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		95.20 66	94.56 71	97.14 53	92.80 299	92.68 64	97.85 48	94.87 282	96.64 1	92.46 126	97.80 61	86.23 98	99.65 39	93.72 78	98.62 73	99.10 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		97.30 8	97.03 9	98.11 6	98.77 34	95.06 9	97.34 99	98.04 64	95.96 2	97.09 26	97.88 52	93.18 10	99.71 27	95.84 35	99.17 52	99.56 14

CNVR-MVS		97.68 2	97.44 5	98.37 2	98.90 31	95.86 2	97.27 105	98.08 50	95.81 3	97.87 10	98.31 31	94.26 3	99.68 35	97.02 4	99.49 22	99.57 12

HPM-MVS++		97.34 7	96.97 11	98.47 1	99.08 26	96.16 1	97.55 82	97.97 78	95.59 4	96.61 34	97.89 50	92.57 18	99.84 12	95.95 32	99.51 18	99.40 34

HSP-MVS		97.53 4	97.49 4	97.63 33	99.40 5	93.77 39	98.53 9	97.85 88	95.55 5	98.56 3	97.81 59	93.90 5	99.65 39	96.62 13	99.21 49	99.48 27

MVS_0304		96.05 49	95.45 51	97.85 13	97.75 100	94.50 14	96.87 141	97.95 81	95.46 6	95.60 71	98.01 46	80.96 190	99.83 13	97.23 2	99.25 45	99.23 48

DeepPCF-MVS	93.97 1	96.61 35	97.09 7	95.15 134	98.09 79	86.63 248	96.00 220	98.15 38	95.43 7	97.95 8	98.56 7	93.40 9	99.36 89	96.77 12	99.48 23	99.45 29

CANet		96.39 41	96.02 43	97.50 37	97.62 106	93.38 48	97.02 126	97.96 79	95.42 8	94.86 81	97.81 59	87.38 88	99.82 16	96.88 7	99.20 50	99.29 44

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 12	98.39 58	94.25 21	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 2	99.71 27	96.57 16	99.62 6	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
Regformer-2		97.16 12	96.99 10	97.67 28	98.32 64	93.84 34	96.83 144	98.10 47	95.24 10	97.49 12	98.25 37	92.57 18	99.61 45	96.80 9	99.29 42	99.56 14

DELS-MVS		96.61 35	96.38 36	97.30 43	97.79 97	93.19 52	95.96 221	98.18 35	95.23 11	95.87 60	97.65 70	91.45 39	99.70 32	95.87 33	99.44 28	99.00 68
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
Regformer-1		97.10 14	96.96 12	97.54 36	98.32 64	93.48 45	96.83 144	97.99 76	95.20 12	97.46 13	98.25 37	92.48 21	99.58 53	96.79 11	99.29 42	99.55 16

Regformer-4		96.97 21	96.87 15	97.25 47	98.34 61	92.66 65	96.96 131	98.01 69	95.12 13	97.14 22	98.42 16	91.82 33	99.61 45	96.90 6	99.13 55	99.50 23

MPTG		97.07 16	96.77 23	97.97 10	99.37 10	94.42 17	97.15 119	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

MTAPA		97.08 15	96.78 22	97.97 10	99.37 10	94.42 17	97.24 107	98.08 50	95.07 14	96.11 50	98.59 5	90.88 48	99.90 1	96.18 27	99.50 20	99.58 10

Regformer-3		96.85 26	96.80 21	97.01 56	98.34 61	92.02 83	96.96 131	97.76 91	95.01 16	97.08 27	98.42 16	91.71 34	99.54 65	96.80 9	99.13 55	99.48 27

XVS		97.18 10	96.96 12	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	98.29 34	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

X-MVStestdata		91.71 172	89.67 227	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	32.69 344	91.70 35	99.80 18	95.66 37	99.40 31	99.62 6

HQP_MVS		93.78 103	93.43 98	94.82 149	96.21 159	89.99 136	97.74 56	97.51 117	94.85 17	91.34 151	96.64 115	81.32 186	98.60 150	93.02 90	92.23 186	95.86 196

plane_prior2							97.74 56		94.85 17

SD-MVS		97.41 6	97.53 2	97.06 55	98.57 50	94.46 15	97.92 42	98.14 40	94.82 21	99.01 1	98.55 9	94.18 4	97.41 270	96.94 5	99.64 3	99.32 42

UA-Net		95.95 53	95.53 50	97.20 52	97.67 103	92.98 58	97.65 68	98.13 41	94.81 22	96.61 34	98.35 22	88.87 65	99.51 72	90.36 131	97.35 105	99.11 59

DeepC-MVS_fast	93.89 2	96.93 24	96.64 26	97.78 19	98.64 45	94.30 19	97.41 91	98.04 64	94.81 22	96.59 36	98.37 21	91.24 41	99.64 44	95.16 47	99.52 16	99.42 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		97.82 1	97.73 1	98.08 7	99.15 24	94.82 11	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 7	99.68 35	97.93 1	99.69 1	99.75 1

EI-MVSNet-Vis-set		96.51 37	96.47 32	96.63 64	98.24 70	91.20 106	96.89 140	97.73 94	94.74 25	96.49 40	98.49 11	90.88 48	99.58 53	96.44 18	98.32 79	99.13 56

EI-MVSNet-UG-set		96.34 42	96.30 37	96.47 75	98.20 74	90.93 117	96.86 142	97.72 97	94.67 26	96.16 49	98.46 12	90.43 52	99.58 53	96.23 21	97.96 88	98.90 77

MSLP-MVS++		96.94 23	97.06 8	96.59 67	98.72 36	91.86 87	97.67 65	98.49 12	94.66 27	97.24 17	98.41 19	92.31 25	98.94 124	96.61 14	99.46 24	98.96 70

3Dnovator+	91.43 4	95.40 59	94.48 76	98.16 5	96.90 129	95.34 5	98.48 14	97.87 85	94.65 28	88.53 228	98.02 45	83.69 126	99.71 27	93.18 89	98.96 65	99.44 31

canonicalmvs		96.02 51	95.45 51	97.75 23	97.59 109	95.15 8	98.28 22	97.60 108	94.52 29	96.27 46	96.12 141	87.65 82	99.18 99	96.20 26	94.82 148	98.91 76

plane_prior3						90.00 134			94.46 30	91.34 151

UGNet		94.04 95	93.28 103	96.31 84	96.85 130	91.19 107	97.88 45	97.68 102	94.40 31	93.00 118	96.18 138	73.39 282	99.61 45	91.72 112	98.46 76	98.13 120
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
alignmvs		95.87 55	95.23 58	97.78 19	97.56 111	95.19 6	97.86 46	97.17 152	94.39 32	96.47 41	96.40 131	85.89 103	99.20 96	96.21 25	95.11 144	98.95 72

CANet_DTU		94.37 83	93.65 89	96.55 68	96.46 151	92.13 79	96.21 208	96.67 203	94.38 33	93.53 101	97.03 100	79.34 219	99.71 27	90.76 127	98.45 77	97.82 135

Vis-MVSNet		95.23 64	94.81 64	96.51 72	97.18 119	91.58 95	98.26 24	98.12 42	94.38 33	94.90 80	98.15 39	82.28 169	98.92 125	91.45 121	98.58 75	99.01 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_111021_HR		96.68 34	96.58 29	96.99 57	98.46 52	92.31 72	96.20 209	98.90 2	94.30 35	95.86 61	97.74 64	92.33 22	99.38 88	96.04 30	99.42 29	99.28 47

TSAR-MVS + GP.		96.69 32	96.49 31	97.27 46	98.31 66	93.39 47	96.79 151	96.72 196	94.17 36	97.44 14	97.66 69	92.76 12	99.33 90	96.86 8	97.76 94	99.08 61

3Dnovator	91.36 5	95.19 67	94.44 78	97.44 38	96.56 143	93.36 50	98.65 6	98.36 16	94.12 37	89.25 218	98.06 43	82.20 172	99.77 20	93.41 86	99.32 40	99.18 51

plane_prior						89.99 136	97.24 107		94.06 38						92.16 190

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 73	91.35 101	96.24 207	98.79 4	93.99 39	95.80 64	97.65 70	89.92 59	99.24 95	95.87 33	99.20 50	98.58 93

DeepC-MVS	93.07 3	96.06 48	95.66 49	97.29 44	97.96 86	93.17 53	97.30 104	98.06 57	93.92 40	93.38 104	98.66 4	86.83 93	99.73 23	95.60 43	99.22 48	98.96 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		95.89 54	95.45 51	97.21 51	98.07 80	92.94 59	97.50 85	98.15 38	93.87 41	97.52 11	97.61 76	85.29 109	99.53 68	95.81 36	95.27 142	99.16 52

Effi-MVS+-dtu		93.08 123	93.21 104	92.68 246	96.02 170	83.25 282	97.14 120	96.72 196	93.85 42	91.20 162	93.44 264	83.08 139	98.30 176	91.69 115	95.73 137	96.50 175

mvs-test1		93.63 107	93.69 87	93.46 220	96.02 170	84.61 270	97.24 107	96.72 196	93.85 42	92.30 132	95.76 160	83.08 139	98.89 129	91.69 115	96.54 124	96.87 166

PS-MVSNAJ		95.37 60	95.33 56	95.49 119	97.35 115	90.66 125	95.31 250	97.48 119	93.85 42	96.51 39	95.70 165	88.65 69	99.65 39	94.80 61	98.27 80	96.17 183

TSAR-MVS + MP.		97.42 5	97.33 6	97.69 27	99.25 19	94.24 22	98.07 34	97.85 88	93.72 45	98.57 2	98.35 22	93.69 8	99.40 85	97.06 3	99.46 24	99.44 31

OPM-MVS		93.28 118	92.76 112	94.82 149	94.63 230	90.77 123	96.65 169	97.18 150	93.72 45	91.68 144	97.26 90	79.33 220	98.63 147	92.13 101	92.28 185	95.07 242

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 117	90.50 128	95.44 245	97.44 131	93.70 47	96.46 42	96.18 138	88.59 72	99.53 68	94.79 63	97.81 91	96.17 183

