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	97.15 137	98.08 64	95.07 20	96.11 76	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	97.24 124	98.08 64	95.07 20	96.11 76	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	97.21 131	98.35 19	95.16 15	98.71 10	98.80 9	95.05 7	99.89 3	96.70 19	99.73 1	99.73 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ZNCC-MVS		96.96 30	96.67 38	97.85 25	99.37 16	94.12 45	98.49 14	98.18 46	92.64 102	96.39 69	98.18 58	91.61 52	99.88 4	95.59 63	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 71	95.54 102	98.34 41	90.59 75	99.88 4	94.83 82	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 75	95.95 85	98.33 44	91.04 66	99.88 4	95.20 69	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 79	97.14 41	98.34 41	91.59 54	99.87 7	95.46 66	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 98	97.59 24	98.20 57	91.96 44	99.86 8	94.21 94	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 99	96.70 51	98.06 64	91.35 59	99.86 8	94.83 82	99.28 59	99.47 44

MP-MVS-pluss		96.70 44	96.27 53	97.98 21	99.23 30	94.71 26	96.96 152	98.06 73	90.67 158	95.55 100	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 91	98.19 44	92.82 95	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 76	97.15 40	98.33 44	91.35 59	99.86 8	95.63 58	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 16	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 16	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	92.22 110	97.14 41	98.44 28	91.17 64	99.85 14	94.35 92	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 85	96.45 67	98.30 49	91.90 45	99.85 14	95.61 60	99.68 4	99.54 29

ACMMP		96.27 58	95.93 60	97.28 59	99.24 28	92.62 87	98.25 28	98.81 3	92.99 85	94.56 115	98.39 35	88.96 89	99.85 14	94.57 91	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 23	98.73 8	98.87 6	95.08 5	99.84 19	97.52 2	99.67 6	99.48 41
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									94.78 32	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 95	97.97 99	95.59 4	96.61 57	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	90.57 167	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 76	97.18 38	98.29 50	92.08 39	99.83 22	95.63 58	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	92.57 103	97.18 38	98.29 50	92.08 39	99.83 22	95.12 72	99.59 15	99.54 29

CANet		96.39 55	96.02 59	97.50 50	97.62 129	93.38 67	97.02 144	97.96 100	95.42 7	94.86 111	97.81 82	87.38 114	99.82 25	96.88 12	99.20 70	99.29 62

QAPM		93.45 136	92.27 157	96.98 74	96.77 164	92.62 87	98.39 19	98.12 56	84.50 299	88.27 263	97.77 85	82.39 191	99.81 26	85.40 261	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 25	96.59 59	98.29 50	91.70 50	99.80 27	95.66 53	99.40 45	99.62 13

X-MVStestdata		91.71 195	89.67 254	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 25	96.59 59	32.69 359	91.70 50	99.80 27	95.66 53	99.40 45	99.62 13

3Dnovator	91.36 5	95.19 87	94.44 100	97.44 52	96.56 174	93.36 69	98.65 6	98.36 16	94.12 47	89.25 241	98.06 64	82.20 194	99.77 29	93.41 114	99.32 53	99.18 69

CSCG		96.05 63	95.91 61	96.46 93	99.24 28	90.47 159	98.30 23	98.57 11	89.01 201	93.97 127	97.57 103	92.62 28	99.76 30	94.66 88	99.27 61	99.15 72

OpenMVS	89.19 12	92.86 158	91.68 174	96.40 96	95.34 231	92.73 83	98.27 26	98.12 56	84.86 294	85.78 300	97.75 86	78.89 252	99.74 31	87.50 227	98.65 95	96.73 199

PVSNet_Blended_VisFu		95.27 82	94.91 85	96.38 99	98.20 98	90.86 148	97.27 122	98.25 33	90.21 172	94.18 122	97.27 116	87.48 112	99.73 32	93.53 109	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	97.30 120	98.06 73	93.92 51	93.38 140	98.66 12	86.83 120	99.73 32	95.60 62	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		93.57 133	92.61 145	96.47 91	97.59 132	91.61 117	97.67 81	97.72 122	85.17 289	90.29 201	98.34 41	84.60 148	99.73 32	83.85 280	98.27 103	98.06 155

xxxxxxxxxxxxxcwj		97.36 11	97.20 10	97.83 26	98.91 49	94.28 35	97.02 144	97.22 183	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	92.37 107	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	92.20 102	97.89 57	98.03 84	93.34 74	97.22 37	98.42 31	87.93 103	99.72 35	95.10 73	99.07 80	99.02 83

CANet_DTU		94.37 105	93.65 113	96.55 84	96.46 181	92.13 104	96.21 221	96.67 233	94.38 43	93.53 136	97.03 128	79.34 241	99.71 38	90.76 160	98.45 100	97.82 167

MCST-MVS		97.18 16	96.84 25	98.20 10	99.30 24	95.35 12	97.12 139	98.07 70	93.54 66	96.08 78	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	97.34 114	98.04 81	95.96 2	97.09 45	97.88 74	93.18 20	99.71 38	95.84 50	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	96.90 157	95.34 13	98.48 15	97.87 108	94.65 37	88.53 257	98.02 67	83.69 160	99.71 38	93.18 118	98.96 85	99.44 47

DELS-MVS		96.61 48	96.38 51	97.30 57	97.79 121	93.19 72	95.96 234	98.18 46	95.23 12	95.87 86	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	96.99 149	98.05 80	89.85 181	97.26 35	98.22 56	91.80 47	99.69 44	94.84 81	99.28 59	99.27 66

DP-MVS		92.76 163	91.51 182	96.52 85	98.77 57	90.99 142	97.38 112	96.08 258	82.38 318	89.29 238	97.87 75	83.77 159	99.69 44	81.37 300	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	89.86 179	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 33	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	97.27 122	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	97.03 141	98.21 40	88.16 232	96.64 56	97.70 89	91.18 63	99.67 49	92.44 126	99.47 36	99.48 41

新几何1				97.32 56	98.60 68	93.59 61		97.75 117	81.58 324	95.75 91	97.85 78	90.04 82	99.67 49	86.50 242	99.13 75	98.69 115

testdata2													99.67 49	85.96 254

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	91.57 129	97.90 21	98.37 36	92.61 29	99.66 52	95.59 63	99.51 29	99.43 49

ZD-MVS					99.05 41	94.59 28		98.08 64	89.22 196	97.03 47	98.10 60	92.52 32	99.65 53	94.58 90	99.31 55

test_241102_ONE					99.42 6	95.30 15		98.27 28	95.09 19	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	91.28 143	97.88 22	98.44 28	93.00 21	99.65 53	95.76 52	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
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PS-MVSNAJ		95.37 79	95.33 76	95.49 148	97.35 136	90.66 155	95.31 261	97.48 147	93.85 53	96.51 62	95.70 202	88.65 94	99.65 53	94.80 85	98.27 103	96.17 211

无先验							95.79 242	97.87 108	83.87 307				99.65 53	87.68 220		98.89 100

1121		94.71 102	93.83 107	97.34 55	98.57 72	93.64 60	96.04 228	97.73 119	81.56 325	95.68 94	97.85 78	90.23 78	99.65 53	87.68 220	99.12 78	98.73 111

EPNet		95.20 86	94.56 93	97.14 68	92.80 320	92.68 84	97.85 62	94.87 310	96.64 1	92.46 156	97.80 84	86.23 127	99.65 53	93.72 107	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	97.41 106	98.04 81	94.81 30	96.59 59	98.37 36	91.24 61	99.64 61	95.16 70	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
hse-mvs3		94.15 110	93.52 118	96.04 118	97.81 119	90.22 165	97.62 90	97.58 138	95.19 14	96.74 50	97.45 109	83.67 161	99.61 62	95.85 49	79.73 329	98.29 146

Regformer-4		96.97 29	96.87 21	97.25 61	98.34 83	92.66 85	96.96 152	98.01 91	95.12 18	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	96.83 164	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	93.51 119	96.06 116	98.27 89	89.38 192	95.18 268	98.48 14	85.60 282	93.76 131	97.11 124	83.15 169	99.61 62	91.33 152	98.72 92	99.19 68

CPTT-MVS		95.57 76	95.19 79	96.70 77	99.27 26	91.48 122	98.33 21	98.11 59	87.79 243	95.17 107	98.03 66	87.09 118	99.61 62	93.51 110	99.42 43	99.02 83

UGNet		94.04 118	93.28 128	96.31 103	96.85 158	91.19 136	97.88 58	97.68 127	94.40 41	93.00 148	96.18 173	73.39 300	99.61 62	91.72 142	98.46 99	98.13 150
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 57	97.45 28	98.48 25	91.43 56	99.59 68	96.22 33	99.27 61	99.54 29

TEST9					98.70 60	94.19 40	96.41 199	98.02 88	88.17 230	96.03 79	97.56 105	92.74 24	99.59 68

train_agg		96.30 57	95.83 63	97.72 39	98.70 60	94.19 40	96.41 199	98.02 88	88.58 218	96.03 79	97.56 105	92.73 25	99.59 68	95.04 74	99.37 52	99.39 54

test_8					98.67 62	94.06 49	96.37 206	98.01 91	88.58 218	95.98 84	97.55 107	92.73 25	99.58 71

EI-MVSNet-UG-set		96.34 56	96.30 52	96.47 91	98.20 98	90.93 146	96.86 160	97.72 122	94.67 35	96.16 75	98.46 26	90.43 76	99.58 71	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	91.20 135	96.89 159	97.73 119	94.74 34	96.49 63	98.49 24	90.88 70	99.58 71	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	96.83 164	97.99 97	95.20 13	97.46 27	98.25 54	92.48 34	99.58 71	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	91.17 147	96.40 68	97.99 69	90.99 67	99.58 71	95.61 60	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	91.50 131	98.01 18	98.32 46	92.33 35	99.58 71	94.85 80	99.51 29	99.53 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_BlendedMVS		94.06 116	93.92 105	94.47 190	98.27 89	89.46 189	96.73 172	98.36 16	90.17 173	94.36 118	95.24 221	88.02 100	99.58 71	93.44 112	90.72 235	94.36 304

