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 55	98.71 52	96.63 78	95.68 115	99.56 49	97.51 102	93.10 90	99.22 44	94.99 52	97.18 86	97.30 71	98.65 67	98.83 46	98.93 30	99.84 5	99.92 1

LTVRE_ROB	93.20 16	92.84 167	94.92 149	90.43 195	92.83 153	98.63 124	97.08 120	87.87 163	97.91 137	68.42 222	93.54 145	79.46 214	96.62 125	97.55 130	97.40 116	99.74 49	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 70	97.81 83	96.72 74	96.20 97	99.45 61	98.21 78	91.60 103	99.22 44	95.39 43	98.48 52	90.95 124	99.16 44	97.66 124	99.05 24	99.76 44	99.90 3

v748		91.12 204	91.95 212	90.16 197	90.60 212	97.35 195	91.11 207	87.92 162	94.75 216	80.54 169	86.26 213	75.97 221	91.13 212	94.63 205	94.81 194	99.65 108	99.90 3

PVSNet_Blended_VisFu		97.41 65	98.49 57	96.15 87	97.49 65	99.76 1	96.02 138	93.75 82	99.26 39	93.38 86	93.73 144	99.35 50	96.47 130	98.96 36	98.46 55	99.77 42	99.90 3

CSCG		98.90 27	98.93 46	98.85 21	99.75 3	99.72 4	99.49 18	96.58 37	99.38 20	98.05 11	98.97 29	97.87 64	99.49 18	97.78 117	98.92 31	99.78 37	99.90 3

Anonymous20240521		94.00 144	95.58 142	92.16 148	91.01 208	98.44 136	95.13 152	88.24 156	96.39 199	84.01 141	92.89 156	87.44 134	94.83 184	97.73 121	96.25 143	99.73 55	99.89 7

MVS_0304		98.14 47	99.03 41	97.10 55	98.05 59	99.63 30	99.27 32	94.33 59	99.63 6	93.06 90	97.32 80	99.05 54	98.09 86	98.82 47	98.87 35	99.81 26	99.89 7

PS-CasMVS		92.72 175	93.36 195	91.98 156	91.62 195	97.52 185	94.13 194	88.98 146	95.94 208	81.51 162	87.35 204	79.95 211	95.91 142	96.37 160	96.49 135	99.70 79	99.89 7

CP-MVSNet		93.25 158	94.00 172	92.38 144	91.65 193	97.56 182	94.38 190	89.20 144	96.05 205	83.16 151	89.51 177	81.97 199	96.16 137	96.43 158	96.56 133	99.71 70	99.89 7

WR-MVS_H		93.54 154	94.67 155	92.22 145	91.95 172	97.91 158	94.58 187	88.75 149	96.64 188	83.88 143	90.66 164	85.13 162	94.40 190	96.54 156	95.91 153	99.73 55	99.89 7

FC-MVSNet-train		97.04 75	97.91 82	96.03 92	96.00 105	98.41 140	96.53 131	93.42 85	99.04 68	93.02 91	98.03 66	94.32 101	97.47 103	97.93 110	97.77 99	99.75 45	99.88 12

IterMVS-LS		96.12 107	97.48 92	94.53 109	95.19 128	97.56 182	97.15 115	89.19 145	99.08 60	88.23 120	94.97 134	94.73 97	97.84 96	97.86 114	98.26 74	99.60 141	99.88 12
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v7n		91.61 203	92.95 204	90.04 198	90.56 213	97.69 171	93.74 197	85.59 180	95.89 209	76.95 202	86.60 211	78.60 218	93.76 201	97.01 146	94.99 185	99.65 108	99.87 14

CHOSEN 1792x2688		96.41 97	96.99 106	95.74 98	98.01 60	99.72 4	97.70 98	90.78 119	99.13 57	90.03 114	87.35 204	95.36 89	98.33 78	98.59 68	98.91 33	99.59 147	99.87 14

CANet		98.46 39	99.16 30	97.64 44	98.48 52	99.64 25	99.35 29	94.71 52	99.53 12	95.17 47	97.63 77	99.59 47	98.38 76	98.88 44	98.99 27	99.74 49	99.86 16

diffmvs		97.50 64	98.63 53	96.18 85	95.88 109	99.26 86	98.19 80	91.08 114	99.36 24	94.32 70	98.24 61	96.83 75	98.22 82	98.45 74	98.42 57	99.42 177	99.86 16

HyFIR lowres test		95.99 109	96.56 113	95.32 103	97.99 61	99.65 20	96.54 129	88.86 147	98.44 113	89.77 117	84.14 216	97.05 73	99.03 58	98.55 70	98.19 77	99.73 55	99.86 16

v52		91.94 199	93.10 202	90.57 191	90.62 211	97.50 187	93.98 195	87.02 168	95.86 210	77.67 199	86.93 208	82.16 197	94.53 187	94.71 204	94.70 197	99.61 134	99.85 19

V4		91.92 200	93.10 202	90.55 192	90.64 210	97.51 186	93.93 196	87.02 168	95.81 212	77.61 200	86.93 208	82.19 196	94.50 188	94.72 203	94.68 198	99.62 131	99.85 19

tfpnnormal		93.85 151	94.12 167	93.54 128	93.22 152	98.24 148	95.45 148	91.96 98	94.61 217	83.91 142	90.74 162	81.75 202	97.04 111	97.49 132	96.16 146	99.68 90	99.84 21

Effi-MVS+		95.81 111	97.31 100	94.06 115	95.09 129	99.35 73	97.24 112	88.22 157	98.54 108	85.38 139	98.52 49	88.68 130	98.70 65	98.32 80	97.93 86	99.74 49	99.84 21

SD-MVS		99.25 9	99.50 7	98.96 16	98.79 48	99.55 51	99.33 30	98.29 9	99.75 1	97.96 13	99.15 20	99.95 15	99.61 6	99.17 26	99.06 23	99.81 26	99.84 21

EPNet		98.05 48	98.86 48	97.10 55	99.02 44	99.43 64	98.47 61	94.73 51	99.05 66	95.62 38	98.93 31	97.62 68	95.48 157	98.59 68	98.55 51	99.29 186	99.84 21
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SteuartSystems-ACMMP		99.20 13	99.51 6	98.83 23	99.66 14	99.66 19	99.71 4	98.12 23	99.14 53	96.62 30	99.16 19	99.98 1	99.12 50	99.63 3	99.19 20	99.78 37	99.83 25
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		99.27 7	99.57 3	98.92 19	98.78 49	99.53 52	99.72 2	98.11 24	99.73 2	97.43 20	99.15 20	99.96 10	99.59 9	99.73 1	99.07 22	99.88 1	99.82 26

anonymousdsp		93.12 159	95.86 136	89.93 201	91.09 206	98.25 147	95.12 153	85.08 185	97.44 152	73.30 214	90.89 161	90.78 125	95.25 178	97.91 111	95.96 152	99.71 70	99.82 26

TSAR-MVS + ACMM		98.77 30	99.45 9	97.98 39	99.37 32	99.46 59	99.44 24	98.13 22	99.65 4	92.30 96	98.91 36	99.95 15	99.05 56	99.42 15	98.95 29	99.58 151	99.82 26

PEN-MVS		92.72 175	93.20 201	92.15 149	91.29 203	97.31 196	94.67 182	89.81 137	96.19 201	81.83 160	88.58 191	79.06 216	95.61 151	95.21 184	96.27 140	99.72 61	99.82 26

WR-MVS		93.43 157	94.48 160	92.21 146	91.52 198	97.69 171	94.66 183	89.98 134	96.86 179	83.43 148	90.12 166	85.03 163	93.94 198	96.02 172	95.82 154	99.71 70	99.82 26