HQP-NCC					95.86 173		96.65 169		93.55 48	90.14 175

ACMP_Plane					95.86 173		96.65 169		93.55 48	90.14 175

HQP-MVS		93.19 121	92.74 116	94.54 165	95.86 173	89.33 171	96.65 169	97.39 136	93.55 48	90.14 175	95.87 150	80.95 191	98.50 159	92.13 101	92.10 191	95.78 203

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 4	97.12 121	98.07 55	93.54 51	96.08 52	97.69 66	93.86 6	99.71 27	96.50 17	99.39 33	99.55 16

MG-MVS		95.61 57	95.38 54	96.31 84	98.42 55	90.53 127	96.04 216	97.48 119	93.47 52	95.67 70	98.10 40	89.17 62	99.25 94	91.27 124	98.77 69	99.13 56

FC-MVSNet-test		93.94 98	93.57 90	95.04 139	95.48 186	91.45 99	98.12 30	98.71 5	93.37 53	90.23 174	96.70 110	87.66 81	97.85 237	91.49 119	90.39 218	95.83 200

MP-MVS		96.77 29	96.45 34	97.72 24	99.39 7	93.80 35	98.41 17	98.06 57	93.37 53	95.54 74	98.34 25	90.59 51	99.88 3	94.83 59	99.54 14	99.49 25

FIs		94.09 92	93.70 86	95.27 127	95.70 180	92.03 82	98.10 31	98.68 7	93.36 55	90.39 171	96.70 110	87.63 83	97.94 227	92.25 97	90.50 217	95.84 199

abl_6		96.40 40	96.21 40	96.98 58	98.89 32	92.20 77	97.89 44	98.03 66	93.34 56	97.22 18	98.42 16	87.93 78	99.72 26	95.10 50	99.07 60	99.02 63

mPP-MVS		96.86 25	96.60 27	97.64 31	99.40 5	93.44 46	98.50 13	98.09 49	93.27 57	95.95 59	98.33 28	91.04 44	99.88 3	95.20 46	99.57 12	99.60 9

HFP-MVS		97.14 13	96.92 14	97.83 14	99.42 3	94.12 26	98.52 10	98.32 19	93.21 58	97.18 19	98.29 34	92.08 27	99.83 13	95.63 39	99.59 8	99.54 18

ACMMPR		97.07 16	96.84 17	97.79 18	99.44 2	93.88 32	98.52 10	98.31 21	93.21 58	97.15 21	98.33 28	91.35 40	99.86 6	95.63 39	99.59 8	99.62 6

IS-MVSNet		94.90 75	94.52 74	96.05 95	97.67 103	90.56 126	98.44 15	96.22 219	93.21 58	93.99 94	97.74 64	85.55 107	98.45 163	89.98 132	97.86 89	99.14 55

region2R		97.07 16	96.84 17	97.77 21	99.46 1	93.79 36	98.52 10	98.24 28	93.19 61	97.14 22	98.34 25	91.59 38	99.87 5	95.46 44	99.59 8	99.64 4

EPNet_dtu		91.71 172	91.28 165	92.99 236	93.76 272	83.71 277	96.69 166	95.28 259	93.15 62	87.02 258	95.95 147	83.37 130	97.38 273	79.46 290	96.84 114	97.88 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet (Re)		93.31 117	92.55 123	95.61 112	95.39 189	93.34 51	97.39 95	98.71 5	93.14 63	90.10 183	94.83 202	87.71 80	98.03 210	91.67 117	83.99 280	95.46 216

APD-MVS_3200maxsize		96.81 27	96.71 25	97.12 54	99.01 29	92.31 72	97.98 40	98.06 57	93.11 64	97.44 14	98.55 9	90.93 46	99.55 63	96.06 29	99.25 45	99.51 22

testdata1							95.26 254		93.10 65

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 216	92.83 60	97.16 118	98.24 28	93.02 66	90.13 179	95.71 163	83.47 128	97.85 237	91.71 113	83.93 281	95.78 203

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 140	91.71 88	96.25 204	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base		95.01 69	94.76 65	95.75 106	96.58 140	91.71 88	96.25 204	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

xiu_mvs_v1_base_debi		95.01 69	94.76 65	95.75 106	96.58 140	91.71 88	96.25 204	97.35 142	92.99 67	96.70 30	96.63 119	82.67 158	99.44 80	96.22 22	97.46 98	96.11 188

CP-MVS		97.02 19	96.81 20	97.64 31	99.33 14	93.54 43	98.80 3	98.28 23	92.99 67	96.45 43	98.30 33	91.90 32	99.85 9	95.61 41	99.68 2	99.54 18

ACMMP		96.27 44	95.93 44	97.28 45	99.24 20	92.62 66	98.25 25	98.81 3	92.99 67	94.56 85	98.39 20	88.96 64	99.85 9	94.57 65	97.63 95	99.36 40
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
UniMVSNet_NR-MVSNet		93.37 115	92.67 118	95.47 122	95.34 192	92.83 60	97.17 117	98.58 10	92.98 72	90.13 179	95.80 155	88.37 74	97.85 237	91.71 113	83.93 281	95.73 209

VPNet		92.23 159	91.31 164	94.99 141	95.56 183	90.96 115	97.22 112	97.86 87	92.96 73	90.96 163	96.62 122	75.06 268	98.20 180	91.90 107	83.65 287	95.80 202

nrg030		94.05 94	93.31 102	96.27 88	95.22 202	94.59 13	98.34 19	97.46 124	92.93 74	91.21 161	96.64 115	87.23 90	98.22 179	94.99 56	85.80 254	95.98 194

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 225	92.07 80	97.53 83	98.11 45	92.90 75	89.56 206	96.12 141	83.16 132	97.60 258	89.30 145	83.20 291	95.75 207

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 25	95.16 7	97.60 77	98.19 33	92.82 76	97.93 9	98.74 3	91.60 37	99.86 6	96.26 20	99.52 16	99.67 2

test_prior3		96.46 39	96.20 41	97.23 48	98.67 39	92.99 56	96.35 194	98.00 71	92.80 77	96.03 53	97.59 77	92.01 29	99.41 83	95.01 53	99.38 34	99.29 44

test_prior2							96.35 194		92.80 77	96.03 53	97.59 77	92.01 29		95.01 53	99.38 34

CLD-MVS		92.98 127	92.53 125	94.32 173	96.12 168	89.20 178	95.28 251	97.47 122	92.66 79	89.90 188	95.62 168	80.58 200	98.40 167	92.73 93	92.40 184	95.38 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		92.34 152	91.27 166	95.53 116	94.95 216	93.05 55	97.39 95	98.07 55	92.65 80	84.46 275	95.71 163	85.00 113	97.77 246	89.71 137	83.52 288	95.78 203

#test#		97.02 19	96.75 24	97.83 14	99.42 3	94.12 26	98.15 29	98.32 19	92.57 81	97.18 19	98.29 34	92.08 27	99.83 13	95.12 49	99.59 8	99.54 18

PS-MVSNAJss		93.74 104	93.51 94	94.44 167	93.91 267	89.28 176	97.75 54	97.56 114	92.50 82	89.94 187	96.54 125	88.65 69	98.18 183	93.83 77	90.90 210	95.86 196

VDD-MVS		93.82 101	93.08 105	96.02 96	97.88 94	89.96 141	97.72 60	95.85 237	92.43 83	95.86 61	98.44 14	68.42 303	99.39 86	96.31 19	94.85 146	98.71 89

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 249	96.82 134	81.89 290	96.92 138	93.71 309	92.41 84	84.30 277	94.60 212	85.08 112	97.03 283	91.51 118	97.36 104	98.40 111

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 180	92.39 71	97.86 46	98.66 9	92.30 85	92.09 137	95.37 181	80.49 202	98.40 167	93.95 71	85.86 253	95.75 207

PGM-MVS		96.81 27	96.53 30	97.65 29	99.35 13	93.53 44	97.65 68	98.98 1	92.22 86	97.14 22	98.44 14	91.17 42	99.85 9	94.35 66	99.46 24	99.57 12

Vis-MVSNet (Re-imp)		94.15 88	93.88 82	94.95 146	97.61 107	87.92 220	98.10 31	95.80 240	92.22 86	93.02 117	97.45 86	84.53 120	97.91 234	88.24 166	97.97 87	99.02 63

conf200view11		92.45 147	91.58 153	95.05 138	97.92 90	89.37 169	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 248	99.08 116	81.40 274	94.08 156	96.70 171

thres100view900		92.43 148	91.58 153	94.98 143	97.92 90	89.37 169	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 248	99.08 116	81.40 274	94.08 156	96.48 176

tfpn200view9		92.38 151	91.52 157	94.95 146	97.85 95	89.29 174	97.41 91	94.88 279	92.19 90	93.27 109	94.46 218	78.17 241	99.08 116	81.40 274	94.08 156	96.48 176

thres400		92.42 149	91.52 157	95.12 137	97.85 95	89.29 174	97.41 91	94.88 279	92.19 90	93.27 109	94.46 218	78.17 241	99.08 116	81.40 274	94.08 156	96.98 156

thres600view7		92.49 146	91.60 152	95.18 129	97.91 92	89.47 160	97.65 68	94.66 284	92.18 92	93.33 105	94.91 195	78.06 248	99.10 111	81.61 267	94.06 160	96.98 156

view600		92.55 140	91.68 146	95.18 129	97.98 82	89.44 164	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 244	99.11 106	81.61 267	94.04 161	96.98 156

view800		92.55 140	91.68 146	95.18 129	97.98 82	89.44 164	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 244	99.11 106	81.61 267	94.04 161	96.98 156

conf0.05thres1000		92.55 140	91.68 146	95.18 129	97.98 82	89.44 164	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 244	99.11 106	81.61 267	94.04 161	96.98 156

tfpn		92.55 140	91.68 146	95.18 129	97.98 82	89.44 164	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 244	99.11 106	81.61 267	94.04 161	96.98 156

