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 69	96.26 44	72.84 31	99.38 1	92.64 32	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 104	71.10 27	97.02 3	96.04 6	88.70 2	91.57 19	96.19 46	70.12 48	98.91 20	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 168	76.68 2	97.29 1	95.35 17	82.87 36	91.58 18	97.22 7	79.93 5	99.10 9	83.12 124	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 86	94.37 60	72.48 229	92.07 11	96.85 25	83.82 2	99.15 2	91.53 44	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 102	92.83 83	64.03 222	93.06 131	94.33 62	82.19 44	93.65 3	96.15 48	85.89 1	97.19 96	91.02 48	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 48	70.23 37	97.00 5	93.73 81	87.30 4	92.15 8	96.15 48	66.38 73	98.94 19	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 35	84.83 16	89.07 41	96.80 28	70.86 44	99.06 15	92.64 32	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 74	73.88 9	97.01 4	94.40 58	88.32 3	85.71 70	94.91 89	74.11 22	98.91 20	87.26 76	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 54	71.65 259	92.11 9	97.21 8	76.79 9	99.11 6	92.34 34	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 160	93.00 79	58.16 361	96.72 9	94.41 56	86.50 9	90.25 32	97.83 1	75.46 15	98.67 28	92.78 31	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 105	96.04 25	63.70 237	95.04 42	95.19 22	86.74 8	91.53 20	95.15 82	73.86 23	97.58 68	93.38 26	92.00 73	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 42	69.71 52	96.53 13	93.78 74	86.89 7	89.68 38	95.78 55	65.94 78	99.10 9	92.99 29	93.91 45	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 50	71.92 245	90.55 28	96.93 19	73.77 24	99.08 11	91.91 40	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 94	95.24 34	94.49 52	82.43 41	88.90 43	96.35 39	71.89 41	98.63 29	88.76 62	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 21	89.81 6	93.66 57	75.15 5	90.61 259	93.43 96	84.06 23	86.20 64	90.17 217	72.42 36	96.98 113	93.09 28	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 69	96.38 15	94.64 45	84.42 20	86.74 59	96.20 45	66.56 72	98.76 26	89.03 61	94.56 34	95.92 48

DPE-MVS		88.77 17	89.21 17	87.45 44	96.26 21	67.56 112	94.17 72	94.15 67	68.77 311	90.74 26	97.27 5	76.09 13	98.49 32	90.58 52	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 18	88.99 19	87.70 33	94.76 34	68.73 77	94.47 60	94.87 32	73.09 216	91.27 23	96.95 16	76.77 11	98.98 16	84.41 108	94.28 37	95.37 68

ME-MVS		88.25 19	88.55 25	87.33 49	96.33 18	67.28 119	93.93 88	94.81 36	70.09 291	88.91 42	96.95 16	70.12 48	98.73 27	91.55 42	94.28 37	95.99 45

fmvsm_l_conf0.5_n_9		88.24 20	89.36 16	84.85 149	88.15 227	61.94 287	95.65 25	89.70 284	85.54 11	92.07 11	97.33 4	67.51 64	97.27 91	96.23 5	92.07 72	95.35 72

fmvsm_s_conf0.5_n_9		88.14 21	89.21 17	84.92 144	89.29 179	61.41 303	92.97 136	88.36 337	86.96 6	91.49 21	97.49 3	69.48 53	97.46 75	97.00 1	89.88 109	95.89 49

SMA-MVS		88.14 21	88.29 29	87.67 34	93.21 71	68.72 79	93.85 94	94.03 70	74.18 190	91.74 15	96.67 31	65.61 83	98.42 36	89.24 58	96.08 7	95.88 50
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 21	87.61 39	89.71 7	92.06 107	76.72 1	95.75 20	93.26 102	83.86 24	89.55 39	96.06 50	53.55 254	97.89 50	91.10 46	93.31 56	94.54 127

TSAR-MVS + MP.		88.11 24	88.64 24	86.54 81	91.73 122	68.04 98	90.36 266	93.55 88	82.89 34	91.29 22	92.89 144	72.27 38	96.03 167	87.99 66	94.77 26	95.54 61
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_8		87.96 25	88.93 20	85.07 139	88.43 214	61.78 290	94.73 55	91.74 175	85.87 10	91.66 17	97.50 2	64.03 104	98.33 37	96.28 4	90.08 105	95.10 89

TSAR-MVS + GP.		87.96 25	88.37 28	86.70 70	93.51 65	65.32 180	95.15 37	93.84 73	78.17 122	85.93 68	94.80 92	75.80 14	98.21 39	89.38 55	88.78 121	96.59 19

DeepC-MVS_fast	79.48 2	87.95 27	88.00 33	87.79 31	95.86 28	68.32 88	95.74 21	94.11 68	83.82 25	83.49 95	96.19 46	64.53 99	98.44 34	83.42 123	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 28	88.67 23	85.71 112	88.69 196	63.71 235	94.56 58	90.22 260	85.04 14	92.27 6	97.05 11	63.67 112	98.15 41	95.09 12	91.39 85	95.27 80

xiu_mvs_v2_base		87.92 29	87.38 43	89.55 12	91.41 134	76.43 3	95.74 21	93.12 110	83.53 28	89.55 39	95.95 53	53.45 258	97.68 58	91.07 47	92.62 63	94.54 127

EPNet		87.84 30	88.38 27	86.23 92	93.30 68	66.05 159	95.26 33	94.84 34	87.09 5	88.06 46	94.53 98	66.79 69	97.34 84	83.89 115	91.68 79	95.29 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 31	87.77 36	87.63 39	89.24 184	71.18 24	96.57 12	92.90 119	82.70 38	87.13 54	95.27 75	64.99 89	95.80 177	89.34 56	91.80 77	95.93 47

test_fmvsm_n_1920		87.69 32	88.50 26	85.27 132	87.05 261	63.55 244	93.69 104	91.08 215	84.18 22	90.17 34	97.04 13	67.58 63	97.99 45	95.72 8	90.03 106	94.26 143

fmvsm_l_conf0.5_n_3		87.54 33	88.29 29	85.30 129	86.92 267	62.63 270	95.02 44	90.28 255	84.95 15	90.27 31	96.86 23	65.36 85	97.52 73	94.93 14	90.03 106	95.76 53

APDe-MVS		87.54 33	87.84 35	86.65 72	96.07 24	66.30 154	94.84 50	93.78 74	69.35 300	88.39 45	96.34 40	67.74 62	97.66 63	90.62 51	93.44 54	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 35	88.72 22	83.84 197	86.89 269	60.04 337	95.05 40	92.17 154	84.80 17	92.27 6	96.37 37	64.62 96	96.54 139	94.43 18	91.86 75	94.94 98

fmvsm_l_conf0.5_n		87.49 36	88.19 31	85.39 123	86.95 262	64.37 210	94.30 69	88.45 335	80.51 68	92.70 4	96.86 23	69.98 50	97.15 101	95.83 7	88.08 129	94.65 120

SD-MVS		87.49 36	87.49 41	87.50 43	93.60 59	68.82 75	93.90 91	92.63 134	76.86 147	87.90 48	95.76 56	66.17 75	97.63 65	89.06 60	91.48 83	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 38	88.15 32	85.30 129	87.10 259	64.19 217	94.41 64	88.14 345	80.24 78	92.54 5	96.97 15	69.52 52	97.17 97	95.89 6	88.51 124	94.56 124

dcpmvs_2		87.37 39	87.55 40	86.85 61	95.04 33	68.20 95	90.36 266	90.66 235	79.37 97	81.20 119	93.67 128	74.73 17	96.55 138	90.88 49	92.00 73	95.82 51

alignmvs		87.28 40	86.97 47	88.24 27	91.30 136	71.14 26	95.61 26	93.56 87	79.30 98	87.07 56	95.25 77	68.43 55	96.93 121	87.87 67	84.33 178	96.65 17

train_agg		87.21 41	87.42 42	86.60 75	94.18 44	67.28 119	94.16 73	93.51 90	71.87 250	85.52 73	95.33 69	68.19 57	97.27 91	89.09 59	94.90 22	95.25 84

MG-MVS		87.11 42	86.27 60	89.62 8	97.79 1	76.27 4	94.96 46	94.49 52	78.74 113	83.87 91	92.94 142	64.34 100	96.94 119	75.19 197	94.09 41	95.66 56

SF-MVS		87.03 43	87.09 45	86.84 62	92.70 89	67.45 117	93.64 107	93.76 77	70.78 283	86.25 62	96.44 36	66.98 67	97.79 54	88.68 63	94.56 34	95.28 79

fmvsm_s_conf0.5_n_3		86.88 44	87.99 34	83.58 211	87.26 253	60.74 317	93.21 128	87.94 352	84.22 21	91.70 16	97.27 5	65.91 80	95.02 219	93.95 23	90.42 101	94.99 95

CSCG		86.87 45	86.26 61	88.72 17	95.05 32	70.79 29	93.83 99	95.33 18	68.48 315	77.63 172	94.35 107	73.04 29	98.45 33	84.92 101	93.71 50	96.92 14

sasdasda		86.85 46	86.25 62	88.66 20	91.80 120	71.92 16	93.54 112	91.71 178	80.26 75	87.55 51	95.25 77	63.59 116	96.93 121	88.18 64	84.34 176	97.11 9

canonicalmvs		86.85 46	86.25 62	88.66 20	91.80 120	71.92 16	93.54 112	91.71 178	80.26 75	87.55 51	95.25 77	63.59 116	96.93 121	88.18 64	84.34 176	97.11 9

UBG		86.83 48	86.70 53	87.20 51	93.07 77	69.81 47	93.43 120	95.56 13	81.52 51	81.50 114	92.12 164	73.58 27	96.28 151	84.37 109	85.20 166	95.51 62

PHI-MVS		86.83 48	86.85 52	86.78 66	93.47 66	65.55 175	95.39 31	95.10 25	71.77 255	85.69 71	96.52 33	62.07 140	98.77 25	86.06 89	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 50	86.90 50	86.58 78	90.42 153	66.38 151	96.09 17	93.87 72	77.73 131	84.01 90	95.66 58	63.39 119	97.94 46	87.40 74	93.55 53	95.42 64
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 51	87.63 37	84.27 184	86.15 285	61.48 300	94.69 56	91.16 204	83.79 27	90.51 30	96.28 42	64.24 101	98.22 38	95.00 13	86.88 141	93.11 192

PVSNet_Blended		86.73 52	86.86 51	86.31 91	93.76 53	67.53 114	96.33 16	93.61 85	82.34 43	81.00 124	93.08 138	63.19 123	97.29 87	87.08 80	91.38 86	94.13 152

testing11		86.71 53	86.44 58	87.55 41	93.54 63	71.35 21	93.65 106	95.58 11	81.36 58	80.69 129	92.21 163	72.30 37	96.46 144	85.18 97	83.43 188	94.82 107

test_fmvsmconf_n		86.58 54	87.17 44	84.82 151	85.28 303	62.55 271	94.26 71	89.78 275	83.81 26	87.78 50	96.33 41	65.33 86	96.98 113	94.40 19	87.55 135	94.95 97

BP-MVS1		86.54 55	86.68 55	86.13 95	87.80 241	67.18 125	92.97 136	95.62 10	79.92 82	82.84 102	94.14 116	74.95 16	96.46 144	82.91 128	88.96 120	94.74 111

jason		86.40 56	86.17 64	87.11 54	86.16 284	70.54 32	95.71 24	92.19 151	82.00 46	84.58 83	94.34 108	61.86 142	95.53 201	87.76 68	90.89 94	95.27 80
jason: jason.
NormalMVS		86.39 57	86.66 56	85.60 117	92.12 104	65.95 164	94.88 47	90.83 223	84.69 18	83.67 93	94.10 117	63.16 125	96.91 125	85.31 93	91.15 90	93.93 163

fmvsm_s_conf0.5_n		86.39 57	86.91 49	84.82 151	87.36 252	63.54 245	94.74 52	90.02 268	82.52 39	90.14 35	96.92 21	62.93 130	97.84 53	95.28 11	82.26 199	93.07 195

fmvsm_s_conf0.5_n_5		86.38 59	86.94 48	84.71 162	84.67 315	63.29 250	94.04 82	89.99 270	82.88 35	87.85 49	96.03 51	62.89 132	96.36 148	94.15 20	89.95 108	94.48 133

SymmetryMVS		86.32 60	86.39 59	86.12 96	90.52 151	65.95 164	94.88 47	94.58 49	84.69 18	83.67 93	94.10 117	63.16 125	96.91 125	85.31 93	86.59 150	95.51 62

WTY-MVS		86.32 60	85.81 72	87.85 29	92.82 85	69.37 61	95.20 35	95.25 20	82.71 37	81.91 110	94.73 93	67.93 61	97.63 65	79.55 162	82.25 201	96.54 22

myMVS_eth3d28		86.31 62	86.15 65	86.78 66	93.56 61	70.49 33	92.94 139	95.28 19	82.47 40	78.70 162	92.07 166	72.45 35	95.41 203	82.11 136	85.78 159	94.44 135

MSLP-MVS++		86.27 63	85.91 71	87.35 47	92.01 111	68.97 72	95.04 42	92.70 125	79.04 108	81.50 114	96.50 35	58.98 185	96.78 129	83.49 122	93.93 44	96.29 35

VNet		86.20 64	85.65 76	87.84 30	93.92 50	69.99 39	95.73 23	95.94 7	78.43 118	86.00 67	93.07 139	58.22 195	97.00 109	85.22 95	84.33 178	96.52 23

MVS_111021_HR		86.19 65	85.80 73	87.37 46	93.17 73	69.79 48	93.99 85	93.76 77	79.08 105	78.88 158	93.99 122	62.25 139	98.15 41	85.93 90	91.15 90	94.15 151

SPE-MVS-test		86.14 66	87.01 46	83.52 212	92.63 91	59.36 349	95.49 28	91.92 164	80.09 79	85.46 75	95.53 64	61.82 144	95.77 180	86.77 84	93.37 55	95.41 65

