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.

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

UA-Net		98.88 7	98.76 13	99.22 2	99.11 81	97.89 13	99.47 3	99.32 7	99.08 10	97.87 134	99.67 2	96.47 83	99.92 4	97.88 22	99.98 2	99.85 3

abl_6		98.42 23	98.19 32	99.09 3	99.16 68	98.10 5	97.73 64	99.11 26	97.76 49	98.62 51	98.27 96	97.88 19	99.80 37	95.67 95	99.50 107	99.38 85

zzz-MVS		98.01 44	97.66 64	99.06 4	99.44 30	97.90 11	95.66 173	98.73 123	97.69 56	97.90 128	97.96 132	95.81 108	99.82 29	96.13 76	99.61 70	99.45 66

MTAPA		98.14 34	97.84 49	99.06 4	99.44 30	97.90 11	97.25 90	98.73 123	97.69 56	97.90 128	97.96 132	95.81 108	99.82 29	96.13 76	99.61 70	99.45 66

test1172		98.08 39	97.76 57	99.05 6	98.78 106	98.07 6	97.41 85	98.85 88	97.57 60	98.15 100	97.96 132	96.60 75	99.76 54	95.30 120	99.18 184	99.33 94

mPP-MVS		97.91 58	97.53 80	99.04 7	99.22 56	97.87 14	97.74 62	98.78 114	96.04 113	97.10 170	97.73 161	96.53 78	99.78 41	95.16 130	99.50 107	99.46 61

MSP-MVS		97.45 92	96.92 119	99.03 8	99.26 47	97.70 18	97.66 65	98.89 75	95.65 134	98.51 60	96.46 251	92.15 208	99.81 31	95.14 133	98.58 252	99.58 28
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
SR-MVS-dyc-post		98.14 34	97.84 49	99.02 9	98.81 101	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.60 75	99.76 54	95.49 105	99.20 180	99.26 113

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 19	98.06 7	99.34 4	99.44 6	98.85 19	99.00 36	99.20 23	97.42 31	99.59 149	97.21 46	99.76 38	99.40 81

SR-MVS		98.00 45	97.66 64	99.01 11	98.77 108	97.93 10	97.38 86	98.83 100	97.32 73	98.06 112	97.85 147	96.65 70	99.77 50	95.00 142	99.11 195	99.32 95

MP-MVS		97.64 77	97.18 103	99.00 12	99.32 44	97.77 17	97.49 79	98.73 123	96.27 101	95.59 245	97.75 158	96.30 91	99.78 41	93.70 196	99.48 115	99.45 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Effi-MVS+-dtu		96.81 129	96.09 160	98.99 13	96.90 285	98.69 2	96.42 127	98.09 211	95.86 126	95.15 253	95.54 289	94.26 159	99.81 31	94.06 180	98.51 255	98.47 225

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 13	97.29 37	98.65 14	99.19 15	95.62 136	99.35 19	99.37 12	97.38 32	99.90 13	98.59 11	99.91 16	99.77 8

CP-MVS		97.92 55	97.56 79	98.99 13	98.99 92	97.82 15	97.93 50	98.96 67	96.11 108	96.89 188	97.45 183	96.85 63	99.78 41	95.19 126	99.63 63	99.38 85

PGM-MVS		97.88 60	97.52 81	98.96 16	99.20 64	97.62 21	97.09 99	99.06 38	95.45 143	97.55 143	97.94 137	97.11 43	99.78 41	94.77 152	99.46 120	99.48 56

RPSCF		97.87 61	97.51 82	98.95 17	99.15 71	98.43 3	97.56 72	99.06 38	96.19 105	98.48 63	98.70 57	94.72 142	99.24 246	94.37 167	99.33 164	99.17 127

XVS		97.96 46	97.63 71	98.94 18	99.15 71	97.66 19	97.77 58	98.83 100	97.42 67	96.32 214	97.64 168	96.49 81	99.72 78	95.66 97	99.37 146	99.45 66

X-MVStestdata		92.86 271	90.83 296	98.94 18	99.15 71	97.66 19	97.77 58	98.83 100	97.42 67	96.32 214	36.50 358	96.49 81	99.72 78	95.66 97	99.37 146	99.45 66

ACMMPR		97.95 49	97.62 73	98.94 18	99.20 64	97.56 25	97.59 70	98.83 100	96.05 111	97.46 155	97.63 169	96.77 66	99.76 54	95.61 101	99.46 120	99.49 51

ACMMP		98.05 41	97.75 59	98.93 21	99.23 53	97.60 22	98.09 43	98.96 67	95.75 133	97.91 127	98.06 122	96.89 59	99.76 54	95.32 119	99.57 80	99.43 77
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
region2R		97.92 55	97.59 76	98.92 22	99.22 56	97.55 26	97.60 69	98.84 93	96.00 116	97.22 162	97.62 170	96.87 62	99.76 54	95.48 108	99.43 132	99.46 61

HPM-MVS		98.11 38	97.83 51	98.92 22	99.42 34	97.46 31	98.57 15	99.05 40	95.43 145	97.41 158	97.50 179	97.98 15	99.79 38	95.58 104	99.57 80	99.50 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 19	97.48 30	98.35 26	99.03 47	95.88 124	97.88 131	98.22 103	98.15 12	99.74 68	96.50 67	99.62 64	99.42 78

ACMM	93.33 11	98.05 41	97.79 53	98.85 25	99.15 71	97.55 26	96.68 120	98.83 100	95.21 151	98.36 75	98.13 109	98.13 14	99.62 141	96.04 81	99.54 92	99.39 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		97.92 55	97.62 73	98.83 26	99.32 44	97.24 39	97.45 80	98.84 93	95.76 131	96.93 185	97.43 184	97.26 39	99.79 38	96.06 78	99.53 95	99.45 66

HFP-MVS		97.94 51	97.64 69	98.83 26	99.15 71	97.50 28	97.59 70	98.84 93	96.05 111	97.49 149	97.54 174	97.07 47	99.70 102	95.61 101	99.46 120	99.30 101

#test#		97.62 79	97.22 101	98.83 26	99.15 71	97.50 28	96.81 110	98.84 93	94.25 188	97.49 149	97.54 174	97.07 47	99.70 102	94.37 167	99.46 120	99.30 101

GST-MVS		97.82 67	97.49 84	98.81 29	99.23 53	97.25 38	97.16 94	98.79 110	95.96 118	97.53 144	97.40 186	96.93 56	99.77 50	95.04 139	99.35 154	99.42 78

HPM-MVS++		96.99 113	96.38 148	98.81 29	98.64 121	97.59 23	95.97 156	98.20 196	95.51 141	95.06 254	96.53 247	94.10 163	99.70 102	94.29 171	99.15 186	99.13 137

APD-MVS_3200maxsize		98.13 37	97.90 44	98.79 31	98.79 104	97.31 36	97.55 73	98.92 72	97.72 53	98.25 89	98.13 109	97.10 44	99.75 61	95.44 112	99.24 178	99.32 95

SteuartSystems-ACMMP		98.02 43	97.76 57	98.79 31	99.43 32	97.21 41	97.15 95	98.90 74	96.58 91	98.08 110	97.87 146	97.02 52	99.76 54	95.25 123	99.59 75	99.40 81
Skip Steuart: Steuart Systems R&D Blog.
mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 59	98.54 18	99.22 10	96.23 104	99.71 4	99.48 7	98.77 6	99.93 2	98.89 3	99.95 5	99.84 5

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 22	96.61 55	98.55 17	99.17 16	99.05 13	99.17 29	98.79 50	95.47 121	99.89 16	97.95 20	99.91 16	99.75 13

jajsoiax		98.77 9	98.79 12	98.74 35	99.66 10	96.48 59	98.45 23	99.12 25	95.83 129	99.67 6	99.37 12	98.25 10	99.92 4	98.77 5	99.94 8	99.82 6

LPG-MVS_test		97.94 51	97.67 63	98.74 35	99.15 71	97.02 42	97.09 99	99.02 49	95.15 155	98.34 78	98.23 100	97.91 17	99.70 102	94.41 164	99.73 44	99.50 43

LGP-MVS_train				98.74 35	99.15 71	97.02 42		99.02 49	95.15 155	98.34 78	98.23 100	97.91 17	99.70 102	94.41 164	99.73 44	99.50 43

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 38	99.71 7	96.99 44	99.69 2	99.57 3	99.02 15	99.62 10	99.36 14	98.53 7	99.52 170	98.58 12	99.95 5	99.66 22
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
MP-MVS-pluss		97.69 75	97.36 90	98.70 39	99.50 25	96.84 47	95.38 189	98.99 60	92.45 240	98.11 104	98.31 85	97.25 40	99.77 50	96.60 61	99.62 64	99.48 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_djsdf		98.73 11	98.74 16	98.69 40	99.63 12	96.30 65	98.67 11	99.02 49	96.50 93	99.32 20	99.44 10	97.43 30	99.92 4	98.73 7	99.95 5	99.86 2

ACMMP_NAP		97.89 59	97.63 71	98.67 41	99.35 41	96.84 47	96.36 132	98.79 110	95.07 159	97.88 131	98.35 81	97.24 41	99.72 78	96.05 80	99.58 77	99.45 66

MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 50	98.76 9	98.89 75	98.49 27	99.38 17	99.14 30	95.44 123	99.84 25	96.47 68	99.80 32	99.47 59

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 63	99.18 5	99.20 13	99.67 2	99.73 3	99.65 4	99.15 3	99.86 20	97.22 44	99.92 13	99.77 8

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 63	97.35 35	97.96 48	99.16 17	98.34 31	98.78 44	98.52 70	97.32 34	99.45 189	94.08 179	99.67 57	99.13 137
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 62	99.17 6	99.05 40	98.05 40	99.61 11	99.52 5	93.72 173	99.88 18	98.72 9	99.88 22	99.65 23

SMA-MVS		97.48 90	97.11 106	98.60 46	98.83 100	96.67 52	96.74 114	98.73 123	91.61 251	98.48 63	98.36 80	96.53 78	99.68 116	95.17 128	99.54 92	99.45 66
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
DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 17	96.12 70	98.48 22	99.10 28	99.36 4	99.29 23	99.06 36	97.27 37	99.93 2	97.71 31	99.91 16	99.70 18

LS3D		97.77 71	97.50 83	98.57 48	96.24 298	97.58 24	98.45 23	98.85 88	98.58 26	97.51 146	97.94 137	95.74 112	99.63 133	95.19 126	98.97 209	98.51 222

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 61	98.87 7	99.30 8	99.01 16	99.63 9	99.66 3	99.27 2	99.68 116	97.75 29	99.89 21	99.62 25