PVSNet_Blended		94.87 97	94.56 93	95.81 127	98.27 89	89.46 189	95.47 254	98.36 16	88.84 209	94.36 118	96.09 180	88.02 100	99.58 71	93.44 112	98.18 106	98.40 138

agg_prior1		96.22 60	95.77 64	97.56 48	98.67 62	93.79 55	96.28 215	98.00 93	88.76 215	95.68 94	97.55 107	92.70 27	99.57 79	95.01 75	99.32 53	99.32 60

agg_prior					98.67 62	93.79 55		98.00 93		95.68 94			99.57 79

test1172		96.93 33	96.86 22	97.15 67	99.10 34	92.34 94	97.96 53	98.04 81	93.79 56	97.35 33	98.53 21	91.40 57	99.56 81	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 94	97.98 48	98.03 84	93.52 67	97.43 31	98.51 22	91.40 57	99.56 81	96.05 42	99.26 63	99.43 49

Anonymous20240529		91.98 189	90.73 210	95.73 133	98.14 103	89.40 191	97.99 47	97.72 122	79.63 335	93.54 135	97.41 112	69.94 318	99.56 81	91.04 157	91.11 228	98.22 147

APD-MVS_3200maxsize		96.81 40	96.71 36	97.12 69	99.01 46	92.31 97	97.98 48	98.06 73	93.11 82	97.44 29	98.55 19	90.93 68	99.55 84	96.06 41	99.25 65	99.51 34

PCF-MVS	89.48 11	91.56 202	89.95 242	96.36 101	96.60 169	92.52 90	92.51 326	97.26 180	79.41 336	88.90 245	96.56 156	84.04 157	99.55 84	77.01 326	97.30 131	97.01 188
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	92.02 108	96.96 152	97.76 116	95.01 22	97.08 46	98.42 31	91.71 49	99.54 86	96.80 14	99.13 75	99.48 41

原ACMM1				96.38 99	98.59 69	91.09 141		97.89 104	87.41 254	95.22 106	97.68 91	90.25 77	99.54 86	87.95 211	99.12 78	98.49 127

AdaColmap		94.34 106	93.68 112	96.31 103	98.59 69	91.68 116	96.59 190	97.81 114	89.87 178	92.15 166	97.06 127	83.62 162	99.54 86	89.34 186	98.07 109	97.70 171

Anonymous202405211		92.07 187	90.83 206	95.76 128	98.19 100	88.75 211	97.58 92	95.00 301	86.00 277	93.64 132	97.45 109	66.24 336	99.53 89	90.68 163	92.71 201	99.01 87

xiu_mvs_v2_base		95.32 81	95.29 77	95.40 153	97.22 139	90.50 158	95.44 255	97.44 162	93.70 60	96.46 66	96.18 173	88.59 97	99.53 89	94.79 87	97.81 115	96.17 211

VNet		95.89 68	95.45 71	97.21 65	98.07 107	92.94 79	97.50 98	98.15 51	93.87 52	97.52 25	97.61 100	85.29 140	99.53 89	95.81 51	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	90.57 167	96.77 49	98.35 38	90.21 79	99.53 89	94.80 85	99.63 12	99.38 56

PLC	91.00 6	94.11 114	93.43 123	96.13 113	98.58 71	91.15 140	96.69 178	97.39 168	87.29 257	91.37 179	96.71 140	88.39 98	99.52 93	87.33 230	97.13 137	97.73 169
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	97.67 126	92.98 78	97.65 84	98.13 54	94.81 30	96.61 57	98.35 38	88.87 90	99.51 94	90.36 166	97.35 129	99.11 78

RPMNet		88.98 272	87.05 287	94.77 180	94.45 279	87.19 248	90.23 340	98.03 84	77.87 343	92.40 157	87.55 344	80.17 227	99.51 94	68.84 347	93.95 189	97.60 178

MAR-MVS		94.22 108	93.46 121	96.51 88	98.00 108	92.19 103	97.67 81	97.47 150	88.13 234	93.00 148	95.84 189	84.86 146	99.51 94	87.99 210	98.17 107	97.83 166
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	95.27 264	97.62 134	90.43 170	95.55 100	97.07 126	91.72 48	99.50 97	89.62 180	98.94 86	98.82 106

F-COLMAP		93.58 132	92.98 132	95.37 154	98.40 78	88.98 207	97.18 133	97.29 179	87.75 246	90.49 196	97.10 125	85.21 141	99.50 97	86.70 239	96.72 144	97.63 173

DP-MVS Recon		95.68 72	95.12 82	97.37 54	99.19 31	94.19 40	97.03 141	98.08 64	88.35 225	95.09 109	97.65 94	89.97 83	99.48 99	92.08 136	98.59 97	98.44 135

CDPH-MVS		95.97 66	95.38 74	97.77 35	98.93 47	94.44 31	96.35 207	97.88 106	86.98 262	96.65 55	97.89 72	91.99 43	99.47 100	92.26 127	99.46 38	99.39 54

test12				97.65 44	98.46 74	94.26 37		97.66 129		95.52 103		90.89 69	99.46 101		99.25 65	99.22 67

ab-mvs		93.57 133	92.55 147	96.64 78	97.28 138	91.96 111	95.40 256	97.45 158	89.81 183	93.22 146	96.28 170	79.62 238	99.46 101	90.74 161	93.11 197	98.50 125

HY-MVS	89.66 9	93.87 122	92.95 133	96.63 80	97.10 147	92.49 91	95.64 248	96.64 234	89.05 200	93.00 148	95.79 195	85.77 136	99.45 103	89.16 195	94.35 182	97.96 156

xiu_mvs_v1_base_debu		95.01 89	94.76 87	95.75 130	96.58 171	91.71 113	96.25 217	97.35 174	92.99 85	96.70 51	96.63 151	82.67 182	99.44 104	96.22 33	97.46 122	96.11 216

xiu_mvs_v1_base		95.01 89	94.76 87	95.75 130	96.58 171	91.71 113	96.25 217	97.35 174	92.99 85	96.70 51	96.63 151	82.67 182	99.44 104	96.22 33	97.46 122	96.11 216

xiu_mvs_v1_base_debi		95.01 89	94.76 87	95.75 130	96.58 171	91.71 113	96.25 217	97.35 174	92.99 85	96.70 51	96.63 151	82.67 182	99.44 104	96.22 33	97.46 122	96.11 216

test_prior3		96.46 53	96.20 56	97.23 62	98.67 62	92.99 76	96.35 207	98.00 93	92.80 96	96.03 79	97.59 101	92.01 41	99.41 107	95.01 75	99.38 48	99.29 62

test_prior				97.23 62	98.67 62	92.99 76		98.00 93					99.41 107			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 58	98.57 11	98.35 38	93.69 15	99.40 109	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		93.82 124	93.08 130	96.02 119	97.88 116	89.96 174	97.72 76	95.85 265	92.43 105	95.86 87	98.44 28	68.42 324	99.39 110	96.31 28	94.85 174	98.71 114

WTY-MVS		94.71 102	94.02 104	96.79 76	97.71 125	92.05 106	96.59 190	97.35 174	90.61 164	94.64 114	96.93 130	86.41 126	99.39 110	91.20 156	94.71 180	98.94 94

MVS_111021_HR		96.68 47	96.58 42	96.99 73	98.46 74	92.31 97	96.20 222	98.90 2	94.30 45	95.86 87	97.74 87	92.33 35	99.38 112	96.04 44	99.42 43	99.28 65

DeepPCF-MVS	93.97 1	96.61 48	97.09 12	95.15 159	98.09 105	86.63 262	96.00 232	98.15 51	95.43 6	97.95 19	98.56 17	93.40 16	99.36 113	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	96.79 168	96.72 225	94.17 46	97.44 29	97.66 93	92.76 23	99.33 114	96.86 13	97.76 118	99.08 80

114514_t		93.95 120	93.06 131	96.63 80	99.07 39	91.61 117	97.46 105	97.96 100	77.99 341	93.00 148	97.57 103	86.14 132	99.33 114	89.22 191	99.15 73	98.94 94

test_yl		94.78 100	94.23 102	96.43 94	97.74 123	91.22 131	96.85 161	97.10 192	91.23 145	95.71 92	96.93 130	84.30 152	99.31 116	93.10 119	95.12 170	98.75 108

DCV-MVSNet		94.78 100	94.23 102	96.43 94	97.74 123	91.22 131	96.85 161	97.10 192	91.23 145	95.71 92	96.93 130	84.30 152	99.31 116	93.10 119	95.12 170	98.75 108

COLMAP_ROB	87.81 15	90.40 252	89.28 261	93.79 222	97.95 110	87.13 251	96.92 156	95.89 264	82.83 316	86.88 293	97.18 120	73.77 297	99.29 118	78.44 317	93.62 193	94.95 272
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	97.07 148	91.97 110	96.32 211	98.06 73	88.94 205	94.50 116	96.78 137	84.60 148	99.27 119	91.90 137	96.02 153	98.68 116

MG-MVS		95.61 74	95.38 74	96.31 103	98.42 77	90.53 157	96.04 228	97.48 147	93.47 69	95.67 97	98.10 60	89.17 87	99.25 120	91.27 154	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	99.24 121	95.36 67	99.59 15	99.56 22

MVS_111021_LR		96.24 59	96.19 57	96.39 98	98.23 97	91.35 128	96.24 220	98.79 4	93.99 50	95.80 89	97.65 94	89.92 84	99.24 121	95.87 47	99.20 70	98.58 118

alignmvs		95.87 69	95.23 78	97.78 33	97.56 134	95.19 18	97.86 59	97.17 186	94.39 42	96.47 65	96.40 165	85.89 133	99.20 123	96.21 37	95.11 172	98.95 93

