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.

SD-MVS		99.41 40	99.52 6	99.05 160	99.74 67	99.68 45	99.46 143	99.52 86	99.11 7	99.88 5	99.91 5	99.43 1	97.70 335	98.72 98	99.93 10	99.77 62
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		99.58 4	99.50 8	99.81 36	99.91 1	99.66 50	99.63 57	99.39 203	98.91 36	99.78 31	99.85 29	99.36 2	99.94 49	98.84 81	99.88 36	99.82 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 21	99.59 73	99.51 96	98.62 57	99.79 26	99.83 42	99.28 3	99.97 10	98.48 130	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
OPU-MVS				99.64 75	99.56 137	99.72 38	99.60 68				99.70 129	99.27 4	99.42 237	98.24 151	99.80 82	99.79 53

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 68	99.48 133	99.08 11	99.91 1	99.81 60	99.20 5	99.96 18	98.91 67	99.85 58	99.79 53

test_241102_ONE					99.84 32	99.90 1		99.48 133	99.07 13	99.91 1	99.74 113	99.20 5	99.76 169

MSLP-MVS++		99.46 24	99.47 9	99.44 118	99.60 128	99.16 116	99.41 164	99.71 13	98.98 27	99.45 104	99.78 92	99.19 7	99.54 222	99.28 29	99.84 65	99.63 119

SMA-MVS		99.44 29	99.30 38	99.85 25	99.73 72	99.83 14	99.56 92	99.47 152	97.45 177	99.78 31	99.82 49	99.18 8	99.91 86	98.79 90	99.89 33	99.81 41

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 49	99.89 3	99.75 25	99.56 54	99.02 15	99.88 5	99.85 29	99.18 8	99.96 18	99.22 34	99.92 11	99.90 1

HPM-MVS_fast		99.51 14	99.40 16	99.85 25	99.91 1	99.79 27	99.76 24	99.56 54	97.72 149	99.76 37	99.75 107	99.13 10	99.92 75	99.07 50	99.92 11	99.85 14

PGM-MVS		99.45 26	99.31 36	99.86 18	99.87 15	99.78 33	99.58 80	99.65 32	97.84 134	99.71 43	99.80 74	99.12 11	99.97 10	98.33 146	99.87 40	99.83 29

test_0728_THIRD									98.99 25	99.81 22	99.80 74	99.09 12	99.96 18	98.85 79	99.90 23	99.88 5

HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 23	99.66 46	99.67 22	98.15 98	99.68 50	99.69 136	99.06 13	99.96 18	98.69 103	99.87 40	99.84 18

#test#		99.43 32	99.29 42	99.86 18	99.87 15	99.80 23	99.55 100	99.67 22	97.83 135	99.68 50	99.69 136	99.06 13	99.96 18	98.39 138	99.87 40	99.84 18

TSAR-MVS + GP.		99.36 47	99.36 21	99.36 124	99.67 96	98.61 182	99.07 254	99.33 234	99.00 22	99.82 20	99.81 60	99.06 13	99.84 132	99.09 48	99.42 130	99.65 109

pcd_1.5k_mvsjas		8.27 325	11.03 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	99.01 16	0.00 354	0.00 351	0.00 351	0.00 350

PS-MVSNAJss		98.92 112	98.92 94	98.90 185	98.78 291	98.53 187	99.78 19	99.54 68	98.07 112	99.00 206	99.76 102	99.01 16	99.37 244	99.13 44	97.23 245	98.81 215

PS-MVSNAJ		99.32 51	99.32 29	99.30 135	99.57 133	98.94 150	98.97 282	99.46 162	98.92 35	99.71 43	99.24 277	99.01 16	99.98 5	99.35 19	99.66 113	98.97 205

EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 75	99.78 44	99.15 120	99.61 67	99.45 174	99.01 18	99.89 4	99.82 49	99.01 16	99.92 75	99.56 4	99.95 6	99.85 14

Regformer-1		99.53 11	99.47 9	99.72 59	99.71 83	99.44 87	99.49 129	99.46 162	98.95 32	99.83 17	99.76 102	99.01 16	99.93 64	99.17 40	99.87 40	99.80 49

Regformer-2		99.54 9	99.47 9	99.75 49	99.71 83	99.52 78	99.49 129	99.49 122	98.94 33	99.83 17	99.76 102	99.01 16	99.94 49	99.15 43	99.87 40	99.80 49

Regformer-4		99.59 3	99.54 4	99.73 56	99.76 52	99.41 90	99.58 80	99.49 122	99.02 15	99.88 5	99.80 74	99.00 22	99.94 49	99.45 15	99.92 11	99.84 18

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 75	99.78 44	99.14 121	99.60 68	99.45 174	99.01 18	99.90 3	99.83 42	98.98 23	99.93 64	99.59 1	99.95 6	99.86 11

Regformer-3		99.57 7	99.53 5	99.68 63	99.76 52	99.29 102	99.58 80	99.44 182	99.01 18	99.87 10	99.80 74	98.97 24	99.91 86	99.44 17	99.92 11	99.83 29

region2R		99.48 19	99.35 24	99.87 11	99.88 11	99.80 23	99.65 53	99.66 27	98.13 100	99.66 61	99.68 141	98.96 25	99.96 18	98.62 111	99.87 40	99.84 18

segment_acmp												98.96 25

CNVR-MVS		99.42 36	99.30 38	99.78 43	99.62 121	99.71 40	99.26 220	99.52 86	98.82 42	99.39 123	99.71 125	98.96 25	99.85 127	98.59 117	99.80 82	99.77 62

xxxxxxxxxxxxxcwj		99.43 32	99.32 29	99.75 49	99.76 52	99.59 63	99.14 242	99.53 80	99.00 22	99.71 43	99.80 74	98.95 28	99.93 64	98.19 154	99.84 65	99.74 70

SF-MVS		99.38 45	99.24 52	99.79 41	99.79 42	99.68 45	99.57 85	99.54 68	97.82 140	99.71 43	99.80 74	98.95 28	99.93 64	98.19 154	99.84 65	99.74 70

ACMMPR		99.49 15	99.36 21	99.86 18	99.87 15	99.79 27	99.66 46	99.67 22	98.15 98	99.67 56	99.69 136	98.95 28	99.96 18	98.69 103	99.87 40	99.84 18

test_241102_TWO								99.48 133	99.08 11	99.88 5	99.81 60	98.94 31	99.96 18	98.91 67	99.84 65	99.88 5

MSP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 85	99.37 217	99.10 8	99.81 22	99.80 74	98.94 31	99.96 18	98.93 64	99.86 51	99.81 41

test0726					99.85 25	99.89 3	99.62 61	99.50 114	99.10 8	99.86 11	99.82 49	98.94 31

xiu_mvs_v2_base		99.26 60	99.25 51	99.29 138	99.53 141	98.91 154	99.02 268	99.45 174	98.80 46	99.71 43	99.26 275	98.94 31	99.98 5	99.34 23	99.23 142	98.98 204

CP-MVS		99.45 26	99.32 29	99.85 25	99.83 36	99.75 34	99.69 35	99.52 86	98.07 112	99.53 91	99.63 166	98.93 35	99.97 10	98.74 94	99.91 16	99.83 29

ZNCC-MVS		99.47 22	99.33 27	99.87 11	99.87 15	99.81 21	99.64 55	99.67 22	98.08 111	99.55 88	99.64 161	98.91 36	99.96 18	98.72 98	99.90 23	99.82 36

MCST-MVS		99.43 32	99.30 38	99.82 33	99.79 42	99.74 37	99.29 206	99.40 199	98.79 47	99.52 93	99.62 171	98.91 36	99.90 101	98.64 109	99.75 93	99.82 36

HPM-MVS		99.42 36	99.28 45	99.83 31	99.90 3	99.72 38	99.81 12	99.54 68	97.59 160	99.68 50	99.63 166	98.91 36	99.94 49	98.58 118	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				99.54 90	99.75 60	98.95 147		99.51 96	97.07 213	99.43 109	99.70 129	98.87 39	99.94 49	97.76 191	99.64 116	99.72 83

APD-MVS_3200maxsize		99.48 19	99.35 24	99.85 25	99.76 52	99.83 14	99.63 57	99.54 68	98.36 76	99.79 26	99.82 49	98.86 40	99.95 41	98.62 111	99.81 80	99.78 60

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 45	99.63 115	99.59 63	99.36 188	99.46 162	99.07 13	99.79 26	99.82 49	98.85 41	99.92 75	98.68 105	99.87 40	99.82 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			99.10 66		99.72 77		99.40 172	99.51 96	97.53 170	99.64 66	99.78 92	98.84 42	99.91 86	97.63 203	99.82 78

CDPH-MVS		99.13 77	98.91 96	99.80 38	99.75 60	99.71 40	99.15 240	99.41 193	96.60 248	99.60 77	99.55 194	98.83 43	99.90 101	97.48 219	99.83 72	99.78 60

ACMMP_NAP		99.47 22	99.34 26	99.88 6	99.87 15	99.86 10	99.47 140	99.48 133	98.05 117	99.76 37	99.86 23	98.82 44	99.93 64	98.82 88	99.91 16	99.84 18

XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 128	99.74 113	98.81 45	99.94 49	98.79 90	99.86 51	99.84 18

X-MVStestdata		96.55 282	95.45 295	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 128	64.01 351	98.81 45	99.94 49	98.79 90	99.86 51	99.84 18

MP-MVS-pluss		99.37 46	99.20 56	99.88 6	99.90 3	99.87 9	99.30 202	99.52 86	97.18 202	99.60 77	99.79 86	98.79 47	99.95 41	98.83 84	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mPP-MVS		99.44 29	99.30 38	99.86 18	99.88 11	99.79 27	99.69 35	99.48 133	98.12 102	99.50 96	99.75 107	98.78 48	99.97 10	98.57 120	99.89 33	99.83 29

APD-MVS		99.27 58	99.08 69	99.84 30	99.75 60	99.79 27	99.50 119	99.50 114	97.16 204	99.77 33	99.82 49	98.78 48	99.94 49	97.56 212	99.86 51	99.80 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAPA-MVS	97.07 15	97.74 238	97.34 258	98.94 175	99.70 89	97.53 238	99.25 222	99.51 96	91.90 326	99.30 141	99.63 166	98.78 48	99.64 208	88.09 336	99.87 40	99.65 109
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TEST9					99.67 96	99.65 53	99.05 259	99.41 193	96.22 277	98.95 213	99.49 215	98.77 51	99.91 86

agg_prior1		99.01 105	98.76 117	99.76 48	99.67 96	99.62 56	98.99 275	99.40 199	96.26 273	98.87 226	99.49 215	98.77 51	99.91 86	97.69 200	99.72 99	99.75 66

train_agg		99.02 102	98.77 115	99.77 45	99.67 96	99.65 53	99.05 259	99.41 193	96.28 270	98.95 213	99.49 215	98.76 53	99.91 86	97.63 203	99.72 99	99.75 66

test_8					99.67 96	99.61 58	99.03 265	99.41 193	96.28 270	98.93 217	99.48 221	98.76 53	99.91 86

