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.

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

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 15	97.10 23	95.17 2	92.11 64	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

MSP-MVS		95.62 6	96.54 1	92.86 92	98.31 49	80.10 164	97.42 88	96.78 44	92.20 13	97.11 8	98.29 28	93.46 1	99.10 95	96.01 20	99.30 3	99.38 10

DPM-MVS		96.21 2	95.53 9	98.26 1	96.26 105	95.09 1	99.15 4	96.98 29	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 106	99.25 4	99.70 3

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 34	86.04 32	97.81 54	96.93 35	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 38	99.18 5	98.65 38

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

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 32	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 27	99.10 7	99.00 23

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 80	96.77 50	85.32 109	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
MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 74	98.64 21	96.93 35	90.71 25	93.08 55	98.70 16	79.98 71	99.21 80	94.12 45	99.07 9	98.63 39

DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 21	84.87 63	97.77 56	96.74 54	86.11 91	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 73	95.85 191	96.42 99	91.26 20	97.49 7	96.80 111	86.50 23	98.49 126	95.54 28	99.03 11	98.33 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test9_res														96.00 21	99.03 11	98.31 58

test_241102_TWO								96.78 44	88.72 49	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

agg_prior2														94.30 42	99.00 13	98.57 41

SED-MVS		95.88 4	96.22 3	94.87 19	99.03 13	85.03 58	99.12 6	96.78 44	88.72 49	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 41	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 80	97.82 52	96.65 67	84.50 133	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 69

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 16	89.42 7	98.75 18	96.80 43	87.28 77	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 44	98.09 8	89.99 32	92.34 63	96.97 103	81.30 59	98.99 101	88.54 111	98.88 19	99.20 18

DVP-MVS		95.58 7	95.91 7	94.57 26	99.05 10	85.18 51	99.06 9	96.46 94	88.75 47	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30

test_0728_SECOND				95.14 15	99.04 12	86.14 31	99.06 9	96.77 50					99.84 12	97.90 5	98.85 20	99.45 8

test_0728_THIRD									88.38 56	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 115	98.37 27	96.40 103	86.08 93	94.57 38	98.02 49	83.14 44	99.06 97	95.05 35	98.79 23	98.29 60

test_prior2							98.37 27		86.08 93	94.57 38	98.02 49	83.14 44		95.05 35	98.79 23

APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 87	98.04 42	96.41 100	85.79 99	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 19	85.68 42	98.06 40	96.64 70	93.64 8	91.74 70	98.54 20	80.17 70	99.90 5	92.28 68	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
agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 93	97.77 56	96.56 81	84.38 137	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 71

CDPH-MVS		93.12 43	92.91 47	93.74 51	98.65 30	83.88 75	97.67 66	96.26 116	83.00 171	93.22 53	98.24 30	81.31 58	99.21 80	89.12 107	98.74 27	98.14 72

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 103	90.59 5	99.21 2	97.02 26	94.40 5	91.46 73	97.08 100	83.32 43	99.69 36	92.83 61	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
DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 181	97.09 97	70.21 303	98.99 14	96.69 61	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 123	98.64 21	97.13 21	82.60 180	94.09 45	98.49 23	80.35 65	99.85 10	94.74 39	98.62 31	98.83 28

ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 95	84.28 70	96.82 134	96.65 67	86.24 89	94.27 40	97.99 52	77.94 96	99.83 15	93.39 52	98.57 32	98.39 52

ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 71	85.78 38	97.98 44	96.53 86	85.29 112	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 66

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 88	98.61 23	92.69 283	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 62	96.43 98	84.02 147	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

原ACMM1				91.22 146	97.77 72	78.10 215		96.61 73	81.05 199	91.28 79	97.42 86	77.92 97	98.98 102	79.85 184	98.51 34	96.59 155

SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 66	97.76 60	96.19 122	89.59 36	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
ZD-MVS					99.09 9	83.22 92		96.60 76	82.88 174	93.61 49	98.06 48	82.93 48	99.14 91	95.51 29	98.49 38

新几何1				93.12 79	97.44 84	81.60 129		96.71 58	74.54 281	91.22 81	97.57 75	79.13 81	99.51 56	77.40 206	98.46 39	98.26 63

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 125	81.35 132	99.02 13	96.59 77	89.50 37	94.18 43	98.36 27	83.68 40	99.45 60	94.77 37	98.45 40	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
1121		90.66 95	89.82 104	93.16 78	97.39 88	81.71 126	93.33 256	96.66 66	74.45 282	91.38 74	97.55 79	79.27 77	99.52 53	79.95 181	98.43 41	98.26 63

9.14			94.26 27		98.10 60		98.14 34	96.52 87	84.74 124	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 90	97.59 71	96.61 73	83.60 161	94.77 36	98.46 24	82.72 52	99.64 42	95.29 33	98.42 42	99.32 13

HFP-MVS		92.89 47	92.86 49	92.98 86	98.71 23	81.12 135	97.58 72	96.70 59	85.20 115	91.75 68	97.97 56	78.47 88	99.71 32	90.95 79	98.41 44	98.12 74

#test#		92.99 45	92.99 45	92.98 86	98.71 23	81.12 135	97.77 56	96.70 59	85.75 100	91.75 68	97.97 56	78.47 88	99.71 32	91.36 75	98.41 44	98.12 74

ACMMPR		92.69 56	92.67 53	92.75 96	98.66 28	80.57 151	97.58 72	96.69 61	85.20 115	91.57 72	97.92 58	77.01 111	99.67 40	90.95 79	98.41 44	98.00 86

ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 101	85.74 39	97.22 95	96.41 100	83.94 150	94.13 44	98.69 18	83.13 46	99.37 68	95.25 34	98.39 47	97.97 89

MP-MVS-pluss		92.58 60	92.35 58	93.29 72	97.30 93	82.53 103	96.44 157	96.04 131	84.68 127	89.12 109	98.37 26	77.48 104	99.74 28	93.31 56	98.38 48	97.59 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		92.72 54	92.70 52	92.79 95	98.68 25	80.53 154	97.53 76	96.51 88	85.22 113	91.94 66	97.98 54	77.26 106	99.67 40	90.83 83	98.37 49	98.18 67

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 22	83.26 91	97.21 97	96.09 127	82.41 182	94.65 37	98.21 31	81.96 57	98.81 113	94.65 40	98.36 50	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		92.75 50	92.60 55	93.23 75	98.24 52	81.82 121	97.63 67	96.50 90	85.00 120	91.05 83	97.74 67	78.38 90	99.80 19	90.48 88	98.34 51	98.07 77

test12				94.25 35	98.34 47	85.55 44		96.35 110		92.36 62		80.84 60	99.22 78		98.31 52	97.98 88

MP-MVS		92.61 59	92.67 53	92.42 109	98.13 59	79.73 173	97.33 93	96.20 120	85.63 102	90.53 89	97.66 69	78.14 94	99.70 35	92.12 70	98.30 53	97.85 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test222					96.15 108	78.41 203	95.87 189	96.46 94	71.97 301	89.66 101	97.45 82	76.33 124			98.24 54	98.30 59

CP-MVS		92.54 61	92.60 55	92.34 111	98.50 40	79.90 167	98.40 26	96.40 103	84.75 123	90.48 91	98.09 43	77.40 105	99.21 80	91.15 78	98.23 55	97.92 92

zzz-MVS		92.74 51	92.71 50	92.86 92	97.90 66	80.85 143	96.47 152	96.33 111	87.92 64	90.20 94	98.18 32	76.71 117	99.76 20	92.57 65	98.09 56	97.96 90

MTAPA		92.45 62	92.31 59	92.86 92	97.90 66	80.85 143	92.88 269	96.33 111	87.92 64	90.20 94	98.18 32	76.71 117	99.76 20	92.57 65	98.09 56	97.96 90

XVS		92.69 56	92.71 50	92.63 102	98.52 38	80.29 157	97.37 91	96.44 96	87.04 84	91.38 74	97.83 64	77.24 108	99.59 47	90.46 89	98.07 58	98.02 81

X-MVStestdata		86.26 172	84.14 187	92.63 102	98.52 38	80.29 157	97.37 91	96.44 96	87.04 84	91.38 74	20.73 354	77.24 108	99.59 47	90.46 89	98.07 58	98.02 81

MVS		90.60 97	88.64 119	96.50 3	94.25 160	90.53 6	93.33 256	97.21 19	77.59 260	78.88 210	97.31 89	71.52 184	99.69 36	89.60 101	98.03 60	99.27 16

mPP-MVS		91.88 69	91.82 68	92.07 121	98.38 45	78.63 197	97.29 94	96.09 127	85.12 117	88.45 117	97.66 69	75.53 138	99.68 38	89.83 98	98.02 61	97.88 93

HPM-MVS		91.62 76	91.53 74	91.89 127	97.88 69	79.22 182	96.99 121	95.73 145	82.07 187	89.50 106	97.19 96	75.59 137	98.93 109	90.91 81	97.94 62	97.54 117
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 92	82.83 99	97.56 74	98.27 6	89.16 42	89.71 99	97.14 97	79.77 73	99.56 51	93.65 48	97.94 62	98.02 81

PGM-MVS		91.93 68	91.80 69	92.32 113	98.27 51	79.74 172	95.28 206	97.27 17	83.83 154	90.89 86	97.78 66	76.12 127	99.56 51	88.82 109	97.93 64	97.66 110

3Dnovator	82.32 10	89.33 118	87.64 134	94.42 30	93.73 173	85.70 41	97.73 62	96.75 53	86.73 87	76.21 241	95.93 125	62.17 237	99.68 38	81.67 169	97.81 65	97.88 93

GST-MVS		92.43 63	92.22 63	93.04 84	98.17 57	81.64 128	97.40 90	96.38 107	84.71 126	90.90 85	97.40 87	77.55 102	99.76 20	89.75 100	97.74 66	97.72 105

PAPM		92.87 49	92.40 57	94.30 33	92.25 208	87.85 16	96.40 161	96.38 107	91.07 21	88.72 114	96.90 104	82.11 56	97.37 171	90.05 96	97.70 67	97.67 109

