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.

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 94	98.97 116	99.12 236	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 94	98.97 116	99.12 236	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 94	98.97 116	99.12 236	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

EPNet		98.86 100	98.71 103	99.30 113	97.20 311	98.18 195	99.62 74	98.91 278	99.28 2	98.63 232	99.81 53	95.96 128	99.99 1	99.24 30	99.72 83	99.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		99.06 79	98.86 87	99.66 53	99.51 117	99.36 74	99.22 220	99.51 84	98.95 24	99.58 60	99.65 127	93.74 225	99.98 5	99.66 1	99.95 6	99.64 94

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 114	98.91 129	99.02 262	99.45 147	98.80 39	99.71 29	99.26 243	98.94 24	99.98 5	99.34 22	99.23 117	98.98 176

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 109	98.94 124	98.97 275	99.46 136	98.92 28	99.71 29	99.24 245	99.01 11	99.98 5	99.35 18	99.66 95	98.97 177

QAPM		98.67 122	98.30 135	99.80 29	99.20 182	99.67 32	99.77 24	99.72 11	94.74 273	98.73 213	99.90 7	95.78 137	99.98 5	96.96 221	99.88 34	99.76 52

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 199	99.66 34	99.84 9	99.74 10	99.09 8	98.92 192	99.90 7	95.94 131	99.98 5	98.95 53	99.92 12	99.79 43

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 214	99.53 55	99.82 13	99.72 11	94.56 279	98.08 258	99.88 14	94.73 186	99.98 5	97.47 191	99.76 76	99.06 168

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 155	98.42 189	99.08 246	99.30 220	99.16 5	99.43 85	99.75 90	95.27 149	99.97 11	98.56 100	99.95 6	99.36 144

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

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 58	99.47 126	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

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

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 16	99.69 44	99.48 112	98.12 84	99.50 73	99.75 90	98.78 36	99.97 11	98.57 97	99.89 32	99.83 23

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

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 11	99.59 83	99.51 84	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 191	99.68 30	99.81 15	99.51 84	99.20 4	98.72 214	99.89 10	95.68 140	99.97 11	98.86 64	99.86 48	99.81 34

UA-Net		99.42 29	99.29 36	99.80 29	99.62 98	99.55 51	99.50 121	99.70 15	98.79 40	99.77 23	99.96 1	97.45 91	99.96 19	98.92 55	99.90 24	99.89 2

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

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

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

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

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

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

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

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

CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 32	99.46 65	99.59 83	99.49 103	97.03 189	99.63 50	99.69 111	97.27 97	99.96 19	97.82 156	99.84 57	99.81 34

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 102	99.47 137	99.93 2	97.66 136	99.71 29	99.86 22	97.73 86	99.96 19	99.47 13	99.82 65	99.79 43

UGNet		98.87 97	98.69 105	99.40 102	99.22 179	98.72 160	99.44 145	99.68 19	99.24 3	99.18 152	99.42 200	92.74 239	99.96 19	99.34 22	99.94 10	99.53 116
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 191	99.71 41	99.66 25	98.11 86	99.41 90	99.80 64	98.37 67	99.96 19	98.99 50	99.96 5	99.72 69

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 32	99.62 74	99.69 18	98.12 84	99.63 50	99.84 35	98.73 46	99.96 19	98.55 103	99.83 61	99.81 34
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CANet		99.25 52	99.14 51	99.59 68	99.41 138	99.16 92	99.35 183	99.57 43	98.82 35	99.51 72	99.61 146	96.46 117	99.95 33	99.59 2	99.98 2	99.65 88

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 193	99.52 75	97.18 174	99.60 56	99.79 72	98.79 35	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 12	99.67 55	99.37 190	98.70 45	99.77 23	99.49 179	98.21 73	99.95 33	98.46 111	99.77 74	99.81 34

mvs-test1		98.86 100	98.84 90	98.89 166	99.33 155	97.77 214	99.44 145	99.30 220	98.47 57	99.10 162	99.43 198	96.78 108	99.95 33	98.73 77	99.02 132	98.96 179

testdata2													99.95 33	96.67 236

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

sss		99.17 58	99.05 58	99.53 79	99.62 98	98.97 116	99.36 179	99.62 31	97.83 117	99.67 40	99.65 127	97.37 95	99.95 33	99.19 33	99.19 120	99.68 81

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

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

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

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	99.74 95	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 255	95.45 276	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	64.01 340	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

旧先验2							98.96 277		96.70 205	99.47 78			99.94 40	98.19 127

新几何1				99.75 38	99.75 47	99.59 46		99.54 61	96.76 201	99.29 117	99.64 134	98.43 61	99.94 40	96.92 225	99.66 95	99.72 69

testdata				99.54 75	99.75 47	98.95 121		99.51 84	97.07 185	99.43 85	99.70 106	98.87 28	99.94 40	97.76 162	99.64 98	99.72 69

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

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

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 47	99.79 16	99.50 121	99.50 98	97.16 176	99.77 23	99.82 44	98.78 36	99.94 40	97.56 181	99.86 48	99.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 65	99.40 72	99.05 253	99.66 25	99.14 6	99.57 63	99.80 64	98.46 59	99.94 40	99.57 4	99.84 57	99.60 102
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 98	99.16 92	99.37 175	99.56 47	98.04 99	99.53 68	99.62 143	96.84 106	99.94 40	98.85 65	98.49 165	99.72 69

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 88	99.62 74	99.55 54	98.94 26	99.63 50	99.95 2	95.82 136	99.94 40	99.37 17	99.97 3	99.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		99.27 49	99.12 53	99.74 43	99.18 186	99.75 21	99.56 100	99.57 43	98.45 59	99.49 76	99.85 26	97.77 85	99.94 40	98.33 121	99.84 57	99.52 117

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 137	99.48 112	98.05 98	99.76 26	99.86 22	98.82 32	99.93 55	98.82 71	99.91 17	99.84 12

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

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

无先验							98.99 268	99.51 84	96.89 196				99.93 55	97.53 184		99.72 69

1121		99.09 75	98.87 84	99.75 38	99.74 57	99.60 44	99.27 203	99.48 112	96.82 200	99.25 130	99.65 127	98.38 65	99.93 55	97.53 184	99.67 94	99.73 63

VDDNet		97.55 228	97.02 243	99.16 127	99.49 124	98.12 199	99.38 173	99.30 220	95.35 266	99.68 34	99.90 7	82.62 324	99.93 55	99.31 25	98.13 184	99.42 139

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

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 34	99.27 84	99.42 157	99.54 61	97.29 165	99.41 90	99.59 151	98.42 64	99.93 55	98.19 127	99.69 90	99.73 63

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

VDD-MVS		97.73 213	97.35 224	98.88 173	99.47 128	97.12 228	99.34 186	98.85 284	98.19 76	99.67 40	99.85 26	82.98 322	99.92 63	99.49 12	98.32 171	99.60 102

VNet		99.11 71	98.90 80	99.73 45	99.52 115	99.56 49	99.41 161	99.39 177	99.01 13	99.74 28	99.78 77	95.56 141	99.92 63	99.52 7	98.18 180	99.72 69

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 166	99.71 68	97.74 215	99.12 236	99.54 61	98.44 62	99.42 88	99.71 103	94.20 206	99.92 63	98.54 105	98.90 144	99.00 173

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

HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 135	99.08 101	99.62 74	99.36 191	97.39 158	99.28 121	99.68 116	96.44 118	99.92 63	98.37 117	98.22 176	99.40 141

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 38	99.53 55	99.41 161	99.50 98	97.03 189	99.04 174	99.88 14	97.39 92	99.92 63	98.66 85	99.90 24	99.87 4

IB-MVS	95.67 18	96.22 269	95.44 277	98.57 205	99.21 180	96.70 252	98.65 303	97.74 326	96.71 204	97.27 275	98.54 291	86.03 312	99.92 63	98.47 110	86.30 323	99.10 158
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 94	99.59 46	99.36 179	99.46 136	99.07 9	99.79 18	99.82 44	98.85 30	99.92 63	98.68 84	99.87 38	99.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					99.67 78	99.65 37	99.05 253	99.41 167	96.22 243	98.95 188	99.49 179	98.77 39	99.91 72

train_agg		99.02 85	98.77 97	99.77 35	99.67 78	99.65 37	99.05 253	99.41 167	96.28 236	98.95 188	99.49 179	98.76 41	99.91 72	97.63 175	99.72 83	99.75 53

test_8					99.67 78	99.61 42	99.03 259	99.41 167	96.28 236	98.93 191	99.48 185	98.76 41	99.91 72

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 78	99.60 44	99.04 258	99.41 167	95.93 258	98.87 198	99.48 185	98.61 53	99.91 72	97.63 175	99.72 83	99.75 53

