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.

region2R		97.07 22	96.84 25	97.77 35	99.46 1	93.79 55	98.52 10	98.24 34	93.19 78	97.14 41	98.34 41	91.59 54	99.87 7	95.46 65	99.59 15	99.64 10

DVP-MVS		97.91 2	97.81 2	98.22 9	99.45 2	95.36 10	98.21 34	97.85 112	94.92 22	98.73 8	98.87 6	95.08 5	99.84 19	97.52 2	99.67 6	99.48 41

test_0728_SECOND				98.51 2	99.45 2	95.93 3	98.21 34	98.28 26					99.86 8	97.52 2	99.67 6	99.75 3

test0726					99.45 2	95.36 10	98.31 22	98.29 24	94.92 22	98.99 4	98.92 2	95.08 5

ACMMPR		97.07 22	96.84 25	97.79 32	99.44 5	93.88 52	98.52 10	98.31 22	93.21 75	97.15 40	98.33 44	91.35 59	99.86 8	95.63 57	99.59 15	99.62 13

SED-MVS		98.05 1	97.99 1	98.24 7	99.42 6	95.30 15	98.25 28	98.27 28	95.13 15	99.19 1	98.89 4	95.54 3	99.85 14	97.52 2	99.66 8	99.56 22

IU-MVS					99.42 6	95.39 9		97.94 102	90.40 169	98.94 5				97.41 7	99.66 8	99.74 5

test_241102_ONE					99.42 6	95.30 15		98.27 28	95.09 18	99.19 1	98.81 8	95.54 3	99.65 53

HFP-MVS		97.14 19	96.92 20	97.83 26	99.42 6	94.12 45	98.52 10	98.32 20	93.21 75	97.18 38	98.29 50	92.08 39	99.83 22	95.63 57	99.59 15	99.54 29

#test#		97.02 26	96.75 33	97.83 26	99.42 6	94.12 45	98.15 37	98.32 20	92.57 102	97.18 38	98.29 50	92.08 39	99.83 22	95.12 71	99.59 15	99.54 29

MSP-MVS		97.59 7	97.54 5	97.73 38	99.40 11	93.77 58	98.53 9	98.29 24	95.55 5	98.56 12	97.81 82	93.90 12	99.65 53	96.62 20	99.21 69	99.77 1

mPP-MVS		96.86 37	96.60 40	97.64 46	99.40 11	93.44 65	98.50 13	98.09 63	93.27 74	95.95 84	98.33 44	91.04 66	99.88 4	95.20 68	99.57 20	99.60 16

MP-MVS		96.77 42	96.45 49	97.72 39	99.39 13	93.80 54	98.41 18	98.06 73	93.37 70	95.54 101	98.34 41	90.59 75	99.88 4	94.83 81	99.54 23	99.49 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
XVS		97.18 16	96.96 18	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 24	96.59 58	98.29 50	91.70 50	99.80 27	95.66 52	99.40 45	99.62 13

X-MVStestdata		91.71 193	89.67 251	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 24	96.59 58	32.69 356	91.70 50	99.80 27	95.66 52	99.40 45	99.62 13

ZNCC-MVS		96.96 30	96.67 38	97.85 25	99.37 16	94.12 45	98.49 14	98.18 46	92.64 101	96.39 68	98.18 58	91.61 52	99.88 4	95.59 62	99.55 21	99.57 19

zzz-MVS		97.07 22	96.77 32	97.97 22	99.37 16	94.42 32	97.15 136	98.08 64	95.07 19	96.11 75	98.59 15	90.88 70	99.90 1	96.18 39	99.50 32	99.58 17

MTAPA		97.08 21	96.78 31	97.97 22	99.37 16	94.42 32	97.24 123	98.08 64	95.07 19	96.11 75	98.59 15	90.88 70	99.90 1	96.18 39	99.50 32	99.58 17

GST-MVS		96.85 38	96.52 45	97.82 29	99.36 19	94.14 44	98.29 24	98.13 54	92.72 98	96.70 50	98.06 64	91.35 59	99.86 8	94.83 81	99.28 59	99.47 44

HPM-MVS		96.69 45	96.45 49	97.40 53	99.36 19	93.11 74	98.87 1	98.06 73	91.17 145	96.40 67	97.99 69	90.99 67	99.58 70	95.61 59	99.61 14	99.49 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		96.81 40	96.53 44	97.65 44	99.35 21	93.53 63	97.65 84	98.98 1	92.22 108	97.14 41	98.44 28	91.17 64	99.85 14	94.35 91	99.46 38	99.57 19

CP-MVS		97.02 26	96.81 28	97.64 46	99.33 22	93.54 62	98.80 3	98.28 26	92.99 84	96.45 66	98.30 49	91.90 45	99.85 14	95.61 59	99.68 4	99.54 29

HPM-MVS_fast		96.51 51	96.27 53	97.22 64	99.32 23	92.74 82	98.74 4	98.06 73	90.57 165	96.77 49	98.35 38	90.21 79	99.53 88	94.80 84	99.63 12	99.38 56

MCST-MVS		97.18 16	96.84 25	98.20 10	99.30 24	95.35 12	97.12 138	98.07 70	93.54 65	96.08 77	97.69 90	93.86 13	99.71 38	96.50 24	99.39 47	99.55 26

test_part2					99.28 25	95.74 6				98.10 17

CPTT-MVS		95.57 76	95.19 79	96.70 77	99.27 26	91.48 121	98.33 21	98.11 59	87.79 241	95.17 106	98.03 66	87.09 118	99.61 62	93.51 108	99.42 43	99.02 83

TSAR-MVS + MP.		97.42 8	97.33 9	97.69 42	99.25 27	94.24 39	98.07 43	97.85 112	93.72 57	98.57 11	98.35 38	93.69 15	99.40 108	97.06 8	99.46 38	99.44 47
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		96.05 63	95.91 61	96.46 93	99.24 28	90.47 158	98.30 23	98.57 11	89.01 199	93.97 126	97.57 103	92.62 28	99.76 30	94.66 87	99.27 61	99.15 72

ACMMP		96.27 58	95.93 60	97.28 59	99.24 28	92.62 86	98.25 28	98.81 3	92.99 84	94.56 114	98.39 35	88.96 89	99.85 14	94.57 90	97.63 119	99.36 58
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
MP-MVS-pluss		96.70 44	96.27 53	97.98 21	99.23 30	94.71 26	96.96 151	98.06 73	90.67 156	95.55 99	98.78 10	91.07 65	99.86 8	96.58 22	99.55 21	99.38 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS Recon		95.68 72	95.12 82	97.37 54	99.19 31	94.19 40	97.03 140	98.08 64	88.35 223	95.09 108	97.65 94	89.97 83	99.48 98	92.08 134	98.59 97	98.44 135

DPE-MVS		97.86 3	97.65 4	98.47 3	99.17 32	95.78 5	97.21 130	98.35 19	95.16 14	98.71 10	98.80 9	95.05 7	99.89 3	96.70 19	99.73 1	99.73 7

APDe-MVS		97.82 4	97.73 3	98.08 15	99.15 33	94.82 25	98.81 2	98.30 23	94.76 32	98.30 13	98.90 3	93.77 14	99.68 47	97.93 1	99.69 3	99.75 3

test1172		96.93 33	96.86 22	97.15 67	99.10 34	92.34 93	97.96 53	98.04 81	93.79 55	97.35 33	98.53 21	91.40 57	99.56 80	96.30 29	99.30 56	99.55 26

testtj		96.93 33	96.56 43	98.05 17	99.10 34	94.66 27	97.78 68	98.22 39	92.74 97	97.59 24	98.20 57	91.96 44	99.86 8	94.21 93	99.25 65	99.63 11

SR-MVS		97.01 28	96.86 22	97.47 51	99.09 36	93.27 71	97.98 48	98.07 70	93.75 56	97.45 28	98.48 25	91.43 56	99.59 67	96.22 33	99.27 61	99.54 29

ACMMP_NAP		97.20 15	96.86 22	98.23 8	99.09 36	95.16 20	97.60 90	98.19 44	92.82 94	97.93 20	98.74 11	91.60 53	99.86 8	96.26 30	99.52 25	99.67 8

HPM-MVS++		97.34 13	96.97 17	98.47 3	99.08 38	96.16 2	97.55 94	97.97 99	95.59 4	96.61 56	97.89 72	92.57 30	99.84 19	95.95 46	99.51 29	99.40 53

114514_t		93.95 119	93.06 130	96.63 80	99.07 39	91.61 116	97.46 104	97.96 100	77.99 338	93.00 147	97.57 103	86.14 132	99.33 113	89.22 190	99.15 73	98.94 94

SMA-MVS		97.35 12	97.03 14	98.30 6	99.06 40	95.42 8	97.94 54	98.18 46	90.57 165	98.85 7	98.94 1	93.33 17	99.83 22	96.72 18	99.68 4	99.63 11
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
ZD-MVS					99.05 41	94.59 28		98.08 64	89.22 193	97.03 47	98.10 60	92.52 32	99.65 53	94.58 89	99.31 55

APD-MVS		96.95 31	96.60 40	98.01 19	99.03 42	94.93 24	97.72 76	98.10 61	91.50 129	98.01 18	98.32 46	92.33 35	99.58 70	94.85 79	99.51 29	99.53 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post		96.88 36	96.80 29	97.11 70	99.02 43	92.34 93	97.98 48	98.03 84	93.52 66	97.43 31	98.51 22	91.40 57	99.56 80	96.05 42	99.26 63	99.43 49

RE-MVS-def			96.72 35		99.02 43	92.34 93	97.98 48	98.03 84	93.52 66	97.43 31	98.51 22	90.71 73		96.05 42	99.26 63	99.43 49

SF-MVS		97.39 10	97.13 11	98.17 11	99.02 43	95.28 17	98.23 31	98.27 28	92.37 106	98.27 14	98.65 13	93.33 17	99.72 35	96.49 25	99.52 25	99.51 34

APD-MVS_3200maxsize		96.81 40	96.71 36	97.12 69	99.01 46	92.31 96	97.98 48	98.06 73	93.11 81	97.44 29	98.55 19	90.93 68	99.55 83	96.06 41	99.25 65	99.51 34

9.14			96.75 33		98.93 47		97.73 73	98.23 38	91.28 141	97.88 22	98.44 28	93.00 21	99.65 53	95.76 51	99.47 36

CDPH-MVS		95.97 66	95.38 74	97.77 35	98.93 47	94.44 31	96.35 205	97.88 106	86.98 260	96.65 54	97.89 72	91.99 43	99.47 99	92.26 125	99.46 38	99.39 54

xxxxxxxxxxxxxcwj		97.36 11	97.20 10	97.83 26	98.91 49	94.28 35	97.02 143	97.22 182	95.35 8	98.27 14	98.65 13	93.33 17	99.72 35	96.49 25	99.52 25	99.51 34

save fliter					98.91 49	94.28 35	97.02 143	98.02 88	95.35 8

ETH3 D test6400		96.16 61	95.52 68	98.07 16	98.90 51	95.06 22	97.03 140	98.21 40	88.16 230	96.64 55	97.70 89	91.18 63	99.67 49	92.44 124	99.47 36	99.48 41