ACMP	92.54 13	97.47 91	97.10 107	98.55 50	99.04 89	96.70 51	96.24 140	98.89 75	93.71 204	97.97 122	97.75 158	97.44 29	99.63 133	93.22 206	99.70 53	99.32 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		97.64 77	97.35 91	98.50 51	98.85 99	96.18 67	95.21 204	98.99 60	95.84 128	98.78 44	98.08 115	96.84 64	99.81 31	93.98 186	99.57 80	99.52 40

XVG-ACMP-BASELINE		97.58 83	97.28 96	98.49 52	99.16 68	96.90 46	96.39 129	98.98 63	95.05 160	98.06 112	98.02 126	95.86 100	99.56 158	94.37 167	99.64 62	99.00 162

CPTT-MVS		96.69 139	96.08 161	98.49 52	98.89 98	96.64 54	97.25 90	98.77 115	92.89 234	96.01 230	97.13 207	92.23 207	99.67 121	92.24 218	99.34 157	99.17 127

APDe-MVS		98.14 34	98.03 40	98.47 54	98.72 112	96.04 72	98.07 44	99.10 28	95.96 118	98.59 55	98.69 58	96.94 54	99.81 31	96.64 60	99.58 77	99.57 32

PEN-MVS		98.75 10	98.85 10	98.44 55	99.58 14	95.67 86	98.45 23	99.15 21	99.33 5	99.30 21	99.00 37	97.27 37	99.92 4	97.64 32	99.92 13	99.75 13

TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 56	99.07 85	95.87 77	96.73 118	99.05 40	98.67 23	98.84 41	98.45 75	97.58 27	99.88 18	96.45 69	99.86 24	99.54 36

OPM-MVS		97.54 85	97.25 97	98.41 57	99.11 81	96.61 55	95.24 202	98.46 163	94.58 178	98.10 107	98.07 117	97.09 46	99.39 211	95.16 130	99.44 125	99.21 122

APD-MVS		97.00 112	96.53 141	98.41 57	98.55 135	96.31 64	96.32 135	98.77 115	92.96 233	97.44 157	97.58 173	95.84 101	99.74 68	91.96 220	99.35 154	99.19 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		98.73 11	98.85 10	98.39 59	99.55 17	95.47 96	98.49 20	99.13 24	99.22 8	99.22 27	98.96 41	97.35 33	99.92 4	97.79 27	99.93 10	99.79 7

UniMVSNet_NR-MVSNet		97.83 65	97.65 66	98.37 60	98.72 112	95.78 79	95.66 173	99.02 49	98.11 39	98.31 84	97.69 166	94.65 147	99.85 22	97.02 55	99.71 50	99.48 56

testtj		96.69 139	96.13 157	98.36 61	98.46 148	96.02 74	96.44 126	98.70 133	94.26 187	96.79 190	97.13 207	94.07 164	99.75 61	90.53 261	98.80 231	99.31 100

DU-MVS		97.79 69	97.60 75	98.36 61	98.73 110	95.78 79	95.65 175	98.87 82	97.57 60	98.31 84	97.83 149	94.69 143	99.85 22	97.02 55	99.71 50	99.46 61

UniMVSNet (Re)		97.83 65	97.65 66	98.35 63	98.80 103	95.86 78	95.92 161	99.04 46	97.51 64	98.22 92	97.81 153	94.68 145	99.78 41	97.14 51	99.75 42	99.41 80

mvs-test1		96.20 158	95.50 183	98.32 64	96.90 285	98.16 4	95.07 212	98.09 211	95.86 126	93.63 294	94.32 313	94.26 159	99.71 93	94.06 180	97.27 304	97.07 299

nrg030		98.54 18	98.62 21	98.32 64	99.22 56	95.66 87	97.90 53	99.08 34	98.31 32	99.02 34	98.74 54	97.68 24	99.61 147	97.77 28	99.85 26	99.70 18

DeepPCF-MVS	94.58 5	96.90 121	96.43 147	98.31 66	97.48 251	97.23 40	92.56 299	98.60 151	92.84 235	98.54 58	97.40 186	96.64 72	98.78 297	94.40 166	99.41 141	98.93 174

CP-MVSNet		98.42 23	98.46 24	98.30 67	99.46 28	95.22 109	98.27 31	98.84 93	99.05 13	99.01 35	98.65 62	95.37 124	99.90 13	97.57 34	99.91 16	99.77 8

XVG-OURS-SEG-HR		97.38 97	97.07 110	98.30 67	99.01 91	97.41 34	94.66 232	99.02 49	95.20 152	98.15 100	97.52 177	98.83 4	98.43 326	94.87 145	96.41 318	99.07 153

NR-MVSNet		97.96 46	97.86 48	98.26 69	98.73 110	95.54 91	98.14 40	98.73 123	97.79 45	99.42 15	97.83 149	94.40 156	99.78 41	95.91 90	99.76 38	99.46 61

XVG-OURS		97.12 110	96.74 128	98.26 69	98.99 92	97.45 32	93.82 267	99.05 40	95.19 153	98.32 82	97.70 164	95.22 130	98.41 327	94.27 172	98.13 267	98.93 174

test_0728_SECOND				98.25 71	99.23 53	95.49 95	96.74 114	98.89 75					99.75 61	95.48 108	99.52 100	99.53 39

PHI-MVS		96.96 117	96.53 141	98.25 71	97.48 251	96.50 58	96.76 113	98.85 88	93.52 207	96.19 223	96.85 226	95.94 98	99.42 195	93.79 192	99.43 132	98.83 191

SF-MVS		97.60 81	97.39 88	98.22 73	98.93 95	95.69 83	97.05 101	99.10 28	95.32 148	97.83 137	97.88 144	96.44 85	99.72 78	94.59 159	99.39 143	99.25 117

PS-MVSNAJss		98.53 19	98.63 19	98.21 74	99.68 9	94.82 120	98.10 42	99.21 11	96.91 81	99.75 2	99.45 9	95.82 104	99.92 4	98.80 4	99.96 4	99.89 1

ETH3D-3000-0.1		96.89 123	96.46 146	98.16 75	98.62 126	95.69 83	95.96 157	98.98 63	93.36 212	97.04 176	97.31 199	94.93 139	99.63 133	92.60 213	99.34 157	99.17 127

DVP-MVS		97.78 70	97.65 66	98.16 75	99.24 51	95.51 93	96.74 114	98.23 192	95.92 121	98.40 70	98.28 92	97.06 49	99.71 93	95.48 108	99.52 100	99.26 113
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
DeepC-MVS	95.41 4	97.82 67	97.70 60	98.16 75	98.78 106	95.72 81	96.23 141	99.02 49	93.92 200	98.62 51	98.99 38	97.69 23	99.62 141	96.18 75	99.87 23	99.15 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	96.13 3	97.73 72	97.59 76	98.15 78	98.11 186	95.60 89	98.04 45	98.70 133	98.13 38	96.93 185	98.45 75	95.30 128	99.62 141	95.64 99	98.96 210	99.24 119

PM-MVS		97.36 100	97.10 107	98.14 79	98.91 97	96.77 49	96.20 142	98.63 149	93.82 201	98.54 58	98.33 83	93.98 166	99.05 271	95.99 86	99.45 124	98.61 216

NCCC		96.52 147	95.99 165	98.10 80	97.81 214	95.68 85	95.00 218	98.20 196	95.39 146	95.40 249	96.36 258	93.81 170	99.45 189	93.55 199	98.42 257	99.17 127

ETH3D cwj APD-0.16		96.23 157	95.61 179	98.09 81	97.91 202	95.65 88	94.94 220	98.74 121	91.31 257	96.02 229	97.08 212	94.05 165	99.69 110	91.51 232	98.94 214	98.93 174

SED-MVS		97.94 51	97.90 44	98.07 82	99.22 56	95.35 101	96.79 111	98.83 100	96.11 108	99.08 31	98.24 98	97.87 20	99.72 78	95.44 112	99.51 105	99.14 134

Vis-MVSNet		98.27 29	98.34 28	98.07 82	99.33 42	95.21 111	98.04 45	99.46 5	97.32 73	97.82 139	99.11 31	96.75 67	99.86 20	97.84 24	99.36 149	99.15 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AllTest		97.20 109	96.92 119	98.06 84	99.08 83	96.16 68	97.14 97	99.16 17	94.35 184	97.78 140	98.07 117	95.84 101	99.12 261	91.41 233	99.42 135	98.91 179

TestCases				98.06 84	99.08 83	96.16 68		99.16 17	94.35 184	97.78 140	98.07 117	95.84 101	99.12 261	91.41 233	99.42 135	98.91 179

N_pmnet		95.18 199	94.23 231	98.06 84	97.85 206	96.55 57	92.49 300	91.63 333	89.34 275	98.09 108	97.41 185	90.33 236	99.06 270	91.58 231	99.31 168	98.56 219

F-COLMAP		95.30 195	94.38 228	98.05 87	98.64 121	96.04 72	95.61 178	98.66 143	89.00 279	93.22 309	96.40 255	92.90 189	99.35 222	87.45 308	97.53 294	98.77 200

CNVR-MVS		96.92 119	96.55 138	98.03 88	98.00 196	95.54 91	94.87 223	98.17 202	94.60 175	96.38 211	97.05 215	95.67 114	99.36 219	95.12 136	99.08 199	99.19 124

TSAR-MVS + MP.		97.42 94	97.23 100	98.00 89	99.38 38	95.00 115	97.63 68	98.20 196	93.00 228	98.16 98	98.06 122	95.89 99	99.72 78	95.67 95	99.10 197	99.28 108
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMH+	93.58 10	98.23 32	98.31 29	97.98 90	99.39 37	95.22 109	97.55 73	99.20 13	98.21 36	99.25 25	98.51 71	98.21 11	99.40 206	94.79 149	99.72 47	99.32 95

v7n		98.73 11	98.99 5	97.95 91	99.64 11	94.20 145	98.67 11	99.14 23	99.08 10	99.42 15	99.23 21	96.53 78	99.91 12	99.27 2	99.93 10	99.73 15

Anonymous20231211		98.55 17	98.76 13	97.94 92	98.79 104	94.37 137	98.84 8	99.15 21	99.37 3	99.67 6	99.43 11	95.61 116	99.72 78	98.12 16	99.86 24	99.73 15

Regformer-2		97.41 95	97.24 99	97.93 93	97.21 272	94.72 123	94.85 225	98.27 187	97.74 50	98.11 104	97.50 179	95.58 117	99.69 110	96.57 64	99.31 168	99.37 90

