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.

SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 61	98.87 55	97.65 9	99.73 1	99.48 6	97.53 5	99.94 3	98.43 21	99.81 10	99.70 50

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 5	99.74 108

SD-MVS		98.64 14	98.68 5	98.53 92	99.33 65	98.36 43	98.90 82	98.85 64	97.28 31	99.72 3	99.39 14	96.63 16	97.60 325	98.17 31	99.85 3	99.64 72
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
IU-MVS					99.71 20	99.23 6		98.64 137	95.28 122	99.63 4				98.35 27	99.81 10	99.83 6

PC_three_1452									95.08 136	99.60 5	99.16 61	97.86 2	98.47 258	97.52 77	99.72 51	99.74 34

test0726					99.72 12	99.25 2	99.06 53	98.88 49	97.62 11	99.56 6	99.50 4	97.42 7

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 41	99.69 25	98.28 49	99.14 39	98.66 132	96.84 53	99.56 6	99.31 35	96.34 20	99.70 116	98.32 28	99.73 44	99.73 38
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 174	98.91 43	97.58 14	99.54 8	99.46 9	97.10 10	99.94 3	97.64 66	99.84 8	99.83 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_TWO								98.87 55	97.65 9	99.53 9	99.48 6	97.34 9	99.94 3	98.43 21	99.80 17	99.83 6

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 71	98.58 148	97.62 11	99.45 10	99.46 9	97.42 7	99.94 3	98.47 18	99.81 10	99.69 53
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									97.32 29	99.45 10	99.46 9	97.88 1	99.94 3	98.47 18	99.86 1	99.85 3

MSP-MVS		98.74 8	98.55 10	99.29 32	99.75 3	98.23 50	99.26 21	98.88 49	97.52 15	99.41 12	98.78 115	96.00 35	99.79 93	97.79 54	99.59 73	99.85 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
APDe-MVS		99.02 3	98.84 2	99.55 7	99.57 33	98.96 13	99.39 6	98.93 37	97.38 26	99.41 12	99.54 1	96.66 14	99.84 54	98.86 2	99.85 3	99.87 1

SMA-MVS		98.58 23	98.25 38	99.56 6	99.51 39	99.04 12	98.95 75	98.80 87	93.67 201	99.37 14	99.52 3	96.52 18	99.89 36	98.06 37	99.81 10	99.76 27
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
SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 22	99.22 94	98.43 34	99.10 48	98.87 55	97.38 26	99.35 15	99.40 13	97.78 4	99.87 45	97.77 55	99.85 3	99.78 14
Skip Steuart: Steuart Systems R&D Blog.
xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 31	99.46 51	98.38 36	98.21 201	98.52 159	97.95 3	99.32 16	99.39 14	96.22 21	99.84 54	97.72 58	99.73 44	99.67 63

SF-MVS		98.59 20	98.32 33	99.41 17	99.54 35	98.71 19	99.04 55	98.81 76	95.12 131	99.32 16	99.39 14	96.22 21	99.84 54	97.72 58	99.73 44	99.67 63

test_part2					99.63 29	99.18 8				99.27 18

abl_6		98.30 53	98.03 52	99.13 55	99.56 34	97.76 76	99.13 42	98.82 70	96.14 82	99.26 19	99.37 22	93.33 105	99.93 16	96.96 96	99.67 56	99.69 53

DeepPCF-MVS	96.37 2	97.93 65	98.48 17	96.30 248	99.00 111	89.54 318	97.43 270	98.87 55	98.16 2	99.26 19	99.38 21	96.12 29	99.64 127	98.30 29	99.77 27	99.72 42

APD-MVS		98.35 46	98.00 54	99.42 16	99.51 39	98.72 18	98.80 108	98.82 70	94.52 160	99.23 21	99.25 43	95.54 50	99.80 81	96.52 121	99.77 27	99.74 34
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		98.69 11	98.52 12	99.19 44	99.35 60	98.01 63	98.37 178	98.81 76	97.48 18	99.21 22	99.21 48	96.13 28	99.80 81	98.40 25	99.73 44	99.75 29

Regformer-1		98.66 12	98.51 13	99.12 58	99.35 60	97.81 75	98.37 178	98.76 99	97.49 17	99.20 23	99.21 48	96.08 30	99.79 93	98.42 23	99.73 44	99.75 29

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 54	99.50 41	97.92 68	99.15 38	98.81 76	96.24 77	99.20 23	99.37 22	95.30 63	99.80 81	97.73 57	99.67 56	99.72 42

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 55	99.49 45	97.86 69	99.11 45	98.80 87	96.49 68	99.17 25	99.35 28	95.34 60	99.82 65	97.72 58	99.65 60	99.71 46

RE-MVS-def			98.34 28		99.49 45	97.86 69	99.11 45	98.80 87	96.49 68	99.17 25	99.35 28	95.29 64		97.72 58	99.65 60	99.71 46

9.14			98.06 50		99.47 48		98.71 126	98.82 70	94.36 165	99.16 27	99.29 39	96.05 33	99.81 72	97.00 92	99.71 52

ACMMP_NAP		98.61 17	98.30 34	99.55 7	99.62 30	98.95 14	98.82 101	98.81 76	95.80 94	99.16 27	99.47 8	95.37 58	99.92 22	97.89 47	99.75 39	99.79 11

ETH3D-3000-0.1		98.35 46	98.00 54	99.38 18	99.47 48	98.68 22	98.67 136	98.84 65	94.66 155	99.11 29	99.25 43	95.46 52	99.81 72	96.80 111	99.73 44	99.63 75

SR-MVS		98.57 26	98.35 24	99.24 41	99.53 36	98.18 54	99.09 49	98.82 70	96.58 64	99.10 30	99.32 33	95.39 56	99.82 65	97.70 63	99.63 66	99.72 42

Regformer-4		98.64 14	98.53 11	98.99 64	99.43 57	97.37 88	98.40 176	98.79 92	97.46 21	99.09 31	99.31 35	95.86 43	99.80 81	98.64 4	99.76 33	99.79 11

Regformer-3		98.59 20	98.50 14	98.86 74	99.43 57	97.05 102	98.40 176	98.68 121	97.43 22	99.06 32	99.31 35	95.80 44	99.77 102	98.62 6	99.76 33	99.78 14

test1172		98.56 28	98.35 24	99.16 51	99.53 36	97.94 67	99.09 49	98.83 68	96.52 67	99.05 33	99.34 31	95.34 60	99.82 65	97.86 49	99.64 64	99.73 38

PGM-MVS		98.49 36	98.23 42	99.27 39	99.72 12	98.08 60	98.99 67	99.49 5	95.43 112	99.03 34	99.32 33	95.56 48	99.94 3	96.80 111	99.77 27	99.78 14

VNet		97.79 71	97.40 83	98.96 68	98.88 120	97.55 82	98.63 142	98.93 37	96.74 58	99.02 35	98.84 108	90.33 165	99.83 57	98.53 11	96.66 191	99.50 93

xiu_mvs_v1_base_debu		97.60 80	97.56 71	97.72 150	98.35 159	95.98 146	97.86 244	98.51 162	97.13 44	99.01 36	98.40 153	91.56 137	99.80 81	98.53 11	98.68 131	97.37 226

xiu_mvs_v1_base		97.60 80	97.56 71	97.72 150	98.35 159	95.98 146	97.86 244	98.51 162	97.13 44	99.01 36	98.40 153	91.56 137	99.80 81	98.53 11	98.68 131	97.37 226

xiu_mvs_v1_base_debi		97.60 80	97.56 71	97.72 150	98.35 159	95.98 146	97.86 244	98.51 162	97.13 44	99.01 36	98.40 153	91.56 137	99.80 81	98.53 11	98.68 131	97.37 226

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 75	99.22 94	97.25 97	98.11 220	98.29 208	97.19 40	98.99 39	99.02 81	96.22 21	99.67 123	98.52 16	98.56 139	99.51 91

HFP-MVS		98.63 16	98.40 18	99.32 29	99.72 12	98.29 47	99.23 24	98.96 32	96.10 86	98.94 40	99.17 56	96.06 31	99.92 22	97.62 67	99.78 24	99.75 29

region2R		98.61 17	98.38 20	99.29 32	99.74 7	98.16 56	99.23 24	98.93 37	96.15 81	98.94 40	99.17 56	95.91 40	99.94 3	97.55 74	99.79 20	99.78 14

#test#		98.54 32	98.27 36	99.32 29	99.72 12	98.29 47	98.98 70	98.96 32	95.65 102	98.94 40	99.17 56	96.06 31	99.92 22	97.21 87	99.78 24	99.75 29

HPM-MVS_fast		98.38 43	98.13 46	99.12 58	99.75 3	97.86 69	99.44 5	98.82 70	94.46 163	98.94 40	99.20 52	95.16 70	99.74 108	97.58 70	99.85 3	99.77 21

ACMMPR		98.59 20	98.36 22	99.29 32	99.74 7	98.15 57	99.23 24	98.95 34	96.10 86	98.93 44	99.19 55	95.70 45	99.94 3	97.62 67	99.79 20	99.78 14

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 48	99.25 86	98.04 61	98.50 163	98.78 95	97.72 6	98.92 45	99.28 40	95.27 65	99.82 65	97.55 74	99.77 27	99.69 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		98.33 50	97.95 56	99.47 12	99.49 45	98.70 20	98.83 98	98.86 61	95.48 109	98.91 46	99.17 56	95.48 51	99.93 16	95.80 146	99.53 87	99.76 27

DROMVSNet		98.21 56	98.11 48	98.49 96	98.34 164	97.26 96	99.61 1	98.43 179	96.78 55	98.87 47	98.84 108	93.72 102	99.01 201	98.91 1	99.50 90	99.19 135

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 79	99.46 51	96.49 127	98.30 192	98.69 118	97.21 38	98.84 48	99.36 26	95.41 55	99.78 97	98.62 6	99.65 60	99.80 10

MSLP-MVS++		98.56 28	98.57 8	98.55 88	99.26 85	96.80 112	98.71 126	99.05 24	97.28 31	98.84 48	99.28 40	96.47 19	99.40 156	98.52 16	99.70 53	99.47 100

PHI-MVS		98.34 48	98.06 50	99.18 48	99.15 102	98.12 59	99.04 55	99.09 20	93.32 214	98.83 50	99.10 70	96.54 17	99.83 57	97.70 63	99.76 33	99.59 82

MVSFormer		97.57 85	97.49 77	97.84 139	98.07 187	95.76 165	99.47 3	98.40 184	94.98 139	98.79 51	98.83 110	92.34 116	98.41 271	96.91 98	99.59 73	99.34 114

lupinMVS		97.44 93	97.22 90	98.12 123	98.07 187	95.76 165	97.68 257	97.76 269	94.50 161	98.79 51	98.61 131	92.34 116	99.30 162	97.58 70	99.59 73	99.31 120

CDPH-MVS		97.94 63	97.49 77	99.28 36	99.47 48	98.44 32	97.91 237	98.67 129	92.57 242	98.77 53	98.85 106	95.93 39	99.72 110	95.56 156	99.69 54	99.68 59