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 78	99.62 40	98.99 268	99.40 174	96.26 239	98.87 198	99.49 179	98.77 39	99.91 72	97.69 172	99.72 83	99.75 53

agg_prior					99.67 78	99.62 40		99.40 174		98.87 198			99.91 72

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

原ACMM1				99.65 57	99.73 62	99.33 76		99.47 126	97.46 149	99.12 157	99.66 126	98.67 51	99.91 72	97.70 171	99.69 90	99.71 76

LFMVS		97.90 189	97.35 224	99.54 75	99.52 115	99.01 109	99.39 168	98.24 316	97.10 184	99.65 48	99.79 72	84.79 318	99.91 72	99.28 27	98.38 169	99.69 77

XVG-OURS		98.73 117	98.68 106	98.88 173	99.70 73	97.73 216	98.92 284	99.55 54	98.52 55	99.45 81	99.84 35	95.27 149	99.91 72	98.08 138	98.84 148	99.00 173

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 88	99.01 109	99.24 215	99.52 75	96.85 198	99.27 125	99.48 185	98.25 72	99.91 72	97.76 162	99.62 101	99.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 224	97.06 242	99.47 91	99.61 102	99.09 100	98.04 322	99.25 239	91.24 311	98.51 238	99.70 106	94.55 194	99.91 72	92.76 301	99.85 52	99.42 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 33	99.74 24	99.29 197	99.40 174	98.79 40	99.52 70	99.62 143	98.91 26	99.90 84	98.64 87	99.75 77	99.82 30

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 47	99.71 26	99.15 232	99.41 167	96.60 213	99.60 56	99.55 163	98.83 31	99.90 84	97.48 189	99.83 61	99.78 47

NCCC		99.34 40	99.19 47	99.79 32	99.61 102	99.65 37	99.30 193	99.48 112	98.86 31	99.21 144	99.63 138	98.72 47	99.90 84	98.25 125	99.63 100	99.80 39

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 55	99.62 74	99.59 37	92.65 304	99.71 29	99.78 77	98.06 78	99.90 84	98.84 66	99.91 17	99.74 58

1112_ss		98.98 90	98.77 97	99.59 68	99.68 77	99.02 107	99.25 213	99.48 112	97.23 171	99.13 155	99.58 154	96.93 105	99.90 84	98.87 61	98.78 152	99.84 12

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 112	99.52 58	99.58 87	99.80 8	97.12 180	99.62 53	99.73 98	98.58 55	99.90 84	98.61 92	99.91 17	99.68 81

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 112	99.54 52	99.18 227	99.70 15	98.18 79	99.35 106	99.63 138	96.32 121	99.90 84	97.48 189	99.77 74	99.55 110

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 126	99.88 11	98.53 177	99.34 186	99.59 37	97.55 143	98.70 221	99.89 10	95.83 135	99.90 84	98.10 133	99.90 24	99.08 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
view600		97.97 178	97.66 187	98.89 166	99.75 47	97.81 209	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

view800		97.97 178	97.66 187	98.89 166	99.75 47	97.81 209	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

conf0.05thres1000		97.97 178	97.66 187	98.89 166	99.75 47	97.81 209	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

tfpn		97.97 178	97.66 187	98.89 166	99.75 47	97.81 209	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

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

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 75	98.95 121	99.03 259	99.47 126	96.98 191	99.15 154	99.23 246	96.77 110	99.89 92	98.83 68	98.78 152	99.86 5

CNLPA		99.14 61	98.99 68	99.59 68	99.58 107	99.41 70	99.16 229	99.44 155	98.45 59	99.19 150	99.49 179	98.08 77	99.89 92	97.73 166	99.75 77	99.48 126

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 137	99.76 41	98.79 155	99.28 200	99.91 3	97.42 155	99.67 40	99.37 215	97.53 89	99.88 99	98.98 51	97.29 222	98.42 280

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 41	98.79 155	98.78 293	99.91 3	96.74 202	99.67 40	99.49 179	97.53 89	99.88 99	98.98 51	99.85 52	99.60 102

MVS		97.28 244	96.55 251	99.48 88	98.78 265	98.95 121	99.27 203	99.39 177	83.53 324	98.08 258	99.54 166	96.97 103	99.87 101	94.23 289	99.16 121	99.63 98

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 109	98.63 168	99.07 247	99.34 203	98.99 18	99.61 55	99.82 44	97.98 80	99.87 101	97.00 217	99.80 68	99.85 8

MSDG		98.98 90	98.80 94	99.53 79	99.76 41	99.19 89	98.75 296	99.55 54	97.25 168	99.47 78	99.77 82	97.82 83	99.87 101	96.93 224	99.90 24	99.54 112

thres600view7		97.86 192	97.51 202	98.92 156	99.72 65	97.95 205	99.59 83	98.74 296	97.94 109	99.27 125	98.62 287	91.75 263	99.86 104	93.73 293	98.19 179	98.96 179

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 117	98.94 124	99.05 253	99.16 248	97.86 112	99.80 16	99.56 160	97.39 92	99.86 104	98.94 54	99.85 52	99.58 108

PVSNet	96.02 17	98.85 106	98.84 90	98.89 166	99.73 62	97.28 221	98.32 315	99.60 34	97.86 112	99.50 73	99.57 158	96.75 111	99.86 104	98.56 100	99.70 89	99.54 112

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 148	99.66 34	99.45 141	99.54 61	96.61 211	99.01 177	99.40 207	97.09 100	99.86 104	97.68 174	99.53 103	99.10 158
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 185	99.56 100	99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

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

jason		99.13 62	99.03 63	99.45 94	99.46 129	98.87 132	99.12 236	99.26 237	98.03 101	99.79 18	99.65 127	97.02 102	99.85 108	99.02 48	99.90 24	99.65 88
jason: jason.
CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 98	99.71 26	99.26 211	99.52 75	98.82 35	99.39 95	99.71 103	98.96 20	99.85 108	98.59 94	99.80 68	99.77 49

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

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 117	99.28 82	99.52 112	99.47 126	96.11 253	99.01 177	99.34 229	96.20 125	99.84 113	97.88 151	98.82 149	99.39 142

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 78	98.61 173	99.07 247	99.33 211	99.00 17	99.82 14	99.81 53	99.06 8	99.84 113	99.09 42	99.42 106	99.65 88

tpmrst		98.33 138	98.48 125	97.90 264	99.16 193	94.78 291	99.31 191	99.11 253	97.27 166	99.45 81	99.59 151	95.33 146	99.84 113	98.48 108	98.61 155	99.09 162

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 34	99.10 99	99.68 53	99.66 25	98.49 56	99.86 7	99.87 19	94.77 183	99.84 113	99.19 33	99.41 107	99.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 126	98.34 131	99.51 85	99.40 143	99.03 106	98.80 292	99.36 191	96.33 233	99.00 184	99.12 256	98.46 59	99.84 113	95.23 266	99.37 112	99.66 85

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 68	99.28 82	99.06 251	99.77 9	97.74 127	99.50 73	99.53 167	95.41 145	99.84 113	97.17 209	99.64 98	99.44 136

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 90	99.06 103	99.81 15	99.33 211	97.43 153	99.60 56	99.88 14	97.14 99	99.84 113	99.13 39	98.94 139	99.69 77

test_normal		97.44 239	96.77 249	99.44 97	97.75 303	99.00 111	99.10 244	98.64 305	97.71 130	93.93 307	98.82 279	87.39 308	99.83 120	98.61 92	98.97 136	99.49 124

test_prior3		99.21 54	99.05 58	99.68 50	99.67 78	99.48 62	98.96 277	99.56 47	98.34 66	99.01 177	99.52 171	98.68 49	99.83 120	97.96 145	99.74 79	99.74 58

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

1314		98.68 121	98.54 123	99.11 131	98.89 248	98.65 166	99.27 203	99.49 103	96.89 196	97.99 263	99.56 160	97.72 87	99.83 120	97.74 165	99.27 116	98.84 189

thres400		97.77 206	97.38 221	98.92 156	99.69 75	97.96 204	99.50 121	98.73 302	97.83 117	99.17 153	98.45 293	91.67 266	99.83 120	93.22 297	98.18 180	98.96 179

DI_MVS_plusplus_test		97.45 238	96.79 247	99.44 97	97.76 302	99.04 105	99.21 223	98.61 308	97.74 127	94.01 304	98.83 278	87.38 309	99.83 120	98.63 88	98.90 144	99.44 136

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 124	99.14 96	99.67 55	99.34 203	97.31 163	99.58 60	99.76 85	97.65 88	99.82 126	98.87 61	99.07 129	99.46 133

dp		97.75 210	97.80 169	97.59 278	99.10 204	93.71 303	99.32 188	98.88 282	96.48 224	99.08 167	99.55 163	92.67 244	99.82 126	96.52 241	98.58 158	99.24 152

