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.

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

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

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

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.
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

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

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

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

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

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
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

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

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

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

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
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_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

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

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
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

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

QAPM		93.45 135	92.27 156	96.98 74	96.77 162	92.62 86	98.39 19	98.12 56	84.50 294	88.27 260	97.77 85	82.39 190	99.81 26	85.40 260	98.81 89	98.51 124

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	32.69 354	91.70 50	99.80 27	95.66 52	99.40 45	99.62 13

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

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

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	84.86 289	85.78 298	97.75 86	78.89 251	99.74 31	87.50 226	98.65 95	96.73 198

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

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
LS3D		93.57 132	92.61 144	96.47 91	97.59 130	91.61 116	97.67 81	97.72 122	85.17 284	90.29 200	98.34 41	84.60 148	99.73 32	83.85 279	98.27 103	98.06 154

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

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

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

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

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

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

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
Skip Steuart: Steuart Systems R&D Blog.
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

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
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

DP-MVS		92.76 162	91.51 181	96.52 85	98.77 57	90.99 141	97.38 111	96.08 257	82.38 312	89.29 236	97.87 75	83.77 159	99.69 44	81.37 299	96.69 145	98.89 100

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

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

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

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

新几何1				97.32 56	98.60 68	93.59 61		97.75 117	81.58 318	95.75 90	97.85 78	90.04 82	99.67 49	86.50 241	99.13 75	98.69 115

testdata2													99.67 49	85.96 253

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

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

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

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

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

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

无先验							95.79 240	97.87 108	83.87 302				99.65 53	87.68 219		98.89 100

1121		94.71 102	93.83 107	97.34 55	98.57 72	93.64 60	96.04 226	97.73 119	81.56 319	95.68 93	97.85 78	90.23 78	99.65 53	87.68 219	99.12 78	98.73 111

EPNet		95.20 86	94.56 93	97.14 68	92.80 319	92.68 84	97.85 62	94.87 306	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
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
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

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

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

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

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
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

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

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

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

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

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
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
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

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

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

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

Anonymous20240529		91.98 187	90.73 208	95.73 132	98.14 103	89.40 189	97.99 47	97.72 122	79.63 329	93.54 134	97.41 111	69.94 314	99.56 80	91.04 156	91.11 228	98.22 146

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

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	79.41 330	88.90 243	96.56 155	84.04 157	99.55 83	77.01 322	97.30 131	97.01 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
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

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

Anonymous202405211		92.07 185	90.83 204	95.76 127	98.19 100	88.75 210	97.58 91	95.00 297	86.00 274	93.64 131	97.45 109	66.24 331	99.53 88	90.68 161	92.71 201	99.01 87

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

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

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

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
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

RPMNet		88.98 270	87.05 285	94.77 178	94.45 278	87.19 247	90.23 335	98.03 84	77.87 338	92.40 156	87.55 339	80.17 226	99.51 93	68.84 342	93.95 189	97.60 177

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
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

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

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

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

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

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

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

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

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

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
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

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

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

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

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

114514_t		93.95 119	93.06 130	96.63 80	99.07 39	91.61 116	97.46 104	97.96 100	77.99 336	93.00 147	97.57 103	86.14 132	99.33 113	89.22 190	99.15 73	98.94 94

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

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	82.83 310	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
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

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

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

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

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

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

IB-MVS	87.33 17	89.91 259	88.28 271	94.79 177	95.26 240	87.70 238	95.12 268	93.95 324	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
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

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

LFMVS		93.60 130	92.63 142	96.52 85	98.13 104	91.27 129	97.94 54	93.39 328	90.57 165	96.29 70	98.31 47	69.00 317	99.16 127	94.18 94	95.87 157	99.12 77

AllTest		90.23 253	88.98 262	93.98 208	97.94 111	86.64 258	96.51 192	95.54 274	85.38 280	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		95.54 274	85.38 280	85.49 301	96.77 137	70.28 310	99.15 128	80.02 306	92.87 198	96.15 212