HSP-MVS		99.31 3	99.43 14	99.17 2	99.68 11	99.75 2	99.72 2	98.31 5	99.45 17	98.16 9	99.28 13	99.98 1	99.30 31	99.34 20	98.41 59	99.81 26	99.81 31

DeepC-MVS	97.63 4	98.33 43	98.57 54	98.04 37	98.62 51	99.65 20	99.45 22	98.15 19	99.51 15	92.80 93	95.74 124	96.44 78	99.46 20	99.37 17	99.50 2	99.78 37	99.81 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
conf0.05thres1000		96.34 100	96.47 121	96.17 86	96.16 98	99.71 8	97.82 93	93.46 84	98.10 125	90.69 106	96.75 95	85.26 161	99.11 52	98.05 104	97.65 101	99.82 13	99.80 33

v8		92.87 166	93.87 180	91.72 166	92.05 168	97.50 187	94.79 171	88.20 158	96.85 180	80.11 177	90.01 169	82.86 186	95.48 157	95.15 195	94.90 191	99.66 102	99.80 33

v10		92.79 172	94.06 170	91.31 174	91.78 182	97.29 198	94.87 164	86.10 178	96.97 173	79.82 180	88.16 196	84.56 167	95.63 148	96.33 163	95.31 166	99.65 108	99.80 33

UniMVSNet_NR-MVSNet		94.59 135	95.47 144	93.55 127	91.85 176	97.89 159	95.03 155	92.00 96	97.33 160	86.12 132	93.19 150	87.29 135	96.60 126	96.12 169	96.70 128	99.72 61	99.80 33

DU-MVS		93.98 146	94.44 161	93.44 130	91.66 191	97.77 161	95.03 155	91.57 104	97.17 165	86.12 132	93.13 152	81.13 204	96.60 126	95.10 198	97.01 123	99.67 97	99.80 33

UGNet		97.66 58	99.07 36	96.01 93	97.19 74	99.65 20	97.09 119	93.39 86	99.35 26	94.40 67	98.79 41	99.59 47	94.24 193	98.04 106	98.29 73	99.73 55	99.80 33
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 51	98.86 48	96.68 75	96.02 103	99.72 4	98.35 74	93.37 88	98.75 99	94.01 73	96.88 94	98.40 60	98.48 73	99.09 30	99.42 5	99.83 9	99.80 33

UniMVSNet (Re)		94.58 136	95.34 145	93.71 122	92.25 163	98.08 152	94.97 157	91.29 113	97.03 171	87.94 123	93.97 143	86.25 151	96.07 138	96.27 166	95.97 151	99.72 61	99.79 40

DELS-MVS		98.19 45	98.77 51	97.52 46	98.29 55	99.71 8	99.12 38	94.58 57	98.80 90	95.38 44	96.24 111	98.24 62	97.92 91	99.06 33	99.52 1	99.82 13	99.79 40
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
SMA-MVS		99.30 5	99.62 2	98.93 17	99.76 2	99.64 25	99.44 24	98.21 14	99.53 12	96.79 29	99.41 9	99.98 1	99.67 4	99.63 3	99.37 9	99.71 70	99.78 42

tfpn		96.22 104	95.62 141	96.93 72	96.29 91	99.72 4	98.34 75	93.94 76	97.96 133	93.94 75	96.45 107	79.09 215	99.22 33	98.28 82	98.06 81	99.83 9	99.78 42

v144192		92.38 187	93.55 192	91.00 182	91.44 199	97.47 190	94.27 191	87.41 166	96.52 194	78.03 196	87.50 203	82.65 188	95.32 173	95.82 175	95.15 171	99.55 159	99.78 42

V42		93.05 163	93.90 179	92.04 152	91.91 173	97.66 173	94.91 159	89.91 135	96.85 180	80.58 168	89.66 176	83.43 176	95.37 169	95.03 201	94.90 191	99.59 147	99.78 42

MVS_Test		97.30 71	98.54 55	95.87 94	95.74 113	99.28 84	98.19 80	91.40 108	99.18 50	91.59 102	98.17 62	96.18 81	98.63 69	98.61 64	98.55 51	99.66 102	99.78 42

TranMVSNet+NR-MVSNet		93.67 153	94.14 165	93.13 136	91.28 205	97.58 180	95.60 145	91.97 97	97.06 169	84.05 140	90.64 165	82.22 195	96.17 136	94.94 202	96.78 126	99.69 81	99.78 42

PVSNet_BlendedMVS		97.51 62	97.71 85	97.28 50	98.06 57	99.61 38	97.31 108	95.02 46	99.08 60	95.51 41	98.05 64	90.11 126	98.07 87	98.91 41	98.40 60	99.72 61	99.78 42

PVSNet_Blended		97.51 62	97.71 85	97.28 50	98.06 57	99.61 38	97.31 108	95.02 46	99.08 60	95.51 41	98.05 64	90.11 126	98.07 87	98.91 41	98.40 60	99.72 61	99.78 42

Fast-Effi-MVS+		95.38 120	96.52 116	94.05 116	94.15 139	99.14 96	97.24 112	86.79 171	98.53 109	87.62 126	94.51 139	87.06 136	98.76 63	98.60 67	98.04 83	99.72 61	99.77 50

v7		92.97 165	94.11 168	91.65 167	91.83 177	97.55 184	94.86 167	88.19 159	96.96 174	79.72 183	88.16 196	84.68 166	95.63 148	96.33 163	95.30 167	99.65 108	99.77 50

APDe-MVS		99.49 1	99.64 1	99.32 1	99.74 4	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 4	99.97 4	99.53 15	99.65 2	99.25 14	99.84 5	99.77 50

ACMMPR		99.30 5	99.54 4	99.03 12	99.66 14	99.64 25	99.68 5	98.25 12	99.56 9	97.12 25	99.19 17	99.95 15	99.72 1	99.43 14	99.25 14	99.72 61	99.77 50

view800		96.70 88	96.45 124	96.99 70	96.29 91	99.69 11	98.39 71	93.95 75	97.92 136	94.25 72	96.23 112	85.57 157	99.22 33	98.28 82	97.71 100	99.82 13	99.76 54

v1144		92.81 168	94.03 171	91.40 171	91.68 190	97.60 179	94.73 178	88.40 154	96.71 183	78.48 195	88.14 198	84.46 168	95.45 164	96.31 165	95.22 169	99.65 108	99.76 54

HFP-MVS		99.32 2	99.53 5	99.07 9	99.69 8	99.59 44	99.63 10	98.31 5	99.56 9	97.37 21	99.27 14	99.97 4	99.70 3	99.35 19	99.24 16	99.71 70	99.76 54

v1neww		93.06 161	93.94 175	92.03 153	91.99 170	97.70 167	94.79 171	90.14 130	96.93 176	80.13 175	89.97 171	83.01 181	95.48 157	95.16 191	95.01 180	99.63 125	99.76 54

v7new		93.06 161	93.94 175	92.03 153	91.99 170	97.70 167	94.79 171	90.14 130	96.93 176	80.13 175	89.97 171	83.01 181	95.48 157	95.16 191	95.01 180	99.63 125	99.76 54

v6		93.11 160	93.98 173	92.10 150	92.01 169	97.71 164	94.86 167	90.15 129	96.96 174	80.47 170	90.01 169	83.26 177	95.48 157	95.17 187	95.01 180	99.64 119	99.76 54

MSLP-MVS++		99.15 15	99.24 27	99.04 11	99.52 27	99.49 56	99.09 41	98.07 25	99.37 22	98.47 5	97.79 71	99.89 30	99.50 16	98.93 39	99.45 4	99.61 134	99.76 54

