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	99.30 199	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	99.47 143	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	99.36 185	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 59	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 49	99.84 6	99.43 156	99.51 85	98.68 47	99.27 128	99.53 168	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	99.37 102	99.74 95	98.81 34	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 261	95.45 282	99.87 6	99.85 23	99.83 7	99.69 45	99.68 19	98.98 19	99.37 102	64.01 346	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 73	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 59	99.46 138	98.09 89	99.48 80	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 86	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	99.49 181	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 59	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 69	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	99.55 110	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 59	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 76	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 49	99.79 17	99.50 126	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 92	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 59	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 71	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	99.18 193	99.75 22	99.56 105	99.57 44	98.45 59	99.49 79	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	99.29 203	99.40 176	98.79 40	99.52 73	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 49	99.71 27	99.15 238	99.41 169	96.60 218	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	99.62 104	99.71 27	99.26 217	99.52 76	98.82 35	99.39 98	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 60	99.70 29	99.27 209	99.57 44	96.40 236	99.42 91	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	99.05 220	99.69 30	99.81 15	99.46 138	98.04 99	99.01 183	99.82 44	96.69 114	99.38 216	99.34 22	94.59 282	98.78 196

SD-MVS		99.41 32	99.52 6	99.05 138	99.74 60	99.68 31	99.46 146	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 320	98.72 79	99.93 11	99.77 50

3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 198	99.68 31	99.81 15	99.51 85	99.20 4	98.72 220	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	99.20 188	99.67 33	99.77 24	99.72 11	94.74 279	98.73 219	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 76	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 73	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	99.37 154	99.66 35	99.45 147	99.54 62	96.61 216	99.01 183	99.40 209	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	99.12 206	99.66 35	99.84 9	99.74 10	99.09 8	98.92 198	99.90 7	95.94 132	99.98 5	98.95 53	99.92 12	99.79 44

TEST9					99.67 84	99.65 38	99.05 259	99.41 169	96.22 249	98.95 194	99.49 181	98.77 40	99.91 72

train_agg		99.02 85	98.77 97	99.77 35	99.67 84	99.65 38	99.05 259	99.41 169	96.28 242	98.95 194	99.49 181	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	99.61 108	99.65 38	99.30 199	99.48 113	98.86 31	99.21 149	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 84	99.62 41	98.99 274	99.40 176	96.26 245	98.87 204	99.49 181	98.77 40	99.91 72	97.69 173	99.72 84	99.75 54

agg_prior					99.67 84	99.62 41		99.40 176		98.87 204			99.91 72

test_8					99.67 84	99.61 43	99.03 265	99.41 169	96.28 242	98.93 197	99.48 187	98.76 42	99.91 72

test12				99.75 38	99.64 97	99.61 43		99.29 226		99.21 149		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 84	99.60 45	99.04 264	99.41 169	95.93 264	98.87 204	99.48 187	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 60	99.60 45	99.27 209	99.48 113	96.82 205	99.25 135	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 49	99.59 47		99.54 62	96.76 206	99.29 120	99.64 135	98.43 62	99.94 40	96.92 227	99.66 96	99.72 70

旧先验1					99.74 60	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	99.63 100	99.59 47	99.36 185	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	98.99 274

VNet		99.11 71	98.90 80	99.73 45	99.52 121	99.56 50	99.41 167	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	99.62 104	99.55 52	99.50 126	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	99.23 124	99.18 193	99.54 53	99.83 12	99.59 38	98.28 70	98.79 214	99.81 53	96.75 112	99.37 219	99.08 43	96.38 244	98.78 196

VPA-MVSNet		98.29 141	97.95 158	99.30 113	99.16 200	99.54 53	99.50 126	99.58 43	98.27 71	99.35 109	99.37 218	92.53 250	99.65 184	99.35 18	94.46 283	98.72 207

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 118	99.54 53	99.18 233	99.70 15	98.18 79	99.35 109	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 76	99.59 38	92.65 310	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	99.41 167	99.50 99	97.03 193	99.04 180	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	99.04 221	99.53 56	99.82 13	99.72 11	94.56 285	98.08 265	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 73	99.52 59	99.49 134	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	99.56 118	99.52 59	99.58 92	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	98.94 289	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 49	99.49 62	98.91 292	99.49 104	96.42 233	99.34 112	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 84	99.48 63	98.96 283	99.56 48	98.34 66	99.01 183	99.52 173	98.68 50	99.83 122	97.96 145	99.74 80	99.74 59

test_prior				99.68 50	99.67 84	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 68	99.47 65	98.95 287	99.85 6	98.82 35	99.54 70	99.73 98	98.51 57	99.74 151	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 86	99.49 104	97.03 193	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	99.15 130	99.08 214	99.45 67	99.86 8	99.60 35	98.23 75	98.70 227	99.82 44	96.80 108	99.22 257	99.07 44	96.38 244	98.79 195

Regformer-1		99.53 9	99.47 8	99.72 47	99.71 73	99.44 68	99.49 134	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 60	99.44 68	99.39 174	99.38 185	97.70 135	99.28 124	99.28 243	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	99.43 140	99.42 70	99.51 121	98.96 273	98.61 50	99.35 109	98.92 274	94.78 180	99.77 146	99.35 18	98.11 191	99.54 113

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 43	99.41 71	99.58 92	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	99.58 113	99.41 71	99.16 235	99.44 157	98.45 59	99.19 155	99.49 181	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 68	99.40 73	99.05 259	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	99.02 268	99.91 3	97.67 138	99.59 62	99.75 90	95.90 134	99.73 159	99.53 6	99.02 133	99.86 5

MVS_0304		99.06 79	98.86 87	99.66 53	99.51 123	99.36 75	99.22 226	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	99.13 132	99.00 226	99.36 75	99.49 134	99.51 85	97.95 108	98.97 193	99.13 256	96.30 123	99.38 216	98.36 119	93.34 299	98.66 245

原ACMM1				99.65 57	99.73 65	99.33 77		99.47 128	97.46 153	99.12 163	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	99.42 141	99.32 78	99.80 19	99.48 113	98.63 48	99.31 115	98.81 283	97.09 101	99.75 150	99.27 29	97.90 197	99.47 131

XXY-MVS		98.38 136	98.09 146	99.24 122	99.26 180	99.32 78	99.56 105	99.55 55	97.45 156	98.71 221	99.83 37	93.23 230	99.63 191	98.88 57	96.32 246	98.76 201

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 65	99.32 78	99.75 34	99.20 246	98.02 102	99.56 67	99.86 22	96.54 117	99.67 180	98.09 134	99.13 124	99.73 64

API-MVS		99.04 82	99.03 63	99.06 136	99.40 149	99.31 81	99.55 110	99.56 48	98.54 53	99.33 113	99.39 213	98.76 42	99.78 144	96.98 221	99.78 73	98.07 296

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 43	99.29 82	99.58 92	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	99.51 123	99.28 83	99.52 117	99.47 128	96.11 259	99.01 183	99.34 232	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 73	99.28 83	99.06 257	99.77 9	97.74 130	99.50 76	99.53 168	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	99.42 163	99.54 62	97.29 169	99.41 93	99.59 152	98.42 65	99.93 55	98.19 127	99.69 91	99.73 64

NR-MVSNet		97.97 180	97.61 197	99.02 140	98.87 259	99.26 86	99.47 143	99.42 166	97.63 140	97.08 286	99.50 178	95.07 160	99.13 267	97.86 153	93.59 297	98.68 223

WR-MVS		98.06 161	97.73 185	99.06 136	98.86 262	99.25 87	99.19 232	99.35 197	97.30 168	98.66 230	99.43 200	93.94 217	99.21 261	98.58 95	94.28 286	98.71 209

CP-MVSNet		98.09 159	97.78 174	99.01 141	98.97 234	99.24 88	99.67 56	99.46 138	97.25 172	98.48 247	99.64 135	93.79 222	99.06 274	98.63 88	94.10 290	98.74 205

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 89	99.62 76	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		97.84 197	97.47 210	98.98 145	99.20 188	99.22 90	99.64 71	99.61 32	96.32 239	98.27 259	99.70 106	93.35 229	99.44 210	95.69 259	95.40 260	98.27 291

diffmvs		98.72 118	98.49 124	99.43 100	99.48 133	99.19 91	99.62 76	99.42 166	95.58 270	99.37 102	99.67 121	96.14 127	99.74 151	98.14 131	98.96 138	99.37 144

