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.

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 28	99.91 1	99.66 36	99.63 79	99.39 179	98.91 29	99.78 22	99.85 26	99.36 2	99.94 42	98.84 66	99.88 34	99.82 32

HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 18	99.76 27	99.56 48	97.72 134	99.76 28	99.75 92	99.13 6	99.92 65	99.07 44	99.92 12	99.85 8

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 207	99.52 76	97.18 180	99.60 60	99.79 72	98.79 37	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 193	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 65	99.47 129	98.79 40	99.68 37	99.81 53	98.43 63	99.97 11	98.88 57	99.90 24	99.83 23

HPM-MVS		99.42 29	99.28 38	99.83 23	99.90 3	99.72 27	99.81 15	99.54 62	97.59 143	99.68 37	99.63 141	98.91 28	99.94 42	98.58 95	99.91 17	99.84 12

HyFIR lowres test		99.11 72	98.92 78	99.65 58	99.90 3	99.37 75	99.02 276	99.91 3	97.67 140	99.59 63	99.75 92	95.90 135	99.73 167	99.53 6	99.02 134	99.86 5

HSP-MVS		99.41 32	99.26 43	99.85 18	99.89 8	99.80 14	99.67 56	99.37 192	98.70 45	99.77 23	99.49 189	98.21 75	99.95 33	98.46 111	99.77 76	99.81 36

CHOSEN 1792x2688		99.19 56	99.10 56	99.45 95	99.89 8	98.52 191	99.39 180	99.94 1	98.73 44	99.11 172	99.89 10	95.50 145	99.94 42	99.50 8	99.97 3	99.89 2

ACMMP		99.45 22	99.32 26	99.82 25	99.89 8	99.67 34	99.62 82	99.69 18	98.12 84	99.63 53	99.84 35	98.73 48	99.96 19	98.55 103	99.83 63	99.81 36
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
region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 14	99.65 75	99.66 25	98.13 82	99.66 48	99.68 119	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 12	99.66 65	99.46 138	98.09 89	99.48 87	99.74 97	98.29 72	99.96 19	97.93 148	99.87 38	99.82 32

mPP-MVS		99.44 25	99.30 33	99.86 13	99.88 11	99.79 18	99.69 45	99.48 113	98.12 84	99.50 83	99.75 92	98.78 38	99.97 11	98.57 97	99.89 32	99.83 23

COLMAP_ROB	97.56 6	98.86 101	98.75 101	99.17 132	99.88 11	98.53 188	99.34 200	99.59 38	97.55 148	98.70 234	99.89 10	95.83 137	99.90 86	98.10 133	99.90 24	99.08 168
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 149	99.48 113	98.05 98	99.76 28	99.86 22	98.82 34	99.93 57	98.82 71	99.91 17	99.84 12

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 14	99.66 65	99.67 22	98.15 80	99.68 37	99.69 114	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

#test#		99.43 27	99.29 36	99.86 13	99.87 15	99.80 14	99.55 116	99.67 22	97.83 121	99.68 37	99.69 114	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 18	99.66 65	99.67 22	98.15 80	99.67 43	99.69 114	98.95 25	99.96 19	98.69 82	99.87 38	99.84 12

PGM-MVS		99.45 22	99.31 31	99.86 13	99.87 15	99.78 22	99.58 98	99.65 30	97.84 120	99.71 31	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

AllTest		98.87 98	98.72 102	99.31 111	99.86 20	98.48 196	99.56 111	99.61 32	97.85 118	99.36 113	99.85 26	95.95 131	99.85 111	96.66 247	99.83 63	99.59 108

TestCases				99.31 111	99.86 20	98.48 196		99.61 32	97.85 118	99.36 113	99.85 26	95.95 131	99.85 111	96.66 247	99.83 63	99.59 108

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 67	99.86 20	99.07 105	99.47 149	99.93 2	97.66 141	99.71 31	99.86 22	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 109	99.74 97	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 268	95.45 289	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 109	64.01 354	98.81 35	99.94 42	98.79 72	99.86 48	99.84 12

abl_6		99.44 25	99.31 31	99.83 23	99.85 23	99.75 23	99.66 65	99.59 38	98.13 82	99.82 14	99.81 53	98.60 56	99.96 19	98.46 111	99.88 34	99.79 45

114514_t		98.93 95	98.67 108	99.72 48	99.85 23	99.53 57	99.62 82	99.59 38	92.65 318	99.71 31	99.78 77	98.06 80	99.90 86	98.84 66	99.91 17	99.74 60

CSCG		99.32 42	99.32 26	99.32 110	99.85 23	98.29 202	99.71 41	99.66 25	98.11 86	99.41 100	99.80 64	98.37 69	99.96 19	98.99 50	99.96 5	99.72 71

CP-MVS		99.45 22	99.32 26	99.85 18	99.83 28	99.75 23	99.69 45	99.52 76	98.07 93	99.53 78	99.63 141	98.93 27	99.97 11	98.74 75	99.91 17	99.83 23

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 13	99.59 92	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
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RPSCF		98.22 149	98.62 116	96.99 300	99.82 29	91.58 328	99.72 39	99.44 157	96.61 224	99.66 48	99.89 10	95.92 134	99.82 133	97.46 194	99.10 128	99.57 111

DeepC-MVS	98.35 2	99.30 45	99.19 47	99.64 63	99.82 29	99.23 90	99.62 82	99.55 55	98.94 26	99.63 53	99.95 2	95.82 138	99.94 42	99.37 17	99.97 3	99.73 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_part2					99.81 32	99.83 7				99.77 23

ESAPD		99.31 44	99.13 52	99.87 6	99.81 32	99.83 7	99.37 187	99.48 113	97.97 108	99.77 23	99.78 77	98.96 20	99.95 33	97.15 212	99.84 57	99.83 23

CPTT-MVS		99.11 72	98.90 81	99.74 44	99.80 34	99.46 67	99.59 92	99.49 104	97.03 201	99.63 53	99.69 114	97.27 99	99.96 19	97.82 156	99.84 57	99.81 36

MCST-MVS		99.43 27	99.30 33	99.82 25	99.79 35	99.74 26	99.29 211	99.40 176	98.79 40	99.52 80	99.62 146	98.91 28	99.90 86	98.64 87	99.75 79	99.82 32

tfpn1000		98.33 139	98.02 153	99.25 124	99.78 36	98.73 169	99.70 42	97.55 340	97.48 154	99.69 36	99.53 176	92.37 264	99.85 111	97.82 156	98.26 178	99.16 159

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 63	99.78 36	99.14 99	99.60 90	99.45 149	99.01 13	99.90 1	99.83 37	98.98 18	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 63	99.78 36	99.15 98	99.61 88	99.45 149	99.01 13	99.89 2	99.82 44	99.01 11	99.92 65	99.56 5	99.95 6	99.85 8

Vis-MVSNet		99.12 68	98.97 72	99.56 75	99.78 36	99.10 102	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 185	99.84 117	99.19 33	99.41 109	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
F-COLMAP		99.19 56	99.04 62	99.64 63	99.78 36	99.27 86	99.42 169	99.54 62	97.29 171	99.41 100	99.59 154	98.42 66	99.93 57	98.19 127	99.69 92	99.73 65

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 41	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

MVS_111021_LR		99.41 32	99.33 25	99.65 58	99.77 41	99.51 62	98.94 297	99.85 6	98.82 35	99.65 51	99.74 97	98.51 58	99.80 141	98.83 68	99.89 32	99.64 96

DP-MVS		99.16 61	98.95 76	99.78 34	99.77 41	99.53 57	99.41 173	99.50 99	97.03 201	99.04 187	99.88 14	97.39 94	99.92 65	98.66 85	99.90 24	99.87 4

conf0.01		98.21 152	97.89 166	99.15 135	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.61 274

conf0.002		98.21 152	97.89 166	99.15 135	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.61 274

thresconf0.02		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpn_n400		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpnconf		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

tfpnview11		98.24 145	97.89 166	99.27 120	99.76 44	99.04 108	99.67 56	97.71 332	97.10 190	99.55 71	99.54 169	92.70 246	99.79 144	96.90 231	98.12 192	98.97 182

Regformer-3		99.57 6	99.53 5	99.68 51	99.76 44	99.29 83	99.58 98	99.44 157	99.01 13	99.87 6	99.80 64	98.97 19	99.91 74	99.44 16	99.92 12	99.83 23

Regformer-4		99.59 2	99.54 4	99.73 46	99.76 44	99.41 72	99.58 98	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 42	99.45 15	99.92 12	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 44	99.83 7	99.63 79	99.54 62	98.36 65	99.79 18	99.82 44	98.86 31	99.95 33	98.62 90	99.81 68	99.78 49

PVSNet_BlendedMVS		98.86 101	98.80 95	99.03 146	99.76 44	98.79 164	99.28 214	99.91 3	97.42 161	99.67 43	99.37 226	97.53 91	99.88 101	98.98 51	97.29 235	98.42 293

PVSNet_Blended		99.08 78	98.97 72	99.42 102	99.76 44	98.79 164	98.78 307	99.91 3	96.74 215	99.67 43	99.49 189	97.53 91	99.88 101	98.98 51	99.85 52	99.60 104

MSDG		98.98 91	98.80 95	99.53 80	99.76 44	99.19 92	98.75 310	99.55 55	97.25 174	99.47 88	99.77 84	97.82 85	99.87 103	96.93 228	99.90 24	99.54 114

tfpn_ndepth		98.17 156	97.84 174	99.15 135	99.75 56	98.76 168	99.61 88	97.39 342	96.92 208	99.61 58	99.38 222	92.19 266	99.86 106	97.57 181	98.13 190	98.82 199

view600		97.97 187	97.66 196	98.89 176	99.75 56	97.81 223	99.69 45	98.80 290	98.02 102	99.25 142	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

view800		97.97 187	97.66 196	98.89 176	99.75 56	97.81 223	99.69 45	98.80 290	98.02 102	99.25 142	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

conf0.05thres1000		97.97 187	97.66 196	98.89 176	99.75 56	97.81 223	99.69 45	98.80 290	98.02 102	99.25 142	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

tfpn		97.97 187	97.66 196	98.89 176	99.75 56	97.81 223	99.69 45	98.80 290	98.02 102	99.25 142	98.88 283	91.95 268	99.89 94	94.36 292	98.29 174	98.96 188