Fast-Effi-MVS+-dtu		92.29 156	91.99 137	93.21 231	95.27 197	85.52 259	97.03 124	96.63 206	92.09 93	89.11 219	95.14 190	80.33 206	98.08 193	87.54 185	94.74 151	96.03 193

thres200		92.23 159	91.39 160	94.75 156	97.61 107	89.03 181	96.60 176	95.09 269	92.08 98	93.28 108	94.00 243	78.39 239	99.04 121	81.26 281	94.18 155	96.19 182

mvs_tets		92.31 154	91.76 142	93.94 191	93.41 282	88.29 193	97.63 75	97.53 115	92.04 99	88.76 223	96.45 129	74.62 272	98.09 192	93.91 73	91.48 201	95.45 217

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 52	91.28 102	96.43 184	97.57 111	92.04 99	94.77 83	97.96 49	87.01 92	99.09 114	91.31 123	96.77 117	98.36 115

jajsoiax		92.42 149	91.89 140	94.03 182	93.33 286	88.50 190	97.73 58	97.53 115	92.00 101	88.85 222	96.50 127	75.62 265	98.11 189	93.88 75	91.56 200	95.48 213

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 123	88.61 187	94.79 260	97.46 124	91.97 102	93.99 94	97.86 55	81.74 181	98.88 131	92.64 94	92.67 182	96.92 164

WR-MVS		92.34 152	91.53 156	94.77 155	95.13 208	90.83 120	96.40 190	97.98 77	91.88 103	89.29 215	95.54 173	82.50 163	97.80 242	89.79 136	85.27 260	95.69 210

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 32	90.68 124	97.24 107	97.73 94	91.80 104	92.93 123	96.62 122	89.13 63	99.14 104	89.21 149	97.78 92	98.97 69

testgi		87.97 268	87.21 265	90.24 296	92.86 297	80.76 296	96.67 168	94.97 275	91.74 105	85.52 269	95.83 153	62.66 319	94.47 318	76.25 302	88.36 237	95.48 213

CP-MVSNet		91.89 169	91.24 167	93.82 194	95.05 211	88.57 188	97.82 50	98.19 33	91.70 106	88.21 235	95.76 160	81.96 176	97.52 262	87.86 173	84.65 274	95.37 226

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 151	97.06 125	88.53 189	95.28 251	97.45 128	91.68 107	94.08 93	97.68 67	82.41 167	98.90 127	93.84 76	92.47 183	96.98 156

OurMVSNet-221017-0		90.51 226	90.19 209	91.44 279	93.41 282	81.25 294	96.98 130	96.28 214	91.68 107	86.55 262	96.30 134	74.20 275	97.98 218	88.96 156	87.40 246	95.09 239

ACMP	89.59 10	92.62 139	92.14 132	94.05 181	96.40 153	88.20 201	97.36 98	97.25 149	91.52 109	88.30 232	96.64 115	78.46 237	98.72 144	91.86 110	91.48 201	95.23 236
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
APD-MVS		96.95 22	96.60 27	98.01 8	99.03 28	94.93 10	97.72 60	98.10 47	91.50 110	98.01 7	98.32 30	92.33 22	99.58 53	94.85 58	99.51 18	99.53 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ITE_SJBPF				92.43 250	95.34 192	85.37 261		95.92 229	91.47 111	87.75 241	96.39 132	71.00 291	97.96 225	82.36 264	89.86 224	93.97 284

PS-CasMVS		91.55 187	90.84 184	93.69 207	94.96 215	88.28 194	97.84 49	98.24 28	91.46 112	88.04 237	95.80 155	79.67 215	97.48 264	87.02 196	84.54 276	95.31 229

WR-MVS_H		92.00 166	91.35 161	93.95 188	95.09 210	89.47 160	98.04 35	98.68 7	91.46 112	88.34 230	94.68 208	85.86 104	97.56 259	85.77 215	84.24 278	94.82 260

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 155	91.21 104	98.22 26	97.57 111	91.42 114	96.22 47	97.32 87	86.20 100	97.92 231	94.07 68	99.05 61	98.85 81

test_djsdf		93.07 124	92.76 112	94.00 183	93.49 280	88.70 186	98.22 26	97.57 111	91.42 114	90.08 185	95.55 172	82.85 155	97.92 231	94.07 68	91.58 199	95.40 223

ACMM	89.79 8	92.96 128	92.50 127	94.35 171	96.30 157	88.71 185	97.58 80	97.36 141	91.40 116	90.53 167	96.65 114	79.77 213	98.75 141	91.24 125	91.64 197	95.59 212
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		91.20 202	90.44 197	93.48 218	94.49 234	87.91 222	97.76 53	98.18 35	91.29 117	87.78 240	95.74 162	80.35 205	97.33 275	85.46 220	82.96 292	95.19 238

LPG-MVS_test		92.94 129	92.56 122	94.10 178	96.16 164	88.26 195	97.65 68	97.46 124	91.29 117	90.12 181	97.16 94	79.05 223	98.73 142	92.25 97	91.89 194	95.31 229

LGP-MVS_train				94.10 178	96.16 164	88.26 195		97.46 124	91.29 117	90.12 181	97.16 94	79.05 223	98.73 142	92.25 97	91.89 194	95.31 229

MVSTER		93.20 120	92.81 111	94.37 170	96.56 143	89.59 154	97.06 123	97.12 159	91.24 120	91.30 154	95.96 146	82.02 175	98.05 205	93.48 83	90.55 215	95.47 215

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 133	89.55 156	96.70 164	97.17 152	91.17 121	95.60 71	96.11 143	87.87 79	98.76 140	93.01 92	97.17 109	98.72 87

HPM-MVS		96.69 32	96.45 34	97.40 39	99.36 12	93.11 54	98.87 1	98.06 57	91.17 121	96.40 44	97.99 48	90.99 45	99.58 53	95.61 41	99.61 7	99.49 25

test-LLR		91.42 193	91.19 170	92.12 261	94.59 231	80.66 297	94.29 270	92.98 318	91.11 123	90.76 165	92.37 279	79.02 225	98.07 197	88.81 161	96.74 118	97.63 140

test0.0.03 1		89.37 247	88.70 241	91.41 280	92.47 304	85.63 257	95.22 255	92.70 323	91.11 123	86.91 260	93.65 255	79.02 225	93.19 324	78.00 296	89.18 228	95.41 219

XVG-ACMP-BASELINE		90.93 211	90.21 208	93.09 233	94.31 241	85.89 253	95.33 248	97.26 147	91.06 125	89.38 211	95.44 180	68.61 301	98.60 150	89.46 143	91.05 208	94.79 264

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 138	91.47 97	96.41 186	97.41 135	91.02 126	94.50 86	95.92 148	87.53 85	98.78 137	93.89 74	96.81 116	98.84 83

Patchmatch-test1		91.54 188	90.85 182	93.59 212	95.59 182	84.95 266	94.72 261	95.58 247	90.82 127	92.25 133	93.58 257	75.80 262	97.41 270	83.35 249	95.98 131	98.40 111

SixPastTwentyTwo		89.15 248	88.54 245	90.98 283	93.49 280	80.28 304	96.70 164	94.70 283	90.78 128	84.15 280	95.57 170	71.78 286	97.71 250	84.63 231	85.07 266	94.94 250

DTE-MVSNet		90.56 224	89.75 225	93.01 235	93.95 265	87.25 232	97.64 72	97.65 105	90.74 129	87.12 254	95.68 166	79.97 211	97.00 286	83.33 251	81.66 299	94.78 265

GA-MVS		91.38 195	90.31 200	94.59 160	94.65 229	87.62 227	94.34 268	96.19 220	90.73 130	90.35 172	93.83 248	71.84 285	97.96 225	87.22 192	93.61 172	98.21 118

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 114	89.56 155	98.67 5	97.00 175	90.69 131	94.24 91	97.62 75	89.79 60	98.81 135	93.39 87	96.49 125	98.92 75

MP-MVS-pluss		96.70 31	96.27 38	97.98 9	99.23 22	94.71 12	96.96 131	98.06 57	90.67 132	95.55 73	98.78 2	91.07 43	99.86 6	96.58 15	99.55 13	99.38 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
IterMVS-LS		92.29 156	91.94 139	93.34 225	96.25 158	86.97 241	96.57 180	97.05 168	90.67 132	89.50 209	94.80 204	86.59 94	97.64 255	89.91 133	86.11 252	95.40 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		93.03 126	92.88 110	93.48 218	95.77 178	86.98 240	96.44 182	97.12 159	90.66 134	91.30 154	97.64 73	86.56 95	98.05 205	89.91 133	90.55 215	95.41 219

K. test v3		87.64 272	86.75 270	90.32 295	93.02 296	79.48 310	96.61 174	92.08 326	90.66 134	80.25 312	94.09 241	67.21 309	96.65 289	85.96 213	80.83 303	94.83 258

test_normal		92.01 164	90.75 187	95.80 104	93.24 288	89.97 139	95.93 223	96.24 218	90.62 136	81.63 293	93.45 263	74.98 269	98.89 129	93.61 79	97.04 112	98.55 94

BH-RMVSNet		92.72 138	91.97 138	94.97 144	97.16 120	87.99 215	96.15 210	95.60 245	90.62 136	91.87 140	97.15 96	78.41 238	98.57 153	83.16 252	97.60 96	98.36 115

semantic-postprocess				91.82 269	95.52 184	84.20 273		96.15 222	90.61 138	87.39 249	94.27 236	75.63 264	96.44 290	87.34 189	86.88 249	94.82 260

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 102	92.05 81	96.59 177	97.35 142	90.61 138	94.64 84	96.93 101	86.41 97	99.39 86	91.20 126	94.71 152	98.94 73

DI_MVS_plusplus_test		92.01 164	90.77 185	95.73 109	93.34 284	89.78 146	96.14 211	96.18 221	90.58 140	81.80 292	93.50 260	74.95 270	98.90 127	93.51 81	96.94 113	98.51 99

LFMVS		93.60 108	92.63 119	96.52 69	98.13 78	91.27 103	97.94 41	93.39 314	90.57 141	96.29 45	98.31 31	69.00 299	99.16 101	94.18 67	95.87 134	99.12 58

