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.

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 40	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 202	99.52 76	97.18 178	99.60 59	99.79 72	98.79 36	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 146	99.48 113	98.05 98	99.76 27	99.86 22	98.82 33	99.93 55	98.82 71	99.91 17	99.84 12

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 188	99.47 128	98.79 40	99.68 36	99.81 53	98.43 62	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 62	99.47 128	98.79 40	99.68 36	99.81 53	98.43 62	99.97 11	98.88 57	99.90 24	99.83 23

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 52	99.84 6	99.43 159	99.51 85	98.68 47	99.27 131	99.53 171	98.64 53	99.96 19	98.44 113	99.80 69	99.79 44

test_part2					99.81 32	99.83 7				99.77 23

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 105	99.74 95	98.81 34	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 264	95.45 285	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 105	64.01 349	98.81 34	99.94 40	98.79 72	99.86 48	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 43	99.83 7	99.63 76	99.54 62	98.36 65	99.79 18	99.82 44	98.86 30	99.95 33	98.62 90	99.81 67	99.78 48

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 11	99.66 62	99.46 138	98.09 89	99.48 83	99.74 95	98.29 71	99.96 19	97.93 148	99.87 38	99.82 31

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 12	99.59 89	99.51 85	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.
HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 13	99.67 56	99.37 192	98.70 45	99.77 23	99.49 184	98.21 74	99.95 33	98.46 111	99.77 75	99.81 35

HFP-MVS		99.49 13	99.37 17	99.86 12	99.87 15	99.80 13	99.66 62	99.67 22	98.15 80	99.68 36	99.69 112	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 13	99.65 72	99.66 25	98.13 82	99.66 47	99.68 117	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 13	99.55 113	99.67 22	97.83 119	99.68 36	99.69 112	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 17	99.66 62	99.67 22	98.15 80	99.67 42	99.69 112	98.95 24	99.96 19	98.69 82	99.87 38	99.84 12

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 17	99.69 45	99.48 113	98.12 84	99.50 79	99.75 90	98.78 37	99.97 11	98.57 97	99.89 32	99.83 23

HPM-MVS_fast		99.51 12	99.40 14	99.85 17	99.91 1	99.79 17	99.76 27	99.56 48	97.72 132	99.76 27	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 52	99.79 17	99.50 129	99.50 99	97.16 180	99.77 23	99.82 44	98.78 37	99.94 40	97.56 183	99.86 48	99.80 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		99.45 22	99.31 31	99.86 12	99.87 15	99.78 21	99.58 95	99.65 30	97.84 118	99.71 30	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

abl_6		99.44 25	99.31 31	99.83 22	99.85 23	99.75 22	99.66 62	99.59 38	98.13 82	99.82 14	99.81 53	98.60 55	99.96 19	98.46 111	99.88 34	99.79 44

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 22	99.69 45	99.52 76	98.07 93	99.53 74	99.63 139	98.93 26	99.97 11	98.74 75	99.91 17	99.83 23

LS3D		99.27 49	99.12 53	99.74 43	99.18 196	99.75 22	99.56 108	99.57 44	98.45 59	99.49 82	99.85 26	97.77 86	99.94 40	98.33 121	99.84 57	99.52 118

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 34	99.74 25	99.29 206	99.40 176	98.79 40	99.52 76	99.62 144	98.91 27	99.90 84	98.64 87	99.75 78	99.82 31

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 26	99.81 15	99.54 62	97.59 141	99.68 36	99.63 139	98.91 27	99.94 40	98.58 95	99.91 17	99.84 12

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 52	99.71 27	99.15 241	99.41 169	96.60 221	99.60 59	99.55 164	98.83 32	99.90 84	97.48 191	99.83 62	99.78 48

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 107	99.71 27	99.26 220	99.52 76	98.82 35	99.39 101	99.71 103	98.96 20	99.85 109	98.59 94	99.80 69	99.77 50

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 63	99.70 29	99.27 212	99.57 44	96.40 239	99.42 94	99.68 117	98.75 45	99.80 139	97.98 144	99.72 84	99.44 137

nrg030		98.64 125	98.42 127	99.28 118	99.05 223	99.69 30	99.81 15	99.46 138	98.04 99	99.01 186	99.82 44	96.69 114	99.38 219	99.34 22	94.59 285	98.78 199

SD-MVS		99.41 32	99.52 6	99.05 141	99.74 63	99.68 31	99.46 149	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 323	98.72 79	99.93 11	99.77 50

3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 201	99.68 31	99.81 15	99.51 85	99.20 4	98.72 223	99.89 10	95.68 141	99.97 11	98.86 64	99.86 48	99.81 35

QAPM		98.67 122	98.30 135	99.80 29	99.20 191	99.67 33	99.77 24	99.72 11	94.74 282	98.73 222	99.90 7	95.78 138	99.98 5	96.96 223	99.88 34	99.76 53

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 33	99.62 79	99.69 18	98.12 84	99.63 52	99.84 35	98.73 47	99.96 19	98.55 103	99.83 62	99.81 35
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
TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 35	99.63 76	99.39 179	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 31

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 157	99.66 35	99.45 150	99.54 62	96.61 219	99.01 186	99.40 212	97.09 101	99.86 104	97.68 175	99.53 104	99.10 160
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
3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 209	99.66 35	99.84 9	99.74 10	99.09 8	98.92 201	99.90 7	95.94 132	99.98 5	98.95 53	99.92 12	99.79 44

TEST9					99.67 87	99.65 38	99.05 262	99.41 169	96.22 252	98.95 197	99.49 184	98.77 40	99.91 72

train_agg		99.02 85	98.77 97	99.77 35	99.67 87	99.65 38	99.05 262	99.41 169	96.28 245	98.95 197	99.49 184	98.76 42	99.91 72	97.63 176	99.72 84	99.75 54

NCCC		99.34 40	99.19 47	99.79 32	99.61 111	99.65 38	99.30 202	99.48 113	98.86 31	99.21 152	99.63 139	98.72 48	99.90 84	98.25 125	99.63 101	99.80 40

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 87	99.62 41	98.99 277	99.40 176	96.26 248	98.87 207	99.49 184	98.77 40	99.91 72	97.69 173	99.72 84	99.75 54

agg_prior					99.67 87	99.62 41		99.40 176		98.87 207			99.91 72

test_8					99.67 87	99.61 43	99.03 268	99.41 169	96.28 245	98.93 200	99.48 190	98.76 42	99.91 72

test12				99.75 38	99.64 100	99.61 43		99.29 226		99.21 152		98.38 66	99.89 92		99.74 80	99.74 59

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 87	99.60 45	99.04 267	99.41 169	95.93 267	98.87 207	99.48 190	98.61 54	99.91 72	97.63 176	99.72 84	99.75 54

1121		99.09 75	98.87 84	99.75 38	99.74 63	99.60 45	99.27 212	99.48 113	96.82 208	99.25 138	99.65 128	98.38 66	99.93 55	97.53 186	99.67 95	99.73 64

新几何1				99.75 38	99.75 52	99.59 47		99.54 62	96.76 209	99.29 123	99.64 135	98.43 62	99.94 40	96.92 227	99.66 96	99.72 70

旧先验1					99.74 63	99.59 47		99.54 62			99.69 112	98.47 59			99.68 94	99.73 64

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 103	99.59 47	99.36 188	99.46 138	99.07 9	99.79 18	99.82 44	98.85 31	99.92 63	98.68 84	99.87 38	99.82 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior4						99.56 50	98.99 277

VNet		99.11 71	98.90 80	99.73 45	99.52 124	99.56 50	99.41 170	99.39 179	99.01 13	99.74 29	99.78 77	95.56 142	99.92 63	99.52 7	98.18 182	99.72 70

UA-Net		99.42 29	99.29 36	99.80 29	99.62 107	99.55 52	99.50 129	99.70 15	98.79 40	99.77 23	99.96 1	97.45 92	99.96 19	98.92 55	99.90 24	99.89 2

FIs		98.78 113	98.63 112	99.23 127	99.18 196	99.54 53	99.83 12	99.59 38	98.28 70	98.79 217	99.81 53	96.75 112	99.37 222	99.08 43	96.38 247	98.78 199

VPA-MVSNet		98.29 141	97.95 158	99.30 113	99.16 203	99.54 53	99.50 129	99.58 43	98.27 71	99.35 112	99.37 221	92.53 253	99.65 187	99.35 18	94.46 286	98.72 210

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 121	99.54 53	99.18 236	99.70 15	98.18 79	99.35 112	99.63 139	96.32 122	99.90 84	97.48 191	99.77 75	99.55 111

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 56	99.62 79	99.59 38	92.65 313	99.71 30	99.78 77	98.06 79	99.90 84	98.84 66	99.91 17	99.74 59

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 40	99.53 56	99.41 170	99.50 99	97.03 196	99.04 183	99.88 14	97.39 93	99.92 63	98.66 85	99.90 24	99.87 4

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 224	99.53 56	99.82 13	99.72 11	94.56 288	98.08 268	99.88 14	94.73 187	99.98 5	97.47 193	99.76 77	99.06 170

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 76	99.52 59	99.49 137	99.49 104	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 40

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 121	99.52 59	99.58 95	99.80 8	97.12 184	99.62 55	99.73 98	98.58 56	99.90 84	98.61 92	99.91 17	99.68 82

