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.

EPNet		97.28 81	96.87 82	98.51 75	94.98 309	96.14 110	98.90 72	97.02 283	98.28 1	95.99 160	99.11 48	91.36 113	99.89 29	96.98 64	99.19 87	99.50 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	96.37 2	97.93 49	98.48 13	96.30 229	99.00 88	89.54 291	97.43 262	98.87 49	98.16 2	99.26 8	99.38 11	96.12 19	99.64 102	98.30 21	99.77 19	99.72 32

NCCC		98.61 14	98.35 21	99.38 11	99.28 62	98.61 12	98.45 164	98.76 75	97.82 3	98.45 50	98.93 75	96.65 8	99.83 45	97.38 57	99.41 78	99.71 34

CNVR-MVS		98.78 3	98.56 6	99.45 9	99.32 47	98.87 7	98.47 163	98.81 61	97.72 4	98.76 35	99.16 44	97.05 4	99.78 76	98.06 25	99.66 44	99.69 37

DeepC-MVS_fast	96.70 1	98.55 23	98.34 22	99.18 33	99.25 66	98.04 41	98.50 160	98.78 71	97.72 4	98.92 28	99.28 27	95.27 46	99.82 50	97.55 50	99.77 19	99.69 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.40 32	98.20 35	98.99 48	99.00 88	97.66 54	97.75 243	98.89 44	97.71 6	98.33 55	98.97 67	94.97 54	99.88 36	98.42 16	99.76 25	99.42 87
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
HSP-MVS		98.70 5	98.52 8	99.24 26	99.75 3	98.23 30	99.26 17	98.58 120	97.52 7	99.41 3	98.78 87	96.00 25	99.79 71	97.79 38	99.59 54	99.69 37

MVS_0304		97.70 58	97.25 66	99.07 44	98.90 97	97.83 50	98.20 191	98.74 79	97.51 8	98.03 65	99.06 58	86.12 225	99.93 9	99.02 1	99.64 47	99.44 86

HPM-MVS++		98.58 19	98.25 30	99.55 2	99.50 29	99.08 3	98.72 121	98.66 107	97.51 8	98.15 57	98.83 83	95.70 35	99.92 15	97.53 52	99.67 41	99.66 50

Regformer-1		98.66 9	98.51 10	99.12 41	99.35 39	97.81 52	98.37 172	98.76 75	97.49 10	99.20 12	99.21 34	96.08 21	99.79 71	98.42 16	99.73 36	99.75 22

Regformer-2		98.69 8	98.52 8	99.19 29	99.35 39	98.01 43	98.37 172	98.81 61	97.48 11	99.21 11	99.21 34	96.13 18	99.80 59	98.40 18	99.73 36	99.75 22

Regformer-4		98.64 11	98.53 7	98.99 48	99.43 37	97.37 65	98.40 170	98.79 69	97.46 12	99.09 15	99.31 21	95.86 33	99.80 59	98.64 4	99.76 25	99.79 4

Regformer-3		98.59 17	98.50 11	98.86 58	99.43 37	97.05 76	98.40 170	98.68 97	97.43 13	99.06 16	99.31 21	95.80 34	99.77 81	98.62 6	99.76 25	99.78 7

XVS		98.70 5	98.49 12	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	99.20 37	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

X-MVStestdata		94.06 241	92.30 259	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	43.50 349	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

UGNet		96.78 100	96.30 104	98.19 95	98.24 142	95.89 134	98.88 78	98.93 36	97.39 16	96.81 123	97.84 167	82.60 281	99.90 27	96.53 88	99.49 69	98.79 141
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
APDe-MVS		99.02 1	98.84 1	99.55 2	99.57 25	98.96 4	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 7	99.84 44	98.86 2	99.85 2	99.87 1

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 13	99.22 73	98.43 18	99.10 50	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 37	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
CANet		98.05 44	97.76 46	98.90 56	98.73 115	97.27 68	98.35 174	98.78 71	97.37 19	97.72 85	98.96 71	91.53 112	99.92 15	98.79 3	99.65 45	99.51 71

PS-MVSNAJ		97.73 56	97.77 45	97.62 130	98.68 121	95.58 143	97.34 271	98.51 132	97.29 20	98.66 39	97.88 163	94.51 62	99.90 27	97.87 33	99.17 88	97.39 195

SD-MVS		98.64 11	98.68 3	98.53 74	99.33 44	98.36 23	98.90 72	98.85 53	97.28 21	99.72 1	99.39 7	96.63 9	97.60 294	98.17 23	99.85 2	99.64 55

MSLP-MVS++		98.56 22	98.57 5	98.55 72	99.26 65	96.80 85	98.71 122	99.05 23	97.28 21	98.84 29	99.28 27	96.47 11	99.40 133	98.52 14	99.70 39	99.47 79

HQP_MVS		96.14 122	95.90 116	96.85 177	97.42 194	94.60 208	98.80 100	98.56 122	97.28 21	95.34 164	98.28 133	87.09 209	99.03 175	96.07 99	94.27 204	96.92 216

plane_prior2							98.80 100		97.28 21

MPTG		98.55 23	98.25 30	99.46 7	99.76 1	98.64 10	98.55 150	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 19	98.29 27	99.46 7	99.76 1	98.64 10	98.90 72	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

CANet_DTU		96.96 93	96.55 96	98.21 92	98.17 151	96.07 112	97.98 218	98.21 178	97.24 27	97.13 102	98.93 75	86.88 214	99.91 24	95.00 135	99.37 82	98.66 149

EI-MVSNet-Vis-set		98.47 29	98.39 15	98.69 63	99.46 34	96.49 98	98.30 183	98.69 94	97.21 28	98.84 29	99.36 16	95.41 41	99.78 76	98.62 6	99.65 45	99.80 3

MVS_111021_HR		98.47 29	98.34 22	98.88 57	99.22 73	97.32 66	97.91 226	99.58 3	97.20 29	98.33 55	99.00 65	95.99 26	99.64 102	98.05 26	99.76 25	99.69 37

TSAR-MVS + GP.		98.38 33	98.24 32	98.81 59	99.22 73	97.25 71	98.11 206	98.29 167	97.19 30	98.99 22	99.02 60	96.22 13	99.67 98	98.52 14	98.56 112	99.51 71

EI-MVSNet-UG-set		98.41 31	98.34 22	98.61 68	99.45 35	96.32 105	98.28 185	98.68 97	97.17 31	98.74 36	99.37 12	95.25 47	99.79 71	98.57 8	99.54 66	99.73 29

xiu_mvs_v2_base		97.66 61	97.70 48	97.56 138	98.61 127	95.46 149	97.44 260	98.46 142	97.15 32	98.65 40	98.15 143	94.33 68	99.80 59	97.84 36	98.66 108	97.41 193

MVS_111021_LR		98.34 37	98.23 33	98.67 65	99.27 63	96.90 82	97.95 221	99.58 3	97.14 33	98.44 51	99.01 64	95.03 53	99.62 107	97.91 29	99.75 31	99.50 73

xiu_mvs_v1_base_debu		97.60 62	97.56 52	97.72 120	98.35 134	95.98 113	97.86 234	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 197

xiu_mvs_v1_base		97.60 62	97.56 52	97.72 120	98.35 134	95.98 113	97.86 234	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 197

xiu_mvs_v1_base_debi		97.60 62	97.56 52	97.72 120	98.35 134	95.98 113	97.86 234	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 197

3Dnovator+	94.38 6	97.43 73	96.78 86	99.38 11	97.83 169	98.52 13	99.37 7	98.71 91	97.09 37	92.99 248	99.13 46	89.36 138	99.89 29	96.97 65	99.57 57	99.71 34

MCST-MVS		98.65 10	98.37 18	99.48 6	99.60 24	98.87 7	98.41 169	98.68 97	97.04 38	98.52 46	98.80 86	96.78 6	99.83 45	97.93 28	99.61 50	99.74 27

plane_prior3						94.61 206			97.02 39	95.34 164

3Dnovator	94.51 5	97.46 68	96.93 79	99.07 44	97.78 171	97.64 55	99.35 10	99.06 21	97.02 39	93.75 227	99.16 44	89.25 141	99.92 15	97.22 59	99.75 31	99.64 55

DeepC-MVS	95.98 3	97.88 50	97.58 51	98.77 60	99.25 66	96.93 80	98.83 89	98.75 78	96.96 41	96.89 117	99.50 3	90.46 126	99.87 37	97.84 36	99.76 25	99.52 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		97.81 54	97.60 50	98.44 81	99.12 82	95.97 117	97.75 243	98.78 71	96.89 42	98.46 47	99.22 33	93.90 75	99.68 97	94.81 139	99.52 68	99.67 48

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 26	99.69 17	98.28 29	99.14 43	98.66 107	96.84 43	99.56 2	99.31 21	96.34 12	99.70 93	98.32 20	99.73 36	99.73 29

EPNet_dtu		95.21 175	94.95 155	95.99 239	96.17 275	90.45 283	98.16 200	97.27 272	96.77 44	93.14 244	98.33 130	90.34 128	98.42 244	85.57 308	98.81 102	99.09 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		97.67 60	97.23 68	98.98 50	98.70 118	98.38 19	99.34 11	98.39 154	96.76 45	97.67 88	97.40 199	92.26 91	99.49 127	98.28 22	96.28 179	99.08 122

alignmvs		97.56 66	97.07 75	99.01 47	98.66 122	98.37 22	98.83 89	98.06 215	96.74 46	98.00 70	97.65 184	90.80 123	99.48 131	98.37 19	96.56 162	99.19 108

VNet		97.79 55	97.40 62	98.96 52	98.88 105	97.55 59	98.63 137	98.93 36	96.74 46	99.02 18	98.84 82	90.33 129	99.83 45	98.53 10	96.66 158	99.50 73

plane_prior						94.60 208	98.44 165		96.74 46						94.22 206

UA-Net		97.96 46	97.62 49	98.98 50	98.86 107	97.47 62	98.89 76	99.08 20	96.67 49	98.72 37	99.54 1	93.15 81	99.81 52	94.87 136	98.83 100	99.65 52

OPM-MVS		95.69 139	95.33 137	96.76 181	96.16 278	94.63 203	98.43 167	98.39 154	96.64 50	95.02 170	98.78 87	85.15 247	99.05 170	95.21 133	94.20 207	96.60 264

