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.

test_0728_SECOND				95.01 15	98.79 1	86.43 41	97.09 11	97.49 5					99.61 3	95.62 5	99.08 7	98.99 5

DVP-MVS		95.67 2	96.02 2	94.64 40	98.78 2	85.93 58	97.09 11	96.73 76	90.27 28	97.04 8	98.05 6	91.47 6	99.55 12	95.62 5	99.08 7	98.45 32
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					98.78 2	85.93 58	97.19 6	97.47 8	90.27 28	97.64 4	98.13 1	91.47 6

SED-MVS		95.91 1	96.28 1	94.80 33	98.77 4	85.99 55	97.13 9	97.44 12	90.31 26	97.71 1	98.07 4	92.31 2	99.58 5	95.66 2	99.13 3	98.84 8

IU-MVS					98.77 4	86.00 54		96.84 62	81.26 244	97.26 6				95.50 7	99.13 3	99.03 4

test_241102_ONE					98.77 4	85.99 55		97.44 12	90.26 30	97.71 1	97.96 8	92.31 2	99.38 29

region2R		94.43 22	94.27 24	94.92 20	98.65 7	86.67 32	96.92 19	97.23 32	88.60 72	93.58 47	97.27 27	85.22 56	99.54 16	92.21 48	98.74 29	98.56 19

ACMMPR		94.43 22	94.28 22	94.91 22	98.63 8	86.69 30	96.94 15	97.32 24	88.63 70	93.53 50	97.26 29	85.04 59	99.54 16	92.35 45	98.78 21	98.50 22

HFP-MVS		94.52 17	94.40 19	94.86 25	98.61 9	86.81 24	96.94 15	97.34 19	88.63 70	93.65 43	97.21 32	86.10 45	99.49 23	92.35 45	98.77 24	98.30 41

#test#		94.32 28	94.14 31	94.86 25	98.61 9	86.81 24	96.43 32	97.34 19	87.51 104	93.65 43	97.21 32	86.10 45	99.49 23	91.68 70	98.77 24	98.30 41

test_part2					98.55 11	87.22 16				96.40 11

XVS		94.45 20	94.32 20	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 61	97.16 37	85.02 60	99.49 23	91.99 57	98.56 47	98.47 28

X-MVStestdata		88.31 165	86.13 207	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 61	23.41 364	85.02 60	99.49 23	91.99 57	98.56 47	98.47 28

ZNCC-MVS		94.47 18	94.28 22	95.03 14	98.52 14	86.96 17	96.85 24	97.32 24	88.24 82	93.15 55	97.04 42	86.17 44	99.62 1	92.40 43	98.81 18	98.52 20

mPP-MVS		93.99 38	93.78 43	94.63 41	98.50 15	85.90 63	96.87 21	96.91 56	88.70 68	91.83 92	97.17 36	83.96 74	99.55 12	91.44 75	98.64 43	98.43 34

MSP-MVS		95.42 5	95.56 5	94.98 19	98.49 16	86.52 38	96.91 20	97.47 8	91.73 8	96.10 13	96.69 58	89.90 9	99.30 39	94.70 9	98.04 66	99.13 1
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
MP-MVS		94.25 29	94.07 34	94.77 35	98.47 17	86.31 47	96.71 27	96.98 48	89.04 60	91.98 86	97.19 34	85.43 54	99.56 7	92.06 56	98.79 19	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		94.45 20	94.20 28	95.19 9	98.46 18	87.50 13	95.00 110	97.12 40	87.13 111	92.51 75	96.30 74	89.24 14	99.34 33	93.46 21	98.62 44	98.73 11

PGM-MVS		93.96 40	93.72 45	94.68 38	98.43 19	86.22 50	95.30 86	97.78 1	87.45 107	93.26 51	97.33 24	84.62 65	99.51 21	90.75 87	98.57 46	98.32 40

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 108	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

MTAPA		94.42 24	94.22 25	95.00 16	98.42 20	86.95 18	94.36 158	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

GST-MVS		94.21 33	93.97 38	94.90 24	98.41 22	86.82 23	96.54 31	97.19 35	88.24 82	93.26 51	96.83 51	85.48 53	99.59 4	91.43 76	98.40 53	98.30 41

HPM-MVS		94.02 37	93.88 39	94.43 50	98.39 23	85.78 65	97.25 5	97.07 44	86.90 119	92.62 72	96.80 55	84.85 63	99.17 50	92.43 41	98.65 42	98.33 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		94.34 26	94.21 27	94.74 37	98.39 23	86.64 34	97.60 1	97.24 30	88.53 74	92.73 68	97.23 30	85.20 57	99.32 37	92.15 51	98.83 17	98.25 50

DPE-MVS		95.57 3	95.67 3	95.25 7	98.36 25	87.28 15	95.56 76	97.51 4	89.13 58	97.14 7	97.91 9	91.64 5	99.62 1	94.61 11	99.17 2	98.86 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS_fast		93.40 55	93.22 55	93.94 61	98.36 25	84.83 76	97.15 8	96.80 68	85.77 141	92.47 76	97.13 38	82.38 88	99.07 58	90.51 89	98.40 53	97.92 75

DP-MVS Recon		91.95 78	91.28 84	93.96 60	98.33 27	85.92 60	94.66 133	96.66 86	82.69 210	90.03 117	95.82 95	82.30 91	99.03 64	84.57 154	96.48 102	96.91 118

APDe-MVS		95.46 4	95.64 4	94.91 22	98.26 28	86.29 49	97.46 2	97.40 17	89.03 61	96.20 12	98.10 2	89.39 13	99.34 33	95.88 1	99.03 9	99.10 3

TSAR-MVS + MP.		94.85 12	94.94 10	94.58 43	98.25 29	86.33 45	96.11 49	96.62 89	88.14 88	96.10 13	96.96 46	89.09 15	98.94 84	94.48 12	98.68 35	98.48 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		95.14 9	94.91 11	95.83 2	98.25 29	89.65 2	95.92 59	96.96 52	91.75 7	94.02 35	96.83 51	88.12 21	99.55 12	93.41 24	98.94 12	98.28 45

testtj		94.39 25	94.18 29	95.00 16	98.24 31	86.77 28	96.16 44	97.23 32	87.28 109	94.85 24	97.04 42	86.99 37	99.52 20	91.54 72	98.33 56	98.71 12

CPTT-MVS		91.99 77	91.80 78	92.55 107	98.24 31	81.98 154	96.76 26	96.49 97	81.89 229	90.24 113	96.44 71	78.59 133	98.61 107	89.68 94	97.85 74	97.06 110

SR-MVS		94.23 31	94.17 30	94.43 50	98.21 33	85.78 65	96.40 34	96.90 57	88.20 85	94.33 27	97.40 21	84.75 64	99.03 64	93.35 26	97.99 67	98.48 24

MP-MVS-pluss		94.21 33	94.00 37	94.85 27	98.17 34	86.65 33	94.82 122	97.17 38	86.26 132	92.83 63	97.87 10	85.57 52	99.56 7	94.37 14	98.92 13	98.34 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZD-MVS					98.15 35	86.62 35		97.07 44	83.63 187	94.19 30	96.91 48	87.57 29	99.26 43	91.99 57	98.44 51

SMA-MVS		95.20 7	95.07 9	95.59 3	98.14 36	88.48 6	96.26 40	97.28 28	85.90 138	97.67 3	98.10 2	88.41 17	99.56 7	94.66 10	99.19 1	98.71 12
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
test1172		93.97 39	94.07 34	93.66 71	98.11 37	83.45 115	96.26 40	96.84 62	88.33 78	94.19 30	97.43 18	84.24 69	99.01 70	93.26 28	97.98 68	98.52 20

CNVR-MVS		95.40 6	95.37 6	95.50 5	98.11 37	88.51 5	95.29 89	96.96 52	92.09 3	95.32 19	97.08 40	89.49 12	99.33 36	95.10 8	98.85 15	98.66 14

114514_t		89.51 130	88.50 140	92.54 108	98.11 37	81.99 153	95.16 100	96.36 105	70.19 342	85.81 183	95.25 111	76.70 151	98.63 105	82.07 189	96.86 92	97.00 114

ACMMP		93.24 59	92.88 64	94.30 54	98.09 40	85.33 72	96.86 23	97.45 11	88.33 78	90.15 115	97.03 44	81.44 102	99.51 21	90.85 86	95.74 108	98.04 66
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
APD-MVS		94.24 30	94.07 34	94.75 36	98.06 41	86.90 21	95.88 60	96.94 54	85.68 144	95.05 23	97.18 35	87.31 31	99.07 58	91.90 65	98.61 45	98.28 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		93.23 60	93.05 59	93.76 69	98.04 42	84.07 98	96.22 42	97.37 18	84.15 176	90.05 116	95.66 100	87.77 23	99.15 53	89.91 92	98.27 58	98.07 63

ACMMP_NAP		94.74 14	94.56 16	95.28 6	98.02 43	87.70 10	95.68 69	97.34 19	88.28 81	95.30 20	97.67 13	85.90 49	99.54 16	93.91 17	98.95 11	98.60 17

OPU-MVS				96.21 1	98.00 44	90.85 1	97.13 9				97.08 40	92.59 1	98.94 84	92.25 47	98.99 10	98.84 8

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 64	97.92 45	84.57 81	96.28 38	96.76 72	87.46 105	93.75 40	97.43 18	84.24 69	99.01 70	92.73 35	97.80 75	97.88 77

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 38	96.76 72	87.46 105	93.75 40	97.43 18	82.94 82		92.73 35	97.80 75	97.88 77

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 68	97.92 45	84.19 96	96.30 36	96.87 61	86.96 115	93.92 37	97.47 16	83.88 75	98.96 83	92.71 38	97.87 73	98.26 49

xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 47	97.85 48	85.63 68	95.21 95	95.47 171	89.44 47	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

save fliter					97.85 48	85.63 68	95.21 95	96.82 66	89.44 47

SF-MVS		94.97 10	94.90 12	95.20 8	97.84 50	87.76 8	96.65 29	97.48 7	87.76 97	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

NCCC		94.81 13	94.69 15	95.17 10	97.83 51	87.46 14	95.66 71	96.93 55	92.34 2	93.94 36	96.58 65	87.74 24	99.44 27	92.83 34	98.40 53	98.62 16

ETH3 D test6400		93.64 49	93.22 55	94.92 20	97.79 52	86.84 22	95.31 83	97.26 29	82.67 211	93.81 39	96.29 75	87.29 32	99.27 42	89.87 93	98.67 37	98.65 15

9.14			94.47 17		97.79 52		96.08 50	97.44 12	86.13 136	95.10 22	97.40 21	88.34 18	99.22 46	93.25 29	98.70 32

CDPH-MVS		92.83 65	92.30 73	94.44 48	97.79 52	86.11 52	94.06 177	96.66 86	80.09 257	92.77 65	96.63 62	86.62 39	99.04 63	87.40 121	98.66 40	98.17 54