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 56	99.84 6	99.43 162	99.51 85	98.68 47	99.27 135	99.53 176	98.64 54	99.96 19	98.44 113	99.80 70	99.79 45

新几何1				99.75 39	99.75 56	99.59 48		99.54 62	96.76 214	99.29 127	99.64 137	98.43 63	99.94 42	96.92 229	99.66 97	99.72 71

test222					99.75 56	99.49 63	98.91 300	99.49 104	96.42 241	99.34 119	99.65 130	98.28 73			99.69 92	99.72 71

testdata				99.54 76	99.75 56	98.95 130		99.51 85	97.07 197	99.43 95	99.70 108	98.87 30	99.94 42	97.76 163	99.64 100	99.72 71

CDPH-MVS		99.13 63	98.91 80	99.80 30	99.75 56	99.71 28	99.15 246	99.41 169	96.60 226	99.60 60	99.55 166	98.83 33	99.90 86	97.48 191	99.83 63	99.78 49

APD-MVS		99.27 50	99.08 57	99.84 22	99.75 56	99.79 18	99.50 132	99.50 99	97.16 182	99.77 23	99.82 44	98.78 38	99.94 42	97.56 183	99.86 48	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
旧先验1					99.74 67	99.59 48		99.54 62			99.69 114	98.47 60			99.68 95	99.73 65

1121		99.09 76	98.87 85	99.75 39	99.74 67	99.60 46	99.27 217	99.48 113	96.82 213	99.25 142	99.65 130	98.38 67	99.93 57	97.53 186	99.67 96	99.73 65

SD-MVS		99.41 32	99.52 6	99.05 145	99.74 67	99.68 32	99.46 152	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 328	98.72 79	99.93 11	99.77 51

DP-MVS Recon		99.12 68	98.95 76	99.65 58	99.74 67	99.70 30	99.27 217	99.57 44	96.40 244	99.42 98	99.68 119	98.75 46	99.80 141	97.98 144	99.72 85	99.44 140

PAPM_NR		99.04 83	98.84 91	99.66 54	99.74 67	99.44 69	99.39 180	99.38 185	97.70 137	99.28 131	99.28 251	98.34 70	99.85 111	96.96 225	99.45 106	99.69 79

原ACMM1				99.65 58	99.73 72	99.33 78		99.47 129	97.46 155	99.12 170	99.66 129	98.67 53	99.91 74	97.70 172	99.69 92	99.71 78

IS-MVSNet		99.05 82	98.87 85	99.57 73	99.73 72	99.32 79	99.75 34	99.20 246	98.02 102	99.56 68	99.86 22	96.54 118	99.67 188	98.09 134	99.13 125	99.73 65

PVSNet	96.02 17	98.85 107	98.84 91	98.89 176	99.73 72	97.28 235	98.32 329	99.60 35	97.86 116	99.50 83	99.57 161	96.75 113	99.86 106	98.56 100	99.70 91	99.54 114

conf200view11		97.78 216	97.45 220	98.77 203	99.72 75	97.86 220	99.59 92	98.74 298	97.93 112	99.26 139	98.62 298	91.75 274	99.83 124	93.22 308	98.18 183	98.61 274

thres100view900		97.76 218	97.45 220	98.69 209	99.72 75	97.86 220	99.59 92	98.74 298	97.93 112	99.26 139	98.62 298	91.75 274	99.83 124	93.22 308	98.18 183	98.37 297

thres600view7		97.86 201	97.51 211	98.92 166	99.72 75	97.95 217	99.59 92	98.74 298	97.94 111	99.27 135	98.62 298	91.75 274	99.86 106	93.73 304	98.19 182	98.96 188

DELS-MVS		99.48 17	99.42 11	99.65 58	99.72 75	99.40 74	99.05 267	99.66 25	99.14 6	99.57 67	99.80 64	98.46 61	99.94 42	99.57 4	99.84 57	99.60 104
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
MVS_111021_HR		99.41 32	99.32 26	99.66 54	99.72 75	99.47 66	98.95 295	99.85 6	98.82 35	99.54 77	99.73 100	98.51 58	99.74 159	98.91 56	99.88 34	99.77 51

Regformer-1		99.53 9	99.47 8	99.72 48	99.71 80	99.44 69	99.49 140	99.46 138	98.95 24	99.83 11	99.76 87	99.01 11	99.93 57	99.17 36	99.87 38	99.80 41

Regformer-2		99.54 7	99.47 8	99.75 39	99.71 80	99.52 60	99.49 140	99.49 104	98.94 26	99.83 11	99.76 87	99.01 11	99.94 42	99.15 38	99.87 38	99.80 41

XVG-OURS-SEG-HR		98.69 121	98.62 116	98.89 176	99.71 80	97.74 229	99.12 250	99.54 62	98.44 62	99.42 98	99.71 105	94.20 208	99.92 65	98.54 105	98.90 146	99.00 178

Vis-MVSNet (Re-imp)		98.87 98	98.72 102	99.31 111	99.71 80	98.88 140	99.80 19	99.44 157	97.91 114	99.36 113	99.78 77	95.49 146	99.43 222	97.91 149	99.11 126	99.62 102

PatchMatch-RL		98.84 109	98.62 116	99.52 84	99.71 80	99.28 84	99.06 265	99.77 9	97.74 132	99.50 83	99.53 176	95.41 147	99.84 117	97.17 211	99.64 100	99.44 140

XVG-OURS		98.73 118	98.68 107	98.88 183	99.70 85	97.73 230	98.92 298	99.55 55	98.52 55	99.45 91	99.84 35	95.27 151	99.91 74	98.08 138	98.84 150	99.00 178

TAPA-MVS	97.07 15	97.74 224	97.34 240	98.94 158	99.70 85	97.53 232	99.25 227	99.51 85	91.90 322	99.30 123	99.63 141	98.78 38	99.64 194	88.09 329	99.87 38	99.65 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn200view9		97.72 227	97.38 233	98.72 207	99.69 87	97.96 215	99.50 132	98.73 306	97.83 121	99.17 165	98.45 306	91.67 279	99.83 124	93.22 308	98.18 183	98.37 297

thres400		97.77 217	97.38 233	98.92 166	99.69 87	97.96 215	99.50 132	98.73 306	97.83 121	99.17 165	98.45 306	91.67 279	99.83 124	93.22 308	98.18 183	98.96 188

Test_1112_low_res		98.89 97	98.66 111	99.57 73	99.69 87	98.95 130	99.03 273	99.47 129	96.98 203	99.15 167	99.23 257	96.77 112	99.89 94	98.83 68	98.78 154	99.86 5

1112_ss		98.98 91	98.77 98	99.59 69	99.68 90	99.02 116	99.25 227	99.48 113	97.23 177	99.13 168	99.58 157	96.93 107	99.90 86	98.87 61	98.78 154	99.84 12

TEST9					99.67 91	99.65 39	99.05 267	99.41 169	96.22 257	98.95 201	99.49 189	98.77 41	99.91 74

train_agg		99.02 86	98.77 98	99.77 36	99.67 91	99.65 39	99.05 267	99.41 169	96.28 250	98.95 201	99.49 189	98.76 43	99.91 74	97.63 176	99.72 85	99.75 55

test_8					99.67 91	99.61 44	99.03 273	99.41 169	96.28 250	98.93 204	99.48 195	98.76 43	99.91 74

agg_prior3		98.97 93	98.71 104	99.75 39	99.67 91	99.60 46	99.04 272	99.41 169	95.93 272	98.87 211	99.48 195	98.61 55	99.91 74	97.63 176	99.72 85	99.75 55

agg_prior1		99.01 89	98.76 100	99.76 38	99.67 91	99.62 42	98.99 282	99.40 176	96.26 253	98.87 211	99.49 189	98.77 41	99.91 74	97.69 173	99.72 85	99.75 55

agg_prior					99.67 91	99.62 42		99.40 176		98.87 211			99.91 74

test_prior3		99.21 55	99.05 59	99.68 51	99.67 91	99.48 64	98.96 291	99.56 48	98.34 66	99.01 190	99.52 181	98.68 51	99.83 124	97.96 145	99.74 81	99.74 60

test_prior				99.68 51	99.67 91	99.48 64		99.56 48					99.83 124			99.74 60

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 105	99.67 91	98.61 184	99.07 261	99.33 213	99.00 17	99.82 14	99.81 53	99.06 8	99.84 117	99.09 42	99.42 108	99.65 90

OMC-MVS		99.08 78	99.04 62	99.20 131	99.67 91	98.22 205	99.28 214	99.52 76	98.07 93	99.66 48	99.81 53	97.79 86	99.78 152	97.79 159	99.81 68	99.60 104

CHOSEN 280x420		99.12 68	99.13 52	99.08 141	99.66 101	97.89 218	98.43 325	99.71 13	98.88 30	99.62 56	99.76 87	96.63 116	99.70 183	99.46 14	99.99 1	99.66 87

PLC	97.94 4	99.02 86	98.85 90	99.53 80	99.66 101	99.01 118	99.24 229	99.52 76	96.85 211	99.27 135	99.48 195	98.25 74	99.91 74	97.76 163	99.62 103	99.65 90
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		99.13 63	98.99 69	99.53 80	99.65 103	99.06 106	99.81 15	99.33 213	97.43 159	99.60 60	99.88 14	97.14 101	99.84 117	99.13 39	98.94 141	99.69 79

thres200		97.61 239	97.28 247	98.62 214	99.64 104	98.03 211	99.26 225	98.74 298	97.68 139	99.09 179	98.32 308	91.66 281	99.81 137	92.88 314	98.22 179	98.03 309

test12				99.75 39	99.64 104	99.61 44		99.29 226		99.21 156		98.38 67	99.89 94		99.74 81	99.74 60

ab-mvs		98.86 101	98.63 113	99.54 76	99.64 104	99.19 92	99.44 157	99.54 62	97.77 128	99.30 123	99.81 53	94.20 208	99.93 57	99.17 36	98.82 151	99.49 128

xiu_mvs_v1_base_debu		99.29 47	99.27 40	99.34 106	99.63 107	98.97 125	99.12 250	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base		99.29 47	99.27 40	99.34 106	99.63 107	98.97 125	99.12 250	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

xiu_mvs_v1_base_debi		99.29 47	99.27 40	99.34 106	99.63 107	98.97 125	99.12 250	99.51 85	98.86 31	99.84 8	99.47 199	98.18 76	99.99 1	99.50 8	99.31 115	99.08 168