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

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

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

MVS		91.71 193	90.44 217	95.51 144	95.20 243	91.59 118	96.04 226	97.45 157	73.44 343	87.36 279	95.60 204	85.42 139	99.10 133	85.97 252	97.46 122	95.83 225

thres600view7		92.49 167	91.60 175	95.18 156	97.91 114	89.47 184	97.65 84	94.66 308	92.18 114	93.33 140	94.91 229	78.06 263	99.10 133	81.61 293	94.06 188	96.98 188

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	85.38 280	89.65 224	95.18 220	85.86 134	99.10 133	87.70 216	93.58 196	98.49 127

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

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

thres100view900		92.43 168	91.58 176	94.98 165	97.92 113	89.37 191	97.71 78	94.66 308	92.20 110	93.31 141	94.90 230	78.06 263	99.08 138	81.40 296	94.08 185	96.48 204

tfpn200view9		92.38 171	91.52 179	94.95 168	97.85 117	89.29 196	97.41 105	94.88 303	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	94.88 303	92.19 112	93.27 143	94.46 254	78.17 260	99.08 138	81.40 296	94.08 185	96.98 188

tttt0517		92.96 151	92.33 154	94.87 171	97.11 144	87.16 249	97.97 52	92.09 336	90.63 160	93.88 128	97.01 128	76.50 273	99.06 141	90.29 166	95.45 165	98.38 140

thisisatest0530		93.03 148	92.21 157	95.49 146	97.07 146	89.11 204	97.49 101	92.19 335	90.16 172	94.09 122	96.41 163	76.43 276	99.05 142	90.38 163	95.68 163	98.31 144

PVSNet	86.66 18	92.24 179	91.74 172	93.73 222	97.77 120	83.69 300	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

thres200		92.23 180	91.39 182	94.75 180	97.61 128	89.03 205	96.60 187	95.09 294	92.08 116	93.28 142	94.00 277	78.39 258	99.04 144	81.26 300	94.18 184	96.19 209

thisisatest0515		92.29 176	91.30 187	95.25 154	96.60 167	88.90 208	94.36 284	92.32 334	87.92 235	93.43 138	94.57 247	77.28 270	99.00 145	89.42 183	95.86 158	97.86 162

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

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

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

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

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

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

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
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	81.83 316	91.52 175	97.23 118	87.94 102	98.91 153	71.31 338	98.37 101	98.17 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
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

mvs-test1		93.63 129	93.69 111	93.46 237	96.02 201	84.61 289	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

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

testdata				95.46 150	98.18 102	88.90 208		97.66 128	82.73 311	97.03 47	98.07 63	90.06 81	98.85 157	89.67 177	98.98 84	98.64 117

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

旧先验2							95.94 233		81.66 317	97.34 34			98.82 159	92.26 125

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

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

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

RPSCF		90.75 239	90.86 200	90.42 309	96.84 157	76.29 340	95.61 247	96.34 247	83.89 300	91.38 177	97.87 75	76.45 274	98.78 162	87.16 234	92.23 208	96.20 208

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.
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

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
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

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

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
baseline2		91.63 196	90.86 200	93.94 214	94.33 282	86.32 264	95.92 234	91.64 340	89.37 189	86.94 287	94.69 240	81.62 204	98.69 171	88.64 202	94.57 181	96.81 196

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

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

HyFIR lowres test		93.66 128	92.92 133	95.87 124	98.24 93	89.88 172	94.58 275	98.49 12	85.06 286	93.78 129	95.78 194	82.86 177	98.67 173	91.77 140	95.71 162	99.07 82

gm-plane-assit					93.22 312	78.89 336			84.82 290		93.52 293		98.64 175	87.72 214

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

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

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
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_prior5								97.51 144					98.60 179	93.02 119	92.23 208	95.86 221

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

BH-RMVSNet		92.72 163	91.97 164	94.97 166	97.16 141	87.99 231	96.15 222	95.60 271	90.62 161	91.87 171	97.15 122	78.41 257	98.57 182	83.16 281	97.60 120	98.36 142

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
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

tpmvs		89.83 263	89.15 261	91.89 280	94.92 256	80.30 324	93.11 315	95.46 276	86.28 269	88.08 265	92.65 305	80.44 220	98.52 185	81.47 295	89.92 244	96.84 195