CANet		94.89 12	94.64 16	95.63 10	97.55 81	88.12 13	99.06 9	96.39 106	94.07 7	95.34 24	97.80 65	76.83 114	99.87 8	97.08 14	97.64 68	98.89 26

testdata				90.13 172	95.92 114	74.17 272		96.49 93	73.49 290	94.82 35	97.99 52	78.80 85	97.93 141	83.53 155	97.52 69	98.29 60

MVSFormer		91.36 82	90.57 86	93.73 53	93.00 187	88.08 14	94.80 225	94.48 213	80.74 204	94.90 32	97.13 98	78.84 83	95.10 274	83.77 147	97.46 70	98.02 81

lupinMVS		93.87 36	93.58 37	94.75 23	93.00 187	88.08 14	99.15 4	95.50 157	91.03 22	94.90 32	97.66 69	78.84 83	97.56 158	94.64 41	97.46 70	98.62 40

HPM-MVS_fast		90.38 104	90.17 95	91.03 150	97.61 76	77.35 233	97.15 106	95.48 158	79.51 233	88.79 113	96.90 104	71.64 183	98.81 113	87.01 127	97.44 72	96.94 141

GG-mvs-BLEND				93.49 66	94.94 141	86.26 29	81.62 328	97.00 27		88.32 120	94.30 164	91.23 3	96.21 218	88.49 113	97.43 73	98.00 86

旧先验1					97.39 88	79.58 177		96.54 84			98.08 46	84.00 36			97.42 74	97.62 114

PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 120	92.35 2	98.21 32	95.79 142	92.42 12	96.24 15	98.18 32	71.04 189	99.17 88	96.77 15	97.39 75	96.79 148

CSCG		92.02 67	91.65 72	93.12 79	98.53 37	80.59 150	97.47 80	97.18 20	77.06 268	84.64 149	97.98 54	83.98 37	99.52 53	90.72 85	97.33 76	99.23 17

SR-MVS		92.16 65	92.27 60	91.83 131	98.37 46	78.41 203	96.67 145	95.76 143	82.19 186	91.97 65	98.07 47	76.44 120	98.64 117	93.71 47	97.27 77	98.45 49

gg-mvs-nofinetune		85.48 183	82.90 203	93.24 74	94.51 155	85.82 37	79.22 332	96.97 31	61.19 331	87.33 128	53.01 344	90.58 4	96.07 219	86.07 130	97.23 78	97.81 100

test1172		91.64 74	92.00 66	90.54 163	98.20 56	74.48 269	96.45 155	95.65 148	81.97 190	91.63 71	98.02 49	75.76 133	98.61 118	93.16 58	97.17 79	98.52 45

MAR-MVS		90.63 96	90.22 92	91.86 128	98.47 42	78.20 213	97.18 101	96.61 73	83.87 153	88.18 122	98.18 32	68.71 202	99.75 26	83.66 152	97.15 80	97.63 113
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
3Dnovator+	82.88 8	89.63 114	87.85 129	94.99 18	94.49 156	86.76 27	97.84 51	95.74 144	86.10 92	75.47 252	96.02 124	65.00 224	99.51 56	82.91 163	97.07 81	98.72 36

Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 69	97.43 86	97.04 25	89.50 37	92.75 59	98.13 38	82.60 54	99.26 75	93.55 50	96.99 82	98.06 78

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 79	97.43 86	97.00 27	89.43 39	92.69 60	98.13 38	82.48 55	99.22 78	93.51 51	96.99 82	98.04 79

DeepC-MVS	86.58 3	91.53 78	91.06 81	92.94 89	94.52 152	81.89 117	95.95 183	95.98 133	90.76 24	83.76 161	96.76 112	73.24 170	99.71 32	91.67 74	96.96 84	97.22 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CPTT-MVS		89.72 112	89.87 103	89.29 192	98.33 48	73.30 276	97.70 64	95.35 168	75.68 272	87.40 126	97.44 85	70.43 194	98.25 134	89.56 103	96.90 85	96.33 164

APD-MVS_3200maxsize		91.23 86	91.35 76	90.89 154	97.89 68	76.35 248	96.30 167	95.52 156	79.82 228	91.03 84	97.88 61	74.70 155	98.54 123	92.11 71	96.89 86	97.77 102

CS-MVS		92.88 48	93.09 44	92.26 115	95.21 131	80.70 147	98.84 16	95.26 174	88.83 46	92.50 61	97.48 81	77.49 103	97.63 154	95.34 32	96.88 87	98.46 47

MVP-Stereo		82.65 226	81.67 220	85.59 261	86.10 298	78.29 206	93.33 256	92.82 280	77.75 258	69.17 296	87.98 246	59.28 257	95.76 237	71.77 252	96.88 87	82.73 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM_NR		91.46 79	90.82 83	93.37 71	98.50 40	81.81 122	95.03 220	96.13 124	84.65 129	86.10 139	97.65 73	79.24 79	99.75 26	83.20 159	96.88 87	98.56 42

EIA-MVS		91.73 71	92.05 65	90.78 158	94.52 152	76.40 247	98.06 40	95.34 169	89.19 41	88.90 112	97.28 93	77.56 101	97.73 151	90.77 84	96.86 90	98.20 65

SR-MVS-dyc-post		91.29 84	91.45 75	90.80 156	97.76 73	76.03 252	96.20 173	95.44 161	80.56 209	90.72 87	97.84 62	75.76 133	98.61 118	91.99 72	96.79 91	97.75 103

RE-MVS-def			91.18 80		97.76 73	76.03 252	96.20 173	95.44 161	80.56 209	90.72 87	97.84 62	73.36 169		91.99 72	96.79 91	97.75 103

jason		92.73 53	92.23 62	94.21 38	90.50 245	87.30 23	98.65 20	95.09 179	90.61 26	92.76 58	97.13 98	75.28 148	97.30 174	93.32 55	96.75 93	98.02 81
jason: jason.
xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 137	92.02 4	98.19 33	95.68 147	92.06 14	96.01 18	98.14 37	70.83 192	98.96 103	96.74 16	96.57 94	96.76 151

MVS_111021_LR		91.60 77	91.64 73	91.47 140	95.74 117	78.79 195	96.15 175	96.77 50	88.49 54	88.64 115	97.07 101	72.33 177	99.19 85	93.13 59	96.48 95	96.43 159

PAPR		92.74 51	92.17 64	94.45 28	98.89 18	84.87 63	97.20 99	96.20 120	87.73 70	88.40 118	98.12 41	78.71 86	99.76 20	87.99 118	96.28 96	98.74 31

Vis-MVSNet		88.67 133	87.82 130	91.24 145	92.68 193	78.82 192	96.95 127	93.85 240	87.55 73	87.07 131	95.13 148	63.43 231	97.21 179	77.58 203	96.15 97	97.70 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 67	98.29 29	97.64 13	94.57 4	95.36 23	96.88 106	79.96 72	99.12 94	91.30 76	96.11 98	97.82 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		90.18 106	88.97 115	93.80 49	98.66 28	82.95 98	97.50 79	95.63 151	75.16 276	86.31 136	97.69 68	72.49 175	99.90 5	81.26 171	96.07 99	98.56 42

QAPM		86.88 162	84.51 179	93.98 43	94.04 166	85.89 36	97.19 100	96.05 130	73.62 287	75.12 255	95.62 134	62.02 240	99.74 28	70.88 261	96.06 100	96.30 166

1314		88.94 124	87.20 146	94.17 39	93.21 181	85.73 40	93.33 256	96.64 70	82.89 173	75.98 244	96.36 118	66.83 213	99.39 63	83.52 156	96.02 101	97.39 128

MS-PatchMatch		83.05 218	81.82 218	86.72 247	89.64 259	79.10 187	94.88 223	94.59 210	79.70 231	70.67 286	89.65 225	50.43 299	96.82 198	70.82 264	95.99 102	84.25 323

CHOSEN 280x420		91.71 73	91.85 67	91.29 143	94.94 141	82.69 100	87.89 308	96.17 123	85.94 96	87.27 129	94.31 163	90.27 6	95.65 245	94.04 46	95.86 103	95.53 180

OpenMVS	79.58 14	86.09 173	83.62 194	93.50 65	90.95 238	86.71 28	97.44 82	95.83 140	75.35 273	72.64 274	95.72 129	57.42 273	99.64 42	71.41 255	95.85 104	94.13 201

PVSNet_Blended		93.13 42	92.98 46	93.57 61	97.47 82	83.86 76	99.32 1	96.73 55	91.02 23	89.53 104	96.21 120	76.42 121	99.57 49	94.29 43	95.81 105	97.29 134

CHOSEN 1792x2688		91.07 88	90.21 93	93.64 57	95.18 133	83.53 84	96.26 169	96.13 124	88.92 44	84.90 144	93.10 182	72.86 172	99.62 45	88.86 108	95.67 106	97.79 101

ETV-MVS		92.72 54	92.87 48	92.28 114	94.54 151	81.89 117	97.98 44	95.21 176	89.77 35	93.11 54	96.83 108	77.23 110	97.50 165	95.74 25	95.38 107	97.44 124

abl_6		89.80 110	89.71 107	90.07 173	96.53 102	75.52 260	94.48 228	95.04 182	81.12 198	89.22 107	97.00 102	68.83 201	98.96 103	89.86 97	95.27 108	95.73 175

Regformer-3		93.19 41	93.19 42	93.19 77	98.10 60	83.01 97	97.08 116	96.98 29	88.98 43	91.35 78	97.89 59	80.80 61	99.23 76	92.30 67	95.20 109	97.32 130

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 60	82.20 110	97.08 116	96.92 37	88.87 45	91.23 80	97.89 59	80.57 64	99.19 85	92.21 69	95.20 109	97.29 134

114514_t		88.79 131	87.57 138	92.45 107	98.21 55	81.74 124	96.99 121	95.45 160	75.16 276	82.48 172	95.69 131	68.59 203	98.50 125	80.33 176	95.18 111	97.10 138

CANet_DTU		90.98 89	90.04 97	93.83 48	94.76 146	86.23 30	96.32 166	93.12 275	93.11 10	93.71 47	96.82 110	63.08 233	99.48 58	84.29 142	95.12 112	95.77 174

