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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

EPP-MVSNet		97.75 62	98.71 58	96.63 78	95.68 115	99.56 47	97.51 104	93.10 92	99.22 44	94.99 55	97.18 92	97.30 81	98.65 71	98.83 53	98.93 33	99.84 5	99.92 1

LTVRE_ROB	93.20 16	92.84 169	94.92 156	90.43 183	92.83 158	98.63 127	97.08 123	87.87 162	97.91 149	68.42 209	93.54 146	79.46 205	96.62 131	97.55 134	97.40 120	99.74 44	99.92 1
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
canonicalmvs		97.31 75	97.81 91	96.72 73	96.20 98	99.45 61	98.21 81	91.60 107	99.22 44	95.39 47	98.48 57	90.95 137	99.16 43	97.66 128	99.05 24	99.76 35	99.90 3

PVSNet_Blended_VisFu		97.41 73	98.49 64	96.15 88	97.49 71	99.76 1	96.02 144	93.75 77	99.26 40	93.38 86	93.73 144	99.35 56	96.47 136	98.96 42	98.46 61	99.77 33	99.90 3

CSCG		98.90 30	98.93 51	98.85 25	99.75 3	99.72 6	99.49 21	96.58 43	99.38 23	98.05 16	98.97 34	97.87 74	99.49 19	97.78 121	98.92 34	99.78 28	99.90 3

MVS_0304		98.14 51	99.03 46	97.10 60	98.05 65	99.63 25	99.27 34	94.33 65	99.63 6	93.06 90	97.32 86	99.05 61	98.09 89	98.82 54	98.87 38	99.81 16	99.89 6

PS-CasMVS		92.72 174	93.36 188	91.98 157	91.62 186	97.52 185	94.13 184	88.98 148	95.94 197	81.51 166	87.35 191	79.95 202	95.91 149	96.37 166	96.49 141	99.70 77	99.89 6

CP-MVSNet		93.25 163	94.00 176	92.38 148	91.65 184	97.56 183	94.38 180	89.20 146	96.05 194	83.16 155	89.51 173	81.97 191	96.16 144	96.43 163	96.56 139	99.71 68	99.89 6

WR-MVS_H		93.54 159	94.67 163	92.22 149	91.95 173	97.91 163	94.58 177	88.75 151	96.64 184	83.88 147	90.66 167	85.13 173	94.40 178	96.54 161	95.91 160	99.73 51	99.89 6

FC-MVSNet-train		97.04 82	97.91 88	96.03 92	96.00 102	98.41 144	96.53 135	93.42 82	99.04 73	93.02 92	98.03 72	94.32 117	97.47 110	97.93 113	97.77 103	99.75 39	99.88 10

IterMVS-LS		96.12 110	97.48 99	94.53 112	95.19 131	97.56 183	97.15 118	89.19 147	99.08 65	88.23 122	94.97 132	94.73 111	97.84 102	97.86 118	98.26 78	99.60 125	99.88 10
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DCV-MVSNet		97.56 68	98.36 67	96.62 79	96.44 89	98.36 148	98.37 72	91.73 104	99.11 61	94.80 57	98.36 63	96.28 92	98.60 75	98.12 96	98.44 62	99.76 35	99.87 12

v7n		91.61 190	92.95 191	90.04 185	90.56 200	97.69 171	93.74 185	85.59 178	95.89 198	76.95 186	86.60 196	78.60 208	93.76 189	97.01 152	94.99 179	99.65 106	99.87 12

CHOSEN 1792x2688		96.41 102	96.99 120	95.74 99	98.01 66	99.72 6	97.70 100	90.78 124	99.13 60	90.03 116	87.35 191	95.36 103	98.33 83	98.59 75	98.91 36	99.59 131	99.87 12

CANet		98.46 42	99.16 34	97.64 49	98.48 58	99.64 22	99.35 31	94.71 58	99.53 13	95.17 51	97.63 83	99.59 53	98.38 82	98.88 51	98.99 29	99.74 44	99.86 15

baseline		97.45 72	98.70 59	95.99 94	95.89 105	99.36 75	98.29 77	91.37 113	99.21 46	92.99 93	98.40 61	96.87 86	97.96 94	98.60 73	98.60 55	99.42 163	99.86 15

HyFIR lowres test		95.99 112	96.56 128	95.32 104	97.99 67	99.65 17	96.54 133	88.86 149	98.44 125	89.77 119	84.14 201	97.05 84	99.03 52	98.55 77	98.19 82	99.73 51	99.86 15

GeoE		95.98 114	97.24 114	94.51 113	95.02 134	99.38 71	98.02 91	87.86 163	98.37 128	87.86 127	92.99 157	93.54 124	98.56 76	98.61 70	97.92 93	99.73 51	99.85 18

tfpnnormal		93.85 157	94.12 172	93.54 133	93.22 157	98.24 152	95.45 154	91.96 101	94.61 203	83.91 146	90.74 165	81.75 193	97.04 117	97.49 136	96.16 151	99.68 89	99.84 19

Effi-MVS+		95.81 115	97.31 112	94.06 120	95.09 132	99.35 78	97.24 114	88.22 158	98.54 119	85.38 143	98.52 55	88.68 147	98.70 68	98.32 87	97.93 92	99.74 44	99.84 19

SD-MVS		99.25 12	99.50 11	98.96 21	98.79 53	99.55 48	99.33 32	98.29 11	99.75 1	97.96 19	99.15 22	99.95 17	99.61 6	99.17 31	99.06 23	99.81 16	99.84 19
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
EPNet		98.05 54	98.86 53	97.10 60	99.02 49	99.43 65	98.47 67	94.73 57	99.05 71	95.62 43	98.93 37	97.62 78	95.48 161	98.59 75	98.55 57	99.29 173	99.84 19
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SteuartSystems-ACMMP		99.20 15	99.51 10	98.83 27	99.66 16	99.66 15	99.71 3	98.12 28	99.14 55	96.62 34	99.16 21	99.98 1	99.12 45	99.63 3	99.19 20	99.78 28	99.83 23
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		99.27 10	99.57 4	98.92 23	98.78 54	99.53 50	99.72 2	98.11 29	99.73 2	97.43 25	99.15 22	99.96 12	99.59 10	99.73 1	99.07 22	99.88 1	99.82 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
anonymousdsp		93.12 165	95.86 147	89.93 188	91.09 197	98.25 151	95.12 158	85.08 180	97.44 162	73.30 199	90.89 164	90.78 138	95.25 169	97.91 114	95.96 159	99.71 68	99.82 24

TSAR-MVS + ACMM		98.77 33	99.45 13	97.98 44	99.37 37	99.46 59	99.44 27	98.13 27	99.65 4	92.30 102	98.91 39	99.95 17	99.05 50	99.42 17	98.95 32	99.58 135	99.82 24

PEN-MVS		92.72 174	93.20 190	92.15 152	91.29 194	97.31 193	94.67 174	89.81 138	96.19 190	81.83 164	88.58 182	79.06 206	95.61 157	95.21 187	96.27 146	99.72 58	99.82 24