HPM-MVS_fast		96.51 37	96.27 38	97.22 50	99.32 15	92.74 62	98.74 4	98.06 57	90.57 141	96.77 29	98.35 22	90.21 55	99.53 68	94.80 61	99.63 4	99.38 38

Test4		89.48 244	87.50 254	95.44 124	90.76 313	89.72 147	95.78 231	97.09 162	90.28 143	77.67 318	91.74 291	55.42 331	98.08 193	91.92 106	96.83 115	98.52 97

PVSNet_Blended_VisFu		95.27 63	94.91 63	96.38 80	98.20 74	90.86 119	97.27 105	98.25 26	90.21 144	94.18 92	97.27 89	87.48 86	99.73 23	93.53 80	97.77 93	98.55 94

PVSNet_BlendedMVS		94.06 93	93.92 81	94.47 166	98.27 67	89.46 162	96.73 156	98.36 16	90.17 145	94.36 88	95.24 187	88.02 75	99.58 53	93.44 84	90.72 213	94.36 277

CNLPA		94.28 85	93.53 93	96.52 69	98.38 59	92.55 68	96.59 177	96.88 190	90.13 146	91.91 139	97.24 91	85.21 110	99.09 114	87.64 182	97.83 90	97.92 128

BH-untuned		92.94 129	92.62 120	93.92 192	97.22 117	86.16 252	96.40 190	96.25 217	90.06 147	89.79 195	96.17 140	83.19 131	98.35 171	87.19 193	97.27 107	97.24 153

IterMVS		90.15 234	89.67 227	91.61 276	95.48 186	83.72 276	94.33 269	96.12 223	89.99 148	87.31 252	94.15 240	75.78 263	96.27 293	86.97 197	86.89 248	94.83 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		94.34 84	93.68 88	96.31 84	98.59 47	91.68 91	96.59 177	97.81 90	89.87 149	92.15 135	97.06 99	83.62 127	99.54 65	89.34 144	98.07 85	97.70 139

UnsupCasMVSNet_eth		85.99 284	84.45 285	90.62 291	89.97 316	82.40 287	93.62 285	97.37 139	89.86 150	78.59 317	92.37 279	65.25 315	95.35 314	82.27 265	70.75 329	94.10 282

PHI-MVS		96.77 29	96.46 33	97.71 26	98.40 56	94.07 28	98.21 28	98.45 15	89.86 150	97.11 25	98.01 46	92.52 20	99.69 33	96.03 31	99.53 15	99.36 40

mvs_anonymous		93.82 101	93.74 85	94.06 180	96.44 152	85.41 260	95.81 228	97.05 168	89.85 152	90.09 184	96.36 133	87.44 87	97.75 247	93.97 70	96.69 121	99.02 63

PatchFormer-LS_test		91.68 180	91.18 171	93.19 232	95.24 201	83.63 280	95.53 241	95.44 251	89.82 153	91.37 149	92.58 276	80.85 198	98.52 157	89.65 140	90.16 220	97.42 151

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 116	91.96 86	95.40 246	97.45 128	89.81 154	93.22 111	96.28 135	79.62 216	99.46 77	90.74 128	93.11 177	98.50 101

FMVSNet3		91.78 171	90.69 190	95.03 140	96.53 145	92.27 74	97.02 126	96.93 185	89.79 155	89.35 212	94.65 210	77.01 256	97.47 265	86.12 208	88.82 230	95.35 227

v2v482		91.59 184	90.85 182	93.80 195	93.87 269	88.17 203	96.94 137	96.88 190	89.54 156	89.53 207	94.90 196	81.70 182	98.02 213	89.25 147	85.04 268	95.20 237

PatchmatchNet		91.91 168	91.35 161	93.59 212	95.38 190	84.11 274	93.15 293	95.39 252	89.54 156	92.10 136	93.68 253	82.82 156	98.13 186	84.81 227	95.32 141	98.52 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		90.70 221	89.81 222	93.37 224	94.73 227	84.21 272	93.67 283	88.02 337	89.50 158	92.38 129	93.49 261	77.82 253	97.78 244	86.03 211	92.68 181	98.11 124

v148		90.99 209	90.38 199	92.81 241	93.83 270	85.80 254	96.78 153	96.68 201	89.45 159	88.75 224	93.93 246	82.96 151	97.82 241	87.83 174	83.25 289	94.80 262

anonymousdsp		92.16 161	91.55 155	93.97 186	92.58 303	89.55 156	97.51 84	97.42 134	89.42 160	88.40 229	94.84 200	80.66 199	97.88 236	91.87 109	91.28 205	94.48 273

IB-MVS	87.33 17	89.91 237	88.28 248	94.79 154	95.26 200	87.70 226	95.12 257	93.95 307	89.35 161	87.03 257	92.49 277	70.74 293	99.19 97	89.18 150	81.37 300	97.49 149
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
jason		94.84 78	94.39 79	96.18 92	95.52 184	90.93 117	96.09 213	96.52 208	89.28 162	96.01 57	97.32 87	84.70 117	98.77 139	95.15 48	98.91 67	98.85 81
jason: jason.
TAMVS		94.01 96	93.46 96	95.64 111	96.16 164	90.45 130	96.71 161	96.89 189	89.27 163	93.46 103	96.92 102	87.29 89	97.94 227	88.70 163	95.74 136	98.53 96

testing_2		87.33 274	85.03 281	94.22 174	87.77 325	89.32 173	94.97 258	97.11 161	89.22 164	71.64 327	88.73 312	55.16 332	97.94 227	91.95 105	88.73 234	95.41 219

v6		91.69 177	91.00 175	93.75 200	94.14 249	88.12 208	97.20 113	96.98 176	89.19 165	89.90 188	94.42 222	83.04 143	98.07 197	89.07 152	85.10 263	95.07 242

API-MVS		94.84 78	94.49 75	95.90 100	97.90 93	92.00 84	97.80 51	97.48 119	89.19 165	94.81 82	96.71 108	88.84 66	99.17 100	88.91 157	98.76 70	96.53 173

v1neww		91.70 175	91.01 173	93.75 200	94.19 244	88.14 206	97.20 113	96.98 176	89.18 167	89.87 191	94.44 220	83.10 137	98.06 202	89.06 153	85.09 264	95.06 245

v7new		91.70 175	91.01 173	93.75 200	94.19 244	88.14 206	97.20 113	96.98 176	89.18 167	89.87 191	94.44 220	83.10 137	98.06 202	89.06 153	85.09 264	95.06 245

v1141		91.61 181	90.89 177	93.78 197	94.01 262	88.24 197	96.96 131	96.96 180	89.17 169	89.75 197	94.29 232	82.99 147	98.03 210	88.85 159	85.00 269	95.07 242

divwei89l23v2f112		91.61 181	90.89 177	93.78 197	94.01 262	88.22 199	96.96 131	96.96 180	89.17 169	89.75 197	94.28 234	83.02 145	98.03 210	88.86 158	84.98 271	95.08 240

v1		91.61 181	90.89 177	93.78 197	94.01 262	88.21 200	96.96 131	96.96 180	89.17 169	89.78 196	94.29 232	82.97 149	98.05 205	88.85 159	84.99 270	95.08 240

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 226	90.94 116	97.47 89	97.43 133	89.14 172	88.90 220	96.43 130	79.71 214	98.24 178	89.56 141	87.68 241	95.67 211

pm-mvs1		90.72 219	89.65 229	93.96 187	94.29 242	89.63 151	97.79 52	96.82 193	89.07 173	86.12 266	95.48 179	78.61 235	97.78 244	86.97 197	81.67 298	94.46 274

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 122	92.49 70	95.64 236	96.64 204	89.05 174	93.00 118	95.79 158	85.77 106	99.45 79	89.16 151	94.35 153	97.96 126

CSCG		96.05 49	95.91 45	96.46 77	99.24 20	90.47 129	98.30 21	98.57 11	89.01 175	93.97 96	97.57 79	92.62 17	99.76 21	94.66 64	99.27 44	99.15 54

tfpn1000		91.99 167	91.05 172	94.80 152	97.78 98	89.66 150	97.91 43	92.90 321	88.99 176	91.73 142	94.84 200	78.99 229	98.33 174	82.41 263	93.91 167	96.40 178

v8		91.29 200	90.53 196	93.57 215	94.15 248	88.12 208	97.34 99	97.06 167	88.99 176	88.32 231	94.26 238	83.08 139	98.01 214	87.62 183	83.92 283	94.57 271

PAPR		94.18 87	93.42 100	96.48 74	97.64 105	91.42 100	95.55 239	97.71 100	88.99 176	92.34 131	95.82 154	89.19 61	99.11 106	86.14 207	97.38 103	98.90 77

CDS-MVSNet		94.14 90	93.54 92	95.93 99	96.18 162	91.46 98	96.33 197	97.04 171	88.97 179	93.56 99	96.51 126	87.55 84	97.89 235	89.80 135	95.95 132	98.44 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sss		94.51 82	93.80 84	96.64 62	97.07 123	91.97 85	96.32 198	98.06 57	88.94 180	94.50 86	96.78 105	84.60 118	99.27 93	91.90 107	96.02 130	98.68 91

tfpn_ndepth		91.88 170	90.96 176	94.62 159	97.73 101	89.93 142	97.75 54	92.92 320	88.93 181	91.73 142	93.80 250	78.91 230	98.49 162	83.02 255	93.86 168	95.45 217

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 155	91.21 104	95.83 227	96.27 215	88.93 181	96.22 47	96.88 103	86.20 100	98.85 132	95.27 45	99.05 61	98.82 84

v7n		90.76 215	89.86 219	93.45 221	93.54 277	87.60 228	97.70 64	97.37 139	88.85 183	87.65 244	94.08 242	81.08 188	98.10 190	84.68 230	83.79 286	94.66 269

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 67	89.46 162	95.47 244	98.36 16	88.84 184	94.36 88	96.09 144	88.02 75	99.58 53	93.44 84	98.18 82	98.40 111

