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.

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 68	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 68	98.58 146	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 15	99.81 10	99.69 51

test0726					99.72 12	99.25 2	99.06 51	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 58	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 18	99.81 10	99.70 48

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 106

IU-MVS					99.71 20	99.23 6		98.64 136	95.28 117	99.63 4				98.35 24	99.81 10	99.83 5

DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 169	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 60	99.84 8	99.83 5

test_part2					99.63 29	99.18 8				99.27 17

HPM-MVS++		98.58 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 121	98.66 131	97.51 16	98.15 81	98.83 107	95.70 44	99.92 21	97.53 71	99.67 54	99.66 65

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 58				99.16 61	97.81 2	99.37 157	97.24 79	99.73 43	99.70 48

SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 72	98.80 86	93.67 195	99.37 13	99.52 3	96.52 17	99.89 35	98.06 33	99.81 10	99.76 26
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 24	99.41 11	99.54 1	96.66 13	99.84 52	98.86 1	99.85 3	99.87 1

ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 97	98.81 76	95.80 90	99.16 26	99.47 8	95.37 57	99.92 21	97.89 41	99.75 38	99.79 10

MP-MVS-pluss		98.31 52	97.92 57	99.49 9	99.72 12	98.88 14	98.43 167	98.78 94	94.10 165	97.69 115	99.42 12	95.25 66	99.92 21	98.09 32	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 170	98.68 120	97.04 46	98.52 67	98.80 110	96.78 12	99.83 55	97.93 37	99.61 67	99.74 33

CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 161	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 95	98.06 33	99.66 57	99.69 51

APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 104	98.82 70	94.52 154	99.23 20	99.25 43	95.54 49	99.80 79	96.52 114	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 53	98.81 76	95.12 126	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

ZD-MVS					99.46 51	98.70 19		98.79 91	93.21 212	98.67 58	98.97 87	95.70 44	99.83 55	96.07 126	99.58 74

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 94	98.86 61	95.48 104	98.91 45	99.17 56	95.48 50	99.93 15	95.80 139	99.53 85	99.76 26

ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 132	98.84 65	94.66 149	99.11 28	99.25 43	95.46 51	99.81 70	96.80 105	99.73 43	99.63 73

zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 151	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 78	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 162	98.76 98	97.82 5	98.45 71	98.93 97	96.65 14	99.83 55	97.38 76	99.41 97	99.71 44

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 198	98.81 76	91.63 266	98.44 72	98.85 104	93.98 98	99.82 63	94.11 195	99.69 52	99.64 70

DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 286	98.35 190	94.85 140	97.93 102	98.58 132	95.07 72	99.71 113	92.60 236	99.34 102	99.43 106

ETH3D cwj APD-0.16		97.96 58	97.52 72	99.29 31	99.05 105	98.52 27	98.33 178	98.68 120	93.18 213	98.68 57	99.13 64	94.62 81	99.83 55	96.45 116	99.55 83	99.52 85

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 196	98.52 27	99.37 7	98.71 113	97.09 45	92.99 271	99.13 64	89.36 174	99.89 35	96.97 88	99.57 75	99.71 44

TEST9					99.31 70	98.50 29	97.92 228	98.73 107	92.63 232	97.74 111	98.68 121	96.20 23	99.80 79

train_agg		97.97 57	97.52 72	99.33 27	99.31 70	98.50 29	97.92 228	98.73 107	92.98 221	97.74 111	98.68 121	96.20 23	99.80 79	96.59 111	99.57 75	99.68 57

test_8					99.29 78	98.44 31	97.89 234	98.72 109	92.98 221	97.70 114	98.66 124	96.20 23	99.80 79

CDPH-MVS		97.94 62	97.49 75	99.28 35	99.47 48	98.44 31	97.91 230	98.67 128	92.57 236	98.77 51	98.85 104	95.93 38	99.72 108	95.56 149	99.69 52	99.68 57

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 45	98.87 55	97.38 24	99.35 14	99.40 13	97.78 3	99.87 44	97.77 49	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 31	98.86 61	95.77 91	98.31 80	99.10 69	95.46 51	99.93 15	97.57 68	99.81 10	99.74 33

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 195	98.52 158	97.95 3	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

save fliter					99.46 51	98.38 35	98.21 195	98.71 113	97.95 3

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 46	98.82 70	95.71 94	98.73 55	99.06 78	95.27 64	99.93 15	97.07 85	99.63 64	99.72 40

agg_prior1		97.95 61	97.51 74	99.28 35	99.30 75	98.38 35	97.81 241	98.72 109	93.16 215	97.57 125	98.66 124	96.14 26	99.81 70	96.63 110	99.56 80	99.66 65

agg_prior					99.30 75	98.38 35		98.72 109		97.57 125			99.81 70

canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 133	98.38 35	99.34 11	98.39 184	96.76 54	97.67 116	97.40 238	92.26 116	99.49 145	98.28 27	96.28 202	99.08 147

alignmvs		97.56 84	97.07 94	99.01 62	98.66 137	98.37 41	98.83 94	98.06 246	96.74 55	98.00 96	97.65 218	90.80 153	99.48 149	98.37 23	96.56 190	99.19 132

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 78	98.85 64	97.28 29	99.72 3	99.39 14	96.63 15	97.60 318	98.17 28	99.85 3	99.64 70
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
XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	99.20 52	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

X-MVStestdata		94.06 260	92.30 280	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	43.50 353	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

DP-MVS Recon		97.86 65	97.46 77	99.06 61	99.53 36	98.35 43	98.33 178	98.89 46	92.62 233	98.05 86	98.94 96	95.34 59	99.65 123	96.04 130	99.42 96	99.19 132

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 21	98.96 32	96.10 82	98.94 39	99.17 56	96.06 30	99.92 21	97.62 61	99.78 23	99.75 28

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 67	98.96 32	95.65 98	98.94 39	99.17 56	96.06 30	99.92 21	97.21 81	99.78 23	99.75 28

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 36	98.66 131	96.84 51	99.56 5	99.31 35	96.34 19	99.70 114	98.32 25	99.73 43	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 18	98.88 49	97.52 15	99.41 11	98.78 112	96.00 34	99.79 91	97.79 48	99.59 71	99.85 2

test_prior3		98.22 55	97.90 58	99.19 43	99.31 70	98.22 50	97.80 242	98.84 65	96.12 80	97.89 105	98.69 119	95.96 36	99.70 114	96.89 95	99.60 68	99.65 67

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 114			99.65 67

test12				99.18 47	99.16 99	98.19 52		98.53 156		98.07 85		95.13 70	99.72 108		99.56 80	99.63 73

SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 46	98.82 70	96.58 61	99.10 29	99.32 33	95.39 55	99.82 63	97.70 57	99.63 64	99.72 40

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 25	98.79 91	96.13 79	97.92 103	99.23 45	94.54 84	99.94 3	96.74 109	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 21	98.93 37	96.15 77	98.94 39	99.17 56	95.91 39	99.94 3	97.55 69	99.79 19	99.78 13

nrg030		96.28 139	95.72 143	97.96 131	96.90 264	98.15 56	99.39 5	98.31 196	95.47 105	94.42 213	98.35 155	92.09 123	98.69 229	97.50 72	89.05 298	97.04 227

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 21	98.95 34	96.10 82	98.93 43	99.19 55	95.70 44	99.94 3	97.62 61	99.79 19	99.78 13

PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 53	99.09 20	93.32 208	98.83 48	99.10 69	96.54 16	99.83 55	97.70 57	99.76 32	99.59 80

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 64	99.49 5	95.43 107	99.03 33	99.32 33	95.56 47	99.94 3	96.80 105	99.77 26	99.78 13

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 16	98.81 76	96.24 73	98.35 77	99.23 45	95.46 51	99.94 3	97.42 74	99.81 10	99.77 20

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 158	98.78 94	97.72 6	98.92 44	99.28 40	95.27 64	99.82 63	97.55 69	99.77 26	99.69 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 173	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 79	98.40 22	99.73 43	99.75 28

test_prior4						98.01 62	97.86 237

新几何1				99.16 50	99.34 62	98.01 62		98.69 117	90.06 301	98.13 82	98.95 95	94.60 82	99.89 35	91.97 256	99.47 90	99.59 80

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 202	98.68 120	90.14 300	98.01 94	98.97 87	94.80 79	99.87 44	93.36 216	99.46 93	99.61 75

test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 46	98.83 68	96.52 64	99.05 32	99.34 31	95.34 59	99.82 63	97.86 43	99.64 62	99.73 36

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 35	98.81 76	96.24 73	99.20 22	99.37 22	95.30 62	99.80 79	97.73 51	99.67 54	99.72 40

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.34 59	99.82 63	97.72 52	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.29 63		97.72 52	99.65 58	99.71 44

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 157	98.94 39	99.20 52	95.16 69	99.74 106	97.58 65	99.85 3	99.77 20

CP-MVS		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 11	98.87 55	95.96 85	98.60 64	99.13 64	96.05 32	99.94 3	97.77 49	99.86 1	99.77 20

HPM-MVS		98.36 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 86	94.63 150	98.61 63	98.97 87	95.13 70	99.77 100	97.65 59	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 73	98.37 173	98.76 98	97.49 17	99.20 22	99.21 48	96.08 29	99.79 91	98.42 20	99.73 43	99.75 28

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 74	99.13 39	98.82 70	96.14 78	99.26 18	99.37 22	93.33 102	99.93 15	96.96 90	99.67 54	99.69 51

DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 109	97.66 75	97.75 246	98.89 46	97.71 8	98.33 78	98.97 87	94.97 74	99.88 43	98.42 20	99.76 32	99.42 107
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
3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 198	97.64 76	99.35 10	99.06 22	97.02 47	93.75 245	99.16 61	89.25 177	99.92 21	97.22 80	99.75 38	99.64 70

114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 77	98.85 90	98.90 44	84.80 333	97.77 108	99.11 67	92.84 107	99.66 122	94.85 167	99.77 26	99.47 98

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 78	99.03 56	99.41 6	95.98 84	97.60 124	99.36 26	94.45 89	99.93 15	97.14 82	98.85 122	99.70 48
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
QAPM		96.29 137	95.40 154	98.96 67	97.85 195	97.60 79	99.23 21	98.93 37	89.76 304	93.11 268	99.02 80	89.11 182	99.93 15	91.99 255	99.62 66	99.34 111

VNet		97.79 69	97.40 81	98.96 67	98.88 118	97.55 80	98.63 137	98.93 37	96.74 55	99.02 34	98.84 106	90.33 162	99.83 55	98.53 9	96.66 186	99.50 91

FIs		96.51 130	96.12 133	97.67 150	97.13 250	97.54 81	99.36 8	99.22 14	95.89 86	94.03 233	98.35 155	91.98 126	98.44 255	96.40 119	92.76 253	97.01 228

旧先验1					99.29 78	97.48 82		98.70 116			99.09 74	95.56 47			99.47 90	99.61 75

UA-Net		97.96 58	97.62 64	98.98 65	98.86 120	97.47 83	98.89 82	99.08 21	96.67 58	98.72 56	99.54 1	93.15 105	99.81 70	94.87 166	98.83 123	99.65 67

UniMVSNet (Re)		95.78 157	95.19 168	97.58 156	96.99 258	97.47 83	98.79 108	99.18 16	95.60 99	93.92 236	97.04 267	91.68 131	98.48 249	95.80 139	87.66 313	96.79 253

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 85	98.07 216	98.53 156	95.32 115	96.80 153	98.53 136	93.32 103	99.72 108	94.31 188	99.31 104	99.02 151

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 86	98.40 171	98.79 91	97.46 19	99.09 30	99.31 35	95.86 42	99.80 79	98.64 3	99.76 32	99.79 10

RRT_MVS		96.04 145	95.53 151	97.56 158	97.07 254	97.32 87	98.57 148	98.09 238	95.15 124	95.02 192	98.44 144	88.20 205	98.58 243	96.17 125	93.09 250	96.79 253

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 87	97.91 230	99.58 3	97.20 37	98.33 78	99.00 85	95.99 35	99.64 125	98.05 35	99.76 32	99.69 51

OpenMVS	93.04 13	95.83 155	95.00 176	98.32 107	97.18 247	97.32 87	99.21 28	98.97 30	89.96 302	91.14 304	99.05 79	86.64 237	99.92 21	93.38 214	99.47 90	97.73 209

ETV-MVS		97.96 58	97.81 59	98.40 103	98.42 152	97.27 90	98.73 117	98.55 151	96.84 51	98.38 75	97.44 235	95.39 55	99.35 158	97.62 61	98.89 118	98.58 184

CANet		98.05 56	97.76 61	98.90 71	98.73 128	97.27 90	98.35 175	98.78 94	97.37 26	97.72 113	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

FC-MVSNet-test		96.42 133	96.05 134	97.53 160	96.95 259	97.27 90	99.36 8	99.23 12	95.83 89	93.93 235	98.37 153	92.00 125	98.32 274	96.02 131	92.72 254	97.00 229

VPA-MVSNet		95.75 158	95.11 172	97.69 148	97.24 239	97.27 90	98.94 74	99.23 12	95.13 125	95.51 185	97.32 241	85.73 252	98.91 208	97.33 78	89.55 291	96.89 242

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 94	98.11 214	98.29 204	97.19 38	98.99 38	99.02 80	96.22 20	99.67 121	98.52 13	98.56 135	99.51 89

NR-MVSNet		94.98 202	94.16 217	97.44 162	96.53 282	97.22 95	98.74 113	98.95 34	94.96 135	89.25 319	97.69 214	89.32 175	98.18 286	94.59 177	87.40 316	96.92 234

LS3D		97.16 107	96.66 116	98.68 79	98.53 147	97.19 96	98.93 75	98.90 44	92.83 229	95.99 181	99.37 22	92.12 122	99.87 44	93.67 208	99.57 75	98.97 156

test222					99.23 93	97.17 97	97.40 265	98.66 131	88.68 315	98.05 86	98.96 93	94.14 94			99.53 85	99.61 75

CS-MVS		97.81 67	97.61 65	98.41 102	98.52 148	97.15 98	99.09 46	98.55 151	96.18 76	97.61 122	97.20 250	94.59 83	99.39 155	97.62 61	99.10 111	98.70 172

CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 99	99.12 41	98.81 76	92.34 244	98.09 84	99.08 76	93.01 106	99.92 21	96.06 129	99.77 26	99.75 28

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 100	98.40 171	98.68 120	97.43 20	99.06 31	99.31 35	95.80 43	99.77 100	98.62 5	99.76 32	99.78 13

HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 151	97.00 101	98.14 209	98.21 212	93.95 174	96.72 155	97.99 187	91.58 133	99.76 102	94.51 180	96.54 191	98.95 159

UniMVSNet_NR-MVSNet		95.71 160	95.15 169	97.40 166	96.84 267	96.97 102	98.74 113	99.24 10	95.16 123	93.88 238	97.72 212	91.68 131	98.31 276	95.81 137	87.25 319	96.92 234

DU-MVS		95.42 173	94.76 186	97.40 166	96.53 282	96.97 102	98.66 135	98.99 29	95.43 107	93.88 238	97.69 214	88.57 196	98.31 276	95.81 137	87.25 319	96.92 234

DeepC-MVS	95.98 3	97.88 64	97.58 67	98.77 75	99.25 86	96.93 104	98.83 94	98.75 101	96.96 49	96.89 148	99.50 4	90.46 159	99.87 44	97.84 46	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPR		96.84 119	96.24 130	98.65 81	98.72 132	96.92 105	97.36 271	98.57 147	93.33 207	96.67 156	97.57 226	94.30 92	99.56 136	91.05 271	98.59 133	99.47 98

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 106	97.95 227	99.58 3	97.14 41	98.44 72	99.01 84	95.03 73	99.62 130	97.91 38	99.75 38	99.50 91

MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 135	96.90 106	98.66 135	98.68 120	92.40 243	97.07 138	97.96 189	91.54 137	99.75 104	93.68 206	98.92 116	98.69 174
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
WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 120	96.89 108	98.31 184	98.71 113	95.26 118	97.67 116	98.56 135	92.21 119	99.78 95	95.89 134	96.85 181	99.48 96

MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 109	98.71 122	99.05 24	97.28 29	98.84 46	99.28 40	96.47 18	99.40 154	98.52 13	99.70 51	99.47 98

API-MVS		97.41 94	97.25 86	97.91 132	98.70 133	96.80 109	98.82 97	98.69 117	94.53 152	98.11 83	98.28 164	94.50 88	99.57 134	94.12 194	99.49 88	97.37 219

PCF-MVS	93.45 11	94.68 216	93.43 260	98.42 101	98.62 141	96.77 111	95.48 331	98.20 214	84.63 334	93.34 259	98.32 161	88.55 198	99.81 70	84.80 328	98.96 115	98.68 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ab-mvs		96.42 133	95.71 146	98.55 87	98.63 140	96.75 112	97.88 235	98.74 102	93.84 179	96.54 165	98.18 174	85.34 260	99.75 104	95.93 133	96.35 196	99.15 138

Effi-MVS+		97.12 109	96.69 113	98.39 104	98.19 172	96.72 113	97.37 269	98.43 178	93.71 188	97.65 119	98.02 183	92.20 120	99.25 164	96.87 101	97.79 162	99.19 132

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 114	98.01 222	98.89 46	94.44 158	96.83 149	98.68 121	90.69 156	99.76 102	94.36 184	99.29 105	98.98 155

原ACMM1				98.65 81	99.32 68	96.62 115		98.67 128	93.27 211	97.81 107	98.97 87	95.18 68	99.83 55	93.84 202	99.46 93	99.50 91

FMVSNet3		94.97 203	94.26 211	97.11 179	98.18 174	96.62 115	98.56 149	98.26 209	93.67 195	94.09 229	97.10 254	84.25 276	98.01 300	92.08 250	92.14 257	96.70 266

sss		97.39 95	96.98 99	98.61 83	98.60 143	96.61 117	98.22 194	98.93 37	93.97 173	98.01 94	98.48 141	91.98 126	99.85 49	96.45 116	98.15 151	99.39 108

test_yl		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 143	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 143	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

VPNet		94.99 200	94.19 214	97.40 166	97.16 248	96.57 120	98.71 122	98.97 30	95.67 96	94.84 196	98.24 170	80.36 306	98.67 233	96.46 115	87.32 317	96.96 231

MVS		94.67 219	93.54 256	98.08 123	96.88 265	96.56 121	98.19 202	98.50 166	78.05 343	92.69 279	98.02 183	91.07 149	99.63 128	90.09 282	98.36 146	98.04 200

XXY-MVS		95.20 189	94.45 203	97.46 161	96.75 272	96.56 121	98.86 89	98.65 135	93.30 210	93.27 261	98.27 167	84.85 267	98.87 215	94.82 169	91.26 270	96.96 231

