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.

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 100	99.60 90	99.45 151	99.01 13	99.90 1	99.83 37	98.98 19	99.93 57	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 99	99.61 88	99.45 151	99.01 13	99.89 2	99.82 44	99.01 12	99.92 65	99.56 5	99.95 6	99.85 8

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 44	99.41 73	99.58 99	99.49 105	99.02 10	99.88 3	99.80 65	99.00 18	99.94 42	99.45 15	99.92 12	99.84 12

SD-MVS		99.41 33	99.52 6	99.05 146	99.74 67	99.68 33	99.46 154	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 332	98.72 80	99.93 11	99.77 52

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 6	99.96 19	99.22 31	99.92 12	99.90 1

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 44	99.29 84	99.58 99	99.44 159	99.01 13	99.87 6	99.80 65	98.97 20	99.91 74	99.44 16	99.92 12	99.83 23

Vis-MVSNet		99.12 69	98.97 73	99.56 76	99.78 36	99.10 103	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 188	99.84 119	99.19 33	99.41 110	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SMA-MVS		99.47 20	99.34 24	99.86 13	99.73 72	99.85 6	99.56 112	99.50 99	97.61 144	99.84 8	99.82 44	99.28 3	99.91 74	98.79 72	99.91 17	99.81 36

xiu_mvs_v1_base_debu		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base_debi		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

Regformer-1		99.53 9	99.47 8	99.72 49	99.71 82	99.44 70	99.49 142	99.46 139	98.95 24	99.83 12	99.76 88	99.01 12	99.93 57	99.17 36	99.87 39	99.80 42

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 82	99.52 61	99.49 142	99.49 105	98.94 26	99.83 12	99.76 88	99.01 12	99.94 42	99.15 38	99.87 39	99.80 42

DeepPCF-MVS	98.18 3	98.81 111	99.37 17	97.12 303	99.60 119	91.75 331	98.61 322	99.44 159	99.35 1	99.83 12	99.85 26	98.70 51	99.81 139	99.02 48	99.91 17	99.81 36

TSAR-MVS + GP.		99.36 39	99.36 19	99.36 106	99.67 93	98.61 185	99.07 263	99.33 215	99.00 17	99.82 15	99.81 54	99.06 9	99.84 119	99.09 42	99.42 109	99.65 91

abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 65	99.59 38	98.13 82	99.82 15	99.81 54	98.60 57	99.96 19	98.46 112	99.88 35	99.79 46

MVSFormer		99.17 60	99.12 55	99.29 118	99.51 132	98.94 134	99.88 1	99.46 139	97.55 150	99.80 17	99.65 131	97.39 95	99.28 252	99.03 46	99.85 53	99.65 91

lupinMVS		99.13 64	99.01 69	99.46 95	99.51 132	98.94 134	99.05 269	99.16 253	97.86 117	99.80 17	99.56 165	97.39 95	99.86 107	98.94 54	99.85 53	99.58 111

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 19	99.76 44	99.83 8	99.63 79	99.54 62	98.36 65	99.79 19	99.82 44	98.86 32	99.95 33	98.62 91	99.81 69	99.78 50

jason		99.13 64	99.03 65	99.45 96	99.46 144	98.87 142	99.12 252	99.26 242	98.03 101	99.79 19	99.65 131	97.02 105	99.85 113	99.02 48	99.90 25	99.65 91
jason: jason.
SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 14	99.59 92	99.51 85	98.62 49	99.79 19	99.83 37	99.28 3	99.97 11	98.48 109	99.90 25	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 109	99.59 49	99.36 195	99.46 139	99.07 9	99.79 19	99.82 44	98.85 33	99.92 65	98.68 85	99.87 39	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
TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 79	99.39 181	98.91 29	99.78 23	99.85 26	99.36 2	99.94 42	98.84 66	99.88 35	99.82 32

test_part2					99.81 32	99.83 8				99.77 24

ESAPD		99.31 45	99.13 53	99.87 6	99.81 32	99.83 8	99.37 189	99.48 114	97.97 108	99.77 24	99.78 78	98.96 21	99.95 33	97.15 213	99.84 58	99.83 23

HSP-MVS		99.41 33	99.26 44	99.85 19	99.89 8	99.80 15	99.67 56	99.37 194	98.70 45	99.77 24	99.49 191	98.21 76	99.95 33	98.46 112	99.77 77	99.81 36

UA-Net		99.42 30	99.29 37	99.80 31	99.62 113	99.55 54	99.50 134	99.70 15	98.79 40	99.77 24	99.96 1	97.45 94	99.96 19	98.92 55	99.90 25	99.89 2

APD-MVS		99.27 51	99.08 58	99.84 23	99.75 56	99.79 19	99.50 134	99.50 99	97.16 184	99.77 24	99.82 44	98.78 39	99.94 42	97.56 184	99.86 49	99.80 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 151	99.48 114	98.05 98	99.76 29	99.86 22	98.82 35	99.93 57	98.82 71	99.91 17	99.84 12

HPM-MVS_fast		99.51 12	99.40 14	99.85 19	99.91 1	99.79 19	99.76 27	99.56 48	97.72 135	99.76 29	99.75 93	99.13 7	99.92 65	99.07 44	99.92 12	99.85 8

VNet		99.11 73	98.90 82	99.73 47	99.52 130	99.56 52	99.41 175	99.39 181	99.01 13	99.74 31	99.78 78	95.56 146	99.92 65	99.52 7	98.18 184	99.72 72

xiu_mvs_v2_base		99.26 53	99.25 45	99.29 118	99.53 129	98.91 139	99.02 278	99.45 151	98.80 39	99.71 32	99.26 257	98.94 27	99.98 5	99.34 22	99.23 120	98.98 182

PS-MVSNAJ		99.32 43	99.32 27	99.30 115	99.57 124	98.94 134	98.97 291	99.46 139	98.92 28	99.71 32	99.24 259	99.01 12	99.98 5	99.35 18	99.66 98	98.97 183

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 99	99.65 30	97.84 121	99.71 32	99.80 65	99.12 8	99.97 11	98.33 122	99.87 39	99.83 23

114514_t		98.93 96	98.67 109	99.72 49	99.85 23	99.53 58	99.62 82	99.59 38	92.65 322	99.71 32	99.78 78	98.06 81	99.90 87	98.84 66	99.91 17	99.74 61

PVSNet_Blended_VisFu		99.36 39	99.28 39	99.61 68	99.86 20	99.07 106	99.47 151	99.93 2	97.66 142	99.71 32	99.86 22	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

tfpn1000		98.33 140	98.02 154	99.25 125	99.78 36	98.73 170	99.70 42	97.55 344	97.48 156	99.69 37	99.53 178	92.37 267	99.85 113	97.82 157	98.26 179	99.16 160

zzz-MVS		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 195	99.47 130	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 65	99.47 130	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 65	99.67 22	98.15 80	99.68 38	99.69 115	99.06 9	99.96 19	98.69 83	99.87 39	99.84 12

#test#		99.43 28	99.29 37	99.86 13	99.87 15	99.80 15	99.55 118	99.67 22	97.83 122	99.68 38	99.69 115	99.06 9	99.96 19	98.39 115	99.87 39	99.84 12

VDDNet		97.55 244	97.02 260	99.16 134	99.49 139	98.12 211	99.38 187	99.30 224	95.35 284	99.68 38	99.90 7	82.62 342	99.93 57	99.31 25	98.13 192	99.42 144

HPM-MVS		99.42 30	99.28 39	99.83 24	99.90 3	99.72 28	99.81 15	99.54 62	97.59 145	99.68 38	99.63 142	98.91 29	99.94 42	98.58 96	99.91 17	99.84 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS		97.73 227	97.35 241	98.88 184	99.47 143	97.12 244	99.34 202	98.85 289	98.19 76	99.67 44	99.85 26	82.98 340	99.92 65	99.49 12	98.32 174	99.60 105

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 65	99.67 22	98.15 80	99.67 44	99.69 115	98.95 26	99.96 19	98.69 83	99.87 39	99.84 12

PVSNet_BlendedMVS		98.86 102	98.80 96	99.03 147	99.76 44	98.79 165	99.28 216	99.91 3	97.42 163	99.67 44	99.37 228	97.53 92	99.88 102	98.98 51	97.29 237	98.42 297

PVSNet_Blended		99.08 79	98.97 73	99.42 103	99.76 44	98.79 165	98.78 311	99.91 3	96.74 218	99.67 44	99.49 191	97.53 92	99.88 102	98.98 51	99.85 53	99.60 105

sss		99.17 60	99.05 60	99.53 81	99.62 113	98.97 126	99.36 195	99.62 31	97.83 122	99.67 44	99.65 131	97.37 98	99.95 33	99.19 33	99.19 123	99.68 84

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 15	99.65 75	99.66 25	98.13 82	99.66 49	99.68 120	98.96 21	99.96 19	98.62 91	99.87 39	99.84 12

RPSCF		98.22 150	98.62 117	96.99 304	99.82 29	91.58 332	99.72 39	99.44 159	96.61 227	99.66 49	99.89 10	95.92 137	99.82 135	97.46 195	99.10 129	99.57 112

OMC-MVS		99.08 79	99.04 63	99.20 132	99.67 93	98.22 206	99.28 216	99.52 76	98.07 93	99.66 49	99.81 54	97.79 87	99.78 154	97.79 160	99.81 69	99.60 105

LFMVS		97.90 199	97.35 241	99.54 77	99.52 130	99.01 119	99.39 182	98.24 325	97.10 192	99.65 52	99.79 73	84.79 336	99.91 74	99.28 27	98.38 172	99.69 80

MVS_111021_LR		99.41 33	99.33 26	99.65 59	99.77 41	99.51 63	98.94 299	99.85 6	98.82 35	99.65 52	99.74 98	98.51 59	99.80 143	98.83 68	99.89 33	99.64 97

CPTT-MVS		99.11 73	98.90 82	99.74 45	99.80 34	99.46 68	99.59 92	99.49 105	97.03 203	99.63 54	99.69 115	97.27 100	99.96 19	97.82 157	99.84 58	99.81 36