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 36	99.63 107	99.59 48	99.36 193	99.46 138	99.07 9	99.79 18	99.82 44	98.85 32	99.92 65	98.68 84	99.87 38	99.82 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		99.42 29	99.29 36	99.80 30	99.62 111	99.55 53	99.50 132	99.70 15	98.79 40	99.77 23	99.96 1	97.45 93	99.96 19	98.92 55	99.90 24	99.89 2

CNVR-MVS		99.42 29	99.30 33	99.78 34	99.62 111	99.71 28	99.26 225	99.52 76	98.82 35	99.39 105	99.71 105	98.96 20	99.85 111	98.59 94	99.80 70	99.77 51

WTY-MVS		99.06 80	98.88 84	99.61 67	99.62 111	99.16 95	99.37 187	99.56 48	98.04 99	99.53 78	99.62 146	96.84 108	99.94 42	98.85 65	98.49 167	99.72 71

sss		99.17 59	99.05 59	99.53 80	99.62 111	98.97 125	99.36 193	99.62 31	97.83 121	99.67 43	99.65 130	97.37 97	99.95 33	99.19 33	99.19 122	99.68 83

NCCC		99.34 40	99.19 47	99.79 33	99.61 115	99.65 39	99.30 207	99.48 113	98.86 31	99.21 156	99.63 141	98.72 49	99.90 86	98.25 125	99.63 102	99.80 41

PCF-MVS	97.08 14	97.66 237	97.06 255	99.47 92	99.61 115	99.09 103	98.04 336	99.25 241	91.24 325	98.51 251	99.70 108	94.55 196	99.91 74	92.76 315	99.85 52	99.42 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSLP-MVS++		99.46 21	99.47 8	99.44 98	99.60 117	99.16 95	99.41 173	99.71 13	98.98 19	99.45 91	99.78 77	99.19 4	99.54 208	99.28 27	99.84 57	99.63 100

DeepPCF-MVS	98.18 3	98.81 110	99.37 17	97.12 299	99.60 117	91.75 327	98.61 318	99.44 157	99.35 1	99.83 11	99.85 26	98.70 50	99.81 137	99.02 48	99.91 17	99.81 36

IterMVS-LS		98.46 131	98.42 128	98.58 217	99.59 119	98.00 212	99.37 187	99.43 165	96.94 206	99.07 181	99.59 154	97.87 83	99.03 286	98.32 123	95.62 264	98.71 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		97.83 206	97.77 185	98.02 268	99.58 120	96.27 280	99.02 276	99.48 113	97.22 178	98.71 228	99.70 108	92.75 240	99.13 275	97.46 194	96.00 258	98.67 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CNLPA		99.14 62	98.99 69	99.59 69	99.58 120	99.41 72	99.16 243	99.44 157	98.45 59	99.19 162	99.49 189	98.08 79	99.89 94	97.73 167	99.75 79	99.48 130

semantic-postprocess				98.06 265	99.57 122	96.36 277		99.49 104	97.18 180	98.71 228	99.72 104	92.70 246	99.14 272	97.44 196	95.86 260	98.67 241

PS-MVSNAJ		99.32 42	99.32 26	99.30 114	99.57 122	98.94 133	98.97 289	99.46 138	98.92 28	99.71 31	99.24 256	99.01 11	99.98 5	99.35 18	99.66 97	98.97 182

MG-MVS		99.13 63	99.02 67	99.45 95	99.57 122	98.63 179	99.07 261	99.34 205	98.99 18	99.61 58	99.82 44	97.98 82	99.87 103	97.00 221	99.80 70	99.85 8

PHI-MVS		99.30 45	99.17 49	99.70 50	99.56 125	99.52 60	99.58 98	99.80 8	97.12 186	99.62 56	99.73 100	98.58 57	99.90 86	98.61 92	99.91 17	99.68 83

AdaColmap		99.01 89	98.80 95	99.66 54	99.56 125	99.54 54	99.18 241	99.70 15	98.18 79	99.35 116	99.63 141	96.32 123	99.90 86	97.48 191	99.77 76	99.55 112

xiu_mvs_v2_base		99.26 52	99.25 44	99.29 117	99.53 127	98.91 138	99.02 276	99.45 149	98.80 39	99.71 31	99.26 254	98.94 26	99.98 5	99.34 22	99.23 119	98.98 181

LFMVS		97.90 198	97.35 237	99.54 76	99.52 128	99.01 118	99.39 180	98.24 321	97.10 190	99.65 51	99.79 72	84.79 332	99.91 74	99.28 27	98.38 171	99.69 79

VNet		99.11 72	98.90 81	99.73 46	99.52 128	99.56 51	99.41 173	99.39 179	99.01 13	99.74 30	99.78 77	95.56 143	99.92 65	99.52 7	98.18 183	99.72 71

MVS_0304		99.06 80	98.86 88	99.66 54	99.51 130	99.36 76	99.22 234	99.51 85	98.95 24	99.58 64	99.65 130	93.74 227	99.98 5	99.66 1	99.95 6	99.64 96

Fast-Effi-MVS+		98.70 120	98.43 127	99.51 86	99.51 130	99.28 84	99.52 123	99.47 129	96.11 267	99.01 190	99.34 240	96.20 127	99.84 117	97.88 151	98.82 151	99.39 146

MVSFormer		99.17 59	99.12 54	99.29 117	99.51 130	98.94 133	99.88 1	99.46 138	97.55 148	99.80 16	99.65 130	97.39 94	99.28 250	99.03 46	99.85 52	99.65 90

lupinMVS		99.13 63	99.01 68	99.46 94	99.51 130	98.94 133	99.05 267	99.16 250	97.86 116	99.80 16	99.56 163	97.39 94	99.86 106	98.94 54	99.85 52	99.58 110

GBi-Net		97.68 233	97.48 215	98.29 245	99.51 130	97.26 237	99.43 162	99.48 113	96.49 231	99.07 181	99.32 245	90.26 293	98.98 291	97.10 215	96.65 244	98.62 265

test1		97.68 233	97.48 215	98.29 245	99.51 130	97.26 237	99.43 162	99.48 113	96.49 231	99.07 181	99.32 245	90.26 293	98.98 291	97.10 215	96.65 244	98.62 265

FMVSNet2		97.72 227	97.36 235	98.80 200	99.51 130	98.84 145	99.45 153	99.42 166	96.49 231	98.86 216	99.29 250	90.26 293	98.98 291	96.44 253	96.56 247	98.58 283

VDDNet		97.55 241	97.02 256	99.16 133	99.49 137	98.12 210	99.38 185	99.30 222	95.35 280	99.68 37	99.90 7	82.62 338	99.93 57	99.31 25	98.13 190	99.42 143

MVS_Test		99.10 75	98.97 72	99.48 89	99.49 137	99.14 99	99.67 56	99.34 205	97.31 169	99.58 64	99.76 87	97.65 90	99.82 133	98.87 61	99.07 131	99.46 137

BH-untuned		98.42 134	98.36 130	98.59 216	99.49 137	96.70 266	99.27 217	99.13 254	97.24 176	98.80 220	99.38 222	95.75 140	99.74 159	97.07 218	99.16 123	99.33 151

diffmvs		98.72 119	98.49 125	99.43 101	99.48 140	99.19 92	99.62 82	99.42 166	95.58 278	99.37 109	99.67 123	96.14 128	99.74 159	98.14 131	98.96 139	99.37 147

VDD-MVS		97.73 225	97.35 237	98.88 183	99.47 141	97.12 242	99.34 200	98.85 286	98.19 76	99.67 43	99.85 26	82.98 336	99.92 65	99.49 12	98.32 173	99.60 104

Effi-MVS+		98.81 110	98.59 121	99.48 89	99.46 142	99.12 101	98.08 335	99.50 99	97.50 153	99.38 107	99.41 213	96.37 122	99.81 137	99.11 41	98.54 164	99.51 124

jason		99.13 63	99.03 64	99.45 95	99.46 142	98.87 141	99.12 250	99.26 239	98.03 101	99.79 18	99.65 130	97.02 104	99.85 111	99.02 48	99.90 24	99.65 90
jason: jason.
TAMVS		99.12 68	99.08 57	99.24 127	99.46 142	98.55 186	99.51 127	99.46 138	98.09 89	99.45 91	99.82 44	98.34 70	99.51 209	98.70 80	98.93 142	99.67 86

ACMH+	97.24 10	97.92 196	97.78 181	98.32 242	99.46 142	96.68 268	99.56 111	99.54 62	98.41 63	97.79 284	99.87 19	90.18 296	99.66 190	98.05 142	97.18 239	98.62 265

MIMVSNet		97.73 225	97.45 220	98.57 218	99.45 146	97.50 233	99.02 276	98.98 270	96.11 267	99.41 100	99.14 263	90.28 292	98.74 303	95.74 265	98.93 142	99.47 134

alignmvs		98.81 110	98.56 123	99.58 72	99.43 147	99.42 71	99.51 127	98.96 273	98.61 50	99.35 116	98.92 282	94.78 181	99.77 154	99.35 18	98.11 198	99.54 114

canonicalmvs		99.02 86	98.86 88	99.51 86	99.42 148	99.32 79	99.80 19	99.48 113	98.63 48	99.31 122	98.81 291	97.09 102	99.75 158	99.27 29	97.90 204	99.47 134

HY-MVS	97.30 7	98.85 107	98.64 112	99.47 92	99.42 148	99.08 104	99.62 82	99.36 193	97.39 164	99.28 131	99.68 119	96.44 120	99.92 65	98.37 117	98.22 179	99.40 145

