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.

CHOSEN 280x420		99.12 67	99.13 52	99.08 132	99.66 88	97.89 205	98.43 310	99.71 13	98.88 30	99.62 53	99.76 85	96.63 114	99.70 169	99.46 14	99.99 1	99.66 85

CANet		99.25 52	99.14 51	99.59 68	99.41 137	99.16 92	99.35 183	99.57 43	98.82 35	99.51 72	99.61 146	96.46 117	99.95 33	99.59 2	99.98 2	99.65 88

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 180	99.39 168	99.94 1	98.73 44	99.11 159	99.89 10	95.50 143	99.94 40	99.50 8	99.97 3	99.89 2

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 88	99.62 74	99.55 54	98.94 26	99.63 50	99.95 2	95.82 136	99.94 40	99.37 17	99.97 3	99.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 191	99.71 41	99.66 25	98.11 86	99.41 90	99.80 64	98.37 67	99.96 19	98.99 50	99.96 5	99.72 69

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 154	98.42 189	99.08 245	99.30 220	99.16 5	99.43 85	99.75 90	95.27 149	99.97 11	98.56 100	99.95 6	99.36 144

MVS_0304		99.06 79	98.86 87	99.66 53	99.51 116	99.36 74	99.22 219	99.51 84	98.95 24	99.58 60	99.65 127	93.74 225	99.98 5	99.66 1	99.95 6	99.64 94

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 62	99.78 34	99.14 96	99.60 81	99.45 147	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 34	99.15 95	99.61 80	99.45 147	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

UGNet		98.87 97	98.69 105	99.40 102	99.22 178	98.72 160	99.44 145	99.68 19	99.24 3	99.18 152	99.42 200	92.74 239	99.96 19	99.34 22	99.94 10	99.53 116
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
SD-MVS		99.41 32	99.52 6	99.05 136	99.74 57	99.68 30	99.46 140	99.52 75	99.11 7	99.88 3	99.91 5	99.43 1	97.70 313	98.72 79	99.93 11	99.77 49

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 41	99.29 81	99.58 87	99.44 155	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 41	99.41 70	99.58 87	99.49 103	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 38	99.89 1	99.75 34	99.56 47	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

HPM-MVS_fast		99.51 12	99.40 14	99.85 17	99.91 1	99.79 16	99.76 27	99.56 47	97.72 129	99.76 26	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 198	99.66 34	99.84 9	99.74 10	99.09 8	98.92 191	99.90 7	95.94 131	99.98 5	98.95 53	99.92 12	99.79 43

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 193	99.52 75	97.18 173	99.60 56	99.79 72	98.79 35	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 137	99.48 112	98.05 98	99.76 26	99.86 22	98.82 32	99.93 55	98.82 71	99.91 17	99.84 12

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 25	99.81 15	99.54 61	97.59 137	99.68 34	99.63 138	98.91 26	99.94 40	98.58 95	99.91 17	99.84 12

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 55	99.62 74	99.59 37	92.65 303	99.71 29	99.78 77	98.06 78	99.90 84	98.84 66	99.91 17	99.74 58

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 21	99.69 44	99.52 75	98.07 93	99.53 68	99.63 138	98.93 25	99.97 11	98.74 75	99.91 17	99.83 23

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 111	99.52 58	99.58 87	99.80 8	97.12 179	99.62 53	99.73 98	98.58 55	99.90 84	98.61 92	99.91 17	99.68 81

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 285	99.60 103	91.75 312	98.61 303	99.44 155	99.35 1	99.83 11	99.85 26	98.70 48	99.81 130	99.02 48	99.91 17	99.81 34

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 179	99.47 126	98.79 40	99.68 34	99.81 53	98.43 61	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 58	99.47 126	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

UA-Net		99.42 29	99.29 36	99.80 29	99.62 97	99.55 51	99.50 121	99.70 15	98.79 40	99.77 23	99.96 1	97.45 91	99.96 19	98.92 55	99.90 24	99.89 2

jason		99.13 62	99.03 63	99.45 94	99.46 128	98.87 132	99.12 235	99.26 237	98.03 101	99.79 18	99.65 127	97.02 102	99.85 108	99.02 48	99.90 24	99.65 88
jason: jason.
SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 11	99.59 83	99.51 84	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
DP-MVS		99.16 60	98.95 75	99.78 33	99.77 38	99.53 55	99.41 161	99.50 98	97.03 188	99.04 173	99.88 14	97.39 92	99.92 63	98.66 85	99.90 24	99.87 4

MSDG		98.98 90	98.80 94	99.53 79	99.76 41	99.19 89	98.75 295	99.55 54	97.25 167	99.47 78	99.77 82	97.82 83	99.87 101	96.93 224	99.90 24	99.54 112

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 126	99.88 11	98.53 177	99.34 186	99.59 37	97.55 142	98.70 220	99.89 10	95.83 135	99.90 84	98.10 133	99.90 24	99.08 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 16	99.69 44	99.48 112	98.12 84	99.50 73	99.75 90	98.78 36	99.97 11	98.57 97	99.89 32	99.83 23

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 38	99.51 60	98.94 282	99.85 6	98.82 35	99.65 48	99.74 95	98.51 56	99.80 133	98.83 68	99.89 32	99.64 94

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 34	99.63 71	99.39 177	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 30

abl_6		99.44 25	99.31 31	99.83 22	99.85 23	99.75 21	99.66 58	99.59 37	98.13 82	99.82 14	99.81 53	98.60 54	99.96 19	98.46 111	99.88 34	99.79 43

QAPM		98.67 122	98.30 135	99.80 29	99.20 181	99.67 32	99.77 24	99.72 11	94.74 272	98.73 212	99.90 7	95.78 137	99.98 5	96.96 221	99.88 34	99.76 52

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 65	99.47 64	98.95 280	99.85 6	98.82 35	99.54 67	99.73 98	98.51 56	99.74 145	98.91 56	99.88 34	99.77 49

HFP-MVS		99.49 13	99.37 17	99.86 12	99.87 15	99.80 12	99.66 58	99.67 22	98.15 80	99.68 34	99.69 111	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 12	99.65 68	99.66 25	98.13 82	99.66 45	99.68 116	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

#test#		99.43 27	99.29 36	99.86 12	99.87 15	99.80 12	99.55 105	99.67 22	97.83 117	99.68 34	99.69 111	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

Regformer-1		99.53 9	99.47 8	99.72 47	99.71 68	99.44 67	99.49 128	99.46 136	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 39

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 68	99.52 58	99.49 128	99.49 103	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 39

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 16	99.66 58	99.67 22	98.15 80	99.67 40	99.69 111	98.95 23	99.96 19	98.69 82	99.87 38	99.84 12

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 10	99.66 58	99.46 136	98.09 89	99.48 77	99.74 95	98.29 70	99.96 19	97.93 148	99.87 38	99.82 30

PGM-MVS		99.45 22	99.31 31	99.86 12	99.87 15	99.78 20	99.58 87	99.65 30	97.84 116	99.71 29	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 93	99.59 46	99.36 179	99.46 136	99.07 9	99.79 18	99.82 44	98.85 30	99.92 63	98.68 84	99.87 38	99.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	97.07 15	97.74 212	97.34 227	98.94 148	99.70 73	97.53 217	99.25 212	99.51 84	91.90 307	99.30 113	99.63 138	98.78 36	99.64 180	88.09 314	99.87 38	99.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	99.74 95	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 254	95.45 275	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	64.01 339	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 47	99.79 16	99.50 121	99.50 98	97.16 175	99.77 23	99.82 44	98.78 36	99.94 40	97.56 181	99.86 48	99.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 190	99.68 30	99.81 15	99.51 84	99.20 4	98.72 213	99.89 10	95.68 140	99.97 11	98.86 64	99.86 48	99.81 34

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 116	98.94 124	99.88 1	99.46 136	97.55 142	99.80 16	99.65 127	97.39 92	99.28 235	99.03 46	99.85 52	99.65 88

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 116	98.94 124	99.05 252	99.16 248	97.86 112	99.80 16	99.56 160	97.39 92	99.86 104	98.94 54	99.85 52	99.58 108

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 41	98.79 155	98.78 292	99.91 3	96.74 201	99.67 40	99.49 179	97.53 89	99.88 99	98.98 51	99.85 52	99.60 102

MVS-HIRNet		95.75 275	95.16 279	97.51 279	99.30 163	93.69 303	98.88 287	95.78 331	85.09 322	98.78 208	92.65 326	91.29 270	99.37 212	94.85 271	99.85 52	99.46 133

PCF-MVS	97.08 14	97.66 224	97.06 241	99.47 91	99.61 101	99.09 100	98.04 321	99.25 239	91.24 310	98.51 237	99.70 106	94.55 194	99.91 72	92.76 300	99.85 52	99.42 139
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 97	99.60 103	99.16 92	99.41 161	99.71 13	98.98 19	99.45 81	99.78 77	99.19 4	99.54 194	99.28 27	99.84 57	99.63 98

DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 65	99.40 72	99.05 252	99.66 25	99.14 6	99.57 63	99.80 64	98.46 59	99.94 40	99.57 4	99.84 57	99.60 102
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
CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 32	99.46 65	99.59 83	99.49 103	97.03 188	99.63 50	99.69 111	97.27 97	99.96 19	97.82 156	99.84 57	99.81 34

LS3D		99.27 49	99.12 53	99.74 43	99.18 185	99.75 21	99.56 100	99.57 43	98.45 59	99.49 76	99.85 26	97.77 85	99.94 40	98.33 121	99.84 57	99.52 117

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 185	99.56 100	99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

TestCases				99.31 110	99.86 20	98.48 185		99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 47	99.71 26	99.15 231	99.41 167	96.60 212	99.60 56	99.55 163	98.83 31	99.90 84	97.48 189	99.83 61	99.78 47

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 32	99.62 74	99.69 18	98.12 84	99.63 50	99.84 35	98.73 46	99.96 19	98.55 103	99.83 61	99.81 34
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
PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 102	99.47 137	99.93 2	97.66 135	99.71 29	99.86 22	97.73 86	99.96 19	99.47 13	99.82 65	99.79 43

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 41	99.83 7	99.63 71	99.54 61	98.36 65	99.79 18	99.82 44	98.86 29	99.95 33	98.62 90	99.81 66	99.78 47

OMC-MVS		99.08 77	99.04 61	99.20 125	99.67 78	98.22 194	99.28 200	99.52 75	98.07 93	99.66 45	99.81 53	97.79 84	99.78 138	97.79 158	99.81 66	99.60 102

MS-PatchMatch		97.24 245	97.32 230	96.99 286	98.45 291	93.51 305	98.82 290	99.32 217	97.41 155	98.13 255	99.30 237	88.99 290	99.56 191	95.68 257	99.80 68	97.90 299

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 47	99.84 6	99.43 150	99.51 84	98.68 47	99.27 125	99.53 167	98.64 52	99.96 19	98.44 113	99.80 68	99.79 43

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 97	99.71 26	99.26 211	99.52 75	98.82 35	99.39 95	99.71 103	98.96 20	99.85 108	98.59 94	99.80 68	99.77 49

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 108	98.63 168	99.07 246	99.34 203	98.99 18	99.61 55	99.82 44	97.98 80	99.87 101	97.00 217	99.80 68	99.85 8

MVP-Stereo		97.81 200	97.75 182	97.99 257	97.53 303	96.60 255	98.96 276	98.85 284	97.22 171	97.23 275	99.36 222	95.28 148	99.46 198	95.51 260	99.78 72	97.92 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
API-MVS		99.04 82	99.03 63	99.06 134	99.40 142	99.31 80	99.55 105	99.56 47	98.54 53	99.33 110	99.39 211	98.76 41	99.78 138	96.98 219	99.78 72	98.07 289

HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 12	99.67 55	99.37 190	98.70 45	99.77 23	99.49 179	98.21 73	99.95 33	98.46 111	99.77 74	99.81 34

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 111	99.54 52	99.18 226	99.70 15	98.18 79	99.35 106	99.63 138	96.32 121	99.90 84	97.48 189	99.77 74	99.55 110

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 213	99.53 55	99.82 13	99.72 11	94.56 278	98.08 257	99.88 14	94.73 186	99.98 5	97.47 191	99.76 76	99.06 168

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 33	99.74 24	99.29 197	99.40 174	98.79 40	99.52 70	99.62 143	98.91 26	99.90 84	98.64 87	99.75 77	99.82 30

CNLPA		99.14 61	98.99 68	99.59 68	99.58 106	99.41 70	99.16 228	99.44 155	98.45 59	99.19 150	99.49 179	98.08 77	99.89 92	97.73 166	99.75 77	99.48 126

test_prior3		99.21 54	99.05 58	99.68 50	99.67 78	99.48 62	98.96 276	99.56 47	98.34 66	99.01 176	99.52 171	98.68 49	99.83 120	97.96 145	99.74 79	99.74 58

test_prior2							98.96 276		98.34 66	99.01 176	99.52 171	98.68 49		97.96 145	99.74 79

test12				99.75 38	99.64 91	99.61 42		99.29 224		99.21 144		98.38 65	99.89 92		99.74 79	99.74 58

agg_prior2														97.21 204	99.73 82	99.75 53

test9_res														97.49 188	99.72 83	99.75 53

train_agg		99.02 85	98.77 97	99.77 35	99.67 78	99.65 37	99.05 252	99.41 167	96.28 235	98.95 187	99.49 179	98.76 41	99.91 72	97.63 175	99.72 83	99.75 53

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 78	99.60 44	99.04 257	99.41 167	95.93 257	98.87 197	99.48 185	98.61 53	99.91 72	97.63 175	99.72 83	99.75 53

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 78	99.62 40	98.99 267	99.40 174	96.26 238	98.87 197	99.49 179	98.77 39	99.91 72	97.69 172	99.72 83	99.75 53

EPNet		98.86 100	98.71 103	99.30 113	97.20 310	98.18 195	99.62 74	98.91 278	99.28 2	98.63 231	99.81 53	95.96 128	99.99 1	99.24 30	99.72 83	99.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 57	99.70 28	99.27 203	99.57 43	96.40 230	99.42 88	99.68 116	98.75 44	99.80 133	97.98 144	99.72 83	99.44 136

PVSNet	96.02 17	98.85 106	98.84 90	98.89 166	99.73 62	97.28 220	98.32 314	99.60 34	97.86 112	99.50 73	99.57 158	96.75 111	99.86 104	98.56 100	99.70 89	99.54 112

原ACMM1				99.65 57	99.73 62	99.33 76		99.47 126	97.46 148	99.12 157	99.66 126	98.67 51	99.91 72	97.70 171	99.69 90	99.71 76

test222					99.75 47	99.49 61	98.91 285	99.49 103	96.42 227	99.34 109	99.65 127	98.28 71			99.69 90	99.72 69

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 34	99.27 84	99.42 157	99.54 61	97.29 164	99.41 90	99.59 151	98.42 64	99.93 55	98.19 127	99.69 90	99.73 63

旧先验1					99.74 57	99.59 46		99.54 61			99.69 111	98.47 58			99.68 93	99.73 63

1121		99.09 75	98.87 84	99.75 38	99.74 57	99.60 44	99.27 203	99.48 112	96.82 199	99.25 130	99.65 127	98.38 65	99.93 55	97.53 184	99.67 94	99.73 63

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 108	98.94 124	98.97 274	99.46 136	98.92 28	99.71 29	99.24 245	99.01 11	99.98 5	99.35 18	99.66 95	98.97 177

新几何1				99.75 38	99.75 47	99.59 46		99.54 61	96.76 200	99.29 117	99.64 134	98.43 61	99.94 40	96.92 225	99.66 95	99.72 69

EPNet_dtu		98.03 168	97.96 156	98.23 241	98.27 294	95.54 277	99.23 215	98.75 293	99.02 10	97.82 267	99.71 103	96.11 127	99.48 196	93.04 298	99.65 97	99.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testdata				99.54 75	99.75 47	98.95 121		99.51 84	97.07 184	99.43 85	99.70 106	98.87 28	99.94 40	97.76 162	99.64 98	99.72 69

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 68	99.28 82	99.06 250	99.77 9	97.74 127	99.50 73	99.53 167	95.41 145	99.84 113	97.17 209	99.64 98	99.44 136

NCCC		99.34 40	99.19 47	99.79 32	99.61 101	99.65 37	99.30 193	99.48 112	98.86 31	99.21 144	99.63 138	98.72 47	99.90 84	98.25 125	99.63 100	99.80 39

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 88	99.01 109	99.24 214	99.52 75	96.85 197	99.27 125	99.48 185	98.25 72	99.91 72	97.76 162	99.62 101	99.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 137	98.83 139	99.30 193	98.77 292	97.70 132	98.94 189	99.65 127	92.91 235	99.74 145	96.52 241	99.55 102	99.64 94

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 147	99.66 34	99.45 141	99.54 61	96.61 210	99.01 176	99.40 207	97.09 100	99.86 104	97.68 174	99.53 103	99.10 158
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
Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 201	99.41 137	96.99 239	99.52 112	99.49 103	98.11 86	99.24 135	99.34 229	96.96 104	99.79 136	97.95 147	99.45 104	99.02 172

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 57	99.44 67	99.39 168	99.38 183	97.70 132	99.28 121	99.28 240	98.34 68	99.85 108	96.96 221	99.45 104	99.69 77

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 78	98.61 173	99.07 246	99.33 211	99.00 17	99.82 14	99.81 53	99.06 8	99.84 113	99.09 42	99.42 106	99.65 88

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 34	99.10 99	99.68 53	99.66 25	98.49 56	99.86 7	99.87 19	94.77 183	99.84 113	99.19 33	99.41 107	99.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		98.78 113	98.89 82	98.47 215	99.33 154	96.91 245	99.57 93	99.30 220	98.47 57	99.41 90	98.99 265	96.78 108	99.74 145	98.73 77	99.38 108	98.74 202

test-LLR		98.06 159	97.90 160	98.55 208	98.79 260	97.10 228	98.67 299	97.75 323	97.34 159	98.61 234	98.85 276	94.45 198	99.45 199	97.25 202	99.38 108	99.10 158

