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	92.80 301	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	97.34 101	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	97.27 107	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 84	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	96.87 143	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	86.63 250	96.00 222	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	97.02 128	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	96.83 146	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	95.96 223	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	96.83 146	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	96.96 133	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	97.15 121	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	97.24 109	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	96.96 133	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	89.67 229	97.81 16	99.38 8	94.03 30	98.59 7	98.20 31	94.85 17	96.59 36	32.69 346	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	96.21 161	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	92.23 188	95.86 198

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	97.41 272	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 93	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	96.89 142	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	96.86 144	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	96.90 131	95.34 5	98.48 14	97.87 85	94.65 28	88.53 230	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	96.85 132	91.19 107	97.88 45	97.68 102	94.40 31	93.00 118	96.18 138	73.39 284	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	96.46 153	92.13 79	96.21 210	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	97.18 121	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	96.20 211	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	96.79 153	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	96.56 145	93.36 50	98.65 6	98.36 16	94.12 37	89.25 220	98.06 43	82.20 172	99.77 20	93.41 86	99.32 40	99.18 51

plane_prior						89.99 136	97.24 109		94.06 38						92.16 192

MVS_111021_LR		96.24 45	96.19 42	96.39 79	98.23 73	91.35 101	96.24 209	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	97.30 106	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 87	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	92.68 248	96.02 172	83.25 284	97.14 122	96.72 196	93.85 42	91.20 164	93.44 266	83.08 139	98.30 178	91.69 115	95.73 137	96.50 175

mvs-test1		93.63 107	93.69 87	93.46 222	96.02 172	84.61 272	97.24 109	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	97.35 117	90.66 125	95.31 252	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	94.63 232	90.77 123	96.65 171	97.18 150	93.72 45	91.68 144	97.26 90	79.33 220	98.63 147	92.13 101	92.28 187	95.07 244

xiu_mvs_v2_base		95.32 62	95.29 57	95.40 125	97.22 119	90.50 128	95.44 247	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					95.86 175		96.65 171		93.55 48	90.14 177

ACMP_Plane					95.86 175		96.65 171		93.55 48	90.14 177

HQP-MVS		93.19 121	92.74 116	94.54 167	95.86 175	89.33 173	96.65 171	97.39 136	93.55 48	90.14 177	95.87 150	80.95 191	98.50 159	92.13 101	92.10 193	95.78 205

MCST-MVS		97.18 10	96.84 17	98.20 4	99.30 16	95.35 4	97.12 123	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	96.04 218	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	95.48 188	91.45 99	98.12 30	98.71 5	93.37 53	90.23 176	96.70 110	87.66 81	97.85 239	91.49 119	90.39 220	95.83 202

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	95.70 182	92.03 82	98.10 31	98.68 7	93.36 55	90.39 173	96.70 110	87.63 83	97.94 229	92.25 97	90.50 219	95.84 201

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	92.99 238	93.76 274	83.71 279	96.69 168	95.28 259	93.15 62	87.02 260	95.95 147	83.37 130	97.38 275	79.46 292	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	95.39 191	93.34 51	97.39 97	98.71 5	93.14 63	90.10 185	94.83 202	87.71 80	98.03 212	91.67 117	83.99 282	95.46 218

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							95.26 256		93.10 65

DU-MVS		92.90 131	92.04 134	95.49 119	94.95 218	92.83 60	97.16 120	98.24 28	93.02 66	90.13 181	95.71 163	83.47 128	97.85 239	91.71 113	83.93 283	95.78 205

xiu_mvs_v1_base_debu		95.01 69	94.76 65	95.75 106	96.58 142	91.71 88	96.25 206	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	96.58 142	91.71 88	96.25 206	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	96.58 142	91.71 88	96.25 206	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	95.34 194	92.83 60	97.17 119	98.58 10	92.98 72	90.13 181	95.80 155	88.37 74	97.85 239	91.71 113	83.93 283	95.73 211

VPNet		92.23 159	91.31 164	94.99 141	95.56 185	90.96 115	97.22 114	97.86 87	92.96 73	90.96 165	96.62 122	75.06 270	98.20 182	91.90 107	83.65 289	95.80 204

nrg030		94.05 94	93.31 102	96.27 88	95.22 204	94.59 13	98.34 19	97.46 124	92.93 74	91.21 163	96.64 115	87.23 90	98.22 181	94.99 56	85.80 256	95.98 196

TranMVSNet+NR-MVSNet		92.50 144	91.63 151	95.14 135	94.76 227	92.07 80	97.53 85	98.11 45	92.90 75	89.56 208	96.12 141	83.16 132	97.60 260	89.30 145	83.20 293	95.75 209

ACMMP_Plus		97.20 9	96.86 16	98.23 3	99.09 25	95.16 7	97.60 79	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	96.35 196	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							96.35 196		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	94.32 175	96.12 170	89.20 180	95.28 253	97.47 122	92.66 79	89.90 190	95.62 168	80.58 200	98.40 169	92.73 93	92.40 186	95.38 227
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	94.95 218	93.05 55	97.39 97	98.07 55	92.65 80	84.46 277	95.71 163	85.00 113	97.77 248	89.71 137	83.52 290	95.78 205

#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	94.44 169	93.91 269	89.28 178	97.75 54	97.56 114	92.50 82	89.94 189	96.54 125	88.65 69	98.18 185	93.83 77	90.90 212	95.86 198

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	68.42 305	99.39 86	96.31 19	94.85 146	98.71 89

LCM-MVSNet-Re		92.50 144	92.52 126	92.44 251	96.82 136	81.89 292	96.92 140	93.71 309	92.41 84	84.30 279	94.60 212	85.08 112	97.03 285	91.51 118	97.36 104	98.40 111

VPA-MVSNet		93.24 119	92.48 128	95.51 117	95.70 182	92.39 71	97.86 46	98.66 9	92.30 85	92.09 137	95.37 181	80.49 202	98.40 169	93.95 71	85.86 255	95.75 209

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	87.92 222	98.10 31	95.80 240	92.22 86	93.02 117	97.45 86	84.53 120	97.91 236	88.24 166	97.97 87	99.02 63

conf200view11		92.45 147	91.58 153	95.05 138	97.92 90	89.37 171	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 250	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	89.37 171	97.71 62	94.66 284	92.20 88	93.31 106	94.90 196	78.06 250	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	89.29 176	97.41 93	94.88 279	92.19 90	93.27 109	94.46 218	78.17 243	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	89.29 176	97.41 93	94.88 279	92.19 90	93.27 109	94.46 218	78.17 243	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	89.47 162	97.65 68	94.66 284	92.18 92	93.33 105	94.91 195	78.06 250	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	89.44 166	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 246	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	89.44 166	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 246	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	89.44 166	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 246	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	89.44 166	98.00 36	94.57 288	92.09 93	93.17 112	95.52 174	78.14 246	99.11 106	81.61 267	94.04 161	96.98 156

Fast-Effi-MVS+-dtu		92.29 156	91.99 137	93.21 233	95.27 199	85.52 261	97.03 126	96.63 206	92.09 93	89.11 221	95.14 190	80.33 206	98.08 195	87.54 185	94.74 151	96.03 195

thres200		92.23 159	91.39 160	94.75 156	97.61 107	89.03 183	96.60 178	95.09 269	92.08 98	93.28 108	94.00 245	78.39 241	99.04 121	81.26 283	94.18 155	96.19 182

mvs_tets		92.31 154	91.76 142	93.94 193	93.41 284	88.29 195	97.63 77	97.53 115	92.04 99	88.76 225	96.45 129	74.62 274	98.09 194	93.91 73	91.48 203	95.45 219

