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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 2

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 29	98.81 32	93.86 40	99.07 2	98.98 8	97.01 17	98.92 5	98.78 19	95.22 47	98.61 196	96.85 11	99.77 9	99.31 33
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
TDRefinement		97.68 3	97.60 8	97.93 2	99.02 13	95.95 8	98.61 3	98.81 10	97.41 13	97.28 71	98.46 35	94.62 77	98.84 149	94.64 53	99.53 39	98.99 66

reproduce_model		97.35 4	97.24 15	97.70 4	98.44 67	95.08 12	95.88 82	98.50 21	96.62 24	98.27 23	97.93 62	94.57 79	99.50 23	95.57 35	99.35 67	98.52 151

UA-Net		97.35 4	97.24 15	97.69 5	98.22 83	93.87 39	98.42 6	98.19 61	96.95 18	95.46 194	99.23 9	93.45 107	99.57 14	95.34 45	99.89 2	99.63 12

lecture		97.32 6	97.64 6	96.33 54	99.01 15	90.77 107	96.90 21	98.60 16	96.30 33	97.74 41	98.00 55	96.87 8	99.39 54	95.95 24	99.42 54	98.84 98

reproduce-ours		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

our_new_method		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

sc_t1		97.21 9	97.71 4	95.71 78	99.06 10	88.89 142	96.72 31	97.79 139	98.34 2	98.97 2	99.40 5	96.81 9	98.79 160	92.58 129	99.72 15	99.45 23

UniMVSNet_ETH3D		97.13 10	97.72 3	95.35 97	99.51 2	87.38 181	97.70 8	97.54 165	98.16 5	98.94 3	99.33 6	97.84 4	99.08 111	90.73 189	99.73 14	99.59 15

HPM-MVS_fast		97.01 11	96.89 21	97.39 24	99.12 8	93.92 36	97.16 14	98.17 67	93.11 89	96.48 118	97.36 120	96.92 6	99.34 70	94.31 61	99.38 63	98.92 87

tt0320-xc		97.00 12	97.67 5	94.98 117	98.89 23	86.94 195	96.72 31	98.46 24	98.28 4	98.86 7	99.43 4	96.80 10	98.51 222	91.79 152	99.76 10	99.50 19

tt0320		96.97 13	97.64 6	94.96 120	98.89 23	86.86 197	96.85 23	98.45 25	98.29 3	98.88 6	99.45 3	96.48 13	98.54 214	91.73 155	99.72 15	99.47 21

SR-MVS-dyc-post		96.84 14	96.60 33	97.56 13	98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 160	94.85 69	99.42 37	93.49 87	98.84 164	98.00 213

mvs_tets		96.83 15	96.71 26	97.17 30	98.83 29	92.51 70	96.58 38	97.61 156	87.57 269	98.80 10	98.90 14	96.50 12	99.59 13	96.15 22	99.47 44	99.40 27

v7n		96.82 16	97.31 14	95.33 99	98.54 55	86.81 198	96.83 24	98.07 86	96.59 25	98.46 20	98.43 37	92.91 131	99.52 19	96.25 21	99.76 10	99.65 11

APD-MVS_3200maxsize		96.82 16	96.65 28	97.32 28	97.95 106	93.82 42	96.31 61	98.25 46	95.51 44	96.99 90	97.05 159	95.63 27	99.39 54	93.31 99	98.88 159	98.75 115

HPM-MVS		96.81 18	96.62 31	97.36 26	98.89 23	93.53 51	97.51 10	98.44 26	92.35 104	95.95 157	96.41 214	96.71 11	99.42 37	93.99 70	99.36 66	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 19	97.36 13	95.15 112	99.12 8	87.82 175	96.68 33	97.86 126	96.10 36	98.14 30	99.28 8	97.94 3	98.21 262	91.38 168	99.69 17	99.42 24