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

DWT-MVSNet_test		90.76 237	89.89 241	93.38 240	95.04 250	83.70 299	95.85 237	94.30 319	88.19 226	90.46 196	92.80 303	73.61 294	98.50 186	88.16 206	90.58 236	97.95 157

HQP4-MVS										90.14 202			98.50 186			95.78 228

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

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

CHOSEN 280x420		93.12 144	92.72 140	94.34 194	96.71 165	87.27 243	90.29 334	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

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

PMMVS		92.86 157	92.34 153	94.42 191	94.92 256	86.73 257	94.53 277	96.38 246	84.78 291	94.27 119	95.12 224	83.13 169	98.40 192	91.47 149	96.49 149	98.12 150

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
cascas		91.20 220	90.08 234	94.58 186	94.97 252	89.16 203	93.65 305	97.59 136	79.90 328	89.40 231	92.92 302	75.36 283	98.36 195	92.14 130	94.75 178	96.23 207

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

TR-MVS		91.48 205	90.59 213	94.16 201	96.40 182	87.33 241	95.67 243	95.34 283	87.68 246	91.46 176	95.52 210	76.77 272	98.35 196	82.85 285	93.61 194	96.79 197

TDRefinement		86.53 294	84.76 302	91.85 281	82.23 350	84.25 291	96.38 203	95.35 280	84.97 288	84.09 314	94.94 227	65.76 334	98.34 198	84.60 270	74.52 338	92.97 322

Effi-MVS+-dtu		93.08 145	93.21 128	92.68 265	96.02 201	83.25 303	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

tpmrst		91.44 206	91.32 185	91.79 285	95.15 244	79.20 333	93.42 309	95.37 279	88.55 219	93.49 136	93.67 290	82.49 187	98.27 200	90.41 162	89.34 249	97.90 159

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

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	66.35 329	98.23 202	90.30 165	90.99 231	97.96 155

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

baseline1		92.82 160	91.90 166	95.55 142	97.20 139	90.77 151	97.19 131	94.58 311	92.20 110	92.36 159	96.34 167	84.16 155	98.21 204	89.20 192	83.90 310	97.68 171

RRT_test8_iter05		91.19 223	90.78 205	92.41 270	95.76 211	83.14 304	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

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

CostFormer		91.18 224	90.70 209	92.62 266	94.84 262	81.76 313	94.09 294	94.43 313	84.15 297	92.72 154	93.77 285	79.43 239	98.20 206	90.70 160	92.18 211	97.90 159

USDC		88.94 271	87.83 276	92.27 273	94.66 269	84.96 284	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

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

tpm cat1		88.36 280	87.21 283	91.81 284	95.13 246	80.55 321	92.58 322	95.70 266	74.97 340	87.45 275	91.96 318	78.01 265	98.17 210	80.39 304	88.74 255	96.72 199

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	75.67 326	95.93 156	97.63 172

Anonymous20231211		90.63 244	89.42 255	94.27 196	98.24 93	89.19 202	98.05 44	97.89 104	79.95 327	88.25 261	94.96 226	72.56 297	98.13 212	89.70 176	85.14 290	95.49 238

PatchmatchNet		91.91 188	91.35 183	93.59 230	95.38 224	84.11 294	93.15 314	95.39 277	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.
TinyColmap		86.82 293	85.35 297	91.21 296	94.91 259	82.99 305	93.94 297	94.02 323	83.58 304	81.56 323	94.68 241	62.34 340	98.13 212	75.78 324	87.35 269	92.52 328

dp		88.90 273	88.26 272	90.81 302	94.58 275	76.62 339	92.85 319	94.93 301	85.12 285	90.07 213	93.07 300	75.81 278	98.12 215	80.53 303	87.42 267	97.71 169

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

patchmatchnet-post											90.45 326	82.65 184	98.10 217

SCA		91.84 190	91.18 194	93.83 218	95.59 214	84.95 285	94.72 272	95.58 273	90.82 151	92.25 163	93.69 287	75.80 279	98.10 217	86.20 245	95.98 154	98.45 132