OMC-MVS		96.48 149	96.00 164	97.91 94	98.30 157	96.01 75	94.86 224	98.60 151	91.88 248	97.18 164	97.21 205	96.11 94	99.04 272	90.49 265	99.34 157	98.69 208

xxxxxxxxxxxxxcwj		97.24 107	97.03 113	97.89 95	98.48 144	94.71 124	94.53 237	99.07 37	95.02 162	97.83 137	97.88 144	96.44 85	99.72 78	94.59 159	99.39 143	99.25 117

train_agg		95.46 188	94.66 211	97.88 96	97.84 211	95.23 106	93.62 273	98.39 174	87.04 298	93.78 287	95.99 273	94.58 150	99.52 170	91.76 228	98.90 219	98.89 183

pm-mvs1		98.47 21	98.67 17	97.86 97	99.52 21	94.58 130	98.28 29	99.00 57	97.57 60	99.27 24	99.22 22	98.32 9	99.50 175	97.09 52	99.75 42	99.50 43

ITE_SJBPF				97.85 98	98.64 121	96.66 53		98.51 160	95.63 135	97.22 162	97.30 200	95.52 118	98.55 320	90.97 243	98.90 219	98.34 237

CDPH-MVS		95.45 189	94.65 212	97.84 99	98.28 160	94.96 116	93.73 271	98.33 183	85.03 320	95.44 247	96.60 243	95.31 127	99.44 192	90.01 271	99.13 191	99.11 146

DP-MVS		97.87 61	97.89 46	97.81 100	98.62 126	94.82 120	97.13 98	98.79 110	98.98 17	98.74 47	98.49 72	95.80 110	99.49 176	95.04 139	99.44 125	99.11 146

MAR-MVS		94.21 241	93.03 258	97.76 101	96.94 283	97.44 33	96.97 106	97.15 262	87.89 293	92.00 327	92.73 331	92.14 209	99.12 261	83.92 332	97.51 295	96.73 316
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
agg_prior1		95.39 191	94.60 217	97.75 102	97.80 218	94.96 116	93.39 281	98.36 178	87.20 296	93.49 301	95.97 276	94.65 147	99.53 166	91.69 230	98.86 225	98.77 200

AUN-MVS		93.95 251	92.69 268	97.74 103	97.80 218	95.38 98	95.57 179	95.46 301	91.26 258	92.64 320	96.10 271	74.67 330	99.55 162	93.72 195	96.97 305	98.30 241

VDD-MVS		97.37 98	97.25 97	97.74 103	98.69 119	94.50 133	97.04 102	95.61 297	98.59 25	98.51 60	98.72 55	92.54 201	99.58 151	96.02 83	99.49 111	99.12 142

Anonymous20240529		97.96 46	98.04 39	97.71 105	98.69 119	94.28 142	97.86 55	98.31 186	98.79 21	99.23 26	98.86 48	95.76 111	99.61 147	95.49 105	99.36 149	99.23 120

Regformer-4		97.53 87	97.47 86	97.71 105	97.35 261	93.91 153	95.26 199	98.14 206	97.97 42	98.34 78	97.89 142	95.49 119	99.71 93	97.41 39	99.42 135	99.51 42

VPA-MVSNet		98.27 29	98.46 24	97.70 107	99.06 86	93.80 159	97.76 60	99.00 57	98.40 29	99.07 33	98.98 39	96.89 59	99.75 61	97.19 49	99.79 34	99.55 35

IS-MVSNet		96.93 118	96.68 131	97.70 107	99.25 50	94.00 151	98.57 15	96.74 278	98.36 30	98.14 102	97.98 131	88.23 261	99.71 93	93.10 209	99.72 47	99.38 85

CSCG		97.40 96	97.30 93	97.69 109	98.95 94	94.83 119	97.28 89	98.99 60	96.35 100	98.13 103	95.95 278	95.99 97	99.66 127	94.36 170	99.73 44	98.59 217

HQP_MVS		96.66 142	96.33 151	97.68 110	98.70 117	94.29 139	96.50 124	98.75 119	96.36 98	96.16 224	96.77 233	91.91 219	99.46 185	92.59 215	99.20 180	99.28 108

EPP-MVSNet		96.84 124	96.58 135	97.65 111	99.18 67	93.78 161	98.68 10	96.34 282	97.91 44	97.30 160	98.06 122	88.46 258	99.85 22	93.85 190	99.40 142	99.32 95

OPU-MVS				97.64 112	98.01 192	95.27 104	96.79 111				97.35 195	96.97 53	98.51 323	91.21 239	99.25 177	99.14 134

MVS_111021_LR		96.82 128	96.55 138	97.62 113	98.27 162	95.34 103	93.81 269	98.33 183	94.59 177	96.56 203	96.63 242	96.61 73	98.73 302	94.80 148	99.34 157	98.78 197

Regformer-1		97.27 104	97.16 104	97.61 114	97.21 272	93.86 156	94.85 225	98.04 220	97.62 59	98.03 116	97.50 179	95.34 125	99.63 133	96.52 65	99.31 168	99.35 92

UGNet		96.81 129	96.56 137	97.58 115	96.64 288	93.84 158	97.75 61	97.12 264	96.47 96	93.62 295	98.88 46	93.22 182	99.53 166	95.61 101	99.69 54	99.36 91
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
FC-MVSNet-test		98.16 33	98.37 27	97.56 116	99.49 26	93.10 181	98.35 26	99.21 11	98.43 28	98.89 39	98.83 49	94.30 158	99.81 31	97.87 23	99.91 16	99.77 8

MCST-MVS		96.24 156	95.80 172	97.56 116	98.75 109	94.13 147	94.66 232	98.17 202	90.17 269	96.21 222	96.10 271	95.14 131	99.43 194	94.13 178	98.85 227	99.13 137

GBi-Net		96.99 113	96.80 125	97.56 116	97.96 198	93.67 164	98.23 32	98.66 143	95.59 138	97.99 118	99.19 24	89.51 250	99.73 74	94.60 156	99.44 125	99.30 101

test1		96.99 113	96.80 125	97.56 116	97.96 198	93.67 164	98.23 32	98.66 143	95.59 138	97.99 118	99.19 24	89.51 250	99.73 74	94.60 156	99.44 125	99.30 101

FMVSNet1		97.95 49	98.08 35	97.56 116	99.14 79	93.67 164	98.23 32	98.66 143	97.41 70	99.00 36	99.19 24	95.47 121	99.73 74	95.83 91	99.76 38	99.30 101

TransMVSNet (Re)		98.38 25	98.67 17	97.51 121	99.51 22	93.39 174	98.20 37	98.87 82	98.23 35	99.48 12	99.27 19	98.47 8	99.55 162	96.52 65	99.53 95	99.60 26

PLC	91.02 16	94.05 248	92.90 260	97.51 121	98.00 196	95.12 113	94.25 245	98.25 190	86.17 304	91.48 330	95.25 293	91.01 227	99.19 251	85.02 327	96.69 313	98.22 249
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	93.61 9	98.44 22	98.76 13	97.51 121	99.43 32	93.54 170	98.23 32	99.05 40	97.40 71	99.37 18	99.08 34	98.79 5	99.47 182	97.74 30	99.71 50	99.50 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
alignmvs		96.01 167	95.52 182	97.50 124	97.77 229	94.71 124	96.07 148	96.84 272	97.48 65	96.78 194	94.28 314	85.50 281	99.40 206	96.22 73	98.73 240	98.40 228

Baseline_NR-MVSNet		97.72 73	97.79 53	97.50 124	99.56 15	93.29 175	95.44 182	98.86 84	98.20 37	98.37 73	99.24 20	94.69 143	99.55 162	95.98 87	99.79 34	99.65 23

3Dnovator	96.53 2	97.61 80	97.64 69	97.50 124	97.74 232	93.65 168	98.49 20	98.88 80	96.86 83	97.11 169	98.55 68	95.82 104	99.73 74	95.94 88	99.42 135	99.13 137

TSAR-MVS + GP.		96.47 150	96.12 158	97.49 127	97.74 232	95.23 106	94.15 252	96.90 271	93.26 216	98.04 115	96.70 238	94.41 155	98.89 288	94.77 152	99.14 187	98.37 231

FIs		97.93 54	98.07 36	97.48 128	99.38 38	92.95 184	98.03 47	99.11 26	98.04 41	98.62 51	98.66 60	93.75 172	99.78 41	97.23 43	99.84 27	99.73 15

ETH3 D test6400		94.77 216	93.87 245	97.47 129	98.12 185	93.73 162	94.56 236	98.70 133	85.45 315	94.70 264	95.93 280	91.77 221	99.63 133	86.45 314	99.14 187	99.05 157

test_part1		96.77 132	96.53 141	97.47 129	98.04 188	92.92 185	97.93 50	98.85 88	98.83 20	99.30 21	99.07 35	79.25 306	99.79 38	97.59 33	99.93 10	99.69 20

test_0402		97.84 64	97.97 41	97.47 129	99.19 66	94.07 148	96.71 119	98.73 123	98.66 24	98.56 57	98.41 77	96.84 64	99.69 110	94.82 147	99.81 29	98.64 211

test_prior3		95.91 170	95.39 184	97.46 132	97.79 224	94.26 143	93.33 284	98.42 170	94.21 189	94.02 282	96.25 262	93.64 174	99.34 224	91.90 222	98.96 210	98.79 195

test_prior				97.46 132	97.79 224	94.26 143		98.42 170					99.34 224			98.79 195

test12				97.46 132	97.61 243	94.07 148		97.78 233		93.57 298		93.31 180	99.42 195		98.78 233	98.89 183

DeepC-MVS_fast	94.34 7	96.74 133	96.51 144	97.44 135	97.69 235	94.15 146	96.02 152	98.43 167	93.17 223	97.30 160	97.38 192	95.48 120	99.28 240	93.74 193	99.34 157	98.88 186
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous202405211		96.34 154	95.98 166	97.43 136	98.25 165	93.85 157	96.74 114	94.41 309	97.72 53	98.37 73	98.03 125	87.15 272	99.53 166	94.06 180	99.07 201	98.92 178

pmmvs-eth3d		96.49 148	96.18 156	97.42 137	98.25 165	94.29 139	94.77 229	98.07 217	89.81 272	97.97 122	98.33 83	93.11 183	99.08 268	95.46 111	99.84 27	98.89 183

VDDNet		96.98 116	96.84 122	97.41 138	99.40 36	93.26 176	97.94 49	95.31 302	99.26 7	98.39 72	99.18 27	87.85 268	99.62 141	95.13 135	99.09 198	99.35 92

EG-PatchMatch MVS		97.69 75	97.79 53	97.40 139	99.06 86	93.52 171	95.96 157	98.97 66	94.55 179	98.82 42	98.76 53	97.31 35	99.29 238	97.20 48	99.44 125	99.38 85

Fast-Effi-MVS+-dtu		96.44 151	96.12 158	97.39 140	97.18 274	94.39 135	95.46 181	98.73 123	96.03 115	94.72 262	94.92 301	96.28 93	99.69 110	93.81 191	97.98 272	98.09 255