RPSCF		98.22 143	98.62 115	96.99 287	99.82 29	91.58 314	99.72 39	99.44 155	96.61 211	99.66 45	99.89 10	95.92 132	99.82 126	97.46 192	99.10 126	99.57 109

PMMVS		98.80 112	98.62 115	99.34 105	99.27 172	98.70 161	98.76 295	99.31 218	97.34 160	99.21 144	99.07 258	97.20 98	99.82 126	98.56 100	98.87 146	99.52 117

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 129	99.12 98	98.08 321	99.50 98	97.50 148	99.38 97	99.41 203	96.37 120	99.81 130	99.11 41	98.54 162	99.51 120

thres200		97.61 226	97.28 234	98.62 201	99.64 91	98.03 200	99.26 211	98.74 296	97.68 134	99.09 166	98.32 294	91.66 267	99.81 130	92.88 300	98.22 176	98.03 293

tpmvs		97.98 175	98.02 152	97.84 268	99.04 214	94.73 293	99.31 191	99.20 244	96.10 256	98.76 211	99.42 200	94.94 166	99.81 130	96.97 220	98.45 166	98.97 177

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 286	99.60 104	91.75 313	98.61 304	99.44 155	99.35 1	99.83 11	99.85 26	98.70 48	99.81 130	99.02 48	99.91 17	99.81 34

PatchFormer-LS_test		98.01 173	98.05 150	97.87 265	99.15 196	94.76 292	99.42 157	98.93 273	97.12 180	98.84 204	98.59 289	93.74 225	99.80 134	98.55 103	98.17 182	99.06 168

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 57	99.70 28	99.27 203	99.57 43	96.40 231	99.42 88	99.68 116	98.75 44	99.80 134	97.98 144	99.72 83	99.44 136

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 38	99.51 60	98.94 283	99.85 6	98.82 35	99.65 48	99.74 95	98.51 56	99.80 134	98.83 68	99.89 32	99.64 94

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 202	99.41 138	96.99 240	99.52 112	99.49 103	98.11 86	99.24 135	99.34 229	96.96 104	99.79 137	97.95 147	99.45 104	99.02 172

PVSNet_0	94.43 19	96.09 273	95.47 275	97.94 260	99.31 163	94.34 297	97.81 323	99.70 15	97.12 180	97.46 272	98.75 284	89.71 285	99.79 137	97.69 172	81.69 327	99.68 81

API-MVS		99.04 82	99.03 63	99.06 134	99.40 143	99.31 80	99.55 105	99.56 47	98.54 53	99.33 110	99.39 211	98.76 41	99.78 139	96.98 219	99.78 72	98.07 289

OMC-MVS		99.08 77	99.04 61	99.20 125	99.67 78	98.22 194	99.28 200	99.52 75	98.07 93	99.66 45	99.81 53	97.79 84	99.78 139	97.79 158	99.81 66	99.60 102

alignmvs		98.81 109	98.56 122	99.58 71	99.43 134	99.42 69	99.51 116	98.96 271	98.61 50	99.35 106	98.92 271	94.78 179	99.77 141	99.35 18	98.11 185	99.54 112

tpm cat1		97.39 241	97.36 222	97.50 281	99.17 191	93.73 301	99.43 150	99.31 218	91.27 310	98.71 215	99.08 257	94.31 204	99.77 141	96.41 245	98.50 164	99.00 173

CostFormer		97.72 215	97.73 183	97.71 276	99.15 196	94.02 299	99.54 108	99.02 265	94.67 274	99.04 174	99.35 226	92.35 255	99.77 141	98.50 107	97.94 190	99.34 146

MDTV_nov1_ep13			98.32 133		99.11 201	94.44 295	99.27 203	98.74 296	97.51 147	99.40 94	99.62 143	94.78 179	99.76 144	97.59 177	98.81 151

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 135	99.32 77	99.80 19	99.48 112	98.63 48	99.31 112	98.81 280	97.09 100	99.75 145	99.27 29	97.90 191	99.47 130

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 216	99.33 155	96.91 246	99.57 93	99.30 220	98.47 57	99.41 90	98.99 265	96.78 108	99.74 146	98.73 77	99.38 108	98.74 202

patchmatchnet-post											98.70 285	94.79 178	99.74 146

diffmvs		98.72 118	98.49 124	99.43 100	99.48 127	99.19 89	99.62 74	99.42 164	95.58 264	99.37 99	99.67 120	96.14 126	99.74 146	98.14 131	98.96 137	99.37 143

DWT-MVSNet_test		97.53 230	97.40 219	97.93 261	99.03 216	94.86 290	99.57 93	98.63 306	96.59 215	98.36 247	98.79 281	89.32 288	99.74 146	98.14 131	98.16 183	99.20 154

tpmp4_e23		97.34 242	97.29 233	97.52 279	99.25 176	93.73 301	99.58 87	99.19 247	94.00 290	98.20 253	99.41 203	90.74 276	99.74 146	97.13 210	98.07 186	99.07 167

BH-untuned		98.42 133	98.36 129	98.59 203	99.49 124	96.70 252	99.27 203	99.13 252	97.24 170	98.80 207	99.38 212	95.75 138	99.74 146	97.07 214	99.16 121	99.33 147

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 138	98.83 139	99.30 193	98.77 292	97.70 132	98.94 190	99.65 127	92.91 235	99.74 146	96.52 241	99.55 102	99.64 94

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 65	99.47 64	98.95 281	99.85 6	98.82 35	99.54 67	99.73 98	98.51 56	99.74 146	98.91 56	99.88 34	99.77 49

test_post											65.99 338	94.65 191	99.73 154

XVG-ACMP-BASELINE		97.83 196	97.71 185	98.20 246	99.11 201	96.33 264	99.41 161	99.52 75	98.06 97	99.05 173	99.50 176	89.64 286	99.73 154	97.73 166	97.38 219	98.53 273

HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 73	99.02 262	99.91 3	97.67 135	99.59 59	99.75 90	95.90 133	99.73 154	99.53 6	99.02 132	99.86 5

DeepMVS_CX				93.34 304	99.29 167	82.27 328		99.22 242	85.15 322	96.33 287	99.05 261	90.97 274	99.73 154	93.57 294	97.77 194	98.01 294

Patchmatch-test		97.93 184	97.65 192	98.77 193	99.18 186	97.07 233	99.03 259	99.14 251	96.16 248	98.74 212	99.57 158	94.56 193	99.72 158	93.36 296	99.11 124	99.52 117

LPG-MVS_test		98.22 143	98.13 142	98.49 212	99.33 155	97.05 235	99.58 87	99.55 54	97.46 149	99.24 135	99.83 37	92.58 246	99.72 158	98.09 134	97.51 206	98.68 220

LGP-MVS_train				98.49 212	99.33 155	97.05 235		99.55 54	97.46 149	99.24 135	99.83 37	92.58 246	99.72 158	98.09 134	97.51 206	98.68 220

BH-w/o		98.00 174	97.89 164	98.32 229	99.35 151	96.20 268	99.01 266	98.90 280	96.42 228	98.38 245	99.00 264	95.26 151	99.72 158	96.06 249	98.61 155	99.03 170

ACMP	97.20 11	98.06 159	97.94 158	98.45 218	99.37 148	97.01 238	99.44 145	99.49 103	97.54 146	98.45 242	99.79 72	91.95 257	99.72 158	97.91 149	97.49 211	98.62 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 170	97.90 160	98.40 224	99.23 177	96.80 250	99.70 42	99.60 34	97.12 180	98.18 254	99.70 106	91.73 264	99.72 158	98.39 114	97.45 213	98.68 220
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_post1							99.23 216				65.14 339	94.18 209	99.71 164	97.58 178

ADS-MVSNet		98.20 146	98.08 147	98.56 207	99.33 155	96.48 259	99.23 216	99.15 249	96.24 241	99.10 162	99.67 120	94.11 211	99.71 164	96.81 228	99.05 130	99.48 126

JIA-IIPM		97.50 235	97.02 243	98.93 151	98.73 271	97.80 213	99.30 193	98.97 269	91.73 309	98.91 193	94.86 325	95.10 158	99.71 164	97.58 178	97.98 189	99.28 150

EPMVS		97.82 199	97.65 192	98.35 227	98.88 249	95.98 270	99.49 128	94.71 335	97.57 141	99.26 129	99.48 185	92.46 253	99.71 164	97.87 152	99.08 128	99.35 145