VDDNet		93.05 148	92.07 160	96.02 119	96.84 159	90.39 163	98.08 42	95.85 265	86.22 274	95.79 90	98.46 26	67.59 327	99.19 124	94.92 79	94.85 174	98.47 130

IB-MVS	87.33 17	89.91 262	88.28 274	94.79 179	95.26 241	87.70 239	95.12 270	93.95 328	89.35 193	87.03 288	92.49 310	70.74 311	99.19 124	89.18 194	81.37 325	97.49 182
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	97.59 132	95.15 21	98.28 25	97.60 135	94.52 39	96.27 72	96.12 176	87.65 107	99.18 126	96.20 38	94.82 176	98.91 97

API-MVS		94.84 98	94.49 97	95.90 124	97.90 115	92.00 109	97.80 66	97.48 147	89.19 197	94.81 112	96.71 140	88.84 91	99.17 127	88.91 198	98.76 91	96.53 202

LFMVS		93.60 131	92.63 143	96.52 85	98.13 104	91.27 130	97.94 54	93.39 333	90.57 167	96.29 71	98.31 47	69.00 320	99.16 128	94.18 96	95.87 157	99.12 77

AllTest		90.23 256	88.98 265	93.98 209	97.94 111	86.64 259	96.51 194	95.54 278	85.38 285	85.49 303	96.77 138	70.28 314	99.15 129	80.02 307	92.87 198	96.15 213

TestCases				93.98 209	97.94 111	86.64 259		95.54 278	85.38 285	85.49 303	96.77 138	70.28 314	99.15 129	80.02 307	92.87 198	96.15 213

1112_ss		93.37 137	92.42 153	96.21 111	97.05 153	90.99 142	96.31 212	96.72 225	86.87 265	89.83 220	96.69 144	86.51 124	99.14 131	88.12 208	93.67 191	98.50 125

PAPM_NR		95.01 89	94.59 92	96.26 108	98.89 54	90.68 154	97.24 124	97.73 119	91.80 124	92.93 153	96.62 154	89.13 88	99.14 131	89.21 192	97.78 116	98.97 90

PAPR		94.18 109	93.42 125	96.48 90	97.64 128	91.42 127	95.55 250	97.71 126	88.99 202	92.34 162	95.82 191	89.19 86	99.11 133	86.14 248	97.38 127	98.90 98

MVS		91.71 195	90.44 220	95.51 145	95.20 244	91.59 119	96.04 228	97.45 158	73.44 348	87.36 282	95.60 206	85.42 139	99.10 134	85.97 253	97.46 122	95.83 226

thres600view7		92.49 168	91.60 176	95.18 158	97.91 114	89.47 187	97.65 84	94.66 312	92.18 116	93.33 141	94.91 231	78.06 266	99.10 134	81.61 294	94.06 188	96.98 189

Test_1112_low_res		92.84 160	91.84 169	95.85 126	97.04 154	89.97 173	95.53 252	96.64 234	85.38 285	89.65 226	95.18 222	85.86 134	99.10 134	87.70 217	93.58 196	98.49 127

CNLPA		94.28 107	93.53 117	96.52 85	98.38 81	92.55 89	96.59 190	96.88 216	90.13 175	91.91 171	97.24 118	85.21 141	99.09 137	87.64 223	97.83 114	97.92 159

OMC-MVS		95.09 88	94.70 90	96.25 109	98.46 74	91.28 129	96.43 197	97.57 139	92.04 119	94.77 113	97.96 71	87.01 119	99.09 137	91.31 153	96.77 141	98.36 142

thres100view900		92.43 169	91.58 177	94.98 167	97.92 113	89.37 193	97.71 78	94.66 312	92.20 112	93.31 142	94.90 232	78.06 266	99.08 139	81.40 297	94.08 185	96.48 205

tfpn200view9		92.38 172	91.52 180	94.95 170	97.85 117	89.29 197	97.41 106	94.88 307	92.19 114	93.27 144	94.46 256	78.17 262	99.08 139	81.40 297	94.08 185	96.48 205

thres400		92.42 170	91.52 180	95.12 162	97.85 117	89.29 197	97.41 106	94.88 307	92.19 114	93.27 144	94.46 256	78.17 262	99.08 139	81.40 297	94.08 185	96.98 189

tttt0517		92.96 152	92.33 155	94.87 173	97.11 146	87.16 250	97.97 52	92.09 341	90.63 162	93.88 129	97.01 129	76.50 277	99.06 142	90.29 168	95.45 165	98.38 140

thisisatest0530		93.03 149	92.21 158	95.49 148	97.07 148	89.11 205	97.49 102	92.19 340	90.16 174	94.09 123	96.41 164	76.43 280	99.05 143	90.38 165	95.68 163	98.31 144

PVSNet	86.66 18	92.24 180	91.74 173	93.73 223	97.77 122	83.69 303	92.88 320	96.72 225	87.91 238	93.00 148	94.86 234	78.51 256	99.05 143	86.53 240	97.45 126	98.47 130

thres200		92.23 181	91.39 183	94.75 182	97.61 130	89.03 206	96.60 189	95.09 298	92.08 118	93.28 143	94.00 279	78.39 260	99.04 145	81.26 301	94.18 184	96.19 210

thisisatest0515		92.29 177	91.30 188	95.25 156	96.60 169	88.90 209	94.36 285	92.32 339	87.92 237	93.43 139	94.57 249	77.28 273	99.00 146	89.42 184	95.86 158	97.86 163

PatchMatch-RL		92.90 156	92.02 163	95.56 141	98.19 100	90.80 150	95.27 264	97.18 184	87.96 236	91.86 173	95.68 203	80.44 221	98.99 147	84.01 276	97.54 121	96.89 194

CS-MVS		95.80 70	95.65 66	96.24 110	97.32 137	91.43 126	98.10 39	97.91 103	93.38 70	95.16 108	94.57 249	90.21 79	98.98 148	95.53 65	98.67 94	98.30 145

MSDG		91.42 210	90.24 230	94.96 169	97.15 145	88.91 208	93.69 305	96.32 248	85.72 281	86.93 291	96.47 160	80.24 225	98.98 148	80.57 303	95.05 173	96.98 189

EIA-MVS		95.53 77	95.47 70	95.71 134	97.06 151	89.63 178	97.82 64	97.87 108	93.57 62	93.92 128	95.04 227	90.61 74	98.95 150	94.62 89	98.68 93	98.54 120

MSLP-MVS++		96.94 32	97.06 13	96.59 83	98.72 59	91.86 112	97.67 81	98.49 12	94.66 36	97.24 36	98.41 34	92.31 37	98.94 151	96.61 21	99.46 38	98.96 91

ETV-MVS		96.02 64	95.89 62	96.40 96	97.16 143	92.44 92	97.47 103	97.77 115	94.55 38	96.48 64	94.51 251	91.23 62	98.92 152	95.65 56	98.19 105	97.82 167

Vis-MVSNet		95.23 84	94.81 86	96.51 88	97.18 142	91.58 120	98.26 27	98.12 56	94.38 43	94.90 110	98.15 59	82.28 192	98.92 152	91.45 151	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	92.30 176	91.22 193	95.56 141	98.33 85	89.60 180	96.79 168	97.65 131	81.83 322	91.52 176	97.23 119	87.94 102	98.91 154	71.31 343	98.37 101	98.17 149
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XVG-OURS-SEG-HR		93.86 123	93.55 115	94.81 176	97.06 151	88.53 217	95.28 262	97.45 158	91.68 127	94.08 124	97.68 91	82.41 190	98.90 155	93.84 105	92.47 205	96.98 189

mvs-test1		93.63 130	93.69 111	93.46 238	96.02 203	84.61 292	97.24 124	96.72 225	93.85 53	92.30 163	95.76 197	83.08 171	98.89 156	91.69 145	96.54 148	96.87 195

XVG-OURS		93.72 128	93.35 126	94.80 178	97.07 148	88.61 214	94.79 272	97.46 152	91.97 122	93.99 125	97.86 77	81.74 203	98.88 157	92.64 125	92.67 203	96.92 193

testdata				95.46 152	98.18 102	88.90 209		97.66 129	82.73 317	97.03 47	98.07 63	90.06 81	98.85 158	89.67 178	98.98 84	98.64 117

lupinMVS		94.99 93	94.56 93	96.29 106	96.34 187	91.21 133	95.83 240	96.27 250	88.93 206	96.22 73	96.88 135	86.20 130	98.85 158	95.27 68	99.05 81	98.82 106

旧先验2							95.94 235		81.66 323	97.34 34			98.82 160	92.26 127

EPP-MVSNet		95.22 85	95.04 83	95.76 128	97.49 135	89.56 182	98.67 5	97.00 205	90.69 157	94.24 121	97.62 99	89.79 85	98.81 161	93.39 115	96.49 149	98.92 96

1314		92.81 162	92.03 162	95.14 160	95.33 234	89.52 186	96.04 228	97.44 162	87.72 247	86.25 297	95.33 217	83.84 158	98.79 162	89.26 189	97.05 138	97.11 187

Effi-MVS+		94.93 94	94.45 99	96.36 101	96.61 168	91.47 123	96.41 199	97.41 167	91.02 152	94.50 116	95.92 185	87.53 110	98.78 163	93.89 103	96.81 140	98.84 105

RPSCF		90.75 242	90.86 202	90.42 312	96.84 159	76.29 345	95.61 249	96.34 247	83.89 305	91.38 178	97.87 75	76.45 278	98.78 163	87.16 235	92.23 208	96.20 209

jason		94.84 98	94.39 101	96.18 112	95.52 220	90.93 146	96.09 226	96.52 241	89.28 194	96.01 83	97.32 114	84.70 147	98.77 165	95.15 71	98.91 88	98.85 103
jason: jason.
MVS_Test		94.89 96	94.62 91	95.68 135	96.83 161	89.55 183	96.70 176	97.17 186	91.17 147	95.60 99	96.11 179	87.87 104	98.76 166	93.01 123	97.17 136	98.72 112