RRT_MVS		94.90 210	94.07 237	97.39 140	93.18 347	93.21 178	95.26 199	97.49 251	93.94 199	98.25 89	97.85 147	72.96 340	99.84 25	97.90 21	99.78 37	99.14 134

LF4IMVS		96.07 163	95.63 178	97.36 142	98.19 171	95.55 90	95.44 182	98.82 108	92.29 242	95.70 243	96.55 245	92.63 197	98.69 306	91.75 229	99.33 164	97.85 275

Gipuma		98.07 40	98.31 29	97.36 142	99.76 5	96.28 66	98.51 19	99.10 28	98.76 22	96.79 190	99.34 17	96.61 73	98.82 293	96.38 70	99.50 107	96.98 302
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		97.33 101	97.33 92	97.32 144	98.13 184	93.79 160	96.99 105	99.65 2	96.74 86	99.47 13	98.93 43	96.91 58	99.84 25	90.11 269	99.06 204	98.32 238

canonicalmvs		97.23 108	97.21 102	97.30 145	97.65 240	94.39 135	97.84 56	99.05 40	97.42 67	96.68 197	93.85 317	97.63 26	99.33 227	96.29 72	98.47 256	98.18 253

1121		94.26 237	93.26 254	97.27 146	98.26 164	94.73 122	95.86 162	97.71 237	77.96 349	94.53 268	96.71 237	91.93 217	99.40 206	87.71 300	98.64 247	97.69 283

MVS_111021_HR		96.73 135	96.54 140	97.27 146	98.35 155	93.66 167	93.42 279	98.36 178	94.74 170	96.58 201	96.76 235	96.54 77	98.99 278	94.87 145	99.27 175	99.15 131

SixPastTwentyTwo		97.49 89	97.57 78	97.26 148	99.56 15	92.33 194	98.28 29	96.97 269	98.30 33	99.45 14	99.35 16	88.43 259	99.89 16	98.01 19	99.76 38	99.54 36

DIV-MVS_2432*1600		97.86 63	98.07 36	97.25 149	99.22 56	92.81 187	97.55 73	98.94 70	97.10 77	98.85 40	98.88 46	95.03 135	99.67 121	97.39 41	99.65 60	99.26 113

新几何1				97.25 149	98.29 158	94.70 127		97.73 235	77.98 348	94.83 261	96.67 240	92.08 212	99.45 189	88.17 298	98.65 246	97.61 286

WR-MVS		96.90 121	96.81 124	97.16 151	98.56 134	92.20 200	94.33 241	98.12 209	97.34 72	98.20 93	97.33 197	92.81 190	99.75 61	94.79 149	99.81 29	99.54 36

TAMVS		95.49 184	94.94 197	97.16 151	98.31 156	93.41 173	95.07 212	96.82 274	91.09 260	97.51 146	97.82 152	89.96 243	99.42 195	88.42 294	99.44 125	98.64 211

CDS-MVSNet		94.88 212	94.12 236	97.14 153	97.64 241	93.57 169	93.96 263	97.06 266	90.05 270	96.30 217	96.55 245	86.10 277	99.47 182	90.10 270	99.31 168	98.40 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		97.32 102	97.39 88	97.11 154	97.36 260	92.08 204	95.34 192	97.65 243	97.74 50	98.29 87	98.11 113	95.05 132	99.68 116	97.50 37	99.50 107	99.56 33

Regformer-3		97.25 106	97.29 94	97.11 154	97.35 261	92.32 195	95.26 199	97.62 248	97.67 58	98.17 97	97.89 142	95.05 132	99.56 158	97.16 50	99.42 135	99.46 61

EI-MVSNet-UG-set		97.32 102	97.40 87	97.09 156	97.34 265	92.01 206	95.33 193	97.65 243	97.74 50	98.30 86	98.14 108	95.04 134	99.69 110	97.55 35	99.52 100	99.58 28

XXY-MVS		97.54 85	97.70 60	97.07 157	99.46 28	92.21 198	97.22 93	99.00 57	94.93 166	98.58 56	98.92 44	97.31 35	99.41 204	94.44 162	99.43 132	99.59 27

lessismore_v0				97.05 158	99.36 40	92.12 202		84.07 357		98.77 46	98.98 39	85.36 282	99.74 68	97.34 42	99.37 146	99.30 101

TAPA-MVS	93.32 12	94.93 209	94.23 231	97.04 159	98.18 174	94.51 131	95.22 203	98.73 123	81.22 337	96.25 220	95.95 278	93.80 171	98.98 280	89.89 273	98.87 223	97.62 285
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		93.72 254	92.62 271	97.03 160	87.61 362	92.25 196	96.27 136	91.28 336	96.74 86	87.65 350	97.39 190	85.00 284	99.64 131	92.14 219	99.48 115	99.20 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchMatch-RL		94.61 228	93.81 246	97.02 161	98.19 171	95.72 81	93.66 272	97.23 258	88.17 289	94.94 259	95.62 287	91.43 223	98.57 317	87.36 309	97.68 287	96.76 315

K. test v3		96.44 151	96.28 152	96.95 162	99.41 35	91.53 214	97.65 66	90.31 345	98.89 18	98.93 38	99.36 14	84.57 288	99.92 4	97.81 25	99.56 83	99.39 83

tfpnnormal		97.72 73	97.97 41	96.94 163	99.26 47	92.23 197	97.83 57	98.45 164	98.25 34	99.13 30	98.66 60	96.65 70	99.69 110	93.92 188	99.62 64	98.91 179

MVP-Stereo		95.69 176	95.28 186	96.92 164	98.15 180	93.03 182	95.64 177	98.20 196	90.39 266	96.63 200	97.73 161	91.63 222	99.10 266	91.84 226	97.31 302	98.63 213
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP-MVS		95.17 201	94.58 220	96.92 164	97.85 206	92.47 192	94.26 242	98.43 167	93.18 220	92.86 314	95.08 295	90.33 236	99.23 248	90.51 263	98.74 237	99.05 157

HyFIR lowres test		93.72 254	92.65 269	96.91 166	98.93 95	91.81 211	91.23 323	98.52 158	82.69 330	96.46 208	96.52 249	80.38 303	99.90 13	90.36 267	98.79 232	99.03 159

VNet		96.84 124	96.83 123	96.88 167	98.06 187	92.02 205	96.35 133	97.57 250	97.70 55	97.88 131	97.80 154	92.40 205	99.54 165	94.73 154	98.96 210	99.08 151

FMVSNet2		96.72 136	96.67 132	96.87 168	97.96 198	91.88 208	97.15 95	98.06 218	95.59 138	98.50 62	98.62 63	89.51 250	99.65 128	94.99 143	99.60 73	99.07 153

EIA-MVS		96.04 165	95.77 174	96.85 169	97.80 218	92.98 183	96.12 146	99.16 17	94.65 173	93.77 289	91.69 342	95.68 113	99.67 121	94.18 175	98.85 227	97.91 273

MVS_0304		95.50 183	95.05 195	96.84 170	96.28 297	93.12 180	97.00 104	96.16 284	95.03 161	89.22 344	97.70 164	90.16 242	99.48 179	94.51 161	99.34 157	97.93 272

ETV-MVS		96.13 162	95.90 170	96.82 171	97.76 230	93.89 154	95.40 187	98.95 69	95.87 125	95.58 246	91.00 347	96.36 90	99.72 78	93.36 200	98.83 229	96.85 309

DP-MVS Recon		95.55 182	95.13 190	96.80 172	98.51 139	93.99 152	94.60 234	98.69 136	90.20 268	95.78 239	96.21 265	92.73 193	98.98 280	90.58 260	98.86 225	97.42 292

QAPM		95.88 172	95.57 181	96.80 172	97.90 204	91.84 210	98.18 39	98.73 123	88.41 285	96.42 209	98.13 109	94.73 141	99.75 61	88.72 289	98.94 214	98.81 193

CMPMVS	73.10 23	92.74 273	91.39 285	96.77 174	93.57 346	94.67 128	94.21 249	97.67 239	80.36 341	93.61 296	96.60 243	82.85 293	97.35 347	84.86 328	98.78 233	98.29 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+		95.49 184	95.07 192	96.75 175	97.67 239	92.82 186	94.22 248	98.60 151	91.61 251	93.42 306	92.90 327	96.73 68	99.70 102	92.60 213	97.89 277	97.74 280

CNLPA		95.04 205	94.47 224	96.75 175	97.81 214	95.25 105	94.12 256	97.89 225	94.41 181	94.57 266	95.69 283	90.30 239	98.35 333	86.72 313	98.76 235	96.64 318

Effi-MVS+		96.19 159	96.01 163	96.71 177	97.43 257	92.19 201	96.12 146	99.10 28	95.45 143	93.33 308	94.71 304	97.23 42	99.56 158	93.21 207	97.54 293	98.37 231

pmmvs4		94.82 214	94.19 234	96.70 178	97.42 258	92.75 189	92.09 309	96.76 276	86.80 301	95.73 242	97.22 204	89.28 253	98.89 288	93.28 203	99.14 187	98.46 227

CS-MVS		95.86 173	95.59 180	96.69 179	97.85 206	93.14 179	96.42 127	99.25 9	94.17 192	93.56 299	90.76 350	96.05 96	99.72 78	93.28 203	98.91 218	97.21 296

CLD-MVS		95.47 187	95.07 192	96.69 179	98.27 162	92.53 191	91.36 317	98.67 141	91.22 259	95.78 239	94.12 315	95.65 115	98.98 280	90.81 248	99.72 47	98.57 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		97.04 111	97.16 104	96.68 181	98.59 131	91.05 219	96.33 134	98.36 178	94.60 175	97.99 118	98.30 89	93.32 179	99.62 141	97.40 40	99.53 95	99.38 85

LFMVS		95.32 194	94.88 202	96.62 182	98.03 189	91.47 216	97.65 66	90.72 342	99.11 9	97.89 130	98.31 85	79.20 307	99.48 179	93.91 189	99.12 194	98.93 174

ab-mvs		96.59 144	96.59 134	96.60 183	98.64 121	92.21 198	98.35 26	97.67 239	94.45 180	96.99 180	98.79 50	94.96 138	99.49 176	90.39 266	99.07 201	98.08 256

VPNet		97.26 105	97.49 84	96.59 184	99.47 27	90.58 229	96.27 136	98.53 157	97.77 46	98.46 66	98.41 77	94.59 149	99.68 116	94.61 155	99.29 172	99.52 40

原ACMM1				96.58 185	98.16 178	92.12 202		98.15 205	85.90 308	93.49 301	96.43 252	92.47 204	99.38 214	87.66 303	98.62 248	98.23 248

AdaColmap		95.11 202	94.62 216	96.58 185	97.33 267	94.45 134	94.92 221	98.08 213	93.15 224	93.98 285	95.53 290	94.34 157	99.10 266	85.69 319	98.61 249	96.20 326