ETH3D-3000-0.1		97.07 22	96.71 36	98.14 13	98.90 51	95.33 14	97.68 80	98.24 34	91.57 127	97.90 21	98.37 36	92.61 29	99.66 52	95.59 62	99.51 29	99.43 49

CNVR-MVS		97.68 5	97.44 8	98.37 5	98.90 51	95.86 4	97.27 121	98.08 64	95.81 3	97.87 23	98.31 47	94.26 10	99.68 47	97.02 9	99.49 34	99.57 19

abl_6		96.40 54	96.21 55	96.98 74	98.89 54	92.20 101	97.89 57	98.03 84	93.34 73	97.22 37	98.42 31	87.93 103	99.72 35	95.10 72	99.07 80	99.02 83

PAPM_NR		95.01 89	94.59 92	96.26 108	98.89 54	90.68 153	97.24 123	97.73 119	91.80 122	92.93 152	96.62 153	89.13 88	99.14 130	89.21 191	97.78 116	98.97 90

OPU-MVS				98.55 1	98.82 56	96.86 1	98.25 28				98.26 53	96.04 1	99.24 120	95.36 66	99.59 15	99.56 22

NCCC		97.30 14	97.03 14	98.11 14	98.77 57	95.06 22	97.34 113	98.04 81	95.96 2	97.09 45	97.88 74	93.18 20	99.71 38	95.84 49	99.17 72	99.56 22

DP-MVS		92.76 162	91.51 181	96.52 85	98.77 57	90.99 141	97.38 111	96.08 257	82.38 314	89.29 236	97.87 75	83.77 159	99.69 44	81.37 299	96.69 145	98.89 100

MSLP-MVS++		96.94 32	97.06 13	96.59 83	98.72 59	91.86 111	97.67 81	98.49 12	94.66 35	97.24 36	98.41 34	92.31 37	98.94 150	96.61 21	99.46 38	98.96 91

TEST9					98.70 60	94.19 40	96.41 197	98.02 88	88.17 228	96.03 78	97.56 105	92.74 24	99.59 67

train_agg		96.30 57	95.83 63	97.72 39	98.70 60	94.19 40	96.41 197	98.02 88	88.58 216	96.03 78	97.56 105	92.73 25	99.59 67	95.04 73	99.37 52	99.39 54

test_8					98.67 62	94.06 49	96.37 204	98.01 91	88.58 216	95.98 83	97.55 107	92.73 25	99.58 70

agg_prior1		96.22 60	95.77 64	97.56 48	98.67 62	93.79 55	96.28 213	98.00 93	88.76 213	95.68 93	97.55 107	92.70 27	99.57 78	95.01 74	99.32 53	99.32 60

agg_prior					98.67 62	93.79 55		98.00 93		95.68 93			99.57 78

test_prior3		96.46 53	96.20 56	97.23 62	98.67 62	92.99 76	96.35 205	98.00 93	92.80 95	96.03 78	97.59 101	92.01 41	99.41 106	95.01 74	99.38 48	99.29 62

test_prior				97.23 62	98.67 62	92.99 76		98.00 93					99.41 106			99.29 62

DeepC-MVS_fast	93.89 2	96.93 33	96.64 39	97.78 33	98.64 67	94.30 34	97.41 105	98.04 81	94.81 29	96.59 58	98.37 36	91.24 61	99.64 61	95.16 69	99.52 25	99.42 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				97.32 56	98.60 68	93.59 61		97.75 117	81.58 320	95.75 90	97.85 78	90.04 82	99.67 49	86.50 241	99.13 75	98.69 115

原ACMM1				96.38 99	98.59 69	91.09 140		97.89 104	87.41 252	95.22 105	97.68 91	90.25 77	99.54 85	87.95 210	99.12 78	98.49 127

AdaColmap		94.34 106	93.68 112	96.31 103	98.59 69	91.68 115	96.59 188	97.81 114	89.87 176	92.15 165	97.06 126	83.62 161	99.54 85	89.34 185	98.07 109	97.70 170

PLC	91.00 6	94.11 113	93.43 122	96.13 113	98.58 71	91.15 139	96.69 176	97.39 167	87.29 255	91.37 178	96.71 139	88.39 98	99.52 92	87.33 229	97.13 137	97.73 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1121		94.71 102	93.83 107	97.34 55	98.57 72	93.64 60	96.04 226	97.73 119	81.56 321	95.68 93	97.85 78	90.23 78	99.65 53	87.68 219	99.12 78	98.73 111

SD-MVS		97.41 9	97.53 6	97.06 71	98.57 72	94.46 30	97.92 56	98.14 53	94.82 28	99.01 3	98.55 19	94.18 11	97.41 297	96.94 10	99.64 11	99.32 60
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				97.65 44	98.46 74	94.26 37		97.66 128		95.52 102		90.89 69	99.46 100		99.25 65	99.22 67

MVS_111021_HR		96.68 47	96.58 42	96.99 73	98.46 74	92.31 96	96.20 220	98.90 2	94.30 44	95.86 86	97.74 87	92.33 35	99.38 111	96.04 44	99.42 43	99.28 65

OMC-MVS		95.09 88	94.70 90	96.25 109	98.46 74	91.28 128	96.43 195	97.57 138	92.04 117	94.77 112	97.96 71	87.01 119	99.09 136	91.31 152	96.77 141	98.36 142

MG-MVS		95.61 74	95.38 74	96.31 103	98.42 77	90.53 156	96.04 226	97.48 146	93.47 68	95.67 96	98.10 60	89.17 87	99.25 119	91.27 153	98.77 90	99.13 74

PHI-MVS		96.77 42	96.46 48	97.71 41	98.40 78	94.07 48	98.21 34	98.45 15	89.86 177	97.11 44	98.01 68	92.52 32	99.69 44	96.03 45	99.53 24	99.36 58

F-COLMAP		93.58 131	92.98 131	95.37 152	98.40 78	88.98 206	97.18 132	97.29 178	87.75 244	90.49 195	97.10 124	85.21 141	99.50 96	86.70 238	96.72 144	97.63 172

SteuartSystems-ACMMP		97.62 6	97.53 6	97.87 24	98.39 80	94.25 38	98.43 17	98.27 28	95.34 10	98.11 16	98.56 17	94.53 9	99.71 38	96.57 23	99.62 13	99.65 9
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旧先验1					98.38 81	93.38 67		97.75 117			98.09 62	92.30 38			99.01 83	99.16 70

CNLPA		94.28 107	93.53 117	96.52 85	98.38 81	92.55 88	96.59 188	96.88 216	90.13 173	91.91 170	97.24 117	85.21 141	99.09 136	87.64 222	97.83 114	97.92 158

Regformer-3		96.85 38	96.80 29	97.01 72	98.34 83	92.02 107	96.96 151	97.76 116	95.01 21	97.08 46	98.42 31	91.71 49	99.54 85	96.80 14	99.13 75	99.48 41

Regformer-4		96.97 29	96.87 21	97.25 61	98.34 83	92.66 85	96.96 151	98.01 91	95.12 17	97.14 41	98.42 31	91.82 46	99.61 62	96.90 11	99.13 75	99.50 37

TAPA-MVS	90.10 7	92.30 175	91.22 192	95.56 140	98.33 85	89.60 177	96.79 167	97.65 130	81.83 318	91.52 175	97.23 118	87.94 102	98.91 153	71.31 340	98.37 101	98.17 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-1		97.10 20	96.96 18	97.54 49	98.32 86	93.48 64	96.83 163	97.99 97	95.20 13	97.46 27	98.25 54	92.48 34	99.58 70	96.79 16	99.29 57	99.55 26

Regformer-2		97.16 18	96.99 16	97.67 43	98.32 86	93.84 53	96.83 163	98.10 61	95.24 11	97.49 26	98.25 54	92.57 30	99.61 62	96.80 14	99.29 57	99.56 22

TSAR-MVS + GP.		96.69 45	96.49 46	97.27 60	98.31 88	93.39 66	96.79 167	96.72 225	94.17 45	97.44 29	97.66 93	92.76 23	99.33 113	96.86 13	97.76 118	99.08 80

CHOSEN 1792x2688		94.15 110	93.51 118	96.06 116	98.27 89	89.38 190	95.18 266	98.48 14	85.60 279	93.76 130	97.11 123	83.15 168	99.61 62	91.33 151	98.72 92	99.19 68

PVSNet_BlendedMVS		94.06 115	93.92 105	94.47 188	98.27 89	89.46 186	96.73 171	98.36 16	90.17 171	94.36 117	95.24 219	88.02 100	99.58 70	93.44 110	90.72 235	94.36 303

PVSNet_Blended		94.87 97	94.56 93	95.81 126	98.27 89	89.46 186	95.47 252	98.36 16	88.84 207	94.36 117	96.09 179	88.02 100	99.58 70	93.44 110	98.18 106	98.40 138

ETH3D cwj APD-0.16		96.56 50	96.06 58	98.05 17	98.26 92	95.19 18	96.99 148	98.05 80	89.85 179	97.26 35	98.22 56	91.80 47	99.69 44	94.84 80	99.28 59	99.27 66

Anonymous20231211		90.63 244	89.42 255	94.27 196	98.24 93	89.19 202	98.05 44	97.89 104	79.95 329	88.25 261	94.96 226	72.56 297	98.13 212	89.70 176	85.14 290	95.49 238

EI-MVSNet-Vis-set		96.51 51	96.47 47	96.63 80	98.24 93	91.20 134	96.89 158	97.73 119	94.74 33	96.49 62	98.49 24	90.88 70	99.58 70	96.44 27	98.32 102	99.13 74

test222					98.24 93	92.21 99	95.33 257	97.60 134	79.22 333	95.25 104	97.84 81	88.80 92			99.15 73	98.72 112

HyFIR lowres test		93.66 128	92.92 133	95.87 124	98.24 93	89.88 172	94.58 275	98.49 12	85.06 288	93.78 129	95.78 194	82.86 177	98.67 173	91.77 140	95.71 162	99.07 82

MVS_111021_LR		96.24 59	96.19 57	96.39 98	98.23 97	91.35 127	96.24 218	98.79 4	93.99 49	95.80 88	97.65 94	89.92 84	99.24 120	95.87 47	99.20 70	98.58 118

EI-MVSNet-UG-set		96.34 56	96.30 52	96.47 91	98.20 98	90.93 145	96.86 159	97.72 122	94.67 34	96.16 74	98.46 26	90.43 76	99.58 70	96.23 32	97.96 112	98.90 98