ACMMP		99.45 23	99.32 27	99.82 26	99.89 8	99.67 35	99.62 82	99.69 18	98.12 84	99.63 54	99.84 35	98.73 49	99.96 19	98.55 104	99.83 64	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
DeepC-MVS	98.35 2	99.30 46	99.19 48	99.64 64	99.82 29	99.23 91	99.62 82	99.55 55	98.94 26	99.63 54	99.95 2	95.82 141	99.94 42	99.37 17	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		99.12 69	99.13 53	99.08 142	99.66 103	97.89 219	98.43 329	99.71 13	98.88 30	99.62 57	99.76 88	96.63 118	99.70 185	99.46 14	99.99 1	99.66 88

PHI-MVS		99.30 46	99.17 50	99.70 51	99.56 127	99.52 61	99.58 99	99.80 8	97.12 188	99.62 57	99.73 101	98.58 58	99.90 87	98.61 93	99.91 17	99.68 84

tfpn_ndepth		98.17 157	97.84 175	99.15 136	99.75 56	98.76 169	99.61 88	97.39 346	96.92 210	99.61 59	99.38 224	92.19 269	99.86 107	97.57 182	98.13 192	98.82 200

MG-MVS		99.13 64	99.02 68	99.45 96	99.57 124	98.63 180	99.07 263	99.34 207	98.99 18	99.61 59	99.82 44	97.98 83	99.87 104	97.00 222	99.80 71	99.85 8

MP-MVS-pluss		99.37 38	99.20 47	99.88 4	99.90 3	99.87 2	99.30 209	99.52 76	97.18 182	99.60 61	99.79 73	98.79 38	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS		99.13 64	98.91 81	99.80 31	99.75 56	99.71 29	99.15 248	99.41 171	96.60 229	99.60 61	99.55 168	98.83 34	99.90 87	97.48 192	99.83 64	99.78 50

EPP-MVSNet		99.13 64	98.99 70	99.53 81	99.65 105	99.06 107	99.81 15	99.33 215	97.43 161	99.60 61	99.88 14	97.14 102	99.84 119	99.13 39	98.94 142	99.69 80

HyFIR lowres test		99.11 73	98.92 79	99.65 59	99.90 3	99.37 76	99.02 278	99.91 3	97.67 141	99.59 64	99.75 93	95.90 138	99.73 169	99.53 6	99.02 135	99.86 5

MVS_0304		99.06 81	98.86 89	99.66 55	99.51 132	99.36 77	99.22 236	99.51 85	98.95 24	99.58 65	99.65 131	93.74 230	99.98 5	99.66 1	99.95 6	99.64 97

MVS_Test		99.10 76	98.97 73	99.48 90	99.49 139	99.14 100	99.67 56	99.34 207	97.31 171	99.58 65	99.76 88	97.65 91	99.82 135	98.87 61	99.07 132	99.46 138

MDTV_nov1_ep13_2view						95.18 304	99.35 199		96.84 214	99.58 65		95.19 160		97.82 157		99.46 138

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 76	99.40 75	99.05 269	99.66 25	99.14 6	99.57 68	99.80 65	98.46 62	99.94 42	99.57 4	99.84 58	99.60 105
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
CR-MVSNet		98.17 157	97.93 162	98.87 188	99.18 202	98.49 195	99.22 236	99.33 215	96.96 206	99.56 69	99.38 224	94.33 207	99.00 292	94.83 285	98.58 161	99.14 161

RPMNet		96.61 271	95.85 279	98.87 188	99.18 202	98.49 195	99.22 236	99.08 261	88.72 338	99.56 69	97.38 334	94.08 218	99.00 292	86.87 338	98.58 161	99.14 161

IS-MVSNet		99.05 83	98.87 86	99.57 74	99.73 72	99.32 80	99.75 34	99.20 249	98.02 102	99.56 69	99.86 22	96.54 120	99.67 190	98.09 135	99.13 126	99.73 66

conf0.01		98.21 153	97.89 167	99.15 136	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.61 276

conf0.002		98.21 153	97.89 167	99.15 136	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.61 276

thresconf0.02		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.97 183

tfpn_n400		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.97 183

tfpnconf		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.97 183

tfpnview11		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 336	97.10 192	99.55 72	99.54 171	92.70 249	99.79 146	96.90 232	98.12 194	98.97 183

MVS_111021_HR		99.41 33	99.32 27	99.66 55	99.72 76	99.47 67	98.95 297	99.85 6	98.82 35	99.54 78	99.73 101	98.51 59	99.74 161	98.91 56	99.88 35	99.77 52

CP-MVS		99.45 23	99.32 27	99.85 19	99.83 28	99.75 24	99.69 45	99.52 76	98.07 93	99.53 79	99.63 142	98.93 28	99.97 11	98.74 76	99.91 17	99.83 23

WTY-MVS		99.06 81	98.88 85	99.61 68	99.62 113	99.16 96	99.37 189	99.56 48	98.04 99	99.53 79	99.62 147	96.84 110	99.94 42	98.85 65	98.49 168	99.72 72

MCST-MVS		99.43 28	99.30 34	99.82 26	99.79 35	99.74 27	99.29 213	99.40 178	98.79 40	99.52 81	99.62 147	98.91 29	99.90 87	98.64 88	99.75 80	99.82 32

PatchT		97.03 268	96.44 269	98.79 202	98.99 236	98.34 202	99.16 245	99.07 264	92.13 323	99.52 81	97.31 336	94.54 200	98.98 294	88.54 331	98.73 157	99.03 176

CANet		99.25 54	99.14 52	99.59 70	99.41 153	99.16 96	99.35 199	99.57 44	98.82 35	99.51 83	99.61 151	96.46 121	99.95 33	99.59 2	99.98 2	99.65 91

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 45	99.48 114	98.12 84	99.50 84	99.75 93	98.78 39	99.97 11	98.57 98	99.89 33	99.83 23

PatchMatch-RL		98.84 110	98.62 117	99.52 85	99.71 82	99.28 85	99.06 267	99.77 9	97.74 133	99.50 84	99.53 178	95.41 150	99.84 119	97.17 212	99.64 101	99.44 141

PVSNet	96.02 17	98.85 108	98.84 92	98.89 177	99.73 72	97.28 237	98.32 333	99.60 35	97.86 117	99.50 84	99.57 163	96.75 115	99.86 107	98.56 101	99.70 92	99.54 115

LS3D		99.27 51	99.12 55	99.74 45	99.18 202	99.75 24	99.56 112	99.57 44	98.45 59	99.49 87	99.85 26	97.77 88	99.94 42	98.33 122	99.84 58	99.52 120

MP-MVS		99.33 42	99.15 51	99.87 6	99.88 11	99.82 13	99.66 65	99.46 139	98.09 89	99.48 88	99.74 98	98.29 73	99.96 19	97.93 149	99.87 39	99.82 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
旧先验2							98.96 293		96.70 221	99.47 89			99.94 42	98.19 128

MSDG		98.98 92	98.80 96	99.53 81	99.76 44	99.19 93	98.75 314	99.55 55	97.25 176	99.47 89	99.77 85	97.82 86	99.87 104	96.93 229	99.90 25	99.54 115

CDS-MVSNet		99.09 77	99.03 65	99.25 125	99.42 150	98.73 170	99.45 155	99.46 139	98.11 86	99.46 91	99.77 85	98.01 82	99.37 229	98.70 81	98.92 145	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSLP-MVS++		99.46 22	99.47 8	99.44 99	99.60 119	99.16 96	99.41 175	99.71 13	98.98 19	99.45 92	99.78 78	99.19 5	99.54 210	99.28 27	99.84 58	99.63 101

XVG-OURS		98.73 119	98.68 108	98.88 184	99.70 87	97.73 232	98.92 300	99.55 55	98.52 55	99.45 92	99.84 35	95.27 154	99.91 74	98.08 139	98.84 151	99.00 179

tpmrst		98.33 140	98.48 127	97.90 279	99.16 209	94.78 309	99.31 207	99.11 258	97.27 174	99.45 92	99.59 156	95.33 151	99.84 119	98.48 109	98.61 158	99.09 168

TAMVS		99.12 69	99.08 58	99.24 128	99.46 144	98.55 187	99.51 129	99.46 139	98.09 89	99.45 92	99.82 44	98.34 71	99.51 211	98.70 81	98.93 143	99.67 87

CANet_DTU		98.97 94	98.87 86	99.25 125	99.33 170	98.42 201	99.08 262	99.30 224	99.16 5	99.43 96	99.75 93	95.27 154	99.97 11	98.56 101	99.95 6	99.36 149

Patchmatch-test1		98.16 159	98.14 143	98.22 259	99.30 179	95.55 292	99.07 263	98.97 274	97.57 148	99.43 96	99.60 154	92.72 246	99.60 204	97.38 200	99.20 122	99.50 128

testdata				99.54 77	99.75 56	98.95 131		99.51 85	97.07 199	99.43 96	99.70 109	98.87 31	99.94 42	97.76 164	99.64 101	99.72 72

XVG-OURS-SEG-HR		98.69 122	98.62 117	98.89 177	99.71 82	97.74 231	99.12 252	99.54 62	98.44 62	99.42 99	99.71 106	94.20 211	99.92 65	98.54 106	98.90 147	99.00 179

DP-MVS Recon		99.12 69	98.95 77	99.65 59	99.74 67	99.70 31	99.27 219	99.57 44	96.40 247	99.42 99	99.68 120	98.75 47	99.80 143	97.98 145	99.72 86	99.44 141

Effi-MVS+-dtu		98.78 115	98.89 84	98.47 231	99.33 170	96.91 262	99.57 105	99.30 224	98.47 57	99.41 101	98.99 279	96.78 112	99.74 161	98.73 78	99.38 111	98.74 213

MIMVSNet		97.73 227	97.45 223	98.57 220	99.45 148	97.50 235	99.02 278	98.98 273	96.11 270	99.41 101	99.14 266	90.28 296	98.74 307	95.74 266	98.93 143	99.47 135

CSCG		99.32 43	99.32 27	99.32 111	99.85 23	98.29 203	99.71 41	99.66 25	98.11 86	99.41 101	99.80 65	98.37 70	99.96 19	98.99 50	99.96 5	99.72 72

F-COLMAP		99.19 57	99.04 63	99.64 64	99.78 36	99.27 87	99.42 171	99.54 62	97.29 173	99.41 101	99.59 156	98.42 67	99.93 57	98.19 128	99.69 93	99.73 66