OMC-MVS		95.09 68	94.70 68	96.25 90	98.46 52	91.28 102	96.43 186	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	94.03 184	93.33 288	88.50 192	97.73 58	97.53 115	92.00 101	88.85 224	96.50 127	75.62 267	98.11 191	93.88 75	91.56 202	95.48 215

XVG-OURS		93.72 105	93.35 101	94.80 152	97.07 125	88.61 189	94.79 262	97.46 124	91.97 102	93.99 94	97.86 55	81.74 181	98.88 131	92.64 94	92.67 184	96.92 164

WR-MVS		92.34 152	91.53 156	94.77 155	95.13 210	90.83 120	96.40 192	97.98 77	91.88 103	89.29 217	95.54 173	82.50 163	97.80 244	89.79 136	85.27 262	95.69 212

PAPM_NR		95.01 69	94.59 70	96.26 89	98.89 32	90.68 124	97.24 109	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		87.97 270	87.21 267	90.24 298	92.86 299	80.76 298	96.67 170	94.97 275	91.74 105	85.52 271	95.83 153	62.66 321	94.47 320	76.25 304	88.36 239	95.48 215

CP-MVSNet		91.89 169	91.24 167	93.82 196	95.05 213	88.57 190	97.82 50	98.19 33	91.70 106	88.21 237	95.76 160	81.96 176	97.52 264	87.86 173	84.65 276	95.37 228

XVG-OURS-SEG-HR		93.86 100	93.55 91	94.81 151	97.06 127	88.53 191	95.28 253	97.45 128	91.68 107	94.08 93	97.68 67	82.41 167	98.90 127	93.84 76	92.47 185	96.98 156

OurMVSNet-221017-0		90.51 228	90.19 211	91.44 281	93.41 284	81.25 296	96.98 132	96.28 214	91.68 107	86.55 264	96.30 134	74.20 277	97.98 220	88.96 156	87.40 248	95.09 241

ACMP	89.59 10	92.62 139	92.14 132	94.05 183	96.40 155	88.20 203	97.36 100	97.25 149	91.52 109	88.30 234	96.64 115	78.46 239	98.72 144	91.86 110	91.48 203	95.23 238
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				92.43 252	95.34 194	85.37 263		95.92 229	91.47 111	87.75 243	96.39 132	71.00 293	97.96 227	82.36 264	89.86 226	93.97 286

PS-CasMVS		91.55 189	90.84 184	93.69 209	94.96 217	88.28 196	97.84 49	98.24 28	91.46 112	88.04 239	95.80 155	79.67 215	97.48 266	87.02 196	84.54 278	95.31 231

WR-MVS_H		92.00 166	91.35 161	93.95 190	95.09 212	89.47 162	98.04 35	98.68 7	91.46 112	88.34 232	94.68 208	85.86 104	97.56 261	85.77 215	84.24 280	94.82 262

MVSFormer		95.37 60	95.16 60	95.99 98	96.34 157	91.21 104	98.22 26	97.57 111	91.42 114	96.22 47	97.32 87	86.20 100	97.92 233	94.07 68	99.05 61	98.85 81

test_djsdf		93.07 124	92.76 112	94.00 185	93.49 282	88.70 188	98.22 26	97.57 111	91.42 114	90.08 187	95.55 172	82.85 155	97.92 233	94.07 68	91.58 201	95.40 225

ACMM	89.79 8	92.96 128	92.50 127	94.35 173	96.30 159	88.71 187	97.58 82	97.36 141	91.40 116	90.53 169	96.65 114	79.77 213	98.75 141	91.24 125	91.64 199	95.59 214
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		91.20 204	90.44 199	93.48 220	94.49 236	87.91 224	97.76 53	98.18 35	91.29 117	87.78 242	95.74 162	80.35 205	97.33 277	85.46 220	82.96 294	95.19 240

LPG-MVS_test		92.94 129	92.56 122	94.10 180	96.16 166	88.26 197	97.65 68	97.46 124	91.29 117	90.12 183	97.16 94	79.05 223	98.73 142	92.25 97	91.89 196	95.31 231

LGP-MVS_train				94.10 180	96.16 166	88.26 197		97.46 124	91.29 117	90.12 183	97.16 94	79.05 223	98.73 142	92.25 97	91.89 196	95.31 231

MVSTER		93.20 120	92.81 111	94.37 172	96.56 145	89.59 156	97.06 125	97.12 159	91.24 120	91.30 154	95.96 146	82.02 175	98.05 207	93.48 83	90.55 217	95.47 217

MVS_Test		94.89 76	94.62 69	95.68 110	96.83 135	89.55 158	96.70 166	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		91.42 195	91.19 170	92.12 263	94.59 233	80.66 299	94.29 272	92.98 320	91.11 123	90.76 167	92.37 281	79.02 225	98.07 199	88.81 161	96.74 118	97.63 140

test0.0.03 1		89.37 249	88.70 243	91.41 282	92.47 306	85.63 259	95.22 257	92.70 325	91.11 123	86.91 262	93.65 257	79.02 225	93.19 326	78.00 298	89.18 230	95.41 221

XVG-ACMP-BASELINE		90.93 213	90.21 210	93.09 235	94.31 243	85.89 255	95.33 250	97.26 147	91.06 125	89.38 213	95.44 180	68.61 303	98.60 150	89.46 143	91.05 210	94.79 266

Effi-MVS+		94.93 74	94.45 77	96.36 82	96.61 140	91.47 97	96.41 188	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		91.54 190	90.85 182	93.59 214	95.59 184	84.95 268	94.72 263	95.58 247	90.82 127	92.25 133	93.58 259	75.80 264	97.41 272	83.35 249	95.98 131	98.40 111

SixPastTwentyTwo		89.15 250	88.54 247	90.98 285	93.49 282	80.28 306	96.70 166	94.70 283	90.78 128	84.15 282	95.57 170	71.78 288	97.71 252	84.63 231	85.07 268	94.94 252

DTE-MVSNet		90.56 226	89.75 227	93.01 237	93.95 267	87.25 234	97.64 72	97.65 105	90.74 129	87.12 256	95.68 166	79.97 211	97.00 288	83.33 251	81.66 301	94.78 267

GA-MVS		91.38 197	90.31 202	94.59 162	94.65 231	87.62 229	94.34 270	96.19 220	90.73 130	90.35 174	93.83 250	71.84 287	97.96 227	87.22 192	93.61 172	98.21 118

EPP-MVSNet		95.22 65	95.04 62	95.76 105	97.49 116	89.56 157	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	96.96 133	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	93.34 227	96.25 160	86.97 243	96.57 182	97.05 168	90.67 132	89.50 211	94.80 204	86.59 94	97.64 257	89.91 133	86.11 254	95.40 225
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	93.48 220	95.77 180	86.98 242	96.44 184	97.12 159	90.66 134	91.30 154	97.64 73	86.56 95	98.05 207	89.91 133	90.55 217	95.41 221

K. test v3		87.64 274	86.75 272	90.32 297	93.02 298	79.48 312	96.61 176	92.08 328	90.66 134	80.25 314	94.09 243	67.21 311	96.65 291	85.96 213	80.83 305	94.83 260

test_normal		92.01 164	90.75 187	95.80 104	93.24 290	89.97 139	95.93 225	96.24 218	90.62 136	81.63 295	93.45 265	74.98 271	98.89 129	93.61 79	97.04 112	98.55 94

BH-RMVSNet		92.72 138	91.97 138	94.97 144	97.16 122	87.99 217	96.15 212	95.60 245	90.62 136	91.87 140	97.15 96	78.41 240	98.57 153	83.16 252	97.60 96	98.36 115

semantic-postprocess				91.82 271	95.52 186	84.20 275		96.15 222	90.61 138	87.39 251	94.27 238	75.63 266	96.44 292	87.34 189	86.88 251	94.82 262

