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.

DP-MVS Recon		91.72 74	90.85 84	94.34 32	99.50 1	85.00 60	98.51 26	95.96 135	80.57 214	88.08 127	97.63 74	76.84 114	99.89 7	85.67 137	94.88 115	98.13 72

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 16	97.10 24	95.17 2	92.11 68	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 46	98.09 8	89.99 34	92.34 65	96.97 105	81.30 59	98.99 101	88.54 115	98.88 19	99.20 18

AdaColmap		88.81 132	87.61 141	92.39 112	99.33 4	79.95 168	96.70 149	95.58 155	77.51 268	83.05 174	96.69 117	61.90 254	99.72 31	84.29 147	93.47 128	97.50 123

CNVR-MVS		96.30 1	96.54 1	95.55 12	99.31 5	87.69 19	99.06 9	97.12 22	94.66 3	96.79 9	98.78 11	86.42 24	99.95 3	97.59 9	99.18 5	99.00 23

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 91	97.59 73	96.61 74	83.60 167	94.77 36	98.46 24	82.72 52	99.64 42	95.29 33	98.42 42	99.32 13

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 33	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 28	99.10 7	99.00 23

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 20	92.06 2	99.84 12	99.11 1	99.37 1	99.74 1

ZD-MVS					99.09 9	83.22 93		96.60 77	82.88 180	93.61 51	98.06 48	82.93 48	99.14 91	95.51 30	98.49 38

DVP-MVS		95.58 7	95.91 7	94.57 26	99.05 10	85.18 51	99.06 9	96.46 95	88.75 49	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30
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
test0726					99.05 10	85.18 51	99.11 8	96.78 45	88.75 49	97.65 6	98.91 3	87.69 18

test_0728_SECOND				95.14 15	99.04 12	86.14 31	99.06 9	96.77 51					99.84 12	97.90 5	98.85 20	99.45 8

SED-MVS		95.88 4	96.22 3	94.87 19	99.03 13	85.03 58	99.12 6	96.78 45	88.72 51	97.79 3	98.91 3	88.48 14	99.82 16	98.15 2	98.97 15	99.74 1

IU-MVS					99.03 13	85.34 47		96.86 42	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

test_241102_ONE					99.03 13	85.03 58		96.78 45	88.72 51	97.79 3	98.90 6	88.48 14	99.82 16

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 16	89.42 7	98.75 19	96.80 44	87.28 81	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

test_part2					98.90 17	85.14 57				96.07 17

PAPR		92.74 52	92.17 66	94.45 28	98.89 18	84.87 63	97.20 102	96.20 121	87.73 73	88.40 122	98.12 41	78.71 88	99.76 20	87.99 122	96.28 98	98.74 31

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 19	85.68 42	98.06 42	96.64 71	93.64 8	91.74 74	98.54 20	80.17 70	99.90 5	92.28 72	98.75 26	99.49 6
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 88	98.04 44	96.41 101	85.79 103	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 21	84.87 63	97.77 58	96.74 55	86.11 95	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APD-MVS		93.61 37	93.59 37	93.69 54	98.76 22	83.26 92	97.21 100	96.09 128	82.41 188	94.65 38	98.21 31	81.96 57	98.81 114	94.65 41	98.36 50	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		92.89 49	92.86 50	92.98 86	98.71 23	81.12 139	97.58 74	96.70 60	85.20 120	91.75 72	97.97 56	78.47 90	99.71 32	90.95 83	98.41 44	98.12 73

#test#		92.99 47	92.99 46	92.98 86	98.71 23	81.12 139	97.77 58	96.70 60	85.75 104	91.75 72	97.97 56	78.47 90	99.71 32	91.36 79	98.41 44	98.12 73

region2R		92.72 55	92.70 53	92.79 95	98.68 25	80.53 157	97.53 78	96.51 89	85.22 118	91.94 70	97.98 54	77.26 107	99.67 40	90.83 87	98.37 49	98.18 66

test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 119	98.37 28	96.40 104	86.08 97	94.57 39	98.02 49	83.14 44	99.06 97	95.05 35	98.79 23	98.29 59

test_prior				93.09 81	98.68 25	81.91 119		96.40 104					99.06 97			98.29 59

ACMMPR		92.69 57	92.67 54	92.75 96	98.66 28	80.57 154	97.58 74	96.69 62	85.20 120	91.57 76	97.92 58	77.01 112	99.67 40	90.95 83	98.41 44	98.00 85

API-MVS		90.18 108	88.97 117	93.80 49	98.66 28	82.95 100	97.50 82	95.63 154	75.16 286	86.31 140	97.69 68	72.49 180	99.90 5	81.26 176	96.07 101	98.56 42

CDPH-MVS		93.12 44	92.91 48	93.74 51	98.65 30	83.88 76	97.67 68	96.26 117	83.00 177	93.22 56	98.24 30	81.31 58	99.21 80	89.12 111	98.74 27	98.14 71

TEST9					98.64 31	83.71 81	97.82 54	96.65 68	84.29 146	95.16 25	98.09 43	84.39 31	99.36 70

train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 81	97.82 54	96.65 68	84.50 138	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 68

test_8					98.63 33	83.64 84	97.81 56	96.63 73	84.50 138	95.10 27	98.11 42	84.33 32	99.23 76

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 34	86.04 32	97.81 56	96.93 36	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 39	99.18 5	98.65 38

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 94	97.77 58	96.56 82	84.38 142	94.19 42	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 70

agg_prior					98.59 35	83.13 94		96.56 82		94.19 42			99.16 89

CSCG		92.02 68	91.65 74	93.12 79	98.53 37	80.59 153	97.47 83	97.18 20	77.06 276	84.64 154	97.98 54	83.98 37	99.52 53	90.72 89	97.33 78	99.23 17

XVS		92.69 57	92.71 51	92.63 103	98.52 38	80.29 160	97.37 94	96.44 97	87.04 88	91.38 78	97.83 64	77.24 109	99.59 47	90.46 93	98.07 58	98.02 80

X-MVStestdata		86.26 177	84.14 193	92.63 103	98.52 38	80.29 160	97.37 94	96.44 97	87.04 88	91.38 78	20.73 366	77.24 109	99.59 47	90.46 93	98.07 58	98.02 80

CP-MVS		92.54 62	92.60 56	92.34 113	98.50 40	79.90 170	98.40 27	96.40 104	84.75 128	90.48 95	98.09 43	77.40 106	99.21 80	91.15 82	98.23 55	97.92 92

PAPM_NR		91.46 81	90.82 85	93.37 71	98.50 40	81.81 126	95.03 227	96.13 125	84.65 134	86.10 143	97.65 73	79.24 80	99.75 26	83.20 165	96.88 90	98.56 42

MAR-MVS		90.63 98	90.22 94	91.86 129	98.47 42	78.20 218	97.18 104	96.61 74	83.87 159	88.18 126	98.18 32	68.71 209	99.75 26	83.66 157	97.15 82	97.63 114
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
Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 70	97.43 89	97.04 26	89.50 39	92.75 62	98.13 38	82.60 54	99.26 75	93.55 52	96.99 85	98.06 77

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 80	97.43 89	97.00 28	89.43 41	92.69 63	98.13 38	82.48 55	99.22 78	93.51 53	96.99 85	98.04 78

mPP-MVS		91.88 71	91.82 70	92.07 122	98.38 45	78.63 202	97.29 97	96.09 128	85.12 122	88.45 121	97.66 69	75.53 139	99.68 38	89.83 102	98.02 61	97.88 93

SR-MVS		92.16 66	92.27 62	91.83 132	98.37 46	78.41 208	96.67 150	95.76 146	82.19 192	91.97 69	98.07 47	76.44 121	98.64 118	93.71 49	97.27 79	98.45 48

test12				94.25 35	98.34 47	85.55 44		96.35 111		92.36 64		80.84 60	99.22 78		98.31 52	97.98 87

CPTT-MVS		89.72 114	89.87 105	89.29 197	98.33 48	73.30 284	97.70 66	95.35 171	75.68 282	87.40 130	97.44 85	70.43 201	98.25 135	89.56 107	96.90 88	96.33 167

MSP-MVS		95.62 6	96.54 1	92.86 92	98.31 49	80.10 167	97.42 91	96.78 45	92.20 13	97.11 8	98.29 28	93.46 1	99.10 95	96.01 20	99.30 3	99.38 10
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
MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 75	98.64 22	96.93 36	90.71 25	93.08 58	98.70 16	79.98 71	99.21 80	94.12 46	99.07 9	98.63 39

PGM-MVS		91.93 70	91.80 71	92.32 115	98.27 51	79.74 175	95.28 213	97.27 17	83.83 160	90.89 90	97.78 66	76.12 128	99.56 51	88.82 113	97.93 64	97.66 111

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 89	98.61 24	92.69 291	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 31	98.51 34	98.32 54

ZNCC-MVS		92.75 51	92.60 56	93.23 75	98.24 52	81.82 125	97.63 69	96.50 91	85.00 125	91.05 87	97.74 67	78.38 92	99.80 19	90.48 92	98.34 51	98.07 76

save fliter					98.24 52	83.34 89	98.61 24	96.57 80	91.32 18

114514_t		88.79 134	87.57 142	92.45 109	98.21 55	81.74 128	96.99 124	95.45 163	75.16 286	82.48 177	95.69 133	68.59 210	98.50 126	80.33 181	95.18 113	97.10 141

test1172		91.64 76	92.00 68	90.54 165	98.20 56	74.48 275	96.45 160	95.65 151	81.97 196	91.63 75	98.02 49	75.76 134	98.61 119	93.16 60	97.17 81	98.52 45

GST-MVS		92.43 64	92.22 65	93.04 84	98.17 57	81.64 132	97.40 93	96.38 108	84.71 131	90.90 89	97.40 88	77.55 104	99.76 20	89.75 104	97.74 68	97.72 106

DP-MVS		81.47 249	78.28 263	91.04 150	98.14 58	78.48 204	95.09 226	86.97 338	61.14 344	71.12 291	92.78 191	59.59 263	99.38 64	53.11 338	86.61 182	95.27 190

MP-MVS		92.61 60	92.67 54	92.42 111	98.13 59	79.73 176	97.33 96	96.20 121	85.63 106	90.53 93	97.66 69	78.14 96	99.70 35	92.12 74	98.30 53	97.85 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
9.14			94.26 27		98.10 60		98.14 36	96.52 88	84.74 129	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

Regformer-3		93.19 42	93.19 44	93.19 77	98.10 60	83.01 99	97.08 119	96.98 30	88.98 46	91.35 82	97.89 59	80.80 61	99.23 76	92.30 71	95.20 111	97.32 131