DP-MVS Recon		91.72 72	90.85 82	94.34 32	99.50 1	85.00 60	98.51 25	95.96 134	80.57 208	88.08 123	97.63 74	76.84 113	99.89 7	85.67 132	94.88 113	98.13 73

BH-w/o		88.24 145	87.47 142	90.54 163	95.03 139	78.54 198	97.41 89	93.82 241	84.08 145	78.23 216	94.51 161	69.34 200	97.21 179	80.21 179	94.58 114	95.87 172

MVS_Test		90.29 105	89.18 112	93.62 59	95.23 129	84.93 61	94.41 231	94.66 203	84.31 139	90.37 93	91.02 205	75.13 150	97.82 148	83.11 161	94.42 115	98.12 74

Vis-MVSNet (Re-imp)		88.88 127	88.87 118	88.91 198	93.89 169	74.43 270	96.93 129	94.19 226	84.39 136	83.22 166	95.67 132	78.24 92	94.70 284	78.88 193	94.40 116	97.61 115

UGNet		87.73 152	86.55 157	91.27 144	95.16 134	79.11 186	96.35 163	96.23 118	88.14 61	87.83 125	90.48 213	50.65 297	99.09 96	80.13 180	94.03 117	95.60 178
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
PVSNet	82.34 9	89.02 122	87.79 131	92.71 99	95.49 123	81.50 130	97.70 64	97.29 16	87.76 69	85.47 141	95.12 149	56.90 274	98.90 110	80.33 176	94.02 118	97.71 107

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 96	98.10 37	95.29 172	91.57 16	93.81 46	97.45 82	86.64 21	99.43 61	96.28 17	94.01 119	99.20 18

PVSNet_Blended_VisFu		91.24 85	90.77 84	92.66 101	95.09 135	82.40 106	97.77 56	95.87 139	88.26 59	86.39 135	93.94 171	76.77 115	99.27 73	88.80 110	94.00 120	96.31 165

PMMVS		89.46 116	89.92 101	88.06 216	94.64 147	69.57 310	96.22 171	94.95 186	87.27 78	91.37 77	96.54 117	65.88 216	97.39 170	88.54 111	93.89 121	97.23 136

BH-untuned		86.95 160	85.94 162	89.99 176	94.52 152	77.46 230	96.78 137	93.37 265	81.80 191	76.62 232	93.81 175	66.64 214	97.02 188	76.06 220	93.88 122	95.48 181

BH-RMVSNet		86.84 163	85.28 168	91.49 139	95.35 127	80.26 160	96.95 127	92.21 287	82.86 175	81.77 186	95.46 138	59.34 256	97.64 153	69.79 266	93.81 123	96.57 156

Effi-MVS+		90.70 94	89.90 102	93.09 81	93.61 174	83.48 85	95.20 211	92.79 281	83.22 165	91.82 67	95.70 130	71.82 180	97.48 166	91.25 77	93.67 124	98.32 55

IS-MVSNet		88.67 133	88.16 125	90.20 171	93.61 174	76.86 240	96.77 139	93.07 276	84.02 147	83.62 162	95.60 135	74.69 156	96.24 217	78.43 196	93.66 125	97.49 123

AdaColmap		88.81 129	87.61 137	92.39 110	99.33 4	79.95 165	96.70 144	95.58 152	77.51 261	83.05 169	96.69 115	61.90 244	99.72 31	84.29 142	93.47 126	97.50 122

xiu_mvs_v1_base_debu		90.54 98	89.54 108	93.55 62	92.31 201	87.58 20	96.99 121	94.87 190	87.23 79	93.27 50	97.56 76	57.43 270	98.32 131	92.72 62	93.46 127	94.74 193

xiu_mvs_v1_base		90.54 98	89.54 108	93.55 62	92.31 201	87.58 20	96.99 121	94.87 190	87.23 79	93.27 50	97.56 76	57.43 270	98.32 131	92.72 62	93.46 127	94.74 193

xiu_mvs_v1_base_debi		90.54 98	89.54 108	93.55 62	92.31 201	87.58 20	96.99 121	94.87 190	87.23 79	93.27 50	97.56 76	57.43 270	98.32 131	92.72 62	93.46 127	94.74 193

mvs_anonymous		88.68 132	87.62 136	91.86 128	94.80 145	81.69 127	93.53 252	94.92 187	82.03 188	78.87 211	90.43 216	75.77 132	95.34 259	85.04 138	93.16 130	98.55 44

LCM-MVSNet-Re		83.75 206	83.54 196	84.39 280	93.54 176	64.14 325	92.51 272	84.03 339	83.90 152	66.14 306	86.59 266	67.36 208	92.68 310	84.89 140	92.87 131	96.35 161

casdiffmvs		90.95 90	90.39 89	92.63 102	92.82 192	82.53 103	96.83 133	94.47 215	87.69 71	88.47 116	95.56 136	74.04 161	97.54 162	90.90 82	92.74 132	97.83 98

TAPA-MVS	81.61 12	85.02 188	83.67 191	89.06 194	96.79 99	73.27 278	95.92 185	94.79 197	74.81 279	80.47 195	96.83 108	71.07 188	98.19 137	49.82 334	92.57 133	95.71 176
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
diffmvs		91.17 87	90.74 85	92.44 108	93.11 186	82.50 105	96.25 170	93.62 254	87.79 68	90.40 92	95.93 125	73.44 168	97.42 168	93.62 49	92.55 134	97.41 126

EPMVS		87.47 155	85.90 163	92.18 118	95.41 125	82.26 109	87.00 313	96.28 115	85.88 98	84.23 152	85.57 283	75.07 152	96.26 215	71.14 260	92.50 135	98.03 80

LS3D		82.22 233	79.94 245	89.06 194	97.43 85	74.06 274	93.20 263	92.05 288	61.90 327	73.33 267	95.21 142	59.35 255	99.21 80	54.54 322	92.48 136	93.90 205

ACMMP		90.39 102	89.97 98	91.64 134	97.58 79	78.21 212	96.78 137	96.72 57	84.73 125	84.72 147	97.23 94	71.22 186	99.63 44	88.37 116	92.41 137	97.08 139
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
TESTMET0.1,1		89.83 109	89.34 111	91.31 141	92.54 199	80.19 162	97.11 110	96.57 79	86.15 90	86.85 134	91.83 196	79.32 75	96.95 190	81.30 170	92.35 138	96.77 150

PLC	83.97 7	88.00 148	87.38 144	89.83 184	98.02 64	76.46 245	97.16 105	94.43 218	79.26 240	81.98 182	96.28 119	69.36 199	99.27 73	77.71 201	92.25 139	93.77 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
baseline		90.76 93	90.10 96	92.74 97	92.90 191	82.56 102	94.60 227	94.56 211	87.69 71	89.06 111	95.67 132	73.76 164	97.51 164	90.43 91	92.23 140	98.16 69

PatchMatch-RL		85.00 189	83.66 192	89.02 196	95.86 115	74.55 268	92.49 273	93.60 255	79.30 238	79.29 208	91.47 197	58.53 262	98.45 128	70.22 265	92.17 141	94.07 202

test-LLR		88.48 138	87.98 127	89.98 177	92.26 206	77.23 235	97.11 110	95.96 134	83.76 156	86.30 137	91.38 199	72.30 178	96.78 201	80.82 172	91.92 142	95.94 170

test-mter		88.95 123	88.60 120	89.98 177	92.26 206	77.23 235	97.11 110	95.96 134	85.32 109	86.30 137	91.38 199	76.37 123	96.78 201	80.82 172	91.92 142	95.94 170

Fast-Effi-MVS+		87.93 150	86.94 154	90.92 153	94.04 166	79.16 184	98.26 30	93.72 250	81.29 196	83.94 158	92.90 183	69.83 198	96.68 204	76.70 212	91.74 144	96.93 142

UA-Net		88.92 125	88.48 122	90.24 169	94.06 165	77.18 237	93.04 265	94.66 203	87.39 75	91.09 82	93.89 172	74.92 153	98.18 138	75.83 223	91.43 145	95.35 184

PatchmatchNet		86.83 164	85.12 172	91.95 125	94.12 163	82.27 108	86.55 317	95.64 150	84.59 131	82.98 170	84.99 295	77.26 106	95.96 226	68.61 272	91.34 146	97.64 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PCF-MVS	84.09 5	86.77 167	85.00 174	92.08 120	92.06 218	83.07 95	92.14 277	94.47 215	79.63 232	76.90 228	94.78 155	71.15 187	99.20 84	72.87 246	91.05 147	93.98 203
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		91.84 70	91.77 70	92.04 123	97.60 77	81.17 134	96.61 146	96.87 39	88.20 60	89.19 108	97.55 79	78.69 87	99.14 91	90.29 94	90.94 148	95.80 173

CNLPA		86.96 159	85.37 167	91.72 133	97.59 78	79.34 180	97.21 97	91.05 304	74.22 283	78.90 209	96.75 113	67.21 210	98.95 106	74.68 233	90.77 149	96.88 146

mvs-test1		86.83 164	87.17 147	85.81 258	91.96 221	65.24 322	97.90 49	93.34 266	85.57 103	84.51 151	95.14 147	61.99 241	97.19 181	83.55 153	90.55 150	95.00 188

CVMVSNet		84.83 191	85.57 164	82.63 298	91.55 229	60.38 335	95.13 214	95.03 183	80.60 207	82.10 181	94.71 156	66.40 215	90.19 332	74.30 238	90.32 151	97.31 132

EPNet_dtu		87.65 153	87.89 128	86.93 241	94.57 149	71.37 297	96.72 140	96.50 90	88.56 53	87.12 130	95.02 151	75.91 131	94.01 296	66.62 279	90.00 152	95.42 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline2		90.39 102	90.21 93	90.93 152	90.86 241	80.99 139	95.20 211	97.41 15	86.03 95	80.07 203	94.61 158	90.58 4	97.47 167	87.29 123	89.86 153	94.35 197