PCF-MVS	89.43 18	92.12 285	90.64 299	96.57 187	97.80 218	93.48 172	89.88 341	98.45 164	74.46 354	96.04 228	95.68 284	90.71 232	99.31 231	73.73 351	99.01 208	96.91 306
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ambc				96.56 188	98.23 168	91.68 213	97.88 54	98.13 208		98.42 69	98.56 67	94.22 161	99.04 272	94.05 183	99.35 154	98.95 168

casdiffmvs		97.50 88	97.81 52	96.56 188	98.51 139	91.04 220	95.83 165	99.09 33	97.23 76	98.33 81	98.30 89	97.03 51	99.37 217	96.58 63	99.38 145	99.28 108

FMVSNet5		93.39 263	92.35 274	96.50 190	95.83 313	90.81 226	97.31 87	98.27 187	92.74 236	96.27 218	98.28 92	62.23 356	99.67 121	90.86 246	99.36 149	99.03 159

CANet		95.86 173	95.65 177	96.49 191	96.41 294	90.82 224	94.36 240	98.41 172	94.94 164	92.62 322	96.73 236	92.68 194	99.71 93	95.12 136	99.60 73	98.94 170

test20.03		96.58 145	96.61 133	96.48 192	98.49 142	91.72 212	95.68 172	97.69 238	96.81 84	98.27 88	97.92 140	94.18 162	98.71 304	90.78 250	99.66 59	99.00 162

UnsupCasMVSNet_eth		95.91 170	95.73 175	96.44 193	98.48 144	91.52 215	95.31 195	98.45 164	95.76 131	97.48 152	97.54 174	89.53 249	98.69 306	94.43 163	94.61 336	99.13 137

baseline		97.44 93	97.78 56	96.43 194	98.52 138	90.75 227	96.84 108	99.03 47	96.51 92	97.86 135	98.02 126	96.67 69	99.36 219	97.09 52	99.47 117	99.19 124

DPM-MVS		93.68 256	92.77 267	96.42 195	97.91 202	92.54 190	91.17 324	97.47 254	84.99 321	93.08 311	94.74 303	89.90 244	99.00 276	87.54 306	98.09 269	97.72 281

PVSNet_Blended_VisFu		95.95 169	95.80 172	96.42 195	99.28 46	90.62 228	95.31 195	99.08 34	88.40 286	96.97 183	98.17 107	92.11 210	99.78 41	93.64 197	99.21 179	98.86 189

ANet_high		98.31 28	98.94 6	96.41 197	99.33 42	89.64 240	97.92 52	99.56 4	99.27 6	99.66 8	99.50 6	97.67 25	99.83 28	97.55 35	99.98 2	99.77 8

SD-MVS		97.37 98	97.70 60	96.35 198	98.14 181	95.13 112	96.54 123	98.92 72	95.94 120	99.19 28	98.08 115	97.74 22	95.06 354	95.24 124	99.54 92	98.87 188
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
Patchmtry		95.03 207	94.59 219	96.33 199	94.83 330	90.82 224	96.38 131	97.20 259	96.59 90	97.49 149	98.57 65	77.67 314	99.38 214	92.95 212	99.62 64	98.80 194

OpenMVS	94.22 8	95.48 186	95.20 187	96.32 200	97.16 275	91.96 207	97.74 62	98.84 93	87.26 295	94.36 273	98.01 128	93.95 167	99.67 121	90.70 256	98.75 236	97.35 295

v10		97.55 84	97.97 41	96.31 201	98.60 129	89.64 240	97.44 81	99.02 49	96.60 89	98.72 49	99.16 29	93.48 177	99.72 78	98.76 6	99.92 13	99.58 28

PMMVS		92.39 278	91.08 290	96.30 202	93.12 350	92.81 187	90.58 332	95.96 289	79.17 345	91.85 329	92.27 335	90.29 240	98.66 311	89.85 274	96.68 314	97.43 291

bset_n11_16_dypcd		94.53 232	93.95 243	96.25 203	97.56 246	89.85 237	88.52 347	91.32 335	94.90 167	97.51 146	96.38 257	82.34 295	99.78 41	97.22 44	99.80 32	99.12 142

v8		97.60 81	98.06 38	96.23 204	98.71 115	89.44 244	97.43 83	98.82 108	97.29 75	98.74 47	99.10 32	93.86 168	99.68 116	98.61 10	99.94 8	99.56 33

1112_ss		94.12 244	93.42 251	96.23 204	98.59 131	90.85 223	94.24 246	98.85 88	85.49 312	92.97 312	94.94 299	86.01 278	99.64 131	91.78 227	97.92 274	98.20 251

FMVSNet3		95.26 197	94.94 197	96.22 206	96.53 291	90.06 233	95.99 154	97.66 241	94.11 194	97.99 118	97.91 141	80.22 304	99.63 133	94.60 156	99.44 125	98.96 167

114514_t		93.96 249	93.22 256	96.19 207	99.06 86	90.97 222	95.99 154	98.94 70	73.88 355	93.43 305	96.93 222	92.38 206	99.37 217	89.09 284	99.28 173	98.25 247

CHOSEN 1792x2688		94.10 245	93.41 252	96.18 208	99.16 68	90.04 234	92.15 306	98.68 138	79.90 342	96.22 221	97.83 149	87.92 267	99.42 195	89.18 283	99.65 60	99.08 151

v1192		96.83 127	97.06 111	96.15 209	98.28 160	89.29 246	95.36 190	98.77 115	93.73 203	98.11 104	98.34 82	93.02 188	99.67 121	98.35 14	99.58 77	99.50 43

v1144		96.84 124	97.08 109	96.13 210	98.42 150	89.28 247	95.41 186	98.67 141	94.21 189	97.97 122	98.31 85	93.06 184	99.65 128	98.06 18	99.62 64	99.45 66

UnsupCasMVSNet_bld		94.72 221	94.26 230	96.08 211	98.62 126	90.54 232	93.38 282	98.05 219	90.30 267	97.02 178	96.80 232	89.54 247	99.16 257	88.44 293	96.18 321	98.56 219

v144192		96.69 139	96.90 121	96.03 212	98.25 165	88.92 251	95.49 180	98.77 115	93.05 226	98.09 108	98.29 91	92.51 203	99.70 102	98.11 17	99.56 83	99.47 59

v1921920		96.72 136	96.96 117	95.99 213	98.21 169	88.79 256	95.42 184	98.79 110	93.22 218	98.19 96	98.26 97	92.68 194	99.70 102	98.34 15	99.55 89	99.49 51

DELS-MVS		96.17 160	96.23 153	95.99 213	97.55 248	90.04 234	92.38 304	98.52 158	94.13 193	96.55 205	97.06 214	94.99 137	99.58 151	95.62 100	99.28 173	98.37 231
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
CANet_DTU		94.65 226	94.21 233	95.96 215	95.90 310	89.68 239	93.92 264	97.83 231	93.19 219	90.12 339	95.64 286	88.52 257	99.57 157	93.27 205	99.47 117	98.62 214

PAPM_NR		94.61 228	94.17 235	95.96 215	98.36 154	91.23 217	95.93 160	97.95 221	92.98 229	93.42 306	94.43 311	90.53 233	98.38 330	87.60 304	96.29 320	98.27 245

v2v482		96.78 131	97.06 111	95.95 217	98.57 133	88.77 257	95.36 190	98.26 189	95.18 154	97.85 136	98.23 100	92.58 198	99.63 133	97.80 26	99.69 54	99.45 66

PMVS	89.60 17	96.71 138	96.97 115	95.95 217	99.51 22	97.81 16	97.42 84	97.49 251	97.93 43	95.95 231	98.58 64	96.88 61	96.91 349	89.59 277	99.36 149	93.12 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSDG		95.33 193	95.13 190	95.94 219	97.40 259	91.85 209	91.02 328	98.37 177	95.30 149	96.31 216	95.99 273	94.51 153	98.38 330	89.59 277	97.65 290	97.60 287

v1240		96.74 133	97.02 114	95.91 220	98.18 174	88.52 259	95.39 188	98.88 80	93.15 224	98.46 66	98.40 79	92.80 191	99.71 93	98.45 13	99.49 111	99.49 51

Anonymous20231206		95.27 196	95.06 194	95.88 221	98.72 112	89.37 245	95.70 169	97.85 227	88.00 291	96.98 182	97.62 170	91.95 215	99.34 224	89.21 282	99.53 95	98.94 170

Vis-MVSNet (Re-imp)		95.11 202	94.85 203	95.87 222	99.12 80	89.17 248	97.54 78	94.92 304	96.50 93	96.58 201	97.27 201	83.64 291	99.48 179	88.42 294	99.67 57	98.97 166

CL-MVSNet_2432*1600		95.04 205	94.79 208	95.82 223	97.51 250	89.79 238	91.14 325	96.82 274	93.05 226	96.72 195	96.40 255	90.82 230	99.16 257	91.95 221	98.66 244	98.50 223

IterMVS-LS		96.92 119	97.29 94	95.79 224	98.51 139	88.13 268	95.10 207	98.66 143	96.99 78	98.46 66	98.68 59	92.55 199	99.74 68	96.91 58	99.79 34	99.50 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		96.63 143	96.93 118	95.74 225	97.26 270	88.13 268	95.29 197	97.65 243	96.99 78	97.94 125	98.19 105	92.55 199	99.58 151	96.91 58	99.56 83	99.50 43

MDA-MVSNet-bldmvs		95.69 176	95.67 176	95.74 225	98.48 144	88.76 258	92.84 291	97.25 257	96.00 116	97.59 142	97.95 136	91.38 224	99.46 185	93.16 208	96.35 319	98.99 165

sss		94.22 239	93.72 247	95.74 225	97.71 234	89.95 236	93.84 266	96.98 268	88.38 287	93.75 290	95.74 282	87.94 263	98.89 288	91.02 242	98.10 268	98.37 231

testdata				95.70 228	98.16 178	90.58 229		97.72 236	80.38 340	95.62 244	97.02 217	92.06 213	98.98 280	89.06 286	98.52 253	97.54 288

test_yl		94.40 234	94.00 240	95.59 229	96.95 281	89.52 242	94.75 230	95.55 299	96.18 106	96.79 190	96.14 268	81.09 299	99.18 252	90.75 251	97.77 278	98.07 258

DCV-MVSNet		94.40 234	94.00 240	95.59 229	96.95 281	89.52 242	94.75 230	95.55 299	96.18 106	96.79 190	96.14 268	81.09 299	99.18 252	90.75 251	97.77 278	98.07 258

tttt0517		93.31 265	92.56 272	95.57 231	98.71 115	87.86 272	97.44 81	87.17 353	95.79 130	97.47 154	96.84 227	64.12 354	99.81 31	96.20 74	99.32 166	99.02 161