Regformer-4		93.06 46	93.12 45	92.89 91	98.10 60	82.20 113	97.08 119	96.92 38	88.87 48	91.23 84	97.89 59	80.57 64	99.19 85	92.21 73	95.20 111	97.29 135

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 127	98.64 22	97.13 21	82.60 186	94.09 47	98.49 23	80.35 65	99.85 10	94.74 40	98.62 31	98.83 28

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 83	96.77 51	85.32 114	97.92 2	98.70 16	83.09 47	99.84 12	95.79 24	99.08 8	98.49 46
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
PLC	83.97 7	88.00 152	87.38 148	89.83 188	98.02 64	76.46 250	97.16 108	94.43 223	79.26 246	81.98 187	96.28 121	69.36 206	99.27 73	77.71 207	92.25 141	93.77 212
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		92.74 52	92.71 51	92.86 92	97.90 66	80.85 147	96.47 157	96.33 112	87.92 67	90.20 98	98.18 32	76.71 118	99.76 20	92.57 69	98.09 56	97.96 89

MTAPA		92.45 63	92.31 61	92.86 92	97.90 66	80.85 147	92.88 277	96.33 112	87.92 67	90.20 98	98.18 32	76.71 118	99.76 20	92.57 69	98.09 56	97.96 89

APD-MVS_3200maxsize		91.23 88	91.35 78	90.89 156	97.89 68	76.35 253	96.30 172	95.52 159	79.82 233	91.03 88	97.88 61	74.70 157	98.54 124	92.11 75	96.89 89	97.77 103

HPM-MVS		91.62 78	91.53 76	91.89 128	97.88 69	79.22 186	96.99 124	95.73 148	82.07 193	89.50 110	97.19 97	75.59 138	98.93 109	90.91 85	97.94 62	97.54 118
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 67	97.76 62	96.19 123	89.59 38	96.66 12	98.17 36	84.33 32	99.60 46	96.09 18	98.50 37	98.66 37
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
ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 71	85.78 38	97.98 46	96.53 87	85.29 117	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 65

原ACMM1				91.22 147	97.77 72	78.10 220		96.61 74	81.05 205	91.28 83	97.42 86	77.92 99	98.98 102	79.85 189	98.51 34	96.59 158

SR-MVS-dyc-post		91.29 86	91.45 77	90.80 158	97.76 73	76.03 258	96.20 178	95.44 164	80.56 215	90.72 91	97.84 62	75.76 134	98.61 119	91.99 76	96.79 93	97.75 104

RE-MVS-def			91.18 82		97.76 73	76.03 258	96.20 178	95.44 164	80.56 215	90.72 91	97.84 62	73.36 174		91.99 76	96.79 93	97.75 104

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 74	95.85 196	96.42 100	91.26 20	97.49 7	96.80 113	86.50 23	98.49 127	95.54 29	99.03 11	98.33 53
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS_fast		90.38 106	90.17 97	91.03 151	97.61 76	77.35 238	97.15 109	95.48 161	79.51 239	88.79 117	96.90 106	71.64 190	98.81 114	87.01 131	97.44 74	96.94 144

EI-MVSNet-Vis-set		91.84 72	91.77 72	92.04 124	97.60 77	81.17 138	96.61 151	96.87 40	88.20 63	89.19 112	97.55 79	78.69 89	99.14 91	90.29 98	90.94 150	95.80 177

CNLPA		86.96 163	85.37 172	91.72 134	97.59 78	79.34 184	97.21 100	91.05 313	74.22 293	78.90 215	96.75 115	67.21 218	98.95 106	74.68 238	90.77 151	96.88 149

ACMMP		90.39 104	89.97 100	91.64 135	97.58 79	78.21 217	96.78 141	96.72 58	84.73 130	84.72 152	97.23 95	71.22 193	99.63 44	88.37 120	92.41 139	97.08 142
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
SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 64	96.43 99	84.02 152	95.07 29	98.74 14	82.93 48	99.38 64	95.42 31	98.51 34	98.32 54

CANet		94.89 12	94.64 16	95.63 10	97.55 81	88.12 13	99.06 9	96.39 107	94.07 7	95.34 24	97.80 65	76.83 115	99.87 8	97.08 14	97.64 70	98.89 26

PVSNet_BlendedMVS		90.05 109	89.96 101	90.33 171	97.47 82	83.86 77	98.02 45	96.73 56	87.98 66	89.53 108	89.61 232	76.42 122	99.57 49	94.29 44	79.59 230	87.57 299

PVSNet_Blended		93.13 43	92.98 47	93.57 61	97.47 82	83.86 77	99.32 1	96.73 56	91.02 23	89.53 108	96.21 122	76.42 122	99.57 49	94.29 44	95.81 107	97.29 135

新几何1				93.12 79	97.44 84	81.60 133		96.71 59	74.54 291	91.22 85	97.57 75	79.13 82	99.51 56	77.40 212	98.46 39	98.26 62

LS3D		82.22 241	79.94 253	89.06 199	97.43 85	74.06 280	93.20 271	92.05 297	61.90 339	73.33 275	95.21 144	59.35 266	99.21 80	54.54 334	92.48 138	93.90 211

test_yl		91.46 81	90.53 89	94.24 36	97.41 86	85.18 51	98.08 40	97.72 10	80.94 206	89.85 100	96.14 123	75.61 136	98.81 114	90.42 96	88.56 168	98.74 31

DCV-MVSNet		91.46 81	90.53 89	94.24 36	97.41 86	85.18 51	98.08 40	97.72 10	80.94 206	89.85 100	96.14 123	75.61 136	98.81 114	90.42 96	88.56 168	98.74 31

EI-MVSNet-UG-set		91.35 85	91.22 79	91.73 133	97.39 88	80.68 151	96.47 157	96.83 43	87.92 67	88.30 125	97.36 89	77.84 100	99.13 93	89.43 109	89.45 157	95.37 187

旧先验1					97.39 88	79.58 179		96.54 85			98.08 46	84.00 36			97.42 76	97.62 115

1121		90.66 97	89.82 106	93.16 78	97.39 88	81.71 130	93.33 264	96.66 67	74.45 292	91.38 78	97.55 79	79.27 78	99.52 53	79.95 186	98.43 41	98.26 62

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 98	98.10 39	95.29 175	91.57 16	93.81 48	97.45 82	86.64 21	99.43 61	96.28 17	94.01 121	99.20 18

MVS_111021_HR		93.41 40	93.39 40	93.47 70	97.34 92	82.83 101	97.56 76	98.27 6	89.16 44	89.71 103	97.14 99	79.77 73	99.56 51	93.65 50	97.94 62	98.02 80

MP-MVS-pluss		92.58 61	92.35 59	93.29 72	97.30 93	82.53 105	96.44 162	96.04 132	84.68 132	89.12 113	98.37 26	77.48 105	99.74 28	93.31 58	98.38 48	97.59 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 68	98.29 30	97.64 13	94.57 4	95.36 23	96.88 108	79.96 72	99.12 94	91.30 80	96.11 100	97.82 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_NAP		93.46 39	93.23 43	94.17 39	97.16 95	84.28 71	96.82 138	96.65 68	86.24 93	94.27 41	97.99 52	77.94 98	99.83 15	93.39 54	98.57 32	98.39 51

LFMVS		89.27 122	87.64 138	94.16 41	97.16 95	85.52 45	97.18 104	94.66 207	79.17 247	89.63 106	96.57 118	55.35 296	98.22 136	89.52 108	89.54 156	98.74 31

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 185	97.09 97	70.21 311	98.99 15	96.69 62	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

VNet		92.11 67	91.22 79	94.79 21	96.91 98	86.98 24	97.91 49	97.96 9	86.38 92	93.65 50	95.74 130	70.16 204	98.95 106	93.39 54	88.87 163	98.43 49

TAPA-MVS	81.61 12	85.02 194	83.67 197	89.06 199	96.79 99	73.27 286	95.92 190	94.79 201	74.81 289	80.47 201	96.83 110	71.07 195	98.19 138	49.82 346	92.57 135	95.71 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		84.41 205	81.93 224	91.85 131	96.78 100	78.41 208	97.44 85	91.34 308	70.29 318	84.06 158	94.26 168	41.09 340	98.96 103	79.46 191	82.65 218	98.17 67

ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 101	85.74 39	97.22 98	96.41 101	83.94 155	94.13 46	98.69 18	83.13 46	99.37 68	95.25 34	98.39 47	97.97 88

abl_6		89.80 112	89.71 109	90.07 177	96.53 102	75.52 266	94.48 235	95.04 184	81.12 204	89.22 111	97.00 104	68.83 208	98.96 103	89.86 101	95.27 110	95.73 179

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 103	90.59 5	99.21 2	97.02 27	94.40 5	91.46 77	97.08 102	83.32 43	99.69 36	92.83 65	98.70 29	99.04 21
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
thres200		88.92 128	87.65 137	92.73 98	96.30 104	85.62 43	97.85 52	98.86 1	84.38 142	84.82 150	93.99 176	75.12 153	98.01 140	70.86 268	86.67 181	94.56 202

DPM-MVS		96.21 2	95.53 9	98.26 1	96.26 105	95.09 1	99.15 4	96.98 30	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 110	99.25 4	99.70 3

tfpn200view9		88.48 141	87.15 152	92.47 107	96.21 106	85.30 49	97.44 85	98.85 2	83.37 169	83.99 160	93.82 179	75.36 146	97.93 142	69.04 274	86.24 187	94.17 204

thres400		88.42 144	87.15 152	92.23 117	96.21 106	85.30 49	97.44 85	98.85 2	83.37 169	83.99 160	93.82 179	75.36 146	97.93 142	69.04 274	86.24 187	93.45 217

test222					96.15 108	78.41 208	95.87 194	96.46 95	71.97 311	89.66 105	97.45 82	76.33 125			98.24 54	98.30 58

HY-MVS	84.06 6	91.63 77	90.37 92	95.39 14	96.12 109	88.25 12	90.22 301	97.58 14	88.33 61	90.50 94	91.96 198	79.26 79	99.06 97	90.29 98	89.07 160	98.88 27

thres100view900		88.30 146	86.95 157	92.33 114	96.10 110	84.90 62	97.14 110	98.85 2	82.69 184	83.41 168	93.66 182	75.43 143	97.93 142	69.04 274	86.24 187	94.17 204

thres600view7		88.06 150	86.70 160	92.15 120	96.10 110	85.17 55	97.14 110	98.85 2	82.70 183	83.41 168	93.66 182	75.43 143	97.82 149	67.13 283	85.88 191	93.45 217