ACMMP		98.74 31	99.03 41	98.40 29	99.36 34	99.64 25	99.20 34	97.75 32	98.82 87	95.24 46	98.85 39	99.87 32	99.17 42	98.74 55	97.50 109	99.71 70	99.76 54
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	95.27 124	95.96 133	94.45 111	96.83 79	98.78 112	94.72 179	91.67 102	98.95 72	86.82 131	96.42 108	83.67 173	97.00 112	97.48 133	96.68 129	99.69 81	99.76 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
X-MVS		98.93 26	99.37 19	98.42 28	99.67 12	99.62 34	99.60 12	98.15 19	99.08 60	93.81 80	98.46 54	99.95 15	99.59 9	99.49 11	99.21 19	99.68 90	99.75 63

NR-MVSNet		94.01 143	94.51 159	93.44 130	92.56 157	97.77 161	95.67 142	91.57 104	97.17 165	85.84 135	93.13 152	80.53 207	95.29 176	97.01 146	96.17 145	99.69 81	99.75 63

Vis-MVSNet (Re-imp)		97.40 66	98.89 47	95.66 100	95.99 106	99.62 34	97.82 93	93.22 89	98.82 87	91.40 103	96.94 92	98.56 58	95.70 146	99.14 28	99.41 6	99.79 34	99.75 63

ACMMP_Plus		99.05 22	99.45 9	98.58 27	99.73 5	99.60 42	99.64 8	98.28 10	99.23 43	94.57 58	99.35 12	99.97 4	99.55 13	99.63 3	98.66 44	99.70 79	99.74 66

v1192		92.43 184	93.61 186	91.05 179	91.53 197	97.43 191	94.61 185	87.99 161	96.60 189	76.72 203	87.11 206	82.74 187	95.85 143	96.35 162	95.30 167	99.60 141	99.74 66

PGM-MVS		98.86 28	99.35 23	98.29 31	99.77 1	99.63 30	99.67 6	95.63 40	98.66 102	95.27 45	99.11 23	99.82 37	99.67 4	99.33 21	99.19 20	99.73 55	99.74 66

CP-MVS		99.27 7	99.44 12	99.08 8	99.62 18	99.58 46	99.53 15	98.16 17	99.21 46	97.79 15	99.15 20	99.96 10	99.59 9	99.54 8	98.86 36	99.78 37	99.74 66

view600		96.70 88	96.44 126	97.01 66	96.28 94	99.67 13	98.42 63	93.99 68	97.87 139	94.34 69	95.99 116	85.94 153	99.20 36	98.26 84	97.64 102	99.82 13	99.73 70

zzz-MVS		99.31 3	99.44 12	99.16 4	99.73 5	99.65 20	99.63 10	98.26 11	99.27 36	98.01 12	99.27 14	99.97 4	99.60 7	99.59 6	98.58 50	99.71 70	99.73 70

v1921920		92.36 189	93.57 189	90.94 184	91.39 201	97.39 193	94.70 180	87.63 165	96.60 189	76.63 204	86.98 207	82.89 185	95.75 144	96.26 167	95.14 172	99.55 159	99.73 70

DI_MVS_plusplus_trai		96.90 80	97.49 91	96.21 84	95.61 117	99.40 67	98.72 55	92.11 93	99.14 53	92.98 92	93.08 154	95.14 91	98.13 85	98.05 104	97.91 88	99.74 49	99.73 70

v1240		91.99 198	93.33 196	90.44 194	91.29 203	97.30 197	94.25 192	86.79 171	96.43 198	75.49 209	86.34 212	81.85 201	95.29 176	96.42 159	95.22 169	99.52 165	99.73 70

v1		92.81 168	93.57 189	91.94 157	91.79 181	97.70 167	94.80 170	90.32 124	96.52 194	79.75 181	88.47 192	82.46 192	95.32 173	95.14 197	94.96 187	99.63 125	99.73 70

thres600view7		96.69 90	96.43 128	97.00 68	96.28 94	99.67 13	98.41 64	93.99 68	97.85 142	94.29 71	95.96 117	85.91 154	99.19 38	98.26 84	97.63 103	99.82 13	99.73 70

MP-MVS		99.07 20	99.36 20	98.74 24	99.63 17	99.57 48	99.66 7	98.25 12	99.00 70	95.62 38	98.97 29	99.94 23	99.54 14	99.51 10	98.79 41	99.71 70	99.73 70
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DTE-MVSNet		92.42 186	92.85 207	91.91 159	90.87 209	96.97 200	94.53 189	89.81 137	95.86 210	81.59 161	88.83 184	77.88 219	95.01 182	94.34 207	96.35 138	99.64 119	99.73 70

Baseline_NR-MVSNet		93.87 149	93.98 173	93.75 120	91.66 191	97.02 199	95.53 146	91.52 107	97.16 167	87.77 125	87.93 202	83.69 172	96.35 131	95.10 198	97.23 118	99.68 90	99.73 70

SixPastTwentyTwo		93.44 156	95.32 146	91.24 176	92.11 166	98.40 141	92.77 202	88.64 152	98.09 126	77.83 197	93.51 146	85.74 155	96.52 129	96.91 148	94.89 193	99.59 147	99.73 70

LGP-MVS_train		96.23 103	96.89 108	95.46 102	97.32 69	98.77 113	98.81 52	93.60 83	98.58 105	85.52 137	99.08 26	86.67 146	97.83 97	97.87 113	97.51 108	99.69 81	99.73 70

v1141		92.79 172	93.61 186	91.84 163	91.75 184	97.71 164	94.74 177	90.33 123	96.58 191	79.21 191	88.59 189	82.53 191	95.36 170	95.16 191	94.96 187	99.63 125	99.72 82

pm-mvs1		94.27 139	95.57 143	92.75 141	92.58 156	98.13 151	94.87 164	90.71 120	96.70 184	83.78 144	89.94 174	89.85 129	94.96 183	97.58 129	97.07 120	99.61 134	99.72 82

divwei89l23v2f112		92.80 170	93.60 188	91.86 162	91.75 184	97.71 164	94.75 176	90.32 124	96.54 193	79.35 188	88.59 189	82.55 190	95.35 171	95.15 195	94.96 187	99.63 125	99.72 82

IterMVS		94.81 130	97.71 85	91.42 170	94.83 135	97.63 175	97.38 105	85.08 185	98.93 77	75.67 207	94.02 141	97.64 66	96.66 124	98.45 74	97.60 105	98.90 195	99.72 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MCST-MVS		99.11 17	99.27 26	98.93 17	99.67 12	99.33 77	99.51 17	98.31 5	99.28 34	96.57 32	99.10 24	99.90 28	99.71 2	99.19 25	98.35 66	99.82 13	99.71 86