ACMMP_NAP		86.05 67	85.80 73	86.80 65	91.58 126	67.53 114	91.79 201	93.49 93	74.93 180	84.61 82	95.30 71	59.42 175	97.92 47	86.13 87	94.92 20	94.94 98

testing99		86.01 68	85.47 78	87.63 39	93.62 58	71.25 23	93.47 118	95.23 21	80.42 71	80.60 131	91.95 173	71.73 42	96.50 142	80.02 159	82.22 202	95.13 87

ETV-MVS		86.01 68	86.11 66	85.70 113	90.21 158	67.02 132	93.43 120	91.92 164	81.21 60	84.13 89	94.07 121	60.93 153	95.63 190	89.28 57	89.81 110	94.46 134

testing91		85.93 70	85.31 82	87.78 32	93.59 60	71.47 19	93.50 115	95.08 28	80.26 75	80.53 133	91.93 174	70.43 46	96.51 141	80.32 157	82.13 204	95.37 68

APD-MVS		85.93 70	85.99 69	85.76 109	95.98 27	65.21 183	93.59 110	92.58 136	66.54 333	86.17 65	95.88 54	63.83 108	97.00 109	86.39 86	92.94 60	95.06 91
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 72	85.46 79	87.18 52	88.20 226	72.42 15	92.41 171	92.77 123	82.11 45	80.34 136	93.07 139	68.27 56	95.02 219	78.39 178	93.59 52	94.09 154

CS-MVS		85.80 73	86.65 57	83.27 224	92.00 112	58.92 353	95.31 32	91.86 169	79.97 80	84.82 81	95.40 67	62.26 138	95.51 202	86.11 88	92.08 71	95.37 68

fmvsm_s_conf0.5_n_a		85.75 74	86.09 67	84.72 160	85.73 296	63.58 242	93.79 100	89.32 295	81.42 56	90.21 33	96.91 22	62.41 137	97.67 60	94.48 17	80.56 227	92.90 201

test_fmvsmconf0.1_n		85.71 75	86.08 68	84.62 170	80.83 364	62.33 276	93.84 97	88.81 323	83.50 29	87.00 57	96.01 52	63.36 120	96.93 121	94.04 22	87.29 138	94.61 122

CDPH-MVS		85.71 75	85.46 79	86.46 83	94.75 37	67.19 123	93.89 92	92.83 121	70.90 279	83.09 100	95.28 73	63.62 114	97.36 82	80.63 153	94.18 40	94.84 103

casdiffmvs_mvg		85.66 77	85.18 84	87.09 55	88.22 225	69.35 62	93.74 103	91.89 167	81.47 52	80.10 138	91.45 185	64.80 94	96.35 149	87.23 77	87.69 133	95.58 59
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 78	85.93 70	84.68 164	82.95 347	63.48 247	94.03 84	89.46 289	81.69 49	89.86 36	96.74 29	61.85 143	97.75 56	94.74 16	82.01 206	92.81 205

MGCFI-Net		85.59 79	85.73 75	85.17 136	91.41 134	62.44 272	92.87 144	91.31 195	79.65 89	86.99 58	95.14 83	62.90 131	96.12 159	87.13 79	84.13 183	96.96 13

GDP-MVS		85.54 80	85.32 81	86.18 93	87.64 244	67.95 102	92.91 142	92.36 141	77.81 128	83.69 92	94.31 110	72.84 31	96.41 146	80.39 156	85.95 157	94.19 147

DeepC-MVS	77.85 3	85.52 81	85.24 83	86.37 87	88.80 194	66.64 145	92.15 180	93.68 83	81.07 62	76.91 184	93.64 129	62.59 134	98.44 34	85.50 91	92.84 62	94.03 158
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 82	84.87 90	86.84 62	88.25 223	69.07 67	93.04 133	91.76 174	81.27 59	80.84 127	92.07 166	64.23 102	96.06 165	84.98 100	87.43 137	95.39 66
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 83	85.08 86	86.06 97	93.09 76	65.65 171	93.89 92	93.41 98	73.75 201	79.94 140	94.68 95	60.61 157	98.03 44	82.63 131	93.72 49	94.52 129

fmvsm_s_conf0.5_n_7		85.24 84	86.69 54	80.91 292	84.52 320	60.10 335	93.35 123	90.35 248	83.41 30	86.54 61	96.27 43	60.50 158	90.02 376	94.84 15	90.38 102	92.61 209

MP-MVS-pluss		85.24 84	85.13 85	85.56 118	91.42 131	65.59 173	91.54 213	92.51 138	74.56 183	80.62 130	95.64 59	59.15 180	97.00 109	86.94 82	93.80 46	94.07 156
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 86	84.69 94	86.63 74	92.91 81	69.91 43	92.61 158	95.80 9	80.31 74	80.38 135	92.27 159	68.73 54	95.19 216	75.94 191	83.27 190	94.81 109

PAPR		85.15 87	84.47 95	87.18 52	96.02 26	68.29 89	91.85 199	93.00 116	76.59 158	79.03 154	95.00 84	61.59 145	97.61 67	78.16 179	89.00 119	95.63 57

fmvsm_s_conf0.5_n_2		85.06 88	85.60 77	83.44 218	86.92 267	60.53 324	94.41 64	87.31 360	83.30 31	88.72 44	96.72 30	54.28 246	97.75 56	94.07 21	84.68 175	92.04 232

MP-MVS		85.02 89	84.97 88	85.17 136	92.60 92	64.27 215	93.24 125	92.27 144	73.13 212	79.63 146	94.43 101	61.90 141	97.17 97	85.00 99	92.56 64	94.06 157
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 90	84.44 96	86.71 69	88.33 220	68.73 77	90.24 271	91.82 173	81.05 63	81.18 120	92.50 151	63.69 111	96.08 164	84.45 107	86.71 148	95.32 75

CHOSEN 1792x2688		84.98 91	83.45 114	89.57 11	89.94 163	75.14 6	92.07 186	92.32 142	81.87 47	75.68 193	88.27 251	60.18 162	98.60 30	80.46 155	90.27 104	94.96 96

MVSMamba_PlusPlus		84.97 92	83.65 107	88.93 14	90.17 159	74.04 8	87.84 329	92.69 128	62.18 371	81.47 116	87.64 265	71.47 43	96.28 151	84.69 103	94.74 31	96.47 28

viewmanbaseed2359cas		84.89 93	84.26 99	86.78 66	88.50 205	69.77 50	92.69 155	91.13 210	81.11 61	81.54 113	91.98 170	60.35 159	95.73 182	84.47 106	86.56 151	94.84 103

EIA-MVS		84.84 94	84.88 89	84.69 163	91.30 136	62.36 275	93.85 94	92.04 157	79.45 93	79.33 151	94.28 112	62.42 136	96.35 149	80.05 158	91.25 89	95.38 67

lecture		84.77 95	84.81 92	84.65 166	92.12 104	62.27 279	94.74 52	92.64 133	68.35 316	85.53 72	95.30 71	59.77 169	97.91 48	83.73 118	91.15 90	93.77 172

fmvsm_s_conf0.1_n_a		84.76 96	84.84 91	84.53 172	80.23 377	63.50 246	92.79 146	88.73 326	80.46 69	89.84 37	96.65 32	60.96 152	97.57 70	93.80 24	80.14 229	92.53 214

viewcassd2359sk11		84.74 97	84.11 100	86.64 73	88.57 199	69.20 65	92.61 158	91.23 201	80.58 66	80.85 126	91.96 171	61.39 147	95.89 172	84.28 110	85.49 163	94.82 107

HFP-MVS		84.73 98	84.40 97	85.72 111	93.75 55	65.01 189	93.50 115	93.19 106	72.19 239	79.22 152	94.93 87	59.04 183	97.67 60	81.55 142	92.21 67	94.49 132

MVS		84.66 99	82.86 135	90.06 2	90.93 143	74.56 7	87.91 327	95.54 14	68.55 313	72.35 250	94.71 94	59.78 168	98.90 22	81.29 148	94.69 32	96.74 16

GST-MVS		84.63 100	84.29 98	85.66 114	92.82 85	65.27 181	93.04 133	93.13 109	73.20 210	78.89 155	94.18 115	59.41 176	97.85 52	81.45 144	92.48 66	93.86 169

EC-MVSNet		84.53 101	85.04 87	83.01 230	89.34 175	61.37 304	94.42 63	91.09 213	77.91 126	83.24 96	94.20 114	58.37 193	95.40 204	85.35 92	91.41 84	92.27 226

E3		84.45 102	83.74 104	86.56 80	87.90 235	69.06 68	92.53 166	91.13 210	80.35 73	80.58 132	91.69 181	60.70 154	95.84 175	83.80 117	84.99 168	94.79 110

fmvsm_s_conf0.1_n_2		84.40 103	84.78 93	83.27 224	85.25 304	60.41 327	94.13 76	85.69 384	83.05 33	87.99 47	96.37 37	52.75 263	97.68 58	93.75 25	84.05 184	91.71 240

ACMMPR		84.37 104	84.06 101	85.28 131	93.56 61	64.37 210	93.50 115	93.15 108	72.19 239	78.85 160	94.86 90	56.69 215	97.45 76	81.55 142	92.20 68	94.02 159

region2R		84.36 105	84.03 102	85.36 127	93.54 63	64.31 213	93.43 120	92.95 117	72.16 242	78.86 159	94.84 91	56.97 210	97.53 72	81.38 146	92.11 70	94.24 145

LFMVS		84.34 106	82.73 137	89.18 13	94.76 34	73.25 11	94.99 45	91.89 167	71.90 247	82.16 109	93.49 133	47.98 316	97.05 104	82.55 132	84.82 171	97.25 8

test_yl		84.28 107	83.16 126	87.64 35	94.52 40	69.24 63	95.78 18	95.09 26	69.19 303	81.09 121	92.88 145	57.00 208	97.44 77	81.11 150	81.76 210	96.23 38

DCV-MVSNet		84.28 107	83.16 126	87.64 35	94.52 40	69.24 63	95.78 18	95.09 26	69.19 303	81.09 121	92.88 145	57.00 208	97.44 77	81.11 150	81.76 210	96.23 38

diffmvs		84.28 107	83.83 103	85.61 116	87.40 250	68.02 99	90.88 244	89.24 298	80.54 67	81.64 112	92.52 150	59.83 167	94.52 248	87.32 75	85.11 167	94.29 142
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	84.28 107	83.36 120	87.02 58	92.22 99	67.74 107	84.65 356	94.50 51	79.15 102	82.23 108	87.93 260	66.88 68	96.94 119	80.53 154	82.20 203	96.39 33

ETVMVS		84.22 111	83.71 105	85.76 109	92.58 93	68.25 93	92.45 169	95.53 15	79.54 92	79.46 148	91.64 183	70.29 47	94.18 262	69.16 260	82.76 196	94.84 103

MAR-MVS		84.18 112	83.43 115	86.44 84	96.25 22	65.93 166	94.28 70	94.27 64	74.41 185	79.16 153	95.61 60	53.99 249	98.88 24	69.62 254	93.26 57	94.50 131
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		84.16 113	83.20 124	87.05 57	91.56 127	69.82 46	89.99 280	92.05 156	77.77 130	82.84 102	86.57 282	63.93 107	96.09 161	74.91 202	89.18 116	95.25 84

CANet_DTU		84.09 114	83.52 108	85.81 106	90.30 156	66.82 139	91.87 197	89.01 314	85.27 12	86.09 66	93.74 126	47.71 322	96.98 113	77.90 181	89.78 112	93.65 175

viewdifsd2359ckpt13		84.08 115	83.21 123	86.70 70	88.49 209	69.55 55	92.25 174	91.14 208	79.71 87	79.73 143	91.72 180	58.83 186	95.89 172	82.06 137	84.99 168	94.66 119

viewmacassd2359aftdt		84.03 116	83.18 125	86.59 77	86.76 270	69.44 56	92.44 170	90.85 222	80.38 72	80.78 128	91.33 190	58.54 190	95.62 192	82.15 135	85.41 164	94.72 113

ET-MVSNet_ETH3D		84.01 117	83.15 128	86.58 78	90.78 148	70.89 28	94.74 52	94.62 46	81.44 55	58.19 389	93.64 129	73.64 26	92.35 331	82.66 130	78.66 249	96.50 27

diffmvs_AUTHOR		83.97 118	83.49 111	85.39 123	86.09 286	67.83 104	90.76 249	89.05 312	79.94 81	81.43 117	92.23 162	59.53 172	94.42 251	87.18 78	85.22 165	93.92 165

PVSNet_Blended_VisFu		83.97 118	83.50 110	85.39 123	90.02 161	66.59 148	93.77 101	91.73 176	77.43 140	77.08 183	89.81 227	63.77 110	96.97 116	79.67 161	88.21 127	92.60 210

MTAPA		83.91 120	83.38 119	85.50 119	91.89 118	65.16 185	81.75 387	92.23 145	75.32 175	80.53 133	95.21 80	56.06 224	97.16 100	84.86 102	92.55 65	94.18 148

XVS		83.87 121	83.47 113	85.05 140	93.22 69	63.78 229	92.92 140	92.66 130	73.99 193	78.18 166	94.31 110	55.25 230	97.41 79	79.16 168	91.58 81	93.95 161

Effi-MVS+		83.82 122	82.76 136	86.99 59	89.56 171	69.40 57	91.35 224	86.12 378	72.59 226	83.22 99	92.81 148	59.60 171	96.01 169	81.76 141	87.80 132	95.56 60

test_fmvsmvis_n_1920		83.80 123	83.48 112	84.77 155	82.51 350	63.72 234	91.37 222	83.99 402	81.42 56	77.68 171	95.74 57	58.37 193	97.58 68	93.38 26	86.87 142	93.00 198

EI-MVSNet-Vis-set		83.77 124	83.67 106	84.06 188	92.79 88	63.56 243	91.76 205	94.81 36	79.65 89	77.87 169	94.09 119	63.35 121	97.90 49	79.35 166	79.36 239	90.74 261