MSLP-MVS++		96.42 153	96.71 129	95.57 231	97.82 213	90.56 231	95.71 168	98.84 93	94.72 171	96.71 196	97.39 190	94.91 140	98.10 341	95.28 121	99.02 206	98.05 265

thisisatest0530		92.71 274	91.76 282	95.56 233	98.42 150	88.23 264	96.03 151	87.35 352	94.04 196	96.56 203	95.47 291	64.03 355	99.77 50	94.78 151	99.11 195	98.68 210

Test_1112_low_res		93.53 261	92.86 261	95.54 234	98.60 129	88.86 254	92.75 294	98.69 136	82.66 331	92.65 319	96.92 224	84.75 286	99.56 158	90.94 244	97.76 280	98.19 252

pmmvs5		94.63 227	94.34 229	95.50 235	97.63 242	88.34 263	94.02 258	97.13 263	87.15 297	95.22 252	97.15 206	87.50 269	99.27 242	93.99 185	99.26 176	98.88 186

MVSFormer		96.14 161	96.36 149	95.49 236	97.68 236	87.81 275	98.67 11	99.02 49	96.50 93	94.48 271	96.15 266	86.90 273	99.92 4	98.73 7	99.13 191	98.74 202

ET-MVSNet_ETH3D		91.12 295	89.67 307	95.47 237	96.41 294	89.15 250	91.54 315	90.23 346	89.07 277	86.78 354	92.84 328	69.39 349	99.44 192	94.16 176	96.61 315	97.82 277

diffmvs		96.04 165	96.23 153	95.46 238	97.35 261	88.03 270	93.42 279	99.08 34	94.09 195	96.66 198	96.93 222	93.85 169	99.29 238	96.01 85	98.67 242	99.06 155

v148		96.58 145	96.97 115	95.42 239	98.63 125	87.57 279	95.09 209	97.90 224	95.91 123	98.24 91	97.96 132	93.42 178	99.39 211	96.04 81	99.52 100	99.29 107

OpenMVS_ROB	91.80 14	93.64 258	93.05 257	95.42 239	97.31 269	91.21 218	95.08 211	96.68 280	81.56 334	96.88 189	96.41 253	90.44 235	99.25 245	85.39 323	97.67 288	95.80 330

jason		94.39 236	94.04 239	95.41 241	98.29 158	87.85 274	92.74 296	96.75 277	85.38 317	95.29 250	96.15 266	88.21 262	99.65 128	94.24 173	99.34 157	98.74 202
jason: jason.
API-MVS		95.09 204	95.01 196	95.31 242	96.61 289	94.02 150	96.83 109	97.18 261	95.60 137	95.79 237	94.33 312	94.54 152	98.37 332	85.70 318	98.52 253	93.52 344

PVSNet_BlendedMVS		95.02 208	94.93 199	95.27 243	97.79 224	87.40 283	94.14 254	98.68 138	88.94 280	94.51 269	98.01 128	93.04 185	99.30 234	89.77 275	99.49 111	99.11 146

lupinMVS		93.77 252	93.28 253	95.24 244	97.68 236	87.81 275	92.12 307	96.05 286	84.52 324	94.48 271	95.06 297	86.90 273	99.63 133	93.62 198	99.13 191	98.27 245

D2MVS		95.18 199	95.17 189	95.21 245	97.76 230	87.76 277	94.15 252	97.94 222	89.77 273	96.99 180	97.68 167	87.45 270	99.14 259	95.03 141	99.81 29	98.74 202

Patchmatch-RL test		94.66 225	94.49 223	95.19 246	98.54 136	88.91 252	92.57 298	98.74 121	91.46 254	98.32 82	97.75 158	77.31 319	98.81 295	96.06 78	99.61 70	97.85 275

WTY-MVS		93.55 260	93.00 259	95.19 246	97.81 214	87.86 272	93.89 265	96.00 287	89.02 278	94.07 280	95.44 292	86.27 276	99.33 227	87.69 302	96.82 309	98.39 230

JIA-IIPM		91.79 289	90.69 298	95.11 248	93.80 343	90.98 221	94.16 251	91.78 332	96.38 97	90.30 338	99.30 18	72.02 342	98.90 286	88.28 296	90.17 348	95.45 336

MIMVSNet		93.42 262	92.86 261	95.10 249	98.17 176	88.19 265	98.13 41	93.69 312	92.07 243	95.04 257	98.21 104	80.95 301	99.03 275	81.42 340	98.06 270	98.07 258

PAPR		92.22 282	91.27 288	95.07 250	95.73 317	88.81 255	91.97 310	97.87 226	85.80 309	90.91 332	92.73 331	91.16 225	98.33 334	79.48 343	95.76 327	98.08 256

MVSTER		94.21 241	93.93 244	95.05 251	95.83 313	86.46 295	95.18 205	97.65 243	92.41 241	97.94 125	98.00 130	72.39 341	99.58 151	96.36 71	99.56 83	99.12 142

cl-mvsnet_		94.73 217	94.64 213	95.01 252	95.85 312	87.00 289	91.33 319	98.08 213	93.34 213	97.10 170	97.33 197	84.01 290	99.30 234	95.14 133	99.56 83	98.71 207

cl-mvsnet1		94.73 217	94.64 213	95.01 252	95.86 311	87.00 289	91.33 319	98.08 213	93.34 213	97.10 170	97.34 196	84.02 289	99.31 231	95.15 132	99.55 89	98.72 205

TinyColmap		96.00 168	96.34 150	94.96 254	97.90 204	87.91 271	94.13 255	98.49 161	94.41 181	98.16 98	97.76 155	96.29 92	98.68 309	90.52 262	99.42 135	98.30 241

PVSNet_Blended		93.96 249	93.65 248	94.91 255	97.79 224	87.40 283	91.43 316	98.68 138	84.50 325	94.51 269	94.48 310	93.04 185	99.30 234	89.77 275	98.61 249	98.02 268

BH-RMVSNet		94.56 230	94.44 227	94.91 255	97.57 244	87.44 282	93.78 270	96.26 283	93.69 205	96.41 210	96.50 250	92.10 211	99.00 276	85.96 316	97.71 284	98.31 239

RPMNet		94.68 224	94.60 217	94.90 257	95.44 322	88.15 266	96.18 143	98.86 84	97.43 66	94.10 278	98.49 72	79.40 305	99.76 54	95.69 94	95.81 323	96.81 313

HY-MVS	91.43 15	92.58 275	91.81 281	94.90 257	96.49 292	88.87 253	97.31 87	94.62 306	85.92 307	90.50 336	96.84 227	85.05 283	99.40 206	83.77 335	95.78 326	96.43 323

GA-MVS		92.83 272	92.15 277	94.87 259	96.97 280	87.27 286	90.03 336	96.12 285	91.83 249	94.05 281	94.57 305	76.01 326	98.97 284	92.46 217	97.34 301	98.36 236

miper_lstm_enhance		94.81 215	94.80 207	94.85 260	96.16 304	86.45 296	91.14 325	98.20 196	93.49 208	97.03 177	97.37 194	84.97 285	99.26 243	95.28 121	99.56 83	98.83 191

IterMVS-SCA-FT		95.86 173	96.19 155	94.85 260	97.68 236	85.53 305	92.42 302	97.63 247	96.99 78	98.36 75	98.54 69	87.94 263	99.75 61	97.07 54	99.08 199	99.27 112

cl_fuxian		95.20 198	95.32 185	94.83 262	96.19 302	86.43 297	91.83 312	98.35 182	93.47 209	97.36 159	97.26 202	88.69 256	99.28 240	95.41 118	99.36 149	98.78 197

testgi		96.07 163	96.50 145	94.80 263	99.26 47	87.69 278	95.96 157	98.58 154	95.08 158	98.02 117	96.25 262	97.92 16	97.60 346	88.68 291	98.74 237	99.11 146

CR-MVSNet		93.29 266	92.79 264	94.78 264	95.44 322	88.15 266	96.18 143	97.20 259	84.94 322	94.10 278	98.57 65	77.67 314	99.39 211	95.17 128	95.81 323	96.81 313

eth_miper_zixun_eth		94.89 211	94.93 199	94.75 265	95.99 309	86.12 300	91.35 318	98.49 161	93.40 210	97.12 168	97.25 203	86.87 275	99.35 222	95.08 138	98.82 230	98.78 197

MVS_Test		96.27 155	96.79 127	94.73 266	96.94 283	86.63 294	96.18 143	98.33 183	94.94 164	96.07 227	98.28 92	95.25 129	99.26 243	97.21 46	97.90 276	98.30 241

miper_ehance_all_eth		94.69 222	94.70 210	94.64 267	95.77 315	86.22 299	91.32 321	98.24 191	91.67 250	97.05 175	96.65 241	88.39 260	99.22 250	94.88 144	98.34 259	98.49 224

Patchmatch-test		93.60 259	93.25 255	94.63 268	96.14 307	87.47 281	96.04 150	94.50 308	93.57 206	96.47 207	96.97 219	76.50 322	98.61 314	90.67 257	98.41 258	97.81 279

baseline1		93.14 269	92.64 270	94.62 269	97.34 265	87.20 287	96.67 121	93.02 320	94.71 172	96.51 206	95.83 281	81.64 296	98.60 316	90.00 272	88.06 351	98.07 258

xiu_mvs_v1_base_debu		95.62 179	95.96 167	94.60 270	98.01 192	88.42 260	93.99 260	98.21 193	92.98 229	95.91 232	94.53 307	96.39 87	99.72 78	95.43 115	98.19 264	95.64 332

xiu_mvs_v1_base		95.62 179	95.96 167	94.60 270	98.01 192	88.42 260	93.99 260	98.21 193	92.98 229	95.91 232	94.53 307	96.39 87	99.72 78	95.43 115	98.19 264	95.64 332

xiu_mvs_v1_base_debi		95.62 179	95.96 167	94.60 270	98.01 192	88.42 260	93.99 260	98.21 193	92.98 229	95.91 232	94.53 307	96.39 87	99.72 78	95.43 115	98.19 264	95.64 332

MS-PatchMatch		94.83 213	94.91 201	94.57 273	96.81 287	87.10 288	94.23 247	97.34 256	88.74 283	97.14 166	97.11 210	91.94 216	98.23 337	92.99 210	97.92 274	98.37 231

USDC		94.56 230	94.57 222	94.55 274	97.78 228	86.43 297	92.75 294	98.65 148	85.96 306	96.91 187	97.93 139	90.82 230	98.74 301	90.71 255	99.59 75	98.47 225