API-MVS		99.04 99	99.03 76	99.06 158	99.40 176	99.31 100	99.55 100	99.56 54	98.54 61	99.33 138	99.39 244	98.76 53	99.78 164	96.98 251	99.78 87	98.07 320

DP-MVS Recon		99.12 83	98.95 92	99.65 70	99.74 67	99.70 42	99.27 212	99.57 49	96.40 266	99.42 112	99.68 141	98.75 56	99.80 157	97.98 173	99.72 99	99.44 164

Test By Simon												98.75 56

ACMMP		99.45 26	99.32 29	99.82 33	99.89 8	99.67 48	99.62 61	99.69 18	98.12 102	99.63 67	99.84 38	98.73 58	99.96 18	98.55 126	99.83 72	99.81 41
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
DPE-MVS		99.46 24	99.32 29	99.91 2	99.78 44	99.88 7	99.36 188	99.51 96	98.73 51	99.88 5	99.84 38	98.72 59	99.96 18	98.16 159	99.87 40	99.88 5

NCCC		99.34 49	99.19 57	99.79 41	99.61 125	99.65 53	99.30 202	99.48 133	98.86 38	99.21 165	99.63 166	98.72 59	99.90 101	98.25 150	99.63 118	99.80 49

DeepPCF-MVS	98.18 3	98.81 128	99.37 19	97.12 305	99.60 128	91.75 336	98.61 318	99.44 182	99.35 1	99.83 17	99.85 29	98.70 61	99.81 152	99.02 54	99.91 16	99.81 41

SR-MVS		99.43 32	99.29 42	99.86 18	99.75 60	99.83 14	99.59 73	99.62 33	98.21 93	99.73 40	99.79 86	98.68 62	99.96 18	98.44 136	99.77 89	99.79 53

test_prior3		99.21 64	99.05 71	99.68 63	99.67 96	99.48 82	98.96 283	99.56 54	98.34 78	99.01 201	99.52 205	98.68 62	99.83 141	97.96 174	99.74 95	99.74 70

test_prior2							98.96 283		98.34 78	99.01 201	99.52 205	98.68 62		97.96 174	99.74 95

DPM-MVS		98.95 110	98.71 121	99.66 66	99.63 115	99.55 70	98.64 317	99.10 273	97.93 126	99.42 112	99.55 194	98.67 65	99.80 157	95.80 285	99.68 110	99.61 123

原ACMM1				99.65 70	99.73 72	99.33 96		99.47 152	97.46 174	99.12 181	99.66 153	98.67 65	99.91 86	97.70 199	99.69 105	99.71 90

HPM-MVS++		99.39 44	99.23 54	99.87 11	99.75 60	99.84 13	99.43 154	99.51 96	98.68 55	99.27 149	99.53 202	98.64 67	99.96 18	98.44 136	99.80 82	99.79 53

abl_6		99.44 29	99.31 36	99.83 31	99.85 25	99.75 34	99.66 46	99.59 42	98.13 100	99.82 20	99.81 60	98.60 68	99.96 18	98.46 134	99.88 36	99.79 53

PHI-MVS		99.30 53	99.17 59	99.70 62	99.56 137	99.52 78	99.58 80	99.80 8	97.12 208	99.62 71	99.73 120	98.58 69	99.90 101	98.61 114	99.91 16	99.68 99

GST-MVS		99.40 43	99.24 52	99.85 25	99.86 21	99.79 27	99.60 68	99.67 22	97.97 123	99.63 67	99.68 141	98.52 70	99.95 41	98.38 140	99.86 51	99.81 41

ETH3D-3000-0.1		99.21 64	99.02 79	99.77 45	99.73 72	99.69 43	99.38 181	99.51 96	97.45 177	99.61 73	99.75 107	98.51 71	99.91 86	97.45 224	99.83 72	99.71 90

MVS_111021_LR		99.41 40	99.33 27	99.65 70	99.77 49	99.51 80	98.94 289	99.85 6	98.82 42	99.65 64	99.74 113	98.51 71	99.80 157	98.83 84	99.89 33	99.64 115

MVS_111021_HR		99.41 40	99.32 29	99.66 66	99.72 77	99.47 84	98.95 287	99.85 6	98.82 42	99.54 89	99.73 120	98.51 71	99.74 172	98.91 67	99.88 36	99.77 62

旧先验1					99.74 67	99.59 63		99.54 68			99.69 136	98.47 74			99.68 110	99.73 77

DELS-MVS		99.48 19	99.42 13	99.65 70	99.72 77	99.40 92	99.05 259	99.66 27	99.14 6	99.57 84	99.80 74	98.46 75	99.94 49	99.57 3	99.84 65	99.60 125
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
PAPR		98.63 145	98.34 153	99.51 103	99.40 176	99.03 132	98.80 302	99.36 218	96.33 267	99.00 206	99.12 293	98.46 75	99.84 132	95.23 298	99.37 136	99.66 105

zzz-MVS		99.49 15	99.36 21	99.89 4	99.90 3	99.86 10	99.36 188	99.47 152	98.79 47	99.68 50	99.81 60	98.43 77	99.97 10	98.88 70	99.90 23	99.83 29

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 46	99.47 152	98.79 47	99.68 50	99.81 60	98.43 77	99.97 10	98.88 70	99.90 23	99.83 29

新几何1				99.75 49	99.75 60	99.59 63		99.54 68	96.76 234	99.29 144	99.64 161	98.43 77	99.94 49	96.92 257	99.66 113	99.72 83

F-COLMAP		99.19 67	99.04 74	99.64 75	99.78 44	99.27 105	99.42 161	99.54 68	97.29 192	99.41 116	99.59 181	98.42 80	99.93 64	98.19 154	99.69 105	99.73 77

ETV-MVS		99.26 60	99.21 55	99.40 120	99.46 161	99.30 101	99.56 92	99.52 86	98.52 63	99.44 108	99.27 274	98.41 81	99.86 121	99.10 47	99.59 122	99.04 197

1121		99.09 92	98.87 101	99.75 49	99.74 67	99.60 60	99.27 212	99.48 133	96.82 233	99.25 156	99.65 154	98.38 82	99.93 64	97.53 215	99.67 112	99.73 77

test12				99.75 49	99.64 112	99.61 58		99.29 254		99.21 165		98.38 82	99.89 109		99.74 95	99.74 70

CSCG		99.32 51	99.32 29	99.32 130	99.85 25	98.29 205	99.71 31	99.66 27	98.11 104	99.41 116	99.80 74	98.37 84	99.96 18	98.99 56	99.96 5	99.72 83

PAPM_NR		99.04 99	98.84 107	99.66 66	99.74 67	99.44 87	99.39 176	99.38 209	97.70 151	99.28 146	99.28 271	98.34 85	99.85 127	96.96 253	99.45 128	99.69 95

TAMVS		99.12 83	99.08 69	99.24 145	99.46 161	98.55 185	99.51 113	99.46 162	98.09 107	99.45 104	99.82 49	98.34 85	99.51 223	98.70 100	98.93 166	99.67 102

ETH3D cwj APD-0.16		99.06 96	98.84 107	99.72 59	99.51 145	99.60 60	99.23 225	99.44 182	97.04 216	99.39 123	99.67 147	98.30 87	99.92 75	97.27 231	99.69 105	99.64 115

MP-MVS		99.33 50	99.15 60	99.87 11	99.88 11	99.82 20	99.66 46	99.46 162	98.09 107	99.48 100	99.74 113	98.29 88	99.96 18	97.93 177	99.87 40	99.82 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test222					99.75 60	99.49 81	98.91 292	99.49 122	96.42 264	99.34 137	99.65 154	98.28 89			99.69 105	99.72 83

CS-MVS		99.21 64	99.13 62	99.45 113	99.54 140	99.34 95	99.71 31	99.54 68	98.26 86	98.99 208	99.24 277	98.25 90	99.88 114	98.98 57	99.63 118	99.12 186

PLC	97.94 4	99.02 102	98.85 106	99.53 96	99.66 105	99.01 135	99.24 224	99.52 86	96.85 230	99.27 149	99.48 221	98.25 90	99.91 86	97.76 191	99.62 120	99.65 109
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DVP-MVS		99.42 36	99.27 47	99.88 6	99.89 8	99.80 23	99.67 42	99.50 114	98.70 53	99.77 33	99.49 215	98.21 92	99.95 41	98.46 134	99.77 89	99.88 5

xiu_mvs_v1_base_debu		99.29 55	99.27 47	99.34 125	99.63 115	98.97 141	99.12 244	99.51 96	98.86 38	99.84 13	99.47 224	98.18 93	99.99 1	99.50 8	99.31 137	99.08 192

xiu_mvs_v1_base		99.29 55	99.27 47	99.34 125	99.63 115	98.97 141	99.12 244	99.51 96	98.86 38	99.84 13	99.47 224	98.18 93	99.99 1	99.50 8	99.31 137	99.08 192

xiu_mvs_v1_base_debi		99.29 55	99.27 47	99.34 125	99.63 115	98.97 141	99.12 244	99.51 96	98.86 38	99.84 13	99.47 224	98.18 93	99.99 1	99.50 8	99.31 137	99.08 192

testtj		99.12 83	98.87 101	99.86 18	99.72 77	99.79 27	99.44 148	99.51 96	97.29 192	99.59 80	99.74 113	98.15 96	99.96 18	96.74 263	99.69 105	99.81 41

EIA-MVS		99.18 69	99.09 68	99.45 113	99.49 154	99.18 113	99.67 42	99.53 80	97.66 156	99.40 121	99.44 230	98.10 97	99.81 152	98.94 62	99.62 120	99.35 172

CNLPA		99.14 75	98.99 84	99.59 82	99.58 131	99.41 90	99.16 236	99.44 182	98.45 69	99.19 171	99.49 215	98.08 98	99.89 109	97.73 195	99.75 93	99.48 154

114514_t		98.93 111	98.67 125	99.72 59	99.85 25	99.53 75	99.62 61	99.59 42	92.65 324	99.71 43	99.78 92	98.06 99	99.90 101	98.84 81	99.91 16	99.74 70