LFMVS		89.27 119	87.64 134	94.16 41	97.16 95	85.52 45	97.18 101	94.66 203	79.17 241	89.63 102	96.57 116	55.35 285	98.22 135	89.52 104	89.54 154	98.74 31

EI-MVSNet-UG-set		91.35 83	91.22 77	91.73 132	97.39 88	80.68 148	96.47 152	96.83 42	87.92 64	88.30 121	97.36 88	77.84 98	99.13 93	89.43 105	89.45 155	95.37 183

sss		90.87 92	89.96 99	93.60 60	94.15 162	83.84 78	97.14 107	98.13 7	85.93 97	89.68 100	96.09 123	71.67 181	99.30 72	87.69 119	89.16 156	97.66 110

HY-MVS	84.06 6	91.63 75	90.37 90	95.39 14	96.12 109	88.25 12	90.22 292	97.58 14	88.33 58	90.50 90	91.96 192	79.26 78	99.06 97	90.29 94	89.07 157	98.88 27

thisisatest0515		90.95 90	90.26 91	93.01 85	94.03 168	84.27 71	97.91 47	96.67 63	83.18 166	86.87 133	95.51 137	88.66 13	97.85 147	80.46 175	89.01 158	96.92 144

CDS-MVSNet		89.50 115	88.96 116	91.14 148	91.94 224	80.93 141	97.09 114	95.81 141	84.26 142	84.72 147	94.20 166	80.31 66	95.64 246	83.37 157	88.96 159	96.85 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VNet		92.11 66	91.22 77	94.79 21	96.91 98	86.98 24	97.91 47	97.96 9	86.38 88	93.65 48	95.74 128	70.16 197	98.95 106	93.39 52	88.87 160	98.43 50

alignmvs		92.97 46	92.26 61	95.12 16	95.54 122	87.77 17	98.67 19	96.38 107	88.04 62	93.01 56	97.45 82	79.20 80	98.60 120	93.25 57	88.76 161	98.99 25

WTY-MVS		92.65 58	91.68 71	95.56 11	96.00 112	88.90 10	98.23 31	97.65 12	88.57 52	89.82 98	97.22 95	79.29 76	99.06 97	89.57 102	88.73 162	98.73 35

DWT-MVSNet_test		90.52 101	89.80 105	92.70 100	95.73 119	82.20 110	93.69 247	96.55 83	88.34 57	87.04 132	95.34 140	86.53 22	97.55 159	76.32 218	88.66 163	98.34 53

canonicalmvs		92.27 64	91.22 77	95.41 13	95.80 116	88.31 11	97.09 114	94.64 206	88.49 54	92.99 57	97.31 89	72.68 174	98.57 122	93.38 54	88.58 164	99.36 12

test_yl		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 200	89.85 96	96.14 121	75.61 135	98.81 113	90.42 92	88.56 165	98.74 31

DCV-MVSNet		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 200	89.85 96	96.14 121	75.61 135	98.81 113	90.42 92	88.56 165	98.74 31

HyFIR lowres test		89.36 117	88.60 120	91.63 136	94.91 143	80.76 146	95.60 199	95.53 154	82.56 181	84.03 154	91.24 202	78.03 95	96.81 199	87.07 126	88.41 167	97.32 130

TAMVS		88.48 138	87.79 131	90.56 162	91.09 236	79.18 183	96.45 155	95.88 138	83.64 159	83.12 167	93.33 178	75.94 130	95.74 241	82.40 164	88.27 168	96.75 152

EPP-MVSNet		89.76 111	89.72 106	89.87 182	93.78 170	76.02 254	97.22 95	96.51 88	79.35 235	85.11 142	95.01 152	84.82 29	97.10 186	87.46 122	88.21 169	96.50 157

MVS-HIRNet		71.36 304	67.00 307	84.46 278	90.58 244	69.74 308	79.15 333	87.74 326	46.09 344	61.96 322	50.50 345	45.14 316	95.64 246	53.74 324	88.11 170	88.00 284

TR-MVS		86.30 171	84.93 176	90.42 165	94.63 148	77.58 228	96.57 148	93.82 241	80.30 217	82.42 174	95.16 145	58.74 260	97.55 159	74.88 231	87.82 171	96.13 168

cascas		86.50 169	84.48 181	92.55 105	92.64 197	85.95 33	97.04 120	95.07 181	75.32 274	80.50 194	91.02 205	54.33 292	97.98 140	86.79 128	87.62 172	93.71 207

OMC-MVS		88.80 130	88.16 125	90.72 159	95.30 128	77.92 221	94.81 224	94.51 212	86.80 86	84.97 143	96.85 107	67.53 206	98.60 120	85.08 137	87.62 172	95.63 177

SCA		85.63 180	83.64 193	91.60 137	92.30 204	81.86 119	92.88 269	95.56 153	84.85 121	82.52 171	85.12 293	58.04 265	95.39 256	73.89 241	87.58 174	97.54 117

thisisatest0530		89.65 113	89.02 114	91.53 138	93.46 178	80.78 145	96.52 149	96.67 63	81.69 193	83.79 160	94.90 154	88.85 12	97.68 152	77.80 197	87.49 175	96.14 167

VDDNet		86.44 170	84.51 179	92.22 117	91.56 228	81.83 120	97.10 113	94.64 206	69.50 311	87.84 124	95.19 143	48.01 306	97.92 146	89.82 99	86.92 176	96.89 145

VDD-MVS		88.28 144	87.02 152	92.06 122	95.09 135	80.18 163	97.55 75	94.45 217	83.09 168	89.10 110	95.92 127	47.97 307	98.49 126	93.08 60	86.91 177	97.52 121

thres200		88.92 125	87.65 133	92.73 98	96.30 104	85.62 43	97.85 50	98.86 1	84.38 137	84.82 145	93.99 170	75.12 151	98.01 139	70.86 262	86.67 178	94.56 196

DP-MVS		81.47 241	78.28 255	91.04 149	98.14 58	78.48 199	95.09 219	86.97 327	61.14 332	71.12 283	92.78 185	59.59 252	99.38 64	53.11 326	86.61 179	95.27 186

F-COLMAP		84.50 197	83.44 198	87.67 222	95.22 130	72.22 283	95.95 183	93.78 246	75.74 271	76.30 238	95.18 144	59.50 254	98.45 128	72.67 248	86.59 180	92.35 215

tttt0517		88.57 137	88.19 124	89.71 187	93.00 187	75.99 255	95.67 196	96.67 63	80.78 203	81.82 185	94.40 162	88.97 11	97.58 157	76.05 221	86.31 181	95.57 179

CR-MVSNet		83.53 209	81.36 225	90.06 174	90.16 251	79.75 170	79.02 334	91.12 301	84.24 143	82.27 179	80.35 320	75.45 140	93.67 302	63.37 297	86.25 182	96.75 152

RPMNet		79.85 255	75.92 273	91.64 134	90.16 251	79.75 170	79.02 334	95.44 161	58.43 340	82.27 179	72.55 337	73.03 171	98.41 130	46.10 340	86.25 182	96.75 152

thres100view900		88.30 143	86.95 153	92.33 112	96.10 110	84.90 62	97.14 107	98.85 2	82.69 178	83.41 163	93.66 176	75.43 142	97.93 141	69.04 268	86.24 184	94.17 198

tfpn200view9		88.48 138	87.15 148	92.47 106	96.21 106	85.30 49	97.44 82	98.85 2	83.37 163	83.99 155	93.82 173	75.36 145	97.93 141	69.04 268	86.24 184	94.17 198

thres400		88.42 141	87.15 148	92.23 116	96.21 106	85.30 49	97.44 82	98.85 2	83.37 163	83.99 155	93.82 173	75.36 145	97.93 141	69.04 268	86.24 184	93.45 211

CostFormer		89.08 121	88.39 123	91.15 147	93.13 184	79.15 185	88.61 304	96.11 126	83.14 167	89.58 103	86.93 261	83.83 39	96.87 196	88.22 117	85.92 187	97.42 125

thres600view7		88.06 146	86.70 156	92.15 119	96.10 110	85.17 55	97.14 107	98.85 2	82.70 177	83.41 163	93.66 176	75.43 142	97.82 148	67.13 277	85.88 188	93.45 211

Effi-MVS+-dtu		84.61 194	84.90 177	83.72 287	91.96 221	63.14 329	94.95 221	93.34 266	85.57 103	79.79 204	87.12 258	61.99 241	95.61 249	83.55 153	85.83 189	92.41 214

JIA-IIPM		79.00 264	77.20 262	84.40 279	89.74 258	64.06 326	75.30 340	95.44 161	62.15 326	81.90 183	59.08 342	78.92 82	95.59 250	66.51 282	85.78 190	93.54 208

tpm2		87.35 156	86.26 159	90.62 161	92.93 190	78.67 196	88.06 307	95.99 132	79.33 236	87.40 126	86.43 272	80.28 67	96.40 209	80.23 178	85.73 191	96.79 148

1112_ss		88.60 136	87.47 142	92.00 124	93.21 181	80.97 140	96.47 152	92.46 285	83.64 159	80.86 191	97.30 91	80.24 68	97.62 155	77.60 202	85.49 192	97.40 127

Test_1112_low_res		88.03 147	86.73 155	91.94 126	93.15 183	80.88 142	96.44 157	92.41 286	83.59 162	80.74 193	91.16 203	80.18 69	97.59 156	77.48 205	85.40 193	97.36 129

GA-MVS		85.79 178	84.04 188	91.02 151	89.47 263	80.27 159	96.90 130	94.84 193	85.57 103	80.88 190	89.08 229	56.56 278	96.47 208	77.72 200	85.35 194	96.34 162

tpmrst		88.36 142	87.38 144	91.31 141	94.36 159	79.92 166	87.32 311	95.26 174	85.32 109	88.34 119	86.13 277	80.60 63	96.70 203	83.78 146	85.34 195	97.30 133

MDTV_nov1_ep13			83.69 190		94.09 164	81.01 138	86.78 315	96.09 127	83.81 155	84.75 146	84.32 301	74.44 157	96.54 205	63.88 293	85.07 196