WR-MVS		93.43 162	94.48 166	92.21 150	91.52 189	97.69 171	94.66 175	89.98 135	96.86 178	83.43 152	90.12 169	85.03 174	93.94 186	96.02 178	95.82 161	99.71 68	99.82 24

UniMVSNet_ETH3D		93.15 164	92.33 197	94.11 119	93.91 146	98.61 130	94.81 168	90.98 119	97.06 173	87.51 130	82.27 205	76.33 211	97.87 101	94.79 194	97.47 116	99.56 142	99.81 29

DeepC-MVS	97.63 4	98.33 46	98.57 60	98.04 42	98.62 57	99.65 17	99.45 25	98.15 24	99.51 16	92.80 95	95.74 125	96.44 89	99.46 21	99.37 19	99.50 2	99.78 28	99.81 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		97.70 64	98.78 56	96.44 84	95.72 112	99.65 17	98.14 84	93.72 78	98.30 131	92.31 101	98.63 52	97.90 73	98.97 55	98.92 47	98.30 76	99.78 28	99.80 31

CS-MVS		98.06 53	99.12 36	96.82 72	95.83 108	99.66 15	98.93 52	93.12 91	98.95 79	94.29 69	98.55 54	99.05 61	98.94 56	99.05 39	98.78 48	99.83 8	99.80 31

v8		92.87 168	93.87 181	91.72 165	92.05 171	97.50 186	94.79 169	88.20 159	96.85 179	80.11 174	90.01 170	82.86 187	95.48 161	95.15 189	94.90 182	99.66 102	99.80 31

v10		92.79 172	94.06 174	91.31 171	91.78 179	97.29 195	94.87 166	86.10 176	96.97 176	79.82 176	88.16 185	84.56 177	95.63 155	96.33 169	95.31 170	99.65 106	99.80 31

UniMVSNet_NR-MVSNet		94.59 142	95.47 151	93.55 132	91.85 177	97.89 164	95.03 159	92.00 99	97.33 166	86.12 135	93.19 151	87.29 151	96.60 132	96.12 174	96.70 133	99.72 58	99.80 31

DU-MVS		93.98 152	94.44 167	93.44 135	91.66 182	97.77 166	95.03 159	91.57 108	97.17 170	86.12 135	93.13 153	81.13 195	96.60 132	95.10 190	97.01 128	99.67 97	99.80 31

UGNet		97.66 65	99.07 41	96.01 93	97.19 80	99.65 17	97.09 122	93.39 83	99.35 28	94.40 67	98.79 44	99.59 53	94.24 181	98.04 107	98.29 77	99.73 51	99.80 31
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
IS_MVSNet		97.86 58	98.86 53	96.68 74	96.02 100	99.72 6	98.35 75	93.37 85	98.75 110	94.01 72	96.88 99	98.40 68	98.48 80	99.09 35	99.42 5	99.83 8	99.80 31

ETV-MVS		98.05 54	99.25 30	96.65 76	95.61 117	99.61 34	98.26 80	93.52 81	98.90 86	93.74 81	99.32 14	99.20 58	98.90 61	99.21 29	98.72 49	99.87 2	99.79 39

DVP-MVS		99.45 2	99.54 6	99.35 1	99.72 7	99.76 1	99.63 11	98.37 2	99.63 6	99.03 3	98.95 36	99.98 1	99.60 7	99.60 6	99.05 24	99.74 44	99.79 39
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
thisisatest0515		94.61 141	96.89 122	91.95 158	92.00 172	98.47 138	92.01 193	90.73 126	98.18 136	83.96 145	94.51 137	95.13 106	93.38 191	97.38 139	94.74 187	99.61 117	99.79 39

MSP-MVS		99.34 6	99.52 9	99.14 8	99.68 12	99.75 4	99.64 8	98.31 8	99.44 20	98.10 14	99.28 15	99.98 1	99.30 33	99.34 22	99.05 24	99.81 16	99.79 39
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
UniMVSNet (Re)		94.58 143	95.34 153	93.71 127	92.25 169	98.08 157	94.97 161	91.29 118	97.03 175	87.94 125	93.97 143	86.25 165	96.07 145	96.27 171	95.97 158	99.72 58	99.79 39

DELS-MVS		98.19 49	98.77 57	97.52 51	98.29 61	99.71 9	99.12 41	94.58 63	98.80 100	95.38 48	96.24 115	98.24 71	97.92 96	99.06 38	99.52 1	99.82 10	99.79 39
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
tttt0517		97.23 77	98.24 73	96.04 91	95.60 119	99.60 39	96.94 127	93.23 86	99.15 52	92.56 99	98.74 49	96.12 96	98.17 84	98.21 92	96.10 153	99.73 51	99.78 45

test_part1		95.56 120	95.38 152	95.78 96	96.07 99	98.16 155	97.57 102	90.78 124	97.43 163	93.04 91	89.12 178	89.41 146	97.93 95	96.38 165	97.38 121	99.29 173	99.78 45

v144192		92.38 183	93.55 186	91.00 176	91.44 190	97.47 188	94.27 181	87.41 166	96.52 187	78.03 183	87.50 190	82.65 189	95.32 166	95.82 182	95.15 175	99.55 144	99.78 45

V42		93.05 166	93.90 180	92.04 154	91.91 174	97.66 173	94.91 163	89.91 136	96.85 179	80.58 170	89.66 172	83.43 183	95.37 165	95.03 192	94.90 182	99.59 131	99.78 45

MVS_Test		97.30 76	98.54 61	95.87 95	95.74 111	99.28 87	98.19 82	91.40 112	99.18 50	91.59 108	98.17 68	96.18 94	98.63 73	98.61 70	98.55 57	99.66 102	99.78 45

TranMVSNet+NR-MVSNet		93.67 158	94.14 170	93.13 141	91.28 196	97.58 181	95.60 151	91.97 100	97.06 173	84.05 144	90.64 168	82.22 190	96.17 143	94.94 193	96.78 131	99.69 80	99.78 45

PVSNet_BlendedMVS		97.51 70	97.71 92	97.28 55	98.06 63	99.61 34	97.31 110	95.02 52	99.08 65	95.51 45	98.05 70	90.11 140	98.07 90	98.91 48	98.40 65	99.72 58	99.78 45

PVSNet_Blended		97.51 70	97.71 92	97.28 55	98.06 63	99.61 34	97.31 110	95.02 52	99.08 65	95.51 45	98.05 70	90.11 140	98.07 90	98.91 48	98.40 65	99.72 58	99.78 45

SED-MVS		99.44 3	99.58 3	99.28 3	99.69 8	99.76 1	99.62 14	98.35 3	99.51 16	99.05 2	99.60 5	99.98 1	99.28 35	99.61 5	98.83 43	99.70 77	99.77 53

thisisatest0530		97.23 77	98.25 70	96.05 90	95.60 119	99.59 41	96.96 126	93.23 86	99.17 51	92.60 98	98.75 48	96.19 93	98.17 84	98.19 94	96.10 153	99.72 58	99.77 53