ACMP	96.25 10	96.62 95	96.72 110	96.50 82	96.96 78	98.75 116	97.80 95	94.30 60	98.85 82	93.12 89	98.78 42	86.61 147	97.23 108	97.73 121	96.61 131	99.62 131	99.71 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tfpn111		96.96 79	96.91 107	97.03 60	96.31 84	99.67 13	98.41 64	93.99 68	97.35 154	94.50 61	98.65 46	86.93 139	99.14 45	98.26 84	97.80 94	99.82 13	99.70 88

tfpn1000		97.60 60	98.21 69	96.89 73	96.32 83	99.60 42	97.99 90	93.85 78	99.21 46	95.03 50	98.49 51	93.69 110	98.31 79	98.50 73	98.31 72	99.86 2	99.70 88

conf200view11		96.75 83	96.51 117	97.03 60	96.31 84	99.67 13	98.41 64	93.99 68	97.35 154	94.50 61	95.90 119	86.93 139	99.14 45	98.26 84	97.80 94	99.82 13	99.70 88

tfpn200view9		96.75 83	96.51 117	97.03 60	96.31 84	99.67 13	98.41 64	93.99 68	97.35 154	94.52 59	95.90 119	86.93 139	99.14 45	98.26 84	97.80 94	99.82 13	99.70 88

v11		92.43 184	93.77 182	90.85 188	91.72 188	95.58 215	94.87 164	84.07 200	96.98 172	79.28 189	88.03 199	84.22 170	95.53 155	96.55 155	95.36 164	99.65 108	99.70 88

thres400		96.71 87	96.45 124	97.02 64	96.28 94	99.63 30	98.41 64	94.00 67	97.82 144	94.42 66	95.74 124	86.26 150	99.18 40	98.20 91	97.79 98	99.81 26	99.70 88

conf0.01		96.35 99	95.71 138	97.10 55	96.30 90	99.65 20	98.41 64	94.10 64	97.35 154	94.82 55	95.44 132	81.88 200	99.14 45	98.16 93	97.80 94	99.82 13	99.69 94

conf0.002		96.31 101	95.63 140	97.11 54	96.29 91	99.64 25	98.41 64	94.11 63	97.35 154	94.86 53	95.49 131	81.06 205	99.14 45	98.14 94	98.02 84	99.82 13	99.69 94

v148		92.36 189	92.88 205	91.75 164	91.63 194	97.66 173	92.64 203	90.55 122	96.09 203	83.34 149	88.19 195	80.00 210	92.74 206	93.98 208	94.58 199	99.58 151	99.69 94

v2v482		92.77 174	93.52 193	91.90 160	91.59 196	97.63 175	94.57 188	90.31 126	96.80 182	79.22 190	88.74 186	81.55 203	96.04 140	95.26 180	94.97 186	99.66 102	99.69 94

CPTT-MVS		99.14 16	99.20 29	99.06 10	99.58 21	99.53 52	99.45 22	97.80 31	99.19 49	98.32 8	98.58 48	99.95 15	99.60 7	99.28 23	98.20 76	99.64 119	99.69 94

FMVSNet1		95.77 112	96.41 129	95.03 104	93.42 151	97.86 160	97.11 118	89.89 136	98.53 109	92.00 98	89.17 179	93.23 114	98.15 84	98.07 100	98.34 68	99.61 134	99.69 94

Vis-MVSNet		96.16 106	98.22 68	93.75 120	95.33 126	99.70 10	97.27 110	90.85 116	98.30 117	85.51 138	95.72 126	96.45 76	93.69 202	98.70 57	99.00 26	99.84 5	99.69 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v17		92.55 180	93.65 185	91.27 175	92.11 166	95.63 209	94.89 160	85.15 183	97.12 168	80.39 174	90.02 168	83.02 180	95.45 164	95.17 187	94.92 190	99.66 102	99.68 101

v16		92.66 178	93.80 181	91.32 173	92.13 164	95.62 210	94.89 160	85.12 184	97.20 163	80.66 166	89.96 173	83.93 171	95.49 156	95.17 187	95.04 175	99.63 125	99.68 101

v13		92.16 196	93.28 200	90.85 188	91.75 184	95.58 215	94.65 184	84.23 198	96.49 197	79.51 187	88.40 194	82.58 189	95.31 175	95.21 184	95.03 177	99.66 102	99.68 101

v12		92.18 195	93.29 199	90.88 186	91.70 189	95.59 213	94.61 185	84.36 195	96.65 187	79.59 185	88.85 183	82.03 198	95.35 171	95.22 181	95.04 175	99.65 108	99.68 101

MVSTER		97.16 73	97.71 85	96.52 80	95.97 107	98.48 132	98.63 57	92.10 94	98.68 101	95.96 37	99.23 16	91.79 121	96.87 117	98.76 52	97.37 117	99.57 155	99.68 101

ESAPD		99.23 11	99.41 16	99.01 14	99.70 7	99.69 11	99.40 27	98.31 5	98.94 75	97.70 17	99.40 10	99.97 4	99.17 42	99.54 8	98.67 43	99.78 37	99.67 106

v18		92.63 179	93.67 184	91.43 169	92.13 164	95.65 208	95.09 154	85.44 182	97.06 169	80.78 165	90.06 167	83.06 179	95.47 162	95.16 191	95.01 180	99.64 119	99.67 106

v15		92.27 192	93.33 196	91.04 180	91.83 177	95.60 211	94.79 171	84.88 188	96.66 186	79.66 184	88.72 187	82.45 193	95.40 167	95.19 186	95.00 184	99.65 108	99.67 106

V14		92.31 191	93.41 194	91.03 181	91.80 180	95.59 213	94.79 171	84.70 189	96.58 191	79.83 179	88.79 185	82.98 183	95.41 166	95.22 181	95.02 179	99.65 108	99.67 106

V9		92.24 193	93.32 198	90.98 183	91.76 183	95.58 215	94.83 169	84.50 193	96.68 185	79.73 182	88.66 188	82.39 194	95.39 168	95.22 181	95.03 177	99.65 108	99.67 106

GBi-Net		96.98 77	98.00 79	95.78 95	93.81 144	97.98 153	98.09 83	91.32 109	98.80 90	93.92 76	97.21 83	95.94 85	97.89 92	98.07 100	98.34 68	99.68 90	99.67 106

test1		96.98 77	98.00 79	95.78 95	93.81 144	97.98 153	98.09 83	91.32 109	98.80 90	93.92 76	97.21 83	95.94 85	97.89 92	98.07 100	98.34 68	99.68 90	99.67 106

FMVSNet2		96.64 93	97.50 90	95.63 101	93.81 144	97.98 153	98.09 83	90.87 115	98.99 71	93.48 84	93.17 151	95.25 90	97.89 92	98.63 63	98.80 40	99.68 90	99.67 106

3Dnovator+	96.92 7	98.71 33	99.05 37	98.32 30	99.53 25	99.34 75	99.06 43	94.61 54	99.65 4	97.49 19	96.75 95	99.86 33	99.44 22	98.78 50	99.30 12	99.81 26	99.67 106

HPM-MVS++		99.10 18	99.30 24	98.86 20	99.69 8	99.48 57	99.59 13	98.34 2	99.26 39	96.55 33	99.10 24	99.96 10	99.36 26	99.25 24	98.37 64	99.64 119	99.66 115

thres200		96.76 82	96.53 115	97.03 60	96.31 84	99.67 13	98.37 72	93.99 68	97.68 149	94.49 63	95.83 123	86.77 144	99.18 40	98.26 84	97.82 93	99.82 13	99.66 115

3Dnovator	96.92 7	98.67 34	99.05 37	98.23 34	99.57 22	99.45 61	99.11 39	94.66 53	99.69 3	96.80 28	96.55 105	99.61 46	99.40 24	98.87 45	99.49 3	99.85 4	99.66 115

TSAR-MVS + GP.		98.66 36	99.36 20	97.85 41	97.16 75	99.46 59	99.03 45	94.59 56	99.09 58	97.19 24	99.73 3	99.95 15	99.39 25	98.95 37	98.69 42	99.75 45	99.65 118

FMVSNet3		97.02 76	98.12 74	95.73 99	93.59 150	97.98 153	98.34 75	91.32 109	98.80 90	93.92 76	97.21 83	95.94 85	97.63 99	98.61 64	98.62 46	99.61 134	99.65 118

CDS-MVSNet		96.59 96	98.02 78	94.92 106	94.45 137	98.96 103	97.46 104	91.75 100	97.86 141	90.07 113	96.02 115	97.25 72	96.21 133	98.04 106	98.38 62	99.60 141	99.65 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMH+	95.51 13	95.40 119	96.00 131	94.70 108	96.33 82	98.79 110	96.79 124	91.32 109	98.77 96	87.18 128	95.60 129	85.46 158	96.97 113	97.15 142	96.59 132	99.59 147	99.65 118

