45 369	62.86 443	79.87 389	22.35 489	76.55 434	71.74 456	77.42 148	55.85 414	87.77 271	51.39 288	80.69 455	31.51 468	65.92 353	85.55 364

pmmvs-eth3d		65.53 395	62.32 401	75.19 385	69.39 457	59.59 356	82.80 393	83.43 419	62.52 382	51.30 435	72.49 430	32.86 420	87.16 419	55.32 372	50.73 438	78.83 439

mamv4		65.18 396	67.43 365	58.44 447	77.88 419	49.36 437	69.40 454	70.99 460	48.31 450	57.78 408	85.53 304	59.01 190	51.88 485	73.67 218	64.32 370	74.07 455

SixPastTwentyTwo		64.92 397	61.78 404	74.34 397	78.74 406	49.76 431	83.42 384	79.51 434	62.86 378	50.27 439	77.35 401	30.92 432	90.49 378	45.89 417	47.06 445	82.78 397

OurMVSNet-221017-0		64.68 398	62.17 402	72.21 414	76.08 430	47.35 444	80.67 411	81.02 427	56.19 426	51.60 432	79.66 385	27.05 444	88.56 399	53.60 381	53.63 428	80.71 421

test_0402		64.54 399	61.09 405	74.92 390	84.10 338	60.75 329	87.95 338	79.71 433	52.03 436	52.41 428	77.20 404	32.21 425	91.64 361	23.14 473	61.03 400	72.36 461

testgi		64.48 400	62.87 398	69.31 428	71.24 447	40.62 466	85.49 365	79.92 432	65.36 355	54.18 420	83.49 328	23.74 451	84.55 431	41.60 434	60.79 403	82.77 398

RPSCF		64.24 401	61.98 403	71.01 422	76.10 429	45.00 456	75.83 439	75.94 440	46.94 453	58.96 398	84.59 314	31.40 428	82.00 451	47.76 409	60.33 408	86.04 350

EU-MVSNet		64.01 402	63.01 396	67.02 437	74.40 440	38.86 472	83.27 385	86.19 389	45.11 458	54.27 419	81.15 365	36.91 405	80.01 457	48.79 401	57.02 417	82.19 409

test20.03		63.83 403	62.65 399	67.38 436	70.58 453	39.94 468	86.57 359	84.17 411	63.29 373	51.86 431	77.30 402	37.09 403	82.47 447	38.87 445	54.13 427	79.73 430

sc_t1		63.81 404	59.39 412	77.10 369	77.62 420	56.03 397	84.32 374	73.56 450	46.66 455	58.22 401	73.06 428	23.28 454	90.62 375	50.93 388	46.84 446	84.64 377

MDA-MVSNet_test_wron		63.78 405	60.16 408	74.64 392	78.15 415	60.41 340	83.49 381	84.03 412	56.17 428	39.17 468	71.59 439	37.22 400	83.24 444	42.87 430	48.73 441	80.26 427

YYNet1		63.76 406	60.14 409	74.62 393	78.06 416	60.19 347	83.46 383	83.99 416	56.18 427	39.25 467	71.56 440	37.18 401	83.34 442	42.90 429	48.70 442	80.32 426

K. test v3		63.09 407	59.61 411	73.53 403	76.26 428	49.38 436	83.27 385	77.15 437	64.35 361	47.77 449	72.32 434	28.73 438	87.79 409	49.93 394	36.69 465	83.41 390

COLMAP_ROB	57.96 20	62.98 408	59.65 410	72.98 407	81.44 369	53.00 413	83.75 379	75.53 444	48.34 449	48.81 446	81.40 358	24.14 449	90.30 379	32.95 459	60.52 405	75.65 453
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 409	59.08 413	71.10 421	67.19 461	48.72 439	83.91 377	85.23 402	50.38 443	47.84 448	71.22 442	20.74 459	85.51 427	46.47 414	58.75 413	79.06 435

tt0320		61.85 410	57.45 419	75.03 387	77.49 421	57.60 381	82.74 394	73.65 449	43.65 464	53.65 423	68.18 450	25.47 447	88.66 396	45.56 419	46.68 447	78.81 440