Vis-MVSNet		97.42 74	97.11 72	98.34 87	98.66 122	96.23 108	99.22 28	99.00 26	96.63 51	98.04 64	99.21 34	88.05 187	99.35 138	96.01 104	99.21 86	99.45 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu		96.29 117	96.56 95	95.51 254	97.89 166	90.22 285	98.80 100	98.10 208	96.57 52	96.45 150	96.66 263	90.81 121	98.91 189	95.72 113	97.99 132	97.40 194

mvs-test1		96.60 104	96.68 92	96.37 223	97.89 166	91.81 262	98.56 148	98.10 208	96.57 52	96.52 138	97.94 158	90.81 121	99.45 132	95.72 113	98.01 131	97.86 181

HQP-NCC					97.20 208		98.05 211		96.43 54	94.45 185

ACMP_Plane					97.20 208		98.05 211		96.43 54	94.45 185

HQP-MVS		95.72 135	95.40 130	96.69 186	97.20 208	94.25 221	98.05 211	98.46 142	96.43 54	94.45 185	97.73 176	86.75 215	98.96 182	95.30 127	94.18 208	96.86 229

test_part3							98.55 150		96.40 57		99.31 21		99.93 9	96.37 95

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 150	98.84 54	96.40 57	99.27 6	99.31 21	97.38 2	99.93 9	96.37 95	99.78 14	99.76 20

testdata1							97.32 273		96.34 59

APD-MVS_3200maxsize		98.53 26	98.33 25	99.15 38	99.50 29	97.92 47	99.15 42	98.81 61	96.24 60	99.20 12	99.37 12	95.30 45	99.80 59	97.73 41	99.67 41	99.72 32

mPP-MVS		98.51 27	98.26 29	99.25 25	99.75 3	98.04 41	99.28 16	98.81 61	96.24 60	98.35 54	99.23 31	95.46 40	99.94 3	97.42 55	99.81 8	99.77 14

region2R		98.61 14	98.38 17	99.29 19	99.74 7	98.16 36	99.23 22	98.93 36	96.15 62	98.94 23	99.17 41	95.91 30	99.94 3	97.55 50	99.79 10	99.78 7

abl_6		98.30 41	98.03 39	99.13 39	99.56 26	97.76 53	99.13 46	98.82 58	96.14 63	99.26 8	99.37 12	93.33 78	99.93 9	96.96 67	99.67 41	99.69 37

MP-MVS		98.33 39	98.01 40	99.28 21	99.75 3	98.18 35	99.22 28	98.79 69	96.13 64	97.92 75	99.23 31	94.54 61	99.94 3	96.74 81	99.78 14	99.73 29

test_prior3		98.22 43	97.90 44	99.19 29	99.31 49	98.22 32	97.80 239	98.84 54	96.12 65	97.89 77	98.69 94	95.96 27	99.70 93	96.89 71	99.60 51	99.65 52

test_prior2							97.80 239		96.12 65	97.89 77	98.69 94	95.96 27		96.89 71	99.60 51

HFP-MVS		98.63 13	98.40 14	99.32 17	99.72 11	98.29 27	99.23 22	98.96 31	96.10 67	98.94 23	99.17 41	96.06 22	99.92 15	97.62 45	99.78 14	99.75 22

ACMMPR		98.59 17	98.36 19	99.29 19	99.74 7	98.15 37	99.23 22	98.95 33	96.10 67	98.93 27	99.19 40	95.70 35	99.94 3	97.62 45	99.79 10	99.78 7

ACMMP		98.23 42	97.95 42	99.09 43	99.74 7	97.62 57	99.03 59	99.41 6	95.98 69	97.60 93	99.36 16	94.45 66	99.93 9	97.14 61	98.85 99	99.70 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
CP-MVS		98.57 21	98.36 19	99.19 29	99.66 19	97.86 48	99.34 11	98.87 49	95.96 70	98.60 43	99.13 46	96.05 24	99.94 3	97.77 39	99.86 1	99.77 14

FIs		96.51 109	96.12 110	97.67 127	97.13 214	97.54 60	99.36 8	99.22 14	95.89 71	94.03 218	98.35 125	91.98 101	98.44 241	96.40 93	92.76 237	97.01 210

PS-MVSNAJss		96.43 111	96.26 106	96.92 176	95.84 291	95.08 163	99.16 41	98.50 137	95.87 72	93.84 225	98.34 129	94.51 62	98.61 213	96.88 74	93.45 227	97.06 207

FC-MVSNet-test		96.42 112	96.05 111	97.53 139	96.95 221	97.27 68	99.36 8	99.23 12	95.83 73	93.93 220	98.37 123	92.00 100	98.32 260	96.02 103	92.72 238	97.00 211

ACMMP_Plus		98.61 14	98.30 26	99.55 2	99.62 23	98.95 5	98.82 91	98.81 61	95.80 74	99.16 14	99.47 4	95.37 42	99.92 15	97.89 32	99.75 31	99.79 4

jajsoiax		95.45 157	95.03 149	96.73 182	95.42 304	94.63 203	99.14 43	98.52 130	95.74 75	93.22 239	98.36 124	83.87 275	98.65 211	96.95 68	94.04 213	96.91 221

mvs_tets		95.41 161	95.00 150	96.65 194	95.58 299	94.42 213	99.00 61	98.55 124	95.73 76	93.21 240	98.38 122	83.45 278	98.63 212	97.09 63	94.00 215	96.91 221

CVMVSNet		95.43 158	96.04 112	93.57 300	97.93 163	83.62 323	98.12 204	98.59 115	95.68 77	96.56 132	99.02 60	87.51 203	97.51 297	93.56 171	97.44 147	99.60 61

VPNet		94.99 182	94.19 195	97.40 150	97.16 212	96.57 94	98.71 122	98.97 29	95.67 78	94.84 173	98.24 139	80.36 296	98.67 210	96.46 90	87.32 297	96.96 213

XVG-OURS		96.55 108	96.41 100	96.99 168	98.75 114	93.76 232	97.50 259	98.52 130	95.67 78	96.83 120	99.30 26	88.95 152	99.53 124	95.88 107	96.26 180	97.69 188

#test#		98.54 25	98.27 28	99.32 17	99.72 11	98.29 27	98.98 65	98.96 31	95.65 80	98.94 23	99.17 41	96.06 22	99.92 15	97.21 60	99.78 14	99.75 22

testgi		93.06 260	92.45 257	94.88 279	96.43 249	89.90 286	98.75 112	97.54 240	95.60 81	91.63 273	97.91 160	74.46 323	97.02 304	86.10 304	93.67 220	97.72 186

UniMVSNet (Re)		95.78 133	95.19 144	97.58 136	96.99 220	97.47 62	98.79 105	99.18 16	95.60 81	93.92 221	97.04 234	91.68 105	98.48 231	95.80 111	87.66 294	96.79 234

Fast-Effi-MVS+-dtu		95.87 129	95.85 117	95.91 242	97.74 173	91.74 266	98.69 126	98.15 193	95.56 83	94.92 171	97.68 183	88.98 150	98.79 204	93.19 179	97.78 141	97.20 205

CLD-MVS		95.62 142	95.34 135	96.46 219	97.52 187	93.75 234	97.27 276	98.46 142	95.53 84	94.42 193	98.00 154	86.21 223	98.97 179	96.25 98	94.37 202	96.66 255
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OMC-MVS		97.55 67	97.34 63	98.20 93	99.33 44	95.92 130	98.28 185	98.59 115	95.52 85	97.97 71	99.10 50	93.28 80	99.49 127	95.09 134	98.88 96	99.19 108

nrg030		96.28 119	95.72 121	97.96 109	96.90 226	98.15 37	99.39 5	98.31 162	95.47 86	94.42 193	98.35 125	92.09 98	98.69 207	97.50 53	89.05 272	97.04 209

XVG-OURS-SEG-HR		96.51 109	96.34 102	97.02 167	98.77 113	93.76 232	97.79 241	98.50 137	95.45 87	96.94 112	99.09 54	87.87 193	99.55 123	96.76 80	95.83 196	97.74 184

PGM-MVS		98.49 28	98.23 33	99.27 24	99.72 11	98.08 40	98.99 62	99.49 5	95.43 88	99.03 17	99.32 20	95.56 37	99.94 3	96.80 79	99.77 19	99.78 7

DU-MVS		95.42 159	94.76 170	97.40 150	96.53 243	96.97 78	98.66 135	98.99 28	95.43 88	93.88 222	97.69 180	88.57 172	98.31 262	95.81 109	87.25 299	96.92 216

IS-MVSNet		97.22 83	96.88 81	98.25 91	98.85 109	96.36 103	99.19 34	97.97 220	95.39 90	97.23 100	98.99 66	91.11 117	98.93 187	94.60 143	98.59 110	99.47 79

conf200view11		95.40 162	94.70 172	97.50 144	98.98 91	94.92 172	98.87 79	96.90 293	95.38 91	96.61 129	96.88 254	84.29 263	99.56 117	88.11 290	96.29 175	98.02 176

thres100view900		95.38 163	94.70 172	97.41 148	98.98 91	94.92 172	98.87 79	96.90 293	95.38 91	96.61 129	96.88 254	84.29 263	99.56 117	88.11 290	96.29 175	97.76 182

thres600view7		95.49 154	94.77 169	97.67 127	98.98 91	95.02 164	98.85 85	96.90 293	95.38 91	96.63 128	96.90 251	84.29 263	99.59 109	88.65 284	96.33 173	98.40 161

view600		95.60 144	94.93 156	97.62 130	99.05 83	94.85 177	99.09 51	97.01 285	95.36 94	96.52 138	97.37 200	84.55 256	99.59 109	89.07 275	96.39 168	98.40 161

view800		95.60 144	94.93 156	97.62 130	99.05 83	94.85 177	99.09 51	97.01 285	95.36 94	96.52 138	97.37 200	84.55 256	99.59 109	89.07 275	96.39 168	98.40 161

conf0.05thres1000		95.60 144	94.93 156	97.62 130	99.05 83	94.85 177	99.09 51	97.01 285	95.36 94	96.52 138	97.37 200	84.55 256	99.59 109	89.07 275	96.39 168	98.40 161

tfpn		95.60 144	94.93 156	97.62 130	99.05 83	94.85 177	99.09 51	97.01 285	95.36 94	96.52 138	97.37 200	84.55 256	99.59 109	89.07 275	96.39 168	98.40 161

tfpn200view9		95.32 170	94.62 175	97.43 147	98.94 95	94.98 168	98.68 130	96.93 291	95.33 98	96.55 134	96.53 268	84.23 267	99.56 117	88.11 290	96.29 175	97.76 182

thres400		95.38 163	94.62 175	97.65 129	98.94 95	94.98 168	98.68 130	96.93 291	95.33 98	96.55 134	96.53 268	84.23 267	99.56 117	88.11 290	96.29 175	98.40 161