QAPM		98.62 37	99.04 40	98.13 35	99.57 22	99.48 57	99.17 36	94.78 50	99.57 8	96.16 35	96.73 97	99.80 38	99.33 28	98.79 49	99.29 13	99.75 45	99.64 122

DeepC-MVS_fast	98.34 1	99.17 14	99.45 9	98.85 21	99.55 24	99.37 70	99.64 8	98.05 26	99.53 12	96.58 31	98.93 31	99.92 25	99.49 18	99.46 12	99.32 11	99.80 33	99.64 122
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 86	96.47 121	97.00 68	96.31 84	99.52 55	98.28 77	94.01 66	97.35 154	94.52 59	95.90 119	86.93 139	99.09 55	98.07 100	97.87 90	99.81 26	99.63 124

Effi-MVS+-dtu		95.74 113	98.04 76	93.06 137	93.92 140	99.16 95	97.90 91	88.16 160	99.07 65	82.02 159	98.02 67	94.32 101	96.74 121	98.53 71	97.56 106	99.61 134	99.62 125

HQP-MVS		96.37 98	96.58 112	96.13 89	97.31 71	98.44 136	98.45 62	95.22 44	98.86 80	88.58 119	98.33 58	87.00 138	97.67 98	97.23 139	96.56 133	99.56 158	99.62 125

tfpn_n400		97.32 67	98.38 60	96.09 90	96.07 100	99.30 81	98.00 88	93.84 79	99.35 26	90.50 109	98.93 31	94.24 103	98.30 80	98.65 59	98.60 48	99.83 9	99.60 127

tfpnconf		97.32 67	98.38 60	96.09 90	96.07 100	99.30 81	98.00 88	93.84 79	99.35 26	90.50 109	98.93 31	94.24 103	98.30 80	98.65 59	98.60 48	99.83 9	99.60 127

tfpnview11		97.32 67	98.33 63	96.14 88	96.07 100	99.31 80	98.08 86	93.96 74	99.25 41	90.50 109	98.93 31	94.24 103	98.38 76	98.61 64	98.36 65	99.84 5	99.59 129

IB-MVS	93.96 15	95.02 127	96.44 126	93.36 133	97.05 77	99.28 84	90.43 212	93.39 86	98.02 128	96.02 36	94.92 135	92.07 120	83.52 224	95.38 178	95.82 154	99.72 61	99.59 129
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 32	99.11 32	98.28 32	99.36 34	99.35 73	99.48 20	97.96 28	98.83 85	93.86 79	98.70 45	99.86 33	99.44 22	99.08 32	98.38 62	99.61 134	99.58 131

CDPH-MVS		98.41 40	99.10 33	97.61 45	99.32 38	99.36 71	99.49 18	96.15 39	98.82 87	91.82 99	98.41 55	99.66 45	99.10 53	98.93 39	98.97 28	99.75 45	99.58 131

APD-MVS		99.25 9	99.38 18	99.09 7	99.69 8	99.58 46	99.56 14	98.32 4	98.85 82	97.87 14	98.91 36	99.92 25	99.30 31	99.45 13	99.38 8	99.79 34	99.58 131
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.67 34	99.41 16	97.81 42	99.37 32	99.53 52	98.51 60	95.52 42	99.27 36	94.85 54	99.56 5	99.69 44	99.04 57	99.36 18	98.88 34	99.60 141	99.58 131

PHI-MVS		99.08 19	99.43 14	98.67 25	99.15 41	99.59 44	99.11 39	97.35 34	99.14 53	97.30 22	99.44 8	99.96 10	99.32 29	98.89 43	99.39 7	99.79 34	99.58 131

MVS_111021_HR		98.59 38	99.36 20	97.68 43	99.42 30	99.61 38	98.14 82	94.81 49	99.31 31	95.00 51	99.51 6	99.79 40	99.00 60	98.94 38	98.83 38	99.69 81	99.57 136

tfpn_ndepth		97.71 56	98.30 64	97.02 64	96.31 84	99.56 49	98.05 87	93.94 76	98.95 72	95.59 40	98.40 56	94.79 96	98.39 75	98.40 78	98.42 57	99.86 2	99.56 137

DeepPCF-MVS	97.74 3	98.34 42	99.46 8	97.04 59	98.82 47	99.33 77	96.28 135	97.47 33	99.58 7	94.70 57	98.99 28	99.85 36	97.24 107	99.55 7	99.34 10	97.73 212	99.56 137

CLD-MVS		96.74 85	96.51 117	97.01 66	96.71 80	98.62 125	98.73 54	94.38 58	98.94 75	94.46 64	97.33 79	87.03 137	98.07 87	97.20 141	96.87 125	99.72 61	99.54 139
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 93	99.08 34	93.81 119	97.10 76	99.42 65	98.85 50	90.01 133	99.31 31	79.98 178	99.78 2	99.10 53	97.42 104	98.35 79	98.05 82	99.47 170	99.53 140

pmmvs6		91.90 201	92.53 211	91.17 177	91.81 179	97.63 175	93.23 198	88.37 155	93.43 222	80.61 167	77.32 226	87.47 133	94.12 194	96.58 153	95.72 157	98.88 196	99.53 140

FC-MVSNet-test		96.07 108	97.94 81	93.89 117	93.60 149	98.67 122	96.62 128	90.30 128	98.76 97	88.62 118	95.57 130	97.63 67	94.48 189	97.97 108	97.48 112	99.71 70	99.52 142

CNVR-MVS		99.23 11	99.28 25	99.17 2	99.65 16	99.34 75	99.46 21	98.21 14	99.28 34	98.47 5	98.89 38	99.94 23	99.50 16	99.42 15	98.61 47	99.73 55	99.52 142

PLC	97.93 2	99.02 25	98.94 45	99.11 6	99.46 29	99.24 89	99.06 43	97.96 28	99.31 31	99.16 1	97.90 69	99.79 40	99.36 26	98.71 56	98.12 79	99.65 108	99.52 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		97.71 56	98.04 76	97.32 48	99.35 36	98.91 105	97.65 99	91.68 101	98.00 129	97.01 26	97.72 75	94.83 94	98.85 62	98.44 76	98.86 36	99.41 178	99.52 142
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 22	99.08 34	99.02 13	99.62 18	99.38 68	99.43 26	98.21 14	99.36 24	97.66 18	97.79 71	99.90 28	99.45 21	99.17 26	98.43 56	99.77 42	99.51 146

OpenMVS	96.23 11	97.95 50	98.45 58	97.35 47	99.52 27	99.42 65	98.91 49	94.61 54	98.87 79	92.24 97	94.61 138	99.05 54	99.10 53	98.64 62	99.05 24	99.74 49	99.51 146

thresconf0.02		97.18 72	97.81 83	96.45 83	96.11 99	99.20 94	98.21 78	94.26 61	99.14 53	91.72 101	98.65 46	91.51 123	98.57 70	98.22 90	98.47 54	99.82 13	99.50 148

Fast-Effi-MVS+-dtu		95.38 120	98.20 70	92.09 151	93.91 141	98.87 107	97.35 107	85.01 187	99.08 60	81.09 163	98.10 63	96.36 79	95.62 150	98.43 77	97.03 121	99.55 159	99.50 148

ACMM	96.26 9	96.67 92	96.69 111	96.66 76	97.29 72	98.46 133	96.48 132	95.09 45	99.21 46	93.19 88	98.78 42	86.73 145	98.17 83	97.84 115	96.32 139	99.74 49	99.49 150
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		97.99 49	99.24 27	96.53 79	98.34 54	99.61 38	98.36 73	89.80 139	99.27 36	95.08 49	99.81 1	98.58 57	98.64 68	99.02 34	98.92 31	98.93 194	99.48 151