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

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

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

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

test_post											17.58 357	81.76 201	98.08 222

MDTV_nov1_ep13			90.76 206		95.22 241	80.33 323	93.03 317	95.28 284	88.14 231	92.84 153	93.83 281	81.34 206	98.08 222	82.86 284	94.34 183

test-LLR		91.42 207	91.19 193	92.12 275	94.59 273	80.66 318	94.29 288	92.98 330	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	80.66 318	94.29 288	92.98 330	87.68 246	90.76 192	92.37 310	67.67 323	98.07 225	88.81 198	96.74 142	97.63 172

BH-w/o		92.14 184	91.75 170	93.31 243	96.99 154	85.73 274	95.67 243	95.69 267	88.73 214	89.26 238	94.82 235	82.97 175	98.07 225	85.26 262	96.32 152	96.13 214

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	74.48 327	84.59 300	92.65 326

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

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

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

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

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

test_part1		89.59 265	88.03 273	94.27 196	95.32 235	89.42 188	98.03 46	97.58 137	78.01 335	86.10 297	94.59 246	69.87 315	98.01 234	89.88 171	82.85 319	95.40 249

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

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

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

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

OurMVSNet-221017-0		90.51 247	90.19 232	91.44 294	93.41 308	81.25 315	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

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

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

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

PVSNet_0	82.17 19	85.46 304	83.64 307	90.92 300	95.27 237	79.49 330	90.55 333	95.60 271	83.76 303	83.00 320	89.95 327	71.09 304	97.97 240	82.75 287	60.79 348	95.31 256

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

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

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

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

testing_2		87.33 289	85.03 298	94.22 198	87.77 346	89.32 195	94.97 269	97.11 191	89.22 193	71.64 342	88.73 332	55.16 347	97.94 247	91.95 135	88.73 256	95.41 245

tpm2		89.96 258	89.21 259	92.23 274	94.91 259	81.25 315	93.78 300	94.42 314	80.62 325	91.56 174	93.44 296	76.44 275	97.94 247	85.60 257	92.08 215	97.49 181

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

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

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

JIA-IIPM		88.26 282	87.04 286	91.91 279	93.52 304	81.42 314	89.38 340	94.38 315	80.84 323	90.93 191	80.74 344	79.22 242	97.92 251	82.76 286	91.62 219	96.38 206

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

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
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

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

ADS-MVSNet		89.89 260	88.68 266	93.53 233	95.86 204	84.89 286	90.93 330	95.07 295	83.23 308	91.28 185	91.81 320	79.01 248	97.85 257	79.52 308	91.39 224	97.84 163

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

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

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

MS-PatchMatch		90.27 251	89.77 247	91.78 286	94.33 282	84.72 288	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

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

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

EPMVS		90.70 242	89.81 245	93.37 241	94.73 267	84.21 292	93.67 304	88.02 347	89.50 186	92.38 158	93.49 294	77.82 267	97.78 264	86.03 251	92.68 202	98.11 153

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

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	92.17 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
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

EG-PatchMatch MVS		87.02 292	85.44 295	91.76 288	92.67 321	85.00 283	96.08 225	96.45 243	83.41 307	79.52 333	93.49 294	57.10 344	97.72 269	79.34 313	90.87 234	92.56 327

SixPastTwentyTwo		89.15 269	88.54 268	90.98 299	93.49 306	80.28 325	96.70 174	94.70 307	90.78 152	84.15 313	95.57 205	71.78 300	97.71 270	84.63 269	85.07 292	94.94 273

test_post1							92.81 320				16.58 358	80.53 218	97.68 271	86.20 245

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

TESTMET0.1,1		90.06 257	89.42 255	91.97 278	94.41 280	80.62 320	94.29 288	91.97 338	87.28 256	90.44 197	92.47 309	68.79 318	97.67 272	88.50 204	96.60 147	97.61 176

LF4IMVS		87.94 284	87.25 281	89.98 313	92.38 327	80.05 328	94.38 283	95.25 287	87.59 248	84.34 309	94.74 239	64.31 336	97.66 274	84.83 265	87.45 265	92.23 331