CDS-MVSNet		99.09 76	99.03 64	99.25 124	99.42 148	98.73 169	99.45 153	99.46 138	98.11 86	99.46 90	99.77 84	98.01 81	99.37 227	98.70 80	98.92 144	99.66 87
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet		99.25 53	99.14 51	99.59 69	99.41 151	99.16 95	99.35 197	99.57 44	98.82 35	99.51 82	99.61 149	96.46 119	99.95 33	99.59 2	99.98 2	99.65 90

Fast-Effi-MVS+-dtu		98.77 116	98.83 94	98.60 215	99.41 151	96.99 254	99.52 123	99.49 104	98.11 86	99.24 147	99.34 240	96.96 106	99.79 144	97.95 147	99.45 106	99.02 177

BH-RMVSNet		98.41 135	98.08 148	99.40 103	99.41 151	98.83 148	99.30 207	98.77 294	97.70 137	98.94 203	99.65 130	92.91 238	99.74 159	96.52 251	99.55 104	99.64 96

ACMM	97.58 5	98.37 138	98.34 132	98.48 227	99.41 151	97.10 243	99.56 111	99.45 149	98.53 54	99.04 187	99.85 26	93.00 234	99.71 177	98.74 75	97.45 226	98.64 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	97.28 8	98.10 165	97.99 156	98.44 234	99.41 151	96.96 258	99.60 90	99.56 48	98.09 89	98.15 269	99.91 5	90.87 289	99.70 183	98.88 57	97.45 226	98.67 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PAPR		98.63 127	98.34 132	99.51 86	99.40 156	99.03 115	98.80 306	99.36 193	96.33 246	99.00 197	99.12 267	98.46 61	99.84 117	95.23 277	99.37 114	99.66 87

API-MVS		99.04 83	99.03 64	99.06 143	99.40 156	99.31 82	99.55 116	99.56 48	98.54 53	99.33 120	99.39 221	98.76 43	99.78 152	96.98 223	99.78 74	98.07 305

FMVSNet3		98.03 177	97.76 188	98.84 195	99.39 158	98.98 122	99.40 179	99.38 185	96.67 220	99.07 181	99.28 251	92.93 235	98.98 291	97.10 215	96.65 244	98.56 285

GA-MVS		97.85 202	97.47 217	99.00 150	99.38 159	97.99 213	98.57 320	99.15 251	97.04 200	98.90 208	99.30 248	89.83 298	99.38 224	96.70 244	98.33 172	99.62 102

mvs_anonymous		99.03 85	98.99 69	99.16 133	99.38 159	98.52 191	99.51 127	99.38 185	97.79 126	99.38 107	99.81 53	97.30 98	99.45 213	99.35 18	98.99 136	99.51 124

ACMP	97.20 11	98.06 168	97.94 160	98.45 231	99.37 161	97.01 252	99.44 157	99.49 104	97.54 151	98.45 255	99.79 72	91.95 268	99.72 171	97.91 149	97.49 224	98.62 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		98.86 101	98.63 113	99.54 76	99.37 161	99.66 36	99.45 153	99.54 62	96.61 224	99.01 190	99.40 217	97.09 102	99.86 106	97.68 175	99.53 105	99.10 163
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
testgi		97.65 238	97.50 213	98.13 263	99.36 163	96.45 274	99.42 169	99.48 113	97.76 129	97.87 280	99.45 206	91.09 286	98.81 302	94.53 287	98.52 165	99.13 162

EI-MVSNet		98.67 123	98.67 108	98.68 210	99.35 164	97.97 214	99.50 132	99.38 185	96.93 207	99.20 159	99.83 37	97.87 83	99.36 231	98.38 116	97.56 216	98.71 216

CVMVSNet		98.57 128	98.67 108	98.30 244	99.35 164	95.59 289	99.50 132	99.55 55	98.60 51	99.39 105	99.83 37	94.48 199	99.45 213	98.75 74	98.56 163	99.85 8

BH-w/o		98.00 183	97.89 166	98.32 242	99.35 164	96.20 282	99.01 280	98.90 282	96.42 241	98.38 258	99.00 275	95.26 153	99.72 171	96.06 259	98.61 157	99.03 175

MVSTER		98.49 129	98.32 134	99.00 150	99.35 164	99.02 116	99.54 119	99.38 185	97.41 162	99.20 159	99.73 100	93.86 222	99.36 231	98.87 61	97.56 216	98.62 265

Effi-MVS+-dtu		98.78 114	98.89 83	98.47 229	99.33 168	96.91 260	99.57 104	99.30 222	98.47 57	99.41 100	98.99 276	96.78 110	99.74 159	98.73 77	99.38 110	98.74 212

CANet_DTU		98.97 93	98.87 85	99.25 124	99.33 168	98.42 200	99.08 260	99.30 222	99.16 5	99.43 95	99.75 92	95.27 151	99.97 11	98.56 100	99.95 6	99.36 148

mvs-test1		98.86 101	98.84 91	98.89 176	99.33 168	97.77 228	99.44 157	99.30 222	98.47 57	99.10 175	99.43 208	96.78 110	99.95 33	98.73 77	99.02 134	98.96 188

ADS-MVSNet2		98.02 179	98.07 150	97.87 278	99.33 168	95.19 300	99.23 230	99.08 258	96.24 255	99.10 175	99.67 123	94.11 213	98.93 299	96.81 238	99.05 132	99.48 130

ADS-MVSNet		98.20 154	98.08 148	98.56 220	99.33 168	96.48 273	99.23 230	99.15 251	96.24 255	99.10 175	99.67 123	94.11 213	99.71 177	96.81 238	99.05 132	99.48 130

LPG-MVS_test		98.22 149	98.13 143	98.49 225	99.33 168	97.05 249	99.58 98	99.55 55	97.46 155	99.24 147	99.83 37	92.58 255	99.72 171	98.09 134	97.51 219	98.68 230

LGP-MVS_train				98.49 225	99.33 168	97.05 249		99.55 55	97.46 155	99.24 147	99.83 37	92.58 255	99.72 171	98.09 134	97.51 219	98.68 230

FMVSNet1		96.84 265	96.36 266	98.29 245	99.32 175	97.26 237	99.43 162	99.48 113	95.11 282	98.55 250	99.32 245	83.95 335	98.98 291	95.81 264	96.26 254	98.62 265

PVSNet_0	94.43 19	96.09 286	95.47 288	97.94 273	99.31 176	94.34 311	97.81 337	99.70 15	97.12 186	97.46 286	98.75 295	89.71 299	99.79 144	97.69 173	81.69 341	99.68 83

Patchmatch-test1		98.16 158	98.14 142	98.22 257	99.30 177	95.55 290	99.07 261	98.97 271	97.57 146	99.43 95	99.60 152	92.72 243	99.60 202	97.38 199	99.20 121	99.50 127

LCM-MVSNet-Re		97.83 206	98.15 141	96.87 304	99.30 177	92.25 326	99.59 92	98.26 320	97.43 159	96.20 302	99.13 264	96.27 125	98.73 304	98.17 129	98.99 136	99.64 96

MVS-HIRNet		95.75 289	95.16 293	97.51 293	99.30 177	93.69 318	98.88 302	95.78 345	85.09 337	98.78 222	92.65 341	91.29 285	99.37 227	94.85 282	99.85 52	99.46 137

HQP_MVS		98.27 144	98.22 140	98.44 234	99.29 180	96.97 256	99.39 180	99.47 129	98.97 22	99.11 172	99.61 149	92.71 244	99.69 186	97.78 160	97.63 209	98.67 241

plane_prior7					99.29 180	97.03 251

ITE_SJBPF				98.08 264	99.29 180	96.37 276		98.92 277	98.34 66	98.83 218	99.75 92	91.09 286	99.62 200	95.82 263	97.40 230	98.25 302

DeepMVS_CX				93.34 317	99.29 180	82.27 342		99.22 244	85.15 336	96.33 301	99.05 272	90.97 288	99.73 167	93.57 305	97.77 207	98.01 310

CLD-MVS		98.16 158	98.10 145	98.33 241	99.29 180	96.82 263	98.75 310	99.44 157	97.83 121	99.13 168	99.55 166	92.92 236	99.67 188	98.32 123	97.69 208	98.48 289
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					99.27 185	96.98 255						92.71 244

PMMVS		98.80 113	98.62 116	99.34 106	99.27 185	98.70 172	98.76 309	99.31 220	97.34 166	99.21 156	99.07 269	97.20 100	99.82 133	98.56 100	98.87 148	99.52 119

plane_prior1					99.26 187

XXY-MVS		98.38 137	98.09 147	99.24 127	99.26 187	99.32 79	99.56 111	99.55 55	97.45 158	98.71 228	99.83 37	93.23 231	99.63 199	98.88 57	96.32 253	98.76 208

tpmp4_e23		97.34 255	97.29 246	97.52 292	99.25 189	93.73 315	99.58 98	99.19 249	94.00 304	98.20 267	99.41 213	90.74 290	99.74 159	97.13 214	98.07 199	99.07 172

NP-MVS					99.23 190	96.92 259					99.40 217

LTVRE_ROB	97.16 12	98.02 179	97.90 162	98.40 237	99.23 190	96.80 264	99.70 42	99.60 35	97.12 186	98.18 268	99.70 108	91.73 277	99.72 171	98.39 114	97.45 226	98.68 230
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
UGNet		98.87 98	98.69 106	99.40 103	99.22 192	98.72 171	99.44 157	99.68 19	99.24 3	99.18 164	99.42 210	92.74 242	99.96 19	99.34 22	99.94 10	99.53 118
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
VPNet		97.84 204	97.44 225	99.01 148	99.21 193	98.94 133	99.48 145	99.57 44	98.38 64	99.28 131	99.73 100	88.89 306	99.39 223	99.19 33	93.27 307	98.71 216

IB-MVS	95.67 18	96.22 282	95.44 290	98.57 218	99.21 193	96.70 266	98.65 317	97.74 331	96.71 217	97.27 289	98.54 304	86.03 326	99.92 65	98.47 110	86.30 337	99.10 163
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
tfpnnormal		97.84 204	97.47 217	98.98 152	99.20 195	99.22 91	99.64 77	99.61 32	96.32 247	98.27 266	99.70 108	93.35 230	99.44 218	95.69 267	95.40 267	98.27 300