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 11	97.70 55	87.71 9	95.98 56	97.44 12	86.67 124	95.25 21	97.31 25	87.73 25	99.24 44	93.11 32	98.76 26	98.40 35

DP-MVS		87.25 203	85.36 233	92.90 91	97.65 56	83.24 120	94.81 123	92.00 284	74.99 311	81.92 274	95.00 119	72.66 207	99.05 60	66.92 325	92.33 167	96.40 131

PAPM_NR		91.22 92	90.78 95	92.52 109	97.60 57	81.46 168	94.37 157	96.24 112	86.39 130	87.41 153	94.80 128	82.06 97	98.48 113	82.80 178	95.37 116	97.61 88

TEST9					97.53 58	86.49 39	94.07 175	96.78 69	81.61 237	92.77 65	96.20 80	87.71 26	99.12 55

train_agg		93.44 53	93.08 58	94.52 45	97.53 58	86.49 39	94.07 175	96.78 69	81.86 230	92.77 65	96.20 80	87.63 27	99.12 55	92.14 52	98.69 33	97.94 72

abl_6		93.18 61	93.05 59	93.57 73	97.52 60	84.27 95	95.53 77	96.67 85	87.85 94	93.20 54	97.22 31	80.35 109	99.18 49	91.91 62	97.21 85	97.26 100

test_8					97.49 61	86.30 48	94.02 180	96.76 72	81.86 230	92.70 69	96.20 80	87.63 27	99.02 68

DeepC-MVS_fast	89.43 2	94.04 36	93.79 42	94.80 33	97.48 62	86.78 26	95.65 73	96.89 58	89.40 50	92.81 64	96.97 45	85.37 55	99.24 44	90.87 85	98.69 33	98.38 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		89.89 122	89.07 128	92.37 118	97.41 63	83.03 126	94.42 149	95.92 135	82.81 208	86.34 176	94.65 134	73.89 190	99.02 68	80.69 215	95.51 111	95.05 180

agg_prior1		93.29 57	92.97 62	94.26 55	97.38 64	85.92 60	93.92 186	96.72 78	81.96 224	92.16 81	96.23 78	87.85 22	98.97 80	91.95 61	98.55 49	97.90 76

agg_prior					97.38 64	85.92 60		96.72 78		92.16 81			98.97 80

原ACMM1				92.01 129	97.34 66	81.05 180		96.81 67	78.89 271	90.45 111	95.92 91	82.65 85	98.84 96	80.68 216	98.26 59	96.14 140

MSLP-MVS++		93.72 46	94.08 33	92.65 103	97.31 67	83.43 116	95.79 64	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 166	92.19 50	98.66 40	96.76 121

新几何1				93.10 81	97.30 68	84.35 94		95.56 163	71.09 339	91.26 103	96.24 77	82.87 84	98.86 91	79.19 237	98.10 63	96.07 147

test_prior3		93.60 50	93.53 49	93.82 64	97.29 69	84.49 85	94.12 168	96.88 59	87.67 100	92.63 70	96.39 72	86.62 39	98.87 88	91.50 73	98.67 37	98.11 61

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 88			98.11 61

1121		90.42 109	89.49 115	93.20 77	97.27 71	84.46 88	92.63 234	95.51 169	71.01 340	91.20 104	96.21 79	82.92 83	99.05 60	80.56 218	98.07 65	96.10 145

PLC	84.53 7	89.06 145	88.03 153	92.15 126	97.27 71	82.69 140	94.29 160	95.44 177	79.71 262	84.01 240	94.18 151	76.68 152	98.75 100	77.28 254	93.41 148	95.02 181
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		94.96 11	95.33 7	93.88 62	97.25 73	86.69 30	96.19 43	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 239	94.38 13	98.85 15	98.03 67
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
test12				94.34 53	97.13 74	86.15 51		96.29 107		91.04 106		85.08 58	99.01 70		98.13 62	97.86 79

MG-MVS		91.77 81	91.70 80	92.00 131	97.08 75	80.03 210	93.60 199	95.18 191	87.85 94	90.89 108	96.47 70	82.06 97	98.36 122	85.07 146	97.04 89	97.62 87

SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 27	96.99 76	86.33 45	97.33 3	97.30 26	91.38 11	95.39 18	97.46 17	88.98 16	99.40 28	94.12 15	98.89 14	98.82 10
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		93.45 52	93.31 53	93.84 63	96.99 76	84.84 75	93.24 216	97.24 30	88.76 67	91.60 97	95.85 94	86.07 47	98.66 102	91.91 62	98.16 61	98.03 67

CNLPA		89.07 144	87.98 155	92.34 119	96.87 78	84.78 77	94.08 174	93.24 255	81.41 240	84.46 224	95.13 116	75.57 167	96.62 249	77.21 255	93.84 139	95.61 165

PHI-MVS		93.89 43	93.65 47	94.62 42	96.84 79	86.43 41	96.69 28	97.49 5	85.15 160	93.56 49	96.28 76	85.60 51	99.31 38	92.45 40	98.79 19	98.12 59

旧先验1					96.79 80	81.81 158		95.67 155			96.81 53	86.69 38			97.66 79	96.97 115

ETH3D cwj APD-0.16		93.91 41	93.53 49	95.06 13	96.76 81	87.78 7	94.92 115	97.21 34	84.33 174	93.89 38	97.09 39	87.20 33	99.29 41	91.90 65	98.44 51	98.12 59

LFMVS		90.08 114	89.13 127	92.95 89	96.71 82	82.32 149	96.08 50	89.91 332	86.79 120	92.15 83	96.81 53	62.60 299	98.34 125	87.18 125	93.90 137	98.19 53

Anonymous202405211		87.68 181	86.13 207	92.31 121	96.66 83	80.74 190	94.87 119	91.49 299	80.47 253	89.46 122	95.44 104	54.72 337	98.23 131	82.19 187	89.89 191	97.97 70

TAPA-MVS	84.62 6	88.16 169	87.01 177	91.62 150	96.64 84	80.65 191	94.39 152	96.21 117	76.38 296	86.19 179	95.44 104	79.75 117	98.08 146	62.75 340	95.29 118	96.13 141
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		90.30 110	89.37 120	93.07 84	96.61 85	84.48 87	95.68 69	95.67 155	82.36 215	87.85 144	92.85 199	76.63 153	98.80 98	80.01 226	96.68 95	95.91 152
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
VNet		92.24 75	91.91 77	93.24 76	96.59 86	83.43 116	94.84 121	96.44 98	89.19 56	94.08 34	95.90 92	77.85 144	98.17 135	88.90 103	93.38 149	98.13 58

TSAR-MVS + GP.		93.66 48	93.41 52	94.41 52	96.59 86	86.78 26	94.40 150	93.93 241	89.77 40	94.21 29	95.59 103	87.35 30	98.61 107	92.72 37	96.15 105	97.83 81

test222					96.55 88	81.70 160	92.22 248	95.01 198	68.36 345	90.20 114	96.14 85	80.26 112			97.80 75	96.05 149

Anonymous20240529		88.09 171	86.59 192	92.58 106	96.53 89	81.92 157	95.99 54	95.84 143	74.11 319	89.06 128	95.21 113	61.44 307	98.81 97	83.67 166	87.47 228	97.01 113

Anonymous20231211		86.59 226	85.13 236	90.98 181	96.52 90	81.50 164	96.14 46	96.16 118	73.78 321	83.65 249	92.15 222	63.26 297	97.37 205	82.82 177	81.74 284	94.06 228

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 110	96.52 90	80.00 212	94.00 182	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 80	93.95 16	98.71 30	98.50 22

testdata				90.49 194	96.40 92	77.89 258		95.37 183	72.51 332	93.63 45	96.69 58	82.08 96	97.65 175	83.08 170	97.39 83	95.94 151

PVSNet_Blended_VisFu		91.38 88	90.91 92	92.80 94	96.39 93	83.17 122	94.87 119	96.66 86	83.29 197	89.27 124	94.46 141	80.29 111	99.17 50	87.57 119	95.37 116	96.05 149

API-MVS		90.66 103	90.07 105	92.45 112	96.36 94	84.57 81	96.06 52	95.22 190	82.39 213	89.13 125	94.27 149	80.32 110	98.46 115	80.16 225	96.71 94	94.33 216

F-COLMAP		87.95 174	86.80 182	91.40 160	96.35 95	80.88 186	94.73 128	95.45 175	79.65 263	82.04 272	94.61 135	71.13 221	98.50 112	76.24 265	91.05 178	94.80 194

VDD-MVS		90.74 99	89.92 111	93.20 77	96.27 96	83.02 127	95.73 66	93.86 245	88.42 77	92.53 73	96.84 50	62.09 302	98.64 104	90.95 83	92.62 163	97.93 74

OMC-MVS		91.23 91	90.62 96	93.08 82	96.27 96	84.07 98	93.52 201	95.93 134	86.95 116	89.51 120	96.13 86	78.50 135	98.35 124	85.84 139	92.90 159	96.83 120

DPM-MVS		92.58 69	91.74 79	95.08 12	96.19 98	89.31 3	92.66 233	96.56 95	83.44 193	91.68 96	95.04 118	86.60 42	98.99 77	85.60 142	97.92 72	96.93 117

CHOSEN 1792x2688		88.84 152	87.69 160	92.30 122	96.14 99	81.42 170	90.01 291	95.86 142	74.52 316	87.41 153	93.94 160	75.46 168	98.36 122	80.36 221	95.53 110	97.12 108

thres100view900		87.63 186	86.71 185	90.38 200	96.12 100	78.55 240	95.03 109	91.58 295	87.15 110	88.06 140	92.29 218	68.91 256	98.10 139	70.13 304	91.10 174	94.48 212

PVSNet_BlendedMVS		89.98 117	89.70 112	90.82 183	96.12 100	81.25 173	93.92 186	96.83 64	83.49 192	89.10 126	92.26 219	81.04 106	98.85 94	86.72 133	87.86 226	92.35 298

PVSNet_Blended		90.73 100	90.32 99	91.98 132	96.12 100	81.25 173	92.55 238	96.83 64	82.04 222	89.10 126	92.56 209	81.04 106	98.85 94	86.72 133	95.91 106	95.84 156

UA-Net		92.83 65	92.54 69	93.68 70	96.10 103	84.71 78	95.66 71	96.39 103	91.92 4	93.22 53	96.49 69	83.16 80	98.87 88	84.47 155	95.47 113	97.45 96

thres600view7		87.65 183	86.67 187	90.59 187	96.08 104	78.72 236	94.88 118	91.58 295	87.06 113	88.08 139	92.30 217	68.91 256	98.10 139	70.05 307	91.10 174	94.96 185