Fast-Effi-MVS+		95.38 125	96.52 131	94.05 121	94.15 144	99.14 98	97.24 114	86.79 169	98.53 120	87.62 129	94.51 137	87.06 152	98.76 66	98.60 73	98.04 90	99.72 58	99.77 53

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 5	99.75 1	98.34 4	99.56 10	98.72 7	99.57 6	99.97 7	99.53 16	99.65 2	99.25 14	99.84 5	99.77 53

ACMMPR		99.30 9	99.54 6	99.03 16	99.66 16	99.64 22	99.68 4	98.25 14	99.56 10	97.12 30	99.19 19	99.95 17	99.72 1	99.43 16	99.25 14	99.72 58	99.77 53

Anonymous202405211			97.40 104		96.45 88	99.54 49	98.08 89	93.79 74	98.24 135		93.55 145	94.41 115	98.88 64	98.04 107	98.24 79	99.75 39	99.76 58

SMA-MVS		99.38 5	99.60 2	99.12 9	99.76 2	99.62 29	99.39 29	98.23 19	99.52 15	98.03 17	99.45 10	99.98 1	99.64 5	99.58 8	99.30 11	99.68 89	99.76 58
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
v1144		92.81 170	94.03 175	91.40 169	91.68 181	97.60 180	94.73 170	88.40 156	96.71 182	78.48 182	88.14 186	84.46 178	95.45 164	96.31 170	95.22 173	99.65 106	99.76 58

HFP-MVS		99.32 7	99.53 8	99.07 13	99.69 8	99.59 41	99.63 11	98.31 8	99.56 10	97.37 26	99.27 16	99.97 7	99.70 3	99.35 21	99.24 16	99.71 68	99.76 58

MSLP-MVS++		99.15 18	99.24 31	99.04 15	99.52 32	99.49 56	99.09 44	98.07 30	99.37 25	98.47 9	97.79 77	99.89 34	99.50 17	98.93 45	99.45 4	99.61 117	99.76 58

ACMMP		98.74 34	99.03 46	98.40 33	99.36 39	99.64 22	99.20 36	97.75 38	98.82 97	95.24 50	98.85 42	99.87 36	99.17 42	98.74 62	97.50 112	99.71 68	99.76 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
ACMH	95.42 14	95.27 128	95.96 144	94.45 115	96.83 85	98.78 114	94.72 171	91.67 106	98.95 79	86.82 134	96.42 112	83.67 180	97.00 118	97.48 137	96.68 134	99.69 80	99.76 58
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		97.10 81	97.06 118	97.14 59	96.32 91	99.52 53	98.16 83	93.76 75	98.84 94	95.98 40	90.92 163	94.58 114	98.90 61	97.72 126	98.10 87	99.71 68	99.75 65

X-MVS		98.93 29	99.37 21	98.42 32	99.67 13	99.62 29	99.60 15	98.15 24	99.08 65	93.81 78	98.46 59	99.95 17	99.59 10	99.49 13	99.21 19	99.68 89	99.75 65

NR-MVSNet		94.01 150	94.51 165	93.44 135	92.56 162	97.77 166	95.67 148	91.57 108	97.17 170	85.84 138	93.13 153	80.53 197	95.29 167	97.01 152	96.17 150	99.69 80	99.75 65

Vis-MVSNet (Re-imp)		97.40 74	98.89 52	95.66 101	95.99 103	99.62 29	97.82 94	93.22 88	98.82 97	91.40 109	96.94 96	98.56 66	95.70 153	99.14 33	99.41 6	99.79 25	99.75 65

ACMMP_NAP		99.05 25	99.45 13	98.58 31	99.73 5	99.60 39	99.64 8	98.28 12	99.23 43	94.57 60	99.35 13	99.97 7	99.55 14	99.63 3	98.66 51	99.70 77	99.74 69

v1192		92.43 181	93.61 183	91.05 175	91.53 188	97.43 189	94.61 176	87.99 161	96.60 185	76.72 187	87.11 193	82.74 188	95.85 150	96.35 168	95.30 171	99.60 125	99.74 69

PGM-MVS		98.86 31	99.35 25	98.29 35	99.77 1	99.63 25	99.67 5	95.63 46	98.66 113	95.27 49	99.11 25	99.82 42	99.67 4	99.33 23	99.19 20	99.73 51	99.74 69

CP-MVS		99.27 10	99.44 16	99.08 12	99.62 23	99.58 44	99.53 18	98.16 22	99.21 46	97.79 21	99.15 22	99.96 12	99.59 10	99.54 11	98.86 39	99.78 28	99.74 69

IterMVS-SCA-FT		94.89 134	97.87 89	91.42 167	94.86 138	97.70 169	97.24 114	84.88 183	98.93 83	75.74 191	94.26 140	98.25 70	96.69 127	98.52 79	97.68 105	99.10 181	99.73 73

zzz-MVS		99.31 8	99.44 16	99.16 6	99.73 5	99.65 17	99.63 11	98.26 13	99.27 37	98.01 18	99.27 16	99.97 7	99.60 7	99.59 7	98.58 56	99.71 68	99.73 73

v1921920		92.36 185	93.57 184	90.94 177	91.39 192	97.39 191	94.70 172	87.63 165	96.60 185	76.63 188	86.98 194	82.89 186	95.75 151	96.26 172	95.14 176	99.55 144	99.73 73

DI_MVS_plusplus_trai		96.90 87	97.49 98	96.21 87	95.61 117	99.40 70	98.72 61	92.11 96	99.14 55	92.98 94	93.08 155	95.14 105	98.13 88	98.05 106	97.91 95	99.74 44	99.73 73

v1240		91.99 188	93.33 189	90.44 182	91.29 194	97.30 194	94.25 182	86.79 169	96.43 188	75.49 194	86.34 197	81.85 192	95.29 167	96.42 164	95.22 173	99.52 151	99.73 73

thres600view7		96.69 95	96.43 139	97.00 69	96.28 95	99.67 12	98.41 69	93.99 71	97.85 153	94.29 69	95.96 119	85.91 167	99.19 38	98.26 89	97.63 106	99.82 10	99.73 73

MP-MVS		99.07 23	99.36 22	98.74 28	99.63 21	99.57 46	99.66 6	98.25 14	99.00 76	95.62 43	98.97 34	99.94 25	99.54 15	99.51 12	98.79 47	99.71 68	99.73 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DTE-MVSNet		92.42 182	92.85 193	91.91 160	90.87 199	96.97 197	94.53 179	89.81 138	95.86 199	81.59 165	88.83 180	77.88 209	95.01 173	94.34 197	96.35 144	99.64 110	99.73 73

Baseline_NR-MVSNet		93.87 155	93.98 177	93.75 125	91.66 182	97.02 196	95.53 152	91.52 111	97.16 172	87.77 128	87.93 189	83.69 179	96.35 138	95.10 190	97.23 123	99.68 89	99.73 73

SixPastTwentyTwo		93.44 161	95.32 154	91.24 172	92.11 170	98.40 145	92.77 189	88.64 154	98.09 140	77.83 184	93.51 147	85.74 168	96.52 135	96.91 154	94.89 184	99.59 131	99.73 73