WTY-MVS		94.71 80	94.02 80	96.79 60	97.71 102	92.05 81	96.59 179	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	93.34 286	89.78 146	96.14 213	96.18 221	90.58 140	81.80 294	93.50 262	74.95 272	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	69.00 301	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		89.48 246	87.50 256	95.44 124	90.76 315	89.72 147	95.78 233	97.09 162	90.28 143	77.67 320	91.74 293	55.42 333	98.08 195	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	97.27 107	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	94.47 168	98.27 67	89.46 164	96.73 158	98.36 16	90.17 145	94.36 88	95.24 187	88.02 75	99.58 53	93.44 84	90.72 215	94.36 279

CNLPA		94.28 85	93.53 93	96.52 69	98.38 59	92.55 68	96.59 179	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	93.92 194	97.22 119	86.16 254	96.40 192	96.25 217	90.06 147	89.79 197	96.17 140	83.19 131	98.35 173	87.19 193	97.27 107	97.24 153

IterMVS		90.15 236	89.67 229	91.61 278	95.48 188	83.72 278	94.33 271	96.12 223	89.99 148	87.31 254	94.15 242	75.78 265	96.27 295	86.97 197	86.89 250	94.83 260
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	96.59 179	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		85.99 286	84.45 287	90.62 293	89.97 318	82.40 289	93.62 287	97.37 139	89.86 150	78.59 319	92.37 281	65.25 317	95.35 316	82.27 265	70.75 331	94.10 284

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	94.06 182	96.44 154	85.41 262	95.81 230	97.05 168	89.85 152	90.09 186	96.36 133	87.44 87	97.75 249	93.97 70	96.69 121	99.02 63

PatchFormer-LS_test		91.68 182	91.18 171	93.19 234	95.24 203	83.63 282	95.53 243	95.44 251	89.82 153	91.37 149	92.58 278	80.85 198	98.52 157	89.65 140	90.16 222	97.42 151

ab-mvs		93.57 110	92.55 123	96.64 62	97.28 118	91.96 86	95.40 248	97.45 128	89.81 154	93.22 111	96.28 135	79.62 216	99.46 77	90.74 128	93.11 179	98.50 101

FMVSNet3		91.78 171	90.69 190	95.03 140	96.53 147	92.27 74	97.02 128	96.93 185	89.79 155	89.35 214	94.65 210	77.01 258	97.47 267	86.12 208	88.82 232	95.35 229

v2v482		91.59 186	90.85 182	93.80 197	93.87 271	88.17 205	96.94 139	96.88 190	89.54 156	89.53 209	94.90 196	81.70 182	98.02 215	89.25 147	85.04 270	95.20 239

PatchmatchNet		91.91 168	91.35 161	93.59 214	95.38 192	84.11 276	93.15 295	95.39 252	89.54 156	92.10 136	93.68 255	82.82 156	98.13 188	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		90.70 223	89.81 224	93.37 226	94.73 229	84.21 274	93.67 285	88.02 339	89.50 158	92.38 129	93.49 263	77.82 255	97.78 246	86.03 211	92.68 183	98.11 124

v148		90.99 211	90.38 201	92.81 243	93.83 272	85.80 256	96.78 155	96.68 201	89.45 159	88.75 226	93.93 248	82.96 151	97.82 243	87.83 174	83.25 291	94.80 264

anonymousdsp		92.16 161	91.55 155	93.97 188	92.58 305	89.55 158	97.51 86	97.42 134	89.42 160	88.40 231	94.84 200	80.66 199	97.88 238	91.87 109	91.28 207	94.48 275

IB-MVS	87.33 17	89.91 239	88.28 250	94.79 154	95.26 202	87.70 228	95.12 259	93.95 307	89.35 161	87.03 259	92.49 279	70.74 295	99.19 97	89.18 150	81.37 302	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	95.52 186	90.93 117	96.09 215	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	96.16 166	90.45 130	96.71 163	96.89 189	89.27 163	93.46 103	96.92 102	87.29 89	97.94 229	88.70 163	95.74 136	98.53 96

testing_2		87.33 276	85.03 283	94.22 176	87.77 327	89.32 175	94.97 260	97.11 161	89.22 164	71.64 329	88.73 314	55.16 334	97.94 229	91.95 105	88.73 236	95.41 221

v6		91.69 177	91.00 175	93.75 202	94.14 251	88.12 210	97.20 115	96.98 176	89.19 165	89.90 190	94.42 222	83.04 143	98.07 199	89.07 152	85.10 265	95.07 244

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	93.75 202	94.19 246	88.14 208	97.20 115	96.98 176	89.18 167	89.87 193	94.44 220	83.10 137	98.06 204	89.06 153	85.09 266	95.06 247

v7new		91.70 175	91.01 173	93.75 202	94.19 246	88.14 208	97.20 115	96.98 176	89.18 167	89.87 193	94.44 220	83.10 137	98.06 204	89.06 153	85.09 266	95.06 247

v1141		91.61 183	90.89 177	93.78 199	94.01 264	88.24 199	96.96 133	96.96 180	89.17 169	89.75 199	94.29 234	82.99 147	98.03 212	88.85 159	85.00 271	95.07 244

divwei89l23v2f112		91.61 183	90.89 177	93.78 199	94.01 264	88.22 201	96.96 133	96.96 180	89.17 169	89.75 199	94.28 236	83.02 145	98.03 212	88.86 158	84.98 273	95.08 242

v1		91.61 183	90.89 177	93.78 199	94.01 264	88.21 202	96.96 133	96.96 180	89.17 169	89.78 198	94.29 234	82.97 149	98.05 207	88.85 159	84.99 272	95.08 242

XXY-MVS		92.16 161	91.23 168	94.95 146	94.75 228	90.94 116	97.47 91	97.43 133	89.14 172	88.90 222	96.43 130	79.71 214	98.24 180	89.56 141	87.68 243	95.67 213

pm-mvs1		90.72 221	89.65 231	93.96 189	94.29 244	89.63 153	97.79 52	96.82 193	89.07 173	86.12 268	95.48 179	78.61 237	97.78 246	86.97 197	81.67 300	94.46 276

HY-MVS	89.66 9	93.87 99	92.95 108	96.63 64	97.10 124	92.49 70	95.64 238	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	89.66 152	97.91 43	92.90 323	88.99 176	91.73 142	94.84 200	78.99 229	98.33 176	82.41 263	93.91 167	96.40 178

v8		91.29 202	90.53 198	93.57 217	94.15 250	88.12 210	97.34 101	97.06 167	88.99 176	88.32 233	94.26 240	83.08 139	98.01 216	87.62 183	83.92 285	94.57 273

PAPR		94.18 87	93.42 100	96.48 74	97.64 105	91.42 100	95.55 241	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	96.18 164	91.46 98	96.33 199	97.04 171	88.97 179	93.56 99	96.51 126	87.55 84	97.89 237	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	97.07 125	91.97 85	96.32 200	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	94.62 161	97.73 101	89.93 142	97.75 54	92.92 322	88.93 181	91.73 142	93.80 252	78.91 230	98.49 162	83.02 255	93.86 168	95.45 219

lupinMVS		94.99 73	94.56 71	96.29 87	96.34 157	91.21 104	95.83 229	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		90.76 217	89.86 221	93.45 223	93.54 279	87.60 230	97.70 64	97.37 139	88.85 183	87.65 246	94.08 244	81.08 188	98.10 192	84.68 230	83.79 288	94.66 271

PVSNet_Blended		94.87 77	94.56 71	95.81 103	98.27 67	89.46 164	95.47 246	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	90.20 234	89.18 238	93.25 230	96.48 151	86.45 251	96.99 131	96.68 201	88.83 185	84.79 276	96.22 137	70.16 299	98.53 156	84.42 236	88.04 240	94.77 268