CNVR-MVS		98.78 6	98.56 9	99.45 15	99.32 68	98.87 16	98.47 166	98.81 76	97.72 6	98.76 54	99.16 61	97.05 11	99.78 97	98.06 37	99.66 59	99.69 53

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 84	99.45 55	96.32 136	98.28 195	98.68 121	97.17 41	98.74 55	99.37 22	95.25 67	99.79 93	98.57 9	99.54 86	99.73 38

diffmvs		97.58 84	97.40 83	98.13 121	98.32 168	95.81 164	98.06 223	98.37 191	96.20 79	98.74 55	98.89 102	91.31 146	99.25 165	98.16 32	98.52 140	99.34 114

GST-MVS		98.43 40	98.12 47	99.34 24	99.72 12	98.38 36	99.09 49	98.82 70	95.71 98	98.73 57	99.06 79	95.27 65	99.93 16	97.07 91	99.63 66	99.72 42

UA-Net		97.96 59	97.62 67	98.98 66	98.86 122	97.47 85	98.89 86	99.08 21	96.67 61	98.72 58	99.54 1	93.15 108	99.81 72	94.87 172	98.83 127	99.65 69

ETH3D cwj APD-0.16		97.96 59	97.52 74	99.29 32	99.05 106	98.52 28	98.33 184	98.68 121	93.18 219	98.68 59	99.13 65	94.62 82	99.83 57	96.45 123	99.55 85	99.52 87

hse-mvs3		96.17 145	95.62 154	97.81 143	99.03 109	94.45 220	98.64 141	98.75 102	97.48 18	98.67 60	98.72 122	89.76 172	99.86 50	97.95 41	81.59 344	99.11 147

hse-mvs2		95.71 164	95.30 169	96.93 196	98.50 151	93.53 252	98.36 180	98.10 239	97.48 18	98.67 60	97.99 193	89.76 172	99.02 198	97.95 41	80.91 348	98.22 202

ZD-MVS					99.46 51	98.70 20		98.79 92	93.21 218	98.67 60	98.97 88	95.70 45	99.83 57	96.07 133	99.58 76

旧先验2							97.57 265		91.30 285	98.67 60			99.80 81	95.70 153

PS-MVSNAJ		97.73 73	97.77 63	97.62 160	98.68 139	95.58 169	97.34 279	98.51 162	97.29 30	98.66 64	97.88 204	94.51 86	99.90 34	97.87 48	99.17 114	97.39 224

xiu_mvs_v2_base		97.66 77	97.70 66	97.56 164	98.61 145	95.46 175	97.44 268	98.46 172	97.15 42	98.65 65	98.15 181	94.33 93	99.80 81	97.84 52	98.66 135	97.41 222

LFMVS		95.86 157	94.98 183	98.47 98	98.87 121	96.32 136	98.84 97	96.02 335	93.40 211	98.62 66	99.20 52	74.99 345	99.63 130	97.72 58	97.20 181	99.46 104

HPM-MVS		98.36 45	98.10 49	99.13 55	99.74 7	97.82 73	99.53 2	98.80 87	94.63 156	98.61 67	98.97 88	95.13 71	99.77 102	97.65 65	99.83 9	99.79 11
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.26 112	99.20 97	95.36 178		98.68 121	91.89 265	98.60 68	99.10 70	94.44 91	99.82 65	94.27 195	99.44 98	99.58 84

CP-MVS		98.57 26	98.36 22	99.19 44	99.66 27	97.86 69	99.34 13	98.87 55	95.96 89	98.60 68	99.13 65	96.05 33	99.94 3	97.77 55	99.86 1	99.77 21

jason		97.32 101	97.08 95	98.06 128	97.45 234	95.59 168	97.87 243	97.91 264	94.79 147	98.55 70	98.83 110	91.12 149	99.23 168	97.58 70	99.60 70	99.34 114
jason: jason.
MCST-MVS		98.65 13	98.37 21	99.48 11	99.60 31	98.87 16	98.41 175	98.68 121	97.04 48	98.52 71	98.80 113	96.78 13	99.83 57	97.93 43	99.61 69	99.74 34

CS-MVS		97.94 63	97.90 59	98.06 128	98.04 191	96.85 111	99.04 55	98.39 187	96.17 80	98.50 72	98.29 169	94.60 83	99.02 198	98.61 8	99.43 99	98.30 200

XVS		98.70 9	98.49 16	99.34 24	99.70 23	98.35 44	99.29 17	98.88 49	97.40 23	98.46 73	99.20 52	95.90 41	99.89 36	97.85 50	99.74 42	99.78 14

X-MVStestdata		94.06 267	92.30 287	99.34 24	99.70 23	98.35 44	99.29 17	98.88 49	97.40 23	98.46 73	43.50 365	95.90 41	99.89 36	97.85 50	99.74 42	99.78 14

MG-MVS		97.81 70	97.60 68	98.44 100	99.12 104	95.97 151	97.75 253	98.78 95	96.89 52	98.46 73	99.22 47	93.90 101	99.68 122	94.81 176	99.52 89	99.67 63

NCCC		98.61 17	98.35 24	99.38 18	99.28 82	98.61 25	98.45 167	98.76 99	97.82 5	98.45 76	98.93 98	96.65 15	99.83 57	97.38 82	99.41 101	99.71 46

ETH3 D test6400		97.59 83	97.01 98	99.34 24	99.40 59	98.56 26	98.20 204	98.81 76	91.63 273	98.44 77	98.85 106	93.98 100	99.82 65	94.11 201	99.69 54	99.64 72

MVS_Test		97.28 102	97.00 99	98.13 121	98.33 166	95.97 151	98.74 117	98.07 248	94.27 167	98.44 77	98.07 186	92.48 114	99.26 164	96.43 125	98.19 154	99.16 141

MVS_111021_LR		98.34 48	98.23 42	98.67 81	99.27 83	96.90 108	97.95 233	99.58 3	97.14 43	98.44 77	99.01 85	95.03 74	99.62 132	97.91 44	99.75 39	99.50 93

ETV-MVS		97.96 59	97.81 62	98.40 104	98.42 155	97.27 92	98.73 121	98.55 153	96.84 53	98.38 80	97.44 243	95.39 56	99.35 159	97.62 67	98.89 122	98.58 189

VDDNet		95.36 184	94.53 201	97.86 138	98.10 186	95.13 189	98.85 94	97.75 270	90.46 301	98.36 81	99.39 14	73.27 351	99.64 127	97.98 40	96.58 194	98.81 171

mPP-MVS		98.51 35	98.26 37	99.25 40	99.75 3	98.04 61	99.28 19	98.81 76	96.24 77	98.35 82	99.23 45	95.46 52	99.94 3	97.42 80	99.81 10	99.77 21

DELS-MVS		98.40 42	98.20 44	98.99 64	99.00 111	97.66 77	97.75 253	98.89 46	97.71 8	98.33 83	98.97 88	94.97 75	99.88 44	98.42 23	99.76 33	99.42 110
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
MVS_111021_HR		98.47 38	98.34 28	98.88 73	99.22 94	97.32 89	97.91 237	99.58 3	97.20 39	98.33 83	99.00 86	95.99 36	99.64 127	98.05 39	99.76 33	99.69 53

ZNCC-MVS		98.49 36	98.20 44	99.35 23	99.73 11	98.39 35	99.19 34	98.86 61	95.77 95	98.31 85	99.10 70	95.46 52	99.93 16	97.57 73	99.81 10	99.74 34

CS-MVS-test		97.90 66	97.83 61	98.11 124	98.14 183	96.49 127	99.35 11	98.40 184	96.31 76	98.27 86	98.31 166	94.42 92	99.05 190	98.07 36	99.20 111	98.80 172

HPM-MVS++		98.58 23	98.25 38	99.55 7	99.50 41	99.08 9	98.72 125	98.66 132	97.51 16	98.15 87	98.83 110	95.70 45	99.92 22	97.53 76	99.67 56	99.66 67

新几何1				99.16 51	99.34 62	98.01 63		98.69 118	90.06 310	98.13 88	98.95 96	94.60 83	99.89 36	91.97 263	99.47 93	99.59 82

API-MVS		97.41 96	97.25 88	97.91 136	98.70 136	96.80 112	98.82 101	98.69 118	94.53 158	98.11 89	98.28 170	94.50 89	99.57 136	94.12 200	99.49 91	97.37 226

CPTT-MVS		97.72 74	97.32 86	98.92 70	99.64 28	97.10 101	99.12 44	98.81 76	92.34 250	98.09 90	99.08 77	93.01 109	99.92 22	96.06 136	99.77 27	99.75 29

test12				99.18 48	99.16 100	98.19 53		98.53 157		98.07 91		95.13 71	99.72 110		99.56 82	99.63 75

test222					99.23 93	97.17 100	97.40 271	98.66 132	88.68 326	98.05 92	98.96 94	94.14 96			99.53 87	99.61 77

DP-MVS Recon		97.86 68	97.46 79	99.06 62	99.53 36	98.35 44	98.33 184	98.89 46	92.62 239	98.05 92	98.94 97	95.34 60	99.65 125	96.04 137	99.42 100	99.19 135

Vis-MVSNet		97.42 95	97.11 93	98.34 107	98.66 140	96.23 139	99.22 28	99.00 27	96.63 63	98.04 94	99.21 48	88.05 217	99.35 159	96.01 139	99.21 110	99.45 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
baseline		97.64 78	97.44 81	98.25 113	98.35 159	96.20 140	99.00 65	98.32 198	96.33 75	98.03 95	99.17 56	91.35 144	99.16 174	98.10 34	98.29 153	99.39 111

test_yl		97.22 104	96.78 109	98.54 90	98.73 131	96.60 121	98.45 167	98.31 200	94.70 149	98.02 96	98.42 151	90.80 156	99.70 116	96.81 109	96.79 188	99.34 114

DCV-MVSNet		97.22 104	96.78 109	98.54 90	98.73 131	96.60 121	98.45 167	98.31 200	94.70 149	98.02 96	98.42 151	90.80 156	99.70 116	96.81 109	96.79 188	99.34 114

zzz-MVS		98.55 30	98.25 38	99.46 13	99.76 1	98.64 23	98.55 156	98.74 104	97.27 35	98.02 96	99.39 14	94.81 78	99.96 1	97.91 44	99.79 20	99.77 21

MTAPA		98.58 23	98.29 35	99.46 13	99.76 1	98.64 23	98.90 82	98.74 104	97.27 35	98.02 96	99.39 14	94.81 78	99.96 1	97.91 44	99.79 20	99.77 21

1121		97.37 99	96.77 113	99.16 51	99.34 62	97.99 66	98.19 208	98.68 121	90.14 309	98.01 100	98.97 88	94.80 80	99.87 45	93.36 222	99.46 96	99.61 77

sss		97.39 97	96.98 101	98.61 84	98.60 146	96.61 120	98.22 200	98.93 37	93.97 179	98.01 100	98.48 145	91.98 129	99.85 51	96.45 123	98.15 155	99.39 111

alignmvs		97.56 86	97.07 96	99.01 63	98.66 140	98.37 42	98.83 98	98.06 253	96.74 58	98.00 102	97.65 225	90.80 156	99.48 151	98.37 26	96.56 195	99.19 135