TDRefinement		95.42 280	94.57 285	97.97 259	89.83 330	96.11 269	99.48 133	98.75 293	96.74 202	96.68 284	99.88 14	88.65 297	99.71 164	98.37 117	82.74 326	98.09 288

ACMM	97.58 5	98.37 137	98.34 131	98.48 214	99.41 138	97.10 229	99.56 100	99.45 147	98.53 54	99.04 174	99.85 26	93.00 231	99.71 164	98.74 75	97.45 213	98.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		99.12 67	99.13 52	99.08 132	99.66 88	97.89 206	98.43 311	99.71 13	98.88 30	99.62 53	99.76 85	96.63 114	99.70 170	99.46 14	99.99 1	99.66 85

PatchmatchNet		98.31 139	98.36 129	98.19 247	99.16 193	95.32 283	99.27 203	98.92 275	97.37 159	99.37 99	99.58 154	94.90 171	99.70 170	97.43 195	99.21 118	99.54 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 156	97.99 154	98.44 221	99.41 138	96.96 244	99.60 81	99.56 47	98.09 89	98.15 255	99.91 5	90.87 275	99.70 170	98.88 57	97.45 213	98.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP_MVS		98.27 142	98.22 139	98.44 221	99.29 167	96.97 242	99.39 168	99.47 126	98.97 22	99.11 159	99.61 146	92.71 241	99.69 173	97.78 159	97.63 196	98.67 231

plane_prior5								99.47 126					99.69 173	97.78 159	97.63 196	98.67 231

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 62	99.32 77	99.75 34	99.20 244	98.02 102	99.56 64	99.86 22	96.54 116	99.67 175	98.09 134	99.13 123	99.73 63

CLD-MVS		98.16 149	98.10 144	98.33 228	99.29 167	96.82 249	98.75 296	99.44 155	97.83 117	99.13 155	99.55 163	92.92 233	99.67 175	98.32 123	97.69 195	98.48 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		98.19 147	98.10 144	98.45 218	98.88 249	97.07 233	99.28 200	99.38 183	98.57 52	99.22 142	99.81 53	92.12 256	99.66 177	98.08 138	97.54 205	98.61 264

ACMH+	97.24 10	97.92 187	97.78 172	98.32 229	99.46 129	96.68 254	99.56 100	99.54 61	98.41 63	97.79 270	99.87 19	90.18 282	99.66 177	98.05 142	97.18 226	98.62 255

VPA-MVSNet		98.29 140	97.95 157	99.30 113	99.16 193	99.54 52	99.50 121	99.58 42	98.27 71	99.35 106	99.37 215	92.53 248	99.65 179	99.35 18	94.46 276	98.72 204

TR-MVS		97.76 207	97.41 218	98.82 187	99.06 210	97.87 207	98.87 289	98.56 310	96.63 210	98.68 223	99.22 247	92.49 249	99.65 179	95.40 263	97.79 193	98.95 186

gm-plane-assit					98.54 289	92.96 308			94.65 275		99.15 251		99.64 181	97.56 181

HQP4-MVS										98.66 224			99.64 181			98.64 247

HQP-MVS		98.02 170	97.90 160	98.37 226	99.19 183	96.83 247	98.98 272	99.39 177	98.24 72	98.66 224	99.40 207	92.47 250	99.64 181	97.19 206	97.58 201	98.64 247

PAPM		97.59 227	97.09 241	99.07 133	99.06 210	98.26 193	98.30 316	99.10 254	94.88 270	98.08 258	99.34 229	96.27 123	99.64 181	89.87 309	98.92 142	99.31 148

TAPA-MVS	97.07 15	97.74 212	97.34 227	98.94 148	99.70 73	97.53 218	99.25 213	99.51 84	91.90 308	99.30 113	99.63 138	98.78 36	99.64 181	88.09 315	99.87 38	99.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 136	98.09 146	99.24 121	99.26 174	99.32 77	99.56 100	99.55 54	97.45 152	98.71 215	99.83 37	93.23 228	99.63 186	98.88 57	96.32 240	98.76 198

ITE_SJBPF				98.08 251	99.29 167	96.37 262		98.92 275	98.34 66	98.83 205	99.75 90	91.09 272	99.62 187	95.82 253	97.40 217	98.25 286

LF4IMVS		97.52 231	97.46 209	97.70 277	98.98 224	95.55 276	99.29 197	98.82 287	98.07 93	98.66 224	99.64 134	89.97 283	99.61 188	97.01 216	96.68 230	97.94 297

Patchmatch-test1		98.16 149	98.14 141	98.22 244	99.30 164	95.55 276	99.07 247	98.97 269	97.57 141	99.43 85	99.60 149	92.72 240	99.60 189	97.38 197	99.20 119	99.50 123

tpm		97.67 223	97.55 198	98.03 253	99.02 217	95.01 289	99.43 150	98.54 311	96.44 226	99.12 157	99.34 229	91.83 262	99.60 189	97.75 164	96.46 236	99.48 126

tpm2		97.44 239	97.34 227	97.74 275	99.15 196	94.36 296	99.45 141	98.94 272	93.45 299	98.90 195	99.44 197	91.35 270	99.59 191	97.31 200	98.07 186	99.29 149

MS-PatchMatch		97.24 246	97.32 230	96.99 287	98.45 292	93.51 306	98.82 291	99.32 217	97.41 156	98.13 256	99.30 237	88.99 291	99.56 192	95.68 257	99.80 68	97.90 300

TinyColmap		97.12 248	96.89 245	97.83 269	99.07 208	95.52 279	98.57 306	98.74 296	97.58 140	97.81 269	99.79 72	88.16 304	99.56 192	95.10 267	97.21 224	98.39 283

USDC		97.34 242	97.20 238	97.75 274	99.07 208	95.20 285	98.51 309	99.04 263	97.99 107	98.31 250	99.86 22	89.02 290	99.55 194	95.67 258	97.36 220	98.49 275

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 104	99.16 92	99.41 161	99.71 13	98.98 19	99.45 81	99.78 77	99.19 4	99.54 195	99.28 27	99.84 57	99.63 98

TAMVS		99.12 67	99.08 56	99.24 121	99.46 129	98.55 175	99.51 116	99.46 136	98.09 89	99.45 81	99.82 44	98.34 68	99.51 196	98.70 80	98.93 140	99.67 84

EPNet_dtu		98.03 168	97.96 156	98.23 242	98.27 295	95.54 278	99.23 216	98.75 293	99.02 10	97.82 268	99.71 103	96.11 127	99.48 197	93.04 298	99.65 97	99.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		95.97 274	95.69 268	96.81 292	97.78 301	92.79 309	99.16 229	98.93 273	96.16 248	94.08 301	99.22 247	82.72 323	99.47 198	95.67 258	97.50 208	98.17 287

MVP-Stereo		97.81 200	97.75 182	97.99 258	97.53 304	96.60 256	98.96 277	98.85 284	97.22 172	97.23 276	99.36 222	95.28 148	99.46 199	95.51 260	99.78 72	97.92 299
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 127	98.67 107	98.30 231	99.35 151	95.59 275	99.50 121	99.55 54	98.60 51	99.39 95	99.83 37	94.48 197	99.45 200	98.75 74	98.56 161	99.85 8

test-LLR		98.06 159	97.90 160	98.55 209	98.79 261	97.10 229	98.67 300	97.75 324	97.34 160	98.61 235	98.85 276	94.45 198	99.45 200	97.25 202	99.38 108	99.10 158

TESTMET0.1,1		97.55 228	97.27 236	98.40 224	98.93 240	96.53 257	98.67 300	97.61 328	96.96 192	98.64 231	99.28 240	88.63 298	99.45 200	97.30 201	99.38 108	99.21 153

test-mter		97.49 237	97.13 240	98.55 209	98.79 261	97.10 229	98.67 300	97.75 324	96.65 208	98.61 235	98.85 276	88.23 303	99.45 200	97.25 202	99.38 108	99.10 158

mvs_anonymous		99.03 84	98.99 68	99.16 127	99.38 146	98.52 180	99.51 116	99.38 183	97.79 121	99.38 97	99.81 53	97.30 96	99.45 200	99.35 18	98.99 134	99.51 120

v7n		97.87 191	97.52 200	98.92 156	98.76 269	98.58 174	99.84 9	99.46 136	96.20 244	98.91 193	99.70 106	94.89 172	99.44 205	96.03 250	93.89 288	98.75 199

jajsoiax		98.43 132	98.28 136	98.88 173	98.60 285	98.43 187	99.82 13	99.53 71	98.19 76	98.63 232	99.80 64	93.22 229	99.44 205	99.22 31	97.50 208	98.77 196

mvs_tets		98.40 135	98.23 138	98.91 160	98.67 280	98.51 182	99.66 58	99.53 71	98.19 76	98.65 230	99.81 53	92.75 237	99.44 205	99.31 25	97.48 212	98.77 196