TESTMET0.1,1		97.55 227	97.27 235	98.40 223	98.93 239	96.53 256	98.67 299	97.61 327	96.96 191	98.64 230	99.28 240	88.63 297	99.45 199	97.30 201	99.38 108	99.21 153

test-mter		97.49 236	97.13 239	98.55 208	98.79 260	97.10 228	98.67 299	97.75 323	96.65 207	98.61 234	98.85 276	88.23 302	99.45 199	97.25 202	99.38 108	99.10 158

PAPR		98.63 126	98.34 131	99.51 85	99.40 142	99.03 106	98.80 291	99.36 191	96.33 232	99.00 183	99.12 256	98.46 59	99.84 113	95.23 266	99.37 112	99.66 85

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 93	98.97 116	99.12 235	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 93	98.97 116	99.12 235	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 93	98.97 116	99.12 235	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

1314		98.68 121	98.54 123	99.11 131	98.89 247	98.65 166	99.27 203	99.49 103	96.89 195	97.99 262	99.56 160	97.72 87	99.83 120	97.74 165	99.27 116	98.84 189

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 113	98.91 129	99.02 261	99.45 147	98.80 39	99.71 29	99.26 243	98.94 24	99.98 5	99.34 22	99.23 117	98.98 176

PatchmatchNet		98.31 139	98.36 129	98.19 246	99.16 192	95.32 282	99.27 203	98.92 275	97.37 158	99.37 99	99.58 154	94.90 171	99.70 169	97.43 195	99.21 118	99.54 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		98.16 149	98.14 141	98.22 243	99.30 163	95.55 275	99.07 246	98.97 269	97.57 140	99.43 85	99.60 149	92.72 240	99.60 188	97.38 197	99.20 119	99.50 123

sss		99.17 58	99.05 58	99.53 79	99.62 97	98.97 116	99.36 179	99.62 31	97.83 117	99.67 40	99.65 127	97.37 95	99.95 33	99.19 33	99.19 120	99.68 81

MVS		97.28 243	96.55 250	99.48 88	98.78 264	98.95 121	99.27 203	99.39 177	83.53 323	98.08 257	99.54 166	96.97 103	99.87 101	94.23 289	99.16 121	99.63 98

BH-untuned		98.42 133	98.36 129	98.59 202	99.49 123	96.70 251	99.27 203	99.13 252	97.24 169	98.80 206	99.38 212	95.75 138	99.74 145	97.07 214	99.16 121	99.33 147

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 62	99.32 77	99.75 34	99.20 244	98.02 102	99.56 64	99.86 22	96.54 116	99.67 174	98.09 134	99.13 123	99.73 63

Patchmatch-test		97.93 184	97.65 192	98.77 193	99.18 185	97.07 232	99.03 258	99.14 251	96.16 247	98.74 211	99.57 158	94.56 193	99.72 157	93.36 296	99.11 124	99.52 117

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 68	98.88 131	99.80 19	99.44 155	97.91 110	99.36 103	99.78 77	95.49 144	99.43 207	97.91 149	99.11 124	99.62 100

RPSCF		98.22 143	98.62 115	96.99 286	99.82 29	91.58 313	99.72 39	99.44 155	96.61 210	99.66 45	99.89 10	95.92 132	99.82 126	97.46 192	99.10 126	99.57 109

gg-mvs-nofinetune		96.17 270	95.32 277	98.73 195	98.79 260	98.14 197	99.38 173	94.09 335	91.07 312	98.07 260	91.04 330	89.62 286	99.35 219	96.75 231	99.09 127	98.68 220

EPMVS		97.82 199	97.65 192	98.35 226	98.88 248	95.98 269	99.49 128	94.71 334	97.57 140	99.26 129	99.48 185	92.46 253	99.71 163	97.87 152	99.08 128	99.35 145

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 123	99.14 96	99.67 55	99.34 203	97.31 162	99.58 60	99.76 85	97.65 88	99.82 126	98.87 61	99.07 129	99.46 133

ADS-MVSNet2		98.02 170	98.07 149	97.87 264	99.33 154	95.19 285	99.23 215	99.08 256	96.24 240	99.10 162	99.67 120	94.11 211	98.93 284	96.81 228	99.05 130	99.48 126

ADS-MVSNet		98.20 146	98.08 147	98.56 206	99.33 154	96.48 258	99.23 215	99.15 249	96.24 240	99.10 162	99.67 120	94.11 211	99.71 163	96.81 228	99.05 130	99.48 126

mvs-test1		98.86 100	98.84 90	98.89 166	99.33 154	97.77 213	99.44 145	99.30 220	98.47 57	99.10 162	99.43 198	96.78 108	99.95 33	98.73 77	99.02 132	98.96 179

HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 73	99.02 261	99.91 3	97.67 134	99.59 59	99.75 90	95.90 133	99.73 153	99.53 6	99.02 132	99.86 5

LCM-MVSNet-Re		97.83 196	98.15 140	96.87 290	99.30 163	92.25 311	99.59 83	98.26 314	97.43 152	96.20 287	99.13 253	96.27 123	98.73 289	98.17 129	98.99 134	99.64 94

mvs_anonymous		99.03 84	98.99 68	99.16 127	99.38 145	98.52 180	99.51 116	99.38 183	97.79 121	99.38 97	99.81 53	97.30 96	99.45 199	99.35 18	98.99 134	99.51 120

test_normal		97.44 238	96.77 248	99.44 97	97.75 302	99.00 111	99.10 243	98.64 304	97.71 130	93.93 306	98.82 279	87.39 307	99.83 120	98.61 92	98.97 136	99.49 124

diffmvs		98.72 118	98.49 124	99.43 100	99.48 126	99.19 89	99.62 74	99.42 164	95.58 263	99.37 99	99.67 120	96.14 126	99.74 145	98.14 131	98.96 137	99.37 143

Test4		95.05 282	93.67 290	99.22 124	96.07 312	98.94 124	99.20 224	99.27 236	97.71 130	89.96 321	97.59 312	66.18 329	99.25 244	98.06 141	98.96 137	99.47 130

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 90	99.06 103	99.81 15	99.33 211	97.43 152	99.60 56	99.88 14	97.14 99	99.84 113	99.13 39	98.94 139	99.69 77

MIMVSNet		97.73 213	97.45 210	98.57 204	99.45 132	97.50 218	99.02 261	98.98 268	96.11 252	99.41 90	99.14 252	90.28 277	98.74 288	95.74 255	98.93 140	99.47 130

TAMVS		99.12 67	99.08 56	99.24 121	99.46 128	98.55 175	99.51 116	99.46 136	98.09 89	99.45 81	99.82 44	98.34 68	99.51 195	98.70 80	98.93 140	99.67 84

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 134	98.73 159	99.45 141	99.46 136	98.11 86	99.46 80	99.77 82	98.01 79	99.37 212	98.70 80	98.92 142	99.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		97.59 226	97.09 240	99.07 133	99.06 209	98.26 193	98.30 315	99.10 254	94.88 269	98.08 257	99.34 229	96.27 123	99.64 180	89.87 308	98.92 142	99.31 148

DI_MVS_plusplus_test		97.45 237	96.79 246	99.44 97	97.76 301	99.04 105	99.21 222	98.61 307	97.74 127	94.01 303	98.83 278	87.38 308	99.83 120	98.63 88	98.90 144	99.44 136

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 166	99.71 68	97.74 214	99.12 235	99.54 61	98.44 62	99.42 88	99.71 103	94.20 206	99.92 63	98.54 105	98.90 144	99.00 173

PMMVS		98.80 112	98.62 115	99.34 105	99.27 171	98.70 161	98.76 294	99.31 218	97.34 159	99.21 144	99.07 258	97.20 98	99.82 126	98.56 100	98.87 146	99.52 117

DSMNet-mixed		97.25 244	97.35 224	96.95 288	97.84 299	93.61 304	99.57 93	96.63 330	96.13 251	98.87 197	98.61 288	94.59 192	97.70 313	95.08 268	98.86 147	99.55 110

XVG-OURS		98.73 117	98.68 106	98.88 173	99.70 73	97.73 215	98.92 283	99.55 54	98.52 55	99.45 81	99.84 35	95.27 149	99.91 72	98.08 138	98.84 148	99.00 173

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 116	99.28 82	99.52 112	99.47 126	96.11 252	99.01 176	99.34 229	96.20 125	99.84 113	97.88 151	98.82 149	99.39 142

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 91	99.19 89	99.44 145	99.54 61	97.77 123	99.30 113	99.81 53	94.20 206	99.93 55	99.17 36	98.82 149	99.49 124

MDTV_nov1_ep13			98.32 133		99.11 200	94.44 294	99.27 203	98.74 296	97.51 146	99.40 94	99.62 143	94.78 179	99.76 143	97.59 177	98.81 151

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 75	98.95 121	99.03 258	99.47 126	96.98 190	99.15 154	99.23 246	96.77 110	99.89 92	98.83 68	98.78 152	99.86 5

1112_ss		98.98 90	98.77 97	99.59 68	99.68 77	99.02 107	99.25 212	99.48 112	97.23 170	99.13 155	99.58 154	96.93 105	99.90 84	98.87 61	98.78 152	99.84 12