OMC-MVS		97.55 87	97.34 85	98.20 116	99.33 65	95.92 158	98.28 195	98.59 143	95.52 108	97.97 103	99.10 70	93.28 107	99.49 147	95.09 169	98.88 123	99.19 135

VDD-MVS		95.82 160	95.23 171	97.61 161	98.84 125	93.98 235	98.68 133	97.40 298	95.02 138	97.95 104	99.34 31	74.37 349	99.78 97	98.64 4	96.80 187	99.08 152

casdiffmvs		97.63 79	97.41 82	98.28 109	98.33 166	96.14 143	98.82 101	98.32 198	96.38 73	97.95 104	99.21 48	91.23 148	99.23 168	98.12 33	98.37 148	99.48 98

PVSNet_BlendedMVS		96.73 124	96.60 119	97.12 184	99.25 86	95.35 180	98.26 198	99.26 8	94.28 166	97.94 106	97.46 240	92.74 112	99.81 72	96.88 104	93.32 251	96.20 317

PVSNet_Blended		97.38 98	97.12 92	98.14 119	99.25 86	95.35 180	97.28 284	99.26 8	93.13 222	97.94 106	98.21 177	92.74 112	99.81 72	96.88 104	99.40 103	99.27 127

DPM-MVS		97.55 87	96.99 100	99.23 43	99.04 108	98.55 27	97.17 292	98.35 194	94.85 146	97.93 108	98.58 136	95.07 73	99.71 115	92.60 243	99.34 106	99.43 108

MP-MVS		98.33 50	98.01 53	99.28 36	99.75 3	98.18 54	99.22 28	98.79 92	96.13 83	97.92 109	99.23 45	94.54 85	99.94 3	96.74 115	99.78 24	99.73 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDTV_nov1_ep13_2view						84.26 350	96.89 311		90.97 295	97.90 110		89.89 171		93.91 206		99.18 140

test_prior3		98.22 55	97.90 59	99.19 44	99.31 70	98.22 51	97.80 249	98.84 65	96.12 84	97.89 111	98.69 123	95.96 37	99.70 116	96.89 101	99.60 70	99.65 69

test_prior2							97.80 249		96.12 84	97.89 111	98.69 123	95.96 37		96.89 101	99.60 70

原ACMM1				98.65 82	99.32 68	96.62 118		98.67 129	93.27 217	97.81 113	98.97 88	95.18 69	99.83 57	93.84 208	99.46 96	99.50 93

114514_t		96.93 117	96.27 130	98.92 70	99.50 41	97.63 79	98.85 94	98.90 44	84.80 345	97.77 114	99.11 68	92.84 110	99.66 124	94.85 173	99.77 27	99.47 100

PMMVS		96.60 127	96.33 128	97.41 170	97.90 199	93.93 236	97.35 278	98.41 182	92.84 234	97.76 115	97.45 242	91.10 151	99.20 171	96.26 129	97.91 162	99.11 147

PVSNet	91.96 18	96.35 138	96.15 134	96.96 194	99.17 98	92.05 279	96.08 329	98.68 121	93.69 197	97.75 116	97.80 215	88.86 197	99.69 121	94.26 196	99.01 117	99.15 142

TEST9					99.31 70	98.50 30	97.92 235	98.73 108	92.63 238	97.74 117	98.68 125	96.20 24	99.80 81

train_agg		97.97 58	97.52 74	99.33 28	99.31 70	98.50 30	97.92 235	98.73 108	92.98 227	97.74 117	98.68 125	96.20 24	99.80 81	96.59 117	99.57 77	99.68 59

CANet		98.05 57	97.76 64	98.90 72	98.73 131	97.27 92	98.35 181	98.78 95	97.37 28	97.72 119	98.96 94	91.53 141	99.92 22	98.79 3	99.65 60	99.51 91

test_8					99.29 78	98.44 32	97.89 241	98.72 110	92.98 227	97.70 120	98.66 128	96.20 24	99.80 81

MP-MVS-pluss		98.31 52	97.92 58	99.49 10	99.72 12	98.88 15	98.43 172	98.78 95	94.10 171	97.69 121	99.42 12	95.25 67	99.92 22	98.09 35	99.80 17	99.67 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		97.67 76	97.23 89	98.98 66	98.70 136	98.38 36	99.34 13	98.39 187	96.76 57	97.67 122	97.40 246	92.26 119	99.49 147	98.28 30	96.28 207	99.08 152

PVSNet_Blended_VisFu		97.70 75	97.46 79	98.44 100	99.27 83	95.91 159	98.63 142	99.16 17	94.48 162	97.67 122	98.88 103	92.80 111	99.91 31	97.11 89	99.12 115	99.50 93

WTY-MVS		97.37 99	96.92 103	98.72 78	98.86 122	96.89 110	98.31 190	98.71 114	95.26 123	97.67 122	98.56 139	92.21 122	99.78 97	95.89 141	96.85 186	99.48 98

Effi-MVS+		97.12 111	96.69 115	98.39 105	98.19 177	96.72 116	97.37 275	98.43 179	93.71 194	97.65 125	98.02 189	92.20 123	99.25 165	96.87 107	97.79 167	99.19 135

thisisatest0530		96.01 150	95.36 163	97.97 133	98.38 157	95.52 173	98.88 89	94.19 356	94.04 173	97.64 126	98.31 166	83.82 296	99.46 153	95.29 164	97.70 172	98.93 165

tttt0517		96.07 147	95.51 157	97.78 145	98.41 156	94.84 202	99.28 19	94.33 354	94.26 168	97.64 126	98.64 130	84.05 289	99.47 152	95.34 160	97.60 175	99.03 155

HyFIR lowres test		96.90 119	96.49 124	98.14 119	99.33 65	95.56 170	97.38 273	99.65 2	92.34 250	97.61 128	98.20 178	89.29 182	99.10 186	96.97 94	97.60 175	99.77 21

ACMMP		98.23 54	97.95 56	99.09 60	99.74 7	97.62 80	99.03 58	99.41 6	95.98 88	97.60 129	99.36 26	94.45 90	99.93 16	97.14 88	98.85 126	99.70 50
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
agg_prior1		97.95 62	97.51 76	99.28 36	99.30 75	98.38 36	97.81 248	98.72 110	93.16 221	97.57 130	98.66 128	96.14 27	99.81 72	96.63 116	99.56 82	99.66 67

agg_prior					99.30 75	98.38 36		98.72 110		97.57 130			99.81 72

tpmrst		95.63 169	95.69 151	95.44 280	97.54 224	88.54 333	96.97 301	97.56 280	93.50 207	97.52 132	96.93 287	89.49 176	99.16 174	95.25 166	96.42 200	98.64 185

MDTV_nov1_ep13			95.40 158		97.48 228	88.34 336	96.85 314	97.29 302	93.74 191	97.48 133	97.26 252	89.18 185	99.05 190	91.92 264	97.43 178

EPMVS		94.99 205	94.48 204	96.52 231	97.22 247	91.75 285	97.23 286	91.66 362	94.11 170	97.28 134	96.81 294	85.70 260	98.84 224	93.04 232	97.28 180	98.97 161

EIA-MVS		97.75 72	97.58 69	98.27 110	98.38 157	96.44 130	99.01 63	98.60 141	95.88 91	97.26 135	97.53 236	94.97 75	99.33 161	97.38 82	99.20 111	99.05 154

IS-MVSNet		97.22 104	96.88 104	98.25 113	98.85 124	96.36 134	99.19 34	97.97 258	95.39 114	97.23 136	98.99 87	91.11 150	98.93 212	94.60 182	98.59 137	99.47 100

EPP-MVSNet		97.46 89	97.28 87	97.99 132	98.64 142	95.38 177	99.33 16	98.31 200	93.61 204	97.19 137	99.07 78	94.05 97	99.23 168	96.89 101	98.43 147	99.37 113

thisisatest0515		95.61 172	94.89 187	97.76 147	98.15 182	95.15 187	96.77 317	94.41 352	92.95 229	97.18 138	97.43 244	84.78 275	99.45 154	94.63 179	97.73 171	98.68 180

CANet_DTU		96.96 116	96.55 121	98.21 115	98.17 181	96.07 145	97.98 231	98.21 216	97.24 37	97.13 139	98.93 98	86.88 241	99.91 31	95.00 171	99.37 105	98.66 183

CHOSEN 1792x2688		97.12 111	96.80 106	98.08 126	99.30 75	94.56 218	98.05 224	99.71 1	93.57 205	97.09 140	98.91 101	88.17 212	99.89 36	96.87 107	99.56 82	99.81 9

PatchT		93.06 286	91.97 291	96.35 245	96.69 281	92.67 272	94.48 349	97.08 309	86.62 334	97.08 141	92.23 351	87.94 219	97.90 315	78.89 354	96.69 190	98.49 191

PatchmatchNet		95.71 164	95.52 156	96.29 249	97.58 219	90.72 305	96.84 315	97.52 287	94.06 172	97.08 141	96.96 283	89.24 184	98.90 217	92.03 261	98.37 148	99.26 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 118	96.40 126	98.45 99	98.69 138	96.90 108	98.66 139	98.68 121	92.40 249	97.07 143	97.96 196	91.54 140	99.75 106	93.68 212	98.92 120	98.69 179
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
PAPM_NR		97.46 89	97.11 93	98.50 94	99.50 41	96.41 132	98.63 142	98.60 141	95.18 127	97.06 144	98.06 187	94.26 95	99.57 136	93.80 210	98.87 125	99.52 87

TAMVS		97.02 114	96.79 108	97.70 153	98.06 189	95.31 182	98.52 158	98.31 200	93.95 180	97.05 145	98.61 131	93.49 104	98.52 253	95.33 161	97.81 166	99.29 125

CSCG		97.85 69	97.74 65	98.20 116	99.67 26	95.16 185	99.22 28	99.32 7	93.04 225	97.02 146	98.92 100	95.36 59	99.91 31	97.43 79	99.64 64	99.52 87

CDS-MVSNet		96.99 115	96.69 115	97.90 137	98.05 190	95.98 146	98.20 204	98.33 197	93.67 201	96.95 147	98.49 144	93.54 103	98.42 264	95.24 167	97.74 170	99.31 120
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 133	96.34 127	97.02 189	98.77 129	93.76 241	97.79 251	98.50 167	95.45 111	96.94 148	99.09 75	87.87 222	99.55 143	96.76 114	95.83 217	97.74 215

CR-MVSNet		94.76 220	94.15 224	96.59 222	97.00 262	93.43 255	94.96 343	97.56 280	92.46 243	96.93 149	96.24 313	88.15 213	97.88 319	87.38 319	96.65 192	98.46 192

RPMNet		92.81 288	91.34 296	97.24 176	97.00 262	93.43 255	94.96 343	98.80 87	82.27 349	96.93 149	92.12 352	86.98 239	99.82 65	76.32 358	96.65 192	98.46 192