ACMH+	87.92 14	90.20 232	89.18 236	93.25 228	96.48 149	86.45 249	96.99 129	96.68 201	88.83 185	84.79 274	96.22 137	70.16 297	98.53 156	84.42 236	88.04 238	94.77 266

GBi-Net		91.35 197	90.27 203	94.59 160	96.51 146	91.18 108	97.50 85	96.93 185	88.82 186	89.35 212	94.51 214	73.87 276	97.29 277	86.12 208	88.82 230	95.31 229

test1		91.35 197	90.27 203	94.59 160	96.51 146	91.18 108	97.50 85	96.93 185	88.82 186	89.35 212	94.51 214	73.87 276	97.29 277	86.12 208	88.82 230	95.31 229

FMVSNet2		91.31 199	90.08 210	94.99 141	96.51 146	92.21 75	97.41 91	96.95 183	88.82 186	88.62 225	94.75 206	73.87 276	97.42 269	85.20 224	88.55 236	95.35 227

V42		91.58 185	90.87 180	93.73 203	94.05 261	88.50 190	97.32 102	96.97 179	88.80 189	89.71 199	94.33 227	82.54 162	98.05 205	89.01 155	85.07 266	94.64 270

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 39	93.79 36	96.28 202	98.00 71	88.76 190	95.68 67	97.55 83	92.70 16	99.57 61	95.01 53	99.32 40	99.32 42

BH-w/o		92.14 163	91.75 143	93.31 226	96.99 128	85.73 255	95.67 233	95.69 242	88.73 191	89.26 217	94.82 203	82.97 149	98.07 197	85.26 223	96.32 128	96.13 187

test20.03		86.14 283	85.40 279	88.35 301	90.12 314	80.06 306	95.90 224	95.20 264	88.59 192	81.29 295	93.62 256	71.43 288	92.65 325	71.26 317	81.17 301	92.34 313

train_agg		96.30 43	95.83 46	97.72 24	98.70 37	94.19 23	96.41 186	98.02 67	88.58 193	96.03 53	97.56 81	92.73 14	99.59 50	95.04 51	99.37 38	99.39 35

test_8					98.67 39	94.06 29	96.37 193	98.01 69	88.58 193	95.98 58	97.55 83	92.73 14	99.58 53

tpmrst		91.44 192	91.32 163	91.79 271	95.15 206	79.20 312	93.42 287	95.37 254	88.55 195	93.49 102	93.67 254	82.49 164	98.27 177	90.41 130	89.34 227	97.90 129

v748		90.34 228	89.54 230	92.75 243	93.25 287	85.71 256	97.61 76	97.17 152	88.54 196	87.20 253	93.54 258	81.02 189	98.01 214	85.73 217	81.80 296	94.52 272

ACMH	87.59 16	90.53 225	89.42 232	93.87 193	96.21 159	87.92 220	97.24 107	96.94 184	88.45 197	83.91 283	96.27 136	71.92 284	98.62 149	84.43 235	89.43 226	95.05 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnconf		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

tfpnview11		91.69 177	90.67 191	94.75 156	97.55 112	89.68 148	97.64 72	93.14 316	88.43 198	91.24 159	94.30 230	78.91 230	98.45 163	81.28 279	93.57 175	96.11 188

Baseline_NR-MVSNet		91.20 202	90.62 193	92.95 237	93.83 270	88.03 214	97.01 128	95.12 268	88.42 200	89.70 200	95.13 191	83.47 128	97.44 267	89.66 139	83.24 290	93.37 292

v7		91.47 191	90.73 188	93.68 208	94.13 250	88.16 204	97.09 122	97.05 168	88.38 201	89.80 194	94.52 213	82.21 171	98.01 214	88.00 170	85.42 257	94.87 254

v1144		91.37 196	90.60 194	93.68 208	93.89 268	88.23 198	96.84 143	97.03 173	88.37 202	89.69 201	94.39 223	82.04 174	97.98 218	87.80 175	85.37 258	94.84 256

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 23	94.19 23	97.03 124	98.08 50	88.35 203	95.09 79	97.65 70	89.97 58	99.48 75	92.08 104	98.59 74	98.44 108

tpm		90.25 230	89.74 226	91.76 274	93.92 266	79.73 308	93.98 277	93.54 313	88.28 204	91.99 138	93.25 267	77.51 255	97.44 267	87.30 191	87.94 239	98.12 121

v10		91.04 208	90.23 206	93.49 217	94.12 252	88.16 204	97.32 102	97.08 164	88.26 205	88.29 233	94.22 239	82.17 173	97.97 221	86.45 203	84.12 279	94.33 278

v52		90.70 221	90.00 214	92.82 238	93.24 288	87.03 238	97.60 77	97.14 156	88.21 206	87.69 242	93.94 245	80.91 194	98.07 197	87.39 187	83.87 285	93.36 293

V4		90.71 220	90.00 214	92.82 238	93.21 291	87.03 238	97.59 79	97.16 155	88.21 206	87.69 242	93.92 247	80.93 193	98.06 202	87.39 187	83.90 284	93.39 291

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 135	90.03 133	96.81 148	97.13 158	88.19 208	91.30 154	94.27 236	86.21 99	98.63 147	87.66 181	96.46 127	98.12 121

DWT-MVSNet_test		90.76 215	89.89 218	93.38 223	95.04 212	83.70 278	95.85 226	94.30 299	88.19 208	90.46 169	92.80 271	73.61 280	98.50 159	88.16 167	90.58 214	97.95 127

TEST9					98.70 37	94.19 23	96.41 186	98.02 67	88.17 210	96.03 53	97.56 81	92.74 13	99.59 50

MDTV_nov1_ep13			90.76 186		95.22 202	80.33 302	93.03 296	95.28 259	88.14 211	92.84 124	93.83 248	81.34 185	98.08 193	82.86 256	94.34 154

MAR-MVS		94.22 86	93.46 96	96.51 72	98.00 81	92.19 78	97.67 65	97.47 122	88.13 212	93.00 118	95.84 152	84.86 116	99.51 72	87.99 171	98.17 83	97.83 134
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
PatchMatch-RL		92.90 131	92.02 136	95.56 114	98.19 76	90.80 121	95.27 253	97.18 150	87.96 213	91.86 141	95.68 166	80.44 203	98.99 122	84.01 243	97.54 97	96.89 165

agg_prior3		96.16 47	95.67 48	97.62 34	98.67 39	93.88 32	96.41 186	98.00 71	87.93 214	95.81 63	97.47 85	92.33 22	99.59 50	95.04 51	99.37 38	99.39 35

PVSNet	86.66 18	92.24 158	91.74 145	93.73 203	97.77 99	83.69 279	92.88 297	96.72 196	87.91 215	93.00 118	94.86 199	78.51 236	99.05 120	86.53 200	97.45 102	98.47 106

LTVRE_ROB	88.41 13	90.99 209	89.92 217	94.19 175	96.18 162	89.55 156	96.31 199	97.09 162	87.88 216	85.67 268	95.91 149	78.79 234	98.57 153	81.50 272	89.98 221	94.44 275
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
CPTT-MVS		95.57 58	95.19 59	96.70 61	99.27 18	91.48 96	98.33 20	98.11 45	87.79 217	95.17 78	98.03 44	87.09 91	99.61 45	93.51 81	99.42 29	99.02 63

v1192		91.07 206	90.23 206	93.58 214	93.70 273	87.82 223	96.73 156	97.07 165	87.77 218	89.58 204	94.32 228	80.90 197	97.97 221	86.52 201	85.48 255	94.95 248

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 56	88.98 182	97.18 116	97.29 146	87.75 219	90.49 168	97.10 98	85.21 110	99.50 74	86.70 199	96.72 120	97.63 140

1314		92.81 136	92.03 135	95.14 135	95.33 195	89.52 159	96.04 216	97.44 131	87.72 220	86.25 264	95.33 183	83.84 124	98.79 136	89.26 146	97.05 111	97.11 154

test-mter		90.19 233	89.54 230	92.12 261	94.59 231	80.66 297	94.29 270	92.98 318	87.68 221	90.76 165	92.37 279	67.67 305	98.07 197	88.81 161	96.74 118	97.63 140

TR-MVS		91.48 190	90.59 195	94.16 177	96.40 153	87.33 229	95.67 233	95.34 258	87.68 221	91.46 147	95.52 174	76.77 257	98.35 171	82.85 257	93.61 172	96.79 168

LF4IMVS		87.94 269	87.25 261	89.98 298	92.38 305	80.05 307	94.38 267	95.25 262	87.59 223	84.34 276	94.74 207	64.31 316	97.66 254	84.83 226	87.45 243	92.23 314

TransMVSNet (Re)		88.94 249	87.56 252	93.08 234	94.35 239	88.45 192	97.73 58	95.23 263	87.47 224	84.26 278	95.29 184	79.86 212	97.33 275	79.44 291	74.44 326	93.45 290

v144192		91.06 207	90.28 202	93.39 222	93.66 275	87.23 234	96.83 144	97.07 165	87.43 225	89.69 201	94.28 234	81.48 183	98.00 217	87.18 194	84.92 272	94.93 252

原ACMM1				96.38 80	98.59 47	91.09 112		97.89 82	87.41 226	95.22 77	97.68 67	90.25 53	99.54 65	87.95 172	99.12 58	98.49 103

v1921920		90.85 213	90.03 213	93.29 227	93.55 276	86.96 242	96.74 155	97.04 171	87.36 227	89.52 208	94.34 226	80.23 208	97.97 221	86.27 204	85.21 261	94.94 250

USDC		88.94 249	87.83 251	92.27 251	94.66 228	84.96 265	93.86 279	95.90 231	87.34 228	83.40 285	95.56 171	67.43 307	98.19 182	82.64 261	89.67 225	93.66 287

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 49	91.15 111	96.69 166	97.39 136	87.29 229	91.37 149	96.71 108	88.39 73	99.52 71	87.33 190	97.13 110	97.73 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		89.70 242	88.40 246	93.60 211	95.15 206	90.10 132	97.56 81	98.16 37	87.28 230	86.16 265	94.63 211	77.57 254	98.05 205	74.48 305	84.59 275	92.65 299