AllTest		61.66 411	58.06 415	72.46 411	79.57 391	51.42 421	80.17 417	68.61 464	51.25 440	45.88 452	81.23 360	19.86 463	86.58 421	38.98 443	57.01 418	79.39 432

UnsupCasMVSNet_bld		61.60 412	57.71 416	73.29 405	68.73 458	51.64 418	78.61 425	89.05 324	57.20 420	46.11 451	61.96 464	28.70 439	88.60 398	50.08 393	38.90 463	79.63 431

MDA-MVSNet-bldmvs		61.54 413	57.70 417	73.05 406	79.53 393	57.00 392	83.08 389	81.23 426	57.57 415	34.91 472	72.45 431	32.79 421	86.26 423	35.81 450	41.95 456	75.89 452

tt0320-xc		61.51 414	56.89 423	75.37 383	78.50 410	58.61 370	82.61 396	71.27 459	44.31 461	53.17 425	68.03 452	23.38 452	88.46 401	47.77 408	43.00 455	79.03 437

mvs5depth		61.03 415	57.65 418	71.18 420	67.16 462	47.04 449	72.74 445	77.49 435	57.47 418	60.52 386	72.53 429	22.84 455	88.38 402	49.15 397	38.94 462	78.11 446

KD-MVS_self_test		60.87 416	58.60 414	67.68 434	66.13 464	39.93 469	75.63 441	84.70 406	57.32 419	49.57 442	68.45 449	29.55 435	82.87 445	48.09 403	47.94 443	80.25 428

kuosan		60.86 417	60.24 407	62.71 444	81.57 367	46.43 451	75.70 440	85.88 394	57.98 414	48.95 445	69.53 446	58.42 198	76.53 460	28.25 469	35.87 467	65.15 468

FE-MVSNET		60.52 418	57.18 422	70.53 423	67.53 460	50.68 426	82.62 395	76.28 438	59.33 409	46.71 450	71.10 443	30.54 433	83.61 439	33.15 458	47.37 444	77.29 449

TinyColmap		60.32 419	56.42 426	72.00 418	78.78 405	53.18 412	78.36 428	75.64 442	52.30 435	41.59 466	75.82 420	14.76 470	88.35 403	35.84 449	54.71 426	74.46 454

MVS-HIRNet		60.25 420	55.55 427	74.35 396	84.37 333	56.57 394	71.64 448	74.11 447	34.44 471	45.54 456	42.24 479	31.11 431	89.81 390	40.36 440	76.10 280	76.67 451

MIMVSNet1		60.16 421	57.33 420	68.67 430	69.71 455	44.13 458	78.92 424	84.21 410	55.05 430	44.63 459	71.85 437	23.91 450	81.54 453	32.63 463	55.03 424	80.35 425

PM-MVS		59.40 422	56.59 424	67.84 432	63.63 467	41.86 462	76.76 433	63.22 472	59.01 410	51.07 436	72.27 435	11.72 474	83.25 443	61.34 345	50.28 440	78.39 444

new-patchmatchnet		59.30 423	56.48 425	67.79 433	65.86 465	44.19 457	82.47 397	81.77 425	59.94 405	43.65 462	66.20 455	27.67 442	81.68 452	39.34 442	41.40 457	77.50 448

test_vis1_rt		59.09 424	57.31 421	64.43 440	68.44 459	46.02 453	83.05 391	48.63 484	51.96 437	49.57 442	63.86 460	16.30 465	80.20 456	71.21 249	62.79 382	67.07 467

test_fmvs3		56.82 425	54.86 429	62.69 445	53.59 478	35.47 475	75.87 438	65.64 469	43.91 462	55.10 416	71.43 441	6.91 482	74.40 465	68.64 274	52.63 430	78.20 445