PVSNet_Blended_VisFu		95.27 82	94.91 85	96.38 99	98.20 98	90.86 147	97.27 121	98.25 33	90.21 170	94.18 121	97.27 115	87.48 112	99.73 32	93.53 107	97.77 117	98.55 119

Anonymous202405211		92.07 185	90.83 204	95.76 127	98.19 100	88.75 210	97.58 91	95.00 299	86.00 274	93.64 131	97.45 109	66.24 333	99.53 88	90.68 161	92.71 201	99.01 87

PatchMatch-RL		92.90 155	92.02 162	95.56 140	98.19 100	90.80 149	95.27 262	97.18 183	87.96 234	91.86 172	95.68 201	80.44 220	98.99 146	84.01 275	97.54 121	96.89 193

testdata				95.46 150	98.18 102	88.90 208		97.66 128	82.73 313	97.03 47	98.07 63	90.06 81	98.85 157	89.67 177	98.98 84	98.64 117

Anonymous20240529		91.98 187	90.73 208	95.73 132	98.14 103	89.40 189	97.99 47	97.72 122	79.63 331	93.54 134	97.41 111	69.94 314	99.56 80	91.04 156	91.11 228	98.22 146

LFMVS		93.60 130	92.63 142	96.52 85	98.13 104	91.27 129	97.94 54	93.39 330	90.57 165	96.29 70	98.31 47	69.00 317	99.16 127	94.18 94	95.87 157	99.12 77

DeepPCF-MVS	93.97 1	96.61 48	97.09 12	95.15 157	98.09 105	86.63 261	96.00 230	98.15 51	95.43 6	97.95 19	98.56 17	93.40 16	99.36 112	96.77 17	99.48 35	99.45 45

DPM-MVS		95.69 71	94.92 84	98.01 19	98.08 106	95.71 7	95.27 262	97.62 133	90.43 168	95.55 99	97.07 125	91.72 48	99.50 96	89.62 179	98.94 86	98.82 106

VNet		95.89 68	95.45 71	97.21 65	98.07 107	92.94 79	97.50 97	98.15 51	93.87 51	97.52 25	97.61 100	85.29 140	99.53 88	95.81 50	95.27 168	99.16 70

MAR-MVS		94.22 108	93.46 120	96.51 88	98.00 108	92.19 102	97.67 81	97.47 149	88.13 232	93.00 147	95.84 187	84.86 146	99.51 93	87.99 209	98.17 107	97.83 165
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
DeepC-MVS	93.07 3	96.06 62	95.66 65	97.29 58	97.96 109	93.17 73	97.30 119	98.06 73	93.92 50	93.38 139	98.66 12	86.83 120	99.73 32	95.60 61	99.22 68	98.96 91
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 249	89.28 258	93.79 221	97.95 110	87.13 250	96.92 155	95.89 262	82.83 312	86.88 290	97.18 119	73.77 293	99.29 117	78.44 316	93.62 193	94.95 271
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 253	88.98 262	93.98 208	97.94 111	86.64 258	96.51 192	95.54 276	85.38 282	85.49 301	96.77 137	70.28 310	99.15 128	80.02 306	92.87 198	96.15 212

TestCases				93.98 208	97.94 111	86.64 258		95.54 276	85.38 282	85.49 301	96.77 137	70.28 310	99.15 128	80.02 306	92.87 198	96.15 212

thres100view900		92.43 168	91.58 176	94.98 165	97.92 113	89.37 191	97.71 78	94.66 310	92.20 110	93.31 141	94.90 230	78.06 263	99.08 138	81.40 296	94.08 185	96.48 204

thres600view7		92.49 167	91.60 175	95.18 156	97.91 114	89.47 184	97.65 84	94.66 310	92.18 114	93.33 140	94.91 229	78.06 263	99.10 133	81.61 293	94.06 188	96.98 188

API-MVS		94.84 98	94.49 97	95.90 123	97.90 115	92.00 108	97.80 66	97.48 146	89.19 195	94.81 111	96.71 139	88.84 91	99.17 126	88.91 197	98.76 91	96.53 201

VDD-MVS		93.82 123	93.08 129	96.02 118	97.88 116	89.96 171	97.72 76	95.85 263	92.43 104	95.86 86	98.44 28	68.42 321	99.39 109	96.31 28	94.85 174	98.71 114

tfpn200view9		92.38 171	91.52 179	94.95 168	97.85 117	89.29 196	97.41 105	94.88 305	92.19 112	93.27 143	94.46 254	78.17 260	99.08 138	81.40 296	94.08 185	96.48 204

thres400		92.42 169	91.52 179	95.12 160	97.85 117	89.29 196	97.41 105	94.88 305	92.19 112	93.27 143	94.46 254	78.17 260	99.08 138	81.40 296	94.08 185	96.98 188

DELS-MVS		96.61 48	96.38 51	97.30 57	97.79 119	93.19 72	95.96 232	98.18 46	95.23 12	95.87 85	97.65 94	91.45 55	99.70 43	95.87 47	99.44 42	99.00 89
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
PVSNet	86.66 18	92.24 179	91.74 172	93.73 222	97.77 120	83.69 302	92.88 318	96.72 225	87.91 236	93.00 147	94.86 232	78.51 255	99.05 142	86.53 239	97.45 126	98.47 130

test_yl		94.78 100	94.23 102	96.43 94	97.74 121	91.22 130	96.85 160	97.10 192	91.23 143	95.71 91	96.93 129	84.30 152	99.31 115	93.10 117	95.12 170	98.75 108

DCV-MVSNet		94.78 100	94.23 102	96.43 94	97.74 121	91.22 130	96.85 160	97.10 192	91.23 143	95.71 91	96.93 129	84.30 152	99.31 115	93.10 117	95.12 170	98.75 108

WTY-MVS		94.71 102	94.02 104	96.79 76	97.71 123	92.05 105	96.59 188	97.35 173	90.61 162	94.64 113	96.93 129	86.41 126	99.39 109	91.20 155	94.71 180	98.94 94

UA-Net		95.95 67	95.53 67	97.20 66	97.67 124	92.98 78	97.65 84	98.13 54	94.81 29	96.61 56	98.35 38	88.87 90	99.51 93	90.36 164	97.35 129	99.11 78

IS-MVSNet		94.90 95	94.52 96	96.05 117	97.67 124	90.56 155	98.44 16	96.22 253	93.21 75	93.99 124	97.74 87	85.55 138	98.45 190	89.98 167	97.86 113	99.14 73

PAPR		94.18 109	93.42 124	96.48 90	97.64 126	91.42 126	95.55 248	97.71 126	88.99 200	92.34 161	95.82 189	89.19 86	99.11 132	86.14 247	97.38 127	98.90 98

CANet		96.39 55	96.02 59	97.50 50	97.62 127	93.38 67	97.02 143	97.96 100	95.42 7	94.86 110	97.81 82	87.38 114	99.82 25	96.88 12	99.20 70	99.29 62

thres200		92.23 180	91.39 182	94.75 180	97.61 128	89.03 205	96.60 187	95.09 296	92.08 116	93.28 142	94.00 277	78.39 258	99.04 144	81.26 300	94.18 184	96.19 209

Vis-MVSNet (Re-imp)		94.15 110	93.88 106	94.95 168	97.61 128	87.92 232	98.10 39	95.80 265	92.22 108	93.02 146	97.45 109	84.53 150	97.91 254	88.24 205	97.97 111	99.02 83

canonicalmvs		96.02 64	95.45 71	97.75 37	97.59 130	95.15 21	98.28 25	97.60 134	94.52 38	96.27 71	96.12 175	87.65 107	99.18 125	96.20 38	94.82 176	98.91 97

LS3D		93.57 132	92.61 144	96.47 91	97.59 130	91.61 116	97.67 81	97.72 122	85.17 286	90.29 200	98.34 41	84.60 148	99.73 32	83.85 279	98.27 103	98.06 154

alignmvs		95.87 69	95.23 78	97.78 33	97.56 132	95.19 18	97.86 59	97.17 185	94.39 41	96.47 64	96.40 164	85.89 133	99.20 122	96.21 37	95.11 172	98.95 93

EPP-MVSNet		95.22 85	95.04 83	95.76 127	97.49 133	89.56 179	98.67 5	97.00 205	90.69 155	94.24 120	97.62 99	89.79 85	98.81 160	93.39 113	96.49 149	98.92 96

PS-MVSNAJ		95.37 79	95.33 76	95.49 146	97.35 134	90.66 154	95.31 259	97.48 146	93.85 52	96.51 61	95.70 200	88.65 94	99.65 53	94.80 84	98.27 103	96.17 210

CS-MVS		95.80 70	95.65 66	96.24 110	97.32 135	91.43 125	98.10 39	97.91 103	93.38 69	95.16 107	94.57 247	90.21 79	98.98 147	95.53 64	98.67 94	98.30 145

ab-mvs		93.57 132	92.55 146	96.64 78	97.28 136	91.96 110	95.40 254	97.45 157	89.81 181	93.22 145	96.28 169	79.62 237	99.46 100	90.74 159	93.11 197	98.50 125

xiu_mvs_v2_base		95.32 81	95.29 77	95.40 151	97.22 137	90.50 157	95.44 253	97.44 161	93.70 59	96.46 65	96.18 172	88.59 97	99.53 88	94.79 86	97.81 115	96.17 210

BH-untuned		92.94 153	92.62 143	93.92 216	97.22 137	86.16 270	96.40 200	96.25 252	90.06 174	89.79 219	96.17 174	83.19 166	98.35 196	87.19 232	97.27 132	97.24 185

baseline1		92.82 160	91.90 166	95.55 142	97.20 139	90.77 151	97.19 131	94.58 313	92.20 110	92.36 159	96.34 167	84.16 155	98.21 204	89.20 192	83.90 310	97.68 171

Vis-MVSNet		95.23 84	94.81 86	96.51 88	97.18 140	91.58 119	98.26 27	98.12 56	94.38 42	94.90 109	98.15 59	82.28 191	98.92 151	91.45 150	98.58 98	99.01 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		96.02 64	95.89 62	96.40 96	97.16 141	92.44 91	97.47 102	97.77 115	94.55 37	96.48 63	94.51 249	91.23 62	98.92 151	95.65 55	98.19 105	97.82 166

BH-RMVSNet		92.72 163	91.97 164	94.97 166	97.16 141	87.99 231	96.15 222	95.60 273	90.62 161	91.87 171	97.15 122	78.41 257	98.57 182	83.16 281	97.60 120	98.36 142

MSDG		91.42 207	90.24 227	94.96 167	97.15 143	88.91 207	93.69 303	96.32 248	85.72 278	86.93 288	96.47 159	80.24 224	98.98 147	80.57 302	95.05 173	96.98 188

tttt0517		92.96 151	92.33 154	94.87 171	97.11 144	87.16 249	97.97 52	92.09 338	90.63 160	93.88 128	97.01 128	76.50 273	99.06 141	90.29 166	95.45 165	98.38 140