WTY-MVS		92.65 59	91.68 73	95.56 11	96.00 112	88.90 10	98.23 32	97.65 12	88.57 55	89.82 102	97.22 96	79.29 77	99.06 97	89.57 106	88.73 165	98.73 35

MVSTER		89.25 123	88.92 120	90.24 173	95.98 113	84.66 66	96.79 140	95.36 169	87.19 86	80.33 204	90.61 218	90.02 9	95.97 229	85.38 140	78.64 239	90.09 242

testdata				90.13 176	95.92 114	74.17 278		96.49 94	73.49 300	94.82 35	97.99 52	78.80 87	97.93 142	83.53 161	97.52 71	98.29 59

PatchMatch-RL		85.00 195	83.66 198	89.02 201	95.86 115	74.55 274	92.49 281	93.60 262	79.30 244	79.29 214	91.47 203	58.53 273	98.45 129	70.22 271	92.17 143	94.07 208

canonicalmvs		92.27 65	91.22 79	95.41 13	95.80 116	88.31 11	97.09 117	94.64 210	88.49 57	92.99 60	97.31 90	72.68 179	98.57 123	93.38 56	88.58 167	99.36 12

Anonymous20240529		83.15 224	80.60 242	90.80 158	95.74 117	78.27 212	96.81 139	94.92 189	60.10 348	81.89 189	92.54 192	45.82 325	98.82 113	79.25 195	78.32 244	95.31 189

MVS_111021_LR		91.60 79	91.64 75	91.47 141	95.74 117	78.79 200	96.15 180	96.77 51	88.49 57	88.64 119	97.07 103	72.33 182	99.19 85	93.13 63	96.48 97	96.43 162

test_part1		84.72 198	82.85 210	90.34 170	95.73 119	84.79 65	96.75 144	94.10 236	79.05 253	75.97 252	89.51 233	67.69 211	95.94 233	79.34 192	67.50 310	90.30 237

DWT-MVSNet_test		90.52 103	89.80 107	92.70 100	95.73 119	82.20 113	93.69 255	96.55 84	88.34 60	87.04 136	95.34 142	86.53 22	97.55 159	76.32 224	88.66 166	98.34 52

PS-MVSNAJ		94.17 25	93.52 39	96.10 6	95.65 121	92.35 2	98.21 34	95.79 145	92.42 12	96.24 15	98.18 32	71.04 196	99.17 88	96.77 15	97.39 77	96.79 151

Anonymous20231211		79.72 265	77.19 271	87.33 239	95.59 122	77.16 243	95.18 220	94.18 231	59.31 350	72.57 283	86.20 284	47.89 319	95.66 249	74.53 242	69.24 293	89.18 260

alignmvs		92.97 48	92.26 63	95.12 16	95.54 123	87.77 17	98.67 20	96.38 108	88.04 65	93.01 59	97.45 82	79.20 81	98.60 121	93.25 59	88.76 164	98.99 25

PVSNet	82.34 9	89.02 125	87.79 135	92.71 99	95.49 124	81.50 134	97.70 66	97.29 16	87.76 72	85.47 145	95.12 151	56.90 285	98.90 110	80.33 181	94.02 120	97.71 108

tpmvs		83.04 227	80.77 238	89.84 187	95.43 125	77.96 223	85.59 333	95.32 174	75.31 285	76.27 246	83.70 313	73.89 166	97.41 169	59.53 314	81.93 221	94.14 206

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 126	81.35 136	99.02 13	96.59 78	89.50 39	94.18 45	98.36 27	83.68 40	99.45 60	94.77 38	98.45 40	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		87.47 159	85.90 167	92.18 119	95.41 126	82.26 112	87.00 324	96.28 116	85.88 102	84.23 157	85.57 291	75.07 154	96.26 220	71.14 266	92.50 137	98.03 79

BH-RMVSNet		86.84 167	85.28 173	91.49 140	95.35 128	80.26 163	96.95 131	92.21 295	82.86 181	81.77 191	95.46 140	59.34 267	97.64 154	69.79 272	93.81 125	96.57 159

OMC-MVS		88.80 133	88.16 129	90.72 161	95.30 129	77.92 226	94.81 231	94.51 216	86.80 90	84.97 148	96.85 109	67.53 214	98.60 121	85.08 142	87.62 175	95.63 181

MVS_Test		90.29 107	89.18 114	93.62 59	95.23 130	84.93 61	94.41 238	94.66 207	84.31 144	90.37 97	91.02 211	75.13 152	97.82 149	83.11 167	94.42 117	98.12 73

F-COLMAP		84.50 204	83.44 204	87.67 229	95.22 131	72.22 291	95.95 188	93.78 253	75.74 281	76.30 245	95.18 146	59.50 265	98.45 129	72.67 254	86.59 183	92.35 221

baseline1		88.85 131	87.49 144	92.93 90	95.21 132	86.85 25	95.47 208	94.61 212	87.29 80	83.11 173	94.99 156	80.70 62	96.89 197	82.28 171	73.72 260	95.05 191

CHOSEN 1792x2688		91.07 90	90.21 95	93.64 57	95.18 133	83.53 85	96.26 174	96.13 125	88.92 47	84.90 149	93.10 188	72.86 177	99.62 45	88.86 112	95.67 108	97.79 101

UGNet		87.73 156	86.55 161	91.27 145	95.16 134	79.11 190	96.35 168	96.23 119	88.14 64	87.83 129	90.48 219	50.65 308	99.09 96	80.13 185	94.03 119	95.60 182
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
VDD-MVS		88.28 147	87.02 156	92.06 123	95.09 135	80.18 166	97.55 77	94.45 222	83.09 174	89.10 114	95.92 129	47.97 318	98.49 127	93.08 64	86.91 180	97.52 122

PVSNet_Blended_VisFu		91.24 87	90.77 86	92.66 101	95.09 135	82.40 108	97.77 58	95.87 142	88.26 62	86.39 139	93.94 177	76.77 116	99.27 73	88.80 114	94.00 122	96.31 168

hse-mvs3		89.30 121	88.95 119	90.36 169	95.07 137	76.04 257	96.96 130	97.11 23	90.39 30	92.22 66	95.10 152	74.70 157	98.86 111	93.14 61	65.89 320	96.16 170

xiu_mvs_v2_base		93.92 33	93.26 42	95.91 8	95.07 137	92.02 4	98.19 35	95.68 150	92.06 14	96.01 18	98.14 37	70.83 199	98.96 103	96.74 16	96.57 96	96.76 154

cl-mvsnet2		85.11 193	84.17 192	87.92 225	95.06 139	78.82 197	95.51 206	94.22 228	79.74 235	76.77 236	87.92 255	75.96 130	95.68 248	79.93 188	72.42 268	89.27 258

BH-w/o		88.24 148	87.47 146	90.54 165	95.03 140	78.54 203	97.41 92	93.82 248	84.08 150	78.23 223	94.51 164	69.34 207	97.21 179	80.21 184	94.58 116	95.87 176

RRT_MVS		86.89 165	85.96 165	89.68 193	95.01 141	84.13 73	96.33 170	94.98 187	84.20 149	80.10 208	92.07 196	70.52 200	95.01 284	83.30 164	77.14 248	89.91 246

CHOSEN 280x420		91.71 75	91.85 69	91.29 144	94.94 142	82.69 102	87.89 318	96.17 124	85.94 100	87.27 133	94.31 166	90.27 6	95.65 251	94.04 47	95.86 105	95.53 184

GG-mvs-BLEND				93.49 66	94.94 142	86.26 29	81.62 339	97.00 28		88.32 124	94.30 167	91.23 3	96.21 223	88.49 117	97.43 75	98.00 85

HyFIR lowres test		89.36 119	88.60 123	91.63 137	94.91 144	80.76 150	95.60 204	95.53 157	82.56 187	84.03 159	91.24 208	78.03 97	96.81 202	87.07 130	88.41 170	97.32 131

miper_enhance_ethall		85.95 181	85.20 174	88.19 222	94.85 145	79.76 172	96.00 185	94.06 239	82.98 178	77.74 226	88.76 242	79.42 75	95.46 261	80.58 179	72.42 268	89.36 257

mvs_anonymous		88.68 135	87.62 140	91.86 129	94.80 146	81.69 131	93.53 260	94.92 189	82.03 194	78.87 217	90.43 222	75.77 133	95.34 265	85.04 143	93.16 132	98.55 44

CANet_DTU		90.98 91	90.04 99	93.83 48	94.76 147	86.23 30	96.32 171	93.12 284	93.11 10	93.71 49	96.82 112	63.08 242	99.48 58	84.29 147	95.12 114	95.77 178

PMMVS		89.46 118	89.92 103	88.06 223	94.64 148	69.57 318	96.22 176	94.95 188	87.27 82	91.37 81	96.54 119	65.88 225	97.39 170	88.54 115	93.89 123	97.23 138

TR-MVS		86.30 176	84.93 182	90.42 167	94.63 149	77.58 233	96.57 153	93.82 248	80.30 223	82.42 179	95.16 147	58.74 271	97.55 159	74.88 236	87.82 174	96.13 172

EPNet_dtu		87.65 157	87.89 132	86.93 248	94.57 150	71.37 305	96.72 145	96.50 91	88.56 56	87.12 134	95.02 154	75.91 132	94.01 302	66.62 285	90.00 154	95.42 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		84.71 199	82.71 214	90.70 162	94.55 151	87.71 18	95.92 190	94.67 206	81.73 198	75.82 255	88.08 253	66.99 219	94.47 294	71.23 263	75.38 254	89.91 246

ETV-MVS		92.72 55	92.87 49	92.28 116	94.54 152	81.89 121	97.98 46	95.21 178	89.77 37	93.11 57	96.83 110	77.23 111	97.50 165	95.74 25	95.38 109	97.44 125

EIA-MVS		91.73 73	92.05 67	90.78 160	94.52 153	76.40 252	98.06 42	95.34 172	89.19 43	88.90 116	97.28 94	77.56 103	97.73 152	90.77 88	96.86 92	98.20 64

BH-untuned		86.95 164	85.94 166	89.99 180	94.52 153	77.46 235	96.78 141	93.37 274	81.80 197	76.62 239	93.81 181	66.64 222	97.02 189	76.06 226	93.88 124	95.48 185

DeepC-MVS	86.58 3	91.53 80	91.06 83	92.94 89	94.52 153	81.89 121	95.95 188	95.98 134	90.76 24	83.76 166	96.76 114	73.24 175	99.71 32	91.67 78	96.96 87	97.22 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
gg-mvs-nofinetune		85.48 189	82.90 209	93.24 74	94.51 156	85.82 37	79.22 343	96.97 32	61.19 343	87.33 132	53.01 356	90.58 4	96.07 225	86.07 135	97.23 80	97.81 100