GBi-Net		91.35 199	90.27 205	94.59 162	96.51 148	91.18 108	97.50 87	96.93 185	88.82 186	89.35 214	94.51 214	73.87 278	97.29 279	86.12 208	88.82 232	95.31 231

test1		91.35 199	90.27 205	94.59 162	96.51 148	91.18 108	97.50 87	96.93 185	88.82 186	89.35 214	94.51 214	73.87 278	97.29 279	86.12 208	88.82 232	95.31 231

FMVSNet2		91.31 201	90.08 212	94.99 141	96.51 148	92.21 75	97.41 93	96.95 183	88.82 186	88.62 227	94.75 206	73.87 278	97.42 271	85.20 224	88.55 238	95.35 229

V42		91.58 187	90.87 180	93.73 205	94.05 263	88.50 192	97.32 104	96.97 179	88.80 189	89.71 201	94.33 227	82.54 162	98.05 207	89.01 155	85.07 268	94.64 272

agg_prior1		96.22 46	95.77 47	97.56 35	98.67 39	93.79 36	96.28 204	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	93.31 228	96.99 130	85.73 257	95.67 235	95.69 242	88.73 191	89.26 219	94.82 203	82.97 149	98.07 199	85.26 223	96.32 128	96.13 187

test20.03		86.14 285	85.40 281	88.35 303	90.12 316	80.06 308	95.90 226	95.20 264	88.59 192	81.29 297	93.62 258	71.43 290	92.65 327	71.26 319	81.17 303	92.34 315

train_agg		96.30 43	95.83 46	97.72 24	98.70 37	94.19 23	96.41 188	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	96.37 195	98.01 69	88.58 193	95.98 58	97.55 83	92.73 14	99.58 53

tpmrst		91.44 194	91.32 163	91.79 273	95.15 208	79.20 314	93.42 289	95.37 254	88.55 195	93.49 102	93.67 256	82.49 164	98.27 179	90.41 130	89.34 229	97.90 129

v748		90.34 230	89.54 232	92.75 245	93.25 289	85.71 258	97.61 78	97.17 152	88.54 196	87.20 255	93.54 260	81.02 189	98.01 216	85.73 217	81.80 298	94.52 274

ACMH	87.59 16	90.53 227	89.42 234	93.87 195	96.21 161	87.92 222	97.24 109	96.94 184	88.45 197	83.91 285	96.27 136	71.92 286	98.62 149	84.43 235	89.43 228	95.05 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thresconf0.02		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

tfpn_n400		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

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		91.20 204	90.62 195	92.95 239	93.83 272	88.03 216	97.01 130	95.12 268	88.42 202	89.70 202	95.13 191	83.47 128	97.44 269	89.66 139	83.24 292	93.37 294

v7		91.47 193	90.73 188	93.68 210	94.13 252	88.16 206	97.09 124	97.05 168	88.38 203	89.80 196	94.52 213	82.21 171	98.01 216	88.00 170	85.42 259	94.87 256

v1144		91.37 198	90.60 196	93.68 210	93.89 270	88.23 200	96.84 145	97.03 173	88.37 204	89.69 203	94.39 223	82.04 174	97.98 220	87.80 175	85.37 260	94.84 258

DP-MVS Recon		95.68 56	95.12 61	97.37 40	99.19 23	94.19 23	97.03 126	98.08 50	88.35 205	95.09 79	97.65 70	89.97 58	99.48 75	92.08 104	98.59 74	98.44 108

tpm		90.25 232	89.74 228	91.76 276	93.92 268	79.73 310	93.98 279	93.54 313	88.28 206	91.99 138	93.25 269	77.51 257	97.44 269	87.30 191	87.94 241	98.12 121

v10		91.04 210	90.23 208	93.49 219	94.12 254	88.16 206	97.32 104	97.08 164	88.26 207	88.29 235	94.22 241	82.17 173	97.97 223	86.45 203	84.12 281	94.33 280

v52		90.70 223	90.00 216	92.82 240	93.24 290	87.03 240	97.60 79	97.14 156	88.21 208	87.69 244	93.94 247	80.91 194	98.07 199	87.39 187	83.87 287	93.36 295

V4		90.71 222	90.00 216	92.82 240	93.21 293	87.03 240	97.59 81	97.16 155	88.21 208	87.69 244	93.92 249	80.93 193	98.06 204	87.39 187	83.90 286	93.39 293

Fast-Effi-MVS+		93.46 112	92.75 114	95.59 113	96.77 137	90.03 133	96.81 150	97.13 158	88.19 210	91.30 154	94.27 238	86.21 99	98.63 147	87.66 181	96.46 127	98.12 121

DWT-MVSNet_test		90.76 217	89.89 220	93.38 225	95.04 214	83.70 280	95.85 228	94.30 299	88.19 210	90.46 171	92.80 273	73.61 282	98.50 159	88.16 167	90.58 216	97.95 127

TEST9					98.70 37	94.19 23	96.41 188	98.02 67	88.17 212	96.03 53	97.56 81	92.74 13	99.59 50

MDTV_nov1_ep13			90.76 186		95.22 204	80.33 304	93.03 298	95.28 259	88.14 213	92.84 124	93.83 250	81.34 185	98.08 195	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	88.13 214	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	95.27 255	97.18 150	87.96 215	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	96.41 188	98.00 71	87.93 216	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	93.73 205	97.77 99	83.69 281	92.88 299	96.72 196	87.91 217	93.00 118	94.86 199	78.51 238	99.05 120	86.53 200	97.45 102	98.47 106

LTVRE_ROB	88.41 13	90.99 211	89.92 219	94.19 177	96.18 164	89.55 158	96.31 201	97.09 162	87.88 218	85.67 270	95.91 149	78.79 236	98.57 153	81.50 272	89.98 223	94.44 277
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	87.79 219	95.17 78	98.03 44	87.09 91	99.61 45	93.51 81	99.42 29	99.02 63

v1192		91.07 208	90.23 208	93.58 216	93.70 275	87.82 225	96.73 158	97.07 165	87.77 220	89.58 206	94.32 228	80.90 197	97.97 223	86.52 201	85.48 257	94.95 250

F-COLMAP		93.58 109	92.98 107	95.37 126	98.40 56	88.98 184	97.18 118	97.29 146	87.75 221	90.49 170	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	95.33 197	89.52 161	96.04 218	97.44 131	87.72 222	86.25 266	95.33 183	83.84 124	98.79 136	89.26 146	97.05 111	97.11 154

test-mter		90.19 235	89.54 232	92.12 263	94.59 233	80.66 299	94.29 272	92.98 320	87.68 223	90.76 167	92.37 281	67.67 307	98.07 199	88.81 161	96.74 118	97.63 140

TR-MVS		91.48 192	90.59 197	94.16 179	96.40 155	87.33 231	95.67 235	95.34 258	87.68 223	91.46 147	95.52 174	76.77 259	98.35 173	82.85 257	93.61 172	96.79 168

LF4IMVS		87.94 271	87.25 263	89.98 300	92.38 307	80.05 309	94.38 269	95.25 262	87.59 225	84.34 278	94.74 207	64.31 318	97.66 256	84.83 226	87.45 245	92.23 316

TransMVSNet (Re)		88.94 251	87.56 254	93.08 236	94.35 241	88.45 194	97.73 58	95.23 263	87.47 226	84.26 280	95.29 184	79.86 212	97.33 277	79.44 293	74.44 328	93.45 292

v144192		91.06 209	90.28 204	93.39 224	93.66 277	87.23 236	96.83 146	97.07 165	87.43 227	89.69 203	94.28 236	81.48 183	98.00 219	87.18 194	84.92 274	94.93 254