QAPM		98.67 123	98.30 136	99.80 30	99.20 195	99.67 34	99.77 24	99.72 11	94.74 287	98.73 226	99.90 7	95.78 139	99.98 5	96.96 225	99.88 34	99.76 54

HQP-NCC					99.19 197		98.98 286		98.24 72	98.66 237

ACMP_Plane					99.19 197		98.98 286		98.24 72	98.66 237

HQP-MVS		98.02 179	97.90 162	98.37 239	99.19 197	96.83 261	98.98 286	99.39 179	98.24 72	98.66 237	99.40 217	92.47 259	99.64 194	97.19 208	97.58 214	98.64 257

Patchmatch-test		97.93 193	97.65 201	98.77 203	99.18 200	97.07 247	99.03 273	99.14 253	96.16 262	98.74 225	99.57 161	94.56 195	99.72 171	93.36 307	99.11 126	99.52 119

FIs		98.78 114	98.63 113	99.23 129	99.18 200	99.54 54	99.83 12	99.59 38	98.28 70	98.79 221	99.81 53	96.75 113	99.37 227	99.08 43	96.38 251	98.78 203

CR-MVSNet		98.17 156	97.93 161	98.87 187	99.18 200	98.49 194	99.22 234	99.33 213	96.96 204	99.56 68	99.38 222	94.33 204	99.00 289	94.83 283	98.58 160	99.14 160

RPMNet		96.61 267	95.85 275	98.87 187	99.18 200	98.49 194	99.22 234	99.08 258	88.72 334	99.56 68	97.38 330	94.08 215	99.00 289	86.87 334	98.58 160	99.14 160

LS3D		99.27 50	99.12 54	99.74 44	99.18 200	99.75 23	99.56 111	99.57 44	98.45 59	99.49 86	99.85 26	97.77 87	99.94 42	98.33 121	99.84 57	99.52 119

tpm cat1		97.39 254	97.36 235	97.50 294	99.17 205	93.73 315	99.43 162	99.31 220	91.27 324	98.71 228	99.08 268	94.31 206	99.77 154	96.41 255	98.50 166	99.00 178

3Dnovator+	97.12 13	99.18 58	98.97 72	99.82 25	99.17 205	99.68 32	99.81 15	99.51 85	99.20 4	98.72 227	99.89 10	95.68 142	99.97 11	98.86 64	99.86 48	99.81 36

VPA-MVSNet		98.29 142	97.95 159	99.30 114	99.16 207	99.54 54	99.50 132	99.58 43	98.27 71	99.35 116	99.37 226	92.53 257	99.65 192	99.35 18	94.46 290	98.72 214

tpmrst		98.33 139	98.48 126	97.90 277	99.16 207	94.78 305	99.31 205	99.11 255	97.27 172	99.45 91	99.59 154	95.33 148	99.84 117	98.48 108	98.61 157	99.09 167

PatchmatchNet		98.31 141	98.36 130	98.19 260	99.16 207	95.32 297	99.27 217	98.92 277	97.37 165	99.37 109	99.58 157	94.90 173	99.70 183	97.43 197	99.21 120	99.54 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchFormer-LS_test		98.01 182	98.05 151	97.87 278	99.15 210	94.76 306	99.42 169	98.93 275	97.12 186	98.84 217	98.59 302	93.74 227	99.80 141	98.55 103	98.17 188	99.06 173

tpm2		97.44 252	97.34 240	97.74 288	99.15 210	94.36 310	99.45 153	98.94 274	93.45 313	98.90 208	99.44 207	91.35 284	99.59 204	97.31 202	98.07 199	99.29 153

CostFormer		97.72 227	97.73 192	97.71 289	99.15 210	94.02 313	99.54 119	99.02 267	94.67 288	99.04 187	99.35 237	92.35 265	99.77 154	98.50 107	97.94 203	99.34 150

TransMVSNet (Re)		97.15 260	96.58 263	98.86 191	99.12 213	98.85 144	99.49 140	98.91 280	95.48 279	97.16 292	99.80 64	93.38 229	99.11 278	94.16 302	91.73 317	98.62 265

3Dnovator	97.25 9	99.24 54	99.05 59	99.81 28	99.12 213	99.66 36	99.84 9	99.74 10	99.09 8	98.92 205	99.90 7	95.94 133	99.98 5	98.95 53	99.92 12	99.79 45

XVG-ACMP-BASELINE		97.83 206	97.71 194	98.20 259	99.11 215	96.33 278	99.41 173	99.52 76	98.06 97	99.05 186	99.50 186	89.64 300	99.73 167	97.73 167	97.38 232	98.53 286

FMVSNet5		96.43 271	96.19 268	97.15 297	99.11 215	95.89 286	99.32 202	99.52 76	94.47 297	98.34 262	99.07 269	87.54 321	97.07 331	92.61 316	95.72 262	98.47 290

MDTV_nov1_ep13			98.32 134		99.11 215	94.44 309	99.27 217	98.74 298	97.51 152	99.40 104	99.62 146	94.78 181	99.76 157	97.59 178	98.81 153

Patchmtry		97.75 222	97.40 231	98.81 198	99.10 218	98.87 141	99.11 256	99.33 213	94.83 285	98.81 219	99.38 222	94.33 204	99.02 287	96.10 258	95.57 265	98.53 286

dp		97.75 222	97.80 178	97.59 291	99.10 218	93.71 317	99.32 202	98.88 284	96.48 237	99.08 180	99.55 166	92.67 253	99.82 133	96.52 251	98.58 160	99.24 156

Baseline_NR-MVSNet		97.76 218	97.45 220	98.68 210	99.09 220	98.29 202	99.41 173	98.85 286	95.65 277	98.63 245	99.67 123	94.82 178	99.10 280	98.07 140	92.89 311	98.64 257

FC-MVSNet-test		98.75 117	98.62 116	99.15 135	99.08 221	99.45 68	99.86 8	99.60 35	98.23 75	98.70 234	99.82 44	96.80 109	99.22 265	99.07 44	96.38 251	98.79 202

USDC		97.34 255	97.20 251	97.75 287	99.07 222	95.20 299	98.51 323	99.04 265	97.99 107	98.31 263	99.86 22	89.02 304	99.55 207	95.67 269	97.36 233	98.49 288

TinyColmap		97.12 261	96.89 258	97.83 282	99.07 222	95.52 293	98.57 320	98.74 298	97.58 145	97.81 283	99.79 72	88.16 318	99.56 205	95.10 278	97.21 237	98.39 296

pm-mvs1		97.68 233	97.28 247	98.88 183	99.06 224	98.62 181	99.50 132	99.45 149	96.32 247	97.87 280	99.79 72	92.47 259	99.35 234	97.54 185	93.54 305	98.67 241

TR-MVS		97.76 218	97.41 230	98.82 197	99.06 224	97.87 219	98.87 303	98.56 315	96.63 223	98.68 236	99.22 258	92.49 258	99.65 192	95.40 274	97.79 206	98.95 195

PAPM		97.59 240	97.09 254	99.07 142	99.06 224	98.26 204	98.30 330	99.10 256	94.88 284	98.08 272	99.34 240	96.27 125	99.64 194	89.87 323	98.92 144	99.31 152

nrg030		98.64 126	98.42 128	99.28 119	99.05 227	99.69 31	99.81 15	99.46 138	98.04 99	99.01 190	99.82 44	96.69 115	99.38 224	99.34 22	94.59 289	98.78 203

tpmvs		97.98 184	98.02 153	97.84 281	99.04 228	94.73 307	99.31 205	99.20 246	96.10 270	98.76 224	99.42 210	94.94 168	99.81 137	96.97 224	98.45 168	98.97 182

OpenMVS	96.50 16	98.47 130	98.12 144	99.52 84	99.04 228	99.53 57	99.82 13	99.72 11	94.56 293	98.08 272	99.88 14	94.73 188	99.98 5	97.47 193	99.76 78	99.06 173

DWT-MVSNet_test		97.53 243	97.40 231	97.93 274	99.03 230	94.86 304	99.57 104	98.63 311	96.59 228	98.36 260	98.79 292	89.32 302	99.74 159	98.14 131	98.16 189	99.20 158

WR-MVS_H		98.13 160	97.87 173	98.90 174	99.02 231	98.84 145	99.70 42	99.59 38	97.27 172	98.40 257	99.19 260	95.53 144	99.23 262	98.34 120	93.78 303	98.61 274

tpm		97.67 236	97.55 207	98.03 266	99.02 231	95.01 303	99.43 162	98.54 316	96.44 239	99.12 170	99.34 240	91.83 273	99.60 202	97.75 165	96.46 249	99.48 130

UniMVSNet (Re)		98.29 142	98.00 155	99.13 139	99.00 233	99.36 76	99.49 140	99.51 85	97.95 110	98.97 200	99.13 264	96.30 124	99.38 224	98.36 119	93.34 306	98.66 252

v7		98.05 174	97.78 181	98.87 187	98.99 234	98.67 174	99.64 77	99.34 205	96.31 249	99.29 127	99.51 184	94.78 181	99.27 253	97.03 219	95.15 273	98.66 252

v10		97.85 202	97.52 209	98.86 191	98.99 234	98.67 174	99.75 34	99.41 169	95.70 276	98.98 199	99.41 213	94.75 187	99.23 262	96.01 261	94.63 288	98.67 241

PS-CasMVS		97.93 193	97.59 206	98.95 157	98.99 234	99.06 106	99.68 54	99.52 76	97.13 184	98.31 263	99.68 119	92.44 263	99.05 283	98.51 106	94.08 298	98.75 209

PatchT		97.03 264	96.44 265	98.79 201	98.99 234	98.34 201	99.16 243	99.07 261	92.13 319	99.52 80	97.31 332	94.54 197	98.98 291	88.54 327	98.73 156	99.03 175

v13		96.24 279	95.58 284	98.25 252	98.98 238	98.83 148	99.75 34	99.29 226	94.35 300	93.89 322	97.60 325	95.17 158	98.11 316	94.27 299	86.86 335	97.81 317

V42		98.06 168	97.79 179	98.86 191	98.98 238	98.84 145	99.69 45	99.34 205	96.53 230	99.30 123	99.37 226	94.67 191	99.32 241	97.57 181	94.66 286	98.42 293