OurMVSNet-221017-0		96.80 19	96.75 25	96.96 38	99.03 12	91.85 82	97.98 7	98.01 102	94.15 64	98.93 4	99.07 10	88.07 251	99.57 14	95.86 27	99.69 17	99.46 22

testf1		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 304	89.32 242	99.23 95	98.19 193

APD_test2		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 304	89.32 242	99.23 95	98.19 193

COLMAP_ROB	91.06 5	96.75 23	96.62 31	97.13 31	98.38 70	94.31 21	96.79 27	98.32 38	96.69 21	96.86 95	97.56 95	95.48 31	98.77 167	90.11 220	99.44 51	98.31 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 24	96.42 38	97.68 7	98.00 102	94.03 29	96.97 19	97.61 156	87.68 266	98.45 21	98.77 20	94.20 90	99.50 23	96.70 13	99.40 61	99.53 17

DTE-MVSNet		96.74 24	97.43 9	94.67 139	99.13 6	84.68 251	96.51 41	97.94 115	98.14 6	98.67 15	98.32 39	95.04 56	99.69 3	93.27 103	99.82 7	99.62 13

SR-MVS		96.70 26	96.42 38	97.54 14	98.05 94	94.69 15	96.13 71	98.07 86	95.17 48	96.82 99	96.73 189	95.09 55	99.43 36	92.99 114	98.71 198	98.50 153

PS-CasMVS		96.69 27	97.43 9	94.49 153	99.13 6	84.09 264	96.61 37	97.97 107	97.91 8	98.64 16	98.13 45	95.24 45	99.65 4	93.39 97	99.84 3	99.72 4

PEN-MVS		96.69 27	97.39 12	94.61 142	99.16 4	84.50 253	96.54 39	98.05 92	98.06 7	98.64 16	98.25 42	95.01 59	99.65 4	92.95 115	99.83 5	99.68 7

MTAPA		96.65 29	96.38 42	97.47 18	98.95 21	94.05 27	95.88 82	97.62 154	94.46 59	96.29 136	96.94 167	93.56 102	99.37 65	94.29 62	99.42 54	98.99 66

test_djsdf		96.62 30	96.49 35	97.01 35	98.55 53	91.77 85	97.15 15	97.37 180	88.98 219	98.26 26	98.86 15	93.35 112	99.60 9	96.41 18	99.45 48	99.66 9

ACMMP		96.61 31	96.34 45	97.43 21	98.61 45	93.88 37	96.95 20	98.18 63	92.26 107	96.33 130	96.84 179	95.10 54	99.40 51	93.47 90	99.33 73	99.02 63
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
Anonymous20231211		96.60 32	97.13 19	95.00 116	97.46 145	86.35 214	97.11 18	98.24 54	97.58 11	98.72 11	98.97 12	93.15 120	99.15 99	93.18 106	99.74 13	99.50 19

WR-MVS_H		96.60 32	97.05 20	95.24 106	99.02 13	86.44 210	96.78 28	98.08 83	97.42 12	98.48 19	97.86 73	91.76 162	99.63 7	94.23 63	99.84 3	99.66 9

jajsoiax		96.59 34	96.42 38	97.12 32	98.76 35	92.49 71	96.44 48	97.42 177	86.96 288	98.71 13	98.72 22	95.36 38	99.56 17	95.92 25	99.45 48	99.32 32

ACMH	88.36 12	96.59 34	97.43 9	94.07 169	98.56 49	85.33 243	96.33 54	98.30 41	94.66 54	98.72 11	98.30 40	97.51 5	98.00 297	94.87 50	99.59 29	98.86 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TestfortrainingZip a		96.50 36	96.80 23	95.62 84	98.69 37	88.28 158	96.32 56	98.06 90	94.10 65	97.65 43	97.37 115	94.54 82	99.28 85	95.41 42	99.04 127	99.30 34

XVS		96.49 37	96.18 53	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	96.49 207	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