HY-MVS	89.66 9	93.87 121	92.95 132	96.63 80	97.10 145	92.49 90	95.64 246	96.64 234	89.05 198	93.00 147	95.79 193	85.77 136	99.45 102	89.16 194	94.35 182	97.96 155

thisisatest0530		93.03 148	92.21 157	95.49 146	97.07 146	89.11 204	97.49 101	92.19 337	90.16 172	94.09 122	96.41 163	76.43 276	99.05 142	90.38 163	95.68 163	98.31 144

XVG-OURS		93.72 127	93.35 125	94.80 176	97.07 146	88.61 213	94.79 271	97.46 151	91.97 120	93.99 124	97.86 77	81.74 202	98.88 156	92.64 123	92.67 203	96.92 192

sss		94.51 104	93.80 108	96.64 78	97.07 146	91.97 109	96.32 209	98.06 73	88.94 203	94.50 115	96.78 136	84.60 148	99.27 118	91.90 136	96.02 153	98.68 116

EIA-MVS		95.53 77	95.47 70	95.71 133	97.06 149	89.63 175	97.82 64	97.87 108	93.57 61	93.92 127	95.04 225	90.61 74	98.95 149	94.62 88	98.68 93	98.54 120

XVG-OURS-SEG-HR		93.86 122	93.55 115	94.81 174	97.06 149	88.53 216	95.28 260	97.45 157	91.68 125	94.08 123	97.68 91	82.41 189	98.90 154	93.84 103	92.47 205	96.98 188

1112_ss		93.37 136	92.42 152	96.21 111	97.05 151	90.99 141	96.31 210	96.72 225	86.87 263	89.83 218	96.69 143	86.51 124	99.14 130	88.12 207	93.67 191	98.50 125

Test_1112_low_res		92.84 159	91.84 168	95.85 125	97.04 152	89.97 170	95.53 250	96.64 234	85.38 282	89.65 224	95.18 220	85.86 134	99.10 133	87.70 216	93.58 196	98.49 127

AUN-MVS		91.76 192	90.75 207	94.81 174	97.00 153	88.57 214	96.65 179	96.49 242	89.63 183	92.15 165	96.12 175	78.66 253	98.50 186	90.83 157	79.18 330	97.36 183

BH-w/o		92.14 184	91.75 170	93.31 243	96.99 154	85.73 274	95.67 243	95.69 269	88.73 214	89.26 238	94.82 235	82.97 175	98.07 225	85.26 262	96.32 152	96.13 214

3Dnovator+	91.43 4	95.40 78	94.48 98	98.16 12	96.90 155	95.34 13	98.48 15	97.87 108	94.65 36	88.53 254	98.02 67	83.69 160	99.71 38	93.18 116	98.96 85	99.44 47

UGNet		94.04 117	93.28 127	96.31 103	96.85 156	91.19 135	97.88 58	97.68 127	94.40 40	93.00 147	96.18 172	73.39 296	99.61 62	91.72 141	98.46 99	98.13 149
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
VDDNet		93.05 147	92.07 159	96.02 118	96.84 157	90.39 162	98.08 42	95.85 263	86.22 271	95.79 89	98.46 26	67.59 324	99.19 123	94.92 78	94.85 174	98.47 130

RPSCF		90.75 239	90.86 200	90.42 311	96.84 157	76.29 342	95.61 247	96.34 247	83.89 302	91.38 177	97.87 75	76.45 274	98.78 162	87.16 234	92.23 208	96.20 208

MVS_Test		94.89 96	94.62 91	95.68 134	96.83 159	89.55 180	96.70 174	97.17 185	91.17 145	95.60 98	96.11 178	87.87 104	98.76 165	93.01 121	97.17 136	98.72 112

LCM-MVSNet-Re		92.50 165	92.52 149	92.44 268	96.82 160	81.89 314	96.92 155	93.71 327	92.41 105	84.30 310	94.60 245	85.08 143	97.03 308	91.51 147	97.36 128	98.40 138

baseline		95.58 75	95.42 73	96.08 114	96.78 161	90.41 161	97.16 134	97.45 157	93.69 60	95.65 97	97.85 78	87.29 115	98.68 172	95.66 52	97.25 133	99.13 74

Fast-Effi-MVS+		93.46 134	92.75 138	95.59 139	96.77 162	90.03 164	96.81 166	97.13 188	88.19 226	91.30 182	94.27 265	86.21 129	98.63 176	87.66 221	96.46 151	98.12 150

QAPM		93.45 135	92.27 156	96.98 74	96.77 162	92.62 86	98.39 19	98.12 56	84.50 296	88.27 260	97.77 85	82.39 190	99.81 26	85.40 260	98.81 89	98.51 124

casdiffmvs		95.64 73	95.49 69	96.08 114	96.76 164	90.45 159	97.29 120	97.44 161	94.00 48	95.46 103	97.98 70	87.52 111	98.73 167	95.64 56	97.33 130	99.08 80

CHOSEN 280x420		93.12 144	92.72 140	94.34 194	96.71 165	87.27 243	90.29 336	97.72 122	86.61 266	91.34 179	95.29 216	84.29 154	98.41 191	93.25 115	98.94 86	97.35 184

Effi-MVS+		94.93 94	94.45 99	96.36 101	96.61 166	91.47 122	96.41 197	97.41 166	91.02 150	94.50 115	95.92 183	87.53 110	98.78 162	93.89 101	96.81 140	98.84 105

thisisatest0515		92.29 176	91.30 187	95.25 154	96.60 167	88.90 208	94.36 284	92.32 336	87.92 235	93.43 138	94.57 247	77.28 270	99.00 145	89.42 183	95.86 158	97.86 162

PCF-MVS	89.48 11	91.56 200	89.95 239	96.36 101	96.60 167	92.52 89	92.51 323	97.26 179	79.41 332	88.90 243	96.56 155	84.04 157	99.55 83	77.01 324	97.30 131	97.01 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		95.01 89	94.76 87	95.75 129	96.58 169	91.71 112	96.25 215	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 215

xiu_mvs_v1_base		95.01 89	94.76 87	95.75 129	96.58 169	91.71 112	96.25 215	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 215

xiu_mvs_v1_base_debi		95.01 89	94.76 87	95.75 129	96.58 169	91.71 112	96.25 215	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 215

MVSTER		93.20 142	92.81 135	94.37 192	96.56 172	89.59 178	97.06 139	97.12 189	91.24 142	91.30 182	95.96 181	82.02 196	98.05 228	93.48 109	90.55 237	95.47 241

3Dnovator	91.36 5	95.19 87	94.44 100	97.44 52	96.56 172	93.36 69	98.65 6	98.36 16	94.12 46	89.25 239	98.06 64	82.20 193	99.77 29	93.41 112	99.32 53	99.18 69

FMVSNet3		91.78 191	90.69 210	95.03 162	96.53 174	92.27 98	97.02 143	96.93 209	89.79 182	89.35 233	94.65 243	77.01 271	97.47 291	86.12 248	88.82 252	95.35 254

GBi-Net		91.35 212	90.27 225	94.59 182	96.51 175	91.18 136	97.50 97	96.93 209	88.82 209	89.35 233	94.51 249	73.87 290	97.29 303	86.12 248	88.82 252	95.31 256

test1		91.35 212	90.27 225	94.59 182	96.51 175	91.18 136	97.50 97	96.93 209	88.82 209	89.35 233	94.51 249	73.87 290	97.29 303	86.12 248	88.82 252	95.31 256

FMVSNet2		91.31 215	90.08 234	94.99 163	96.51 175	92.21 99	97.41 105	96.95 207	88.82 209	88.62 251	94.75 238	73.87 290	97.42 296	85.20 263	88.55 258	95.35 254

ACMH+	87.92 14	90.20 254	89.18 260	93.25 245	96.48 178	86.45 263	96.99 148	96.68 231	88.83 208	84.79 307	96.22 171	70.16 312	98.53 184	84.42 273	88.04 260	94.77 292

CANet_DTU		94.37 105	93.65 113	96.55 84	96.46 179	92.13 103	96.21 219	96.67 233	94.38 42	93.53 135	97.03 127	79.34 240	99.71 38	90.76 158	98.45 100	97.82 166

mvs_anonymous		93.82 123	93.74 109	94.06 204	96.44 180	85.41 279	95.81 239	97.05 199	89.85 179	90.09 211	96.36 166	87.44 113	97.75 267	93.97 97	96.69 145	99.02 83

diffmvs		95.25 83	95.13 81	95.63 136	96.43 181	89.34 192	95.99 231	97.35 173	92.83 93	96.31 69	97.37 112	86.44 125	98.67 173	96.26 30	97.19 135	98.87 102

ET-MVSNet_ETH3D		91.49 204	90.11 233	95.63 136	96.40 182	91.57 120	95.34 256	93.48 329	90.60 164	75.58 341	95.49 211	80.08 227	96.79 317	94.25 92	89.76 246	98.52 122

TR-MVS		91.48 205	90.59 213	94.16 201	96.40 182	87.33 241	95.67 243	95.34 285	87.68 246	91.46 176	95.52 210	76.77 272	98.35 196	82.85 285	93.61 194	96.79 197

ACMP	89.59 10	92.62 164	92.14 158	94.05 205	96.40 182	88.20 225	97.36 112	97.25 181	91.52 128	88.30 258	96.64 146	78.46 256	98.72 170	91.86 139	91.48 222	95.23 263
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		95.37 79	95.16 80	95.99 120	96.34 185	91.21 132	98.22 32	97.57 138	91.42 133	96.22 72	97.32 113	86.20 130	97.92 251	94.07 95	99.05 81	98.85 103

lupinMVS		94.99 93	94.56 93	96.29 106	96.34 185	91.21 132	95.83 238	96.27 250	88.93 204	96.22 72	96.88 134	86.20 130	98.85 157	95.27 67	99.05 81	98.82 106

ACMM	89.79 8	92.96 151	92.50 150	94.35 193	96.30 187	88.71 211	97.58 91	97.36 172	91.40 136	90.53 194	96.65 145	79.77 233	98.75 166	91.24 154	91.64 218	95.59 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 176	91.94 165	93.34 242	96.25 188	86.97 253	96.57 191	97.05 199	90.67 156	89.50 230	94.80 236	86.59 121	97.64 275	89.91 169	86.11 279	95.40 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP_MVS		93.78 125	93.43 122	94.82 172	96.21 189	89.99 167	97.74 71	97.51 144	94.85 24	91.34 179	96.64 146	81.32 207	98.60 179	93.02 119	92.23 208	95.86 221

plane_prior7					96.21 189	89.98 169

ACMH	87.59 16	90.53 246	89.42 255	93.87 217	96.21 189	87.92 232	97.24 123	96.94 208	88.45 220	83.91 317	96.27 170	71.92 298	98.62 178	84.43 272	89.43 248	95.05 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDS-MVSNet		94.14 112	93.54 116	95.93 121	96.18 192	91.46 123	96.33 208	97.04 201	88.97 202	93.56 132	96.51 157	87.55 109	97.89 255	89.80 173	95.95 155	98.44 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 229	89.92 240	94.19 199	96.18 192	89.55 180	96.31 210	97.09 194	87.88 237	85.67 299	95.91 184	78.79 252	98.57 182	81.50 294	89.98 243	94.44 301
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
LPG-MVS_test		92.94 153	92.56 145	94.10 202	96.16 194	88.26 222	97.65 84	97.46 151	91.29 138	90.12 208	97.16 120	79.05 244	98.73 167	92.25 127	91.89 216	95.31 256