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 97	99.19 91	99.44 151	99.54 62	97.77 126	99.30 116	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	98.75 302	99.55 55	97.25 172	99.47 81	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	99.41 144	99.16 94	99.35 189	99.57 44	98.82 35	99.51 75	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	99.60 110	99.16 94	99.41 167	99.71 13	98.98 19	99.45 84	99.78 77	99.19 4	99.54 200	99.28 27	99.84 57	99.63 99

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 104	99.16 94	99.37 181	99.56 48	98.04 99	99.53 71	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 82	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 84	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	99.49 130	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	99.46 135	99.12 100	98.08 327	99.50 99	97.50 151	99.38 100	99.41 205	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	97.66 230	97.06 248	99.47 91	99.61 108	99.09 102	98.04 328	99.25 241	91.24 317	98.51 244	99.70 106	94.55 195	99.91 72	92.76 307	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	99.42 141	99.08 103	99.62 76	99.36 193	97.39 162	99.28 124	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	99.47 143	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		97.93 186	97.59 199	98.95 150	98.99 227	99.06 105	99.68 54	99.52 76	97.13 182	98.31 256	99.68 117	92.44 256	99.05 275	98.51 106	94.08 291	98.75 202

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 96	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

DI_MVS_plusplus_test		97.45 244	96.79 253	99.44 97	97.76 309	99.04 107	99.21 229	98.61 313	97.74 130	94.01 311	98.83 281	87.38 316	99.83 122	98.63 88	98.90 145	99.44 137

PAPR		98.63 126	98.34 131	99.51 85	99.40 149	99.03 108	98.80 298	99.36 193	96.33 238	99.00 190	99.12 259	98.46 60	99.84 115	95.23 269	99.37 113	99.66 86

MVSTER		98.49 128	98.32 133	99.00 143	99.35 157	99.02 109	99.54 113	99.38 185	97.41 160	99.20 152	99.73 98	93.86 221	99.36 223	98.87 61	97.56 209	98.62 258

1112_ss		98.98 90	98.77 97	99.59 68	99.68 83	99.02 109	99.25 219	99.48 113	97.23 175	99.13 161	99.58 155	96.93 106	99.90 84	98.87 61	98.78 153	99.84 12

LFMVS		97.90 191	97.35 230	99.54 75	99.52 121	99.01 111	99.39 174	98.24 321	97.10 188	99.65 50	99.79 72	84.79 325	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 94	99.01 111	99.24 221	99.52 76	96.85 203	99.27 128	99.48 187	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		94.44 294	92.93 300	98.98 145	94.16 328	99.00 113	99.42 163	99.28 233	96.60 218	84.86 331	96.84 325	70.91 334	99.27 245	98.23 126	96.08 250	98.68 223

test_normal		97.44 245	96.77 255	99.44 97	97.75 310	99.00 113	99.10 250	98.64 310	97.71 133	93.93 314	98.82 282	87.39 315	99.83 122	98.61 92	98.97 137	99.49 125

UniMVSNet_NR-MVSNet		98.22 144	97.97 156	98.96 148	98.92 249	98.98 115	99.48 139	99.53 72	97.76 127	98.71 221	99.46 195	96.43 120	99.22 257	98.57 97	92.87 305	98.69 218

DU-MVS		98.08 160	97.79 172	98.96 148	98.87 259	98.98 115	99.41 167	99.45 149	97.87 113	98.71 221	99.50 178	94.82 177	99.22 257	98.57 97	92.87 305	98.68 223

FMVSNet3		98.03 170	97.76 181	98.84 188	99.39 151	98.98 115	99.40 173	99.38 185	96.67 212	99.07 174	99.28 243	92.93 234	98.98 283	97.10 213	96.65 237	98.56 276

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 100	98.97 118	99.12 242	99.51 85	98.86 31	99.84 8	99.47 191	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	99.63 100	98.97 118	99.12 242	99.51 85	98.86 31	99.84 8	99.47 191	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	99.63 100	98.97 118	99.12 242	99.51 85	98.86 31	99.84 8	99.47 191	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	99.62 104	98.97 118	99.36 185	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	98.94 151	98.50 297	98.96 122	99.77 24	99.50 99	97.07 189	98.87 204	99.77 82	94.76 185	99.28 242	98.66 85	97.60 205	98.57 275

testdata				99.54 75	99.75 49	98.95 123		99.51 85	97.07 189	99.43 88	99.70 106	98.87 29	99.94 40	97.76 163	99.64 99	99.72 70

MVS		97.28 250	96.55 257	99.48 88	98.78 272	98.95 123	99.27 209	99.39 179	83.53 330	98.08 265	99.54 167	96.97 104	99.87 101	94.23 292	99.16 122	99.63 99

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 80	98.95 123	99.03 265	99.47 128	96.98 195	99.15 160	99.23 249	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	99.57 115	98.94 126	98.97 281	99.46 138	98.92 28	99.71 30	99.24 248	99.01 11	99.98 5	99.35 18	99.66 96	98.97 179

VPNet		97.84 197	97.44 218	99.01 141	99.21 186	98.94 126	99.48 139	99.57 44	98.38 64	99.28 124	99.73 98	88.89 299	99.39 215	99.19 33	93.27 300	98.71 209

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 123	98.94 126	99.88 1	99.46 138	97.55 146	99.80 16	99.65 128	97.39 93	99.28 242	99.03 46	99.85 52	99.65 89

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 123	98.94 126	99.05 259	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		95.05 289	93.67 297	99.22 125	96.07 320	98.94 126	99.20 231	99.27 238	97.71 133	89.96 329	97.59 319	66.18 337	99.25 251	98.06 141	98.96 138	99.47 131

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 120	98.91 131	99.02 268	99.45 149	98.80 39	99.71 30	99.26 246	98.94 25	99.98 5	99.34 22	99.23 118	98.98 178

test_djsdf		98.67 122	98.57 121	98.98 145	98.70 283	98.91 131	99.88 1	99.46 138	97.55 146	99.22 147	99.88 14	95.73 140	99.28 242	99.03 46	97.62 204	98.75 202

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 73	98.88 133	99.80 19	99.44 157	97.91 112	99.36 106	99.78 77	95.49 145	99.43 214	97.91 149	99.11 125	99.62 101

pmmvs4		98.13 153	97.90 161	98.81 191	98.61 291	98.87 134	98.99 274	99.21 245	96.44 231	99.06 178	99.58 155	95.90 134	99.11 270	97.18 210	96.11 249	98.46 283

jason		99.13 62	99.03 63	99.45 94	99.46 135	98.87 134	99.12 242	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		97.75 215	97.40 224	98.81 191	99.10 211	98.87 134	99.11 248	99.33 213	94.83 277	98.81 212	99.38 214	94.33 203	99.02 279	96.10 250	95.57 258	98.53 277

TransMVSNet (Re)		97.15 253	96.58 256	98.86 184	99.12 206	98.85 137	99.49 134	98.91 280	95.48 271	97.16 285	99.80 64	93.38 228	99.11 270	94.16 294	91.73 310	98.62 258

V42		98.06 161	97.79 172	98.86 184	98.98 231	98.84 138	99.69 45	99.34 205	96.53 222	99.30 116	99.37 218	94.67 190	99.32 233	97.57 181	94.66 279	98.42 284

WR-MVS_H		98.13 153	97.87 166	98.90 167	99.02 224	98.84 138	99.70 42	99.59 38	97.27 170	98.40 250	99.19 252	95.53 143	99.23 254	98.34 120	93.78 296	98.61 267