ACMH+	88.43 11	96.48 38	96.82 22	95.47 92	98.54 55	89.06 138	95.65 91	98.61 15	96.10 36	98.16 29	97.52 100	96.90 7	98.62 195	90.30 209	99.60 27	98.72 121

APDe-MVS		96.46 39	96.64 29	95.93 66	97.68 129	89.38 131	96.90 21	98.41 29	92.52 98	97.43 58	97.92 67	95.11 52	99.50 23	94.45 57	99.30 80	98.92 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		96.46 39	96.14 56	97.41 23	98.60 46	93.82 42	96.30 65	97.96 109	92.35 104	95.57 187	96.61 199	94.93 64	99.41 43	93.78 76	99.15 111	99.00 64

mPP-MVS		96.46 39	96.05 62	97.69 5	98.62 43	94.65 17	96.45 46	97.74 143	92.59 97	95.47 192	96.68 193	94.50 83	99.42 37	93.10 109	99.26 90	98.99 66

CP-MVS		96.44 42	96.08 60	97.54 14	98.29 77	94.62 18	96.80 26	98.08 83	92.67 96	95.08 229	96.39 220	94.77 73	99.42 37	93.17 107	99.44 51	98.58 146

ZNCC-MVS		96.42 43	96.20 52	97.07 33	98.80 34	92.79 64	96.08 73	98.16 70	91.74 136	95.34 201	96.36 223	95.68 25	99.44 33	94.41 59	99.28 88	98.97 73

region2R		96.41 44	96.09 58	97.38 25	98.62 43	93.81 44	96.32 56	97.96 109	92.26 107	95.28 207	96.57 202	95.02 58	99.41 43	93.63 80	99.11 114	98.94 81

SteuartSystems-ACMMP		96.40 45	96.30 47	96.71 43	98.63 42	91.96 80	95.70 88	98.01 102	93.34 86	96.64 112	96.57 202	94.99 60	99.36 66	93.48 89	99.34 71	98.82 99
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 46	96.17 55	97.04 34	98.51 58	93.37 52	96.30 65	97.98 105	92.35 104	95.63 184	96.47 208	95.37 36	99.27 88	93.78 76	99.14 112	98.48 156

MED-MVS		96.38 47	96.63 30	95.63 83	98.69 37	88.21 161	96.32 56	98.58 18	94.10 65	97.38 65	97.37 115	95.11 52	99.39 54	92.89 117	99.19 102	99.30 34

LPG-MVS_test		96.38 47	96.23 50	96.84 41	98.36 75	92.13 77	95.33 106	98.25 46	91.78 132	97.07 83	97.22 139	96.38 16	99.28 85	92.07 142	99.59 29	99.11 54

nrg030		96.32 49	96.55 34	95.62 84	97.83 114	88.55 153	95.77 86	98.29 44	92.68 94	98.03 34	97.91 70	95.13 50	98.95 135	93.85 74	99.49 43	99.36 30

PGM-MVS		96.32 49	95.94 69	97.43 21	98.59 48	93.84 41	95.33 106	98.30 41	91.40 153	95.76 170	96.87 175	95.26 44	99.45 32	92.77 120	99.21 99	99.00 64

ACMM	88.83 9	96.30 51	96.07 61	96.97 37	98.39 69	92.95 61	94.74 131	98.03 99	90.82 168	97.15 78	96.85 176	96.25 18	99.00 125	93.10 109	99.33 73	98.95 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 52	95.99 66	97.00 36	98.65 41	92.71 66	95.69 90	98.01 102	92.08 116	95.74 175	96.28 229	95.22 47	99.42 37	93.17 107	99.06 119	98.88 93

ACMMP_NAP		96.21 53	96.12 57	96.49 51	98.90 22	91.42 92	94.57 142	98.03 99	90.42 184	96.37 127	97.35 123	95.68 25	99.25 89	94.44 58	99.34 71	98.80 104