LGP-MVS_train		96.23 106	96.89 122	95.46 103	97.32 75	98.77 115	98.81 58	93.60 80	98.58 116	85.52 141	99.08 30	86.67 159	97.83 103	97.87 117	97.51 111	99.69 80	99.73 73

pm-mvs1		94.27 146	95.57 150	92.75 145	92.58 161	98.13 156	94.87 166	90.71 127	96.70 183	83.78 148	89.94 171	89.85 144	94.96 174	97.58 133	97.07 125	99.61 117	99.72 84

casdiffmvs		96.93 86	97.43 103	96.34 85	95.70 113	99.50 55	97.75 98	93.22 88	98.98 78	92.64 96	94.97 132	91.71 135	98.93 57	98.62 69	98.52 60	99.82 10	99.72 84

IterMVS		94.81 136	97.71 92	91.42 167	94.83 139	97.63 176	97.38 107	85.08 180	98.93 83	75.67 192	94.02 141	97.64 76	96.66 130	98.45 82	97.60 108	98.90 184	99.72 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MCST-MVS		99.11 20	99.27 29	98.93 22	99.67 13	99.33 83	99.51 20	98.31 8	99.28 35	96.57 36	99.10 28	99.90 32	99.71 2	99.19 30	98.35 70	99.82 10	99.71 87

ACMP	96.25 10	96.62 100	96.72 125	96.50 83	96.96 84	98.75 119	97.80 95	94.30 66	98.85 90	93.12 89	98.78 45	86.61 160	97.23 115	97.73 125	96.61 137	99.62 115	99.71 87
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		99.39 4	99.55 5	99.20 4	99.63 21	99.71 9	99.66 6	98.33 6	99.29 34	98.40 12	99.64 4	99.98 1	99.31 31	99.56 9	98.96 31	99.85 3	99.70 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
tfpn200view9		96.75 91	96.51 132	97.03 64	96.31 92	99.67 12	98.41 69	93.99 71	97.35 164	94.52 61	95.90 121	86.93 155	99.14 44	98.26 89	97.80 101	99.82 10	99.70 89

thres400		96.71 94	96.45 137	97.02 66	96.28 95	99.63 25	98.41 69	94.00 70	97.82 154	94.42 66	95.74 125	86.26 164	99.18 40	98.20 93	97.79 102	99.81 16	99.70 89

diffmvs		96.83 88	97.33 108	96.25 86	95.76 110	99.34 80	98.06 90	93.22 88	99.43 21	92.30 102	96.90 98	89.83 145	98.55 77	98.00 110	98.14 83	99.64 110	99.70 89

v148		92.36 185	92.88 192	91.75 163	91.63 185	97.66 173	92.64 190	90.55 129	96.09 192	83.34 153	88.19 184	80.00 200	92.74 195	93.98 198	94.58 188	99.58 135	99.69 93

v2v482		92.77 173	93.52 187	91.90 161	91.59 187	97.63 176	94.57 178	90.31 131	96.80 181	79.22 178	88.74 181	81.55 194	96.04 147	95.26 186	94.97 180	99.66 102	99.69 93

CPTT-MVS		99.14 19	99.20 33	99.06 14	99.58 26	99.53 50	99.45 25	97.80 37	99.19 49	98.32 13	98.58 53	99.95 17	99.60 7	99.28 25	98.20 81	99.64 110	99.69 93

FMVSNet1		95.77 116	96.41 140	95.03 106	93.42 156	97.86 165	97.11 121	89.89 137	98.53 120	92.00 105	89.17 175	93.23 128	98.15 87	98.07 102	98.34 72	99.61 117	99.69 93

Vis-MVSNet		96.16 109	98.22 74	93.75 125	95.33 129	99.70 11	97.27 112	90.85 121	98.30 131	85.51 142	95.72 127	96.45 87	93.69 190	98.70 64	99.00 28	99.84 5	99.69 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVSTER		97.16 79	97.71 92	96.52 81	95.97 104	98.48 137	98.63 63	92.10 97	98.68 112	95.96 41	99.23 18	91.79 134	96.87 122	98.76 59	97.37 122	99.57 139	99.68 98

GBi-Net		96.98 84	98.00 85	95.78 96	93.81 149	97.98 158	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 97	98.07 102	98.34 72	99.68 89	99.67 99

test1		96.98 84	98.00 85	95.78 96	93.81 149	97.98 158	98.09 86	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.89 97	98.07 102	98.34 72	99.68 89	99.67 99

FMVSNet2		96.64 98	97.50 97	95.63 102	93.81 149	97.98 158	98.09 86	90.87 120	98.99 77	93.48 84	93.17 152	95.25 104	97.89 97	98.63 68	98.80 46	99.68 89	99.67 99

3Dnovator+	96.92 7	98.71 36	99.05 42	98.32 34	99.53 30	99.34 80	99.06 46	94.61 60	99.65 4	97.49 24	96.75 100	99.86 37	99.44 23	98.78 57	99.30 11	99.81 16	99.67 99

HPM-MVS++		99.10 21	99.30 27	98.86 24	99.69 8	99.48 57	99.59 16	98.34 4	99.26 40	96.55 37	99.10 28	99.96 12	99.36 27	99.25 26	98.37 69	99.64 110	99.66 103

thres200		96.76 90	96.53 130	97.03 64	96.31 92	99.67 12	98.37 72	93.99 71	97.68 159	94.49 63	95.83 124	86.77 157	99.18 40	98.26 89	97.82 100	99.82 10	99.66 103

3Dnovator	96.92 7	98.67 37	99.05 42	98.23 38	99.57 27	99.45 61	99.11 42	94.66 59	99.69 3	96.80 33	96.55 110	99.61 52	99.40 25	98.87 52	99.49 3	99.85 3	99.66 103

TSAR-MVS + GP.		98.66 39	99.36 22	97.85 46	97.16 81	99.46 59	99.03 48	94.59 62	99.09 63	97.19 29	99.73 3	99.95 17	99.39 26	98.95 43	98.69 50	99.75 39	99.65 106

FMVSNet3		97.02 83	98.12 79	95.73 100	93.59 155	97.98 158	98.34 76	91.32 114	98.80 100	93.92 74	97.21 89	95.94 99	97.63 106	98.61 70	98.62 53	99.61 117	99.65 106

CDS-MVSNet		96.59 101	98.02 84	94.92 108	94.45 142	98.96 106	97.46 106	91.75 103	97.86 152	90.07 115	96.02 118	97.25 82	96.21 140	98.04 107	98.38 67	99.60 125	99.65 106
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMH+	95.51 13	95.40 124	96.00 142	94.70 110	96.33 90	98.79 112	96.79 128	91.32 114	98.77 106	87.18 131	95.60 129	85.46 170	96.97 119	97.15 148	96.59 138	99.59 131	99.65 106

QAPM		98.62 40	99.04 45	98.13 39	99.57 27	99.48 57	99.17 38	94.78 56	99.57 9	96.16 38	96.73 101	99.80 43	99.33 29	98.79 56	99.29 13	99.75 39	99.64 110