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

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.
OpenMVS_ROB	81.14 20	84.42 307	82.28 310	90.83 301	90.06 335	84.05 295	95.73 242	94.04 322	73.89 342	80.17 332	91.53 324	59.15 342	97.64 275	66.92 344	89.05 251	90.80 339

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

CMPMVS	62.92 21	85.62 303	84.92 300	87.74 321	89.14 341	73.12 345	94.17 291	96.80 223	73.98 341	73.65 341	94.93 228	66.36 328	97.61 279	83.95 277	91.28 226	92.48 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		91.02 228	90.59 213	92.34 272	95.33 232	84.35 290	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

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

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

lessismore_v0				90.45 308	91.96 330	79.09 335		87.19 350		80.32 330	94.39 256	66.31 330	97.55 283	84.00 276	76.84 334	94.70 294

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

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

gg-mvs-nofinetune		87.82 285	85.61 294	94.44 189	94.46 277	89.27 199	91.21 329	84.61 352	80.88 322	89.89 217	74.98 346	71.50 301	97.53 286	85.75 256	97.21 134	96.51 202

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

Patchmatch-test		89.42 267	87.99 274	93.70 225	95.27 237	85.11 281	88.98 341	94.37 316	81.11 320	87.10 284	93.69 287	82.28 191	97.50 289	74.37 329	94.76 177	98.48 129

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

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

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

pmmvs4		90.93 233	89.85 243	94.17 200	93.34 310	90.79 150	94.60 274	96.02 258	84.62 292	87.45 275	95.15 221	81.88 200	97.45 293	87.70 216	87.87 262	94.27 308

Baseline_NR-MVSNet		91.20 220	90.62 211	92.95 256	93.83 296	88.03 230	97.01 147	95.12 293	88.42 221	89.70 221	95.13 223	83.47 162	97.44 294	89.66 178	83.24 315	93.37 321

tpm		90.25 252	89.74 250	91.76 288	93.92 292	79.73 329	93.98 295	93.54 326	88.28 224	91.99 169	93.25 299	77.51 269	97.44 294	87.30 230	87.94 261	98.12 150

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

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
MVS-HIRNet		82.47 311	81.21 313	86.26 326	95.38 224	69.21 349	88.96 342	89.49 346	66.28 345	80.79 326	74.08 348	68.48 320	97.39 298	71.93 336	95.47 164	92.18 332

EPNet_dtu		91.71 193	91.28 188	92.99 254	93.76 298	83.71 298	96.69 176	95.28 284	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
pmmvs5		89.86 262	88.87 264	92.82 260	92.86 317	86.23 267	96.26 214	95.39 277	84.24 296	87.12 282	94.51 249	74.27 288	97.36 300	87.61 224	87.57 264	94.86 280

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

TransMVSNet (Re)		88.94 271	87.56 278	93.08 252	94.35 281	88.45 219	97.73 73	95.23 288	87.47 250	84.26 311	95.29 216	79.86 232	97.33 301	79.44 312	74.44 339	93.45 320

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

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	65.94 333	97.29 303	83.04 283	87.43 266	95.31 256

test_0402		86.46 295	84.79 301	91.45 293	95.02 251	85.55 276	96.29 212	94.89 302	80.90 321	82.21 321	93.97 279	68.21 322	97.29 303	62.98 346	88.68 257	91.51 336

CR-MVSNet		90.82 236	89.77 247	93.95 212	94.45 278	87.19 247	90.23 335	95.68 269	86.89 262	92.40 156	92.36 313	80.91 212	97.05 307	81.09 301	93.95 189	97.60 177

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	56.68 345	97.03 308	84.47 271	92.58 204	94.19 309

LCM-MVSNet-Re		92.50 165	92.52 149	92.44 268	96.82 160	81.89 312	96.92 155	93.71 325	92.41 105	84.30 310	94.60 245	85.08 143	97.03 308	91.51 147	97.36 128	98.40 138

Patchmtry		88.64 278	87.25 281	92.78 262	94.09 288	86.64 258	89.82 338	95.68 269	80.81 324	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	85.10 282	89.51 339	94.64 310	81.92 315	92.36 159	88.15 337	80.05 228	97.01 311	72.43 334	93.65 192	97.54 180