CDS-MVSNet		99.09 92	99.03 76	99.25 143	99.42 168	98.73 171	99.45 144	99.46 162	98.11 104	99.46 103	99.77 98	98.01 100	99.37 244	98.70 100	98.92 168	99.66 105
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		99.13 77	99.02 79	99.45 113	99.57 133	98.63 179	99.07 254	99.34 227	98.99 25	99.61 73	99.82 49	97.98 101	99.87 118	97.00 249	99.80 82	99.85 14

EI-MVSNet		98.67 141	98.67 125	98.68 218	99.35 185	97.97 219	99.50 119	99.38 209	96.93 227	99.20 168	99.83 42	97.87 102	99.36 248	98.38 140	97.56 225	98.71 232

IterMVS-LS		98.46 150	98.42 148	98.58 223	99.59 130	98.00 217	99.37 184	99.43 189	96.94 226	99.07 192	99.59 181	97.87 102	99.03 299	98.32 148	95.62 282	98.71 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		98.98 107	98.80 112	99.53 96	99.76 52	99.19 111	98.75 307	99.55 61	97.25 196	99.47 101	99.77 98	97.82 104	99.87 118	96.93 256	99.90 23	99.54 138

OMC-MVS		99.08 94	99.04 74	99.20 148	99.67 96	98.22 208	99.28 208	99.52 86	98.07 112	99.66 61	99.81 60	97.79 105	99.78 164	97.79 188	99.81 80	99.60 125

LS3D		99.27 58	99.12 64	99.74 54	99.18 229	99.75 34	99.56 92	99.57 49	98.45 69	99.49 99	99.85 29	97.77 106	99.94 49	98.33 146	99.84 65	99.52 143

PVSNet_Blended_VisFu		99.36 47	99.28 45	99.61 80	99.86 21	99.07 129	99.47 140	99.93 2	97.66 156	99.71 43	99.86 23	97.73 107	99.96 18	99.47 13	99.82 78	99.79 53

1314		98.68 140	98.54 143	99.11 155	98.89 275	98.65 177	99.27 212	99.49 122	96.89 228	97.99 297	99.56 191	97.72 108	99.83 141	97.74 194	99.27 140	98.84 214

MVS_Test		99.10 91	98.97 88	99.48 107	99.49 154	99.14 121	99.67 42	99.34 227	97.31 190	99.58 82	99.76 102	97.65 109	99.82 148	98.87 74	99.07 157	99.46 161

PVSNet_BlendedMVS		98.86 117	98.80 112	99.03 163	99.76 52	98.79 168	99.28 208	99.91 3	97.42 182	99.67 56	99.37 248	97.53 110	99.88 114	98.98 57	97.29 244	98.42 305

PVSNet_Blended		99.08 94	98.97 88	99.42 119	99.76 52	98.79 168	98.78 304	99.91 3	96.74 235	99.67 56	99.49 215	97.53 110	99.88 114	98.98 57	99.85 58	99.60 125

UA-Net		99.42 36	99.29 42	99.80 38	99.62 121	99.55 70	99.50 119	99.70 15	98.79 47	99.77 33	99.96 1	97.45 112	99.96 18	98.92 66	99.90 23	99.89 2

ETH3 D test6400		98.70 137	98.35 152	99.73 56	99.69 91	99.60 60	99.16 236	99.45 174	95.42 298	99.27 149	99.60 178	97.39 113	99.91 86	95.36 296	99.83 72	99.70 92

MVSFormer		99.17 71	99.12 64	99.29 138	99.51 145	98.94 150	99.88 1	99.46 162	97.55 165	99.80 24	99.65 154	97.39 113	99.28 262	99.03 52	99.85 58	99.65 109

lupinMVS		99.13 77	99.01 83	99.46 112	99.51 145	98.94 150	99.05 259	99.16 267	97.86 130	99.80 24	99.56 191	97.39 113	99.86 121	98.94 62	99.85 58	99.58 133

DP-MVS		99.16 73	98.95 92	99.78 43	99.77 49	99.53 75	99.41 164	99.50 114	97.03 218	99.04 198	99.88 15	97.39 113	99.92 75	98.66 107	99.90 23	99.87 10

sss		99.17 71	99.05 71	99.53 96	99.62 121	98.97 141	99.36 188	99.62 33	97.83 135	99.67 56	99.65 154	97.37 117	99.95 41	99.19 37	99.19 145	99.68 99

mvs_anonymous		99.03 101	98.99 84	99.16 151	99.38 180	98.52 191	99.51 113	99.38 209	97.79 141	99.38 126	99.81 60	97.30 118	99.45 227	99.35 19	98.99 163	99.51 149

miper_ehance_all_eth		98.18 174	98.10 167	98.41 245	99.23 216	97.72 234	98.72 310	99.31 245	96.60 248	98.88 224	99.29 269	97.29 119	99.13 286	97.60 205	95.99 271	98.38 310

CPTT-MVS		99.11 88	98.90 97	99.74 54	99.80 41	99.46 85	99.59 73	99.49 122	97.03 218	99.63 67	99.69 136	97.27 120	99.96 18	97.82 186	99.84 65	99.81 41

PMMVS		98.80 131	98.62 135	99.34 125	99.27 208	98.70 173	98.76 306	99.31 245	97.34 187	99.21 165	99.07 295	97.20 121	99.82 148	98.56 123	98.87 171	99.52 143

EPP-MVSNet		99.13 77	98.99 84	99.53 96	99.65 110	99.06 130	99.81 12	99.33 234	97.43 180	99.60 77	99.88 15	97.14 122	99.84 132	99.13 44	98.94 165	99.69 95

cl_fuxian		98.12 181	98.04 175	98.38 249	99.30 199	97.69 237	98.81 301	99.33 234	96.67 240	98.83 232	99.34 257	97.11 123	98.99 306	97.58 207	95.34 288	98.48 296

canonicalmvs		99.02 102	98.86 105	99.51 103	99.42 168	99.32 97	99.80 16	99.48 133	98.63 56	99.31 140	98.81 311	97.09 124	99.75 171	99.27 31	97.90 213	99.47 159

MAR-MVS		98.86 117	98.63 130	99.54 90	99.37 182	99.66 50	99.45 144	99.54 68	96.61 246	99.01 201	99.40 240	97.09 124	99.86 121	97.68 202	99.53 126	99.10 187
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
miper_enhance_ethall		98.16 176	98.08 171	98.41 245	98.96 270	97.72 234	98.45 327	99.32 242	96.95 224	98.97 211	99.17 285	97.06 126	99.22 273	97.86 182	95.99 271	98.29 313

jason		99.13 77	99.03 76	99.45 113	99.46 161	98.87 157	99.12 244	99.26 257	98.03 120	99.79 26	99.65 154	97.02 127	99.85 127	99.02 54	99.90 23	99.65 109
jason: jason.
our_test_3		97.65 253	97.68 214	97.55 298	98.62 309	94.97 314	98.84 298	99.30 249	96.83 232	98.19 288	99.34 257	97.01 128	99.02 301	95.00 302	96.01 269	98.64 265

MVS		97.28 271	96.55 278	99.48 107	98.78 291	98.95 147	99.27 212	99.39 203	83.53 339	98.08 292	99.54 199	96.97 129	99.87 118	94.23 310	99.16 146	99.63 119

Fast-Effi-MVS+-dtu		98.77 134	98.83 111	98.60 221	99.41 171	96.99 262	99.52 109	99.49 122	98.11 104	99.24 157	99.34 257	96.96 130	99.79 160	97.95 176	99.45 128	99.02 200

1112_ss		98.98 107	98.77 115	99.59 82	99.68 95	99.02 133	99.25 222	99.48 133	97.23 199	99.13 179	99.58 184	96.93 131	99.90 101	98.87 74	98.78 177	99.84 18

WTY-MVS		99.06 96	98.88 100	99.61 80	99.62 121	99.16 116	99.37 184	99.56 54	98.04 118	99.53 91	99.62 171	96.84 132	99.94 49	98.85 79	98.49 190	99.72 83

FC-MVSNet-test		98.75 135	98.62 135	99.15 153	99.08 251	99.45 86	99.86 5	99.60 39	98.23 90	98.70 251	99.82 49	96.80 133	99.22 273	99.07 50	96.38 262	98.79 217

Effi-MVS+-dtu		98.78 132	98.89 99	98.47 238	99.33 190	96.91 268	99.57 85	99.30 249	98.47 66	99.41 116	98.99 302	96.78 134	99.74 172	98.73 96	99.38 132	98.74 228

mvs-test1		98.86 117	98.84 107	98.89 188	99.33 190	97.77 231	99.44 148	99.30 249	98.47 66	99.10 186	99.43 232	96.78 134	99.95 41	98.73 96	99.02 161	98.96 207

Test_1112_low_res		98.89 113	98.66 128	99.57 86	99.69 91	98.95 147	99.03 265	99.47 152	96.98 220	99.15 177	99.23 279	96.77 136	99.89 109	98.83 84	98.78 177	99.86 11

FIs		98.78 132	98.63 130	99.23 147	99.18 229	99.54 72	99.83 9	99.59 42	98.28 84	98.79 238	99.81 60	96.75 137	99.37 244	99.08 49	96.38 262	98.78 218

PVSNet	96.02 17	98.85 125	98.84 107	98.89 188	99.73 72	97.28 244	98.32 331	99.60 39	97.86 130	99.50 96	99.57 188	96.75 137	99.86 121	98.56 123	99.70 104	99.54 138

nrg030		98.64 144	98.42 148	99.28 140	99.05 257	99.69 43	99.81 12	99.46 162	98.04 118	99.01 201	99.82 49	96.69 139	99.38 241	99.34 23	94.59 301	98.78 218

CHOSEN 280x420		99.12 83	99.13 62	99.08 156	99.66 105	97.89 225	98.43 328	99.71 13	98.88 37	99.62 71	99.76 102	96.63 140	99.70 195	99.46 14	99.99 1	99.66 105

eth_miper_zixun_eth		98.05 191	97.96 183	98.33 252	99.26 210	97.38 242	98.56 323	99.31 245	96.65 242	98.88 224	99.52 205	96.58 141	99.12 290	97.39 228	95.53 285	98.47 298

cdsmvs_eth3d_5k		24.64 323	32.85 325	0.00 337	0.00 356	0.00 357	0.00 348	99.51 96	0.00 352	0.00 353	99.56 191	96.58 141	0.00 354	0.00 351	0.00 351	0.00 350