原ACMM1				96.38 80	98.59 47	91.09 112		97.89 82	87.41 228	95.22 77	97.68 67	90.25 53	99.54 65	87.95 172	99.12 58	98.49 103

v1921920		90.85 215	90.03 215	93.29 229	93.55 278	86.96 244	96.74 157	97.04 171	87.36 229	89.52 210	94.34 226	80.23 208	97.97 223	86.27 204	85.21 263	94.94 252

USDC		88.94 251	87.83 253	92.27 253	94.66 230	84.96 267	93.86 281	95.90 231	87.34 230	83.40 287	95.56 171	67.43 309	98.19 184	82.64 261	89.67 227	93.66 289

PLC	91.00 6	94.11 91	93.43 98	96.13 93	98.58 49	91.15 111	96.69 168	97.39 136	87.29 231	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		89.70 244	88.40 248	93.60 213	95.15 208	90.10 132	97.56 83	98.16 37	87.28 232	86.16 267	94.63 211	77.57 256	98.05 207	74.48 307	84.59 277	92.65 301

TESTMET0.1,1		90.06 237	89.42 234	91.97 267	94.41 240	80.62 301	94.29 272	91.97 329	87.28 232	90.44 172	92.47 280	68.79 302	97.67 254	88.50 165	96.60 123	97.61 144

v1240		90.70 223	89.85 222	93.23 231	93.51 281	86.80 245	96.61 176	97.02 174	87.16 234	89.58 206	94.31 229	79.55 217	97.98 220	85.52 219	85.44 258	94.90 255

Patchmatch-RL test		87.38 275	86.24 274	90.81 289	88.74 323	78.40 317	88.12 330	93.17 315	87.11 235	82.17 290	89.29 311	81.95 177	95.60 312	88.64 164	77.02 312	98.41 110

v18		88.71 256	87.52 255	92.27 253	94.16 249	88.11 212	96.82 149	95.96 226	87.03 236	80.76 301	89.81 301	83.15 133	96.22 296	84.69 229	75.31 319	92.49 305

v17		88.67 258	87.47 258	92.26 255	94.13 252	88.09 214	96.81 150	95.95 227	87.02 237	80.72 302	89.75 303	83.11 136	96.20 297	84.61 232	75.15 321	92.49 305

v16		88.69 257	87.50 256	92.26 255	94.19 246	88.11 212	96.81 150	95.95 227	87.01 238	80.71 303	89.80 302	83.08 139	96.20 297	84.61 232	75.34 318	92.48 307

v15		88.53 260	87.31 260	92.20 258	94.09 258	88.05 215	96.72 161	95.90 231	87.01 238	80.53 306	89.60 307	83.02 145	96.13 299	84.29 237	74.64 322	92.41 311

V14		88.52 261	87.30 261	92.17 260	94.12 254	87.99 217	96.72 161	95.91 230	86.98 240	80.50 307	89.63 304	83.03 144	96.12 301	84.23 238	74.60 324	92.40 312

CDPH-MVS		95.97 52	95.38 54	97.77 21	98.93 30	94.44 16	96.35 196	97.88 83	86.98 240	96.65 33	97.89 50	91.99 31	99.47 76	92.26 95	99.46 24	99.39 35

V9		88.49 264	87.26 262	92.18 259	94.12 254	87.97 220	96.73 158	95.90 231	86.95 242	80.40 309	89.61 305	82.98 148	96.13 299	84.14 239	74.55 325	92.44 309

v12		88.46 265	87.23 265	92.17 260	94.10 257	87.99 217	96.71 163	95.90 231	86.91 243	80.34 311	89.58 308	82.92 152	96.11 303	84.09 240	74.50 327	92.42 310

PM-MVS		83.48 295	81.86 298	88.31 304	87.83 326	77.59 318	93.43 288	91.75 330	86.91 243	80.63 304	89.91 299	44.42 340	95.84 308	85.17 225	76.73 314	91.50 323

CR-MVSNet		90.82 216	89.77 225	93.95 190	94.45 238	87.19 237	90.23 321	95.68 243	86.89 245	92.40 127	92.36 284	80.91 194	97.05 283	81.09 284	93.95 165	97.60 145

1112_ss		93.37 115	92.42 129	96.21 91	97.05 128	90.99 113	96.31 201	96.72 196	86.87 246	89.83 195	96.69 112	86.51 96	99.14 104	88.12 168	93.67 169	98.50 101

v13		88.45 266	87.22 266	92.16 262	94.08 260	87.95 221	96.71 163	95.90 231	86.86 247	80.27 313	89.55 309	82.92 152	96.12 301	84.02 242	74.63 323	92.40 312

v11		88.41 267	87.19 269	92.08 265	94.08 260	87.77 226	96.75 156	95.85 237	86.74 248	80.50 307	89.50 310	82.49 164	96.08 304	83.55 248	75.20 320	92.38 314

FMVSNet1		89.88 241	88.31 249	94.59 162	95.41 190	91.18 108	97.50 87	96.93 185	86.62 249	87.41 250	94.51 214	65.94 315	97.29 279	83.04 254	87.43 246	95.31 231

CHOSEN 280x420		93.12 122	92.72 117	94.34 174	96.71 139	87.27 233	90.29 320	97.72 97	86.61 250	91.34 151	95.29 184	84.29 122	98.41 168	93.25 88	98.94 66	97.35 152

MIMVSNet		88.50 263	86.76 271	93.72 207	94.84 224	87.77 226	91.39 311	94.05 304	86.41 251	87.99 240	92.59 277	63.27 319	95.82 309	77.44 299	92.84 182	97.57 147

tpmvs		89.83 243	89.15 239	91.89 269	94.92 220	80.30 305	93.11 296	95.46 250	86.28 252	88.08 238	92.65 275	80.44 203	98.52 157	81.47 273	89.92 225	96.84 167

PAPM		91.52 191	90.30 203	95.20 128	95.30 198	89.83 144	93.38 290	96.85 192	86.26 253	88.59 229	95.80 155	84.88 114	98.15 187	75.67 306	95.93 133	97.63 140

VDDNet		93.05 125	92.07 133	96.02 96	96.84 133	90.39 131	98.08 33	95.85 237	86.22 254	95.79 65	98.46 12	67.59 308	99.19 97	94.92 57	94.85 146	98.47 106

MS-PatchMatch		90.27 231	89.77 225	91.78 274	94.33 242	84.72 271	95.55 241	96.73 195	86.17 255	86.36 265	95.28 186	71.28 291	97.80 244	84.09 240	98.14 84	92.81 300

MVP-Stereo		90.74 220	90.08 212	92.71 246	93.19 295	88.20 203	95.86 227	96.27 215	86.07 256	84.86 275	94.76 205	77.84 254	97.75 249	83.88 246	98.01 86	92.17 318
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	96.47 152	89.61 154	96.09 215	97.14 156	85.97 257	93.09 116	95.35 182	84.87 115	98.55 155	89.51 142	96.26 129	98.28 117

CVMVSNet		91.23 203	91.75 143	89.67 302	95.77 180	74.69 322	96.44 184	94.88 279	85.81 258	92.18 134	97.64 73	79.07 222	95.58 313	88.06 169	95.86 135	98.74 85

MSDG		91.42 195	90.24 207	94.96 145	97.15 123	88.91 185	93.69 284	96.32 213	85.72 259	86.93 261	96.47 128	80.24 207	98.98 123	80.57 285	95.05 145	96.98 156

CHOSEN 1792x2688		94.15 88	93.51 94	96.06 94	98.27 67	89.38 170	95.18 258	98.48 14	85.60 260	93.76 98	97.11 97	83.15 133	99.61 45	91.33 122	98.72 71	99.19 50