3Dnovator+	82.88 8	89.63 116	87.85 133	94.99 18	94.49 157	86.76 27	97.84 53	95.74 147	86.10 96	75.47 260	96.02 126	65.00 233	99.51 56	82.91 169	97.07 84	98.72 36

RRT_test8_iter05		87.14 161	86.41 162	89.32 196	94.41 158	81.10 141	97.06 121	95.33 173	84.67 133	76.27 246	90.48 219	83.60 41	96.33 217	85.10 141	70.78 276	90.53 231

ET-MVSNet_ETH3D		90.01 110	89.03 115	92.95 88	94.38 159	86.77 26	98.14 36	96.31 115	89.30 42	63.33 327	96.72 116	90.09 8	93.63 309	90.70 90	82.29 220	98.46 47

tpmrst		88.36 145	87.38 148	91.31 142	94.36 160	79.92 169	87.32 322	95.26 177	85.32 114	88.34 123	86.13 285	80.60 63	96.70 206	83.78 151	85.34 198	97.30 134

MVS		90.60 99	88.64 122	96.50 3	94.25 161	90.53 6	93.33 264	97.21 19	77.59 267	78.88 216	97.31 90	71.52 191	99.69 36	89.60 105	98.03 60	99.27 16

dp		84.30 208	82.31 219	90.28 172	94.24 162	77.97 222	86.57 327	95.53 157	79.94 232	80.75 198	85.16 299	71.49 192	96.39 215	63.73 300	83.36 208	96.48 161

sss		90.87 94	89.96 101	93.60 60	94.15 163	83.84 79	97.14 110	98.13 7	85.93 101	89.68 104	96.09 125	71.67 188	99.30 72	87.69 123	89.16 159	97.66 111

PatchmatchNet		86.83 168	85.12 178	91.95 126	94.12 164	82.27 111	86.55 328	95.64 153	84.59 136	82.98 175	84.99 303	77.26 107	95.96 232	68.61 278	91.34 148	97.64 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.69 196		94.09 165	81.01 142	86.78 326	96.09 128	83.81 161	84.75 151	84.32 308	74.44 161	96.54 210	63.88 299	85.07 199

CS-MVS-test		93.32 41	93.61 36	92.46 108	94.06 166	82.39 109	99.02 13	94.92 189	89.03 45	94.70 37	97.52 81	79.57 74	97.07 187	95.74 25	97.88 65	97.93 91

UA-Net		88.92 128	88.48 125	90.24 173	94.06 166	77.18 242	93.04 273	94.66 207	87.39 79	91.09 86	93.89 178	74.92 155	98.18 139	75.83 229	91.43 147	95.35 188

Fast-Effi-MVS+		87.93 154	86.94 158	90.92 155	94.04 168	79.16 188	98.26 31	93.72 257	81.29 202	83.94 163	92.90 189	69.83 205	96.68 207	76.70 218	91.74 146	96.93 145

QAPM		86.88 166	84.51 185	93.98 43	94.04 168	85.89 36	97.19 103	96.05 131	73.62 297	75.12 263	95.62 136	62.02 250	99.74 28	70.88 267	96.06 102	96.30 169

thisisatest0515		90.95 92	90.26 93	93.01 85	94.03 170	84.27 72	97.91 49	96.67 64	83.18 172	86.87 137	95.51 139	88.66 13	97.85 148	80.46 180	89.01 161	96.92 147

Vis-MVSNet (Re-imp)		88.88 130	88.87 121	88.91 203	93.89 171	74.43 276	96.93 133	94.19 230	84.39 141	83.22 171	95.67 134	78.24 94	94.70 290	78.88 199	94.40 118	97.61 116

CS-MVS		93.12 44	93.27 41	92.64 102	93.86 172	83.12 96	98.85 17	94.85 196	88.61 54	94.19 42	97.42 86	79.02 83	97.02 189	94.89 37	97.77 67	97.78 102

ADS-MVSNet2		79.57 266	77.53 268	85.71 267	93.78 173	72.13 293	79.48 341	86.11 343	73.09 303	80.14 206	79.99 333	62.15 248	90.14 342	59.49 315	83.52 205	94.85 194

ADS-MVSNet		81.26 252	78.36 262	89.96 183	93.78 173	79.78 171	79.48 341	93.60 262	73.09 303	80.14 206	79.99 333	62.15 248	95.24 271	59.49 315	83.52 205	94.85 194

EPP-MVSNet		89.76 113	89.72 108	89.87 186	93.78 173	76.02 260	97.22 98	96.51 89	79.35 241	85.11 147	95.01 155	84.82 29	97.10 186	87.46 126	88.21 172	96.50 160

3Dnovator	82.32 10	89.33 120	87.64 138	94.42 30	93.73 176	85.70 41	97.73 64	96.75 54	86.73 91	76.21 248	95.93 127	62.17 247	99.68 38	81.67 174	97.81 66	97.88 93

DROMVSNet		92.00 69	92.32 60	91.03 151	93.66 177	78.95 195	98.22 33	94.47 219	87.62 76	93.27 52	97.17 98	75.32 149	96.91 196	94.02 48	97.11 83	97.24 137

Effi-MVS+		90.70 96	89.90 104	93.09 81	93.61 178	83.48 86	95.20 218	92.79 289	83.22 171	91.82 71	95.70 132	71.82 187	97.48 166	91.25 81	93.67 126	98.32 54

IS-MVSNet		88.67 136	88.16 129	90.20 175	93.61 178	76.86 245	96.77 143	93.07 285	84.02 152	83.62 167	95.60 137	74.69 160	96.24 222	78.43 202	93.66 127	97.49 124

AUN-MVS		86.25 178	85.57 168	88.26 218	93.57 180	73.38 282	95.45 209	95.88 140	83.94 155	85.47 145	94.21 170	73.70 171	96.67 208	83.54 160	64.41 324	94.73 200

hse-mvs2		88.22 149	88.21 127	88.25 219	93.54 181	73.41 281	95.41 211	95.89 139	90.39 30	92.22 66	94.22 169	74.70 157	96.66 209	93.14 61	64.37 325	94.69 201

LCM-MVSNet-Re		83.75 214	83.54 202	84.39 288	93.54 181	64.14 334	92.51 280	84.03 350	83.90 158	66.14 316	86.59 274	67.36 216	92.68 316	84.89 145	92.87 133	96.35 164

tpm cat1		83.63 216	81.38 232	90.39 168	93.53 183	78.19 219	85.56 334	95.09 181	70.78 316	78.51 220	83.28 316	74.80 156	97.03 188	66.77 284	84.05 203	95.95 173

thisisatest0530		89.65 115	89.02 116	91.53 139	93.46 184	80.78 149	96.52 154	96.67 64	81.69 199	83.79 165	94.90 157	88.85 12	97.68 153	77.80 203	87.49 178	96.14 171

MSDG		80.62 259	77.77 267	89.14 198	93.43 185	77.24 239	91.89 288	90.18 320	69.86 321	68.02 305	91.94 200	52.21 306	98.84 112	59.32 317	83.12 209	91.35 223

ab-mvs		87.08 162	84.94 181	93.48 67	93.34 186	83.67 83	88.82 309	95.70 149	81.18 203	84.55 155	90.14 228	62.72 243	98.94 108	85.49 139	82.54 219	97.85 96

1314		88.94 127	87.20 150	94.17 39	93.21 187	85.73 40	93.33 264	96.64 71	82.89 179	75.98 251	96.36 120	66.83 221	99.39 63	83.52 162	96.02 103	97.39 129

1112_ss		88.60 139	87.47 146	92.00 125	93.21 187	80.97 144	96.47 157	92.46 293	83.64 165	80.86 197	97.30 92	80.24 68	97.62 155	77.60 208	85.49 195	97.40 128

GeoE		86.36 175	85.20 174	89.83 188	93.17 189	76.13 255	97.53 78	92.11 296	79.58 238	80.99 195	94.01 175	66.60 223	96.17 224	73.48 250	89.30 158	97.20 140

Test_1112_low_res		88.03 151	86.73 159	91.94 127	93.15 190	80.88 146	96.44 162	92.41 294	83.59 168	80.74 199	91.16 209	80.18 69	97.59 156	77.48 211	85.40 196	97.36 130

CostFormer		89.08 124	88.39 126	91.15 148	93.13 191	79.15 189	88.61 312	96.11 127	83.14 173	89.58 107	86.93 269	83.83 39	96.87 199	88.22 121	85.92 190	97.42 126

IB-MVS	85.34 4	88.67 136	87.14 154	93.26 73	93.12 192	84.32 69	98.76 18	97.27 17	87.19 86	79.36 213	90.45 221	83.92 38	98.53 125	84.41 146	69.79 287	96.93 145
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
diffmvs		91.17 89	90.74 87	92.44 110	93.11 193	82.50 107	96.25 175	93.62 261	87.79 71	90.40 96	95.93 127	73.44 173	97.42 168	93.62 51	92.55 136	97.41 127

tttt0517		88.57 140	88.19 128	89.71 192	93.00 194	75.99 261	95.67 201	96.67 64	80.78 209	81.82 190	94.40 165	88.97 11	97.58 157	76.05 227	86.31 184	95.57 183

MVSFormer		91.36 84	90.57 88	93.73 53	93.00 194	88.08 14	94.80 232	94.48 217	80.74 210	94.90 32	97.13 100	78.84 85	95.10 280	83.77 152	97.46 72	98.02 80

lupinMVS		93.87 36	93.58 38	94.75 23	93.00 194	88.08 14	99.15 4	95.50 160	91.03 22	94.90 32	97.66 69	78.84 85	97.56 158	94.64 42	97.46 72	98.62 40

tpm2		87.35 160	86.26 163	90.62 163	92.93 197	78.67 201	88.06 317	95.99 133	79.33 242	87.40 130	86.43 280	80.28 67	96.40 214	80.23 183	85.73 194	96.79 151

baseline		90.76 95	90.10 98	92.74 97	92.90 198	82.56 104	94.60 234	94.56 215	87.69 74	89.06 115	95.67 134	73.76 168	97.51 164	90.43 95	92.23 142	98.16 68

casdiffmvs		90.95 92	90.39 91	92.63 103	92.82 199	82.53 105	96.83 137	94.47 219	87.69 74	88.47 120	95.56 138	74.04 165	97.54 162	90.90 86	92.74 134	97.83 98