MDTV_nov1_ep13			98.32 135		99.11 217	94.44 313	99.27 219	98.74 301	97.51 154	99.40 105	99.62 147	94.78 184	99.76 159	97.59 179	98.81 154

CVMVSNet		98.57 129	98.67 109	98.30 246	99.35 166	95.59 291	99.50 134	99.55 55	98.60 51	99.39 106	99.83 37	94.48 202	99.45 215	98.75 75	98.56 164	99.85 8

CNVR-MVS		99.42 30	99.30 34	99.78 35	99.62 113	99.71 29	99.26 227	99.52 76	98.82 35	99.39 106	99.71 106	98.96 21	99.85 113	98.59 95	99.80 71	99.77 52

Effi-MVS+		98.81 111	98.59 122	99.48 90	99.46 144	99.12 102	98.08 339	99.50 99	97.50 155	99.38 108	99.41 215	96.37 125	99.81 139	99.11 41	98.54 165	99.51 125

mvs_anonymous		99.03 86	98.99 70	99.16 134	99.38 161	98.52 192	99.51 129	99.38 187	97.79 127	99.38 108	99.81 54	97.30 99	99.45 215	99.35 18	98.99 137	99.51 125

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 8	99.69 45	99.68 19	98.98 19	99.37 110	99.74 98	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

X-MVStestdata		96.55 272	95.45 293	99.87 6	99.85 23	99.83 8	99.69 45	99.68 19	98.98 19	99.37 110	64.01 358	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

diffmvs		98.72 120	98.49 126	99.43 102	99.48 142	99.19 93	99.62 82	99.42 168	95.58 282	99.37 110	99.67 124	96.14 131	99.74 161	98.14 132	98.96 140	99.37 148

PatchmatchNet		98.31 142	98.36 131	98.19 262	99.16 209	95.32 299	99.27 219	98.92 280	97.37 167	99.37 110	99.58 159	94.90 176	99.70 185	97.43 198	99.21 121	99.54 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AllTest		98.87 99	98.72 103	99.31 112	99.86 20	98.48 197	99.56 112	99.61 32	97.85 119	99.36 114	99.85 26	95.95 134	99.85 113	96.66 248	99.83 64	99.59 109

TestCases				99.31 112	99.86 20	98.48 197		99.61 32	97.85 119	99.36 114	99.85 26	95.95 134	99.85 113	96.66 248	99.83 64	99.59 109

Vis-MVSNet (Re-imp)		98.87 99	98.72 103	99.31 112	99.71 82	98.88 141	99.80 19	99.44 159	97.91 115	99.36 114	99.78 78	95.49 149	99.43 224	97.91 150	99.11 127	99.62 103

alignmvs		98.81 111	98.56 124	99.58 73	99.43 149	99.42 72	99.51 129	98.96 276	98.61 50	99.35 117	98.92 285	94.78 184	99.77 156	99.35 18	98.11 200	99.54 115

VPA-MVSNet		98.29 143	97.95 160	99.30 115	99.16 209	99.54 55	99.50 134	99.58 43	98.27 71	99.35 117	99.37 228	92.53 260	99.65 194	99.35 18	94.46 294	98.72 215

AdaColmap		99.01 90	98.80 96	99.66 55	99.56 127	99.54 55	99.18 243	99.70 15	98.18 79	99.35 117	99.63 142	96.32 126	99.90 87	97.48 192	99.77 77	99.55 113

test222					99.75 56	99.49 64	98.91 302	99.49 105	96.42 244	99.34 120	99.65 131	98.28 74			99.69 93	99.72 72

API-MVS		99.04 84	99.03 65	99.06 144	99.40 158	99.31 83	99.55 118	99.56 48	98.54 53	99.33 121	99.39 223	98.76 44	99.78 154	96.98 224	99.78 75	98.07 309

v144192		97.92 197	97.60 207	98.87 188	98.83 274	98.65 178	99.55 118	99.34 207	96.20 261	99.32 122	99.40 219	94.36 206	99.26 260	96.37 257	95.03 280	98.70 221

canonicalmvs		99.02 87	98.86 89	99.51 87	99.42 150	99.32 80	99.80 19	99.48 114	98.63 48	99.31 123	98.81 294	97.09 103	99.75 160	99.27 29	97.90 206	99.47 135

v6		98.12 163	97.84 175	98.94 159	98.94 251	98.83 149	99.66 65	99.34 207	96.49 234	99.30 124	99.37 228	94.95 170	99.34 239	97.77 163	94.74 284	98.67 242

V42		98.06 169	97.79 180	98.86 192	98.98 240	98.84 146	99.69 45	99.34 207	96.53 233	99.30 124	99.37 228	94.67 194	99.32 243	97.57 182	94.66 290	98.42 297

ab-mvs		98.86 102	98.63 114	99.54 77	99.64 106	99.19 93	99.44 159	99.54 62	97.77 129	99.30 124	99.81 54	94.20 211	99.93 57	99.17 36	98.82 152	99.49 129

TAPA-MVS	97.07 15	97.74 226	97.34 244	98.94 159	99.70 87	97.53 234	99.25 229	99.51 85	91.90 326	99.30 124	99.63 142	98.78 39	99.64 196	88.09 333	99.87 39	99.65 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1neww		98.12 163	97.84 175	98.93 162	98.97 243	98.81 158	99.66 65	99.35 199	96.49 234	99.29 128	99.37 228	95.02 166	99.32 243	97.73 168	94.73 285	98.67 242

v7new		98.12 163	97.84 175	98.93 162	98.97 243	98.81 158	99.66 65	99.35 199	96.49 234	99.29 128	99.37 228	95.02 166	99.32 243	97.73 168	94.73 285	98.67 242

新几何1				99.75 40	99.75 56	99.59 49		99.54 62	96.76 217	99.29 128	99.64 138	98.43 64	99.94 42	96.92 230	99.66 98	99.72 72

v7		98.05 175	97.78 182	98.87 188	98.99 236	98.67 175	99.64 77	99.34 207	96.31 252	99.29 128	99.51 186	94.78 184	99.27 255	97.03 220	95.15 277	98.66 253

VPNet		97.84 205	97.44 229	99.01 149	99.21 195	98.94 134	99.48 147	99.57 44	98.38 64	99.28 132	99.73 101	88.89 310	99.39 225	99.19 33	93.27 311	98.71 217

v1		98.05 175	97.76 189	98.93 162	98.92 258	98.80 163	99.57 105	99.35 199	96.39 248	99.28 132	99.36 235	94.86 179	99.32 243	97.38 200	94.72 287	98.68 231

HY-MVS	97.30 7	98.85 108	98.64 113	99.47 93	99.42 150	99.08 105	99.62 82	99.36 195	97.39 166	99.28 132	99.68 120	96.44 123	99.92 65	98.37 118	98.22 180	99.40 146

PAPM_NR		99.04 84	98.84 92	99.66 55	99.74 67	99.44 70	99.39 182	99.38 187	97.70 138	99.28 132	99.28 254	98.34 71	99.85 113	96.96 226	99.45 107	99.69 80

HPM-MVS++		99.39 37	99.23 46	99.87 6	99.75 56	99.84 7	99.43 164	99.51 85	98.68 47	99.27 136	99.53 178	98.64 55	99.96 19	98.44 114	99.80 71	99.79 46

v1240		97.69 233	97.32 247	98.79 202	98.85 272	98.43 199	99.48 147	99.36 195	96.11 270	99.27 136	99.36 235	93.76 228	99.24 263	94.46 291	95.23 274	98.70 221

thres600view7		97.86 202	97.51 213	98.92 167	99.72 76	97.95 218	99.59 92	98.74 301	97.94 111	99.27 136	98.62 301	91.75 277	99.86 107	93.73 307	98.19 183	98.96 189

PLC	97.94 4	99.02 87	98.85 91	99.53 81	99.66 103	99.01 119	99.24 231	99.52 76	96.85 213	99.27 136	99.48 197	98.25 75	99.91 74	97.76 164	99.62 104	99.65 91
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpn111		97.81 211	97.49 217	98.78 204	99.72 76	97.86 221	99.59 92	98.74 301	97.93 112	99.26 140	98.62 301	91.75 277	99.86 107	93.57 308	98.18 184	98.61 276

conf200view11		97.78 218	97.45 223	98.77 205	99.72 76	97.86 221	99.59 92	98.74 301	97.93 112	99.26 140	98.62 301	91.75 277	99.83 126	93.22 312	98.18 184	98.61 276

thres100view900		97.76 220	97.45 223	98.69 211	99.72 76	97.86 221	99.59 92	98.74 301	97.93 112	99.26 140	98.62 301	91.75 277	99.83 126	93.22 312	98.18 184	98.37 301

EPMVS		97.82 210	97.65 203	98.35 242	98.88 265	95.98 286	99.49 142	94.71 352	97.57 148	99.26 140	99.48 197	92.46 265	99.71 179	97.87 153	99.08 131	99.35 150

view600		97.97 188	97.66 198	98.89 177	99.75 56	97.81 225	99.69 45	98.80 293	98.02 102	99.25 144	98.88 286	91.95 271	99.89 95	94.36 294	98.29 175	98.96 189

view800		97.97 188	97.66 198	98.89 177	99.75 56	97.81 225	99.69 45	98.80 293	98.02 102	99.25 144	98.88 286	91.95 271	99.89 95	94.36 294	98.29 175	98.96 189

conf0.05thres1000		97.97 188	97.66 198	98.89 177	99.75 56	97.81 225	99.69 45	98.80 293	98.02 102	99.25 144	98.88 286	91.95 271	99.89 95	94.36 294	98.29 175	98.96 189

tfpn		97.97 188	97.66 198	98.89 177	99.75 56	97.81 225	99.69 45	98.80 293	98.02 102	99.25 144	98.88 286	91.95 271	99.89 95	94.36 294	98.29 175	98.96 189

1121		99.09 77	98.87 86	99.75 40	99.74 67	99.60 47	99.27 219	99.48 114	96.82 216	99.25 144	99.65 131	98.38 68	99.93 57	97.53 187	99.67 97	99.73 66