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 68	98.88 131	99.80 19	99.44 155	97.91 110	99.36 103	99.78 77	95.49 144	99.43 208	97.91 149	99.11 124	99.62 100

VPNet		97.84 195	97.44 213	99.01 139	99.21 180	98.94 124	99.48 133	99.57 43	98.38 64	99.28 121	99.73 98	88.89 292	99.39 209	99.19 33	93.27 293	98.71 206

nrg030		98.64 125	98.42 127	99.28 118	99.05 213	99.69 29	99.81 15	99.46 136	98.04 99	99.01 177	99.82 44	96.69 113	99.38 210	99.34 22	94.59 275	98.78 193

GA-MVS		97.85 193	97.47 208	99.00 141	99.38 146	97.99 202	98.57 306	99.15 249	97.04 188	98.90 195	99.30 237	89.83 284	99.38 210	96.70 234	98.33 170	99.62 100

UniMVSNet (Re)		98.29 140	98.00 153	99.13 130	99.00 219	99.36 74	99.49 128	99.51 84	97.95 108	98.97 187	99.13 253	96.30 122	99.38 210	98.36 119	93.34 292	98.66 242

FIs		98.78 113	98.63 112	99.23 123	99.18 186	99.54 52	99.83 12	99.59 37	98.28 70	98.79 208	99.81 53	96.75 111	99.37 213	99.08 43	96.38 238	98.78 193

PS-MVSNAJss		98.92 95	98.92 77	98.90 164	98.78 265	98.53 177	99.78 22	99.54 61	98.07 93	99.00 184	99.76 85	99.01 11	99.37 213	99.13 39	97.23 223	98.81 190

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 135	98.73 159	99.45 141	99.46 136	98.11 86	99.46 80	99.77 82	98.01 79	99.37 213	98.70 80	98.92 142	99.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 276	95.16 280	97.51 280	99.30 164	93.69 304	98.88 288	95.78 332	85.09 323	98.78 209	92.65 327	91.29 271	99.37 213	94.85 271	99.85 52	99.46 133

v1192		97.81 200	97.44 213	98.91 160	98.88 249	98.68 162	99.51 116	99.34 203	96.18 246	99.20 147	99.34 229	94.03 214	99.36 217	95.32 265	95.18 257	98.69 215

EI-MVSNet		98.67 122	98.67 107	98.68 197	99.35 151	97.97 203	99.50 121	99.38 183	96.93 195	99.20 147	99.83 37	97.87 81	99.36 217	98.38 116	97.56 203	98.71 206

MVSTER		98.49 128	98.32 133	99.00 141	99.35 151	99.02 107	99.54 108	99.38 183	97.41 156	99.20 147	99.73 98	93.86 220	99.36 217	98.87 61	97.56 203	98.62 255

gg-mvs-nofinetune		96.17 271	95.32 278	98.73 195	98.79 261	98.14 197	99.38 173	94.09 336	91.07 313	98.07 261	91.04 331	89.62 287	99.35 220	96.75 231	99.09 127	98.68 220

pm-mvs1		97.68 220	97.28 234	98.88 173	99.06 210	98.62 170	99.50 121	99.45 147	96.32 234	97.87 266	99.79 72	92.47 250	99.35 220	97.54 183	93.54 291	98.67 231

OurMVSNet-221017-0		97.88 190	97.77 176	98.19 247	98.71 275	96.53 257	99.88 1	99.00 266	97.79 121	98.78 209	99.94 3	91.68 265	99.35 220	97.21 204	96.99 229	98.69 215

v6		98.12 153	97.84 166	98.94 148	98.94 235	98.83 139	99.66 58	99.34 203	96.49 218	99.30 113	99.37 215	94.95 165	99.34 223	97.77 161	94.74 266	98.67 231

pmmvs6		96.53 256	96.09 257	97.82 270	98.69 277	95.47 280	99.37 175	99.47 126	93.46 298	97.41 273	99.78 77	87.06 310	99.33 224	96.92 225	92.70 300	98.65 245

v52		97.79 204	97.50 204	98.66 200	98.80 259	98.62 170	99.87 4	99.44 155	95.87 259	99.01 177	99.46 193	94.44 200	99.33 224	96.65 239	93.96 287	98.05 290

V4		97.80 202	97.51 202	98.67 199	98.79 261	98.63 168	99.87 4	99.44 155	95.87 259	99.01 177	99.46 193	94.52 196	99.33 224	96.64 240	93.97 286	98.05 290

v1neww		98.12 153	97.84 166	98.93 151	98.97 227	98.81 148	99.66 58	99.35 195	96.49 218	99.29 117	99.37 215	95.02 161	99.32 227	97.73 166	94.73 267	98.67 231

v7new		98.12 153	97.84 166	98.93 151	98.97 227	98.81 148	99.66 58	99.35 195	96.49 218	99.29 117	99.37 215	95.02 161	99.32 227	97.73 166	94.73 267	98.67 231

v1		98.05 165	97.76 179	98.93 151	98.92 242	98.80 153	99.57 93	99.35 195	96.39 232	99.28 121	99.36 222	94.86 174	99.32 227	97.38 197	94.72 269	98.68 220

V42		98.06 159	97.79 170	98.86 181	98.98 224	98.84 136	99.69 44	99.34 203	96.53 217	99.30 113	99.37 215	94.67 189	99.32 227	97.57 180	94.66 272	98.42 280

lessismore_v0				97.79 272	98.69 277	95.44 282		94.75 334		95.71 293	99.87 19	88.69 295	99.32 227	95.89 252	94.93 265	98.62 255

OpenMVS_ROB	92.34 20	94.38 289	93.70 290	96.41 297	97.38 306	93.17 307	99.06 251	98.75 293	86.58 321	94.84 297	98.26 296	81.53 325	99.32 227	89.01 312	97.87 192	96.76 316

v748		97.52 231	97.23 237	98.41 223	98.69 277	97.23 226	99.87 4	99.45 147	95.72 261	98.51 238	99.53 167	94.13 210	99.30 233	96.78 230	92.39 302	98.70 210

v8		97.95 183	97.63 194	98.93 151	98.95 232	98.81 148	99.80 19	99.41 167	96.03 257	99.10 162	99.42 200	94.92 169	99.30 233	96.94 223	94.08 284	98.66 242

v1921920		97.80 202	97.45 210	98.84 185	98.80 259	98.53 177	99.52 112	99.34 203	96.15 250	99.24 135	99.47 189	93.98 215	99.29 235	95.40 263	95.13 260	98.69 215

anonymousdsp		98.44 131	98.28 136	98.94 148	98.50 290	98.96 120	99.77 24	99.50 98	97.07 185	98.87 198	99.77 82	94.76 184	99.28 236	98.66 85	97.60 199	98.57 271

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 117	98.94 124	99.88 1	99.46 136	97.55 143	99.80 16	99.65 127	97.39 92	99.28 236	99.03 46	99.85 52	99.65 88

test_djsdf		98.67 122	98.57 121	98.98 143	98.70 276	98.91 129	99.88 1	99.46 136	97.55 143	99.22 142	99.88 14	95.73 139	99.28 236	99.03 46	97.62 198	98.75 199

v1141		98.05 165	97.76 179	98.91 160	98.91 244	98.78 157	99.57 93	99.35 195	96.41 230	99.23 140	99.36 222	94.93 168	99.27 239	97.38 197	94.72 269	98.68 220

testing_2		94.44 288	92.93 294	98.98 143	94.16 321	99.00 111	99.42 157	99.28 231	96.60 213	84.86 324	96.84 319	70.91 327	99.27 239	98.23 126	96.08 244	98.68 220

divwei89l23v2f112		98.06 159	97.78 172	98.91 160	98.90 245	98.77 158	99.57 93	99.35 195	96.45 225	99.24 135	99.37 215	94.92 169	99.27 239	97.50 187	94.71 271	98.68 220

v7		98.05 165	97.78 172	98.87 177	98.99 220	98.67 163	99.64 70	99.34 203	96.31 235	99.29 117	99.51 174	94.78 179	99.27 239	97.03 215	95.15 259	98.66 242

cascas		97.69 218	97.43 216	98.48 214	98.60 285	97.30 220	98.18 320	99.39 177	92.96 301	98.41 243	98.78 283	93.77 222	99.27 239	98.16 130	98.61 155	98.86 188

v144192		97.92 187	97.60 196	98.87 177	98.83 258	98.65 166	99.55 105	99.34 203	96.20 244	99.32 111	99.40 207	94.36 201	99.26 244	96.37 246	95.03 262	98.70 210

v2v482		98.06 159	97.77 176	98.92 156	98.90 245	98.82 146	99.57 93	99.36 191	96.65 208	99.19 150	99.35 226	94.20 206	99.25 245	97.72 170	94.97 263	98.69 215