TESTMET0.1,1		90.06 235	89.42 232	91.97 265	94.41 238	80.62 299	94.29 270	91.97 327	87.28 230	90.44 170	92.47 278	68.79 300	97.67 252	88.50 165	96.60 123	97.61 144

v1240		90.70 221	89.85 220	93.23 229	93.51 279	86.80 243	96.61 174	97.02 174	87.16 232	89.58 204	94.31 229	79.55 217	97.98 218	85.52 219	85.44 256	94.90 253

Patchmatch-RL test		87.38 273	86.24 272	90.81 287	88.74 321	78.40 315	88.12 328	93.17 315	87.11 233	82.17 288	89.29 309	81.95 177	95.60 310	88.64 164	77.02 310	98.41 110

v18		88.71 254	87.52 253	92.27 251	94.16 247	88.11 210	96.82 147	95.96 226	87.03 234	80.76 299	89.81 299	83.15 133	96.22 294	84.69 229	75.31 317	92.49 303

v17		88.67 256	87.47 256	92.26 253	94.13 250	88.09 212	96.81 148	95.95 227	87.02 235	80.72 300	89.75 301	83.11 136	96.20 295	84.61 232	75.15 319	92.49 303

v16		88.69 255	87.50 254	92.26 253	94.19 244	88.11 210	96.81 148	95.95 227	87.01 236	80.71 301	89.80 300	83.08 139	96.20 295	84.61 232	75.34 316	92.48 305

v15		88.53 258	87.31 258	92.20 256	94.09 256	88.05 213	96.72 159	95.90 231	87.01 236	80.53 304	89.60 305	83.02 145	96.13 297	84.29 237	74.64 320	92.41 309

V14		88.52 259	87.30 259	92.17 258	94.12 252	87.99 215	96.72 159	95.91 230	86.98 238	80.50 305	89.63 302	83.03 144	96.12 299	84.23 238	74.60 322	92.40 310

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 30	94.44 16	96.35 194	97.88 83	86.98 238	96.65 33	97.89 50	91.99 31	99.47 76	92.26 95	99.46 24	99.39 35

V9		88.49 262	87.26 260	92.18 257	94.12 252	87.97 218	96.73 156	95.90 231	86.95 240	80.40 307	89.61 303	82.98 148	96.13 297	84.14 239	74.55 323	92.44 307

v12		88.46 263	87.23 263	92.17 258	94.10 255	87.99 215	96.71 161	95.90 231	86.91 241	80.34 309	89.58 306	82.92 152	96.11 301	84.09 240	74.50 325	92.42 308

PM-MVS		83.48 293	81.86 296	88.31 302	87.83 324	77.59 316	93.43 286	91.75 328	86.91 241	80.63 302	89.91 297	44.42 338	95.84 306	85.17 225	76.73 312	91.50 321

CR-MVSNet		90.82 214	89.77 223	93.95 188	94.45 236	87.19 235	90.23 319	95.68 243	86.89 243	92.40 127	92.36 282	80.91 194	97.05 281	81.09 282	93.95 165	97.60 145

1112_ss		93.37 115	92.42 129	96.21 91	97.05 126	90.99 113	96.31 199	96.72 196	86.87 244	89.83 193	96.69 112	86.51 96	99.14 104	88.12 168	93.67 169	98.50 101

v13		88.45 264	87.22 264	92.16 260	94.08 258	87.95 219	96.71 161	95.90 231	86.86 245	80.27 311	89.55 307	82.92 152	96.12 299	84.02 242	74.63 321	92.40 310

v11		88.41 265	87.19 267	92.08 263	94.08 258	87.77 224	96.75 154	95.85 237	86.74 246	80.50 305	89.50 308	82.49 164	96.08 302	83.55 248	75.20 318	92.38 312

FMVSNet1		89.88 239	88.31 247	94.59 160	95.41 188	91.18 108	97.50 85	96.93 185	86.62 247	87.41 248	94.51 214	65.94 313	97.29 277	83.04 254	87.43 244	95.31 229

CHOSEN 280x420		93.12 122	92.72 117	94.34 172	96.71 137	87.27 231	90.29 318	97.72 97	86.61 248	91.34 151	95.29 184	84.29 122	98.41 166	93.25 88	98.94 66	97.35 152

MIMVSNet		88.50 261	86.76 269	93.72 205	94.84 222	87.77 224	91.39 309	94.05 304	86.41 249	87.99 238	92.59 275	63.27 317	95.82 307	77.44 297	92.84 180	97.57 147

tpmvs		89.83 241	89.15 237	91.89 267	94.92 218	80.30 303	93.11 294	95.46 250	86.28 250	88.08 236	92.65 273	80.44 203	98.52 157	81.47 273	89.92 223	96.84 167

PAPM		91.52 189	90.30 201	95.20 128	95.30 196	89.83 144	93.38 288	96.85 192	86.26 251	88.59 227	95.80 155	84.88 114	98.15 185	75.67 304	95.93 133	97.63 140

VDDNet		93.05 125	92.07 133	96.02 96	96.84 131	90.39 131	98.08 33	95.85 237	86.22 252	95.79 65	98.46 12	67.59 306	99.19 97	94.92 57	94.85 146	98.47 106

MS-PatchMatch		90.27 229	89.77 223	91.78 272	94.33 240	84.72 269	95.55 239	96.73 195	86.17 253	86.36 263	95.28 186	71.28 289	97.80 242	84.09 240	98.14 84	92.81 298

MVP-Stereo		90.74 218	90.08 210	92.71 244	93.19 293	88.20 201	95.86 225	96.27 215	86.07 254	84.86 273	94.76 205	77.84 252	97.75 247	83.88 246	98.01 86	92.17 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
diffmvs		93.43 114	92.75 114	95.48 121	96.47 150	89.61 152	96.09 213	97.14 156	85.97 255	93.09 116	95.35 182	84.87 115	98.55 155	89.51 142	96.26 129	98.28 117

CVMVSNet		91.23 201	91.75 143	89.67 300	95.77 178	74.69 320	96.44 182	94.88 279	85.81 256	92.18 134	97.64 73	79.07 222	95.58 311	88.06 169	95.86 135	98.74 85

MSDG		91.42 193	90.24 205	94.96 145	97.15 121	88.91 183	93.69 282	96.32 213	85.72 257	86.93 259	96.47 128	80.24 207	98.98 123	80.57 283	95.05 145	96.98 156

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 67	89.38 168	95.18 256	98.48 14	85.60 258	93.76 98	97.11 97	83.15 133	99.61 45	91.33 122	98.72 71	99.19 50

AllTest		90.23 231	88.98 238	93.98 184	97.94 88	86.64 245	96.51 181	95.54 248	85.38 259	85.49 270	96.77 106	70.28 295	99.15 102	80.02 286	92.87 178	96.15 185

TestCases				93.98 184	97.94 88	86.64 245		95.54 248	85.38 259	85.49 270	96.77 106	70.28 295	99.15 102	80.02 286	92.87 178	96.15 185

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 127	89.97 139	95.53 241	96.64 204	85.38 259	89.65 203	95.18 188	85.86 104	99.10 111	87.70 177	93.58 174	98.49 103

EU-MVSNet		88.72 253	88.90 239	88.20 303	93.15 294	74.21 321	96.63 173	94.22 302	85.18 262	87.32 251	95.97 145	76.16 260	94.98 316	85.27 222	86.17 250	95.41 219

LS3D		93.57 110	92.61 121	96.47 75	97.59 109	91.61 92	97.67 65	97.72 97	85.17 263	90.29 173	98.34 25	84.60 118	99.73 23	83.85 247	98.27 80	98.06 125

dp		88.90 251	88.26 249	90.81 287	94.58 233	76.62 317	92.85 298	94.93 277	85.12 264	90.07 186	93.07 268	75.81 261	98.12 188	80.53 284	87.42 245	97.71 138

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 70	89.88 143	94.58 263	98.49 12	85.06 265	93.78 97	95.78 159	82.86 154	98.67 145	91.77 111	95.71 138	99.07 62

new-patchmatchnet		83.18 294	81.87 295	87.11 307	86.88 327	75.99 319	93.70 281	95.18 265	85.02 266	77.30 319	88.40 315	65.99 312	93.88 321	74.19 309	70.18 330	91.47 322

TDRefinement		86.53 279	84.76 284	91.85 268	82.23 335	84.25 271	96.38 192	95.35 255	84.97 267	84.09 281	94.94 193	65.76 314	98.34 173	84.60 234	74.52 324	92.97 294

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 192	92.73 63	98.27 23	98.12 42	84.86 268	85.78 267	97.75 63	78.89 233	99.74 22	87.50 186	98.65 72	96.73 169

gm-plane-assit					93.22 290	78.89 314			84.82 269		93.52 259		98.64 146	87.72 176

PMMVS		92.86 133	92.34 130	94.42 169	94.92 218	86.73 244	94.53 265	96.38 211	84.78 270	94.27 90	95.12 192	83.13 135	98.40 167	91.47 120	96.49 125	98.12 121

pmmvs4		90.93 211	89.85 220	94.17 176	93.34 284	90.79 122	94.60 262	96.02 225	84.62 271	87.45 246	95.15 189	81.88 179	97.45 266	87.70 177	87.87 240	94.27 281

MDA-MVSNet-bldmvs		85.00 289	82.95 291	91.17 282	93.13 295	83.33 281	94.56 264	95.00 273	84.57 272	65.13 333	92.65 273	70.45 294	95.85 305	73.57 310	77.49 309	94.33 278

QAPM		93.45 113	92.27 131	96.98 58	96.77 135	92.62 66	98.39 18	98.12 42	84.50 273	88.27 234	97.77 62	82.39 168	99.81 17	85.40 221	98.81 68	98.51 99

pmmvs5		89.86 240	88.87 240	92.82 238	92.86 297	86.23 251	96.26 203	95.39 252	84.24 274	87.12 254	94.51 214	74.27 274	97.36 274	87.61 184	87.57 242	94.86 255