LGP-MVS_train				94.10 202	96.16 194	88.26 222		97.46 151	91.29 138	90.12 208	97.16 120	79.05 244	98.73 167	92.25 127	91.89 216	95.31 256

TAMVS		94.01 118	93.46 120	95.64 135	96.16 194	90.45 159	96.71 173	96.89 215	89.27 192	93.46 137	96.92 132	87.29 115	97.94 247	88.70 201	95.74 160	98.53 121

plane_prior1					96.14 197

CLD-MVS		92.98 150	92.53 148	94.32 195	96.12 198	89.20 200	95.28 260	97.47 149	92.66 99	89.90 215	95.62 203	80.58 217	98.40 192	92.73 122	92.40 206	95.38 252
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					96.10 199	90.00 165						81.32 207

cl-mvsnet2		91.21 219	90.56 215	93.14 250	96.09 200	86.80 255	94.41 282	96.58 240	87.80 240	88.58 253	93.99 278	80.85 215	97.62 278	89.87 172	86.93 270	94.99 270

Effi-MVS+-dtu		93.08 145	93.21 128	92.68 265	96.02 201	83.25 305	97.14 137	96.72 225	93.85 52	91.20 189	93.44 296	83.08 170	98.30 199	91.69 144	95.73 161	96.50 203

mvs-test1		93.63 129	93.69 111	93.46 237	96.02 201	84.61 291	97.24 123	96.72 225	93.85 52	92.30 162	95.76 195	83.08 170	98.89 155	91.69 144	96.54 148	96.87 194

NP-MVS					95.99 203	89.81 174					95.87 185

ADS-MVSNet2		89.45 266	88.59 267	92.03 277	95.86 204	82.26 313	90.93 332	94.32 320	83.23 310	91.28 185	91.81 320	79.01 248	95.99 324	79.52 308	91.39 224	97.84 163

ADS-MVSNet		89.89 260	88.68 266	93.53 233	95.86 204	84.89 288	90.93 332	95.07 297	83.23 310	91.28 185	91.81 320	79.01 248	97.85 257	79.52 308	91.39 224	97.84 163

HQP-NCC					95.86 204		96.65 179		93.55 62	90.14 202

ACMP_Plane					95.86 204		96.65 179		93.55 62	90.14 202

HQP-MVS		93.19 143	92.74 139	94.54 187	95.86 204	89.33 193	96.65 179	97.39 167	93.55 62	90.14 202	95.87 185	80.95 210	98.50 186	92.13 131	92.10 213	95.78 228

EI-MVSNet		93.03 148	92.88 134	93.48 235	95.77 209	86.98 252	96.44 193	97.12 189	90.66 158	91.30 182	97.64 97	86.56 122	98.05 228	89.91 169	90.55 237	95.41 245

CVMVSNet		91.23 218	91.75 170	89.67 317	95.77 209	74.69 344	96.44 193	94.88 305	85.81 276	92.18 164	97.64 97	79.07 243	95.58 333	88.06 208	95.86 158	98.74 110

RRT_test8_iter05		91.19 223	90.78 205	92.41 270	95.76 211	83.14 306	97.32 116	97.46 151	91.37 137	89.07 242	95.57 205	70.33 309	98.21 204	93.56 106	86.62 275	95.89 220

FIs		94.09 114	93.70 110	95.27 153	95.70 212	92.03 106	98.10 39	98.68 7	93.36 72	90.39 198	96.70 141	87.63 108	97.94 247	92.25 127	90.50 239	95.84 224

VPA-MVSNet		93.24 140	92.48 151	95.51 144	95.70 212	92.39 92	97.86 59	98.66 9	92.30 107	92.09 168	95.37 214	80.49 219	98.40 192	93.95 98	85.86 280	95.75 232

SCA		91.84 190	91.18 194	93.83 218	95.59 214	84.95 287	94.72 272	95.58 275	90.82 151	92.25 163	93.69 287	75.80 279	98.10 217	86.20 245	95.98 154	98.45 132

cl_fuxian		91.38 209	90.89 198	92.88 258	95.58 215	86.30 265	94.68 273	96.84 221	88.17 228	88.83 248	94.23 268	85.65 137	97.47 291	89.36 184	84.63 298	94.89 279

VPNet		92.23 180	91.31 186	94.99 163	95.56 216	90.96 143	97.22 129	97.86 111	92.96 90	90.96 190	96.62 153	75.06 284	98.20 206	91.90 136	83.65 312	95.80 227

miper_ehance_all_eth		91.59 197	91.13 195	92.97 255	95.55 217	86.57 262	94.47 278	96.88 216	87.77 242	88.88 245	94.01 276	86.22 128	97.54 284	89.49 181	86.93 270	94.79 289

IterMVS-SCA-FT		90.31 250	89.81 245	91.82 283	95.52 218	84.20 295	94.30 287	96.15 255	90.61 162	87.39 278	94.27 265	75.80 279	96.44 320	87.34 228	86.88 274	94.82 284

jason		94.84 98	94.39 101	96.18 112	95.52 218	90.93 145	96.09 224	96.52 241	89.28 191	96.01 82	97.32 113	84.70 147	98.77 164	95.15 70	98.91 88	98.85 103
jason: jason.
FC-MVSNet-test		93.94 120	93.57 114	95.04 161	95.48 220	91.45 124	98.12 38	98.71 5	93.37 70	90.23 201	96.70 141	87.66 106	97.85 257	91.49 148	90.39 240	95.83 225

IterMVS		90.15 256	89.67 251	91.61 290	95.48 220	83.72 299	94.33 286	96.12 256	89.99 175	87.31 281	94.15 273	75.78 281	96.27 323	86.97 236	86.89 273	94.83 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		89.88 261	88.31 270	94.59 182	95.41 222	91.18 136	97.50 97	96.93 209	86.62 265	87.41 277	94.51 249	65.94 335	97.29 303	83.04 283	87.43 266	95.31 256

UniMVSNet (Re)		93.31 138	92.55 146	95.61 138	95.39 223	93.34 70	97.39 109	98.71 5	93.14 80	90.10 210	94.83 234	87.71 105	98.03 232	91.67 146	83.99 306	95.46 242

MVS-HIRNet		82.47 313	81.21 315	86.26 328	95.38 224	69.21 351	88.96 344	89.49 348	66.28 347	80.79 328	74.08 350	68.48 320	97.39 298	71.93 338	95.47 164	92.18 334

PatchmatchNet		91.91 188	91.35 183	93.59 230	95.38 224	84.11 296	93.15 314	95.39 279	89.54 184	92.10 167	93.68 289	82.82 179	98.13 212	84.81 266	95.32 167	98.52 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl-mvsnet_		90.96 232	90.32 221	92.89 257	95.37 226	86.21 268	94.46 280	96.64 234	87.82 238	88.15 264	94.18 271	82.98 174	97.54 284	87.70 216	85.59 282	94.92 277

cl-mvsnet1		90.97 231	90.33 220	92.88 258	95.36 227	86.19 269	94.46 280	96.63 237	87.82 238	88.18 263	94.23 268	82.99 173	97.53 286	87.72 214	85.57 283	94.93 275

miper_enhance_ethall		91.54 202	91.01 196	93.15 249	95.35 228	87.07 251	93.97 296	96.90 213	86.79 264	89.17 240	93.43 298	86.55 123	97.64 275	89.97 168	86.93 270	94.74 293

UniMVSNet_NR-MVSNet		93.37 136	92.67 141	95.47 149	95.34 229	92.83 80	97.17 133	98.58 10	92.98 89	90.13 206	95.80 190	88.37 99	97.85 257	91.71 142	83.93 307	95.73 234

ITE_SJBPF				92.43 269	95.34 229	85.37 280		95.92 260	91.47 130	87.75 272	96.39 165	71.00 305	97.96 244	82.36 290	89.86 245	93.97 313

OpenMVS	89.19 12	92.86 157	91.68 173	96.40 96	95.34 229	92.73 83	98.27 26	98.12 56	84.86 291	85.78 298	97.75 86	78.89 251	99.74 31	87.50 226	98.65 95	96.73 198

eth_miper_zixun_eth		91.02 228	90.59 213	92.34 272	95.33 232	84.35 292	94.10 293	96.90 213	88.56 218	88.84 247	94.33 260	84.08 156	97.60 280	88.77 200	84.37 303	95.06 268

miper_lstm_enhance		90.50 248	90.06 237	91.83 282	95.33 232	83.74 298	93.86 298	96.70 230	87.56 249	87.79 270	93.81 284	83.45 164	96.92 314	87.39 227	84.62 299	94.82 284

1314		92.81 161	92.03 161	95.14 158	95.33 232	89.52 183	96.04 226	97.44 161	87.72 245	86.25 294	95.33 215	83.84 158	98.79 161	89.26 188	97.05 138	97.11 186

test_part1		89.59 265	88.03 273	94.27 196	95.32 235	89.42 188	98.03 46	97.58 137	78.01 337	86.10 297	94.59 246	69.87 315	98.01 234	89.88 171	82.85 319	95.40 249

PAPM		91.52 203	90.30 223	95.20 155	95.30 236	89.83 173	93.38 310	96.85 220	86.26 270	88.59 252	95.80 190	84.88 145	98.15 211	75.67 328	95.93 156	97.63 172

Fast-Effi-MVS+-dtu		92.29 176	91.99 163	93.21 248	95.27 237	85.52 277	97.03 140	96.63 237	92.09 115	89.11 241	95.14 222	80.33 223	98.08 222	87.54 225	94.74 179	96.03 218

Patchmatch-test		89.42 267	87.99 274	93.70 225	95.27 237	85.11 283	88.98 343	94.37 318	81.11 322	87.10 284	93.69 287	82.28 191	97.50 289	74.37 331	94.76 177	98.48 129

PVSNet_0	82.17 19	85.46 304	83.64 307	90.92 302	95.27 237	79.49 332	90.55 335	95.60 273	83.76 305	83.00 322	89.95 329	71.09 304	97.97 240	82.75 287	60.79 350	95.31 256