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 40	99.51 61	98.94 292	99.85 6	98.82 35	99.65 50	99.74 95	98.51 57	99.80 139	98.83 68	99.89 32	99.64 95

test222					99.75 52	99.49 62	98.91 295	99.49 104	96.42 236	99.34 115	99.65 128	98.28 72			99.69 91	99.72 70

test_prior3		99.21 54	99.05 58	99.68 50	99.67 87	99.48 63	98.96 286	99.56 48	98.34 66	99.01 186	99.52 176	98.68 50	99.83 122	97.96 145	99.74 80	99.74 59

test_prior				99.68 50	99.67 87	99.48 63		99.56 48					99.83 122			99.74 59

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 71	99.47 65	98.95 290	99.85 6	98.82 35	99.54 73	99.73 98	98.51 57	99.74 154	98.91 56	99.88 34	99.77 50

CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 33	99.46 66	99.59 89	99.49 104	97.03 196	99.63 52	99.69 112	97.27 98	99.96 19	97.82 156	99.84 57	99.81 35

FC-MVSNet-test		98.75 116	98.62 115	99.15 133	99.08 217	99.45 67	99.86 8	99.60 35	98.23 75	98.70 230	99.82 44	96.80 108	99.22 260	99.07 44	96.38 247	98.79 198

Regformer-1		99.53 9	99.47 8	99.72 47	99.71 76	99.44 68	99.49 137	99.46 138	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 40

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 63	99.44 68	99.39 177	99.38 185	97.70 135	99.28 127	99.28 246	98.34 69	99.85 109	96.96 223	99.45 105	99.69 78

alignmvs		98.81 109	98.56 122	99.58 71	99.43 143	99.42 70	99.51 124	98.96 273	98.61 50	99.35 112	98.92 277	94.78 180	99.77 149	99.35 18	98.11 194	99.54 113

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 43	99.41 71	99.58 95	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

CNLPA		99.14 61	98.99 68	99.59 68	99.58 116	99.41 71	99.16 238	99.44 157	98.45 59	99.19 158	99.49 184	98.08 78	99.89 92	97.73 167	99.75 78	99.48 127

DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 71	99.40 73	99.05 262	99.66 25	99.14 6	99.57 66	99.80 64	98.46 60	99.94 40	99.57 4	99.84 57	99.60 103
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
HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 74	99.02 271	99.91 3	97.67 138	99.59 62	99.75 90	95.90 134	99.73 162	99.53 6	99.02 133	99.86 5

MVS_0304		99.06 79	98.86 87	99.66 53	99.51 126	99.36 75	99.22 229	99.51 85	98.95 24	99.58 63	99.65 128	93.74 226	99.98 5	99.66 1	99.95 6	99.64 95

UniMVSNet (Re)		98.29 141	98.00 154	99.13 135	99.00 229	99.36 75	99.49 137	99.51 85	97.95 108	98.97 196	99.13 259	96.30 123	99.38 219	98.36 119	93.34 302	98.66 248

原ACMM1				99.65 57	99.73 68	99.33 77		99.47 128	97.46 153	99.12 166	99.66 127	98.67 52	99.91 72	97.70 172	99.69 91	99.71 77

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 144	99.32 78	99.80 19	99.48 113	98.63 48	99.31 118	98.81 286	97.09 101	99.75 153	99.27 29	97.90 200	99.47 131

XXY-MVS		98.38 136	98.09 146	99.24 125	99.26 183	99.32 78	99.56 108	99.55 55	97.45 156	98.71 224	99.83 37	93.23 230	99.63 194	98.88 57	96.32 249	98.76 204

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 68	99.32 78	99.75 34	99.20 246	98.02 102	99.56 67	99.86 22	96.54 117	99.67 183	98.09 134	99.13 124	99.73 64

API-MVS		99.04 82	99.03 63	99.06 139	99.40 152	99.31 81	99.55 113	99.56 48	98.54 53	99.33 116	99.39 216	98.76 42	99.78 147	96.98 221	99.78 73	98.07 299

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 43	99.29 82	99.58 95	99.44 157	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 126	99.28 83	99.52 120	99.47 128	96.11 262	99.01 186	99.34 235	96.20 126	99.84 115	97.88 151	98.82 150	99.39 143

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 76	99.28 83	99.06 260	99.77 9	97.74 130	99.50 79	99.53 171	95.41 146	99.84 115	97.17 211	99.64 99	99.44 137

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 35	99.27 85	99.42 166	99.54 62	97.29 169	99.41 96	99.59 152	98.42 65	99.93 55	98.19 127	99.69 91	99.73 64

NR-MVSNet		97.97 183	97.61 200	99.02 143	98.87 262	99.26 86	99.47 146	99.42 166	97.63 140	97.08 289	99.50 181	95.07 160	99.13 270	97.86 153	93.59 300	98.68 226

WR-MVS		98.06 164	97.73 188	99.06 139	98.86 265	99.25 87	99.19 235	99.35 197	97.30 168	98.66 233	99.43 203	93.94 217	99.21 264	98.58 95	94.28 289	98.71 212

CP-MVSNet		98.09 162	97.78 177	99.01 144	98.97 237	99.24 88	99.67 56	99.46 138	97.25 172	98.48 250	99.64 135	93.79 222	99.06 277	98.63 88	94.10 293	98.74 208

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 89	99.62 79	99.55 55	98.94 26	99.63 52	99.95 2	95.82 137	99.94 40	99.37 17	99.97 3	99.73 64
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpnnormal		97.84 200	97.47 213	98.98 148	99.20 191	99.22 90	99.64 74	99.61 32	96.32 242	98.27 262	99.70 106	93.35 229	99.44 213	95.69 262	95.40 263	98.27 294

diffmvs		98.72 118	98.49 124	99.43 100	99.48 136	99.19 91	99.62 79	99.42 166	95.58 273	99.37 105	99.67 121	96.14 127	99.74 154	98.14 131	98.96 138	99.37 144

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 100	99.19 91	99.44 154	99.54 62	97.77 126	99.30 119	99.81 53	94.20 207	99.93 55	99.17 36	98.82 150	99.49 125

MSDG		98.98 90	98.80 94	99.53 79	99.76 43	99.19 91	98.75 305	99.55 55	97.25 172	99.47 84	99.77 82	97.82 84	99.87 101	96.93 226	99.90 24	99.54 113

CANet		99.25 52	99.14 51	99.59 68	99.41 147	99.16 94	99.35 192	99.57 44	98.82 35	99.51 78	99.61 147	96.46 118	99.95 33	99.59 2	99.98 2	99.65 89

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 113	99.16 94	99.41 170	99.71 13	98.98 19	99.45 87	99.78 77	99.19 4	99.54 203	99.28 27	99.84 57	99.63 99

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 107	99.16 94	99.37 184	99.56 48	98.04 99	99.53 74	99.62 144	96.84 107	99.94 40	98.85 65	98.49 166	99.72 70

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 35	99.15 97	99.61 85	99.45 149	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 62	99.78 35	99.14 98	99.60 87	99.45 149	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 133	99.14 98	99.67 56	99.34 205	97.31 167	99.58 63	99.76 85	97.65 89	99.82 131	98.87 61	99.07 130	99.46 134

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 138	99.12 100	98.08 330	99.50 99	97.50 151	99.38 103	99.41 208	96.37 121	99.81 135	99.11 41	98.54 163	99.51 121

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 35	99.10 101	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 184	99.84 115	99.19 33	99.41 108	99.74 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	97.08 14	97.66 233	97.06 251	99.47 91	99.61 111	99.09 102	98.04 331	99.25 241	91.24 320	98.51 247	99.70 106	94.55 195	99.91 72	92.76 310	99.85 52	99.42 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 144	99.08 103	99.62 79	99.36 193	97.39 162	99.28 127	99.68 117	96.44 119	99.92 63	98.37 117	98.22 178	99.40 142

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 104	99.47 146	99.93 2	97.66 139	99.71 30	99.86 22	97.73 87	99.96 19	99.47 13	99.82 66	99.79 44

PS-CasMVS		97.93 189	97.59 202	98.95 153	98.99 230	99.06 105	99.68 54	99.52 76	97.13 182	98.31 259	99.68 117	92.44 259	99.05 278	98.51 106	94.08 294	98.75 205

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 99	99.06 105	99.81 15	99.33 213	97.43 157	99.60 59	99.88 14	97.14 100	99.84 115	99.13 39	98.94 140	99.69 78

tfpn_n400		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

tfpnconf		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

tfpnview11		98.24 144	97.89 165	99.27 119	99.76 43	99.04 107	99.67 56	97.71 332	97.10 188	99.55 70	99.54 167	92.70 245	99.79 142	96.90 229	98.12 191	98.97 179

DI_MVS_plusplus_test		97.45 247	96.79 256	99.44 97	97.76 312	99.04 107	99.21 232	98.61 313	97.74 130	94.01 314	98.83 284	87.38 319	99.83 122	98.63 88	98.90 145	99.44 137

PAPR		98.63 126	98.34 131	99.51 85	99.40 152	99.03 111	98.80 301	99.36 193	96.33 241	99.00 193	99.12 262	98.46 60	99.84 115	95.23 272	99.37 113	99.66 86

MVSTER		98.49 128	98.32 133	99.00 146	99.35 160	99.02 112	99.54 116	99.38 185	97.41 160	99.20 155	99.73 98	93.86 221	99.36 226	98.87 61	97.56 212	98.62 261

1112_ss		98.98 90	98.77 97	99.59 68	99.68 86	99.02 112	99.25 222	99.48 113	97.23 175	99.13 164	99.58 155	96.93 106	99.90 84	98.87 61	98.78 153	99.84 12