Fast-Effi-MVS+-dtu		98.77 117	98.83 95	98.60 217	99.41 153	96.99 256	99.52 125	99.49 105	98.11 86	99.24 149	99.34 242	96.96 108	99.79 146	97.95 148	99.45 107	99.02 178

v1921920		97.80 214	97.45 223	98.84 196	98.80 275	98.53 189	99.52 125	99.34 207	96.15 267	99.24 149	99.47 201	93.98 220	99.29 251	95.40 275	95.13 278	98.69 226

divwei89l23v2f112		98.06 169	97.78 182	98.91 171	98.90 261	98.77 168	99.57 105	99.35 199	96.45 241	99.24 149	99.37 228	94.92 174	99.27 255	97.50 190	94.71 289	98.68 231

LPG-MVS_test		98.22 150	98.13 144	98.49 227	99.33 170	97.05 251	99.58 99	99.55 55	97.46 157	99.24 149	99.83 37	92.58 258	99.72 173	98.09 135	97.51 221	98.68 231

LGP-MVS_train				98.49 227	99.33 170	97.05 251		99.55 55	97.46 157	99.24 149	99.83 37	92.58 258	99.72 173	98.09 135	97.51 221	98.68 231

v1144		97.98 185	97.69 196	98.85 195	98.87 268	98.66 177	99.54 121	99.35 199	96.27 255	99.23 154	99.35 239	94.67 194	99.23 264	96.73 243	95.16 276	98.68 231

v1141		98.05 175	97.76 189	98.91 171	98.91 260	98.78 167	99.57 105	99.35 199	96.41 246	99.23 154	99.36 235	94.93 173	99.27 255	97.38 200	94.72 287	98.68 231

OPM-MVS		98.19 156	98.10 146	98.45 233	98.88 265	97.07 249	99.28 216	99.38 187	98.57 52	99.22 156	99.81 54	92.12 270	99.66 192	98.08 139	97.54 220	98.61 276

test_djsdf		98.67 124	98.57 123	98.98 153	98.70 292	98.91 139	99.88 1	99.46 139	97.55 150	99.22 156	99.88 14	95.73 144	99.28 252	99.03 46	97.62 213	98.75 210

test12				99.75 40	99.64 106	99.61 45		99.29 229		99.21 158		98.38 68	99.89 95		99.74 82	99.74 61

NCCC		99.34 41	99.19 48	99.79 34	99.61 117	99.65 40	99.30 209	99.48 114	98.86 31	99.21 158	99.63 142	98.72 50	99.90 87	98.25 126	99.63 103	99.80 42

PMMVS		98.80 114	98.62 117	99.34 107	99.27 187	98.70 173	98.76 313	99.31 222	97.34 168	99.21 158	99.07 272	97.20 101	99.82 135	98.56 101	98.87 149	99.52 120

v1192		97.81 211	97.44 229	98.91 171	98.88 265	98.68 174	99.51 129	99.34 207	96.18 263	99.20 161	99.34 242	94.03 219	99.36 233	95.32 277	95.18 275	98.69 226

EI-MVSNet		98.67 124	98.67 109	98.68 212	99.35 166	97.97 215	99.50 134	99.38 187	96.93 209	99.20 161	99.83 37	97.87 84	99.36 233	98.38 117	97.56 218	98.71 217

MVSTER		98.49 130	98.32 135	99.00 151	99.35 166	99.02 117	99.54 121	99.38 187	97.41 164	99.20 161	99.73 101	93.86 225	99.36 233	98.87 61	97.56 218	98.62 267

v2v482		98.06 169	97.77 186	98.92 167	98.90 261	98.82 156	99.57 105	99.36 195	96.65 224	99.19 164	99.35 239	94.20 211	99.25 261	97.72 172	94.97 281	98.69 226

CNLPA		99.14 63	98.99 70	99.59 70	99.58 122	99.41 73	99.16 245	99.44 159	98.45 59	99.19 164	99.49 191	98.08 80	99.89 95	97.73 168	99.75 80	99.48 131

UGNet		98.87 99	98.69 107	99.40 104	99.22 194	98.72 172	99.44 159	99.68 19	99.24 3	99.18 166	99.42 212	92.74 245	99.96 19	99.34 22	99.94 10	99.53 119
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
tfpn200view9		97.72 229	97.38 237	98.72 209	99.69 89	97.96 216	99.50 134	98.73 310	97.83 122	99.17 167	98.45 310	91.67 283	99.83 126	93.22 312	98.18 184	98.37 301

thres400		97.77 219	97.38 237	98.92 167	99.69 89	97.96 216	99.50 134	98.73 310	97.83 122	99.17 167	98.45 310	91.67 283	99.83 126	93.22 312	98.18 184	98.96 189

Test_1112_low_res		98.89 98	98.66 112	99.57 74	99.69 89	98.95 131	99.03 275	99.47 130	96.98 205	99.15 169	99.23 260	96.77 114	99.89 95	98.83 68	98.78 155	99.86 5

1112_ss		98.98 92	98.77 99	99.59 70	99.68 92	99.02 117	99.25 229	99.48 114	97.23 179	99.13 170	99.58 159	96.93 109	99.90 87	98.87 61	98.78 155	99.84 12

CLD-MVS		98.16 159	98.10 146	98.33 243	99.29 182	96.82 265	98.75 314	99.44 159	97.83 122	99.13 170	99.55 168	92.92 239	99.67 190	98.32 124	97.69 210	98.48 293
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
原ACMM1				99.65 59	99.73 72	99.33 79		99.47 130	97.46 157	99.12 172	99.66 130	98.67 54	99.91 74	97.70 173	99.69 93	99.71 79

tpm		97.67 238	97.55 209	98.03 268	99.02 233	95.01 306	99.43 164	98.54 320	96.44 242	99.12 172	99.34 242	91.83 276	99.60 204	97.75 166	96.46 251	99.48 131

HQP_MVS		98.27 145	98.22 141	98.44 236	99.29 182	96.97 258	99.39 182	99.47 130	98.97 22	99.11 174	99.61 151	92.71 247	99.69 188	97.78 161	97.63 211	98.67 242

plane_prior3						97.00 255			98.69 46	99.11 174

CHOSEN 1792x2688		99.19 57	99.10 57	99.45 96	99.89 8	98.52 192	99.39 182	99.94 1	98.73 44	99.11 174	99.89 10	95.50 148	99.94 42	99.50 8	99.97 3	99.89 2

mvs-test1		98.86 102	98.84 92	98.89 177	99.33 170	97.77 230	99.44 159	99.30 224	98.47 57	99.10 177	99.43 210	96.78 112	99.95 33	98.73 78	99.02 135	98.96 189

v8		97.95 193	97.63 205	98.93 162	98.95 248	98.81 158	99.80 19	99.41 171	96.03 275	99.10 177	99.42 212	94.92 174	99.30 249	96.94 228	94.08 302	98.66 253

ADS-MVSNet2		98.02 180	98.07 151	97.87 280	99.33 170	95.19 303	99.23 232	99.08 261	96.24 258	99.10 177	99.67 124	94.11 216	98.93 303	96.81 239	99.05 133	99.48 131

ADS-MVSNet		98.20 155	98.08 149	98.56 222	99.33 170	96.48 275	99.23 232	99.15 254	96.24 258	99.10 177	99.67 124	94.11 216	99.71 179	96.81 239	99.05 133	99.48 131

thres200		97.61 242	97.28 251	98.62 216	99.64 106	98.03 212	99.26 227	98.74 301	97.68 140	99.09 181	98.32 312	91.66 285	99.81 139	92.88 318	98.22 180	98.03 313

dp		97.75 224	97.80 179	97.59 294	99.10 220	93.71 321	99.32 204	98.88 287	96.48 240	99.08 182	99.55 168	92.67 256	99.82 135	96.52 252	98.58 161	99.24 157

GBi-Net		97.68 235	97.48 218	98.29 247	99.51 132	97.26 239	99.43 164	99.48 114	96.49 234	99.07 183	99.32 248	90.26 297	98.98 294	97.10 216	96.65 246	98.62 267

test1		97.68 235	97.48 218	98.29 247	99.51 132	97.26 239	99.43 164	99.48 114	96.49 234	99.07 183	99.32 248	90.26 297	98.98 294	97.10 216	96.65 246	98.62 267

FMVSNet3		98.03 178	97.76 189	98.84 196	99.39 160	98.98 123	99.40 181	99.38 187	96.67 223	99.07 183	99.28 254	92.93 238	98.98 294	97.10 216	96.65 246	98.56 289

IterMVS-LS		98.46 132	98.42 129	98.58 219	99.59 121	98.00 213	99.37 189	99.43 167	96.94 208	99.07 183	99.59 156	97.87 84	99.03 288	98.32 124	95.62 268	98.71 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		98.13 161	97.90 163	98.81 199	98.61 302	98.87 142	98.99 284	99.21 248	96.44 242	99.06 187	99.58 159	95.90 138	99.11 280	97.18 211	96.11 258	98.46 296

XVG-ACMP-BASELINE		97.83 207	97.71 195	98.20 261	99.11 217	96.33 280	99.41 175	99.52 76	98.06 97	99.05 188	99.50 188	89.64 304	99.73 169	97.73 168	97.38 234	98.53 290

CostFormer		97.72 229	97.73 193	97.71 291	99.15 212	94.02 317	99.54 121	99.02 270	94.67 292	99.04 189	99.35 239	92.35 268	99.77 156	98.50 108	97.94 205	99.34 151

DP-MVS		99.16 62	98.95 77	99.78 35	99.77 41	99.53 58	99.41 175	99.50 99	97.03 203	99.04 189	99.88 14	97.39 95	99.92 65	98.66 86	99.90 25	99.87 4

ACMM	97.58 5	98.37 139	98.34 133	98.48 229	99.41 153	97.10 245	99.56 112	99.45 151	98.53 54	99.04 189	99.85 26	93.00 237	99.71 179	98.74 76	97.45 228	98.64 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		98.70 121	98.43 128	99.51 87	99.51 132	99.28 85	99.52 125	99.47 130	96.11 270	99.01 192	99.34 242	96.20 130	99.84 119	97.88 152	98.82 152	99.39 147

nrg030		98.64 127	98.42 129	99.28 120	99.05 229	99.69 32	99.81 15	99.46 139	98.04 99	99.01 192	99.82 44	96.69 117	99.38 226	99.34 22	94.59 293	98.78 204

test_prior3		99.21 56	99.05 60	99.68 52	99.67 93	99.48 65	98.96 293	99.56 48	98.34 66	99.01 192	99.52 183	98.68 52	99.83 126	97.96 146	99.74 82	99.74 61

test_prior2							98.96 293		98.34 66	99.01 192	99.52 183	98.68 52		97.96 146	99.74 82

v52		97.79 216	97.50 215	98.66 215	98.80 275	98.62 182	99.87 4	99.44 159	95.87 277	99.01 192	99.46 205	94.44 205	99.33 240	96.65 250	93.96 305	98.05 310