DeepC-MVS_fast	98.34 1	99.17 17	99.45 13	98.85 25	99.55 29	99.37 74	99.64 8	98.05 32	99.53 13	96.58 35	98.93 37	99.92 28	99.49 19	99.46 14	99.32 10	99.80 24	99.64 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres100view900		96.72 93	96.47 135	97.00 69	96.31 92	99.52 53	98.28 78	94.01 69	97.35 164	94.52 61	95.90 121	86.93 155	99.09 49	98.07 102	97.87 97	99.81 16	99.63 112

Effi-MVS+-dtu		95.74 117	98.04 82	93.06 142	93.92 145	99.16 96	97.90 92	88.16 160	99.07 70	82.02 163	98.02 73	94.32 117	96.74 126	98.53 78	97.56 109	99.61 117	99.62 113

HQP-MVS		96.37 103	96.58 127	96.13 89	97.31 77	98.44 141	98.45 68	95.22 50	98.86 88	88.58 121	98.33 64	87.00 154	97.67 105	97.23 145	96.56 139	99.56 142	99.62 113

IB-MVS	93.96 15	95.02 131	96.44 138	93.36 138	97.05 83	99.28 87	90.43 198	93.39 83	98.02 142	96.02 39	94.92 134	92.07 133	83.52 207	95.38 184	95.82 161	99.72 58	99.59 115
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
train_agg		98.73 35	99.11 37	98.28 36	99.36 39	99.35 78	99.48 23	97.96 34	98.83 95	93.86 77	98.70 51	99.86 37	99.44 23	99.08 37	98.38 67	99.61 117	99.58 116

CDPH-MVS		98.41 43	99.10 38	97.61 50	99.32 43	99.36 75	99.49 21	96.15 45	98.82 97	91.82 106	98.41 60	99.66 51	99.10 47	98.93 45	98.97 30	99.75 39	99.58 116

APD-MVS		99.25 12	99.38 20	99.09 11	99.69 8	99.58 44	99.56 17	98.32 7	98.85 90	97.87 20	98.91 39	99.92 28	99.30 33	99.45 15	99.38 8	99.79 25	99.58 116
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.67 37	99.41 19	97.81 47	99.37 37	99.53 50	98.51 66	95.52 48	99.27 37	94.85 56	99.56 7	99.69 50	99.04 51	99.36 20	98.88 37	99.60 125	99.58 116

PHI-MVS		99.08 22	99.43 18	98.67 29	99.15 46	99.59 41	99.11 42	97.35 40	99.14 55	97.30 27	99.44 11	99.96 12	99.32 30	98.89 50	99.39 7	99.79 25	99.58 116

MVS_111021_HR		98.59 41	99.36 22	97.68 48	99.42 35	99.61 34	98.14 84	94.81 55	99.31 31	95.00 54	99.51 8	99.79 45	99.00 54	98.94 44	98.83 43	99.69 80	99.57 121

xxxxxxxxxxxxxcwj		98.14 51	97.38 105	99.03 16	99.65 18	99.41 68	98.87 54	98.24 17	99.14 55	98.73 5	99.11 25	86.38 163	98.92 58	99.22 27	98.84 41	99.76 35	99.56 122

SF-MVS		99.18 16	99.32 26	99.03 16	99.65 18	99.41 68	98.87 54	98.24 17	99.14 55	98.73 5	99.11 25	99.92 28	98.92 58	99.22 27	98.84 41	99.76 35	99.56 122

DeepPCF-MVS	97.74 3	98.34 45	99.46 12	97.04 63	98.82 52	99.33 83	96.28 140	97.47 39	99.58 8	94.70 59	98.99 33	99.85 40	97.24 114	99.55 10	99.34 9	97.73 198	99.56 122

CLD-MVS		96.74 92	96.51 132	97.01 68	96.71 86	98.62 128	98.73 60	94.38 64	98.94 82	94.46 64	97.33 85	87.03 153	98.07 90	97.20 147	96.87 130	99.72 58	99.54 125
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet_DTU		96.64 98	99.08 39	93.81 124	97.10 82	99.42 66	98.85 56	90.01 134	99.31 31	79.98 175	99.78 2	99.10 60	97.42 111	98.35 86	98.05 89	99.47 156	99.53 126

pmmvs6		91.90 189	92.53 196	91.17 173	91.81 178	97.63 176	93.23 186	88.37 157	93.43 208	80.61 169	77.32 209	87.47 150	94.12 182	96.58 159	95.72 163	98.88 185	99.53 126

baseline1		97.58 67	98.05 81	97.02 66	96.21 97	99.45 61	97.71 99	93.71 79	98.47 124	95.75 42	98.78 45	93.20 129	98.91 60	98.52 79	98.44 62	99.81 16	99.53 126

FC-MVSNet-test		96.07 111	97.94 87	93.89 122	93.60 154	98.67 125	96.62 132	90.30 133	98.76 107	88.62 120	95.57 130	97.63 77	94.48 177	97.97 111	97.48 115	99.71 68	99.52 129

CNVR-MVS		99.23 14	99.28 28	99.17 5	99.65 18	99.34 80	99.46 24	98.21 20	99.28 35	98.47 9	98.89 41	99.94 25	99.50 17	99.42 17	98.61 54	99.73 51	99.52 129

PLC	97.93 2	99.02 28	98.94 50	99.11 10	99.46 34	99.24 91	99.06 46	97.96 34	99.31 31	99.16 1	97.90 75	99.79 45	99.36 27	98.71 63	98.12 85	99.65 106	99.52 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		97.71 63	98.04 82	97.32 53	99.35 41	98.91 108	97.65 101	91.68 105	98.00 143	97.01 31	97.72 81	94.83 109	98.85 65	98.44 84	98.86 39	99.41 164	99.52 129
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
NCCC		99.05 25	99.08 39	99.02 19	99.62 23	99.38 71	99.43 28	98.21 20	99.36 27	97.66 23	97.79 77	99.90 32	99.45 22	99.17 31	98.43 64	99.77 33	99.51 133

OpenMVS	96.23 11	97.95 57	98.45 65	97.35 52	99.52 32	99.42 66	98.91 53	94.61 60	98.87 87	92.24 104	94.61 136	99.05 61	99.10 47	98.64 67	99.05 24	99.74 44	99.51 133

Fast-Effi-MVS+-dtu		95.38 125	98.20 75	92.09 153	93.91 146	98.87 109	97.35 109	85.01 182	99.08 65	81.09 167	98.10 69	96.36 90	95.62 156	98.43 85	97.03 126	99.55 144	99.50 135

ACMM	96.26 9	96.67 97	96.69 126	96.66 75	97.29 78	98.46 139	96.48 136	95.09 51	99.21 46	93.19 88	98.78 45	86.73 158	98.17 84	97.84 119	96.32 145	99.74 44	99.49 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		97.99 56	99.24 31	96.53 80	98.34 60	99.61 34	98.36 74	89.80 140	99.27 37	95.08 53	99.81 1	98.58 65	98.64 72	99.02 40	98.92 34	98.93 183	99.48 137