LFMVS		97.90 194	97.35 233	99.54 75	99.52 124	99.01 114	99.39 177	98.24 321	97.10 188	99.65 50	99.79 72	84.79 328	99.91 72	99.28 27	98.38 170	99.69 78

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 97	99.01 114	99.24 224	99.52 76	96.85 206	99.27 131	99.48 190	98.25 73	99.91 72	97.76 163	99.62 102	99.65 89
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testing_2		94.44 297	92.93 303	98.98 148	94.16 331	99.00 116	99.42 166	99.28 233	96.60 221	84.86 334	96.84 328	70.91 337	99.27 248	98.23 126	96.08 253	98.68 226

test_normal		97.44 248	96.77 258	99.44 97	97.75 313	99.00 116	99.10 253	98.64 310	97.71 133	93.93 317	98.82 285	87.39 318	99.83 122	98.61 92	98.97 137	99.49 125

UniMVSNet_NR-MVSNet		98.22 147	97.97 156	98.96 151	98.92 252	98.98 118	99.48 142	99.53 72	97.76 127	98.71 224	99.46 198	96.43 120	99.22 260	98.57 97	92.87 308	98.69 221

DU-MVS		98.08 163	97.79 175	98.96 151	98.87 262	98.98 118	99.41 170	99.45 149	97.87 113	98.71 224	99.50 181	94.82 177	99.22 260	98.57 97	92.87 308	98.68 226

FMVSNet3		98.03 173	97.76 184	98.84 191	99.39 154	98.98 118	99.40 176	99.38 185	96.67 215	99.07 177	99.28 246	92.93 234	98.98 286	97.10 213	96.65 240	98.56 279

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 103	98.97 121	99.12 245	99.51 85	98.86 31	99.84 8	99.47 194	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 103	98.97 121	99.12 245	99.51 85	98.86 31	99.84 8	99.47 194	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 103	98.97 121	99.12 245	99.51 85	98.86 31	99.84 8	99.47 194	98.18 75	99.99 1	99.50 8	99.31 114	99.08 165

sss		99.17 58	99.05 58	99.53 79	99.62 107	98.97 121	99.36 188	99.62 31	97.83 119	99.67 42	99.65 128	97.37 96	99.95 33	99.19 33	99.19 121	99.68 82

anonymousdsp		98.44 131	98.28 136	98.94 154	98.50 300	98.96 125	99.77 24	99.50 99	97.07 192	98.87 207	99.77 82	94.76 185	99.28 245	98.66 85	97.60 208	98.57 278

testdata				99.54 75	99.75 52	98.95 126		99.51 85	97.07 192	99.43 91	99.70 106	98.87 29	99.94 40	97.76 163	99.64 99	99.72 70

MVS		97.28 253	96.55 260	99.48 88	98.78 275	98.95 126	99.27 212	99.39 179	83.53 333	98.08 268	99.54 167	96.97 104	99.87 101	94.23 295	99.16 122	99.63 99

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 83	98.95 126	99.03 268	99.47 128	96.98 198	99.15 163	99.23 252	96.77 111	99.89 92	98.83 68	98.78 153	99.86 5

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 118	98.94 129	98.97 284	99.46 138	98.92 28	99.71 30	99.24 251	99.01 11	99.98 5	99.35 18	99.66 96	98.97 179

VPNet		97.84 200	97.44 221	99.01 144	99.21 189	98.94 129	99.48 142	99.57 44	98.38 64	99.28 127	99.73 98	88.89 302	99.39 218	99.19 33	93.27 303	98.71 212

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 126	98.94 129	99.88 1	99.46 138	97.55 146	99.80 16	99.65 128	97.39 93	99.28 245	99.03 46	99.85 52	99.65 89

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 126	98.94 129	99.05 262	99.16 250	97.86 114	99.80 16	99.56 161	97.39 93	99.86 104	98.94 54	99.85 52	99.58 109

Test4		95.05 292	93.67 300	99.22 128	96.07 323	98.94 129	99.20 234	99.27 238	97.71 133	89.96 332	97.59 322	66.18 340	99.25 254	98.06 141	98.96 138	99.47 131

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 123	98.91 134	99.02 271	99.45 149	98.80 39	99.71 30	99.26 249	98.94 25	99.98 5	99.34 22	99.23 118	98.98 178

test_djsdf		98.67 122	98.57 121	98.98 148	98.70 286	98.91 134	99.88 1	99.46 138	97.55 146	99.22 150	99.88 14	95.73 140	99.28 245	99.03 46	97.62 207	98.75 205

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 76	98.88 136	99.80 19	99.44 157	97.91 112	99.36 109	99.78 77	95.49 145	99.43 217	97.91 149	99.11 125	99.62 101

pmmvs4		98.13 156	97.90 161	98.81 194	98.61 294	98.87 137	98.99 277	99.21 245	96.44 234	99.06 181	99.58 155	95.90 134	99.11 273	97.18 210	96.11 252	98.46 286

jason		99.13 62	99.03 63	99.45 94	99.46 138	98.87 137	99.12 245	99.26 239	98.03 101	99.79 18	99.65 128	97.02 103	99.85 109	99.02 48	99.90 24	99.65 89
jason: jason.
Patchmtry		97.75 218	97.40 227	98.81 194	99.10 214	98.87 137	99.11 251	99.33 213	94.83 280	98.81 215	99.38 217	94.33 203	99.02 282	96.10 253	95.57 261	98.53 280

TransMVSNet (Re)		97.15 256	96.58 259	98.86 187	99.12 209	98.85 140	99.49 137	98.91 280	95.48 274	97.16 288	99.80 64	93.38 228	99.11 273	94.16 297	91.73 313	98.62 261

V42		98.06 164	97.79 175	98.86 187	98.98 234	98.84 141	99.69 45	99.34 205	96.53 225	99.30 119	99.37 221	94.67 190	99.32 236	97.57 181	94.66 282	98.42 287

WR-MVS_H		98.13 156	97.87 169	98.90 170	99.02 227	98.84 141	99.70 42	99.59 38	97.27 170	98.40 253	99.19 255	95.53 143	99.23 257	98.34 120	93.78 299	98.61 270

FMVSNet2		97.72 223	97.36 231	98.80 196	99.51 126	98.84 141	99.45 150	99.42 166	96.49 226	98.86 212	99.29 245	90.26 289	98.98 286	96.44 248	96.56 243	98.58 277

v15		96.28 272	95.62 278	98.25 248	98.94 245	98.83 144	99.76 27	99.29 226	94.52 290	94.02 313	97.61 319	95.02 162	98.13 309	94.53 282	86.92 328	97.80 313

v13		96.24 275	95.58 280	98.25 248	98.98 234	98.83 144	99.75 34	99.29 226	94.35 295	93.89 318	97.60 320	95.17 157	98.11 311	94.27 294	86.86 331	97.81 311

v6		98.12 158	97.84 170	98.94 154	98.94 245	98.83 144	99.66 62	99.34 205	96.49 226	99.30 119	99.37 221	94.95 166	99.34 232	97.77 162	94.74 276	98.67 237

v11		96.23 277	95.57 283	98.21 254	98.93 250	98.83 144	99.72 39	99.29 226	94.29 296	94.05 312	97.64 317	94.88 174	98.04 313	92.89 308	88.43 321	97.77 319

V14		96.26 273	95.60 279	98.26 244	98.94 245	98.83 144	99.76 27	99.29 226	94.49 291	93.96 315	97.66 315	94.99 165	98.13 309	94.41 285	86.90 329	97.80 313

V9		96.25 274	95.58 280	98.26 244	98.94 245	98.83 144	99.75 34	99.29 226	94.45 293	93.96 315	97.62 318	94.94 167	98.14 308	94.40 286	86.87 330	97.81 311

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 147	98.83 144	99.30 202	98.77 294	97.70 135	98.94 199	99.65 128	92.91 237	99.74 154	96.52 246	99.55 103	99.64 95

v18		96.42 268	95.80 275	98.26 244	98.95 242	98.82 151	99.76 27	99.28 233	94.58 285	94.12 309	97.70 312	95.22 155	98.16 305	94.83 278	87.80 323	97.79 318

v2v482		98.06 164	97.77 181	98.92 162	98.90 255	98.82 151	99.57 101	99.36 193	96.65 216	99.19 158	99.35 232	94.20 207	99.25 254	97.72 171	94.97 273	98.69 221

v1neww		98.12 158	97.84 170	98.93 157	98.97 237	98.81 153	99.66 62	99.35 197	96.49 226	99.29 123	99.37 221	95.02 162	99.32 236	97.73 167	94.73 277	98.67 237

v7new		98.12 158	97.84 170	98.93 157	98.97 237	98.81 153	99.66 62	99.35 197	96.49 226	99.29 123	99.37 221	95.02 162	99.32 236	97.73 167	94.73 277	98.67 237

v16		96.39 270	95.76 276	98.26 244	98.96 240	98.81 153	99.76 27	99.28 233	94.57 286	94.10 310	97.70 312	95.04 161	98.16 305	94.70 280	87.77 324	97.80 313

v12		96.24 275	95.58 280	98.23 251	98.96 240	98.81 153	99.76 27	99.29 226	94.42 294	93.85 319	97.60 320	95.12 158	98.09 312	94.32 291	86.85 332	97.80 313

v8		97.95 188	97.63 199	98.93 157	98.95 242	98.81 153	99.80 19	99.41 169	96.03 266	99.10 171	99.42 205	94.92 170	99.30 242	96.94 225	94.08 294	98.66 248

v17		96.42 268	95.81 273	98.25 248	98.94 245	98.80 158	99.76 27	99.28 233	94.57 286	94.18 308	97.71 311	95.23 154	98.16 305	94.86 276	87.73 325	97.80 313

v1		98.05 170	97.76 184	98.93 157	98.92 252	98.80 158	99.57 101	99.35 197	96.39 240	99.28 127	99.36 228	94.86 175	99.32 236	97.38 199	94.72 279	98.68 226