CostFormer		91.18 205	90.70 189	92.62 247	94.84 222	81.76 291	94.09 276	94.43 293	84.15 275	92.72 125	93.77 251	79.43 218	98.20 180	90.70 129	92.18 189	97.90 129

FMVSNet5		87.29 275	85.79 276	91.78 272	94.80 224	87.28 230	95.49 243	95.28 259	84.09 276	83.85 284	91.82 288	62.95 318	94.17 319	78.48 294	85.34 259	93.91 285

MIMVSNet1		84.93 290	83.05 290	90.56 292	89.56 319	84.84 268	95.40 246	95.35 255	83.91 277	80.38 308	92.21 286	57.23 326	93.34 323	70.69 319	82.75 295	93.50 288

RPSCF		90.75 217	90.86 181	90.42 294	96.84 131	76.29 318	95.61 238	96.34 212	83.89 278	91.38 148	97.87 53	76.45 258	98.78 137	87.16 195	92.23 186	96.20 181

MDTV_nov1_ep13_2view						70.35 328	93.10 295		83.88 279	93.55 100		82.47 166		86.25 205		98.38 114

无先验							95.79 229	97.87 85	83.87 280				99.65 39	87.68 179		98.89 79

PVSNet_0	82.17 19	85.46 288	83.64 289	90.92 285	95.27 197	79.49 309	90.55 317	95.60 245	83.76 281	83.00 286	89.95 296	71.09 290	97.97 221	82.75 259	60.79 336	95.31 229

TinyColmap		86.82 278	85.35 280	91.21 281	94.91 220	82.99 283	93.94 278	94.02 306	83.58 282	81.56 294	94.68 208	62.34 320	98.13 186	75.78 303	87.35 247	92.52 302

Anonymous20231206		87.09 276	86.14 274	89.93 299	91.22 311	80.35 301	96.11 212	95.35 255	83.57 283	84.16 279	93.02 269	73.54 281	95.61 309	72.16 313	86.14 251	93.84 286

pmmvs-eth3d		86.22 282	84.45 285	91.53 277	88.34 322	87.25 232	94.47 266	95.01 272	83.47 284	79.51 315	89.61 303	69.75 298	95.71 308	83.13 253	76.73 312	91.64 318

EG-PatchMatch MVS		87.02 277	85.44 278	91.76 274	92.67 301	85.00 264	96.08 215	96.45 209	83.41 285	79.52 314	93.49 261	57.10 327	97.72 249	79.34 292	90.87 211	92.56 301

ADS-MVSNet2		89.45 245	88.59 243	92.03 264	95.86 173	82.26 288	90.93 314	94.32 298	83.23 286	91.28 157	91.81 289	79.01 227	95.99 303	79.52 288	91.39 203	97.84 132

ADS-MVSNet		89.89 238	88.68 242	93.53 216	95.86 173	84.89 267	90.93 314	95.07 271	83.23 286	91.28 157	91.81 289	79.01 227	97.85 237	79.52 288	91.39 203	97.84 132

COLMAP_ROB	87.81 15	90.40 227	89.28 234	93.79 196	97.95 87	87.13 237	96.92 138	95.89 236	82.83 288	86.88 261	97.18 93	73.77 279	99.29 92	78.44 295	93.62 171	94.95 248
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testdata				95.46 123	98.18 77	88.90 184		97.66 103	82.73 289	97.03 28	98.07 42	90.06 56	98.85 132	89.67 138	98.98 64	98.64 92

testus		82.63 297	82.15 293	84.07 313	87.31 326	67.67 332	93.18 289	94.29 300	82.47 290	82.14 289	90.69 294	53.01 333	91.94 328	66.30 323	89.96 222	92.62 300

DP-MVS		92.76 137	91.51 159	96.52 69	98.77 34	90.99 113	97.38 97	96.08 224	82.38 291	89.29 215	97.87 53	83.77 125	99.69 33	81.37 278	96.69 121	98.89 79

MDA-MVSNet_test_wron		85.87 285	84.23 287	90.80 289	92.38 305	82.57 284	93.17 291	95.15 266	82.15 292	67.65 329	92.33 285	78.20 240	95.51 312	77.33 298	79.74 304	94.31 280

YYNet1		85.87 285	84.23 287	90.78 290	92.38 305	82.46 286	93.17 291	95.14 267	82.12 293	67.69 328	92.36 282	78.16 243	95.50 313	77.31 299	79.73 305	94.39 276

PatchT		88.87 252	87.42 257	93.22 230	94.08 258	85.10 263	89.51 323	94.64 287	81.92 294	92.36 130	88.15 318	80.05 210	97.01 285	72.43 312	93.65 170	97.54 148

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 63	89.60 153	96.79 151	97.65 105	81.83 295	91.52 146	97.23 92	87.94 77	98.91 126	71.31 316	98.37 78	98.17 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
旧先验2							95.94 222		81.66 296	97.34 16			98.82 134	92.26 95

tpmp4_e23		89.58 243	88.59 243	92.54 248	95.16 205	81.53 292	94.11 275	95.09 269	81.66 296	88.60 226	93.44 264	75.11 267	98.33 174	82.45 262	91.72 196	97.75 136

新几何1				97.32 42	98.60 46	93.59 42		97.75 92	81.58 298	95.75 66	97.85 56	90.04 57	99.67 37	86.50 202	99.13 55	98.69 90

test2356		82.77 296	82.14 294	84.65 312	85.77 329	70.36 327	91.22 312	93.69 312	81.58 298	81.82 291	89.00 311	60.63 323	90.77 331	64.74 324	90.80 212	92.82 296

1121		94.71 80	93.83 83	97.34 41	98.57 50	93.64 41	96.04 216	97.73 94	81.56 300	95.68 67	97.85 56	90.23 54	99.65 39	87.68 179	99.12 58	98.73 86

Patchmatch-test		89.42 246	87.99 250	93.70 206	95.27 197	85.11 262	88.98 325	94.37 296	81.11 301	87.10 256	93.69 252	82.28 169	97.50 263	74.37 307	94.76 149	98.48 105

test_0402		86.46 280	84.79 283	91.45 278	95.02 213	85.55 258	96.29 201	94.89 278	80.90 302	82.21 287	93.97 244	68.21 304	97.29 277	62.98 326	88.68 235	91.51 320

gg-mvs-nofinetune		87.82 270	85.61 277	94.44 167	94.46 235	89.27 177	91.21 313	84.61 343	80.88 303	89.89 190	74.98 333	71.50 287	97.53 261	85.75 216	97.21 108	96.51 174

JIA-IIPM		88.26 267	87.04 268	91.91 266	93.52 278	81.42 293	89.38 324	94.38 295	80.84 304	90.93 164	80.74 330	79.22 221	97.92 231	82.76 258	91.62 198	96.38 179

Patchmtry		88.64 257	87.25 261	92.78 242	94.09 256	86.64 245	89.82 322	95.68 243	80.81 305	87.63 245	92.36 282	80.91 194	97.03 283	78.86 293	85.12 262	94.67 268

tpm2		89.96 236	89.21 235	92.23 255	94.91 220	81.25 294	93.78 280	94.42 294	80.62 306	91.56 145	93.44 264	76.44 259	97.94 227	85.60 218	92.08 193	97.49 149

pmmvs6		87.81 271	86.19 273	92.69 245	91.32 310	86.30 250	97.34 99	96.41 210	80.59 307	84.05 282	94.37 225	67.37 308	97.67 252	84.75 228	79.51 306	94.09 283

cascas		91.20 202	90.08 210	94.58 164	94.97 214	89.16 180	93.65 284	97.59 110	79.90 308	89.40 210	92.92 270	75.36 266	98.36 170	92.14 100	94.75 150	96.23 180

PCF-MVS	89.48 11	91.56 186	89.95 216	96.36 82	96.60 139	92.52 69	92.51 302	97.26 147	79.41 309	88.90 220	96.56 124	84.04 123	99.55 63	77.01 301	97.30 106	97.01 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test1235678		79.82 302	78.53 303	83.69 314	82.55 334	67.55 333	92.50 303	94.13 303	79.28 310	72.10 326	86.45 326	57.27 325	90.68 332	61.60 329	80.90 302	92.82 296

test222					98.24 70	92.21 75	95.33 248	97.60 108	79.22 311	95.25 76	97.84 58	88.80 67			99.15 53	98.72 87

UnsupCasMVSNet_bld		82.13 299	79.46 301	90.14 297	88.00 323	82.47 285	90.89 316	96.62 207	78.94 312	75.61 320	84.40 328	56.63 328	96.31 292	77.30 300	66.77 335	91.63 319

testpf		80.97 300	81.40 298	79.65 319	91.53 309	72.43 325	73.47 340	89.55 335	78.63 313	80.81 297	89.06 310	61.36 321	91.36 330	83.34 250	84.89 273	75.15 336

N_pmnet		78.73 303	78.71 302	78.79 321	92.80 299	46.50 348	94.14 274	43.71 352	78.61 314	80.83 296	91.66 292	74.94 271	96.36 291	67.24 321	84.45 277	93.50 288

ANet_high		63.94 314	59.58 315	77.02 323	61.24 348	66.06 334	85.66 333	87.93 338	78.53 315	42.94 340	71.04 337	25.42 348	80.71 341	52.60 338	30.83 343	84.28 331

114514_t		93.95 97	93.06 106	96.63 64	99.07 27	91.61 92	97.46 90	97.96 79	77.99 316	93.00 118	97.57 79	86.14 102	99.33 90	89.22 148	99.15 53	98.94 73

DSMNet-mixed		86.34 281	86.12 275	87.00 308	89.88 317	70.43 326	94.93 259	90.08 334	77.97 317	85.42 272	92.78 272	74.44 273	93.96 320	74.43 306	95.14 143	96.62 172

RPMNet		88.52 259	86.72 271	93.95 188	94.45 236	87.19 235	90.23 319	94.99 274	77.87 318	92.40 127	87.55 323	80.17 209	97.05 281	68.84 320	93.95 165	97.60 145