FMVSNet2		97.72 220	97.36 228	98.80 193	99.51 123	98.84 138	99.45 147	99.42 166	96.49 223	98.86 209	99.29 242	90.26 286	98.98 283	96.44 245	96.56 240	98.58 274

v15		96.28 269	95.62 275	98.25 245	98.94 242	98.83 141	99.76 27	99.29 226	94.52 287	94.02 310	97.61 316	95.02 162	98.13 306	94.53 279	86.92 325	97.80 310

v13		96.24 272	95.58 277	98.25 245	98.98 231	98.83 141	99.75 34	99.29 226	94.35 292	93.89 315	97.60 317	95.17 157	98.11 308	94.27 291	86.86 328	97.81 308

v6		98.12 155	97.84 167	98.94 151	98.94 242	98.83 141	99.66 59	99.34 205	96.49 223	99.30 116	99.37 218	94.95 166	99.34 229	97.77 162	94.74 273	98.67 234

v11		96.23 274	95.57 280	98.21 251	98.93 247	98.83 141	99.72 39	99.29 226	94.29 293	94.05 309	97.64 314	94.88 174	98.04 310	92.89 305	88.43 318	97.77 316

V14		96.26 270	95.60 276	98.26 241	98.94 242	98.83 141	99.76 27	99.29 226	94.49 288	93.96 312	97.66 312	94.99 165	98.13 306	94.41 282	86.90 326	97.80 310

V9		96.25 271	95.58 277	98.26 241	98.94 242	98.83 141	99.75 34	99.29 226	94.45 290	93.96 312	97.62 315	94.94 167	98.14 305	94.40 283	86.87 327	97.81 308

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 144	98.83 141	99.30 199	98.77 294	97.70 135	98.94 196	99.65 128	92.91 237	99.74 151	96.52 243	99.55 103	99.64 95

v18		96.42 265	95.80 272	98.26 241	98.95 239	98.82 148	99.76 27	99.28 233	94.58 282	94.12 306	97.70 309	95.22 155	98.16 302	94.83 275	87.80 320	97.79 315

v2v482		98.06 161	97.77 178	98.92 159	98.90 252	98.82 148	99.57 98	99.36 193	96.65 213	99.19 155	99.35 229	94.20 207	99.25 251	97.72 171	94.97 270	98.69 218

v1neww		98.12 155	97.84 167	98.93 154	98.97 234	98.81 150	99.66 59	99.35 197	96.49 223	99.29 120	99.37 218	95.02 162	99.32 233	97.73 167	94.73 274	98.67 234

v7new		98.12 155	97.84 167	98.93 154	98.97 234	98.81 150	99.66 59	99.35 197	96.49 223	99.29 120	99.37 218	95.02 162	99.32 233	97.73 167	94.73 274	98.67 234

v16		96.39 267	95.76 273	98.26 241	98.96 237	98.81 150	99.76 27	99.28 233	94.57 283	94.10 307	97.70 309	95.04 161	98.16 302	94.70 277	87.77 321	97.80 310

v12		96.24 272	95.58 277	98.23 248	98.96 237	98.81 150	99.76 27	99.29 226	94.42 291	93.85 316	97.60 317	95.12 158	98.09 309	94.32 288	86.85 329	97.80 310

v8		97.95 185	97.63 196	98.93 154	98.95 239	98.81 150	99.80 19	99.41 169	96.03 263	99.10 168	99.42 202	94.92 170	99.30 239	96.94 225	94.08 291	98.66 245

v17		96.42 265	95.81 270	98.25 245	98.94 242	98.80 155	99.76 27	99.28 233	94.57 283	94.18 305	97.71 308	95.23 154	98.16 302	94.86 273	87.73 322	97.80 310

v1		98.05 167	97.76 181	98.93 154	98.92 249	98.80 155	99.57 98	99.35 197	96.39 237	99.28 124	99.36 225	94.86 175	99.32 233	97.38 199	94.72 276	98.68 223

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 139	99.76 43	98.79 157	99.28 206	99.91 3	97.42 159	99.67 42	99.37 218	97.53 90	99.88 99	98.98 51	97.29 228	98.42 284

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 43	98.79 157	98.78 299	99.91 3	96.74 207	99.67 42	99.49 181	97.53 90	99.88 99	98.98 51	99.85 52	99.60 103

v1141		98.05 167	97.76 181	98.91 163	98.91 251	98.78 159	99.57 98	99.35 197	96.41 235	99.23 145	99.36 225	94.93 169	99.27 245	97.38 199	94.72 276	98.68 223

divwei89l23v2f112		98.06 161	97.78 174	98.91 163	98.90 252	98.77 160	99.57 98	99.35 197	96.45 230	99.24 140	99.37 218	94.92 170	99.27 245	97.50 189	94.71 278	98.68 223

tfpn_ndepth		98.17 149	97.84 167	99.15 130	99.75 49	98.76 161	99.61 82	97.39 336	96.92 200	99.61 57	99.38 214	92.19 259	99.86 104	97.57 181	98.13 189	98.82 192

tfpn1000		98.33 138	98.02 152	99.25 119	99.78 35	98.73 162	99.70 42	97.55 334	97.48 152	99.69 35	99.53 168	92.37 257	99.85 109	97.82 156	98.26 177	99.16 156

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 141	98.73 162	99.45 147	99.46 138	98.11 86	99.46 83	99.77 82	98.01 80	99.37 219	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	99.22 185	98.72 164	99.44 151	99.68 19	99.24 3	99.18 157	99.42 202	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	99.27 178	98.70 165	98.76 301	99.31 220	97.34 164	99.21 149	99.07 261	97.20 99	99.82 131	98.56 100	98.87 147	99.52 118

v1192		97.81 203	97.44 218	98.91 163	98.88 256	98.68 166	99.51 121	99.34 205	96.18 252	99.20 152	99.34 232	94.03 215	99.36 223	95.32 268	95.18 264	98.69 218

v7		98.05 167	97.78 174	98.87 180	98.99 227	98.67 167	99.64 71	99.34 205	96.31 241	99.29 120	99.51 176	94.78 180	99.27 245	97.03 217	95.15 266	98.66 245

v10		97.85 195	97.52 202	98.86 184	98.99 227	98.67 167	99.75 34	99.41 169	95.70 268	98.98 192	99.41 205	94.75 186	99.23 254	96.01 253	94.63 281	98.67 234

v1144		97.98 177	97.69 188	98.85 187	98.87 259	98.66 169	99.54 113	99.35 197	96.27 244	99.23 145	99.35 229	94.67 190	99.23 254	96.73 234	95.16 265	98.68 223

v144192		97.92 189	97.60 198	98.87 180	98.83 265	98.65 170	99.55 110	99.34 205	96.20 250	99.32 114	99.40 209	94.36 202	99.26 250	96.37 248	95.03 269	98.70 213

1314		98.68 121	98.54 123	99.11 133	98.89 255	98.65 170	99.27 209	99.49 104	96.89 201	97.99 270	99.56 161	97.72 88	99.83 122	97.74 166	99.27 117	98.84 191

V4		97.80 205	97.51 204	98.67 205	98.79 268	98.63 172	99.87 4	99.44 157	95.87 265	99.01 183	99.46 195	94.52 197	99.33 230	96.64 242	93.97 293	98.05 297

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 115	98.63 172	99.07 253	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		97.68 226	97.28 240	98.88 176	99.06 217	98.62 174	99.50 126	99.45 149	96.32 239	97.87 273	99.79 72	92.47 252	99.35 226	97.54 185	93.54 298	98.67 234

v52		97.79 207	97.50 206	98.66 206	98.80 266	98.62 174	99.87 4	99.44 157	95.87 265	99.01 183	99.46 195	94.44 201	99.33 230	96.65 241	93.96 294	98.05 297

TranMVSNet+NR-MVSNet		97.93 186	97.66 189	98.76 198	98.78 272	98.62 174	99.65 69	99.49 104	97.76 127	98.49 246	99.60 150	94.23 206	98.97 290	98.00 143	92.90 303	98.70 213

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 84	98.61 177	99.07 253	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		97.87 193	97.52 202	98.92 159	98.76 276	98.58 178	99.84 9	99.46 138	96.20 250	98.91 199	99.70 106	94.89 173	99.44 210	96.03 252	93.89 295	98.75 202

TAMVS		99.12 67	99.08 56	99.24 122	99.46 135	98.55 179	99.51 121	99.46 138	98.09 89	99.45 84	99.82 44	98.34 69	99.51 201	98.70 80	98.93 141	99.67 85