AllTest		90.23 233	88.98 240	93.98 186	97.94 88	86.64 247	96.51 183	95.54 248	85.38 261	85.49 272	96.77 106	70.28 297	99.15 102	80.02 288	92.87 180	96.15 185

TestCases				93.98 186	97.94 88	86.64 247		95.54 248	85.38 261	85.49 272	96.77 106	70.28 297	99.15 102	80.02 288	92.87 180	96.15 185

Test_1112_low_res		92.84 135	91.84 141	95.85 102	97.04 129	89.97 139	95.53 243	96.64 204	85.38 261	89.65 205	95.18 188	85.86 104	99.10 111	87.70 177	93.58 174	98.49 103

EU-MVSNet		88.72 255	88.90 241	88.20 305	93.15 296	74.21 323	96.63 175	94.22 302	85.18 264	87.32 253	95.97 145	76.16 262	94.98 318	85.27 222	86.17 252	95.41 221

LS3D		93.57 110	92.61 121	96.47 75	97.59 109	91.61 92	97.67 65	97.72 97	85.17 265	90.29 175	98.34 25	84.60 118	99.73 23	83.85 247	98.27 80	98.06 125

dp		88.90 253	88.26 251	90.81 289	94.58 235	76.62 319	92.85 300	94.93 277	85.12 266	90.07 188	93.07 270	75.81 263	98.12 190	80.53 286	87.42 247	97.71 138

HyFIR lowres test		93.66 106	92.92 109	95.87 101	98.24 70	89.88 143	94.58 265	98.49 12	85.06 267	93.78 97	95.78 159	82.86 154	98.67 145	91.77 111	95.71 138	99.07 62

new-patchmatchnet		83.18 296	81.87 297	87.11 309	86.88 329	75.99 321	93.70 283	95.18 265	85.02 268	77.30 321	88.40 317	65.99 314	93.88 323	74.19 311	70.18 332	91.47 324

TDRefinement		86.53 281	84.76 286	91.85 270	82.23 337	84.25 273	96.38 194	95.35 255	84.97 269	84.09 283	94.94 193	65.76 316	98.34 175	84.60 234	74.52 326	92.97 296

OpenMVS	89.19 12	92.86 133	91.68 146	96.40 78	95.34 194	92.73 63	98.27 23	98.12 42	84.86 270	85.78 269	97.75 63	78.89 235	99.74 22	87.50 186	98.65 72	96.73 169

gm-plane-assit					93.22 292	78.89 316			84.82 271		93.52 261		98.64 146	87.72 176

PMMVS		92.86 133	92.34 130	94.42 171	94.92 220	86.73 246	94.53 267	96.38 211	84.78 272	94.27 90	95.12 192	83.13 135	98.40 169	91.47 120	96.49 125	98.12 121

pmmvs4		90.93 213	89.85 222	94.17 178	93.34 286	90.79 122	94.60 264	96.02 225	84.62 273	87.45 248	95.15 189	81.88 179	97.45 268	87.70 177	87.87 242	94.27 283

MDA-MVSNet-bldmvs		85.00 291	82.95 293	91.17 284	93.13 297	83.33 283	94.56 266	95.00 273	84.57 274	65.13 335	92.65 275	70.45 296	95.85 307	73.57 312	77.49 311	94.33 280

QAPM		93.45 113	92.27 131	96.98 58	96.77 137	92.62 66	98.39 18	98.12 42	84.50 275	88.27 236	97.77 62	82.39 168	99.81 17	85.40 221	98.81 68	98.51 99

pmmvs5		89.86 242	88.87 242	92.82 240	92.86 299	86.23 253	96.26 205	95.39 252	84.24 276	87.12 256	94.51 214	74.27 276	97.36 276	87.61 184	87.57 244	94.86 257

CostFormer		91.18 207	90.70 189	92.62 249	94.84 224	81.76 293	94.09 278	94.43 293	84.15 277	92.72 125	93.77 253	79.43 218	98.20 182	90.70 129	92.18 191	97.90 129

FMVSNet5		87.29 277	85.79 278	91.78 274	94.80 226	87.28 232	95.49 245	95.28 259	84.09 278	83.85 286	91.82 290	62.95 320	94.17 321	78.48 296	85.34 261	93.91 287

MIMVSNet1		84.93 292	83.05 292	90.56 294	89.56 321	84.84 270	95.40 248	95.35 255	83.91 279	80.38 310	92.21 288	57.23 328	93.34 325	70.69 321	82.75 297	93.50 290

RPSCF		90.75 219	90.86 181	90.42 296	96.84 133	76.29 320	95.61 240	96.34 212	83.89 280	91.38 148	97.87 53	76.45 260	98.78 137	87.16 195	92.23 188	96.20 181

MDTV_nov1_ep13_2view						70.35 330	93.10 297		83.88 281	93.55 100		82.47 166		86.25 205		98.38 114

无先验							95.79 231	97.87 85	83.87 282				99.65 39	87.68 179		98.89 79

PVSNet_0	82.17 19	85.46 290	83.64 291	90.92 287	95.27 199	79.49 311	90.55 319	95.60 245	83.76 283	83.00 288	89.95 298	71.09 292	97.97 223	82.75 259	60.79 338	95.31 231

TinyColmap		86.82 280	85.35 282	91.21 283	94.91 222	82.99 285	93.94 280	94.02 306	83.58 284	81.56 296	94.68 208	62.34 322	98.13 188	75.78 305	87.35 249	92.52 304

Anonymous20231206		87.09 278	86.14 276	89.93 301	91.22 313	80.35 303	96.11 214	95.35 255	83.57 285	84.16 281	93.02 271	73.54 283	95.61 311	72.16 315	86.14 253	93.84 288

pmmvs-eth3d		86.22 284	84.45 287	91.53 279	88.34 324	87.25 234	94.47 268	95.01 272	83.47 286	79.51 317	89.61 305	69.75 300	95.71 310	83.13 253	76.73 314	91.64 320

EG-PatchMatch MVS		87.02 279	85.44 280	91.76 276	92.67 303	85.00 266	96.08 217	96.45 209	83.41 287	79.52 316	93.49 263	57.10 329	97.72 251	79.34 294	90.87 213	92.56 303

ADS-MVSNet2		89.45 247	88.59 245	92.03 266	95.86 175	82.26 290	90.93 316	94.32 298	83.23 288	91.28 157	91.81 291	79.01 227	95.99 305	79.52 290	91.39 205	97.84 132

ADS-MVSNet		89.89 240	88.68 244	93.53 218	95.86 175	84.89 269	90.93 316	95.07 271	83.23 288	91.28 157	91.81 291	79.01 227	97.85 239	79.52 290	91.39 205	97.84 132

COLMAP_ROB	87.81 15	90.40 229	89.28 236	93.79 198	97.95 87	87.13 239	96.92 140	95.89 236	82.83 290	86.88 263	97.18 93	73.77 281	99.29 92	78.44 297	93.62 171	94.95 250
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	88.90 186		97.66 103	82.73 291	97.03 28	98.07 42	90.06 56	98.85 132	89.67 138	98.98 64	98.64 92

testus		82.63 299	82.15 295	84.07 315	87.31 328	67.67 334	93.18 291	94.29 300	82.47 292	82.14 291	90.69 296	53.01 335	91.94 330	66.30 325	89.96 224	92.62 302

DP-MVS		92.76 137	91.51 159	96.52 69	98.77 34	90.99 113	97.38 99	96.08 224	82.38 293	89.29 217	97.87 53	83.77 125	99.69 33	81.37 278	96.69 121	98.89 79