Fast-Effi-MVS+-dtu		83.33 212	82.60 208	85.50 262	89.55 261	69.38 311	96.09 179	91.38 296	82.30 183	75.96 245	91.41 198	56.71 275	95.58 251	75.13 230	84.90 197	91.54 216

PatchT		79.75 256	76.85 266	88.42 206	89.55 261	75.49 261	77.37 338	94.61 208	63.07 323	82.46 173	73.32 336	75.52 139	93.41 306	51.36 329	84.43 198	96.36 160

XVG-OURS-SEG-HR		85.74 179	85.16 171	87.49 230	90.22 249	71.45 296	91.29 286	94.09 232	81.37 195	83.90 159	95.22 141	60.30 249	97.53 163	85.58 133	84.42 199	93.50 209

tpm cat1		83.63 208	81.38 224	90.39 166	93.53 177	78.19 214	85.56 323	95.09 179	70.78 306	78.51 213	83.28 309	74.80 154	97.03 187	66.77 278	84.05 200	95.95 169

DSMNet-mixed		73.13 297	72.45 293	75.19 323	77.51 337	46.82 347	85.09 324	82.01 344	67.61 318	69.27 295	81.33 315	50.89 296	86.28 340	54.54 322	83.80 201	92.46 213

ADS-MVSNet2		79.57 258	77.53 260	85.71 259	93.78 170	72.13 285	79.48 330	86.11 332	73.09 293	80.14 200	79.99 323	62.15 238	90.14 333	59.49 306	83.52 202	94.85 190

ADS-MVSNet		81.26 244	78.36 254	89.96 179	93.78 170	79.78 168	79.48 330	93.60 255	73.09 293	80.14 200	79.99 323	62.15 238	95.24 266	59.49 306	83.52 202	94.85 190

XVG-OURS		85.18 186	84.38 183	87.59 225	90.42 247	71.73 293	91.06 289	94.07 233	82.00 189	83.29 165	95.08 150	56.42 279	97.55 159	83.70 151	83.42 204	93.49 210

dp		84.30 200	82.31 211	90.28 168	94.24 161	77.97 217	86.57 316	95.53 154	79.94 227	80.75 192	85.16 291	71.49 185	96.39 210	63.73 294	83.36 205	96.48 158

MSDG		80.62 251	77.77 259	89.14 193	93.43 179	77.24 234	91.89 280	90.18 309	69.86 310	68.02 297	91.94 194	52.21 295	98.84 111	59.32 308	83.12 206	91.35 217

MIMVSNet		79.18 263	75.99 272	88.72 203	87.37 284	80.66 149	79.96 329	91.82 291	77.38 263	74.33 260	81.87 313	41.78 326	90.74 328	66.36 284	83.10 207	94.76 192

HQP3-MVS								94.80 195							83.01 208

HQP-MVS		87.91 151	87.55 139	88.98 197	92.08 215	78.48 199	97.63 67	94.80 195	90.52 27	82.30 175	94.56 159	65.40 220	97.32 172	87.67 120	83.01 208	91.13 218

plane_prior						77.96 218	97.52 78		90.36 30						82.96 210

CLD-MVS		87.97 149	87.48 141	89.44 189	92.16 213	80.54 153	98.14 34	94.92 187	91.41 17	79.43 206	95.40 139	62.34 236	97.27 177	90.60 87	82.90 211	90.50 226
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		87.50 154	87.09 151	88.74 202	91.86 225	77.96 218	97.18 101	94.69 199	89.89 33	81.33 187	94.15 167	64.77 225	97.30 174	87.08 124	82.82 212	90.96 220

plane_prior5								94.69 199					97.30 174	87.08 124	82.82 212	90.96 220

OPM-MVS		85.84 176	85.10 173	88.06 216	88.34 274	77.83 224	95.72 194	94.20 225	87.89 67	80.45 196	94.05 169	58.57 261	97.26 178	83.88 145	82.76 214	89.09 256

Anonymous202405211		84.41 198	81.93 216	91.85 130	96.78 100	78.41 203	97.44 82	91.34 299	70.29 308	84.06 153	94.26 165	41.09 329	98.96 103	79.46 186	82.65 215	98.17 68

ab-mvs		87.08 158	84.94 175	93.48 67	93.34 180	83.67 82	88.82 301	95.70 146	81.18 197	84.55 150	90.14 222	62.72 234	98.94 108	85.49 134	82.54 216	97.85 96

ET-MVSNet_ETH3D		90.01 108	89.03 113	92.95 88	94.38 158	86.77 26	98.14 34	96.31 114	89.30 40	63.33 317	96.72 114	90.09 8	93.63 303	90.70 86	82.29 217	98.46 47

tpmvs		83.04 219	80.77 230	89.84 183	95.43 124	77.96 218	85.59 322	95.32 171	75.31 275	76.27 239	83.70 306	73.89 162	97.41 169	59.53 305	81.93 218	94.14 200

CMPMVS	54.94 21	75.71 287	74.56 282	79.17 314	79.69 331	55.98 341	89.59 295	93.30 268	60.28 334	53.85 338	89.07 230	47.68 310	96.33 212	76.55 213	81.02 219	85.22 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LPG-MVS_test		84.20 201	83.49 197	86.33 249	90.88 239	73.06 279	95.28 206	94.13 229	82.20 184	76.31 236	93.20 179	54.83 290	96.95 190	83.72 149	80.83 220	88.98 263

LGP-MVS_train				86.33 249	90.88 239	73.06 279		94.13 229	82.20 184	76.31 236	93.20 179	54.83 290	96.95 190	83.72 149	80.83 220	88.98 263

ACMM	80.70 13	83.72 207	82.85 204	86.31 252	91.19 234	72.12 286	95.88 188	94.29 221	80.44 212	77.02 226	91.96 192	55.24 286	97.14 185	79.30 188	80.38 222	89.67 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_0304		78.43 266	76.70 267	83.60 289	88.22 276	69.81 306	92.91 268	95.10 178	72.32 300	78.71 212	80.29 322	33.78 340	93.37 307	68.77 271	80.23 223	87.63 290

jajsoiax		82.12 234	81.15 227	85.03 267	84.19 318	70.70 299	94.22 241	93.95 236	83.07 169	73.48 264	89.75 224	49.66 302	95.37 258	82.24 167	79.76 224	89.02 261

test_djsdf		83.00 221	82.45 210	84.64 273	84.07 320	69.78 307	94.80 225	94.48 213	80.74 204	75.41 253	87.70 249	61.32 246	95.10 274	83.77 147	79.76 224	89.04 260

ACMP	81.66 11	84.00 202	83.22 200	86.33 249	91.53 231	72.95 281	95.91 187	93.79 245	83.70 158	73.79 262	92.22 188	54.31 293	96.89 194	83.98 144	79.74 226	89.16 254
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		90.05 107	89.96 99	90.33 167	97.47 82	83.86 76	98.02 43	96.73 55	87.98 63	89.53 104	89.61 226	76.42 121	99.57 49	94.29 43	79.59 227	87.57 293

Patchmatch-test		78.25 268	74.72 280	88.83 200	91.20 233	74.10 273	73.91 343	88.70 323	59.89 337	66.82 302	85.12 293	78.38 90	94.54 287	48.84 336	79.58 228	97.86 95

mvs_tets		81.74 237	80.71 232	84.84 268	84.22 317	70.29 302	93.91 244	93.78 246	82.77 176	73.37 265	89.46 227	47.36 311	95.31 262	81.99 168	79.55 229	88.92 267

FIs		86.73 168	86.10 160	88.61 204	90.05 253	80.21 161	96.14 176	96.95 33	85.56 106	78.37 215	92.30 187	76.73 116	95.28 263	79.51 185	79.27 230	90.35 228

D2MVS		82.67 225	81.55 221	86.04 256	87.77 280	76.47 244	95.21 210	96.58 78	82.66 179	70.26 289	85.46 286	60.39 248	95.80 235	76.40 216	79.18 231	85.83 315

ACMMP++															79.05 232

PS-MVSNAJss		84.91 190	84.30 184	86.74 243	85.89 301	74.40 271	94.95 221	94.16 228	83.93 151	76.45 234	90.11 223	71.04 189	95.77 236	83.16 160	79.02 233	90.06 237

FC-MVSNet-test		85.96 174	85.39 166	87.66 223	89.38 265	78.02 216	95.65 198	96.87 39	85.12 117	77.34 221	91.94 194	76.28 125	94.74 283	77.09 207	78.82 234	90.21 231

EG-PatchMatch MVS		74.92 289	72.02 294	83.62 288	83.76 323	73.28 277	93.62 249	92.04 289	68.57 313	58.88 329	83.80 305	31.87 344	95.57 252	56.97 315	78.67 235	82.00 335

EI-MVSNet		85.80 177	85.20 169	87.59 225	91.55 229	77.41 231	95.13 214	95.36 166	80.43 214	80.33 198	94.71 156	73.72 165	95.97 223	76.96 210	78.64 236	89.39 245

MVSTER		89.25 120	88.92 117	90.24 169	95.98 113	84.66 65	96.79 136	95.36 166	87.19 82	80.33 198	90.61 212	90.02 9	95.97 223	85.38 135	78.64 236	90.09 235

anonymousdsp		80.98 248	79.97 244	84.01 281	81.73 325	70.44 301	92.49 273	93.58 257	77.10 267	72.98 271	86.31 274	57.58 269	94.90 279	79.32 187	78.63 238	86.69 305

UniMVSNet_ETH3D		80.86 249	78.75 253	87.22 237	86.31 292	72.02 287	91.95 278	93.76 249	73.51 288	75.06 256	90.16 221	43.04 323	95.66 243	76.37 217	78.55 239	93.98 203

ACMMP++_ref															78.45 240

Anonymous20240529		83.15 216	80.60 234	90.80 156	95.74 117	78.27 207	96.81 135	94.92 187	60.10 336	81.89 184	92.54 186	45.82 315	98.82 112	79.25 189	78.32 241	95.31 185