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 142	99.76 43	98.79 160	99.28 209	99.91 3	97.42 159	99.67 42	99.37 221	97.53 90	99.88 99	98.98 51	97.29 231	98.42 287

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 43	98.79 160	98.78 302	99.91 3	96.74 210	99.67 42	99.49 184	97.53 90	99.88 99	98.98 51	99.85 52	99.60 103

v1141		98.05 170	97.76 184	98.91 166	98.91 254	98.78 162	99.57 101	99.35 197	96.41 238	99.23 148	99.36 228	94.93 169	99.27 248	97.38 199	94.72 279	98.68 226

divwei89l23v2f112		98.06 164	97.78 177	98.91 166	98.90 255	98.77 163	99.57 101	99.35 197	96.45 233	99.24 143	99.37 221	94.92 170	99.27 248	97.50 189	94.71 281	98.68 226

tfpn_ndepth		98.17 152	97.84 170	99.15 133	99.75 52	98.76 164	99.61 85	97.39 339	96.92 203	99.61 57	99.38 217	92.19 262	99.86 104	97.57 181	98.13 189	98.82 195

tfpn1000		98.33 138	98.02 152	99.25 122	99.78 35	98.73 165	99.70 42	97.55 337	97.48 152	99.69 35	99.53 171	92.37 260	99.85 109	97.82 156	98.26 177	99.16 156

CDS-MVSNet		99.09 75	99.03 63	99.25 122	99.42 144	98.73 165	99.45 150	99.46 138	98.11 86	99.46 86	99.77 82	98.01 80	99.37 222	98.70 80	98.92 143	99.66 86
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		98.87 97	98.69 105	99.40 102	99.22 188	98.72 167	99.44 154	99.68 19	99.24 3	99.18 160	99.42 205	92.74 241	99.96 19	99.34 22	99.94 10	99.53 117
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
PMMVS		98.80 112	98.62 115	99.34 105	99.27 181	98.70 168	98.76 304	99.31 220	97.34 164	99.21 152	99.07 264	97.20 99	99.82 131	98.56 100	98.87 147	99.52 118

v1192		97.81 206	97.44 221	98.91 166	98.88 259	98.68 169	99.51 124	99.34 205	96.18 255	99.20 155	99.34 235	94.03 215	99.36 226	95.32 271	95.18 267	98.69 221

v7		98.05 170	97.78 177	98.87 183	98.99 230	98.67 170	99.64 74	99.34 205	96.31 244	99.29 123	99.51 179	94.78 180	99.27 248	97.03 217	95.15 269	98.66 248

v10		97.85 198	97.52 205	98.86 187	98.99 230	98.67 170	99.75 34	99.41 169	95.70 271	98.98 195	99.41 208	94.75 186	99.23 257	96.01 256	94.63 284	98.67 237

v1144		97.98 180	97.69 191	98.85 190	98.87 262	98.66 172	99.54 116	99.35 197	96.27 247	99.23 148	99.35 232	94.67 190	99.23 257	96.73 237	95.16 268	98.68 226

v144192		97.92 192	97.60 201	98.87 183	98.83 268	98.65 173	99.55 113	99.34 205	96.20 253	99.32 117	99.40 212	94.36 202	99.26 253	96.37 251	95.03 272	98.70 216

1314		98.68 121	98.54 123	99.11 136	98.89 258	98.65 173	99.27 212	99.49 104	96.89 204	97.99 273	99.56 161	97.72 88	99.83 122	97.74 166	99.27 117	98.84 194

V4		97.80 208	97.51 207	98.67 208	98.79 271	98.63 175	99.87 4	99.44 157	95.87 268	99.01 186	99.46 198	94.52 197	99.33 233	96.64 245	93.97 296	98.05 300

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 118	98.63 175	99.07 256	99.34 205	98.99 18	99.61 57	99.82 44	97.98 81	99.87 101	97.00 219	99.80 69	99.85 8

pm-mvs1		97.68 229	97.28 243	98.88 179	99.06 220	98.62 177	99.50 129	99.45 149	96.32 242	97.87 276	99.79 72	92.47 255	99.35 229	97.54 185	93.54 301	98.67 237

v52		97.79 210	97.50 209	98.66 209	98.80 269	98.62 177	99.87 4	99.44 157	95.87 268	99.01 186	99.46 198	94.44 201	99.33 233	96.65 244	93.96 297	98.05 300

TranMVSNet+NR-MVSNet		97.93 189	97.66 192	98.76 201	98.78 275	98.62 177	99.65 72	99.49 104	97.76 127	98.49 249	99.60 150	94.23 206	98.97 293	98.00 143	92.90 306	98.70 216

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 87	98.61 180	99.07 256	99.33 213	99.00 17	99.82 14	99.81 53	99.06 8	99.84 115	99.09 42	99.42 107	99.65 89

v7n		97.87 196	97.52 205	98.92 162	98.76 279	98.58 181	99.84 9	99.46 138	96.20 253	98.91 202	99.70 106	94.89 173	99.44 213	96.03 255	93.89 298	98.75 205

TAMVS		99.12 67	99.08 56	99.24 125	99.46 138	98.55 182	99.51 124	99.46 138	98.09 89	99.45 87	99.82 44	98.34 69	99.51 204	98.70 80	98.93 141	99.67 85

PEN-MVS		97.76 214	97.44 221	98.72 203	98.77 278	98.54 183	99.78 22	99.51 85	97.06 194	98.29 261	99.64 135	92.63 250	98.89 295	98.09 134	93.16 304	98.72 210

v1921920		97.80 208	97.45 216	98.84 191	98.80 269	98.53 184	99.52 120	99.34 205	96.15 259	99.24 143	99.47 194	93.98 216	99.29 244	95.40 269	95.13 270	98.69 221

PS-MVSNAJss		98.92 95	98.92 77	98.90 170	98.78 275	98.53 184	99.78 22	99.54 62	98.07 93	99.00 193	99.76 85	99.01 11	99.37 222	99.13 39	97.23 232	98.81 196

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 130	99.88 11	98.53 184	99.34 195	99.59 38	97.55 146	98.70 230	99.89 10	95.83 136	99.90 84	98.10 133	99.90 24	99.08 165
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		99.03 84	98.99 68	99.16 131	99.38 155	98.52 187	99.51 124	99.38 185	97.79 124	99.38 103	99.81 53	97.30 97	99.45 208	99.35 18	98.99 135	99.51 121

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 187	99.39 177	99.94 1	98.73 44	99.11 168	99.89 10	95.50 144	99.94 40	99.50 8	99.97 3	99.89 2

mvs_tets		98.40 135	98.23 138	98.91 166	98.67 290	98.51 189	99.66 62	99.53 72	98.19 76	98.65 239	99.81 53	92.75 239	99.44 213	99.31 25	97.48 221	98.77 202

CR-MVSNet		98.17 152	97.93 160	98.87 183	99.18 196	98.49 190	99.22 229	99.33 213	96.96 199	99.56 67	99.38 217	94.33 203	99.00 284	94.83 278	98.58 159	99.14 157

RPMNet		96.61 263	95.85 271	98.87 183	99.18 196	98.49 190	99.22 229	99.08 258	88.72 329	99.56 67	97.38 325	94.08 214	99.00 284	86.87 329	98.58 159	99.14 157

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 192	99.56 108	99.61 32	97.85 116	99.36 109	99.85 26	95.95 130	99.85 109	96.66 242	99.83 62	99.59 107

TestCases				99.31 110	99.86 20	98.48 192		99.61 32	97.85 116	99.36 109	99.85 26	95.95 130	99.85 109	96.66 242	99.83 62	99.59 107

jajsoiax		98.43 132	98.28 136	98.88 179	98.60 295	98.43 194	99.82 13	99.53 72	98.19 76	98.63 241	99.80 64	93.22 231	99.44 213	99.22 31	97.50 217	98.77 202

v1240		97.69 227	97.32 239	98.79 197	98.85 266	98.43 194	99.48 142	99.36 193	96.11 262	99.27 131	99.36 228	93.76 224	99.24 256	94.46 284	95.23 266	98.70 216

CANet_DTU		98.97 92	98.87 84	99.25 122	99.33 164	98.42 196	99.08 255	99.30 222	99.16 5	99.43 91	99.75 90	95.27 150	99.97 11	98.56 100	99.95 6	99.36 145

PatchT		97.03 260	96.44 261	98.79 197	98.99 230	98.34 197	99.16 238	99.07 261	92.13 314	99.52 76	97.31 327	94.54 196	98.98 286	88.54 322	98.73 155	99.03 172

Baseline_NR-MVSNet		97.76 214	97.45 216	98.68 206	99.09 216	98.29 198	99.41 170	98.85 286	95.65 272	98.63 241	99.67 121	94.82 177	99.10 275	98.07 140	92.89 307	98.64 253

CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 198	99.71 41	99.66 25	98.11 86	99.41 96	99.80 64	98.37 68	99.96 19	98.99 50	99.96 5	99.72 70

PAPM		97.59 236	97.09 250	99.07 138	99.06 220	98.26 200	98.30 325	99.10 256	94.88 279	98.08 268	99.34 235	96.27 124	99.64 189	89.87 318	98.92 143	99.31 149