PatchMatch-RL		96.59 127	96.03 136	98.27 109	99.31 70	96.51 123	97.91 230	99.06 22	93.72 187	96.92 146	98.06 181	88.50 200	99.65 123	91.77 260	99.00 114	98.66 178

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 124	98.30 186	98.69 117	97.21 36	98.84 46	99.36 26	95.41 54	99.78 95	98.62 5	99.65 58	99.80 9

WR-MVS		95.15 191	94.46 201	97.22 171	96.67 277	96.45 125	98.21 195	98.81 76	94.15 163	93.16 264	97.69 214	87.51 222	98.30 278	95.29 158	88.62 304	96.90 241

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 154	96.44 126	99.01 60	98.60 139	95.88 87	97.26 130	97.53 229	94.97 74	99.33 160	97.38 76	99.20 107	99.05 149

FMVSNet2		94.47 234	93.61 253	97.04 182	98.21 169	96.43 127	98.79 108	98.27 205	92.46 237	93.50 254	97.09 258	81.16 298	98.00 302	91.09 267	91.93 261	96.70 266

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 128	98.63 137	98.60 139	95.18 122	97.06 139	98.06 181	94.26 93	99.57 134	93.80 204	98.87 121	99.52 85

1112_ss		96.63 124	96.00 137	98.50 93	98.56 144	96.37 129	98.18 206	98.10 234	92.92 224	94.84 196	98.43 145	92.14 121	99.58 133	94.35 185	96.51 192	99.56 84

TranMVSNet+NR-MVSNet		95.14 192	94.48 199	97.11 179	96.45 287	96.36 130	99.03 56	99.03 25	95.04 131	93.58 248	97.93 192	88.27 203	98.03 299	94.13 193	86.90 324	96.95 233

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 122	96.36 130	99.19 31	97.97 252	95.39 109	97.23 131	98.99 86	91.11 147	98.93 206	94.60 175	98.59 133	99.47 98

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 132	98.28 189	98.68 120	97.17 39	98.74 53	99.37 22	95.25 66	99.79 91	98.57 7	99.54 84	99.73 36

LFMVS		95.86 153	94.98 178	98.47 96	98.87 119	96.32 132	98.84 93	96.02 324	93.40 205	98.62 62	99.20 52	74.99 333	99.63 128	97.72 52	97.20 176	99.46 102

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 134	98.14 209	98.76 98	92.41 242	96.39 172	98.31 162	94.92 76	99.78 95	94.06 197	98.77 126	99.23 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Vis-MVSNet		97.42 93	97.11 91	98.34 106	98.66 137	96.23 135	99.22 25	99.00 27	96.63 60	98.04 88	99.21 48	88.05 211	99.35 158	96.01 132	99.21 106	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ET-MVSNet_ETH3D		94.13 253	92.98 268	97.58 156	98.22 168	96.20 136	97.31 276	95.37 331	94.53 152	79.56 341	97.63 222	86.51 238	97.53 321	96.91 92	90.74 276	99.02 151

baseline		97.64 76	97.44 79	98.25 112	98.35 156	96.20 136	99.00 62	98.32 194	96.33 72	98.03 89	99.17 56	91.35 141	99.16 173	98.10 31	98.29 149	99.39 108

DP-MVS		96.59 127	95.93 138	98.57 85	99.34 62	96.19 138	98.70 126	98.39 184	89.45 309	94.52 205	99.35 28	91.85 128	99.85 49	92.89 232	98.88 119	99.68 57

casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 161	96.14 139	98.82 97	98.32 194	96.38 70	97.95 98	99.21 48	91.23 145	99.23 167	98.12 30	98.37 144	99.48 96

EPNet		97.28 100	96.87 103	98.51 92	94.98 326	96.14 139	98.90 78	97.02 305	98.28 1	95.99 181	99.11 67	91.36 140	99.89 35	96.98 87	99.19 108	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 176	96.07 141	97.98 225	98.21 212	97.24 35	97.13 134	98.93 97	86.88 234	99.91 30	95.00 165	99.37 101	98.66 178

xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 237	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 237	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 237	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

baseline1		95.84 154	95.12 171	98.01 127	98.49 150	95.98 142	98.73 117	97.03 303	95.37 112	96.22 175	98.19 173	89.96 167	99.16 173	94.60 175	87.48 314	98.90 162

CDS-MVSNet		96.99 113	96.69 113	97.90 133	98.05 184	95.98 142	98.20 198	98.33 193	93.67 195	96.95 142	98.49 140	93.54 100	98.42 258	95.24 161	97.74 165	99.31 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		96.28 139	95.70 147	98.03 126	98.29 165	95.97 147	98.58 143	98.25 210	91.74 261	95.29 189	97.23 247	91.03 150	99.15 176	92.90 230	97.96 156	98.97 156

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 161	95.97 147	98.74 113	98.07 242	94.27 161	98.44 72	98.07 180	92.48 111	99.26 163	96.43 118	98.19 150	99.16 137

MG-MVS		97.81 67	97.60 66	98.44 98	99.12 103	95.97 147	97.75 246	98.78 94	96.89 50	98.46 68	99.22 47	93.90 99	99.68 120	94.81 170	99.52 87	99.67 61

tfpnnormal		93.66 265	92.70 274	96.55 222	96.94 260	95.94 150	98.97 68	99.19 15	91.04 287	91.38 302	97.34 239	84.94 265	98.61 236	85.45 325	89.02 300	95.11 326

pmmvs4		94.69 214	93.99 229	96.81 198	95.74 311	95.94 150	97.40 265	97.67 266	90.42 295	93.37 258	97.59 224	89.08 183	98.20 285	92.97 228	91.67 264	96.30 308

Test_1112_low_res		96.34 136	95.66 150	98.36 105	98.56 144	95.94 150	97.71 248	98.07 242	92.10 253	94.79 200	97.29 243	91.75 130	99.56 136	94.17 192	96.50 193	99.58 82

MVSTER		96.06 144	95.72 143	97.08 181	98.23 167	95.93 153	98.73 117	98.27 205	94.86 139	95.07 190	98.09 179	88.21 204	98.54 245	96.59 111	93.46 241	96.79 253

OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 154	98.28 189	98.59 141	95.52 103	97.97 97	99.10 69	93.28 104	99.49 145	95.09 163	98.88 119	99.19 132

PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 155	98.63 137	99.16 17	94.48 156	97.67 116	98.88 102	92.80 108	99.91 30	97.11 83	99.12 110	99.50 91

anonymousdsp		95.42 173	94.91 181	96.94 190	95.10 325	95.90 156	99.14 36	98.41 180	93.75 183	93.16 264	97.46 232	87.50 224	98.41 265	95.63 148	94.03 229	96.50 296

UGNet		96.78 121	96.30 127	98.19 117	98.24 166	95.89 157	98.88 85	98.93 37	97.39 23	96.81 152	97.84 201	82.60 292	99.90 33	96.53 113	99.49 88	98.79 167
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
WR-MVS_H		95.05 197	94.46 201	96.81 198	96.86 266	95.82 158	99.24 20	99.24 10	93.87 178	92.53 284	96.84 286	90.37 160	98.24 284	93.24 219	87.93 310	96.38 303

diffmvs		97.58 82	97.40 81	98.13 120	98.32 163	95.81 159	98.06 217	98.37 187	96.20 75	98.74 53	98.89 101	91.31 143	99.25 164	98.16 29	98.52 136	99.34 111

MVSFormer		97.57 83	97.49 75	97.84 135	98.07 181	95.76 160	99.47 2	98.40 182	94.98 133	98.79 49	98.83 107	92.34 113	98.41 265	96.91 92	99.59 71	99.34 111

lupinMVS		97.44 91	97.22 88	98.12 122	98.07 181	95.76 160	97.68 251	97.76 262	94.50 155	98.79 49	98.61 127	92.34 113	99.30 161	97.58 65	99.59 71	99.31 117

PAPM		94.95 204	94.00 227	97.78 139	97.04 255	95.65 162	96.03 325	98.25 210	91.23 282	94.19 225	97.80 207	91.27 144	98.86 217	82.61 332	97.61 169	98.84 165

jason		97.32 99	97.08 93	98.06 125	97.45 228	95.59 163	97.87 236	97.91 257	94.79 141	98.55 66	98.83 107	91.12 146	99.23 167	97.58 65	99.60 68	99.34 111
jason: jason.
PS-MVSNAJ		97.73 71	97.77 60	97.62 154	98.68 136	95.58 164	97.34 273	98.51 161	97.29 28	98.66 60	97.88 196	94.51 85	99.90 33	97.87 42	99.17 109	97.39 217

testing_2		90.61 301	88.50 307	96.95 189	90.08 346	95.57 165	97.69 250	98.06 246	93.02 219	76.55 342	92.48 338	61.18 349	98.44 255	95.45 153	91.98 260	96.84 249

CP-MVSNet		94.94 206	94.30 210	96.83 197	96.72 274	95.56 166	99.11 42	98.95 34	93.89 176	92.42 289	97.90 194	87.19 228	98.12 291	94.32 187	88.21 307	96.82 252

HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 166	97.38 267	99.65 2	92.34 244	97.61 122	98.20 172	89.29 176	99.10 185	96.97 88	97.60 170	99.77 20