CNLPA		97.45 71	97.03 76	98.73 61	99.05 83	97.44 64	98.07 210	98.53 128	95.32 100	96.80 124	98.53 109	93.32 79	99.72 88	94.31 152	99.31 84	99.02 125

OurMVSNet-221017-0		94.21 229	94.00 207	94.85 280	95.60 298	89.22 296	98.89 76	97.43 258	95.29 101	92.18 268	98.52 112	82.86 280	98.59 216	93.46 172	91.76 248	96.74 239

WTY-MVS		97.37 78	96.92 80	98.72 62	98.86 107	96.89 84	98.31 181	98.71 91	95.26 102	97.67 88	98.56 108	92.21 94	99.78 76	95.89 106	96.85 155	99.48 78

CHOSEN 280x420		97.18 85	97.18 70	97.20 156	98.81 111	93.27 243	95.78 318	99.15 18	95.25 103	96.79 125	98.11 146	92.29 90	99.07 169	98.56 9	99.85 2	99.25 102

ACMM	93.85 9	95.69 139	95.38 134	96.61 200	97.61 179	93.84 230	98.91 71	98.44 146	95.25 103	94.28 203	98.47 115	86.04 234	99.12 159	95.50 122	93.95 217	96.87 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres200		95.25 172	94.57 177	97.28 154	98.81 111	94.92 172	98.20 191	97.11 277	95.24 105	96.54 136	96.22 281	84.58 255	99.53 124	87.93 294	96.50 165	97.39 195

PAPM_NR		97.46 68	97.11 72	98.50 76	99.50 29	96.41 101	98.63 137	98.60 114	95.18 106	97.06 107	98.06 149	94.26 70	99.57 115	93.80 165	98.87 98	99.52 68

UniMVSNet_NR-MVSNet		95.71 137	95.15 145	97.40 150	96.84 229	96.97 78	98.74 116	99.24 10	95.16 107	93.88 222	97.72 179	91.68 105	98.31 262	95.81 109	87.25 299	96.92 216

VPA-MVSNet		95.75 134	95.11 146	97.69 125	97.24 204	97.27 68	98.94 69	99.23 12	95.13 108	95.51 163	97.32 206	85.73 237	98.91 189	97.33 58	89.55 266	96.89 224

test-LLR		95.10 179	94.87 162	95.80 247	96.77 231	89.70 289	96.91 289	95.21 327	95.11 109	94.83 175	95.72 294	87.71 197	98.97 179	93.06 182	98.50 114	98.72 143

test0.0.03 1		94.08 239	93.51 239	95.80 247	95.53 301	92.89 251	97.38 265	95.97 314	95.11 109	92.51 260	96.66 263	87.71 197	96.94 305	87.03 299	93.67 220	97.57 190

LCM-MVSNet-Re		95.22 174	95.32 138	94.91 277	98.18 149	87.85 314	98.75 112	95.66 323	95.11 109	88.96 295	96.85 256	90.26 131	97.65 292	95.65 118	98.44 117	99.22 105

ITE_SJBPF				95.44 260	97.42 194	91.32 270		97.50 247	95.09 112	93.59 228	98.35 125	81.70 285	98.88 194	89.71 263	93.39 229	96.12 289

TranMVSNet+NR-MVSNet		95.14 178	94.48 180	97.11 163	96.45 248	96.36 103	99.03 59	99.03 24	95.04 113	93.58 229	97.93 159	88.27 180	98.03 278	94.13 156	86.90 304	96.95 215

VDD-MVS		95.82 132	95.23 142	97.61 135	98.84 110	93.98 226	98.68 130	97.40 261	95.02 114	97.95 72	99.34 19	74.37 324	99.78 76	98.64 4	96.80 156	99.08 122

MVSFormer		97.57 65	97.49 57	97.84 113	98.07 154	95.76 138	99.47 2	98.40 152	94.98 115	98.79 32	98.83 83	92.34 88	98.41 251	96.91 69	99.59 54	99.34 90

test_djsdf		96.00 124	95.69 126	96.93 174	95.72 295	95.49 148	99.47 2	98.40 152	94.98 115	94.58 180	97.86 164	89.16 144	98.41 251	96.91 69	94.12 212	96.88 226

NR-MVSNet		94.98 184	94.16 196	97.44 146	96.53 243	97.22 72	98.74 116	98.95 33	94.96 117	89.25 293	97.69 180	89.32 139	98.18 270	94.59 144	87.40 296	96.92 216

XVG-ACMP-BASELINE		94.54 215	94.14 198	95.75 250	96.55 242	91.65 267	98.11 206	98.44 146	94.96 117	94.22 207	97.90 161	79.18 302	99.11 163	94.05 159	93.85 218	96.48 279

Vis-MVSNet (Re-imp)		96.87 97	96.55 96	97.83 114	98.73 115	95.46 149	99.20 32	98.30 165	94.96 117	96.60 131	98.87 80	90.05 133	98.59 216	93.67 168	98.60 109	99.46 83

ACMP	93.49 10	95.34 168	94.98 152	96.43 220	97.67 175	93.48 239	98.73 119	98.44 146	94.94 120	92.53 258	98.53 109	84.50 261	99.14 157	95.48 123	94.00 215	96.66 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSTER		96.06 123	95.72 121	97.08 165	98.23 143	95.93 124	98.73 119	98.27 168	94.86 121	95.07 168	98.09 147	88.21 181	98.54 220	96.59 85	93.46 225	96.79 234

jason		97.32 80	97.08 74	98.06 105	97.45 193	95.59 142	97.87 233	97.91 223	94.79 122	98.55 45	98.83 83	91.12 116	99.23 145	97.58 47	99.60 51	99.34 90
jason: jason.
EU-MVSNet		93.66 248	94.14 198	92.25 309	95.96 285	83.38 324	98.52 155	98.12 198	94.69 123	92.61 255	98.13 145	87.36 207	96.39 323	91.82 218	90.00 260	96.98 212

Patchmatch-test1		95.32 170	94.97 154	96.35 225	97.67 175	91.29 271	97.33 272	97.60 233	94.68 124	96.92 115	96.95 243	83.97 272	98.50 230	91.33 231	98.32 123	99.25 102

LPG-MVS_test		95.62 142	95.34 135	96.47 216	97.46 190	93.54 237	98.99 62	98.54 125	94.67 125	94.36 195	98.77 89	85.39 242	99.11 163	95.71 115	94.15 210	96.76 237

LGP-MVS_train				96.47 216	97.46 190	93.54 237		98.54 125	94.67 125	94.36 195	98.77 89	85.39 242	99.11 163	95.71 115	94.15 210	96.76 237

DI_MVS_plusplus_test		94.74 201	93.62 231	98.09 101	95.34 305	95.92 130	98.09 209	97.34 265	94.66 127	85.89 307	95.91 288	80.49 295	99.38 136	96.66 83	98.22 125	98.97 130

test_normal		94.72 202	93.59 233	98.11 100	95.30 306	95.95 120	97.91 226	97.39 263	94.64 128	85.70 310	95.88 289	80.52 294	99.36 137	96.69 82	98.30 124	99.01 128

HPM-MVS		98.36 35	98.10 37	99.13 39	99.74 7	97.82 51	99.53 1	98.80 68	94.63 129	98.61 42	98.97 67	95.13 51	99.77 81	97.65 44	99.83 7	99.79 4

BH-RMVSNet		95.92 128	95.32 138	97.69 125	98.32 139	94.64 202	98.19 195	97.45 256	94.56 130	96.03 158	98.61 102	85.02 248	99.12 159	90.68 240	99.06 90	99.30 96

API-MVS		97.41 75	97.25 66	97.91 110	98.70 118	96.80 85	98.82 91	98.69 94	94.53 131	98.11 59	98.28 133	94.50 65	99.57 115	94.12 157	99.49 69	97.37 197

APD-MVS		98.35 36	98.00 41	99.42 10	99.51 28	98.72 9	98.80 100	98.82 58	94.52 132	99.23 10	99.25 30	95.54 39	99.80 59	96.52 89	99.77 19	99.74 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lupinMVS		97.44 72	97.22 69	98.12 99	98.07 154	95.76 138	97.68 248	97.76 227	94.50 133	98.79 32	98.61 102	92.34 88	99.30 139	97.58 47	99.59 54	99.31 93

Test4		92.21 267	90.34 283	97.82 116	92.83 323	95.87 136	97.94 222	98.05 218	94.50 133	82.12 326	94.48 305	59.54 341	98.54 220	95.39 125	98.22 125	99.06 124

PVSNet_Blended_VisFu		97.70 58	97.46 59	98.44 81	99.27 63	95.91 132	98.63 137	99.16 17	94.48 135	97.67 88	98.88 79	92.80 84	99.91 24	97.11 62	99.12 89	99.50 73

HPM-MVS_fast		98.38 33	98.13 36	99.12 41	99.75 3	97.86 48	99.44 4	98.82 58	94.46 136	98.94 23	99.20 37	95.16 50	99.74 87	97.58 47	99.85 2	99.77 14

AdaColmap		97.15 87	96.70 89	98.48 78	99.16 78	96.69 90	98.01 215	98.89 44	94.44 137	96.83 120	98.68 96	90.69 124	99.76 83	94.36 149	99.29 85	98.98 129

PVSNet_BlendedMVS		96.73 101	96.60 94	97.12 162	99.25 66	95.35 154	98.26 187	99.26 8	94.28 138	97.94 73	97.46 195	92.74 85	99.81 52	96.88 74	93.32 230	96.20 287

MVS_Test		97.28 81	97.00 77	98.13 98	98.33 138	95.97 117	98.74 116	98.07 213	94.27 139	98.44 51	98.07 148	92.48 87	99.26 142	96.43 92	98.19 127	99.16 113

WR-MVS		95.15 177	94.46 182	97.22 155	96.67 239	96.45 99	98.21 190	98.81 61	94.15 140	93.16 241	97.69 180	87.51 203	98.30 264	95.29 129	88.62 283	96.90 223