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

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

miper_lstm_enhance		90.50 248	90.06 237	91.83 282	95.33 232	83.74 296	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

GG-mvs-BLEND				93.62 228	93.69 300	89.20 200	92.39 325	83.33 353		87.98 269	89.84 329	71.00 305	96.87 315	82.08 292	95.40 166	94.80 287

ambc				86.56 325	83.60 348	70.00 348	85.69 345	94.97 299		80.60 328	88.45 333	37.42 352	96.84 316	82.69 288	75.44 337	92.86 324

ET-MVSNet_ETH3D		91.49 204	90.11 233	95.63 136	96.40 182	91.57 120	95.34 256	93.48 327	90.60 164	75.58 339	95.49 211	80.08 227	96.79 317	94.25 92	89.76 246	98.52 122

our_test_3		88.78 276	87.98 275	91.20 297	92.45 325	82.53 307	93.61 307	95.69 267	85.77 277	84.88 305	93.71 286	79.99 229	96.78 318	79.47 310	86.24 276	94.28 307

K. test v3		87.64 287	86.75 288	90.32 310	93.02 316	79.48 331	96.61 185	92.08 337	90.66 158	80.25 331	94.09 274	67.21 327	96.65 319	85.96 253	80.83 326	94.83 282

IterMVS-SCA-FT		90.31 250	89.81 245	91.82 283	95.52 218	84.20 293	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

N_pmnet		78.73 314	78.71 316	78.79 329	92.80 319	46.50 358	94.14 292	43.71 360	78.61 333	80.83 325	91.66 323	74.94 285	96.36 321	67.24 343	84.45 302	93.50 318

UnsupCasMVSNet_bld		82.13 312	79.46 315	90.14 312	88.00 344	82.47 308	90.89 332	96.62 239	78.94 332	75.61 338	84.40 342	56.63 346	96.31 322	77.30 321	66.77 347	91.63 335

IterMVS		90.15 256	89.67 251	91.61 290	95.48 220	83.72 297	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.
ADS-MVSNet2		89.45 266	88.59 267	92.03 277	95.86 204	82.26 311	90.93 330	94.32 318	83.23 308	91.28 185	91.81 320	79.01 248	95.99 324	79.52 308	91.39 224	97.84 163

MDA-MVSNet-bldmvs		85.00 305	82.95 309	91.17 298	93.13 315	83.33 302	94.56 276	95.00 297	84.57 293	65.13 347	92.65 305	70.45 308	95.85 325	73.57 332	77.49 332	94.33 304

PM-MVS		83.48 308	81.86 312	88.31 318	87.83 345	77.59 338	93.43 308	91.75 339	86.91 261	80.63 327	89.91 328	44.42 351	95.84 326	85.17 264	76.73 335	91.50 337

MIMVSNet		88.50 279	86.76 287	93.72 224	94.84 262	87.77 237	91.39 327	94.05 321	86.41 268	87.99 268	92.59 307	63.27 337	95.82 327	77.44 318	92.84 200	97.57 179

pmmvs-eth3d		86.22 297	84.45 303	91.53 291	88.34 343	87.25 244	94.47 278	95.01 296	83.47 306	79.51 334	89.61 330	69.75 316	95.71 328	83.13 282	76.73 335	91.64 334

Anonymous20231206		87.09 291	86.14 291	89.93 314	91.22 332	80.35 322	96.11 223	95.35 280	83.57 305	84.16 312	93.02 301	73.54 295	95.61 329	72.16 335	86.14 278	93.84 315

Patchmatch-RL test		87.38 288	86.24 289	90.81 302	88.74 342	78.40 337	88.12 343	93.17 329	87.11 259	82.17 322	89.29 331	81.95 198	95.60 330	88.64 202	77.02 333	98.41 137

CVMVSNet		91.23 218	91.75 170	89.67 315	95.77 209	74.69 342	96.44 193	94.88 303	85.81 276	92.18 164	97.64 97	79.07 243	95.58 331	88.06 208	95.86 158	98.74 110

MDA-MVSNet_test_wron		85.87 301	84.23 305	90.80 304	92.38 327	82.57 306	93.17 312	95.15 291	82.15 313	67.65 344	92.33 316	78.20 259	95.51 332	77.33 319	79.74 327	94.31 306