1314		96.25 141	95.73 142	97.79 138	97.13 250	95.55 168	98.19 202	98.59 141	93.47 202	92.03 296	97.82 205	91.33 142	99.49 145	94.62 174	98.44 141	98.32 194

thisisatest0530		96.01 146	95.36 159	97.97 129	98.38 154	95.52 169	98.88 85	94.19 344	94.04 167	97.64 120	98.31 162	83.82 288	99.46 151	95.29 158	97.70 167	98.93 160

test_djsdf		96.00 147	95.69 148	96.93 191	95.72 312	95.49 170	99.47 2	98.40 182	94.98 133	94.58 203	97.86 198	89.16 180	98.41 265	96.91 92	94.12 227	96.88 243

xiu_mvs_v2_base		97.66 75	97.70 63	97.56 158	98.61 142	95.46 171	97.44 262	98.46 171	97.15 40	98.65 61	98.15 175	94.33 91	99.80 79	97.84 46	98.66 131	97.41 215

Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 136	98.73 128	95.46 171	99.20 29	98.30 202	94.96 135	96.60 160	98.87 103	90.05 165	98.59 241	93.67 208	98.60 132	99.46 102

EPP-MVSNet		97.46 87	97.28 85	97.99 128	98.64 139	95.38 173	99.33 13	98.31 196	93.61 198	97.19 132	99.07 77	94.05 95	99.23 167	96.89 95	98.43 143	99.37 110

testdata				98.26 111	99.20 97	95.36 174		98.68 120	91.89 258	98.60 64	99.10 69	94.44 90	99.82 63	94.27 189	99.44 95	99.58 82

MSDG		95.93 150	95.30 165	97.83 136	98.90 116	95.36 174	96.83 310	98.37 187	91.32 277	94.43 212	98.73 118	90.27 163	99.60 131	90.05 285	98.82 124	98.52 185

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 178	99.25 86	95.35 176	98.26 192	99.26 8	94.28 160	97.94 100	97.46 232	92.74 109	99.81 70	96.88 98	93.32 246	96.20 310

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 176	97.28 278	99.26 8	93.13 216	97.94 100	98.21 171	92.74 109	99.81 70	96.88 98	99.40 99	99.27 124

TAMVS		97.02 112	96.79 106	97.70 147	98.06 183	95.31 178	98.52 153	98.31 196	93.95 174	97.05 140	98.61 127	93.49 101	98.52 247	95.33 155	97.81 161	99.29 122

PS-CasMVS		94.67 219	93.99 229	96.71 202	96.68 276	95.26 179	99.13 39	99.03 25	93.68 193	92.33 290	97.95 190	85.35 259	98.10 292	93.59 210	88.16 309	96.79 253

V42		94.78 212	94.14 219	96.70 204	96.33 292	95.22 180	98.97 68	98.09 238	92.32 246	94.31 218	97.06 264	88.39 201	98.55 244	92.90 230	88.87 302	96.34 304

pm-mvs1		93.94 263	93.06 267	96.59 215	96.49 285	95.16 181	98.95 72	98.03 249	92.32 246	91.08 305	97.84 201	84.54 272	98.41 265	92.16 248	86.13 330	96.19 311

CSCG		97.85 66	97.74 62	98.20 115	99.67 26	95.16 181	99.22 25	99.32 7	93.04 218	97.02 141	98.92 99	95.36 58	99.91 30	97.43 73	99.64 62	99.52 85

thisisatest0515		95.61 167	94.89 182	97.76 141	98.15 177	95.15 183	96.77 311	94.41 340	92.95 223	97.18 133	97.43 236	84.78 268	99.45 152	94.63 172	97.73 166	98.68 175

VDDNet		95.36 179	94.53 196	97.86 134	98.10 180	95.13 184	98.85 90	97.75 263	90.46 293	98.36 76	99.39 14	73.27 339	99.64 125	97.98 36	96.58 189	98.81 166

gg-mvs-nofinetune		92.21 288	90.58 295	97.13 177	96.75 272	95.09 185	95.85 327	89.40 353	85.43 332	94.50 206	81.98 347	80.80 304	98.40 271	92.16 248	98.33 147	97.88 203

PS-MVSNAJss		96.43 132	96.26 129	96.92 193	95.84 310	95.08 186	99.16 34	98.50 166	95.87 88	93.84 241	98.34 159	94.51 85	98.61 236	96.88 98	93.45 243	97.06 226

thres600view7		95.49 168	94.77 185	97.67 150	98.98 112	95.02 187	98.85 90	96.90 311	95.38 110	96.63 158	96.90 281	84.29 274	99.59 132	88.65 305	96.33 197	98.40 189

GBi-Net		94.49 232	93.80 240	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 229	97.07 261	81.16 298	97.95 304	92.08 250	92.14 257	96.72 262

test1		94.49 232	93.80 240	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 229	97.07 261	81.16 298	97.95 304	92.08 250	92.14 257	96.72 262

FMVSNet1		93.19 277	92.07 282	96.56 219	97.54 218	95.00 188	98.82 97	98.18 218	90.38 296	92.27 291	97.07 261	73.68 338	97.95 304	89.36 299	91.30 268	96.72 262

tfpn200view9		95.32 183	94.62 192	97.43 163	98.94 114	94.98 191	98.68 129	96.93 309	95.33 113	96.55 163	96.53 298	84.23 277	99.56 136	88.11 306	96.29 199	97.76 206

GG-mvs-BLEND				96.59 215	96.34 291	94.98 191	96.51 320	88.58 354		93.10 269	94.34 331	80.34 307	98.05 298	89.53 295	96.99 179	96.74 259

thres400		95.38 176	94.62 192	97.65 153	98.94 114	94.98 191	98.68 129	96.93 309	95.33 113	96.55 163	96.53 298	84.23 277	99.56 136	88.11 306	96.29 199	98.40 189

F-COLMAP		97.09 111	96.80 104	97.97 129	99.45 55	94.95 194	98.55 151	98.62 138	93.02 219	96.17 177	98.58 132	94.01 96	99.81 70	93.95 199	98.90 117	99.14 140

thres100view900		95.38 176	94.70 189	97.41 164	98.98 112	94.92 195	98.87 87	96.90 311	95.38 110	96.61 159	96.88 282	84.29 274	99.56 136	88.11 306	96.29 199	97.76 206

thres200		95.25 185	94.57 194	97.28 169	98.81 124	94.92 195	98.20 198	97.11 298	95.24 121	96.54 165	96.22 310	84.58 271	99.53 142	87.93 310	96.50 193	97.39 217

tttt0517		96.07 143	95.51 153	97.78 139	98.41 153	94.84 197	99.28 16	94.33 342	94.26 162	97.64 120	98.64 126	84.05 281	99.47 150	95.34 154	97.60 170	99.03 150

PEN-MVS		94.42 236	93.73 247	96.49 226	96.28 293	94.84 197	99.17 33	99.00 27	93.51 200	92.23 292	97.83 204	86.10 247	97.90 308	92.55 241	86.92 323	96.74 259

v8		94.47 234	93.77 243	96.57 218	96.36 290	94.83 199	99.05 52	98.19 215	91.92 257	93.16 264	96.97 274	88.82 193	98.48 249	91.69 262	87.79 311	96.39 302

TAPA-MVS	93.98 7	95.35 180	94.56 195	97.74 143	99.13 102	94.83 199	98.33 178	98.64 136	86.62 322	96.29 174	98.61 127	94.00 97	99.29 162	80.00 337	99.41 97	99.09 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		94.29 243	93.55 255	96.51 225	96.39 289	94.80 201	98.99 64	98.19 215	91.35 275	93.02 270	96.99 272	88.09 209	98.41 265	90.50 278	88.41 306	96.33 306

v2v482		94.69 214	94.03 223	96.65 207	96.17 297	94.79 202	98.67 132	98.08 240	92.72 230	94.00 234	97.16 252	87.69 221	98.45 253	92.91 229	88.87 302	96.72 262

v1144		94.59 224	93.92 232	96.60 214	96.21 294	94.78 203	98.59 141	98.14 228	91.86 260	94.21 224	97.02 269	87.97 212	98.41 265	91.72 261	89.57 289	96.61 276

TransMVSNet (Re)		92.67 284	91.51 289	96.15 247	96.58 280	94.65 204	98.90 78	96.73 317	90.86 289	89.46 318	97.86 198	85.62 254	98.09 294	86.45 317	81.12 338	95.71 320

BH-RMVSNet		95.92 151	95.32 163	97.69 148	98.32 163	94.64 205	98.19 202	97.45 284	94.56 151	96.03 179	98.61 127	85.02 263	99.12 179	90.68 276	99.06 112	99.30 120

OPM-MVS		95.69 162	95.33 162	96.76 200	96.16 299	94.63 206	98.43 167	98.39 184	96.64 59	95.02 192	98.78 112	85.15 262	99.05 189	95.21 162	94.20 222	96.60 277

jajsoiax		95.45 171	95.03 175	96.73 201	95.42 323	94.63 206	99.14 36	98.52 158	95.74 92	93.22 262	98.36 154	83.87 286	98.65 234	96.95 91	94.04 228	96.91 239

plane_prior7					97.42 229	94.63 206

plane_prior6					97.35 234	94.61 209						87.09 229

plane_prior3						94.61 209			97.02 47	95.34 186

HQP_MVS		96.14 142	95.90 139	96.85 196	97.42 229	94.60 211	98.80 104	98.56 149	97.28 29	95.34 186	98.28 164	87.09 229	99.03 193	96.07 126	94.27 219	96.92 234

plane_prior						94.60 211	98.44 165		96.74 55						94.22 221

CHOSEN 1792x2688		97.12 109	96.80 104	98.08 123	99.30 75	94.56 213	98.05 218	99.71 1	93.57 199	97.09 135	98.91 100	88.17 206	99.89 35	96.87 101	99.56 80	99.81 8