EPMVS		94.99 182	94.48 180	96.52 212	97.22 206	91.75 265	97.23 277	91.66 344	94.11 141	97.28 99	96.81 258	85.70 238	98.84 198	93.04 184	97.28 149	98.97 130

MP-MVS-pluss		98.31 40	97.92 43	99.49 5	99.72 11	98.88 6	98.43 167	98.78 71	94.10 142	97.69 87	99.42 5	95.25 47	99.92 15	98.09 24	99.80 9	99.67 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
tfpn1000		95.72 135	95.11 146	97.58 136	99.00 88	95.73 140	99.24 20	95.49 325	94.08 143	96.87 119	97.45 197	85.81 236	99.30 139	91.78 220	96.22 184	97.71 187

PatchmatchNet		95.71 137	95.52 129	96.29 230	97.58 182	90.72 278	96.84 296	97.52 241	94.06 144	97.08 104	96.96 242	89.24 142	98.90 192	92.03 213	98.37 120	99.26 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
K. test v3		92.55 263	91.91 264	94.48 290	95.64 297	89.24 295	99.07 55	94.88 331	94.04 145	86.78 303	97.59 189	77.64 310	97.64 293	92.08 209	89.43 268	96.57 268

mvs_anonymous		96.70 102	96.53 98	97.18 158	98.19 147	93.78 231	98.31 181	98.19 182	94.01 146	94.47 184	98.27 136	92.08 99	98.46 236	97.39 56	97.91 134	99.31 93

GA-MVS		94.81 195	94.03 205	97.14 160	97.15 213	93.86 229	96.76 298	97.58 234	94.00 147	94.76 178	97.04 234	80.91 289	98.48 231	91.79 219	96.25 181	99.09 119

ACMH+	92.99 14	94.30 225	93.77 222	95.88 244	97.81 170	92.04 260	98.71 122	98.37 157	93.99 148	90.60 283	98.47 115	80.86 291	99.05 170	92.75 195	92.40 240	96.55 271

PatchFormer-LS_test		95.47 155	95.27 141	96.08 238	97.59 181	90.66 279	98.10 208	97.34 265	93.98 149	96.08 156	96.15 283	87.65 201	99.12 159	95.27 130	95.24 200	98.44 160

sss		97.39 76	96.98 78	98.61 68	98.60 128	96.61 93	98.22 189	98.93 36	93.97 150	98.01 68	98.48 114	91.98 101	99.85 42	96.45 91	98.15 128	99.39 88

HY-MVS	93.96 8	96.82 99	96.23 108	98.57 70	98.46 133	97.00 77	98.14 201	98.21 178	93.95 151	96.72 126	97.99 155	91.58 107	99.76 83	94.51 147	96.54 163	98.95 134

TAMVS		97.02 91	96.79 85	97.70 124	98.06 156	95.31 156	98.52 155	98.31 162	93.95 151	97.05 108	98.61 102	93.49 77	98.52 227	95.33 126	97.81 139	99.29 98

CP-MVSNet		94.94 188	94.30 189	96.83 178	96.72 236	95.56 145	99.11 49	98.95 33	93.89 153	92.42 263	97.90 161	87.19 208	98.12 272	94.32 151	88.21 286	96.82 233

SixPastTwentyTwo		93.34 253	92.86 250	94.75 284	95.67 296	89.41 294	98.75 112	96.67 304	93.89 153	90.15 286	98.25 138	80.87 290	98.27 267	90.90 237	90.64 256	96.57 268

WR-MVS_H		95.05 180	94.46 182	96.81 179	96.86 228	95.82 137	99.24 20	99.24 10	93.87 155	92.53 258	96.84 257	90.37 127	98.24 268	93.24 177	87.93 289	96.38 282

ab-mvs		96.42 112	95.71 124	98.55 72	98.63 125	96.75 88	97.88 232	98.74 79	93.84 156	96.54 136	98.18 142	85.34 245	99.75 85	95.93 105	96.35 172	99.15 114

USDC		93.33 254	92.71 253	95.21 270	96.83 230	90.83 275	96.91 289	97.50 247	93.84 156	90.72 281	98.14 144	77.69 307	98.82 201	89.51 268	93.21 234	95.97 293

LF4IMVS		93.14 259	92.79 252	94.20 295	95.88 289	88.67 304	97.66 250	97.07 279	93.81 158	91.71 272	97.65 184	77.96 306	98.81 202	91.47 229	91.92 246	95.12 306

tfpn_ndepth		95.53 149	94.90 161	97.39 153	98.96 94	95.88 135	99.05 56	95.27 326	93.80 159	96.95 110	96.93 249	85.53 240	99.40 133	91.54 226	96.10 187	96.89 224

semantic-postprocess				94.85 280	97.98 162	90.56 282		98.11 203	93.75 160	92.58 256	97.48 194	83.91 273	97.41 299	92.48 204	91.30 252	96.58 266

anonymousdsp		95.42 159	94.91 160	96.94 173	95.10 308	95.90 133	99.14 43	98.41 150	93.75 160	93.16 241	97.46 195	87.50 205	98.41 251	95.63 119	94.03 214	96.50 277

MDTV_nov1_ep13			95.40 130		97.48 188	88.34 309	96.85 295	97.29 270	93.74 162	97.48 98	97.26 209	89.18 143	99.05 170	91.92 217	97.43 148

BH-untuned		95.95 126	95.72 121	96.65 194	98.55 131	92.26 256	98.23 188	97.79 226	93.73 163	94.62 179	98.01 153	88.97 151	99.00 178	93.04 184	98.51 113	98.68 147

PatchMatch-RL		96.59 106	96.03 113	98.27 89	99.31 49	96.51 97	97.91 226	99.06 21	93.72 164	96.92 115	98.06 149	88.50 177	99.65 100	91.77 221	99.00 92	98.66 149

Effi-MVS+		97.12 88	96.69 90	98.39 85	98.19 147	96.72 89	97.37 267	98.43 149	93.71 165	97.65 91	98.02 151	92.20 95	99.25 143	96.87 77	97.79 140	99.19 108

IterMVS-LS		95.46 156	95.21 143	96.22 232	98.12 152	93.72 235	98.32 180	98.13 196	93.71 165	94.26 204	97.31 207	92.24 92	98.10 273	94.63 141	90.12 258	96.84 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 125	95.83 118	96.36 224	97.93 163	93.70 236	98.12 204	98.27 168	93.70 167	95.07 168	99.02 60	92.23 93	98.54 220	94.68 140	93.46 225	96.84 230

UnsupCasMVSNet_eth		90.99 287	89.92 288	94.19 296	94.08 318	89.83 287	97.13 282	98.67 104	93.69 168	85.83 309	96.19 282	75.15 318	96.74 315	89.14 273	79.41 327	96.00 292

PVSNet	91.96 18	96.35 114	96.15 109	96.96 171	99.17 77	92.05 259	96.08 310	98.68 97	93.69 168	97.75 82	97.80 173	88.86 155	99.69 96	94.26 154	99.01 91	99.15 114

PS-CasMVS		94.67 207	93.99 209	96.71 183	96.68 238	95.26 157	99.13 46	99.03 24	93.68 170	92.33 264	97.95 157	85.35 244	98.10 273	93.59 170	88.16 288	96.79 234

IterMVS		94.09 238	93.85 217	94.80 283	97.99 160	90.35 284	97.18 280	98.12 198	93.68 170	92.46 262	97.34 204	84.05 271	97.41 299	92.51 203	91.33 251	96.62 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		94.97 185	94.26 190	97.11 163	98.18 149	96.62 91	98.56 148	98.26 172	93.67 172	94.09 214	97.10 222	84.25 266	98.01 279	92.08 209	92.14 241	96.70 246

CDS-MVSNet		96.99 92	96.69 90	97.90 111	98.05 157	95.98 113	98.20 191	98.33 161	93.67 172	96.95 110	98.49 113	93.54 76	98.42 244	95.24 132	97.74 143	99.31 93
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		97.46 68	97.28 65	97.99 107	98.64 124	95.38 151	99.33 13	98.31 162	93.61 174	97.19 101	99.07 57	94.05 72	99.23 145	96.89 71	98.43 119	99.37 89

CHOSEN 1792x2688		97.12 88	96.80 83	98.08 102	99.30 54	94.56 210	98.05 211	99.71 1	93.57 175	97.09 103	98.91 78	88.17 182	99.89 29	96.87 77	99.56 63	99.81 2

PEN-MVS		94.42 220	93.73 226	96.49 214	96.28 268	94.84 186	99.17 35	99.00 26	93.51 176	92.23 266	97.83 170	86.10 231	97.90 285	92.55 201	86.92 303	96.74 239

tpmrst		95.63 141	95.69 126	95.44 260	97.54 185	88.54 307	96.97 285	97.56 235	93.50 177	97.52 97	96.93 249	89.49 135	99.16 154	95.25 131	96.42 167	98.64 151

1314		96.25 121	95.73 120	97.79 117	97.13 214	95.55 147	98.19 195	98.59 115	93.47 178	92.03 270	97.82 171	91.33 114	99.49 127	94.62 142	98.44 117	98.32 170

ACMH	92.88 16	94.55 214	93.95 211	96.34 227	97.63 177	93.26 244	98.81 97	98.49 141	93.43 179	89.74 288	98.53 109	81.91 284	99.08 168	93.69 166	93.30 231	96.70 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
conf0.002		95.56 148	94.84 164	97.72 120	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	98.02 176

thresconf0.02		95.50 150	94.84 164	97.51 140	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 197

tfpn_n400		95.50 150	94.84 164	97.51 140	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 197

tfpnconf		95.50 150	94.84 164	97.51 140	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 197

tfpnview11		95.50 150	94.84 164	97.51 140	98.90 97	95.93 124	99.17 35	95.70 318	93.42 180	96.50 143	97.16 214	86.12 225	99.22 147	90.51 244	96.06 188	97.37 197

LFMVS		95.86 130	94.98 152	98.47 79	98.87 106	96.32 105	98.84 88	96.02 312	93.40 185	98.62 41	99.20 37	74.99 319	99.63 105	97.72 42	97.20 150	99.46 83

test20.03		90.89 288	90.38 282	92.43 307	93.48 320	88.14 311	98.33 176	97.56 235	93.40 185	87.96 299	96.71 262	80.69 293	94.13 332	79.15 324	86.17 308	95.01 311

PAPR		96.84 98	96.24 107	98.65 66	98.72 117	96.92 81	97.36 269	98.57 121	93.33 187	96.67 127	97.57 191	94.30 69	99.56 117	91.05 236	98.59 110	99.47 79