ACMM	89.79 8	92.96 152	92.50 151	94.35 196	96.30 189	88.71 212	97.58 92	97.36 173	91.40 138	90.53 195	96.65 146	79.77 234	98.75 167	91.24 155	91.64 218	95.59 239
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	96.76 166	90.45 160	97.29 121	97.44 162	94.00 49	95.46 104	97.98 70	87.52 111	98.73 168	95.64 57	97.33 130	99.08 80

LPG-MVS_test		92.94 154	92.56 146	94.10 203	96.16 196	88.26 223	97.65 84	97.46 152	91.29 140	90.12 210	97.16 121	79.05 245	98.73 168	92.25 129	91.89 216	95.31 256

LGP-MVS_train				94.10 203	96.16 196	88.26 223		97.46 152	91.29 140	90.12 210	97.16 121	79.05 245	98.73 168	92.25 129	91.89 216	95.31 256

ACMP	89.59 10	92.62 165	92.14 159	94.05 206	96.40 184	88.20 226	97.36 113	97.25 182	91.52 130	88.30 261	96.64 147	78.46 257	98.72 171	91.86 140	91.48 222	95.23 264
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
baseline2		91.63 198	90.86 202	93.94 215	94.33 283	86.32 265	95.92 236	91.64 345	89.37 192	86.94 290	94.69 243	81.62 205	98.69 172	88.64 203	94.57 181	96.81 197

baseline		95.58 75	95.42 73	96.08 114	96.78 163	90.41 162	97.16 135	97.45 158	93.69 61	95.65 98	97.85 78	87.29 115	98.68 173	95.66 53	97.25 133	99.13 74

diffmvs		95.25 83	95.13 81	95.63 137	96.43 183	89.34 194	95.99 233	97.35 174	92.83 94	96.31 70	97.37 113	86.44 125	98.67 174	96.26 30	97.19 135	98.87 102

HyFIR lowres test		93.66 129	92.92 134	95.87 125	98.24 93	89.88 175	94.58 276	98.49 12	85.06 291	93.78 130	95.78 196	82.86 178	98.67 174	91.77 141	95.71 162	99.07 82

gm-plane-assit					93.22 313	78.89 341			84.82 295		93.52 295		98.64 176	87.72 215

OPM-MVS		93.28 140	92.76 137	94.82 174	94.63 273	90.77 152	96.65 181	97.18 184	93.72 58	91.68 174	97.26 117	79.33 242	98.63 177	92.13 133	92.28 207	95.07 268

Fast-Effi-MVS+		93.46 135	92.75 139	95.59 140	96.77 164	90.03 167	96.81 167	97.13 189	88.19 228	91.30 183	94.27 267	86.21 129	98.63 177	87.66 222	96.46 151	98.12 151

ACMH	87.59 16	90.53 249	89.42 258	93.87 218	96.21 191	87.92 233	97.24 124	96.94 208	88.45 222	83.91 320	96.27 171	71.92 302	98.62 179	84.43 273	89.43 248	95.05 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP_MVS		93.78 126	93.43 123	94.82 174	96.21 191	89.99 170	97.74 71	97.51 145	94.85 25	91.34 180	96.64 147	81.32 208	98.60 180	93.02 121	92.23 208	95.86 222

plane_prior5								97.51 145					98.60 180	93.02 121	92.23 208	95.86 222

XVG-ACMP-BASELINE		90.93 236	90.21 234	93.09 252	94.31 285	85.89 273	95.33 259	97.26 180	91.06 151	89.38 234	95.44 215	68.61 322	98.60 180	89.46 183	91.05 229	94.79 290

BH-RMVSNet		92.72 164	91.97 165	94.97 168	97.16 143	87.99 232	96.15 224	95.60 275	90.62 163	91.87 172	97.15 123	78.41 259	98.57 183	83.16 282	97.60 120	98.36 142

LTVRE_ROB	88.41 13	90.99 232	89.92 243	94.19 200	96.18 194	89.55 183	96.31 212	97.09 194	87.88 239	85.67 301	95.91 186	78.79 253	98.57 183	81.50 295	89.98 243	94.44 302
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	90.20 257	89.18 263	93.25 246	96.48 180	86.45 264	96.99 149	96.68 231	88.83 210	84.79 310	96.22 172	70.16 316	98.53 185	84.42 274	88.04 259	94.77 293

tpmvs		89.83 266	89.15 264	91.89 281	94.92 257	80.30 328	93.11 317	95.46 280	86.28 272	88.08 268	92.65 307	80.44 221	98.52 186	81.47 296	89.92 244	96.84 196

AUN-MVS		91.76 194	90.75 209	94.81 176	97.00 155	88.57 215	96.65 181	96.49 242	89.63 186	92.15 166	96.12 176	78.66 254	98.50 187	90.83 159	79.18 332	97.36 184

DWT-MVSNet_test		90.76 240	89.89 244	93.38 241	95.04 251	83.70 302	95.85 239	94.30 323	88.19 228	90.46 197	92.80 305	73.61 298	98.50 187	88.16 207	90.58 236	97.95 158

HQP4-MVS										90.14 204			98.50 187			95.78 229

HQP-MVS		93.19 144	92.74 140	94.54 189	95.86 206	89.33 195	96.65 181	97.39 168	93.55 63	90.14 204	95.87 187	80.95 211	98.50 187	92.13 133	92.10 213	95.78 229

IS-MVSNet		94.90 95	94.52 96	96.05 117	97.67 126	90.56 156	98.44 16	96.22 253	93.21 76	93.99 125	97.74 87	85.55 138	98.45 191	89.98 169	97.86 113	99.14 73

CHOSEN 280x420		93.12 145	92.72 141	94.34 197	96.71 167	87.27 244	90.29 339	97.72 122	86.61 269	91.34 180	95.29 218	84.29 154	98.41 192	93.25 117	98.94 86	97.35 185

VPA-MVSNet		93.24 141	92.48 152	95.51 145	95.70 214	92.39 93	97.86 59	98.66 9	92.30 108	92.09 169	95.37 216	80.49 220	98.40 193	93.95 100	85.86 280	95.75 233

PMMVS		92.86 158	92.34 154	94.42 194	94.92 257	86.73 258	94.53 278	96.38 246	84.78 296	94.27 120	95.12 226	83.13 170	98.40 193	91.47 150	96.49 149	98.12 151

CLD-MVS		92.98 151	92.53 149	94.32 198	96.12 200	89.20 201	95.28 262	97.47 150	92.66 100	89.90 217	95.62 205	80.58 218	98.40 193	92.73 124	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		91.20 223	90.08 237	94.58 188	94.97 253	89.16 204	93.65 307	97.59 137	79.90 334	89.40 233	92.92 304	75.36 287	98.36 196	92.14 132	94.75 178	96.23 208

BH-untuned		92.94 154	92.62 144	93.92 217	97.22 139	86.16 271	96.40 202	96.25 252	90.06 176	89.79 221	96.17 175	83.19 167	98.35 197	87.19 233	97.27 132	97.24 186

TR-MVS		91.48 208	90.59 215	94.16 202	96.40 184	87.33 242	95.67 245	95.34 287	87.68 248	91.46 177	95.52 212	76.77 276	98.35 197	82.85 286	93.61 194	96.79 198

TDRefinement		86.53 295	84.76 305	91.85 282	82.23 355	84.25 294	96.38 205	95.35 284	84.97 293	84.09 317	94.94 229	65.76 339	98.34 199	84.60 271	74.52 340	92.97 324

Effi-MVS+-dtu		93.08 146	93.21 129	92.68 266	96.02 203	83.25 306	97.14 138	96.72 225	93.85 53	91.20 190	93.44 298	83.08 171	98.30 200	91.69 145	95.73 161	96.50 204

tpmrst		91.44 209	91.32 186	91.79 286	95.15 245	79.20 338	93.42 311	95.37 283	88.55 221	93.49 137	93.67 292	82.49 188	98.27 201	90.41 164	89.34 249	97.90 160

XXY-MVS		92.16 184	91.23 192	94.95 170	94.75 267	90.94 145	97.47 103	97.43 165	89.14 198	88.90 245	96.43 162	79.71 235	98.24 202	89.56 181	87.68 263	95.67 238

UniMVSNet_ETH3D		91.34 217	90.22 233	94.68 183	94.86 262	87.86 236	97.23 129	97.46 152	87.99 235	89.90 217	96.92 133	66.35 334	98.23 203	90.30 167	90.99 231	97.96 156

nrg030		94.05 117	93.31 127	96.27 107	95.22 242	94.59 28	98.34 20	97.46 152	92.93 92	91.21 189	96.64 147	87.23 117	98.22 204	94.99 78	85.80 281	95.98 220

baseline1		92.82 161	91.90 167	95.55 143	97.20 141	90.77 152	97.19 132	94.58 315	92.20 112	92.36 160	96.34 168	84.16 155	98.21 205	89.20 193	83.90 312	97.68 172

RRT_test8_iter05		91.19 226	90.78 207	92.41 271	95.76 213	83.14 307	97.32 117	97.46 152	91.37 139	89.07 244	95.57 207	70.33 313	98.21 205	93.56 108	86.62 275	95.89 221

VPNet		92.23 181	91.31 187	94.99 165	95.56 218	90.96 144	97.22 130	97.86 111	92.96 91	90.96 191	96.62 154	75.06 288	98.20 207	91.90 137	83.65 314	95.80 228