IS-MVSNet		99.05 98	98.87 101	99.57 86	99.73 72	99.32 97	99.75 25	99.20 264	98.02 121	99.56 85	99.86 23	96.54 143	99.67 200	98.09 163	99.13 150	99.73 77

diffmvs		99.14 75	99.02 79	99.51 103	99.61 125	98.96 145	99.28 208	99.49 122	98.46 68	99.72 42	99.71 125	96.50 144	99.88 114	99.31 26	99.11 151	99.67 102

CANet		99.25 62	99.14 61	99.59 82	99.41 171	99.16 116	99.35 193	99.57 49	98.82 42	99.51 95	99.61 175	96.46 145	99.95 41	99.59 1	99.98 2	99.65 109

ppachtmachnet_test		97.49 265	97.45 238	97.61 295	98.62 309	95.24 308	98.80 302	99.46 162	96.11 287	98.22 287	99.62 171	96.45 146	98.97 314	93.77 314	95.97 274	98.61 284

HY-MVS	97.30 7	98.85 125	98.64 129	99.47 110	99.42 168	99.08 128	99.62 61	99.36 218	97.39 185	99.28 146	99.68 141	96.44 147	99.92 75	98.37 142	98.22 199	99.40 169

UniMVSNet_NR-MVSNet		98.22 168	97.97 182	98.96 172	98.92 273	98.98 138	99.48 135	99.53 80	97.76 144	98.71 245	99.46 228	96.43 148	99.22 273	98.57 120	92.87 323	98.69 240

Effi-MVS+		98.81 128	98.59 140	99.48 107	99.46 161	99.12 125	98.08 337	99.50 114	97.50 173	99.38 126	99.41 238	96.37 149	99.81 152	99.11 46	98.54 187	99.51 149

AdaColmap		99.01 105	98.80 112	99.66 66	99.56 137	99.54 72	99.18 234	99.70 15	98.18 97	99.35 134	99.63 166	96.32 150	99.90 101	97.48 219	99.77 89	99.55 136

UniMVSNet (Re)		98.29 165	98.00 179	99.13 154	99.00 263	99.36 94	99.49 129	99.51 96	97.95 124	98.97 211	99.13 290	96.30 151	99.38 241	98.36 144	93.34 316	98.66 261

LCM-MVSNet-Re		97.83 222	98.15 163	96.87 310	99.30 199	92.25 335	99.59 73	98.26 325	97.43 180	96.20 321	99.13 290	96.27 152	98.73 322	98.17 158	98.99 163	99.64 115

PAPM		97.59 256	97.09 270	99.07 157	99.06 254	98.26 207	98.30 332	99.10 273	94.88 303	98.08 292	99.34 257	96.27 152	99.64 208	89.87 331	98.92 168	99.31 176

Fast-Effi-MVS+		98.70 137	98.43 147	99.51 103	99.51 145	99.28 103	99.52 109	99.47 152	96.11 287	99.01 201	99.34 257	96.20 154	99.84 132	97.88 180	98.82 174	99.39 170

EPNet_dtu		98.03 192	97.96 183	98.23 262	98.27 321	95.54 302	99.23 225	98.75 307	99.02 15	97.82 302	99.71 125	96.11 155	99.48 224	93.04 322	99.65 115	99.69 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline		99.15 74	99.02 79	99.53 96	99.66 105	99.14 121	99.72 29	99.48 133	98.35 77	99.42 112	99.84 38	96.07 156	99.79 160	99.51 7	99.14 149	99.67 102

D2MVS		98.41 155	98.50 144	98.15 267	99.26 210	96.62 278	99.40 172	99.61 35	97.71 150	98.98 209	99.36 251	96.04 157	99.67 200	98.70 100	97.41 240	98.15 318

miper_lstm_enhance		98.00 199	97.91 189	98.28 260	99.34 189	97.43 241	98.88 294	99.36 218	96.48 259	98.80 236	99.55 194	95.98 158	98.91 317	97.27 231	95.50 286	98.51 294

EPNet		98.86 117	98.71 121	99.30 135	97.20 335	98.18 209	99.62 61	98.91 296	99.28 2	98.63 262	99.81 60	95.96 159	99.99 1	99.24 33	99.72 99	99.73 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest		98.87 114	98.72 119	99.31 131	99.86 21	98.48 197	99.56 92	99.61 35	97.85 132	99.36 131	99.85 29	95.95 160	99.85 127	96.66 269	99.83 72	99.59 129

TestCases				99.31 131	99.86 21	98.48 197		99.61 35	97.85 132	99.36 131	99.85 29	95.95 160	99.85 127	96.66 269	99.83 72	99.59 129

3Dnovator	97.25 9	99.24 63	99.05 71	99.81 36	99.12 242	99.66 50	99.84 6	99.74 10	99.09 10	98.92 218	99.90 7	95.94 162	99.98 5	98.95 61	99.92 11	99.79 53

casdiffmvs		99.13 77	98.98 87	99.56 88	99.65 110	99.16 116	99.56 92	99.50 114	98.33 81	99.41 116	99.86 23	95.92 163	99.83 141	99.45 15	99.16 146	99.70 92

RPSCF		98.22 168	98.62 135	96.99 306	99.82 37	91.58 337	99.72 29	99.44 182	96.61 246	99.66 61	99.89 10	95.92 163	99.82 148	97.46 222	99.10 154	99.57 134

pmmvs4		98.13 179	97.90 190	98.81 207	98.61 311	98.87 157	98.99 275	99.21 263	96.44 262	99.06 196	99.58 184	95.90 165	99.11 291	97.18 241	96.11 267	98.46 302

HyFIR lowres test		99.11 88	98.92 94	99.65 70	99.90 3	99.37 93	99.02 268	99.91 3	97.67 155	99.59 80	99.75 107	95.90 165	99.73 179	99.53 5	99.02 161	99.86 11

COLMAP_ROB	97.56 6	98.86 117	98.75 118	99.17 150	99.88 11	98.53 187	99.34 196	99.59 42	97.55 165	98.70 251	99.89 10	95.83 167	99.90 101	98.10 162	99.90 23	99.08 192
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	98.35 2	99.30 53	99.19 57	99.64 75	99.82 37	99.23 109	99.62 61	99.55 61	98.94 33	99.63 67	99.95 2	95.82 168	99.94 49	99.37 18	99.97 3	99.73 77
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
QAPM		98.67 141	98.30 157	99.80 38	99.20 224	99.67 48	99.77 21	99.72 11	94.74 306	98.73 243	99.90 7	95.78 169	99.98 5	96.96 253	99.88 36	99.76 65

BH-untuned		98.42 153	98.36 150	98.59 222	99.49 154	96.70 274	99.27 212	99.13 271	97.24 198	98.80 236	99.38 245	95.75 170	99.74 172	97.07 247	99.16 146	99.33 175

test_djsdf		98.67 141	98.57 141	98.98 169	98.70 302	98.91 154	99.88 1	99.46 162	97.55 165	99.22 162	99.88 15	95.73 171	99.28 262	99.03 52	97.62 220	98.75 225

cl-mvsnet1		98.01 197	97.85 196	98.48 234	99.24 215	97.95 223	98.71 311	99.35 223	96.50 254	98.60 267	99.54 199	95.72 172	99.03 299	97.21 235	95.77 277	98.46 302

3Dnovator+	97.12 13	99.18 69	98.97 88	99.82 33	99.17 235	99.68 45	99.81 12	99.51 96	99.20 4	98.72 244	99.89 10	95.68 173	99.97 10	98.86 77	99.86 51	99.81 41

cl-mvsnet_		98.01 197	97.84 197	98.55 228	99.25 214	97.97 219	98.71 311	99.34 227	96.47 261	98.59 268	99.54 199	95.65 174	99.21 278	97.21 235	95.77 277	98.46 302

VNet		99.11 88	98.90 97	99.73 56	99.52 143	99.56 68	99.41 164	99.39 203	99.01 18	99.74 39	99.78 92	95.56 175	99.92 75	99.52 6	98.18 203	99.72 83

WR-MVS_H		98.13 179	97.87 195	98.90 185	99.02 261	98.84 161	99.70 33	99.59 42	97.27 194	98.40 277	99.19 284	95.53 176	99.23 270	98.34 145	93.78 313	98.61 284

CHOSEN 1792x2688		99.19 67	99.10 66	99.45 113	99.89 8	98.52 191	99.39 176	99.94 1	98.73 51	99.11 183	99.89 10	95.50 177	99.94 49	99.50 8	99.97 3	99.89 2

Vis-MVSNet (Re-imp)		98.87 114	98.72 119	99.31 131	99.71 83	98.88 156	99.80 16	99.44 182	97.91 128	99.36 131	99.78 92	95.49 178	99.43 236	97.91 178	99.11 151	99.62 121

PatchMatch-RL		98.84 127	98.62 135	99.52 101	99.71 83	99.28 103	99.06 257	99.77 9	97.74 148	99.50 96	99.53 202	95.41 179	99.84 132	97.17 242	99.64 116	99.44 164

RRT_MVS		98.60 146	98.44 146	99.05 160	98.88 276	99.14 121	99.49 129	99.38 209	97.76 144	99.29 144	99.86 23	95.38 180	99.36 248	98.81 89	97.16 249	98.64 265

test_yl		98.86 117	98.63 130	99.54 90	99.49 154	99.18 113	99.50 119	99.07 279	98.22 91	99.61 73	99.51 209	95.37 181	99.84 132	98.60 115	98.33 193	99.59 129

DCV-MVSNet		98.86 117	98.63 130	99.54 90	99.49 154	99.18 113	99.50 119	99.07 279	98.22 91	99.61 73	99.51 209	95.37 181	99.84 132	98.60 115	98.33 193	99.59 129

tpmrst		98.33 161	98.48 145	97.90 283	99.16 237	94.78 317	99.31 200	99.11 272	97.27 194	99.45 104	99.59 181	95.33 183	99.84 132	98.48 130	98.61 180	99.09 191

MVP-Stereo		97.81 227	97.75 208	97.99 277	97.53 328	96.60 279	98.96 283	98.85 302	97.22 200	97.23 310	99.36 251	95.28 184	99.46 226	95.51 291	99.78 87	97.92 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CANet_DTU		98.97 109	98.87 101	99.25 143	99.33 190	98.42 202	99.08 253	99.30 249	99.16 5	99.43 109	99.75 107	95.27 185	99.97 10	98.56 123	99.95 6	99.36 171

XVG-OURS		98.73 136	98.68 124	98.88 191	99.70 89	97.73 233	98.92 290	99.55 61	98.52 63	99.45 104	99.84 38	95.27 185	99.91 86	98.08 167	98.84 173	99.00 201