LF4IMVS		97.52 244	97.46 219	97.70 290	98.98 238	95.55 290	99.29 211	98.82 289	98.07 93	98.66 237	99.64 137	89.97 297	99.61 201	97.01 220	96.68 243	97.94 313

v1neww		98.12 162	97.84 174	98.93 161	98.97 241	98.81 157	99.66 65	99.35 197	96.49 231	99.29 127	99.37 226	95.02 163	99.32 241	97.73 167	94.73 281	98.67 241

v7new		98.12 162	97.84 174	98.93 161	98.97 241	98.81 157	99.66 65	99.35 197	96.49 231	99.29 127	99.37 226	95.02 163	99.32 241	97.73 167	94.73 281	98.67 241

CP-MVSNet		98.09 166	97.78 181	99.01 148	98.97 241	99.24 89	99.67 56	99.46 138	97.25 174	98.48 254	99.64 137	93.79 223	99.06 282	98.63 88	94.10 297	98.74 212

v16		96.39 274	95.76 280	98.26 248	98.96 244	98.81 157	99.76 27	99.28 233	94.57 291	94.10 314	97.70 317	95.04 162	98.16 310	94.70 285	87.77 328	97.80 319

v12		96.24 279	95.58 284	98.23 255	98.96 244	98.81 157	99.76 27	99.29 226	94.42 299	93.85 323	97.60 325	95.12 159	98.09 317	94.32 296	86.85 336	97.80 319

pcd1.5k->3k		40.85 327	43.49 329	32.93 341	98.95 246	0.00 359	0.00 350	99.53 72	0.00 354	0.00 355	0.27 356	95.32 149	0.00 357	0.00 354	97.30 234	98.80 201

v18		96.42 272	95.80 279	98.26 248	98.95 246	98.82 155	99.76 27	99.28 233	94.58 290	94.12 313	97.70 317	95.22 156	98.16 310	94.83 283	87.80 327	97.79 324

v8		97.95 192	97.63 203	98.93 161	98.95 246	98.81 157	99.80 19	99.41 169	96.03 271	99.10 175	99.42 210	94.92 171	99.30 247	96.94 227	94.08 298	98.66 252

v17		96.42 272	95.81 277	98.25 252	98.94 249	98.80 162	99.76 27	99.28 233	94.57 291	94.18 312	97.71 316	95.23 155	98.16 310	94.86 281	87.73 329	97.80 319

v15		96.28 276	95.62 282	98.25 252	98.94 249	98.83 148	99.76 27	99.29 226	94.52 295	94.02 317	97.61 324	95.02 163	98.13 314	94.53 287	86.92 332	97.80 319

v6		98.12 162	97.84 174	98.94 158	98.94 249	98.83 148	99.66 65	99.34 205	96.49 231	99.30 123	99.37 226	94.95 167	99.34 237	97.77 162	94.74 280	98.67 241

V14		96.26 277	95.60 283	98.26 248	98.94 249	98.83 148	99.76 27	99.29 226	94.49 296	93.96 319	97.66 320	94.99 166	98.13 314	94.41 290	86.90 333	97.80 319

V9		96.25 278	95.58 284	98.26 248	98.94 249	98.83 148	99.75 34	99.29 226	94.45 298	93.96 319	97.62 323	94.94 168	98.14 313	94.40 291	86.87 334	97.81 317

v11		96.23 281	95.57 287	98.21 258	98.93 254	98.83 148	99.72 39	99.29 226	94.29 301	94.05 316	97.64 322	94.88 175	98.04 318	92.89 313	88.43 325	97.77 325

TESTMET0.1,1		97.55 241	97.27 249	98.40 237	98.93 254	96.53 271	98.67 314	97.61 339	96.96 204	98.64 244	99.28 251	88.63 312	99.45 213	97.30 203	99.38 110	99.21 157

v1		98.05 174	97.76 188	98.93 161	98.92 256	98.80 162	99.57 104	99.35 197	96.39 245	99.28 131	99.36 233	94.86 176	99.32 241	97.38 199	94.72 283	98.68 230

UniMVSNet_NR-MVSNet		98.22 149	97.97 157	98.96 155	98.92 256	98.98 122	99.48 145	99.53 72	97.76 129	98.71 228	99.46 203	96.43 121	99.22 265	98.57 97	92.87 312	98.69 225

v1141		98.05 174	97.76 188	98.91 170	98.91 258	98.78 166	99.57 104	99.35 197	96.41 243	99.23 152	99.36 233	94.93 170	99.27 253	97.38 199	94.72 283	98.68 230

divwei89l23v2f112		98.06 168	97.78 181	98.91 170	98.90 259	98.77 167	99.57 104	99.35 197	96.45 238	99.24 147	99.37 226	94.92 171	99.27 253	97.50 189	94.71 285	98.68 230

v2v482		98.06 168	97.77 185	98.92 166	98.90 259	98.82 155	99.57 104	99.36 193	96.65 221	99.19 162	99.35 237	94.20 208	99.25 259	97.72 171	94.97 277	98.69 225

LP		97.04 263	96.80 259	97.77 286	98.90 259	95.23 298	98.97 289	99.06 263	94.02 303	98.09 271	99.41 213	93.88 220	98.82 301	90.46 321	98.42 170	99.26 155

1314		98.68 122	98.54 124	99.11 140	98.89 262	98.65 177	99.27 217	99.49 104	96.89 209	97.99 277	99.56 163	97.72 89	99.83 124	97.74 166	99.27 118	98.84 198

OPM-MVS		98.19 155	98.10 145	98.45 231	98.88 263	97.07 247	99.28 214	99.38 185	98.57 52	99.22 154	99.81 53	92.12 267	99.66 190	98.08 138	97.54 218	98.61 274

v1192		97.81 210	97.44 225	98.91 170	98.88 263	98.68 173	99.51 127	99.34 205	96.18 260	99.20 159	99.34 240	94.03 216	99.36 231	95.32 276	95.18 271	98.69 225

EPMVS		97.82 209	97.65 201	98.35 240	98.88 263	95.98 284	99.49 140	94.71 348	97.57 146	99.26 139	99.48 195	92.46 262	99.71 177	97.87 152	99.08 130	99.35 149

v1144		97.98 184	97.69 195	98.85 194	98.87 266	98.66 176	99.54 119	99.35 197	96.27 252	99.23 152	99.35 237	94.67 191	99.23 262	96.73 242	95.16 272	98.68 230

DU-MVS		98.08 167	97.79 179	98.96 155	98.87 266	98.98 122	99.41 173	99.45 149	97.87 115	98.71 228	99.50 186	94.82 178	99.22 265	98.57 97	92.87 312	98.68 230

NR-MVSNet		97.97 187	97.61 204	99.02 147	98.87 266	99.26 87	99.47 149	99.42 166	97.63 142	97.08 293	99.50 186	95.07 161	99.13 275	97.86 153	93.59 304	98.68 230

WR-MVS		98.06 168	97.73 192	99.06 143	98.86 269	99.25 88	99.19 240	99.35 197	97.30 170	98.66 237	99.43 208	93.94 218	99.21 269	98.58 95	94.28 293	98.71 216

v1240		97.69 231	97.32 243	98.79 201	98.85 270	98.43 198	99.48 145	99.36 193	96.11 267	99.27 135	99.36 233	93.76 225	99.24 261	94.46 289	95.23 270	98.70 220

test_0402		96.64 266	96.24 267	97.85 280	98.85 270	96.43 275	99.44 157	99.26 239	93.52 310	96.98 296	99.52 181	88.52 313	99.20 270	92.58 317	97.50 221	97.93 314

v144192		97.92 196	97.60 205	98.87 187	98.83 272	98.65 177	99.55 116	99.34 205	96.20 258	99.32 121	99.40 217	94.36 203	99.26 258	96.37 256	95.03 276	98.70 220

v1921920		97.80 212	97.45 220	98.84 195	98.80 273	98.53 188	99.52 123	99.34 205	96.15 264	99.24 147	99.47 199	93.98 217	99.29 249	95.40 274	95.13 274	98.69 225

v52		97.79 214	97.50 213	98.66 213	98.80 273	98.62 181	99.87 4	99.44 157	95.87 273	99.01 190	99.46 203	94.44 202	99.33 238	96.65 249	93.96 301	98.05 306

gg-mvs-nofinetune		96.17 284	95.32 291	98.73 206	98.79 275	98.14 208	99.38 185	94.09 349	91.07 327	98.07 275	91.04 345	89.62 301	99.35 234	96.75 241	99.09 129	98.68 230

V4		97.80 212	97.51 211	98.67 212	98.79 275	98.63 179	99.87 4	99.44 157	95.87 273	99.01 190	99.46 203	94.52 198	99.33 238	96.64 250	93.97 300	98.05 306

test-LLR		98.06 168	97.90 162	98.55 222	98.79 275	97.10 243	98.67 314	97.75 329	97.34 166	98.61 248	98.85 287	94.45 200	99.45 213	97.25 204	99.38 110	99.10 163

test-mter		97.49 250	97.13 253	98.55 222	98.79 275	97.10 243	98.67 314	97.75 329	96.65 221	98.61 248	98.85 287	88.23 317	99.45 213	97.25 204	99.38 110	99.10 163

PS-MVSNAJss		98.92 96	98.92 78	98.90 174	98.78 279	98.53 188	99.78 22	99.54 62	98.07 93	99.00 197	99.76 87	99.01 11	99.37 227	99.13 39	97.23 236	98.81 200

MVS		97.28 257	96.55 264	99.48 89	98.78 279	98.95 130	99.27 217	99.39 179	83.53 338	98.08 272	99.54 169	96.97 105	99.87 103	94.23 300	99.16 123	99.63 100

TranMVSNet+NR-MVSNet		97.93 193	97.66 196	98.76 205	98.78 279	98.62 181	99.65 75	99.49 104	97.76 129	98.49 253	99.60 152	94.23 207	98.97 298	98.00 143	92.90 310	98.70 220