CostFormer		91.18 227	90.70 211	92.62 267	94.84 263	81.76 316	94.09 295	94.43 317	84.15 302	92.72 155	93.77 287	79.43 240	98.20 207	90.70 162	92.18 211	97.90 160

USDC		88.94 273	87.83 278	92.27 274	94.66 270	84.96 287	93.86 300	95.90 263	87.34 256	83.40 322	95.56 209	67.43 328	98.19 209	82.64 290	89.67 247	93.66 317

test_part1		92.21 183	91.10 197	95.51 145	97.80 120	92.66 85	98.02 46	97.68 127	89.79 184	88.80 251	96.02 181	76.85 275	98.18 210	90.86 158	84.11 307	95.69 236

PS-MVSNAJss		93.74 127	93.51 119	94.44 191	93.91 294	89.28 199	97.75 70	97.56 142	92.50 104	89.94 216	96.54 157	88.65 94	98.18 210	93.83 106	90.90 233	95.86 222

tpm cat1		88.36 282	87.21 285	91.81 285	95.13 247	80.55 325	92.58 325	95.70 270	74.97 345	87.45 278	91.96 320	78.01 268	98.17 212	80.39 305	88.74 255	96.72 200

PAPM		91.52 206	90.30 226	95.20 157	95.30 237	89.83 176	93.38 312	96.85 220	86.26 273	88.59 255	95.80 192	84.88 145	98.15 213	75.67 330	95.93 156	97.63 173

Anonymous20231211		90.63 247	89.42 258	94.27 199	98.24 93	89.19 203	98.05 44	97.89 104	79.95 333	88.25 264	94.96 228	72.56 301	98.13 214	89.70 177	85.14 291	95.49 240

PatchmatchNet		91.91 190	91.35 184	93.59 231	95.38 226	84.11 297	93.15 316	95.39 281	89.54 187	92.10 168	93.68 291	82.82 180	98.13 214	84.81 267	95.32 167	98.52 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		86.82 294	85.35 300	91.21 299	94.91 260	82.99 308	93.94 298	94.02 327	83.58 310	81.56 329	94.68 244	62.34 346	98.13 214	75.78 328	87.35 269	92.52 332

dp		88.90 275	88.26 275	90.81 305	94.58 276	76.62 344	92.85 321	94.93 305	85.12 290	90.07 215	93.07 302	75.81 282	98.12 217	80.53 304	87.42 267	97.71 170

jajsoiax		92.42 170	91.89 168	94.03 207	93.33 312	88.50 218	97.73 73	97.53 143	92.00 121	88.85 248	96.50 159	75.62 286	98.11 218	93.88 104	91.56 221	95.48 241

patchmatchnet-post											90.45 330	82.65 185	98.10 219

SCA		91.84 192	91.18 195	93.83 219	95.59 216	84.95 288	94.72 273	95.58 277	90.82 153	92.25 164	93.69 289	75.80 283	98.10 219	86.20 246	95.98 154	98.45 132

v7n		90.76 240	89.86 245	93.45 239	93.54 304	87.60 241	97.70 79	97.37 171	88.85 208	87.65 276	94.08 277	81.08 210	98.10 219	84.68 269	83.79 313	94.66 297

RRT_MVS		93.21 142	92.32 156	95.91 123	94.92 257	94.15 43	96.92 156	96.86 219	91.42 135	91.28 186	96.43 162	79.66 237	98.10 219	93.29 116	90.06 242	95.46 244

mvs_tets		92.31 175	91.76 170	93.94 215	93.41 309	88.29 221	97.63 89	97.53 143	92.04 119	88.76 252	96.45 161	74.62 290	98.09 223	93.91 102	91.48 222	95.45 246

Fast-Effi-MVS+-dtu		92.29 177	91.99 164	93.21 249	95.27 238	85.52 278	97.03 141	96.63 237	92.09 117	89.11 243	95.14 224	80.33 224	98.08 224	87.54 226	94.74 179	96.03 219

test_post											17.58 362	81.76 202	98.08 224

MDTV_nov1_ep13			90.76 208		95.22 242	80.33 327	93.03 319	95.28 288	88.14 233	92.84 154	93.83 283	81.34 207	98.08 224	82.86 285	94.34 183

test-LLR		91.42 210	91.19 194	92.12 276	94.59 274	80.66 322	94.29 289	92.98 335	91.11 149	90.76 193	92.37 312	79.02 247	98.07 227	88.81 199	96.74 142	97.63 173

test-mter		90.19 258	89.54 257	92.12 276	94.59 274	80.66 322	94.29 289	92.98 335	87.68 248	90.76 193	92.37 312	67.67 326	98.07 227	88.81 199	96.74 142	97.63 173

BH-w/o		92.14 186	91.75 171	93.31 244	96.99 156	85.73 275	95.67 245	95.69 271	88.73 216	89.26 240	94.82 237	82.97 176	98.07 227	85.26 263	96.32 152	96.13 215

tfpnnormal		89.70 267	88.40 272	93.60 230	95.15 245	90.10 166	97.56 94	98.16 50	87.28 258	86.16 298	94.63 247	77.57 271	98.05 230	74.48 332	84.59 301	92.65 330

V42		91.58 201	90.87 201	93.73 223	94.05 291	88.50 218	97.32 117	96.97 206	88.80 214	89.71 222	94.33 262	82.54 186	98.05 230	89.01 196	85.07 293	94.64 298

EI-MVSNet		93.03 149	92.88 135	93.48 236	95.77 211	86.98 253	96.44 195	97.12 190	90.66 160	91.30 183	97.64 97	86.56 122	98.05 230	89.91 171	90.55 237	95.41 247

MVSTER		93.20 143	92.81 136	94.37 195	96.56 174	89.59 181	97.06 140	97.12 190	91.24 144	91.30 183	95.96 183	82.02 197	98.05 230	93.48 111	90.55 237	95.47 243

UniMVSNet (Re)		93.31 139	92.55 147	95.61 139	95.39 225	93.34 70	97.39 110	98.71 5	93.14 81	90.10 212	94.83 236	87.71 105	98.03 234	91.67 147	83.99 308	95.46 244

v2v482		91.59 199	90.85 204	93.80 221	93.87 296	88.17 228	96.94 155	96.88 216	89.54 187	89.53 230	94.90 232	81.70 204	98.02 235	89.25 190	85.04 295	95.20 265

v8		91.29 220	90.53 219	93.57 233	94.15 287	88.12 230	97.34 114	97.06 198	88.99 202	88.32 260	94.26 269	83.08 171	98.01 236	87.62 224	83.92 311	94.57 299

v144192		91.06 229	90.28 227	93.39 240	93.66 302	87.23 247	96.83 164	97.07 196	87.43 253	89.69 224	94.28 266	81.48 206	98.00 237	87.18 234	84.92 297	94.93 276

v1144		91.37 214	90.60 214	93.68 228	93.89 295	88.23 225	96.84 163	97.03 203	88.37 224	89.69 224	94.39 258	82.04 196	97.98 238	87.80 214	85.37 286	94.84 282

v1240		90.70 245	89.85 246	93.23 247	93.51 306	86.80 256	96.61 187	97.02 204	87.16 260	89.58 227	94.31 265	79.55 239	97.98 238	85.52 259	85.44 285	94.90 279

OurMVSNet-221017-0		90.51 250	90.19 235	91.44 295	93.41 309	81.25 319	96.98 151	96.28 249	91.68 127	86.55 295	96.30 169	74.20 293	97.98 238	88.96 197	87.40 268	95.09 267

v1921920		90.85 238	90.03 241	93.29 245	93.55 303	86.96 255	96.74 171	97.04 201	87.36 255	89.52 231	94.34 261	80.23 226	97.97 241	86.27 244	85.21 290	94.94 274

v1192		91.07 228	90.23 231	93.58 232	93.70 300	87.82 237	96.73 172	97.07 196	87.77 244	89.58 227	94.32 264	80.90 215	97.97 241	86.52 241	85.48 284	94.95 272

v10		91.04 230	90.23 231	93.49 235	94.12 288	88.16 229	97.32 117	97.08 195	88.26 227	88.29 262	94.22 272	82.17 195	97.97 241	86.45 243	84.12 306	94.33 305

PVSNet_0	82.17 19	85.46 307	83.64 310	90.92 303	95.27 238	79.49 335	90.55 338	95.60 275	83.76 308	83.00 326	89.95 333	71.09 308	97.97 241	82.75 288	60.79 353	95.31 256

GA-MVS		91.38 212	90.31 225	94.59 184	94.65 271	87.62 240	94.34 286	96.19 255	90.73 156	90.35 200	93.83 283	71.84 303	97.96 245	87.22 232	93.61 194	98.21 148

ITE_SJBPF				92.43 270	95.34 231	85.37 281		95.92 261	91.47 132	87.75 275	96.39 166	71.00 309	97.96 245	82.36 291	89.86 245	93.97 314

D2MVS		91.30 219	90.95 199	92.35 272	94.71 269	85.52 278	96.18 223	98.21 40	88.89 207	86.60 294	93.82 285	79.92 232	97.95 247	89.29 188	90.95 232	93.56 318

FIs		94.09 115	93.70 110	95.27 155	95.70 214	92.03 107	98.10 39	98.68 7	93.36 73	90.39 199	96.70 142	87.63 108	97.94 248	92.25 129	90.50 239	95.84 225

tpm2		89.96 261	89.21 262	92.23 275	94.91 260	81.25 319	93.78 302	94.42 318	80.62 331	91.56 175	93.44 298	76.44 279	97.94 248	85.60 258	92.08 215	97.49 182

TAMVS		94.01 119	93.46 121	95.64 136	96.16 196	90.45 160	96.71 175	96.89 215	89.27 195	93.46 138	96.92 133	87.29 115	97.94 248	88.70 202	95.74 160	98.53 121