DeepC-MVS	88.79 3	93.31 56	92.99 61	94.26 55	96.07 105	85.83 64	94.89 117	96.99 47	89.02 62	89.56 119	97.37 23	82.51 87	99.38 29	92.20 49	98.30 57	97.57 91
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		87.89 175	86.32 202	92.59 105	96.07 105	82.92 131	95.23 93	94.92 206	75.66 303	82.89 262	95.98 89	72.48 210	99.21 47	68.43 314	95.23 121	95.64 164

hse-mvs3		90.80 97	90.15 103	92.75 97	96.01 107	82.66 141	95.43 79	95.53 167	89.80 37	93.08 58	95.64 101	75.77 160	99.00 75	92.07 54	78.05 322	96.60 126

HyFIR lowres test		88.09 171	86.81 181	91.93 136	96.00 108	80.63 192	90.01 291	95.79 147	73.42 324	87.68 149	92.10 227	73.86 191	97.96 157	80.75 214	91.70 170	97.19 104

tfpn200view9		87.58 190	86.64 188	90.41 197	95.99 109	78.64 238	94.58 136	91.98 286	86.94 117	88.09 137	91.77 237	69.18 253	98.10 139	70.13 304	91.10 174	94.48 212

thres400		87.62 188	86.64 188	90.57 188	95.99 109	78.64 238	94.58 136	91.98 286	86.94 117	88.09 137	91.77 237	69.18 253	98.10 139	70.13 304	91.10 174	94.96 185

MVS_111021_LR		92.47 72	92.29 74	92.98 87	95.99 109	84.43 92	93.08 221	96.09 122	88.20 85	91.12 105	95.72 99	81.33 104	97.76 166	91.74 68	97.37 84	96.75 122

test_part1		89.00 149	87.99 154	92.04 128	95.94 112	83.81 105	96.14 46	96.05 127	86.44 128	85.69 186	93.73 174	71.57 216	97.66 173	85.80 140	80.54 303	94.66 197

PatchMatch-RL		86.77 222	85.54 227	90.47 196	95.88 113	82.71 139	90.54 280	92.31 275	79.82 261	84.32 232	91.57 247	68.77 258	96.39 268	73.16 288	93.48 147	92.32 299

EPP-MVSNet		91.70 84	91.56 81	92.13 127	95.88 113	80.50 197	97.33 3	95.25 187	86.15 134	89.76 118	95.60 102	83.42 78	98.32 128	87.37 123	93.25 152	97.56 92

IS-MVSNet		91.43 87	91.09 89	92.46 111	95.87 115	81.38 171	96.95 14	93.69 250	89.72 42	89.50 121	95.98 89	78.57 134	97.77 165	83.02 172	96.50 101	98.22 52

PAPR		90.02 116	89.27 125	92.29 123	95.78 116	80.95 184	92.68 232	96.22 114	81.91 227	86.66 169	93.75 173	82.23 92	98.44 119	79.40 236	94.79 123	97.48 94

Vis-MVSNet (Re-imp)		89.59 128	89.44 117	90.03 214	95.74 117	75.85 289	95.61 74	90.80 317	87.66 102	87.83 145	95.40 108	76.79 149	96.46 265	78.37 242	96.73 93	97.80 82

test_yl		90.69 101	90.02 109	92.71 99	95.72 118	82.41 147	94.11 170	95.12 193	85.63 145	91.49 98	94.70 130	74.75 175	98.42 120	86.13 137	92.53 164	97.31 98

DCV-MVSNet		90.69 101	90.02 109	92.71 99	95.72 118	82.41 147	94.11 170	95.12 193	85.63 145	91.49 98	94.70 130	74.75 175	98.42 120	86.13 137	92.53 164	97.31 98

canonicalmvs		93.27 58	92.75 66	94.85 27	95.70 120	87.66 11	96.33 35	96.41 101	90.00 34	94.09 33	94.60 136	82.33 90	98.62 106	92.40 43	92.86 160	98.27 47

CANet		93.54 51	93.20 57	94.55 44	95.65 121	85.73 67	94.94 113	96.69 82	91.89 5	90.69 109	95.88 93	81.99 99	99.54 16	93.14 31	97.95 71	98.39 36

3Dnovator+	87.14 4	92.42 73	91.37 82	95.55 4	95.63 122	88.73 4	97.07 13	96.77 71	90.84 16	84.02 239	96.62 63	75.95 159	99.34 33	87.77 116	97.68 78	98.59 18

alignmvs		93.08 63	92.50 70	94.81 32	95.62 123	87.61 12	95.99 54	96.07 124	89.77 40	94.12 32	94.87 123	80.56 108	98.66 102	92.42 42	93.10 155	98.15 56

Regformer-1		94.22 32	94.13 32	94.51 46	95.54 124	86.36 44	94.57 138	96.44 98	91.69 9	94.32 28	96.56 67	87.05 36	99.03 64	93.35 26	97.65 80	98.15 56

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 124	86.75 29	94.57 138	96.70 80	91.84 6	94.41 25	96.56 67	87.19 34	99.13 54	93.50 20	97.65 80	98.16 55

WTY-MVS		89.60 127	88.92 132	91.67 149	95.47 126	81.15 178	92.38 242	94.78 216	83.11 200	89.06 128	94.32 144	78.67 132	96.61 252	81.57 201	90.89 180	97.24 101

DELS-MVS		93.43 54	93.25 54	93.97 59	95.42 127	85.04 74	93.06 223	97.13 39	90.74 20	91.84 90	95.09 117	86.32 43	99.21 47	91.22 77	98.45 50	97.65 86
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
Regformer-3		93.68 47	93.64 48	93.81 67	95.36 128	84.61 79	94.68 130	95.83 144	91.27 12	93.60 46	96.71 56	85.75 50	98.86 91	92.87 33	96.65 96	97.96 71

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 128	85.47 70	94.68 130	96.41 101	91.60 10	93.75 40	96.71 56	85.95 48	99.10 57	93.21 30	96.65 96	98.01 69

thres200		87.21 207	86.24 205	90.12 210	95.36 128	78.53 241	93.26 213	92.10 280	86.42 129	88.00 142	91.11 260	69.24 252	98.00 154	69.58 308	91.04 179	93.83 241

Vis-MVSNet		91.75 82	91.23 85	93.29 74	95.32 131	83.78 106	96.14 46	95.98 130	89.89 35	90.45 111	96.58 65	75.09 171	98.31 129	84.75 152	96.90 90	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		88.37 163	87.48 165	91.02 176	95.28 132	79.45 222	92.89 228	93.07 259	85.45 151	86.91 164	94.84 127	70.35 235	97.76 166	73.97 283	94.59 128	95.85 155

COLMAP_ROB	80.39 16	83.96 267	82.04 274	89.74 226	95.28 132	79.75 217	94.25 162	92.28 276	75.17 309	78.02 312	93.77 171	58.60 326	97.84 163	65.06 333	85.92 240	91.63 309
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PS-MVSNAJ		91.18 93	90.92 91	91.96 134	95.26 134	82.60 144	92.09 253	95.70 153	86.27 131	91.84 90	92.46 211	79.70 119	98.99 77	89.08 101	95.86 107	94.29 217

BH-untuned		88.60 159	88.13 152	90.01 216	95.24 135	78.50 243	93.29 211	94.15 235	84.75 168	84.46 224	93.40 178	75.76 162	97.40 201	77.59 251	94.52 130	94.12 223

ETV-MVS		92.74 67	92.66 67	92.97 88	95.20 136	84.04 100	95.07 105	96.51 96	90.73 21	92.96 60	91.19 254	84.06 71	98.34 125	91.72 69	96.54 99	96.54 130

GeoE		90.05 115	89.43 118	91.90 139	95.16 137	80.37 199	95.80 63	94.65 220	83.90 181	87.55 152	94.75 129	78.18 139	97.62 179	81.28 204	93.63 141	97.71 85

EIA-MVS		91.95 78	91.94 76	91.98 132	95.16 137	80.01 211	95.36 80	96.73 76	88.44 75	89.34 123	92.16 221	83.82 77	98.45 118	89.35 98	97.06 88	97.48 94

ab-mvs		89.41 136	88.35 144	92.60 104	95.15 139	82.65 142	92.20 249	95.60 162	83.97 180	88.55 132	93.70 175	74.16 186	98.21 134	82.46 183	89.37 199	96.94 116

VDDNet		89.56 129	88.49 142	92.76 96	95.07 140	82.09 151	96.30 36	93.19 257	81.05 249	91.88 88	96.86 49	61.16 312	98.33 127	88.43 109	92.49 166	97.84 80

AllTest		83.42 273	81.39 279	89.52 234	95.01 141	77.79 262	93.12 218	90.89 315	77.41 288	76.12 324	93.34 179	54.08 340	97.51 185	68.31 315	84.27 253	93.26 264

TestCases				89.52 234	95.01 141	77.79 262		90.89 315	77.41 288	76.12 324	93.34 179	54.08 340	97.51 185	68.31 315	84.27 253	93.26 264

EI-MVSNet-Vis-set		93.01 64	92.92 63	93.29 74	95.01 141	83.51 114	94.48 142	95.77 148	90.87 15	92.52 74	96.67 60	84.50 66	99.00 75	91.99 57	94.44 133	97.36 97

DROMVSNet		93.18 61	93.44 51	92.40 113	94.99 144	81.96 156	96.87 21	96.69 82	89.72 42	92.47 76	95.44 104	83.30 79	98.15 136	93.40 25	98.10 63	97.10 109

xiu_mvs_v2_base		91.13 94	90.89 93	91.86 140	94.97 145	82.42 145	92.24 247	95.64 160	86.11 137	91.74 95	93.14 190	79.67 122	98.89 87	89.06 102	95.46 114	94.28 218

tttt0517		88.61 158	87.78 159	91.11 171	94.96 146	77.81 261	95.35 81	89.69 336	85.09 162	88.05 141	94.59 137	66.93 271	98.48 113	83.27 169	92.13 169	97.03 112

baseline1		88.10 170	87.28 171	90.57 188	94.96 146	80.07 206	94.27 161	91.29 304	86.74 121	87.41 153	94.00 157	76.77 150	96.20 276	80.77 213	79.31 318	95.44 170

Test_1112_low_res		87.65 183	86.51 195	91.08 172	94.94 148	79.28 230	91.77 258	94.30 229	76.04 301	83.51 253	92.37 214	77.86 143	97.73 170	78.69 241	89.13 205	96.22 137

1112_ss		88.42 161	87.33 169	91.72 147	94.92 149	80.98 182	92.97 226	94.54 221	78.16 285	83.82 244	93.88 165	78.78 130	97.91 161	79.45 232	89.41 198	96.26 136

QAPM		89.51 130	88.15 151	93.59 72	94.92 149	84.58 80	96.82 25	96.70 80	78.43 280	83.41 255	96.19 83	73.18 202	99.30 39	77.11 257	96.54 99	96.89 119