YYNet1		85.87 301	84.23 305	90.78 305	92.38 327	82.46 309	93.17 312	95.14 292	82.12 314	67.69 343	92.36 313	78.16 262	95.50 333	77.31 320	79.73 328	94.39 302

UnsupCasMVSNet_eth		85.99 299	84.45 303	90.62 306	89.97 336	82.40 310	93.62 306	97.37 170	89.86 177	78.59 336	92.37 310	65.25 335	95.35 334	82.27 291	70.75 343	94.10 310

EU-MVSNet		88.72 277	88.90 263	88.20 319	93.15 314	74.21 343	96.63 184	94.22 320	85.18 283	87.32 280	95.97 180	76.16 277	94.98 335	85.27 261	86.17 277	95.41 245

CL-MVSNet_2432*1600		85.95 300	84.95 299	88.96 316	89.55 340	79.11 334	95.13 267	96.42 244	85.91 275	84.07 315	90.48 325	70.03 313	94.82 336	80.04 305	72.94 342	92.94 323

new_pmnet		82.89 310	81.12 314	88.18 320	89.63 338	80.18 326	91.77 326	92.57 333	76.79 339	75.56 340	88.23 336	61.22 341	94.48 337	71.43 337	82.92 318	89.87 341

testgi		87.97 283	87.21 283	90.24 311	92.86 317	80.76 317	96.67 178	94.97 299	91.74 123	85.52 300	95.83 188	62.66 339	94.47 338	76.25 323	88.36 259	95.48 239

FMVSNet5		87.29 290	85.79 293	91.78 286	94.80 264	87.28 242	95.49 251	95.28 284	84.09 298	83.85 318	91.82 319	62.95 338	94.17 339	78.48 315	85.34 287	93.91 314

DSMNet-mixed		86.34 296	86.12 292	87.00 324	89.88 337	70.43 346	94.93 270	90.08 345	77.97 337	85.42 303	92.78 304	74.44 287	93.96 340	74.43 328	95.14 169	96.62 200

new-patchmatchnet		83.18 309	81.87 311	87.11 323	86.88 347	75.99 341	93.70 302	95.18 290	85.02 287	77.30 337	88.40 334	65.99 332	93.88 341	74.19 331	70.18 344	91.47 338

pmmvs3		79.97 313	77.50 317	87.39 322	82.80 349	79.38 332	92.70 321	90.75 344	70.69 344	78.66 335	87.47 340	51.34 349	93.40 342	73.39 333	69.65 345	89.38 342

MIMVSNet1		84.93 306	83.05 308	90.56 307	89.56 339	84.84 287	95.40 254	95.35 280	83.91 299	80.38 329	92.21 317	57.23 343	93.34 343	70.69 341	82.75 321	93.50 318

test0.0.03 1		89.37 268	88.70 265	91.41 295	92.47 324	85.63 275	95.22 265	92.70 332	91.11 147	86.91 289	93.65 291	79.02 246	93.19 344	78.00 317	89.18 250	95.41 245

test20.03		86.14 298	85.40 296	88.35 317	90.12 334	80.06 327	95.90 235	95.20 289	88.59 215	81.29 324	93.62 292	71.43 302	92.65 345	71.26 339	81.17 325	92.34 330

LCM-MVSNet		72.55 315	69.39 319	82.03 327	70.81 356	65.42 352	90.12 337	94.36 317	55.02 349	65.88 346	81.72 343	24.16 359	89.96 346	74.32 330	68.10 346	90.71 340

Gipuma		67.86 318	65.41 321	75.18 332	92.66 322	73.45 344	66.50 352	94.52 312	53.33 350	57.80 350	66.07 350	30.81 353	89.20 347	48.15 350	78.88 331	62.90 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		70.19 317	66.92 320	80.01 328	76.35 351	65.67 351	86.22 344	87.58 349	64.83 347	62.38 348	80.29 345	26.78 357	88.49 348	63.79 345	54.07 349	85.88 343