IB-MVS	91.98 17	93.27 255	91.97 262	97.19 157	97.47 189	93.41 242	97.09 283	95.99 313	93.32 188	92.47 261	95.73 292	78.06 305	99.53 124	94.59 144	82.98 316	98.62 152
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
PHI-MVS		98.34 37	98.06 38	99.18 33	99.15 80	98.12 39	99.04 58	99.09 19	93.32 188	98.83 31	99.10 50	96.54 10	99.83 45	97.70 43	99.76 25	99.59 63

XXY-MVS		95.20 176	94.45 184	97.46 145	96.75 234	96.56 95	98.86 84	98.65 111	93.30 190	93.27 238	98.27 136	84.85 252	98.87 195	94.82 138	91.26 254	96.96 213

原ACMM1				98.65 66	99.32 47	96.62 91		98.67 104	93.27 191	97.81 79	98.97 67	95.18 49	99.83 45	93.84 163	99.46 74	99.50 73

TESTMET0.1,1		94.18 233	93.69 228	95.63 252	96.92 223	89.12 297	96.91 289	94.78 332	93.17 192	94.88 172	96.45 272	78.52 303	98.92 188	93.09 181	98.50 114	98.85 137

agg_prior1		97.95 47	97.51 56	99.28 21	99.30 54	98.38 19	97.81 238	98.72 86	93.16 193	97.57 95	98.66 99	96.14 17	99.81 52	96.63 84	99.56 63	99.66 50

PVSNet_Blended		97.38 77	97.12 71	98.14 96	99.25 66	95.35 154	97.28 275	99.26 8	93.13 194	97.94 73	98.21 140	92.74 85	99.81 52	96.88 74	99.40 80	99.27 100

DTE-MVSNet		93.98 243	93.26 246	96.14 235	96.06 281	94.39 215	99.20 32	98.86 52	93.06 195	91.78 271	97.81 172	85.87 235	97.58 295	90.53 243	86.17 308	96.46 280

CSCG		97.85 53	97.74 47	98.20 93	99.67 18	95.16 159	99.22 28	99.32 7	93.04 196	97.02 109	98.92 77	95.36 43	99.91 24	97.43 54	99.64 47	99.52 68

testing_2		90.61 291	88.50 298	96.95 172	90.08 331	95.57 144	97.69 247	98.06 215	93.02 197	76.55 333	92.48 329	61.18 340	98.44 241	95.45 124	91.98 244	96.84 230

F-COLMAP		97.09 90	96.80 83	97.97 108	99.45 35	94.95 171	98.55 150	98.62 113	93.02 197	96.17 155	98.58 107	94.01 73	99.81 52	93.95 160	98.90 95	99.14 116

train_agg		97.97 45	97.52 55	99.33 16	99.31 49	98.50 14	97.92 223	98.73 84	92.98 199	97.74 83	98.68 96	96.20 14	99.80 59	96.59 85	99.57 57	99.68 43

test_8					99.29 57	98.44 16	97.89 231	98.72 86	92.98 199	97.70 86	98.66 99	96.20 14	99.80 59

1112_ss		96.63 103	96.00 114	98.50 76	98.56 129	96.37 102	98.18 199	98.10 208	92.92 201	94.84 173	98.43 117	92.14 96	99.58 114	94.35 150	96.51 164	99.56 67

DWT-MVSNet_test		94.82 194	94.36 187	96.20 233	97.35 199	90.79 276	98.34 175	96.57 307	92.91 202	95.33 166	96.44 273	82.00 283	99.12 159	94.52 146	95.78 197	98.70 145

test-mter		94.08 239	93.51 239	95.80 247	96.77 231	89.70 289	96.91 289	95.21 327	92.89 203	94.83 175	95.72 294	77.69 307	98.97 179	93.06 182	98.50 114	98.72 143

BH-w/o		95.38 163	95.08 148	96.26 231	98.34 137	91.79 263	97.70 246	97.43 258	92.87 204	94.24 206	97.22 212	88.66 170	98.84 198	91.55 225	97.70 144	98.16 173

PMMVS		96.60 104	96.33 103	97.41 148	97.90 165	93.93 227	97.35 270	98.41 150	92.84 205	97.76 81	97.45 197	91.10 118	99.20 152	96.26 97	97.91 134	99.11 118

LS3D		97.16 86	96.66 93	98.68 64	98.53 132	97.19 73	98.93 70	98.90 42	92.83 206	95.99 160	99.37 12	92.12 97	99.87 37	93.67 168	99.57 57	98.97 130

v2v482		94.69 203	94.03 205	96.65 194	96.17 275	94.79 196	98.67 133	98.08 212	92.72 207	94.00 219	97.16 214	87.69 200	98.45 238	92.91 190	88.87 278	96.72 242

TEST9					99.31 49	98.50 14	97.92 223	98.73 84	92.63 208	97.74 83	98.68 96	96.20 14	99.80 59

tpm		94.13 237	93.80 219	95.12 273	96.50 245	87.91 313	97.44 260	95.89 317	92.62 209	96.37 152	96.30 276	84.13 270	98.30 264	93.24 177	91.66 250	99.14 116

DP-MVS Recon		97.86 52	97.46 59	99.06 46	99.53 27	98.35 24	98.33 176	98.89 44	92.62 209	98.05 62	98.94 74	95.34 44	99.65 100	96.04 102	99.42 77	99.19 108

v148		94.29 226	93.76 224	95.91 242	96.10 279	92.93 250	98.58 143	97.97 220	92.59 211	93.47 235	96.95 243	88.53 175	98.32 260	92.56 200	87.06 301	96.49 278

CDPH-MVS		97.94 48	97.49 57	99.28 21	99.47 33	98.44 16	97.91 226	98.67 104	92.57 212	98.77 34	98.85 81	95.93 29	99.72 88	95.56 120	99.69 40	99.68 43

v6		94.83 191	94.21 193	96.69 186	96.36 255	94.85 177	98.87 79	98.11 203	92.46 213	94.44 191	97.05 233	88.76 166	98.57 218	92.95 187	88.92 275	96.65 257

CR-MVSNet		94.76 197	94.15 197	96.59 202	97.00 218	93.43 240	94.96 324	97.56 235	92.46 213	96.93 113	96.24 277	88.15 183	97.88 289	87.38 296	96.65 159	98.46 158

GBi-Net		94.49 216	93.80 219	96.56 207	98.21 144	95.00 165	98.82 91	98.18 185	92.46 213	94.09 214	97.07 226	81.16 286	97.95 282	92.08 209	92.14 241	96.72 242

test1		94.49 216	93.80 219	96.56 207	98.21 144	95.00 165	98.82 91	98.18 185	92.46 213	94.09 214	97.07 226	81.16 286	97.95 282	92.08 209	92.14 241	96.72 242

FMVSNet2		94.47 218	93.61 232	97.04 166	98.21 144	96.43 100	98.79 105	98.27 168	92.46 213	93.50 234	97.09 224	81.16 286	98.00 280	91.09 232	91.93 245	96.70 246

v1neww		94.83 191	94.22 191	96.68 189	96.39 251	94.85 177	98.87 79	98.11 203	92.45 218	94.45 185	97.06 229	88.82 160	98.54 220	92.93 188	88.91 276	96.65 257

v7new		94.83 191	94.22 191	96.68 189	96.39 251	94.85 177	98.87 79	98.11 203	92.45 218	94.45 185	97.06 229	88.82 160	98.54 220	92.93 188	88.91 276	96.65 257

divwei89l23v2f112		94.76 197	94.12 201	96.67 192	96.28 268	94.85 177	98.69 126	98.12 198	92.44 220	94.29 202	96.94 245	88.85 157	98.48 231	92.67 196	88.79 282	96.67 252

v1141		94.75 199	94.11 202	96.67 192	96.27 270	94.86 176	98.69 126	98.12 198	92.43 221	94.31 199	96.94 245	88.78 165	98.48 231	92.63 198	88.85 280	96.67 252

v1		94.75 199	94.11 202	96.69 186	96.27 270	94.87 175	98.69 126	98.12 198	92.43 221	94.32 198	96.94 245	88.71 169	98.54 220	92.66 197	88.84 281	96.67 252

PLC	95.07 4	97.20 84	96.78 86	98.44 81	99.29 57	96.31 107	98.14 201	98.76 75	92.41 223	96.39 151	98.31 132	94.92 55	99.78 76	94.06 158	98.77 103	99.23 104
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		96.91 95	96.40 101	98.45 80	98.69 120	96.90 82	98.66 135	98.68 97	92.40 224	97.07 106	97.96 156	91.54 111	99.75 85	93.68 167	98.92 94	98.69 146
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
agg_prior3		97.87 51	97.42 61	99.23 28	99.29 57	98.23 30	97.92 223	98.72 86	92.38 225	97.59 94	98.64 101	96.09 20	99.79 71	96.59 85	99.57 57	99.68 43

CPTT-MVS		97.72 57	97.32 64	98.92 54	99.64 20	97.10 75	99.12 48	98.81 61	92.34 226	98.09 60	99.08 56	93.01 82	99.92 15	96.06 101	99.77 19	99.75 22

HyFIR lowres test		96.90 96	96.49 99	98.14 96	99.33 44	95.56 145	97.38 265	99.65 2	92.34 226	97.61 92	98.20 141	89.29 140	99.10 166	96.97 65	97.60 146	99.77 14

pm-mvs1		93.94 244	93.06 247	96.59 202	96.49 246	95.16 159	98.95 68	98.03 219	92.32 228	91.08 277	97.84 167	84.54 260	98.41 251	92.16 207	86.13 310	96.19 288

V42		94.78 196	94.14 198	96.70 185	96.33 262	95.22 158	98.97 66	98.09 211	92.32 228	94.31 199	97.06 229	88.39 178	98.55 219	92.90 191	88.87 278	96.34 284

TR-MVS		94.94 188	94.20 194	97.17 159	97.75 172	94.14 223	97.59 254	97.02 283	92.28 230	95.75 162	97.64 186	83.88 274	98.96 182	89.77 260	96.15 185	98.40 161

MS-PatchMatch		93.84 246	93.63 230	94.46 292	96.18 274	89.45 292	97.76 242	98.27 168	92.23 231	92.13 269	97.49 193	79.50 299	98.69 207	89.75 262	99.38 81	95.25 305