PatchT		97.03 250	96.44 251	98.79 191	98.99 219	98.34 190	99.16 228	99.07 259	92.13 304	99.52 70	97.31 317	94.54 195	98.98 276	88.54 312	98.73 154	99.03 170

tpmrst		98.33 138	98.48 125	97.90 263	99.16 192	94.78 290	99.31 191	99.11 253	97.27 165	99.45 81	99.59 151	95.33 146	99.84 113	98.48 108	98.61 155	99.09 162

BH-w/o		98.00 174	97.89 164	98.32 228	99.35 150	96.20 267	99.01 265	98.90 280	96.42 227	98.38 244	99.00 264	95.26 151	99.72 157	96.06 249	98.61 155	99.03 170

cascas		97.69 218	97.43 216	98.48 213	98.60 284	97.30 219	98.18 319	99.39 177	92.96 300	98.41 242	98.78 283	93.77 222	99.27 238	98.16 130	98.61 155	98.86 188

CR-MVSNet		98.17 148	97.93 159	98.87 177	99.18 185	98.49 183	99.22 219	99.33 211	96.96 191	99.56 64	99.38 212	94.33 202	99.00 274	94.83 272	98.58 158	99.14 155

RPMNet		96.61 253	95.85 261	98.87 177	99.18 185	98.49 183	99.22 219	99.08 256	88.72 319	99.56 64	97.38 315	94.08 213	99.00 274	86.87 319	98.58 158	99.14 155

dp		97.75 210	97.80 169	97.59 277	99.10 203	93.71 302	99.32 188	98.88 282	96.48 223	99.08 166	99.55 163	92.67 244	99.82 126	96.52 241	98.58 158	99.24 152

CVMVSNet		98.57 127	98.67 107	98.30 230	99.35 150	95.59 274	99.50 121	99.55 54	98.60 51	99.39 95	99.83 37	94.48 197	99.45 199	98.75 74	98.56 161	99.85 8

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 128	99.12 98	98.08 320	99.50 98	97.50 147	99.38 97	99.41 203	96.37 120	99.81 130	99.11 41	98.54 162	99.51 120

testgi		97.65 225	97.50 204	98.13 249	99.36 149	96.45 259	99.42 157	99.48 112	97.76 124	97.87 265	99.45 196	91.09 271	98.81 287	94.53 276	98.52 163	99.13 157

tpm cat1		97.39 240	97.36 222	97.50 280	99.17 190	93.73 300	99.43 150	99.31 218	91.27 309	98.71 214	99.08 257	94.31 204	99.77 140	96.41 245	98.50 164	99.00 173

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 97	99.16 92	99.37 175	99.56 47	98.04 99	99.53 68	99.62 143	96.84 106	99.94 40	98.85 65	98.49 165	99.72 69

testus		94.61 285	95.30 278	92.54 307	96.44 311	84.18 322	98.36 311	99.03 264	94.18 287	96.49 284	98.57 290	88.74 292	95.09 325	87.41 316	98.45 166	98.36 284

tpmvs		97.98 175	98.02 152	97.84 267	99.04 213	94.73 292	99.31 191	99.20 244	96.10 255	98.76 210	99.42 200	94.94 166	99.81 130	96.97 220	98.45 166	98.97 177

LP		97.04 249	96.80 245	97.77 272	98.90 244	95.23 283	98.97 274	99.06 261	94.02 288	98.09 256	99.41 203	93.88 218	98.82 286	90.46 306	98.42 168	99.26 151

LFMVS		97.90 189	97.35 224	99.54 75	99.52 114	99.01 109	99.39 168	98.24 315	97.10 183	99.65 48	99.79 72	84.79 317	99.91 72	99.28 27	98.38 169	99.69 77

GA-MVS		97.85 193	97.47 208	99.00 141	99.38 145	97.99 201	98.57 305	99.15 249	97.04 187	98.90 194	99.30 237	89.83 283	99.38 209	96.70 234	98.33 170	99.62 100

VDD-MVS		97.73 213	97.35 224	98.88 173	99.47 127	97.12 227	99.34 186	98.85 284	98.19 76	99.67 40	99.85 26	82.98 321	99.92 63	99.49 12	98.32 171	99.60 102

view600		97.97 178	97.66 187	98.89 166	99.75 47	97.81 208	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

view800		97.97 178	97.66 187	98.89 166	99.75 47	97.81 208	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

conf0.05thres1000		97.97 178	97.66 187	98.89 166	99.75 47	97.81 208	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

tfpn		97.97 178	97.66 187	98.89 166	99.75 47	97.81 208	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

GG-mvs-BLEND				98.45 217	98.55 287	98.16 196	99.43 150	93.68 336		97.23 275	98.46 292	89.30 288	99.22 250	95.43 262	98.22 176	97.98 294

HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 134	99.08 101	99.62 74	99.36 191	97.39 157	99.28 121	99.68 116	96.44 118	99.92 63	98.37 117	98.22 176	99.40 141

thres600view7		97.86 192	97.51 202	98.92 156	99.72 65	97.95 204	99.59 83	98.74 296	97.94 109	99.27 125	98.62 287	91.75 263	99.86 104	93.73 293	98.19 178	98.96 179

VNet		99.11 71	98.90 80	99.73 45	99.52 114	99.56 49	99.41 161	99.39 177	99.01 13	99.74 28	99.78 77	95.56 141	99.92 63	99.52 7	98.18 179	99.72 69

thres400		97.77 206	97.38 221	98.92 156	99.69 75	97.96 203	99.50 121	98.73 301	97.83 117	99.17 153	98.45 293	91.67 266	99.83 120	93.22 297	98.18 179	98.96 179

PatchFormer-LS_test		98.01 173	98.05 150	97.87 264	99.15 195	94.76 291	99.42 157	98.93 273	97.12 179	98.84 203	98.59 289	93.74 225	99.80 133	98.55 103	98.17 181	99.06 168

DWT-MVSNet_test		97.53 229	97.40 219	97.93 260	99.03 215	94.86 289	99.57 93	98.63 305	96.59 214	98.36 246	98.79 281	89.32 287	99.74 145	98.14 131	98.16 182	99.20 154

VDDNet		97.55 227	97.02 242	99.16 127	99.49 123	98.12 199	99.38 173	99.30 220	95.35 265	99.68 34	99.90 7	82.62 323	99.93 55	99.31 25	98.13 183	99.42 139

alignmvs		98.81 109	98.56 122	99.58 71	99.43 133	99.42 69	99.51 116	98.96 271	98.61 50	99.35 106	98.92 271	94.78 179	99.77 140	99.35 18	98.11 184	99.54 112

tpm2		97.44 238	97.34 227	97.74 274	99.15 195	94.36 295	99.45 141	98.94 272	93.45 298	98.90 194	99.44 197	91.35 269	99.59 190	97.31 200	98.07 185	99.29 149

tpmp4_e23		97.34 241	97.29 233	97.52 278	99.25 175	93.73 300	99.58 87	99.19 247	94.00 289	98.20 252	99.41 203	90.74 275	99.74 145	97.13 210	98.07 185	99.07 167

test2356		94.07 291	94.46 286	92.89 305	95.18 316	86.13 320	97.60 325	99.06 261	93.61 294	96.15 290	98.28 294	85.60 314	93.95 327	86.68 320	98.00 187	98.59 267

JIA-IIPM		97.50 234	97.02 242	98.93 151	98.73 270	97.80 212	99.30 193	98.97 269	91.73 308	98.91 192	94.86 324	95.10 158	99.71 163	97.58 178	97.98 188	99.28 150

CostFormer		97.72 215	97.73 183	97.71 275	99.15 195	94.02 298	99.54 108	99.02 265	94.67 273	99.04 173	99.35 226	92.35 255	99.77 140	98.50 107	97.94 189	99.34 146

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 134	99.32 77	99.80 19	99.48 112	98.63 48	99.31 112	98.81 280	97.09 100	99.75 144	99.27 29	97.90 190	99.47 130

OpenMVS_ROB	92.34 20	94.38 288	93.70 289	96.41 296	97.38 305	93.17 306	99.06 250	98.75 293	86.58 320	94.84 296	98.26 295	81.53 324	99.32 226	89.01 311	97.87 191	96.76 315

TR-MVS		97.76 207	97.41 218	98.82 187	99.06 209	97.87 206	98.87 288	98.56 309	96.63 209	98.68 222	99.22 247	92.49 249	99.65 178	95.40 263	97.79 192	98.95 186

DeepMVS_CX				93.34 303	99.29 166	82.27 327		99.22 242	85.15 321	96.33 286	99.05 261	90.97 273	99.73 153	93.57 294	97.77 193	98.01 293