OMC-MVS		99.08 77	99.04 61	99.20 129	99.67 87	98.22 201	99.28 209	99.52 76	98.07 93	99.66 47	99.81 53	97.79 85	99.78 147	97.79 159	99.81 67	99.60 103

EPNet		98.86 100	98.71 103	99.30 113	97.20 321	98.18 202	99.62 79	98.91 280	99.28 2	98.63 241	99.81 53	95.96 129	99.99 1	99.24 30	99.72 84	99.73 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				98.45 227	98.55 298	98.16 203	99.43 159	93.68 347		97.23 286	98.46 300	89.30 299	99.22 260	95.43 268	98.22 178	97.98 305

gg-mvs-nofinetune		96.17 280	95.32 287	98.73 202	98.79 271	98.14 204	99.38 182	94.09 346	91.07 322	98.07 271	91.04 340	89.62 297	99.35 229	96.75 236	99.09 128	98.68 226

DTE-MVSNet		97.51 243	97.19 248	98.46 226	98.63 293	98.13 205	99.84 9	99.48 113	96.68 214	97.97 274	99.67 121	92.92 235	98.56 301	96.88 232	92.60 311	98.70 216

VDDNet		97.55 237	97.02 252	99.16 131	99.49 133	98.12 206	99.38 182	99.30 222	95.35 275	99.68 36	99.90 7	82.62 334	99.93 55	99.31 25	98.13 189	99.42 140

thres200		97.61 235	97.28 243	98.62 210	99.64 100	98.03 207	99.26 220	98.74 298	97.68 137	99.09 175	98.32 303	91.66 277	99.81 135	92.88 309	98.22 178	98.03 303

IterMVS-LS		98.46 130	98.42 127	98.58 213	99.59 115	98.00 208	99.37 184	99.43 165	96.94 201	99.07 177	99.59 152	97.87 82	99.03 281	98.32 123	95.62 260	98.71 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		97.85 198	97.47 213	99.00 146	99.38 155	97.99 209	98.57 315	99.15 251	97.04 195	98.90 204	99.30 243	89.83 294	99.38 219	96.70 239	98.33 171	99.62 101

EI-MVSNet		98.67 122	98.67 107	98.68 206	99.35 160	97.97 210	99.50 129	99.38 185	96.93 202	99.20 155	99.83 37	97.87 82	99.36 226	98.38 116	97.56 212	98.71 212

tfpn200view9		97.72 223	97.38 229	98.72 203	99.69 83	97.96 211	99.50 129	98.73 306	97.83 119	99.17 161	98.45 301	91.67 275	99.83 122	93.22 303	98.18 182	98.37 291

thres400		97.77 213	97.38 229	98.92 162	99.69 83	97.96 211	99.50 129	98.73 306	97.83 119	99.17 161	98.45 301	91.67 275	99.83 122	93.22 303	98.18 182	98.96 184

thres600view7		97.86 197	97.51 207	98.92 162	99.72 71	97.95 213	99.59 89	98.74 298	97.94 109	99.27 131	98.62 293	91.75 270	99.86 104	93.73 299	98.19 181	98.96 184

CHOSEN 280x420		99.12 67	99.13 52	99.08 137	99.66 97	97.89 214	98.43 320	99.71 13	98.88 30	99.62 55	99.76 85	96.63 115	99.70 178	99.46 14	99.99 1	99.66 86

TR-MVS		97.76 214	97.41 226	98.82 193	99.06 220	97.87 215	98.87 298	98.56 315	96.63 218	98.68 232	99.22 253	92.49 254	99.65 187	95.40 269	97.79 202	98.95 191

conf200view11		97.78 212	97.45 216	98.77 199	99.72 71	97.86 216	99.59 89	98.74 298	97.93 110	99.26 135	98.62 293	91.75 270	99.83 122	93.22 303	98.18 182	98.61 270

thres100view900		97.76 214	97.45 216	98.69 205	99.72 71	97.86 216	99.59 89	98.74 298	97.93 110	99.26 135	98.62 293	91.75 270	99.83 122	93.22 303	98.18 182	98.37 291

test0.0.03 1		97.71 226	97.42 225	98.56 216	98.41 303	97.82 218	98.78 302	98.63 311	97.34 164	98.05 272	98.98 274	94.45 199	98.98 286	95.04 275	97.15 236	98.89 192

view600		97.97 183	97.66 192	98.89 172	99.75 52	97.81 219	99.69 45	98.80 290	98.02 102	99.25 138	98.88 278	91.95 264	99.89 92	94.36 287	98.29 173	98.96 184

view800		97.97 183	97.66 192	98.89 172	99.75 52	97.81 219	99.69 45	98.80 290	98.02 102	99.25 138	98.88 278	91.95 264	99.89 92	94.36 287	98.29 173	98.96 184

conf0.05thres1000		97.97 183	97.66 192	98.89 172	99.75 52	97.81 219	99.69 45	98.80 290	98.02 102	99.25 138	98.88 278	91.95 264	99.89 92	94.36 287	98.29 173	98.96 184

tfpn		97.97 183	97.66 192	98.89 172	99.75 52	97.81 219	99.69 45	98.80 290	98.02 102	99.25 138	98.88 278	91.95 264	99.89 92	94.36 287	98.29 173	98.96 184

JIA-IIPM		97.50 244	97.02 252	98.93 157	98.73 281	97.80 223	99.30 202	98.97 271	91.73 318	98.91 202	94.86 334	95.10 159	99.71 172	97.58 179	97.98 198	99.28 151

mvs-test1		98.86 100	98.84 90	98.89 172	99.33 164	97.77 224	99.44 154	99.30 222	98.47 57	99.10 171	99.43 203	96.78 109	99.95 33	98.73 77	99.02 133	98.96 184

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 172	99.71 76	97.74 225	99.12 245	99.54 62	98.44 62	99.42 94	99.71 103	94.20 207	99.92 63	98.54 105	98.90 145	99.00 175

XVG-OURS		98.73 117	98.68 106	98.88 179	99.70 81	97.73 226	98.92 293	99.55 55	98.52 55	99.45 87	99.84 35	95.27 150	99.91 72	98.08 138	98.84 149	99.00 175

v148		97.79 210	97.55 203	98.50 220	98.74 280	97.72 227	99.54 116	99.33 213	96.26 248	98.90 204	99.51 179	94.68 189	99.14 267	97.83 155	93.15 305	98.63 259

TAPA-MVS	97.07 15	97.74 220	97.34 236	98.94 154	99.70 81	97.53 228	99.25 222	99.51 85	91.90 317	99.30 119	99.63 139	98.78 37	99.64 189	88.09 324	99.87 38	99.65 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		97.73 221	97.45 216	98.57 214	99.45 142	97.50 229	99.02 271	98.98 270	96.11 262	99.41 96	99.14 258	90.28 288	98.74 298	95.74 260	98.93 141	99.47 131

cascas		97.69 227	97.43 224	98.48 223	98.60 295	97.30 230	98.18 329	99.39 179	92.96 310	98.41 252	98.78 289	93.77 223	99.27 248	98.16 130	98.61 156	98.86 193

PVSNet	96.02 17	98.85 106	98.84 90	98.89 172	99.73 68	97.28 231	98.32 324	99.60 35	97.86 114	99.50 79	99.57 159	96.75 112	99.86 104	98.56 100	99.70 90	99.54 113

MDA-MVSNet-bldmvs		94.96 293	93.98 298	97.92 271	98.24 306	97.27 232	99.15 241	99.33 213	93.80 302	80.09 340	99.03 269	88.31 312	97.86 319	93.49 301	94.36 288	98.62 261

GBi-Net		97.68 229	97.48 211	98.29 241	99.51 126	97.26 233	99.43 159	99.48 113	96.49 226	99.07 177	99.32 240	90.26 289	98.98 286	97.10 213	96.65 240	98.62 261

test1		97.68 229	97.48 211	98.29 241	99.51 126	97.26 233	99.43 159	99.48 113	96.49 226	99.07 177	99.32 240	90.26 289	98.98 286	97.10 213	96.65 240	98.62 261

FMVSNet1		96.84 261	96.36 262	98.29 241	99.32 171	97.26 233	99.43 159	99.48 113	95.11 277	98.55 246	99.32 240	83.95 331	98.98 286	95.81 259	96.26 250	98.62 261

v748		97.52 240	97.23 246	98.41 232	98.69 287	97.23 236	99.87 4	99.45 149	95.72 270	98.51 247	99.53 171	94.13 211	99.30 242	96.78 235	92.39 312	98.70 216

MDA-MVSNet_test_wron		95.45 288	94.60 293	98.01 265	98.16 307	97.21 237	99.11 251	99.24 242	93.49 306	80.73 339	98.98 274	93.02 232	98.18 303	94.22 296	94.45 287	98.64 253

VDD-MVS		97.73 221	97.35 233	98.88 179	99.47 137	97.12 238	99.34 195	98.85 286	98.19 76	99.67 42	99.85 26	82.98 332	99.92 63	99.49 12	98.32 172	99.60 103

test-LLR		98.06 164	97.90 161	98.55 218	98.79 271	97.10 239	98.67 309	97.75 329	97.34 164	98.61 244	98.85 282	94.45 199	99.45 208	97.25 204	99.38 109	99.10 160

test-mter		97.49 246	97.13 249	98.55 218	98.79 271	97.10 239	98.67 309	97.75 329	96.65 216	98.61 244	98.85 282	88.23 313	99.45 208	97.25 204	99.38 109	99.10 160