Test4		95.05 283	93.67 291	99.22 124	96.07 313	98.94 124	99.20 225	99.27 236	97.71 130	89.96 322	97.59 313	66.18 330	99.25 245	98.06 141	98.96 137	99.47 130

v1240		97.69 218	97.32 230	98.79 191	98.85 256	98.43 187	99.48 133	99.36 191	96.11 253	99.27 125	99.36 222	93.76 223	99.24 247	94.46 278	95.23 256	98.70 210

v1144		97.98 175	97.69 186	98.85 184	98.87 252	98.66 165	99.54 108	99.35 195	96.27 238	99.23 140	99.35 226	94.67 189	99.23 248	96.73 232	95.16 258	98.68 220

v10		97.85 193	97.52 200	98.86 181	98.99 220	98.67 163	99.75 34	99.41 167	95.70 262	98.98 186	99.41 203	94.75 185	99.23 248	96.01 251	94.63 274	98.67 231

WR-MVS_H		98.13 151	97.87 165	98.90 164	99.02 217	98.84 136	99.70 42	99.59 37	97.27 166	98.40 244	99.19 249	95.53 142	99.23 248	98.34 120	93.78 289	98.61 264

GG-mvs-BLEND				98.45 218	98.55 288	98.16 196	99.43 150	93.68 337		97.23 276	98.46 292	89.30 289	99.22 251	95.43 262	98.22 176	97.98 295

FC-MVSNet-test		98.75 116	98.62 115	99.15 129	99.08 207	99.45 66	99.86 8	99.60 34	98.23 75	98.70 221	99.82 44	96.80 107	99.22 251	99.07 44	96.38 238	98.79 192

UniMVSNet_NR-MVSNet		98.22 143	97.97 155	98.96 145	98.92 242	98.98 113	99.48 133	99.53 71	97.76 124	98.71 215	99.46 193	96.43 119	99.22 251	98.57 97	92.87 298	98.69 215

DU-MVS		98.08 158	97.79 170	98.96 145	98.87 252	98.98 113	99.41 161	99.45 147	97.87 111	98.71 215	99.50 176	94.82 176	99.22 251	98.57 97	92.87 298	98.68 220

WR-MVS		98.06 159	97.73 183	99.06 134	98.86 255	99.25 86	99.19 226	99.35 195	97.30 164	98.66 224	99.43 198	93.94 216	99.21 255	98.58 95	94.28 279	98.71 206

test_0402		96.64 253	96.24 254	97.85 267	98.85 256	96.43 261	99.44 145	99.26 237	93.52 296	96.98 282	99.52 171	88.52 299	99.20 256	92.58 303	97.50 208	97.93 298

SixPastTwentyTwo		97.50 235	97.33 229	98.03 253	98.65 281	96.23 267	99.77 24	98.68 304	97.14 177	97.90 265	99.93 4	90.45 277	99.18 257	97.00 217	96.43 237	98.67 231

semantic-postprocess				98.06 252	99.57 109	96.36 263		99.49 103	97.18 174	98.71 215	99.72 102	92.70 243	99.14 258	97.44 194	95.86 247	98.67 231

pmmvs5		97.52 231	97.30 232	98.16 249	98.57 287	96.73 251	99.27 203	98.90 280	96.14 251	98.37 246	99.53 167	91.54 269	99.14 258	97.51 186	95.87 246	98.63 253

v148		97.79 204	97.55 198	98.50 211	98.74 270	97.72 217	99.54 108	99.33 211	96.26 239	98.90 195	99.51 174	94.68 188	99.14 258	97.83 155	93.15 295	98.63 253

NR-MVSNet		97.97 178	97.61 195	99.02 138	98.87 252	99.26 85	99.47 137	99.42 164	97.63 137	97.08 279	99.50 176	95.07 159	99.13 261	97.86 153	93.59 290	98.68 220

IterMVS		97.83 196	97.77 176	98.02 255	99.58 107	96.27 266	99.02 262	99.48 112	97.22 172	98.71 215	99.70 106	92.75 237	99.13 261	97.46 192	96.00 245	98.67 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 290	94.90 282	91.84 310	97.24 310	80.01 330	98.52 308	99.48 112	89.01 318	91.99 316	99.67 120	85.67 314	99.13 261	95.44 261	97.03 228	96.39 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		98.13 151	97.90 160	98.81 188	98.61 284	98.87 132	98.99 268	99.21 243	96.44 226	99.06 172	99.58 154	95.90 133	99.11 264	97.18 208	96.11 243	98.46 279

TransMVSNet (Re)		97.15 247	96.58 250	98.86 181	99.12 199	98.85 135	99.49 128	98.91 278	95.48 265	97.16 278	99.80 64	93.38 227	99.11 264	94.16 291	91.73 303	98.62 255

ambc				93.06 305	92.68 325	82.36 327	98.47 310	98.73 302		95.09 295	97.41 315	55.55 335	99.10 266	96.42 244	91.32 304	97.71 311

Baseline_NR-MVSNet		97.76 207	97.45 210	98.68 197	99.09 206	98.29 191	99.41 161	98.85 284	95.65 263	98.63 232	99.67 120	94.82 176	99.10 266	98.07 140	92.89 297	98.64 247

CP-MVSNet		98.09 157	97.78 172	99.01 139	98.97 227	99.24 87	99.67 55	99.46 136	97.25 168	98.48 241	99.64 134	93.79 221	99.06 268	98.63 88	94.10 283	98.74 202

PS-CasMVS		97.93 184	97.59 197	98.95 147	98.99 220	99.06 103	99.68 53	99.52 75	97.13 178	98.31 250	99.68 116	92.44 254	99.05 269	98.51 106	94.08 284	98.75 199

K. test v3		97.10 249	96.79 247	98.01 256	98.72 273	96.33 264	99.87 4	97.05 330	97.59 138	96.16 289	99.80 64	88.71 294	99.04 270	96.69 235	96.55 235	98.65 245

new_pmnet		96.38 262	96.03 258	97.41 282	98.13 298	95.16 288	99.05 253	99.20 244	93.94 291	97.39 274	98.79 281	91.61 268	99.04 270	90.43 308	95.77 248	98.05 290

IterMVS-LS		98.46 130	98.42 127	98.58 204	99.59 106	98.00 201	99.37 175	99.43 163	96.94 194	99.07 168	99.59 151	97.87 81	99.03 272	98.32 123	95.62 251	98.71 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		97.75 210	97.40 219	98.81 188	99.10 204	98.87 132	99.11 242	99.33 211	94.83 271	98.81 206	99.38 212	94.33 202	99.02 273	96.10 248	95.57 252	98.53 273

N_pmnet		94.95 285	95.83 263	92.31 309	98.47 291	79.33 331	99.12 236	92.81 341	93.87 292	97.68 271	99.13 253	93.87 219	99.01 274	91.38 305	96.19 242	98.59 267

CR-MVSNet		98.17 148	97.93 159	98.87 177	99.18 186	98.49 183	99.22 220	99.33 211	96.96 192	99.56 64	99.38 212	94.33 202	99.00 275	94.83 272	98.58 158	99.14 155

RPMNet		96.61 254	95.85 262	98.87 177	99.18 186	98.49 183	99.22 220	99.08 256	88.72 320	99.56 64	97.38 316	94.08 213	99.00 275	86.87 320	98.58 158	99.14 155

test0.0.03 1		97.71 217	97.42 217	98.56 207	98.41 293	97.82 208	98.78 293	98.63 306	97.34 160	98.05 262	98.98 268	94.45 198	98.98 277	95.04 269	97.15 227	98.89 187

PatchT		97.03 251	96.44 252	98.79 191	98.99 220	98.34 190	99.16 229	99.07 259	92.13 305	99.52 70	97.31 318	94.54 195	98.98 277	88.54 313	98.73 154	99.03 170

GBi-Net		97.68 220	97.48 206	98.29 232	99.51 117	97.26 223	99.43 150	99.48 112	96.49 218	99.07 168	99.32 234	90.26 279	98.98 277	97.10 211	96.65 231	98.62 255

test1		97.68 220	97.48 206	98.29 232	99.51 117	97.26 223	99.43 150	99.48 112	96.49 218	99.07 168	99.32 234	90.26 279	98.98 277	97.10 211	96.65 231	98.62 255

FMVSNet3		98.03 168	97.76 179	98.84 185	99.39 145	98.98 113	99.40 167	99.38 183	96.67 207	99.07 168	99.28 240	92.93 232	98.98 277	97.10 211	96.65 231	98.56 272