SCA		95.46 174	95.13 175	96.46 238	97.67 212	91.29 296	97.33 280	97.60 278	94.68 152	96.92 151	97.10 261	83.97 291	98.89 218	92.59 245	98.32 152	99.20 132

PatchMatch-RL		96.59 129	96.03 139	98.27 110	99.31 70	96.51 126	97.91 237	99.06 22	93.72 193	96.92 151	98.06 187	88.50 206	99.65 125	91.77 267	99.00 118	98.66 183

DeepC-MVS	95.98 3	97.88 67	97.58 69	98.77 76	99.25 86	96.93 106	98.83 98	98.75 102	96.96 51	96.89 153	99.50 4	90.46 162	99.87 45	97.84 52	99.76 33	99.52 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVG-OURS		96.55 132	96.41 125	96.99 190	98.75 130	93.76 241	97.50 267	98.52 159	95.67 100	96.83 154	99.30 38	88.95 196	99.53 144	95.88 142	96.26 208	97.69 218

AdaColmap		97.15 110	96.70 114	98.48 97	99.16 100	96.69 117	98.01 228	98.89 46	94.44 164	96.83 154	98.68 125	90.69 159	99.76 104	94.36 190	99.29 109	98.98 160

CostFormer		94.95 209	94.73 193	95.60 275	97.28 243	89.06 325	97.53 266	96.89 323	89.66 317	96.82 156	96.72 297	86.05 255	98.95 211	95.53 157	96.13 213	98.79 173

UGNet		96.78 123	96.30 129	98.19 118	98.24 171	95.89 161	98.88 89	98.93 37	97.39 25	96.81 157	97.84 209	82.60 300	99.90 34	96.53 120	99.49 91	98.79 173
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
CNLPA		97.45 92	97.03 97	98.73 77	99.05 106	97.44 87	98.07 222	98.53 157	95.32 120	96.80 158	98.53 140	93.32 106	99.72 110	94.31 194	99.31 108	99.02 156

CHOSEN 280x420		97.18 108	97.18 91	97.20 178	98.81 127	93.27 262	95.78 336	99.15 18	95.25 124	96.79 159	98.11 184	92.29 118	99.07 189	98.56 10	99.85 3	99.25 129

HY-MVS	93.96 8	96.82 122	96.23 133	98.57 86	98.46 154	97.00 103	98.14 215	98.21 216	93.95 180	96.72 160	97.99 193	91.58 136	99.76 104	94.51 187	96.54 196	98.95 164

PAPR		96.84 121	96.24 132	98.65 82	98.72 135	96.92 107	97.36 277	98.57 149	93.33 213	96.67 161	97.57 233	94.30 94	99.56 138	91.05 278	98.59 137	99.47 100

Anonymous20240529		95.10 199	94.22 218	97.75 148	99.01 110	94.26 229	98.87 91	98.83 68	85.79 342	96.64 162	98.97 88	78.73 324	99.85 51	96.27 128	94.89 221	99.12 146

thres600view7		95.49 173	94.77 190	97.67 156	98.98 114	95.02 192	98.85 94	96.90 321	95.38 115	96.63 163	96.90 288	84.29 282	99.59 134	88.65 312	96.33 202	98.40 194

thres100view900		95.38 181	94.70 194	97.41 170	98.98 114	94.92 200	98.87 91	96.90 321	95.38 115	96.61 164	96.88 289	84.29 282	99.56 138	88.11 313	96.29 204	97.76 213

Vis-MVSNet (Re-imp)		96.87 120	96.55 121	97.83 140	98.73 131	95.46 175	99.20 32	98.30 206	94.96 141	96.60 165	98.87 104	90.05 168	98.59 247	93.67 214	98.60 136	99.46 104

CVMVSNet		95.43 177	96.04 138	93.57 319	97.93 197	83.62 351	98.12 218	98.59 143	95.68 99	96.56 166	99.02 81	87.51 228	97.51 329	93.56 218	97.44 177	99.60 80

RPSCF		94.87 214	95.40 158	93.26 325	98.89 119	82.06 356	98.33 184	98.06 253	90.30 306	96.56 166	99.26 42	87.09 236	99.49 147	93.82 209	96.32 203	98.24 201

tfpn200view9		95.32 188	94.62 197	97.43 169	98.94 116	94.98 196	98.68 133	96.93 319	95.33 118	96.55 168	96.53 305	84.23 285	99.56 138	88.11 313	96.29 204	97.76 213

thres400		95.38 181	94.62 197	97.65 159	98.94 116	94.98 196	98.68 133	96.93 319	95.33 118	96.55 168	96.53 305	84.23 285	99.56 138	88.11 313	96.29 204	98.40 194

thres200		95.25 190	94.57 199	97.28 175	98.81 127	94.92 200	98.20 204	97.11 308	95.24 126	96.54 170	96.22 317	84.58 279	99.53 144	87.93 317	96.50 198	97.39 224

ab-mvs		96.42 136	95.71 149	98.55 88	98.63 143	96.75 115	97.88 242	98.74 104	93.84 185	96.54 170	98.18 180	85.34 267	99.75 106	95.93 140	96.35 201	99.15 142

mvs-test1		96.60 127	96.68 117	96.37 243	97.89 200	91.81 282	98.56 154	98.10 239	96.57 65	96.52 172	97.94 198	90.81 154	99.45 154	95.72 149	98.01 159	97.86 212

Anonymous202405211		95.28 189	94.49 203	97.67 156	99.00 111	93.75 243	98.70 130	97.04 312	90.66 297	96.49 173	98.80 113	78.13 329	99.83 57	96.21 131	95.36 220	99.44 107

ADS-MVSNet2		94.58 232	94.40 212	95.11 289	98.00 192	88.74 330	96.04 330	97.30 301	90.15 307	96.47 174	96.64 302	87.89 220	97.56 327	90.08 290	97.06 182	99.02 156

ADS-MVSNet		95.00 204	94.45 208	96.63 217	98.00 192	91.91 281	96.04 330	97.74 271	90.15 307	96.47 174	96.64 302	87.89 220	98.96 207	90.08 290	97.06 182	99.02 156

Effi-MVS+-dtu		96.29 140	96.56 120	95.51 276	97.89 200	90.22 311	98.80 108	98.10 239	96.57 65	96.45 176	96.66 299	90.81 154	98.91 214	95.72 149	97.99 160	97.40 223

PLC	95.07 4	97.20 107	96.78 109	98.44 100	99.29 78	96.31 138	98.14 215	98.76 99	92.41 248	96.39 177	98.31 166	94.92 77	99.78 97	94.06 203	98.77 130	99.23 130
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 260	93.80 247	95.12 288	96.50 290	87.91 341	97.44 268	95.89 340	92.62 239	96.37 178	96.30 312	84.13 288	98.30 284	93.24 225	91.66 269	99.14 144

TAPA-MVS	93.98 7	95.35 185	94.56 200	97.74 149	99.13 103	94.83 204	98.33 184	98.64 137	86.62 334	96.29 179	98.61 131	94.00 99	99.29 163	80.00 350	99.41 101	99.09 149
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.84 158	95.12 176	98.01 131	98.49 153	95.98 146	98.73 121	97.03 313	95.37 117	96.22 180	98.19 179	89.96 170	99.16 174	94.60 182	87.48 321	98.90 167

tpm2		94.19 256	93.76 252	95.46 279	97.23 246	89.04 326	97.31 282	96.85 327	87.08 333	96.21 181	96.79 295	83.75 297	98.74 233	92.43 253	96.23 210	98.59 187

F-COLMAP		97.09 113	96.80 106	97.97 133	99.45 55	94.95 199	98.55 156	98.62 140	93.02 226	96.17 182	98.58 136	94.01 98	99.81 72	93.95 205	98.90 121	99.14 144

GeoE		96.58 131	96.07 136	98.10 125	98.35 159	95.89 161	99.34 13	98.12 234	93.12 223	96.09 183	98.87 104	89.71 174	98.97 203	92.95 235	98.08 158	99.43 108

JIA-IIPM		93.35 277	92.49 284	95.92 262	96.48 292	90.65 306	95.01 342	96.96 317	85.93 340	96.08 184	87.33 356	87.70 226	98.78 231	91.35 273	95.58 219	98.34 197

BH-RMVSNet		95.92 155	95.32 167	97.69 154	98.32 168	94.64 210	98.19 208	97.45 294	94.56 157	96.03 185	98.61 131	85.02 270	99.12 180	90.68 283	99.06 116	99.30 123

dp		94.15 259	93.90 240	94.90 295	97.31 242	86.82 347	96.97 301	97.19 307	91.22 290	96.02 186	96.61 304	85.51 263	99.02 198	90.00 294	94.30 223	98.85 168

EPNet		97.28 102	96.87 105	98.51 93	94.98 332	96.14 143	98.90 82	97.02 315	98.28 1	95.99 187	99.11 68	91.36 143	99.89 36	96.98 93	99.19 113	99.50 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 109	96.66 118	98.68 80	98.53 150	97.19 99	98.93 79	98.90 44	92.83 235	95.99 187	99.37 22	92.12 125	99.87 45	93.67 214	99.57 77	98.97 161

AUN-MVS		94.53 236	93.73 254	96.92 199	98.50 151	93.52 253	98.34 182	98.10 239	93.83 187	95.94 189	97.98 195	85.59 262	99.03 195	94.35 191	80.94 347	98.22 202

TR-MVS		94.94 211	94.20 219	97.17 181	97.75 206	94.14 232	97.59 263	97.02 315	92.28 255	95.75 190	97.64 227	83.88 293	98.96 207	89.77 296	96.15 212	98.40 194

VPA-MVSNet		95.75 162	95.11 177	97.69 154	97.24 245	97.27 92	98.94 77	99.23 12	95.13 130	95.51 191	97.32 249	85.73 259	98.91 214	97.33 84	89.55 296	96.89 250

HQP_MVS		96.14 146	95.90 142	96.85 202	97.42 235	94.60 216	98.80 108	98.56 151	97.28 31	95.34 192	98.28 170	87.09 236	99.03 195	96.07 133	94.27 224	96.92 242

plane_prior3						94.61 214			97.02 49	95.34 192

DWT-MVSNet_test		94.82 215	94.36 213	96.20 252	97.35 240	90.79 303	98.34 182	96.57 334	92.91 231	95.33 194	96.44 309	82.00 302	99.12 180	94.52 186	95.78 218	98.70 178

Fast-Effi-MVS+		96.28 142	95.70 150	98.03 130	98.29 170	95.97 151	98.58 148	98.25 214	91.74 268	95.29 195	97.23 255	91.03 153	99.15 177	92.90 237	97.96 161	98.97 161

EI-MVSNet		95.96 152	95.83 144	96.36 244	97.93 197	93.70 247	98.12 218	98.27 209	93.70 196	95.07 196	99.02 81	92.23 121	98.54 251	94.68 178	93.46 246	96.84 256

MVSTER		96.06 148	95.72 146	97.08 187	98.23 172	95.93 157	98.73 121	98.27 209	94.86 145	95.07 196	98.09 185	88.21 210	98.54 251	96.59 117	93.46 246	96.79 260