V4		97.80 214	97.51 213	98.67 214	98.79 277	98.63 180	99.87 4	99.44 159	95.87 277	99.01 192	99.46 205	94.52 201	99.33 240	96.64 251	93.97 304	98.05 310

MAR-MVS		98.86 102	98.63 114	99.54 77	99.37 163	99.66 37	99.45 155	99.54 62	96.61 227	99.01 192	99.40 219	97.09 103	99.86 107	97.68 176	99.53 106	99.10 164
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
PS-MVSNAJss		98.92 97	98.92 79	98.90 175	98.78 281	98.53 189	99.78 22	99.54 62	98.07 93	99.00 199	99.76 88	99.01 12	99.37 229	99.13 39	97.23 238	98.81 201

PAPR		98.63 128	98.34 133	99.51 87	99.40 158	99.03 116	98.80 309	99.36 195	96.33 249	99.00 199	99.12 270	98.46 62	99.84 119	95.23 278	99.37 115	99.66 88

v10		97.85 203	97.52 211	98.86 192	98.99 236	98.67 175	99.75 34	99.41 171	95.70 280	98.98 201	99.41 215	94.75 190	99.23 264	96.01 262	94.63 292	98.67 242

UniMVSNet (Re)		98.29 143	98.00 156	99.13 140	99.00 235	99.36 77	99.49 142	99.51 85	97.95 110	98.97 202	99.13 267	96.30 127	99.38 226	98.36 120	93.34 310	98.66 253

TEST9					99.67 93	99.65 40	99.05 269	99.41 171	96.22 260	98.95 203	99.49 191	98.77 42	99.91 74

train_agg		99.02 87	98.77 99	99.77 37	99.67 93	99.65 40	99.05 269	99.41 171	96.28 253	98.95 203	99.49 191	98.76 44	99.91 74	97.63 177	99.72 86	99.75 56

BH-RMVSNet		98.41 136	98.08 149	99.40 104	99.41 153	98.83 149	99.30 209	98.77 297	97.70 138	98.94 205	99.65 131	92.91 241	99.74 161	96.52 252	99.55 105	99.64 97

test_8					99.67 93	99.61 45	99.03 275	99.41 171	96.28 253	98.93 206	99.48 197	98.76 44	99.91 74

3Dnovator	97.25 9	99.24 55	99.05 60	99.81 29	99.12 215	99.66 37	99.84 9	99.74 10	99.09 8	98.92 207	99.90 7	95.94 136	99.98 5	98.95 53	99.92 12	99.79 46

v7n		97.87 201	97.52 211	98.92 167	98.76 285	98.58 186	99.84 9	99.46 139	96.20 261	98.91 208	99.70 109	94.89 177	99.44 220	96.03 261	93.89 306	98.75 210

JIA-IIPM		97.50 251	97.02 260	98.93 162	98.73 287	97.80 229	99.30 209	98.97 274	91.73 327	98.91 208	94.86 343	95.10 163	99.71 179	97.58 180	97.98 204	99.28 155

v148		97.79 216	97.55 209	98.50 226	98.74 286	97.72 233	99.54 121	99.33 215	96.26 256	98.90 210	99.51 186	94.68 193	99.14 274	97.83 156	93.15 313	98.63 265

GA-MVS		97.85 203	97.47 220	99.00 151	99.38 161	97.99 214	98.57 324	99.15 254	97.04 202	98.90 210	99.30 251	89.83 302	99.38 226	96.70 245	98.33 173	99.62 103

tpm2		97.44 256	97.34 244	97.74 290	99.15 212	94.36 314	99.45 155	98.94 277	93.45 317	98.90 210	99.44 209	91.35 288	99.59 206	97.31 203	98.07 201	99.29 154

agg_prior3		98.97 94	98.71 105	99.75 40	99.67 93	99.60 47	99.04 274	99.41 171	95.93 276	98.87 213	99.48 197	98.61 56	99.91 74	97.63 177	99.72 86	99.75 56

agg_prior1		99.01 90	98.76 101	99.76 39	99.67 93	99.62 43	98.99 284	99.40 178	96.26 256	98.87 213	99.49 191	98.77 42	99.91 74	97.69 174	99.72 86	99.75 56

agg_prior					99.67 93	99.62 43		99.40 178		98.87 213			99.91 74

anonymousdsp		98.44 133	98.28 138	98.94 159	98.50 308	98.96 130	99.77 24	99.50 99	97.07 199	98.87 213	99.77 85	94.76 189	99.28 252	98.66 86	97.60 214	98.57 288

DSMNet-mixed		97.25 262	97.35 241	96.95 306	97.84 318	93.61 323	99.57 105	96.63 348	96.13 269	98.87 213	98.61 305	94.59 197	97.70 332	95.08 280	98.86 150	99.55 113

FMVSNet2		97.72 229	97.36 239	98.80 201	99.51 132	98.84 146	99.45 155	99.42 168	96.49 234	98.86 218	99.29 253	90.26 297	98.98 294	96.44 254	96.56 249	98.58 287

PatchFormer-LS_test		98.01 183	98.05 152	97.87 280	99.15 212	94.76 310	99.42 171	98.93 278	97.12 188	98.84 219	98.59 306	93.74 230	99.80 143	98.55 104	98.17 190	99.06 174

ITE_SJBPF				98.08 266	99.29 182	96.37 278		98.92 280	98.34 66	98.83 220	99.75 93	91.09 290	99.62 202	95.82 264	97.40 232	98.25 306

Patchmtry		97.75 224	97.40 235	98.81 199	99.10 220	98.87 142	99.11 258	99.33 215	94.83 289	98.81 221	99.38 224	94.33 207	99.02 289	96.10 259	95.57 269	98.53 290

BH-untuned		98.42 135	98.36 131	98.59 218	99.49 139	96.70 268	99.27 219	99.13 257	97.24 178	98.80 222	99.38 224	95.75 143	99.74 161	97.07 219	99.16 124	99.33 152

FIs		98.78 115	98.63 114	99.23 130	99.18 202	99.54 55	99.83 12	99.59 38	98.28 70	98.79 223	99.81 54	96.75 115	99.37 229	99.08 43	96.38 253	98.78 204

OurMVSNet-221017-0		97.88 200	97.77 186	98.19 262	98.71 291	96.53 273	99.88 1	99.00 271	97.79 127	98.78 224	99.94 3	91.68 282	99.35 236	97.21 207	96.99 244	98.69 226

MVS-HIRNet		95.75 293	95.16 297	97.51 297	99.30 179	93.69 322	98.88 304	95.78 349	85.09 341	98.78 224	92.65 345	91.29 289	99.37 229	94.85 284	99.85 53	99.46 138

tpmvs		97.98 185	98.02 154	97.84 283	99.04 230	94.73 311	99.31 207	99.20 249	96.10 274	98.76 226	99.42 212	94.94 171	99.81 139	96.97 225	98.45 169	98.97 183

Patchmatch-test		97.93 194	97.65 203	98.77 205	99.18 202	97.07 249	99.03 275	99.14 256	96.16 265	98.74 227	99.57 163	94.56 198	99.72 173	93.36 311	99.11 127	99.52 120

QAPM		98.67 124	98.30 137	99.80 31	99.20 197	99.67 35	99.77 24	99.72 11	94.74 291	98.73 228	99.90 7	95.78 142	99.98 5	96.96 226	99.88 35	99.76 55

3Dnovator+	97.12 13	99.18 59	98.97 73	99.82 26	99.17 207	99.68 33	99.81 15	99.51 85	99.20 4	98.72 229	99.89 10	95.68 145	99.97 11	98.86 64	99.86 49	99.81 36

semantic-postprocess				98.06 267	99.57 124	96.36 279		99.49 105	97.18 182	98.71 230	99.72 105	92.70 249	99.14 274	97.44 197	95.86 264	98.67 242

UniMVSNet_NR-MVSNet		98.22 150	97.97 158	98.96 156	98.92 258	98.98 123	99.48 147	99.53 72	97.76 130	98.71 230	99.46 205	96.43 124	99.22 267	98.57 98	92.87 316	98.69 226

DU-MVS		98.08 168	97.79 180	98.96 156	98.87 268	98.98 123	99.41 175	99.45 151	97.87 116	98.71 230	99.50 188	94.82 181	99.22 267	98.57 98	92.87 316	98.68 231

tpm cat1		97.39 258	97.36 239	97.50 298	99.17 207	93.73 319	99.43 164	99.31 222	91.27 328	98.71 230	99.08 271	94.31 209	99.77 156	96.41 256	98.50 167	99.00 179

XXY-MVS		98.38 138	98.09 148	99.24 128	99.26 189	99.32 80	99.56 112	99.55 55	97.45 160	98.71 230	99.83 37	93.23 234	99.63 201	98.88 57	96.32 255	98.76 209

IterMVS		97.83 207	97.77 186	98.02 270	99.58 122	96.27 282	99.02 278	99.48 114	97.22 180	98.71 230	99.70 109	92.75 243	99.13 277	97.46 195	96.00 261	98.67 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		98.75 118	98.62 117	99.15 136	99.08 223	99.45 69	99.86 8	99.60 35	98.23 75	98.70 236	99.82 44	96.80 111	99.22 267	99.07 44	96.38 253	98.79 203

COLMAP_ROB	97.56 6	98.86 102	98.75 102	99.17 133	99.88 11	98.53 189	99.34 202	99.59 38	97.55 150	98.70 236	99.89 10	95.83 140	99.90 87	98.10 134	99.90 25	99.08 169
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		97.76 220	97.41 234	98.82 198	99.06 226	97.87 220	98.87 305	98.56 319	96.63 226	98.68 238	99.22 261	92.49 261	99.65 194	95.40 275	97.79 208	98.95 196

WR-MVS		98.06 169	97.73 193	99.06 144	98.86 271	99.25 89	99.19 242	99.35 199	97.30 172	98.66 239	99.43 210	93.94 221	99.21 271	98.58 96	94.28 297	98.71 217