BH-untuned		94.69 222	94.75 209	94.52 275	97.95 201	87.53 280	94.07 257	97.01 267	93.99 197	97.10 170	95.65 285	92.65 196	98.95 285	87.60 304	96.74 312	97.09 298

cl-mvsnet2		93.25 267	92.84 263	94.46 276	94.30 336	86.00 301	91.09 327	96.64 281	90.74 262	95.79 237	96.31 260	78.24 311	98.77 298	94.15 177	98.34 259	98.62 214

MDA-MVSNet_test_wron		94.73 217	94.83 206	94.42 277	97.48 251	85.15 312	90.28 335	95.87 291	92.52 237	97.48 152	97.76 155	91.92 218	99.17 256	93.32 201	96.80 311	98.94 170

YYNet1		94.73 217	94.84 204	94.41 278	97.47 255	85.09 314	90.29 334	95.85 292	92.52 237	97.53 144	97.76 155	91.97 214	99.18 252	93.31 202	96.86 308	98.95 168

ADS-MVSNet2		91.47 293	90.51 301	94.36 279	95.51 320	85.63 303	95.05 215	95.70 293	83.46 328	92.69 317	96.84 227	79.15 308	99.41 204	85.66 320	90.52 346	98.04 266

new_pmnet		92.34 280	91.69 283	94.32 280	96.23 300	89.16 249	92.27 305	92.88 322	84.39 327	95.29 250	96.35 259	85.66 280	96.74 352	84.53 330	97.56 292	97.05 300

MG-MVS		94.08 247	94.00 240	94.32 280	97.09 277	85.89 302	93.19 288	95.96 289	92.52 237	94.93 260	97.51 178	89.54 247	98.77 298	87.52 307	97.71 284	98.31 239

PatchT		93.75 253	93.57 249	94.29 282	95.05 328	87.32 285	96.05 149	92.98 321	97.54 63	94.25 274	98.72 55	75.79 327	99.24 246	95.92 89	95.81 323	96.32 324

miper_enhance_ethall		93.14 269	92.78 266	94.20 283	93.65 344	85.29 309	89.97 337	97.85 227	85.05 319	96.15 226	94.56 306	85.74 279	99.14 259	93.74 193	98.34 259	98.17 254

IterMVS		95.42 190	95.83 171	94.20 283	97.52 249	83.78 326	92.41 303	97.47 254	95.49 142	98.06 112	98.49 72	87.94 263	99.58 151	96.02 83	99.02 206	99.23 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thisisatest0515		90.43 301	89.18 313	94.17 285	97.07 278	85.44 306	89.75 342	87.58 351	88.28 288	93.69 293	91.72 341	65.27 353	99.58 151	90.59 259	98.67 242	97.50 290

thres600view7		92.03 286	91.43 284	93.82 286	98.19 171	84.61 319	96.27 136	90.39 343	96.81 84	96.37 212	93.11 320	73.44 338	99.49 176	80.32 342	97.95 273	97.36 293

FPMVS		89.92 308	88.63 315	93.82 286	98.37 153	96.94 45	91.58 314	93.34 318	88.00 291	90.32 337	97.10 211	70.87 346	91.13 357	71.91 354	96.16 322	93.39 346

thres400		91.68 291	91.00 291	93.71 288	98.02 190	84.35 322	95.70 169	90.79 340	96.26 102	95.90 235	92.13 337	73.62 336	99.42 195	78.85 346	97.74 281	97.36 293

IB-MVS	85.98 20	88.63 316	86.95 325	93.68 289	95.12 327	84.82 318	90.85 329	90.17 347	87.55 294	88.48 347	91.34 344	58.01 358	99.59 149	87.24 310	93.80 340	96.63 320
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
EU-MVSNet		94.25 238	94.47 224	93.60 290	98.14 181	82.60 330	97.24 92	92.72 325	85.08 318	98.48 63	98.94 42	82.59 294	98.76 300	97.47 38	99.53 95	99.44 76

TR-MVS		92.54 276	92.20 276	93.57 291	96.49 292	86.66 293	93.51 277	94.73 305	89.96 271	94.95 258	93.87 316	90.24 241	98.61 314	81.18 341	94.88 333	95.45 336

cascas		91.89 288	91.35 286	93.51 292	94.27 337	85.60 304	88.86 346	98.61 150	79.32 344	92.16 326	91.44 343	89.22 254	98.12 340	90.80 249	97.47 298	96.82 312

ppachtmachnet_test		94.49 233	94.84 204	93.46 293	96.16 304	82.10 332	90.59 331	97.48 253	90.53 265	97.01 179	97.59 172	91.01 227	99.36 219	93.97 187	99.18 184	98.94 170

pmmvs3		90.00 305	88.90 314	93.32 294	94.20 340	85.34 307	91.25 322	92.56 327	78.59 346	93.82 286	95.17 294	67.36 352	98.69 306	89.08 285	98.03 271	95.92 327

EPNet_dtu		91.39 294	90.75 297	93.31 295	90.48 359	82.61 329	94.80 227	92.88 322	93.39 211	81.74 358	94.90 302	81.36 298	99.11 264	88.28 296	98.87 223	98.21 250
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		91.76 290	91.26 289	93.26 296	98.21 169	84.50 320	96.39 129	90.39 343	96.87 82	96.33 213	93.08 324	73.44 338	99.42 195	78.85 346	97.74 281	95.85 328

baseline2		89.65 310	88.44 317	93.25 297	95.62 318	82.71 328	93.82 267	85.94 355	88.89 281	87.35 352	92.54 333	71.23 344	99.33 227	86.01 315	94.60 337	97.72 281

DSMNet-mixed		92.19 283	91.83 280	93.25 297	96.18 303	83.68 327	96.27 136	93.68 314	76.97 352	92.54 323	99.18 27	89.20 255	98.55 320	83.88 333	98.60 251	97.51 289

tfpn200view9		91.55 292	91.00 291	93.21 299	98.02 190	84.35 322	95.70 169	90.79 340	96.26 102	95.90 235	92.13 337	73.62 336	99.42 195	78.85 346	97.74 281	95.85 328

mvs_anonymous		95.36 192	96.07 162	93.21 299	96.29 296	81.56 335	94.60 234	97.66 241	93.30 215	96.95 184	98.91 45	93.03 187	99.38 214	96.60 61	97.30 303	98.69 208

our_test_3		94.20 243	94.58 220	93.07 301	96.16 304	81.20 337	90.42 333	96.84 272	90.72 263	97.14 166	97.13 207	90.47 234	99.11 264	94.04 184	98.25 263	98.91 179

ADS-MVSNet		90.95 299	90.26 303	93.04 302	95.51 320	82.37 331	95.05 215	93.41 317	83.46 328	92.69 317	96.84 227	79.15 308	98.70 305	85.66 320	90.52 346	98.04 266

PAPM		87.64 324	85.84 329	93.04 302	96.54 290	84.99 315	88.42 348	95.57 298	79.52 343	83.82 355	93.05 326	80.57 302	98.41 327	62.29 357	92.79 342	95.71 331

PS-MVSNAJ		94.10 245	94.47 224	93.00 304	97.35 261	84.88 316	91.86 311	97.84 229	91.96 246	94.17 276	92.50 334	95.82 104	99.71 93	91.27 236	97.48 296	94.40 342

xiu_mvs_v2_base		94.22 239	94.63 215	92.99 305	97.32 268	84.84 317	92.12 307	97.84 229	91.96 246	94.17 276	93.43 318	96.07 95	99.71 93	91.27 236	97.48 296	94.42 341

SCA		93.38 264	93.52 250	92.96 306	96.24 298	81.40 336	93.24 286	94.00 311	91.58 253	94.57 266	96.97 219	87.94 263	99.42 195	89.47 279	97.66 289	98.06 262

new-patchmatchnet		95.67 178	96.58 135	92.94 307	97.48 251	80.21 340	92.96 290	98.19 201	94.83 168	98.82 42	98.79 50	93.31 180	99.51 174	95.83 91	99.04 205	99.12 142

test0.0.03 1		90.11 303	89.21 310	92.83 308	93.89 342	86.87 292	91.74 313	88.74 350	92.02 244	94.71 263	91.14 346	73.92 333	94.48 355	83.75 336	92.94 341	97.16 297

thres200		91.00 298	90.42 302	92.77 309	97.47 255	83.98 325	94.01 259	91.18 338	95.12 157	95.44 247	91.21 345	73.93 332	99.31 231	77.76 349	97.63 291	95.01 338

BH-w/o		92.14 284	91.94 278	92.73 310	97.13 276	85.30 308	92.46 301	95.64 294	89.33 276	94.21 275	92.74 330	89.60 246	98.24 336	81.68 339	94.66 335	94.66 340

1314		92.38 279	92.30 275	92.64 311	95.42 324	85.15 312	95.86 162	96.97 269	85.40 316	90.62 333	93.06 325	91.12 226	97.80 344	86.74 312	95.49 330	94.97 339

KD-MVS_2432*1600		88.93 314	87.74 319	92.49 312	88.04 360	81.99 333	89.63 343	95.62 295	91.35 255	95.06 254	93.11 320	56.58 361	98.63 312	85.19 324	95.07 331	96.85 309

miper_refine_blended		88.93 314	87.74 319	92.49 312	88.04 360	81.99 333	89.63 343	95.62 295	91.35 255	95.06 254	93.11 320	56.58 361	98.63 312	85.19 324	95.07 331	96.85 309

MVS		90.02 304	89.20 311	92.47 314	94.71 331	86.90 291	95.86 162	96.74 278	64.72 357	90.62 333	92.77 329	92.54 201	98.39 329	79.30 344	95.56 329	92.12 348

PMMVS2		93.66 257	94.07 237	92.45 315	97.57 244	80.67 339	86.46 350	96.00 287	93.99 197	97.10 170	97.38 192	89.90 244	97.82 343	88.76 288	99.47 117	98.86 189

CHOSEN 280x420		89.98 306	89.19 312	92.37 316	95.60 319	81.13 338	86.22 351	97.09 265	81.44 336	87.44 351	93.15 319	73.99 331	99.47 182	88.69 290	99.07 201	96.52 322

PatchmatchNet		91.98 287	91.87 279	92.30 317	94.60 333	79.71 341	95.12 206	93.59 316	89.52 274	93.61 296	97.02 217	77.94 312	99.18 252	90.84 247	94.57 338	98.01 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RRT_test8_iter05		92.46 277	92.52 273	92.29 318	95.33 325	77.43 348	95.73 167	98.55 156	94.41 181	97.46 155	97.72 163	57.44 359	99.74 68	96.92 57	99.14 187	99.69 20

gg-mvs-nofinetune		88.28 319	86.96 324	92.23 319	92.84 353	84.44 321	98.19 38	74.60 360	99.08 10	87.01 353	99.47 8	56.93 360	98.23 337	78.91 345	95.61 328	94.01 343

tpm		91.08 297	90.85 295	91.75 320	95.33 325	78.09 344	95.03 217	91.27 337	88.75 282	93.53 300	97.40 186	71.24 343	99.30 234	91.25 238	93.87 339	97.87 274