OPM-MVS		95.69 167	95.33 166	96.76 206	96.16 305	94.63 211	98.43 172	98.39 187	96.64 62	95.02 198	98.78 115	85.15 269	99.05 190	95.21 168	94.20 227	96.60 284
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RRT_MVS		96.04 149	95.53 155	97.56 164	97.07 260	97.32 89	98.57 153	98.09 244	95.15 129	95.02 198	98.44 148	88.20 211	98.58 249	96.17 132	93.09 255	96.79 260

Fast-Effi-MVS+-dtu		95.87 156	95.85 143	95.91 263	97.74 209	91.74 286	98.69 132	98.15 230	95.56 105	94.92 200	97.68 224	88.98 194	98.79 230	93.19 227	97.78 168	97.20 230

TESTMET0.1,1		94.18 258	93.69 257	95.63 274	96.92 267	89.12 324	96.91 306	94.78 349	93.17 220	94.88 201	96.45 308	78.52 325	98.92 213	93.09 229	98.50 142	98.85 168

VPNet		94.99 205	94.19 220	97.40 172	97.16 254	96.57 123	98.71 126	98.97 30	95.67 100	94.84 202	98.24 176	80.36 315	98.67 239	96.46 122	87.32 324	96.96 239

1112_ss		96.63 126	96.00 140	98.50 94	98.56 147	96.37 133	98.18 212	98.10 239	92.92 230	94.84 202	98.43 149	92.14 124	99.58 135	94.35 191	96.51 197	99.56 86

test-LLR		95.10 199	94.87 188	95.80 268	96.77 275	89.70 315	96.91 306	95.21 344	95.11 132	94.83 204	95.72 327	87.71 224	98.97 203	93.06 230	98.50 142	98.72 176

test-mter		94.08 265	93.51 264	95.80 268	96.77 275	89.70 315	96.91 306	95.21 344	92.89 232	94.83 204	95.72 327	77.69 332	98.97 203	93.06 230	98.50 142	98.72 176

Test_1112_low_res		96.34 139	95.66 153	98.36 106	98.56 147	95.94 154	97.71 255	98.07 248	92.10 260	94.79 206	97.29 251	91.75 133	99.56 138	94.17 198	96.50 198	99.58 84

GA-MVS		94.81 217	94.03 229	97.14 182	97.15 255	93.86 238	96.76 318	97.58 279	94.00 177	94.76 207	97.04 274	80.91 310	98.48 255	91.79 266	96.25 209	99.09 149

bset_n11_16_dypcd		94.89 213	94.27 216	96.76 206	94.41 339	95.15 187	95.67 337	95.64 342	95.53 106	94.65 208	97.52 237	87.10 235	98.29 287	96.58 119	91.35 271	96.83 258

BH-untuned		95.95 153	95.72 146	96.65 214	98.55 149	92.26 275	98.23 199	97.79 268	93.73 192	94.62 209	98.01 191	88.97 195	99.00 202	93.04 232	98.51 141	98.68 180

test_djsdf		96.00 151	95.69 151	96.93 196	95.72 318	95.49 174	99.47 3	98.40 184	94.98 139	94.58 210	97.86 206	89.16 186	98.41 271	96.91 98	94.12 232	96.88 251

cascas		94.63 228	93.86 243	96.93 196	96.91 269	94.27 228	96.00 333	98.51 162	85.55 343	94.54 211	96.23 315	84.20 287	98.87 221	95.80 146	96.98 185	97.66 219

DP-MVS		96.59 129	95.93 141	98.57 86	99.34 62	96.19 142	98.70 130	98.39 187	89.45 320	94.52 212	99.35 28	91.85 131	99.85 51	92.89 239	98.88 123	99.68 59

gg-mvs-nofinetune		92.21 294	90.58 301	97.13 183	96.75 278	95.09 190	95.85 334	89.40 365	85.43 344	94.50 213	81.98 359	80.80 313	98.40 277	92.16 255	98.33 151	97.88 210

mvs_anonymous		96.70 125	96.53 123	97.18 180	98.19 177	93.78 240	98.31 190	98.19 219	94.01 176	94.47 214	98.27 173	92.08 127	98.46 259	97.39 81	97.91 162	99.31 120

HQP-NCC					97.20 249		98.05 224		96.43 70	94.45 215

ACMP_Plane					97.20 249		98.05 224		96.43 70	94.45 215

HQP4-MVS										94.45 215			98.96 207			96.87 253

HQP-MVS		95.72 163	95.40 158	96.69 212	97.20 249	94.25 230	98.05 224	98.46 172	96.43 70	94.45 215	97.73 218	86.75 242	98.96 207	95.30 162	94.18 228	96.86 255

MSDG		95.93 154	95.30 169	97.83 140	98.90 118	95.36 178	96.83 316	98.37 191	91.32 284	94.43 219	98.73 121	90.27 166	99.60 133	90.05 292	98.82 128	98.52 190

nrg030		96.28 142	95.72 146	97.96 135	96.90 270	98.15 57	99.39 6	98.31 200	95.47 110	94.42 220	98.35 159	92.09 126	98.69 235	97.50 78	89.05 304	97.04 234

CLD-MVS		95.62 170	95.34 164	96.46 238	97.52 227	93.75 243	97.27 285	98.46 172	95.53 106	94.42 220	98.00 192	86.21 252	98.97 203	96.25 130	94.37 222	96.66 279
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		95.62 170	95.34 164	96.47 235	97.46 230	93.54 250	98.99 67	98.54 155	94.67 153	94.36 222	98.77 117	85.39 264	99.11 183	95.71 151	94.15 230	96.76 264

LGP-MVS_train				96.47 235	97.46 230	93.54 250		98.54 155	94.67 153	94.36 222	98.77 117	85.39 264	99.11 183	95.71 151	94.15 230	96.76 264

v144192		94.39 245	93.70 256	96.48 234	96.06 308	94.35 226	98.58 148	98.16 229	91.45 277	94.33 224	97.02 276	87.50 230	98.45 260	91.08 275	89.11 303	96.63 281

V42		94.78 219	94.14 225	96.70 211	96.33 298	95.22 184	98.97 71	98.09 244	92.32 252	94.31 225	97.06 271	88.39 207	98.55 250	92.90 237	88.87 308	96.34 311

ACMM	93.85 9	95.69 167	95.38 162	96.61 219	97.61 216	93.84 239	98.91 81	98.44 176	95.25 124	94.28 226	98.47 146	86.04 257	99.12 180	95.50 158	93.95 237	96.87 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 174	95.21 172	96.22 251	98.12 184	93.72 246	98.32 189	98.13 233	93.71 194	94.26 227	97.31 250	92.24 120	98.10 299	94.63 179	90.12 287	96.84 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 255	93.47 266	96.40 242	95.98 311	94.08 233	98.52 158	98.15 230	91.33 283	94.25 228	97.20 258	86.41 249	98.42 264	90.04 293	89.39 300	96.69 278

BH-w/o		95.38 181	95.08 178	96.26 250	98.34 164	91.79 283	97.70 256	97.43 296	92.87 233	94.24 229	97.22 256	88.66 200	98.84 224	91.55 271	97.70 172	98.16 205

XVG-ACMP-BASELINE		94.54 235	94.14 225	95.75 271	96.55 287	91.65 288	98.11 220	98.44 176	94.96 141	94.22 230	97.90 201	79.18 322	99.11 183	94.05 204	93.85 239	96.48 305

v1144		94.59 231	93.92 238	96.60 221	96.21 300	94.78 208	98.59 146	98.14 232	91.86 267	94.21 231	97.02 276	87.97 218	98.41 271	91.72 268	89.57 294	96.61 283

v1192		94.32 248	93.58 261	96.53 230	96.10 306	94.45 220	98.50 163	98.17 227	91.54 275	94.19 232	97.06 271	86.95 240	98.43 263	90.14 288	89.57 294	96.70 273

PAPM		94.95 209	94.00 233	97.78 145	97.04 261	95.65 167	96.03 332	98.25 214	91.23 289	94.19 232	97.80 215	91.27 147	98.86 223	82.61 344	97.61 174	98.84 170

Patchmatch-test		94.42 243	93.68 258	96.63 217	97.60 217	91.76 284	94.83 347	97.49 291	89.45 320	94.14 234	97.10 261	88.99 191	98.83 226	85.37 333	98.13 156	99.29 125

v1240		94.06 267	93.29 271	96.34 246	96.03 310	93.90 237	98.44 170	98.17 227	91.18 292	94.13 235	97.01 278	86.05 255	98.42 264	89.13 309	89.50 298	96.70 273

GBi-Net		94.49 239	93.80 247	96.56 226	98.21 174	95.00 193	98.82 101	98.18 222	92.46 243	94.09 236	97.07 268	81.16 307	97.95 311	92.08 257	92.14 262	96.72 269

test1		94.49 239	93.80 247	96.56 226	98.21 174	95.00 193	98.82 101	98.18 222	92.46 243	94.09 236	97.07 268	81.16 307	97.95 311	92.08 257	92.14 262	96.72 269

FMVSNet3		94.97 208	94.26 217	97.11 185	98.18 179	96.62 118	98.56 154	98.26 213	93.67 201	94.09 236	97.10 261	84.25 284	98.01 307	92.08 257	92.14 262	96.70 273

MIMVSNet		93.26 281	92.21 288	96.41 241	97.73 210	93.13 267	95.65 338	97.03 313	91.27 288	94.04 239	96.06 320	75.33 343	97.19 333	86.56 323	96.23 210	98.92 166

FIs		96.51 133	96.12 135	97.67 156	97.13 256	97.54 83	99.36 9	99.22 14	95.89 90	94.03 240	98.35 159	91.98 129	98.44 262	96.40 126	92.76 258	97.01 235

v2v482		94.69 221	94.03 229	96.65 214	96.17 303	94.79 207	98.67 136	98.08 246	92.72 236	94.00 241	97.16 259	87.69 227	98.45 260	92.91 236	88.87 308	96.72 269

FC-MVSNet-test		96.42 136	96.05 137	97.53 166	96.95 265	97.27 92	99.36 9	99.23 12	95.83 93	93.93 242	98.37 157	92.00 128	98.32 280	96.02 138	92.72 259	97.00 236

UniMVSNet (Re)		95.78 161	95.19 173	97.58 162	96.99 264	97.47 85	98.79 112	99.18 16	95.60 103	93.92 243	97.04 274	91.68 134	98.48 255	95.80 146	87.66 320	96.79 260

miper_enhance_ethall		95.10 199	94.75 192	96.12 256	97.53 226	93.73 245	96.61 323	98.08 246	92.20 259	93.89 244	96.65 301	92.44 115	98.30 284	94.21 197	91.16 276	96.34 311

UniMVSNet_NR-MVSNet		95.71 164	95.15 174	97.40 172	96.84 273	96.97 104	98.74 117	99.24 10	95.16 128	93.88 245	97.72 220	91.68 134	98.31 282	95.81 144	87.25 325	96.92 242