NP-MVS					97.28 237	94.51 214					97.73 210

v1192		94.32 241	93.58 254	96.53 223	96.10 300	94.45 215	98.50 158	98.17 223	91.54 268	94.19 225	97.06 264	86.95 233	98.43 257	90.14 281	89.57 289	96.70 266

mvs_tets		95.41 175	95.00 176	96.65 207	95.58 316	94.42 216	99.00 62	98.55 151	95.73 93	93.21 263	98.38 152	83.45 290	98.63 235	97.09 84	94.00 230	96.91 239

LTVRE_ROB	92.95 15	94.60 222	93.90 234	96.68 206	97.41 232	94.42 216	98.52 153	98.59 141	91.69 264	91.21 303	98.35 155	84.87 266	99.04 192	91.06 269	93.44 244	96.60 277
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DTE-MVSNet		93.98 262	93.26 265	96.14 248	96.06 302	94.39 218	99.20 29	98.86 61	93.06 217	91.78 298	97.81 206	85.87 251	97.58 319	90.53 277	86.17 328	96.46 300

v7n		94.19 249	93.43 260	96.47 228	95.90 307	94.38 219	99.26 18	98.34 192	91.99 255	92.76 276	97.13 253	88.31 202	98.52 247	89.48 297	87.70 312	96.52 291

v144192		94.39 238	93.70 249	96.48 227	96.06 302	94.35 220	98.58 143	98.16 225	91.45 270	94.33 217	97.02 269	87.50 224	98.45 253	91.08 268	89.11 297	96.63 274

Anonymous20231211		94.10 256	93.26 265	96.61 212	99.11 104	94.28 221	99.01 60	98.88 49	86.43 324	92.81 274	97.57 226	81.66 297	98.68 232	94.83 168	89.02 300	96.88 243

cascas		94.63 221	93.86 237	96.93 191	96.91 263	94.27 222	96.00 326	98.51 161	85.55 331	94.54 204	96.23 308	84.20 279	98.87 215	95.80 139	96.98 180	97.66 212

Anonymous20240529		95.10 194	94.22 212	97.75 142	99.01 108	94.26 223	98.87 87	98.83 68	85.79 330	96.64 157	98.97 87	78.73 314	99.85 49	96.27 121	94.89 216	99.12 142

HQP5-MVS						94.25 224

HQP-MVS		95.72 159	95.40 154	96.69 205	97.20 243	94.25 224	98.05 218	98.46 171	96.43 67	94.45 208	97.73 210	86.75 235	98.96 201	95.30 156	94.18 223	96.86 247

TR-MVS		94.94 206	94.20 213	97.17 175	97.75 199	94.14 226	97.59 257	97.02 305	92.28 248	95.75 184	97.64 220	83.88 285	98.96 201	89.77 289	96.15 207	98.40 189

v1921920		94.20 248	93.47 259	96.40 235	95.98 305	94.08 227	98.52 153	98.15 226	91.33 276	94.25 221	97.20 250	86.41 242	98.42 258	90.04 286	89.39 294	96.69 271

Baseline_NR-MVSNet		94.35 239	93.81 239	95.96 255	96.20 295	94.05 228	98.61 140	96.67 321	91.44 271	93.85 240	97.60 223	88.57 196	98.14 289	94.39 183	86.93 322	95.68 321

VDD-MVS		95.82 156	95.23 166	97.61 155	98.84 123	93.98 229	98.68 129	97.40 288	95.02 132	97.95 98	99.34 31	74.37 337	99.78 95	98.64 3	96.80 182	99.08 147

PMMVS		96.60 125	96.33 126	97.41 164	97.90 192	93.93 230	97.35 272	98.41 180	92.84 228	97.76 109	97.45 234	91.10 148	99.20 170	96.26 122	97.91 157	99.11 143

v1240		94.06 260	93.29 264	96.34 239	96.03 304	93.90 231	98.44 165	98.17 223	91.18 285	94.13 228	97.01 271	86.05 248	98.42 258	89.13 302	89.50 292	96.70 266

GA-MVS		94.81 210	94.03 223	97.14 176	97.15 249	93.86 232	96.76 312	97.58 270	94.00 171	94.76 201	97.04 267	80.91 301	98.48 249	91.79 259	96.25 204	99.09 144

ACMM	93.85 9	95.69 162	95.38 158	96.61 212	97.61 209	93.84 233	98.91 77	98.44 175	95.25 119	94.28 219	98.47 142	86.04 250	99.12 179	95.50 151	93.95 232	96.87 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_anonymous		96.70 123	96.53 121	97.18 174	98.19 172	93.78 234	98.31 184	98.19 215	94.01 170	94.47 207	98.27 167	92.08 124	98.46 252	97.39 75	97.91 157	99.31 117

XVG-OURS-SEG-HR		96.51 130	96.34 125	97.02 183	98.77 126	93.76 235	97.79 244	98.50 166	95.45 106	96.94 143	99.09 74	87.87 216	99.55 141	96.76 108	95.83 212	97.74 208

XVG-OURS		96.55 129	96.41 123	96.99 184	98.75 127	93.76 235	97.50 261	98.52 158	95.67 96	96.83 149	99.30 38	88.95 190	99.53 142	95.88 135	96.26 203	97.69 211

Anonymous202405211		95.28 184	94.49 198	97.67 150	99.00 109	93.75 237	98.70 126	97.04 302	90.66 290	96.49 168	98.80 110	78.13 317	99.83 55	96.21 124	95.36 215	99.44 105

CLD-MVS		95.62 165	95.34 160	96.46 231	97.52 221	93.75 237	97.27 279	98.46 171	95.53 102	94.42 213	98.00 186	86.21 245	98.97 198	96.25 123	94.37 217	96.66 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_enhance_ethall		95.10 194	94.75 187	96.12 250	97.53 220	93.73 239	96.61 317	98.08 240	92.20 252	93.89 237	96.65 294	92.44 112	98.30 278	94.21 191	91.16 271	96.34 304

IterMVS-LS		95.46 169	95.21 167	96.22 245	98.12 178	93.72 240	98.32 183	98.13 229	93.71 188	94.26 220	97.31 242	92.24 117	98.10 292	94.63 172	90.12 282	96.84 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 148	95.83 141	96.36 237	97.93 190	93.70 241	98.12 212	98.27 205	93.70 190	95.07 190	99.02 80	92.23 118	98.54 245	94.68 171	93.46 241	96.84 249

cl-mvsnet2		94.68 216	94.19 214	96.13 249	98.11 179	93.60 242	96.94 297	98.31 196	92.43 241	93.32 260	96.87 284	86.51 238	98.28 282	94.10 196	91.16 271	96.51 294

baseline2		95.11 193	94.52 197	96.87 195	96.65 278	93.56 243	98.27 191	94.10 346	93.45 203	92.02 297	97.43 236	87.45 226	99.19 171	93.88 201	97.41 174	97.87 204

LPG-MVS_test		95.62 165	95.34 160	96.47 228	97.46 224	93.54 244	98.99 64	98.54 154	94.67 147	94.36 215	98.77 114	85.39 257	99.11 182	95.71 144	94.15 225	96.76 257

LGP-MVS_train				96.47 228	97.46 224	93.54 244		98.54 154	94.67 147	94.36 215	98.77 114	85.39 257	99.11 182	95.71 144	94.15 225	96.76 257

AUN-MVS		94.53 229	93.73 247	96.92 193	98.50 149	93.52 246	98.34 176	98.10 234	93.83 181	95.94 183	97.98 188	85.59 255	99.03 193	94.35 185	80.94 340	98.22 196

test_part1		92.87 281	91.72 287	96.32 241	97.55 217	93.50 247	99.04 53	98.74 102	83.31 336	90.81 308	97.70 213	76.61 326	98.60 240	94.43 182	87.30 318	96.85 248

ACMP	93.49 10	95.34 181	94.98 178	96.43 233	97.67 205	93.48 248	98.73 117	98.44 175	94.94 138	92.53 284	98.53 136	84.50 273	99.14 177	95.48 152	94.00 230	96.66 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CR-MVSNet		94.76 213	94.15 218	96.59 215	97.00 256	93.43 249	94.96 333	97.56 271	92.46 237	96.93 144	96.24 306	88.15 207	97.88 312	87.38 312	96.65 187	98.46 187

RPMNet		92.81 282	91.34 290	97.24 170	97.00 256	93.43 249	94.96 333	98.80 86	82.27 338	96.93 144	92.12 340	86.98 232	99.82 63	76.32 345	96.65 187	98.46 187

IB-MVS	91.98 17	93.27 273	91.97 284	97.19 173	97.47 223	93.41 251	97.09 290	95.99 325	93.32 208	92.47 287	95.73 318	78.06 318	99.53 142	94.59 177	82.98 333	98.62 181
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
cl-mvsnet_		94.51 231	94.01 226	96.02 251	97.58 212	93.40 252	97.05 291	97.96 254	91.73 263	92.76 276	97.08 260	89.06 184	98.13 290	92.61 235	90.29 281	96.52 291

cl-mvsnet1		94.52 230	94.03 223	95.99 252	97.57 216	93.38 253	97.05 291	97.94 255	91.74 261	92.81 274	97.10 254	89.12 181	98.07 296	92.60 236	90.30 280	96.53 288