TAPA-MVS	97.53 5	98.41 40	98.84 50	97.91 40	99.08 43	99.33 77	99.15 37	97.13 35	99.34 29	93.20 87	97.75 73	99.19 52	99.20 36	98.66 58	98.13 78	99.66 102	99.48 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GA-MVS		93.93 148	96.31 130	91.16 178	93.61 148	98.79 110	95.39 150	90.69 121	98.25 119	73.28 215	96.15 113	88.42 131	94.39 191	97.76 119	95.35 165	99.58 151	99.45 153

CVMVSNet		95.33 123	97.09 103	93.27 135	95.23 127	98.39 142	95.49 147	92.58 92	97.71 148	83.00 153	94.44 140	93.28 113	93.92 199	97.79 116	98.54 53	99.41 178	99.45 153

LS3D		97.79 52	98.25 66	97.26 52	98.40 53	99.63 30	99.53 15	98.63 1	99.25 41	88.13 121	96.93 93	94.14 106	99.19 38	99.14 28	99.23 17	99.69 81	99.42 155

abl_6				98.09 36	99.33 37	99.22 91	98.79 53	94.96 48	98.52 111	97.00 27	97.30 81	99.86 33	98.76 63			99.69 81	99.41 156

test0.0.03 1		96.69 90	98.12 74	95.01 105	95.49 120	98.99 100	95.86 140	90.82 117	98.38 115	92.54 95	96.66 99	97.33 69	95.75 144	97.75 120	98.34 68	99.60 141	99.40 157

testgi		95.67 114	97.48 92	93.56 126	95.07 130	99.00 98	95.33 151	88.47 153	98.80 90	86.90 130	97.30 81	92.33 118	95.97 141	97.66 124	97.91 88	99.60 141	99.38 158

TAMVS		95.53 116	96.50 120	94.39 112	93.86 143	99.03 97	96.67 126	89.55 142	97.33 160	90.64 107	93.02 155	91.58 122	96.21 133	97.72 123	97.43 115	99.43 175	99.36 159

AdaColmap		99.06 21	98.98 44	99.15 5	99.60 20	99.30 81	99.38 28	98.16 17	99.02 69	98.55 4	98.71 44	99.57 49	99.58 12	99.09 30	97.84 92	99.64 119	99.36 159

PM-MVS		89.55 211	90.30 216	88.67 207	87.06 220	95.60 211	90.88 210	84.51 192	96.14 202	75.75 206	86.89 210	63.47 231	94.64 185	96.85 149	93.89 204	99.17 191	99.29 161

pmmvs4		95.09 125	95.90 134	94.14 114	92.29 161	97.70 167	95.45 148	90.31 126	98.60 103	90.70 105	93.25 149	89.90 128	96.67 123	97.13 143	95.42 162	99.44 174	99.28 162

EG-PatchMatch MVS		92.45 182	93.92 178	90.72 190	92.56 157	98.43 139	94.88 163	84.54 191	97.18 164	79.55 186	86.12 214	83.23 178	93.15 205	97.22 140	96.00 148	99.67 97	99.27 163

UA-Net		97.13 74	99.14 31	94.78 107	97.21 73	99.38 68	97.56 100	92.04 95	98.48 112	88.03 122	98.39 57	99.91 27	94.03 196	99.33 21	99.23 17	99.81 26	99.25 164

pmmvs-eth3d		89.81 210	89.65 217	90.00 199	86.94 221	95.38 218	91.08 208	86.39 176	94.57 218	82.27 158	83.03 220	64.94 228	93.96 197	96.57 154	93.82 205	99.35 182	99.24 165

gg-mvs-nofinetune		90.85 205	94.14 165	87.02 211	94.89 134	99.25 87	98.64 56	76.29 228	88.24 229	57.50 233	79.93 224	95.45 88	95.18 179	98.77 51	98.07 80	99.62 131	99.24 165

PMMVS		97.52 61	98.39 59	96.51 81	95.82 112	98.73 119	97.80 95	93.05 91	98.76 97	94.39 68	99.07 27	97.03 74	98.55 71	98.31 81	97.61 104	99.43 175	99.21 167

CNLPA		99.03 24	99.05 37	99.01 14	99.27 39	99.22 91	99.03 45	97.98 27	99.34 29	99.00 2	98.25 60	99.71 43	99.31 30	98.80 48	98.82 39	99.48 168	99.17 168

CR-MVSNet		94.57 137	97.34 96	91.33 172	94.90 133	98.59 127	97.15 115	79.14 217	97.98 130	80.42 171	96.59 104	93.50 112	96.85 118	98.10 95	97.49 110	99.50 167	99.15 169

PatchT		93.96 147	97.36 95	90.00 199	94.76 136	98.65 123	90.11 215	78.57 222	97.96 133	80.42 171	96.07 114	94.10 107	96.85 118	98.10 95	97.49 110	99.26 187	99.15 169

COLMAP_ROB	96.15 12	97.78 53	98.17 71	97.32 48	98.84 46	99.45 61	99.28 31	95.43 43	99.48 16	91.80 100	94.83 136	98.36 61	98.90 61	98.09 97	97.85 91	99.68 90	99.15 169
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MSDG		98.27 44	98.29 65	98.24 33	99.20 40	99.22 91	99.20 34	97.82 30	99.37 22	94.43 65	95.90 119	97.31 70	99.12 50	98.76 52	98.35 66	99.67 97	99.14 172

test-mter		94.86 129	97.32 97	92.00 155	92.41 159	98.82 109	96.18 137	86.35 177	98.05 127	82.28 157	96.48 106	94.39 100	95.46 163	98.17 92	96.20 144	99.32 184	99.13 173

RPMNet		94.66 132	97.16 102	91.75 164	94.98 131	98.59 127	97.00 122	78.37 223	97.98 130	83.78 144	96.27 110	94.09 108	96.91 115	97.36 135	96.73 127	99.48 168	99.09 174

OMC-MVS		98.84 29	99.01 43	98.65 26	99.39 31	99.23 90	99.22 33	96.70 36	99.40 19	97.77 16	97.89 70	99.80 38	99.21 35	99.02 34	98.65 45	99.57 155	99.07 175

TSAR-MVS + COLMAP		96.79 81	96.55 114	97.06 58	97.70 64	98.46 133	99.07 42	96.23 38	99.38 20	91.32 104	98.80 40	85.61 156	98.69 66	97.64 127	96.92 124	99.37 181	99.06 176

tpm		92.38 187	94.79 153	89.56 202	94.30 138	97.50 187	94.24 193	78.97 220	97.72 147	74.93 211	97.97 68	82.91 184	96.60 126	93.65 211	94.81 194	98.33 203	98.98 177

PatchMatch-RL		97.77 54	98.25 66	97.21 53	99.11 42	99.25 87	97.06 121	94.09 65	98.72 100	95.14 48	98.47 53	96.29 80	98.43 74	98.65 59	97.44 114	99.45 172	98.94 178

pmmvs5		92.71 177	94.27 164	90.90 185	91.42 200	97.74 163	93.23 198	86.66 174	95.99 207	78.96 194	91.45 158	83.44 175	95.55 152	97.30 137	95.05 174	99.58 151	98.93 179

test-LLR		95.50 117	97.32 97	93.37 132	95.49 120	98.74 117	96.44 133	90.82 117	98.18 121	82.75 154	96.60 102	94.67 98	95.54 153	98.09 97	96.00 148	99.20 189	98.93 179