CP-MVSNet		96.19 54	96.80 23	94.38 158	98.99 19	83.82 267	96.31 61	97.53 168	97.60 10	98.34 22	97.52 100	91.98 156	99.63 7	93.08 111	99.81 8	99.70 5

MP-MVS		96.14 55	95.68 86	97.51 16	98.81 32	94.06 25	96.10 72	97.78 141	92.73 93	93.48 291	96.72 190	94.23 89	99.42 37	91.99 145	99.29 83	99.05 61
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 56	95.83 79	96.95 39	94.75 359	94.20 23	97.34 13	97.98 105	97.31 14	95.32 202	96.77 182	93.08 123	99.20 95	91.79 152	98.16 273	97.44 289

MP-MVS-pluss		96.08 57	95.92 72	96.57 47	99.06 10	91.21 94	93.25 202	98.32 38	87.89 258	96.86 95	97.38 114	95.55 30	99.39 54	95.47 38	99.47 44	99.11 54
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 58	96.26 49	95.50 90	98.26 80	87.69 177	93.75 180	97.86 126	95.96 41	97.48 56	97.14 148	95.33 40	99.44 33	90.79 187	99.76 10	99.38 28

PS-MVSNAJss		96.01 59	96.04 63	95.89 71	98.82 30	88.51 154	95.57 97	97.88 123	88.72 227	98.81 9	98.86 15	90.77 196	99.60 9	95.43 40	99.53 39	99.57 16

Elysia		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 131	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

StellarMVS		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 131	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

SED-MVS		96.00 60	96.41 41	94.76 132	98.51 58	86.97 192	95.21 114	98.10 80	91.95 118	97.63 45	97.25 134	96.48 13	99.35 67	93.29 101	99.29 83	97.95 223

DVP-MVS++		95.93 63	96.34 45	94.70 135	96.54 225	86.66 204	98.45 4	98.22 58	93.26 87	97.54 50	97.36 120	93.12 121	99.38 63	93.88 72	98.68 203	98.04 208

APD_test1		95.91 64	95.42 100	97.36 26	98.82 30	96.62 6	95.64 92	97.64 152	93.38 85	95.89 162	97.23 137	93.35 112	97.66 334	88.20 286	98.66 207	97.79 253

test_fmvsmconf0.01_n		95.90 65	96.09 58	95.31 102	97.30 155	89.21 133	94.24 155	98.76 12	86.25 304	97.56 49	98.66 23	95.73 23	98.44 235	97.35 3	98.99 133	98.27 183

DPE-MVS		95.89 66	95.88 75	95.92 68	97.93 108	89.83 121	93.46 194	98.30 41	92.37 102	97.75 39	96.95 166	95.14 49	99.51 20	91.74 154	99.28 88	98.41 164
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		95.88 67	95.88 75	95.87 72	98.12 88	89.65 123	95.58 96	98.56 20	91.84 128	96.36 129	96.68 193	94.37 87	99.32 77	92.41 134	99.05 122	98.64 138

3Dnovator+	92.74 2	95.86 68	95.77 83	96.13 57	96.81 194	90.79 106	96.30 65	97.82 134	96.13 35	94.74 244	97.23 137	91.33 176	99.16 98	93.25 104	98.30 256	98.46 157

mmtdpeth		95.82 69	96.02 65	95.23 107	96.91 185	88.62 148	96.49 44	99.26 3	95.07 49	93.41 293	99.29 7	90.25 210	97.27 366	94.49 55	99.01 131	99.80 3

DVP-MVS		95.82 69	96.18 53	94.72 134	98.51 58	86.69 202	95.20 116	97.00 215	91.85 125	97.40 63	97.35 123	95.58 28	99.34 70	93.44 93	99.31 78	98.13 201
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
CS-MVS		95.77 71	95.58 91	96.37 53	96.84 191	91.72 87	96.73 30	99.06 7	94.23 62	92.48 342	94.79 328	93.56 102	99.49 29	93.47 90	99.05 122	97.89 238