UniMVSNet_ETH3D		94.24 246	93.33 262	96.97 187	97.19 246	93.38 253	98.74 113	98.57 147	91.21 284	93.81 242	98.58 132	72.85 340	98.77 226	95.05 164	93.93 233	98.77 169

miper_ehance_all_eth		95.01 198	94.69 190	95.97 254	97.70 204	93.31 255	97.02 293	98.07 242	92.23 249	93.51 253	96.96 276	91.85 128	98.15 288	93.68 206	91.16 271	96.44 301

CHOSEN 280x420		97.18 106	97.18 89	97.20 172	98.81 124	93.27 256	95.78 329	99.15 18	95.25 119	96.79 154	98.11 178	92.29 115	99.07 188	98.56 8	99.85 3	99.25 126

ACMH	92.88 16	94.55 227	93.95 231	96.34 239	97.63 208	93.26 257	98.81 103	98.49 170	93.43 204	89.74 315	98.53 136	81.91 295	99.08 187	93.69 205	93.30 247	96.70 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	93.27 12	95.33 182	94.87 183	96.71 202	99.29 78	93.24 258	98.58 143	98.11 232	89.92 303	93.57 249	99.10 69	86.37 243	99.79 91	90.78 274	98.10 153	97.09 224
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		95.24 186	94.65 191	96.99 184	99.25 86	93.21 259	98.59 141	98.18 218	91.36 273	93.52 251	98.77 114	84.67 269	99.72 108	89.70 292	97.87 159	98.02 201

TestCases				96.99 184	99.25 86	93.21 259		98.18 218	91.36 273	93.52 251	98.77 114	84.67 269	99.72 108	89.70 292	97.87 159	98.02 201

MIMVSNet		93.26 274	92.21 281	96.41 234	97.73 203	93.13 261	95.65 330	97.03 303	91.27 281	94.04 232	96.06 313	75.33 331	97.19 326	86.56 316	96.23 205	98.92 161

cl_fuxian		94.79 211	94.43 205	95.89 259	97.75 199	93.12 262	97.16 287	98.03 249	92.23 249	93.46 256	97.05 266	91.39 139	98.01 300	93.58 211	89.21 296	96.53 288

Patchmtry		93.22 275	92.35 279	95.84 261	96.77 269	93.09 263	94.66 338	97.56 271	87.37 320	92.90 272	96.24 306	88.15 207	97.90 308	87.37 313	90.10 283	96.53 288

v148		94.29 243	93.76 245	95.91 257	96.10 300	92.93 264	98.58 143	97.97 252	92.59 235	93.47 255	96.95 278	88.53 199	98.32 274	92.56 240	87.06 321	96.49 297

test0.0.03 1		94.08 258	93.51 257	95.80 262	95.53 318	92.89 265	97.38 267	95.97 326	95.11 127	92.51 286	96.66 292	87.71 218	96.94 329	87.03 314	93.67 236	97.57 213

PatchT		93.06 279	91.97 284	96.35 238	96.69 275	92.67 266	94.48 339	97.08 299	86.62 322	97.08 136	92.23 339	87.94 213	97.90 308	78.89 341	96.69 185	98.49 186

MVP-Stereo		94.28 245	93.92 232	95.35 276	94.95 327	92.60 267	97.97 226	97.65 267	91.61 267	90.68 310	97.09 258	86.32 244	98.42 258	89.70 292	99.34 102	95.02 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		93.65 267	92.97 269	95.68 266	95.49 319	92.37 268	98.20 198	97.28 293	89.66 306	92.58 282	97.26 244	82.14 293	98.09 294	93.18 222	90.95 275	96.58 279

BH-untuned		95.95 149	95.72 143	96.65 207	98.55 146	92.26 269	98.23 193	97.79 261	93.73 186	94.62 202	98.01 185	88.97 189	99.00 197	93.04 226	98.51 137	98.68 175

pmmvs-eth3d		90.36 302	89.05 305	94.32 305	91.10 343	92.12 270	97.63 256	96.95 308	88.86 314	84.91 336	93.13 334	78.32 316	96.74 330	88.70 304	81.81 337	94.09 336

FMVSNet5		91.81 289	90.92 292	94.49 300	97.21 242	92.09 271	98.00 224	97.55 275	89.31 311	90.86 307	95.61 324	74.48 335	95.32 341	85.57 323	89.70 287	96.07 314

D2MVS		95.18 190	95.08 173	95.48 271	97.10 252	92.07 272	98.30 186	99.13 19	94.02 169	92.90 272	96.73 289	89.48 171	98.73 228	94.48 181	93.60 240	95.65 322

PVSNet	91.96 18	96.35 135	96.15 132	96.96 188	99.17 98	92.05 273	96.08 322	98.68 120	93.69 191	97.75 110	97.80 207	88.86 191	99.69 119	94.26 190	99.01 113	99.15 138

ACMH+	92.99 14	94.30 242	93.77 243	95.88 260	97.81 197	92.04 274	98.71 122	98.37 187	93.99 172	90.60 311	98.47 142	80.86 303	99.05 189	92.75 234	92.40 256	96.55 285

ADS-MVSNet		95.00 199	94.45 203	96.63 210	98.00 185	91.91 275	96.04 323	97.74 264	90.15 298	96.47 169	96.64 295	87.89 214	98.96 201	90.08 283	97.06 177	99.02 151

mvs-test1		96.60 125	96.68 115	96.37 236	97.89 193	91.81 276	98.56 149	98.10 234	96.57 62	96.52 167	97.94 191	90.81 151	99.45 152	95.72 142	98.01 154	97.86 205

BH-w/o		95.38 176	95.08 173	96.26 244	98.34 160	91.79 277	97.70 249	97.43 286	92.87 227	94.24 222	97.22 248	88.66 194	98.84 218	91.55 264	97.70 167	98.16 198

Patchmatch-test		94.42 236	93.68 251	96.63 210	97.60 210	91.76 278	94.83 337	97.49 281	89.45 309	94.14 227	97.10 254	88.99 185	98.83 220	85.37 326	98.13 152	99.29 122

EPMVS		94.99 200	94.48 199	96.52 224	97.22 241	91.75 279	97.23 280	91.66 350	94.11 164	97.28 129	96.81 287	85.70 253	98.84 218	93.04 226	97.28 175	98.97 156

Fast-Effi-MVS+-dtu		95.87 152	95.85 140	95.91 257	97.74 202	91.74 280	98.69 128	98.15 226	95.56 101	94.92 194	97.68 217	88.98 188	98.79 224	93.19 221	97.78 163	97.20 223

eth_miper_zixun_eth		94.68 216	94.41 206	95.47 272	97.64 207	91.71 281	96.73 314	98.07 242	92.71 231	93.64 246	97.21 249	90.54 158	98.17 287	93.38 214	89.76 286	96.54 286

XVG-ACMP-BASELINE		94.54 228	94.14 219	95.75 265	96.55 281	91.65 282	98.11 214	98.44 175	94.96 135	94.22 223	97.90 194	79.18 312	99.11 182	94.05 198	93.85 234	96.48 298

TDRefinement		91.06 296	89.68 301	95.21 279	85.35 349	91.49 283	98.51 157	97.07 300	91.47 269	88.83 321	97.84 201	77.31 324	99.09 186	92.79 233	77.98 342	95.04 328

MDA-MVSNet-bldmvs		89.97 304	88.35 309	94.83 293	95.21 324	91.34 284	97.64 254	97.51 278	88.36 316	71.17 348	96.13 312	79.22 311	96.63 335	83.65 329	86.27 327	96.52 291

RRT_test8_iter05		94.56 226	94.19 214	95.67 267	97.60 210	91.34 284	98.93 75	98.42 179	94.75 142	93.39 257	97.87 197	79.00 313	98.61 236	96.78 107	90.99 274	97.07 225

MVS_0304		92.81 282	92.01 283	95.23 278	97.46 224	91.33 286	98.17 207	98.81 76	91.13 286	93.80 243	95.68 323	66.08 346	98.06 297	90.79 273	96.13 208	96.32 307

ITE_SJBPF				95.44 274	97.42 229	91.32 287		97.50 279	95.09 130	93.59 247	98.35 155	81.70 296	98.88 214	89.71 291	93.39 245	96.12 312

SCA		95.46 169	95.13 170	96.46 231	97.67 205	91.29 288	97.33 274	97.60 269	94.68 146	96.92 146	97.10 254	83.97 283	98.89 212	92.59 238	98.32 148	99.20 129

pmmvs6		91.77 290	90.63 294	95.17 281	94.69 332	91.24 289	98.67 132	97.92 256	86.14 326	89.62 316	97.56 228	75.79 330	98.34 272	90.75 275	84.56 332	95.94 317

test_0402		91.32 293	90.27 297	94.48 301	96.60 279	91.12 290	98.50 158	97.22 296	86.10 327	88.30 323	96.98 273	77.65 322	97.99 303	78.13 343	92.94 252	94.34 332

MIMVSNet1		89.67 306	88.28 310	93.82 309	92.81 340	91.08 291	98.01 222	97.45 284	87.95 317	87.90 325	95.87 316	67.63 344	94.56 344	78.73 342	88.18 308	95.83 319

miper_lstm_enhance		94.33 240	94.07 222	95.11 283	97.75 199	90.97 292	97.22 281	98.03 249	91.67 265	92.76 276	96.97 274	90.03 166	97.78 314	92.51 243	89.64 288	96.56 283

ppachtmachnet_test		93.22 275	92.63 275	94.97 287	95.45 321	90.84 293	96.88 306	97.88 258	90.60 291	92.08 295	97.26 244	88.08 210	97.86 313	85.12 327	90.33 279	96.22 309