Test_1112_low_res		96.34 115	95.66 128	98.36 86	98.56 129	95.94 121	97.71 245	98.07 213	92.10 232	94.79 177	97.29 208	91.75 104	99.56 117	94.17 155	96.50 165	99.58 65

PVSNet_0	88.72 19	91.28 283	90.03 286	95.00 276	97.99 160	87.29 317	94.84 327	98.50 137	92.06 233	89.86 287	95.19 298	79.81 298	99.39 135	92.27 206	69.79 340	98.33 169

v7n		94.19 231	93.43 242	96.47 216	95.90 287	94.38 216	99.26 17	98.34 160	91.99 234	92.76 252	97.13 221	88.31 179	98.52 227	89.48 269	87.70 293	96.52 274

v8		94.47 218	93.77 222	96.57 206	96.36 255	94.83 188	99.05 56	98.19 182	91.92 235	93.16 241	96.97 241	88.82 160	98.48 231	91.69 223	87.79 292	96.39 281

testdata				98.26 90	99.20 76	95.36 152		98.68 97	91.89 236	98.60 43	99.10 50	94.44 67	99.82 50	94.27 153	99.44 76	99.58 65

v7		94.69 203	94.04 204	96.62 199	96.41 250	94.79 196	98.78 107	98.13 196	91.89 236	94.30 201	97.16 214	88.13 185	98.45 238	91.96 216	89.65 263	96.61 262

Patchmatch-RL test		91.49 281	90.85 273	93.41 301	91.37 327	84.40 321	92.81 336	95.93 316	91.87 238	87.25 301	94.87 302	88.99 147	96.53 321	92.54 202	82.00 318	99.30 96

v1144		94.59 212	93.92 212	96.60 201	96.21 272	94.78 198	98.59 141	98.14 195	91.86 239	94.21 208	97.02 236	87.97 188	98.41 251	91.72 222	89.57 264	96.61 262

Fast-Effi-MVS+		96.28 119	95.70 125	98.03 106	98.29 140	95.97 117	98.58 143	98.25 173	91.74 240	95.29 167	97.23 211	91.03 120	99.15 155	92.90 191	97.96 133	98.97 130

LTVRE_ROB	92.95 15	94.60 210	93.90 214	96.68 189	97.41 197	94.42 213	98.52 155	98.59 115	91.69 241	91.21 275	98.35 125	84.87 251	99.04 174	91.06 234	93.44 228	96.60 264
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
MVP-Stereo		94.28 228	93.92 212	95.35 268	94.95 310	92.60 254	97.97 219	97.65 232	91.61 242	90.68 282	97.09 224	86.32 222	98.42 244	89.70 264	99.34 83	95.02 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v748		93.75 247	93.06 247	95.82 246	95.73 294	92.64 253	99.25 19	98.24 175	91.60 243	92.22 267	96.52 270	87.60 202	98.46 236	90.64 241	85.72 311	96.36 283

v1192		94.32 224	93.58 234	96.53 211	96.10 279	94.45 212	98.50 160	98.17 190	91.54 244	94.19 209	97.06 229	86.95 213	98.43 243	90.14 252	89.57 264	96.70 246

TDRefinement		91.06 286	89.68 289	95.21 270	85.35 339	91.49 268	98.51 159	97.07 279	91.47 245	88.83 296	97.84 167	77.31 311	99.09 167	92.79 194	77.98 333	95.04 309

v144192		94.39 222	93.70 227	96.48 215	96.06 281	94.35 217	98.58 143	98.16 192	91.45 246	94.33 197	97.02 236	87.50 205	98.45 238	91.08 233	89.11 271	96.63 260

Baseline_NR-MVSNet		94.35 223	93.81 218	95.96 240	96.20 273	94.05 225	98.61 140	96.67 304	91.44 247	93.85 224	97.60 188	88.57 172	98.14 271	94.39 148	86.93 302	95.68 300

v52		94.18 233	93.52 237	96.13 236	95.95 286	94.29 219	99.23 22	98.21 178	91.42 248	92.84 250	96.89 252	87.85 194	98.53 226	91.51 227	87.81 290	95.57 303

V4		94.18 233	93.52 237	96.13 236	95.89 288	94.31 218	99.23 22	98.22 177	91.42 248	92.82 251	96.89 252	87.93 190	98.52 227	91.51 227	87.81 290	95.58 302

无先验							97.58 255	98.72 86	91.38 250				99.87 37	93.36 174		99.60 61

AllTest		95.24 173	94.65 174	96.99 168	99.25 66	93.21 246	98.59 141	98.18 185	91.36 251	93.52 232	98.77 89	84.67 253	99.72 88	89.70 264	97.87 136	98.02 176

TestCases				96.99 168	99.25 66	93.21 246		98.18 185	91.36 251	93.52 232	98.77 89	84.67 253	99.72 88	89.70 264	97.87 136	98.02 176

v10		94.29 226	93.55 235	96.51 213	96.39 251	94.80 193	98.99 62	98.19 182	91.35 253	93.02 247	96.99 239	88.09 186	98.41 251	90.50 249	88.41 285	96.33 285

v1921920		94.20 230	93.47 241	96.40 222	95.98 284	94.08 224	98.52 155	98.15 193	91.33 254	94.25 205	97.20 213	86.41 220	98.42 244	90.04 257	89.39 269	96.69 251

MSDG		95.93 127	95.30 140	97.83 114	98.90 97	95.36 152	96.83 297	98.37 157	91.32 255	94.43 192	98.73 93	90.27 130	99.60 108	90.05 256	98.82 101	98.52 155

旧先验2							97.57 256		91.30 256	98.67 38			99.80 59	95.70 117

tpmvs		94.60 210	94.36 187	95.33 269	97.46 190	88.60 305	96.88 294	97.68 230	91.29 257	93.80 226	96.42 274	88.58 171	99.24 144	91.06 234	96.04 193	98.17 172

PM-MVS		87.77 303	86.55 305	91.40 312	91.03 329	83.36 325	96.92 287	95.18 329	91.28 258	86.48 306	93.42 311	53.27 342	96.74 315	89.43 270	81.97 319	94.11 326

MIMVSNet		93.26 256	92.21 260	96.41 221	97.73 174	93.13 248	95.65 319	97.03 282	91.27 259	94.04 217	96.06 285	75.33 317	97.19 302	86.56 301	96.23 182	98.92 135

PAPM		94.95 186	94.00 207	97.78 118	97.04 217	95.65 141	96.03 313	98.25 173	91.23 260	94.19 209	97.80 173	91.27 115	98.86 197	82.61 316	97.61 145	98.84 139

dp		94.15 236	93.90 214	94.90 278	97.31 201	86.82 319	96.97 285	97.19 276	91.22 261	96.02 159	96.61 267	85.51 241	99.02 177	90.00 258	94.30 203	98.85 137

v1240		94.06 241	93.29 245	96.34 227	96.03 283	93.90 228	98.44 165	98.17 190	91.18 262	94.13 213	97.01 238	86.05 232	98.42 244	89.13 274	89.50 267	96.70 246

tfpnnormal		93.66 248	92.70 254	96.55 210	96.94 222	95.94 121	98.97 66	99.19 15	91.04 263	91.38 274	97.34 204	84.94 250	98.61 213	85.45 310	89.02 274	95.11 307

MDTV_nov1_ep13_2view						84.26 322	96.89 293		90.97 264	97.90 76		89.89 134		93.91 161		99.18 112

TransMVSNet (Re)		92.67 262	91.51 266	96.15 234	96.58 241	94.65 201	98.90 72	96.73 300	90.86 265	89.46 291	97.86 164	85.62 239	98.09 275	86.45 302	81.12 321	95.71 299

Anonymous20231206		91.66 280	91.10 268	93.33 302	94.02 319	87.35 316	98.58 143	97.26 273	90.48 266	90.16 285	96.31 275	83.83 276	96.53 321	79.36 323	89.90 261	96.12 289

VDDNet		95.36 166	94.53 179	97.86 112	98.10 153	95.13 161	98.85 85	97.75 228	90.46 267	98.36 53	99.39 7	73.27 326	99.64 102	97.98 27	96.58 161	98.81 140

TinyColmap		92.31 266	91.53 265	94.65 286	96.92 223	89.75 288	96.92 287	96.68 303	90.45 268	89.62 289	97.85 166	76.06 315	98.81 202	86.74 300	92.51 239	95.41 304

pmmvs4		94.69 203	93.99 209	96.81 179	95.74 293	95.94 121	97.40 263	97.67 231	90.42 269	93.37 236	97.59 189	89.08 146	98.20 269	92.97 186	91.67 249	96.30 286

FMVSNet1		93.19 258	92.07 261	96.56 207	97.54 185	95.00 165	98.82 91	98.18 185	90.38 270	92.27 265	97.07 226	73.68 325	97.95 282	89.36 271	91.30 252	96.72 242

RPSCF		94.87 190	95.40 130	93.26 304	98.89 104	82.06 329	98.33 176	98.06 215	90.30 271	96.56 132	99.26 29	87.09 209	99.49 127	93.82 164	96.32 174	98.24 171

ADS-MVSNet2		94.58 213	94.40 186	95.11 274	98.00 158	88.74 302	96.04 311	97.30 269	90.15 272	96.47 148	96.64 265	87.89 191	97.56 296	90.08 254	97.06 151	99.02 125

ADS-MVSNet		95.00 181	94.45 184	96.63 197	98.00 158	91.91 261	96.04 311	97.74 229	90.15 272	96.47 148	96.64 265	87.89 191	98.96 182	90.08 254	97.06 151	99.02 125

1121		97.37 78	96.77 88	99.16 36	99.34 41	97.99 46	98.19 195	98.68 97	90.14 274	98.01 68	98.97 67	94.80 58	99.87 37	93.36 174	99.46 74	99.61 58

diffmvs		96.32 116	95.74 119	98.07 104	98.26 141	96.14 110	98.53 154	98.23 176	90.10 275	96.88 118	97.73 176	90.16 132	99.15 155	93.90 162	97.85 138	98.91 136

新几何1				99.16 36	99.34 41	98.01 43		98.69 94	90.06 276	98.13 58	98.95 73	94.60 60	99.89 29	91.97 215	99.47 71	99.59 63

v18		92.10 269	90.97 269	95.50 255	96.34 258	94.85 177	98.82 91	97.52 241	89.99 277	85.31 314	93.26 313	88.90 154	96.92 306	88.82 280	79.77 325	94.73 313