YYNet1		95.36 290	94.51 295	97.92 271	97.89 309	97.10 239	99.10 253	99.23 243	93.26 309	80.77 338	99.04 268	92.81 238	98.02 314	94.30 292	94.18 292	98.64 253

ACMM	97.58 5	98.37 137	98.34 131	98.48 223	99.41 147	97.10 239	99.56 108	99.45 149	98.53 54	99.04 183	99.85 26	93.00 233	99.71 172	98.74 75	97.45 222	98.64 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		98.19 151	98.10 144	98.45 227	98.88 259	97.07 243	99.28 209	99.38 185	98.57 52	99.22 150	99.81 53	92.12 263	99.66 185	98.08 138	97.54 214	98.61 270

Patchmatch-test		97.93 189	97.65 197	98.77 199	99.18 196	97.07 243	99.03 268	99.14 253	96.16 257	98.74 221	99.57 159	94.56 194	99.72 166	93.36 302	99.11 125	99.52 118

LPG-MVS_test		98.22 147	98.13 142	98.49 221	99.33 164	97.05 245	99.58 95	99.55 55	97.46 153	99.24 143	99.83 37	92.58 251	99.72 166	98.09 134	97.51 215	98.68 226

LGP-MVS_train				98.49 221	99.33 164	97.05 245		99.55 55	97.46 153	99.24 143	99.83 37	92.58 251	99.72 166	98.09 134	97.51 215	98.68 226

plane_prior7					99.29 176	97.03 247

ACMP	97.20 11	98.06 164	97.94 159	98.45 227	99.37 157	97.01 248	99.44 154	99.49 104	97.54 149	98.45 251	99.79 72	91.95 264	99.72 166	97.91 149	97.49 220	98.62 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior3						97.00 249			98.69 46	99.11 168

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 211	99.41 147	96.99 250	99.52 120	99.49 104	98.11 86	99.24 143	99.34 235	96.96 105	99.79 142	97.95 147	99.45 105	99.02 174

plane_prior6					99.27 181	96.98 251						92.71 243

HQP_MVS		98.27 143	98.22 139	98.44 230	99.29 176	96.97 252	99.39 177	99.47 128	98.97 22	99.11 168	99.61 147	92.71 243	99.69 181	97.78 160	97.63 205	98.67 237

plane_prior						96.97 252	99.21 232		98.45 59						97.60 208

ACMH	97.28 8	98.10 161	97.99 155	98.44 230	99.41 147	96.96 254	99.60 87	99.56 48	98.09 89	98.15 265	99.91 5	90.87 285	99.70 178	98.88 57	97.45 222	98.67 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NP-MVS					99.23 186	96.92 255					99.40 212

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 225	99.33 164	96.91 256	99.57 101	99.30 222	98.47 57	99.41 96	98.99 271	96.78 109	99.74 154	98.73 77	99.38 109	98.74 208

HQP5-MVS						96.83 257

HQP-MVS		98.02 175	97.90 161	98.37 235	99.19 193	96.83 257	98.98 281	99.39 179	98.24 72	98.66 233	99.40 212	92.47 255	99.64 189	97.19 208	97.58 210	98.64 253

CLD-MVS		98.16 154	98.10 144	98.33 237	99.29 176	96.82 259	98.75 305	99.44 157	97.83 119	99.13 164	99.55 164	92.92 235	99.67 183	98.32 123	97.69 204	98.48 283
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LTVRE_ROB	97.16 12	98.02 175	97.90 161	98.40 233	99.23 186	96.80 260	99.70 42	99.60 35	97.12 184	98.18 264	99.70 106	91.73 273	99.72 166	98.39 114	97.45 222	98.68 226
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
pmmvs5		97.52 240	97.30 241	98.16 258	98.57 297	96.73 261	99.27 212	98.90 282	96.14 260	98.37 255	99.53 171	91.54 279	99.14 267	97.51 188	95.87 255	98.63 259

BH-untuned		98.42 133	98.36 129	98.59 212	99.49 133	96.70 262	99.27 212	99.13 254	97.24 174	98.80 216	99.38 217	95.75 139	99.74 154	97.07 216	99.16 122	99.33 148

IB-MVS	95.67 18	96.22 278	95.44 286	98.57 214	99.21 189	96.70 262	98.65 312	97.74 331	96.71 212	97.27 285	98.54 299	86.03 322	99.92 63	98.47 110	86.30 333	99.10 160
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
ACMH+	97.24 10	97.92 192	97.78 177	98.32 238	99.46 138	96.68 264	99.56 108	99.54 62	98.41 63	97.79 280	99.87 19	90.18 292	99.66 185	98.05 142	97.18 235	98.62 261

EU-MVSNet		97.98 180	98.03 151	97.81 280	98.72 283	96.65 265	99.66 62	99.66 25	98.09 89	98.35 257	99.82 44	95.25 153	98.01 315	97.41 198	95.30 265	98.78 199

MVP-Stereo		97.81 206	97.75 187	97.99 267	97.53 314	96.60 266	98.96 286	98.85 286	97.22 176	97.23 286	99.36 228	95.28 149	99.46 207	95.51 266	99.78 73	97.92 309
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TESTMET0.1,1		97.55 237	97.27 245	98.40 233	98.93 250	96.53 267	98.67 309	97.61 336	96.96 199	98.64 240	99.28 246	88.63 308	99.45 208	97.30 203	99.38 109	99.21 154

OurMVSNet-221017-0		97.88 195	97.77 181	98.19 256	98.71 285	96.53 267	99.88 1	99.00 268	97.79 124	98.78 218	99.94 3	91.68 274	99.35 229	97.21 206	96.99 238	98.69 221

ADS-MVSNet		98.20 150	98.08 147	98.56 216	99.33 164	96.48 269	99.23 225	99.15 251	96.24 250	99.10 171	99.67 121	94.11 212	99.71 172	96.81 233	99.05 131	99.48 127

testgi		97.65 234	97.50 209	98.13 259	99.36 159	96.45 270	99.42 166	99.48 113	97.76 127	97.87 276	99.45 201	91.09 282	98.81 297	94.53 282	98.52 164	99.13 159

test_0402		96.64 262	96.24 263	97.85 276	98.85 266	96.43 271	99.44 154	99.26 239	93.52 305	96.98 292	99.52 176	88.52 309	99.20 265	92.58 312	97.50 217	97.93 308

ITE_SJBPF				98.08 260	99.29 176	96.37 272		98.92 277	98.34 66	98.83 214	99.75 90	91.09 282	99.62 195	95.82 258	97.40 226	98.25 296

semantic-postprocess				98.06 261	99.57 118	96.36 273		99.49 104	97.18 178	98.71 224	99.72 102	92.70 245	99.14 267	97.44 196	95.86 256	98.67 237

K. test v3		97.10 258	96.79 256	98.01 265	98.72 283	96.33 274	99.87 4	97.05 340	97.59 141	96.16 299	99.80 64	88.71 304	99.04 279	96.69 240	96.55 244	98.65 251

XVG-ACMP-BASELINE		97.83 202	97.71 190	98.20 255	99.11 211	96.33 274	99.41 170	99.52 76	98.06 97	99.05 182	99.50 181	89.64 296	99.73 162	97.73 167	97.38 228	98.53 280

IterMVS		97.83 202	97.77 181	98.02 264	99.58 116	96.27 276	99.02 271	99.48 113	97.22 176	98.71 224	99.70 106	92.75 239	99.13 270	97.46 194	96.00 254	98.67 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		97.50 244	97.33 238	98.03 262	98.65 291	96.23 277	99.77 24	98.68 309	97.14 181	97.90 275	99.93 4	90.45 287	99.18 266	97.00 219	96.43 246	98.67 237

BH-w/o		98.00 179	97.89 165	98.32 238	99.35 160	96.20 278	99.01 275	98.90 282	96.42 236	98.38 254	99.00 270	95.26 152	99.72 166	96.06 254	98.61 156	99.03 172

TDRefinement		95.42 289	94.57 294	97.97 268	89.83 340	96.11 279	99.48 142	98.75 295	96.74 210	96.68 294	99.88 14	88.65 307	99.71 172	98.37 117	82.74 336	98.09 298

EPMVS		97.82 205	97.65 197	98.35 236	98.88 259	95.98 280	99.49 137	94.71 345	97.57 144	99.26 135	99.48 190	92.46 258	99.71 172	97.87 152	99.08 129	99.35 146

pmmvs-eth3d		95.34 291	94.73 292	97.15 293	95.53 326	95.94 281	99.35 192	99.10 256	95.13 276	93.55 320	97.54 323	88.15 315	97.91 317	94.58 281	89.69 319	97.61 322

FMVSNet5		96.43 267	96.19 264	97.15 293	99.11 211	95.89 282	99.32 197	99.52 76	94.47 292	98.34 258	99.07 264	87.54 317	97.07 326	92.61 311	95.72 258	98.47 284

UnsupCasMVSNet_eth		96.44 266	96.12 265	97.40 292	98.65 291	95.65 283	99.36 188	99.51 85	97.13 182	96.04 302	98.99 271	88.40 311	98.17 304	96.71 238	90.27 316	98.40 289

MIMVSNet1		95.51 287	95.04 290	96.92 299	97.38 316	95.60 284	99.52 120	99.50 99	93.65 303	96.97 293	99.17 256	85.28 326	96.56 330	88.36 323	95.55 262	98.60 273