USDC		93.33 272	92.71 273	95.21 279	96.83 268	90.83 294	96.91 300	97.50 279	93.84 179	90.72 309	98.14 176	77.69 320	98.82 221	89.51 296	93.21 249	95.97 316

DWT-MVSNet_test		94.82 209	94.36 208	96.20 246	97.35 234	90.79 295	98.34 176	96.57 323	92.91 225	95.33 188	96.44 302	82.00 294	99.12 179	94.52 179	95.78 213	98.70 172

MDA-MVSNet_test_wron		90.71 299	89.38 304	94.68 297	94.83 329	90.78 296	97.19 283	97.46 282	87.60 318	72.41 347	95.72 320	86.51 238	96.71 333	85.92 321	86.80 325	96.56 283

PatchmatchNet		95.71 160	95.52 152	96.29 243	97.58 212	90.72 297	96.84 309	97.52 277	94.06 166	97.08 136	96.96 276	89.24 178	98.90 211	92.03 254	98.37 144	99.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
YYNet1		90.70 300	89.39 303	94.62 299	94.79 330	90.65 298	97.20 282	97.46 282	87.54 319	72.54 346	95.74 317	86.51 238	96.66 334	86.00 320	86.76 326	96.54 286

JIA-IIPM		93.35 270	92.49 277	95.92 256	96.48 286	90.65 298	95.01 332	96.96 307	85.93 328	96.08 178	87.33 344	87.70 220	98.78 225	91.35 266	95.58 214	98.34 192

IterMVS-SCA-FT		94.11 255	93.87 236	94.85 291	97.98 189	90.56 300	97.18 284	98.11 232	93.75 183	92.58 282	97.48 231	83.97 283	97.41 323	92.48 245	91.30 268	96.58 279

EPNet_dtu		95.21 188	94.95 180	95.99 252	96.17 297	90.45 301	98.16 208	97.27 294	96.77 53	93.14 267	98.33 160	90.34 161	98.42 258	85.57 323	98.81 125	99.09 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS		94.09 257	93.85 238	94.80 294	97.99 187	90.35 302	97.18 284	98.12 230	93.68 193	92.46 288	97.34 239	84.05 281	97.41 323	92.51 243	91.33 267	96.62 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		96.29 137	96.56 118	95.51 270	97.89 193	90.22 303	98.80 104	98.10 234	96.57 62	96.45 171	96.66 292	90.81 151	98.91 208	95.72 142	97.99 155	97.40 216

testgi		93.06 279	92.45 278	94.88 290	96.43 288	89.90 304	98.75 110	97.54 276	95.60 99	91.63 301	97.91 193	74.46 336	97.02 328	86.10 319	93.67 236	97.72 210

UnsupCasMVSNet_eth		90.99 297	89.92 300	94.19 307	94.08 335	89.83 305	97.13 289	98.67 128	93.69 191	85.83 333	96.19 311	75.15 332	96.74 330	89.14 301	79.41 341	96.00 315

TinyColmap		92.31 287	91.53 288	94.65 298	96.92 261	89.75 306	96.92 298	96.68 320	90.45 294	89.62 316	97.85 200	76.06 329	98.81 222	86.74 315	92.51 255	95.41 323

test-LLR		95.10 194	94.87 183	95.80 262	96.77 269	89.70 307	96.91 300	95.21 332	95.11 127	94.83 198	95.72 320	87.71 218	98.97 198	93.06 224	98.50 138	98.72 170

test-mter		94.08 258	93.51 257	95.80 262	96.77 269	89.70 307	96.91 300	95.21 332	92.89 226	94.83 198	95.72 320	77.69 320	98.97 198	93.06 224	98.50 138	98.72 170

our_test_3		93.65 267	93.30 263	94.69 296	95.45 321	89.68 309	96.91 300	97.65 267	91.97 256	91.66 300	96.88 282	89.67 169	97.93 307	88.02 309	91.49 266	96.48 298

DeepPCF-MVS	96.37 2	97.93 63	98.48 17	96.30 242	99.00 109	89.54 310	97.43 264	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 125	98.30 26	99.77 26	99.72 40

MS-PatchMatch		93.84 264	93.63 252	94.46 303	96.18 296	89.45 311	97.76 245	98.27 205	92.23 249	92.13 294	97.49 230	79.50 309	98.69 229	89.75 290	99.38 100	95.25 324

OpenMVS_ROB	86.42 20	89.00 308	87.43 313	93.69 310	93.08 339	89.42 312	97.91 230	96.89 313	78.58 342	85.86 332	94.69 329	69.48 342	98.29 281	77.13 344	93.29 248	93.36 341

SixPastTwentyTwo		93.34 271	92.86 270	94.75 295	95.67 313	89.41 313	98.75 110	96.67 321	93.89 176	90.15 313	98.25 169	80.87 302	98.27 283	90.90 272	90.64 277	96.57 281

K. test v3		92.55 285	91.91 286	94.48 301	95.64 314	89.24 314	99.07 50	94.88 336	94.04 167	86.78 328	97.59 224	77.64 323	97.64 317	92.08 250	89.43 293	96.57 281

OurMVSNet-221017-0		94.21 247	94.00 227	94.85 291	95.60 315	89.22 315	98.89 82	97.43 286	95.29 116	92.18 293	98.52 139	82.86 291	98.59 241	93.46 213	91.76 263	96.74 259

TESTMET0.1,1		94.18 251	93.69 250	95.63 268	96.92 261	89.12 316	96.91 300	94.78 337	93.17 214	94.88 195	96.45 301	78.52 315	98.92 207	93.09 223	98.50 138	98.85 163

CostFormer		94.95 204	94.73 188	95.60 269	97.28 237	89.06 317	97.53 260	96.89 313	89.66 306	96.82 151	96.72 290	86.05 248	98.95 205	95.53 150	96.13 208	98.79 167

tpm2		94.19 249	93.76 245	95.46 273	97.23 240	89.04 318	97.31 276	96.85 316	87.08 321	96.21 176	96.79 288	83.75 289	98.74 227	92.43 246	96.23 205	98.59 182

EG-PatchMatch MVS		91.13 295	90.12 298	94.17 308	94.73 331	89.00 319	98.13 211	97.81 260	89.22 312	85.32 335	96.46 300	67.71 343	98.42 258	87.89 311	93.82 235	95.08 327

UnsupCasMVSNet_bld		87.17 311	85.12 315	93.31 314	91.94 341	88.77 320	94.92 335	98.30 202	84.30 335	82.30 339	90.04 341	63.96 348	97.25 325	85.85 322	74.47 346	93.93 339

ADS-MVSNet2		94.58 225	94.40 207	95.11 283	98.00 185	88.74 321	96.04 323	97.30 291	90.15 298	96.47 169	96.64 295	87.89 214	97.56 320	90.08 283	97.06 177	99.02 151

LF4IMVS		93.14 278	92.79 272	94.20 306	95.88 308	88.67 322	97.66 253	97.07 300	93.81 182	91.71 299	97.65 218	77.96 319	98.81 222	91.47 265	91.92 262	95.12 325

tpmvs		94.60 222	94.36 208	95.33 277	97.46 224	88.60 323	96.88 306	97.68 265	91.29 279	93.80 243	96.42 303	88.58 195	99.24 166	91.06 269	96.04 210	98.17 197

tpmrst		95.63 164	95.69 148	95.44 274	97.54 218	88.54 324	96.97 295	97.56 271	93.50 201	97.52 127	96.93 280	89.49 170	99.16 173	95.25 160	96.42 195	98.64 180

lessismore_v0				94.45 304	94.93 328	88.44 325		91.03 351		86.77 329	97.64 220	76.23 328	98.42 258	90.31 280	85.64 331	96.51 294

MDTV_nov1_ep13			95.40 154		97.48 222	88.34 326	96.85 308	97.29 292	93.74 185	97.48 128	97.26 244	89.18 179	99.05 189	91.92 257	97.43 173

new_pmnet		90.06 303	89.00 306	93.22 316	94.18 333	88.32 327	96.42 321	96.89 313	86.19 325	85.67 334	93.62 332	77.18 325	97.10 327	81.61 334	89.29 295	94.23 333

test20.03		90.89 298	90.38 296	92.43 318	93.48 337	88.14 328	98.33 178	97.56 271	93.40 205	87.96 324	96.71 291	80.69 305	94.13 345	79.15 340	86.17 328	95.01 330

tpm cat1		93.36 269	92.80 271	95.07 285	97.58 212	87.97 329	96.76 312	97.86 259	82.17 339	93.53 250	96.04 314	86.13 246	99.13 178	89.24 300	95.87 211	98.10 199

tpm		94.13 253	93.80 240	95.12 282	96.50 284	87.91 330	97.44 262	95.89 329	92.62 233	96.37 173	96.30 305	84.13 280	98.30 278	93.24 219	91.66 265	99.14 140

LCM-MVSNet-Re		95.22 187	95.32 163	94.91 288	98.18 174	87.85 331	98.75 110	95.66 330	95.11 127	88.96 320	96.85 285	90.26 164	97.65 316	95.65 147	98.44 141	99.22 128

gm-plane-assit					95.88 308	87.47 332			89.74 305		96.94 279		99.19 171	93.32 218