XVG-ACMP-BASELINE		79.38 261	77.90 258	83.81 283	84.98 312	67.14 319	89.03 300	93.18 272	80.26 220	72.87 272	88.15 244	38.55 332	96.26 215	76.05 221	78.05 242	88.02 283

tpm		85.55 181	84.47 182	88.80 201	90.19 250	75.39 262	88.79 302	94.69 199	84.83 122	83.96 157	85.21 289	78.22 93	94.68 285	76.32 218	78.02 243	96.34 162

test0.0.03 1		82.79 223	82.48 209	83.74 286	86.81 288	72.22 283	96.52 149	95.03 183	83.76 156	73.00 270	93.20 179	72.30 178	88.88 334	64.15 292	77.52 244	90.12 233

RRT_MVS		86.89 161	85.96 161	89.68 188	95.01 140	84.13 72	96.33 165	94.98 185	84.20 144	80.10 202	92.07 190	70.52 193	95.01 278	83.30 158	77.14 245	89.91 239

RPSCF		77.73 274	76.63 268	81.06 306	88.66 272	55.76 343	87.77 309	87.88 325	64.82 322	74.14 261	92.79 184	49.22 303	96.81 199	67.47 276	76.88 246	90.62 223

LTVRE_ROB	73.68 18	77.99 270	75.74 274	84.74 269	90.45 246	72.02 287	86.41 318	91.12 301	72.57 298	66.63 303	87.27 254	54.95 289	96.98 189	56.29 317	75.98 247	85.21 318
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
OpenMVS_ROB	68.52 20	73.02 298	69.57 303	83.37 292	80.54 329	71.82 291	93.60 250	88.22 324	62.37 325	61.98 321	83.15 310	35.31 338	95.47 254	45.08 341	75.88 248	82.82 327

USDC		78.65 265	76.25 270	85.85 257	87.58 282	74.60 267	89.58 296	90.58 308	84.05 146	63.13 318	88.23 242	40.69 331	96.86 197	66.57 281	75.81 249	86.09 313

COLMAP_ROB	73.24 19	75.74 286	73.00 292	83.94 282	92.38 200	69.08 312	91.85 281	86.93 328	61.48 330	65.32 309	90.27 218	42.27 325	96.93 193	50.91 331	75.63 250	85.80 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GBi-Net		82.42 229	80.43 237	88.39 208	92.66 194	81.95 112	94.30 237	93.38 262	79.06 244	75.82 247	85.66 279	56.38 280	93.84 298	71.23 257	75.38 251	89.38 247

test1		82.42 229	80.43 237	88.39 208	92.66 194	81.95 112	94.30 237	93.38 262	79.06 244	75.82 247	85.66 279	56.38 280	93.84 298	71.23 257	75.38 251	89.38 247

FMVSNet3		84.71 192	82.71 206	90.70 160	94.55 150	87.71 18	95.92 185	94.67 202	81.73 192	75.82 247	88.08 245	66.99 211	94.47 288	71.23 257	75.38 251	89.91 239

testing_2		76.96 279	73.18 290	88.30 211	75.87 342	79.64 175	89.92 294	94.21 224	80.16 221	51.23 340	75.94 331	33.94 339	95.81 233	82.28 165	75.12 254	89.46 244

FMVSNet2		82.79 223	80.44 236	89.83 184	92.66 194	85.43 46	95.42 204	94.35 219	79.06 244	74.46 259	87.28 253	56.38 280	94.31 291	69.72 267	74.68 255	89.76 241

ITE_SJBPF				82.38 299	87.00 286	65.59 321		89.55 313	79.99 226	69.37 294	91.30 201	41.60 328	95.33 260	62.86 299	74.63 256	86.24 310

ACMH	75.40 17	77.99 270	74.96 278	87.10 239	90.67 243	76.41 246	93.19 264	91.64 295	72.47 299	63.44 316	87.61 251	43.34 320	97.16 182	58.34 310	73.94 257	87.72 287
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		88.85 128	87.49 140	92.93 90	95.21 131	86.85 25	95.47 203	94.61 208	87.29 76	83.11 168	94.99 153	80.70 62	96.89 194	82.28 165	73.72 258	95.05 187

pmmvs4		82.54 227	80.79 229	87.79 220	86.11 297	80.49 155	93.55 251	93.18 272	77.29 264	73.35 266	89.40 228	65.26 223	95.05 277	75.32 227	73.61 259	87.83 286

AllTest		75.92 285	73.06 291	84.47 276	92.18 211	67.29 316	91.07 288	84.43 337	67.63 314	63.48 314	90.18 219	38.20 333	97.16 182	57.04 313	73.37 260	88.97 265

TestCases				84.47 276	92.18 211	67.29 316		84.43 337	67.63 314	63.48 314	90.18 219	38.20 333	97.16 182	57.04 313	73.37 260	88.97 265

pmmvs5		81.34 243	79.54 247	86.73 246	85.02 311	76.91 239	96.22 171	91.65 294	77.65 259	73.55 263	88.61 236	55.70 283	94.43 289	74.12 240	73.35 262	88.86 269

XXY-MVS		83.84 204	82.00 215	89.35 190	87.13 285	81.38 131	95.72 194	94.26 222	80.15 222	75.92 246	90.63 211	61.96 243	96.52 206	78.98 192	73.28 263	90.14 232

FMVSNet1		79.50 259	76.54 269	88.39 208	88.47 273	81.95 112	94.30 237	93.38 262	73.14 292	72.04 279	85.66 279	43.86 317	93.84 298	65.48 286	72.53 264	89.38 247

cl-mvsnet2		85.11 187	84.17 186	87.92 218	95.06 138	78.82 192	95.51 201	94.22 223	79.74 230	76.77 229	87.92 247	75.96 129	95.68 242	79.93 183	72.42 265	89.27 251

miper_ehance_all_eth		84.57 195	83.60 195	87.50 229	92.64 197	78.25 208	95.40 205	93.47 258	79.28 239	76.41 235	87.64 250	76.53 119	95.24 266	78.58 194	72.42 265	89.01 262

miper_enhance_ethall		85.95 175	85.20 169	88.19 215	94.85 144	79.76 169	96.00 180	94.06 234	82.98 172	77.74 219	88.76 234	79.42 74	95.46 255	80.58 174	72.42 265	89.36 250

test_0402		72.68 299	69.54 304	82.09 302	88.67 271	71.81 292	92.72 271	86.77 329	61.52 329	62.21 320	83.91 304	43.22 321	93.76 301	34.60 345	72.23 268	80.72 337

testgi		74.88 290	73.40 289	79.32 313	80.13 330	61.75 332	93.21 262	86.64 330	79.49 234	66.56 305	91.06 204	35.51 337	88.67 335	56.79 316	71.25 269	87.56 294

nrg030		86.79 166	85.43 165	90.87 155	88.76 268	85.34 47	97.06 118	94.33 220	84.31 139	80.45 196	91.98 191	72.36 176	96.36 211	88.48 114	71.13 270	90.93 222

ACMH+	76.62 16	77.47 275	74.94 279	85.05 266	91.07 237	71.58 295	93.26 261	90.01 310	71.80 302	64.76 311	88.55 237	41.62 327	96.48 207	62.35 300	71.00 271	87.09 301

VPA-MVSNet		85.32 184	83.83 189	89.77 186	90.25 248	82.63 101	96.36 162	97.07 24	83.03 170	81.21 189	89.02 231	61.58 245	96.31 214	85.02 139	70.95 272	90.36 227

RRT_test8_iter05		87.14 157	86.41 158	89.32 191	94.41 157	81.10 137	97.06 118	95.33 170	84.67 128	76.27 239	90.48 213	83.60 41	96.33 212	85.10 136	70.78 273	90.53 225

IterMVS		80.67 250	79.16 250	85.20 265	89.79 255	76.08 251	92.97 267	91.86 290	80.28 218	71.20 282	85.14 292	57.93 268	91.34 323	72.52 249	70.74 274	88.18 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-LS		83.93 203	82.80 205	87.31 234	91.46 232	77.39 232	95.66 197	93.43 260	80.44 212	75.51 251	87.26 255	73.72 165	95.16 270	76.99 208	70.72 275	89.39 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		80.51 252	79.10 251	84.73 270	89.63 260	74.66 266	92.98 266	91.81 292	80.05 224	71.06 284	85.18 290	58.04 265	91.40 322	72.48 250	70.70 276	88.12 282

v1240		81.70 238	79.83 246	87.30 235	85.50 304	77.70 227	95.48 202	93.44 259	78.46 252	76.53 233	86.44 270	60.85 247	95.84 231	71.59 254	70.17 277	88.35 277

V42		83.04 219	81.53 222	87.57 227	86.27 294	79.09 188	95.87 189	94.11 231	80.35 216	77.22 224	86.79 264	65.32 222	96.02 221	77.74 199	70.14 278	87.61 292

v1192		82.31 232	80.55 235	87.60 224	85.94 299	78.47 202	95.85 191	93.80 244	79.33 236	76.97 227	86.51 267	63.33 232	95.87 230	73.11 245	70.13 279	88.46 274

v1144		82.90 222	81.27 226	87.78 221	86.29 293	79.07 189	96.14 176	93.93 237	80.05 224	77.38 220	86.80 263	65.50 218	95.93 228	75.21 229	70.13 279	88.33 278

Anonymous20231206		75.29 288	73.64 288	80.22 309	80.75 326	63.38 328	93.36 255	90.71 307	73.09 293	67.12 300	83.70 306	50.33 300	90.85 327	53.63 325	70.10 281	86.44 307

WR-MVS		84.32 199	82.96 201	88.41 207	89.38 265	80.32 156	96.59 147	96.25 117	83.97 149	76.63 231	90.36 217	67.53 206	94.86 281	75.82 224	70.09 282	90.06 237

EU-MVSNet		76.92 281	76.95 265	76.83 318	84.10 319	54.73 344	91.77 282	92.71 282	72.74 296	69.57 293	88.69 235	58.03 267	87.43 338	64.91 289	70.00 283	88.33 278