PEN-MVS		97.76 211	97.44 218	98.72 200	98.77 275	98.54 180	99.78 22	99.51 85	97.06 191	98.29 258	99.64 135	92.63 247	98.89 292	98.09 134	93.16 301	98.72 207

v1921920		97.80 205	97.45 213	98.84 188	98.80 266	98.53 181	99.52 117	99.34 205	96.15 256	99.24 140	99.47 191	93.98 216	99.29 241	95.40 266	95.13 267	98.69 218

PS-MVSNAJss		98.92 95	98.92 77	98.90 167	98.78 272	98.53 181	99.78 22	99.54 62	98.07 93	99.00 190	99.76 85	99.01 11	99.37 219	99.13 39	97.23 229	98.81 193

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 127	99.88 11	98.53 181	99.34 192	99.59 38	97.55 146	98.70 227	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	99.16 128	99.38 152	98.52 184	99.51 121	99.38 185	97.79 124	99.38 100	99.81 53	97.30 97	99.45 205	99.35 18	98.99 135	99.51 121

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 184	99.39 174	99.94 1	98.73 44	99.11 165	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	98.91 163	98.67 287	98.51 186	99.66 59	99.53 72	98.19 76	98.65 236	99.81 53	92.75 239	99.44 210	99.31 25	97.48 218	98.77 199

CR-MVSNet		98.17 149	97.93 160	98.87 180	99.18 193	98.49 187	99.22 226	99.33 213	96.96 196	99.56 67	99.38 214	94.33 203	99.00 281	94.83 275	98.58 159	99.14 157

RPMNet		96.61 260	95.85 268	98.87 180	99.18 193	98.49 187	99.22 226	99.08 258	88.72 326	99.56 67	97.38 322	94.08 214	99.00 281	86.87 326	98.58 159	99.14 157

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 189	99.56 105	99.61 32	97.85 116	99.36 106	99.85 26	95.95 130	99.85 109	96.66 239	99.83 62	99.59 107

TestCases				99.31 110	99.86 20	98.48 189		99.61 32	97.85 116	99.36 106	99.85 26	95.95 130	99.85 109	96.66 239	99.83 62	99.59 107

jajsoiax		98.43 132	98.28 136	98.88 176	98.60 292	98.43 191	99.82 13	99.53 72	98.19 76	98.63 238	99.80 64	93.22 231	99.44 210	99.22 31	97.50 214	98.77 199

v1240		97.69 224	97.32 236	98.79 194	98.85 263	98.43 191	99.48 139	99.36 193	96.11 259	99.27 128	99.36 225	93.76 224	99.24 253	94.46 281	95.23 263	98.70 213

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 161	98.42 193	99.08 252	99.30 222	99.16 5	99.43 88	99.75 90	95.27 150	99.97 11	98.56 100	99.95 6	99.36 145

PatchT		97.03 257	96.44 258	98.79 194	98.99 227	98.34 194	99.16 235	99.07 261	92.13 311	99.52 73	97.31 324	94.54 196	98.98 283	88.54 319	98.73 155	99.03 172

Baseline_NR-MVSNet		97.76 211	97.45 213	98.68 203	99.09 213	98.29 195	99.41 167	98.85 286	95.65 269	98.63 238	99.67 121	94.82 177	99.10 272	98.07 140	92.89 304	98.64 250

CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 195	99.71 41	99.66 25	98.11 86	99.41 93	99.80 64	98.37 68	99.96 19	98.99 50	99.96 5	99.72 70

PAPM		97.59 233	97.09 247	99.07 135	99.06 217	98.26 197	98.30 322	99.10 256	94.88 276	98.08 265	99.34 232	96.27 124	99.64 186	89.87 315	98.92 143	99.31 149

OMC-MVS		99.08 77	99.04 61	99.20 126	99.67 84	98.22 198	99.28 206	99.52 76	98.07 93	99.66 47	99.81 53	97.79 85	99.78 144	97.79 159	99.81 67	99.60 103

EPNet		98.86 100	98.71 103	99.30 113	97.20 318	98.18 199	99.62 76	98.91 280	99.28 2	98.63 238	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				98.45 224	98.55 295	98.16 200	99.43 156	93.68 344		97.23 283	98.46 297	89.30 296	99.22 257	95.43 265	98.22 178	97.98 302

gg-mvs-nofinetune		96.17 277	95.32 284	98.73 199	98.79 268	98.14 201	99.38 179	94.09 343	91.07 319	98.07 268	91.04 337	89.62 294	99.35 226	96.75 233	99.09 128	98.68 223

DTE-MVSNet		97.51 240	97.19 245	98.46 223	98.63 290	98.13 202	99.84 9	99.48 113	96.68 211	97.97 271	99.67 121	92.92 235	98.56 298	96.88 229	92.60 308	98.70 213

VDDNet		97.55 234	97.02 249	99.16 128	99.49 130	98.12 203	99.38 179	99.30 222	95.35 272	99.68 36	99.90 7	82.62 331	99.93 55	99.31 25	98.13 189	99.42 140

thres200		97.61 232	97.28 240	98.62 207	99.64 97	98.03 204	99.26 217	98.74 298	97.68 137	99.09 172	98.32 300	91.66 274	99.81 135	92.88 306	98.22 178	98.03 300

IterMVS-LS		98.46 130	98.42 127	98.58 210	99.59 112	98.00 205	99.37 181	99.43 165	96.94 198	99.07 174	99.59 152	97.87 82	99.03 278	98.32 123	95.62 257	98.71 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		97.85 195	97.47 210	99.00 143	99.38 152	97.99 206	98.57 312	99.15 251	97.04 192	98.90 201	99.30 240	89.83 291	99.38 216	96.70 236	98.33 171	99.62 101

EI-MVSNet		98.67 122	98.67 107	98.68 203	99.35 157	97.97 207	99.50 126	99.38 185	96.93 199	99.20 152	99.83 37	97.87 82	99.36 223	98.38 116	97.56 209	98.71 209

tfpn200view9		97.72 220	97.38 226	98.72 200	99.69 80	97.96 208	99.50 126	98.73 306	97.83 119	99.17 158	98.45 298	91.67 272	99.83 122	93.22 300	98.18 182	98.37 288

thres400		97.77 210	97.38 226	98.92 159	99.69 80	97.96 208	99.50 126	98.73 306	97.83 119	99.17 158	98.45 298	91.67 272	99.83 122	93.22 300	98.18 182	98.96 181

thres600view7		97.86 194	97.51 204	98.92 159	99.72 68	97.95 210	99.59 86	98.74 298	97.94 109	99.27 128	98.62 290	91.75 267	99.86 104	93.73 296	98.19 181	98.96 181

CHOSEN 280x420		99.12 67	99.13 52	99.08 134	99.66 94	97.89 211	98.43 317	99.71 13	98.88 30	99.62 55	99.76 85	96.63 115	99.70 175	99.46 14	99.99 1	99.66 86

TR-MVS		97.76 211	97.41 223	98.82 190	99.06 217	97.87 212	98.87 295	98.56 315	96.63 215	98.68 229	99.22 250	92.49 251	99.65 184	95.40 266	97.79 199	98.95 188

conf200view11		97.78 209	97.45 213	98.77 196	99.72 68	97.86 213	99.59 86	98.74 298	97.93 110	99.26 132	98.62 290	91.75 267	99.83 122	93.22 300	98.18 182	98.61 267

thres100view900		97.76 211	97.45 213	98.69 202	99.72 68	97.86 213	99.59 86	98.74 298	97.93 110	99.26 132	98.62 290	91.75 267	99.83 122	93.22 300	98.18 182	98.37 288

test0.0.03 1		97.71 223	97.42 222	98.56 213	98.41 300	97.82 215	98.78 299	98.63 311	97.34 164	98.05 269	98.98 271	94.45 199	98.98 283	95.04 272	97.15 233	98.89 189

view600		97.97 180	97.66 189	98.89 169	99.75 49	97.81 216	99.69 45	98.80 290	98.02 102	99.25 135	98.88 275	91.95 261	99.89 92	94.36 284	98.29 173	98.96 181

view800		97.97 180	97.66 189	98.89 169	99.75 49	97.81 216	99.69 45	98.80 290	98.02 102	99.25 135	98.88 275	91.95 261	99.89 92	94.36 284	98.29 173	98.96 181

conf0.05thres1000		97.97 180	97.66 189	98.89 169	99.75 49	97.81 216	99.69 45	98.80 290	98.02 102	99.25 135	98.88 275	91.95 261	99.89 92	94.36 284	98.29 173	98.96 181

tfpn		97.97 180	97.66 189	98.89 169	99.75 49	97.81 216	99.69 45	98.80 290	98.02 102	99.25 135	98.88 275	91.95 261	99.89 92	94.36 284	98.29 173	98.96 181