CLD-MVS		98.16 149	98.10 144	98.33 227	99.29 166	96.82 248	98.75 295	99.44 155	97.83 117	99.13 155	99.55 163	92.92 233	99.67 174	98.32 123	97.69 194	98.48 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		98.27 142	98.22 139	98.44 220	99.29 166	96.97 241	99.39 168	99.47 126	98.97 22	99.11 159	99.61 146	92.71 241	99.69 172	97.78 159	97.63 195	98.67 231

plane_prior5								99.47 126					99.69 172	97.78 159	97.63 195	98.67 231

test_djsdf		98.67 122	98.57 121	98.98 143	98.70 275	98.91 129	99.88 1	99.46 136	97.55 142	99.22 142	99.88 14	95.73 139	99.28 235	99.03 46	97.62 197	98.75 199

anonymousdsp		98.44 131	98.28 136	98.94 148	98.50 289	98.96 120	99.77 24	99.50 98	97.07 184	98.87 197	99.77 82	94.76 184	99.28 235	98.66 85	97.60 198	98.57 271

plane_prior						96.97 241	99.21 222		98.45 59						97.60 198

HQP3-MVS								99.39 177							97.58 200

HQP-MVS		98.02 170	97.90 160	98.37 225	99.19 182	96.83 246	98.98 271	99.39 177	98.24 72	98.66 223	99.40 207	92.47 250	99.64 180	97.19 206	97.58 200	98.64 247

EI-MVSNet		98.67 122	98.67 107	98.68 197	99.35 150	97.97 202	99.50 121	99.38 183	96.93 194	99.20 147	99.83 37	97.87 81	99.36 216	98.38 116	97.56 202	98.71 206

MVSTER		98.49 128	98.32 133	99.00 141	99.35 150	99.02 107	99.54 108	99.38 183	97.41 155	99.20 147	99.73 98	93.86 220	99.36 216	98.87 61	97.56 202	98.62 255

OPM-MVS		98.19 147	98.10 144	98.45 217	98.88 248	97.07 232	99.28 200	99.38 183	98.57 52	99.22 142	99.81 53	92.12 256	99.66 176	98.08 138	97.54 204	98.61 264

LPG-MVS_test		98.22 143	98.13 142	98.49 211	99.33 154	97.05 234	99.58 87	99.55 54	97.46 148	99.24 135	99.83 37	92.58 246	99.72 157	98.09 134	97.51 205	98.68 220

LGP-MVS_train				98.49 211	99.33 154	97.05 234		99.55 54	97.46 148	99.24 135	99.83 37	92.58 246	99.72 157	98.09 134	97.51 205	98.68 220

jajsoiax		98.43 132	98.28 136	98.88 173	98.60 284	98.43 187	99.82 13	99.53 71	98.19 76	98.63 231	99.80 64	93.22 229	99.44 204	99.22 31	97.50 207	98.77 196

EG-PatchMatch MVS		95.97 273	95.69 267	96.81 291	97.78 300	92.79 308	99.16 228	98.93 273	96.16 247	94.08 300	99.22 247	82.72 322	99.47 197	95.67 258	97.50 207	98.17 287

test_0402		96.64 252	96.24 253	97.85 266	98.85 255	96.43 260	99.44 145	99.26 237	93.52 295	96.98 281	99.52 171	88.52 298	99.20 255	92.58 302	97.50 207	97.93 297

ACMP	97.20 11	98.06 159	97.94 158	98.45 217	99.37 147	97.01 237	99.44 145	99.49 103	97.54 145	98.45 241	99.79 72	91.95 257	99.72 157	97.91 149	97.49 210	98.62 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
mvs_tets		98.40 135	98.23 138	98.91 160	98.67 279	98.51 182	99.66 58	99.53 71	98.19 76	98.65 229	99.81 53	92.75 237	99.44 204	99.31 25	97.48 211	98.77 196

ACMM	97.58 5	98.37 137	98.34 131	98.48 213	99.41 137	97.10 228	99.56 100	99.45 147	98.53 54	99.04 173	99.85 26	93.00 231	99.71 163	98.74 75	97.45 212	98.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	97.28 8	98.10 156	97.99 154	98.44 220	99.41 137	96.96 243	99.60 81	99.56 47	98.09 89	98.15 254	99.91 5	90.87 274	99.70 169	98.88 57	97.45 212	98.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	97.16 12	98.02 170	97.90 160	98.40 223	99.23 176	96.80 249	99.70 42	99.60 34	97.12 179	98.18 253	99.70 106	91.73 264	99.72 157	98.39 114	97.45 212	98.68 220
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
ACMMP++															97.43 215

testpf		95.66 276	96.02 259	94.58 300	98.35 293	92.32 310	97.25 327	97.91 322	92.83 301	97.03 280	98.99 265	88.69 294	98.61 290	95.72 256	97.40 216	92.80 324

ITE_SJBPF				98.08 250	99.29 166	96.37 261		98.92 275	98.34 66	98.83 204	99.75 90	91.09 271	99.62 186	95.82 253	97.40 216	98.25 286

XVG-ACMP-BASELINE		97.83 196	97.71 185	98.20 245	99.11 200	96.33 263	99.41 161	99.52 75	98.06 97	99.05 172	99.50 176	89.64 285	99.73 153	97.73 166	97.38 218	98.53 273

USDC		97.34 241	97.20 237	97.75 273	99.07 207	95.20 284	98.51 308	99.04 263	97.99 107	98.31 249	99.86 22	89.02 289	99.55 193	95.67 258	97.36 219	98.49 275

pcd1.5k->3k		40.85 313	43.49 315	32.93 327	98.95 231	0.00 344	0.00 335	99.53 71	0.00 339	0.00 340	0.27 341	95.32 147	0.00 342	0.00 339	97.30 220	98.80 191

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 137	99.76 41	98.79 155	99.28 200	99.91 3	97.42 154	99.67 40	99.37 215	97.53 89	99.88 99	98.98 51	97.29 221	98.42 280

PS-MVSNAJss		98.92 95	98.92 77	98.90 164	98.78 264	98.53 177	99.78 22	99.54 61	98.07 93	99.00 183	99.76 85	99.01 11	99.37 212	99.13 39	97.23 222	98.81 190

TinyColmap		97.12 247	96.89 244	97.83 268	99.07 207	95.52 278	98.57 305	98.74 296	97.58 139	97.81 268	99.79 72	88.16 303	99.56 191	95.10 267	97.21 223	98.39 283

ACMMP++_ref															97.19 224

ACMH+	97.24 10	97.92 187	97.78 172	98.32 228	99.46 128	96.68 253	99.56 100	99.54 61	98.41 63	97.79 269	99.87 19	90.18 281	99.66 176	98.05 142	97.18 225	98.62 255

test0.0.03 1		97.71 217	97.42 217	98.56 206	98.41 292	97.82 207	98.78 292	98.63 305	97.34 159	98.05 261	98.98 268	94.45 198	98.98 276	95.04 269	97.15 226	98.89 187

CMPMVS	69.68 23	94.13 289	94.90 281	91.84 309	97.24 309	80.01 329	98.52 307	99.48 112	89.01 317	91.99 315	99.67 120	85.67 313	99.13 260	95.44 261	97.03 227	96.39 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-0		97.88 190	97.77 176	98.19 246	98.71 274	96.53 256	99.88 1	99.00 266	97.79 121	98.78 208	99.94 3	91.68 265	99.35 219	97.21 204	96.99 228	98.69 215

LF4IMVS		97.52 230	97.46 209	97.70 276	98.98 223	95.55 275	99.29 197	98.82 287	98.07 93	98.66 223	99.64 134	89.97 282	99.61 187	97.01 216	96.68 229	97.94 296

GBi-Net		97.68 220	97.48 206	98.29 231	99.51 116	97.26 222	99.43 150	99.48 112	96.49 217	99.07 167	99.32 234	90.26 278	98.98 276	97.10 211	96.65 230	98.62 255

test1		97.68 220	97.48 206	98.29 231	99.51 116	97.26 222	99.43 150	99.48 112	96.49 217	99.07 167	99.32 234	90.26 278	98.98 276	97.10 211	96.65 230	98.62 255

FMVSNet3		98.03 168	97.76 179	98.84 185	99.39 144	98.98 113	99.40 167	99.38 183	96.67 206	99.07 167	99.28 240	92.93 232	98.98 276	97.10 211	96.65 230	98.56 272

FMVSNet2		97.72 215	97.36 222	98.80 190	99.51 116	98.84 136	99.45 141	99.42 164	96.49 217	98.86 202	99.29 239	90.26 278	98.98 276	96.44 243	96.56 233	98.58 270

K. test v3		97.10 248	96.79 246	98.01 255	98.72 272	96.33 263	99.87 4	97.05 329	97.59 137	96.16 288	99.80 64	88.71 293	99.04 269	96.69 235	96.55 234	98.65 245

tpm		97.67 223	97.55 198	98.03 252	99.02 216	95.01 288	99.43 150	98.54 310	96.44 225	99.12 157	99.34 229	91.83 262	99.60 188	97.75 164	96.46 235	99.48 126

SixPastTwentyTwo		97.50 234	97.33 229	98.03 252	98.65 280	96.23 266	99.77 24	98.68 303	97.14 176	97.90 264	99.93 4	90.45 276	99.18 256	97.00 217	96.43 236	98.67 231

FIs		98.78 113	98.63 112	99.23 123	99.18 185	99.54 52	99.83 12	99.59 37	98.28 70	98.79 207	99.81 53	96.75 111	99.37 212	99.08 43	96.38 237	98.78 193