MVSFormer		95.37 79	95.16 80	95.99 121	96.34 187	91.21 133	98.22 32	97.57 139	91.42 135	96.22 73	97.32 114	86.20 130	97.92 251	94.07 97	99.05 81	98.85 103

test_djsdf		93.07 147	92.76 137	94.00 208	93.49 307	88.70 213	98.22 32	97.57 139	91.42 135	90.08 214	95.55 210	82.85 179	97.92 251	94.07 97	91.58 220	95.40 250

JIA-IIPM		88.26 284	87.04 288	91.91 280	93.52 305	81.42 318	89.38 345	94.38 319	80.84 329	90.93 192	80.74 349	79.22 243	97.92 251	82.76 287	91.62 219	96.38 207

Vis-MVSNet (Re-imp)		94.15 110	93.88 106	94.95 170	97.61 130	87.92 233	98.10 39	95.80 267	92.22 110	93.02 147	97.45 109	84.53 150	97.91 254	88.24 206	97.97 111	99.02 83

CDS-MVSNet		94.14 113	93.54 116	95.93 122	96.18 194	91.46 124	96.33 210	97.04 201	88.97 204	93.56 133	96.51 158	87.55 109	97.89 255	89.80 174	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		92.16 184	91.55 178	93.97 211	92.58 324	89.55 183	97.51 97	97.42 166	89.42 191	88.40 258	94.84 235	80.66 217	97.88 256	91.87 139	91.28 226	94.48 300

FC-MVSNet-test		93.94 121	93.57 114	95.04 163	95.48 222	91.45 125	98.12 38	98.71 5	93.37 71	90.23 202	96.70 142	87.66 106	97.85 257	91.49 149	90.39 240	95.83 226

ADS-MVSNet		89.89 263	88.68 269	93.53 234	95.86 206	84.89 289	90.93 335	95.07 299	83.23 314	91.28 186	91.81 322	79.01 249	97.85 257	79.52 309	91.39 224	97.84 164

UniMVSNet_NR-MVSNet		93.37 137	92.67 142	95.47 151	95.34 231	92.83 80	97.17 134	98.58 10	92.98 90	90.13 208	95.80 192	88.37 99	97.85 257	91.71 143	83.93 309	95.73 235

DU-MVS		92.90 156	92.04 161	95.49 148	94.95 255	92.83 80	97.16 135	98.24 34	93.02 84	90.13 208	95.71 200	83.47 163	97.85 257	91.71 143	83.93 309	95.78 229

v148		90.99 232	90.38 222	92.81 262	93.83 297	85.80 274	96.78 170	96.68 231	89.45 190	88.75 253	93.93 282	82.96 177	97.82 261	87.83 213	83.25 316	94.80 288

MS-PatchMatch		90.27 254	89.77 250	91.78 287	94.33 283	84.72 291	95.55 250	96.73 224	86.17 275	86.36 296	95.28 220	71.28 307	97.80 262	84.09 275	98.14 108	92.81 327

bset_n11_16_dypcd		91.55 203	90.59 215	94.44 191	91.51 332	90.25 164	92.70 323	93.42 332	92.27 109	90.22 203	94.74 241	78.42 258	97.80 262	94.19 95	87.86 262	95.29 263

WR-MVS		92.34 173	91.53 179	94.77 180	95.13 247	90.83 149	96.40 202	97.98 98	91.88 123	89.29 238	95.54 211	82.50 187	97.80 262	89.79 175	85.27 289	95.69 236

pm-mvs1		90.72 244	89.65 256	93.96 212	94.29 286	89.63 178	97.79 67	96.82 222	89.07 199	86.12 299	95.48 214	78.61 255	97.78 265	86.97 237	81.67 323	94.46 301

EPMVS		90.70 245	89.81 248	93.37 242	94.73 268	84.21 295	93.67 306	88.02 352	89.50 189	92.38 159	93.49 296	77.82 270	97.78 265	86.03 252	92.68 202	98.11 154

NR-MVSNet		92.34 173	91.27 190	95.53 144	94.95 255	93.05 75	97.39 110	98.07 70	92.65 101	84.46 311	95.71 200	85.00 144	97.77 267	89.71 176	83.52 315	95.78 229

MVP-Stereo		90.74 243	90.08 237	92.71 264	93.19 314	88.20 226	95.86 238	96.27 250	86.07 276	84.86 309	94.76 239	77.84 269	97.75 268	83.88 279	98.01 110	92.17 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs_anonymous		93.82 124	93.74 109	94.06 205	96.44 182	85.41 280	95.81 241	97.05 199	89.85 181	90.09 213	96.36 167	87.44 113	97.75 268	93.97 99	96.69 145	99.02 83

EG-PatchMatch MVS		87.02 293	85.44 297	91.76 289	92.67 322	85.00 286	96.08 227	96.45 243	83.41 313	79.52 339	93.49 296	57.10 350	97.72 270	79.34 314	90.87 234	92.56 331

SixPastTwentyTwo		89.15 271	88.54 271	90.98 302	93.49 307	80.28 329	96.70 176	94.70 311	90.78 154	84.15 316	95.57 207	71.78 304	97.71 271	84.63 270	85.07 293	94.94 274

test_post1							92.81 322				16.58 363	80.53 219	97.68 272	86.20 246

pmmvs6		87.81 288	86.19 292	92.69 265	91.32 333	86.30 266	97.34 114	96.41 245	80.59 332	84.05 319	94.37 260	67.37 329	97.67 273	84.75 268	79.51 331	94.09 313

TESTMET0.1,1		90.06 260	89.42 258	91.97 279	94.41 281	80.62 324	94.29 289	91.97 343	87.28 258	90.44 198	92.47 311	68.79 321	97.67 273	88.50 205	96.60 147	97.61 177

LF4IMVS		87.94 286	87.25 283	89.98 316	92.38 328	80.05 332	94.38 284	95.25 291	87.59 250	84.34 312	94.74 241	64.31 341	97.66 275	84.83 266	87.45 265	92.23 335

miper_enhance_ethall		91.54 205	91.01 198	93.15 250	95.35 230	87.07 252	93.97 297	96.90 213	86.79 266	89.17 242	93.43 300	86.55 123	97.64 276	89.97 170	86.93 270	94.74 294

IterMVS-LS		92.29 177	91.94 166	93.34 243	96.25 190	86.97 254	96.57 193	97.05 199	90.67 158	89.50 232	94.80 238	86.59 121	97.64 276	89.91 171	86.11 279	95.40 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	81.14 20	84.42 312	82.28 315	90.83 304	90.06 340	84.05 298	95.73 244	94.04 326	73.89 347	80.17 338	91.53 326	59.15 348	97.64 276	66.92 349	89.05 251	90.80 344

cl-mvsnet2		91.21 222	90.56 218	93.14 251	96.09 202	86.80 256	94.41 283	96.58 240	87.80 242	88.58 256	93.99 280	80.85 216	97.62 279	89.87 173	86.93 270	94.99 271

CMPMVS	62.92 21	85.62 306	84.92 303	87.74 326	89.14 346	73.12 350	94.17 292	96.80 223	73.98 346	73.65 347	94.93 230	66.36 333	97.61 280	83.95 278	91.28 226	92.48 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		91.02 231	90.59 215	92.34 273	95.33 234	84.35 293	94.10 294	96.90 213	88.56 220	88.84 249	94.33 262	84.08 156	97.60 281	88.77 201	84.37 304	95.06 269

TranMVSNet+NR-MVSNet		92.50 166	91.63 175	95.14 160	94.76 266	92.07 105	97.53 96	98.11 59	92.90 93	89.56 229	96.12 176	83.16 168	97.60 281	89.30 187	83.20 318	95.75 233

WR-MVS_H		92.00 188	91.35 184	93.95 213	95.09 249	89.47 187	98.04 45	98.68 7	91.46 133	88.34 259	94.68 244	85.86 134	97.56 283	85.77 256	84.24 305	94.82 285

lessismore_v0				90.45 311	91.96 331	79.09 340		87.19 355		80.32 336	94.39 258	66.31 335	97.55 284	84.00 277	76.84 336	94.70 295

miper_ehance_all_eth		91.59 199	91.13 196	92.97 256	95.55 219	86.57 263	94.47 279	96.88 216	87.77 244	88.88 247	94.01 278	86.22 128	97.54 285	89.49 182	86.93 270	94.79 290

cl-mvsnet_		90.96 235	90.32 224	92.89 258	95.37 228	86.21 269	94.46 281	96.64 234	87.82 240	88.15 267	94.18 273	82.98 175	97.54 285	87.70 217	85.59 282	94.92 278

cl-mvsnet1		90.97 234	90.33 223	92.88 259	95.36 229	86.19 270	94.46 281	96.63 237	87.82 240	88.18 266	94.23 270	82.99 174	97.53 287	87.72 215	85.57 283	94.93 276

gg-mvs-nofinetune		87.82 287	85.61 296	94.44 191	94.46 278	89.27 200	91.21 334	84.61 357	80.88 328	89.89 219	74.98 351	71.50 305	97.53 287	85.75 257	97.21 134	96.51 203

CP-MVSNet		91.89 191	91.24 191	93.82 220	95.05 250	88.57 215	97.82 64	98.19 44	91.70 126	88.21 265	95.76 197	81.96 198	97.52 289	87.86 212	84.65 298	95.37 253

Patchmatch-test		89.42 269	87.99 276	93.70 226	95.27 238	85.11 284	88.98 346	94.37 320	81.11 326	87.10 287	93.69 289	82.28 192	97.50 290	74.37 334	94.76 177	98.48 129

PS-CasMVS		91.55 203	90.84 205	93.69 227	94.96 254	88.28 222	97.84 63	98.24 34	91.46 133	88.04 269	95.80 192	79.67 236	97.48 291	87.02 236	84.54 302	95.31 256

cl_fuxian		91.38 212	90.89 200	92.88 259	95.58 217	86.30 266	94.68 274	96.84 221	88.17 230	88.83 250	94.23 270	85.65 137	97.47 292	89.36 185	84.63 299	94.89 280