JIA-IIPM		97.50 241	97.02 249	98.93 154	98.73 278	97.80 220	99.30 199	98.97 271	91.73 315	98.91 199	94.86 331	95.10 159	99.71 169	97.58 179	97.98 195	99.28 151

mvs-test1		98.86 100	98.84 90	98.89 169	99.33 161	97.77 221	99.44 151	99.30 222	98.47 57	99.10 168	99.43 200	96.78 109	99.95 33	98.73 77	99.02 133	98.96 181

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 169	99.71 73	97.74 222	99.12 242	99.54 62	98.44 62	99.42 91	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	98.88 176	99.70 78	97.73 223	98.92 290	99.55 55	98.52 55	99.45 84	99.84 35	95.27 150	99.91 72	98.08 138	98.84 149	99.00 175

v148		97.79 207	97.55 200	98.50 217	98.74 277	97.72 224	99.54 113	99.33 213	96.26 245	98.90 201	99.51 176	94.68 189	99.14 264	97.83 155	93.15 302	98.63 256

TAPA-MVS	97.07 15	97.74 217	97.34 233	98.94 151	99.70 78	97.53 225	99.25 219	99.51 85	91.90 314	99.30 116	99.63 139	98.78 37	99.64 186	88.09 321	99.87 38	99.65 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		97.73 218	97.45 213	98.57 211	99.45 139	97.50 226	99.02 268	98.98 270	96.11 259	99.41 93	99.14 255	90.28 285	98.74 295	95.74 257	98.93 141	99.47 131

cascas		97.69 224	97.43 221	98.48 220	98.60 292	97.30 227	98.18 326	99.39 179	92.96 307	98.41 249	98.78 286	93.77 223	99.27 245	98.16 130	98.61 156	98.86 190

PVSNet	96.02 17	98.85 106	98.84 90	98.89 169	99.73 65	97.28 228	98.32 321	99.60 35	97.86 114	99.50 76	99.57 159	96.75 112	99.86 104	98.56 100	99.70 90	99.54 113

MDA-MVSNet-bldmvs		94.96 290	93.98 295	97.92 268	98.24 303	97.27 229	99.15 238	99.33 213	93.80 299	80.09 337	99.03 266	88.31 309	97.86 316	93.49 298	94.36 285	98.62 258

GBi-Net		97.68 226	97.48 208	98.29 238	99.51 123	97.26 230	99.43 156	99.48 113	96.49 223	99.07 174	99.32 237	90.26 286	98.98 283	97.10 213	96.65 237	98.62 258

test1		97.68 226	97.48 208	98.29 238	99.51 123	97.26 230	99.43 156	99.48 113	96.49 223	99.07 174	99.32 237	90.26 286	98.98 283	97.10 213	96.65 237	98.62 258

FMVSNet1		96.84 258	96.36 259	98.29 238	99.32 168	97.26 230	99.43 156	99.48 113	95.11 274	98.55 243	99.32 237	83.95 328	98.98 283	95.81 256	96.26 247	98.62 258

v748		97.52 237	97.23 243	98.41 229	98.69 284	97.23 233	99.87 4	99.45 149	95.72 267	98.51 244	99.53 168	94.13 211	99.30 239	96.78 232	92.39 309	98.70 213

MDA-MVSNet_test_wron		95.45 285	94.60 290	98.01 262	98.16 304	97.21 234	99.11 248	99.24 242	93.49 303	80.73 336	98.98 271	93.02 232	98.18 300	94.22 293	94.45 284	98.64 250

VDD-MVS		97.73 218	97.35 230	98.88 176	99.47 134	97.12 235	99.34 192	98.85 286	98.19 76	99.67 42	99.85 26	82.98 329	99.92 63	99.49 12	98.32 172	99.60 103

test-LLR		98.06 161	97.90 161	98.55 215	98.79 268	97.10 236	98.67 306	97.75 329	97.34 164	98.61 241	98.85 279	94.45 199	99.45 205	97.25 204	99.38 109	99.10 160

test-mter		97.49 243	97.13 246	98.55 215	98.79 268	97.10 236	98.67 306	97.75 329	96.65 213	98.61 241	98.85 279	88.23 310	99.45 205	97.25 204	99.38 109	99.10 160

YYNet1		95.36 287	94.51 292	97.92 268	97.89 306	97.10 236	99.10 250	99.23 243	93.26 306	80.77 335	99.04 265	92.81 238	98.02 311	94.30 289	94.18 289	98.64 250

ACMM	97.58 5	98.37 137	98.34 131	98.48 220	99.41 144	97.10 236	99.56 105	99.45 149	98.53 54	99.04 180	99.85 26	93.00 233	99.71 169	98.74 75	97.45 219	98.64 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		98.19 148	98.10 144	98.45 224	98.88 256	97.07 240	99.28 206	99.38 185	98.57 52	99.22 147	99.81 53	92.12 260	99.66 182	98.08 138	97.54 211	98.61 267

Patchmatch-test		97.93 186	97.65 194	98.77 196	99.18 193	97.07 240	99.03 265	99.14 253	96.16 254	98.74 218	99.57 159	94.56 194	99.72 163	93.36 299	99.11 125	99.52 118

LPG-MVS_test		98.22 144	98.13 142	98.49 218	99.33 161	97.05 242	99.58 92	99.55 55	97.46 153	99.24 140	99.83 37	92.58 248	99.72 163	98.09 134	97.51 212	98.68 223

LGP-MVS_train				98.49 218	99.33 161	97.05 242		99.55 55	97.46 153	99.24 140	99.83 37	92.58 248	99.72 163	98.09 134	97.51 212	98.68 223

plane_prior7					99.29 173	97.03 244

ACMP	97.20 11	98.06 161	97.94 159	98.45 224	99.37 154	97.01 245	99.44 151	99.49 104	97.54 149	98.45 248	99.79 72	91.95 261	99.72 163	97.91 149	97.49 217	98.62 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior3						97.00 246			98.69 46	99.11 165

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 208	99.41 144	96.99 247	99.52 117	99.49 104	98.11 86	99.24 140	99.34 232	96.96 105	99.79 142	97.95 147	99.45 105	99.02 174

plane_prior6					99.27 178	96.98 248						92.71 243

HQP_MVS		98.27 143	98.22 139	98.44 227	99.29 173	96.97 249	99.39 174	99.47 128	98.97 22	99.11 165	99.61 147	92.71 243	99.69 178	97.78 160	97.63 202	98.67 234

plane_prior						96.97 249	99.21 229		98.45 59						97.60 205

ACMH	97.28 8	98.10 158	97.99 155	98.44 227	99.41 144	96.96 251	99.60 84	99.56 48	98.09 89	98.15 262	99.91 5	90.87 282	99.70 175	98.88 57	97.45 219	98.67 234
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NP-MVS					99.23 183	96.92 252					99.40 209

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 222	99.33 161	96.91 253	99.57 98	99.30 222	98.47 57	99.41 93	98.99 268	96.78 109	99.74 151	98.73 77	99.38 109	98.74 205

HQP5-MVS						96.83 254

HQP-MVS		98.02 172	97.90 161	98.37 232	99.19 190	96.83 254	98.98 278	99.39 179	98.24 72	98.66 230	99.40 209	92.47 252	99.64 186	97.19 208	97.58 207	98.64 250

CLD-MVS		98.16 151	98.10 144	98.33 234	99.29 173	96.82 256	98.75 302	99.44 157	97.83 119	99.13 161	99.55 164	92.92 235	99.67 180	98.32 123	97.69 201	98.48 280
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	98.02 172	97.90 161	98.40 230	99.23 183	96.80 257	99.70 42	99.60 35	97.12 184	98.18 261	99.70 106	91.73 270	99.72 163	98.39 114	97.45 219	98.68 223
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		97.52 237	97.30 238	98.16 255	98.57 294	96.73 258	99.27 209	98.90 282	96.14 257	98.37 252	99.53 168	91.54 276	99.14 264	97.51 188	95.87 252	98.63 256