CVMVSNet		98.57 127	98.67 107	98.30 240	99.35 160	95.59 285	99.50 129	99.55 55	98.60 51	99.39 101	99.83 37	94.48 198	99.45 208	98.75 74	98.56 162	99.85 8

Patchmatch-test1		98.16 154	98.14 141	98.22 253	99.30 173	95.55 286	99.07 256	98.97 271	97.57 144	99.43 91	99.60 150	92.72 242	99.60 197	97.38 199	99.20 120	99.50 124

LF4IMVS		97.52 240	97.46 215	97.70 286	98.98 234	95.55 286	99.29 206	98.82 289	98.07 93	98.66 233	99.64 135	89.97 293	99.61 196	97.01 218	96.68 239	97.94 307

EPNet_dtu		98.03 173	97.96 157	98.23 251	98.27 305	95.54 288	99.23 225	98.75 295	99.02 10	97.82 278	99.71 103	96.11 128	99.48 205	93.04 307	99.65 98	99.69 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 257	96.89 254	97.83 278	99.07 218	95.52 289	98.57 315	98.74 298	97.58 143	97.81 279	99.79 72	88.16 314	99.56 200	95.10 273	97.21 233	98.39 290

pmmvs6		96.53 265	96.09 266	97.82 279	98.69 287	95.47 290	99.37 184	99.47 128	93.46 307	97.41 283	99.78 77	87.06 320	99.33 233	96.92 227	92.70 310	98.65 251

test20.03		96.12 281	95.96 270	96.63 303	97.44 315	95.45 291	99.51 124	99.38 185	96.55 224	96.16 299	99.25 250	93.76 224	96.17 331	87.35 327	94.22 291	98.27 294

lessismore_v0				97.79 281	98.69 287	95.44 292		94.75 344		95.71 303	99.87 19	88.69 305	99.32 236	95.89 257	94.93 275	98.62 261

PatchmatchNet		98.31 140	98.36 129	98.19 256	99.16 203	95.32 293	99.27 212	98.92 277	97.37 163	99.37 105	99.58 155	94.90 172	99.70 178	97.43 197	99.21 119	99.54 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LP		97.04 259	96.80 255	97.77 282	98.90 255	95.23 294	98.97 284	99.06 263	94.02 298	98.09 267	99.41 208	93.88 219	98.82 296	90.46 316	98.42 169	99.26 152

USDC		97.34 251	97.20 247	97.75 283	99.07 218	95.20 295	98.51 318	99.04 265	97.99 107	98.31 259	99.86 22	89.02 300	99.55 202	95.67 264	97.36 229	98.49 282

ADS-MVSNet2		98.02 175	98.07 149	97.87 274	99.33 164	95.19 296	99.23 225	99.08 258	96.24 250	99.10 171	99.67 121	94.11 212	98.93 294	96.81 233	99.05 131	99.48 127

MDTV_nov1_ep13_2view						95.18 297	99.35 192		96.84 207	99.58 63		95.19 156		97.82 156		99.46 134

new_pmnet		96.38 271	96.03 267	97.41 291	98.13 308	95.16 298	99.05 262	99.20 246	93.94 300	97.39 284	98.79 287	91.61 278	99.04 279	90.43 317	95.77 257	98.05 300

tpm		97.67 232	97.55 203	98.03 262	99.02 227	95.01 299	99.43 159	98.54 316	96.44 234	99.12 166	99.34 235	91.83 269	99.60 197	97.75 165	96.46 245	99.48 127

DWT-MVSNet_test		97.53 239	97.40 227	97.93 270	99.03 226	94.86 300	99.57 101	98.63 311	96.59 223	98.36 256	98.79 287	89.32 298	99.74 154	98.14 131	98.16 188	99.20 155

tpmrst		98.33 138	98.48 125	97.90 273	99.16 203	94.78 301	99.31 200	99.11 255	97.27 170	99.45 87	99.59 152	95.33 147	99.84 115	98.48 108	98.61 156	99.09 164

PatchFormer-LS_test		98.01 178	98.05 150	97.87 274	99.15 206	94.76 302	99.42 166	98.93 275	97.12 184	98.84 213	98.59 297	93.74 226	99.80 139	98.55 103	98.17 187	99.06 170

tpmvs		97.98 180	98.02 152	97.84 277	99.04 224	94.73 303	99.31 200	99.20 246	96.10 265	98.76 220	99.42 205	94.94 167	99.81 135	96.97 222	98.45 167	98.97 179

pmmvs3		94.09 300	93.25 302	96.60 304	94.76 329	94.49 304	98.92 293	98.18 324	89.66 324	96.48 296	98.06 306	86.28 321	97.33 325	89.68 319	87.20 327	97.97 306

MDTV_nov1_ep13			98.32 133		99.11 211	94.44 305	99.27 212	98.74 298	97.51 150	99.40 100	99.62 144	94.78 180	99.76 152	97.59 178	98.81 152

tpm2		97.44 248	97.34 236	97.74 284	99.15 206	94.36 306	99.45 150	98.94 274	93.45 308	98.90 204	99.44 202	91.35 280	99.59 199	97.31 202	98.07 195	99.29 150

PVSNet_0	94.43 19	96.09 282	95.47 284	97.94 269	99.31 172	94.34 307	97.81 332	99.70 15	97.12 184	97.46 282	98.75 290	89.71 295	99.79 142	97.69 173	81.69 337	99.68 82

Anonymous20231206		96.22 278	96.03 267	96.79 302	97.31 319	94.14 308	99.63 76	99.08 258	96.17 256	97.04 290	99.06 266	93.94 217	97.76 322	86.96 328	95.06 271	98.47 284

CostFormer		97.72 223	97.73 188	97.71 285	99.15 206	94.02 309	99.54 116	99.02 267	94.67 283	99.04 183	99.35 232	92.35 261	99.77 149	98.50 107	97.94 199	99.34 147

UnsupCasMVSNet_bld		93.53 302	92.51 304	96.58 305	97.38 316	93.82 310	98.24 326	99.48 113	91.10 321	93.10 322	96.66 329	74.89 336	98.37 302	94.03 298	87.71 326	97.56 324

tpm cat1		97.39 250	97.36 231	97.50 290	99.17 201	93.73 311	99.43 159	99.31 220	91.27 319	98.71 224	99.08 263	94.31 205	99.77 149	96.41 250	98.50 165	99.00 175

tpmp4_e23		97.34 251	97.29 242	97.52 288	99.25 185	93.73 311	99.58 95	99.19 249	94.00 299	98.20 263	99.41 208	90.74 286	99.74 154	97.13 212	98.07 195	99.07 169

dp		97.75 218	97.80 174	97.59 287	99.10 214	93.71 313	99.32 197	98.88 284	96.48 232	99.08 176	99.55 164	92.67 249	99.82 131	96.52 246	98.58 159	99.24 153

MVS-HIRNet		95.75 285	95.16 289	97.51 289	99.30 173	93.69 314	98.88 297	95.78 342	85.09 332	98.78 218	92.65 336	91.29 281	99.37 222	94.85 277	99.85 52	99.46 134

DSMNet-mixed		97.25 254	97.35 233	96.95 298	97.84 310	93.61 315	99.57 101	96.63 341	96.13 261	98.87 207	98.61 296	94.59 193	97.70 323	95.08 274	98.86 148	99.55 111

MS-PatchMatch		97.24 255	97.32 239	96.99 296	98.45 302	93.51 316	98.82 300	99.32 219	97.41 160	98.13 266	99.30 243	88.99 301	99.56 200	95.68 263	99.80 69	97.90 310

OpenMVS_ROB	92.34 20	94.38 298	93.70 299	96.41 306	97.38 316	93.17 317	99.06 260	98.75 295	86.58 330	94.84 307	98.26 305	81.53 335	99.32 236	89.01 321	97.87 201	96.76 326

gm-plane-assit					98.54 299	92.96 318			94.65 284		99.15 257		99.64 189	97.56 183

EG-PatchMatch MVS		95.97 283	95.69 277	96.81 301	97.78 311	92.79 319	99.16 238	98.93 275	96.16 257	94.08 311	99.22 253	82.72 333	99.47 206	95.67 264	97.50 217	98.17 297

new-patchmatchnet		94.48 296	94.08 297	95.67 308	95.08 328	92.41 320	99.18 236	99.28 233	94.55 289	93.49 321	97.37 326	87.86 316	97.01 327	91.57 313	88.36 322	97.61 322

testpf		95.66 286	96.02 269	94.58 310	98.35 304	92.32 321	97.25 337	97.91 328	92.83 311	97.03 291	98.99 271	88.69 305	98.61 300	95.72 261	97.40 226	92.80 335

LCM-MVSNet-Re		97.83 202	98.15 140	96.87 300	99.30 173	92.25 322	99.59 89	98.26 320	97.43 157	96.20 298	99.13 259	96.27 124	98.73 299	98.17 129	98.99 135	99.64 95

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 295	99.60 113	91.75 323	98.61 313	99.44 157	99.35 1	99.83 11	99.85 26	98.70 49	99.81 135	99.02 48	99.91 17	99.81 35

RPSCF		98.22 147	98.62 115	96.99 296	99.82 29	91.58 324	99.72 39	99.44 157	96.61 219	99.66 47	99.89 10	95.92 133	99.82 131	97.46 194	99.10 127	99.57 110

Patchmatch-RL test		95.84 284	95.81 273	95.95 307	95.61 324	90.57 325	98.24 326	98.39 317	95.10 278	95.20 304	98.67 292	94.78 180	97.77 321	96.28 252	90.02 317	99.51 121