HQP-NCC					99.19 199		98.98 288		98.24 72	98.66 239

ACMP_Plane					99.19 199		98.98 288		98.24 72	98.66 239

HQP4-MVS										98.66 239			99.64 196			98.64 258

HQP-MVS		98.02 180	97.90 163	98.37 241	99.19 199	96.83 263	98.98 288	99.39 181	98.24 72	98.66 239	99.40 219	92.47 262	99.64 196	97.19 209	97.58 216	98.64 258

LF4IMVS		97.52 247	97.46 222	97.70 292	98.98 240	95.55 292	99.29 213	98.82 292	98.07 93	98.66 239	99.64 138	89.97 301	99.61 203	97.01 221	96.68 245	97.94 317

mvs_tets		98.40 137	98.23 140	98.91 171	98.67 296	98.51 194	99.66 65	99.53 72	98.19 76	98.65 245	99.81 54	92.75 243	99.44 220	99.31 25	97.48 227	98.77 207

TESTMET0.1,1		97.55 244	97.27 253	98.40 239	98.93 256	96.53 273	98.67 318	97.61 343	96.96 206	98.64 246	99.28 254	88.63 316	99.45 215	97.30 204	99.38 111	99.21 158

jajsoiax		98.43 134	98.28 138	98.88 184	98.60 303	98.43 199	99.82 13	99.53 72	98.19 76	98.63 247	99.80 65	93.22 235	99.44 220	99.22 31	97.50 223	98.77 207

Baseline_NR-MVSNet		97.76 220	97.45 223	98.68 212	99.09 222	98.29 203	99.41 175	98.85 289	95.65 281	98.63 247	99.67 124	94.82 181	99.10 282	98.07 141	92.89 315	98.64 258

EPNet		98.86 102	98.71 105	99.30 115	97.20 329	98.18 207	99.62 82	98.91 283	99.28 2	98.63 247	99.81 54	95.96 133	99.99 1	99.24 30	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR		98.06 169	97.90 163	98.55 224	98.79 277	97.10 245	98.67 318	97.75 333	97.34 168	98.61 250	98.85 290	94.45 203	99.45 215	97.25 205	99.38 111	99.10 164

test-mter		97.49 253	97.13 257	98.55 224	98.79 277	97.10 245	98.67 318	97.75 333	96.65 224	98.61 250	98.85 290	88.23 321	99.45 215	97.25 205	99.38 111	99.10 164

FMVSNet1		96.84 269	96.36 270	98.29 247	99.32 177	97.26 239	99.43 164	99.48 114	95.11 286	98.55 252	99.32 248	83.95 339	98.98 294	95.81 265	96.26 256	98.62 267

v748		97.52 247	97.23 254	98.41 238	98.69 293	97.23 242	99.87 4	99.45 151	95.72 279	98.51 253	99.53 178	94.13 215	99.30 249	96.78 241	92.39 320	98.70 221

PCF-MVS	97.08 14	97.66 239	97.06 259	99.47 93	99.61 117	99.09 104	98.04 340	99.25 244	91.24 329	98.51 253	99.70 109	94.55 199	99.91 74	92.76 319	99.85 53	99.42 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		97.93 194	97.66 198	98.76 207	98.78 281	98.62 182	99.65 75	99.49 105	97.76 130	98.49 255	99.60 154	94.23 210	98.97 301	98.00 144	92.90 314	98.70 221

CP-MVSNet		98.09 167	97.78 182	99.01 149	98.97 243	99.24 90	99.67 56	99.46 139	97.25 176	98.48 256	99.64 138	93.79 226	99.06 284	98.63 89	94.10 301	98.74 213

ACMP	97.20 11	98.06 169	97.94 161	98.45 233	99.37 163	97.01 254	99.44 159	99.49 105	97.54 153	98.45 257	99.79 73	91.95 271	99.72 173	97.91 150	97.49 226	98.62 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		97.69 233	97.43 232	98.48 229	98.60 303	97.30 236	98.18 338	99.39 181	92.96 319	98.41 258	98.78 297	93.77 227	99.27 255	98.16 131	98.61 158	98.86 198

WR-MVS_H		98.13 161	97.87 174	98.90 175	99.02 233	98.84 146	99.70 42	99.59 38	97.27 174	98.40 259	99.19 263	95.53 147	99.23 264	98.34 121	93.78 307	98.61 276

BH-w/o		98.00 184	97.89 167	98.32 244	99.35 166	96.20 284	99.01 282	98.90 285	96.42 244	98.38 260	99.00 278	95.26 156	99.72 173	96.06 260	98.61 158	99.03 176

pmmvs5		97.52 247	97.30 249	98.16 264	98.57 305	96.73 267	99.27 219	98.90 285	96.14 268	98.37 261	99.53 178	91.54 287	99.14 274	97.51 189	95.87 263	98.63 265

DWT-MVSNet_test		97.53 246	97.40 235	97.93 276	99.03 232	94.86 308	99.57 105	98.63 315	96.59 231	98.36 262	98.79 295	89.32 306	99.74 161	98.14 132	98.16 191	99.20 159

EU-MVSNet		97.98 185	98.03 153	97.81 286	98.72 289	96.65 271	99.66 65	99.66 25	98.09 89	98.35 263	99.82 44	95.25 157	98.01 324	97.41 199	95.30 273	98.78 204

FMVSNet5		96.43 275	96.19 272	97.15 301	99.11 217	95.89 288	99.32 204	99.52 76	94.47 301	98.34 264	99.07 272	87.54 325	97.07 335	92.61 320	95.72 266	98.47 294

PS-CasMVS		97.93 194	97.59 208	98.95 158	98.99 236	99.06 107	99.68 54	99.52 76	97.13 186	98.31 265	99.68 120	92.44 266	99.05 285	98.51 107	94.08 302	98.75 210

USDC		97.34 259	97.20 255	97.75 289	99.07 224	95.20 302	98.51 327	99.04 268	97.99 107	98.31 265	99.86 22	89.02 308	99.55 209	95.67 270	97.36 235	98.49 292

PEN-MVS		97.76 220	97.44 229	98.72 209	98.77 284	98.54 188	99.78 22	99.51 85	97.06 201	98.29 267	99.64 138	92.63 257	98.89 304	98.09 135	93.16 312	98.72 215

tfpnnormal		97.84 205	97.47 220	98.98 153	99.20 197	99.22 92	99.64 77	99.61 32	96.32 250	98.27 268	99.70 109	93.35 233	99.44 220	95.69 268	95.40 271	98.27 304

ppachtmachnet_test		97.49 253	97.45 223	97.61 293	98.62 300	95.24 300	98.80 309	99.46 139	96.11 270	98.22 269	99.62 147	96.45 122	98.97 301	93.77 306	95.97 262	98.61 276

tpmp4_e23		97.34 259	97.29 250	97.52 296	99.25 191	93.73 319	99.58 99	99.19 252	94.00 308	98.20 270	99.41 215	90.74 294	99.74 161	97.13 215	98.07 201	99.07 173

our_test_3		97.65 240	97.68 197	97.55 295	98.62 300	94.97 307	98.84 307	99.30 224	96.83 215	98.19 271	99.34 242	97.01 106	99.02 289	95.00 282	96.01 260	98.64 258

LTVRE_ROB	97.16 12	98.02 180	97.90 163	98.40 239	99.23 192	96.80 266	99.70 42	99.60 35	97.12 188	98.18 272	99.70 109	91.73 281	99.72 173	98.39 115	97.45 228	98.68 231
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
ACMH	97.28 8	98.10 166	97.99 157	98.44 236	99.41 153	96.96 260	99.60 90	99.56 48	98.09 89	98.15 273	99.91 5	90.87 293	99.70 185	98.88 57	97.45 228	98.67 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		97.24 263	97.32 247	96.99 304	98.45 310	93.51 324	98.82 308	99.32 221	97.41 164	98.13 274	99.30 251	88.99 309	99.56 207	95.68 269	99.80 71	97.90 320

LP		97.04 267	96.80 263	97.77 288	98.90 261	95.23 301	98.97 291	99.06 266	94.02 307	98.09 275	99.41 215	93.88 223	98.82 305	90.46 325	98.42 171	99.26 156

MVS		97.28 261	96.55 268	99.48 90	98.78 281	98.95 131	99.27 219	99.39 181	83.53 342	98.08 276	99.54 171	96.97 107	99.87 104	94.23 302	99.16 124	99.63 101

PAPM		97.59 243	97.09 258	99.07 143	99.06 226	98.26 205	98.30 334	99.10 259	94.88 288	98.08 276	99.34 242	96.27 128	99.64 196	89.87 327	98.92 145	99.31 153

OpenMVS	96.50 16	98.47 131	98.12 145	99.52 85	99.04 230	99.53 58	99.82 13	99.72 11	94.56 297	98.08 276	99.88 14	94.73 191	99.98 5	97.47 194	99.76 79	99.06 174

gg-mvs-nofinetune		96.17 288	95.32 295	98.73 208	98.79 277	98.14 209	99.38 187	94.09 353	91.07 331	98.07 279	91.04 349	89.62 305	99.35 236	96.75 242	99.09 130	98.68 231

test0.0.03 1		97.71 232	97.42 233	98.56 222	98.41 311	97.82 224	98.78 311	98.63 315	97.34 168	98.05 280	98.98 282	94.45 203	98.98 294	95.04 281	97.15 242	98.89 197

1314		98.68 123	98.54 125	99.11 141	98.89 264	98.65 178	99.27 219	99.49 105	96.89 211	97.99 281	99.56 165	97.72 90	99.83 126	97.74 167	99.27 119	98.84 199

DTE-MVSNet		97.51 250	97.19 256	98.46 232	98.63 299	98.13 210	99.84 9	99.48 114	96.68 222	97.97 282	99.67 124	92.92 239	98.56 310	96.88 238	92.60 319	98.70 221