IB-MVS	87.33 17	89.91 259	88.28 271	94.79 177	95.26 240	87.70 238	95.12 268	93.95 326	89.35 190	87.03 285	92.49 308	70.74 307	99.19 123	89.18 193	81.37 324	97.49 181
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
nrg030		94.05 116	93.31 126	96.27 107	95.22 241	94.59 28	98.34 20	97.46 151	92.93 91	91.21 188	96.64 146	87.23 117	98.22 203	94.99 77	85.80 281	95.98 219

MDTV_nov1_ep13			90.76 206		95.22 241	80.33 325	93.03 317	95.28 286	88.14 231	92.84 153	93.83 281	81.34 206	98.08 222	82.86 284	94.34 183

MVS		91.71 193	90.44 217	95.51 144	95.20 243	91.59 118	96.04 226	97.45 157	73.44 345	87.36 279	95.60 204	85.42 139	99.10 133	85.97 252	97.46 122	95.83 225

tfpnnormal		89.70 264	88.40 269	93.60 229	95.15 244	90.10 163	97.56 93	98.16 50	87.28 256	86.16 295	94.63 244	77.57 268	98.05 228	74.48 329	84.59 300	92.65 328

tpmrst		91.44 206	91.32 185	91.79 285	95.15 244	79.20 335	93.42 309	95.37 281	88.55 219	93.49 136	93.67 290	82.49 187	98.27 200	90.41 162	89.34 249	97.90 159

WR-MVS		92.34 172	91.53 178	94.77 178	95.13 246	90.83 148	96.40 200	97.98 98	91.88 121	89.29 236	95.54 209	82.50 186	97.80 262	89.79 174	85.27 288	95.69 235

tpm cat1		88.36 280	87.21 283	91.81 284	95.13 246	80.55 323	92.58 322	95.70 268	74.97 342	87.45 275	91.96 318	78.01 265	98.17 210	80.39 304	88.74 255	96.72 199

WR-MVS_H		92.00 186	91.35 183	93.95 212	95.09 248	89.47 184	98.04 45	98.68 7	91.46 131	88.34 256	94.68 241	85.86 134	97.56 282	85.77 255	84.24 304	94.82 284

CP-MVSNet		91.89 189	91.24 190	93.82 219	95.05 249	88.57 214	97.82 64	98.19 44	91.70 124	88.21 262	95.76 195	81.96 197	97.52 288	87.86 211	84.65 297	95.37 253

DWT-MVSNet_test		90.76 237	89.89 241	93.38 240	95.04 250	83.70 301	95.85 237	94.30 321	88.19 226	90.46 196	92.80 303	73.61 294	98.50 186	88.16 206	90.58 236	97.95 157

test_0402		86.46 295	84.79 301	91.45 293	95.02 251	85.55 276	96.29 212	94.89 304	80.90 323	82.21 323	93.97 279	68.21 322	97.29 303	62.98 348	88.68 257	91.51 338

cascas		91.20 220	90.08 234	94.58 186	94.97 252	89.16 203	93.65 305	97.59 136	79.90 330	89.40 231	92.92 302	75.36 283	98.36 195	92.14 130	94.75 178	96.23 207

PS-CasMVS		91.55 201	90.84 203	93.69 226	94.96 253	88.28 221	97.84 63	98.24 34	91.46 131	88.04 266	95.80 190	79.67 235	97.48 290	87.02 235	84.54 301	95.31 256

DU-MVS		92.90 155	92.04 160	95.49 146	94.95 254	92.83 80	97.16 134	98.24 34	93.02 83	90.13 206	95.71 198	83.47 162	97.85 257	91.71 142	83.93 307	95.78 228

NR-MVSNet		92.34 172	91.27 189	95.53 143	94.95 254	93.05 75	97.39 109	98.07 70	92.65 100	84.46 308	95.71 198	85.00 144	97.77 266	89.71 175	83.52 313	95.78 228

RRT_MVS		93.21 141	92.32 155	95.91 122	94.92 256	94.15 43	96.92 155	96.86 219	91.42 133	91.28 185	96.43 161	79.66 236	98.10 217	93.29 114	90.06 242	95.46 242

tpmvs		89.83 263	89.15 261	91.89 280	94.92 256	80.30 326	93.11 315	95.46 278	86.28 269	88.08 265	92.65 305	80.44 220	98.52 185	81.47 295	89.92 244	96.84 195

PMMVS		92.86 157	92.34 153	94.42 191	94.92 256	86.73 257	94.53 277	96.38 246	84.78 293	94.27 119	95.12 224	83.13 169	98.40 192	91.47 149	96.49 149	98.12 150

tpm2		89.96 258	89.21 259	92.23 274	94.91 259	81.25 317	93.78 300	94.42 316	80.62 327	91.56 174	93.44 296	76.44 275	97.94 247	85.60 257	92.08 215	97.49 181

TinyColmap		86.82 293	85.35 297	91.21 298	94.91 259	82.99 307	93.94 297	94.02 325	83.58 306	81.56 325	94.68 241	62.34 342	98.13 212	75.78 326	87.35 269	92.52 330

UniMVSNet_ETH3D		91.34 214	90.22 230	94.68 181	94.86 261	87.86 235	97.23 128	97.46 151	87.99 233	89.90 215	96.92 132	66.35 331	98.23 202	90.30 165	90.99 231	97.96 155

CostFormer		91.18 224	90.70 209	92.62 266	94.84 262	81.76 315	94.09 294	94.43 315	84.15 299	92.72 154	93.77 285	79.43 239	98.20 206	90.70 160	92.18 211	97.90 159

MIMVSNet		88.50 279	86.76 287	93.72 224	94.84 262	87.77 237	91.39 327	94.05 323	86.41 268	87.99 268	92.59 307	63.27 339	95.82 329	77.44 318	92.84 200	97.57 179

FMVSNet5		87.29 290	85.79 293	91.78 286	94.80 264	87.28 242	95.49 251	95.28 286	84.09 300	83.85 318	91.82 319	62.95 340	94.17 341	78.48 315	85.34 287	93.91 314

TranMVSNet+NR-MVSNet		92.50 165	91.63 174	95.14 158	94.76 265	92.07 104	97.53 95	98.11 59	92.90 92	89.56 227	96.12 175	83.16 167	97.60 280	89.30 186	83.20 316	95.75 232

XXY-MVS		92.16 182	91.23 191	94.95 168	94.75 266	90.94 144	97.47 102	97.43 164	89.14 196	88.90 243	96.43 161	79.71 234	98.24 201	89.56 180	87.68 263	95.67 236

EPMVS		90.70 242	89.81 245	93.37 241	94.73 267	84.21 294	93.67 304	88.02 349	89.50 186	92.38 158	93.49 294	77.82 267	97.78 264	86.03 251	92.68 202	98.11 153

D2MVS		91.30 216	90.95 197	92.35 271	94.71 268	85.52 277	96.18 221	98.21 40	88.89 205	86.60 291	93.82 283	79.92 231	97.95 246	89.29 187	90.95 232	93.56 317

USDC		88.94 271	87.83 276	92.27 273	94.66 269	84.96 286	93.86 298	95.90 261	87.34 254	83.40 319	95.56 207	67.43 325	98.19 208	82.64 289	89.67 247	93.66 316

MVS_0304		88.79 275	87.57 277	92.46 267	94.65 270	86.15 271	96.40 200	97.17 185	86.44 267	88.02 267	91.71 322	56.68 347	97.03 308	84.47 271	92.58 204	94.19 309

GA-MVS		91.38 209	90.31 222	94.59 182	94.65 270	87.62 239	94.34 285	96.19 254	90.73 154	90.35 199	93.83 281	71.84 299	97.96 244	87.22 231	93.61 194	98.21 147

OPM-MVS		93.28 139	92.76 136	94.82 172	94.63 272	90.77 151	96.65 179	97.18 183	93.72 57	91.68 173	97.26 116	79.33 241	98.63 176	92.13 131	92.28 207	95.07 267

test-LLR		91.42 207	91.19 193	92.12 275	94.59 273	80.66 320	94.29 288	92.98 332	91.11 147	90.76 192	92.37 310	79.02 246	98.07 225	88.81 198	96.74 142	97.63 172

test-mter		90.19 255	89.54 254	92.12 275	94.59 273	80.66 320	94.29 288	92.98 332	87.68 246	90.76 192	92.37 310	67.67 323	98.07 225	88.81 198	96.74 142	97.63 172

dp		88.90 273	88.26 272	90.81 304	94.58 275	76.62 341	92.85 319	94.93 303	85.12 287	90.07 213	93.07 300	75.81 278	98.12 215	80.53 303	87.42 267	97.71 169

PEN-MVS		91.20 220	90.44 217	93.48 235	94.49 276	87.91 234	97.76 69	98.18 46	91.29 138	87.78 271	95.74 197	80.35 222	97.33 301	85.46 259	82.96 317	95.19 265

gg-mvs-nofinetune		87.82 285	85.61 294	94.44 189	94.46 277	89.27 199	91.21 331	84.61 354	80.88 324	89.89 217	74.98 348	71.50 301	97.53 286	85.75 256	97.21 134	96.51 202

CR-MVSNet		90.82 236	89.77 247	93.95 212	94.45 278	87.19 247	90.23 337	95.68 271	86.89 262	92.40 156	92.36 313	80.91 212	97.05 307	81.09 301	93.95 189	97.60 177

RPMNet		88.98 270	87.05 285	94.77 178	94.45 278	87.19 247	90.23 337	98.03 84	77.87 340	92.40 156	87.55 341	80.17 226	99.51 93	68.84 344	93.95 189	97.60 177

TESTMET0.1,1		90.06 257	89.42 255	91.97 278	94.41 280	80.62 322	94.29 288	91.97 340	87.28 256	90.44 197	92.47 309	68.79 318	97.67 272	88.50 204	96.60 147	97.61 176

TransMVSNet (Re)		88.94 271	87.56 278	93.08 252	94.35 281	88.45 219	97.73 73	95.23 290	87.47 250	84.26 311	95.29 216	79.86 232	97.33 301	79.44 312	74.44 339	93.45 320

MS-PatchMatch		90.27 251	89.77 247	91.78 286	94.33 282	84.72 290	95.55 248	96.73 224	86.17 272	86.36 293	95.28 218	71.28 303	97.80 262	84.09 274	98.14 108	92.81 325

baseline2		91.63 196	90.86 200	93.94 214	94.33 282	86.32 264	95.92 234	91.64 342	89.37 189	86.94 287	94.69 240	81.62 204	98.69 171	88.64 202	94.57 181	96.81 196