TAPA-MVS	97.53 5	98.41 43	98.84 55	97.91 45	99.08 48	99.33 83	99.15 39	97.13 41	99.34 29	93.20 87	97.75 79	99.19 59	99.20 37	98.66 65	98.13 84	99.66 102	99.48 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GA-MVS		93.93 154	96.31 141	91.16 174	93.61 153	98.79 112	95.39 156	90.69 128	98.25 134	73.28 200	96.15 116	88.42 148	94.39 179	97.76 123	95.35 169	99.58 135	99.45 139

CVMVSNet		95.33 127	97.09 116	93.27 140	95.23 130	98.39 146	95.49 153	92.58 95	97.71 158	83.00 157	94.44 139	93.28 127	93.92 187	97.79 120	98.54 59	99.41 164	99.45 139

LS3D		97.79 59	98.25 70	97.26 57	98.40 59	99.63 25	99.53 18	98.63 1	99.25 42	88.13 123	96.93 97	94.14 119	99.19 38	99.14 33	99.23 17	99.69 80	99.42 141

ET-MVSNet_ETH3D		96.17 108	96.99 120	95.21 105	88.53 205	98.54 134	98.28 78	92.61 94	98.85 90	93.60 83	99.06 32	90.39 139	98.63 73	95.98 179	96.68 134	99.61 117	99.41 142

baseline2		96.36 104	97.82 90	94.65 111	94.60 141	99.09 99	96.45 137	89.63 142	98.36 129	91.29 111	97.60 84	94.13 120	96.37 137	98.45 82	97.70 104	99.54 148	99.41 142

abl_6				98.09 40	99.33 42	99.22 93	98.79 59	94.96 54	98.52 122	97.00 32	97.30 87	99.86 37	98.76 66			99.69 80	99.41 142

test0.0.03 1		96.69 95	98.12 79	95.01 107	95.49 124	98.99 103	95.86 146	90.82 122	98.38 127	92.54 100	96.66 104	97.33 79	95.75 151	97.75 124	98.34 72	99.60 125	99.40 145

testgi		95.67 118	97.48 99	93.56 131	95.07 133	99.00 101	95.33 157	88.47 155	98.80 100	86.90 133	97.30 87	92.33 131	95.97 148	97.66 128	97.91 95	99.60 125	99.38 146

TAMVS		95.53 121	96.50 134	94.39 116	93.86 148	99.03 100	96.67 130	89.55 144	97.33 166	90.64 113	93.02 156	91.58 136	96.21 140	97.72 126	97.43 119	99.43 161	99.36 147

AdaColmap		99.06 24	98.98 49	99.15 7	99.60 25	99.30 86	99.38 30	98.16 22	99.02 74	98.55 8	98.71 50	99.57 55	99.58 13	99.09 35	97.84 99	99.64 110	99.36 147

PM-MVS		89.55 197	90.30 202	88.67 193	87.06 206	95.60 205	90.88 196	84.51 186	96.14 191	75.75 190	86.89 195	63.47 217	94.64 176	96.85 155	93.89 192	99.17 179	99.29 149

DPM-MVS		98.31 47	98.53 62	98.05 41	98.76 55	98.77 115	99.13 40	98.07 30	99.10 62	94.27 71	96.70 102	99.84 41	98.70 68	97.90 115	98.11 86	99.40 166	99.28 150

pmmvs4		95.09 129	95.90 145	94.14 118	92.29 167	97.70 169	95.45 154	90.31 131	98.60 114	90.70 112	93.25 150	89.90 143	96.67 129	97.13 149	95.42 168	99.44 160	99.28 150

EG-PatchMatch MVS		92.45 178	93.92 179	90.72 180	92.56 162	98.43 143	94.88 165	84.54 185	97.18 169	79.55 177	86.12 198	83.23 184	93.15 194	97.22 146	96.00 155	99.67 97	99.27 152

UA-Net		97.13 80	99.14 35	94.78 109	97.21 79	99.38 71	97.56 103	92.04 98	98.48 123	88.03 124	98.39 62	99.91 31	94.03 184	99.33 23	99.23 17	99.81 16	99.25 153

pmmvs-eth3d		89.81 196	89.65 203	90.00 186	86.94 207	95.38 206	91.08 194	86.39 174	94.57 204	82.27 162	83.03 204	64.94 214	93.96 185	96.57 160	93.82 193	99.35 169	99.24 154

gg-mvs-nofinetune		90.85 191	94.14 170	87.02 197	94.89 137	99.25 89	98.64 62	76.29 211	88.24 212	57.50 216	79.93 207	95.45 102	95.18 170	98.77 58	98.07 88	99.62 115	99.24 154

PMMVS		97.52 69	98.39 66	96.51 82	95.82 109	98.73 122	97.80 95	93.05 93	98.76 107	94.39 68	99.07 31	97.03 85	98.55 77	98.31 88	97.61 107	99.43 161	99.21 156

CNLPA		99.03 27	99.05 42	99.01 20	99.27 44	99.22 93	99.03 48	97.98 33	99.34 29	99.00 4	98.25 66	99.71 49	99.31 31	98.80 55	98.82 45	99.48 154	99.17 157

CR-MVSNet		94.57 144	97.34 107	91.33 170	94.90 136	98.59 131	97.15 118	79.14 201	97.98 144	80.42 171	96.59 109	93.50 126	96.85 123	98.10 97	97.49 113	99.50 153	99.15 158

PatchT		93.96 153	97.36 106	90.00 186	94.76 140	98.65 126	90.11 201	78.57 206	97.96 147	80.42 171	96.07 117	94.10 121	96.85 123	98.10 97	97.49 113	99.26 175	99.15 158

COLMAP_ROB	96.15 12	97.78 60	98.17 76	97.32 53	98.84 51	99.45 61	99.28 33	95.43 49	99.48 18	91.80 107	94.83 135	98.36 69	98.90 61	98.09 99	97.85 98	99.68 89	99.15 158
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MSDG		98.27 48	98.29 69	98.24 37	99.20 45	99.22 93	99.20 36	97.82 36	99.37 25	94.43 65	95.90 121	97.31 80	99.12 45	98.76 59	98.35 70	99.67 97	99.14 161

test-mter		94.86 135	97.32 109	92.00 156	92.41 165	98.82 111	96.18 143	86.35 175	98.05 141	82.28 161	96.48 111	94.39 116	95.46 163	98.17 95	96.20 149	99.32 171	99.13 162

RPMNet		94.66 138	97.16 115	91.75 163	94.98 135	98.59 131	97.00 125	78.37 207	97.98 144	83.78 148	96.27 114	94.09 122	96.91 121	97.36 140	96.73 132	99.48 154	99.09 163

OMC-MVS		98.84 32	99.01 48	98.65 30	99.39 36	99.23 92	99.22 35	96.70 42	99.40 22	97.77 22	97.89 76	99.80 43	99.21 36	99.02 40	98.65 52	99.57 139	99.07 164

TSAR-MVS + COLMAP		96.79 89	96.55 129	97.06 62	97.70 70	98.46 139	99.07 45	96.23 44	99.38 23	91.32 110	98.80 43	85.61 169	98.69 70	97.64 131	96.92 129	99.37 168	99.06 165

tpm		92.38 183	94.79 160	89.56 190	94.30 143	97.50 186	94.24 183	78.97 204	97.72 157	74.93 196	97.97 74	82.91 185	96.60 132	93.65 199	94.81 185	98.33 190	98.98 166