DSMNet-mixed		56.78 426	54.44 430	63.79 441	63.21 468	29.44 484	64.43 465	64.10 471	42.12 468	51.32 434	71.60 438	31.76 426	75.04 463	36.23 448	65.20 361	86.87 328

pmmvs3		55.51 427	51.50 433	67.53 435	57.90 476	50.93 425	80.37 413	73.66 448	40.63 469	44.15 461	64.75 458	16.30 465	78.97 459	44.77 424	40.98 460	72.69 459

TDRefinement		55.28 428	51.58 432	66.39 438	59.53 475	46.15 452	76.23 436	72.80 451	44.60 459	42.49 464	76.28 416	15.29 468	82.39 448	33.20 457	43.75 452	70.62 463

dongtai		55.18 429	55.46 428	54.34 455	76.03 431	36.88 473	76.07 437	84.61 408	51.28 439	43.41 463	64.61 459	56.56 227	67.81 473	18.09 478	28.50 478	58.32 471

LF4IMVS		54.01 430	52.12 431	59.69 446	62.41 470	39.91 470	68.59 456	68.28 466	42.96 466	44.55 460	75.18 421	14.09 472	68.39 472	41.36 436	51.68 433	70.78 462

ttmdpeth		53.34 431	49.96 434	63.45 442	62.07 472	40.04 467	72.06 446	65.64 469	42.54 467	51.88 430	77.79 398	13.94 473	76.48 461	32.93 460	30.82 476	73.84 456

MVStest1		51.35 432	46.89 436	64.74 439	65.06 466	51.10 423	67.33 461	72.58 452	30.20 475	35.30 470	74.82 423	27.70 441	69.89 470	24.44 472	24.57 479	73.22 457

N_pmnet		50.55 433	49.11 435	54.88 453	77.17 424	4.02 497	84.36 372	2.00 495	48.59 447	45.86 454	68.82 447	32.22 424	82.80 446	31.58 466	51.38 434	77.81 447

new_pmnet		49.31 434	46.44 437	57.93 448	62.84 469	40.74 465	68.47 457	62.96 473	36.48 470	35.09 471	57.81 468	14.97 469	72.18 467	32.86 461	46.44 448	60.88 470

mvsany_test3		48.86 435	46.35 438	56.41 449	46.00 484	31.67 480	62.26 467	47.25 485	43.71 463	45.54 456	68.15 451	10.84 475	64.44 481	57.95 361	35.44 470	73.13 458

test_f		46.58 436	43.45 440	55.96 450	45.18 485	32.05 479	61.18 468	49.49 483	33.39 472	42.05 465	62.48 463	7.00 481	65.56 477	47.08 412	43.21 454	70.27 464

WB-MVS		46.23 437	44.94 439	50.11 458	62.13 471	21.23 491	76.48 435	55.49 477	45.89 456	35.78 469	61.44 466	35.54 409	72.83 466	9.96 485	21.75 480	56.27 473

FPMVS		45.64 438	43.10 442	53.23 456	51.42 481	36.46 474	64.97 464	71.91 455	29.13 476	27.53 476	61.55 465	9.83 477	65.01 479	16.00 482	55.58 422	58.22 472

SSC-MVS		44.51 439	43.35 441	47.99 462	61.01 474	18.90 493	74.12 443	54.36 478	43.42 465	34.10 473	60.02 467	34.42 414	70.39 469	9.14 487	19.57 481	54.68 474

EGC-MVSNET		42.35 440	38.09 443	55.11 452	74.57 438	46.62 450	71.63 449	55.77 476	0.04 490	0.24 491	62.70 462	14.24 471	74.91 464	17.59 479	46.06 449	43.80 476

LCM-MVSNet		40.54 441	35.79 446	54.76 454	36.92 491	30.81 481	51.41 479	69.02 463	22.07 478	24.63 478	45.37 475	4.56 486	65.81 476	33.67 455	34.50 471	67.67 465