DU-MVS		95.42 178	94.76 191	97.40 172	96.53 288	96.97 104	98.66 139	98.99 29	95.43 112	93.88 245	97.69 221	88.57 202	98.31 282	95.81 144	87.25 325	96.92 242

Baseline_NR-MVSNet		94.35 246	93.81 246	95.96 261	96.20 301	94.05 234	98.61 145	96.67 332	91.44 278	93.85 247	97.60 230	88.57 202	98.14 296	94.39 189	86.93 328	95.68 329

PS-MVSNAJss		96.43 135	96.26 131	96.92 199	95.84 316	95.08 191	99.16 37	98.50 167	95.87 92	93.84 248	98.34 163	94.51 86	98.61 243	96.88 104	93.45 248	97.06 233

UniMVSNet_ETH3D		94.24 253	93.33 269	96.97 193	97.19 252	93.38 259	98.74 117	98.57 149	91.21 291	93.81 249	98.58 136	72.85 352	98.77 232	95.05 170	93.93 238	98.77 175

MVS_0304		92.81 288	92.01 290	95.23 284	97.46 230	91.33 294	98.17 213	98.81 76	91.13 293	93.80 250	95.68 330	66.08 358	98.06 304	90.79 280	96.13 213	96.32 314

tpmvs		94.60 229	94.36 213	95.33 283	97.46 230	88.60 332	96.88 312	97.68 272	91.29 286	93.80 250	96.42 310	88.58 201	99.24 167	91.06 276	96.04 215	98.17 204

3Dnovator	94.51 5	97.46 89	96.93 102	99.07 61	97.78 205	97.64 78	99.35 11	99.06 22	97.02 49	93.75 252	99.16 61	89.25 183	99.92 22	97.22 86	99.75 39	99.64 72

eth_miper_zixun_eth		94.68 223	94.41 211	95.47 278	97.64 214	91.71 287	96.73 320	98.07 248	92.71 237	93.64 253	97.21 257	90.54 161	98.17 294	93.38 220	89.76 291	96.54 293

ITE_SJBPF				95.44 280	97.42 235	91.32 295		97.50 289	95.09 135	93.59 254	98.35 159	81.70 305	98.88 220	89.71 298	93.39 250	96.12 319

TranMVSNet+NR-MVSNet		95.14 197	94.48 204	97.11 185	96.45 293	96.36 134	99.03 58	99.03 25	95.04 137	93.58 255	97.93 199	88.27 209	98.03 306	94.13 199	86.90 330	96.95 241

COLMAP_ROB	93.27 12	95.33 187	94.87 188	96.71 209	99.29 78	93.24 264	98.58 148	98.11 237	89.92 312	93.57 256	99.10 70	86.37 250	99.79 93	90.78 281	98.10 157	97.09 231
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 276	92.80 278	95.07 291	97.58 219	87.97 340	96.76 318	97.86 266	82.17 350	93.53 257	96.04 321	86.13 253	99.13 179	89.24 307	95.87 216	98.10 206

AllTest		95.24 191	94.65 196	96.99 190	99.25 86	93.21 265	98.59 146	98.18 222	91.36 280	93.52 258	98.77 117	84.67 277	99.72 110	89.70 299	97.87 164	98.02 208

TestCases				96.99 190	99.25 86	93.21 265		98.18 222	91.36 280	93.52 258	98.77 117	84.67 277	99.72 110	89.70 299	97.87 164	98.02 208

miper_ehance_all_eth		95.01 203	94.69 195	95.97 260	97.70 211	93.31 261	97.02 299	98.07 248	92.23 256	93.51 260	96.96 283	91.85 131	98.15 295	93.68 212	91.16 276	96.44 308

FMVSNet2		94.47 241	93.61 260	97.04 188	98.21 174	96.43 131	98.79 112	98.27 209	92.46 243	93.50 261	97.09 265	81.16 307	98.00 309	91.09 274	91.93 265	96.70 273

v148		94.29 250	93.76 252	95.91 263	96.10 306	92.93 270	98.58 148	97.97 258	92.59 241	93.47 262	96.95 285	88.53 205	98.32 280	92.56 247	87.06 327	96.49 304

cl_fuxian		94.79 218	94.43 210	95.89 265	97.75 206	93.12 268	97.16 293	98.03 255	92.23 256	93.46 263	97.05 273	91.39 142	98.01 307	93.58 217	89.21 302	96.53 295

RRT_test8_iter05		94.56 233	94.19 220	95.67 273	97.60 217	91.34 292	98.93 79	98.42 181	94.75 148	93.39 264	97.87 205	79.00 323	98.61 243	96.78 113	90.99 279	97.07 232

pmmvs4		94.69 221	93.99 235	96.81 204	95.74 317	95.94 154	97.40 271	97.67 273	90.42 303	93.37 265	97.59 231	89.08 189	98.20 292	92.97 234	91.67 268	96.30 315

PCF-MVS	93.45 11	94.68 223	93.43 267	98.42 103	98.62 144	96.77 114	95.48 341	98.20 218	84.63 346	93.34 266	98.32 165	88.55 204	99.81 72	84.80 337	98.96 119	98.68 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl-mvsnet2		94.68 223	94.19 220	96.13 255	98.11 185	93.60 248	96.94 303	98.31 200	92.43 247	93.32 267	96.87 291	86.51 245	98.28 289	94.10 202	91.16 276	96.51 301

XXY-MVS		95.20 194	94.45 208	97.46 167	96.75 278	96.56 124	98.86 93	98.65 136	93.30 216	93.27 268	98.27 173	84.85 274	98.87 221	94.82 175	91.26 275	96.96 239

jajsoiax		95.45 176	95.03 180	96.73 208	95.42 329	94.63 211	99.14 39	98.52 159	95.74 96	93.22 269	98.36 158	83.87 294	98.65 241	96.95 97	94.04 233	96.91 247

mvs_tets		95.41 180	95.00 181	96.65 214	95.58 322	94.42 222	99.00 65	98.55 153	95.73 97	93.21 270	98.38 156	83.45 298	98.63 242	97.09 90	94.00 235	96.91 247

anonymousdsp		95.42 178	94.91 186	96.94 195	95.10 331	95.90 160	99.14 39	98.41 182	93.75 189	93.16 271	97.46 240	87.50 230	98.41 271	95.63 155	94.03 234	96.50 303

v8		94.47 241	93.77 250	96.57 225	96.36 296	94.83 204	99.05 54	98.19 219	91.92 264	93.16 271	96.97 281	88.82 199	98.48 255	91.69 269	87.79 318	96.39 309

WR-MVS		95.15 196	94.46 206	97.22 177	96.67 283	96.45 129	98.21 201	98.81 76	94.15 169	93.16 271	97.69 221	87.51 228	98.30 284	95.29 164	88.62 310	96.90 249

EPNet_dtu		95.21 193	94.95 185	95.99 258	96.17 303	90.45 309	98.16 214	97.27 304	96.77 56	93.14 274	98.33 164	90.34 164	98.42 264	85.57 330	98.81 129	99.09 149
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 140	95.40 158	98.96 68	97.85 202	97.60 81	99.23 24	98.93 37	89.76 315	93.11 275	99.02 81	89.11 188	99.93 16	91.99 262	99.62 68	99.34 114

GG-mvs-BLEND				96.59 222	96.34 297	94.98 196	96.51 326	88.58 366		93.10 276	94.34 343	80.34 316	98.05 305	89.53 302	96.99 184	96.74 266

v10		94.29 250	93.55 262	96.51 232	96.39 295	94.80 206	98.99 67	98.19 219	91.35 282	93.02 277	96.99 279	88.09 215	98.41 271	90.50 285	88.41 312	96.33 313

3Dnovator+	94.38 6	97.43 94	96.78 109	99.38 18	97.83 203	98.52 28	99.37 8	98.71 114	97.09 47	92.99 278	99.13 65	89.36 180	99.89 36	96.97 94	99.57 77	99.71 46

D2MVS		95.18 195	95.08 178	95.48 277	97.10 258	92.07 278	98.30 192	99.13 19	94.02 175	92.90 279	96.73 296	89.48 177	98.73 234	94.48 188	93.60 245	95.65 330

Patchmtry		93.22 282	92.35 286	95.84 267	96.77 275	93.09 269	94.66 348	97.56 280	87.37 332	92.90 279	96.24 313	88.15 213	97.90 315	87.37 320	90.10 288	96.53 295

cl-mvsnet1		94.52 237	94.03 229	95.99 258	97.57 223	93.38 259	97.05 297	97.94 261	91.74 268	92.81 281	97.10 261	89.12 187	98.07 303	92.60 243	90.30 285	96.53 295

Anonymous20231211		94.10 263	93.26 272	96.61 219	99.11 105	94.28 227	99.01 63	98.88 49	86.43 336	92.81 281	97.57 233	81.66 306	98.68 238	94.83 174	89.02 306	96.88 251

cl-mvsnet____		94.51 238	94.01 232	96.02 257	97.58 219	93.40 258	97.05 297	97.96 260	91.73 270	92.76 283	97.08 267	89.06 190	98.13 297	92.61 242	90.29 286	96.52 298

miper_lstm_enhance		94.33 247	94.07 228	95.11 289	97.75 206	90.97 300	97.22 287	98.03 255	91.67 272	92.76 283	96.97 281	90.03 169	97.78 321	92.51 250	89.64 293	96.56 290

v7n		94.19 256	93.43 267	96.47 235	95.90 313	94.38 225	99.26 21	98.34 196	91.99 262	92.76 283	97.13 260	88.31 208	98.52 253	89.48 304	87.70 319	96.52 298

MVS		94.67 226	93.54 263	98.08 126	96.88 271	96.56 124	98.19 208	98.50 167	78.05 354	92.69 286	98.02 189	91.07 152	99.63 130	90.09 289	98.36 150	98.04 207

DSMNet-mixed		92.52 292	92.58 283	92.33 330	94.15 341	82.65 354	98.30 192	94.26 355	89.08 324	92.65 287	95.73 325	85.01 271	95.76 350	86.24 325	97.76 169	98.59 187

EU-MVSNet		93.66 272	94.14 225	92.25 331	95.96 312	83.38 352	98.52 158	98.12 234	94.69 151	92.61 288	98.13 183	87.36 233	96.39 348	91.82 265	90.00 289	96.98 237

IterMVS-SCA-FT		94.11 262	93.87 242	94.85 297	97.98 196	90.56 308	97.18 290	98.11 237	93.75 189	92.58 289	97.48 239	83.97 291	97.41 330	92.48 252	91.30 273	96.58 286

pmmvs5		93.65 274	92.97 276	95.68 272	95.49 325	92.37 274	98.20 204	97.28 303	89.66 317	92.58 289	97.26 252	82.14 301	98.09 301	93.18 228	90.95 280	96.58 286

WR-MVS_H		95.05 202	94.46 206	96.81 204	96.86 272	95.82 163	99.24 23	99.24 10	93.87 184	92.53 291	96.84 293	90.37 163	98.24 291	93.24 225	87.93 317	96.38 310