Vis-MVSNet		88.67 136	87.82 134	91.24 146	92.68 200	78.82 197	96.95 131	93.85 247	87.55 77	87.07 135	95.13 150	63.43 240	97.21 179	77.58 209	96.15 99	97.70 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		82.42 237	80.43 245	88.39 214	92.66 201	81.95 116	94.30 243	93.38 271	79.06 250	75.82 255	85.66 287	56.38 291	93.84 304	71.23 263	75.38 254	89.38 254

test1		82.42 237	80.43 245	88.39 214	92.66 201	81.95 116	94.30 243	93.38 271	79.06 250	75.82 255	85.66 287	56.38 291	93.84 304	71.23 263	75.38 254	89.38 254

FMVSNet2		82.79 231	80.44 244	89.83 188	92.66 201	85.43 46	95.42 210	94.35 224	79.06 250	74.46 267	87.28 261	56.38 291	94.31 297	69.72 273	74.68 257	89.76 248

miper_ehance_all_eth		84.57 202	83.60 201	87.50 236	92.64 204	78.25 213	95.40 212	93.47 266	79.28 245	76.41 242	87.64 258	76.53 120	95.24 271	78.58 200	72.42 268	89.01 268

cascas		86.50 173	84.48 187	92.55 106	92.64 204	85.95 33	97.04 123	95.07 183	75.32 284	80.50 200	91.02 211	54.33 303	97.98 141	86.79 132	87.62 175	93.71 213

TESTMET0.1,1		89.83 111	89.34 113	91.31 142	92.54 206	80.19 165	97.11 113	96.57 80	86.15 94	86.85 138	91.83 202	79.32 76	96.95 192	81.30 175	92.35 140	96.77 153

COLMAP_ROB	73.24 19	75.74 295	73.00 301	83.94 290	92.38 207	69.08 320	91.85 289	86.93 339	61.48 342	65.32 319	90.27 224	42.27 336	96.93 195	50.91 343	75.63 253	85.80 324
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		90.54 100	89.54 110	93.55 62	92.31 208	87.58 20	96.99 124	94.87 193	87.23 83	93.27 52	97.56 76	57.43 281	98.32 132	92.72 66	93.46 129	94.74 197

xiu_mvs_v1_base		90.54 100	89.54 110	93.55 62	92.31 208	87.58 20	96.99 124	94.87 193	87.23 83	93.27 52	97.56 76	57.43 281	98.32 132	92.72 66	93.46 129	94.74 197

xiu_mvs_v1_base_debi		90.54 100	89.54 110	93.55 62	92.31 208	87.58 20	96.99 124	94.87 193	87.23 83	93.27 52	97.56 76	57.43 281	98.32 132	92.72 66	93.46 129	94.74 197

SCA		85.63 186	83.64 199	91.60 138	92.30 211	81.86 123	92.88 277	95.56 156	84.85 126	82.52 176	85.12 301	58.04 276	95.39 262	73.89 246	87.58 177	97.54 118

gm-plane-assit					92.27 212	79.64 178			84.47 140		95.15 148		97.93 142	85.81 136

test-LLR		88.48 141	87.98 131	89.98 181	92.26 213	77.23 240	97.11 113	95.96 135	83.76 162	86.30 141	91.38 205	72.30 183	96.78 204	80.82 177	91.92 144	95.94 174

test-mter		88.95 126	88.60 123	89.98 181	92.26 213	77.23 240	97.11 113	95.96 135	85.32 114	86.30 141	91.38 205	76.37 124	96.78 204	80.82 177	91.92 144	95.94 174

PAPM		92.87 50	92.40 58	94.30 33	92.25 215	87.85 16	96.40 166	96.38 108	91.07 21	88.72 118	96.90 106	82.11 56	97.37 171	90.05 100	97.70 69	97.67 110

cl-mvsnet____		83.27 221	82.12 220	86.74 249	92.20 216	75.95 262	95.11 223	93.27 278	78.44 260	74.82 265	87.02 268	74.19 163	95.19 273	74.67 239	69.32 291	89.09 263

cl-mvsnet1		83.27 221	82.12 220	86.74 249	92.19 217	75.92 263	95.11 223	93.26 279	78.44 260	74.81 266	87.08 267	74.19 163	95.19 273	74.66 240	69.30 292	89.11 262

AllTest		75.92 293	73.06 300	84.47 284	92.18 218	67.29 325	91.07 297	84.43 348	67.63 325	63.48 324	90.18 225	38.20 344	97.16 182	57.04 325	73.37 263	88.97 271

TestCases				84.47 284	92.18 218	67.29 325		84.43 348	67.63 325	63.48 324	90.18 225	38.20 344	97.16 182	57.04 325	73.37 263	88.97 271

CLD-MVS		87.97 153	87.48 145	89.44 194	92.16 220	80.54 156	98.14 36	94.92 189	91.41 17	79.43 212	95.40 141	62.34 245	97.27 177	90.60 91	82.90 214	90.50 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cl_fuxian		83.80 213	82.65 215	87.25 243	92.10 221	77.74 231	95.25 216	93.04 286	78.58 257	76.01 250	87.21 265	75.25 151	95.11 278	77.54 210	68.89 295	88.91 274

HQP-NCC					92.08 222		97.63 69		90.52 27	82.30 180

ACMP_Plane					92.08 222		97.63 69		90.52 27	82.30 180

HQP-MVS		87.91 155	87.55 143	88.98 202	92.08 222	78.48 204	97.63 69	94.80 199	90.52 27	82.30 180	94.56 162	65.40 229	97.32 172	87.67 124	83.01 211	91.13 224

PCF-MVS	84.09 5	86.77 171	85.00 180	92.08 121	92.06 225	83.07 97	92.14 285	94.47 219	79.63 237	76.90 235	94.78 158	71.15 194	99.20 84	72.87 252	91.05 149	93.98 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					92.04 226	78.22 214					94.56 162

plane_prior6					91.98 227	77.92 226						64.77 234

Effi-MVS+-dtu		84.61 201	84.90 183	83.72 295	91.96 228	63.14 338	94.95 228	93.34 275	85.57 107	79.79 210	87.12 266	61.99 251	95.61 255	83.55 158	85.83 192	92.41 220

mvs-test1		86.83 168	87.17 151	85.81 264	91.96 228	65.24 331	97.90 51	93.34 275	85.57 107	84.51 156	95.14 149	61.99 251	97.19 181	83.55 158	90.55 152	95.00 192

plane_prior1					91.95 230

CDS-MVSNet		89.50 117	88.96 118	91.14 149	91.94 231	80.93 145	97.09 117	95.81 144	84.26 147	84.72 152	94.20 171	80.31 66	95.64 252	83.37 163	88.96 162	96.85 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 158	87.09 155	88.74 208	91.86 232	77.96 223	97.18 104	94.69 203	89.89 35	81.33 192	94.15 172	64.77 234	97.30 174	87.08 128	82.82 215	90.96 226

plane_prior7					91.86 232	77.55 234

eth_miper_zixun_eth		83.12 225	82.01 222	86.47 254	91.85 234	74.80 271	94.33 241	93.18 281	79.11 248	75.74 258	87.25 264	72.71 178	95.32 267	76.78 217	67.13 314	89.27 258

VDDNet		86.44 174	84.51 185	92.22 118	91.56 235	81.83 124	97.10 116	94.64 210	69.50 322	87.84 128	95.19 145	48.01 317	97.92 147	89.82 103	86.92 179	96.89 148

EI-MVSNet		85.80 183	85.20 174	87.59 232	91.55 236	77.41 236	95.13 221	95.36 169	80.43 220	80.33 204	94.71 159	73.72 169	95.97 229	76.96 216	78.64 239	89.39 252

CVMVSNet		84.83 197	85.57 168	82.63 306	91.55 236	60.38 345	95.13 221	95.03 185	80.60 213	82.10 186	94.71 159	66.40 224	90.19 341	74.30 243	90.32 153	97.31 133

ACMP	81.66 11	84.00 210	83.22 206	86.33 255	91.53 238	72.95 289	95.91 192	93.79 252	83.70 164	73.79 270	92.22 194	54.31 304	96.89 197	83.98 149	79.74 229	89.16 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 211	82.80 213	87.31 241	91.46 239	77.39 237	95.66 202	93.43 269	80.44 218	75.51 259	87.26 263	73.72 169	95.16 275	76.99 214	70.72 278	89.39 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		78.25 276	74.72 289	88.83 206	91.20 240	74.10 279	73.91 355	88.70 334	59.89 349	66.82 312	85.12 301	78.38 92	94.54 293	48.84 348	79.58 231	97.86 95

miper_lstm_enhance		81.66 248	80.66 241	84.67 280	91.19 241	71.97 297	91.94 287	93.19 280	77.86 264	72.27 285	85.26 295	73.46 172	93.42 311	73.71 249	67.05 315	88.61 276

ACMM	80.70 13	83.72 215	82.85 210	86.31 258	91.19 241	72.12 294	95.88 193	94.29 226	80.44 218	77.02 233	91.96 198	55.24 297	97.14 185	79.30 194	80.38 225	89.67 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		88.48 141	87.79 135	90.56 164	91.09 243	79.18 187	96.45 160	95.88 140	83.64 165	83.12 172	93.33 184	75.94 131	95.74 247	82.40 170	88.27 171	96.75 155

ACMH+	76.62 16	77.47 284	74.94 286	85.05 274	91.07 244	71.58 303	93.26 269	90.01 321	71.80 312	64.76 321	88.55 245	41.62 338	96.48 212	62.35 306	71.00 274	87.09 307

OpenMVS	79.58 14	86.09 179	83.62 200	93.50 65	90.95 245	86.71 28	97.44 85	95.83 143	75.35 283	72.64 282	95.72 131	57.42 284	99.64 42	71.41 261	95.85 106	94.13 207

LPG-MVS_test		84.20 209	83.49 203	86.33 255	90.88 246	73.06 287	95.28 213	94.13 233	82.20 190	76.31 243	93.20 185	54.83 301	96.95 192	83.72 154	80.83 223	88.98 269

LGP-MVS_train				86.33 255	90.88 246	73.06 287		94.13 233	82.20 190	76.31 243	93.20 185	54.83 301	96.95 192	83.72 154	80.83 223	88.98 269

KD-MVS_2432*1600		77.63 282	74.92 287	85.77 265	90.86 248	79.44 180	88.08 315	93.92 243	76.26 278	67.05 310	82.78 318	72.15 185	91.92 325	61.53 307	41.62 356	85.94 321

miper_refine_blended		77.63 282	74.92 287	85.77 265	90.86 248	79.44 180	88.08 315	93.92 243	76.26 278	67.05 310	82.78 318	72.15 185	91.92 325	61.53 307	41.62 356	85.94 321

baseline2		90.39 104	90.21 95	90.93 154	90.86 248	80.99 143	95.20 218	97.41 15	86.03 99	80.07 209	94.61 161	90.58 4	97.47 167	87.29 127	89.86 155	94.35 203