TESTMET0.1,1		94.95 128	97.32 97	92.20 147	92.62 155	98.74 117	96.44 133	86.67 173	98.18 121	82.75 154	96.60 102	94.67 98	95.54 153	98.09 97	96.00 148	99.20 189	98.93 179

EU-MVSNet		92.80 170	94.76 154	90.51 193	91.88 174	96.74 204	92.48 204	88.69 150	96.21 200	79.00 193	91.51 157	87.82 132	91.83 210	95.87 174	96.27 140	99.21 188	98.92 182

Anonymous20231211		83.86 219	83.39 225	84.40 218	85.29 224	93.44 226	86.29 224	84.24 196	85.55 232	68.63 221	61.25 232	59.57 234	84.33 223	92.50 217	92.52 215	97.65 214	98.89 183

PCF-MVS	97.50 6	98.18 46	98.35 62	97.99 38	98.65 50	99.36 71	98.94 48	98.14 21	98.59 104	93.62 83	96.61 101	99.76 42	99.03 58	97.77 118	97.45 113	99.57 155	98.89 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet_dtu		96.30 102	98.53 56	93.70 123	98.97 45	98.24 148	97.36 106	94.23 62	98.85 82	79.18 192	99.19 17	98.47 59	94.09 195	97.89 112	98.21 75	98.39 202	98.85 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		96.22 104	95.85 137	96.65 77	97.75 62	98.54 130	99.00 47	95.53 41	96.88 178	89.88 115	95.95 118	86.46 149	98.07 87	97.65 126	96.63 130	99.67 97	98.83 186

GG-mvs-BLEND		69.11 229	98.13 73	35.26 235	3.49 242	98.20 150	94.89 160	2.38 239	98.42 114	5.82 244	96.37 109	98.60 56	5.97 239	98.75 54	97.98 85	99.01 193	98.61 187

ambc			80.99 227		80.04 234	90.84 227	90.91 209		96.09 203	74.18 212	62.81 231	30.59 242	82.44 225	96.25 168	91.77 222	95.91 230	98.56 188

MDTV_nov1_ep13_2view		92.44 183	95.66 139	88.68 206	91.05 207	97.92 157	92.17 205	79.64 212	98.83 85	76.20 205	91.45 158	93.51 111	95.04 181	95.68 176	93.70 206	97.96 208	98.53 189

USDC		94.26 140	94.83 152	93.59 125	96.02 103	98.44 136	97.84 92	88.65 151	98.86 80	82.73 156	94.02 141	80.56 206	96.76 120	97.28 138	96.15 147	99.55 159	98.50 190

MDA-MVSNet-bldmvs		87.84 217	89.22 218	86.23 213	81.74 231	96.77 203	83.74 226	89.57 141	94.50 219	72.83 217	96.64 100	64.47 230	92.71 207	81.43 231	92.28 219	96.81 226	98.47 191

gm-plane-assit		89.44 212	92.82 209	85.49 215	91.37 202	95.34 219	79.55 230	82.12 203	91.68 225	64.79 228	87.98 200	80.26 209	95.66 147	98.51 72	97.56 106	99.45 172	98.41 192

MS-PatchMatch		95.99 109	97.26 101	94.51 110	97.46 66	98.76 115	97.27 110	86.97 170	99.09 58	89.83 116	93.51 146	97.78 65	96.18 135	97.53 131	95.71 158	99.35 182	98.41 192

TransMVSNet (Re)		93.45 155	94.08 169	92.72 142	92.83 153	97.62 178	94.94 158	91.54 106	95.65 213	83.06 152	88.93 182	83.53 174	94.25 192	97.41 134	97.03 121	99.67 97	98.40 194

TinyColmap		94.00 144	94.35 163	93.60 124	95.89 108	98.26 146	97.49 103	88.82 148	98.56 107	83.21 150	91.28 160	80.48 208	96.68 122	97.34 136	96.26 142	99.53 164	98.24 195

TDRefinement		93.04 164	93.57 189	92.41 143	96.58 81	98.77 113	97.78 97	91.96 98	98.12 124	80.84 164	89.13 181	79.87 212	87.78 215	96.44 157	94.50 200	99.54 163	98.15 196

MDTV_nov1_ep13		95.57 115	97.48 92	93.35 134	95.43 122	98.97 102	97.19 114	83.72 201	98.92 78	87.91 124	97.75 73	96.12 83	97.88 95	96.84 150	95.64 159	97.96 208	98.10 197

tpmp4_e23		93.84 152	94.58 158	92.98 139	95.41 125	98.29 145	96.81 123	80.57 207	98.15 123	90.53 108	97.00 89	84.39 169	96.91 115	93.69 209	92.45 216	97.67 213	98.06 198

MIMVSNet		94.49 138	97.59 89	90.87 187	91.74 187	98.70 121	94.68 181	78.73 221	97.98 130	83.71 147	97.71 76	94.81 95	96.96 114	97.97 108	97.92 87	99.40 180	98.04 199

CostFormer		94.25 141	94.88 151	93.51 129	95.43 122	98.34 144	96.21 136	80.64 206	97.94 135	94.01 73	98.30 59	86.20 152	97.52 100	92.71 213	92.69 213	97.23 224	98.02 200

RPSCF		97.61 59	98.16 72	96.96 71	98.10 56	99.00 98	98.84 51	93.76 81	99.45 17	94.78 56	99.39 11	99.31 51	98.53 72	96.61 151	95.43 161	97.74 210	97.93 201

testus		88.77 214	92.77 210	84.10 219	88.24 218	93.95 223	87.16 222	84.24 196	97.37 153	61.54 232	95.70 127	73.10 224	84.90 221	95.56 177	95.82 154	98.51 198	97.88 202

Anonymous20231206		90.70 207	93.93 177	86.92 212	90.21 216	96.79 202	90.30 214	86.61 175	96.05 205	69.25 220	88.46 193	84.86 165	85.86 219	97.11 144	96.47 136	99.30 185	97.80 203

DWT-MVSNet_training		95.38 120	95.05 148	95.78 95	95.86 110	98.88 106	97.55 101	90.09 132	98.23 120	96.49 34	97.62 78	86.92 143	97.16 109	92.03 221	94.12 202	97.52 216	97.50 204

pmmvs3		88.19 216	91.27 213	84.60 217	85.60 223	93.66 224	85.68 225	81.13 204	92.36 224	63.66 230	89.51 177	77.10 220	93.22 204	96.37 160	92.40 217	98.30 204	97.46 205

test2356		88.81 213	92.86 206	84.09 220	87.85 219	93.46 225	87.07 223	83.60 202	96.50 196	62.08 231	97.06 88	75.04 222	85.17 220	95.08 200	95.42 162	98.75 197	97.46 205

N_pmnet		92.21 194	94.60 156	89.42 203	91.88 174	97.38 194	89.15 218	89.74 140	97.89 138	73.75 213	87.94 201	92.23 119	93.85 200	96.10 170	93.20 209	98.15 206	97.43 207

PatchmatchNet		94.70 131	97.08 104	91.92 158	95.53 118	98.85 108	95.77 141	79.54 214	98.95 72	85.98 134	98.52 49	96.45 76	97.39 105	95.32 179	94.09 203	97.32 220	97.38 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		94.65 133	97.04 105	91.88 161	95.68 115	98.99 100	95.89 139	79.03 219	99.15 51	85.81 136	96.96 91	98.21 63	97.10 110	94.48 206	94.24 201	97.74 210	97.21 209