OpenMVS	93.04 13	95.83 131	95.00 150	98.32 88	97.18 211	97.32 66	99.21 31	98.97 29	89.96 278	91.14 276	99.05 59	86.64 217	99.92 15	93.38 173	99.47 71	97.73 185

v17		92.08 270	90.94 270	95.48 257	96.34 258	94.83 188	98.81 97	97.52 241	89.95 279	85.32 312	93.24 314	88.91 153	96.91 307	88.76 281	79.63 326	94.71 315

v16		92.08 270	90.94 270	95.49 256	96.38 254	94.84 186	98.81 97	97.51 244	89.94 280	85.25 315	93.28 312	88.86 155	96.91 307	88.70 282	79.78 324	94.72 314

v15		91.94 272	90.77 274	95.43 262	96.31 266	94.83 188	98.77 108	97.50 247	89.92 281	85.13 316	93.08 317	88.76 166	96.86 309	88.40 285	79.10 328	94.61 319

COLMAP_ROB	93.27 12	95.33 169	94.87 162	96.71 183	99.29 57	93.24 245	98.58 143	98.11 203	89.92 281	93.57 230	99.10 50	86.37 221	99.79 71	90.78 238	98.10 130	97.09 206
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V14		91.93 273	90.76 275	95.42 265	96.33 262	94.81 192	98.77 108	97.51 244	89.86 283	85.09 317	93.13 315	88.80 164	96.83 311	88.32 286	79.06 330	94.60 320

V9		91.91 274	90.73 276	95.45 259	96.32 265	94.80 193	98.77 108	97.50 247	89.81 284	85.03 319	93.08 317	88.76 166	96.86 309	88.24 287	79.03 331	94.69 316

v12		91.89 275	90.70 277	95.43 262	96.31 266	94.80 193	98.76 111	97.50 247	89.76 285	84.95 320	93.00 320	88.82 160	96.82 313	88.23 288	79.00 332	94.68 318

QAPM		96.29 117	95.40 130	98.96 52	97.85 168	97.60 58	99.23 22	98.93 36	89.76 285	93.11 245	99.02 60	89.11 145	99.93 9	91.99 214	99.62 49	99.34 90

gm-plane-assit					95.88 289	87.47 315			89.74 287		96.94 245		99.19 153	93.32 176

v13		91.88 276	90.69 278	95.43 262	96.33 262	94.78 198	98.75 112	97.50 247	89.68 288	84.93 321	92.98 321	88.84 158	96.83 311	88.14 289	79.09 329	94.69 316

pmmvs5		93.65 250	92.97 249	95.68 251	95.49 302	92.37 255	98.20 191	97.28 271	89.66 289	92.58 256	97.26 209	82.14 282	98.09 275	93.18 180	90.95 255	96.58 266

CostFormer		94.95 186	94.73 171	95.60 253	97.28 202	89.06 298	97.53 257	96.89 296	89.66 289	96.82 122	96.72 261	86.05 232	98.95 186	95.53 121	96.13 186	98.79 141

v11		91.85 277	90.68 279	95.36 267	96.34 258	94.74 200	98.80 100	97.43 258	89.60 291	85.09 317	93.03 319	88.53 175	96.75 314	87.37 297	79.96 323	94.58 321

new-patchmatchnet		88.50 302	87.45 303	91.67 311	90.31 330	85.89 320	97.16 281	97.33 268	89.47 292	83.63 324	92.77 325	76.38 313	95.06 330	82.70 315	77.29 334	94.06 328

Patchmatch-test		94.42 220	93.68 229	96.63 197	97.60 180	91.76 264	94.83 328	97.49 253	89.45 293	94.14 212	97.10 222	88.99 147	98.83 200	85.37 311	98.13 129	99.29 98

DP-MVS		96.59 106	95.93 115	98.57 70	99.34 41	96.19 109	98.70 125	98.39 154	89.45 293	94.52 182	99.35 18	91.85 103	99.85 42	92.89 193	98.88 96	99.68 43

testus		88.91 299	89.08 294	88.40 317	91.39 326	76.05 335	96.56 304	96.48 308	89.38 295	89.39 292	95.17 300	70.94 329	93.56 335	77.04 329	95.41 199	95.61 301

FMVSNet5		91.81 278	90.92 272	94.49 289	97.21 207	92.09 258	98.00 217	97.55 239	89.31 296	90.86 280	95.61 297	74.48 322	95.32 328	85.57 308	89.70 262	96.07 291

EG-PatchMatch MVS		91.13 284	90.12 285	94.17 297	94.73 314	89.00 300	98.13 203	97.81 225	89.22 297	85.32 312	96.46 271	67.71 334	98.42 244	87.89 295	93.82 219	95.08 308

DSMNet-mixed		92.52 264	92.58 255	92.33 308	94.15 317	82.65 327	98.30 183	94.26 337	89.08 298	92.65 254	95.73 292	85.01 249	95.76 326	86.24 303	97.76 142	98.59 153

pmmvs-eth3d		90.36 292	89.05 295	94.32 294	91.10 328	92.12 257	97.63 253	96.95 290	88.86 299	84.91 322	93.13 315	78.32 304	96.74 315	88.70 282	81.81 320	94.09 327

test222					99.23 72	97.17 74	97.40 263	98.66 107	88.68 300	98.05 62	98.96 71	94.14 71			99.53 67	99.61 58

test2356		88.68 301	88.61 297	88.87 316	89.90 332	78.23 332	95.11 322	96.66 306	88.66 301	89.06 294	94.33 309	73.14 327	92.56 339	75.56 332	95.11 201	95.81 297

MDA-MVSNet-bldmvs		89.97 294	88.35 300	94.83 282	95.21 307	91.34 269	97.64 251	97.51 244	88.36 302	71.17 339	96.13 284	79.22 301	96.63 320	83.65 313	86.27 307	96.52 274

MIMVSNet1		89.67 296	88.28 301	93.82 298	92.81 324	91.08 274	98.01 215	97.45 256	87.95 303	87.90 300	95.87 290	67.63 335	94.56 331	78.73 326	88.18 287	95.83 296

tpmp4_e23		93.91 245	93.42 244	95.38 266	97.62 178	88.59 306	97.52 258	97.34 265	87.94 304	94.17 211	96.79 259	82.91 279	99.05 170	90.62 242	95.91 194	98.50 156

MDA-MVSNet_test_wron		90.71 289	89.38 292	94.68 285	94.83 312	90.78 277	97.19 279	97.46 254	87.60 305	72.41 338	95.72 294	86.51 218	96.71 318	85.92 306	86.80 305	96.56 270

YYNet1		90.70 290	89.39 291	94.62 287	94.79 313	90.65 280	97.20 278	97.46 254	87.54 306	72.54 337	95.74 291	86.51 218	96.66 319	86.00 305	86.76 306	96.54 272

Patchmtry		93.22 257	92.35 258	95.84 245	96.77 231	93.09 249	94.66 330	97.56 235	87.37 307	92.90 249	96.24 277	88.15 183	97.90 285	87.37 297	90.10 259	96.53 273

tpm2		94.19 231	93.76 224	95.46 258	97.23 205	89.04 299	97.31 274	96.85 299	87.08 308	96.21 154	96.79 259	83.75 277	98.74 206	92.43 205	96.23 182	98.59 153

PatchT		93.06 260	91.97 262	96.35 225	96.69 237	92.67 252	94.48 331	97.08 278	86.62 309	97.08 104	92.23 331	87.94 189	97.90 285	78.89 325	96.69 157	98.49 157

TAPA-MVS	93.98 7	95.35 167	94.56 178	97.74 119	99.13 81	94.83 188	98.33 176	98.64 112	86.62 309	96.29 153	98.61 102	94.00 74	99.29 141	80.00 321	99.41 78	99.09 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
new_pmnet		90.06 293	89.00 296	93.22 305	94.18 316	88.32 310	96.42 309	96.89 296	86.19 311	85.67 311	93.62 310	77.18 312	97.10 303	81.61 318	89.29 270	94.23 324

pmmvs6		91.77 279	90.63 280	95.17 272	94.69 315	91.24 272	98.67 133	97.92 222	86.14 312	89.62 289	97.56 192	75.79 316	98.34 258	90.75 239	84.56 315	95.94 294

test_0402		91.32 282	90.27 284	94.48 290	96.60 240	91.12 273	98.50 160	97.22 275	86.10 313	88.30 298	96.98 240	77.65 309	97.99 281	78.13 327	92.94 236	94.34 323

test1235678		86.26 307	85.81 306	87.62 319	86.97 337	75.00 339	96.55 306	96.32 311	86.08 314	81.32 329	92.98 321	73.10 328	92.05 340	71.64 336	87.32 297	95.81 297

JIA-IIPM		93.35 252	92.49 256	95.92 241	96.48 247	90.65 280	95.01 323	96.96 289	85.93 315	96.08 156	87.33 336	87.70 199	98.78 205	91.35 230	95.58 198	98.34 168

N_pmnet		87.12 305	87.77 302	85.17 325	95.46 303	61.92 348	97.37 267	70.66 355	85.83 316	88.73 297	96.04 286	85.33 246	97.76 291	80.02 320	90.48 257	95.84 295

cascas		94.63 209	93.86 216	96.93 174	96.91 225	94.27 220	96.00 314	98.51 132	85.55 317	94.54 181	96.23 279	84.20 269	98.87 195	95.80 111	96.98 154	97.66 189

gg-mvs-nofinetune		92.21 267	90.58 281	97.13 161	96.75 234	95.09 162	95.85 316	89.40 347	85.43 318	94.50 183	81.98 340	80.80 292	98.40 257	92.16 207	98.33 122	97.88 180

testpf		88.74 300	89.09 293	87.69 318	95.78 292	83.16 326	84.05 346	94.13 340	85.22 319	90.30 284	94.39 307	74.92 320	95.80 325	89.77 260	93.28 233	84.10 342

LP		91.12 285	89.99 287	94.53 288	96.35 257	88.70 303	93.86 335	97.35 264	84.88 320	90.98 278	94.77 303	84.40 262	97.43 298	75.41 333	91.89 247	97.47 191

114514_t		96.93 94	96.27 105	98.92 54	99.50 29	97.63 56	98.85 85	98.90 42	84.80 321	97.77 80	99.11 48	92.84 83	99.66 99	94.85 137	99.77 19	99.47 79