SixPastTwentyTwo		97.50 251	97.33 246	98.03 268	98.65 297	96.23 283	99.77 24	98.68 313	97.14 185	97.90 283	99.93 4	90.45 295	99.18 273	97.00 222	96.43 252	98.67 242

pm-mvs1		97.68 235	97.28 251	98.88 184	99.06 226	98.62 182	99.50 134	99.45 151	96.32 250	97.87 284	99.79 73	92.47 262	99.35 236	97.54 186	93.54 309	98.67 242

testgi		97.65 240	97.50 215	98.13 265	99.36 165	96.45 276	99.42 171	99.48 114	97.76 130	97.87 284	99.45 208	91.09 290	98.81 306	94.53 289	98.52 166	99.13 163

EPNet_dtu		98.03 178	97.96 159	98.23 257	98.27 313	95.54 294	99.23 232	98.75 298	99.02 10	97.82 286	99.71 106	96.11 132	99.48 212	93.04 316	99.65 100	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 265	96.89 262	97.83 284	99.07 224	95.52 295	98.57 324	98.74 301	97.58 147	97.81 287	99.79 73	88.16 322	99.56 207	95.10 279	97.21 239	98.39 300

ACMH+	97.24 10	97.92 197	97.78 182	98.32 244	99.46 144	96.68 270	99.56 112	99.54 62	98.41 63	97.79 288	99.87 19	90.18 300	99.66 192	98.05 143	97.18 241	98.62 267

N_pmnet		94.95 302	95.83 280	92.31 326	98.47 309	79.33 349	99.12 252	92.81 358	93.87 310	97.68 289	99.13 267	93.87 224	99.01 291	91.38 323	96.19 257	98.59 284

PVSNet_0	94.43 19	96.09 290	95.47 292	97.94 275	99.31 178	94.34 315	97.81 341	99.70 15	97.12 188	97.46 290	98.75 298	89.71 303	99.79 146	97.69 174	81.69 345	99.68 84

pmmvs6		96.53 273	96.09 274	97.82 285	98.69 293	95.47 296	99.37 189	99.47 130	93.46 316	97.41 291	99.78 78	87.06 328	99.33 240	96.92 230	92.70 318	98.65 256

new_pmnet		96.38 279	96.03 275	97.41 299	98.13 316	95.16 305	99.05 269	99.20 249	93.94 309	97.39 292	98.79 295	91.61 286	99.04 286	90.43 326	95.77 265	98.05 310

IB-MVS	95.67 18	96.22 286	95.44 294	98.57 220	99.21 195	96.70 268	98.65 321	97.74 335	96.71 220	97.27 293	98.54 308	86.03 330	99.92 65	98.47 111	86.30 341	99.10 164
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
GG-mvs-BLEND				98.45 233	98.55 306	98.16 208	99.43 164	93.68 354		97.23 294	98.46 309	89.30 307	99.22 267	95.43 274	98.22 180	97.98 315

MVP-Stereo		97.81 211	97.75 192	97.99 273	97.53 322	96.60 272	98.96 293	98.85 289	97.22 180	97.23 294	99.36 235	95.28 153	99.46 214	95.51 272	99.78 75	97.92 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TransMVSNet (Re)		97.15 264	96.58 267	98.86 192	99.12 215	98.85 145	99.49 142	98.91 283	95.48 283	97.16 296	99.80 65	93.38 232	99.11 280	94.16 304	91.73 321	98.62 267

NR-MVSNet		97.97 188	97.61 206	99.02 148	98.87 268	99.26 88	99.47 151	99.42 168	97.63 143	97.08 297	99.50 188	95.07 164	99.13 277	97.86 154	93.59 308	98.68 231

Anonymous20231206		96.22 286	96.03 275	96.79 310	97.31 327	94.14 316	99.63 79	99.08 261	96.17 264	97.04 298	99.06 274	93.94 221	97.76 331	86.96 337	95.06 279	98.47 294

testpf		95.66 294	96.02 277	94.58 318	98.35 312	92.32 329	97.25 346	97.91 332	92.83 320	97.03 299	98.99 279	88.69 313	98.61 309	95.72 267	97.40 232	92.80 345

test_0402		96.64 270	96.24 271	97.85 282	98.85 272	96.43 277	99.44 159	99.26 242	93.52 314	96.98 300	99.52 183	88.52 317	99.20 272	92.58 321	97.50 223	97.93 318

MIMVSNet1		95.51 295	95.04 298	96.92 307	97.38 324	95.60 290	99.52 125	99.50 99	93.65 312	96.97 301	99.17 264	85.28 334	96.56 339	88.36 332	95.55 270	98.60 283

TDRefinement		95.42 297	94.57 302	97.97 274	89.83 348	96.11 285	99.48 147	98.75 298	96.74 218	96.68 302	99.88 14	88.65 315	99.71 179	98.37 118	82.74 344	98.09 308

testus		94.61 303	95.30 296	92.54 325	96.44 330	84.18 341	98.36 330	99.03 269	94.18 306	96.49 303	98.57 307	88.74 311	95.09 344	87.41 335	98.45 169	98.36 303

pmmvs3		94.09 308	93.25 310	96.60 312	94.76 337	94.49 312	98.92 300	98.18 328	89.66 333	96.48 304	98.06 315	86.28 329	97.33 334	89.68 328	87.20 335	97.97 316

DeepMVS_CX				93.34 321	99.29 182	82.27 346		99.22 247	85.15 340	96.33 305	99.05 275	90.97 292	99.73 169	93.57 308	97.77 209	98.01 314

LCM-MVSNet-Re		97.83 207	98.15 142	96.87 308	99.30 179	92.25 330	99.59 92	98.26 324	97.43 161	96.20 306	99.13 267	96.27 128	98.73 308	98.17 130	98.99 137	99.64 97

test20.03		96.12 289	95.96 278	96.63 311	97.44 323	95.45 297	99.51 129	99.38 187	96.55 232	96.16 307	99.25 258	93.76 228	96.17 340	87.35 336	94.22 299	98.27 304

K. test v3		97.10 266	96.79 264	98.01 271	98.72 289	96.33 280	99.87 4	97.05 347	97.59 145	96.16 307	99.80 65	88.71 312	99.04 286	96.69 246	96.55 250	98.65 256

test2356		94.07 309	94.46 304	92.89 323	95.18 335	86.13 339	97.60 344	99.06 266	93.61 313	96.15 309	98.28 313	85.60 333	93.95 346	86.68 339	98.00 203	98.59 284

UnsupCasMVSNet_eth		96.44 274	96.12 273	97.40 300	98.65 297	95.65 289	99.36 195	99.51 85	97.13 186	96.04 310	98.99 279	88.40 319	98.17 313	96.71 244	90.27 324	98.40 299

lessismore_v0				97.79 287	98.69 293	95.44 298		94.75 351		95.71 311	99.87 19	88.69 313	99.32 243	95.89 263	94.93 283	98.62 267

Patchmatch-RL test		95.84 292	95.81 281	95.95 315	95.61 332	90.57 333	98.24 335	98.39 321	95.10 287	95.20 312	98.67 300	94.78 184	97.77 330	96.28 258	90.02 325	99.51 125

ambc				93.06 322	92.68 343	82.36 345	98.47 328	98.73 310		95.09 313	97.41 333	55.55 353	99.10 282	96.42 255	91.32 322	97.71 331

PM-MVS		92.96 311	92.23 313	95.14 317	95.61 332	89.98 335	99.37 189	98.21 326	94.80 290	95.04 314	97.69 323	65.06 349	97.90 327	94.30 299	89.98 326	97.54 335

OpenMVS_ROB	92.34 20	94.38 306	93.70 307	96.41 314	97.38 324	93.17 325	99.06 267	98.75 298	86.58 339	94.84 315	98.26 314	81.53 343	99.32 243	89.01 330	97.87 207	96.76 336

v17		96.42 276	95.81 281	98.25 254	98.94 251	98.80 163	99.76 27	99.28 236	94.57 295	94.18 316	97.71 320	95.23 158	98.16 314	94.86 283	87.73 333	97.80 323

v18		96.42 276	95.80 283	98.26 250	98.95 248	98.82 156	99.76 27	99.28 236	94.58 294	94.12 317	97.70 321	95.22 159	98.16 314	94.83 285	87.80 331	97.79 328

v16		96.39 278	95.76 284	98.26 250	98.96 246	98.81 158	99.76 27	99.28 236	94.57 295	94.10 318	97.70 321	95.04 165	98.16 314	94.70 287	87.77 332	97.80 323

EG-PatchMatch MVS		95.97 291	95.69 285	96.81 309	97.78 319	92.79 327	99.16 245	98.93 278	96.16 265	94.08 319	99.22 261	82.72 341	99.47 213	95.67 270	97.50 223	98.17 307

v11		96.23 285	95.57 291	98.21 260	98.93 256	98.83 149	99.72 39	99.29 229	94.29 305	94.05 320	97.64 326	94.88 178	98.04 322	92.89 317	88.43 329	97.77 329

v15		96.28 280	95.62 286	98.25 254	98.94 251	98.83 149	99.76 27	99.29 229	94.52 299	94.02 321	97.61 328	95.02 166	98.13 318	94.53 289	86.92 336	97.80 323

DI_MVS_plusplus_test		97.45 255	96.79 264	99.44 99	97.76 320	99.04 109	99.21 239	98.61 317	97.74 133	94.01 322	98.83 292	87.38 327	99.83 126	98.63 89	98.90 147	99.44 141

V14		96.26 281	95.60 287	98.26 250	98.94 251	98.83 149	99.76 27	99.29 229	94.49 300	93.96 323	97.66 324	94.99 169	98.13 318	94.41 292	86.90 337	97.80 323

V9		96.25 282	95.58 288	98.26 250	98.94 251	98.83 149	99.75 34	99.29 229	94.45 302	93.96 323	97.62 327	94.94 171	98.14 317	94.40 293	86.87 338	97.81 321

test_normal		97.44 256	96.77 266	99.44 99	97.75 321	99.00 121	99.10 260	98.64 314	97.71 136	93.93 325	98.82 293	87.39 326	99.83 126	98.61 93	98.97 139	99.49 129

v13		96.24 283	95.58 288	98.25 254	98.98 240	98.83 149	99.75 34	99.29 229	94.35 304	93.89 326	97.60 329	95.17 161	98.11 320	94.27 301	86.86 339	97.81 321

v12		96.24 283	95.58 288	98.23 257	98.96 246	98.81 158	99.76 27	99.29 229	94.42 303	93.85 327	97.60 329	95.12 162	98.09 321	94.32 298	86.85 340	97.80 323

pmmvs-eth3d		95.34 299	94.73 300	97.15 301	95.53 334	95.94 287	99.35 199	99.10 259	95.13 285	93.55 328	97.54 332	88.15 323	97.91 326	94.58 288	89.69 327	97.61 332

new-patchmatchnet		94.48 304	94.08 305	95.67 316	95.08 336	92.41 328	99.18 243	99.28 236	94.55 298	93.49 329	97.37 335	87.86 324	97.01 336	91.57 322	88.36 330	97.61 332