MDA-MVSNet_test_wron		85.87 287	84.23 289	90.80 291	92.38 307	82.57 286	93.17 293	95.15 266	82.15 294	67.65 331	92.33 287	78.20 242	95.51 314	77.33 300	79.74 306	94.31 282

YYNet1		85.87 287	84.23 289	90.78 292	92.38 307	82.46 288	93.17 293	95.14 267	82.12 295	67.69 330	92.36 284	78.16 245	95.50 315	77.31 301	79.73 307	94.39 278

PatchT		88.87 254	87.42 259	93.22 232	94.08 260	85.10 265	89.51 325	94.64 287	81.92 296	92.36 130	88.15 320	80.05 210	97.01 287	72.43 314	93.65 170	97.54 148

TAPA-MVS	90.10 7	92.30 155	91.22 169	95.56 114	98.33 63	89.60 155	96.79 153	97.65 105	81.83 297	91.52 146	97.23 92	87.94 77	98.91 126	71.31 318	98.37 78	98.17 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
旧先验2							95.94 224		81.66 298	97.34 16			98.82 134	92.26 95

tpmp4_e23		89.58 245	88.59 245	92.54 250	95.16 207	81.53 294	94.11 277	95.09 269	81.66 298	88.60 228	93.44 266	75.11 269	98.33 176	82.45 262	91.72 198	97.75 136

新几何1				97.32 42	98.60 46	93.59 42		97.75 92	81.58 300	95.75 66	97.85 56	90.04 57	99.67 37	86.50 202	99.13 55	98.69 90

test2356		82.77 298	82.14 296	84.65 314	85.77 331	70.36 329	91.22 314	93.69 312	81.58 300	81.82 293	89.00 313	60.63 325	90.77 333	64.74 326	90.80 214	92.82 298

1121		94.71 80	93.83 83	97.34 41	98.57 50	93.64 41	96.04 218	97.73 94	81.56 302	95.68 67	97.85 56	90.23 54	99.65 39	87.68 179	99.12 58	98.73 86

Patchmatch-test		89.42 248	87.99 252	93.70 208	95.27 199	85.11 264	88.98 327	94.37 296	81.11 303	87.10 258	93.69 254	82.28 169	97.50 265	74.37 309	94.76 149	98.48 105

test_0402		86.46 282	84.79 285	91.45 280	95.02 215	85.55 260	96.29 203	94.89 278	80.90 304	82.21 289	93.97 246	68.21 306	97.29 279	62.98 328	88.68 237	91.51 322

gg-mvs-nofinetune		87.82 272	85.61 279	94.44 169	94.46 237	89.27 179	91.21 315	84.61 345	80.88 305	89.89 192	74.98 335	71.50 289	97.53 263	85.75 216	97.21 108	96.51 174

JIA-IIPM		88.26 269	87.04 270	91.91 268	93.52 280	81.42 295	89.38 326	94.38 295	80.84 306	90.93 166	80.74 332	79.22 221	97.92 233	82.76 258	91.62 200	96.38 179

Patchmtry		88.64 259	87.25 263	92.78 244	94.09 258	86.64 247	89.82 324	95.68 243	80.81 307	87.63 247	92.36 284	80.91 194	97.03 285	78.86 295	85.12 264	94.67 270

tpm2		89.96 238	89.21 237	92.23 257	94.91 222	81.25 296	93.78 282	94.42 294	80.62 308	91.56 145	93.44 266	76.44 261	97.94 229	85.60 218	92.08 195	97.49 149

pmmvs6		87.81 273	86.19 275	92.69 247	91.32 312	86.30 252	97.34 101	96.41 210	80.59 309	84.05 284	94.37 225	67.37 310	97.67 254	84.75 228	79.51 308	94.09 285

cascas		91.20 204	90.08 212	94.58 166	94.97 216	89.16 182	93.65 286	97.59 110	79.90 310	89.40 212	92.92 272	75.36 268	98.36 172	92.14 100	94.75 150	96.23 180

PCF-MVS	89.48 11	91.56 188	89.95 218	96.36 82	96.60 141	92.52 69	92.51 304	97.26 147	79.41 311	88.90 222	96.56 124	84.04 123	99.55 63	77.01 303	97.30 106	97.01 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test1235678		79.82 304	78.53 305	83.69 316	82.55 336	67.55 335	92.50 305	94.13 303	79.28 312	72.10 328	86.45 328	57.27 327	90.68 334	61.60 331	80.90 304	92.82 298

test222					98.24 70	92.21 75	95.33 250	97.60 108	79.22 313	95.25 76	97.84 58	88.80 67			99.15 53	98.72 87

UnsupCasMVSNet_bld		82.13 301	79.46 303	90.14 299	88.00 325	82.47 287	90.89 318	96.62 207	78.94 314	75.61 322	84.40 330	56.63 330	96.31 294	77.30 302	66.77 337	91.63 321

testpf		80.97 302	81.40 300	79.65 321	91.53 311	72.43 327	73.47 342	89.55 337	78.63 315	80.81 299	89.06 312	61.36 323	91.36 332	83.34 250	84.89 275	75.15 338

N_pmnet		78.73 305	78.71 304	78.79 323	92.80 301	46.50 350	94.14 276	43.71 354	78.61 316	80.83 298	91.66 294	74.94 273	96.36 293	67.24 323	84.45 279	93.50 290

ANet_high		63.94 316	59.58 317	77.02 325	61.24 350	66.06 336	85.66 335	87.93 340	78.53 317	42.94 342	71.04 339	25.42 350	80.71 343	52.60 340	30.83 345	84.28 333

114514_t		93.95 97	93.06 106	96.63 64	99.07 27	91.61 92	97.46 92	97.96 79	77.99 318	93.00 118	97.57 79	86.14 102	99.33 90	89.22 148	99.15 53	98.94 73

DSMNet-mixed		86.34 283	86.12 277	87.00 310	89.88 319	70.43 328	94.93 261	90.08 336	77.97 319	85.42 274	92.78 274	74.44 275	93.96 322	74.43 308	95.14 143	96.62 172

RPMNet		88.52 261	86.72 273	93.95 190	94.45 238	87.19 237	90.23 321	94.99 274	77.87 320	92.40 127	87.55 325	80.17 209	97.05 283	68.84 322	93.95 165	97.60 145

LP		84.13 294	81.85 299	90.97 286	93.20 294	82.12 291	87.68 331	94.27 301	76.80 321	81.93 292	88.52 315	72.97 285	95.95 306	59.53 333	81.73 299	94.84 258

new_pmnet		82.89 297	81.12 302	88.18 306	89.63 320	80.18 307	91.77 310	92.57 326	76.79 322	75.56 323	88.23 319	61.22 324	94.48 319	71.43 317	82.92 295	89.87 327

test12356		74.97 308	74.13 309	77.49 324	78.81 338	56.23 346	88.53 329	92.75 324	75.14 323	67.50 332	85.07 329	44.88 339	89.96 335	58.71 334	75.75 316	86.26 330

1111		78.29 306	77.55 306	80.50 319	83.89 332	59.98 342	91.89 308	93.71 309	75.06 324	73.60 326	87.67 323	55.66 331	92.60 328	58.54 335	77.92 310	88.93 329

.test1245		65.38 315	69.22 313	53.86 335	83.89 332	59.98 342	91.89 308	93.71 309	75.06 324	73.60 326	87.67 323	55.66 331	92.60 328	58.54 335	2.96 349	9.00 347

tpm cat1		88.36 268	87.21 267	91.81 272	95.13 210	80.55 302	92.58 303	95.70 241	74.97 326	87.45 248	91.96 289	78.01 253	98.17 186	80.39 287	88.74 235	96.72 170

testmv		72.22 310	70.02 310	78.82 322	73.06 345	61.75 340	91.24 313	92.31 327	74.45 327	61.06 337	80.51 333	34.21 343	88.63 338	55.31 338	68.07 336	86.06 331