BH-w/o		87.57 191	87.05 176	89.12 243	94.90 151	77.90 257	92.41 240	93.51 252	82.89 207	83.70 247	91.34 248	75.75 163	97.07 228	75.49 270	93.49 145	92.39 296

thisisatest0530		88.67 156	87.61 163	91.86 140	94.87 152	80.07 206	94.63 134	89.90 333	84.00 179	88.46 134	93.78 170	66.88 273	98.46 115	83.30 168	92.65 162	97.06 110

EI-MVSNet-UG-set		92.74 67	92.62 68	93.12 80	94.86 153	83.20 121	94.40 150	95.74 151	90.71 22	92.05 85	96.60 64	84.00 73	98.99 77	91.55 71	93.63 141	97.17 105

HY-MVS	83.01 12	89.03 146	87.94 157	92.29 123	94.86 153	82.77 133	92.08 254	94.49 222	81.52 239	86.93 162	92.79 205	78.32 138	98.23 131	79.93 227	90.55 181	95.88 154

hse-mvs2		89.88 123	89.34 121	91.51 155	94.83 155	81.12 179	93.94 185	93.91 244	89.80 37	93.08 58	93.60 176	75.77 160	97.66 173	92.07 54	77.07 329	95.74 161

AUN-MVS		87.78 179	86.54 194	91.48 157	94.82 156	81.05 180	93.91 189	93.93 241	83.00 203	86.93 162	93.53 177	69.50 246	97.67 172	86.14 136	77.12 328	95.73 162

Fast-Effi-MVS+		89.41 136	88.64 137	91.71 148	94.74 157	80.81 188	93.54 200	95.10 195	83.11 200	86.82 167	90.67 272	79.74 118	97.75 169	80.51 220	93.55 143	96.57 128

ACMP	84.23 8	89.01 148	88.35 144	90.99 179	94.73 158	81.27 172	95.07 105	95.89 140	86.48 126	83.67 248	94.30 145	69.33 248	97.99 155	87.10 130	88.55 210	93.72 250
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet	78.82 18	85.55 244	84.65 247	88.23 268	94.72 159	71.93 321	87.12 326	92.75 266	78.80 274	84.95 215	90.53 274	64.43 293	96.71 246	74.74 278	93.86 138	96.06 148

LCM-MVSNet-Re		88.30 166	88.32 147	88.27 265	94.71 160	72.41 320	93.15 217	90.98 311	87.77 96	79.25 306	91.96 233	78.35 137	95.75 296	83.04 171	95.62 109	96.65 125

HQP_MVS		90.60 107	90.19 101	91.82 143	94.70 161	82.73 137	95.85 61	96.22 114	90.81 17	86.91 164	94.86 124	74.23 182	98.12 137	88.15 111	89.99 187	94.63 198

plane_prior7					94.70 161	82.74 136

ACMH+	81.04 14	85.05 255	83.46 263	89.82 222	94.66 163	79.37 224	94.44 147	94.12 238	82.19 218	78.04 311	92.82 202	58.23 327	97.54 183	73.77 285	82.90 270	92.54 290

ACMM	84.12 9	89.14 142	88.48 143	91.12 168	94.65 164	81.22 175	95.31 83	96.12 121	85.31 155	85.92 182	94.34 142	70.19 238	98.06 148	85.65 141	88.86 208	94.08 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
plane_prior1					94.59 165

3Dnovator	86.66 5	91.73 83	90.82 94	94.44 48	94.59 165	86.37 43	97.18 7	97.02 46	89.20 55	84.31 234	96.66 61	73.74 194	99.17 50	86.74 131	97.96 70	97.79 83

plane_prior6					94.52 167	82.75 134						74.23 182

UGNet		89.95 119	88.95 131	92.95 89	94.51 168	83.31 119	95.70 68	95.23 188	89.37 51	87.58 150	93.94 160	64.00 294	98.78 99	83.92 161	96.31 104	96.74 123
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
LPG-MVS_test		89.45 133	88.90 133	91.12 168	94.47 169	81.49 166	95.30 86	96.14 119	86.73 122	85.45 199	95.16 114	69.89 240	98.10 139	87.70 117	89.23 203	93.77 246

LGP-MVS_train				91.12 168	94.47 169	81.49 166		96.14 119	86.73 122	85.45 199	95.16 114	69.89 240	98.10 139	87.70 117	89.23 203	93.77 246

baseline		92.39 74	92.29 74	92.69 102	94.46 171	81.77 159	94.14 167	96.27 108	89.22 54	91.88 88	96.00 88	82.35 89	97.99 155	91.05 79	95.27 120	98.30 41

ACMH	80.38 17	85.36 247	83.68 259	90.39 198	94.45 172	80.63 192	94.73 128	94.85 210	82.09 219	77.24 316	92.65 207	60.01 319	97.58 180	72.25 292	84.87 248	92.96 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	82.13 13	86.26 234	84.90 242	90.34 203	94.44 173	81.50 164	92.31 246	94.89 207	83.03 202	79.63 303	92.67 206	69.69 243	97.79 164	71.20 295	86.26 239	91.72 307
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
casdiffmvs		92.51 71	92.43 71	92.74 98	94.41 174	81.98 154	94.54 140	96.23 113	89.57 45	91.96 87	96.17 84	82.58 86	98.01 153	90.95 83	95.45 115	98.23 51

MVS_Test		91.31 90	91.11 87	91.93 136	94.37 175	80.14 203	93.46 204	95.80 146	86.46 127	91.35 102	93.77 171	82.21 93	98.09 145	87.57 119	94.95 122	97.55 93

NP-MVS					94.37 175	82.42 145					93.98 158

TR-MVS		86.78 219	85.76 224	89.82 222	94.37 175	78.41 245	92.47 239	92.83 263	81.11 248	86.36 175	92.40 213	68.73 259	97.48 187	73.75 286	89.85 193	93.57 254

Effi-MVS+		91.59 86	91.11 87	93.01 86	94.35 178	83.39 118	94.60 135	95.10 195	87.10 112	90.57 110	93.10 192	81.43 103	98.07 147	89.29 99	94.48 131	97.59 90

CLD-MVS		89.47 132	88.90 133	91.18 167	94.22 179	82.07 152	92.13 251	96.09 122	87.90 92	85.37 208	92.45 212	74.38 180	97.56 182	87.15 126	90.43 182	93.93 233
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		92.55 70	92.87 65	91.58 152	94.21 180	80.54 195	95.30 86	96.68 84	88.18 87	92.09 84	94.57 139	84.06 71	98.05 150	92.56 39	98.19 60	96.15 138

HQP-NCC					94.17 181		94.39 152		88.81 64	85.43 202

ACMP_Plane					94.17 181		94.39 152		88.81 64	85.43 202

HQP-MVS		89.80 124	89.28 124	91.34 162	94.17 181	81.56 162	94.39 152	96.04 128	88.81 64	85.43 202	93.97 159	73.83 192	97.96 157	87.11 128	89.77 194	94.50 209

CS-MVS-test		92.16 76	92.35 72	91.57 153	94.15 184	81.18 177	95.09 104	96.62 89	87.64 103	90.92 107	93.10 192	83.86 76	98.06 148	91.82 67	97.98 68	95.49 167

XVG-OURS		89.40 138	88.70 136	91.52 154	94.06 185	81.46 168	91.27 269	96.07 124	86.14 135	88.89 130	95.77 97	68.73 259	97.26 213	87.39 122	89.96 189	95.83 157

sss		88.93 150	88.26 150	90.94 182	94.05 186	80.78 189	91.71 261	95.38 181	81.55 238	88.63 131	93.91 164	75.04 172	95.47 307	82.47 182	91.61 171	96.57 128

PCF-MVS	84.11 10	87.74 180	86.08 211	92.70 101	94.02 187	84.43 92	89.27 301	95.87 141	73.62 323	84.43 226	94.33 143	78.48 136	98.86 91	70.27 300	94.45 132	94.81 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 201	85.98 214	91.08 172	94.01 188	83.10 123	95.14 101	94.94 201	83.57 188	84.37 227	91.64 240	66.59 278	96.34 272	78.23 245	85.36 244	93.79 242

test1		87.26 201	85.98 214	91.08 172	94.01 188	83.10 123	95.14 101	94.94 201	83.57 188	84.37 227	91.64 240	66.59 278	96.34 272	78.23 245	85.36 244	93.79 242

FMVSNet2		87.19 208	85.82 220	91.30 163	94.01 188	83.67 109	94.79 124	94.94 201	83.57 188	83.88 242	92.05 231	66.59 278	96.51 260	77.56 252	85.01 247	93.73 249

XVG-OURS-SEG-HR		89.95 119	89.45 116	91.47 158	94.00 191	81.21 176	91.87 256	96.06 126	85.78 140	88.55 132	95.73 98	74.67 178	97.27 211	88.71 106	89.64 196	95.91 152

FIs		90.51 108	90.35 98	90.99 179	93.99 192	80.98 182	95.73 66	97.54 3	89.15 57	86.72 168	94.68 132	81.83 101	97.24 215	85.18 145	88.31 218	94.76 195

xiu_mvs_v1_base_debu		90.64 104	90.05 106	92.40 113	93.97 193	84.46 88	93.32 206	95.46 172	85.17 157	92.25 78	94.03 152	70.59 230	98.57 109	90.97 80	94.67 124	94.18 219

xiu_mvs_v1_base		90.64 104	90.05 106	92.40 113	93.97 193	84.46 88	93.32 206	95.46 172	85.17 157	92.25 78	94.03 152	70.59 230	98.57 109	90.97 80	94.67 124	94.18 219

xiu_mvs_v1_base_debi		90.64 104	90.05 106	92.40 113	93.97 193	84.46 88	93.32 206	95.46 172	85.17 157	92.25 78	94.03 152	70.59 230	98.57 109	90.97 80	94.67 124	94.18 219

VPA-MVSNet		89.62 126	88.96 130	91.60 151	93.86 196	82.89 132	95.46 78	97.33 22	87.91 91	88.43 135	93.31 182	74.17 185	97.40 201	87.32 124	82.86 271	94.52 207

MVSFormer		91.68 85	91.30 83	92.80 94	93.86 196	83.88 103	95.96 57	95.90 138	84.66 170	91.76 93	94.91 121	77.92 141	97.30 207	89.64 95	97.11 86	97.24 101

lupinMVS		90.92 96	90.21 100	93.03 85	93.86 196	83.88 103	92.81 230	93.86 245	79.84 260	91.76 93	94.29 146	77.92 141	98.04 151	90.48 90	97.11 86	97.17 105

IterMVS-LS		88.36 164	87.91 158	89.70 229	93.80 199	78.29 249	93.73 193	95.08 197	85.73 142	84.75 217	91.90 235	79.88 115	96.92 238	83.83 162	82.51 272	93.89 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 259	83.09 266	90.14 209	93.80 199	80.05 208	89.18 304	93.09 258	78.89 271	78.19 309	91.91 234	65.86 287	97.27 211	68.47 313	88.45 214	93.11 274

FMVSNet3		87.40 198	86.11 209	91.30 163	93.79 201	83.64 110	94.20 165	94.81 214	83.89 182	84.37 227	91.87 236	68.45 262	96.56 257	78.23 245	85.36 244	93.70 251