UnsupCasMVSNet_bld		93.53 310	92.51 312	96.58 313	97.38 324	93.82 318	98.24 335	99.48 114	91.10 330	93.10 330	96.66 338	74.89 344	98.37 311	94.03 305	87.71 334	97.56 334

Anonymous20231211		90.69 316	89.39 317	94.58 318	94.25 338	88.18 336	99.29 213	99.07 264	82.45 344	92.95 331	97.65 325	63.96 351	97.79 329	89.27 329	85.63 342	97.77 329

test1235678		92.91 312	93.30 309	91.71 329	93.14 342	83.01 343	98.75 314	98.58 318	92.80 321	92.45 332	97.91 317	88.51 318	93.54 347	82.26 343	95.35 272	98.59 284

Gipuma		90.99 315	90.15 316	93.51 320	98.73 287	90.12 334	93.98 350	99.45 151	79.32 345	92.28 333	94.91 342	69.61 346	97.98 325	87.42 334	95.67 267	92.45 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	69.68 23	94.13 307	94.90 299	91.84 327	97.24 328	80.01 348	98.52 326	99.48 114	89.01 336	91.99 334	99.67 124	85.67 332	99.13 277	95.44 273	97.03 243	96.39 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		91.74 314	92.19 315	90.37 332	91.22 344	82.41 344	98.61 322	98.28 323	90.66 332	91.82 335	97.92 316	84.90 335	92.61 348	81.64 344	94.66 290	96.09 340

PMMVS2		86.87 318	85.37 321	91.35 331	90.21 347	83.80 342	98.89 303	97.45 345	83.13 343	91.67 336	95.03 341	48.49 355	94.70 345	85.86 340	77.62 346	95.54 341

1111		92.30 313	92.21 314	92.55 324	93.30 340	86.27 337	99.15 248	98.74 301	91.94 324	90.85 337	97.82 318	84.18 337	95.21 342	79.65 345	94.27 298	96.19 339

.test1245		83.42 321	86.17 319	75.15 343	93.30 340	86.27 337	99.15 248	98.74 301	91.94 324	90.85 337	97.82 318	84.18 337	95.21 342	79.65 345	39.90 355	43.98 356

LCM-MVSNet		86.80 319	85.22 322	91.53 330	87.81 350	80.96 347	98.23 337	98.99 272	71.05 348	90.13 339	96.51 339	48.45 356	96.88 337	90.51 324	85.30 343	96.76 336

Test4		95.05 300	93.67 308	99.22 131	96.07 331	98.94 134	99.20 241	99.27 241	97.71 136	89.96 340	97.59 331	66.18 348	99.25 261	98.06 142	98.96 140	99.47 135

testmv		87.91 317	87.80 318	88.24 333	87.68 351	77.50 351	99.07 263	97.66 342	89.27 334	86.47 341	96.22 340	68.35 347	92.49 350	76.63 349	88.82 328	94.72 343

testing_2		94.44 305	92.93 311	98.98 153	94.16 339	99.00 121	99.42 171	99.28 236	96.60 229	84.86 342	96.84 337	70.91 345	99.27 255	98.23 127	96.08 259	98.68 231

E-PMN		80.61 324	79.88 325	82.81 339	90.75 346	76.38 353	97.69 342	95.76 350	66.44 352	83.52 343	92.25 346	62.54 352	87.16 355	68.53 353	61.40 349	84.89 354

FPMVS		84.93 320	85.65 320	82.75 340	86.77 352	63.39 358	98.35 332	98.92 280	74.11 347	83.39 344	98.98 282	50.85 354	92.40 351	84.54 341	94.97 281	92.46 346

EMVS		80.02 325	79.22 326	82.43 341	91.19 345	76.40 352	97.55 345	92.49 360	66.36 353	83.01 345	91.27 347	64.63 350	85.79 356	65.82 354	60.65 350	85.08 353

YYNet1		95.36 298	94.51 303	97.92 277	97.89 317	97.10 245	99.10 260	99.23 246	93.26 318	80.77 346	99.04 276	92.81 242	98.02 323	94.30 299	94.18 300	98.64 258

MDA-MVSNet_test_wron		95.45 296	94.60 301	98.01 271	98.16 315	97.21 243	99.11 258	99.24 245	93.49 315	80.73 347	98.98 282	93.02 236	98.18 312	94.22 303	94.45 295	98.64 258

MDA-MVSNet-bldmvs		94.96 301	93.98 306	97.92 277	98.24 314	97.27 238	99.15 248	99.33 215	93.80 311	80.09 348	99.03 277	88.31 320	97.86 328	93.49 310	94.36 296	98.62 267

tmp_tt		82.80 323	81.52 323	86.66 334	66.61 359	68.44 357	92.79 352	97.92 330	68.96 350	80.04 349	99.85 26	85.77 331	96.15 341	97.86 154	43.89 354	95.39 342

no-one		83.04 322	80.12 324	91.79 328	89.44 349	85.65 340	99.32 204	98.32 322	89.06 335	79.79 350	89.16 351	44.86 357	96.67 338	84.33 342	46.78 353	93.05 344

PNet_i23d		79.43 326	77.68 327	84.67 336	86.18 353	71.69 356	96.50 348	93.68 354	75.17 346	71.33 351	91.18 348	32.18 360	90.62 352	78.57 348	74.34 347	91.71 349

MVE	76.82 21	76.91 328	74.31 330	84.70 335	85.38 355	76.05 354	96.88 347	93.17 356	67.39 351	71.28 352	89.01 352	21.66 365	87.69 354	71.74 352	72.29 348	90.35 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 327	74.86 329	84.62 337	75.88 357	77.61 350	97.63 343	93.15 357	88.81 337	64.27 353	89.29 350	36.51 358	83.93 357	75.89 350	52.31 352	92.33 348

wuykxyi23d		74.42 330	71.19 331	84.14 338	76.16 356	74.29 355	96.00 349	92.57 359	69.57 349	63.84 354	87.49 353	21.98 362	88.86 353	75.56 351	57.50 351	89.26 352

PMVS	70.75 22	75.98 329	74.97 328	79.01 342	70.98 358	55.18 359	93.37 351	98.21 326	65.08 354	61.78 355	93.83 344	21.74 364	92.53 349	78.59 347	91.12 323	89.34 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		39.01 334	42.50 334	28.53 346	39.17 360	20.91 361	98.75 314	19.17 363	19.83 357	38.57 356	66.67 355	33.16 359	15.42 359	37.50 357	29.66 357	49.26 355

testmvs		39.17 333	43.78 332	25.37 347	36.04 361	16.84 362	98.36 330	26.56 361	20.06 356	38.51 357	67.32 354	29.64 361	15.30 360	37.59 356	39.90 355	43.98 356

wuyk23d		40.18 332	41.29 335	36.84 344	86.18 353	49.12 360	79.73 353	22.81 362	27.64 355	25.46 358	28.45 359	21.98 362	48.89 358	55.80 355	23.56 358	12.51 358

cdsmvs_eth3d_5k		24.64 335	32.85 336	0.00 348	0.00 362	0.00 363	0.00 354	99.51 85	0.00 358	0.00 359	99.56 165	96.58 119	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		8.27 337	11.03 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	99.01 12	0.00 361	0.00 358	0.00 359	0.00 359

pcd1.5k->3k		40.85 331	43.49 333	32.93 345	98.95 248	0.00 363	0.00 354	99.53 72	0.00 358	0.00 359	0.27 360	95.32 152	0.00 361	0.00 358	97.30 236	98.80 202

sosnet-low-res		0.02 338	0.03 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.02 338	0.03 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.02 338	0.03 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.02 338	0.03 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		8.30 336	11.06 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	99.58 159	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.02 338	0.03 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.27 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																99.52 120

test_part3							99.37 189		97.97 108		99.78 78		99.95 33	97.15 213

test_part1								99.48 114				98.96 21			99.84 58	99.83 23

sam_mvs1												94.86 179				99.52 120

sam_mvs												94.72 192

MTGPA								99.47 130

test_post1							99.23 232				65.14 357	94.18 214	99.71 179	97.58 180

test_post											65.99 356	94.65 196	99.73 169

patchmatchnet-post											98.70 299	94.79 183	99.74 161

MTMP								98.88 287

gm-plane-assit					98.54 307	92.96 326			94.65 293		99.15 265		99.64 196	97.56 184

test9_res														97.49 191	99.72 86	99.75 56

agg_prior2														97.21 207	99.73 85	99.75 56

test_prior4						99.56 52	98.99 284

test_prior				99.68 52	99.67 93	99.48 65		99.56 48					99.83 126			99.74 61

新几何2							99.01 282

旧先验1					99.74 67	99.59 49		99.54 62			99.69 115	98.47 61			99.68 96	99.73 66

无先验							98.99 284	99.51 85	96.89 211				99.93 57	97.53 187		99.72 72

原ACMM2							98.95 297

testdata2													99.95 33	96.67 247

segment_acmp												98.96 21

testdata1							98.85 306		98.32 69

plane_prior7					99.29 182	97.03 253

plane_prior6					99.27 187	96.98 257						92.71 247

plane_prior5								99.47 130					99.69 188	97.78 161	97.63 211	98.67 242

plane_prior4											99.61 151

plane_prior2							99.39 182		98.97 22

plane_prior1					99.26 189

plane_prior						96.97 258	99.21 239		98.45 59						97.60 214

n2								0.00 364

nn								0.00 364

door-mid								98.05 329

test11								99.35 199

door								97.92 330

HQP5-MVS						96.83 263

BP-MVS														97.19 209

HQP3-MVS								99.39 181							97.58 216

HQP2-MVS												92.47 262

NP-MVS					99.23 192	96.92 261					99.40 219

ACMMP++_ref															97.19 240

ACMMP++															97.43 231

Test By Simon												98.75 47

High-resolution multi-view benchmark