SMA-MVS		95.77 71	95.54 92	96.47 52	98.27 79	91.19 95	95.09 119	97.79 139	86.48 296	97.42 61	97.51 104	94.47 86	99.29 81	93.55 84	99.29 83	98.93 83
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
test_0402		95.73 73	96.22 51	94.26 161	98.19 85	85.77 233	93.24 203	97.24 198	96.88 20	97.69 42	97.77 79	94.12 92	99.13 104	91.54 164	99.29 83	97.88 239

ACMP	88.15 13	95.71 74	95.43 99	96.54 48	98.17 86	91.73 86	94.24 155	98.08 83	89.46 207	96.61 114	96.47 208	95.85 22	99.12 105	90.45 198	99.56 36	98.77 114
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 75	95.34 105	96.69 44	98.40 68	93.04 58	94.54 146	98.05 92	90.45 183	96.31 133	96.76 184	92.91 131	98.72 174	91.19 172	99.42 54	98.32 176

DP-MVS		95.62 76	95.84 78	94.97 118	97.16 163	88.62 148	94.54 146	97.64 152	96.94 19	96.58 116	97.32 127	93.07 125	98.72 174	90.45 198	98.84 164	97.57 277

ME-MVS		95.61 77	95.65 88	95.49 91	97.62 133	88.21 161	94.21 158	97.87 125	92.48 99	96.38 125	96.22 235	94.06 94	99.32 77	92.89 117	99.10 115	98.96 77

test_fmvsmconf0.1_n		95.61 77	95.72 85	95.26 104	96.85 190	89.20 134	93.51 192	98.60 16	85.68 325	97.42 61	98.30 40	95.34 39	98.39 236	96.85 11	98.98 135	98.19 193

OPM-MVS		95.61 77	95.45 95	96.08 58	98.49 65	91.00 98	92.65 238	97.33 188	90.05 194	96.77 103	96.85 176	95.04 56	98.56 211	92.77 120	99.06 119	98.70 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_9		95.58 80	95.91 73	94.59 146	97.25 156	86.26 216	92.96 216	97.86 126	91.88 123	97.52 53	98.13 45	91.45 173	98.54 214	97.17 4	98.99 133	98.98 70

RPSCF		95.58 80	94.89 130	97.62 8	97.58 136	96.30 7	95.97 78	97.53 168	92.42 100	93.41 293	97.78 75	91.21 181	97.77 323	91.06 179	97.06 360	98.80 104

MIMVSNet1		95.52 82	95.45 95	95.72 77	99.14 5	89.02 139	96.23 68	96.87 232	93.73 76	97.87 35	98.49 33	90.73 200	99.05 118	86.43 328	99.60 27	99.10 57

Anonymous20240529		95.50 83	95.83 79	94.50 151	97.33 153	85.93 229	95.19 118	96.77 243	96.64 23	97.61 48	98.05 50	93.23 117	98.79 160	88.60 275	99.04 127	98.78 111

Vis-MVSNet		95.50 83	95.48 94	95.56 88	98.11 89	89.40 130	95.35 104	98.22 58	92.36 103	94.11 263	98.07 49	92.02 154	99.44 33	93.38 98	97.67 323	97.85 245
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Casviewmamba		95.48 85	95.97 67	94.04 170	96.94 181	84.57 252	93.96 171	98.29 44	93.94 71	96.76 104	97.14 148	95.27 43	98.72 174	92.37 136	99.02 130	98.82 99

EC-MVSNet		95.44 86	95.62 89	94.89 124	96.93 184	87.69 177	96.48 45	99.14 6	93.93 72	92.77 332	94.52 341	93.95 97	99.49 29	93.62 81	99.22 98	97.51 282

test_fmvsmconf_n		95.43 87	95.50 93	95.22 109	96.48 234	89.19 135	93.23 204	98.36 35	85.61 328	96.92 93	98.02 54	95.23 46	98.38 240	96.69 14	98.95 145	98.09 203

pm-mvs1		95.43 87	95.94 69	93.93 177	98.38 70	85.08 247	95.46 102	97.12 208	91.84 128	97.28 71	98.46 35	95.30 42	97.71 331	90.17 218	99.42 54	98.99 66