CMPMVS	62.92 21	85.62 289	84.92 284	87.74 307	89.14 322	73.12 326	94.17 275	96.80 194	73.98 328	73.65 325	94.93 194	66.36 312	97.61 259	83.95 245	91.28 207	92.48 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	81.14 20	84.42 293	82.28 294	90.83 288	90.06 317	84.05 277	95.73 234	94.04 305	73.89 329	80.17 315	91.53 295	59.15 326	97.64 257	66.92 324	89.05 231	90.80 325

MVS		91.71 172	90.44 199	95.51 117	95.20 206	91.59 94	96.04 218	97.45 128	73.44 330	87.36 252	95.60 169	85.42 108	99.10 111	85.97 212	97.46 98	95.83 202

no-one		68.12 313	63.78 316	81.13 318	74.01 342	70.22 331	87.61 332	90.71 335	72.63 331	53.13 340	71.89 338	30.29 345	91.45 331	61.53 332	32.21 343	81.72 335

pmmvs3		79.97 303	77.50 307	87.39 308	82.80 335	79.38 313	92.70 302	90.75 334	70.69 332	78.66 318	87.47 326	51.34 337	93.40 324	73.39 313	69.65 333	89.38 328

Anonymous20231211		78.22 307	75.30 308	86.99 311	86.14 330	74.16 324	95.62 239	93.88 308	66.43 333	74.44 324	87.86 322	41.39 341	95.11 317	62.49 329	69.46 334	91.71 319

MVS-HIRNet		82.47 300	81.21 301	86.26 313	95.38 192	69.21 333	88.96 328	89.49 338	66.28 334	80.79 300	74.08 337	68.48 304	97.39 274	71.93 316	95.47 139	92.18 317

DeepMVS_CX				74.68 328	90.84 314	64.34 339		81.61 349	65.34 335	67.47 333	88.01 321	48.60 338	80.13 344	62.33 330	73.68 330	79.58 336

PMMVS2		70.19 312	66.92 314	80.01 320	76.35 339	65.67 337	86.22 333	87.58 341	64.83 336	62.38 336	80.29 334	26.78 349	88.49 339	63.79 327	54.07 339	85.88 332

FPMVS		71.27 311	69.85 311	75.50 326	74.64 340	59.03 344	91.30 312	91.50 331	58.80 337	57.92 338	88.28 318	29.98 347	85.53 341	53.43 339	82.84 296	81.95 334

LCM-MVSNet		72.55 309	69.39 312	82.03 317	70.81 347	65.42 338	90.12 323	94.36 297	55.02 338	65.88 334	81.72 331	24.16 351	89.96 335	74.32 310	68.10 335	90.71 326

Gipuma		67.86 314	65.41 315	75.18 327	92.66 304	73.45 325	66.50 344	94.52 292	53.33 339	57.80 339	66.07 341	30.81 344	89.20 337	48.15 342	78.88 309	62.90 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		59.01 317	55.87 318	68.44 330	73.98 343	51.37 347	81.36 338	82.41 347	52.37 340	42.49 344	70.39 340	11.39 352	79.99 345	49.77 341	38.71 341	73.97 339

wuykxyi23d		56.92 319	51.11 323	74.38 329	62.30 349	61.47 341	80.09 339	84.87 344	49.62 341	30.80 348	57.20 345	7.03 354	82.94 342	55.69 337	32.36 342	78.72 337

PMVS	53.92 22	58.58 318	55.40 319	68.12 331	51.00 351	48.64 348	78.86 340	87.10 343	46.77 342	35.84 347	74.28 336	8.76 353	86.34 340	42.07 343	73.91 329	69.38 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 320	52.56 321	55.43 333	74.43 341	47.13 349	83.63 337	76.30 350	42.23 343	42.59 343	62.22 343	28.57 348	74.40 346	31.53 345	31.51 344	44.78 343

EMVS		52.08 322	51.31 322	54.39 334	72.62 346	45.39 351	83.84 336	75.51 351	41.13 344	40.77 345	59.65 344	30.08 346	73.60 347	28.31 346	29.90 346	44.18 344

MVE	50.73 23	53.25 321	48.81 324	66.58 332	65.34 348	57.50 345	72.49 343	70.94 352	40.15 345	39.28 346	63.51 342	6.89 356	73.48 348	38.29 344	42.38 340	68.76 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		51.94 323	53.82 320	46.29 336	33.73 352	45.30 352	78.32 341	67.24 353	18.02 346	50.93 341	87.05 327	52.99 336	53.11 349	70.76 320	25.29 347	40.46 345

wuyk23d		25.11 325	24.57 327	26.74 338	73.98 343	39.89 353	57.88 345	9.80 355	12.27 347	10.39 349	6.97 351	7.03 354	36.44 350	25.43 347	17.39 348	3.89 349

testmvs		13.36 327	16.33 328	4.48 340	5.04 353	2.26 355	93.18 291	3.28 356	2.70 348	8.24 350	21.66 347	2.29 358	2.19 351	7.58 348	2.96 349	9.00 347

test123		13.04 328	15.66 329	5.18 339	4.51 354	3.45 354	92.50 305	1.81 357	2.50 349	7.58 351	20.15 348	3.67 357	2.18 352	7.13 349	1.07 351	9.90 346

cdsmvs_eth3d_5k		23.24 326	30.99 326	0.00 341	0.00 355	0.00 356	0.00 346	97.63 107	0.00 350	0.00 352	96.88 103	84.38 121	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		7.39 330	9.85 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	88.65 69	0.00 353	0.00 350	0.00 352	0.00 350

pcd1.5k->3k		38.37 324	40.51 325	31.96 337	94.29 244	0.00 356	0.00 346	97.69 101	0.00 350	0.00 352	0.00 352	81.45 184	0.00 353	0.00 350	91.11 209	95.89 197

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		8.06 329	10.74 330	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	96.69 112	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

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_all								98.25 26

sam_mvs1												82.76 157

sam_mvs												81.94 178

ambc				86.56 312	83.60 334	70.00 332	85.69 334	94.97 275		80.60 305	88.45 316	37.42 342	96.84 290	82.69 260	75.44 317	92.86 297

MTGPA								98.08 50

test_post1							92.81 301				16.58 350	80.53 201	97.68 253	86.20 206

test_post											17.58 349	81.76 180	98.08 195

patchmatchnet-post											90.45 297	82.65 161	98.10 192

GG-mvs-BLEND				93.62 212	93.69 276	89.20 180	92.39 307	83.33 346		87.98 241	89.84 300	71.00 293	96.87 289	82.08 266	95.40 140	94.80 264

MTMP								82.03 348

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	96.42 187

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

新几何2							95.79 231

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							95.67 235

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					96.21 161	89.98 138

plane_prior6					96.10 171	90.00 134						81.32 186

plane_prior5								97.51 117					98.60 150	93.02 90	92.23 188	95.86 198

plane_prior4											96.64 115

plane_prior1					96.14 169

n2								0.00 358

nn								0.00 358

door-mid								91.06 333

lessismore_v0				90.45 295	91.96 310	79.09 315		87.19 342		80.32 312	94.39 223	66.31 313	97.55 262	84.00 244	76.84 313	94.70 269

test11								97.88 83

door								91.13 332

HQP5-MVS						89.33 173

BP-MVS														92.13 101

HQP4-MVS										90.14 177			98.50 159			95.78 205

HQP3-MVS								97.39 136							92.10 193

HQP2-MVS												80.95 191

NP-MVS					95.99 174	89.81 145					95.87 150

ACMMP++_ref															90.30 221

ACMMP++															91.02 211

Test By Simon												88.73 68

High-resolution multi-view benchmark