PVSNet	86.72 19	91.10 296	90.97 293	91.49 321	97.56 246	78.04 345	87.17 349	94.60 307	84.65 323	92.34 324	92.20 336	87.37 271	98.47 324	85.17 326	97.69 286	97.96 270

DWT-MVSNet_test		87.92 322	86.77 326	91.39 322	93.18 347	78.62 343	95.10 207	91.42 334	85.58 311	88.00 348	88.73 353	60.60 357	98.90 286	90.60 258	87.70 352	96.65 317

EPMVS		89.26 312	88.55 316	91.39 322	92.36 355	79.11 342	95.65 175	79.86 358	88.60 284	93.12 310	96.53 247	70.73 347	98.10 341	90.75 251	89.32 350	96.98 302

CostFormer		89.75 309	89.25 308	91.26 324	94.69 332	78.00 346	95.32 194	91.98 330	81.50 335	90.55 335	96.96 221	71.06 345	98.89 288	88.59 292	92.63 343	96.87 307

CVMVSNet		92.33 281	92.79 264	90.95 325	97.26 270	75.84 353	95.29 197	92.33 328	81.86 332	96.27 218	98.19 105	81.44 297	98.46 325	94.23 174	98.29 262	98.55 221

tpm2		88.47 317	87.69 321	90.79 326	94.98 329	77.34 349	95.09 209	91.83 331	77.51 351	89.40 342	96.41 253	67.83 351	98.73 302	83.58 337	92.60 344	96.29 325

GG-mvs-BLEND				90.60 327	91.00 357	84.21 324	98.23 32	72.63 363		82.76 356	84.11 356	56.14 363	96.79 351	72.20 353	92.09 345	90.78 352

tpmvs		90.79 300	90.87 294	90.57 328	92.75 354	76.30 351	95.79 166	93.64 315	91.04 261	91.91 328	96.26 261	77.19 320	98.86 292	89.38 281	89.85 349	96.56 321

test-LLR		89.97 307	89.90 305	90.16 329	94.24 338	74.98 354	89.89 338	89.06 348	92.02 244	89.97 340	90.77 348	73.92 333	98.57 317	91.88 224	97.36 299	96.92 304

test-mter		87.92 322	87.17 323	90.16 329	94.24 338	74.98 354	89.89 338	89.06 348	86.44 303	89.97 340	90.77 348	54.96 365	98.57 317	91.88 224	97.36 299	96.92 304

tpm cat1		88.01 321	87.33 322	90.05 331	94.48 334	76.28 352	94.47 239	94.35 310	73.84 356	89.26 343	95.61 288	73.64 335	98.30 335	84.13 331	86.20 354	95.57 335

tpmrst		90.31 302	90.61 300	89.41 332	94.06 341	72.37 359	95.06 214	93.69 312	88.01 290	92.32 325	96.86 225	77.45 316	98.82 293	91.04 241	87.01 353	97.04 301

TESTMET0.1,1		87.20 325	86.57 327	89.07 333	93.62 345	72.84 358	89.89 338	87.01 354	85.46 314	89.12 345	90.20 351	56.00 364	97.72 345	90.91 245	96.92 306	96.64 318

E-PMN		89.52 311	89.78 306	88.73 334	93.14 349	77.61 347	83.26 354	92.02 329	94.82 169	93.71 291	93.11 320	75.31 328	96.81 350	85.81 317	96.81 310	91.77 350

EMVS		89.06 313	89.22 309	88.61 335	93.00 351	77.34 349	82.91 355	90.92 339	94.64 174	92.63 321	91.81 340	76.30 324	97.02 348	83.83 334	96.90 307	91.48 351

PVSNet_0	81.89 21	84.49 327	83.21 330	88.34 336	95.76 316	74.97 356	83.49 353	92.70 326	78.47 347	87.94 349	86.90 355	83.38 292	96.63 353	73.44 352	66.86 358	93.40 345

MVE	73.61 22	86.48 326	85.92 328	88.18 337	96.23 300	85.28 310	81.78 356	75.79 359	86.01 305	82.53 357	91.88 339	92.74 192	87.47 358	71.42 355	94.86 334	91.78 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		88.08 320	88.05 318	88.16 338	92.85 352	68.81 361	94.17 250	92.88 322	85.47 313	91.38 331	96.14 268	68.87 350	98.81 295	86.88 311	83.80 356	96.87 307

wuyk23d		93.25 267	95.20 187	87.40 339	96.07 308	95.38 98	97.04 102	94.97 303	95.33 147	99.70 5	98.11 113	98.14 13	91.94 356	77.76 349	99.68 56	74.89 355

MVS-HIRNet		88.40 318	90.20 304	82.99 340	97.01 279	60.04 362	93.11 289	85.61 356	84.45 326	88.72 346	99.09 33	84.72 287	98.23 337	82.52 338	96.59 316	90.69 353

DeepMVS_CX				77.17 341	90.94 358	85.28 310		74.08 362	52.51 358	80.87 359	88.03 354	75.25 329	70.63 359	59.23 358	84.94 355	75.62 354

tmp_tt		57.23 328	62.50 331	41.44 342	34.77 363	49.21 364	83.93 352	60.22 364	15.31 359	71.11 360	79.37 357	70.09 348	44.86 360	64.76 356	82.93 357	30.25 356

test123		12.59 330	15.49 333	3.87 343	6.07 364	2.55 365	90.75 330	2.59 366	2.52 360	5.20 362	13.02 360	4.96 366	1.85 362	5.20 359	9.09 359	7.23 357

testmvs		12.33 331	15.23 334	3.64 344	5.77 365	2.23 366	88.99 345	3.62 365	2.30 361	5.29 361	13.09 359	4.52 367	1.95 361	5.16 360	8.32 360	6.75 358

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		24.22 329	32.30 332	0.00 345	0.00 366	0.00 367	0.00 357	98.10 210	0.00 362	0.00 363	95.06 297	97.54 28	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		7.98 332	10.65 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	95.82 104	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		7.91 333	10.55 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	94.94 299	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					98.43 149	95.94 76		98.56 155	90.72 263	96.66 198	97.07 213	95.02 136	99.74 68	91.08 240	98.93 216

RE-MVS-def			97.88 47		98.81 101	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.94 54		95.49 105	99.20 180	99.26 113

IU-MVS					99.22 56	95.40 97		98.14 206	85.77 310	98.36 75				95.23 125	99.51 105	99.49 51

test_241102_TWO								98.83 100	96.11 108	98.62 51	98.24 98	96.92 57	99.72 78	95.44 112	99.49 111	99.49 51

test_241102_ONE					99.22 56	95.35 101		98.83 100	96.04 113	99.08 31	98.13 109	97.87 20	99.33 227

9.14			96.69 130		98.53 137		96.02 152	98.98 63	93.23 217	97.18 164	97.46 182	96.47 83	99.62 141	92.99 210	99.32 166

save fliter					98.48 144	94.71 124	94.53 237	98.41 172	95.02 162

test_0728_THIRD									96.62 88	98.40 70	98.28 92	97.10 44	99.71 93	95.70 93	99.62 64	99.58 28

test0726					99.24 51	95.51 93	96.89 107	98.89 75	95.92 121	98.64 50	98.31 85	97.06 49

GSMVS																98.06 262

test_part2					99.03 90	96.07 71				98.08 110

sam_mvs1												77.80 313				98.06 262

sam_mvs												77.38 317

MTGPA								98.73 123

test_post1							94.98 219				10.37 362	76.21 325	99.04 272	89.47 279

test_post											10.87 361	76.83 321	99.07 269

patchmatchnet-post											96.84 227	77.36 318	99.42 195

MTMP							96.55 122	74.60 360

gm-plane-assit					91.79 356	71.40 360			81.67 333		90.11 352		98.99 278	84.86 328

test9_res														91.29 235	98.89 222	99.00 162

TEST9					97.84 211	95.23 106	93.62 273	98.39 174	86.81 300	93.78 287	95.99 273	94.68 145	99.52 170

test_8					97.81 214	95.07 114	93.54 276	98.38 176	87.04 298	93.71 291	95.96 277	94.58 150	99.52 170

agg_prior2														90.34 268	98.90 219	99.10 150

agg_prior					97.80 218	94.96 116		98.36 178		93.49 301			99.53 166

test_prior4						95.38 98	93.61 275

test_prior2							93.33 284		94.21 189	94.02 282	96.25 262	93.64 174		91.90 222	98.96 210

旧先验2							93.35 283		77.95 350	95.77 241			98.67 310	90.74 254

新几何2							93.43 278

旧先验1					97.80 218	93.87 155		97.75 234			97.04 216	93.57 176			98.68 241	98.72 205

无先验							93.20 287	97.91 223	80.78 338				99.40 206	87.71 300		97.94 271

原ACMM2							92.82 292

test222					98.17 176	93.24 177	92.74 296	97.61 249	75.17 353	94.65 265	96.69 239	90.96 229			98.66 244	97.66 284

testdata2													99.46 185	87.84 299

segment_acmp												95.34 125

testdata1							92.77 293		93.78 202

plane_prior7					98.70 117	94.67 128

plane_prior6					98.38 152	94.37 137						91.91 219

plane_prior5								98.75 119					99.46 185	92.59 215	99.20 180	99.28 108

plane_prior4											96.77 233

plane_prior3						94.51 131			95.29 150	96.16 224

plane_prior2							96.50 124		96.36 98

plane_prior1					98.49 142

plane_prior						94.29 139	95.42 184		94.31 186						98.93 216

n2								0.00 367

nn								0.00 367

door-mid								98.17 202

test11								98.08 213

door								97.81 232

HQP5-MVS						92.47 192

HQP-NCC					97.85 206		94.26 242		93.18 220	92.86 314

ACMP_Plane					97.85 206		94.26 242		93.18 220	92.86 314

BP-MVS														90.51 263

HQP4-MVS										92.87 313			99.23 248			99.06 155

HQP3-MVS								98.43 167							98.74 237

HQP2-MVS												90.33 236

NP-MVS					98.14 181	93.72 163					95.08 295

MDTV_nov1_ep13_2view						57.28 363	94.89 222		80.59 339	94.02 282		78.66 310		85.50 322		97.82 277

MDTV_nov1_ep13			91.28 287		94.31 335	73.51 357	94.80 227	93.16 319	86.75 302	93.45 304	97.40 186	76.37 323	98.55 320	88.85 287	96.43 317

ACMMP++_ref															99.52 100

ACMMP++															99.55 89

Test By Simon												94.51 153

High-resolution multi-view benchmark