BH-untuned		98.42 133	98.36 129	98.59 209	99.49 130	96.70 259	99.27 209	99.13 254	97.24 174	98.80 213	99.38 214	95.75 139	99.74 151	97.07 216	99.16 122	99.33 148

IB-MVS	95.67 18	96.22 275	95.44 283	98.57 211	99.21 186	96.70 259	98.65 309	97.74 331	96.71 209	97.27 282	98.54 296	86.03 319	99.92 63	98.47 110	86.30 330	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	97.92 189	97.78 174	98.32 235	99.46 135	96.68 261	99.56 105	99.54 62	98.41 63	97.79 277	99.87 19	90.18 289	99.66 182	98.05 142	97.18 232	98.62 258

EU-MVSNet		97.98 177	98.03 151	97.81 277	98.72 280	96.65 262	99.66 59	99.66 25	98.09 89	98.35 254	99.82 44	95.25 153	98.01 312	97.41 198	95.30 262	98.78 196

MVP-Stereo		97.81 203	97.75 184	97.99 264	97.53 311	96.60 263	98.96 283	98.85 286	97.22 176	97.23 283	99.36 225	95.28 149	99.46 204	95.51 263	99.78 73	97.92 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TESTMET0.1,1		97.55 234	97.27 242	98.40 230	98.93 247	96.53 264	98.67 306	97.61 333	96.96 196	98.64 237	99.28 243	88.63 305	99.45 205	97.30 203	99.38 109	99.21 154

OurMVSNet-221017-0		97.88 192	97.77 178	98.19 253	98.71 282	96.53 264	99.88 1	99.00 268	97.79 124	98.78 215	99.94 3	91.68 271	99.35 226	97.21 206	96.99 235	98.69 218

ADS-MVSNet		98.20 147	98.08 147	98.56 213	99.33 161	96.48 266	99.23 222	99.15 251	96.24 247	99.10 168	99.67 121	94.11 212	99.71 169	96.81 230	99.05 131	99.48 127

testgi		97.65 231	97.50 206	98.13 256	99.36 156	96.45 267	99.42 163	99.48 113	97.76 127	97.87 273	99.45 198	91.09 279	98.81 294	94.53 279	98.52 164	99.13 159

test_0402		96.64 259	96.24 260	97.85 273	98.85 263	96.43 268	99.44 151	99.26 239	93.52 302	96.98 289	99.52 173	88.52 306	99.20 262	92.58 309	97.50 214	97.93 305

ITE_SJBPF				98.08 257	99.29 173	96.37 269		98.92 277	98.34 66	98.83 211	99.75 90	91.09 279	99.62 192	95.82 255	97.40 223	98.25 293

semantic-postprocess				98.06 258	99.57 115	96.36 270		99.49 104	97.18 178	98.71 221	99.72 102	92.70 245	99.14 264	97.44 196	95.86 253	98.67 234

K. test v3		97.10 255	96.79 253	98.01 262	98.72 280	96.33 271	99.87 4	97.05 337	97.59 141	96.16 296	99.80 64	88.71 301	99.04 276	96.69 237	96.55 241	98.65 248

XVG-ACMP-BASELINE		97.83 199	97.71 187	98.20 252	99.11 208	96.33 271	99.41 167	99.52 76	98.06 97	99.05 179	99.50 178	89.64 293	99.73 159	97.73 167	97.38 225	98.53 277

IterMVS		97.83 199	97.77 178	98.02 261	99.58 113	96.27 273	99.02 268	99.48 113	97.22 176	98.71 221	99.70 106	92.75 239	99.13 267	97.46 194	96.00 251	98.67 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		97.50 241	97.33 235	98.03 259	98.65 288	96.23 274	99.77 24	98.68 309	97.14 181	97.90 272	99.93 4	90.45 284	99.18 263	97.00 219	96.43 243	98.67 234

BH-w/o		98.00 176	97.89 165	98.32 235	99.35 157	96.20 275	99.01 272	98.90 282	96.42 233	98.38 251	99.00 267	95.26 152	99.72 163	96.06 251	98.61 156	99.03 172

TDRefinement		95.42 286	94.57 291	97.97 265	89.83 337	96.11 276	99.48 139	98.75 295	96.74 207	96.68 291	99.88 14	88.65 304	99.71 169	98.37 117	82.74 333	98.09 295

EPMVS		97.82 202	97.65 194	98.35 233	98.88 256	95.98 277	99.49 134	94.71 342	97.57 144	99.26 132	99.48 187	92.46 255	99.71 169	97.87 152	99.08 129	99.35 146

pmmvs-eth3d		95.34 288	94.73 289	97.15 290	95.53 323	95.94 278	99.35 189	99.10 256	95.13 273	93.55 317	97.54 320	88.15 312	97.91 314	94.58 278	89.69 316	97.61 319

FMVSNet5		96.43 264	96.19 261	97.15 290	99.11 208	95.89 279	99.32 194	99.52 76	94.47 289	98.34 255	99.07 261	87.54 314	97.07 323	92.61 308	95.72 255	98.47 281

UnsupCasMVSNet_eth		96.44 263	96.12 262	97.40 289	98.65 288	95.65 280	99.36 185	99.51 85	97.13 182	96.04 299	98.99 268	88.40 308	98.17 301	96.71 235	90.27 313	98.40 286

MIMVSNet1		95.51 284	95.04 287	96.92 296	97.38 313	95.60 281	99.52 117	99.50 99	93.65 300	96.97 290	99.17 253	85.28 323	96.56 327	88.36 320	95.55 259	98.60 270

CVMVSNet		98.57 127	98.67 107	98.30 237	99.35 157	95.59 282	99.50 126	99.55 55	98.60 51	99.39 98	99.83 37	94.48 198	99.45 205	98.75 74	98.56 162	99.85 8

Patchmatch-test1		98.16 151	98.14 141	98.22 250	99.30 170	95.55 283	99.07 253	98.97 271	97.57 144	99.43 88	99.60 150	92.72 242	99.60 194	97.38 199	99.20 120	99.50 124

LF4IMVS		97.52 237	97.46 212	97.70 283	98.98 231	95.55 283	99.29 203	98.82 289	98.07 93	98.66 230	99.64 135	89.97 290	99.61 193	97.01 218	96.68 236	97.94 304

EPNet_dtu		98.03 170	97.96 157	98.23 248	98.27 302	95.54 285	99.23 222	98.75 295	99.02 10	97.82 275	99.71 103	96.11 128	99.48 202	93.04 304	99.65 98	99.69 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 254	96.89 251	97.83 275	99.07 215	95.52 286	98.57 312	98.74 298	97.58 143	97.81 276	99.79 72	88.16 311	99.56 197	95.10 270	97.21 230	98.39 287

pmmvs6		96.53 262	96.09 263	97.82 276	98.69 284	95.47 287	99.37 181	99.47 128	93.46 304	97.41 280	99.78 77	87.06 317	99.33 230	96.92 227	92.70 307	98.65 248

test20.03		96.12 278	95.96 267	96.63 300	97.44 312	95.45 288	99.51 121	99.38 185	96.55 221	96.16 296	99.25 247	93.76 224	96.17 328	87.35 324	94.22 288	98.27 291

lessismore_v0				97.79 278	98.69 284	95.44 289		94.75 341		95.71 300	99.87 19	88.69 302	99.32 233	95.89 254	94.93 272	98.62 258

PatchmatchNet		98.31 140	98.36 129	98.19 253	99.16 200	95.32 290	99.27 209	98.92 277	97.37 163	99.37 102	99.58 155	94.90 172	99.70 175	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		97.04 256	96.80 252	97.77 279	98.90 252	95.23 291	98.97 281	99.06 263	94.02 295	98.09 264	99.41 205	93.88 219	98.82 293	90.46 313	98.42 169	99.26 152

USDC		97.34 248	97.20 244	97.75 280	99.07 215	95.20 292	98.51 315	99.04 265	97.99 107	98.31 256	99.86 22	89.02 297	99.55 199	95.67 261	97.36 226	98.49 279