FMVSNet3		91.78 193	90.69 212	95.03 164	96.53 176	92.27 99	97.02 144	96.93 209	89.79 184	89.35 235	94.65 246	77.01 274	97.47 292	86.12 249	88.82 252	95.35 254

pmmvs4		90.93 236	89.85 246	94.17 201	93.34 311	90.79 151	94.60 275	96.02 259	84.62 297	87.45 278	95.15 223	81.88 201	97.45 294	87.70 217	87.87 261	94.27 309

Baseline_NR-MVSNet		91.20 223	90.62 213	92.95 257	93.83 297	88.03 231	97.01 148	95.12 297	88.42 223	89.70 223	95.13 225	83.47 163	97.44 295	89.66 179	83.24 317	93.37 322

tpm		90.25 255	89.74 253	91.76 289	93.92 293	79.73 334	93.98 296	93.54 330	88.28 226	91.99 170	93.25 301	77.51 272	97.44 295	87.30 231	87.94 260	98.12 151

FMVSNet2		91.31 218	90.08 237	94.99 165	96.51 177	92.21 100	97.41 106	96.95 207	88.82 211	88.62 254	94.75 240	73.87 294	97.42 297	85.20 264	88.55 257	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 29	99.01 3	98.55 19	94.18 11	97.41 298	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		82.47 316	81.21 318	86.26 331	95.38 226	69.21 354	88.96 347	89.49 351	66.28 350	80.79 332	74.08 353	68.48 323	97.39 299	71.93 341	95.47 164	92.18 337

EPNet_dtu		91.71 195	91.28 189	92.99 255	93.76 299	83.71 301	96.69 178	95.28 288	93.15 80	87.02 289	95.95 184	83.37 166	97.38 300	79.46 312	96.84 139	97.88 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		89.86 265	88.87 267	92.82 261	92.86 318	86.23 268	96.26 216	95.39 281	84.24 301	87.12 285	94.51 251	74.27 292	97.36 301	87.61 225	87.57 264	94.86 281

PEN-MVS		91.20 223	90.44 220	93.48 236	94.49 277	87.91 235	97.76 69	98.18 46	91.29 140	87.78 274	95.74 199	80.35 223	97.33 302	85.46 260	82.96 319	95.19 266

TransMVSNet (Re)		88.94 273	87.56 280	93.08 253	94.35 282	88.45 220	97.73 73	95.23 292	87.47 252	84.26 314	95.29 218	79.86 233	97.33 302	79.44 313	74.44 341	93.45 321

GBi-Net		91.35 215	90.27 228	94.59 184	96.51 177	91.18 137	97.50 98	96.93 209	88.82 211	89.35 235	94.51 251	73.87 294	97.29 304	86.12 249	88.82 252	95.31 256

test1		91.35 215	90.27 228	94.59 184	96.51 177	91.18 137	97.50 98	96.93 209	88.82 211	89.35 235	94.51 251	73.87 294	97.29 304	86.12 249	88.82 252	95.31 256

FMVSNet1		89.88 264	88.31 273	94.59 184	95.41 224	91.18 137	97.50 98	96.93 209	86.62 268	87.41 280	94.51 251	65.94 338	97.29 304	83.04 284	87.43 266	95.31 256

test_0402		86.46 296	84.79 304	91.45 294	95.02 252	85.55 277	96.29 214	94.89 306	80.90 327	82.21 327	93.97 281	68.21 325	97.29 304	62.98 351	88.68 256	91.51 341

CR-MVSNet		90.82 239	89.77 250	93.95 213	94.45 279	87.19 248	90.23 340	95.68 273	86.89 264	92.40 157	92.36 315	80.91 213	97.05 308	81.09 302	93.95 189	97.60 178

MVS_0304		88.79 277	87.57 279	92.46 268	94.65 271	86.15 272	96.40 202	97.17 186	86.44 270	88.02 270	91.71 324	56.68 351	97.03 309	84.47 272	92.58 204	94.19 310

LCM-MVSNet-Re		92.50 166	92.52 150	92.44 269	96.82 162	81.89 315	96.92 156	93.71 329	92.41 106	84.30 313	94.60 248	85.08 143	97.03 309	91.51 148	97.36 128	98.40 138

Patchmtry		88.64 280	87.25 283	92.78 263	94.09 289	86.64 259	89.82 343	95.68 273	80.81 330	87.63 277	92.36 315	80.91 213	97.03 309	78.86 315	85.12 292	94.67 296

PatchT		88.87 276	87.42 281	93.22 248	94.08 290	85.10 285	89.51 344	94.64 314	81.92 321	92.36 160	88.15 342	80.05 229	97.01 312	72.43 339	93.65 192	97.54 181

DTE-MVSNet		90.56 248	89.75 252	93.01 254	93.95 292	87.25 245	97.64 88	97.65 131	90.74 155	87.12 285	95.68 203	79.97 231	97.00 313	83.33 281	81.66 324	94.78 292

ppachtmachnet_test		88.35 283	87.29 282	91.53 292	92.45 326	83.57 304	93.75 303	95.97 260	84.28 300	85.32 306	94.18 273	79.00 251	96.93 314	75.71 329	84.99 296	94.10 311

miper_lstm_enhance		90.50 251	90.06 240	91.83 283	95.33 234	83.74 299	93.86 300	96.70 230	87.56 251	87.79 273	93.81 286	83.45 165	96.92 315	87.39 228	84.62 300	94.82 285

GG-mvs-BLEND				93.62 229	93.69 301	89.20 201	92.39 328	83.33 358		87.98 272	89.84 335	71.00 309	96.87 316	82.08 293	95.40 166	94.80 288

ambc				86.56 330	83.60 353	70.00 353	85.69 350	94.97 303		80.60 334	88.45 338	37.42 357	96.84 317	82.69 289	75.44 339	92.86 326

ET-MVSNet_ETH3D		91.49 207	90.11 236	95.63 137	96.40 184	91.57 121	95.34 258	93.48 331	90.60 166	75.58 345	95.49 213	80.08 228	96.79 318	94.25 93	89.76 246	98.52 122

our_test_3		88.78 278	87.98 277	91.20 300	92.45 326	82.53 310	93.61 309	95.69 271	85.77 280	84.88 308	93.71 288	79.99 230	96.78 319	79.47 311	86.24 276	94.28 308

K. test v3		87.64 289	86.75 290	90.32 313	93.02 317	79.48 336	96.61 187	92.08 342	90.66 160	80.25 337	94.09 276	67.21 330	96.65 320	85.96 254	80.83 327	94.83 283

IterMVS-SCA-FT		90.31 253	89.81 248	91.82 284	95.52 220	84.20 296	94.30 288	96.15 256	90.61 164	87.39 281	94.27 267	75.80 283	96.44 321	87.34 229	86.88 274	94.82 285

N_pmnet		78.73 319	78.71 321	78.79 334	92.80 320	46.50 363	94.14 293	43.71 365	78.61 339	80.83 331	91.66 325	74.94 289	96.36 322	67.24 348	84.45 303	93.50 319

UnsupCasMVSNet_bld		82.13 317	79.46 320	90.14 315	88.00 350	82.47 311	90.89 337	96.62 239	78.94 338	75.61 344	84.40 347	56.63 352	96.31 323	77.30 323	66.77 350	91.63 340

IterMVS		90.15 259	89.67 254	91.61 291	95.48 222	83.72 300	94.33 287	96.12 257	89.99 177	87.31 284	94.15 275	75.78 285	96.27 324	86.97 237	86.89 273	94.83 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240521		86.42 297	85.44 297	89.34 320	90.33 338	79.79 333	96.73 172	95.92 261	83.71 309	83.25 323	91.36 327	63.92 342	96.01 325	78.39 318	85.36 287	92.22 336

ADS-MVSNet2		89.45 268	88.59 270	92.03 278	95.86 206	82.26 314	90.93 335	94.32 322	83.23 314	91.28 186	91.81 322	79.01 249	95.99 326	79.52 309	91.39 224	97.84 164

KD-MVS_2432*1600		84.81 310	82.64 313	91.31 297	91.07 335	85.34 282	91.22 332	95.75 268	85.56 283	83.09 324	90.21 331	67.21 330	95.89 327	77.18 324	62.48 351	92.69 328

miper_refine_blended		84.81 310	82.64 313	91.31 297	91.07 335	85.34 282	91.22 332	95.75 268	85.56 283	83.09 324	90.21 331	67.21 330	95.89 327	77.18 324	62.48 351	92.69 328

MDA-MVSNet-bldmvs		85.00 308	82.95 312	91.17 301	93.13 316	83.33 305	94.56 277	95.00 301	84.57 298	65.13 352	92.65 307	70.45 312	95.85 329	73.57 337	77.49 334	94.33 305

PM-MVS		83.48 313	81.86 317	88.31 323	87.83 351	77.59 343	93.43 310	91.75 344	86.91 263	80.63 333	89.91 334	44.42 356	95.84 330	85.17 265	76.73 337	91.50 342

MIMVSNet		88.50 281	86.76 289	93.72 225	94.84 263	87.77 238	91.39 330	94.05 325	86.41 271	87.99 271	92.59 309	63.27 343	95.82 331	77.44 320	92.84 200	97.57 180

pmmvs-eth3d		86.22 300	84.45 306	91.53 292	88.34 349	87.25 245	94.47 279	95.01 300	83.47 312	79.51 340	89.61 336	69.75 319	95.71 332	83.13 283	76.73 337	91.64 339

Anonymous20231206		87.09 292	86.14 293	89.93 317	91.22 334	80.35 326	96.11 225	95.35 284	83.57 311	84.16 315	93.02 303	73.54 299	95.61 333	72.16 340	86.14 278	93.84 316