PVSNet_0	77.72 15	81.70 246	78.95 260	89.94 184	90.77 251	76.72 248	95.96 187	96.95 34	85.01 124	70.24 298	88.53 247	52.32 305	98.20 137	86.68 134	44.08 355	94.89 193

ACMH	75.40 17	77.99 278	74.96 285	87.10 246	90.67 252	76.41 251	93.19 272	91.64 304	72.47 309	63.44 326	87.61 259	43.34 331	97.16 182	58.34 319	73.94 259	87.72 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS-HIRNet		71.36 314	67.00 318	84.46 286	90.58 253	69.74 316	79.15 344	87.74 337	46.09 355	61.96 334	50.50 357	45.14 326	95.64 252	53.74 336	88.11 173	88.00 290

jason		92.73 54	92.23 64	94.21 38	90.50 254	87.30 23	98.65 21	95.09 181	90.61 26	92.76 61	97.13 100	75.28 150	97.30 174	93.32 57	96.75 95	98.02 80
jason: jason.
LTVRE_ROB	73.68 18	77.99 278	75.74 282	84.74 277	90.45 255	72.02 295	86.41 329	91.12 310	72.57 308	66.63 313	87.27 262	54.95 300	96.98 191	56.29 329	75.98 250	85.21 327
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
XVG-OURS		85.18 192	84.38 189	87.59 232	90.42 256	71.73 301	91.06 298	94.07 238	82.00 195	83.29 170	95.08 153	56.42 290	97.55 159	83.70 156	83.42 207	93.49 216

VPA-MVSNet		85.32 190	83.83 195	89.77 191	90.25 257	82.63 103	96.36 167	97.07 25	83.03 176	81.21 194	89.02 238	61.58 255	96.31 219	85.02 144	70.95 275	90.36 233

XVG-OURS-SEG-HR		85.74 185	85.16 177	87.49 237	90.22 258	71.45 304	91.29 295	94.09 237	81.37 201	83.90 164	95.22 143	60.30 260	97.53 163	85.58 138	84.42 202	93.50 215

tpm		85.55 187	84.47 188	88.80 207	90.19 259	75.39 268	88.79 310	94.69 203	84.83 127	83.96 162	85.21 297	78.22 95	94.68 291	76.32 224	78.02 246	96.34 165

CR-MVSNet		83.53 217	81.36 233	90.06 178	90.16 260	79.75 173	79.02 345	91.12 310	84.24 148	82.27 184	80.35 330	75.45 141	93.67 308	63.37 303	86.25 185	96.75 155

RPMNet		79.85 263	75.92 281	91.64 135	90.16 260	79.75 173	79.02 345	95.44 164	58.43 352	82.27 184	72.55 348	73.03 176	98.41 131	46.10 352	86.25 185	96.75 155

FIs		86.73 172	86.10 164	88.61 210	90.05 262	80.21 164	96.14 181	96.95 34	85.56 110	78.37 222	92.30 193	76.73 117	95.28 269	79.51 190	79.27 233	90.35 234

FMVSNet5		76.46 291	74.16 295	83.35 301	90.05 262	76.17 254	89.58 304	89.85 322	71.39 315	65.29 320	80.42 329	50.61 309	87.70 348	61.05 312	69.24 293	86.18 317

IterMVS		80.67 258	79.16 258	85.20 273	89.79 264	76.08 256	92.97 275	91.86 299	80.28 224	71.20 290	85.14 300	57.93 279	91.34 331	72.52 255	70.74 277	88.18 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		85.31 191	84.23 191	88.55 211	89.75 265	80.55 155	96.72 145	96.89 39	85.42 111	78.40 221	88.93 240	75.38 145	95.52 259	78.58 200	68.02 304	89.57 250

Patchmtry		77.36 285	74.59 290	85.67 268	89.75 265	75.75 265	77.85 348	91.12 310	60.28 346	71.23 289	80.35 330	75.45 141	93.56 310	57.94 320	67.34 313	87.68 295

JIA-IIPM		79.00 272	77.20 270	84.40 287	89.74 267	64.06 335	75.30 352	95.44 164	62.15 338	81.90 188	59.08 354	78.92 84	95.59 256	66.51 288	85.78 193	93.54 214

MS-PatchMatch		83.05 226	81.82 226	86.72 253	89.64 268	79.10 191	94.88 230	94.59 214	79.70 236	70.67 294	89.65 231	50.43 310	96.82 201	70.82 270	95.99 104	84.25 333

IterMVS-SCA-FT		80.51 260	79.10 259	84.73 278	89.63 269	74.66 272	92.98 274	91.81 301	80.05 229	71.06 292	85.18 298	58.04 276	91.40 330	72.48 256	70.70 279	88.12 288

Fast-Effi-MVS+-dtu		83.33 220	82.60 216	85.50 270	89.55 270	69.38 319	96.09 184	91.38 305	82.30 189	75.96 253	91.41 204	56.71 286	95.58 257	75.13 235	84.90 200	91.54 222

PatchT		79.75 264	76.85 274	88.42 212	89.55 270	75.49 267	77.37 349	94.61 212	63.07 335	82.46 178	73.32 347	75.52 140	93.41 312	51.36 341	84.43 201	96.36 163

GA-MVS		85.79 184	84.04 194	91.02 153	89.47 272	80.27 162	96.90 134	94.84 197	85.57 107	80.88 196	89.08 236	56.56 289	96.47 213	77.72 206	85.35 197	96.34 165

UniMVSNet_NR-MVSNet		85.49 188	84.59 184	88.21 221	89.44 273	79.36 182	96.71 147	96.41 101	85.22 118	78.11 224	90.98 213	76.97 113	95.14 276	79.14 196	68.30 301	90.12 240

FC-MVSNet-test		85.96 180	85.39 171	87.66 230	89.38 274	78.02 221	95.65 203	96.87 40	85.12 122	77.34 228	91.94 200	76.28 126	94.74 289	77.09 213	78.82 237	90.21 238

WR-MVS		84.32 207	82.96 207	88.41 213	89.38 274	80.32 159	96.59 152	96.25 118	83.97 154	76.63 238	90.36 223	67.53 214	94.86 287	75.82 230	70.09 285	90.06 244

VPNet		84.69 200	82.92 208	90.01 179	89.01 276	83.45 87	96.71 147	95.46 162	85.71 105	79.65 211	92.18 195	56.66 288	96.01 228	83.05 168	67.84 307	90.56 230

nrg030		86.79 170	85.43 170	90.87 157	88.76 277	85.34 47	97.06 121	94.33 225	84.31 144	80.45 202	91.98 197	72.36 181	96.36 216	88.48 118	71.13 273	90.93 228

DU-MVS		84.57 202	83.33 205	88.28 217	88.76 277	79.36 182	96.43 164	95.41 168	85.42 111	78.11 224	90.82 214	67.61 212	95.14 276	79.14 196	68.30 301	90.33 235

NR-MVSNet		83.35 219	81.52 231	88.84 205	88.76 277	81.31 137	94.45 237	95.16 179	84.65 134	67.81 306	90.82 214	70.36 202	94.87 286	74.75 237	66.89 317	90.33 235

test_0402		72.68 308	69.54 314	82.09 310	88.67 280	71.81 300	92.72 279	86.77 340	61.52 341	62.21 332	83.91 311	43.22 332	93.76 307	34.60 357	72.23 271	80.72 349

RPSCF		77.73 281	76.63 276	81.06 314	88.66 281	55.76 353	87.77 319	87.88 336	64.82 334	74.14 269	92.79 190	49.22 314	96.81 202	67.47 282	76.88 249	90.62 229

FMVSNet1		79.50 267	76.54 277	88.39 214	88.47 282	81.95 116	94.30 243	93.38 271	73.14 302	72.04 287	85.66 287	43.86 328	93.84 304	65.48 292	72.53 267	89.38 254

OPM-MVS		85.84 182	85.10 179	88.06 223	88.34 283	77.83 229	95.72 199	94.20 229	87.89 70	80.45 202	94.05 174	58.57 272	97.26 178	83.88 150	82.76 217	89.09 263

tfpnnormal		78.14 277	75.42 283	86.31 258	88.33 284	79.24 185	94.41 238	96.22 120	73.51 298	69.81 300	85.52 293	55.43 295	95.75 244	47.65 350	67.86 306	83.95 336

MVS_0304		78.43 274	76.70 275	83.60 297	88.22 285	69.81 314	92.91 276	95.10 180	72.32 310	78.71 218	80.29 332	33.78 351	93.37 313	68.77 277	80.23 226	87.63 296

TinyColmap		72.41 309	68.99 316	82.68 305	88.11 286	69.59 317	88.41 313	85.20 345	65.55 331	57.91 344	84.82 305	30.80 357	95.94 233	51.38 340	68.70 296	82.49 344

WR-MVS_H		81.02 254	80.09 248	83.79 292	88.08 287	71.26 306	94.46 236	96.54 85	80.08 228	72.81 281	86.82 270	70.36 202	92.65 317	64.18 297	67.50 310	87.46 303

CP-MVSNet		81.01 255	80.08 249	83.79 292	87.91 288	70.51 308	94.29 246	95.65 151	80.83 208	72.54 284	88.84 241	63.71 238	92.32 320	68.58 279	68.36 300	88.55 277

D2MVS		82.67 233	81.55 229	86.04 262	87.77 289	76.47 249	95.21 217	96.58 79	82.66 185	70.26 297	85.46 294	60.39 259	95.80 241	76.40 222	79.18 234	85.83 323

TranMVSNet+NR-MVSNet		83.24 223	81.71 227	87.83 226	87.71 290	78.81 199	96.13 183	94.82 198	84.52 137	76.18 249	90.78 216	64.07 237	94.60 292	74.60 241	66.59 319	90.09 242

USDC		78.65 273	76.25 278	85.85 263	87.58 291	74.60 273	89.58 304	90.58 319	84.05 151	63.13 328	88.23 250	40.69 342	96.86 200	66.57 287	75.81 252	86.09 319

PS-CasMVS		80.27 261	79.18 257	83.52 299	87.56 292	69.88 313	94.08 250	95.29 175	80.27 225	72.08 286	88.51 248	59.22 269	92.23 322	67.49 281	68.15 303	88.45 281

MIMVSNet		79.18 271	75.99 280	88.72 209	87.37 293	80.66 152	79.96 340	91.82 300	77.38 270	74.33 268	81.87 322	41.78 337	90.74 337	66.36 290	83.10 210	94.76 196

XXY-MVS		83.84 212	82.00 223	89.35 195	87.13 294	81.38 135	95.72 199	94.26 227	80.15 227	75.92 254	90.63 217	61.96 253	96.52 211	78.98 198	73.28 266	90.14 239