ADS-MVSNet2		98.02 172	98.07 149	97.87 271	99.33 161	95.19 293	99.23 222	99.08 258	96.24 247	99.10 168	99.67 121	94.11 212	98.93 291	96.81 230	99.05 131	99.48 127

MDTV_nov1_ep13_2view						95.18 294	99.35 189		96.84 204	99.58 63		95.19 156		97.82 156		99.46 134

new_pmnet		96.38 268	96.03 264	97.41 288	98.13 305	95.16 295	99.05 259	99.20 246	93.94 297	97.39 281	98.79 284	91.61 275	99.04 276	90.43 314	95.77 254	98.05 297

tpm		97.67 229	97.55 200	98.03 259	99.02 224	95.01 296	99.43 156	98.54 316	96.44 231	99.12 163	99.34 232	91.83 266	99.60 194	97.75 165	96.46 242	99.48 127

DWT-MVSNet_test		97.53 236	97.40 224	97.93 267	99.03 223	94.86 297	99.57 98	98.63 311	96.59 220	98.36 253	98.79 284	89.32 295	99.74 151	98.14 131	98.16 188	99.20 155

tpmrst		98.33 138	98.48 125	97.90 270	99.16 200	94.78 298	99.31 197	99.11 255	97.27 170	99.45 84	99.59 152	95.33 147	99.84 115	98.48 108	98.61 156	99.09 164

PatchFormer-LS_test		98.01 175	98.05 150	97.87 271	99.15 203	94.76 299	99.42 163	98.93 275	97.12 184	98.84 210	98.59 294	93.74 226	99.80 139	98.55 103	98.17 187	99.06 170

tpmvs		97.98 177	98.02 152	97.84 274	99.04 221	94.73 300	99.31 197	99.20 246	96.10 262	98.76 217	99.42 202	94.94 167	99.81 135	96.97 222	98.45 167	98.97 179

pmmvs3		94.09 297	93.25 299	96.60 301	94.76 326	94.49 301	98.92 290	98.18 324	89.66 321	96.48 293	98.06 303	86.28 318	97.33 322	89.68 316	87.20 324	97.97 303

MDTV_nov1_ep13			98.32 133		99.11 208	94.44 302	99.27 209	98.74 298	97.51 150	99.40 97	99.62 144	94.78 180	99.76 149	97.59 178	98.81 152

tpm2		97.44 245	97.34 233	97.74 281	99.15 203	94.36 303	99.45 147	98.94 274	93.45 305	98.90 201	99.44 199	91.35 277	99.59 196	97.31 202	98.07 192	99.29 150

PVSNet_0	94.43 19	96.09 279	95.47 281	97.94 266	99.31 169	94.34 304	97.81 329	99.70 15	97.12 184	97.46 279	98.75 287	89.71 292	99.79 142	97.69 173	81.69 334	99.68 82

Anonymous20231206		96.22 275	96.03 264	96.79 299	97.31 316	94.14 305	99.63 73	99.08 258	96.17 253	97.04 287	99.06 263	93.94 217	97.76 319	86.96 325	95.06 268	98.47 281

CostFormer		97.72 220	97.73 185	97.71 282	99.15 203	94.02 306	99.54 113	99.02 267	94.67 280	99.04 180	99.35 229	92.35 258	99.77 146	98.50 107	97.94 196	99.34 147

UnsupCasMVSNet_bld		93.53 299	92.51 301	96.58 302	97.38 313	93.82 307	98.24 323	99.48 113	91.10 318	93.10 319	96.66 326	74.89 333	98.37 299	94.03 295	87.71 323	97.56 321

tpm cat1		97.39 247	97.36 228	97.50 287	99.17 198	93.73 308	99.43 156	99.31 220	91.27 316	98.71 221	99.08 260	94.31 205	99.77 146	96.41 247	98.50 165	99.00 175

tpmp4_e23		97.34 248	97.29 239	97.52 285	99.25 182	93.73 308	99.58 92	99.19 249	94.00 296	98.20 260	99.41 205	90.74 283	99.74 151	97.13 212	98.07 192	99.07 169

dp		97.75 215	97.80 171	97.59 284	99.10 211	93.71 310	99.32 194	98.88 284	96.48 229	99.08 173	99.55 164	92.67 246	99.82 131	96.52 243	98.58 159	99.24 153

MVS-HIRNet		95.75 282	95.16 286	97.51 286	99.30 170	93.69 311	98.88 294	95.78 339	85.09 329	98.78 215	92.65 333	91.29 278	99.37 219	94.85 274	99.85 52	99.46 134

DSMNet-mixed		97.25 251	97.35 230	96.95 295	97.84 307	93.61 312	99.57 98	96.63 338	96.13 258	98.87 204	98.61 293	94.59 193	97.70 320	95.08 271	98.86 148	99.55 111

MS-PatchMatch		97.24 252	97.32 236	96.99 293	98.45 299	93.51 313	98.82 297	99.32 219	97.41 160	98.13 263	99.30 240	88.99 298	99.56 197	95.68 260	99.80 69	97.90 307

OpenMVS_ROB	92.34 20	94.38 295	93.70 296	96.41 303	97.38 313	93.17 314	99.06 257	98.75 295	86.58 327	94.84 304	98.26 302	81.53 332	99.32 233	89.01 318	97.87 198	96.76 323

gm-plane-assit					98.54 296	92.96 315			94.65 281		99.15 254		99.64 186	97.56 183

EG-PatchMatch MVS		95.97 280	95.69 274	96.81 298	97.78 308	92.79 316	99.16 235	98.93 275	96.16 254	94.08 308	99.22 250	82.72 330	99.47 203	95.67 261	97.50 214	98.17 294

new-patchmatchnet		94.48 293	94.08 294	95.67 305	95.08 325	92.41 317	99.18 233	99.28 233	94.55 286	93.49 318	97.37 323	87.86 313	97.01 324	91.57 310	88.36 319	97.61 319

testpf		95.66 283	96.02 266	94.58 307	98.35 301	92.32 318	97.25 334	97.91 328	92.83 308	97.03 288	98.99 268	88.69 302	98.61 297	95.72 258	97.40 223	92.80 332

LCM-MVSNet-Re		97.83 199	98.15 140	96.87 297	99.30 170	92.25 319	99.59 86	98.26 320	97.43 157	96.20 295	99.13 256	96.27 124	98.73 296	98.17 129	98.99 135	99.64 95

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 292	99.60 110	91.75 320	98.61 310	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		98.22 144	98.62 115	96.99 293	99.82 29	91.58 321	99.72 39	99.44 157	96.61 216	99.66 47	99.89 10	95.92 133	99.82 131	97.46 194	99.10 127	99.57 110

Patchmatch-RL test		95.84 281	95.81 270	95.95 304	95.61 321	90.57 322	98.24 323	98.39 317	95.10 275	95.20 301	98.67 289	94.78 180	97.77 318	96.28 249	90.02 314	99.51 121

Gipuma		90.99 304	90.15 305	93.51 309	98.73 278	90.12 323	93.98 338	99.45 149	79.32 333	92.28 322	94.91 330	69.61 335	97.98 313	87.42 322	95.67 256	92.45 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 300	92.23 302	95.14 306	95.61 321	89.98 324	99.37 181	98.21 322	94.80 278	95.04 303	97.69 311	65.06 338	97.90 315	94.30 289	89.98 315	97.54 322

Anonymous20231211		90.69 305	89.39 306	94.58 307	94.25 327	88.18 325	99.29 203	99.07 261	82.45 332	92.95 320	97.65 313	63.96 340	97.79 317	89.27 317	85.63 331	97.77 316

1111		92.30 302	92.21 303	92.55 313	93.30 329	86.27 326	99.15 238	98.74 298	91.94 312	90.85 326	97.82 306	84.18 326	95.21 330	79.65 333	94.27 287	96.19 326