DeepC-MVS	91.39 4	95.43 87	95.33 107	95.71 78	97.67 130	90.17 117	93.86 176	98.02 101	87.35 273	96.22 142	97.99 58	94.48 85	99.05 118	92.73 123	99.68 20	97.93 228
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0805		95.42 90	95.93 71	93.86 181	98.75 36	88.47 155	97.68 9	94.29 356	96.48 26	95.38 197	93.63 380	94.89 66	97.94 303	95.38 43	96.92 369	95.17 413

XVG-OURS-SEG-HR		95.38 91	95.00 127	96.51 49	98.10 90	94.07 24	92.46 248	98.13 73	90.69 172	93.75 279	96.25 233	98.03 2	97.02 387	92.08 141	95.55 421	98.45 158

UniMVSNet_NR-MVSNet		95.35 92	95.21 112	95.76 75	97.69 128	88.59 151	92.26 265	97.84 130	94.91 52	96.80 100	95.78 270	90.42 206	99.41 43	91.60 160	99.58 33	99.29 36

MSP-MVS		95.34 93	94.63 148	97.48 17	98.67 40	94.05 27	96.41 50	98.18 63	91.26 156	95.12 224	95.15 306	86.60 287	99.50 23	93.43 96	96.81 374	98.89 91
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
SPE-MVS-test		95.32 94	95.10 123	95.96 62	96.86 189	90.75 108	96.33 54	99.20 4	93.99 68	91.03 393	93.73 377	93.52 104	99.55 18	91.81 151	99.45 48	97.58 276

FC-MVSNet-test		95.32 94	95.88 75	93.62 193	98.49 65	81.77 312	95.90 81	98.32 38	93.93 72	97.53 52	97.56 95	88.48 242	99.40 51	92.91 116	99.83 5	99.68 7

UniMVSNet (Re)		95.32 94	95.15 114	95.80 74	97.79 118	88.91 141	92.91 223	98.07 86	93.46 83	96.31 133	95.97 258	90.14 214	99.34 70	92.11 139	99.64 25	99.16 47

Gipuma		95.31 97	95.80 82	93.81 184	97.99 105	90.91 101	96.42 49	97.95 112	96.69 21	91.78 371	98.85 17	91.77 160	95.49 438	91.72 156	99.08 118	95.02 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mvs5depth		95.28 98	95.82 81	93.66 191	96.42 239	83.08 287	97.35 12	99.28 2	96.44 28	96.20 144	99.65 2	84.10 313	98.01 295	94.06 67	98.93 148	99.87 1

DU-MVS		95.28 98	95.12 120	95.75 76	97.75 120	88.59 151	92.58 242	97.81 135	93.99 68	96.80 100	95.90 259	90.10 217	99.41 43	91.60 160	99.58 33	99.26 37

NR-MVSNet		95.28 98	95.28 110	95.26 104	97.75 120	87.21 185	95.08 120	97.37 180	93.92 74	97.65 43	95.90 259	90.10 217	99.33 76	90.11 220	99.66 23	99.26 37

TransMVSNet (Re)		95.27 101	96.04 63	92.97 227	98.37 72	81.92 311	95.07 121	96.76 244	93.97 70	97.77 38	98.57 28	95.72 24	97.90 304	88.89 263	99.23 95	99.08 58

fmvsm_s_conf0.5_n_3		95.20 102	95.95 68	92.94 231	96.60 219	82.18 308	93.13 207	98.39 32	91.44 151	97.16 77	97.68 84	93.03 128	97.82 315	97.54 2	98.63 208	98.81 102

fmvsm_l_conf0.5_n_3		95.19 103	95.36 103	94.68 137	96.79 197	87.49 179	93.05 210	98.38 33	87.21 278	96.59 115	97.76 80	94.20 90	98.11 276	95.90 26	98.40 238	98.42 161