MVSFormer		83.75 125	82.88 134	86.37 87	89.24 184	71.18 24	89.07 305	90.69 232	65.80 338	87.13 54	94.34 108	64.99 89	92.67 317	72.83 219	91.80 77	95.27 80

CP-MVS		83.71 126	83.40 118	84.65 166	93.14 74	63.84 227	94.59 57	92.28 143	71.03 277	77.41 176	94.92 88	55.21 233	96.19 156	81.32 147	90.70 96	93.91 166

test_fmvsmconf0.01_n		83.70 127	83.52 108	84.25 185	75.26 423	61.72 294	92.17 179	87.24 362	82.36 42	84.91 80	95.41 66	55.60 228	96.83 128	92.85 30	85.87 158	94.21 146

baseline2		83.68 128	83.42 117	84.48 175	87.37 251	66.00 161	90.06 275	95.93 8	79.71 87	69.08 288	90.39 205	77.92 6	96.28 151	78.91 173	81.38 214	91.16 254

viewdifsd2359ckpt09		83.52 129	82.57 140	86.37 87	88.02 232	68.47 84	91.78 203	89.63 285	79.61 91	78.56 164	92.00 169	59.28 178	95.96 170	81.94 139	82.35 197	94.69 114

reproduce-ours		83.51 130	83.33 121	84.06 188	92.18 102	60.49 325	90.74 251	92.04 157	64.35 348	83.24 96	95.59 62	59.05 181	97.27 91	83.61 119	89.17 117	94.41 140

our_new_method		83.51 130	83.33 121	84.06 188	92.18 102	60.49 325	90.74 251	92.04 157	64.35 348	83.24 96	95.59 62	59.05 181	97.27 91	83.61 119	89.17 117	94.41 140

thisisatest0515		83.41 132	82.49 142	86.16 94	89.46 174	68.26 91	93.54 112	94.70 42	74.31 188	75.75 191	90.92 195	72.62 33	96.52 140	69.64 252	81.50 213	93.71 173

PVSNet_BlendedMVS		83.38 133	83.43 115	83.22 226	93.76 53	67.53 114	94.06 78	93.61 85	79.13 103	81.00 124	85.14 300	63.19 123	97.29 87	87.08 80	73.91 287	84.83 361

test2506		83.29 134	82.92 133	84.37 179	88.39 217	63.18 256	92.01 189	91.35 194	77.66 133	78.49 165	91.42 186	64.58 98	95.09 218	73.19 215	89.23 114	94.85 100

PGM-MVS		83.25 135	82.70 138	84.92 144	92.81 87	64.07 221	90.44 261	92.20 149	71.28 271	77.23 180	94.43 101	55.17 234	97.31 86	79.33 167	91.38 86	93.37 182

HPM-MVS		83.25 135	82.95 132	84.17 186	92.25 98	62.88 265	90.91 241	91.86 169	70.30 288	77.12 181	93.96 123	56.75 213	96.28 151	82.04 138	91.34 88	93.34 183
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 137	82.96 130	83.73 203	92.02 108	59.74 341	90.37 265	92.08 155	63.70 355	82.86 101	95.48 65	58.62 188	97.17 97	83.06 125	88.42 125	94.26 143

EI-MVSNet-UG-set		83.14 138	82.96 130	83.67 208	92.28 97	63.19 255	91.38 221	94.68 43	79.22 100	76.60 186	93.75 125	62.64 133	97.76 55	78.07 180	78.01 252	90.05 270

testing3-2		83.11 139	83.15 128	82.98 231	91.92 115	64.01 223	94.39 67	95.37 16	78.32 119	75.53 198	90.06 223	73.18 28	93.18 296	74.34 207	75.27 276	91.77 239

VDD-MVS		83.06 140	81.81 153	86.81 64	90.86 146	67.70 108	95.40 30	91.50 189	75.46 170	81.78 111	92.34 158	40.09 366	97.13 102	86.85 83	82.04 205	95.60 58

h-mvs33		83.01 141	82.56 141	84.35 180	89.34 175	62.02 283	92.72 149	93.76 77	81.45 53	82.73 105	92.25 161	60.11 163	97.13 102	87.69 69	62.96 370	93.91 166

PAPM_NR		82.97 142	81.84 152	86.37 87	94.10 47	66.76 142	87.66 333	92.84 120	69.96 293	74.07 223	93.57 131	63.10 128	97.50 74	70.66 247	90.58 98	94.85 100

mPP-MVS		82.96 143	82.44 143	84.52 173	92.83 83	62.92 263	92.76 147	91.85 171	71.52 267	75.61 196	94.24 113	53.48 257	96.99 112	78.97 171	90.73 95	93.64 176

viewdifsd2359ckpt07		82.95 144	82.04 147	85.66 114	87.19 256	66.73 143	91.56 212	90.39 247	77.58 136	77.58 175	91.19 192	58.57 189	95.65 189	82.32 133	82.01 206	94.60 123

SR-MVS		82.81 145	82.58 139	83.50 215	93.35 67	61.16 307	92.23 177	91.28 200	64.48 347	81.27 118	95.28 73	53.71 253	95.86 174	82.87 129	88.77 122	93.49 180

DP-MVS Recon		82.73 146	81.65 154	85.98 99	97.31 4	67.06 128	95.15 37	91.99 161	69.08 308	76.50 188	93.89 124	54.48 242	98.20 40	70.76 245	85.66 161	92.69 206

CLD-MVS		82.73 146	82.35 145	83.86 196	87.90 235	67.65 110	95.45 29	92.18 152	85.06 13	72.58 241	92.27 159	52.46 266	95.78 178	84.18 111	79.06 244	88.16 298
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 148	82.38 144	83.73 203	89.25 181	59.58 344	92.24 176	94.89 31	77.96 124	79.86 141	92.38 156	56.70 214	97.05 104	77.26 184	80.86 222	94.55 125

3Dnovator	73.91 6	82.69 149	80.82 167	88.31 26	89.57 170	71.26 22	92.60 160	94.39 59	78.84 110	67.89 309	92.48 154	48.42 311	98.52 31	68.80 265	94.40 36	95.15 86

RRT-MVS		82.61 150	81.16 158	86.96 60	91.10 140	68.75 76	87.70 332	92.20 149	76.97 145	72.68 237	87.10 276	51.30 280	96.41 146	83.56 121	87.84 131	95.74 54

viewmambaseed2359dif		82.60 151	81.91 151	84.67 165	85.83 293	66.09 158	90.50 260	89.01 314	75.46 170	79.64 145	92.01 168	59.51 173	94.38 253	82.99 127	82.26 199	93.54 178

MVSTER		82.47 152	82.05 146	83.74 201	92.68 90	69.01 70	91.90 196	93.21 103	79.83 83	72.14 251	85.71 295	74.72 18	94.72 233	75.72 193	72.49 297	87.50 305

TESTMET0.1,1		82.41 153	81.98 150	83.72 205	88.08 228	63.74 231	92.70 151	93.77 76	79.30 98	77.61 173	87.57 267	58.19 196	94.08 267	73.91 209	86.68 149	93.33 185

CostFormer		82.33 154	81.15 159	85.86 104	89.01 189	68.46 85	82.39 384	93.01 114	75.59 168	80.25 137	81.57 345	72.03 40	94.96 223	79.06 170	77.48 260	94.16 150

API-MVS		82.28 155	80.53 176	87.54 42	96.13 23	70.59 31	93.63 108	91.04 219	65.72 340	75.45 199	92.83 147	56.11 223	98.89 23	64.10 313	89.75 113	93.15 190

IB-MVS	77.80 4	82.18 156	80.46 178	87.35 47	89.14 186	70.28 36	95.59 27	95.17 24	78.85 109	70.19 276	85.82 293	70.66 45	97.67 60	72.19 231	66.52 340	94.09 154
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		82.16 157	81.12 160	85.26 133	86.42 277	68.72 79	92.59 162	90.44 244	73.12 213	84.20 86	94.36 103	38.04 379	95.73 182	84.12 112	86.81 143	91.33 247

xiu_mvs_v1_base		82.16 157	81.12 160	85.26 133	86.42 277	68.72 79	92.59 162	90.44 244	73.12 213	84.20 86	94.36 103	38.04 379	95.73 182	84.12 112	86.81 143	91.33 247

xiu_mvs_v1_base_debi		82.16 157	81.12 160	85.26 133	86.42 277	68.72 79	92.59 162	90.44 244	73.12 213	84.20 86	94.36 103	38.04 379	95.73 182	84.12 112	86.81 143	91.33 247

3Dnovator+	73.60 7	82.10 160	80.60 174	86.60 75	90.89 145	66.80 141	95.20 35	93.44 95	74.05 192	67.42 316	92.49 153	49.46 301	97.65 64	70.80 244	91.68 79	95.33 73

MVS_111021_LR		82.02 161	81.52 155	83.51 214	88.42 215	62.88 265	89.77 283	88.93 319	76.78 150	75.55 197	93.10 136	50.31 290	95.38 206	83.82 116	87.02 140	92.26 227

PMMVS		81.98 162	82.04 147	81.78 266	89.76 167	56.17 381	91.13 237	90.69 232	77.96 124	80.09 139	93.57 131	46.33 335	94.99 222	81.41 145	87.46 136	94.17 149

baseline1		81.84 163	81.03 164	84.28 183	91.60 125	66.62 146	91.08 238	91.66 183	81.87 47	74.86 209	91.67 182	69.98 50	94.92 226	71.76 234	64.75 357	91.29 252

EPP-MVSNet		81.79 164	81.52 155	82.61 241	88.77 195	60.21 333	93.02 135	93.66 84	68.52 314	72.90 235	90.39 205	72.19 39	94.96 223	74.93 201	79.29 242	92.67 207

WBMVS		81.67 165	80.98 166	83.72 205	93.07 77	69.40 57	94.33 68	93.05 112	76.84 148	72.05 253	84.14 311	74.49 20	93.88 281	72.76 222	68.09 326	87.88 300

test_vis1_n_1920		81.66 166	82.01 149	80.64 295	82.24 352	55.09 390	94.76 51	86.87 366	81.67 50	84.40 85	94.63 96	38.17 376	94.67 239	91.98 39	83.34 189	92.16 230

APD-MVS_3200maxsize		81.64 167	81.32 157	82.59 243	92.36 95	58.74 355	91.39 219	91.01 220	63.35 359	79.72 144	94.62 97	51.82 269	96.14 158	79.71 160	87.93 130	92.89 202

mvsmamba		81.55 168	80.72 169	84.03 192	91.42 131	66.93 137	83.08 375	89.13 306	78.55 117	67.50 314	87.02 277	51.79 271	90.07 375	87.48 72	90.49 100	95.10 89

ACMMP		81.49 169	80.67 171	83.93 194	91.71 123	62.90 264	92.13 181	92.22 148	71.79 254	71.68 259	93.49 133	50.32 289	96.96 117	78.47 177	84.22 182	91.93 237
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		81.43 170	80.11 180	85.38 126	86.60 273	65.47 179	92.90 143	93.54 89	75.33 174	77.31 178	90.39 205	46.81 327	96.75 130	71.65 237	86.46 154	93.93 163

CDS-MVSNet		81.43 170	80.74 168	83.52 212	86.26 281	64.45 204	92.09 184	90.65 236	75.83 166	73.95 225	89.81 227	63.97 106	92.91 307	71.27 238	82.82 193	93.20 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 172	79.99 184	85.46 120	90.39 155	68.40 86	86.88 344	90.61 237	74.41 185	70.31 275	84.67 305	63.79 109	92.32 333	73.13 216	85.70 160	95.67 55

ECVR-MVS		81.29 173	80.38 179	84.01 193	88.39 217	61.96 285	92.56 165	86.79 368	77.66 133	76.63 185	91.42 186	46.34 334	95.24 215	74.36 206	89.23 114	94.85 100

guyue		81.23 174	80.57 175	83.21 228	86.64 271	61.85 288	92.52 167	92.78 122	78.69 114	74.92 208	89.42 231	50.07 293	95.35 207	80.79 152	79.31 241	92.42 216

IMVS_0403		81.19 175	79.88 186	85.13 138	88.54 200	64.75 194	88.84 310	90.80 226	76.73 153	75.21 202	90.18 211	54.22 247	96.21 155	73.47 211	80.95 217	94.43 136

thisisatest0530		81.15 176	80.07 181	84.39 178	88.26 222	65.63 172	91.40 217	94.62 46	71.27 272	70.93 266	89.18 236	72.47 34	96.04 166	65.62 302	76.89 267	91.49 243

Fast-Effi-MVS+		81.14 177	80.01 183	84.51 174	90.24 157	65.86 167	94.12 77	89.15 304	73.81 200	75.37 201	88.26 252	57.26 203	94.53 247	66.97 287	84.92 170	93.15 190

HQP-MVS		81.14 177	80.64 172	82.64 240	87.54 246	63.66 240	94.06 78	91.70 181	79.80 84	74.18 216	90.30 208	51.63 274	95.61 193	77.63 182	78.90 245	88.63 289

hse-mvs2		81.12 179	81.11 163	81.16 280	86.52 276	57.48 370	89.40 296	91.16 204	81.45 53	82.73 105	90.49 203	60.11 163	94.58 240	87.69 69	60.41 397	91.41 246

SR-MVS-dyc-post		81.06 180	80.70 170	82.15 257	92.02 108	58.56 358	90.90 242	90.45 240	62.76 366	78.89 155	94.46 99	51.26 281	95.61 193	78.77 175	86.77 146	92.28 223

HyFIR lowres test		81.03 181	79.56 193	85.43 121	87.81 240	68.11 97	90.18 272	90.01 269	70.65 285	72.95 234	86.06 289	63.61 115	94.50 249	75.01 200	79.75 233	93.67 174

nrg030		80.93 182	79.86 187	84.13 187	83.69 336	68.83 74	93.23 126	91.20 202	75.55 169	75.06 204	88.22 255	63.04 129	94.74 232	81.88 140	66.88 337	88.82 287