.test1245		83.42 310	86.17 308	75.15 332	93.30 329	86.27 326	99.15 238	98.74 298	91.94 312	90.85 326	97.82 306	84.18 326	95.21 330	79.65 333	39.90 344	43.98 343

test2356		94.07 298	94.46 293	92.89 312	95.18 324	86.13 328	97.60 332	99.06 263	93.61 301	96.15 298	98.28 301	85.60 322	93.95 334	86.68 327	98.00 194	98.59 271

no-one		83.04 311	80.12 313	91.79 317	89.44 338	85.65 329	99.32 194	98.32 318	89.06 323	79.79 339	89.16 339	44.86 346	96.67 326	84.33 330	46.78 342	93.05 331

testus		94.61 292	95.30 285	92.54 314	96.44 319	84.18 330	98.36 318	99.03 266	94.18 294	96.49 292	98.57 295	88.74 300	95.09 332	87.41 323	98.45 167	98.36 290

PMMVS2		86.87 307	85.37 310	91.35 320	90.21 336	83.80 331	98.89 293	97.45 335	83.13 331	91.67 325	95.03 329	48.49 344	94.70 333	85.86 328	77.62 335	95.54 328

test1235678		92.91 301	93.30 298	91.71 318	93.14 331	83.01 332	98.75 302	98.58 314	92.80 309	92.45 321	97.91 305	88.51 307	93.54 335	82.26 331	95.35 261	98.59 271

test12356		91.74 303	92.19 304	90.37 321	91.22 333	82.41 333	98.61 310	98.28 319	90.66 320	91.82 324	97.92 304	84.90 324	92.61 336	81.64 332	94.66 279	96.09 327

ambc				93.06 311	92.68 332	82.36 334	98.47 316	98.73 306		95.09 302	97.41 321	55.55 342	99.10 272	96.42 246	91.32 311	97.71 318

DeepMVS_CX				93.34 310	99.29 173	82.27 335		99.22 244	85.15 328	96.33 294	99.05 264	90.97 281	99.73 159	93.57 297	97.77 200	98.01 301

LCM-MVSNet		86.80 308	85.22 311	91.53 319	87.81 339	80.96 336	98.23 325	98.99 269	71.05 336	90.13 328	96.51 327	48.45 345	96.88 325	90.51 312	85.30 332	96.76 323

CMPMVS	69.68 23	94.13 296	94.90 288	91.84 316	97.24 317	80.01 337	98.52 314	99.48 113	89.01 324	91.99 323	99.67 121	85.67 321	99.13 267	95.44 264	97.03 234	96.39 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		94.95 291	95.83 269	92.31 315	98.47 298	79.33 338	99.12 242	92.81 348	93.87 298	97.68 278	99.13 256	93.87 220	99.01 280	91.38 311	96.19 248	98.59 271

ANet_high		77.30 316	74.86 318	84.62 326	75.88 346	77.61 339	97.63 331	93.15 347	88.81 325	64.27 342	89.29 338	36.51 347	83.93 345	75.89 338	52.31 341	92.33 335

testmv		87.91 306	87.80 307	88.24 322	87.68 340	77.50 340	99.07 253	97.66 332	89.27 322	86.47 330	96.22 328	68.35 336	92.49 338	76.63 337	88.82 317	94.72 330

EMVS		80.02 314	79.22 315	82.43 330	91.19 334	76.40 341	97.55 333	92.49 350	66.36 341	83.01 334	91.27 335	64.63 339	85.79 344	65.82 342	60.65 339	85.08 340

E-PMN		80.61 313	79.88 314	82.81 328	90.75 335	76.38 342	97.69 330	95.76 340	66.44 340	83.52 332	92.25 334	62.54 341	87.16 343	68.53 341	61.40 338	84.89 341

MVE	76.82 21	76.91 317	74.31 319	84.70 324	85.38 344	76.05 343	96.88 335	93.17 346	67.39 339	71.28 341	89.01 340	21.66 354	87.69 342	71.74 340	72.29 337	90.35 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		74.42 319	71.19 320	84.14 327	76.16 345	74.29 344	96.00 337	92.57 349	69.57 337	63.84 343	87.49 341	21.98 351	88.86 341	75.56 339	57.50 340	89.26 339

PNet_i23d		79.43 315	77.68 316	84.67 325	86.18 342	71.69 345	96.50 336	93.68 344	75.17 334	71.33 340	91.18 336	32.18 349	90.62 340	78.57 336	74.34 336	91.71 336

tmp_tt		82.80 312	81.52 312	86.66 323	66.61 348	68.44 346	92.79 340	97.92 326	68.96 338	80.04 338	99.85 26	85.77 320	96.15 329	97.86 153	43.89 343	95.39 329

FPMVS		84.93 309	85.65 309	82.75 329	86.77 341	63.39 347	98.35 320	98.92 277	74.11 335	83.39 333	98.98 271	50.85 343	92.40 339	84.54 329	94.97 270	92.46 333

PMVS	70.75 22	75.98 318	74.97 317	79.01 331	70.98 347	55.18 348	93.37 339	98.21 322	65.08 342	61.78 344	93.83 332	21.74 353	92.53 337	78.59 335	91.12 312	89.34 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 321	41.29 324	36.84 333	86.18 342	49.12 349	79.73 341	22.81 352	27.64 343	25.46 347	28.45 347	21.98 351	48.89 346	55.80 343	23.56 347	12.51 345

test123		39.01 323	42.50 323	28.53 335	39.17 349	20.91 350	98.75 302	19.17 353	19.83 345	38.57 345	66.67 343	33.16 348	15.42 347	37.50 345	29.66 346	49.26 342

testmvs		39.17 322	43.78 321	25.37 336	36.04 350	16.84 351	98.36 318	26.56 351	20.06 344	38.51 346	67.32 342	29.64 350	15.30 348	37.59 344	39.90 344	43.98 343

cdsmvs_eth3d_5k		24.64 324	32.85 325	0.00 337	0.00 351	0.00 352	0.00 342	99.51 85	0.00 346	0.00 348	99.56 161	96.58 116	0.00 349	0.00 346	0.00 348	0.00 346

pcd_1.5k_mvsjas		8.27 326	11.03 327	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	99.01 11	0.00 349	0.00 346	0.00 348	0.00 346

pcd1.5k->3k		40.85 320	43.49 322	32.93 334	98.95 239	0.00 352	0.00 342	99.53 72	0.00 346	0.00 348	0.27 348	95.32 148	0.00 349	0.00 346	97.30 227	98.80 194

sosnet-low-res		0.02 327	0.03 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

sosnet		0.02 327	0.03 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uncertanet		0.02 327	0.03 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

Regformer		0.02 327	0.03 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

ab-mvs-re		8.30 325	11.06 326	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	99.58 155	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uanet		0.02 327	0.03 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.27 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

test_part1								99.48 113				98.96 20			99.84 57	99.83 23

test11111								99.47 128

sam_mvs1												94.86 175

sam_mvs												94.72 188

MTGPA								99.47 128

test_post1							99.23 222				65.14 345	94.18 210	99.71 169	97.58 179

test_post											65.99 344	94.65 192	99.73 159

patchmatchnet-post											98.70 288	94.79 179	99.74 151

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							98.96 283		98.34 66	99.01 183	99.52 173	98.68 50		97.96 145	99.74 80

旧先验2							98.96 283		96.70 210	99.47 81			99.94 40	98.19 127

新几何2							99.01 272

无先验							98.99 274	99.51 85	96.89 201				99.93 55	97.53 186		99.72 70

原ACMM2							98.95 287

testdata2													99.95 33	96.67 238

segment_acmp												98.96 20

testdata1							98.85 296		98.32 69

plane_prior5								99.47 128					99.69 178	97.78 160	97.63 202	98.67 234

plane_prior4											99.61 147

plane_prior2							99.39 174		98.97 22

plane_prior1					99.26 180

n2								0.00 354

nn								0.00 354

door-mid								98.05 325

test11								99.35 197

door								97.92 326

HQP-NCC					99.19 190		98.98 278		98.24 72	98.66 230

ACMP_Plane					99.19 190		98.98 278		98.24 72	98.66 230

BP-MVS														97.19 208

HQP4-MVS										98.66 230			99.64 186			98.64 250

HQP3-MVS								99.39 179							97.58 207

HQP2-MVS												92.47 252

ACMMP++_ref															97.19 231

ACMMP++															97.43 222

Test By Simon												98.75 45

High-resolution multi-view benchmark