FC-MVSNet-test		90.27 111	90.18 102	90.53 190	93.71 202	79.85 216	95.77 65	97.59 2	89.31 52	86.27 177	94.67 133	81.93 100	97.01 233	84.26 157	88.09 222	94.71 196

TAMVS		89.21 141	88.29 148	91.96 134	93.71 202	82.62 143	93.30 210	94.19 233	82.22 217	87.78 147	93.94 160	78.83 128	96.95 236	77.70 250	92.98 158	96.32 133

ET-MVSNet_ETH3D		87.51 193	85.91 218	92.32 120	93.70 204	83.93 101	92.33 244	90.94 313	84.16 175	72.09 342	92.52 210	69.90 239	95.85 291	89.20 100	88.36 217	97.17 105

CDS-MVSNet		89.45 133	88.51 139	92.29 123	93.62 205	83.61 112	93.01 224	94.68 219	81.95 225	87.82 146	93.24 186	78.69 131	96.99 234	80.34 222	93.23 153	96.28 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UniMVSNet (Re)		89.80 124	89.07 128	92.01 129	93.60 206	84.52 84	94.78 125	97.47 8	89.26 53	86.44 174	92.32 216	82.10 95	97.39 204	84.81 151	80.84 299	94.12 223

VPNet		88.20 168	87.47 166	90.39 198	93.56 207	79.46 221	94.04 178	95.54 166	88.67 69	86.96 161	94.58 138	69.33 248	97.15 220	84.05 160	80.53 305	94.56 205

thisisatest0515		87.33 199	85.99 213	91.37 161	93.49 208	79.55 219	90.63 279	89.56 339	80.17 255	87.56 151	90.86 265	67.07 270	98.28 130	81.50 202	93.02 157	96.29 134

mvs_anonymous		89.37 139	89.32 122	89.51 236	93.47 209	74.22 300	91.65 264	94.83 212	82.91 206	85.45 199	93.79 169	81.23 105	96.36 271	86.47 135	94.09 135	97.94 72

CANet_DTU		90.26 112	89.41 119	92.81 93	93.46 210	83.01 128	93.48 202	94.47 223	89.43 49	87.76 148	94.23 150	70.54 234	99.03 64	84.97 147	96.39 103	96.38 132

UniMVSNet_NR-MVSNet		89.92 121	89.29 123	91.81 145	93.39 211	83.72 107	94.43 148	97.12 40	89.80 37	86.46 171	93.32 181	83.16 80	97.23 216	84.92 148	81.02 295	94.49 211

Effi-MVS+-dtu		88.65 157	88.35 144	89.54 233	93.33 212	76.39 284	94.47 145	94.36 226	87.70 98	85.43 202	89.56 294	73.45 197	97.26 213	85.57 143	91.28 173	94.97 182

mvs-test1		89.45 133	89.14 126	90.38 200	93.33 212	77.63 267	94.95 112	94.36 226	87.70 98	87.10 160	92.81 203	73.45 197	98.03 152	85.57 143	93.04 156	95.48 168

WR-MVS		88.38 162	87.67 162	90.52 192	93.30 214	80.18 201	93.26 213	95.96 132	88.57 73	85.47 198	92.81 203	76.12 155	96.91 239	81.24 205	82.29 274	94.47 214

WR-MVS_H		87.80 178	87.37 168	89.10 244	93.23 215	78.12 252	95.61 74	97.30 26	87.90 92	83.72 246	92.01 232	79.65 123	96.01 284	76.36 262	80.54 303	93.16 272

test_0402		81.30 295	79.17 303	87.67 278	93.19 216	78.17 251	92.98 225	91.71 291	75.25 308	76.02 326	90.31 278	59.23 323	96.37 269	50.22 355	83.63 260	88.47 345

OPM-MVS		90.12 113	89.56 114	91.82 143	93.14 217	83.90 102	94.16 166	95.74 151	88.96 63	87.86 143	95.43 107	72.48 210	97.91 161	88.10 114	90.18 186	93.65 252

CP-MVSNet		87.63 186	87.26 173	88.74 254	93.12 218	76.59 281	95.29 89	96.58 93	88.43 76	83.49 254	92.98 196	75.28 169	95.83 292	78.97 238	81.15 291	93.79 242

diffmvs		91.37 89	91.23 85	91.77 146	93.09 219	80.27 200	92.36 243	95.52 168	87.03 114	91.40 101	94.93 120	80.08 113	97.44 192	92.13 53	94.56 129	97.61 88

nrg030		91.08 95	90.39 97	93.17 79	93.07 220	86.91 20	96.41 33	96.26 109	88.30 80	88.37 136	94.85 126	82.19 94	97.64 177	91.09 78	82.95 266	94.96 185

PAPM		86.68 223	85.39 231	90.53 190	93.05 221	79.33 229	89.79 294	94.77 217	78.82 273	81.95 273	93.24 186	76.81 148	97.30 207	66.94 323	93.16 154	94.95 188

DU-MVS		89.34 140	88.50 140	91.85 142	93.04 222	83.72 107	94.47 145	96.59 92	89.50 46	86.46 171	93.29 184	77.25 145	97.23 216	84.92 148	81.02 295	94.59 202

NR-MVSNet		88.58 160	87.47 166	91.93 136	93.04 222	84.16 97	94.77 126	96.25 111	89.05 59	80.04 298	93.29 184	79.02 127	97.05 230	81.71 200	80.05 310	94.59 202

jason		90.80 97	90.10 104	92.90 91	93.04 222	83.53 113	93.08 221	94.15 235	80.22 254	91.41 100	94.91 121	76.87 147	97.93 160	90.28 91	96.90 90	97.24 101
jason: jason.
RRT_test8_iter05		86.90 214	86.36 199	88.52 259	93.00 225	73.27 308	94.32 159	95.96 132	85.50 150	84.26 235	92.86 198	60.76 314	97.70 171	88.32 110	82.29 274	94.60 201

PS-CasMVS		87.32 200	86.88 178	88.63 257	92.99 226	76.33 286	95.33 82	96.61 91	88.22 84	83.30 259	93.07 194	73.03 204	95.79 295	78.36 243	81.00 297	93.75 248

MVSTER		88.84 152	88.29 148	90.51 193	92.95 227	80.44 198	93.73 193	95.01 198	84.66 170	87.15 157	93.12 191	72.79 206	97.21 218	87.86 115	87.36 231	93.87 237

RPSCF		85.07 254	84.27 251	87.48 284	92.91 228	70.62 333	91.69 263	92.46 271	76.20 300	82.67 265	95.22 112	63.94 295	97.29 210	77.51 253	85.80 242	94.53 206

FMVSNet1		85.85 240	84.11 253	91.08 172	92.81 229	83.10 123	95.14 101	94.94 201	81.64 235	82.68 264	91.64 240	59.01 325	96.34 272	75.37 272	83.78 256	93.79 242

tfpnnormal		84.72 261	83.23 265	89.20 241	92.79 230	80.05 208	94.48 142	95.81 145	82.38 214	81.08 282	91.21 253	69.01 255	96.95 236	61.69 342	80.59 302	90.58 329

OpenMVS	83.78 11	88.74 155	87.29 170	93.08 82	92.70 231	85.39 71	96.57 30	96.43 100	78.74 276	80.85 284	96.07 87	69.64 244	99.01 70	78.01 248	96.65 96	94.83 192

TranMVSNet+NR-MVSNet		88.84 152	87.95 156	91.49 156	92.68 232	83.01 128	94.92 115	96.31 106	89.88 36	85.53 192	93.85 167	76.63 153	96.96 235	81.91 193	79.87 313	94.50 209

MVS		87.44 196	86.10 210	91.44 159	92.61 233	83.62 111	92.63 234	95.66 157	67.26 346	81.47 276	92.15 222	77.95 140	98.22 133	79.71 229	95.48 112	92.47 293

CHOSEN 280x420		85.15 253	83.99 255	88.65 256	92.47 234	78.40 246	79.68 353	92.76 265	74.90 313	81.41 278	89.59 292	69.85 242	95.51 303	79.92 228	95.29 118	92.03 303

UniMVSNet_ETH3D		87.53 192	86.37 198	91.00 178	92.44 235	78.96 235	94.74 127	95.61 161	84.07 178	85.36 209	94.52 140	59.78 321	97.34 206	82.93 173	87.88 225	96.71 124

1314		87.51 193	86.57 193	90.34 203	92.42 236	79.74 218	92.63 234	95.35 185	78.35 281	80.14 295	91.62 244	74.05 187	97.15 220	81.05 206	93.53 144	94.12 223

cl-mvsnet2		86.78 219	85.98 214	89.18 242	92.34 237	77.62 268	90.84 276	94.13 237	81.33 242	83.97 241	90.15 281	73.96 189	96.60 254	84.19 158	82.94 267	93.33 262

PEN-MVS		86.80 218	86.27 204	88.40 261	92.32 238	75.71 291	95.18 98	96.38 104	87.97 89	82.82 263	93.15 189	73.39 200	95.92 287	76.15 266	79.03 320	93.59 253

cl_fuxian		87.14 210	86.50 196	89.04 246	92.20 239	77.26 273	91.22 271	94.70 218	82.01 223	84.34 231	90.43 276	78.81 129	96.61 252	83.70 165	81.09 292	93.25 266

SCA		86.32 233	85.18 235	89.73 228	92.15 240	76.60 280	91.12 272	91.69 293	83.53 191	85.50 195	88.81 301	66.79 274	96.48 262	76.65 260	90.35 184	96.12 142

XXY-MVS		87.65 183	86.85 180	90.03 214	92.14 241	80.60 194	93.76 192	95.23 188	82.94 205	84.60 219	94.02 155	74.27 181	95.49 306	81.04 207	83.68 259	94.01 231

miper_ehance_all_eth		87.22 206	86.62 191	89.02 247	92.13 242	77.40 272	90.91 275	94.81 214	81.28 243	84.32 232	90.08 283	79.26 125	96.62 249	83.81 163	82.94 267	93.04 277

IB-MVS	80.51 15	85.24 252	83.26 264	91.19 166	92.13 242	79.86 215	91.75 259	91.29 304	83.28 198	80.66 287	88.49 307	61.28 308	98.46 115	80.99 210	79.46 316	95.25 176
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
cascas		86.43 232	84.98 239	90.80 184	92.10 244	80.92 185	90.24 285	95.91 137	73.10 327	83.57 252	88.39 308	65.15 289	97.46 189	84.90 150	91.43 172	94.03 230