Gipuma		90.99 307	90.15 308	93.51 312	98.73 281	90.12 326	93.98 341	99.45 149	79.32 336	92.28 325	94.91 333	69.61 338	97.98 316	87.42 325	95.67 259	92.45 337
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 303	92.23 305	95.14 309	95.61 324	89.98 327	99.37 184	98.21 322	94.80 281	95.04 306	97.69 314	65.06 341	97.90 318	94.30 292	89.98 318	97.54 325

Anonymous20231211		90.69 308	89.39 309	94.58 310	94.25 330	88.18 328	99.29 206	99.07 261	82.45 335	92.95 323	97.65 316	63.96 343	97.79 320	89.27 320	85.63 334	97.77 319

1111		92.30 305	92.21 306	92.55 316	93.30 332	86.27 329	99.15 241	98.74 298	91.94 315	90.85 329	97.82 309	84.18 329	95.21 333	79.65 336	94.27 290	96.19 329

.test1245		83.42 313	86.17 311	75.15 335	93.30 332	86.27 329	99.15 241	98.74 298	91.94 315	90.85 329	97.82 309	84.18 329	95.21 333	79.65 336	39.90 347	43.98 346

test2356		94.07 301	94.46 296	92.89 315	95.18 327	86.13 331	97.60 335	99.06 263	93.61 304	96.15 301	98.28 304	85.60 325	93.95 337	86.68 330	98.00 197	98.59 274

no-one		83.04 314	80.12 316	91.79 320	89.44 341	85.65 332	99.32 197	98.32 318	89.06 326	79.79 342	89.16 342	44.86 349	96.67 329	84.33 333	46.78 345	93.05 334

testus		94.61 295	95.30 288	92.54 317	96.44 322	84.18 333	98.36 321	99.03 266	94.18 297	96.49 295	98.57 298	88.74 303	95.09 335	87.41 326	98.45 167	98.36 293

PMMVS2		86.87 310	85.37 313	91.35 323	90.21 339	83.80 334	98.89 296	97.45 338	83.13 334	91.67 328	95.03 332	48.49 347	94.70 336	85.86 331	77.62 338	95.54 331

test1235678		92.91 304	93.30 301	91.71 321	93.14 334	83.01 335	98.75 305	98.58 314	92.80 312	92.45 324	97.91 308	88.51 310	93.54 338	82.26 334	95.35 264	98.59 274

test12356		91.74 306	92.19 307	90.37 324	91.22 336	82.41 336	98.61 313	98.28 319	90.66 323	91.82 327	97.92 307	84.90 327	92.61 339	81.64 335	94.66 282	96.09 330

ambc				93.06 314	92.68 335	82.36 337	98.47 319	98.73 306		95.09 305	97.41 324	55.55 345	99.10 275	96.42 249	91.32 314	97.71 321

DeepMVS_CX				93.34 313	99.29 176	82.27 338		99.22 244	85.15 331	96.33 297	99.05 267	90.97 284	99.73 162	93.57 300	97.77 203	98.01 304

LCM-MVSNet		86.80 311	85.22 314	91.53 322	87.81 342	80.96 339	98.23 328	98.99 269	71.05 339	90.13 331	96.51 330	48.45 348	96.88 328	90.51 315	85.30 335	96.76 326

CMPMVS	69.68 23	94.13 299	94.90 291	91.84 319	97.24 320	80.01 340	98.52 317	99.48 113	89.01 327	91.99 326	99.67 121	85.67 324	99.13 270	95.44 267	97.03 237	96.39 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		94.95 294	95.83 272	92.31 318	98.47 301	79.33 341	99.12 245	92.81 351	93.87 301	97.68 281	99.13 259	93.87 220	99.01 283	91.38 314	96.19 251	98.59 274

ANet_high		77.30 319	74.86 321	84.62 329	75.88 349	77.61 342	97.63 334	93.15 350	88.81 328	64.27 345	89.29 341	36.51 350	83.93 348	75.89 341	52.31 344	92.33 338

testmv		87.91 309	87.80 310	88.24 325	87.68 343	77.50 343	99.07 256	97.66 335	89.27 325	86.47 333	96.22 331	68.35 339	92.49 341	76.63 340	88.82 320	94.72 333

EMVS		80.02 317	79.22 318	82.43 333	91.19 337	76.40 344	97.55 336	92.49 353	66.36 344	83.01 337	91.27 338	64.63 342	85.79 347	65.82 345	60.65 342	85.08 343

E-PMN		80.61 316	79.88 317	82.81 331	90.75 338	76.38 345	97.69 333	95.76 343	66.44 343	83.52 335	92.25 337	62.54 344	87.16 346	68.53 344	61.40 341	84.89 344

MVE	76.82 21	76.91 320	74.31 322	84.70 327	85.38 347	76.05 346	96.88 338	93.17 349	67.39 342	71.28 344	89.01 343	21.66 357	87.69 345	71.74 343	72.29 340	90.35 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 322	71.19 323	84.14 330	76.16 348	74.29 347	96.00 340	92.57 352	69.57 340	63.84 346	87.49 344	21.98 354	88.86 344	75.56 342	57.50 343	89.26 342

PNet_i23d		79.43 318	77.68 319	84.67 328	86.18 345	71.69 348	96.50 339	93.68 347	75.17 337	71.33 343	91.18 339	32.18 352	90.62 343	78.57 339	74.34 339	91.71 339

tmp_tt		82.80 315	81.52 315	86.66 326	66.61 351	68.44 349	92.79 343	97.92 326	68.96 341	80.04 341	99.85 26	85.77 323	96.15 332	97.86 153	43.89 346	95.39 332

FPMVS		84.93 312	85.65 312	82.75 332	86.77 344	63.39 350	98.35 323	98.92 277	74.11 338	83.39 336	98.98 274	50.85 346	92.40 342	84.54 332	94.97 273	92.46 336

PMVS	70.75 22	75.98 321	74.97 320	79.01 334	70.98 350	55.18 351	93.37 342	98.21 322	65.08 345	61.78 347	93.83 335	21.74 356	92.53 340	78.59 338	91.12 315	89.34 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 324	41.29 327	36.84 336	86.18 345	49.12 352	79.73 344	22.81 355	27.64 346	25.46 350	28.45 350	21.98 354	48.89 349	55.80 346	23.56 350	12.51 348

test123		39.01 326	42.50 326	28.53 338	39.17 352	20.91 353	98.75 305	19.17 356	19.83 348	38.57 348	66.67 346	33.16 351	15.42 350	37.50 348	29.66 349	49.26 345

testmvs		39.17 325	43.78 324	25.37 339	36.04 353	16.84 354	98.36 321	26.56 354	20.06 347	38.51 349	67.32 345	29.64 353	15.30 351	37.59 347	39.90 347	43.98 346

cdsmvs_eth3d_5k		24.64 327	32.85 328	0.00 340	0.00 354	0.00 355	0.00 345	99.51 85	0.00 349	0.00 351	99.56 161	96.58 116	0.00 352	0.00 349	0.00 351	0.00 349

pcd_1.5k_mvsjas		8.27 329	11.03 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	99.01 11	0.00 352	0.00 349	0.00 351	0.00 349

pcd1.5k->3k		40.85 323	43.49 325	32.93 337	98.95 242	0.00 355	0.00 345	99.53 72	0.00 349	0.00 351	0.27 351	95.32 148	0.00 352	0.00 349	97.30 230	98.80 197

sosnet-low-res		0.02 330	0.03 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

sosnet		0.02 330	0.03 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uncertanet		0.02 330	0.03 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

Regformer		0.02 330	0.03 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

ab-mvs-re		8.30 328	11.06 329	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	99.58 155	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

uanet		0.02 330	0.03 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.27 351	0.00 358	0.00 352	0.00 349	0.00 351	0.00 349

test_part1								99.48 113				98.96 20			99.84 57	99.83 23

test_full								99.47 128

sam_mvs1												94.86 175

sam_mvs												94.72 188

MTGPA								99.47 128

test_post1							99.23 225				65.14 348	94.18 210	99.71 172	97.58 179

test_post											65.99 347	94.65 192	99.73 162

patchmatchnet-post											98.70 291	94.79 179	99.74 154

MTMP								98.88 284

test9_res														97.49 190	99.72 84	99.75 54

agg_prior2														97.21 206	99.73 83	99.75 54

test_prior2							98.96 286		98.34 66	99.01 186	99.52 176	98.68 50		97.96 145	99.74 80

旧先验2							98.96 286		96.70 213	99.47 84			99.94 40	98.19 127

新几何2							99.01 275

无先验							98.99 277	99.51 85	96.89 204				99.93 55	97.53 186		99.72 70

原ACMM2							98.95 290

testdata2													99.95 33	96.67 241

segment_acmp												98.96 20

testdata1							98.85 299		98.32 69

plane_prior5								99.47 128					99.69 181	97.78 160	97.63 205	98.67 237

plane_prior4											99.61 147

plane_prior2							99.39 177		98.97 22

plane_prior1					99.26 183

n2								0.00 357

nn								0.00 357

door-mid								98.05 325

test11								99.35 197

door								97.92 326

HQP-NCC					99.19 193		98.98 281		98.24 72	98.66 233

ACMP_Plane					99.19 193		98.98 281		98.24 72	98.66 233

BP-MVS														97.19 208

HQP4-MVS										98.66 233			99.64 189			98.64 253

HQP3-MVS								99.39 179							97.58 210

HQP2-MVS												92.47 255

ACMMP++_ref															97.19 234

ACMMP++															97.43 225

Test By Simon												98.75 45

High-resolution multi-view benchmark