SD-MVS		95.19 103	95.73 84	93.55 197	96.62 218	88.88 144	94.67 136	98.05 92	91.26 156	97.25 74	96.40 215	95.42 34	94.36 465	92.72 124	99.19 102	97.40 295
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
VPA-MVSNet		95.14 105	95.67 87	93.58 196	97.76 119	83.15 283	94.58 141	97.58 161	93.39 84	97.05 86	98.04 52	93.25 115	98.51 222	89.75 232	99.59 29	99.08 58

casdiffmvs_mvg		95.10 106	95.62 89	93.53 201	96.25 264	83.23 279	92.66 237	98.19 61	93.06 90	97.49 55	97.15 147	94.78 72	98.71 181	92.27 137	98.72 196	98.65 132
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		95.09 107	95.40 101	94.15 164	97.42 148	84.35 256	93.91 174	96.69 249	94.41 60	96.67 109	97.25 134	87.67 260	99.14 101	95.78 29	98.81 172	98.97 73

test_fmvsmvis_n_1920		95.08 108	95.40 101	94.13 167	96.66 208	87.75 176	93.44 196	98.49 23	85.57 329	98.27 23	97.11 152	94.11 93	97.75 327	96.26 20	98.72 196	96.89 328

HPM-MVS++		95.02 109	94.39 158	96.91 40	97.88 111	93.58 50	94.09 165	96.99 217	91.05 161	92.40 347	95.22 304	91.03 190	99.25 89	92.11 139	98.69 202	97.90 236

APD-MVS		95.00 110	94.69 141	95.93 66	97.38 149	90.88 102	94.59 139	97.81 135	89.22 214	95.46 194	96.17 243	93.42 110	99.34 70	89.30 244	98.87 162	97.56 279
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 111	95.33 107	93.91 178	98.97 20	97.16 2	95.54 100	95.85 297	96.47 27	93.40 296	97.46 107	95.31 41	95.47 439	86.18 332	98.78 181	89.11 507
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 112	94.75 137	95.57 87	98.86 27	88.69 145	96.37 51	96.81 238	85.23 338	94.75 243	97.12 151	91.85 158	99.40 51	93.45 92	98.33 250	98.62 142
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_11		94.91 113	95.44 98	93.33 212	96.45 235	83.11 286	93.56 190	98.64 14	89.76 200	95.70 179	97.97 59	92.32 146	98.08 281	95.62 31	98.95 145	98.79 106

SixPastTwentyTwo		94.91 113	95.21 112	93.98 172	98.52 57	83.19 282	95.93 79	94.84 338	94.86 53	98.49 18	98.74 21	81.45 344	99.60 9	94.69 52	99.39 62	99.15 48

FIs		94.90 115	95.35 104	93.55 197	98.28 78	81.76 313	95.33 106	98.14 72	93.05 91	97.07 83	97.18 143	87.65 262	99.29 81	91.72 156	99.69 17	99.61 14

AllTest		94.88 116	94.51 155	96.00 59	98.02 98	92.17 74	95.26 112	98.43 27	90.48 181	95.04 231	96.74 187	92.54 140	97.86 312	85.11 350	98.98 135	97.98 217

FMVSNet1		94.84 117	95.13 119	93.97 173	97.60 134	84.29 257	95.99 75	96.56 261	92.38 101	97.03 87	98.53 30	90.12 215	98.98 127	88.78 268	99.16 110	98.65 132

ANet_high		94.83 118	96.28 48	90.47 372	96.65 209	73.16 479	94.33 150	98.74 13	96.39 30	98.09 33	98.93 13	93.37 111	98.70 182	90.38 201	99.68 20	99.53 17