ITE_SJBPF				82.38 307	87.00 295	65.59 330		89.55 324	79.99 231	69.37 302	91.30 207	41.60 339	95.33 266	62.86 305	74.63 258	86.24 316

test0.0.03 1		82.79 231	82.48 217	83.74 294	86.81 296	72.22 291	96.52 154	95.03 185	83.76 162	73.00 278	93.20 185	72.30 183	88.88 344	64.15 298	77.52 247	90.12 240

v8		81.88 244	80.06 251	87.32 240	86.63 297	79.04 194	94.41 238	93.65 260	78.77 255	73.19 277	85.57 291	66.87 220	95.81 240	73.84 248	67.61 309	87.11 306

v10		81.43 250	79.53 256	87.11 245	86.38 298	78.87 196	94.31 242	93.43 269	77.88 263	73.24 276	85.26 295	65.44 228	95.75 244	72.14 257	67.71 308	86.72 310

PEN-MVS		79.47 268	78.26 264	83.08 302	86.36 299	68.58 321	93.85 253	94.77 202	79.76 234	71.37 288	88.55 245	59.79 261	92.46 318	64.50 296	65.40 321	88.19 286

UniMVSNet_ETH3D		80.86 257	78.75 261	87.22 244	86.31 300	72.02 295	91.95 286	93.76 256	73.51 298	75.06 264	90.16 227	43.04 334	95.66 249	76.37 223	78.55 242	93.98 209

v1144		82.90 230	81.27 234	87.78 228	86.29 301	79.07 193	96.14 181	93.93 242	80.05 229	77.38 227	86.80 271	65.50 227	95.93 235	75.21 234	70.13 282	88.33 284

V42		83.04 227	81.53 230	87.57 234	86.27 302	79.09 192	95.87 194	94.11 235	80.35 222	77.22 231	86.79 272	65.32 231	96.02 227	77.74 205	70.14 281	87.61 298

v2v482		83.46 218	81.86 225	88.25 219	86.19 303	79.65 177	96.34 169	94.02 240	81.56 200	77.32 229	88.23 250	65.62 226	96.03 226	77.77 204	69.72 289	89.09 263

v148		82.41 239	80.89 236	86.99 247	86.18 304	76.81 246	96.27 173	93.82 248	80.49 217	75.28 262	86.11 286	67.32 217	95.75 244	75.48 232	67.03 316	88.42 282

pmmvs4		82.54 235	80.79 237	87.79 227	86.11 305	80.49 158	93.55 259	93.18 281	77.29 271	73.35 274	89.40 235	65.26 232	95.05 283	75.32 233	73.61 261	87.83 292

MVP-Stereo		82.65 234	81.67 228	85.59 269	86.10 306	78.29 211	93.33 264	92.82 288	77.75 265	69.17 304	87.98 254	59.28 268	95.76 243	71.77 258	96.88 90	82.73 341
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		82.31 240	80.55 243	87.60 231	85.94 307	78.47 207	95.85 196	93.80 251	79.33 242	76.97 234	86.51 275	63.33 241	95.87 237	73.11 251	70.13 282	88.46 280

TransMVSNet (Re)		76.94 288	74.38 292	84.62 282	85.92 308	75.25 269	95.28 213	89.18 328	73.88 296	67.22 307	86.46 277	59.64 262	94.10 300	59.24 318	52.57 346	84.50 331

PS-MVSNAJss		84.91 196	84.30 190	86.74 249	85.89 309	74.40 277	94.95 228	94.16 232	83.93 157	76.45 241	90.11 229	71.04 196	95.77 242	83.16 166	79.02 236	90.06 244

v144192		82.43 236	80.73 239	87.54 235	85.81 310	78.22 214	95.98 186	93.78 253	79.09 249	77.11 232	86.49 276	64.66 236	95.91 236	74.20 244	69.42 290	88.49 278

v1921920		82.02 243	80.23 247	87.41 238	85.62 311	77.92 226	95.79 198	93.69 258	78.86 254	76.67 237	86.44 278	62.50 244	95.83 239	72.69 253	69.77 288	88.47 279

v1240		81.70 246	79.83 254	87.30 242	85.50 312	77.70 232	95.48 207	93.44 268	78.46 259	76.53 240	86.44 278	60.85 258	95.84 238	71.59 260	70.17 280	88.35 283

pm-mvs1		80.05 262	78.02 265	86.15 260	85.42 313	75.81 264	95.11 223	92.69 291	77.13 273	70.36 296	87.43 260	58.44 274	95.27 270	71.36 262	64.25 326	87.36 304

our_test_3		77.90 280	75.37 284	85.48 271	85.39 314	76.74 247	93.63 256	91.67 302	73.39 301	65.72 318	84.65 306	58.20 275	93.13 315	57.82 321	67.87 305	86.57 312

ppachtmachnet_test		77.19 286	74.22 294	86.13 261	85.39 314	78.22 214	93.98 251	91.36 307	71.74 313	67.11 309	84.87 304	56.67 287	93.37 313	52.21 339	64.59 323	86.80 309

MDA-MVSNet-bldmvs		71.45 313	67.94 317	81.98 311	85.33 316	68.50 322	92.35 284	88.76 332	70.40 317	42.99 355	81.96 321	46.57 323	91.31 332	48.75 349	54.39 340	86.11 318

Baseline_NR-MVSNet		81.22 253	80.07 250	84.68 279	85.32 317	75.12 270	96.48 156	88.80 331	76.24 280	77.28 230	86.40 281	67.61 212	94.39 296	75.73 231	66.73 318	84.54 330

DTE-MVSNet		78.37 275	77.06 272	82.32 309	85.22 318	67.17 327	93.40 261	93.66 259	78.71 256	70.53 295	88.29 249	59.06 270	92.23 322	61.38 310	63.28 330	87.56 300

pmmvs5		81.34 251	79.54 255	86.73 252	85.02 319	76.91 244	96.22 176	91.65 303	77.65 266	73.55 271	88.61 244	55.70 294	94.43 295	74.12 245	73.35 265	88.86 275

XVG-ACMP-BASELINE		79.38 269	77.90 266	83.81 291	84.98 320	67.14 328	89.03 308	93.18 281	80.26 226	72.87 280	88.15 252	38.55 343	96.26 220	76.05 227	78.05 245	88.02 289

MDA-MVSNet_test_wron		73.54 303	70.43 310	82.86 303	84.55 321	71.85 298	91.74 291	91.32 309	67.63 325	46.73 354	81.09 327	55.11 298	90.42 340	55.91 331	59.76 334	86.31 315

SixPastTwentyTwo		76.04 292	74.32 293	81.22 313	84.54 322	61.43 344	91.16 296	89.30 327	77.89 262	64.04 323	86.31 282	48.23 315	94.29 298	63.54 302	63.84 328	87.93 291

YYNet1		73.53 304	70.43 310	82.85 304	84.52 323	71.73 301	91.69 292	91.37 306	67.63 325	46.79 353	81.21 326	55.04 299	90.43 339	55.93 330	59.70 335	86.38 314

N_pmnet		61.30 323	60.20 326	64.60 338	84.32 324	17.00 371	91.67 293	10.98 370	61.77 340	58.45 343	78.55 337	49.89 312	91.83 327	42.27 355	63.94 327	84.97 328

mvs_tets		81.74 245	80.71 240	84.84 276	84.22 325	70.29 310	93.91 252	93.78 253	82.77 182	73.37 273	89.46 234	47.36 322	95.31 268	81.99 173	79.55 232	88.92 273

jajsoiax		82.12 242	81.15 235	85.03 275	84.19 326	70.70 307	94.22 247	93.95 241	83.07 175	73.48 272	89.75 230	49.66 313	95.37 264	82.24 172	79.76 227	89.02 267

EU-MVSNet		76.92 289	76.95 273	76.83 328	84.10 327	54.73 355	91.77 290	92.71 290	72.74 306	69.57 301	88.69 243	58.03 278	87.43 349	64.91 295	70.00 286	88.33 284

test_djsdf		83.00 229	82.45 218	84.64 281	84.07 328	69.78 315	94.80 232	94.48 217	80.74 210	75.41 261	87.70 257	61.32 257	95.10 280	83.77 152	79.76 227	89.04 266

v7n		79.32 270	77.34 269	85.28 272	84.05 329	72.89 290	93.38 262	93.87 246	75.02 288	70.68 293	84.37 307	59.58 264	95.62 254	67.60 280	67.50 310	87.32 305

OurMVSNet-221017-0		77.18 287	76.06 279	80.55 317	83.78 330	60.00 346	90.35 300	91.05 313	77.01 277	66.62 314	87.92 255	47.73 320	94.03 301	71.63 259	68.44 299	87.62 297

EG-PatchMatch MVS		74.92 298	72.02 303	83.62 296	83.76 331	73.28 285	93.62 257	92.04 298	68.57 324	58.88 341	83.80 312	31.87 355	95.57 258	56.97 327	78.67 238	82.00 347

K. test v3		73.62 301	71.59 305	79.69 320	82.98 332	59.85 347	90.85 299	88.83 330	77.13 273	58.90 340	82.11 320	43.62 329	91.72 328	65.83 291	54.10 341	87.50 302

bset_n11_16_dypcd		84.35 206	82.83 212	88.91 203	82.54 333	82.07 115	94.12 249	93.47 266	85.39 113	78.55 219	88.98 239	62.23 246	95.11 278	86.75 133	73.42 262	89.55 251

anonymousdsp		80.98 256	79.97 252	84.01 289	81.73 334	70.44 309	92.49 281	93.58 264	77.10 275	72.98 279	86.31 282	57.58 280	94.90 285	79.32 193	78.63 241	86.69 311

Anonymous20231206		75.29 297	73.64 298	80.22 318	80.75 335	63.38 337	93.36 263	90.71 318	73.09 303	67.12 308	83.70 313	50.33 311	90.85 336	53.63 337	70.10 284	86.44 313

Gipuma		45.11 329	42.05 331	54.30 343	80.69 336	51.30 357	35.80 362	83.81 351	28.13 359	27.94 361	34.53 361	11.41 367	76.70 358	21.45 360	54.65 339	34.90 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
lessismore_v0				79.98 319	80.59 337	58.34 349		80.87 356		58.49 342	83.46 315	43.10 333	93.89 303	63.11 304	48.68 348	87.72 293

OpenMVS_ROB	68.52 20	73.02 307	69.57 313	83.37 300	80.54 338	71.82 299	93.60 258	88.22 335	62.37 337	61.98 333	83.15 317	35.31 350	95.47 260	45.08 353	75.88 251	82.82 339

testgi		74.88 299	73.40 299	79.32 322	80.13 339	61.75 341	93.21 270	86.64 341	79.49 240	66.56 315	91.06 210	35.51 349	88.67 345	56.79 328	71.25 272	87.56 300