Vis-MVSNet		80.92 183	79.98 185	83.74 201	88.48 211	61.80 289	93.44 119	88.26 344	73.96 196	77.73 170	91.76 177	49.94 295	94.76 230	65.84 299	90.37 103	94.65 120
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 184	80.02 182	83.33 219	87.87 237	60.76 315	92.62 157	86.86 367	77.86 127	75.73 192	91.39 188	46.35 333	94.70 238	72.79 221	88.68 123	94.52 129

UWE-MVS		80.81 185	81.01 165	80.20 305	89.33 177	57.05 375	91.91 195	94.71 41	75.67 167	75.01 205	89.37 232	63.13 127	91.44 358	67.19 284	82.80 195	92.12 231

IMVS_0407		80.80 186	79.39 199	85.00 143	88.54 200	64.75 194	88.40 318	90.80 226	76.73 153	73.95 225	90.18 211	51.55 276	95.81 176	73.47 211	80.95 217	94.43 136

1314		80.70 187	78.95 207	85.94 101	87.77 243	67.56 112	87.91 327	92.55 137	72.17 241	67.44 315	93.09 137	50.27 291	97.04 107	71.68 236	87.64 134	93.23 187

AstraMVS		80.66 188	79.79 189	83.28 223	85.07 310	61.64 296	92.19 178	90.58 238	79.40 95	74.77 211	90.18 211	45.93 339	95.61 193	83.04 126	76.96 266	92.60 210

tpmrst		80.57 189	79.14 205	84.84 150	90.10 160	68.28 90	81.70 388	89.72 282	77.63 135	75.96 190	79.54 377	64.94 91	92.71 314	75.43 195	77.28 263	93.55 177

1112_ss		80.56 190	79.83 188	82.77 235	88.65 197	60.78 313	92.29 173	88.36 337	72.58 227	72.46 247	94.95 85	65.09 88	93.42 293	66.38 293	77.71 254	94.10 153

VDDNet		80.50 191	78.26 215	87.21 50	86.19 282	69.79 48	94.48 59	91.31 195	60.42 387	79.34 150	90.91 196	38.48 374	96.56 137	82.16 134	81.05 216	95.27 80

BH-w/o		80.49 192	79.30 201	84.05 191	90.83 147	64.36 212	93.60 109	89.42 292	74.35 187	69.09 287	90.15 219	55.23 232	95.61 193	64.61 310	86.43 155	92.17 229

test_cas_vis1_n_1920		80.45 193	80.61 173	79.97 314	78.25 404	57.01 377	94.04 82	88.33 339	79.06 107	82.81 104	93.70 127	38.65 371	91.63 349	90.82 50	79.81 231	91.27 253

icg_test_0407_2		80.38 194	79.22 203	83.88 195	88.54 200	64.75 194	86.79 345	90.80 226	76.73 153	73.95 225	90.18 211	51.55 276	92.45 326	73.47 211	80.95 217	94.43 136

TAMVS		80.37 195	79.45 196	83.13 229	85.14 307	63.37 248	91.23 231	90.76 231	74.81 182	72.65 239	88.49 245	60.63 156	92.95 302	69.41 256	81.95 208	93.08 194

HQP_MVS		80.34 196	79.75 190	82.12 259	86.94 263	62.42 273	93.13 129	91.31 195	78.81 111	72.53 242	89.14 238	50.66 286	95.55 199	76.74 185	78.53 250	88.39 295

SDMVSNet		80.26 197	78.88 208	84.40 177	89.25 181	67.63 111	85.35 352	93.02 113	76.77 151	70.84 267	87.12 274	47.95 319	96.09 161	85.04 98	74.55 278	89.48 280

HPM-MVS_fast		80.25 198	79.55 195	82.33 249	91.55 128	59.95 338	91.32 226	89.16 303	65.23 344	74.71 213	93.07 139	47.81 321	95.74 181	74.87 204	88.23 126	91.31 251

ab-mvs		80.18 199	78.31 214	85.80 107	88.44 213	65.49 178	83.00 378	92.67 129	71.82 253	77.36 177	85.01 301	54.50 239	96.59 134	76.35 190	75.63 274	95.32 75

IS-MVSNet		80.14 200	79.41 197	82.33 249	87.91 234	60.08 336	91.97 193	88.27 342	72.90 222	71.44 263	91.73 179	61.44 146	93.66 288	62.47 327	86.53 152	93.24 186

test-LLR		80.10 201	79.56 193	81.72 268	86.93 265	61.17 305	92.70 151	91.54 186	71.51 268	75.62 194	86.94 278	53.83 250	92.38 328	72.21 229	84.76 173	91.60 241

PVSNet	73.49 8	80.05 202	78.63 210	84.31 181	90.92 144	64.97 190	92.47 168	91.05 218	79.18 101	72.43 248	90.51 202	37.05 391	94.06 269	68.06 271	86.00 156	93.90 168

UA-Net		80.02 203	79.65 191	81.11 283	89.33 177	57.72 365	86.33 349	89.00 318	77.44 139	81.01 123	89.15 237	59.33 177	95.90 171	61.01 334	84.28 180	89.73 276

test-mter		79.96 204	79.38 200	81.72 268	86.93 265	61.17 305	92.70 151	91.54 186	73.85 198	75.62 194	86.94 278	49.84 297	92.38 328	72.21 229	84.76 173	91.60 241

QAPM		79.95 205	77.39 236	87.64 35	89.63 169	71.41 20	93.30 124	93.70 82	65.34 343	67.39 318	91.75 178	47.83 320	98.96 18	57.71 350	89.81 110	92.54 213

UGNet		79.87 206	78.68 209	83.45 217	89.96 162	61.51 298	92.13 181	90.79 230	76.83 149	78.85 160	86.33 286	38.16 377	96.17 157	67.93 274	87.17 139	92.67 207
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		79.80 207	77.95 222	85.34 128	88.28 221	68.26 91	81.56 390	91.42 192	70.11 290	77.59 174	80.50 363	67.40 65	94.26 260	67.34 281	77.35 261	93.51 179

thres200		79.66 208	78.33 213	83.66 209	92.54 94	65.82 169	93.06 131	96.31 3	74.90 181	73.30 231	88.66 243	59.67 170	95.61 193	47.84 393	78.67 248	89.56 279

CPTT-MVS		79.59 209	79.16 204	80.89 293	91.54 129	59.80 340	92.10 183	88.54 334	60.42 387	72.96 233	93.28 135	48.27 312	92.80 311	78.89 174	86.50 153	90.06 269

Test_1112_low_res		79.56 210	78.60 211	82.43 245	88.24 224	60.39 329	92.09 184	87.99 349	72.10 243	71.84 255	87.42 269	64.62 96	93.04 298	65.80 300	77.30 262	93.85 170

tttt0517		79.50 211	78.53 212	82.41 248	87.22 255	61.43 302	89.75 284	94.76 38	69.29 301	67.91 307	88.06 259	72.92 30	95.63 190	62.91 323	73.90 288	90.16 268

reproduce_monomvs		79.49 212	79.11 206	80.64 295	92.91 81	61.47 301	91.17 236	93.28 101	83.09 32	64.04 348	82.38 331	66.19 74	94.57 242	81.19 149	57.71 405	85.88 344

FIs		79.47 213	79.41 197	79.67 322	85.95 289	59.40 346	91.68 209	93.94 71	78.06 123	68.96 293	88.28 250	66.61 71	91.77 345	66.20 296	74.99 277	87.82 301

SSM_0404		79.46 214	77.65 226	84.91 146	88.37 219	67.04 130	89.59 285	87.03 363	67.99 319	75.45 199	89.32 233	47.98 316	95.34 209	71.23 239	81.90 209	92.34 219

BH-RMVSNet		79.46 214	77.65 226	84.89 147	91.68 124	65.66 170	93.55 111	88.09 347	72.93 219	73.37 230	91.12 194	46.20 337	96.12 159	56.28 356	85.61 162	92.91 200

viewdifsd2359ckpt11		79.42 216	77.95 222	83.81 198	83.87 333	63.85 225	89.54 290	87.38 356	77.39 142	74.94 206	89.95 224	51.11 282	94.72 233	79.52 163	67.90 329	92.88 203

viewmsd2359difaftdt		79.42 216	77.96 221	83.81 198	83.88 332	63.85 225	89.54 290	87.38 356	77.39 142	74.94 206	89.95 224	51.11 282	94.72 233	79.52 163	67.90 329	92.88 203

PCF-MVS	73.15 9	79.29 218	77.63 228	84.29 182	86.06 287	65.96 163	87.03 340	91.10 212	69.86 295	69.79 283	90.64 198	57.54 202	96.59 134	64.37 312	82.29 198	90.32 266
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 219	79.57 192	78.24 343	88.46 212	52.29 401	90.41 263	89.12 307	74.24 189	69.13 286	91.91 175	65.77 81	90.09 374	59.00 346	88.09 128	92.33 220

114514_t		79.17 220	77.67 225	83.68 207	95.32 30	65.53 176	92.85 145	91.60 185	63.49 357	67.92 306	90.63 200	46.65 330	95.72 187	67.01 286	83.54 187	89.79 274

FA-MVS(test-final)		79.12 221	77.23 238	84.81 154	90.54 150	63.98 224	81.35 393	91.71 178	71.09 276	74.85 210	82.94 324	52.85 261	97.05 104	67.97 272	81.73 212	93.41 181

SSM_0407		79.09 222	77.21 239	84.75 158	88.50 205	66.98 133	89.21 301	87.03 363	67.99 319	74.12 220	89.32 233	47.98 316	95.29 214	71.23 239	79.52 234	91.98 234

VPA-MVSNet		79.03 223	78.00 219	82.11 262	85.95 289	64.48 203	93.22 127	94.66 44	75.05 179	74.04 224	84.95 302	52.17 268	93.52 290	74.90 203	67.04 336	88.32 297

OPM-MVS		79.00 224	78.09 217	81.73 267	83.52 339	63.83 228	91.64 211	90.30 253	76.36 162	71.97 254	89.93 226	46.30 336	95.17 217	75.10 198	77.70 255	86.19 332
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 225	78.22 216	81.25 277	85.33 300	62.73 268	89.53 293	93.21 103	72.39 234	72.14 251	90.13 220	60.99 150	94.72 233	67.73 276	72.49 297	86.29 329

AdaColmap		78.94 226	77.00 243	84.76 157	96.34 17	65.86 167	92.66 156	87.97 351	62.18 371	70.56 269	92.37 157	43.53 351	97.35 83	64.50 311	82.86 192	91.05 256

GeoE		78.90 227	77.43 232	83.29 222	88.95 190	62.02 283	92.31 172	86.23 374	70.24 289	71.34 264	89.27 235	54.43 243	94.04 272	63.31 319	80.81 224	93.81 171

miper_enhance_ethall		78.86 228	77.97 220	81.54 272	88.00 233	65.17 184	91.41 215	89.15 304	75.19 177	68.79 296	83.98 314	67.17 66	92.82 309	72.73 223	65.30 347	86.62 326

VPNet		78.82 229	77.53 231	82.70 238	84.52 320	66.44 150	93.93 88	92.23 145	80.46 69	72.60 240	88.38 249	49.18 305	93.13 297	72.47 227	63.97 367	88.55 292

EPNet_dtu		78.80 230	79.26 202	77.43 351	88.06 229	49.71 418	91.96 194	91.95 163	77.67 132	76.56 187	91.28 191	58.51 191	90.20 372	56.37 355	80.95 217	92.39 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 231	77.43 232	82.88 233	92.21 100	64.49 201	92.05 187	96.28 4	73.48 207	71.75 257	88.26 252	60.07 165	95.32 210	45.16 406	77.58 257	88.83 285

TR-MVS		78.77 232	77.37 237	82.95 232	90.49 152	60.88 311	93.67 105	90.07 264	70.08 292	74.51 214	91.37 189	45.69 340	95.70 188	60.12 340	80.32 228	92.29 222

thres400		78.68 233	77.43 232	82.43 245	92.21 100	64.49 201	92.05 187	96.28 4	73.48 207	71.75 257	88.26 252	60.07 165	95.32 210	45.16 406	77.58 257	87.48 306

BH-untuned		78.68 233	77.08 240	83.48 216	89.84 164	63.74 231	92.70 151	88.59 332	71.57 265	66.83 325	88.65 244	51.75 272	95.39 205	59.03 345	84.77 172	91.32 250

OMC-MVS		78.67 235	77.91 224	80.95 290	85.76 295	57.40 372	88.49 316	88.67 329	73.85 198	72.43 248	92.10 165	49.29 304	94.55 246	72.73 223	77.89 253	90.91 260

tpm		78.58 236	77.03 241	83.22 226	85.94 291	64.56 199	83.21 374	91.14 208	78.31 120	73.67 228	79.68 375	64.01 105	92.09 339	66.07 297	71.26 307	93.03 196

OpenMVS	70.45 11	78.54 237	75.92 260	86.41 86	85.93 292	71.68 18	92.74 148	92.51 138	66.49 334	64.56 342	91.96 171	43.88 350	98.10 43	54.61 361	90.65 97	89.44 282

EPMVS		78.49 238	75.98 259	86.02 98	91.21 138	69.68 53	80.23 402	91.20 202	75.25 176	72.48 246	78.11 386	54.65 238	93.69 287	57.66 351	83.04 191	94.69 114

AUN-MVS		78.37 239	77.43 232	81.17 279	86.60 273	57.45 371	89.46 295	91.16 204	74.11 191	74.40 215	90.49 203	55.52 229	94.57 242	74.73 205	60.43 396	91.48 244

thres100view900		78.37 239	77.01 242	82.46 244	91.89 118	63.21 254	91.19 235	96.33 1	72.28 237	70.45 272	87.89 261	60.31 160	95.32 210	45.16 406	77.58 257	88.83 285

GA-MVS		78.33 241	76.23 255	84.65 166	83.65 337	66.30 154	91.44 214	90.14 262	76.01 164	70.32 274	84.02 313	42.50 355	94.72 233	70.98 242	77.00 265	92.94 199

cascas		78.18 242	75.77 262	85.41 122	87.14 258	69.11 66	92.96 138	91.15 207	66.71 332	70.47 270	86.07 288	37.49 385	96.48 143	70.15 250	79.80 232	90.65 262