XVG-ACMP-BASELINE		90.93 233	90.21 231	93.09 251	94.31 284	85.89 272	95.33 257	97.26 179	91.06 149	89.38 232	95.44 213	68.61 319	98.60 179	89.46 182	91.05 229	94.79 289

pm-mvs1		90.72 241	89.65 253	93.96 211	94.29 285	89.63 175	97.79 67	96.82 222	89.07 197	86.12 296	95.48 212	78.61 254	97.78 264	86.97 236	81.67 322	94.46 300

v8		91.29 217	90.53 216	93.57 232	94.15 286	88.12 229	97.34 113	97.06 198	88.99 200	88.32 257	94.26 267	83.08 170	98.01 234	87.62 223	83.92 309	94.57 298

v10		91.04 227	90.23 228	93.49 234	94.12 287	88.16 228	97.32 116	97.08 195	88.26 225	88.29 259	94.22 270	82.17 194	97.97 240	86.45 242	84.12 305	94.33 304

Patchmtry		88.64 278	87.25 281	92.78 262	94.09 288	86.64 258	89.82 340	95.68 271	80.81 326	87.63 274	92.36 313	80.91 212	97.03 308	78.86 314	85.12 291	94.67 295

PatchT		88.87 274	87.42 279	93.22 247	94.08 289	85.10 284	89.51 341	94.64 312	81.92 317	92.36 159	88.15 339	80.05 228	97.01 311	72.43 336	93.65 192	97.54 180

V42		91.58 199	90.87 199	93.73 222	94.05 290	88.50 217	97.32 116	96.97 206	88.80 212	89.71 220	94.33 260	82.54 185	98.05 228	89.01 195	85.07 292	94.64 297

DTE-MVSNet		90.56 245	89.75 249	93.01 253	93.95 291	87.25 244	97.64 88	97.65 130	90.74 153	87.12 282	95.68 201	79.97 230	97.00 312	83.33 280	81.66 323	94.78 291

tpm		90.25 252	89.74 250	91.76 288	93.92 292	79.73 331	93.98 295	93.54 328	88.28 224	91.99 169	93.25 299	77.51 269	97.44 294	87.30 230	87.94 261	98.12 150

PS-MVSNAJss		93.74 126	93.51 118	94.44 189	93.91 293	89.28 198	97.75 70	97.56 141	92.50 103	89.94 214	96.54 156	88.65 94	98.18 209	93.83 104	90.90 233	95.86 221

v1144		91.37 211	90.60 212	93.68 227	93.89 294	88.23 224	96.84 162	97.03 203	88.37 222	89.69 222	94.39 256	82.04 195	97.98 237	87.80 213	85.37 286	94.84 281

v2v482		91.59 197	90.85 202	93.80 220	93.87 295	88.17 227	96.94 154	96.88 216	89.54 184	89.53 228	94.90 230	81.70 203	98.02 233	89.25 189	85.04 294	95.20 264

v148		90.99 229	90.38 219	92.81 261	93.83 296	85.80 273	96.78 169	96.68 231	89.45 187	88.75 250	93.93 280	82.96 176	97.82 261	87.83 212	83.25 314	94.80 287

Baseline_NR-MVSNet		91.20 220	90.62 211	92.95 256	93.83 296	88.03 230	97.01 147	95.12 295	88.42 221	89.70 221	95.13 223	83.47 162	97.44 294	89.66 178	83.24 315	93.37 321

EPNet_dtu		91.71 193	91.28 188	92.99 254	93.76 298	83.71 300	96.69 176	95.28 286	93.15 79	87.02 286	95.95 182	83.37 165	97.38 299	79.46 311	96.84 139	97.88 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 225	90.23 228	93.58 231	93.70 299	87.82 236	96.73 171	97.07 196	87.77 242	89.58 225	94.32 262	80.90 214	97.97 240	86.52 240	85.48 284	94.95 271

GG-mvs-BLEND				93.62 228	93.69 300	89.20 200	92.39 325	83.33 355		87.98 269	89.84 331	71.00 305	96.87 315	82.08 292	95.40 166	94.80 287

v144192		91.06 226	90.28 224	93.39 239	93.66 301	87.23 246	96.83 163	97.07 196	87.43 251	89.69 222	94.28 264	81.48 205	98.00 236	87.18 233	84.92 296	94.93 275

v1921920		90.85 235	90.03 238	93.29 244	93.55 302	86.96 254	96.74 170	97.04 201	87.36 253	89.52 229	94.34 259	80.23 225	97.97 240	86.27 243	85.21 289	94.94 273

v7n		90.76 237	89.86 242	93.45 238	93.54 303	87.60 240	97.70 79	97.37 170	88.85 206	87.65 273	94.08 275	81.08 209	98.10 217	84.68 268	83.79 311	94.66 296

JIA-IIPM		88.26 282	87.04 286	91.91 279	93.52 304	81.42 316	89.38 342	94.38 317	80.84 325	90.93 191	80.74 346	79.22 242	97.92 251	82.76 286	91.62 219	96.38 206

v1240		90.70 242	89.85 243	93.23 246	93.51 305	86.80 255	96.61 185	97.02 204	87.16 258	89.58 225	94.31 263	79.55 238	97.98 237	85.52 258	85.44 285	94.90 278

test_djsdf		93.07 146	92.76 136	94.00 207	93.49 306	88.70 212	98.22 32	97.57 138	91.42 133	90.08 212	95.55 208	82.85 178	97.92 251	94.07 95	91.58 220	95.40 249

SixPastTwentyTwo		89.15 269	88.54 268	90.98 301	93.49 306	80.28 327	96.70 174	94.70 309	90.78 152	84.15 313	95.57 205	71.78 300	97.71 270	84.63 269	85.07 292	94.94 273

mvs_tets		92.31 174	91.76 169	93.94 214	93.41 308	88.29 220	97.63 89	97.53 142	92.04 117	88.76 249	96.45 160	74.62 286	98.09 221	93.91 100	91.48 222	95.45 244

OurMVSNet-221017-0		90.51 247	90.19 232	91.44 294	93.41 308	81.25 317	96.98 150	96.28 249	91.68 125	86.55 292	96.30 168	74.20 289	97.98 237	88.96 196	87.40 268	95.09 266

pmmvs4		90.93 233	89.85 243	94.17 200	93.34 310	90.79 150	94.60 274	96.02 258	84.62 294	87.45 275	95.15 221	81.88 200	97.45 293	87.70 216	87.87 262	94.27 308

jajsoiax		92.42 169	91.89 167	94.03 206	93.33 311	88.50 217	97.73 73	97.53 142	92.00 119	88.85 246	96.50 158	75.62 282	98.11 216	93.88 102	91.56 221	95.48 239

gm-plane-assit					93.22 312	78.89 338			84.82 292		93.52 293		98.64 175	87.72 214

MVP-Stereo		90.74 240	90.08 234	92.71 263	93.19 313	88.20 225	95.86 236	96.27 250	86.07 273	84.86 306	94.76 237	77.84 266	97.75 267	83.88 278	98.01 110	92.17 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 277	88.90 263	88.20 321	93.15 314	74.21 345	96.63 184	94.22 322	85.18 285	87.32 280	95.97 180	76.16 277	94.98 337	85.27 261	86.17 277	95.41 245

MDA-MVSNet-bldmvs		85.00 305	82.95 309	91.17 300	93.13 315	83.33 304	94.56 276	95.00 299	84.57 295	65.13 349	92.65 305	70.45 308	95.85 327	73.57 334	77.49 332	94.33 304

K. test v3		87.64 287	86.75 288	90.32 312	93.02 316	79.48 333	96.61 185	92.08 339	90.66 158	80.25 333	94.09 274	67.21 327	96.65 319	85.96 253	80.83 326	94.83 282

pmmvs5		89.86 262	88.87 264	92.82 260	92.86 317	86.23 267	96.26 214	95.39 279	84.24 298	87.12 282	94.51 249	74.27 288	97.36 300	87.61 224	87.57 264	94.86 280

testgi		87.97 283	87.21 283	90.24 313	92.86 317	80.76 319	96.67 178	94.97 301	91.74 123	85.52 300	95.83 188	62.66 341	94.47 340	76.25 325	88.36 259	95.48 239

EPNet		95.20 86	94.56 93	97.14 68	92.80 319	92.68 84	97.85 62	94.87 308	96.64 1	92.46 155	97.80 84	86.23 127	99.65 53	93.72 105	98.62 96	99.10 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 316	78.71 318	78.79 331	92.80 319	46.50 360	94.14 292	43.71 362	78.61 335	80.83 327	91.66 323	74.94 285	96.36 321	67.24 345	84.45 302	93.50 318

EG-PatchMatch MVS		87.02 292	85.44 295	91.76 288	92.67 321	85.00 285	96.08 225	96.45 243	83.41 309	79.52 335	93.49 294	57.10 346	97.72 269	79.34 313	90.87 234	92.56 329

Gipuma		67.86 320	65.41 323	75.18 334	92.66 322	73.45 346	66.50 354	94.52 314	53.33 352	57.80 352	66.07 352	30.81 355	89.20 349	48.15 352	78.88 331	62.90 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 182	91.55 177	93.97 210	92.58 323	89.55 180	97.51 96	97.42 165	89.42 188	88.40 255	94.84 233	80.66 216	97.88 256	91.87 138	91.28 226	94.48 299

test0.0.03 1		89.37 268	88.70 265	91.41 295	92.47 324	85.63 275	95.22 265	92.70 334	91.11 147	86.91 289	93.65 291	79.02 246	93.19 346	78.00 317	89.18 250	95.41 245

our_test_3		88.78 276	87.98 275	91.20 299	92.45 325	82.53 309	93.61 307	95.69 269	85.77 277	84.88 305	93.71 286	79.99 229	96.78 318	79.47 310	86.24 276	94.28 307

ppachtmachnet_test		88.35 281	87.29 280	91.53 291	92.45 325	83.57 303	93.75 301	95.97 259	84.28 297	85.32 304	94.18 271	79.00 250	96.93 313	75.71 327	84.99 295	94.10 310

YYNet1		85.87 301	84.23 305	90.78 307	92.38 327	82.46 311	93.17 312	95.14 294	82.12 316	67.69 345	92.36 313	78.16 262	95.50 335	77.31 320	79.73 328	94.39 302

MDA-MVSNet_test_wron		85.87 301	84.23 305	90.80 306	92.38 327	82.57 308	93.17 312	95.15 293	82.15 315	67.65 346	92.33 316	78.20 259	95.51 334	77.33 319	79.74 327	94.31 306

LF4IMVS		87.94 284	87.25 281	89.98 315	92.38 327	80.05 330	94.38 283	95.25 289	87.59 248	84.34 309	94.74 239	64.31 338	97.66 274	84.83 265	87.45 265	92.23 333

lessismore_v0				90.45 310	91.96 330	79.09 337		87.19 352		80.32 332	94.39 256	66.31 332	97.55 283	84.00 276	76.84 334	94.70 294

pmmvs6		87.81 286	86.19 290	92.69 264	91.32 331	86.30 265	97.34 113	96.41 245	80.59 328	84.05 316	94.37 258	67.37 326	97.67 272	84.75 267	79.51 329	94.09 312