FC-MVSNet-test		98.75 116	98.62 115	99.15 129	99.08 206	99.45 66	99.86 8	99.60 34	98.23 75	98.70 220	99.82 44	96.80 107	99.22 250	99.07 44	96.38 237	98.79 192

XXY-MVS		98.38 136	98.09 146	99.24 121	99.26 173	99.32 77	99.56 100	99.55 54	97.45 151	98.71 214	99.83 37	93.23 228	99.63 185	98.88 57	96.32 239	98.76 198

FMVSNet1		96.84 251	96.36 252	98.29 231	99.32 161	97.26 222	99.43 150	99.48 112	95.11 267	98.55 236	99.32 234	83.95 320	98.98 276	95.81 254	96.26 240	98.62 255

N_pmnet		94.95 284	95.83 262	92.31 308	98.47 290	79.33 330	99.12 235	92.81 340	93.87 291	97.68 270	99.13 253	93.87 219	99.01 273	91.38 304	96.19 241	98.59 267

pmmvs4		98.13 151	97.90 160	98.81 188	98.61 283	98.87 132	98.99 267	99.21 243	96.44 225	99.06 171	99.58 154	95.90 133	99.11 263	97.18 208	96.11 242	98.46 279

testing_2		94.44 287	92.93 293	98.98 143	94.16 320	99.00 111	99.42 157	99.28 231	96.60 212	84.86 323	96.84 318	70.91 326	99.27 238	98.23 126	96.08 243	98.68 220

IterMVS		97.83 196	97.77 176	98.02 254	99.58 106	96.27 265	99.02 261	99.48 112	97.22 171	98.71 214	99.70 106	92.75 237	99.13 260	97.46 192	96.00 244	98.67 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs5		97.52 230	97.30 232	98.16 248	98.57 286	96.73 250	99.27 203	98.90 280	96.14 250	98.37 245	99.53 167	91.54 268	99.14 257	97.51 186	95.87 245	98.63 253

semantic-postprocess				98.06 251	99.57 108	96.36 262		99.49 103	97.18 173	98.71 214	99.72 102	92.70 243	99.14 257	97.44 194	95.86 246	98.67 231

new_pmnet		96.38 261	96.03 257	97.41 281	98.13 297	95.16 287	99.05 252	99.20 244	93.94 290	97.39 273	98.79 281	91.61 267	99.04 269	90.43 307	95.77 247	98.05 290

FMVSNet5		96.43 257	96.19 254	97.15 283	99.11 200	95.89 271	99.32 188	99.52 75	94.47 282	98.34 248	99.07 258	87.54 306	97.07 316	92.61 301	95.72 248	98.47 277

Gipuma		90.99 297	90.15 298	93.51 302	98.73 270	90.12 315	93.98 331	99.45 147	79.32 326	92.28 314	94.91 323	69.61 327	97.98 306	87.42 315	95.67 249	92.45 326
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IterMVS-LS		98.46 130	98.42 127	98.58 203	99.59 105	98.00 200	99.37 175	99.43 163	96.94 193	99.07 167	99.59 151	97.87 81	99.03 271	98.32 123	95.62 250	98.71 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		97.75 210	97.40 219	98.81 188	99.10 203	98.87 132	99.11 241	99.33 211	94.83 270	98.81 205	99.38 212	94.33 202	99.02 272	96.10 248	95.57 251	98.53 273

MIMVSNet1		95.51 277	95.04 280	96.92 289	97.38 305	95.60 273	99.52 112	99.50 98	93.65 293	96.97 282	99.17 250	85.28 315	96.56 320	88.36 313	95.55 252	98.60 266

test1235678		92.91 294	93.30 291	91.71 311	93.14 323	83.01 324	98.75 295	98.58 308	92.80 302	92.45 313	97.91 298	88.51 299	93.54 328	82.26 324	95.35 253	98.59 267

EU-MVSNet		97.98 175	98.03 151	97.81 270	98.72 272	96.65 254	99.66 58	99.66 25	98.09 89	98.35 247	99.82 44	95.25 152	98.01 305	97.41 196	95.30 254	98.78 193

v1240		97.69 218	97.32 230	98.79 191	98.85 255	98.43 187	99.48 133	99.36 191	96.11 252	99.27 125	99.36 222	93.76 223	99.24 246	94.46 278	95.23 255	98.70 210

v1192		97.81 200	97.44 213	98.91 160	98.88 248	98.68 162	99.51 116	99.34 203	96.18 245	99.20 147	99.34 229	94.03 214	99.36 216	95.32 265	95.18 256	98.69 215

v1144		97.98 175	97.69 186	98.85 184	98.87 251	98.66 165	99.54 108	99.35 195	96.27 237	99.23 140	99.35 226	94.67 189	99.23 247	96.73 232	95.16 257	98.68 220

v7		98.05 165	97.78 172	98.87 177	98.99 219	98.67 163	99.64 70	99.34 203	96.31 234	99.29 117	99.51 174	94.78 179	99.27 238	97.03 215	95.15 258	98.66 242

v1921920		97.80 202	97.45 210	98.84 185	98.80 258	98.53 177	99.52 112	99.34 203	96.15 249	99.24 135	99.47 189	93.98 215	99.29 234	95.40 263	95.13 259	98.69 215

Anonymous20231206		96.22 268	96.03 257	96.79 292	97.31 308	94.14 297	99.63 71	99.08 256	96.17 246	97.04 279	99.06 260	93.94 216	97.76 312	86.96 318	95.06 260	98.47 277

v144192		97.92 187	97.60 196	98.87 177	98.83 257	98.65 166	99.55 105	99.34 203	96.20 243	99.32 111	99.40 207	94.36 201	99.26 243	96.37 246	95.03 261	98.70 210

v2v482		98.06 159	97.77 176	98.92 156	98.90 244	98.82 146	99.57 93	99.36 191	96.65 207	99.19 150	99.35 226	94.20 206	99.25 244	97.72 170	94.97 262	98.69 215

FPMVS		84.93 302	85.65 302	82.75 322	86.77 333	63.39 339	98.35 313	98.92 275	74.11 328	83.39 325	98.98 268	50.85 335	92.40 332	84.54 322	94.97 262	92.46 325

lessismore_v0				97.79 271	98.69 276	95.44 281		94.75 333		95.71 292	99.87 19	88.69 294	99.32 226	95.89 252	94.93 264	98.62 255

v6		98.12 153	97.84 166	98.94 148	98.94 234	98.83 139	99.66 58	99.34 203	96.49 217	99.30 113	99.37 215	94.95 165	99.34 222	97.77 161	94.74 265	98.67 231

v1neww		98.12 153	97.84 166	98.93 151	98.97 226	98.81 148	99.66 58	99.35 195	96.49 217	99.29 117	99.37 215	95.02 161	99.32 226	97.73 166	94.73 266	98.67 231

v7new		98.12 153	97.84 166	98.93 151	98.97 226	98.81 148	99.66 58	99.35 195	96.49 217	99.29 117	99.37 215	95.02 161	99.32 226	97.73 166	94.73 266	98.67 231

v1141		98.05 165	97.76 179	98.91 160	98.91 243	98.78 157	99.57 93	99.35 195	96.41 229	99.23 140	99.36 222	94.93 168	99.27 238	97.38 197	94.72 268	98.68 220

v1		98.05 165	97.76 179	98.93 151	98.92 241	98.80 153	99.57 93	99.35 195	96.39 231	99.28 121	99.36 222	94.86 174	99.32 226	97.38 197	94.72 268	98.68 220

divwei89l23v2f112		98.06 159	97.78 172	98.91 160	98.90 244	98.77 158	99.57 93	99.35 195	96.45 224	99.24 135	99.37 215	94.92 169	99.27 238	97.50 187	94.71 270	98.68 220

V42		98.06 159	97.79 170	98.86 181	98.98 223	98.84 136	99.69 44	99.34 203	96.53 216	99.30 113	99.37 215	94.67 189	99.32 226	97.57 180	94.66 271	98.42 280

test12356		91.74 296	92.19 297	90.37 314	91.22 325	82.41 325	98.61 303	98.28 313	90.66 313	91.82 316	97.92 297	84.90 316	92.61 329	81.64 325	94.66 271	96.09 319

v10		97.85 193	97.52 200	98.86 181	98.99 219	98.67 163	99.75 34	99.41 167	95.70 261	98.98 185	99.41 203	94.75 185	99.23 247	96.01 251	94.63 273	98.67 231

nrg030		98.64 125	98.42 127	99.28 118	99.05 212	99.69 29	99.81 15	99.46 136	98.04 99	99.01 176	99.82 44	96.69 113	99.38 209	99.34 22	94.59 274	98.78 193

VPA-MVSNet		98.29 140	97.95 157	99.30 113	99.16 192	99.54 52	99.50 121	99.58 42	98.27 71	99.35 106	99.37 215	92.53 248	99.65 178	99.35 18	94.46 275	98.72 204

MDA-MVSNet_test_wron		95.45 278	94.60 283	98.01 255	98.16 296	97.21 226	99.11 241	99.24 240	93.49 296	80.73 328	98.98 268	93.02 230	98.18 293	94.22 290	94.45 276	98.64 247