UniMVSNet_NR-MVSNet		78.15 243	77.55 230	79.98 312	84.46 323	60.26 331	92.25 174	93.20 105	77.50 138	68.88 294	86.61 281	66.10 76	92.13 337	66.38 293	62.55 374	87.54 304

LuminaMVS		78.14 244	76.66 247	82.60 242	80.82 365	64.64 198	89.33 297	90.45 240	68.25 317	74.73 212	85.51 297	41.15 361	94.14 263	78.96 172	80.69 226	89.04 283

IMVS_0404		78.11 245	76.29 254	83.59 210	88.54 200	64.75 194	84.63 357	90.80 226	76.73 153	61.16 370	90.18 211	40.17 365	91.58 351	73.47 211	80.95 217	94.43 136

thres600view7		78.00 246	76.66 247	82.03 264	91.93 114	63.69 238	91.30 227	96.33 1	72.43 232	70.46 271	87.89 261	60.31 160	94.92 226	42.64 418	76.64 268	87.48 306

FC-MVSNet-test		77.99 247	78.08 218	77.70 346	84.89 313	55.51 387	90.27 269	93.75 80	76.87 146	66.80 326	87.59 266	65.71 82	90.23 371	62.89 324	73.94 286	87.37 309

Anonymous202405211		77.96 248	75.33 268	85.87 103	93.73 56	64.52 200	94.85 49	85.36 387	62.52 369	76.11 189	90.18 211	29.43 423	97.29 87	68.51 267	77.24 264	95.81 52

cl22		77.94 249	76.78 245	81.42 274	87.57 245	64.93 192	90.67 254	88.86 322	72.45 231	67.63 313	82.68 328	64.07 103	92.91 307	71.79 232	65.30 347	86.44 327

XXY-MVS		77.94 249	76.44 250	82.43 245	82.60 349	64.44 205	92.01 189	91.83 172	73.59 206	70.00 279	85.82 293	54.43 243	94.76 230	69.63 253	68.02 328	88.10 299

MS-PatchMatch		77.90 251	76.50 249	82.12 259	85.99 288	69.95 42	91.75 207	92.70 125	73.97 195	62.58 365	84.44 309	41.11 362	95.78 178	63.76 316	92.17 69	80.62 409

FMVSNet3		77.73 252	76.04 258	82.80 234	91.20 139	68.99 71	91.87 197	91.99 161	73.35 209	67.04 321	83.19 323	56.62 216	92.14 336	59.80 342	69.34 314	87.28 312

VortexMVS		77.62 253	76.44 250	81.13 281	88.58 198	63.73 233	91.24 230	91.30 199	77.81 128	65.76 331	81.97 337	49.69 299	93.72 285	76.40 189	65.26 350	85.94 342

miper_ehance_all_eth		77.60 254	76.44 250	81.09 287	85.70 297	64.41 208	90.65 255	88.64 331	72.31 235	67.37 319	82.52 329	64.77 95	92.64 320	70.67 246	65.30 347	86.24 331

UniMVSNet (Re)		77.58 255	76.78 245	79.98 312	84.11 329	60.80 312	91.76 205	93.17 107	76.56 159	69.93 282	84.78 304	63.32 122	92.36 330	64.89 309	62.51 376	86.78 320

PatchmatchNet		77.46 256	74.63 275	85.96 100	89.55 172	70.35 35	79.97 407	89.55 287	72.23 238	70.94 265	76.91 398	57.03 206	92.79 312	54.27 363	81.17 215	94.74 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 257	75.65 264	82.73 236	80.38 373	67.13 127	91.85 199	90.23 258	75.09 178	69.37 284	83.39 320	53.79 252	94.44 250	71.77 233	65.00 354	86.63 325

CHOSEN 280x420		77.35 258	76.95 244	78.55 338	87.07 260	62.68 269	69.71 439	82.95 409	68.80 310	71.48 262	87.27 273	66.03 77	84.00 422	76.47 188	82.81 194	88.95 284

PS-MVSNAJss		77.26 259	76.31 253	80.13 307	80.64 369	59.16 351	90.63 258	91.06 217	72.80 223	68.58 300	84.57 307	53.55 254	93.96 277	72.97 217	71.96 301	87.27 313

gg-mvs-nofinetune		77.18 260	74.31 282	85.80 107	91.42 131	68.36 87	71.78 433	94.72 40	49.61 431	77.12 181	45.92 460	77.41 8	93.98 276	67.62 277	93.16 58	95.05 92

WB-MVSnew		77.14 261	76.18 257	80.01 311	86.18 283	63.24 252	91.26 228	94.11 68	71.72 257	73.52 229	87.29 272	45.14 345	93.00 300	56.98 353	79.42 237	83.80 370

MVP-Stereo		77.12 262	76.23 255	79.79 319	81.72 357	66.34 153	89.29 298	90.88 221	70.56 286	62.01 368	82.88 325	49.34 302	94.13 264	65.55 304	93.80 46	78.88 424
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 263	75.37 266	82.20 255	89.25 181	62.11 282	82.06 385	89.09 309	76.77 151	70.84 267	87.12 274	41.43 360	95.01 221	67.23 283	74.55 278	89.48 280

MonoMVSNet		76.99 264	75.08 271	82.73 236	83.32 341	63.24 252	86.47 348	86.37 370	79.08 105	66.31 329	79.30 379	49.80 298	91.72 346	79.37 165	65.70 345	93.23 187

dmvs_re		76.93 265	75.36 267	81.61 270	87.78 242	60.71 319	80.00 406	87.99 349	79.42 94	69.02 290	89.47 230	46.77 328	94.32 254	63.38 318	74.45 281	89.81 273

X-MVStestdata		76.86 266	74.13 288	85.05 140	93.22 69	63.78 229	92.92 140	92.66 130	73.99 193	78.18 166	10.19 475	55.25 230	97.41 79	79.16 168	91.58 81	93.95 161

DU-MVS		76.86 266	75.84 261	79.91 315	82.96 345	60.26 331	91.26 228	91.54 186	76.46 161	68.88 294	86.35 284	56.16 221	92.13 337	66.38 293	62.55 374	87.35 310

Anonymous20240529		76.84 268	74.15 287	84.88 148	91.02 141	64.95 191	93.84 97	91.09 213	53.57 419	73.00 232	87.42 269	35.91 395	97.32 85	69.14 261	72.41 299	92.36 218

UWE-MVS-28		76.83 269	77.60 229	74.51 380	84.58 319	50.34 414	88.22 321	94.60 48	74.46 184	66.66 327	88.98 242	62.53 135	85.50 414	57.55 352	80.80 225	87.69 303

c3_l		76.83 269	75.47 265	80.93 291	85.02 311	64.18 218	90.39 264	88.11 346	71.66 258	66.65 328	81.64 343	63.58 118	92.56 321	69.31 258	62.86 371	86.04 337

WR-MVS		76.76 271	75.74 263	79.82 318	84.60 317	62.27 279	92.60 160	92.51 138	76.06 163	67.87 310	85.34 298	56.76 212	90.24 370	62.20 328	63.69 369	86.94 318

v1144		76.73 272	74.88 272	82.27 251	80.23 377	66.60 147	91.68 209	90.21 261	73.69 203	69.06 289	81.89 338	52.73 264	94.40 252	69.21 259	65.23 351	85.80 345

IterMVS-LS		76.49 273	75.18 270	80.43 299	84.49 322	62.74 267	90.64 256	88.80 324	72.40 233	65.16 337	81.72 341	60.98 151	92.27 334	67.74 275	64.65 359	86.29 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 274	74.55 278	82.19 256	79.14 391	67.82 105	90.26 270	89.42 292	73.75 201	68.63 299	81.89 338	51.31 279	94.09 266	71.69 235	64.84 355	84.66 362

Elysia		76.45 275	74.17 285	83.30 220	80.43 371	64.12 219	89.58 286	90.83 223	61.78 379	72.53 242	85.92 291	34.30 402	94.81 228	68.10 269	84.01 185	90.97 257

StellarMVS		76.45 275	74.17 285	83.30 220	80.43 371	64.12 219	89.58 286	90.83 223	61.78 379	72.53 242	85.92 291	34.30 402	94.81 228	68.10 269	84.01 185	90.97 257

mamba_0408		76.22 277	73.37 299	84.77 155	88.50 205	66.98 133	58.80 460	86.18 376	69.12 306	74.12 220	89.01 240	47.50 323	95.35 207	67.57 278	79.52 234	91.98 234

v148		76.19 278	74.47 280	81.36 275	80.05 379	64.44 205	91.75 207	90.23 258	73.68 204	67.13 320	80.84 358	55.92 226	93.86 284	68.95 263	61.73 385	85.76 348

Effi-MVS+-dtu		76.14 279	75.28 269	78.72 337	83.22 342	55.17 389	89.87 281	87.78 353	75.42 172	67.98 305	81.43 347	45.08 346	92.52 323	75.08 199	71.63 302	88.48 293

cl____		76.07 280	74.67 273	80.28 302	85.15 306	61.76 292	90.12 273	88.73 326	71.16 273	65.43 334	81.57 345	61.15 148	92.95 302	66.54 290	62.17 378	86.13 335

DIV-MVS_self_test		76.07 280	74.67 273	80.28 302	85.14 307	61.75 293	90.12 273	88.73 326	71.16 273	65.42 335	81.60 344	61.15 148	92.94 306	66.54 290	62.16 380	86.14 333

FMVSNet2		76.07 280	74.01 290	82.26 253	88.85 191	67.66 109	91.33 225	91.61 184	70.84 280	65.98 330	82.25 333	48.03 313	92.00 341	58.46 347	68.73 322	87.10 315

v144192		76.05 283	74.03 289	82.12 259	79.50 385	66.55 149	91.39 219	89.71 283	72.30 236	68.17 303	81.33 350	51.75 272	94.03 274	67.94 273	64.19 362	85.77 346

NR-MVSNet		76.05 283	74.59 276	80.44 298	82.96 345	62.18 281	90.83 246	91.73 176	77.12 144	60.96 372	86.35 284	59.28 178	91.80 344	60.74 335	61.34 389	87.35 310

v1192		75.98 285	73.92 291	82.15 257	79.73 381	66.24 156	91.22 232	89.75 277	72.67 225	68.49 301	81.42 348	49.86 296	94.27 258	67.08 285	65.02 353	85.95 340

FE-MVS		75.97 286	73.02 305	84.82 151	89.78 165	65.56 174	77.44 418	91.07 216	64.55 346	72.66 238	79.85 373	46.05 338	96.69 132	54.97 360	80.82 223	92.21 228

eth_miper_zixun_eth		75.96 287	74.40 281	80.66 294	84.66 316	63.02 258	89.28 299	88.27 342	71.88 249	65.73 332	81.65 342	59.45 174	92.81 310	68.13 268	60.53 394	86.14 333

TranMVSNet+NR-MVSNet		75.86 288	74.52 279	79.89 316	82.44 351	60.64 322	91.37 222	91.37 193	76.63 157	67.65 312	86.21 287	52.37 267	91.55 352	61.84 330	60.81 392	87.48 306

SCA		75.82 289	72.76 308	85.01 142	86.63 272	70.08 38	81.06 395	89.19 301	71.60 264	70.01 278	77.09 396	45.53 341	90.25 367	60.43 337	73.27 290	94.68 116

LPG-MVS_test		75.82 289	74.58 277	79.56 326	84.31 326	59.37 347	90.44 261	89.73 280	69.49 298	64.86 338	88.42 247	38.65 371	94.30 256	72.56 225	72.76 294	85.01 359

GBi-Net		75.65 291	73.83 292	81.10 284	88.85 191	65.11 186	90.01 277	90.32 249	70.84 280	67.04 321	80.25 368	48.03 313	91.54 353	59.80 342	69.34 314	86.64 322

test1		75.65 291	73.83 292	81.10 284	88.85 191	65.11 186	90.01 277	90.32 249	70.84 280	67.04 321	80.25 368	48.03 313	91.54 353	59.80 342	69.34 314	86.64 322

v1921920		75.63 293	73.49 297	82.06 263	79.38 386	66.35 152	91.07 240	89.48 288	71.98 244	67.99 304	81.22 353	49.16 307	93.90 280	66.56 289	64.56 360	85.92 343

ACMP	71.68 10	75.58 294	74.23 284	79.62 324	84.97 312	59.64 342	90.80 247	89.07 311	70.39 287	62.95 361	87.30 271	38.28 375	93.87 282	72.89 218	71.45 305	85.36 355
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 295	73.26 303	81.61 270	80.67 368	66.82 139	89.54 290	89.27 297	71.65 259	63.30 356	80.30 367	54.99 236	94.06 269	67.33 282	62.33 377	83.94 368

tpm cat1		75.30 296	72.21 317	84.58 171	88.52 204	67.77 106	78.16 416	88.02 348	61.88 377	68.45 302	76.37 402	60.65 155	94.03 274	53.77 366	74.11 284	91.93 237

PLC	68.80 14	75.23 297	73.68 295	79.86 317	92.93 80	58.68 356	90.64 256	88.30 340	60.90 384	64.43 346	90.53 201	42.38 356	94.57 242	56.52 354	76.54 269	86.33 328
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 298	72.98 306	81.88 265	79.20 388	66.00 161	90.75 250	89.11 308	71.63 263	67.41 317	81.22 353	47.36 325	93.87 282	65.46 305	64.72 358	85.77 346

Fast-Effi-MVS+-dtu		75.04 299	73.37 299	80.07 308	80.86 363	59.52 345	91.20 234	85.38 386	71.90 247	65.20 336	84.84 303	41.46 359	92.97 301	66.50 292	72.96 293	87.73 302

dp		75.01 300	72.09 318	83.76 200	89.28 180	66.22 157	79.96 408	89.75 277	71.16 273	67.80 311	77.19 395	51.81 270	92.54 322	50.39 376	71.44 306	92.51 215