IB-MVS	85.34 4	88.67 133	87.14 150	93.26 73	93.12 185	84.32 68	98.76 17	97.27 17	87.19 82	79.36 207	90.45 215	83.92 38	98.53 124	84.41 141	69.79 284	96.93 142
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
v1921920		82.02 235	80.23 239	87.41 231	85.62 303	77.92 221	95.79 193	93.69 251	78.86 247	76.67 230	86.44 270	62.50 235	95.83 232	72.69 247	69.77 285	88.47 273

v2v482		83.46 210	81.86 217	88.25 213	86.19 295	79.65 174	96.34 164	94.02 235	81.56 194	77.32 222	88.23 242	65.62 217	96.03 220	77.77 198	69.72 286	89.09 256

v144192		82.43 228	80.73 231	87.54 228	85.81 302	78.22 209	95.98 181	93.78 246	79.09 243	77.11 225	86.49 268	64.66 227	95.91 229	74.20 239	69.42 287	88.49 272

cl-mvsnet_		83.27 213	82.12 212	86.74 243	92.20 209	75.95 256	95.11 216	93.27 269	78.44 253	74.82 257	87.02 260	74.19 159	95.19 268	74.67 234	69.32 288	89.09 256

cl-mvsnet1		83.27 213	82.12 212	86.74 243	92.19 210	75.92 257	95.11 216	93.26 270	78.44 253	74.81 258	87.08 259	74.19 159	95.19 268	74.66 235	69.30 289	89.11 255

Anonymous20231211		79.72 257	77.19 263	87.33 232	95.59 121	77.16 238	95.18 213	94.18 227	59.31 338	72.57 275	86.20 276	47.89 308	95.66 243	74.53 237	69.24 290	89.18 253

FMVSNet5		76.46 283	74.16 286	83.35 293	90.05 253	76.17 249	89.58 296	89.85 311	71.39 305	65.29 310	80.42 319	50.61 298	87.70 337	61.05 303	69.24 290	86.18 311

cl_fuxian		83.80 205	82.65 207	87.25 236	92.10 214	77.74 226	95.25 209	93.04 277	78.58 250	76.01 243	87.21 257	75.25 149	95.11 273	77.54 204	68.89 292	88.91 268

TinyColmap		72.41 300	68.99 305	82.68 297	88.11 277	69.59 309	88.41 305	85.20 334	65.55 320	57.91 332	84.82 297	30.80 346	95.94 227	51.38 328	68.70 293	82.49 332

LF4IMVS		72.36 301	70.82 298	76.95 317	79.18 332	56.33 340	86.12 319	86.11 332	69.30 312	63.06 319	86.66 265	33.03 342	92.25 315	65.33 287	68.64 294	82.28 333

OurMVSNet-221017-0		77.18 278	76.06 271	80.55 308	83.78 322	60.00 336	90.35 291	91.05 304	77.01 269	66.62 304	87.92 247	47.73 309	94.03 295	71.63 253	68.44 295	87.62 291

CP-MVSNet		81.01 247	80.08 241	83.79 284	87.91 279	70.51 300	94.29 240	95.65 148	80.83 202	72.54 276	88.84 233	63.71 229	92.32 314	68.58 273	68.36 296	88.55 271

UniMVSNet_NR-MVSNet		85.49 182	84.59 178	88.21 214	89.44 264	79.36 178	96.71 142	96.41 100	85.22 113	78.11 217	90.98 207	76.97 112	95.14 271	79.14 190	68.30 297	90.12 233

DU-MVS		84.57 195	83.33 199	88.28 212	88.76 268	79.36 178	96.43 159	95.41 165	85.42 107	78.11 217	90.82 208	67.61 204	95.14 271	79.14 190	68.30 297	90.33 229

PS-CasMVS		80.27 253	79.18 249	83.52 291	87.56 283	69.88 305	94.08 242	95.29 172	80.27 219	72.08 278	88.51 240	59.22 258	92.23 316	67.49 275	68.15 299	88.45 275

UniMVSNet (Re)		85.31 185	84.23 185	88.55 205	89.75 256	80.55 152	96.72 140	96.89 38	85.42 107	78.40 214	88.93 232	75.38 144	95.52 253	78.58 194	68.02 300	89.57 243

our_test_3		77.90 273	75.37 276	85.48 263	85.39 306	76.74 242	93.63 248	91.67 293	73.39 291	65.72 308	84.65 298	58.20 264	93.13 309	57.82 312	67.87 301	86.57 306

tfpnnormal		78.14 269	75.42 275	86.31 252	88.33 275	79.24 181	94.41 231	96.22 119	73.51 288	69.81 291	85.52 285	55.43 284	95.75 238	47.65 338	67.86 302	83.95 325

VPNet		84.69 193	82.92 202	90.01 175	89.01 267	83.45 86	96.71 142	95.46 159	85.71 101	79.65 205	92.18 189	56.66 277	96.01 222	83.05 162	67.84 303	90.56 224

v10		81.43 242	79.53 248	87.11 238	86.38 290	78.87 191	94.31 235	93.43 260	77.88 256	73.24 268	85.26 287	65.44 219	95.75 238	72.14 251	67.71 304	86.72 304

v8		81.88 236	80.06 243	87.32 233	86.63 289	79.04 190	94.41 231	93.65 253	78.77 248	73.19 269	85.57 283	66.87 212	95.81 233	73.84 243	67.61 305	87.11 300

v7n		79.32 262	77.34 261	85.28 264	84.05 321	72.89 282	93.38 254	93.87 239	75.02 278	70.68 285	84.37 300	59.58 253	95.62 248	67.60 274	67.50 306	87.32 299

WR-MVS_H		81.02 246	80.09 240	83.79 284	88.08 278	71.26 298	94.46 229	96.54 84	80.08 223	72.81 273	86.82 262	70.36 195	92.65 311	64.18 291	67.50 306	87.46 297

Patchmtry		77.36 276	74.59 281	85.67 260	89.75 256	75.75 259	77.85 337	91.12 301	60.28 334	71.23 281	80.35 320	75.45 140	93.56 304	57.94 311	67.34 308	87.68 289

eth_miper_zixun_eth		83.12 217	82.01 214	86.47 248	91.85 227	74.80 265	94.33 234	93.18 272	79.11 242	75.74 250	87.25 256	72.71 173	95.32 261	76.78 211	67.13 309	89.27 251

miper_lstm_enhance		81.66 240	80.66 233	84.67 272	91.19 234	71.97 289	91.94 279	93.19 271	77.86 257	72.27 277	85.26 287	73.46 167	93.42 305	73.71 244	67.05 310	88.61 270

v148		82.41 231	80.89 228	86.99 240	86.18 296	76.81 241	96.27 168	93.82 241	80.49 211	75.28 254	86.11 278	67.32 209	95.75 238	75.48 226	67.03 311	88.42 276

NR-MVSNet		83.35 211	81.52 223	88.84 199	88.76 268	81.31 133	94.45 230	95.16 177	84.65 129	67.81 298	90.82 208	70.36 195	94.87 280	74.75 232	66.89 312	90.33 229

Baseline_NR-MVSNet		81.22 245	80.07 242	84.68 271	85.32 309	75.12 264	96.48 151	88.80 320	76.24 270	77.28 223	86.40 273	67.61 204	94.39 290	75.73 225	66.73 313	84.54 321

TranMVSNet+NR-MVSNet		83.24 215	81.71 219	87.83 219	87.71 281	78.81 194	96.13 178	94.82 194	84.52 132	76.18 242	90.78 210	64.07 228	94.60 286	74.60 236	66.59 314	90.09 235

test_part1		77.94 272	74.98 277	86.83 242	86.85 287	76.14 250	94.31 235	93.03 278	58.41 341	69.77 292	84.53 299	47.22 312	95.27 264	75.23 228	65.46 315	89.06 259

PEN-MVS		79.47 260	78.26 256	83.08 294	86.36 291	68.58 313	93.85 245	94.77 198	79.76 229	71.37 280	88.55 237	59.79 250	92.46 312	64.50 290	65.40 316	88.19 280

FPMVS		55.09 313	52.93 316	61.57 329	55.98 349	40.51 352	83.11 327	83.41 342	37.61 346	34.95 346	71.95 338	14.40 351	76.95 345	29.81 346	65.16 317	67.25 344

ppachtmachnet_test		77.19 277	74.22 285	86.13 255	85.39 306	78.22 209	93.98 243	91.36 298	71.74 303	67.11 301	84.87 296	56.67 276	93.37 307	52.21 327	64.59 318	86.80 303

pm-mvs1		80.05 254	78.02 257	86.15 254	85.42 305	75.81 258	95.11 216	92.69 283	77.13 265	70.36 288	87.43 252	58.44 263	95.27 264	71.36 256	64.25 319	87.36 298

N_pmnet		61.30 312	60.20 315	64.60 327	84.32 316	17.00 359	91.67 285	10.98 358	61.77 328	58.45 331	78.55 326	49.89 301	91.83 319	42.27 343	63.94 320	84.97 319

SixPastTwentyTwo		76.04 284	74.32 284	81.22 305	84.54 314	61.43 334	91.16 287	89.30 316	77.89 255	64.04 313	86.31 274	48.23 304	94.29 292	63.54 296	63.84 321	87.93 285

MIMVSNet1		69.44 305	66.65 309	77.84 315	76.48 339	62.84 330	87.42 310	88.97 318	66.96 319	57.75 334	79.72 325	32.77 343	85.83 342	46.32 339	63.42 322	84.85 320

DTE-MVSNet		78.37 267	77.06 264	82.32 301	85.22 310	67.17 318	93.40 253	93.66 252	78.71 249	70.53 287	88.29 241	59.06 259	92.23 316	61.38 302	63.28 323	87.56 294

new_pmnet		66.18 310	63.18 313	75.18 324	76.27 341	61.74 333	83.79 326	84.66 336	56.64 342	51.57 339	71.85 339	31.29 345	87.93 336	49.98 333	62.55 324	75.86 340

test20.03		72.36 301	71.15 297	75.98 322	77.79 335	59.16 338	92.40 275	89.35 315	74.09 284	61.50 323	84.32 301	48.09 305	85.54 343	50.63 332	62.15 325	83.24 326

pmmvs6		74.65 291	71.67 295	83.60 289	79.13 333	69.94 304	93.31 260	90.88 306	61.05 333	65.83 307	84.15 303	43.43 319	94.83 282	66.62 279	60.63 326	86.02 314