Anonymous20231206		91.66 291	91.10 291	93.33 313	94.02 336	87.35 333	98.58 143	97.26 295	90.48 292	90.16 312	96.31 304	83.83 287	96.53 336	79.36 339	89.90 285	96.12 312

PVSNet_0	88.72 19	91.28 294	90.03 299	95.00 286	97.99 187	87.29 334	94.84 336	98.50 166	92.06 254	89.86 314	95.19 325	79.81 308	99.39 155	92.27 247	69.79 347	98.33 193

pmmvs3		86.67 313	84.86 316	92.11 321	88.16 347	87.19 335	96.63 316	94.75 338	79.88 341	87.22 327	92.75 336	66.56 345	95.20 342	81.24 335	76.56 344	93.96 338

dp		94.15 252	93.90 234	94.90 289	97.31 236	86.82 336	96.97 295	97.19 297	91.22 283	96.02 180	96.61 297	85.51 256	99.02 196	90.00 287	94.30 218	98.85 163

new-patchmatchnet		88.50 309	87.45 312	91.67 322	90.31 345	85.89 337	97.16 287	97.33 290	89.47 308	83.63 338	92.77 335	76.38 327	95.06 343	82.70 331	77.29 343	94.06 337

Patchmatch-RL test		91.49 292	90.85 293	93.41 312	91.37 342	84.40 338	92.81 343	95.93 328	91.87 259	87.25 326	94.87 328	88.99 185	96.53 336	92.54 242	82.00 335	99.30 120

MDTV_nov1_ep13_2view						84.26 339	96.89 305		90.97 288	97.90 104		89.89 168		93.91 200		99.18 136

CVMVSNet		95.43 172	96.04 135	93.57 311	97.93 190	83.62 340	98.12 212	98.59 141	95.68 95	96.56 161	99.02 80	87.51 222	97.51 322	93.56 212	97.44 172	99.60 78

EU-MVSNet		93.66 265	94.14 219	92.25 320	95.96 306	83.38 341	98.52 153	98.12 230	94.69 145	92.61 281	98.13 177	87.36 227	96.39 338	91.82 258	90.00 284	96.98 230

PM-MVS		87.77 310	86.55 314	91.40 323	91.03 344	83.36 342	96.92 298	95.18 334	91.28 280	86.48 331	93.42 333	53.27 350	96.74 330	89.43 298	81.97 336	94.11 335

DSMNet-mixed		92.52 286	92.58 276	92.33 319	94.15 334	82.65 343	98.30 186	94.26 343	89.08 313	92.65 280	95.73 318	85.01 264	95.76 339	86.24 318	97.76 164	98.59 182

MVS-HIRNet		89.46 307	88.40 308	92.64 317	97.58 212	82.15 344	94.16 342	93.05 349	75.73 345	90.90 306	82.52 346	79.42 310	98.33 273	83.53 330	98.68 127	97.43 214

RPSCF		94.87 208	95.40 154	93.26 315	98.89 117	82.06 345	98.33 178	98.06 246	90.30 297	96.56 161	99.26 42	87.09 229	99.49 145	93.82 203	96.32 198	98.24 195

Gipuma		78.40 315	76.75 318	83.38 330	95.54 317	80.43 346	79.42 351	97.40 288	64.67 348	73.46 345	80.82 348	45.65 352	93.14 346	66.32 348	87.43 315	76.56 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	66.06 21	89.70 305	89.67 302	89.78 324	93.19 338	76.56 347	97.00 294	98.35 190	80.97 340	81.57 340	97.75 209	74.75 334	98.61 236	89.85 288	93.63 238	94.17 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ambc				89.49 325	86.66 348	75.78 348	92.66 344	96.72 318		86.55 330	92.50 337	46.01 351	97.90 308	90.32 279	82.09 334	94.80 331

PMMVS2		77.95 316	75.44 320	85.46 328	82.54 350	74.95 349	94.23 341	93.08 348	72.80 346	74.68 344	87.38 343	36.36 356	91.56 348	73.95 346	63.94 348	89.87 343

DeepMVS_CX				86.78 326	97.09 253	72.30 350		95.17 335	75.92 344	84.34 337	95.19 325	70.58 341	95.35 340	79.98 338	89.04 299	92.68 342

LCM-MVSNet		78.70 314	76.24 319	86.08 327	77.26 355	71.99 351	94.34 340	96.72 318	61.62 349	76.53 343	89.33 342	33.91 357	92.78 347	81.85 333	74.60 345	93.46 340

ANet_high		69.08 318	65.37 322	80.22 331	65.99 357	71.96 352	90.91 347	90.09 352	82.62 337	49.93 354	78.39 349	29.36 358	81.75 351	62.49 349	38.52 352	86.95 346

MVE	62.14 22	63.28 323	59.38 326	74.99 333	74.33 356	65.47 353	85.55 349	80.50 358	52.02 352	51.10 353	75.00 352	10.91 362	80.50 352	51.60 351	53.40 349	78.99 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		87.12 312	87.77 311	85.17 329	95.46 320	61.92 354	97.37 269	70.66 359	85.83 329	88.73 322	96.04 314	85.33 261	97.76 315	80.02 336	90.48 278	95.84 318

FPMVS		77.62 317	77.14 317	79.05 332	79.25 353	60.97 355	95.79 328	95.94 327	65.96 347	67.93 349	94.40 330	37.73 355	88.88 350	68.83 347	88.46 305	87.29 344

tmp_tt		68.90 319	66.97 321	74.68 334	50.78 359	59.95 356	87.13 348	83.47 357	38.80 354	62.21 350	96.23 308	64.70 347	76.91 355	88.91 303	30.49 353	87.19 345

E-PMN		64.94 321	64.25 323	67.02 336	82.28 351	59.36 357	91.83 346	85.63 355	52.69 351	60.22 351	77.28 350	41.06 354	80.12 353	46.15 352	41.14 350	61.57 351

EMVS		64.07 322	63.26 325	66.53 337	81.73 352	58.81 358	91.85 345	84.75 356	51.93 353	59.09 352	75.13 351	43.32 353	79.09 354	42.03 353	39.47 351	61.69 350

PMVS	61.03 23	65.95 320	63.57 324	73.09 335	57.90 358	51.22 359	85.05 350	93.93 347	54.45 350	44.32 355	83.57 345	13.22 359	89.15 349	58.68 350	81.00 339	78.91 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		30.17 324	30.18 328	30.16 338	78.61 354	43.29 360	66.79 352	14.21 360	17.31 355	14.82 358	11.93 358	11.55 361	41.43 356	37.08 354	19.30 354	5.76 354

test123		20.95 327	23.72 330	12.64 339	13.54 361	8.19 361	96.55 319	6.13 362	7.48 357	16.74 357	37.98 355	12.97 360	6.05 357	16.69 355	5.43 356	23.68 352

testmvs		21.48 326	24.95 329	11.09 340	14.89 360	6.47 362	96.56 318	9.87 361	7.55 356	17.93 356	39.02 354	9.43 363	5.90 358	16.56 356	12.72 355	20.91 353

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		23.98 325	31.98 327	0.00 341	0.00 362	0.00 363	0.00 353	98.59 141	0.00 358	0.00 359	98.61 127	90.60 157	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		7.88 329	10.50 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	94.51 85	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		8.20 328	10.94 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	98.43 145	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 18	99.80 17	99.83 5

9.14			98.06 49		99.47 48		98.71 122	98.82 70	94.36 159	99.16 26	99.29 39	96.05 32	99.81 70	97.00 86	99.71 50

test_0728_THIRD									97.32 27	99.45 9	99.46 9	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

GSMVS																99.20 129

sam_mvs1												89.45 172				99.20 129

sam_mvs												88.99 185

MTGPA								98.74 102

test_post1							96.68 315				30.43 357	87.85 217	98.69 229	92.59 238

test_post											31.83 356	88.83 192	98.91 208

patchmatchnet-post											95.10 327	89.42 173	98.89 212

MTMP							98.89 82	94.14 345

test9_res														96.39 120	99.57 75	99.69 51

agg_prior2														95.87 136	99.57 75	99.68 57

test_prior2							97.80 242		96.12 80	97.89 105	98.69 119	95.96 36		96.89 95	99.60 68

旧先验2							97.57 259		91.30 278	98.67 58			99.80 79	95.70 146

新几何2							97.64 254

无先验							97.58 258	98.72 109	91.38 272				99.87 44	93.36 216		99.60 78

原ACMM2							97.67 252

testdata2													99.89 35	91.65 263

segment_acmp												96.85 11

testdata1							97.32 275		96.34 71

plane_prior5								98.56 149					99.03 193	96.07 126	94.27 219	96.92 234

plane_prior4											98.28 164

plane_prior2							98.80 104		97.28 29

plane_prior1					97.37 233

n2								0.00 363

nn								0.00 363

door-mid								94.37 341

test11								98.66 131

door								94.64 339

HQP-NCC					97.20 243		98.05 218		96.43 67	94.45 208

ACMP_Plane					97.20 243		98.05 218		96.43 67	94.45 208

BP-MVS														95.30 156

HQP4-MVS										94.45 208			98.96 201			96.87 245

HQP3-MVS								98.46 171							94.18 223

HQP2-MVS												86.75 235

ACMMP++_ref															92.97 251

ACMMP++															93.61 239

Test By Simon												94.64 80

High-resolution multi-view benchmark