TAPA-MVS	70.22 12	74.94 301	73.53 296	79.17 332	90.40 154	52.07 402	89.19 303	89.61 286	62.69 368	70.07 277	92.67 149	48.89 310	94.32 254	38.26 432	79.97 230	91.12 255
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 302	73.32 302	79.74 321	86.53 275	60.31 330	89.03 308	92.70 125	78.61 116	68.98 292	83.34 321	41.93 358	92.23 335	52.77 370	65.97 343	86.69 321

SSM_04072		74.86 303	73.37 299	79.35 329	88.50 205	66.98 133	58.80 460	86.18 376	69.12 306	74.12 220	89.01 240	47.50 323	79.09 444	67.57 278	79.52 234	91.98 234

v10		74.77 304	72.54 314	81.46 273	80.33 375	66.71 144	89.15 304	89.08 310	70.94 278	63.08 359	79.86 372	52.52 265	94.04 272	65.70 301	62.17 378	83.64 371

XVG-OURS-SEG-HR		74.70 305	73.08 304	79.57 325	78.25 404	57.33 373	80.49 398	87.32 358	63.22 361	68.76 297	90.12 222	44.89 347	91.59 350	70.55 248	74.09 285	89.79 274

ACMM	69.62 13	74.34 306	72.73 310	79.17 332	84.25 328	57.87 363	90.36 266	89.93 271	63.17 363	65.64 333	86.04 290	37.79 383	94.10 265	65.89 298	71.52 304	85.55 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 307	72.30 316	80.32 300	91.49 130	61.66 295	90.85 245	80.72 415	56.67 411	63.85 351	90.64 198	46.75 329	90.84 361	53.79 365	75.99 273	88.47 294

XVG-OURS		74.25 308	72.46 315	79.63 323	78.45 402	57.59 369	80.33 400	87.39 355	63.86 353	68.76 297	89.62 229	40.50 364	91.72 346	69.00 262	74.25 283	89.58 277

test_fmvs1		74.07 309	73.69 294	75.22 371	78.91 395	47.34 431	89.06 307	74.69 432	63.68 356	79.41 149	91.59 184	24.36 434	87.77 397	85.22 95	76.26 271	90.55 265

CVMVSNet		74.04 310	74.27 283	73.33 390	85.33 300	43.94 445	89.53 293	88.39 336	54.33 418	70.37 273	90.13 220	49.17 306	84.05 420	61.83 331	79.36 239	91.99 233

Baseline_NR-MVSNet		73.99 311	72.83 307	77.48 350	80.78 366	59.29 350	91.79 201	84.55 395	68.85 309	68.99 291	80.70 359	56.16 221	92.04 340	62.67 325	60.98 391	81.11 403

pmmvs4		73.92 312	71.81 322	80.25 304	79.17 389	65.24 182	87.43 336	87.26 361	67.64 325	63.46 354	83.91 315	48.96 309	91.53 356	62.94 322	65.49 346	83.96 367

D2MVS		73.80 313	72.02 319	79.15 334	79.15 390	62.97 259	88.58 315	90.07 264	72.94 218	59.22 382	78.30 383	42.31 357	92.70 316	65.59 303	72.00 300	81.79 398

SD_0403		73.79 314	73.48 298	74.69 377	85.33 300	45.56 441	83.80 364	85.57 385	76.55 160	62.96 360	88.45 246	50.62 288	87.59 401	48.80 386	79.28 243	90.92 259

CR-MVSNet		73.79 314	70.82 330	82.70 238	83.15 343	67.96 100	70.25 436	84.00 400	73.67 205	69.97 280	72.41 419	57.82 199	89.48 380	52.99 369	73.13 291	90.64 263

test_djsdf		73.76 316	72.56 313	77.39 352	77.00 416	53.93 395	89.07 305	90.69 232	65.80 338	63.92 349	82.03 336	43.14 354	92.67 317	72.83 219	68.53 323	85.57 350

pmmvs5		73.35 317	71.52 324	78.86 336	78.64 399	60.61 323	91.08 238	86.90 365	67.69 322	63.32 355	83.64 316	44.33 349	90.53 364	62.04 329	66.02 342	85.46 353

Anonymous20231211		73.08 318	70.39 334	81.13 281	90.62 149	63.33 249	91.40 217	90.06 266	51.84 424	64.46 345	80.67 361	36.49 393	94.07 268	63.83 315	64.17 363	85.98 339

tt0805		73.07 319	70.73 331	80.07 308	78.37 403	57.05 375	87.78 330	92.18 152	61.23 383	67.04 321	86.49 283	31.35 415	94.58 240	65.06 308	67.12 335	88.57 291

miper_lstm_enhance		73.05 320	71.73 323	77.03 357	83.80 334	58.32 360	81.76 386	88.88 320	69.80 296	61.01 371	78.23 385	57.19 204	87.51 402	65.34 306	59.53 399	85.27 358

jajsoiax		73.05 320	71.51 325	77.67 347	77.46 413	54.83 391	88.81 311	90.04 267	69.13 305	62.85 363	83.51 318	31.16 416	92.75 313	70.83 243	69.80 310	85.43 354

LCM-MVSNet-Re		72.93 322	71.84 321	76.18 366	88.49 209	48.02 426	80.07 405	70.17 447	73.96 196	52.25 416	80.09 371	49.98 294	88.24 391	67.35 280	84.23 181	92.28 223

pm-mvs1		72.89 323	71.09 327	78.26 342	79.10 392	57.62 367	90.80 247	89.30 296	67.66 323	62.91 362	81.78 340	49.11 308	92.95 302	60.29 339	58.89 402	84.22 366

tpmvs		72.88 324	69.76 340	82.22 254	90.98 142	67.05 129	78.22 415	88.30 340	63.10 364	64.35 347	74.98 409	55.09 235	94.27 258	43.25 412	69.57 313	85.34 356

test0.0.03 1		72.76 325	72.71 311	72.88 394	80.25 376	47.99 427	91.22 232	89.45 290	71.51 268	62.51 366	87.66 264	53.83 250	85.06 416	50.16 378	67.84 333	85.58 349

UniMVSNet_ETH3D		72.74 326	70.53 333	79.36 328	78.62 400	56.64 379	85.01 354	89.20 300	63.77 354	64.84 340	84.44 309	34.05 404	91.86 343	63.94 314	70.89 309	89.57 278

mvs_tets		72.71 327	71.11 326	77.52 348	77.41 414	54.52 393	88.45 317	89.76 276	68.76 312	62.70 364	83.26 322	29.49 422	92.71 314	70.51 249	69.62 312	85.34 356

FMVSNet1		72.71 327	69.91 338	81.10 284	83.60 338	65.11 186	90.01 277	90.32 249	63.92 352	63.56 353	80.25 368	36.35 394	91.54 353	54.46 362	66.75 338	86.64 322

test_fmvs1_n		72.69 329	71.92 320	74.99 375	71.15 436	47.08 433	87.34 338	75.67 427	63.48 358	78.08 168	91.17 193	20.16 448	87.87 394	84.65 104	75.57 275	90.01 271

IterMVS		72.65 330	70.83 328	78.09 344	82.17 353	62.96 260	87.64 334	86.28 372	71.56 266	60.44 375	78.85 381	45.42 343	86.66 406	63.30 320	61.83 382	84.65 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 331	72.74 309	72.10 402	87.87 237	49.45 420	88.07 323	89.01 314	72.91 220	63.11 357	88.10 256	63.63 113	85.54 411	32.73 448	69.23 317	81.32 401

PatchMatch-RL		72.06 332	69.98 335	78.28 341	89.51 173	55.70 386	83.49 367	83.39 407	61.24 382	63.72 352	82.76 326	34.77 399	93.03 299	53.37 368	77.59 256	86.12 336

PVSNet_0	68.08 15	71.81 333	68.32 349	82.27 251	84.68 314	62.31 278	88.68 313	90.31 252	75.84 165	57.93 394	80.65 362	37.85 382	94.19 261	69.94 251	29.05 463	90.31 267

MIMVSNet		71.64 334	68.44 347	81.23 278	81.97 356	64.44 205	73.05 430	88.80 324	69.67 297	64.59 341	74.79 411	32.79 407	87.82 395	53.99 364	76.35 270	91.42 245

test_vis1_n		71.63 335	70.73 331	74.31 384	69.63 442	47.29 432	86.91 342	72.11 440	63.21 362	75.18 203	90.17 217	20.40 446	85.76 410	84.59 105	74.42 282	89.87 272

IterMVS-SCA-FT		71.55 336	69.97 336	76.32 364	81.48 359	60.67 321	87.64 334	85.99 379	66.17 336	59.50 380	78.88 380	45.53 341	83.65 424	62.58 326	61.93 381	84.63 365

v7n		71.31 337	68.65 344	79.28 330	76.40 418	60.77 314	86.71 346	89.45 290	64.17 351	58.77 387	78.24 384	44.59 348	93.54 289	57.76 349	61.75 384	83.52 374

anonymousdsp		71.14 338	69.37 342	76.45 363	72.95 431	54.71 392	84.19 361	88.88 320	61.92 376	62.15 367	79.77 374	38.14 378	91.44 358	68.90 264	67.45 334	83.21 380

F-COLMAP		70.66 339	68.44 347	77.32 353	86.37 280	55.91 384	88.00 325	86.32 371	56.94 409	57.28 398	88.07 258	33.58 405	92.49 324	51.02 373	68.37 324	83.55 372

WR-MVS_H		70.59 340	69.94 337	72.53 396	81.03 362	51.43 406	87.35 337	92.03 160	67.38 326	60.23 377	80.70 359	55.84 227	83.45 427	46.33 401	58.58 404	82.72 387

CP-MVSNet		70.50 341	69.91 338	72.26 399	80.71 367	51.00 410	87.23 339	90.30 253	67.84 321	59.64 379	82.69 327	50.23 292	82.30 435	51.28 372	59.28 400	83.46 376

RPMNet		70.42 342	65.68 363	84.63 169	83.15 343	67.96 100	70.25 436	90.45 240	46.83 440	69.97 280	65.10 443	56.48 220	95.30 213	35.79 437	73.13 291	90.64 263

testing3		70.38 343	70.83 328	69.03 415	85.82 294	43.93 446	90.72 253	90.56 239	68.06 318	60.24 376	86.82 280	64.83 93	84.12 418	26.33 456	64.10 364	79.04 422

tfpnnormal		70.10 344	67.36 353	78.32 340	83.45 340	60.97 310	88.85 309	92.77 123	64.85 345	60.83 373	78.53 382	43.52 352	93.48 291	31.73 451	61.70 386	80.52 410

TransMVSNet (Re)		70.07 345	67.66 351	77.31 354	80.62 370	59.13 352	91.78 203	84.94 391	65.97 337	60.08 378	80.44 364	50.78 285	91.87 342	48.84 385	45.46 436	80.94 405

CL-MVSNet_self_test		69.92 346	68.09 350	75.41 369	73.25 430	55.90 385	90.05 276	89.90 272	69.96 293	61.96 369	76.54 399	51.05 284	87.64 398	49.51 382	50.59 425	82.70 389

DP-MVS		69.90 347	66.48 355	80.14 306	95.36 29	62.93 261	89.56 288	76.11 425	50.27 430	57.69 396	85.23 299	39.68 367	95.73 182	33.35 442	71.05 308	81.78 399

PS-CasMVS		69.86 348	69.13 343	72.07 403	80.35 374	50.57 413	87.02 341	89.75 277	67.27 327	59.19 383	82.28 332	46.58 331	82.24 436	50.69 375	59.02 401	83.39 378

Syy-MVS		69.65 349	69.52 341	70.03 411	87.87 237	43.21 447	88.07 323	89.01 314	72.91 220	63.11 357	88.10 256	45.28 344	85.54 411	22.07 461	69.23 317	81.32 401

MSDG		69.54 350	65.73 362	80.96 289	85.11 309	63.71 235	84.19 361	83.28 408	56.95 408	54.50 405	84.03 312	31.50 413	96.03 167	42.87 416	69.13 319	83.14 382

PEN-MVS		69.46 351	68.56 345	72.17 401	79.27 387	49.71 418	86.90 343	89.24 298	67.24 330	59.08 384	82.51 330	47.23 326	83.54 426	48.42 388	57.12 406	83.25 379

LS3D		69.17 352	66.40 357	77.50 349	91.92 115	56.12 382	85.12 353	80.37 417	46.96 438	56.50 400	87.51 268	37.25 386	93.71 286	32.52 450	79.40 238	82.68 390

PatchT		69.11 353	65.37 367	80.32 300	82.07 355	63.68 239	67.96 446	87.62 354	50.86 428	69.37 284	65.18 442	57.09 205	88.53 387	41.59 421	66.60 339	88.74 288

KD-MVS_2432*1600		69.03 354	66.37 358	77.01 358	85.56 298	61.06 308	81.44 391	90.25 256	67.27 327	58.00 392	76.53 400	54.49 240	87.63 399	48.04 390	35.77 454	82.34 393

miper_refine_blended		69.03 354	66.37 358	77.01 358	85.56 298	61.06 308	81.44 391	90.25 256	67.27 327	58.00 392	76.53 400	54.49 240	87.63 399	48.04 390	35.77 454	82.34 393

mvsany_test1		68.77 356	68.56 345	69.39 413	73.57 429	45.88 440	80.93 396	60.88 461	59.65 393	71.56 260	90.26 210	43.22 353	75.05 448	74.26 208	62.70 373	87.25 314

ACMH	63.93 17	68.62 357	64.81 369	80.03 310	85.22 305	63.25 251	87.72 331	84.66 393	60.83 385	51.57 420	79.43 378	27.29 429	94.96 223	41.76 419	64.84 355	81.88 397
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 358	65.41 366	77.96 345	78.69 398	62.93 261	89.86 282	89.17 302	60.55 386	50.27 425	77.73 390	22.60 442	94.06 269	47.18 397	72.65 296	76.88 436

ADS-MVSNet		68.54 359	64.38 376	81.03 288	88.06 229	66.90 138	68.01 444	84.02 399	57.57 402	64.48 343	69.87 430	38.68 369	89.21 382	40.87 423	67.89 331	86.97 316