ACMP	93.49 10	95.34 186	94.98 183	96.43 240	97.67 212	93.48 254	98.73 121	98.44 176	94.94 144	92.53 291	98.53 140	84.50 281	99.14 178	95.48 159	94.00 235	96.66 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part1		94.82 215	93.82 245	97.82 142	98.84 125	97.82 73	99.03 58	98.81 76	92.31 254	92.51 293	97.89 203	81.96 303	98.67 239	94.80 177	88.24 313	96.98 237

test0.0.03 1		94.08 265	93.51 264	95.80 268	95.53 324	92.89 271	97.38 273	95.97 337	95.11 132	92.51 293	96.66 299	87.71 224	96.94 337	87.03 321	93.67 241	97.57 220

IB-MVS	91.98 17	93.27 280	91.97 291	97.19 179	97.47 229	93.41 257	97.09 296	95.99 336	93.32 214	92.47 295	95.73 325	78.06 330	99.53 144	94.59 184	82.98 339	98.62 186
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
IterMVS		94.09 264	93.85 244	94.80 300	97.99 194	90.35 310	97.18 290	98.12 234	93.68 199	92.46 296	97.34 247	84.05 289	97.41 330	92.51 250	91.33 272	96.62 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 211	94.30 215	96.83 203	96.72 280	95.56 170	99.11 45	98.95 34	93.89 182	92.42 297	97.90 201	87.19 234	98.12 298	94.32 193	88.21 314	96.82 259

PS-CasMVS		94.67 226	93.99 235	96.71 209	96.68 282	95.26 183	99.13 42	99.03 25	93.68 199	92.33 298	97.95 197	85.35 266	98.10 299	93.59 216	88.16 316	96.79 260

FMVSNet1		93.19 284	92.07 289	96.56 226	97.54 224	95.00 193	98.82 101	98.18 222	90.38 304	92.27 299	97.07 268	73.68 350	97.95 311	89.36 306	91.30 273	96.72 269

PEN-MVS		94.42 243	93.73 254	96.49 233	96.28 299	94.84 202	99.17 36	99.00 27	93.51 206	92.23 300	97.83 212	86.10 254	97.90 315	92.55 248	86.92 329	96.74 266

OurMVSNet-221017-0		94.21 254	94.00 233	94.85 297	95.60 321	89.22 323	98.89 86	97.43 296	95.29 121	92.18 301	98.52 143	82.86 299	98.59 247	93.46 219	91.76 267	96.74 266

MS-PatchMatch		93.84 271	93.63 259	94.46 311	96.18 302	89.45 319	97.76 252	98.27 209	92.23 256	92.13 302	97.49 238	79.50 319	98.69 235	89.75 297	99.38 104	95.25 334

ppachtmachnet_test		93.22 282	92.63 282	94.97 293	95.45 327	90.84 301	96.88 312	97.88 265	90.60 298	92.08 303	97.26 252	88.08 216	97.86 320	85.12 334	90.33 284	96.22 316

1314		96.25 144	95.73 145	97.79 144	97.13 256	95.55 172	98.19 208	98.59 143	93.47 208	92.03 304	97.82 213	91.33 145	99.49 147	94.62 181	98.44 145	98.32 199

baseline2		95.11 198	94.52 202	96.87 201	96.65 284	93.56 249	98.27 197	94.10 358	93.45 209	92.02 305	97.43 244	87.45 232	99.19 172	93.88 207	97.41 179	97.87 211

DTE-MVSNet		93.98 269	93.26 272	96.14 254	96.06 308	94.39 224	99.20 32	98.86 61	93.06 224	91.78 306	97.81 214	85.87 258	97.58 326	90.53 284	86.17 334	96.46 307

LF4IMVS		93.14 285	92.79 279	94.20 314	95.88 314	88.67 331	97.66 259	97.07 310	93.81 188	91.71 307	97.65 225	77.96 331	98.81 228	91.47 272	91.92 266	95.12 337

our_test_3		93.65 274	93.30 270	94.69 302	95.45 327	89.68 317	96.91 306	97.65 274	91.97 263	91.66 308	96.88 289	89.67 175	97.93 314	88.02 316	91.49 270	96.48 305

testgi		93.06 286	92.45 285	94.88 296	96.43 294	89.90 312	98.75 114	97.54 286	95.60 103	91.63 309	97.91 200	74.46 348	97.02 335	86.10 326	93.67 241	97.72 217

tfpnnormal		93.66 272	92.70 281	96.55 229	96.94 266	95.94 154	98.97 71	99.19 15	91.04 294	91.38 310	97.34 247	84.94 272	98.61 243	85.45 332	89.02 306	95.11 338

LTVRE_ROB	92.95 15	94.60 229	93.90 240	96.68 213	97.41 238	94.42 222	98.52 158	98.59 143	91.69 271	91.21 311	98.35 159	84.87 273	99.04 194	91.06 276	93.44 249	96.60 284
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
OpenMVS	93.04 13	95.83 159	95.00 181	98.32 108	97.18 253	97.32 89	99.21 31	98.97 30	89.96 311	91.14 312	99.05 80	86.64 244	99.92 22	93.38 220	99.47 93	97.73 216

pm-mvs1		93.94 270	93.06 274	96.59 222	96.49 291	95.16 185	98.95 75	98.03 255	92.32 252	91.08 313	97.84 209	84.54 280	98.41 271	92.16 255	86.13 336	96.19 318

MVS-HIRNet		89.46 317	88.40 316	92.64 328	97.58 219	82.15 355	94.16 352	93.05 361	75.73 356	90.90 314	82.52 358	79.42 320	98.33 279	83.53 342	98.68 131	97.43 221

FMVSNet5		91.81 295	90.92 298	94.49 308	97.21 248	92.09 277	98.00 230	97.55 285	89.31 322	90.86 315	95.61 331	74.48 347	95.32 353	85.57 330	89.70 292	96.07 321

USDC		93.33 279	92.71 280	95.21 285	96.83 274	90.83 302	96.91 306	97.50 289	93.84 185	90.72 316	98.14 182	77.69 332	98.82 227	89.51 303	93.21 254	95.97 323

MVP-Stereo		94.28 252	93.92 238	95.35 282	94.95 333	92.60 273	97.97 232	97.65 274	91.61 274	90.68 317	97.09 265	86.32 251	98.42 264	89.70 299	99.34 106	95.02 341
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 249	93.77 250	95.88 266	97.81 204	92.04 280	98.71 126	98.37 191	93.99 178	90.60 318	98.47 146	80.86 312	99.05 190	92.75 241	92.40 261	96.55 292

CL-MVSNet_2432*1600		90.11 310	89.14 313	93.02 327	91.86 353	88.23 338	96.51 326	98.07 248	90.49 299	90.49 319	94.41 339	84.75 276	95.34 352	80.79 348	74.95 355	95.50 331

DIV-MVS_2432*1600		90.38 308	89.38 311	93.40 322	92.85 350	88.94 328	97.95 233	97.94 261	90.35 305	90.25 320	93.96 344	79.82 317	95.94 349	84.62 339	76.69 353	95.33 333

Anonymous20231206		91.66 297	91.10 297	93.33 323	94.02 345	87.35 344	98.58 148	97.26 305	90.48 300	90.16 321	96.31 311	83.83 295	96.53 346	79.36 352	89.90 290	96.12 319

SixPastTwentyTwo		93.34 278	92.86 277	94.75 301	95.67 319	89.41 321	98.75 114	96.67 332	93.89 182	90.15 322	98.25 175	80.87 311	98.27 290	90.90 279	90.64 282	96.57 288

PVSNet_0	88.72 19	91.28 300	90.03 306	95.00 292	97.99 194	87.29 345	94.84 346	98.50 167	92.06 261	89.86 323	95.19 333	79.81 318	99.39 157	92.27 254	69.79 358	98.33 198

ACMH	92.88 16	94.55 234	93.95 237	96.34 246	97.63 215	93.26 263	98.81 107	98.49 171	93.43 210	89.74 324	98.53 140	81.91 304	99.08 188	93.69 211	93.30 252	96.70 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		91.77 296	90.63 300	95.17 287	94.69 338	91.24 297	98.67 136	97.92 263	86.14 338	89.62 325	97.56 235	75.79 342	98.34 278	90.75 282	84.56 338	95.94 324

TinyColmap		92.31 293	91.53 294	94.65 304	96.92 267	89.75 314	96.92 304	96.68 331	90.45 302	89.62 325	97.85 208	76.06 341	98.81 228	86.74 322	92.51 260	95.41 332

Anonymous20240521		91.18 301	90.44 302	93.42 320	93.70 346	88.47 334	98.94 77	97.56 280	88.46 327	89.56 327	95.08 336	77.15 338	96.97 336	83.92 340	89.55 296	94.82 343

TransMVSNet (Re)		92.67 290	91.51 295	96.15 253	96.58 286	94.65 209	98.90 82	96.73 328	90.86 296	89.46 328	97.86 206	85.62 261	98.09 301	86.45 324	81.12 345	95.71 328

NR-MVSNet		94.98 207	94.16 223	97.44 168	96.53 288	97.22 98	98.74 117	98.95 34	94.96 141	89.25 329	97.69 221	89.32 181	98.18 293	94.59 184	87.40 323	96.92 242

LCM-MVSNet-Re		95.22 192	95.32 167	94.91 294	98.18 179	87.85 342	98.75 114	95.66 341	95.11 132	88.96 330	96.85 292	90.26 167	97.65 323	95.65 154	98.44 145	99.22 131

KD-MVS_2432*1600		89.61 315	87.96 319	94.54 306	94.06 343	91.59 289	95.59 339	97.63 276	89.87 313	88.95 331	94.38 341	78.28 327	96.82 338	84.83 335	68.05 359	95.21 335

miper_refine_blended		89.61 315	87.96 319	94.54 306	94.06 343	91.59 289	95.59 339	97.63 276	89.87 313	88.95 331	94.38 341	78.28 327	96.82 338	84.83 335	68.05 359	95.21 335

TDRefinement		91.06 303	89.68 308	95.21 285	85.35 361	91.49 291	98.51 162	97.07 310	91.47 276	88.83 333	97.84 209	77.31 336	99.09 187	92.79 240	77.98 351	95.04 340

N_pmnet		87.12 322	87.77 321	85.17 340	95.46 326	61.92 365	97.37 275	70.66 371	85.83 341	88.73 334	96.04 321	85.33 268	97.76 322	80.02 349	90.48 283	95.84 325

test_0402		91.32 299	90.27 304	94.48 309	96.60 285	91.12 298	98.50 163	97.22 306	86.10 339	88.30 335	96.98 280	77.65 334	97.99 310	78.13 356	92.94 257	94.34 345

test20.03		90.89 305	90.38 303	92.43 329	93.48 347	88.14 339	98.33 184	97.56 280	93.40 211	87.96 336	96.71 298	80.69 314	94.13 358	79.15 353	86.17 334	95.01 342

MIMVSNet1		89.67 314	88.28 318	93.82 317	92.81 351	91.08 299	98.01 228	97.45 294	87.95 329	87.90 337	95.87 323	67.63 356	94.56 357	78.73 355	88.18 315	95.83 326