APD_test1		40.50 442	37.31 445	50.09 459	51.88 479	35.27 476	59.45 472	52.59 480	21.64 479	26.12 477	57.80 469	4.56 486	66.56 475	22.64 474	39.09 461	48.43 475

test_vis3_rt		40.46 443	37.79 444	48.47 461	44.49 486	33.35 478	66.56 463	32.84 492	32.39 473	29.65 474	39.13 482	3.91 489	68.65 471	50.17 391	40.99 459	43.40 477

ANet_high		40.27 444	35.20 447	55.47 451	34.74 492	34.47 477	63.84 466	71.56 457	48.42 448	18.80 481	41.08 480	9.52 478	64.45 480	20.18 476	8.66 488	67.49 466

test_method		38.59 445	35.16 448	48.89 460	54.33 477	21.35 490	45.32 482	53.71 479	7.41 487	28.74 475	51.62 471	8.70 479	52.87 484	33.73 454	32.89 472	72.47 460

PMMVS2		37.93 446	33.61 449	50.92 457	46.31 483	24.76 487	60.55 471	50.05 481	28.94 477	20.93 479	47.59 472	4.41 488	65.13 478	25.14 471	18.55 483	62.87 469

Gipuma		34.91 447	31.44 450	45.30 463	70.99 450	39.64 471	19.85 486	72.56 453	20.10 481	16.16 485	21.47 486	5.08 485	71.16 468	13.07 483	43.70 453	25.08 483
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 448	29.47 451	42.67 465	41.89 488	30.81 481	52.07 477	43.45 486	15.45 482	18.52 482	44.82 476	2.12 490	58.38 482	16.05 480	30.87 474	38.83 478

APD_test2		32.77 448	29.47 451	42.67 465	41.89 488	30.81 481	52.07 477	43.45 486	15.45 482	18.52 482	44.82 476	2.12 490	58.38 482	16.05 480	30.87 474	38.83 478

PMVS	26.43 22	31.84 450	28.16 453	42.89 464	25.87 494	27.58 485	50.92 480	49.78 482	21.37 480	14.17 486	40.81 481	2.01 492	66.62 474	9.61 486	38.88 464	34.49 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 451	24.00 455	26.45 469	43.74 487	18.44 494	60.86 469	39.66 488	15.11 484	9.53 488	22.10 485	6.52 483	46.94 487	8.31 488	10.14 485	13.98 485

MVE	24.84 23	24.35 452	19.77 458	38.09 467	34.56 493	26.92 486	26.57 484	38.87 490	11.73 486	11.37 487	27.44 483	1.37 493	50.42 486	11.41 484	14.60 484	36.93 480
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 453	23.20 457	25.46 470	41.52 490	16.90 495	60.56 470	38.79 491	14.62 485	8.99 489	20.24 488	7.35 480	45.82 488	7.25 489	9.46 486	13.64 486

tmp_tt		22.26 454	23.75 456	17.80 471	5.23 495	12.06 496	35.26 483	39.48 489	2.82 489	18.94 480	44.20 478	22.23 457	24.64 490	36.30 447	9.31 487	16.69 484

cdsmvs_eth3d_5k		19.86 455	26.47 454	0.00 475	0.00 498	0.00 500	0.00 487	93.45 95	0.00 493	0.00 494	95.27 77	49.56 310	0.00 494	0.00 493	0.00 491	0.00 490

wuyk23d		11.30 456	10.95 459	12.33 472	48.05 482	19.89 492	25.89 485	1.92 496	3.58 488	3.12 490	1.37 490	0.64 494	15.77 491	6.23 490	7.77 489	1.35 487

ab-mvs-re		7.91 457	10.55 460	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	94.95 87	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

testmvs		7.23 458	9.62 461	0.06 474	0.04 496	0.02 499	84.98 369	0.02 497	0.03 491	0.18 492	1.21 491	0.01 496	0.02 492	0.14 491	0.01 490	0.13 489