Fast-Effi-MVS+-dtu		87.44 196	86.72 184	89.63 231	92.04 245	77.68 266	94.03 179	93.94 240	85.81 139	82.42 266	91.32 251	70.33 236	97.06 229	80.33 223	90.23 185	94.14 222

cl-mvsnet____		86.52 228	85.78 221	88.75 252	92.03 246	76.46 282	90.74 277	94.30 229	81.83 232	83.34 257	90.78 269	75.74 165	96.57 255	81.74 198	81.54 286	93.22 269

cl-mvsnet1		86.53 227	85.78 221	88.75 252	92.02 247	76.45 283	90.74 277	94.30 229	81.83 232	83.34 257	90.82 267	75.75 163	96.57 255	81.73 199	81.52 287	93.24 267

RRT_MVS		88.86 151	87.68 161	92.39 117	92.02 247	86.09 53	94.38 156	94.94 201	85.45 151	87.14 159	93.84 168	65.88 286	97.11 224	88.73 105	86.77 238	93.98 232

eth_miper_zixun_eth		86.50 229	85.77 223	88.68 255	91.94 249	75.81 290	90.47 281	94.89 207	82.05 220	84.05 238	90.46 275	75.96 158	96.77 243	82.76 179	79.36 317	93.46 260

PS-MVSNAJss		89.97 118	89.62 113	91.02 176	91.90 250	80.85 187	95.26 92	95.98 130	86.26 132	86.21 178	94.29 146	79.70 119	97.65 175	88.87 104	88.10 220	94.57 204

ITE_SJBPF				88.24 267	91.88 251	77.05 276		92.92 261	85.54 148	80.13 296	93.30 183	57.29 329	96.20 276	72.46 291	84.71 249	91.49 311

EI-MVSNet		89.10 143	88.86 135	89.80 225	91.84 252	78.30 248	93.70 196	95.01 198	85.73 142	87.15 157	95.28 109	79.87 116	97.21 218	83.81 163	87.36 231	93.88 236

CVMVSNet		84.69 262	84.79 245	84.37 321	91.84 252	64.92 352	93.70 196	91.47 300	66.19 348	86.16 180	95.28 109	67.18 268	93.33 333	80.89 212	90.42 183	94.88 190

MVS-HIRNet		73.70 320	72.20 323	78.18 335	91.81 254	56.42 359	82.94 348	82.58 354	55.24 354	68.88 347	66.48 356	55.32 335	95.13 311	58.12 349	88.42 215	83.01 350

PatchmatchNet		85.85 240	84.70 246	89.29 239	91.76 255	75.54 292	88.49 313	91.30 303	81.63 236	85.05 213	88.70 305	71.71 214	96.24 275	74.61 280	89.05 206	96.08 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		84.43 264	83.06 267	88.54 258	91.72 256	78.44 244	95.18 98	92.82 264	82.73 209	79.67 302	92.12 224	73.49 196	95.96 286	71.10 299	68.73 346	91.21 318

IterMVS-SCA-FT		85.45 245	84.53 250	88.18 269	91.71 257	76.87 278	90.19 288	92.65 269	85.40 153	81.44 277	90.54 273	66.79 274	95.00 315	81.04 207	81.05 293	92.66 288

TinyColmap		79.76 307	77.69 309	85.97 307	91.71 257	73.12 309	89.55 295	90.36 323	75.03 310	72.03 343	90.19 279	46.22 355	96.19 278	63.11 338	81.03 294	88.59 344

MDTV_nov1_ep13			83.56 262		91.69 259	69.93 337	87.75 321	91.54 297	78.60 278	84.86 216	88.90 300	69.54 245	96.03 282	70.25 301	88.93 207

miper_enhance_ethall		86.90 214	86.18 206	89.06 245	91.66 260	77.58 269	90.22 287	94.82 213	79.16 268	84.48 223	89.10 297	79.19 126	96.66 247	84.06 159	82.94 267	92.94 280

DTE-MVSNet		86.11 235	85.48 229	87.98 273	91.65 261	74.92 294	94.93 114	95.75 150	87.36 108	82.26 268	93.04 195	72.85 205	95.82 293	74.04 282	77.46 326	93.20 270

MIMVSNet		82.59 279	80.53 284	88.76 251	91.51 262	78.32 247	86.57 329	90.13 326	79.32 264	80.70 286	88.69 306	52.98 344	93.07 337	66.03 328	88.86 208	94.90 189

pm-mvs1		86.61 224	85.54 227	89.82 222	91.44 263	80.18 201	95.28 91	94.85 210	83.84 183	81.66 275	92.62 208	72.45 212	96.48 262	79.67 230	78.06 321	92.82 285

Baseline_NR-MVSNet		87.07 211	86.63 190	88.40 261	91.44 263	77.87 259	94.23 164	92.57 270	84.12 177	85.74 185	92.08 228	77.25 145	96.04 281	82.29 186	79.94 311	91.30 315

IterMVS		84.88 258	83.98 256	87.60 279	91.44 263	76.03 288	90.18 289	92.41 272	83.24 199	81.06 283	90.42 277	66.60 277	94.28 322	79.46 231	80.98 298	92.48 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DWT-MVSNet_test		84.95 257	83.68 259	88.77 250	91.43 266	73.75 304	91.74 260	90.98 311	80.66 252	83.84 243	87.36 323	62.44 300	97.11 224	78.84 240	85.81 241	95.46 169

MS-PatchMatch		85.05 255	84.16 252	87.73 277	91.42 267	78.51 242	91.25 270	93.53 251	77.50 287	80.15 294	91.58 245	61.99 303	95.51 303	75.69 269	94.35 134	89.16 339

tpm2		84.08 266	82.94 268	87.48 284	91.39 268	71.27 325	89.23 303	90.37 322	71.95 335	84.64 218	89.33 295	67.30 265	96.55 259	75.17 274	87.09 235	94.63 198

v8		87.50 195	86.71 185	89.89 219	91.37 269	79.40 223	94.50 141	95.38 181	84.81 167	83.60 251	91.33 249	76.05 156	97.42 194	82.84 176	80.51 307	92.84 284

ADS-MVSNet2		81.66 288	79.71 296	87.50 282	91.35 270	74.19 301	83.33 345	88.48 342	72.90 329	82.24 269	85.77 335	64.98 290	93.20 335	64.57 334	83.74 257	95.12 178

ADS-MVSNet		81.56 290	79.78 294	86.90 298	91.35 270	71.82 322	83.33 345	89.16 340	72.90 329	82.24 269	85.77 335	64.98 290	93.76 328	64.57 334	83.74 257	95.12 178

GA-MVS		86.61 224	85.27 234	90.66 185	91.33 272	78.71 237	90.40 282	93.81 248	85.34 154	85.12 212	89.57 293	61.25 309	97.11 224	80.99 210	89.59 197	96.15 138

miper_lstm_enhance		85.27 251	84.59 249	87.31 286	91.28 273	74.63 295	87.69 322	94.09 239	81.20 247	81.36 279	89.85 289	74.97 174	94.30 321	81.03 209	79.84 314	93.01 278

XVG-ACMP-BASELINE		86.00 236	84.84 244	89.45 237	91.20 274	78.00 254	91.70 262	95.55 164	85.05 163	82.97 261	92.25 220	54.49 338	97.48 187	82.93 173	87.45 230	92.89 282

v10		87.25 203	86.38 197	89.85 220	91.19 275	79.50 220	94.48 142	95.45 175	83.79 184	83.62 250	91.19 254	75.13 170	97.42 194	81.94 192	80.60 301	92.63 289

FMVSNet5		81.52 291	79.60 297	87.27 287	91.17 276	77.95 255	91.49 266	92.26 277	76.87 293	76.16 323	87.91 317	51.67 345	92.34 341	67.74 319	81.16 289	91.52 310

USDC		82.76 276	81.26 281	87.26 288	91.17 276	74.55 296	89.27 301	93.39 254	78.26 283	75.30 329	92.08 228	54.43 339	96.63 248	71.64 293	85.79 243	90.61 326

CostFormer		85.77 242	84.94 241	88.26 266	91.16 278	72.58 318	89.47 299	91.04 310	76.26 299	86.45 173	89.97 286	70.74 228	96.86 242	82.35 184	87.07 236	95.34 175

baseline2		86.50 229	85.39 231	89.84 221	91.12 279	76.70 279	91.88 255	88.58 341	82.35 216	79.95 299	90.95 264	73.42 199	97.63 178	80.27 224	89.95 190	95.19 177

tpm cat1		81.96 282	80.27 288	87.01 295	91.09 280	71.02 329	87.38 325	91.53 298	66.25 347	80.17 293	86.35 331	68.22 264	96.15 279	69.16 309	82.29 274	93.86 239

tpmvs		83.35 275	82.07 273	87.20 293	91.07 281	71.00 330	88.31 316	91.70 292	78.91 270	80.49 290	87.18 327	69.30 251	97.08 227	68.12 318	83.56 261	93.51 258

v1144		87.61 189	86.79 183	90.06 213	91.01 282	79.34 226	93.95 184	95.42 180	83.36 196	85.66 188	91.31 252	74.98 173	97.42 194	83.37 167	82.06 277	93.42 261

v2v482		87.84 176	87.06 175	90.17 206	90.99 283	79.23 233	94.00 182	95.13 192	84.87 165	85.53 192	92.07 230	74.45 179	97.45 190	84.71 153	81.75 283	93.85 240

SixPastTwentyTwo		83.91 269	82.90 269	86.92 297	90.99 283	70.67 332	93.48 202	91.99 285	85.54 148	77.62 315	92.11 226	60.59 315	96.87 241	76.05 267	77.75 323	93.20 270

test-LLR		85.87 239	85.41 230	87.25 289	90.95 285	71.67 323	89.55 295	89.88 334	83.41 194	84.54 221	87.95 315	67.25 266	95.11 312	81.82 195	93.37 150	94.97 182

test-mter		84.54 263	83.64 261	87.25 289	90.95 285	71.67 323	89.55 295	89.88 334	79.17 267	84.54 221	87.95 315	55.56 333	95.11 312	81.82 195	93.37 150	94.97 182

v148		87.04 212	86.32 202	89.21 240	90.94 287	77.26 273	93.71 195	94.43 224	84.84 166	84.36 230	90.80 268	76.04 157	97.05 230	82.12 188	79.60 315	93.31 263

mvs_tets		88.06 173	87.28 171	90.38 200	90.94 287	79.88 214	95.22 94	95.66 157	85.10 161	84.21 237	93.94 160	63.53 296	97.40 201	88.50 108	88.40 216	93.87 237

MVP-Stereo		85.97 237	84.86 243	89.32 238	90.92 289	82.19 150	92.11 252	94.19 233	78.76 275	78.77 308	91.63 243	68.38 263	96.56 257	75.01 277	93.95 136	89.20 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		81.37 293	79.30 299	87.58 280	90.92 289	74.16 302	80.99 351	87.68 346	70.52 341	76.63 321	88.81 301	71.21 220	92.76 339	60.01 348	86.93 237	95.83 157

jajsoiax		88.24 167	87.50 164	90.48 195	90.89 291	80.14 203	95.31 83	95.65 159	84.97 164	84.24 236	94.02 155	65.31 288	97.42 194	88.56 107	88.52 212	93.89 234

tpmrst		85.35 248	84.99 238	86.43 303	90.88 292	67.88 344	88.71 310	91.43 301	80.13 256	86.08 181	88.80 303	73.05 203	96.02 283	82.48 181	83.40 265	95.40 172

gg-mvs-nofinetune		81.77 285	79.37 298	88.99 248	90.85 293	77.73 265	86.29 330	79.63 360	74.88 314	83.19 260	69.05 355	60.34 316	96.11 280	75.46 271	94.64 127	93.11 274