Patchmatch-RL test		91.49 298	90.85 299	93.41 321	91.37 354	84.40 349	92.81 353	95.93 339	91.87 266	87.25 338	94.87 337	88.99 191	96.53 346	92.54 249	82.00 341	99.30 123

pmmvs3		86.67 323	84.86 326	92.11 332	88.16 358	87.19 346	96.63 322	94.75 350	79.88 352	87.22 339	92.75 349	66.56 357	95.20 354	81.24 347	76.56 354	93.96 351

K. test v3		92.55 291	91.91 293	94.48 309	95.64 320	89.24 322	99.07 52	94.88 348	94.04 173	86.78 340	97.59 231	77.64 335	97.64 324	92.08 257	89.43 299	96.57 288

lessismore_v0				94.45 312	94.93 334	88.44 335		91.03 363		86.77 341	97.64 227	76.23 340	98.42 264	90.31 287	85.64 337	96.51 301

ambc				89.49 336	86.66 359	75.78 359	92.66 354	96.72 329		86.55 342	92.50 350	46.01 363	97.90 315	90.32 286	82.09 340	94.80 344

PM-MVS		87.77 320	86.55 324	91.40 334	91.03 356	83.36 353	96.92 304	95.18 346	91.28 287	86.48 343	93.42 346	53.27 362	96.74 340	89.43 305	81.97 342	94.11 348

OpenMVS_ROB	86.42 20	89.00 318	87.43 323	93.69 318	93.08 349	89.42 320	97.91 237	96.89 323	78.58 353	85.86 344	94.69 338	69.48 354	98.29 287	77.13 357	93.29 253	93.36 354

UnsupCasMVSNet_eth		90.99 304	89.92 307	94.19 315	94.08 342	89.83 313	97.13 295	98.67 129	93.69 197	85.83 345	96.19 318	75.15 344	96.74 340	89.14 308	79.41 349	96.00 322

new_pmnet		90.06 311	89.00 315	93.22 326	94.18 340	88.32 337	96.42 328	96.89 323	86.19 337	85.67 346	93.62 345	77.18 337	97.10 334	81.61 346	89.29 301	94.23 346

EG-PatchMatch MVS		91.13 302	90.12 305	94.17 316	94.73 337	89.00 327	98.13 217	97.81 267	89.22 323	85.32 347	96.46 307	67.71 355	98.42 264	87.89 318	93.82 240	95.08 339

pmmvs-eth3d		90.36 309	89.05 314	94.32 313	91.10 355	92.12 276	97.63 262	96.95 318	88.86 325	84.91 348	93.13 347	78.32 326	96.74 340	88.70 311	81.81 343	94.09 349

DeepMVS_CX				86.78 337	97.09 259	72.30 361		95.17 347	75.92 355	84.34 349	95.19 333	70.58 353	95.35 351	79.98 351	89.04 305	92.68 355

new-patchmatchnet		88.50 319	87.45 322	91.67 333	90.31 357	85.89 348	97.16 293	97.33 300	89.47 319	83.63 350	92.77 348	76.38 339	95.06 355	82.70 343	77.29 352	94.06 350

UnsupCasMVSNet_bld		87.17 321	85.12 325	93.31 324	91.94 352	88.77 329	94.92 345	98.30 206	84.30 347	82.30 351	90.04 353	63.96 360	97.25 332	85.85 329	74.47 357	93.93 352

CMPMVS	66.06 21	89.70 313	89.67 309	89.78 335	93.19 348	76.56 358	97.00 300	98.35 194	80.97 351	81.57 352	97.75 217	74.75 346	98.61 243	89.85 295	93.63 243	94.17 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		79.03 324	78.17 327	81.63 342	86.06 360	54.40 370	82.75 361	96.89 323	39.54 365	80.98 353	95.57 332	58.37 361	94.73 356	84.74 338	78.61 350	95.75 327

ET-MVSNet_ETH3D		94.13 260	92.98 275	97.58 162	98.22 173	96.20 140	97.31 282	95.37 343	94.53 158	79.56 354	97.63 229	86.51 245	97.53 328	96.91 98	90.74 281	99.02 156

LCM-MVSNet		78.70 325	76.24 330	86.08 338	77.26 367	71.99 362	94.34 350	96.72 329	61.62 360	76.53 355	89.33 354	33.91 369	92.78 360	81.85 345	74.60 356	93.46 353

PMMVS2		77.95 327	75.44 331	85.46 339	82.54 362	74.95 360	94.23 351	93.08 360	72.80 357	74.68 356	87.38 355	36.36 368	91.56 361	73.95 359	63.94 361	89.87 356

Gipuma		78.40 326	76.75 329	83.38 341	95.54 323	80.43 357	79.42 362	97.40 298	64.67 359	73.46 357	80.82 360	45.65 364	93.14 359	66.32 361	87.43 322	76.56 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		90.70 307	89.39 310	94.62 305	94.79 336	90.65 306	97.20 288	97.46 292	87.54 331	72.54 358	95.74 324	86.51 245	96.66 344	86.00 327	86.76 332	96.54 293

MDA-MVSNet_test_wron		90.71 306	89.38 311	94.68 303	94.83 335	90.78 304	97.19 289	97.46 292	87.60 330	72.41 359	95.72 327	86.51 245	96.71 343	85.92 328	86.80 331	96.56 290

MDA-MVSNet-bldmvs		89.97 312	88.35 317	94.83 299	95.21 330	91.34 292	97.64 260	97.51 288	88.36 328	71.17 360	96.13 319	79.22 321	96.63 345	83.65 341	86.27 333	96.52 298

FPMVS		77.62 328	77.14 328	79.05 344	79.25 365	60.97 366	95.79 335	95.94 338	65.96 358	67.93 361	94.40 340	37.73 367	88.88 363	68.83 360	88.46 311	87.29 357

tmp_tt		68.90 330	66.97 332	74.68 346	50.78 371	59.95 367	87.13 358	83.47 369	38.80 366	62.21 362	96.23 315	64.70 359	76.91 368	88.91 310	30.49 366	87.19 358

E-PMN		64.94 332	64.25 334	67.02 348	82.28 363	59.36 368	91.83 356	85.63 367	52.69 362	60.22 363	77.28 362	41.06 366	80.12 366	46.15 365	41.14 363	61.57 364

EMVS		64.07 333	63.26 336	66.53 349	81.73 364	58.81 369	91.85 355	84.75 368	51.93 364	59.09 364	75.13 363	43.32 365	79.09 367	42.03 366	39.47 364	61.69 363

MVE	62.14 22	63.28 334	59.38 337	74.99 345	74.33 368	65.47 364	85.55 359	80.50 370	52.02 363	51.10 365	75.00 364	10.91 374	80.50 365	51.60 364	53.40 362	78.99 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 329	65.37 333	80.22 343	65.99 369	71.96 363	90.91 357	90.09 364	82.62 348	49.93 366	78.39 361	29.36 370	81.75 364	62.49 362	38.52 365	86.95 359

PMVS	61.03 23	65.95 331	63.57 335	73.09 347	57.90 370	51.22 371	85.05 360	93.93 359	54.45 361	44.32 367	83.57 357	13.22 371	89.15 362	58.68 363	81.00 346	78.91 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		21.48 337	24.95 340	11.09 352	14.89 372	6.47 374	96.56 324	9.87 373	7.55 368	17.93 368	39.02 366	9.43 375	5.90 371	16.56 369	12.72 368	20.91 366

test123		20.95 338	23.72 341	12.64 351	13.54 373	8.19 373	96.55 325	6.13 374	7.48 369	16.74 369	37.98 367	12.97 372	6.05 370	16.69 368	5.43 369	23.68 365

wuyk23d		30.17 335	30.18 339	30.16 350	78.61 366	43.29 372	66.79 363	14.21 372	17.31 367	14.82 370	11.93 370	11.55 373	41.43 369	37.08 367	19.30 367	5.76 367

uanet_test		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

cdsmvs_eth3d_5k		23.98 336	31.98 338	0.00 353	0.00 374	0.00 375	0.00 364	98.59 143	0.00 370	0.00 371	98.61 131	90.60 160	0.00 372	0.00 370	0.00 370	0.00 368

pcd_1.5k_mvsjas		7.88 340	10.50 343	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	94.51 86	0.00 372	0.00 370	0.00 370	0.00 368

sosnet-low-res		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

sosnet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uncertanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

Regformer		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

ab-mvs-re		8.20 339	10.94 342	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	98.43 149	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

No_MVS				99.62 5	99.17 98	99.08 9		98.63 139					99.94 3	98.53 11	99.80 17	99.86 2

eth-test2					0.00 374

eth-test					0.00 374

OPU-MVS				99.37 21	99.24 92	99.05 11	99.02 61				99.16 61	97.81 3	99.37 158	97.24 85	99.73 44	99.70 50

save fliter					99.46 51	98.38 36	98.21 201	98.71 114	97.95 3

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 71	98.88 49					99.94 3	98.47 18	99.81 10	99.84 5

GSMVS																99.20 132

sam_mvs1												89.45 178				99.20 132

sam_mvs												88.99 191

MTGPA								98.74 104

test_post1							96.68 321				30.43 369	87.85 223	98.69 235	92.59 245

test_post											31.83 368	88.83 198	98.91 214

patchmatchnet-post											95.10 335	89.42 179	98.89 218

MTMP							98.89 86	94.14 357

gm-plane-assit					95.88 314	87.47 343			89.74 316		96.94 286		99.19 172	93.32 224

test9_res														96.39 127	99.57 77	99.69 53

agg_prior2														95.87 143	99.57 77	99.68 59

test_prior4						98.01 63	97.86 244

test_prior				99.19 44	99.31 70	98.22 51		98.84 65					99.70 116			99.65 69

新几何2							97.64 260

旧先验1					99.29 78	97.48 84		98.70 117			99.09 75	95.56 48			99.47 93	99.61 77

无先验							97.58 264	98.72 110	91.38 279				99.87 45	93.36 222		99.60 80

原ACMM2							97.67 258

testdata2													99.89 36	91.65 270

segment_acmp												96.85 12

testdata1							97.32 281		96.34 74

plane_prior7					97.42 235	94.63 211

plane_prior6					97.35 240	94.61 214						87.09 236

plane_prior5								98.56 151					99.03 195	96.07 133	94.27 224	96.92 242

plane_prior4											98.28 170

plane_prior2							98.80 108		97.28 31

plane_prior1					97.37 239

plane_prior						94.60 216	98.44 170		96.74 58						94.22 226

n2								0.00 375

nn								0.00 375

door-mid								94.37 353

test11								98.66 132

door								94.64 351

HQP5-MVS						94.25 230

BP-MVS														95.30 162

HQP3-MVS								98.46 172							94.18 228

HQP2-MVS												86.75 242

NP-MVS					97.28 243	94.51 219					97.73 218

ACMMP++_ref															92.97 256

ACMMP++															93.61 244

Test By Simon												94.64 81

High-resolution multi-view benchmark