PCF-MVS	93.45 11	94.68 206	93.43 242	98.42 84	98.62 126	96.77 87	95.48 320	98.20 181	84.63 322	93.34 237	98.32 131	88.55 174	99.81 52	84.80 312	98.96 93	98.68 147
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UnsupCasMVSNet_bld		87.17 304	85.12 307	93.31 303	91.94 325	88.77 301	94.92 326	98.30 165	84.30 323	82.30 325	90.04 333	63.96 339	97.25 301	85.85 307	74.47 339	93.93 330

test12356		83.47 310	83.37 310	83.78 326	84.43 340	70.09 344	95.12 321	95.60 324	82.98 324	78.89 332	92.43 330	64.99 337	91.41 342	70.36 337	85.55 313	89.82 336

ANet_high		69.08 318	65.37 320	80.22 329	65.99 352	71.96 343	90.91 340	90.09 346	82.62 325	49.93 348	78.39 344	29.36 353	81.75 348	62.49 345	38.52 347	86.95 340

1111		84.94 308	84.30 309	86.86 320	87.59 335	75.10 337	96.63 301	96.43 309	82.53 326	80.75 330	92.91 323	68.94 332	93.79 333	68.24 339	84.66 314	91.70 334

.test1245		73.05 317	76.31 315	63.27 338	87.59 335	75.10 337	96.63 301	96.43 309	82.53 326	80.75 330	92.91 323	68.94 332	93.79 333	68.24 339	12.72 351	20.91 351

RPMNet		92.52 264	91.17 267	96.59 202	97.00 218	93.43 240	94.96 324	97.26 273	82.27 328	96.93 113	92.12 332	86.98 212	97.88 289	76.32 330	96.65 159	98.46 158

tpm cat1		93.36 251	92.80 251	95.07 275	97.58 182	87.97 312	96.76 298	97.86 224	82.17 329	93.53 231	96.04 286	86.13 224	99.13 158	89.24 272	95.87 195	98.10 174

testmv		78.74 311	77.35 312	82.89 328	78.16 348	69.30 345	95.87 315	94.65 334	81.11 330	70.98 340	87.11 337	46.31 344	90.42 343	65.28 342	76.72 335	88.95 337

CMPMVS	66.06 21	89.70 295	89.67 290	89.78 314	93.19 321	76.56 334	97.00 284	98.35 159	80.97 331	81.57 328	97.75 175	74.75 321	98.61 213	89.85 259	93.63 222	94.17 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
no-one		74.41 316	70.76 318	85.35 324	79.88 344	76.83 333	94.68 329	94.22 338	80.33 332	63.81 342	79.73 343	35.45 351	93.36 336	71.78 335	36.99 348	85.86 341

pmmvs3		86.67 306	84.86 308	92.11 310	88.16 334	87.19 318	96.63 301	94.75 333	79.88 333	87.22 302	92.75 326	66.56 336	95.20 329	81.24 319	76.56 336	93.96 329

OpenMVS_ROB	86.42 20	89.00 298	87.43 304	93.69 299	93.08 322	89.42 293	97.91 226	96.89 296	78.58 334	85.86 308	94.69 304	69.48 331	98.29 266	77.13 328	93.29 232	93.36 332

MVS		94.67 207	93.54 236	98.08 102	96.88 227	96.56 95	98.19 195	98.50 137	78.05 335	92.69 253	98.02 151	91.07 119	99.63 105	90.09 253	98.36 121	98.04 175

DeepMVS_CX				86.78 321	97.09 216	72.30 341		95.17 330	75.92 336	84.34 323	95.19 298	70.58 330	95.35 327	79.98 322	89.04 273	92.68 333

MVS-HIRNet		89.46 297	88.40 299	92.64 306	97.58 182	82.15 328	94.16 334	93.05 343	75.73 337	90.90 279	82.52 339	79.42 300	98.33 259	83.53 314	98.68 104	97.43 192

PMMVS2		77.95 314	75.44 317	85.46 323	82.54 341	74.95 340	94.23 333	93.08 342	72.80 338	74.68 335	87.38 335	36.36 350	91.56 341	73.95 334	63.94 341	89.87 335

Anonymous20231211		83.69 309	81.50 311	90.26 313	89.23 333	80.10 331	97.97 219	97.06 281	72.79 339	82.05 327	92.57 327	50.28 343	96.32 324	76.15 331	75.38 337	94.37 322

FPMVS		77.62 315	77.14 313	79.05 330	79.25 345	60.97 349	95.79 317	95.94 315	65.96 340	67.93 341	94.40 306	37.73 349	88.88 345	68.83 338	88.46 284	87.29 338

Gipuma		78.40 313	76.75 314	83.38 327	95.54 300	80.43 330	79.42 347	97.40 261	64.67 341	73.46 336	80.82 342	45.65 346	93.14 337	66.32 341	87.43 295	76.56 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		67.70 320	65.07 321	75.60 332	78.61 346	59.61 351	89.14 341	88.24 349	61.83 342	52.37 346	80.89 341	18.91 354	84.91 347	62.70 344	52.93 343	82.28 343

LCM-MVSNet		78.70 312	76.24 316	86.08 322	77.26 349	71.99 342	94.34 332	96.72 301	61.62 343	76.53 334	89.33 334	33.91 352	92.78 338	81.85 317	74.60 338	93.46 331

wuykxyi23d		63.73 324	58.86 326	78.35 331	67.62 351	67.90 346	86.56 343	87.81 350	58.26 344	42.49 350	70.28 348	11.55 357	85.05 346	63.66 343	41.50 344	82.11 344

PMVS	61.03 23	65.95 321	63.57 323	73.09 335	57.90 353	51.22 354	85.05 345	93.93 341	54.45 345	44.32 349	83.57 338	13.22 355	89.15 344	58.68 346	81.00 322	78.91 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		64.94 322	64.25 322	67.02 336	82.28 342	59.36 352	91.83 339	85.63 351	52.69 346	60.22 344	77.28 345	41.06 348	80.12 350	46.15 348	41.14 345	61.57 349

MVE	62.14 22	63.28 325	59.38 325	74.99 333	74.33 350	65.47 347	85.55 344	80.50 354	52.02 347	51.10 347	75.00 347	10.91 359	80.50 349	51.60 347	53.40 342	78.99 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		64.07 323	63.26 324	66.53 337	81.73 343	58.81 353	91.85 338	84.75 352	51.93 348	59.09 345	75.13 346	43.32 347	79.09 351	42.03 349	39.47 346	61.69 348

tmp_tt		68.90 319	66.97 319	74.68 334	50.78 354	59.95 350	87.13 342	83.47 353	38.80 349	62.21 343	96.23 279	64.70 338	76.91 352	88.91 279	30.49 349	87.19 339

wuyk23d		30.17 327	30.18 329	30.16 340	78.61 346	43.29 355	66.79 348	14.21 356	17.31 350	14.82 353	11.93 354	11.55 357	41.43 353	37.08 350	19.30 350	5.76 353

testmvs		21.48 329	24.95 330	11.09 342	14.89 355	6.47 357	96.56 304	9.87 357	7.55 351	17.93 351	39.02 350	9.43 360	5.90 355	16.56 352	12.72 351	20.91 351

test123		20.95 330	23.72 331	12.64 341	13.54 356	8.19 356	96.55 306	6.13 358	7.48 352	16.74 352	37.98 351	12.97 356	6.05 354	16.69 351	5.43 353	23.68 350

cdsmvs_eth3d_5k		23.98 328	31.98 328	0.00 343	0.00 357	0.00 358	0.00 349	98.59 115	0.00 353	0.00 354	98.61 102	90.60 125	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		7.88 332	10.50 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	94.51 62	0.00 356	0.00 353	0.00 354	0.00 354

pcd1.5k->3k		39.42 326	41.78 327	32.35 339	96.17 275	0.00 358	0.00 349	98.54 125	0.00 353	0.00 354	0.00 355	87.78 196	0.00 356	0.00 353	93.56 224	97.06 207

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

ab-mvs-re		8.20 331	10.94 332	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	98.43 117	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

GSMVS																99.20 106

test_part2					99.63 21	99.18 1				99.27 6

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

sam_mvs1												89.45 136				99.20 106

sam_mvs												88.99 147

ambc				89.49 315	86.66 338	75.78 336	92.66 337	96.72 301		86.55 305	92.50 328	46.01 345	97.90 285	90.32 250	82.09 317	94.80 312

MTGPA								98.74 79

test_post1							96.68 300				30.43 353	87.85 194	98.69 207	92.59 199

test_post											31.83 352	88.83 159	98.91 189

patchmatchnet-post											95.10 301	89.42 137	98.89 193

GG-mvs-BLEND				96.59 202	96.34 258	94.98 168	96.51 308	88.58 348		93.10 246	94.34 308	80.34 297	98.05 277	89.53 267	96.99 153	96.74 239

MTMP								94.14 339

test9_res														96.39 94	99.57 57	99.69 37

agg_prior2														95.87 108	99.57 57	99.68 43

agg_prior					99.30 54	98.38 19		98.72 86		97.57 95			99.81 52

test_prior4						98.01 43	97.86 234

test_prior				99.19 29	99.31 49	98.22 32		98.84 54					99.70 93			99.65 52

新几何2							97.64 251

旧先验1					99.29 57	97.48 61		98.70 93			99.09 54	95.56 37			99.47 71	99.61 58

原ACMM2							97.67 249

testdata2													99.89 29	91.65 224

segment_acmp												96.85 5

test12				99.18 33	99.16 78	98.19 34		98.53 128		98.07 61		95.13 51	99.72 88		99.56 63	99.63 57

plane_prior7					97.42 194	94.63 203

plane_prior6					97.35 199	94.61 206						87.09 209

plane_prior5								98.56 122					99.03 175	96.07 99	94.27 204	96.92 216

plane_prior4											98.28 133

plane_prior1					97.37 198

n2								0.00 359

nn								0.00 359

door-mid								94.37 336

lessismore_v0				94.45 293	94.93 311	88.44 308		91.03 345		86.77 304	97.64 186	76.23 314	98.42 244	90.31 251	85.64 312	96.51 276

test11								98.66 107

door								94.64 335

HQP5-MVS						94.25 221

BP-MVS														95.30 127

HQP4-MVS										94.45 185			98.96 182			96.87 227

HQP3-MVS								98.46 142							94.18 208

HQP2-MVS												86.75 215

NP-MVS					97.28 202	94.51 211					97.73 176

ACMMP++_ref															92.97 235

ACMMP++															93.61 223

Test By Simon												94.64 59

High-resolution multi-view benchmark