CMPMVS	54.94 21	75.71 296	74.56 291	79.17 323	79.69 340	55.98 351	89.59 303	93.30 277	60.28 346	53.85 350	89.07 237	47.68 321	96.33 217	76.55 219	81.02 222	85.22 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		72.36 310	70.82 307	76.95 327	79.18 341	56.33 350	86.12 330	86.11 343	69.30 323	63.06 329	86.66 273	33.03 353	92.25 321	65.33 293	68.64 297	82.28 345

pmmvs6		74.65 300	71.67 304	83.60 297	79.13 342	69.94 312	93.31 268	90.88 317	61.05 345	65.83 317	84.15 310	43.43 330	94.83 288	66.62 285	60.63 333	86.02 320

DeepMVS_CX				64.06 339	78.53 343	43.26 361		68.11 364	69.94 320	38.55 356	76.14 342	18.53 361	79.34 355	43.72 354	41.62 356	69.57 355

CL-MVSNet_2432*1600		75.81 294	74.14 296	80.83 316	78.33 344	67.79 324	94.22 247	93.52 265	77.28 272	69.82 299	81.54 324	61.47 256	89.22 343	57.59 323	53.51 342	85.48 325

test20.03		72.36 310	71.15 306	75.98 332	77.79 345	59.16 348	92.40 283	89.35 326	74.09 294	61.50 335	84.32 308	48.09 316	85.54 354	50.63 344	62.15 332	83.24 337

UnsupCasMVSNet_eth		73.25 305	70.57 309	81.30 312	77.53 346	66.33 329	87.24 323	93.89 245	80.38 221	57.90 345	81.59 323	42.91 335	90.56 338	65.18 294	48.51 349	87.01 308

DSMNet-mixed		73.13 306	72.45 302	75.19 334	77.51 347	46.82 358	85.09 335	82.01 355	67.61 329	69.27 303	81.33 325	50.89 307	86.28 351	54.54 334	83.80 204	92.46 219

Patchmatch-RL test		76.65 290	74.01 297	84.55 283	77.37 348	64.23 333	78.49 347	82.84 354	78.48 258	64.63 322	73.40 346	76.05 129	91.70 329	76.99 214	57.84 336	97.72 106

Anonymous20240521		72.06 312	69.91 312	78.50 324	77.11 349	61.67 343	91.62 294	90.97 315	65.52 332	62.37 331	79.05 336	36.32 346	90.96 335	57.75 322	68.52 298	82.87 338

test_method		56.77 324	54.53 327	63.49 340	76.49 350	40.70 363	75.68 351	74.24 361	19.47 363	48.73 352	71.89 350	19.31 360	65.80 362	57.46 324	47.51 352	83.97 335

MIMVSNet1		69.44 316	66.65 320	77.84 325	76.48 351	62.84 339	87.42 321	88.97 329	66.96 330	57.75 346	79.72 335	32.77 354	85.83 353	46.32 351	63.42 329	84.85 329

pmmvs-eth3d		73.59 302	70.66 308	82.38 307	76.40 352	73.38 282	89.39 307	89.43 325	72.69 307	60.34 339	77.79 339	46.43 324	91.26 333	66.42 289	57.06 337	82.51 342

new_pmnet		66.18 321	63.18 324	75.18 335	76.27 353	61.74 342	83.79 337	84.66 347	56.64 353	51.57 351	71.85 351	31.29 356	87.93 347	49.98 345	62.55 331	75.86 352

DIV-MVS_2432*1600		70.97 315	69.31 315	75.95 333	76.24 354	55.39 354	87.45 320	90.94 316	70.20 319	62.96 330	77.48 340	44.01 327	88.09 346	61.25 311	53.26 343	84.37 332

UnsupCasMVSNet_bld		68.60 320	64.50 323	80.92 315	74.63 355	67.80 323	83.97 336	92.94 287	65.12 333	54.63 349	68.23 352	35.97 347	92.17 324	60.13 313	44.83 353	82.78 340

PM-MVS		69.32 317	66.93 319	76.49 329	73.60 356	55.84 352	85.91 331	79.32 359	74.72 290	61.09 336	78.18 338	21.76 359	91.10 334	70.86 268	56.90 338	82.51 342

new-patchmatchnet		68.85 319	65.93 321	77.61 326	73.57 357	63.94 336	90.11 302	88.73 333	71.62 314	55.08 348	73.60 345	40.84 341	87.22 350	51.35 342	48.49 350	81.67 348

ambc				76.02 331	68.11 358	51.43 356	64.97 358	89.59 323		60.49 338	74.49 343	17.17 362	92.46 318	61.50 309	52.85 345	84.17 334

pmmvs3		65.75 322	62.18 325	76.45 330	67.12 359	64.54 332	88.68 311	85.05 346	54.77 354	57.54 347	73.79 344	29.40 358	86.21 352	55.49 333	47.77 351	78.62 350

TDRefinement		69.20 318	65.78 322	79.48 321	66.04 360	62.21 340	88.21 314	86.12 342	62.92 336	61.03 337	85.61 290	33.23 352	94.16 299	55.82 332	53.02 344	82.08 346

FPMVS		55.09 325	52.93 328	61.57 341	55.98 361	40.51 364	83.11 338	83.41 353	37.61 357	34.95 358	71.95 349	14.40 363	76.95 356	29.81 358	65.16 322	67.25 356

PMMVS2		50.90 327	46.31 330	64.67 337	55.53 362	46.67 359	77.30 350	71.02 362	40.89 356	34.16 359	59.32 353	9.83 368	76.14 359	40.09 356	28.63 359	71.21 353

wuyk23d		14.10 336	13.89 339	14.72 349	55.23 363	22.91 370	33.83 363	3.56 371	4.94 366	4.11 367	2.28 369	2.06 372	19.66 367	10.23 365	8.74 365	1.59 365

E-PMN		32.70 333	32.39 335	33.65 347	53.35 364	25.70 368	74.07 354	53.33 368	21.08 361	17.17 365	33.63 363	11.85 366	54.84 364	12.98 363	14.04 361	20.42 361

EMVS		31.70 334	31.45 336	32.48 348	50.72 365	23.95 369	74.78 353	52.30 369	20.36 362	16.08 366	31.48 364	12.80 364	53.60 365	11.39 364	13.10 364	19.88 362

LCM-MVSNet		52.52 326	48.24 329	65.35 336	47.63 366	41.45 362	72.55 356	83.62 352	31.75 358	37.66 357	57.92 355	9.19 369	76.76 357	49.26 347	44.60 354	77.84 351

MVE	35.65 22	33.85 332	29.49 337	46.92 345	41.86 367	36.28 365	50.45 361	56.52 367	18.75 364	18.28 363	37.84 360	2.41 371	58.41 363	18.71 361	20.62 360	46.06 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		46.22 328	41.28 333	61.04 342	39.91 368	46.25 360	70.59 357	76.18 360	58.87 351	23.09 362	48.00 359	12.58 365	66.54 361	28.65 359	13.62 362	70.35 354

PMVS	34.80 23	39.19 331	35.53 334	50.18 344	29.72 369	30.30 366	59.60 360	66.20 365	26.06 360	17.91 364	49.53 358	3.12 370	74.09 360	18.19 362	49.40 347	46.14 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 330	41.93 332	40.38 346	20.10 370	26.84 367	61.93 359	59.09 366	14.81 365	28.51 360	80.58 328	35.53 348	48.33 366	63.70 301	13.11 363	45.96 359

testmvs		9.92 337	12.94 340	0.84 351	0.65 371	0.29 373	93.78 254	0.39 372	0.42 367	2.85 368	15.84 367	0.17 374	0.30 369	2.18 366	0.21 366	1.91 364

test123		9.07 338	11.73 341	1.11 350	0.50 372	0.77 372	89.44 306	0.20 373	0.34 368	2.15 369	10.72 368	0.34 373	0.32 368	1.79 367	0.08 367	2.23 363

eth-test2					0.00 373

eth-test					0.00 373

uanet_test		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

cdsmvs_eth3d_5k		21.43 335	28.57 338	0.00 352	0.00 373	0.00 374	0.00 364	95.93 138	0.00 369	0.00 370	97.66 69	63.57 239	0.00 370	0.00 368	0.00 368	0.00 366

pcd_1.5k_mvsjas		5.92 340	7.89 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	71.04 196	0.00 370	0.00 368	0.00 368	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

ab-mvs-re		8.11 339	10.81 342	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	97.30 92	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

test_241102_TWO								96.78 45	88.72 51	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

test_0728_THIRD									88.38 59	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

GSMVS																97.54 118

sam_mvs1												77.59 102				97.54 118

sam_mvs												75.35 148

MTGPA								96.33 112

test_post1							85.88 332				30.24 365	73.77 167	95.07 282	73.89 246

test_post											33.80 362	76.17 127	95.97 229

patchmatchnet-post											77.09 341	77.78 101	95.39 262

MTMP							97.53 78	68.16 363

test9_res														96.00 21	99.03 11	98.31 57

agg_prior2														94.30 43	99.00 13	98.57 41

test_prior4						82.34 110	97.75 63

test_prior2							98.37 28		86.08 97	94.57 39	98.02 49	83.14 44		95.05 35	98.79 23

旧先验2							96.97 129		74.06 295	96.10 16			97.76 151	88.38 119

新几何2							96.42 165

无先验							96.87 135	96.78 45	77.39 269				99.52 53	79.95 186		98.43 49

原ACMM2							96.84 136

testdata2													99.48 58	76.45 221

segment_acmp												82.69 53

testdata1							95.57 205		87.44 78

plane_prior5								94.69 203					97.30 174	87.08 128	82.82 215	90.96 226

plane_prior4											94.15 172

plane_prior3						77.75 230			90.17 33	81.33 192

plane_prior2							97.18 104		89.89 35

plane_prior						77.96 223	97.52 81		90.36 32						82.96 213

n2								0.00 374

nn								0.00 374

door-mid								79.75 358

test11								96.50 91

door								80.13 357

HQP5-MVS						78.48 204

BP-MVS														87.67 124

HQP4-MVS										82.30 180			97.32 172			91.13 224

HQP3-MVS								94.80 199							83.01 211

HQP2-MVS												65.40 229

MDTV_nov1_ep13_2view						81.74 128	86.80 325		80.65 212	85.65 144		74.26 162		76.52 220		96.98 143

ACMMP++_ref															78.45 243

ACMMP++															79.05 235

Test By Simon												71.65 189

High-resolution multi-view benchmark