FMVSNet2		97.72 215	97.36 222	98.80 190	99.51 117	98.84 136	99.45 141	99.42 164	96.49 218	98.86 203	99.29 239	90.26 279	98.98 277	96.44 243	96.56 234	98.58 270

FMVSNet1		96.84 252	96.36 253	98.29 232	99.32 162	97.26 223	99.43 150	99.48 112	95.11 268	98.55 237	99.32 234	83.95 321	98.98 277	95.81 254	96.26 241	98.62 255

TranMVSNet+NR-MVSNet		97.93 184	97.66 187	98.76 194	98.78 265	98.62 170	99.65 68	99.49 103	97.76 124	98.49 240	99.60 149	94.23 205	98.97 284	98.00 143	92.90 296	98.70 210

ADS-MVSNet2		98.02 170	98.07 149	97.87 265	99.33 155	95.19 286	99.23 216	99.08 256	96.24 241	99.10 162	99.67 120	94.11 211	98.93 285	96.81 228	99.05 130	99.48 126

PEN-MVS		97.76 207	97.44 213	98.72 196	98.77 268	98.54 176	99.78 22	99.51 84	97.06 187	98.29 252	99.64 134	92.63 245	98.89 286	98.09 134	93.16 294	98.72 204

LP		97.04 250	96.80 246	97.77 273	98.90 245	95.23 284	98.97 275	99.06 261	94.02 289	98.09 257	99.41 203	93.88 218	98.82 287	90.46 307	98.42 168	99.26 151

testgi		97.65 225	97.50 204	98.13 250	99.36 150	96.45 260	99.42 157	99.48 112	97.76 124	97.87 266	99.45 196	91.09 272	98.81 288	94.53 276	98.52 163	99.13 157

MIMVSNet		97.73 213	97.45 210	98.57 205	99.45 133	97.50 219	99.02 262	98.98 268	96.11 253	99.41 90	99.14 252	90.28 278	98.74 289	95.74 255	98.93 140	99.47 130

LCM-MVSNet-Re		97.83 196	98.15 140	96.87 291	99.30 164	92.25 312	99.59 83	98.26 315	97.43 153	96.20 288	99.13 253	96.27 123	98.73 290	98.17 129	98.99 134	99.64 94

testpf		95.66 277	96.02 260	94.58 301	98.35 294	92.32 311	97.25 328	97.91 323	92.83 302	97.03 281	98.99 265	88.69 295	98.61 291	95.72 256	97.40 217	92.80 325

DTE-MVSNet		97.51 234	97.19 239	98.46 217	98.63 283	98.13 198	99.84 9	99.48 112	96.68 206	97.97 264	99.67 120	92.92 233	98.56 292	96.88 227	92.60 301	98.70 210

UnsupCasMVSNet_bld		93.53 293	92.51 295	96.58 296	97.38 306	93.82 300	98.24 317	99.48 112	91.10 312	93.10 312	96.66 320	74.89 326	98.37 293	94.03 292	87.71 316	97.56 314

MDA-MVSNet_test_wron		95.45 279	94.60 284	98.01 256	98.16 297	97.21 227	99.11 242	99.24 240	93.49 297	80.73 329	98.98 268	93.02 230	98.18 294	94.22 290	94.45 277	98.64 247

UnsupCasMVSNet_eth		96.44 257	96.12 256	97.40 283	98.65 281	95.65 273	99.36 179	99.51 84	97.13 178	96.04 292	98.99 265	88.40 301	98.17 295	96.71 233	90.27 306	98.40 282

v18		96.42 259	95.80 266	98.26 235	98.95 232	98.82 146	99.76 27	99.28 231	94.58 276	94.12 299	97.70 303	95.22 154	98.16 296	94.83 272	87.80 313	97.79 308

v17		96.42 259	95.81 264	98.25 239	98.94 235	98.80 153	99.76 27	99.28 231	94.57 277	94.18 298	97.71 302	95.23 153	98.16 296	94.86 270	87.73 315	97.80 303

v16		96.39 261	95.76 267	98.26 235	98.96 230	98.81 148	99.76 27	99.28 231	94.57 277	94.10 300	97.70 303	95.04 160	98.16 296	94.70 274	87.77 314	97.80 303

V9		96.25 265	95.58 271	98.26 235	98.94 235	98.83 139	99.75 34	99.29 224	94.45 284	93.96 305	97.62 309	94.94 166	98.14 299	94.40 280	86.87 320	97.81 301

v15		96.28 263	95.62 269	98.25 239	98.94 235	98.83 139	99.76 27	99.29 224	94.52 281	94.02 303	97.61 310	95.02 161	98.13 300	94.53 276	86.92 318	97.80 303

V14		96.26 264	95.60 270	98.26 235	98.94 235	98.83 139	99.76 27	99.29 224	94.49 282	93.96 305	97.66 306	94.99 164	98.13 300	94.41 279	86.90 319	97.80 303

v13		96.24 266	95.58 271	98.25 239	98.98 224	98.83 139	99.75 34	99.29 224	94.35 286	93.89 308	97.60 311	95.17 156	98.11 302	94.27 288	86.86 321	97.81 301

v12		96.24 266	95.58 271	98.23 242	98.96 230	98.81 148	99.76 27	99.29 224	94.42 285	93.85 309	97.60 311	95.12 157	98.09 303	94.32 285	86.85 322	97.80 303

v11		96.23 268	95.57 274	98.21 245	98.93 240	98.83 139	99.72 39	99.29 224	94.29 287	94.05 302	97.64 308	94.88 173	98.04 304	92.89 299	88.43 311	97.77 309

YYNet1		95.36 281	94.51 286	97.92 262	97.89 299	97.10 229	99.10 244	99.23 241	93.26 300	80.77 328	99.04 262	92.81 236	98.02 305	94.30 286	94.18 282	98.64 247

EU-MVSNet		97.98 175	98.03 151	97.81 271	98.72 273	96.65 255	99.66 58	99.66 25	98.09 89	98.35 248	99.82 44	95.25 152	98.01 306	97.41 196	95.30 255	98.78 193

Gipuma		90.99 298	90.15 299	93.51 303	98.73 271	90.12 316	93.98 332	99.45 147	79.32 327	92.28 315	94.91 324	69.61 328	97.98 307	87.42 316	95.67 250	92.45 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 282	94.73 283	97.15 284	95.53 316	95.94 271	99.35 183	99.10 254	95.13 267	93.55 310	97.54 314	88.15 305	97.91 308	94.58 275	89.69 309	97.61 312

PM-MVS		92.96 294	92.23 296	95.14 300	95.61 314	89.98 317	99.37 175	98.21 317	94.80 272	95.04 296	97.69 305	65.06 331	97.90 309	94.30 286	89.98 308	97.54 315

MDA-MVSNet-bldmvs		94.96 284	93.98 289	97.92 262	98.24 296	97.27 222	99.15 232	99.33 211	93.80 293	80.09 330	99.03 263	88.31 302	97.86 310	93.49 295	94.36 278	98.62 255

Anonymous20231211		90.69 299	89.39 300	94.58 301	94.25 320	88.18 318	99.29 197	99.07 259	82.45 326	92.95 313	97.65 307	63.96 333	97.79 311	89.27 311	85.63 324	97.77 309

Patchmatch-RL test		95.84 275	95.81 264	95.95 298	95.61 314	90.57 315	98.24 317	98.39 312	95.10 269	95.20 294	98.67 286	94.78 179	97.77 312	96.28 247	90.02 307	99.51 120

Anonymous20231206		96.22 269	96.03 258	96.79 293	97.31 309	94.14 298	99.63 71	99.08 256	96.17 247	97.04 280	99.06 260	93.94 216	97.76 313	86.96 319	95.06 261	98.47 277

SD-MVS		99.41 32	99.52 6	99.05 136	99.74 57	99.68 30	99.46 140	99.52 75	99.11 7	99.88 3	99.91 5	99.43 1	97.70 314	98.72 79	99.93 11	99.77 49

DSMNet-mixed		97.25 245	97.35 224	96.95 289	97.84 300	93.61 305	99.57 93	96.63 331	96.13 252	98.87 198	98.61 288	94.59 192	97.70 314	95.08 268	98.86 147	99.55 110

pmmvs3		94.09 291	93.25 293	96.60 295	94.76 319	94.49 294	98.92 284	98.18 319	89.66 315	96.48 286	98.06 297	86.28 311	97.33 316	89.68 310	87.20 317	97.97 296

FMVSNet5		96.43 258	96.19 255	97.15 284	99.11 201	95.89 272	99.32 188	99.52 75	94.47 283	98.34 249	99.07 258	87.54 307	97.07 317	92.61 302	95.72 249	98.47 277

new-patchmatchnet		94.48 287	94.08 288	95.67 299	95.08 318	92.41 310	99.18 227	99.28 231	94.55 280	93.49 311	97.37 317	87.86 306	97.01 318	91.57 304	88.36 312	97.61 312