BH-w/o		98.00 199	97.89 194	98.32 254	99.35 185	96.20 291	99.01 273	98.90 298	96.42 264	98.38 278	99.00 301	95.26 187	99.72 183	96.06 279	98.61 180	99.03 198

EU-MVSNet		97.98 201	98.03 176	97.81 289	98.72 299	96.65 277	99.66 46	99.66 27	98.09 107	98.35 281	99.82 49	95.25 188	98.01 328	97.41 227	95.30 289	98.78 218

MDTV_nov1_ep13_2view						95.18 311	99.35 193		96.84 231	99.58 82		95.19 189		97.82 186		99.46 161

JIA-IIPM		97.50 263	97.02 272	98.93 177	98.73 297	97.80 230	99.30 202	98.97 287	91.73 327	98.91 219	94.86 339	95.10 190	99.71 189	97.58 207	97.98 211	99.28 178

NR-MVSNet		97.97 204	97.61 221	99.02 164	98.87 280	99.26 106	99.47 140	99.42 191	97.63 158	97.08 313	99.50 212	95.07 191	99.13 286	97.86 182	93.59 314	98.68 245

tpmvs		97.98 201	98.02 178	97.84 286	99.04 258	94.73 318	99.31 200	99.20 264	96.10 291	98.76 241	99.42 235	94.94 192	99.81 152	96.97 252	98.45 191	98.97 205

v8		97.95 205	97.63 220	98.93 177	98.95 271	98.81 167	99.80 16	99.41 193	96.03 292	99.10 186	99.42 235	94.92 193	99.30 260	96.94 255	94.08 310	98.66 261

PatchmatchNet		98.31 162	98.36 150	98.19 264	99.16 237	95.32 307	99.27 212	98.92 293	97.37 186	99.37 128	99.58 184	94.90 194	99.70 195	97.43 226	99.21 143	99.54 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v7n		97.87 214	97.52 229	98.92 179	98.76 295	98.58 183	99.84 6	99.46 162	96.20 278	98.91 219	99.70 129	94.89 195	99.44 232	96.03 280	93.89 312	98.75 225

sam_mvs1												94.86 196				99.52 143

DU-MVS		98.08 185	97.79 199	98.96 172	98.87 280	98.98 138	99.41 164	99.45 174	97.87 129	98.71 245	99.50 212	94.82 197	99.22 273	98.57 120	92.87 323	98.68 245

Baseline_NR-MVSNet		97.76 232	97.45 238	98.68 218	99.09 249	98.29 205	99.41 164	98.85 302	95.65 296	98.63 262	99.67 147	94.82 197	99.10 293	98.07 170	92.89 322	98.64 265

patchmatchnet-post											98.70 316	94.79 199	99.74 172

Patchmatch-RL test		95.84 294	95.81 291	95.95 317	95.61 336	90.57 338	98.24 333	98.39 324	95.10 302	95.20 326	98.67 317	94.78 200	97.77 333	96.28 277	90.02 332	99.51 149

alignmvs		98.81 128	98.56 142	99.58 85	99.43 167	99.42 89	99.51 113	98.96 289	98.61 58	99.35 134	98.92 308	94.78 200	99.77 166	99.35 19	98.11 209	99.54 138

MDTV_nov1_ep13			98.32 155		99.11 244	94.44 320	99.27 212	98.74 310	97.51 172	99.40 121	99.62 171	94.78 200	99.76 169	97.59 206	98.81 176

Vis-MVSNet		99.12 83	98.97 88	99.56 88	99.78 44	99.10 126	99.68 40	99.66 27	98.49 65	99.86 11	99.87 20	94.77 203	99.84 132	99.19 37	99.41 131	99.74 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
anonymousdsp		98.44 151	98.28 158	98.94 175	98.50 317	98.96 145	99.77 21	99.50 114	97.07 213	98.87 226	99.77 98	94.76 204	99.28 262	98.66 107	97.60 221	98.57 290

v10		97.85 217	97.52 229	98.86 199	98.99 264	98.67 175	99.75 25	99.41 193	95.70 295	98.98 209	99.41 238	94.75 205	99.23 270	96.01 281	94.63 300	98.67 253

OpenMVS	96.50 16	98.47 149	98.12 166	99.52 101	99.04 258	99.53 75	99.82 10	99.72 11	94.56 309	98.08 292	99.88 15	94.73 206	99.98 5	97.47 221	99.76 92	99.06 196

sam_mvs												94.72 207

v148		97.79 230	97.55 225	98.50 231	98.74 296	97.72 234	99.54 103	99.33 234	96.26 273	98.90 221	99.51 209	94.68 208	99.14 283	97.83 185	93.15 320	98.63 273

v1144		97.98 201	97.69 213	98.85 202	98.87 280	98.66 176	99.54 103	99.35 223	96.27 272	99.23 161	99.35 254	94.67 209	99.23 270	96.73 264	95.16 292	98.68 245

V42		98.06 186	97.79 199	98.86 199	98.98 267	98.84 161	99.69 35	99.34 227	96.53 253	99.30 141	99.37 248	94.67 209	99.32 257	97.57 211	94.66 299	98.42 305

test_post											65.99 349	94.65 211	99.73 179

baseline1		98.31 162	97.95 185	99.38 123	99.50 152	98.74 170	99.59 73	98.93 291	98.41 73	99.14 178	99.60 178	94.59 212	99.79 160	98.48 130	93.29 317	99.61 123

DSMNet-mixed		97.25 272	97.35 255	96.95 308	97.84 326	93.61 329	99.57 85	96.63 343	96.13 286	98.87 226	98.61 320	94.59 212	97.70 335	95.08 300	98.86 172	99.55 136

Patchmatch-test		97.93 206	97.65 217	98.77 212	99.18 229	97.07 255	99.03 265	99.14 270	96.16 282	98.74 242	99.57 188	94.56 214	99.72 183	93.36 318	99.11 151	99.52 143

PCF-MVS	97.08 14	97.66 252	97.06 271	99.47 110	99.61 125	99.09 127	98.04 338	99.25 259	91.24 329	98.51 271	99.70 129	94.55 215	99.91 86	92.76 324	99.85 58	99.42 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchT		97.03 277	96.44 280	98.79 210	98.99 264	98.34 204	99.16 236	99.07 279	92.13 325	99.52 93	97.31 333	94.54 216	98.98 307	88.54 334	98.73 179	99.03 198

CVMVSNet		98.57 147	98.67 125	98.30 256	99.35 185	95.59 299	99.50 119	99.55 61	98.60 59	99.39 123	99.83 42	94.48 217	99.45 227	98.75 93	98.56 186	99.85 14

test-LLR		98.06 186	97.90 190	98.55 228	98.79 288	97.10 251	98.67 313	97.75 333	97.34 187	98.61 265	98.85 309	94.45 218	99.45 227	97.25 233	99.38 132	99.10 187

test0.0.03 1		97.71 245	97.42 247	98.56 226	98.41 320	97.82 229	98.78 304	98.63 319	97.34 187	98.05 296	98.98 305	94.45 218	98.98 307	95.04 301	97.15 250	98.89 211

v144192		97.92 209	97.60 222	98.87 195	98.83 286	98.65 177	99.55 100	99.34 227	96.20 278	99.32 139	99.40 240	94.36 220	99.26 267	96.37 276	95.03 295	98.70 236

CR-MVSNet		98.17 175	97.93 188	98.87 195	99.18 229	98.49 195	99.22 230	99.33 234	96.96 222	99.56 85	99.38 245	94.33 221	99.00 304	94.83 304	98.58 183	99.14 183

Patchmtry		97.75 236	97.40 249	98.81 207	99.10 247	98.87 157	99.11 250	99.33 234	94.83 304	98.81 234	99.38 245	94.33 221	99.02 301	96.10 278	95.57 283	98.53 292

tpm cat1		97.39 268	97.36 253	97.50 300	99.17 235	93.73 326	99.43 154	99.31 245	91.27 328	98.71 245	99.08 294	94.31 223	99.77 166	96.41 275	98.50 189	99.00 201

TranMVSNet+NR-MVSNet		97.93 206	97.66 216	98.76 213	98.78 291	98.62 180	99.65 53	99.49 122	97.76 144	98.49 272	99.60 178	94.23 224	98.97 314	98.00 172	92.90 321	98.70 236

v2v482		98.06 186	97.77 204	98.92 179	98.90 274	98.82 165	99.57 85	99.36 218	96.65 242	99.19 171	99.35 254	94.20 225	99.25 268	97.72 197	94.97 296	98.69 240

XVG-OURS-SEG-HR		98.69 139	98.62 135	98.89 188	99.71 83	97.74 232	99.12 244	99.54 68	98.44 72	99.42 112	99.71 125	94.20 225	99.92 75	98.54 127	98.90 170	99.00 201

ab-mvs		98.86 117	98.63 130	99.54 90	99.64 112	99.19 111	99.44 148	99.54 68	97.77 143	99.30 141	99.81 60	94.20 225	99.93 64	99.17 40	98.82 174	99.49 153

test_post1							99.23 225				65.14 350	94.18 228	99.71 189	97.58 207

ADS-MVSNet2		98.02 194	98.07 174	97.87 284	99.33 190	95.19 310	99.23 225	99.08 276	96.24 275	99.10 186	99.67 147	94.11 229	98.93 316	96.81 260	99.05 158	99.48 154

ADS-MVSNet		98.20 171	98.08 171	98.56 226	99.33 190	96.48 282	99.23 225	99.15 268	96.24 275	99.10 186	99.67 147	94.11 229	99.71 189	96.81 260	99.05 158	99.48 154

RPMNet		96.61 281	95.85 289	98.87 195	99.18 229	98.49 195	99.22 230	99.08 276	88.72 335	99.56 85	97.38 331	94.08 231	99.00 304	86.87 340	98.58 183	99.14 183

v1192		97.81 227	97.44 243	98.91 183	98.88 276	98.68 174	99.51 113	99.34 227	96.18 280	99.20 168	99.34 257	94.03 232	99.36 248	95.32 297	95.18 291	98.69 240

v1921920		97.80 229	97.45 238	98.84 203	98.80 287	98.53 187	99.52 109	99.34 227	96.15 284	99.24 157	99.47 224	93.98 233	99.29 261	95.40 294	95.13 293	98.69 240

Anonymous20231206		96.22 288	96.03 286	96.79 312	97.31 333	94.14 323	99.63 57	99.08 276	96.17 281	97.04 314	99.06 297	93.94 234	97.76 334	86.96 339	95.06 294	98.47 298