MVS-HIRNet		92.51 181	95.97 132	88.48 208	93.73 147	98.37 143	90.33 213	75.36 231	98.32 116	77.78 198	89.15 180	94.87 93	95.14 180	97.62 128	96.39 137	98.51 198	97.11 210

dps		94.63 134	95.31 147	93.84 118	95.53 118	98.71 120	96.54 129	80.12 209	97.81 146	97.21 23	96.98 90	92.37 117	96.34 132	92.46 218	91.77 222	97.26 222	97.08 211

test20.03		90.65 208	93.71 183	87.09 210	90.44 214	96.24 205	89.74 217	85.46 181	95.59 214	72.99 216	90.68 163	85.33 159	84.41 222	95.94 173	95.10 173	99.52 165	97.06 212

EPMVS		95.05 126	96.86 109	92.94 140	95.84 111	98.96 103	96.68 125	79.87 210	99.05 66	90.15 112	97.12 87	95.99 84	97.49 102	95.17 187	94.75 196	97.59 215	96.96 213

testpf		91.80 202	94.43 162	88.74 205	93.89 142	95.30 220	92.05 206	71.77 232	97.52 151	87.24 127	94.77 137	92.68 116	91.48 211	91.75 224	92.11 221	96.02 229	96.89 214

LP		92.12 197	94.60 156	89.22 204	94.96 132	98.45 135	93.01 200	77.58 224	97.85 142	77.26 201	89.80 175	93.00 115	94.54 186	93.69 209	92.58 214	98.00 207	96.83 215

tpmrst		93.86 150	95.88 135	91.50 168	95.69 114	98.62 125	95.64 144	79.41 215	98.80 90	83.76 146	95.63 128	96.13 82	97.25 106	92.92 212	92.31 218	97.27 221	96.74 216

new-patchmatchnet		86.12 218	87.30 219	84.74 216	86.92 222	95.19 222	83.57 227	84.42 194	92.67 223	65.66 225	80.32 223	64.72 229	89.41 214	92.33 220	89.21 226	98.43 201	96.69 217

tpm cat1		94.06 142	94.90 150	93.06 137	95.42 124	98.52 131	96.64 127	80.67 205	97.82 144	92.63 94	93.39 148	95.00 92	96.06 139	91.36 225	91.58 224	96.98 225	96.66 218

FMVSNet5		95.42 118	96.47 121	94.20 113	92.26 162	95.99 207	95.66 143	87.15 167	97.87 139	93.46 85	96.68 98	93.79 109	97.52 100	97.10 145	97.21 119	99.11 192	96.62 219

DeepMVS_CX						96.85 201	87.43 221	89.27 143	98.30 117	75.55 208	95.05 133	79.47 213	92.62 208	89.48 227		95.18 231	95.96 220

MIMVSNet1		88.61 215	90.68 215	86.19 214	81.56 232	95.30 220	87.78 220	85.98 179	94.19 220	72.30 218	78.84 225	78.90 217	90.06 213	96.59 152	95.47 160	99.46 171	95.49 221

CMPMVS	70.31 18	90.74 206	91.06 214	90.36 196	97.32 69	97.43 191	92.97 201	87.82 164	93.50 221	75.34 210	83.27 219	84.90 164	92.19 209	92.64 216	91.21 225	96.50 227	94.46 222
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		90.45 209	92.84 208	87.66 209	88.96 217	96.16 206	88.71 219	84.66 190	97.56 150	71.91 219	85.60 215	86.58 148	93.28 203	96.07 171	93.54 207	98.46 200	94.39 223

Gipuma		81.40 224	81.78 226	80.96 223	83.21 227	85.61 236	79.73 229	76.25 229	97.33 160	64.21 229	55.32 233	55.55 236	86.04 218	92.43 219	92.20 220	96.32 228	93.99 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		81.83 222	86.26 220	76.66 225	84.10 225	89.42 231	74.29 234	79.65 211	90.61 226	51.85 237	82.11 221	63.06 233	72.61 229	91.94 222	92.75 211	97.49 217	93.94 225

test1235678		81.83 222	86.26 220	76.66 225	84.10 225	89.41 232	74.29 234	79.64 212	90.60 227	51.84 238	82.11 221	63.07 232	72.61 229	91.94 222	92.75 211	97.49 217	93.94 225

PMMVS2		77.26 226	79.47 228	74.70 229	76.00 235	88.37 234	74.22 236	76.34 227	78.31 234	54.13 234	69.96 230	52.50 237	70.14 232	84.83 229	88.71 227	97.35 219	93.58 227

test12356		80.53 225	84.80 223	75.54 227	82.31 228	88.05 235	75.99 231	79.31 216	88.53 228	53.24 236	83.30 218	56.38 235	65.16 235	90.87 226	93.10 210	97.25 223	93.34 228

1111		82.87 221	85.67 222	79.62 224	81.86 229	89.62 229	74.44 232	68.81 234	87.44 230	66.59 223	76.83 227	70.33 226	87.71 216	92.65 214	93.37 208	98.28 205	89.42 229

MVE	67.97 19	65.53 233	67.43 234	63.31 234	59.33 239	74.20 238	53.09 241	70.43 233	66.27 237	43.13 239	45.98 238	30.62 241	70.65 231	79.34 233	86.30 228	83.25 238	89.33 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
FPMVS		83.82 220	84.61 224	82.90 221	90.39 215	90.71 228	90.85 211	84.10 199	95.47 215	65.15 226	83.44 217	74.46 223	75.48 226	81.63 230	79.42 232	91.42 233	87.14 231

no-one		66.79 232	67.62 233	65.81 233	73.06 238	81.79 237	51.90 242	76.20 230	61.07 238	54.05 235	51.62 237	41.72 239	49.18 236	67.26 235	82.83 230	90.47 234	87.07 232

EMVS		68.12 231	68.11 232	68.14 231	75.51 236	71.76 239	55.38 240	77.20 226	77.78 235	37.79 241	53.59 234	43.61 238	74.72 227	67.05 236	76.70 234	88.27 237	86.24 233

E-PMN		68.30 230	68.43 231	68.15 230	74.70 237	71.56 240	55.64 239	77.24 225	77.48 236	39.46 240	51.95 236	41.68 240	73.28 228	70.65 234	79.51 231	88.61 236	86.20 234

PMVS	72.60 17	76.39 227	77.66 229	74.92 228	81.04 233	69.37 241	68.47 237	80.54 208	85.39 233	65.07 227	73.52 229	72.91 225	65.67 234	80.35 232	76.81 233	88.71 235	85.25 235
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		69.67 228	72.22 230	66.70 232	81.86 229	89.62 229	74.44 232	68.81 234	87.44 230	66.59 223	76.83 227	70.33 226	87.71 216	92.65 214	37.65 235	20.79 239	51.04 236

testmvs		31.24 234	40.15 235	20.86 236	12.61 240	17.99 242	25.16 243	13.30 237	48.42 239	24.82 242	53.07 235	30.13 243	28.47 237	42.73 237	37.65 235	20.79 239	51.04 236

test123		26.75 235	34.25 236	18.01 237	7.93 241	17.18 243	24.85 244	12.36 238	44.83 240	16.52 243	41.80 239	18.10 244	28.29 238	33.08 238	34.79 237	18.10 241	49.95 238

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					92.30 160	97.58 180	90.09 216

MTAPA										98.09 10		99.97 4

MTMP										98.46 7		99.96 10

Patchmatch-RL test							66.86 238

tmp_tt				82.25 222	97.73 63	88.71 233	80.18 228	68.65 236	99.15 51	86.98 129	99.47 7	85.31 160	68.35 233	87.51 228	83.81 229	91.64 232

XVS					97.42 67	99.62 34	98.59 58			93.81 80		99.95 15				99.69 81

X-MVStestdata					97.42 67	99.62 34	98.59 58			93.81 80		99.95 15				99.69 81

mPP-MVS					99.53 25							99.89 30

NP-MVS									98.57 106

Patchmtry						98.59 127	97.15 115	79.14 217		80.42 171

High-resolution multi-view benchmark