Patchmatch-RL test		87.38 290	86.24 291	90.81 305	88.74 348	78.40 342	88.12 348	93.17 334	87.11 261	82.17 328	89.29 337	81.95 199	95.60 334	88.64 203	77.02 335	98.41 137

CVMVSNet		91.23 221	91.75 171	89.67 319	95.77 211	74.69 347	96.44 195	94.88 307	85.81 279	92.18 165	97.64 97	79.07 244	95.58 335	88.06 209	95.86 158	98.74 110

MDA-MVSNet_test_wron		85.87 304	84.23 308	90.80 307	92.38 328	82.57 309	93.17 314	95.15 295	82.15 319	67.65 349	92.33 318	78.20 261	95.51 336	77.33 321	79.74 328	94.31 307

YYNet1		85.87 304	84.23 308	90.78 308	92.38 328	82.46 312	93.17 314	95.14 296	82.12 320	67.69 348	92.36 315	78.16 264	95.50 337	77.31 322	79.73 329	94.39 303

UnsupCasMVSNet_eth		85.99 302	84.45 306	90.62 309	89.97 341	82.40 313	93.62 308	97.37 171	89.86 179	78.59 342	92.37 312	65.25 340	95.35 338	82.27 292	70.75 346	94.10 311

EU-MVSNet		88.72 279	88.90 266	88.20 324	93.15 315	74.21 348	96.63 186	94.22 324	85.18 288	87.32 283	95.97 182	76.16 281	94.98 339	85.27 262	86.17 277	95.41 247

DIV-MVS_2432*1600		85.95 303	84.95 302	88.96 321	89.55 345	79.11 339	95.13 269	96.42 244	85.91 278	84.07 318	90.48 329	70.03 317	94.82 340	80.04 306	72.94 344	92.94 325

CL-MVSNet_2432*1600		86.31 299	85.15 301	89.80 318	88.83 347	81.74 317	93.93 299	96.22 253	86.67 267	85.03 307	90.80 328	78.09 265	94.50 341	74.92 331	71.86 345	93.15 323

new_pmnet		82.89 315	81.12 319	88.18 325	89.63 343	80.18 330	91.77 329	92.57 338	76.79 344	75.56 346	88.23 341	61.22 347	94.48 342	71.43 342	82.92 320	89.87 346

testgi		87.97 285	87.21 285	90.24 314	92.86 318	80.76 321	96.67 180	94.97 303	91.74 125	85.52 302	95.83 190	62.66 345	94.47 343	76.25 327	88.36 258	95.48 241

FMVSNet5		87.29 291	85.79 295	91.78 287	94.80 265	87.28 243	95.49 253	95.28 288	84.09 303	83.85 321	91.82 321	62.95 344	94.17 344	78.48 316	85.34 288	93.91 315

DSMNet-mixed		86.34 298	86.12 294	87.00 329	89.88 342	70.43 351	94.93 271	90.08 350	77.97 342	85.42 305	92.78 306	74.44 291	93.96 345	74.43 333	95.14 169	96.62 201

new-patchmatchnet		83.18 314	81.87 316	87.11 328	86.88 352	75.99 346	93.70 304	95.18 294	85.02 292	77.30 343	88.40 339	65.99 337	93.88 346	74.19 336	70.18 347	91.47 343

pmmvs3		79.97 318	77.50 322	87.39 327	82.80 354	79.38 337	92.70 323	90.75 349	70.69 349	78.66 341	87.47 345	51.34 354	93.40 347	73.39 338	69.65 348	89.38 347

MIMVSNet1		84.93 309	83.05 311	90.56 310	89.56 344	84.84 290	95.40 256	95.35 284	83.91 304	80.38 335	92.21 319	57.23 349	93.34 348	70.69 346	82.75 322	93.50 319

test0.0.03 1		89.37 270	88.70 268	91.41 296	92.47 325	85.63 276	95.22 267	92.70 337	91.11 149	86.91 292	93.65 293	79.02 247	93.19 349	78.00 319	89.18 250	95.41 247

test20.03		86.14 301	85.40 299	88.35 322	90.12 339	80.06 331	95.90 237	95.20 293	88.59 217	81.29 330	93.62 294	71.43 306	92.65 350	71.26 344	81.17 326	92.34 334

LCM-MVSNet		72.55 320	69.39 324	82.03 332	70.81 361	65.42 357	90.12 342	94.36 321	55.02 354	65.88 351	81.72 348	24.16 364	89.96 351	74.32 335	68.10 349	90.71 345

Gipuma		67.86 323	65.41 326	75.18 337	92.66 323	73.45 349	66.50 357	94.52 316	53.33 355	57.80 355	66.07 355	30.81 358	89.20 352	48.15 355	78.88 333	62.90 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		70.19 322	66.92 325	80.01 333	76.35 356	65.67 356	86.22 349	87.58 354	64.83 352	62.38 353	80.29 350	26.78 362	88.49 353	63.79 350	54.07 354	85.88 348

PMVS	53.92 22	58.58 325	55.40 328	68.12 339	51.00 364	48.64 361	78.86 354	87.10 356	46.77 356	35.84 361	74.28 352	8.76 365	86.34 354	42.07 356	73.91 342	69.38 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		71.27 321	69.85 323	75.50 336	74.64 357	59.03 359	91.30 331	91.50 346	58.80 353	57.92 354	88.28 340	29.98 360	85.53 355	53.43 353	82.84 321	81.95 350

ANet_high		63.94 324	59.58 327	77.02 335	61.24 363	66.06 355	85.66 351	87.93 353	78.53 340	42.94 357	71.04 354	25.42 363	80.71 356	52.60 354	30.83 357	84.28 349

DeepMVS_CX				74.68 338	90.84 337	64.34 358		81.61 360	65.34 351	67.47 350	88.01 343	48.60 355	80.13 357	62.33 352	73.68 343	79.58 351

E-PMN		53.28 326	52.56 330	55.43 341	74.43 358	47.13 362	83.63 353	76.30 361	42.23 357	42.59 358	62.22 357	28.57 361	74.40 358	31.53 358	31.51 356	44.78 355

EMVS		52.08 328	51.31 331	54.39 342	72.62 360	45.39 364	83.84 352	75.51 362	41.13 358	40.77 359	59.65 358	30.08 359	73.60 359	28.31 359	29.90 358	44.18 356

MVE	50.73 23	53.25 327	48.81 332	66.58 340	65.34 362	57.50 360	72.49 356	70.94 363	40.15 359	39.28 360	63.51 356	6.89 367	73.48 360	38.29 357	42.38 355	68.76 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 329	53.82 329	46.29 343	33.73 365	45.30 365	78.32 355	67.24 364	18.02 360	50.93 356	87.05 346	52.99 353	53.11 361	70.76 345	25.29 359	40.46 357

wuyk23d		25.11 330	24.57 334	26.74 344	73.98 359	39.89 366	57.88 358	9.80 366	12.27 361	10.39 362	6.97 364	7.03 366	36.44 362	25.43 360	17.39 360	3.89 360

testmvs		13.36 332	16.33 335	4.48 346	5.04 366	2.26 368	93.18 313	3.28 367	2.70 362	8.24 363	21.66 360	2.29 369	2.19 363	7.58 361	2.96 361	9.00 359

test123		13.04 333	15.66 336	5.18 345	4.51 367	3.45 367	92.50 327	1.81 368	2.50 363	7.58 364	20.15 361	3.67 368	2.18 364	7.13 362	1.07 362	9.90 358

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		23.24 331	30.99 333	0.00 347	0.00 368	0.00 369	0.00 359	97.63 133	0.00 364	0.00 365	96.88 135	84.38 151	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		7.39 335	9.85 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	88.65 94	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		8.06 334	10.74 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	96.69 144	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

RE-MVS-def			96.72 35		99.02 43	92.34 94	97.98 48	98.03 84	93.52 67	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	90.40 171	98.94 5				97.41 7	99.66 8	99.74 5

save fliter					98.91 49	94.28 35	97.02 144	98.02 88	95.35 8

test0726					99.45 2	95.36 10	98.31 22	98.29 24	94.92 23	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												82.76 181				98.45 132

sam_mvs												81.94 200

MTGPA								98.08 64

MTMP							97.86 59	82.03 359

test9_res														94.81 84	99.38 48	99.45 45

agg_prior2														93.94 101	99.38 48	99.50 37

test_prior4						93.66 59	96.42 198

test_prior2							96.35 207		92.80 96	96.03 79	97.59 101	92.01 41		95.01 75	99.38 48

新几何2							95.79 242

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							95.67 245

test222					98.24 93	92.21 100	95.33 259	97.60 135	79.22 337	95.25 105	97.84 81	88.80 92			99.15 73	98.72 112

segment_acmp												92.89 22

testdata1							95.26 266		93.10 83

plane_prior7					96.21 191	89.98 172

plane_prior6					96.10 201	90.00 168						81.32 208

plane_prior4											96.64 147

plane_prior3						90.00 168			94.46 40	91.34 180

plane_prior2							97.74 71		94.85 25

plane_prior1					96.14 199

plane_prior						89.99 170	97.24 124		94.06 48						92.16 212

n2								0.00 369

nn								0.00 369

door-mid								91.06 348

test11								97.88 106

door								91.13 347

HQP5-MVS						89.33 195

HQP-NCC					95.86 206		96.65 181		93.55 63	90.14 204

ACMP_Plane					95.86 206		96.65 181		93.55 63	90.14 204

BP-MVS														92.13 133

HQP3-MVS								97.39 168							92.10 213

HQP2-MVS												80.95 211

NP-MVS					95.99 205	89.81 177					95.87 187

MDTV_nov1_ep13_2view						70.35 352	93.10 318		83.88 306	93.55 134		82.47 189		86.25 245		98.38 140

ACMMP++_ref															90.30 241

ACMMP++															91.02 230

Test By Simon												88.73 93

High-resolution multi-view benchmark