WR-MVS		98.06 186	97.73 210	99.06 158	98.86 283	99.25 107	99.19 233	99.35 223	97.30 191	98.66 254	99.43 232	93.94 234	99.21 278	98.58 118	94.28 306	98.71 232

N_pmnet		94.95 302	95.83 290	92.31 323	98.47 318	79.33 346	99.12 244	92.81 353	93.87 314	97.68 305	99.13 290	93.87 236	99.01 303	91.38 328	96.19 266	98.59 288

MVSTER		98.49 148	98.32 155	99.00 167	99.35 185	99.02 133	99.54 103	99.38 209	97.41 183	99.20 168	99.73 120	93.86 237	99.36 248	98.87 74	97.56 225	98.62 275

CP-MVSNet		98.09 183	97.78 202	99.01 165	98.97 269	99.24 108	99.67 42	99.46 162	97.25 196	98.48 273	99.64 161	93.79 238	99.06 295	98.63 110	94.10 309	98.74 228

cascas		97.69 246	97.43 246	98.48 234	98.60 312	97.30 243	98.18 336	99.39 203	92.96 323	98.41 276	98.78 314	93.77 239	99.27 265	98.16 159	98.61 180	98.86 212

v1240		97.69 246	97.32 261	98.79 210	98.85 284	98.43 200	99.48 135	99.36 218	96.11 287	99.27 149	99.36 251	93.76 240	99.24 269	94.46 307	95.23 290	98.70 236

test20.03		96.12 291	95.96 288	96.63 313	97.44 329	95.45 305	99.51 113	99.38 209	96.55 252	96.16 322	99.25 276	93.76 240	96.17 342	87.35 338	94.22 307	98.27 314

baseline2		97.87 214	97.55 225	98.82 205	99.18 229	98.02 216	99.41 164	96.58 344	96.97 221	96.51 318	99.17 285	93.43 242	99.57 218	97.71 198	99.03 160	98.86 212

TransMVSNet (Re)		97.15 274	96.58 277	98.86 199	99.12 242	98.85 160	99.49 129	98.91 296	95.48 297	97.16 312	99.80 74	93.38 243	99.11 291	94.16 312	91.73 328	98.62 275

tfpnnormal		97.84 220	97.47 235	98.98 169	99.20 224	99.22 110	99.64 55	99.61 35	96.32 268	98.27 286	99.70 129	93.35 244	99.44 232	95.69 287	95.40 287	98.27 314

Anonymous20231211		97.88 212	97.54 228	98.90 185	99.71 83	98.53 187	99.48 135	99.57 49	94.16 312	98.81 234	99.68 141	93.23 245	99.42 237	98.84 81	94.42 304	98.76 223

XXY-MVS		98.38 158	98.09 170	99.24 145	99.26 210	99.32 97	99.56 92	99.55 61	97.45 177	98.71 245	99.83 42	93.23 245	99.63 213	98.88 70	96.32 264	98.76 223

jajsoiax		98.43 152	98.28 158	98.88 191	98.60 312	98.43 200	99.82 10	99.53 80	98.19 94	98.63 262	99.80 74	93.22 247	99.44 232	99.22 34	97.50 231	98.77 221

MDA-MVSNet_test_wron		95.45 297	94.60 302	98.01 275	98.16 323	97.21 249	99.11 250	99.24 260	93.49 319	80.73 344	98.98 305	93.02 248	98.18 325	94.22 311	94.45 303	98.64 265

ACMM	97.58 5	98.37 159	98.34 153	98.48 234	99.41 171	97.10 251	99.56 92	99.45 174	98.53 62	99.04 198	99.85 29	93.00 249	99.71 189	98.74 94	97.45 236	98.64 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		98.03 192	97.76 207	98.84 203	99.39 179	98.98 138	99.40 172	99.38 209	96.67 240	99.07 192	99.28 271	92.93 250	98.98 307	97.10 244	96.65 254	98.56 291

DTE-MVSNet		97.51 262	97.19 268	98.46 239	98.63 308	98.13 213	99.84 6	99.48 133	96.68 239	97.97 298	99.67 147	92.92 251	98.56 323	96.88 259	92.60 326	98.70 236

CLD-MVS		98.16 176	98.10 167	98.33 252	99.29 203	96.82 271	98.75 307	99.44 182	97.83 135	99.13 179	99.55 194	92.92 251	99.67 200	98.32 148	97.69 217	98.48 296
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-RMVSNet		98.41 155	98.08 171	99.40 120	99.41 171	98.83 164	99.30 202	98.77 306	97.70 151	98.94 215	99.65 154	92.91 253	99.74 172	96.52 271	99.55 125	99.64 115

YYNet1		95.36 299	94.51 304	97.92 281	97.89 325	97.10 251	99.10 252	99.23 261	93.26 322	80.77 343	99.04 299	92.81 254	98.02 327	94.30 308	94.18 308	98.64 265

mvs_tets		98.40 157	98.23 160	98.91 183	98.67 305	98.51 193	99.66 46	99.53 80	98.19 94	98.65 260	99.81 60	92.75 255	99.44 232	99.31 26	97.48 235	98.77 221

IterMVS		97.83 222	97.77 204	98.02 274	99.58 131	96.27 289	99.02 268	99.48 133	97.22 200	98.71 245	99.70 129	92.75 255	99.13 286	97.46 222	96.00 270	98.67 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UGNet		98.87 114	98.69 123	99.40 120	99.22 220	98.72 172	99.44 148	99.68 19	99.24 3	99.18 174	99.42 235	92.74 257	99.96 18	99.34 23	99.94 9	99.53 142
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
IterMVS-SCA-FT		97.82 225	97.75 208	98.06 271	99.57 133	96.36 286	99.02 268	99.49 122	97.18 202	98.71 245	99.72 124	92.72 258	99.14 283	97.44 225	95.86 276	98.67 253

SCA		98.19 172	98.16 162	98.27 261	99.30 199	95.55 300	99.07 254	98.97 287	97.57 163	99.43 109	99.57 188	92.72 258	99.74 172	97.58 207	99.20 144	99.52 143

HQP_MVS		98.27 167	98.22 161	98.44 243	99.29 203	96.97 264	99.39 176	99.47 152	98.97 30	99.11 183	99.61 175	92.71 260	99.69 198	97.78 189	97.63 218	98.67 253

plane_prior6					99.27 208	96.98 263						92.71 260

dp		97.75 236	97.80 198	97.59 296	99.10 247	93.71 327	99.32 198	98.88 300	96.48 259	99.08 191	99.55 194	92.67 262	99.82 148	96.52 271	98.58 183	99.24 179

PEN-MVS		97.76 232	97.44 243	98.72 215	98.77 294	98.54 186	99.78 19	99.51 96	97.06 215	98.29 285	99.64 161	92.63 263	98.89 319	98.09 163	93.16 319	98.72 230

LPG-MVS_test		98.22 168	98.13 165	98.49 232	99.33 190	97.05 257	99.58 80	99.55 61	97.46 174	99.24 157	99.83 42	92.58 264	99.72 183	98.09 163	97.51 229	98.68 245

LGP-MVS_train				98.49 232	99.33 190	97.05 257		99.55 61	97.46 174	99.24 157	99.83 42	92.58 264	99.72 183	98.09 163	97.51 229	98.68 245

VPA-MVSNet		98.29 165	97.95 185	99.30 135	99.16 237	99.54 72	99.50 119	99.58 48	98.27 85	99.35 134	99.37 248	92.53 266	99.65 206	99.35 19	94.46 302	98.72 230

TR-MVS		97.76 232	97.41 248	98.82 205	99.06 254	97.87 226	98.87 296	98.56 321	96.63 245	98.68 253	99.22 280	92.49 267	99.65 206	95.40 294	97.79 215	98.95 210

pm-mvs1		97.68 248	97.28 264	98.88 191	99.06 254	98.62 180	99.50 119	99.45 174	96.32 268	97.87 300	99.79 86	92.47 268	99.35 252	97.54 214	93.54 315	98.67 253

HQP2-MVS												92.47 268

HQP-MVS		98.02 194	97.90 190	98.37 250	99.19 226	96.83 269	98.98 279	99.39 203	98.24 87	98.66 254	99.40 240	92.47 268	99.64 208	97.19 239	97.58 223	98.64 265

EPMVS		97.82 225	97.65 217	98.35 251	98.88 276	95.98 294	99.49 129	94.71 348	97.57 163	99.26 154	99.48 221	92.46 271	99.71 189	97.87 181	99.08 156	99.35 172

PS-CasMVS		97.93 206	97.59 224	98.95 174	98.99 264	99.06 130	99.68 40	99.52 86	97.13 206	98.31 283	99.68 141	92.44 272	99.05 296	98.51 128	94.08 310	98.75 225

cl-mvsnet2		97.85 217	97.64 219	98.48 234	99.09 249	97.87 226	98.60 320	99.33 234	97.11 211	98.87 226	99.22 280	92.38 273	99.17 282	98.21 153	95.99 271	98.42 305

CostFormer		97.72 241	97.73 210	97.71 293	99.15 240	94.02 324	99.54 103	99.02 283	94.67 307	99.04 198	99.35 254	92.35 274	99.77 166	98.50 129	97.94 212	99.34 174

OPM-MVS		98.19 172	98.10 167	98.45 240	98.88 276	97.07 255	99.28 208	99.38 209	98.57 60	99.22 162	99.81 60	92.12 275	99.66 203	98.08 167	97.54 227	98.61 284

ET-MVSNet_ETH3D		96.49 284	95.64 293	99.05 160	99.53 141	98.82 165	98.84 298	97.51 337	97.63 158	84.77 339	99.21 283	92.09 276	98.91 317	98.98 57	92.21 327	99.41 168

ACMP	97.20 11	98.06 186	97.94 187	98.45 240	99.37 182	97.01 260	99.44 148	99.49 122	97.54 168	98.45 274	99.79 86	91.95 277	99.72 183	97.91 178	97.49 234	98.62 275
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous202405211		98.30 164	97.98 181	99.26 142	99.57 133	98.16 210	99.41 164	98.55 322	96.03 292	99.19 171	99.74 113	91.87 278	99.92 75	99.16 42	98.29 198	99.70 92

tpm		97.67 251	97.55 225	98.03 272	99.02 261	95.01 313	99.43 154	98.54 323	96.44 262	99.12 181	99.34 257	91.83 279	99.60 216	97.75 193	96.46 260	99.48 154

thres100view900		97.76 232	97.45 238	98.69 217	99.72 77	97.86 228	99.59 73	98.74 310	97.93 126	99.26 154	98.62 318	91.75 280	99.83 141	93.22 319	98.18 203	98.37 311

thres600view7		97.86 216	97.51 231	98.92 179	99.72 77	97.95 223	99.59 73	98.74 310	97.94 125	99.27 149	98.62 318	91.75 280	99.86 121	93.73 315	98.19 202	98.96 207