PMVS	53.92 22	58.58 320	55.40 323	68.12 334	51.00 359	48.64 356	78.86 349	87.10 351	46.77 351	35.84 356	74.28 347	8.76 360	86.34 349	42.07 351	73.91 340	69.38 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		71.27 316	69.85 318	75.50 331	74.64 352	59.03 354	91.30 328	91.50 341	58.80 348	57.92 349	88.28 335	29.98 355	85.53 350	53.43 348	82.84 320	81.95 345

ANet_high		63.94 319	59.58 322	77.02 330	61.24 358	66.06 350	85.66 346	87.93 348	78.53 334	42.94 352	71.04 349	25.42 358	80.71 351	52.60 349	30.83 352	84.28 344

DeepMVS_CX				74.68 333	90.84 333	64.34 353		81.61 355	65.34 346	67.47 345	88.01 338	48.60 350	80.13 352	62.33 347	73.68 341	79.58 346

E-PMN		53.28 321	52.56 325	55.43 336	74.43 353	47.13 357	83.63 348	76.30 356	42.23 352	42.59 353	62.22 352	28.57 356	74.40 353	31.53 353	31.51 351	44.78 350

EMVS		52.08 323	51.31 326	54.39 337	72.62 355	45.39 359	83.84 347	75.51 357	41.13 353	40.77 354	59.65 353	30.08 354	73.60 354	28.31 354	29.90 353	44.18 351

MVE	50.73 23	53.25 322	48.81 327	66.58 335	65.34 357	57.50 355	72.49 351	70.94 358	40.15 354	39.28 355	63.51 351	6.89 362	73.48 355	38.29 352	42.38 350	68.76 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 324	53.82 324	46.29 338	33.73 360	45.30 360	78.32 350	67.24 359	18.02 355	50.93 351	87.05 341	52.99 348	53.11 356	70.76 340	25.29 354	40.46 352

wuyk23d		25.11 325	24.57 329	26.74 339	73.98 354	39.89 361	57.88 353	9.80 361	12.27 356	10.39 357	6.97 359	7.03 361	36.44 357	25.43 355	17.39 355	3.89 355

testmvs		13.36 327	16.33 330	4.48 341	5.04 361	2.26 363	93.18 311	3.28 362	2.70 357	8.24 358	21.66 355	2.29 364	2.19 358	7.58 356	2.96 356	9.00 354

test123		13.04 328	15.66 331	5.18 340	4.51 362	3.45 362	92.50 324	1.81 363	2.50 358	7.58 359	20.15 356	3.67 363	2.18 359	7.13 357	1.07 357	9.90 353

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

cdsmvs_eth3d_5k		23.24 326	30.99 328	0.00 342	0.00 363	0.00 364	0.00 354	97.63 132	0.00 359	0.00 360	96.88 134	84.38 151	0.00 360	0.00 358	0.00 358	0.00 356

pcd_1.5k_mvsjas		7.39 330	9.85 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	88.65 94	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		8.06 329	10.74 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	96.69 143	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

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

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

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

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

GSMVS																98.45 132

test_part2					99.28 25	95.74 6				98.10 17

sam_mvs1												82.76 180				98.45 132

sam_mvs												81.94 199

MTGPA								98.08 64

MTMP							97.86 59	82.03 354

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

新几何2							95.79 240

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

原ACMM2							95.67 243

test222					98.24 93	92.21 99	95.33 257	97.60 134	79.22 331	95.25 104	97.84 81	88.80 92			99.15 73	98.72 112

segment_acmp												92.89 22

testdata1							95.26 264		93.10 82

plane_prior7					96.21 189	89.98 169

plane_prior6					96.10 199	90.00 165						81.32 207

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_prior1					96.14 197

plane_prior						89.99 167	97.24 123		94.06 47						92.16 212

n2								0.00 364

nn								0.00 364

door-mid								91.06 343

test11								97.88 106

door								91.13 342

HQP5-MVS						89.33 193

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

BP-MVS														92.13 131

HQP3-MVS								97.39 167							92.10 213

HQP2-MVS												80.95 210

NP-MVS					95.99 203	89.81 174					95.87 185

MDTV_nov1_ep13_2view						70.35 347	93.10 316		83.88 301	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