PEN-MVS		97.76 218	97.44 225	98.72 207	98.77 282	98.54 187	99.78 22	99.51 85	97.06 199	98.29 265	99.64 137	92.63 254	98.89 300	98.09 134	93.16 308	98.72 214

v7n		97.87 200	97.52 209	98.92 166	98.76 283	98.58 185	99.84 9	99.46 138	96.20 258	98.91 206	99.70 108	94.89 174	99.44 218	96.03 260	93.89 302	98.75 209

v148		97.79 214	97.55 207	98.50 224	98.74 284	97.72 231	99.54 119	99.33 213	96.26 253	98.90 208	99.51 184	94.68 190	99.14 272	97.83 155	93.15 309	98.63 263

JIA-IIPM		97.50 248	97.02 256	98.93 161	98.73 285	97.80 227	99.30 207	98.97 271	91.73 323	98.91 206	94.86 339	95.10 160	99.71 177	97.58 179	97.98 202	99.28 154

Gipuma		90.99 311	90.15 312	93.51 316	98.73 285	90.12 330	93.98 346	99.45 149	79.32 341	92.28 329	94.91 338	69.61 342	97.98 321	87.42 330	95.67 263	92.45 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		97.98 184	98.03 152	97.81 284	98.72 287	96.65 269	99.66 65	99.66 25	98.09 89	98.35 261	99.82 44	95.25 154	98.01 320	97.41 198	95.30 269	98.78 203

K. test v3		97.10 262	96.79 260	98.01 269	98.72 287	96.33 278	99.87 4	97.05 343	97.59 143	96.16 303	99.80 64	88.71 308	99.04 284	96.69 245	96.55 248	98.65 255

OurMVSNet-221017-0		97.88 199	97.77 185	98.19 260	98.71 289	96.53 271	99.88 1	99.00 268	97.79 126	98.78 222	99.94 3	91.68 278	99.35 234	97.21 206	96.99 242	98.69 225

test_djsdf		98.67 123	98.57 122	98.98 152	98.70 290	98.91 138	99.88 1	99.46 138	97.55 148	99.22 154	99.88 14	95.73 141	99.28 250	99.03 46	97.62 211	98.75 209

pmmvs6		96.53 269	96.09 270	97.82 283	98.69 291	95.47 294	99.37 187	99.47 129	93.46 312	97.41 287	99.78 77	87.06 324	99.33 238	96.92 229	92.70 314	98.65 255

v748		97.52 244	97.23 250	98.41 236	98.69 291	97.23 240	99.87 4	99.45 149	95.72 275	98.51 251	99.53 176	94.13 212	99.30 247	96.78 240	92.39 316	98.70 220

lessismore_v0				97.79 285	98.69 291	95.44 296		94.75 347		95.71 307	99.87 19	88.69 309	99.32 241	95.89 262	94.93 279	98.62 265

mvs_tets		98.40 136	98.23 139	98.91 170	98.67 294	98.51 193	99.66 65	99.53 72	98.19 76	98.65 243	99.81 53	92.75 240	99.44 218	99.31 25	97.48 225	98.77 206

SixPastTwentyTwo		97.50 248	97.33 242	98.03 266	98.65 295	96.23 281	99.77 24	98.68 309	97.14 183	97.90 279	99.93 4	90.45 291	99.18 271	97.00 221	96.43 250	98.67 241

UnsupCasMVSNet_eth		96.44 270	96.12 269	97.40 296	98.65 295	95.65 287	99.36 193	99.51 85	97.13 184	96.04 306	98.99 276	88.40 315	98.17 309	96.71 243	90.27 320	98.40 295

DTE-MVSNet		97.51 247	97.19 252	98.46 230	98.63 297	98.13 209	99.84 9	99.48 113	96.68 219	97.97 278	99.67 123	92.92 236	98.56 306	96.88 237	92.60 315	98.70 220

pmmvs4		98.13 160	97.90 162	98.81 198	98.61 298	98.87 141	98.99 282	99.21 245	96.44 239	99.06 185	99.58 157	95.90 135	99.11 278	97.18 210	96.11 256	98.46 292

jajsoiax		98.43 133	98.28 137	98.88 183	98.60 299	98.43 198	99.82 13	99.53 72	98.19 76	98.63 245	99.80 64	93.22 232	99.44 218	99.22 31	97.50 221	98.77 206

cascas		97.69 231	97.43 228	98.48 227	98.60 299	97.30 234	98.18 334	99.39 179	92.96 315	98.41 256	98.78 294	93.77 224	99.27 253	98.16 130	98.61 157	98.86 197

pmmvs5		97.52 244	97.30 245	98.16 262	98.57 301	96.73 265	99.27 217	98.90 282	96.14 265	98.37 259	99.53 176	91.54 283	99.14 272	97.51 188	95.87 259	98.63 263

GG-mvs-BLEND				98.45 231	98.55 302	98.16 207	99.43 162	93.68 350		97.23 290	98.46 305	89.30 303	99.22 265	95.43 273	98.22 179	97.98 311

gm-plane-assit					98.54 303	92.96 322			94.65 289		99.15 262		99.64 194	97.56 183

anonymousdsp		98.44 132	98.28 137	98.94 158	98.50 304	98.96 129	99.77 24	99.50 99	97.07 197	98.87 211	99.77 84	94.76 186	99.28 250	98.66 85	97.60 212	98.57 284

N_pmnet		94.95 298	95.83 276	92.31 322	98.47 305	79.33 345	99.12 250	92.81 354	93.87 306	97.68 285	99.13 264	93.87 221	99.01 288	91.38 319	96.19 255	98.59 280

MS-PatchMatch		97.24 259	97.32 243	96.99 300	98.45 306	93.51 320	98.82 305	99.32 219	97.41 162	98.13 270	99.30 248	88.99 305	99.56 205	95.68 268	99.80 70	97.90 316

test0.0.03 1		97.71 230	97.42 229	98.56 220	98.41 307	97.82 222	98.78 307	98.63 311	97.34 166	98.05 276	98.98 279	94.45 200	98.98 291	95.04 280	97.15 240	98.89 196

testpf		95.66 290	96.02 273	94.58 314	98.35 308	92.32 325	97.25 342	97.91 328	92.83 316	97.03 295	98.99 276	88.69 309	98.61 305	95.72 266	97.40 230	92.80 341

EPNet_dtu		98.03 177	97.96 158	98.23 255	98.27 309	95.54 292	99.23 230	98.75 295	99.02 10	97.82 282	99.71 105	96.11 129	99.48 210	93.04 312	99.65 99	99.69 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDA-MVSNet-bldmvs		94.96 297	93.98 302	97.92 275	98.24 310	97.27 236	99.15 246	99.33 213	93.80 307	80.09 344	99.03 274	88.31 316	97.86 324	93.49 306	94.36 292	98.62 265

MDA-MVSNet_test_wron		95.45 292	94.60 297	98.01 269	98.16 311	97.21 241	99.11 256	99.24 242	93.49 311	80.73 343	98.98 279	93.02 233	98.18 308	94.22 301	94.45 291	98.64 257

new_pmnet		96.38 275	96.03 271	97.41 295	98.13 312	95.16 302	99.05 267	99.20 246	93.94 305	97.39 288	98.79 292	91.61 282	99.04 284	90.43 322	95.77 261	98.05 306

YYNet1		95.36 294	94.51 299	97.92 275	97.89 313	97.10 243	99.10 258	99.23 243	93.26 314	80.77 342	99.04 273	92.81 239	98.02 319	94.30 297	94.18 296	98.64 257

DSMNet-mixed		97.25 258	97.35 237	96.95 302	97.84 314	93.61 319	99.57 104	96.63 344	96.13 266	98.87 211	98.61 301	94.59 194	97.70 328	95.08 279	98.86 149	99.55 112

EG-PatchMatch MVS		95.97 287	95.69 281	96.81 305	97.78 315	92.79 323	99.16 243	98.93 275	96.16 262	94.08 315	99.22 258	82.72 337	99.47 211	95.67 269	97.50 221	98.17 303

DI_MVS_plusplus_test		97.45 251	96.79 260	99.44 98	97.76 316	99.04 108	99.21 237	98.61 313	97.74 132	94.01 318	98.83 289	87.38 323	99.83 124	98.63 88	98.90 146	99.44 140

test_normal		97.44 252	96.77 262	99.44 98	97.75 317	99.00 120	99.10 258	98.64 310	97.71 135	93.93 321	98.82 290	87.39 322	99.83 124	98.61 92	98.97 138	99.49 128

MVP-Stereo		97.81 210	97.75 191	97.99 271	97.53 318	96.60 270	98.96 291	98.85 286	97.22 178	97.23 290	99.36 233	95.28 150	99.46 212	95.51 271	99.78 74	97.92 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		96.12 285	95.96 274	96.63 307	97.44 319	95.45 295	99.51 127	99.38 185	96.55 229	96.16 303	99.25 255	93.76 225	96.17 336	87.35 332	94.22 295	98.27 300

UnsupCasMVSNet_bld		93.53 306	92.51 308	96.58 309	97.38 320	93.82 314	98.24 331	99.48 113	91.10 326	93.10 326	96.66 334	74.89 340	98.37 307	94.03 303	87.71 330	97.56 330

MIMVSNet1		95.51 291	95.04 294	96.92 303	97.38 320	95.60 288	99.52 123	99.50 99	93.65 308	96.97 297	99.17 261	85.28 330	96.56 335	88.36 328	95.55 266	98.60 279

OpenMVS_ROB	92.34 20	94.38 302	93.70 303	96.41 310	97.38 320	93.17 321	99.06 265	98.75 295	86.58 335	94.84 311	98.26 310	81.53 339	99.32 241	89.01 326	97.87 205	96.76 332

Anonymous20231206		96.22 282	96.03 271	96.79 306	97.31 323	94.14 312	99.63 79	99.08 258	96.17 261	97.04 294	99.06 271	93.94 218	97.76 327	86.96 333	95.06 275	98.47 290

CMPMVS	69.68 23	94.13 303	94.90 295	91.84 323	97.24 324	80.01 344	98.52 322	99.48 113	89.01 332	91.99 330	99.67 123	85.67 328	99.13 275	95.44 272	97.03 241	96.39 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EPNet		98.86 101	98.71 104	99.30 114	97.20 325	98.18 206	99.62 82	98.91 280	99.28 2	98.63 245	99.81 53	95.96 130	99.99 1	99.24 30	99.72 85	99.73 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testus		94.61 299	95.30 292	92.54 321	96.44 326	84.18 337	98.36 326	99.03 266	94.18 302	96.49 299	98.57 303	88.74 307	95.09 340	87.41 331	98.45 168	98.36 299