LTVRE_ROB	97.16 12	98.02 194	97.90 190	98.40 247	99.23 216	96.80 272	99.70 33	99.60 39	97.12 208	98.18 289	99.70 129	91.73 282	99.72 183	98.39 138	97.45 236	98.68 245
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
OurMVSNet-221017-0		97.88 212	97.77 204	98.19 264	98.71 301	96.53 280	99.88 1	99.00 284	97.79 141	98.78 239	99.94 3	91.68 283	99.35 252	97.21 235	96.99 252	98.69 240

tfpn200view9		97.72 241	97.38 251	98.72 215	99.69 91	97.96 221	99.50 119	98.73 315	97.83 135	99.17 175	98.45 323	91.67 284	99.83 141	93.22 319	98.18 203	98.37 311

thres400		97.77 231	97.38 251	98.92 179	99.69 91	97.96 221	99.50 119	98.73 315	97.83 135	99.17 175	98.45 323	91.67 284	99.83 141	93.22 319	98.18 203	98.96 207

thisisatest0515		98.14 178	97.79 199	99.19 149	99.50 152	98.50 194	98.61 318	96.82 341	96.95 224	99.54 89	99.43 232	91.66 286	99.86 121	98.08 167	99.51 127	99.22 180

thres200		97.61 255	97.28 264	98.62 220	99.64 112	98.03 215	99.26 220	98.74 310	97.68 153	99.09 190	98.32 325	91.66 286	99.81 152	92.88 323	98.22 199	98.03 322

new_pmnet		96.38 287	96.03 286	97.41 301	98.13 324	95.16 312	99.05 259	99.20 264	93.94 313	97.39 308	98.79 312	91.61 288	99.04 297	90.43 330	95.77 277	98.05 321

pmmvs5		97.52 260	97.30 263	98.16 266	98.57 314	96.73 273	99.27 212	98.90 298	96.14 285	98.37 279	99.53 202	91.54 289	99.14 283	97.51 217	95.87 275	98.63 273

tttt0517		98.42 153	98.14 164	99.28 140	99.66 105	98.38 203	99.74 28	96.85 340	97.68 153	99.79 26	99.74 113	91.39 290	99.89 109	98.83 84	99.56 123	99.57 134

tpm2		97.44 267	97.34 258	97.74 292	99.15 240	94.36 321	99.45 144	98.94 290	93.45 321	98.90 221	99.44 230	91.35 291	99.59 217	97.31 229	98.07 210	99.29 177

MVS-HIRNet		95.75 295	95.16 298	97.51 299	99.30 199	93.69 328	98.88 294	95.78 345	85.09 338	98.78 239	92.65 341	91.29 292	99.37 244	94.85 303	99.85 58	99.46 161

thisisatest0530		98.35 160	98.03 176	99.31 131	99.63 115	98.56 184	99.54 103	96.75 342	97.53 170	99.73 40	99.65 154	91.25 293	99.89 109	98.62 111	99.56 123	99.48 154

testgi		97.65 253	97.50 232	98.13 268	99.36 184	96.45 283	99.42 161	99.48 133	97.76 144	97.87 300	99.45 229	91.09 294	98.81 320	94.53 306	98.52 188	99.13 185

ITE_SJBPF				98.08 269	99.29 203	96.37 285		98.92 293	98.34 78	98.83 232	99.75 107	91.09 294	99.62 214	95.82 283	97.40 241	98.25 316

DeepMVS_CX				93.34 321	99.29 203	82.27 343		99.22 262	85.15 337	96.33 320	99.05 298	90.97 296	99.73 179	93.57 316	97.77 216	98.01 323

ACMH	97.28 8	98.10 182	97.99 180	98.44 243	99.41 171	96.96 266	99.60 68	99.56 54	98.09 107	98.15 290	99.91 5	90.87 297	99.70 195	98.88 70	97.45 236	98.67 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		97.50 263	97.33 260	98.03 272	98.65 306	96.23 290	99.77 21	98.68 318	97.14 205	97.90 299	99.93 4	90.45 298	99.18 281	97.00 249	96.43 261	98.67 253

MIMVSNet		97.73 239	97.45 238	98.57 224	99.45 166	97.50 239	99.02 268	98.98 286	96.11 287	99.41 116	99.14 289	90.28 299	98.74 321	95.74 286	98.93 166	99.47 159

GBi-Net		97.68 248	97.48 233	98.29 257	99.51 145	97.26 246	99.43 154	99.48 133	96.49 255	99.07 192	99.32 264	90.26 300	98.98 307	97.10 244	96.65 254	98.62 275

test1		97.68 248	97.48 233	98.29 257	99.51 145	97.26 246	99.43 154	99.48 133	96.49 255	99.07 192	99.32 264	90.26 300	98.98 307	97.10 244	96.65 254	98.62 275

FMVSNet2		97.72 241	97.36 253	98.80 209	99.51 145	98.84 161	99.45 144	99.42 191	96.49 255	98.86 231	99.29 269	90.26 300	98.98 307	96.44 273	96.56 257	98.58 289

Anonymous20240529		98.09 183	97.68 214	99.34 125	99.66 105	98.44 199	99.40 172	99.43 189	93.67 316	99.22 162	99.89 10	90.23 303	99.93 64	99.26 32	98.33 193	99.66 105

ACMH+	97.24 10	97.92 209	97.78 202	98.32 254	99.46 161	96.68 276	99.56 92	99.54 68	98.41 73	97.79 304	99.87 20	90.18 304	99.66 203	98.05 171	97.18 248	98.62 275

LF4IMVS		97.52 260	97.46 237	97.70 294	98.98 267	95.55 300	99.29 206	98.82 305	98.07 112	98.66 254	99.64 161	89.97 305	99.61 215	97.01 248	96.68 253	97.94 326

GA-MVS		97.85 217	97.47 235	99.00 167	99.38 180	97.99 218	98.57 321	99.15 268	97.04 216	98.90 221	99.30 267	89.83 306	99.38 241	96.70 266	98.33 193	99.62 121

PVSNet_0	94.43 19	96.09 292	95.47 294	97.94 279	99.31 198	94.34 322	97.81 339	99.70 15	97.12 208	97.46 306	98.75 315	89.71 307	99.79 160	97.69 200	81.69 341	99.68 99

XVG-ACMP-BASELINE		97.83 222	97.71 212	98.20 263	99.11 244	96.33 287	99.41 164	99.52 86	98.06 116	99.05 197	99.50 212	89.64 308	99.73 179	97.73 195	97.38 242	98.53 292

gg-mvs-nofinetune		96.17 290	95.32 297	98.73 214	98.79 288	98.14 212	99.38 181	94.09 349	91.07 331	98.07 295	91.04 344	89.62 309	99.35 252	96.75 262	99.09 155	98.68 245

DWT-MVSNet_test		97.53 259	97.40 249	97.93 280	99.03 260	94.86 316	99.57 85	98.63 319	96.59 251	98.36 280	98.79 312	89.32 310	99.74 172	98.14 161	98.16 207	99.20 182

GG-mvs-BLEND				98.45 240	98.55 315	98.16 210	99.43 154	93.68 350		97.23 310	98.46 322	89.30 311	99.22 273	95.43 293	98.22 199	97.98 324

USDC		97.34 269	97.20 267	97.75 291	99.07 252	95.20 309	98.51 325	99.04 282	97.99 122	98.31 283	99.86 23	89.02 312	99.55 221	95.67 289	97.36 243	98.49 295

MS-PatchMatch		97.24 273	97.32 261	96.99 306	98.45 319	93.51 330	98.82 300	99.32 242	97.41 183	98.13 291	99.30 267	88.99 313	99.56 219	95.68 288	99.80 82	97.90 329

VPNet		97.84 220	97.44 243	99.01 165	99.21 222	98.94 150	99.48 135	99.57 49	98.38 75	99.28 146	99.73 120	88.89 314	99.39 239	99.19 37	93.27 318	98.71 232

K. test v3		97.10 276	96.79 276	98.01 275	98.72 299	96.33 287	99.87 4	97.05 339	97.59 160	96.16 322	99.80 74	88.71 315	99.04 297	96.69 267	96.55 258	98.65 263

lessismore_v0				97.79 290	98.69 303	95.44 306		94.75 347		95.71 325	99.87 20	88.69 316	99.32 257	95.89 282	94.93 298	98.62 275

TDRefinement		95.42 298	94.57 303	97.97 278	89.83 346	96.11 292	99.48 135	98.75 307	96.74 235	96.68 317	99.88 15	88.65 317	99.71 189	98.37 142	82.74 340	98.09 319

TESTMET0.1,1		97.55 257	97.27 266	98.40 247	98.93 272	96.53 280	98.67 313	97.61 336	96.96 222	98.64 261	99.28 271	88.63 318	99.45 227	97.30 230	99.38 132	99.21 181

test_0402		96.64 280	96.24 282	97.85 285	98.85 284	96.43 284	99.44 148	99.26 257	93.52 318	96.98 315	99.52 205	88.52 319	99.20 280	92.58 326	97.50 231	97.93 327

UnsupCasMVSNet_eth		96.44 285	96.12 284	97.40 302	98.65 306	95.65 297	99.36 188	99.51 96	97.13 206	96.04 324	98.99 302	88.40 320	98.17 326	96.71 265	90.27 331	98.40 308

MDA-MVSNet-bldmvs		94.96 301	93.98 306	97.92 281	98.24 322	97.27 245	99.15 240	99.33 234	93.80 315	80.09 345	99.03 300	88.31 321	97.86 332	93.49 317	94.36 305	98.62 275

test-mter		97.49 265	97.13 269	98.55 228	98.79 288	97.10 251	98.67 313	97.75 333	96.65 242	98.61 265	98.85 309	88.23 322	99.45 227	97.25 233	99.38 132	99.10 187

TinyColmap		97.12 275	96.89 274	97.83 287	99.07 252	95.52 303	98.57 321	98.74 310	97.58 162	97.81 303	99.79 86	88.16 323	99.56 219	95.10 299	97.21 246	98.39 309

pmmvs-eth3d		95.34 300	94.73 301	97.15 303	95.53 338	95.94 295	99.35 193	99.10 273	95.13 300	93.55 331	97.54 329	88.15 324	97.91 330	94.58 305	89.69 334	97.61 331