MDA-MVSNet-bldmvs		94.96 283	93.98 288	97.92 261	98.24 295	97.27 221	99.15 231	99.33 211	93.80 292	80.09 329	99.03 263	88.31 301	97.86 309	93.49 295	94.36 277	98.62 255

WR-MVS		98.06 159	97.73 183	99.06 134	98.86 254	99.25 86	99.19 225	99.35 195	97.30 163	98.66 223	99.43 198	93.94 216	99.21 254	98.58 95	94.28 278	98.71 206

1111		92.30 295	92.21 296	92.55 306	93.30 321	86.27 318	99.15 231	98.74 296	91.94 305	90.85 318	97.82 299	84.18 318	95.21 323	79.65 326	94.27 279	96.19 318

test20.03		96.12 271	95.96 260	96.63 293	97.44 304	95.45 280	99.51 116	99.38 183	96.55 215	96.16 288	99.25 244	93.76 223	96.17 321	87.35 317	94.22 280	98.27 285

YYNet1		95.36 280	94.51 285	97.92 261	97.89 298	97.10 228	99.10 243	99.23 241	93.26 299	80.77 327	99.04 262	92.81 236	98.02 304	94.30 286	94.18 281	98.64 247

CP-MVSNet		98.09 157	97.78 172	99.01 139	98.97 226	99.24 87	99.67 55	99.46 136	97.25 167	98.48 240	99.64 134	93.79 221	99.06 267	98.63 88	94.10 282	98.74 202

v8		97.95 183	97.63 194	98.93 151	98.95 231	98.81 148	99.80 19	99.41 167	96.03 256	99.10 162	99.42 200	94.92 169	99.30 232	96.94 223	94.08 283	98.66 242

PS-CasMVS		97.93 184	97.59 197	98.95 147	98.99 219	99.06 103	99.68 53	99.52 75	97.13 177	98.31 249	99.68 116	92.44 254	99.05 268	98.51 106	94.08 283	98.75 199

V4		97.80 202	97.51 202	98.67 199	98.79 260	98.63 168	99.87 4	99.44 155	95.87 258	99.01 176	99.46 193	94.52 196	99.33 223	96.64 240	93.97 285	98.05 290

v52		97.79 204	97.50 204	98.66 200	98.80 258	98.62 170	99.87 4	99.44 155	95.87 258	99.01 176	99.46 193	94.44 200	99.33 223	96.65 239	93.96 286	98.05 290

v7n		97.87 191	97.52 200	98.92 156	98.76 268	98.58 174	99.84 9	99.46 136	96.20 243	98.91 192	99.70 106	94.89 172	99.44 204	96.03 250	93.89 287	98.75 199

WR-MVS_H		98.13 151	97.87 165	98.90 164	99.02 216	98.84 136	99.70 42	99.59 37	97.27 165	98.40 243	99.19 249	95.53 142	99.23 247	98.34 120	93.78 288	98.61 264

NR-MVSNet		97.97 178	97.61 195	99.02 138	98.87 251	99.26 85	99.47 137	99.42 164	97.63 136	97.08 278	99.50 176	95.07 159	99.13 260	97.86 153	93.59 289	98.68 220

pm-mvs1		97.68 220	97.28 234	98.88 173	99.06 209	98.62 170	99.50 121	99.45 147	96.32 233	97.87 265	99.79 72	92.47 250	99.35 219	97.54 183	93.54 290	98.67 231

UniMVSNet (Re)		98.29 140	98.00 153	99.13 130	99.00 218	99.36 74	99.49 128	99.51 84	97.95 108	98.97 186	99.13 253	96.30 122	99.38 209	98.36 119	93.34 291	98.66 242

VPNet		97.84 195	97.44 213	99.01 139	99.21 179	98.94 124	99.48 133	99.57 43	98.38 64	99.28 121	99.73 98	88.89 291	99.39 208	99.19 33	93.27 292	98.71 206

PEN-MVS		97.76 207	97.44 213	98.72 196	98.77 267	98.54 176	99.78 22	99.51 84	97.06 186	98.29 251	99.64 134	92.63 245	98.89 285	98.09 134	93.16 293	98.72 204

v148		97.79 204	97.55 198	98.50 210	98.74 269	97.72 216	99.54 108	99.33 211	96.26 238	98.90 194	99.51 174	94.68 188	99.14 257	97.83 155	93.15 294	98.63 253

TranMVSNet+NR-MVSNet		97.93 184	97.66 187	98.76 194	98.78 264	98.62 170	99.65 68	99.49 103	97.76 124	98.49 239	99.60 149	94.23 205	98.97 283	98.00 143	92.90 295	98.70 210

Baseline_NR-MVSNet		97.76 207	97.45 210	98.68 197	99.09 205	98.29 191	99.41 161	98.85 284	95.65 262	98.63 231	99.67 120	94.82 176	99.10 265	98.07 140	92.89 296	98.64 247

UniMVSNet_NR-MVSNet		98.22 143	97.97 155	98.96 145	98.92 241	98.98 113	99.48 133	99.53 71	97.76 124	98.71 214	99.46 193	96.43 119	99.22 250	98.57 97	92.87 297	98.69 215

DU-MVS		98.08 158	97.79 170	98.96 145	98.87 251	98.98 113	99.41 161	99.45 147	97.87 111	98.71 214	99.50 176	94.82 176	99.22 250	98.57 97	92.87 297	98.68 220

pmmvs6		96.53 255	96.09 256	97.82 269	98.69 276	95.47 279	99.37 175	99.47 126	93.46 297	97.41 272	99.78 77	87.06 309	99.33 223	96.92 225	92.70 299	98.65 245

DTE-MVSNet		97.51 233	97.19 238	98.46 216	98.63 282	98.13 198	99.84 9	99.48 112	96.68 205	97.97 263	99.67 120	92.92 233	98.56 291	96.88 227	92.60 300	98.70 210

v748		97.52 230	97.23 236	98.41 222	98.69 276	97.23 225	99.87 4	99.45 147	95.72 260	98.51 237	99.53 167	94.13 210	99.30 232	96.78 230	92.39 301	98.70 210

TransMVSNet (Re)		97.15 246	96.58 249	98.86 181	99.12 198	98.85 135	99.49 128	98.91 278	95.48 264	97.16 277	99.80 64	93.38 227	99.11 263	94.16 291	91.73 302	98.62 255

ambc				93.06 304	92.68 324	82.36 326	98.47 309	98.73 301		95.09 294	97.41 314	55.55 334	99.10 265	96.42 244	91.32 303	97.71 310

PMVS	70.75 22	75.98 311	74.97 310	79.01 324	70.98 339	55.18 340	93.37 332	98.21 316	65.08 335	61.78 336	93.83 325	21.74 345	92.53 330	78.59 328	91.12 304	89.34 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UnsupCasMVSNet_eth		96.44 256	96.12 255	97.40 282	98.65 280	95.65 272	99.36 179	99.51 84	97.13 177	96.04 291	98.99 265	88.40 300	98.17 294	96.71 233	90.27 305	98.40 282

Patchmatch-RL test		95.84 274	95.81 263	95.95 297	95.61 313	90.57 314	98.24 316	98.39 311	95.10 268	95.20 293	98.67 286	94.78 179	97.77 311	96.28 247	90.02 306	99.51 120

PM-MVS		92.96 293	92.23 295	95.14 299	95.61 313	89.98 316	99.37 175	98.21 316	94.80 271	95.04 295	97.69 304	65.06 330	97.90 308	94.30 286	89.98 307	97.54 314

pmmvs-eth3d		95.34 281	94.73 282	97.15 283	95.53 315	95.94 270	99.35 183	99.10 254	95.13 266	93.55 309	97.54 313	88.15 304	97.91 307	94.58 275	89.69 308	97.61 311

testmv		87.91 299	87.80 300	88.24 315	87.68 332	77.50 332	99.07 246	97.66 326	89.27 315	86.47 322	96.22 321	68.35 328	92.49 331	76.63 330	88.82 309	94.72 322

v11		96.23 267	95.57 273	98.21 244	98.93 239	98.83 139	99.72 39	99.29 224	94.29 286	94.05 301	97.64 307	94.88 173	98.04 303	92.89 299	88.43 310	97.77 308

new-patchmatchnet		94.48 286	94.08 287	95.67 298	95.08 317	92.41 309	99.18 226	99.28 231	94.55 279	93.49 310	97.37 316	87.86 305	97.01 317	91.57 303	88.36 311	97.61 311

v18		96.42 258	95.80 265	98.26 234	98.95 231	98.82 146	99.76 27	99.28 231	94.58 275	94.12 298	97.70 302	95.22 154	98.16 295	94.83 272	87.80 312	97.79 307

v16		96.39 260	95.76 266	98.26 234	98.96 229	98.81 148	99.76 27	99.28 231	94.57 276	94.10 299	97.70 302	95.04 160	98.16 295	94.70 274	87.77 313	97.80 302

v17		96.42 258	95.81 263	98.25 238	98.94 234	98.80 153	99.76 27	99.28 231	94.57 276	94.18 297	97.71 301	95.23 153	98.16 295	94.86 270	87.73 314	97.80 302