test123		6.92 459	9.21 462	0.08 473	0.03 497	0.05 498	81.65 403	0.01 498	0.02 492	0.14 493	0.85 492	0.03 495	0.02 492	0.12 492	0.00 491	0.16 488

pcd_1.5k_mvsjas		4.46 460	5.95 463	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	53.55 264	0.00 494	0.00 493	0.00 491	0.00 490

mmdepth		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

monomultidepth		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

test_blank		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

uanet_test		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

DCPMVS		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

sosnet-low-res		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

sosnet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

uncertanet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

Regformer		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

uanet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 491	0.00 490

MED-MVS test				87.42 45	94.76 34	67.28 125	94.47 61	94.87 32	73.09 224	91.27 23	96.95 17		98.98 16	91.55 43	94.28 37	95.99 45

TestfortrainingZip							94.47 61

WAC-MVS						49.45 434								31.56 467

FOURS1					93.95 50	61.77 303	93.96 88	91.92 166	62.14 386	86.57 62

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

PC_three_1452									80.91 65	94.07 2	96.83 29	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

test_one_0601					96.32 19	69.74 51		94.18 66	71.42 278	90.67 29	96.85 27	74.45 22

eth-test2					0.00 498

eth-test					0.00 498

ZD-MVS					96.63 9	65.50 185		93.50 93	70.74 292	85.26 80	95.19 83	64.92 94	97.29 89	87.51 73	93.01 60

RE-MVS-def			80.48 185		92.02 109	58.56 371	90.90 254	90.45 251	62.76 379	78.89 163	94.46 101	49.30 313		78.77 183	86.77 148	92.28 231

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 66	95.28 1				92.34 35	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 15	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 57	71.65 267	92.07 11	97.21 9	74.58 20	99.11 6	92.34 35	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 59		94.44 55	71.65 267	92.11 9	97.05 12	76.79 9	99.11 6

9.14			87.63 39		93.86 52		94.41 66	94.18 66	72.76 232	86.21 65	96.51 36	66.64 72	97.88 52	90.08 55	94.04 43

save fliter					93.84 53	67.89 109	95.05 40	92.66 132	78.19 128

test_0728_THIRD									72.48 237	90.55 30	96.93 21	76.24 13	99.08 11	91.53 46	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 61					99.15 2	91.91 41	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 63	71.92 253	91.89 14	97.11 11	73.77 25

GSMVS																94.68 120

test_part2					96.29 20	68.16 102				90.78 27

sam_mvs1												57.85 207				94.68 120

sam_mvs												54.91 246

ambc				69.61 426	61.38 473	41.35 464	49.07 481	85.86 396		50.18 441	66.40 454	10.16 476	88.14 405	45.73 418	44.20 451	79.32 434

MTGPA								92.23 147

test_post1							78.95 423				20.70 487	53.05 269	91.50 370	60.43 350

test_post											23.01 484	56.49 228	92.67 330

patchmatchnet-post											67.62 453	57.62 210	90.25 380

GG-mvs-BLEND				86.53 88	91.91 118	69.67 54	75.02 442	94.75 40		78.67 171	90.85 205	77.91 7	94.56 253	72.25 236	93.74 49	95.36 72

MTMP							93.77 103	32.52 493

gm-plane-assit					88.42 218	67.04 137			78.62 122		91.83 179		97.37 83	76.57 195

test9_res														89.41 56	94.96 19	95.29 78

TEST9					94.18 45	67.28 125	94.16 75	93.51 91	71.75 264	85.52 75	95.33 71	68.01 61	97.27 93

test_8					94.19 44	67.19 130	94.15 77	93.42 98	71.87 258	85.38 78	95.35 70	68.19 59	96.95 120

agg_prior2														86.41 87	94.75 30	95.33 74

agg_prior					94.16 47	66.97 144		93.31 101		84.49 86			96.75 132

TestCases				72.46 411	79.57 391	51.42 421		68.61 464	51.25 440	45.88 452	81.23 360	19.86 463	86.58 421	38.98 443	57.01 418	79.39 432

test_prior4						67.18 132	93.92 92

test_prior2							95.10 39		75.40 181	85.25 81	95.61 62	67.94 62		87.47 75	94.77 26

test_prior				86.42 92	94.71 39	67.35 124		93.10 112					96.84 129			95.05 93

旧先验2							92.00 197		59.37 408	87.54 55			93.47 302	75.39 204

新几何2							91.41 226

新几何1				84.73 166	92.32 97	64.28 224		91.46 193	59.56 407	79.77 150	92.90 145	56.95 220	96.57 138	63.40 330	92.91 62	93.34 191