RRT_test8_iter05		97.72 241	97.60 222	98.08 269	99.23 216	96.08 293	99.63 57	99.49 122	97.54 168	98.94 215	99.81 60	87.99 325	99.35 252	99.21 36	96.51 259	98.81 215

new-patchmatchnet		94.48 303	94.08 305	95.67 318	95.08 339	92.41 334	99.18 234	99.28 255	94.55 310	93.49 332	97.37 332	87.86 326	97.01 339	91.57 327	88.36 335	97.61 331

FMVSNet5		96.43 286	96.19 283	97.15 303	99.11 244	95.89 296	99.32 198	99.52 86	94.47 311	98.34 282	99.07 295	87.54 327	97.07 338	92.61 325	95.72 280	98.47 298

pmmvs6		96.53 283	96.09 285	97.82 288	98.69 303	95.47 304	99.37 184	99.47 152	93.46 320	97.41 307	99.78 92	87.06 328	99.33 256	96.92 257	92.70 325	98.65 263

pmmvs3		94.09 307	93.25 308	96.60 314	94.76 340	94.49 319	98.92 290	98.18 329	89.66 332	96.48 319	98.06 327	86.28 329	97.33 337	89.68 332	87.20 337	97.97 325

IB-MVS	95.67 18	96.22 288	95.44 296	98.57 224	99.21 222	96.70 274	98.65 316	97.74 335	96.71 237	97.27 309	98.54 321	86.03 330	99.92 75	98.47 133	86.30 338	99.10 187
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
tmp_tt		82.80 314	81.52 316	86.66 327	66.61 353	68.44 351	92.79 346	97.92 331	68.96 344	80.04 346	99.85 29	85.77 331	96.15 343	97.86 182	43.89 347	95.39 339

CMPMVS	69.68 23	94.13 306	94.90 300	91.84 324	97.24 334	80.01 345	98.52 324	99.48 133	89.01 333	91.99 335	99.67 147	85.67 332	99.13 286	95.44 292	97.03 251	96.39 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		95.51 296	95.04 299	96.92 309	97.38 330	95.60 298	99.52 109	99.50 114	93.65 317	96.97 316	99.17 285	85.28 333	96.56 341	88.36 335	95.55 284	98.60 287

LFMVS		97.90 211	97.35 255	99.54 90	99.52 143	99.01 135	99.39 176	98.24 326	97.10 212	99.65 64	99.79 86	84.79 334	99.91 86	99.28 29	98.38 192	99.69 95

FMVSNet1		96.84 278	96.36 281	98.29 257	99.32 197	97.26 246	99.43 154	99.48 133	95.11 301	98.55 269	99.32 264	83.95 335	98.98 307	95.81 284	96.26 265	98.62 275

VDD-MVS		97.73 239	97.35 255	98.88 191	99.47 160	97.12 250	99.34 196	98.85 302	98.19 94	99.67 56	99.85 29	82.98 336	99.92 75	99.49 12	98.32 197	99.60 125

EG-PatchMatch MVS		95.97 293	95.69 292	96.81 311	97.78 327	92.79 333	99.16 236	98.93 291	96.16 282	94.08 330	99.22 280	82.72 337	99.47 225	95.67 289	97.50 231	98.17 317

VDDNet		97.55 257	97.02 272	99.16 151	99.49 154	98.12 214	99.38 181	99.30 249	95.35 299	99.68 50	99.90 7	82.62 338	99.93 64	99.31 26	98.13 208	99.42 166

UniMVSNet_ETH3D		97.32 270	96.81 275	98.87 195	99.40 176	97.46 240	99.51 113	99.53 80	95.86 294	98.54 270	99.77 98	82.44 339	99.66 203	98.68 105	97.52 228	99.50 152

OpenMVS_ROB	92.34 20	94.38 305	93.70 307	96.41 316	97.38 330	93.17 331	99.06 257	98.75 307	86.58 336	94.84 329	98.26 326	81.53 340	99.32 257	89.01 333	97.87 214	96.76 335

MVS_0304		96.79 279	96.52 279	97.59 296	99.22 220	94.92 315	99.04 264	99.59 42	96.49 255	98.43 275	98.99 302	80.48 341	99.39 239	97.15 243	99.27 140	98.47 298

UnsupCasMVSNet_bld		93.53 308	92.51 310	96.58 315	97.38 330	93.82 325	98.24 333	99.48 133	91.10 330	93.10 333	96.66 335	74.89 342	98.37 324	94.03 313	87.71 336	97.56 333

testing_2		94.44 304	92.93 309	98.98 169	94.16 341	99.00 137	99.42 161	99.28 255	96.60 248	84.86 338	96.84 334	70.91 343	99.27 265	98.23 152	96.08 268	98.68 245

Gipuma		90.99 310	90.15 312	93.51 320	98.73 297	90.12 339	93.98 344	99.45 174	79.32 341	92.28 334	94.91 338	69.61 344	97.98 329	87.42 337	95.67 281	92.45 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 309	92.23 311	95.14 319	95.61 336	89.98 340	99.37 184	98.21 327	94.80 305	95.04 328	97.69 328	65.06 345	97.90 331	94.30 308	89.98 333	97.54 334

EMVS		80.02 316	79.22 318	82.43 332	91.19 343	76.40 348	97.55 342	92.49 354	66.36 347	83.01 342	91.27 343	64.63 346	85.79 349	65.82 347	60.65 345	85.08 345

E-PMN		80.61 315	79.88 317	82.81 330	90.75 344	76.38 349	97.69 340	95.76 346	66.44 346	83.52 340	92.25 342	62.54 347	87.16 348	68.53 346	61.40 344	84.89 346

ambc				93.06 322	92.68 342	82.36 342	98.47 326	98.73 315		95.09 327	97.41 330	55.55 348	99.10 293	96.42 274	91.32 329	97.71 330

FPMVS		84.93 313	85.65 313	82.75 331	86.77 348	63.39 352	98.35 330	98.92 293	74.11 342	83.39 341	98.98 305	50.85 349	92.40 346	84.54 342	94.97 296	92.46 340

PMMVS2		86.87 311	85.37 314	91.35 326	90.21 345	83.80 341	98.89 293	97.45 338	83.13 340	91.67 336	95.03 337	48.49 350	94.70 344	85.86 341	77.62 342	95.54 338

LCM-MVSNet		86.80 312	85.22 315	91.53 325	87.81 347	80.96 344	98.23 335	98.99 285	71.05 343	90.13 337	96.51 336	48.45 351	96.88 340	90.51 329	85.30 339	96.76 335

ANet_high		77.30 317	74.86 320	84.62 329	75.88 351	77.61 347	97.63 341	93.15 352	88.81 334	64.27 348	89.29 345	36.51 352	83.93 350	75.89 344	52.31 346	92.33 342

test123		39.01 322	42.50 323	28.53 335	39.17 354	20.91 355	98.75 307	19.17 357	19.83 351	38.57 350	66.67 348	33.16 353	15.42 352	37.50 350	29.66 349	49.26 347

testmvs		39.17 321	43.78 322	25.37 336	36.04 355	16.84 356	98.36 329	26.56 355	20.06 350	38.51 351	67.32 347	29.64 354	15.30 353	37.59 349	39.90 348	43.98 348

wuyk23d		40.18 320	41.29 324	36.84 334	86.18 349	49.12 354	79.73 347	22.81 356	27.64 349	25.46 352	28.45 352	21.98 355	48.89 351	55.80 348	23.56 350	12.51 349

PMVS	70.75 22	75.98 319	74.97 319	79.01 333	70.98 352	55.18 353	93.37 345	98.21 327	65.08 348	61.78 349	93.83 340	21.74 356	92.53 345	78.59 343	91.12 330	89.34 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	76.82 21	76.91 318	74.31 321	84.70 328	85.38 350	76.05 350	96.88 343	93.17 351	67.39 345	71.28 347	89.01 346	21.66 357	87.69 347	71.74 345	72.29 343	90.35 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
uanet_test		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

test_part1				0.00 337		0.00 357	0.00 348	99.48 133				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		8.30 324	11.06 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	99.58 184	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.02 326	0.03 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.27 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

IU-MVS					99.84 32	99.88 7		99.32 242	98.30 83	99.84 13				98.86 77	99.85 58	99.89 2

save fliter					99.76 52	99.59 63	99.14 242	99.40 199	99.00 22

test_0728_SECOND				99.91 2	99.84 32	99.89 3	99.57 85	99.51 96					99.96 18	98.93 64	99.86 51	99.88 5

GSMVS																99.52 143

test_part2					99.81 40	99.83 14				99.77 33

MTGPA								99.47 152

MTMP							99.54 103	98.88 300

gm-plane-assit					98.54 316	92.96 332			94.65 308		99.15 288		99.64 208	97.56 212

test9_res														97.49 218	99.72 99	99.75 66

agg_prior2														97.21 235	99.73 98	99.75 66

agg_prior					99.67 96	99.62 56		99.40 199		98.87 226			99.91 86

test_prior4						99.56 68	98.99 275

test_prior				99.68 63	99.67 96	99.48 82		99.56 54					99.83 141			99.74 70

旧先验2							98.96 283		96.70 238	99.47 101			99.94 49	98.19 154

新几何2							99.01 273

无先验							98.99 275	99.51 96	96.89 228				99.93 64	97.53 215		99.72 83

原ACMM2							98.95 287

testdata2													99.95 41	96.67 268

testdata1							98.85 297		98.32 82

plane_prior7					99.29 203	97.03 259

plane_prior5								99.47 152					99.69 198	97.78 189	97.63 218	98.67 253

plane_prior4											99.61 175

plane_prior3						97.00 261			98.69 54	99.11 183

plane_prior2							99.39 176		98.97 30

plane_prior1					99.26 210

plane_prior						96.97 264	99.21 232		98.45 69						97.60 221

n2								0.00 358

nn								0.00 358

door-mid								98.05 330

test11								99.35 223

door								97.92 331

HQP5-MVS						96.83 269

HQP-NCC					99.19 226		98.98 279		98.24 87	98.66 254

ACMP_Plane					99.19 226		98.98 279		98.24 87	98.66 254

BP-MVS														97.19 239

HQP4-MVS										98.66 254			99.64 208			98.64 265

HQP3-MVS								99.39 203							97.58 223

NP-MVS					99.23 216	96.92 267					99.40 240

ACMMP++_ref															97.19 247

ACMMP++															97.43 239

High-resolution multi-view benchmark