Anonymous20231206		87.09 291	86.14 291	89.93 316	91.22 332	80.35 324	96.11 223	95.35 282	83.57 307	84.16 312	93.02 301	73.54 295	95.61 331	72.16 337	86.14 278	93.84 315

KD-MVS_2432*1600		84.81 307	82.64 310	91.31 296	91.07 333	85.34 281	91.22 329	95.75 266	85.56 280	83.09 320	90.21 327	67.21 327	95.89 325	77.18 322	62.48 348	92.69 326

CL-MVSNet_2432×1600		84.81 307	82.64 310	91.31 296	91.07 333	85.34 281	91.22 329	95.75 266	85.56 280	83.09 320	90.21 327	67.21 327	95.89 325	77.18 322	62.48 348	92.69 326

DeepMVS_CX				74.68 335	90.84 335	64.34 355		81.61 357	65.34 348	67.47 347	88.01 340	48.60 352	80.13 354	62.33 349	73.68 341	79.58 348

test20.03		86.14 298	85.40 296	88.35 319	90.12 336	80.06 329	95.90 235	95.20 291	88.59 215	81.29 326	93.62 292	71.43 302	92.65 347	71.26 341	81.17 325	92.34 332

OpenMVS_ROB	81.14 20	84.42 309	82.28 312	90.83 303	90.06 337	84.05 297	95.73 242	94.04 324	73.89 344	80.17 334	91.53 324	59.15 344	97.64 275	66.92 346	89.05 251	90.80 341

UnsupCasMVSNet_eth		85.99 299	84.45 303	90.62 308	89.97 338	82.40 312	93.62 306	97.37 170	89.86 177	78.59 338	92.37 310	65.25 337	95.35 336	82.27 291	70.75 343	94.10 310

DSMNet-mixed		86.34 296	86.12 292	87.00 326	89.88 339	70.43 348	94.93 270	90.08 347	77.97 339	85.42 303	92.78 304	74.44 287	93.96 342	74.43 330	95.14 169	96.62 200

new_pmnet		82.89 312	81.12 316	88.18 322	89.63 340	80.18 328	91.77 326	92.57 335	76.79 341	75.56 342	88.23 338	61.22 343	94.48 339	71.43 339	82.92 318	89.87 343

MIMVSNet1		84.93 306	83.05 308	90.56 309	89.56 341	84.84 289	95.40 254	95.35 282	83.91 301	80.38 331	92.21 317	57.23 345	93.34 345	70.69 343	82.75 321	93.50 318

DIV-MVS_2432*1600		85.95 300	84.95 299	88.96 318	89.55 342	79.11 336	95.13 267	96.42 244	85.91 275	84.07 315	90.48 325	70.03 313	94.82 338	80.04 305	72.94 342	92.94 323

CMPMVS	62.92 21	85.62 303	84.92 300	87.74 323	89.14 343	73.12 347	94.17 291	96.80 223	73.98 343	73.65 343	94.93 228	66.36 330	97.61 279	83.95 277	91.28 226	92.48 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		87.38 288	86.24 289	90.81 304	88.74 344	78.40 339	88.12 345	93.17 331	87.11 259	82.17 324	89.29 333	81.95 198	95.60 332	88.64 202	77.02 333	98.41 137

pmmvs-eth3d		86.22 297	84.45 303	91.53 291	88.34 345	87.25 244	94.47 278	95.01 298	83.47 308	79.51 336	89.61 332	69.75 316	95.71 330	83.13 282	76.73 335	91.64 336

UnsupCasMVSNet_bld		82.13 314	79.46 317	90.14 314	88.00 346	82.47 310	90.89 334	96.62 239	78.94 334	75.61 340	84.40 344	56.63 348	96.31 322	77.30 321	66.77 347	91.63 337

PM-MVS		83.48 310	81.86 314	88.31 320	87.83 347	77.59 340	93.43 308	91.75 341	86.91 261	80.63 329	89.91 330	44.42 353	95.84 328	85.17 264	76.73 335	91.50 339

testing_2		87.33 289	85.03 298	94.22 198	87.77 348	89.32 195	94.97 269	97.11 191	89.22 193	71.64 344	88.73 334	55.16 349	97.94 247	91.95 135	88.73 256	95.41 245

new-patchmatchnet		83.18 311	81.87 313	87.11 325	86.88 349	75.99 343	93.70 302	95.18 292	85.02 289	77.30 339	88.40 336	65.99 334	93.88 343	74.19 333	70.18 344	91.47 340

ambc				86.56 327	83.60 350	70.00 350	85.69 347	94.97 301		80.60 330	88.45 335	37.42 354	96.84 316	82.69 288	75.44 337	92.86 324

pmmvs3		79.97 315	77.50 319	87.39 324	82.80 351	79.38 334	92.70 321	90.75 346	70.69 346	78.66 337	87.47 342	51.34 351	93.40 344	73.39 335	69.65 345	89.38 344

TDRefinement		86.53 294	84.76 302	91.85 281	82.23 352	84.25 293	96.38 203	95.35 282	84.97 290	84.09 314	94.94 227	65.76 336	98.34 198	84.60 270	74.52 338	92.97 322

PMMVS2		70.19 319	66.92 322	80.01 330	76.35 353	65.67 353	86.22 346	87.58 351	64.83 349	62.38 350	80.29 347	26.78 359	88.49 350	63.79 347	54.07 351	85.88 345

FPMVS		71.27 318	69.85 320	75.50 333	74.64 354	59.03 356	91.30 328	91.50 343	58.80 350	57.92 351	88.28 337	29.98 357	85.53 352	53.43 350	82.84 320	81.95 347

E-PMN		53.28 323	52.56 327	55.43 338	74.43 355	47.13 359	83.63 350	76.30 358	42.23 354	42.59 355	62.22 354	28.57 358	74.40 355	31.53 355	31.51 353	44.78 352

wuyk23d		25.11 327	24.57 331	26.74 341	73.98 356	39.89 363	57.88 355	9.80 363	12.27 358	10.39 359	6.97 361	7.03 363	36.44 359	25.43 357	17.39 357	3.89 357

EMVS		52.08 325	51.31 328	54.39 339	72.62 357	45.39 361	83.84 349	75.51 359	41.13 355	40.77 356	59.65 355	30.08 356	73.60 356	28.31 356	29.90 355	44.18 353

LCM-MVSNet		72.55 317	69.39 321	82.03 329	70.81 358	65.42 354	90.12 339	94.36 319	55.02 351	65.88 348	81.72 345	24.16 361	89.96 348	74.32 332	68.10 346	90.71 342

MVE	50.73 23	53.25 324	48.81 329	66.58 337	65.34 359	57.50 357	72.49 353	70.94 360	40.15 356	39.28 357	63.51 353	6.89 364	73.48 357	38.29 354	42.38 352	68.76 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		63.94 321	59.58 324	77.02 332	61.24 360	66.06 352	85.66 348	87.93 350	78.53 336	42.94 354	71.04 351	25.42 360	80.71 353	52.60 351	30.83 354	84.28 346

PMVS	53.92 22	58.58 322	55.40 325	68.12 336	51.00 361	48.64 358	78.86 351	87.10 353	46.77 353	35.84 358	74.28 349	8.76 362	86.34 351	42.07 353	73.91 340	69.38 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 326	53.82 326	46.29 340	33.73 362	45.30 362	78.32 352	67.24 361	18.02 357	50.93 353	87.05 343	52.99 350	53.11 358	70.76 342	25.29 356	40.46 354

testmvs		13.36 329	16.33 332	4.48 343	5.04 363	2.26 365	93.18 311	3.28 364	2.70 359	8.24 360	21.66 357	2.29 366	2.19 360	7.58 358	2.96 358	9.00 356

test123		13.04 330	15.66 333	5.18 342	4.51 364	3.45 364	92.50 324	1.81 365	2.50 360	7.58 361	20.15 358	3.67 365	2.18 361	7.13 359	1.07 359	9.90 355

uanet_test		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

cdsmvs_eth3d_5k		23.24 328	30.99 330	0.00 344	0.00 365	0.00 366	0.00 356	97.63 132	0.00 361	0.00 362	96.88 134	84.38 151	0.00 362	0.00 360	0.00 360	0.00 358

pcd_1.5k_mvsjas		7.39 332	9.85 335	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	88.65 94	0.00 362	0.00 360	0.00 360	0.00 358

sosnet-low-res		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

sosnet		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

uncertanet		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

Regformer		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

ab-mvs-re		8.06 331	10.74 334	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	96.69 143	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

uanet		0.00 333	0.00 336	0.00 344	0.00 365	0.00 366	0.00 356	0.00 366	0.00 361	0.00 362	0.00 362	0.00 367	0.00 362	0.00 360	0.00 360	0.00 358

test_241102_TWO								98.27 28	95.13 15	98.93 6	98.89 4	94.99 8	99.85 14	97.52 2	99.65 10	99.74 5

test_0728_THIRD									94.78 31	98.73 8	98.87 6	95.87 2	99.84 19	97.45 6	99.72 2	99.77 1

GSMVS																98.45 132

sam_mvs1												82.76 180				98.45 132

sam_mvs												81.94 199

MTGPA								98.08 64

test_post1							92.81 320				16.58 360	80.53 218	97.68 271	86.20 245

test_post											17.58 359	81.76 201	98.08 222

patchmatchnet-post											90.45 326	82.65 184	98.10 217

MTMP							97.86 59	82.03 356

test9_res														94.81 83	99.38 48	99.45 45

agg_prior2														93.94 99	99.38 48	99.50 37

test_prior4						93.66 59	96.42 196

test_prior2							96.35 205		92.80 95	96.03 78	97.59 101	92.01 41		95.01 74	99.38 48

旧先验2							95.94 233		81.66 319	97.34 34			98.82 159	92.26 125

新几何2							95.79 240

无先验							95.79 240	97.87 108	83.87 304				99.65 53	87.68 219		98.89 100

原ACMM2							95.67 243

testdata2													99.67 49	85.96 253

segment_acmp												92.89 22

testdata1							95.26 264		93.10 82

plane_prior5								97.51 144					98.60 179	93.02 119	92.23 208	95.86 221

plane_prior4											96.64 146

plane_prior3						90.00 165			94.46 39	91.34 179

plane_prior2							97.74 71		94.85 24

plane_prior						89.99 167	97.24 123		94.06 47						92.16 212

n2								0.00 366

nn								0.00 366

door-mid								91.06 345

test11								97.88 106

door								91.13 344

HQP5-MVS						89.33 193

BP-MVS														92.13 131

HQP4-MVS										90.14 202			98.50 186			95.78 228

HQP3-MVS								97.39 167							92.10 213

HQP2-MVS												80.95 210

MDTV_nov1_ep13_2view						70.35 349	93.10 316		83.88 303	93.55 133		82.47 188		86.25 244		98.38 140

ACMMP++_ref															90.30 241

ACMMP++															91.02 230

Test By Simon												88.73 93

High-resolution multi-view benchmark