MDA-MVSNet_test_wron		73.54 294	70.43 301	82.86 295	84.55 313	71.85 290	91.74 283	91.32 300	67.63 314	46.73 342	81.09 317	55.11 287	90.42 331	55.91 319	59.76 327	86.31 309

YYNet1		73.53 295	70.43 301	82.85 296	84.52 315	71.73 293	91.69 284	91.37 297	67.63 314	46.79 341	81.21 316	55.04 288	90.43 330	55.93 318	59.70 328	86.38 308

Patchmatch-RL test		76.65 282	74.01 287	84.55 275	77.37 338	64.23 324	78.49 336	82.84 343	78.48 251	64.63 312	73.40 335	76.05 128	91.70 321	76.99 208	57.84 329	97.72 105

pmmvs-eth3d		73.59 293	70.66 299	82.38 299	76.40 340	73.38 275	89.39 299	89.43 314	72.69 297	60.34 327	77.79 328	46.43 314	91.26 325	66.42 283	57.06 330	82.51 330

PM-MVS		69.32 306	66.93 308	76.49 319	73.60 344	55.84 342	85.91 320	79.32 348	74.72 280	61.09 324	78.18 327	21.76 348	91.10 326	70.86 262	56.90 331	82.51 330

Gipuma		45.11 317	42.05 319	54.30 331	80.69 327	51.30 346	35.80 350	83.81 340	28.13 348	27.94 349	34.53 349	11.41 355	76.70 347	21.45 348	54.65 332	34.90 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		71.45 303	67.94 306	81.98 303	85.33 308	68.50 314	92.35 276	88.76 321	70.40 307	42.99 343	81.96 312	46.57 313	91.31 324	48.75 337	54.39 333	86.11 312

K. test v3		73.62 292	71.59 296	79.69 311	82.98 324	59.85 337	90.85 290	88.83 319	77.13 265	58.90 328	82.11 311	43.62 318	91.72 320	65.83 285	54.10 334	87.50 296

TDRefinement		69.20 307	65.78 311	79.48 312	66.04 348	62.21 331	88.21 306	86.12 331	62.92 324	61.03 325	85.61 282	33.23 341	94.16 293	55.82 320	53.02 335	82.08 334

ambc				76.02 321	68.11 346	51.43 345	64.97 346	89.59 312		60.49 326	74.49 332	17.17 350	92.46 312	61.50 301	52.85 336	84.17 324

TransMVSNet (Re)		76.94 280	74.38 283	84.62 274	85.92 300	75.25 263	95.28 206	89.18 317	73.88 286	67.22 299	86.46 269	59.64 251	94.10 294	59.24 309	52.57 337	84.50 322

PMVS	34.80 23	39.19 319	35.53 322	50.18 332	29.72 357	30.30 354	59.60 348	66.20 353	26.06 349	17.91 352	49.53 346	3.12 358	74.09 349	18.19 350	49.40 338	46.14 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lessismore_v0				79.98 310	80.59 328	58.34 339		80.87 345		58.49 330	83.46 308	43.10 322	93.89 297	63.11 298	48.68 339	87.72 287

UnsupCasMVSNet_eth		73.25 296	70.57 300	81.30 304	77.53 336	66.33 320	87.24 312	93.89 238	80.38 215	57.90 333	81.59 314	42.91 324	90.56 329	65.18 288	48.51 340	87.01 302

new-patchmatchnet		68.85 308	65.93 310	77.61 316	73.57 345	63.94 327	90.11 293	88.73 322	71.62 304	55.08 336	73.60 334	40.84 330	87.22 339	51.35 330	48.49 341	81.67 336

pmmvs3		65.75 311	62.18 314	76.45 320	67.12 347	64.54 323	88.68 303	85.05 335	54.77 343	57.54 335	73.79 333	29.40 347	86.21 341	55.49 321	47.77 342	78.62 338

UnsupCasMVSNet_bld		68.60 309	64.50 312	80.92 307	74.63 343	67.80 315	83.97 325	92.94 279	65.12 321	54.63 337	68.23 340	35.97 335	92.17 318	60.13 304	44.83 343	82.78 328

LCM-MVSNet		52.52 314	48.24 317	65.35 325	47.63 354	41.45 351	72.55 344	83.62 341	31.75 347	37.66 345	57.92 343	9.19 357	76.76 346	49.26 335	44.60 344	77.84 339

PVSNet_0	77.72 15	81.70 238	78.95 252	89.94 180	90.77 242	76.72 243	95.96 182	96.95 33	85.01 119	70.24 290	88.53 239	52.32 294	98.20 136	86.68 129	44.08 345	94.89 189

DeepMVS_CX				64.06 328	78.53 334	43.26 350		68.11 352	69.94 309	38.55 344	76.14 330	18.53 349	79.34 344	43.72 342	41.62 346	69.57 343

PMMVS2		50.90 315	46.31 318	64.67 326	55.53 350	46.67 348	77.30 339	71.02 350	40.89 345	34.16 347	59.32 341	9.83 356	76.14 348	40.09 344	28.63 347	71.21 341

MVE	35.65 22	33.85 320	29.49 325	46.92 333	41.86 355	36.28 353	50.45 349	56.52 355	18.75 352	18.28 351	37.84 348	2.41 359	58.41 351	18.71 349	20.62 348	46.06 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 321	32.39 323	33.65 335	53.35 352	25.70 356	74.07 342	53.33 356	21.08 350	17.17 353	33.63 351	11.85 354	54.84 352	12.98 351	14.04 349	20.42 349

ANet_high		46.22 316	41.28 321	61.04 330	39.91 356	46.25 349	70.59 345	76.18 349	58.87 339	23.09 350	48.00 347	12.58 353	66.54 350	28.65 347	13.62 350	70.35 342

tmp_tt		41.54 318	41.93 320	40.38 334	20.10 358	26.84 355	61.93 347	59.09 354	14.81 353	28.51 348	80.58 318	35.53 336	48.33 354	63.70 295	13.11 351	45.96 347

EMVS		31.70 322	31.45 324	32.48 336	50.72 353	23.95 357	74.78 341	52.30 357	20.36 351	16.08 354	31.48 352	12.80 352	53.60 353	11.39 352	13.10 352	19.88 350

wuyk23d		14.10 324	13.89 327	14.72 337	55.23 351	22.91 358	33.83 351	3.56 359	4.94 354	4.11 355	2.28 357	2.06 360	19.66 355	10.23 353	8.74 353	1.59 353

testmvs		9.92 325	12.94 328	0.84 339	0.65 359	0.29 361	93.78 246	0.39 360	0.42 355	2.85 356	15.84 355	0.17 362	0.30 357	2.18 354	0.21 354	1.91 352

test123		9.07 326	11.73 329	1.11 338	0.50 360	0.77 360	89.44 298	0.20 361	0.34 356	2.15 357	10.72 356	0.34 361	0.32 356	1.79 355	0.08 355	2.23 351

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		21.43 323	28.57 326	0.00 340	0.00 361	0.00 362	0.00 352	95.93 137	0.00 357	0.00 358	97.66 69	63.57 230	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		5.92 328	7.89 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	71.04 189	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		8.11 327	10.81 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	97.30 91	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

test_241102_ONE					99.03 13	85.03 58		96.78 44	88.72 49	97.79 3	98.90 6	88.48 14	99.82 16

save fliter					98.24 52	83.34 88	98.61 23	96.57 79	91.32 18

test0726					99.05 10	85.18 51	99.11 8	96.78 44	88.75 47	97.65 6	98.91 3	87.69 18

GSMVS																97.54 117

test_part2					98.90 17	85.14 57				96.07 17

sam_mvs1												77.59 100				97.54 117

sam_mvs												75.35 147

MTGPA								96.33 111

test_post1							85.88 321				30.24 353	73.77 163	95.07 276	73.89 241

test_post											33.80 350	76.17 126	95.97 223

patchmatchnet-post											77.09 329	77.78 99	95.39 256

MTMP							97.53 76	68.16 351

gm-plane-assit					92.27 205	79.64 175			84.47 135		95.15 146		97.93 141	85.81 131

TEST9					98.64 31	83.71 80	97.82 52	96.65 67	84.29 141	95.16 25	98.09 43	84.39 31	99.36 70

test_8					98.63 33	83.64 83	97.81 54	96.63 72	84.50 133	95.10 27	98.11 42	84.33 32	99.23 76

agg_prior					98.59 35	83.13 93		96.56 81		94.19 41			99.16 89

test_prior4						82.34 107	97.75 61

test_prior				93.09 81	98.68 25	81.91 115		96.40 103					99.06 97			98.29 60

旧先验2							96.97 126		74.06 285	96.10 16			97.76 150	88.38 115

新几何2							96.42 160

无先验							96.87 131	96.78 44	77.39 262				99.52 53	79.95 181		98.43 50

原ACMM2							96.84 132

testdata2													99.48 58	76.45 215

segment_acmp												82.69 53

testdata1							95.57 200		87.44 74

plane_prior7					91.86 225	77.55 229

plane_prior6					91.98 220	77.92 221						64.77 225

plane_prior4											94.15 167

plane_prior3						77.75 225			90.17 31	81.33 187

plane_prior2							97.18 101		89.89 33

plane_prior1					91.95 223

n2								0.00 362

nn								0.00 362

door-mid								79.75 347

test11								96.50 90

door								80.13 346

HQP5-MVS						78.48 199

HQP-NCC					92.08 215		97.63 67		90.52 27	82.30 175

ACMP_Plane					92.08 215		97.63 67		90.52 27	82.30 175

BP-MVS														87.67 120

HQP4-MVS										82.30 175			97.32 172			91.13 218

HQP2-MVS												65.40 220

NP-MVS					92.04 219	78.22 209					94.56 159

MDTV_nov1_ep13_2view						81.74 124	86.80 314		80.65 206	85.65 140		74.26 158		76.52 214		96.98 140

Test By Simon												71.65 182

High-resolution multi-view benchmark