旧先验1					91.94 114	60.74 330		91.50 191			94.36 105	65.23 89			91.84 78	94.55 129

无先验							92.71 152	92.61 137	62.03 387				97.01 110	66.63 298		93.97 167

原ACMM2							92.01 194

原ACMM1				84.42 183	93.21 72	64.27 225		93.40 100	65.39 354	79.51 155	92.50 153	58.11 203	96.69 134	65.27 318	93.96 44	92.32 229

test222					89.77 167	61.60 309	89.55 301	89.42 303	56.83 423	77.28 187	92.43 157	52.76 272			91.14 95	93.09 201

testdata2													96.09 163	61.26 346

segment_acmp												65.94 80

testdata				81.34 288	89.02 191	57.72 378		89.84 285	58.65 412	85.32 79	94.09 121	57.03 215	93.28 306	69.34 265	90.56 101	93.03 204

testdata1							89.21 313		77.55 144

test12				87.09 56	94.60 40	68.86 78		92.91 120		82.67 109		65.44 86	97.55 72		93.69 52	94.84 105

plane_prior7					86.94 271	61.51 311

plane_prior6					87.23 258	62.32 289						50.66 296

plane_prior5								91.31 197					95.55 207	76.74 193	78.53 258	88.39 303

plane_prior4											89.14 246

plane_prior3						61.95 298			79.09 111	72.53 250

plane_prior2							93.13 131		78.81 118

plane_prior1					87.15 261

plane_prior						62.42 285	93.85 96		79.38 103						78.80 255

n2								0.00 499

nn								0.00 499

door-mid								66.01 468

lessismore_v0				73.72 402	72.93 445	47.83 442		61.72 474		45.86 454	73.76 426	28.63 440	89.81 390	47.75 410	31.37 473	83.53 386

LGP-MVS_train				79.56 339	84.31 334	59.37 360		89.73 291	69.49 307	64.86 347	88.42 255	38.65 384	94.30 264	72.56 233	72.76 302	85.01 372

test11								93.01 115

door								66.57 467

HQP5-MVS						63.66 252

HQP-NCC					87.54 250		94.06 80		79.80 88	74.18 224

ACMP_Plane					87.54 250		94.06 80		79.80 88	74.18 224

BP-MVS														77.63 190

HQP4-MVS										74.18 224			95.61 201			88.63 297

HQP3-MVS								91.70 183							78.90 253

HQP2-MVS												51.63 284

NP-MVS					87.41 253	63.04 269					90.30 216

MDTV_nov1_ep13_2view						59.90 352	80.13 418		67.65 334	72.79 244		54.33 255		59.83 354		92.58 220

MDTV_nov1_ep13			72.61 322		89.06 189	68.48 89	80.33 414	90.11 274	71.84 260	71.81 264	75.92 419	53.01 270	93.92 287	48.04 404	73.38 297

ACMMP++_ref															71.63 310

ACMMP++															69.72 320

Test By Simon												54.21 258

ITE_SJBPF				70.43 424	74.44 439	47.06 448		77.32 436	60.16 403	54.04 421	83.53 326	23.30 453	84.01 435	43.07 427	61.58 398	80.21 429

DeepMVS_CX				34.71 468	51.45 480	24.73 488		28.48 494	31.46 474	17.49 484	52.75 470	5.80 484	42.60 489	18.18 477	19.42 482	36.81 481

High-resolution multi-view benchmark