D2MVS		85.90 238	85.09 237	88.35 263	90.79 294	77.42 271	91.83 257	95.70 153	80.77 251	80.08 297	90.02 284	66.74 276	96.37 269	81.88 194	87.97 224	91.26 316

OurMVSNet-221017-0		85.35 248	84.64 248	87.49 283	90.77 295	72.59 317	94.01 181	94.40 225	84.72 169	79.62 304	93.17 188	61.91 304	96.72 244	81.99 191	81.16 289	93.16 272

v1192		87.25 203	86.33 201	90.00 217	90.76 296	79.04 234	93.80 190	95.48 170	82.57 212	85.48 197	91.18 256	73.38 201	97.42 194	82.30 185	82.06 277	93.53 255

test_djsdf		89.03 146	88.64 137	90.21 205	90.74 297	79.28 230	95.96 57	95.90 138	84.66 170	85.33 210	92.94 197	74.02 188	97.30 207	89.64 95	88.53 211	94.05 229

v7n		86.81 217	85.76 224	89.95 218	90.72 298	79.25 232	95.07 105	95.92 135	84.45 173	82.29 267	90.86 265	72.60 209	97.53 184	79.42 235	80.52 306	93.08 276

PVSNet_0	73.20 20	77.22 316	74.83 321	84.37 321	90.70 299	71.10 328	83.09 347	89.67 337	72.81 331	73.93 336	83.13 343	60.79 313	93.70 329	68.54 312	50.84 357	88.30 346

v144192		87.19 208	86.35 200	89.74 226	90.64 300	78.24 250	93.92 186	95.43 178	81.93 226	85.51 194	91.05 262	74.21 184	97.45 190	82.86 175	81.56 285	93.53 255

MVS_0304		83.46 272	81.92 275	88.10 271	90.63 301	77.49 270	93.26 213	93.75 249	80.04 258	80.44 291	87.24 326	47.94 352	95.55 300	75.79 268	88.16 219	91.26 316

V42		87.68 181	86.86 179	90.15 208	90.58 302	80.14 203	94.24 163	95.28 186	83.66 186	85.67 187	91.33 249	74.73 177	97.41 199	84.43 156	81.83 281	92.89 282

CR-MVSNet		85.35 248	83.76 258	90.12 210	90.58 302	79.34 226	85.24 336	91.96 288	78.27 282	85.55 190	87.87 318	71.03 223	95.61 298	73.96 284	89.36 200	95.40 172

RPMNet		83.95 268	81.53 278	91.21 165	90.58 302	79.34 226	85.24 336	96.76 72	71.44 337	85.55 190	82.97 344	70.87 226	98.91 86	61.01 344	89.36 200	95.40 172

v1921920		86.97 213	86.06 212	89.69 230	90.53 305	78.11 253	93.80 190	95.43 178	81.90 228	85.33 210	91.05 262	72.66 207	97.41 199	82.05 190	81.80 282	93.53 255

v1240		86.78 219	85.85 219	89.56 232	90.45 306	77.79 262	93.61 198	95.37 183	81.65 234	85.43 202	91.15 258	71.50 218	97.43 193	81.47 203	82.05 279	93.47 259

tpm		84.73 260	84.02 254	86.87 300	90.33 307	68.90 340	89.06 305	89.94 331	80.85 250	85.75 184	89.86 288	68.54 261	95.97 285	77.76 249	84.05 255	95.75 160

EG-PatchMatch MVS		82.37 281	80.34 287	88.46 260	90.27 308	79.35 225	92.80 231	94.33 228	77.14 292	73.26 339	90.18 280	47.47 354	96.72 244	70.25 301	87.32 233	89.30 336

EPNet_dtu		86.49 231	85.94 217	88.14 270	90.24 309	72.82 312	94.11 170	92.20 278	86.66 125	79.42 305	92.36 215	73.52 195	95.81 294	71.26 294	93.66 140	95.80 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		83.90 270	82.70 272	87.51 281	90.23 310	72.67 314	88.62 312	81.96 356	81.37 241	85.01 214	88.34 309	66.31 281	94.45 317	75.30 273	87.12 234	95.43 171

EPNet		91.79 80	91.02 90	94.10 58	90.10 311	85.25 73	96.03 53	92.05 282	92.83 1	87.39 156	95.78 96	79.39 124	99.01 70	88.13 113	97.48 82	98.05 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		82.68 278	81.27 280	86.89 299	90.09 312	70.94 331	84.06 342	90.15 325	74.91 312	85.63 189	83.57 341	69.37 247	94.87 316	65.19 330	88.50 213	94.84 191

Patchmtry		82.71 277	80.93 283	88.06 272	90.05 313	76.37 285	84.74 340	91.96 288	72.28 334	81.32 280	87.87 318	71.03 223	95.50 305	68.97 310	80.15 309	92.32 299

pmmvs4		85.43 246	83.86 257	90.16 207	90.02 314	82.97 130	90.27 283	92.67 268	75.93 302	80.73 285	91.74 239	71.05 222	95.73 297	78.85 239	83.46 263	91.78 306

TESTMET0.1,1		83.74 271	82.85 270	86.42 304	89.96 315	71.21 327	89.55 295	87.88 343	77.41 288	83.37 256	87.31 324	56.71 330	93.65 330	80.62 217	92.85 161	94.40 215

dp		81.47 292	80.23 289	85.17 316	89.92 316	65.49 350	86.74 327	90.10 327	76.30 298	81.10 281	87.12 328	62.81 298	95.92 287	68.13 317	79.88 312	94.09 226

K. test v3		81.59 289	80.15 291	85.91 310	89.89 317	69.42 339	92.57 237	87.71 345	85.56 147	73.44 338	89.71 291	55.58 332	95.52 302	77.17 256	69.76 340	92.78 286

MDA-MVSNet-bldmvs		78.85 312	76.31 315	86.46 302	89.76 318	73.88 303	88.79 309	90.42 320	79.16 268	59.18 354	88.33 310	60.20 317	94.04 324	62.00 341	68.96 344	91.48 312

GG-mvs-BLEND				87.94 275	89.73 319	77.91 256	87.80 319	78.23 362		80.58 288	83.86 339	59.88 320	95.33 309	71.20 295	92.22 168	90.60 328

gm-plane-assit					89.60 320	68.00 342			77.28 291		88.99 298		97.57 181	79.44 233

anonymousdsp		87.84 176	87.09 174	90.12 210	89.13 321	80.54 195	94.67 132	95.55 164	82.05 220	83.82 244	92.12 224	71.47 219	97.15 220	87.15 126	87.80 227	92.67 287

N_pmnet		68.89 323	68.44 326	70.23 339	89.07 322	28.79 369	88.06 317	19.50 370	69.47 343	71.86 344	84.93 337	61.24 310	91.75 346	54.70 352	77.15 327	90.15 330

pmmvs5		84.21 265	82.84 271	88.34 264	88.95 323	76.94 277	92.41 240	91.91 290	75.63 304	80.28 292	91.18 256	64.59 292	95.57 299	77.09 258	83.47 262	92.53 291

PMMVS		85.71 243	84.96 240	87.95 274	88.90 324	77.09 275	88.68 311	90.06 328	72.32 333	86.47 170	90.76 270	72.15 213	94.40 318	81.78 197	93.49 145	92.36 297

JIA-IIPM		81.04 296	78.98 306	87.25 289	88.64 325	73.48 306	81.75 350	89.61 338	73.19 326	82.05 271	73.71 352	66.07 285	95.87 290	71.18 297	84.60 250	92.41 295

Gipuma		57.99 328	54.91 330	67.24 341	88.51 326	65.59 349	52.21 361	90.33 324	43.58 359	42.84 360	51.18 361	20.29 366	85.07 356	34.77 360	70.45 339	51.05 359
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		81.32 294	80.95 282	82.42 330	88.50 327	63.67 353	93.32 206	91.33 302	64.02 350	80.57 289	92.83 201	61.21 311	92.27 342	76.34 263	80.38 308	91.32 314

our_test_3		81.93 283	80.46 286	86.33 305	88.46 328	73.48 306	88.46 314	91.11 306	76.46 294	76.69 320	88.25 311	66.89 272	94.36 319	68.75 311	79.08 319	91.14 320

ppachtmachnet_test		81.84 284	80.07 292	87.15 294	88.46 328	74.43 299	89.04 306	92.16 279	75.33 307	77.75 313	88.99 298	66.20 282	95.37 308	65.12 332	77.60 324	91.65 308

lessismore_v0				86.04 306	88.46 328	68.78 341		80.59 358		73.01 340	90.11 282	55.39 334	96.43 267	75.06 276	65.06 348	92.90 281

test0.0.03 1		82.41 280	81.69 276	84.59 319	88.23 331	72.89 311	90.24 285	87.83 344	83.41 194	79.86 300	89.78 290	67.25 266	88.99 352	65.18 331	83.42 264	91.90 305

bset_n11_16_dypcd		86.83 216	85.55 226	90.65 186	88.22 332	81.70 160	88.88 308	90.42 320	85.26 156	85.49 196	90.69 271	67.11 269	97.02 232	89.51 97	84.39 251	93.23 268

MDA-MVSNet_test_wron		79.21 311	77.19 313	85.29 314	88.22 332	72.77 313	85.87 332	90.06 328	74.34 317	62.62 353	87.56 321	66.14 283	91.99 344	66.90 326	73.01 334	91.10 323

YYNet1		79.22 310	77.20 312	85.28 315	88.20 334	72.66 315	85.87 332	90.05 330	74.33 318	62.70 352	87.61 320	66.09 284	92.03 343	66.94 323	72.97 335	91.15 319

pmmvs6		83.42 273	81.60 277	88.87 249	88.01 335	77.87 259	94.96 111	94.24 232	74.67 315	78.80 307	91.09 261	60.17 318	96.49 261	77.06 259	75.40 332	92.23 301

testgi		80.94 299	80.20 290	83.18 326	87.96 336	66.29 347	91.28 268	90.70 319	83.70 185	78.12 310	92.84 200	51.37 346	90.82 349	63.34 337	82.46 273	92.43 294

Anonymous20231206		81.03 297	79.77 295	84.82 318	87.85 337	70.26 335	91.42 267	92.08 281	73.67 322	77.75 313	89.25 296	62.43 301	93.08 336	61.50 343	82.00 280	91.12 321