LCM-MVSNet		86.80 302	85.22 305	91.53 313	87.81 332	80.96 329	98.23 319	98.99 267	71.05 330	90.13 321	96.51 321	48.45 338	96.88 319	90.51 306	85.30 325	96.76 316

no-one		83.04 305	80.12 307	91.79 311	89.44 331	85.65 322	99.32 188	98.32 313	89.06 317	79.79 332	89.16 333	44.86 339	96.67 320	84.33 324	46.78 335	93.05 324

MIMVSNet1		95.51 278	95.04 281	96.92 290	97.38 306	95.60 274	99.52 112	99.50 98	93.65 294	96.97 283	99.17 250	85.28 316	96.56 321	88.36 314	95.55 253	98.60 266

test20.03		96.12 272	95.96 261	96.63 294	97.44 305	95.45 281	99.51 116	99.38 183	96.55 216	96.16 289	99.25 244	93.76 223	96.17 322	87.35 318	94.22 281	98.27 285

tmp_tt		82.80 306	81.52 306	86.66 317	66.61 341	68.44 339	92.79 334	97.92 321	68.96 332	80.04 331	99.85 26	85.77 313	96.15 323	97.86 153	43.89 336	95.39 322

1111		92.30 296	92.21 297	92.55 307	93.30 322	86.27 319	99.15 232	98.74 296	91.94 306	90.85 319	97.82 300	84.18 319	95.21 324	79.65 327	94.27 280	96.19 319

.test1245		83.42 304	86.17 302	75.15 326	93.30 322	86.27 319	99.15 232	98.74 296	91.94 306	90.85 319	97.82 300	84.18 319	95.21 324	79.65 327	39.90 337	43.98 336

testus		94.61 286	95.30 279	92.54 308	96.44 312	84.18 323	98.36 312	99.03 264	94.18 288	96.49 285	98.57 290	88.74 293	95.09 326	87.41 317	98.45 166	98.36 284

PMMVS2		86.87 301	85.37 304	91.35 314	90.21 329	83.80 324	98.89 287	97.45 329	83.13 325	91.67 318	95.03 323	48.49 337	94.70 327	85.86 322	77.62 328	95.54 321

test2356		94.07 292	94.46 287	92.89 306	95.18 317	86.13 321	97.60 326	99.06 261	93.61 295	96.15 291	98.28 295	85.60 315	93.95 328	86.68 321	98.00 188	98.59 267

test1235678		92.91 295	93.30 292	91.71 312	93.14 324	83.01 325	98.75 296	98.58 309	92.80 303	92.45 314	97.91 299	88.51 300	93.54 329	82.26 325	95.35 254	98.59 267

test12356		91.74 297	92.19 298	90.37 315	91.22 326	82.41 326	98.61 304	98.28 314	90.66 314	91.82 317	97.92 298	84.90 317	92.61 330	81.64 326	94.66 272	96.09 320

PMVS	70.75 22	75.98 312	74.97 311	79.01 325	70.98 340	55.18 341	93.37 333	98.21 317	65.08 336	61.78 337	93.83 326	21.74 346	92.53 331	78.59 329	91.12 305	89.34 331
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmv		87.91 300	87.80 301	88.24 316	87.68 333	77.50 333	99.07 247	97.66 327	89.27 316	86.47 323	96.22 322	68.35 329	92.49 332	76.63 331	88.82 310	94.72 323

FPMVS		84.93 303	85.65 303	82.75 323	86.77 334	63.39 340	98.35 314	98.92 275	74.11 329	83.39 326	98.98 268	50.85 336	92.40 333	84.54 323	94.97 263	92.46 326

PNet_i23d		79.43 309	77.68 310	84.67 319	86.18 335	71.69 338	96.50 330	93.68 337	75.17 328	71.33 333	91.18 330	32.18 342	90.62 334	78.57 330	74.34 329	91.71 329

wuykxyi23d		74.42 313	71.19 314	84.14 321	76.16 338	74.29 337	96.00 331	92.57 342	69.57 331	63.84 336	87.49 335	21.98 344	88.86 335	75.56 333	57.50 333	89.26 332

MVE	76.82 21	76.91 311	74.31 313	84.70 318	85.38 337	76.05 336	96.88 329	93.17 339	67.39 333	71.28 334	89.01 334	21.66 347	87.69 336	71.74 334	72.29 330	90.35 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 307	79.88 308	82.81 322	90.75 328	76.38 335	97.69 324	95.76 333	66.44 334	83.52 325	92.25 328	62.54 334	87.16 337	68.53 335	61.40 331	84.89 334

EMVS		80.02 308	79.22 309	82.43 324	91.19 327	76.40 334	97.55 327	92.49 343	66.36 335	83.01 327	91.27 329	64.63 332	85.79 338	65.82 336	60.65 332	85.08 333

ANet_high		77.30 310	74.86 312	84.62 320	75.88 339	77.61 332	97.63 325	93.15 340	88.81 319	64.27 335	89.29 332	36.51 340	83.93 339	75.89 332	52.31 334	92.33 328

wuyk23d		40.18 315	41.29 318	36.84 327	86.18 335	49.12 342	79.73 335	22.81 345	27.64 337	25.46 340	28.45 341	21.98 344	48.89 340	55.80 337	23.56 340	12.51 338

test123		39.01 317	42.50 317	28.53 329	39.17 342	20.91 343	98.75 296	19.17 346	19.83 339	38.57 338	66.67 337	33.16 341	15.42 341	37.50 339	29.66 339	49.26 335

testmvs		39.17 316	43.78 315	25.37 330	36.04 343	16.84 344	98.36 312	26.56 344	20.06 338	38.51 339	67.32 336	29.64 343	15.30 342	37.59 338	39.90 337	43.98 336

cdsmvs_eth3d_5k		24.64 318	32.85 319	0.00 331	0.00 344	0.00 345	0.00 336	99.51 84	0.00 340	0.00 341	99.56 160	96.58 115	0.00 343	0.00 340	0.00 341	0.00 339

pcd_1.5k_mvsjas		8.27 320	11.03 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	99.01 11	0.00 343	0.00 340	0.00 341	0.00 339

pcd1.5k->3k		40.85 314	43.49 316	32.93 328	98.95 232	0.00 345	0.00 336	99.53 71	0.00 340	0.00 341	0.27 342	95.32 147	0.00 343	0.00 340	97.30 221	98.80 191

sosnet-low-res		0.02 321	0.03 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

sosnet		0.02 321	0.03 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uncertanet		0.02 321	0.03 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

Regformer		0.02 321	0.03 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ab-mvs-re		8.30 319	11.06 320	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	99.58 154	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uanet		0.02 321	0.03 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.27 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ESAPD								99.47 126

sam_mvs1												94.86 174

sam_mvs												94.72 187

MTGPA								99.47 126

MTMP								98.88 282

test9_res														97.49 188	99.72 83	99.75 53

agg_prior2														97.21 204	99.73 82	99.75 53

test_prior4						99.56 49	98.99 268

test_prior2							98.96 277		98.34 66	99.01 177	99.52 171	98.68 49		97.96 145	99.74 79

新几何2							99.01 266

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

原ACMM2							98.95 281

test222					99.75 47	99.49 61	98.91 286	99.49 103	96.42 228	99.34 109	99.65 127	98.28 71			99.69 90	99.72 69

segment_acmp												98.96 20

testdata1							98.85 290		98.32 69

plane_prior7					99.29 167	97.03 237

plane_prior6					99.27 172	96.98 241						92.71 241

plane_prior4											99.61 146

plane_prior3						97.00 239			98.69 46	99.11 159

plane_prior2							99.39 168		98.97 22

plane_prior1					99.26 174

plane_prior						96.97 242	99.21 223		98.45 59						97.60 199

n2								0.00 347

nn								0.00 347

door-mid								98.05 320

test11								99.35 195

door								97.92 321

HQP5-MVS						96.83 247

HQP-NCC					99.19 183		98.98 272		98.24 72	98.66 224

ACMP_Plane					99.19 183		98.98 272		98.24 72	98.66 224

BP-MVS														97.19 206

HQP3-MVS								99.39 177							97.58 201

HQP2-MVS												92.47 250

NP-MVS					99.23 177	96.92 245					99.40 207

MDTV_nov1_ep13_2view						95.18 287	99.35 183		96.84 199	99.58 60		95.19 155		97.82 156		99.46 133

ACMMP++_ref															97.19 225

ACMMP++															97.43 216

Test By Simon												98.75 44

High-resolution multi-view benchmark