UnsupCasMVSNet_bld		93.53 292	92.51 294	96.58 295	97.38 305	93.82 299	98.24 316	99.48 112	91.10 311	93.10 311	96.66 319	74.89 325	98.37 292	94.03 292	87.71 315	97.56 313

pmmvs3		94.09 290	93.25 292	96.60 294	94.76 318	94.49 293	98.92 283	98.18 318	89.66 314	96.48 285	98.06 296	86.28 310	97.33 315	89.68 309	87.20 316	97.97 295

v15		96.28 262	95.62 268	98.25 238	98.94 234	98.83 139	99.76 27	99.29 224	94.52 280	94.02 302	97.61 309	95.02 161	98.13 299	94.53 276	86.92 317	97.80 302

V14		96.26 263	95.60 269	98.26 234	98.94 234	98.83 139	99.76 27	99.29 224	94.49 281	93.96 304	97.66 305	94.99 164	98.13 299	94.41 279	86.90 318	97.80 302

V9		96.25 264	95.58 270	98.26 234	98.94 234	98.83 139	99.75 34	99.29 224	94.45 283	93.96 304	97.62 308	94.94 166	98.14 298	94.40 280	86.87 319	97.81 300

v13		96.24 265	95.58 270	98.25 238	98.98 223	98.83 139	99.75 34	99.29 224	94.35 285	93.89 307	97.60 310	95.17 156	98.11 301	94.27 288	86.86 320	97.81 300

v12		96.24 265	95.58 270	98.23 241	98.96 229	98.81 148	99.76 27	99.29 224	94.42 284	93.85 308	97.60 310	95.12 157	98.09 302	94.32 285	86.85 321	97.80 302

IB-MVS	95.67 18	96.22 268	95.44 276	98.57 204	99.21 179	96.70 251	98.65 302	97.74 325	96.71 203	97.27 274	98.54 291	86.03 311	99.92 63	98.47 110	86.30 322	99.10 158
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
Anonymous20231211		90.69 298	89.39 299	94.58 300	94.25 319	88.18 317	99.29 197	99.07 259	82.45 325	92.95 312	97.65 306	63.96 332	97.79 310	89.27 310	85.63 323	97.77 308

LCM-MVSNet		86.80 301	85.22 304	91.53 312	87.81 331	80.96 328	98.23 318	98.99 267	71.05 329	90.13 320	96.51 320	48.45 337	96.88 318	90.51 305	85.30 324	96.76 315

TDRefinement		95.42 279	94.57 284	97.97 258	89.83 329	96.11 268	99.48 133	98.75 293	96.74 201	96.68 283	99.88 14	88.65 296	99.71 163	98.37 117	82.74 325	98.09 288

PVSNet_0	94.43 19	96.09 272	95.47 274	97.94 259	99.31 162	94.34 296	97.81 322	99.70 15	97.12 179	97.46 271	98.75 284	89.71 284	99.79 136	97.69 172	81.69 326	99.68 81

PMMVS2		86.87 300	85.37 303	91.35 313	90.21 328	83.80 323	98.89 286	97.45 328	83.13 324	91.67 317	95.03 322	48.49 336	94.70 326	85.86 321	77.62 327	95.54 320

PNet_i23d		79.43 308	77.68 309	84.67 318	86.18 334	71.69 337	96.50 329	93.68 336	75.17 327	71.33 332	91.18 329	32.18 341	90.62 333	78.57 329	74.34 328	91.71 328

MVE	76.82 21	76.91 310	74.31 312	84.70 317	85.38 336	76.05 335	96.88 328	93.17 338	67.39 332	71.28 333	89.01 333	21.66 346	87.69 335	71.74 333	72.29 329	90.35 329
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 306	79.88 307	82.81 321	90.75 327	76.38 334	97.69 323	95.76 332	66.44 333	83.52 324	92.25 327	62.54 333	87.16 336	68.53 334	61.40 330	84.89 333

EMVS		80.02 307	79.22 308	82.43 323	91.19 326	76.40 333	97.55 326	92.49 342	66.36 334	83.01 326	91.27 328	64.63 331	85.79 337	65.82 335	60.65 331	85.08 332

wuykxyi23d		74.42 312	71.19 313	84.14 320	76.16 337	74.29 336	96.00 330	92.57 341	69.57 330	63.84 335	87.49 334	21.98 343	88.86 334	75.56 332	57.50 332	89.26 331

ANet_high		77.30 309	74.86 311	84.62 319	75.88 338	77.61 331	97.63 324	93.15 339	88.81 318	64.27 334	89.29 331	36.51 339	83.93 338	75.89 331	52.31 333	92.33 327

no-one		83.04 304	80.12 306	91.79 310	89.44 330	85.65 321	99.32 188	98.32 312	89.06 316	79.79 331	89.16 332	44.86 338	96.67 319	84.33 323	46.78 334	93.05 323

tmp_tt		82.80 305	81.52 305	86.66 316	66.61 340	68.44 338	92.79 333	97.92 320	68.96 331	80.04 330	99.85 26	85.77 312	96.15 322	97.86 153	43.89 335	95.39 321

.test1245		83.42 303	86.17 301	75.15 325	93.30 321	86.27 318	99.15 231	98.74 296	91.94 305	90.85 318	97.82 299	84.18 318	95.21 323	79.65 326	39.90 336	43.98 335

testmvs		39.17 315	43.78 314	25.37 329	36.04 342	16.84 343	98.36 311	26.56 343	20.06 337	38.51 338	67.32 335	29.64 342	15.30 341	37.59 337	39.90 336	43.98 335

test123		39.01 316	42.50 316	28.53 328	39.17 341	20.91 342	98.75 295	19.17 345	19.83 338	38.57 337	66.67 336	33.16 340	15.42 340	37.50 338	29.66 338	49.26 334

wuyk23d		40.18 314	41.29 317	36.84 326	86.18 334	49.12 341	79.73 334	22.81 344	27.64 336	25.46 339	28.45 340	21.98 343	48.89 339	55.80 336	23.56 339	12.51 337

cdsmvs_eth3d_5k		24.64 317	32.85 318	0.00 330	0.00 343	0.00 344	0.00 335	99.51 84	0.00 339	0.00 340	99.56 160	96.58 115	0.00 342	0.00 339	0.00 340	0.00 338

pcd_1.5k_mvsjas		8.27 319	11.03 320	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	99.01 11	0.00 342	0.00 339	0.00 340	0.00 338

sosnet-low-res		0.02 320	0.03 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

sosnet		0.02 320	0.03 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uncertanet		0.02 320	0.03 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

Regformer		0.02 320	0.03 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ab-mvs-re		8.30 318	11.06 319	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	99.58 154	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uanet		0.02 320	0.03 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.27 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ESAPD								99.47 126

sam_mvs1												94.86 174

sam_mvs												94.72 187

MTGPA								99.47 126

test_post1							99.23 215				65.14 338	94.18 209	99.71 163	97.58 178

test_post											65.99 337	94.65 191	99.73 153

patchmatchnet-post											98.70 285	94.79 178	99.74 145

MTMP								98.88 282

gm-plane-assit					98.54 288	92.96 307			94.65 274		99.15 251		99.64 180	97.56 181

TEST9					99.67 78	99.65 37	99.05 252	99.41 167	96.22 242	98.95 187	99.49 179	98.77 39	99.91 72

test_8					99.67 78	99.61 42	99.03 258	99.41 167	96.28 235	98.93 190	99.48 185	98.76 41	99.91 72

agg_prior					99.67 78	99.62 40		99.40 174		98.87 197			99.91 72

test_prior4						99.56 49	98.99 267

test_prior				99.68 50	99.67 78	99.48 62		99.56 47					99.83 120			99.74 58

旧先验2							98.96 276		96.70 204	99.47 78			99.94 40	98.19 127

新几何2							99.01 265

无先验							98.99 267	99.51 84	96.89 195				99.93 55	97.53 184		99.72 69

原ACMM2							98.95 280

testdata2													99.95 33	96.67 236

segment_acmp												98.96 20

testdata1							98.85 289		98.32 69

plane_prior7					99.29 166	97.03 236

plane_prior6					99.27 171	96.98 240						92.71 241

plane_prior4											99.61 146

plane_prior3						97.00 238			98.69 46	99.11 159

plane_prior2							99.39 168		98.97 22

plane_prior1					99.26 173

n2								0.00 346

nn								0.00 346

door-mid								98.05 319

test11								99.35 195

door								97.92 320

HQP5-MVS						96.83 246

HQP-NCC					99.19 182		98.98 271		98.24 72	98.66 223

ACMP_Plane					99.19 182		98.98 271		98.24 72	98.66 223

BP-MVS														97.19 206

HQP4-MVS										98.66 223			99.64 180			98.64 247

HQP2-MVS												92.47 250

NP-MVS					99.23 176	96.92 244					99.40 207

MDTV_nov1_ep13_2view						95.18 286	99.35 183		96.84 198	99.58 60		95.19 155		97.82 156		99.46 133

Test By Simon												98.75 44

High-resolution multi-view benchmark