LP		84.13 292	81.85 297	90.97 284	93.20 292	82.12 289	87.68 329	94.27 301	76.80 319	81.93 290	88.52 313	72.97 283	95.95 304	59.53 331	81.73 297	94.84 256

new_pmnet		82.89 295	81.12 300	88.18 304	89.63 318	80.18 305	91.77 308	92.57 324	76.79 320	75.56 321	88.23 317	61.22 322	94.48 317	71.43 315	82.92 293	89.87 325

test12356		74.97 306	74.13 307	77.49 322	78.81 336	56.23 344	88.53 327	92.75 322	75.14 321	67.50 330	85.07 327	44.88 337	89.96 333	58.71 332	75.75 314	86.26 328

1111		78.29 304	77.55 304	80.50 317	83.89 330	59.98 340	91.89 306	93.71 309	75.06 322	73.60 324	87.67 321	55.66 329	92.60 326	58.54 333	77.92 308	88.93 327

.test1245		65.38 313	69.22 311	53.86 333	83.89 330	59.98 340	91.89 306	93.71 309	75.06 322	73.60 324	87.67 321	55.66 329	92.60 326	58.54 333	2.96 347	9.00 345

tpm cat1		88.36 266	87.21 265	91.81 270	95.13 208	80.55 300	92.58 301	95.70 241	74.97 324	87.45 246	91.96 287	78.01 251	98.17 184	80.39 285	88.74 233	96.72 170

testmv		72.22 308	70.02 308	78.82 320	73.06 343	61.75 338	91.24 311	92.31 325	74.45 325	61.06 335	80.51 331	34.21 341	88.63 336	55.31 336	68.07 334	86.06 329

CMPMVS	62.92 21	85.62 287	84.92 282	87.74 305	89.14 320	73.12 324	94.17 273	96.80 194	73.98 326	73.65 323	94.93 194	66.36 310	97.61 257	83.95 245	91.28 205	92.48 305
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	81.14 20	84.42 291	82.28 292	90.83 286	90.06 315	84.05 275	95.73 232	94.04 305	73.89 327	80.17 313	91.53 293	59.15 324	97.64 255	66.92 322	89.05 229	90.80 323

MVS		91.71 172	90.44 197	95.51 117	95.20 204	91.59 94	96.04 216	97.45 128	73.44 328	87.36 250	95.60 169	85.42 108	99.10 111	85.97 212	97.46 98	95.83 200

no-one		68.12 311	63.78 314	81.13 316	74.01 340	70.22 329	87.61 330	90.71 333	72.63 329	53.13 338	71.89 336	30.29 343	91.45 329	61.53 330	32.21 341	81.72 333

pmmvs3		79.97 301	77.50 305	87.39 306	82.80 333	79.38 311	92.70 300	90.75 332	70.69 330	78.66 316	87.47 324	51.34 335	93.40 322	73.39 311	69.65 331	89.38 326

Anonymous20231211		78.22 305	75.30 306	86.99 309	86.14 328	74.16 322	95.62 237	93.88 308	66.43 331	74.44 322	87.86 320	41.39 339	95.11 315	62.49 327	69.46 332	91.71 317

MVS-HIRNet		82.47 298	81.21 299	86.26 311	95.38 190	69.21 331	88.96 326	89.49 336	66.28 332	80.79 298	74.08 335	68.48 302	97.39 272	71.93 314	95.47 139	92.18 315

DeepMVS_CX				74.68 326	90.84 312	64.34 337		81.61 347	65.34 333	67.47 331	88.01 319	48.60 336	80.13 342	62.33 328	73.68 328	79.58 334

PMMVS2		70.19 310	66.92 312	80.01 318	76.35 337	65.67 335	86.22 331	87.58 339	64.83 334	62.38 334	80.29 332	26.78 347	88.49 337	63.79 325	54.07 337	85.88 330

FPMVS		71.27 309	69.85 309	75.50 324	74.64 338	59.03 342	91.30 310	91.50 329	58.80 335	57.92 336	88.28 316	29.98 345	85.53 339	53.43 337	82.84 294	81.95 332

LCM-MVSNet		72.55 307	69.39 310	82.03 315	70.81 345	65.42 336	90.12 321	94.36 297	55.02 336	65.88 332	81.72 329	24.16 349	89.96 333	74.32 308	68.10 333	90.71 324

Gipuma		67.86 312	65.41 313	75.18 325	92.66 302	73.45 323	66.50 342	94.52 292	53.33 337	57.80 337	66.07 339	30.81 342	89.20 335	48.15 340	78.88 307	62.90 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		59.01 315	55.87 316	68.44 328	73.98 341	51.37 345	81.36 336	82.41 345	52.37 338	42.49 342	70.39 338	11.39 350	79.99 343	49.77 339	38.71 339	73.97 337

wuykxyi23d		56.92 317	51.11 321	74.38 327	62.30 347	61.47 339	80.09 337	84.87 342	49.62 339	30.80 346	57.20 343	7.03 352	82.94 340	55.69 335	32.36 340	78.72 335

PMVS	53.92 22	58.58 316	55.40 317	68.12 329	51.00 349	48.64 346	78.86 338	87.10 341	46.77 340	35.84 345	74.28 334	8.76 351	86.34 338	42.07 341	73.91 327	69.38 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 318	52.56 319	55.43 331	74.43 339	47.13 347	83.63 335	76.30 348	42.23 341	42.59 341	62.22 341	28.57 346	74.40 344	31.53 343	31.51 342	44.78 341

EMVS		52.08 320	51.31 320	54.39 332	72.62 344	45.39 349	83.84 334	75.51 349	41.13 342	40.77 343	59.65 342	30.08 344	73.60 345	28.31 344	29.90 344	44.18 342

MVE	50.73 23	53.25 319	48.81 322	66.58 330	65.34 346	57.50 343	72.49 341	70.94 350	40.15 343	39.28 344	63.51 340	6.89 354	73.48 346	38.29 342	42.38 338	68.76 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 321	53.82 318	46.29 334	33.73 350	45.30 350	78.32 339	67.24 351	18.02 344	50.93 339	87.05 325	52.99 334	53.11 347	70.76 318	25.29 345	40.46 343

wuyk23d		25.11 323	24.57 325	26.74 336	73.98 341	39.89 351	57.88 343	9.80 353	12.27 345	10.39 347	6.97 349	7.03 352	36.44 348	25.43 345	17.39 346	3.89 347

testmvs		13.36 325	16.33 326	4.48 338	5.04 351	2.26 353	93.18 289	3.28 354	2.70 346	8.24 348	21.66 345	2.29 356	2.19 349	7.58 346	2.96 347	9.00 345

test123		13.04 326	15.66 327	5.18 337	4.51 352	3.45 352	92.50 303	1.81 355	2.50 347	7.58 349	20.15 346	3.67 355	2.18 350	7.13 347	1.07 349	9.90 344

cdsmvs_eth3d_5k		23.24 324	30.99 324	0.00 339	0.00 353	0.00 354	0.00 344	97.63 107	0.00 348	0.00 350	96.88 103	84.38 121	0.00 351	0.00 348	0.00 350	0.00 348

pcd_1.5k_mvsjas		7.39 328	9.85 329	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	88.65 69	0.00 351	0.00 348	0.00 350	0.00 348

pcd1.5k->3k		38.37 322	40.51 323	31.96 335	94.29 242	0.00 354	0.00 344	97.69 101	0.00 348	0.00 350	0.00 350	81.45 184	0.00 351	0.00 348	91.11 207	95.89 195

sosnet-low-res		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

sosnet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

uncertanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

Regformer		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

ab-mvs-re		8.06 327	10.74 328	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	96.69 112	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

uanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

test_part2					99.28 17	95.74 3				98.10 6

test_part1								98.26 25				95.31 1			99.63 4	99.63 5

test_full								98.25 26

sam_mvs1												82.76 157

sam_mvs												81.94 178

ambc				86.56 310	83.60 332	70.00 330	85.69 332	94.97 275		80.60 303	88.45 314	37.42 340	96.84 288	82.69 260	75.44 315	92.86 295

MTGPA								98.08 50

test_post1							92.81 299				16.58 348	80.53 201	97.68 251	86.20 206

test_post											17.58 347	81.76 180	98.08 193

patchmatchnet-post											90.45 295	82.65 161	98.10 190

GG-mvs-BLEND				93.62 210	93.69 274	89.20 178	92.39 305	83.33 344		87.98 239	89.84 298	71.00 291	96.87 287	82.08 266	95.40 140	94.80 262

MTMP								82.03 346

test9_res														94.81 60	99.38 34	99.45 29

agg_prior2														93.94 72	99.38 34	99.50 23

agg_prior					98.67 39	93.79 36		98.00 71		95.68 67			99.57 61

test_prior4						93.66 40	96.42 185

test_prior				97.23 48	98.67 39	92.99 56		98.00 71					99.41 83			99.29 44

新几何2							95.79 229

旧先验1					98.38 59	93.38 48		97.75 92			98.09 41	92.30 26			99.01 63	99.16 52

原ACMM2							95.67 233

testdata2													99.67 37	85.96 213

segment_acmp												92.89 11

test12				97.65 29	98.46 52	94.26 20		97.66 103		95.52 75		90.89 47	99.46 77		99.25 45	99.22 49

plane_prior7					96.21 159	89.98 138

plane_prior6					96.10 169	90.00 134						81.32 186

plane_prior5								97.51 117					98.60 150	93.02 90	92.23 186	95.86 196

plane_prior4											96.64 115

plane_prior1					96.14 167

n2								0.00 356

nn								0.00 356

door-mid								91.06 331

lessismore_v0				90.45 293	91.96 308	79.09 313		87.19 340		80.32 310	94.39 223	66.31 311	97.55 260	84.00 244	76.84 311	94.70 267

test11								97.88 83

door								91.13 330

HQP5-MVS						89.33 171

BP-MVS														92.13 101

HQP4-MVS										90.14 175			98.50 159			95.78 203

HQP3-MVS								97.39 136							92.10 191

HQP2-MVS												80.95 191

NP-MVS					95.99 172	89.81 145					95.87 150

ACMMP++_ref															90.30 219

ACMMP++															91.02 209

Test By Simon												88.73 68

High-resolution multi-view benchmark