PatchMatch-RL		97.77 61	98.25 70	97.21 58	99.11 47	99.25 89	97.06 124	94.09 68	98.72 111	95.14 52	98.47 58	96.29 91	98.43 81	98.65 66	97.44 118	99.45 158	98.94 167

pmmvs5		92.71 176	94.27 169	90.90 178	91.42 191	97.74 168	93.23 186	86.66 172	95.99 196	78.96 181	91.45 160	83.44 182	95.55 158	97.30 143	95.05 178	99.58 135	98.93 168

test-LLR		95.50 122	97.32 109	93.37 137	95.49 124	98.74 120	96.44 138	90.82 122	98.18 136	82.75 158	96.60 107	94.67 112	95.54 159	98.09 99	96.00 155	99.20 177	98.93 168

TESTMET0.1,1		94.95 132	97.32 109	92.20 151	92.62 160	98.74 120	96.44 138	86.67 171	98.18 136	82.75 158	96.60 107	94.67 112	95.54 159	98.09 99	96.00 155	99.20 177	98.93 168

EU-MVSNet		92.80 171	94.76 161	90.51 181	91.88 175	96.74 201	92.48 191	88.69 152	96.21 189	79.00 180	91.51 159	87.82 149	91.83 199	95.87 181	96.27 146	99.21 176	98.92 171

PCF-MVS	97.50 6	98.18 50	98.35 68	97.99 43	98.65 56	99.36 75	98.94 51	98.14 26	98.59 115	93.62 82	96.61 106	99.76 48	99.03 52	97.77 122	97.45 117	99.57 139	98.89 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet_dtu		96.30 105	98.53 62	93.70 128	98.97 50	98.24 152	97.36 108	94.23 67	98.85 90	79.18 179	99.19 19	98.47 67	94.09 183	97.89 116	98.21 80	98.39 189	98.85 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		96.22 107	95.85 148	96.65 76	97.75 68	98.54 134	99.00 50	95.53 47	96.88 177	89.88 117	95.95 120	86.46 162	98.07 90	97.65 130	96.63 136	99.67 97	98.83 174

GG-mvs-BLEND		69.11 208	98.13 78	35.26 213	3.49 222	98.20 154	94.89 164	2.38 219	98.42 126	5.82 223	96.37 113	98.60 64	5.97 218	98.75 61	97.98 91	99.01 182	98.61 175

ambc			80.99 208		80.04 214	90.84 211	90.91 195		96.09 192	74.18 197	62.81 212	30.59 223	82.44 208	96.25 173	91.77 203	95.91 211	98.56 176

MDTV_nov1_ep13_2view		92.44 179	95.66 149	88.68 192	91.05 198	97.92 162	92.17 192	79.64 197	98.83 95	76.20 189	91.45 160	93.51 125	95.04 172	95.68 183	93.70 194	97.96 194	98.53 177

USDC		94.26 147	94.83 159	93.59 130	96.02 100	98.44 141	97.84 93	88.65 153	98.86 88	82.73 160	94.02 141	80.56 196	96.76 125	97.28 144	96.15 152	99.55 144	98.50 178

MDA-MVSNet-bldmvs		87.84 201	89.22 204	86.23 199	81.74 211	96.77 200	83.74 211	89.57 143	94.50 205	72.83 204	96.64 105	64.47 216	92.71 196	81.43 211	92.28 200	96.81 207	98.47 179

test_method		87.27 202	91.58 198	82.25 205	75.65 216	87.52 215	86.81 209	72.60 214	97.51 161	73.20 201	85.07 200	79.97 201	88.69 202	97.31 142	95.24 172	96.53 208	98.41 180

gm-plane-assit		89.44 198	92.82 195	85.49 201	91.37 193	95.34 207	79.55 215	82.12 190	91.68 211	64.79 213	87.98 187	80.26 199	95.66 154	98.51 81	97.56 109	99.45 158	98.41 180

MS-PatchMatch		95.99 112	97.26 113	94.51 113	97.46 72	98.76 118	97.27 112	86.97 168	99.09 63	89.83 118	93.51 147	97.78 75	96.18 142	97.53 135	95.71 164	99.35 169	98.41 180

TransMVSNet (Re)		93.45 160	94.08 173	92.72 146	92.83 158	97.62 179	94.94 162	91.54 110	95.65 200	83.06 156	88.93 179	83.53 181	94.25 180	97.41 138	97.03 126	99.67 97	98.40 183

TinyColmap		94.00 151	94.35 168	93.60 129	95.89 105	98.26 150	97.49 105	88.82 150	98.56 118	83.21 154	91.28 162	80.48 198	96.68 128	97.34 141	96.26 148	99.53 150	98.24 184

TDRefinement		93.04 167	93.57 184	92.41 147	96.58 87	98.77 115	97.78 97	91.96 101	98.12 139	80.84 168	89.13 177	79.87 203	87.78 203	96.44 162	94.50 189	99.54 148	98.15 185

MDTV_nov1_ep13		95.57 119	97.48 99	93.35 139	95.43 126	98.97 105	97.19 117	83.72 189	98.92 85	87.91 126	97.75 79	96.12 96	97.88 100	96.84 156	95.64 165	97.96 194	98.10 186

MIMVSNet		94.49 145	97.59 96	90.87 179	91.74 180	98.70 124	94.68 173	78.73 205	97.98 144	83.71 151	97.71 82	94.81 110	96.96 120	97.97 111	97.92 93	99.40 166	98.04 187

CostFormer		94.25 148	94.88 158	93.51 134	95.43 126	98.34 149	96.21 142	80.64 193	97.94 148	94.01 72	98.30 65	86.20 166	97.52 107	92.71 201	92.69 197	97.23 205	98.02 188

pmnet_mix02		92.44 179	94.68 162	89.83 189	92.46 164	97.65 175	89.92 203	90.49 130	98.76 107	73.05 202	91.78 158	90.08 142	94.86 175	94.53 195	91.94 202	98.21 192	98.01 189

RPSCF		97.61 66	98.16 77	96.96 71	98.10 62	99.00 101	98.84 57	93.76 75	99.45 19	94.78 58	99.39 12	99.31 57	98.53 79	96.61 157	95.43 167	97.74 196	97.93 190

Anonymous20231206		90.70 193	93.93 178	86.92 198	90.21 203	96.79 199	90.30 200	86.61 173	96.05 194	69.25 207	88.46 183	84.86 176	85.86 205	97.11 150	96.47 142	99.30 172	97.80 191

SCA		94.95 132	97.44 102	92.04 154	95.55 121	99.16 96	96.26 141	79.30 200	99.02 74	85.73 140	98.18 67	97.13 83	97.69 104	96.03 177	94.91 181	97.69 199	97.65 192

pmmvs3		88.19 200	91.27 199	84.60 203	85.60 209	93.66 210	85.68 210	81.13 191	92.36 210	63.66 215	89.51 173	77.10 210	93.22 193	96.37 166	92.40 198	98.30 191	97.46 193