OpenMVS_ROB	74.94 19	79.51 308	77.03 314	86.93 296	87.00 338	76.23 287	92.33 244	90.74 318	68.93 344	74.52 333	88.23 312	49.58 349	96.62 249	57.64 350	84.29 252	87.94 347

LF4IMVS		80.37 302	79.07 305	84.27 323	86.64 339	69.87 338	89.39 300	91.05 309	76.38 296	74.97 331	90.00 285	47.85 353	94.25 323	74.55 281	80.82 300	88.69 343

MIMVSNet1		79.38 309	77.28 311	85.69 311	86.35 340	73.67 305	91.61 265	92.75 266	78.11 286	72.64 341	88.12 313	48.16 351	91.97 345	60.32 345	77.49 325	91.43 313

KD-MVS_2432*1600		78.50 313	76.02 318	85.93 308	86.22 341	74.47 297	84.80 338	92.33 273	79.29 265	76.98 318	85.92 333	53.81 342	93.97 325	67.39 320	57.42 354	89.36 334

miper_refine_blended		78.50 313	76.02 318	85.93 308	86.22 341	74.47 297	84.80 338	92.33 273	79.29 265	76.98 318	85.92 333	53.81 342	93.97 325	67.39 320	57.42 354	89.36 334

CL-MVSNet_2432*1600		81.74 286	80.53 284	85.36 313	85.96 343	72.45 319	90.25 284	93.07 259	81.24 245	79.85 301	87.29 325	70.93 225	92.52 340	66.95 322	69.23 342	91.11 322

test20.03		79.95 305	79.08 304	82.55 329	85.79 344	67.74 345	91.09 273	91.08 307	81.23 246	74.48 334	89.96 287	61.63 305	90.15 350	60.08 346	76.38 330	89.76 332

Anonymous20240521		80.44 301	79.21 301	84.11 324	85.75 345	67.89 343	92.86 229	93.23 256	75.61 305	75.59 328	87.47 322	50.03 347	94.33 320	71.14 298	81.21 288	90.12 331

DIV-MVS_2432*1600		80.20 303	79.24 300	83.07 327	85.64 346	65.29 351	91.01 274	93.93 241	78.71 277	76.32 322	86.40 330	59.20 324	92.93 338	72.59 290	69.35 341	91.00 324

Patchmatch-RL test		81.67 287	79.96 293	86.81 301	85.42 347	71.23 326	82.17 349	87.50 347	78.47 279	77.19 317	82.50 345	70.81 227	93.48 331	82.66 180	72.89 336	95.71 163

UnsupCasMVSNet_eth		80.07 304	78.27 308	85.46 312	85.24 348	72.63 316	88.45 315	94.87 209	82.99 204	71.64 345	88.07 314	56.34 331	91.75 346	73.48 287	63.36 351	92.01 304

pmmvs-eth3d		80.97 298	78.72 307	87.74 276	84.99 349	79.97 213	90.11 290	91.65 294	75.36 306	73.51 337	86.03 332	59.45 322	93.96 327	75.17 274	72.21 337	89.29 337

CMPMVS	59.16 21	80.52 300	79.20 302	84.48 320	83.98 350	67.63 346	89.95 293	93.84 247	64.79 349	66.81 350	91.14 259	57.93 328	95.17 310	76.25 264	88.10 220	90.65 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		76.23 319	73.27 322	85.09 317	83.79 351	72.92 310	85.65 335	93.47 253	71.52 336	68.84 348	79.08 349	49.77 348	93.21 334	66.81 327	60.52 353	89.13 341

PM-MVS		78.11 315	76.12 317	84.09 325	83.54 352	70.08 336	88.97 307	85.27 351	79.93 259	74.73 332	86.43 329	34.70 359	93.48 331	79.43 234	72.06 338	88.72 342

DSMNet-mixed		76.94 317	76.29 316	78.89 333	83.10 353	56.11 360	87.78 320	79.77 359	60.65 352	75.64 327	88.71 304	61.56 306	88.34 353	60.07 347	89.29 202	92.21 302

new_pmnet		72.15 321	70.13 324	78.20 334	82.95 354	65.68 348	83.91 343	82.40 355	62.94 351	64.47 351	79.82 348	42.85 357	86.26 355	57.41 351	74.44 333	82.65 352

new-patchmatchnet		76.41 318	75.17 320	80.13 332	82.65 355	59.61 355	87.66 323	91.08 307	78.23 284	69.85 346	83.22 342	54.76 336	91.63 348	64.14 336	64.89 349	89.16 339

ambc				83.06 328	79.99 356	63.51 354	77.47 354	92.86 262		74.34 335	84.45 338	28.74 360	95.06 314	73.06 289	68.89 345	90.61 326

TDRefinement		79.81 306	77.34 310	87.22 292	79.24 357	75.48 293	93.12 218	92.03 283	76.45 295	75.01 330	91.58 245	49.19 350	96.44 266	70.22 303	69.18 343	89.75 333

pmmvs3		71.81 322	68.71 325	81.11 331	75.86 358	70.42 334	86.74 327	83.66 353	58.95 353	68.64 349	80.89 347	36.93 358	89.52 351	63.10 339	63.59 350	83.39 349

DeepMVS_CX				56.31 345	74.23 359	51.81 362		56.67 368	44.85 358	48.54 358	75.16 350	27.87 362	58.74 365	40.92 358	52.22 356	58.39 358

FPMVS		64.63 325	62.55 327	70.88 338	70.80 360	56.71 357	84.42 341	84.42 352	51.78 356	49.57 356	81.61 346	23.49 363	81.48 358	40.61 359	76.25 331	74.46 355

PMMVS2		59.60 326	56.40 329	69.21 340	68.83 361	46.58 364	73.02 358	77.48 363	55.07 355	49.21 357	72.95 354	17.43 368	80.04 359	49.32 356	44.33 359	80.99 354

wuyk23d		21.27 336	20.48 339	23.63 349	68.59 362	36.41 367	49.57 362	6.85 371	9.37 365	7.89 367	4.46 369	4.03 372	31.37 366	17.47 365	16.07 365	3.12 363

E-PMN		43.23 332	42.29 334	46.03 346	65.58 363	37.41 366	73.51 356	64.62 364	33.99 361	28.47 365	47.87 362	19.90 367	67.91 362	22.23 363	24.45 361	32.77 360

LCM-MVSNet		66.00 324	62.16 328	77.51 336	64.51 364	58.29 356	83.87 344	90.90 314	48.17 357	54.69 355	73.31 353	16.83 369	86.75 354	65.47 329	61.67 352	87.48 348

EMVS		42.07 333	41.12 335	44.92 347	63.45 365	35.56 368	73.65 355	63.48 365	33.05 362	26.88 366	45.45 363	21.27 365	67.14 363	19.80 364	23.02 363	32.06 361

MVE	39.65 23	43.39 331	38.59 337	57.77 343	56.52 366	48.77 363	55.38 360	58.64 367	29.33 363	28.96 364	52.65 360	4.68 371	64.62 364	28.11 362	33.07 360	59.93 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		58.88 327	54.22 331	72.86 337	56.50 367	56.67 358	80.75 352	86.00 348	73.09 328	37.39 361	64.63 358	22.17 364	79.49 360	43.51 357	23.96 362	82.43 353

test_method		50.52 330	48.47 332	56.66 344	52.26 368	18.98 371	41.51 363	81.40 357	10.10 364	44.59 359	75.01 351	28.51 361	68.16 361	53.54 353	49.31 358	82.83 351

PMVS	47.18 22	52.22 329	48.46 333	63.48 342	45.72 369	46.20 365	73.41 357	78.31 361	41.03 360	30.06 363	65.68 357	6.05 370	83.43 357	30.04 361	65.86 347	60.80 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		35.64 334	39.24 336	24.84 348	14.87 370	23.90 370	62.71 359	51.51 369	6.58 366	36.66 362	62.08 359	44.37 356	30.34 367	52.40 354	22.00 364	20.27 362

testmvs		8.92 337	11.52 340	1.12 351	1.06 371	0.46 373	86.02 331	0.65 372	0.62 367	2.74 368	9.52 367	0.31 374	0.45 369	2.38 366	0.39 366	2.46 365

test123		8.76 338	11.22 341	1.39 350	0.85 372	0.97 372	85.76 334	0.35 373	0.54 368	2.45 369	8.14 368	0.60 373	0.48 368	2.16 367	0.17 367	2.71 364

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		22.14 335	29.52 338	0.00 352	0.00 373	0.00 374	0.00 364	95.76 149	0.00 369	0.00 370	94.29 146	75.66 166	0.00 370	0.00 368	0.00 368	0.00 366

pcd_1.5k_mvsjas		6.64 340	8.86 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	79.70 119	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		7.82 339	10.43 342	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	93.88 165	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								97.44 12	90.31 26	97.62 5	98.07 4	91.46 8	99.58 5	95.66 2	99.12 6	98.98 6

test_0728_THIRD									90.75 19	97.04 8	98.05 6	92.09 4	99.55 12	95.64 4	99.13 3	99.13 1

GSMVS																96.12 142

sam_mvs1												71.70 215				96.12 142

sam_mvs												70.60 229

MTGPA								96.97 49

test_post1							88.00 318				9.81 366	69.31 250	95.53 301	76.65 260

test_post											10.29 365	70.57 233	95.91 289

patchmatchnet-post											83.76 340	71.53 217	96.48 262

MTMP							96.16 44	60.64 366

test9_res														91.91 62	98.71 30	98.07 63

agg_prior2														90.54 88	98.68 35	98.27 47

test_prior4						85.96 57	94.11 170

test_prior2							94.12 168		87.67 100	92.63 70	96.39 72	86.62 39		91.50 73	98.67 37

旧先验2							93.36 205		71.25 338	94.37 26			97.13 223	86.74 131

新几何2							93.11 220

无先验							93.28 212	96.26 109	73.95 320				99.05 60	80.56 218		96.59 127

原ACMM2							92.94 227

testdata2													98.75 100	78.30 244

segment_acmp												87.16 35

testdata1							92.15 250		87.94 90

plane_prior5								96.22 114					98.12 137	88.15 111	89.99 187	94.63 198

plane_prior4											94.86 124

plane_prior3						82.75 134			90.26 30	86.91 164

plane_prior2							95.85 61		90.81 17

plane_prior						82.73 137	95.21 95		89.66 44						89.88 192

n2								0.00 374

nn								0.00 374

door-mid								85.49 349

test11								96.57 94

door								85.33 350

HQP5-MVS						81.56 162

BP-MVS														87.11 128

HQP4-MVS										85.43 202			97.96 157			94.51 208

HQP3-MVS								96.04 128							89.77 194

HQP2-MVS												73.83 192

MDTV_nov1_ep13_2view						55.91 361	87.62 324		73.32 325	84.59 220		70.33 236		74.65 279		95.50 166

ACMMP++_ref															87.47 228

ACMMP++															88.01 223

Test By Simon												80.02 114

High-resolution multi-view benchmark