DTE-MVSNet		68.46 360	67.33 354	71.87 405	77.94 408	49.00 424	86.16 350	88.58 333	66.36 335	58.19 389	82.21 334	46.36 332	83.87 423	44.97 409	55.17 413	82.73 386

mmtdpeth		68.33 361	66.37 358	74.21 385	82.81 348	51.73 403	84.34 359	80.42 416	67.01 331	71.56 260	68.58 434	30.52 420	92.35 331	75.89 192	36.21 452	78.56 429

our_test_3		68.29 362	64.69 371	79.11 335	78.92 393	64.85 193	88.40 318	85.06 389	60.32 389	52.68 414	76.12 404	40.81 363	89.80 379	44.25 411	55.65 411	82.67 391

Patchmatch-RL test		68.17 363	64.49 374	79.19 331	71.22 435	53.93 395	70.07 438	71.54 444	69.22 302	56.79 399	62.89 447	56.58 217	88.61 384	69.53 255	52.61 420	95.03 94

XVG-ACMP-BASELINE		68.04 364	65.53 365	75.56 368	74.06 428	52.37 400	78.43 412	85.88 380	62.03 374	58.91 386	81.21 355	20.38 447	91.15 360	60.69 336	68.18 325	83.16 381

FMVSNet5		68.04 364	65.66 364	75.18 373	84.43 324	57.89 362	83.54 366	86.26 373	61.83 378	53.64 411	73.30 414	37.15 389	85.08 415	48.99 384	61.77 383	82.56 392

ppachtmachnet_test		67.72 366	63.70 378	79.77 320	78.92 393	66.04 160	88.68 313	82.90 410	60.11 391	55.45 402	75.96 405	39.19 368	90.55 363	39.53 427	52.55 421	82.71 388

ACMH+	65.35 16	67.65 367	64.55 372	76.96 360	84.59 318	57.10 374	88.08 322	80.79 414	58.59 400	53.00 413	81.09 357	26.63 431	92.95 302	46.51 399	61.69 387	80.82 406

pmmvs6		67.57 368	64.76 370	76.00 367	72.82 433	53.37 397	88.71 312	86.78 369	53.19 420	57.58 397	78.03 387	35.33 398	92.41 327	55.56 358	54.88 415	82.21 395

Anonymous20231206		67.53 369	65.78 361	72.79 395	74.95 424	47.59 429	88.23 320	87.32 358	61.75 381	58.07 391	77.29 393	37.79 383	87.29 404	42.91 414	63.71 368	83.48 375

Patchmtry		67.53 369	63.93 377	78.34 339	82.12 354	64.38 209	68.72 441	84.00 400	48.23 437	59.24 381	72.41 419	57.82 199	89.27 381	46.10 402	56.68 410	81.36 400

USDC		67.43 371	64.51 373	76.19 365	77.94 408	55.29 388	78.38 413	85.00 390	73.17 211	48.36 433	80.37 365	21.23 444	92.48 325	52.15 371	64.02 366	80.81 407

ADS-MVSNet2		66.90 372	63.44 380	77.26 355	88.06 229	60.70 320	68.01 444	75.56 429	57.57 402	64.48 343	69.87 430	38.68 369	84.10 419	40.87 423	67.89 331	86.97 316

CMPMVS	48.56 21	66.77 373	64.41 375	73.84 387	70.65 439	50.31 415	77.79 417	85.73 383	45.54 443	44.76 444	82.14 335	35.40 397	90.14 373	63.18 321	74.54 280	81.07 404
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 374	62.92 383	76.80 362	76.51 417	57.77 364	89.22 300	83.41 406	55.48 415	53.86 409	77.84 388	26.28 432	93.95 278	34.90 439	68.76 321	78.68 427

LTVRE_ROB	59.60 19	66.27 375	63.54 379	74.45 381	84.00 331	51.55 405	67.08 448	83.53 404	58.78 398	54.94 404	80.31 366	34.54 400	93.23 295	40.64 425	68.03 327	78.58 428
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		66.06 376	62.45 386	76.88 361	81.42 361	54.45 394	57.49 462	88.67 329	49.36 432	63.86 350	46.86 459	56.06 224	90.25 367	49.53 381	68.83 320	85.95 340

Patchmatch-test		65.86 377	60.94 392	80.62 297	83.75 335	58.83 354	58.91 459	75.26 431	44.50 446	50.95 424	77.09 396	58.81 187	87.90 393	35.13 438	64.03 365	95.12 88

UnsupCasMVSNet_eth		65.79 378	63.10 381	73.88 386	70.71 438	50.29 416	81.09 394	89.88 273	72.58 227	49.25 430	74.77 412	32.57 409	87.43 403	55.96 357	41.04 444	83.90 369

test_fmvs2		65.78 379	64.84 368	68.60 417	66.54 449	41.71 449	83.27 371	69.81 448	54.38 417	67.91 307	84.54 308	15.35 453	81.22 440	75.65 194	66.16 341	82.88 383

dmvs_testset		65.55 380	66.45 356	62.86 429	79.87 380	22.35 475	76.55 420	71.74 442	77.42 141	55.85 401	87.77 263	51.39 278	80.69 441	31.51 454	65.92 344	85.55 351

pmmvs-eth3d		65.53 381	62.32 387	75.19 372	69.39 443	59.59 343	82.80 379	83.43 405	62.52 369	51.30 422	72.49 417	32.86 406	87.16 405	55.32 359	50.73 424	78.83 425

mamv4		65.18 382	67.43 352	58.44 433	77.88 410	49.36 423	69.40 440	70.99 446	48.31 436	57.78 395	85.53 296	59.01 184	51.88 471	73.67 210	64.32 361	74.07 441

SixPastTwentyTwo		64.92 383	61.78 390	74.34 383	78.74 397	49.76 417	83.42 370	79.51 420	62.86 365	50.27 425	77.35 391	30.92 418	90.49 365	45.89 403	47.06 431	82.78 384

OurMVSNet-221017-0		64.68 384	62.17 388	72.21 400	76.08 421	47.35 430	80.67 397	81.02 413	56.19 412	51.60 419	79.66 376	27.05 430	88.56 386	53.60 367	53.63 418	80.71 408

test_0402		64.54 385	61.09 391	74.92 376	84.10 330	60.75 316	87.95 326	79.71 419	52.03 422	52.41 415	77.20 394	32.21 411	91.64 348	23.14 459	61.03 390	72.36 447

testgi		64.48 386	62.87 384	69.31 414	71.24 434	40.62 452	85.49 351	79.92 418	65.36 342	54.18 407	83.49 319	23.74 437	84.55 417	41.60 420	60.79 393	82.77 385

RPSCF		64.24 387	61.98 389	71.01 408	76.10 420	45.00 442	75.83 425	75.94 426	46.94 439	58.96 385	84.59 306	31.40 414	82.00 437	47.76 395	60.33 398	86.04 337

EU-MVSNet		64.01 388	63.01 382	67.02 423	74.40 427	38.86 458	83.27 371	86.19 375	45.11 444	54.27 406	81.15 356	36.91 392	80.01 443	48.79 387	57.02 407	82.19 396

test20.03		63.83 389	62.65 385	67.38 422	70.58 440	39.94 454	86.57 347	84.17 397	63.29 360	51.86 418	77.30 392	37.09 390	82.47 433	38.87 431	54.13 417	79.73 416

sc_t1		63.81 390	59.39 398	77.10 356	77.62 411	56.03 383	84.32 360	73.56 436	46.66 441	58.22 388	73.06 415	23.28 440	90.62 362	50.93 374	46.84 432	84.64 364

MDA-MVSNet_test_wron		63.78 391	60.16 394	74.64 378	78.15 406	60.41 327	83.49 367	84.03 398	56.17 414	39.17 454	71.59 425	37.22 387	83.24 430	42.87 416	48.73 427	80.26 413

YYNet1		63.76 392	60.14 395	74.62 379	78.06 407	60.19 334	83.46 369	83.99 402	56.18 413	39.25 453	71.56 426	37.18 388	83.34 428	42.90 415	48.70 428	80.32 412

K. test v3		63.09 393	59.61 397	73.53 389	76.26 419	49.38 422	83.27 371	77.15 423	64.35 348	47.77 435	72.32 421	28.73 424	87.79 396	49.93 380	36.69 451	83.41 377

COLMAP_ROB	57.96 20	62.98 394	59.65 396	72.98 393	81.44 360	53.00 399	83.75 365	75.53 430	48.34 435	48.81 432	81.40 349	24.14 435	90.30 366	32.95 445	60.52 395	75.65 439
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 395	59.08 399	71.10 407	67.19 447	48.72 425	83.91 363	85.23 388	50.38 429	47.84 434	71.22 428	20.74 445	85.51 413	46.47 400	58.75 403	79.06 421

tt0320		61.85 396	57.45 405	75.03 374	77.49 412	57.60 368	82.74 380	73.65 435	43.65 450	53.65 410	68.18 436	25.47 433	88.66 383	45.56 405	46.68 433	78.81 426

AllTest		61.66 397	58.06 401	72.46 397	79.57 382	51.42 407	80.17 403	68.61 450	51.25 426	45.88 438	81.23 351	19.86 449	86.58 407	38.98 429	57.01 408	79.39 418

UnsupCasMVSNet_bld		61.60 398	57.71 402	73.29 391	68.73 444	51.64 404	78.61 411	89.05 312	57.20 407	46.11 437	61.96 450	28.70 425	88.60 385	50.08 379	38.90 449	79.63 417

MDA-MVSNet-bldmvs		61.54 399	57.70 403	73.05 392	79.53 384	57.00 378	83.08 375	81.23 412	57.57 402	34.91 458	72.45 418	32.79 407	86.26 409	35.81 436	41.95 442	75.89 438

tt0320-xc		61.51 400	56.89 409	75.37 370	78.50 401	58.61 357	82.61 382	71.27 445	44.31 447	53.17 412	68.03 438	23.38 438	88.46 388	47.77 394	43.00 441	79.03 423

mvs5depth		61.03 401	57.65 404	71.18 406	67.16 448	47.04 435	72.74 431	77.49 421	57.47 405	60.52 374	72.53 416	22.84 441	88.38 389	49.15 383	38.94 448	78.11 432

KD-MVS_self_test		60.87 402	58.60 400	67.68 420	66.13 450	39.93 455	75.63 427	84.70 392	57.32 406	49.57 428	68.45 435	29.55 421	82.87 431	48.09 389	47.94 429	80.25 414

kuosan		60.86 403	60.24 393	62.71 430	81.57 358	46.43 437	75.70 426	85.88 380	57.98 401	48.95 431	69.53 432	58.42 192	76.53 446	28.25 455	35.87 453	65.15 454

FE-MVSNET		60.52 404	57.18 408	70.53 409	67.53 446	50.68 412	82.62 381	76.28 424	59.33 396	46.71 436	71.10 429	30.54 419	83.61 425	33.15 444	47.37 430	77.29 435

TinyColmap		60.32 405	56.42 412	72.00 404	78.78 396	53.18 398	78.36 414	75.64 428	52.30 421	41.59 452	75.82 407	14.76 456	88.35 390	35.84 435	54.71 416	74.46 440

MVS-HIRNet		60.25 406	55.55 413	74.35 382	84.37 325	56.57 380	71.64 434	74.11 433	34.44 457	45.54 442	42.24 465	31.11 417	89.81 377	40.36 426	76.10 272	76.67 437

MIMVSNet1		60.16 407	57.33 406	68.67 416	69.71 441	44.13 444	78.92 410	84.21 396	55.05 416	44.63 445	71.85 423	23.91 436	81.54 439	32.63 449	55.03 414	80.35 411

PM-MVS		59.40 408	56.59 410	67.84 418	63.63 453	41.86 448	76.76 419	63.22 458	59.01 397	51.07 423	72.27 422	11.72 460	83.25 429	61.34 332	50.28 426	78.39 430

new-patchmatchnet		59.30 409	56.48 411	67.79 419	65.86 451	44.19 443	82.47 383	81.77 411	59.94 392	43.65 448	66.20 441	27.67 428	81.68 438	39.34 428	41.40 443	77.50 434

test_vis1_rt		59.09 410	57.31 407	64.43 426	68.44 445	46.02 439	83.05 377	48.63 470	51.96 423	49.57 428	63.86 446	16.30 451	80.20 442	71.21 241	62.79 372	67.07 453

test_fmvs3		56.82 411	54.86 415	62.69 431	53.59 464	35.47 461	75.87 424	65.64 455	43.91 448	55.10 403	71.43 427	6.91 468	74.40 451	68.64 266	52.63 419	78.20 431

DSMNet-mixed		56.78 412	54.44 416	63.79 427	63.21 454	29.44 470	64.43 451	64.10 457	42.12 454	51.32 421	71.60 424	31.76 412	75.04 449	36.23 434	65.20 352	86.87 319

pmmvs3		55.51 413	51.50 419	67.53 421	57.90 462	50.93 411	80.37 399	73.66 434	40.63 455	44.15 447	64.75 444	16.30 451	78.97 445	44.77 410	40.98 446	72.69 445

TDRefinement		55.28 414	51.58 418	66.39 424	59.53 461	46.15 438	76.23 422	72.80 437	44.60 445	42.49 450	76.28 403	15.29 454	82.39 434	33.20 443	43.75 438	70.62 449

dongtai		55.18 415	55.46 414	54.34 441	76.03 422	36.88 459	76.07 423	84.61 394	51.28 425	43.41 449	64.61 445	56.56 218	67.81 459	18.09 464	28.50 464	58.32 457

LF4IMVS		54.01 416	52.12 417	59.69 432	62.41 456	39.91 456	68.59 442	68.28 452	42.96 452	44.55 446	75.18 408	14.09 458	68.39 458	41.36 422	51.68 422	70.78 448

ttmdpeth		53.34 417	49.96 420	63.45 428	62.07 458	40.04 453	72.06 432	65.64 455	42.54 453	51.88 417	77.79 389	13.94 459	76.48 447	32.93 446	30.82 462	73.84 442