Test4		95.05 296	93.67 304	99.22 130	96.07 327	98.94 133	99.20 239	99.27 238	97.71 135	89.96 336	97.59 327	66.18 344	99.25 259	98.06 141	98.96 139	99.47 134

Patchmatch-RL test		95.84 288	95.81 277	95.95 311	95.61 328	90.57 329	98.24 331	98.39 317	95.10 283	95.20 308	98.67 297	94.78 181	97.77 326	96.28 257	90.02 321	99.51 124

PM-MVS		92.96 307	92.23 309	95.14 313	95.61 328	89.98 331	99.37 187	98.21 322	94.80 286	95.04 310	97.69 319	65.06 345	97.90 323	94.30 297	89.98 322	97.54 331

pmmvs-eth3d		95.34 295	94.73 296	97.15 297	95.53 330	95.94 285	99.35 197	99.10 256	95.13 281	93.55 324	97.54 328	88.15 319	97.91 322	94.58 286	89.69 323	97.61 328

test2356		94.07 305	94.46 300	92.89 319	95.18 331	86.13 335	97.60 340	99.06 263	93.61 309	96.15 305	98.28 309	85.60 329	93.95 342	86.68 335	98.00 201	98.59 280

new-patchmatchnet		94.48 300	94.08 301	95.67 312	95.08 332	92.41 324	99.18 241	99.28 233	94.55 294	93.49 325	97.37 331	87.86 320	97.01 332	91.57 318	88.36 326	97.61 328

pmmvs3		94.09 304	93.25 306	96.60 308	94.76 333	94.49 308	98.92 298	98.18 324	89.66 329	96.48 300	98.06 311	86.28 325	97.33 330	89.68 324	87.20 331	97.97 312

Anonymous20231211		90.69 312	89.39 313	94.58 314	94.25 334	88.18 332	99.29 211	99.07 261	82.45 340	92.95 327	97.65 321	63.96 347	97.79 325	89.27 325	85.63 338	97.77 325

testing_2		94.44 301	92.93 307	98.98 152	94.16 335	99.00 120	99.42 169	99.28 233	96.60 226	84.86 338	96.84 333	70.91 341	99.27 253	98.23 126	96.08 257	98.68 230

1111		92.30 309	92.21 310	92.55 320	93.30 336	86.27 333	99.15 246	98.74 298	91.94 320	90.85 333	97.82 314	84.18 333	95.21 338	79.65 341	94.27 294	96.19 335

.test1245		83.42 317	86.17 315	75.15 339	93.30 336	86.27 333	99.15 246	98.74 298	91.94 320	90.85 333	97.82 314	84.18 333	95.21 338	79.65 341	39.90 351	43.98 352

test1235678		92.91 308	93.30 305	91.71 325	93.14 338	83.01 339	98.75 310	98.58 314	92.80 317	92.45 328	97.91 313	88.51 314	93.54 343	82.26 339	95.35 268	98.59 280

ambc				93.06 318	92.68 339	82.36 341	98.47 324	98.73 306		95.09 309	97.41 329	55.55 349	99.10 280	96.42 254	91.32 318	97.71 327

test12356		91.74 310	92.19 311	90.37 328	91.22 340	82.41 340	98.61 318	98.28 319	90.66 328	91.82 331	97.92 312	84.90 331	92.61 344	81.64 340	94.66 286	96.09 336

EMVS		80.02 321	79.22 322	82.43 337	91.19 341	76.40 348	97.55 341	92.49 356	66.36 349	83.01 341	91.27 343	64.63 346	85.79 352	65.82 350	60.65 346	85.08 349

E-PMN		80.61 320	79.88 321	82.81 335	90.75 342	76.38 349	97.69 338	95.76 346	66.44 348	83.52 339	92.25 342	62.54 348	87.16 351	68.53 349	61.40 345	84.89 350

PMMVS2		86.87 314	85.37 317	91.35 327	90.21 343	83.80 338	98.89 301	97.45 341	83.13 339	91.67 332	95.03 337	48.49 351	94.70 341	85.86 336	77.62 342	95.54 337

TDRefinement		95.42 293	94.57 298	97.97 272	89.83 344	96.11 283	99.48 145	98.75 295	96.74 215	96.68 298	99.88 14	88.65 311	99.71 177	98.37 117	82.74 340	98.09 304

no-one		83.04 318	80.12 320	91.79 324	89.44 345	85.65 336	99.32 202	98.32 318	89.06 331	79.79 346	89.16 347	44.86 353	96.67 334	84.33 338	46.78 349	93.05 340

LCM-MVSNet		86.80 315	85.22 318	91.53 326	87.81 346	80.96 343	98.23 333	98.99 269	71.05 344	90.13 335	96.51 335	48.45 352	96.88 333	90.51 320	85.30 339	96.76 332

testmv		87.91 313	87.80 314	88.24 329	87.68 347	77.50 347	99.07 261	97.66 338	89.27 330	86.47 337	96.22 336	68.35 343	92.49 346	76.63 345	88.82 324	94.72 339

FPMVS		84.93 316	85.65 316	82.75 336	86.77 348	63.39 354	98.35 328	98.92 277	74.11 343	83.39 340	98.98 279	50.85 350	92.40 347	84.54 337	94.97 277	92.46 342

PNet_i23d		79.43 322	77.68 323	84.67 332	86.18 349	71.69 352	96.50 344	93.68 350	75.17 342	71.33 347	91.18 344	32.18 356	90.62 348	78.57 344	74.34 343	91.71 345

wuyk23d		40.18 328	41.29 331	36.84 340	86.18 349	49.12 356	79.73 349	22.81 358	27.64 351	25.46 354	28.45 355	21.98 358	48.89 354	55.80 351	23.56 354	12.51 354

MVE	76.82 21	76.91 324	74.31 326	84.70 331	85.38 351	76.05 350	96.88 343	93.17 352	67.39 347	71.28 348	89.01 348	21.66 361	87.69 350	71.74 348	72.29 344	90.35 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 326	71.19 327	84.14 334	76.16 352	74.29 351	96.00 345	92.57 355	69.57 345	63.84 350	87.49 349	21.98 358	88.86 349	75.56 347	57.50 347	89.26 348

ANet_high		77.30 323	74.86 325	84.62 333	75.88 353	77.61 346	97.63 339	93.15 353	88.81 333	64.27 349	89.29 346	36.51 354	83.93 353	75.89 346	52.31 348	92.33 344

PMVS	70.75 22	75.98 325	74.97 324	79.01 338	70.98 354	55.18 355	93.37 347	98.21 322	65.08 350	61.78 351	93.83 340	21.74 360	92.53 345	78.59 343	91.12 319	89.34 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 319	81.52 319	86.66 330	66.61 355	68.44 353	92.79 348	97.92 326	68.96 346	80.04 345	99.85 26	85.77 327	96.15 337	97.86 153	43.89 350	95.39 338

test123		39.01 330	42.50 330	28.53 342	39.17 356	20.91 357	98.75 310	19.17 359	19.83 353	38.57 352	66.67 351	33.16 355	15.42 355	37.50 353	29.66 353	49.26 351

testmvs		39.17 329	43.78 328	25.37 343	36.04 357	16.84 358	98.36 326	26.56 357	20.06 352	38.51 353	67.32 350	29.64 357	15.30 356	37.59 352	39.90 351	43.98 352

cdsmvs_eth3d_5k		24.64 331	32.85 332	0.00 344	0.00 358	0.00 359	0.00 350	99.51 85	0.00 354	0.00 355	99.56 163	96.58 117	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		8.27 333	11.03 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	99.01 11	0.00 357	0.00 354	0.00 355	0.00 355

sosnet-low-res		0.02 334	0.03 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.02 334	0.03 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.02 334	0.03 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.02 334	0.03 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		8.30 332	11.06 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	99.58 157	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.02 334	0.03 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.27 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																99.52 119

test_part3							99.37 187		97.97 108		99.78 77		99.95 33	97.15 212

test_part1								99.48 113				98.96 20			99.84 57	99.83 23

sam_mvs1												94.86 176				99.52 119

sam_mvs												94.72 189

MTGPA								99.47 129

test_post1							99.23 230				65.14 353	94.18 211	99.71 177	97.58 179

test_post											65.99 352	94.65 193	99.73 167

patchmatchnet-post											98.70 296	94.79 180	99.74 159

MTMP								98.88 284

test9_res														97.49 190	99.72 85	99.75 55

agg_prior2														97.21 206	99.73 84	99.75 55

test_prior4						99.56 51	98.99 282

test_prior2							98.96 291		98.34 66	99.01 190	99.52 181	98.68 51		97.96 145	99.74 81

旧先验2							98.96 291		96.70 218	99.47 88			99.94 42	98.19 127

新几何2							99.01 280

无先验							98.99 282	99.51 85	96.89 209				99.93 57	97.53 186		99.72 71

原ACMM2							98.95 295

testdata2													99.95 33	96.67 246

segment_acmp												98.96 20

testdata1							98.85 304		98.32 69

plane_prior5								99.47 129					99.69 186	97.78 160	97.63 209	98.67 241

plane_prior4											99.61 149

plane_prior3						97.00 253			98.69 46	99.11 172

plane_prior2							99.39 180		98.97 22

plane_prior						96.97 256	99.21 237		98.45 59						97.60 212

n2								0.00 360

nn								0.00 360

door-mid								98.05 325

test11								99.35 197

door								97.92 326

HQP5-MVS						96.83 261

BP-MVS														97.19 208

HQP4-MVS										98.66 237			99.64 194			98.64 257

HQP3-MVS								99.39 179							97.58 214

HQP2-MVS												92.47 259

MDTV_nov1_ep13_2view						95.18 301	99.35 197		96.84 212	99.58 64		95.19 157		97.82 156		99.46 137

ACMMP++_ref															97.19 238

ACMMP++															97.43 229

Test By Simon												98.75 46

High-resolution multi-view benchmark