N_pmnet		92.21 187	94.60 164	89.42 191	91.88 175	97.38 192	89.15 205	89.74 141	97.89 150	73.75 198	87.94 188	92.23 132	93.85 188	96.10 175	93.20 196	98.15 193	97.43 194

PatchmatchNet		94.70 137	97.08 117	91.92 159	95.53 122	98.85 110	95.77 147	79.54 198	98.95 79	85.98 137	98.52 55	96.45 87	97.39 112	95.32 185	94.09 191	97.32 202	97.38 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		94.65 139	97.04 119	91.88 162	95.68 115	98.99 103	95.89 145	79.03 203	99.15 52	85.81 139	96.96 95	98.21 72	97.10 116	94.48 196	94.24 190	97.74 196	97.21 196

MVS-HIRNet		92.51 177	95.97 143	88.48 194	93.73 152	98.37 147	90.33 199	75.36 213	98.32 130	77.78 185	89.15 176	94.87 108	95.14 171	97.62 132	96.39 143	98.51 186	97.11 197

dps		94.63 140	95.31 155	93.84 123	95.53 122	98.71 123	96.54 133	80.12 195	97.81 156	97.21 28	96.98 94	92.37 130	96.34 139	92.46 203	91.77 203	97.26 204	97.08 198

test20.03		90.65 194	93.71 182	87.09 196	90.44 201	96.24 202	89.74 204	85.46 179	95.59 201	72.99 203	90.68 166	85.33 171	84.41 206	95.94 180	95.10 177	99.52 151	97.06 199

EPMVS		95.05 130	96.86 124	92.94 144	95.84 107	98.96 106	96.68 129	79.87 196	99.05 71	90.15 114	97.12 93	95.99 98	97.49 109	95.17 188	94.75 186	97.59 200	96.96 200

tpmrst		93.86 156	95.88 146	91.50 166	95.69 114	98.62 128	95.64 150	79.41 199	98.80 100	83.76 150	95.63 128	96.13 95	97.25 113	92.92 200	92.31 199	97.27 203	96.74 201

new-patchmatchnet		86.12 203	87.30 205	84.74 202	86.92 208	95.19 209	83.57 212	84.42 187	92.67 209	65.66 210	80.32 206	64.72 215	89.41 201	92.33 205	89.21 207	98.43 188	96.69 202

tpm cat1		94.06 149	94.90 157	93.06 142	95.42 128	98.52 136	96.64 131	80.67 192	97.82 154	92.63 97	93.39 149	95.00 107	96.06 146	91.36 206	91.58 205	96.98 206	96.66 203

FMVSNet5		95.42 123	96.47 135	94.20 117	92.26 168	95.99 204	95.66 149	87.15 167	97.87 151	93.46 85	96.68 103	93.79 123	97.52 107	97.10 151	97.21 124	99.11 180	96.62 204

DeepMVS_CX						96.85 198	87.43 208	89.27 145	98.30 131	75.55 193	95.05 131	79.47 204	92.62 197	89.48 207		95.18 212	95.96 205

MIMVSNet1		88.61 199	90.68 201	86.19 200	81.56 212	95.30 208	87.78 207	85.98 177	94.19 206	72.30 205	78.84 208	78.90 207	90.06 200	96.59 158	95.47 166	99.46 157	95.49 206

CMPMVS	70.31 18	90.74 192	91.06 200	90.36 184	97.32 75	97.43 189	92.97 188	87.82 164	93.50 207	75.34 195	83.27 203	84.90 175	92.19 198	92.64 202	91.21 206	96.50 209	94.46 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		90.45 195	92.84 194	87.66 195	88.96 204	96.16 203	88.71 206	84.66 184	97.56 160	71.91 206	85.60 199	86.58 161	93.28 192	96.07 176	93.54 195	98.46 187	94.39 208

Gipuma		81.40 205	81.78 207	80.96 207	83.21 210	85.61 216	79.73 214	76.25 212	97.33 166	64.21 214	55.32 213	55.55 218	86.04 204	92.43 204	92.20 201	96.32 210	93.99 209
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		77.26 206	79.47 209	74.70 209	76.00 215	88.37 214	74.22 216	76.34 210	78.31 214	54.13 217	69.96 211	52.50 219	70.14 213	84.83 209	88.71 208	97.35 201	93.58 210

MVE	67.97 19	65.53 211	67.43 213	63.31 212	59.33 219	74.20 217	53.09 221	70.43 215	66.27 217	43.13 218	45.98 217	30.62 222	70.65 212	79.34 213	86.30 209	83.25 218	89.33 211
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
FPMVS		83.82 204	84.61 206	82.90 204	90.39 202	90.71 212	90.85 197	84.10 188	95.47 202	65.15 211	83.44 202	74.46 212	75.48 209	81.63 210	79.42 212	91.42 214	87.14 212

EMVS		68.12 210	68.11 212	68.14 211	75.51 217	71.76 218	55.38 220	77.20 209	77.78 215	37.79 220	53.59 214	43.61 220	74.72 210	67.05 215	76.70 214	88.27 217	86.24 213

E-PMN		68.30 209	68.43 211	68.15 210	74.70 218	71.56 219	55.64 219	77.24 208	77.48 216	39.46 219	51.95 216	41.68 221	73.28 211	70.65 214	79.51 211	88.61 216	86.20 214

PMVS	72.60 17	76.39 207	77.66 210	74.92 208	81.04 213	69.37 220	68.47 217	80.54 194	85.39 213	65.07 212	73.52 210	72.91 213	65.67 215	80.35 212	76.81 213	88.71 215	85.25 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		31.24 212	40.15 214	20.86 214	12.61 220	17.99 221	25.16 222	13.30 217	48.42 218	24.82 221	53.07 215	30.13 224	28.47 216	42.73 216	37.65 215	20.79 219	51.04 216

test123		26.75 213	34.25 215	18.01 215	7.93 221	17.18 222	24.85 223	12.36 218	44.83 219	16.52 222	41.80 218	18.10 225	28.29 217	33.08 217	34.79 216	18.10 220	49.95 217

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										69.05 208

9.14												99.79 45

SR-MVS					99.67 13			98.25 14				99.94 25

our_test_3					92.30 166	97.58 181	90.09 202

MTAPA										98.09 15		99.97 7

MTMP										98.46 11		99.96 12

Patchmatch-RL test							66.86 218

tmp_tt				82.25 205	97.73 69	88.71 213	80.18 213	68.65 216	99.15 52	86.98 132	99.47 9	85.31 172	68.35 214	87.51 208	83.81 210	91.64 213

XVS					97.42 73	99.62 29	98.59 64			93.81 78		99.95 17				99.69 80

X-MVStestdata					97.42 73	99.62 29	98.59 64			93.81 78		99.95 17				99.69 80

mPP-MVS					99.53 30							99.89 34

NP-MVS									98.57 117

Patchmtry						98.59 131	97.15 118	79.14 201		80.42 171

High-resolution multi-view benchmark