MVStest1		51.35 418	46.89 422	64.74 425	65.06 452	51.10 409	67.33 447	72.58 438	30.20 461	35.30 456	74.82 410	27.70 427	69.89 456	24.44 458	24.57 465	73.22 443

N_pmnet		50.55 419	49.11 421	54.88 439	77.17 415	4.02 483	84.36 358	2.00 481	48.59 433	45.86 440	68.82 433	32.22 410	82.80 432	31.58 452	51.38 423	77.81 433

new_pmnet		49.31 420	46.44 423	57.93 434	62.84 455	40.74 451	68.47 443	62.96 459	36.48 456	35.09 457	57.81 454	14.97 455	72.18 453	32.86 447	46.44 434	60.88 456

mvsany_test3		48.86 421	46.35 424	56.41 435	46.00 470	31.67 466	62.26 453	47.25 471	43.71 449	45.54 442	68.15 437	10.84 461	64.44 467	57.95 348	35.44 456	73.13 444

test_f		46.58 422	43.45 426	55.96 436	45.18 471	32.05 465	61.18 454	49.49 469	33.39 458	42.05 451	62.48 449	7.00 467	65.56 463	47.08 398	43.21 440	70.27 450

WB-MVS		46.23 423	44.94 425	50.11 444	62.13 457	21.23 477	76.48 421	55.49 463	45.89 442	35.78 455	61.44 452	35.54 396	72.83 452	9.96 471	21.75 466	56.27 459

FPMVS		45.64 424	43.10 428	53.23 442	51.42 467	36.46 460	64.97 450	71.91 441	29.13 462	27.53 462	61.55 451	9.83 463	65.01 465	16.00 468	55.58 412	58.22 458

SSC-MVS		44.51 425	43.35 427	47.99 448	61.01 460	18.90 479	74.12 429	54.36 464	43.42 451	34.10 459	60.02 453	34.42 401	70.39 455	9.14 473	19.57 467	54.68 460

EGC-MVSNET		42.35 426	38.09 429	55.11 438	74.57 425	46.62 436	71.63 435	55.77 462	0.04 476	0.24 477	62.70 448	14.24 457	74.91 450	17.59 465	46.06 435	43.80 462

LCM-MVSNet		40.54 427	35.79 432	54.76 440	36.92 477	30.81 467	51.41 465	69.02 449	22.07 464	24.63 464	45.37 461	4.56 472	65.81 462	33.67 441	34.50 457	67.67 451

APD_test1		40.50 428	37.31 431	50.09 445	51.88 465	35.27 462	59.45 458	52.59 466	21.64 465	26.12 463	57.80 455	4.56 472	66.56 461	22.64 460	39.09 447	48.43 461

test_vis3_rt		40.46 429	37.79 430	48.47 447	44.49 472	33.35 464	66.56 449	32.84 478	32.39 459	29.65 460	39.13 468	3.91 475	68.65 457	50.17 377	40.99 445	43.40 463

ANet_high		40.27 430	35.20 433	55.47 437	34.74 478	34.47 463	63.84 452	71.56 443	48.42 434	18.80 467	41.08 466	9.52 464	64.45 466	20.18 462	8.66 474	67.49 452

test_method		38.59 431	35.16 434	48.89 446	54.33 463	21.35 476	45.32 468	53.71 465	7.41 473	28.74 461	51.62 457	8.70 465	52.87 470	33.73 440	32.89 458	72.47 446

PMMVS2		37.93 432	33.61 435	50.92 443	46.31 469	24.76 473	60.55 457	50.05 467	28.94 463	20.93 465	47.59 458	4.41 474	65.13 464	25.14 457	18.55 469	62.87 455

Gipuma		34.91 433	31.44 436	45.30 449	70.99 437	39.64 457	19.85 472	72.56 439	20.10 467	16.16 471	21.47 472	5.08 471	71.16 454	13.07 469	43.70 439	25.08 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 434	29.47 437	42.67 451	41.89 474	30.81 467	52.07 463	43.45 472	15.45 468	18.52 468	44.82 462	2.12 476	58.38 468	16.05 466	30.87 460	38.83 464

APD_test2		32.77 434	29.47 437	42.67 451	41.89 474	30.81 467	52.07 463	43.45 472	15.45 468	18.52 468	44.82 462	2.12 476	58.38 468	16.05 466	30.87 460	38.83 464

PMVS	26.43 22	31.84 436	28.16 439	42.89 450	25.87 480	27.58 471	50.92 466	49.78 468	21.37 466	14.17 472	40.81 467	2.01 478	66.62 460	9.61 472	38.88 450	34.49 468
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 437	24.00 441	26.45 455	43.74 473	18.44 480	60.86 455	39.66 474	15.11 470	9.53 474	22.10 471	6.52 469	46.94 473	8.31 474	10.14 471	13.98 471

MVE	24.84 23	24.35 438	19.77 444	38.09 453	34.56 479	26.92 472	26.57 470	38.87 476	11.73 472	11.37 473	27.44 469	1.37 479	50.42 472	11.41 470	14.60 470	36.93 466
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 439	23.20 443	25.46 456	41.52 476	16.90 481	60.56 456	38.79 477	14.62 471	8.99 475	20.24 474	7.35 466	45.82 474	7.25 475	9.46 472	13.64 472

tmp_tt		22.26 440	23.75 442	17.80 457	5.23 481	12.06 482	35.26 469	39.48 475	2.82 475	18.94 466	44.20 464	22.23 443	24.64 476	36.30 433	9.31 473	16.69 470

cdsmvs_eth3d_5k		19.86 441	26.47 440	0.00 461	0.00 484	0.00 486	0.00 473	93.45 94	0.00 479	0.00 480	95.27 75	49.56 300	0.00 480	0.00 479	0.00 477	0.00 476

wuyk23d		11.30 442	10.95 445	12.33 458	48.05 468	19.89 478	25.89 471	1.92 482	3.58 474	3.12 476	1.37 476	0.64 480	15.77 477	6.23 476	7.77 475	1.35 473

ab-mvs-re		7.91 443	10.55 446	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	94.95 85	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

testmvs		7.23 444	9.62 447	0.06 460	0.04 482	0.02 485	84.98 355	0.02 483	0.03 477	0.18 478	1.21 477	0.01 482	0.02 478	0.14 477	0.01 476	0.13 475

test123		6.92 445	9.21 448	0.08 459	0.03 483	0.05 484	81.65 389	0.01 484	0.02 478	0.14 479	0.85 478	0.03 481	0.02 478	0.12 478	0.00 477	0.16 474

pcd_1.5k_mvsjas		4.46 446	5.95 449	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	53.55 254	0.00 480	0.00 479	0.00 477	0.00 476

mmdepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

monomultidepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

test_blank		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

uanet_test		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

DCPMVS		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

sosnet-low-res		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

sosnet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

uncertanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

Regformer		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

uanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 477	0.00 476

MED-MVS test				87.42 45	94.76 34	67.28 119	94.47 60	94.87 32	73.09 216	91.27 23	96.95 16		98.98 16	91.55 42	94.28 37	95.99 45

TestfortrainingZip							94.47 60

WAC-MVS						49.45 420								31.56 453

FOURS1					93.95 49	61.77 291	93.96 86	91.92 164	62.14 373	86.57 60

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 37	94.77 26	96.51 24

PC_three_1452									80.91 64	94.07 2	96.83 27	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 37	94.77 26	96.51 24

test_one_0601					96.32 19	69.74 51		94.18 65	71.42 270	90.67 27	96.85 25	74.45 21

eth-test2					0.00 484

eth-test					0.00 484

ZD-MVS					96.63 9	65.50 177		93.50 92	70.74 284	85.26 78	95.19 81	64.92 92	97.29 87	87.51 71	93.01 59

RE-MVS-def			80.48 177		92.02 108	58.56 358	90.90 242	90.45 240	62.76 366	78.89 155	94.46 99	49.30 303		78.77 175	86.77 146	92.28 223

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 65	95.28 1				92.34 34	95.36 14	96.47 28

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

test_241102_TWO								94.41 56	71.65 259	92.07 11	97.21 8	74.58 19	99.11 6	92.34 34	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 59		94.44 54	71.65 259	92.11 9	97.05 11	76.79 9	99.11 6

9.14			87.63 37		93.86 51		94.41 64	94.18 65	72.76 224	86.21 63	96.51 34	66.64 70	97.88 51	90.08 53	94.04 42

save fliter					93.84 52	67.89 103	95.05 40	92.66 130	78.19 121

test_0728_THIRD									72.48 229	90.55 28	96.93 19	76.24 12	99.08 11	91.53 44	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 60					99.15 2	91.91 40	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 62	71.92 245	91.89 14	97.11 10	73.77 24

GSMVS																94.68 116

test_part2					96.29 20	68.16 96				90.78 25

sam_mvs1												57.85 198				94.68 116

sam_mvs												54.91 237

ambc				69.61 412	61.38 459	41.35 450	49.07 467	85.86 382		50.18 427	66.40 440	10.16 462	88.14 392	45.73 404	44.20 437	79.32 420

MTGPA								92.23 145

test_post1							78.95 409				20.70 473	53.05 259	91.50 357	60.43 337

test_post											23.01 470	56.49 219	92.67 317

patchmatchnet-post											67.62 439	57.62 201	90.25 367

GG-mvs-BLEND				86.53 82	91.91 117	69.67 54	75.02 428	94.75 39		78.67 163	90.85 197	77.91 7	94.56 245	72.25 228	93.74 48	95.36 71

MTMP							93.77 101	32.52 479

gm-plane-assit					88.42 215	67.04 130			78.62 115		91.83 176		97.37 81	76.57 187

test9_res														89.41 54	94.96 19	95.29 77

TEST9					94.18 44	67.28 119	94.16 73	93.51 90	71.75 256	85.52 73	95.33 69	68.01 59	97.27 91

test_8					94.19 43	67.19 123	94.15 75	93.42 97	71.87 250	85.38 76	95.35 68	68.19 57	96.95 118

agg_prior2														86.41 85	94.75 30	95.33 73

agg_prior					94.16 46	66.97 136		93.31 100		84.49 84			96.75 130

TestCases				72.46 397	79.57 382	51.42 407		68.61 450	51.25 426	45.88 438	81.23 351	19.86 449	86.58 407	38.98 429	57.01 408	79.39 418

test_prior4						67.18 125	93.92 90

test_prior2							95.10 39		75.40 173	85.25 79	95.61 60	67.94 60		87.47 73	94.77 26

test_prior				86.42 85	94.71 38	67.35 118		93.10 111					96.84 127			95.05 92

旧先验2							92.00 192		59.37 395	87.54 53			93.47 292	75.39 196

新几何2							91.41 215

新几何1				84.73 159	92.32 96	64.28 214		91.46 191	59.56 394	79.77 142	92.90 143	56.95 211	96.57 136	63.40 317	92.91 61	93.34 183

旧先验1					91.94 113	60.74 317		91.50 189			94.36 103	65.23 87			91.84 76	94.55 125

无先验							92.71 150	92.61 135	62.03 374				97.01 108	66.63 288		93.97 160

原ACMM2							92.01 189

原ACMM1				84.42 176	93.21 71	64.27 215		93.40 99	65.39 341	79.51 147	92.50 151	58.11 197	96.69 132	65.27 307	93.96 43	92.32 221

test222					89.77 166	61.60 297	89.55 289	89.42 292	56.83 410	77.28 179	92.43 155	52.76 262			91.14 93	93.09 193

testdata2													96.09 161	61.26 333

segment_acmp												65.94 78

testdata				81.34 276	89.02 188	57.72 365		89.84 274	58.65 399	85.32 77	94.09 119	57.03 206	93.28 294	69.34 257	90.56 99	93.03 196

testdata1							89.21 301		77.55 137

test12				87.09 55	94.60 39	68.86 73		92.91 118		82.67 107		65.44 84	97.55 71		93.69 51	94.84 103

plane_prior7					86.94 263	61.51 298

plane_prior6					87.23 254	62.32 277						50.66 286

plane_prior5								91.31 195					95.55 199	76.74 185	78.53 250	88.39 295

plane_prior4											89.14 238

plane_prior3						61.95 286			79.09 104	72.53 242

plane_prior2							93.13 129		78.81 111

plane_prior1					87.15 257

plane_prior						62.42 273	93.85 94		79.38 96						78.80 247

n2								0.00 485

nn								0.00 485

door-mid								66.01 454

lessismore_v0				73.72 388	72.93 432	47.83 428		61.72 460		45.86 440	73.76 413	28.63 426	89.81 377	47.75 396	31.37 459	83.53 373

LGP-MVS_train				79.56 326	84.31 326	59.37 347		89.73 280	69.49 298	64.86 338	88.42 247	38.65 371	94.30 256	72.56 225	72.76 294	85.01 359

test11								93.01 114

door								66.57 453

HQP5-MVS						63.66 240

HQP-NCC					87.54 246		94.06 78		79.80 84	74.18 216

ACMP_Plane					87.54 246		94.06 78		79.80 84	74.18 216

BP-MVS														77.63 182

HQP4-MVS										74.18 216			95.61 193			88.63 289

HQP3-MVS								91.70 181							78.90 245

HQP2-MVS												51.63 274

NP-MVS					87.41 249	63.04 257					90.30 208

MDTV_nov1_ep13_2view						59.90 339	80.13 404		67.65 324	72.79 236		54.33 245		59.83 341		92.58 212

MDTV_nov1_ep13			72.61 312		89.06 187	68.48 83	80.33 400	90.11 263	71.84 252	71.81 256	75.92 406	53.01 260	93.92 279	48.04 390	73.38 289

ACMMP++_ref															71.63 302

ACMMP++															69.72 311

Test By Simon												54.21 248

ITE_SJBPF				70.43 410	74.44 426	47.06 434		77.32 422	60.16 390	54.04 408	83.53 317	23.30 439	84.01 421	43.07 413	61.58 388	80.21 415

DeepMVS_CX				34.71 454	51.45 466	24.73 474		28.48 480	31.46 460	17.49 470	52.75 456	5.80 470	42.60 475	18.18 463	19.42 468	36.81 467

High-resolution multi-view benchmark