MVSMamba_PlusPlus		94.82 119	95.89 74	91.62 307	97.82 115	78.88 392	96.52 40	97.60 158	97.14 16	94.23 259	98.48 34	87.01 277	99.71 2	95.43 40	98.80 176	96.28 365

hybridcas		94.81 120	95.45 95	92.88 236	96.74 201	81.36 323	93.32 201	98.13 73	92.16 113	96.79 102	96.98 165	94.91 65	98.53 218	91.16 173	98.90 154	98.75 115

3Dnovator	92.54 3	94.80 121	94.90 128	94.47 154	95.47 330	87.06 189	96.63 36	97.28 195	91.82 131	94.34 258	97.41 112	90.60 203	98.65 191	92.47 132	98.11 279	97.70 265

CPTT-MVS		94.74 122	94.12 174	96.60 46	98.15 87	93.01 59	95.84 84	97.66 151	89.21 215	93.28 302	95.46 288	88.89 233	98.98 127	89.80 228	98.82 170	97.80 252

test_fmvsm_n_1920		94.72 123	94.74 139	94.67 139	96.30 257	88.62 148	93.19 205	98.07 86	85.63 327	97.08 82	97.35 123	90.86 193	97.66 334	95.70 30	98.48 230	97.74 263

XVG-OURS		94.72 123	94.12 174	96.50 50	98.00 102	94.23 22	91.48 299	98.17 67	90.72 171	95.30 203	96.47 208	87.94 256	96.98 388	91.41 167	97.61 327	98.30 180

fmvsm_s_conf0.5_n_8		94.70 125	95.34 105	92.78 243	96.77 199	81.50 320	92.64 239	98.50 21	91.51 148	97.22 75	97.93 62	88.07 251	98.45 233	96.62 16	98.80 176	98.39 169

CSCG		94.69 126	94.75 137	94.52 150	97.55 138	87.87 173	95.01 124	97.57 162	92.68 94	96.20 144	93.44 386	91.92 157	98.78 164	89.11 256	99.24 93	96.92 325

v10		94.68 127	95.27 111	92.90 234	96.57 222	80.15 344	94.65 138	97.57 162	90.68 173	97.43 58	98.00 55	88.18 248	99.15 99	94.84 51	99.55 37	99.41 26

v8		94.65 128	95.29 109	92.74 244	96.65 209	79.77 361	94.59 139	97.17 202	91.86 124	97.47 57	97.93 62	88.16 249	99.08 111	94.32 60	99.47 44	99.38 28

RoMa-HiRes		94.64 129	94.29 165	95.68 81	97.47 144	93.88 37	93.83 178	96.23 280	88.05 253	97.75 39	96.20 238	88.58 240	94.93 456	91.33 169	99.17 109	98.22 188

fmvsm_s_conf0.5_n_10		94.63 130	95.11 121	93.18 221	96.28 258	83.51 271	93.00 213	98.25 46	88.37 243	97.43 58	97.70 82	88.90 232	98.63 194	97.15 5	98.90 154	97.41 291

sasdasda		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 437	94.95 61	98.66 188	91.45 165	97.57 330	97.20 306

canonicalmvs		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 437	94.95 61	98.66 188	91.45 165	97.57 330	97.20 306

CNVR-MVS		94.58 133	94.29 165	95.46 93	96.94 181	89.35 132	91.81 288	96.80 239	89.66 203	93.90 275	95.44 290	92.80 135	98.72 174	92.74 122	98.52 225	98.32 176

casdiffseed414692147		94.56 134	94.90 128	93.54 199	96.60 219	83.33 275	93.57 189	98.06 90	91.57 142	95.26 210	97.31 128	94.06 94	98.39 236	88.67 271	98.95 145	98.91 89

GeoE		94.55 135	94.68 145	94.15 164	97.23 158	85.11 246	94.14 162	97.34 187	88.71 228	95.26 210	95.50 286	94.65 76	99.12 105	90.94 183	98.40 238	98.23 186

EG-PatchMatch MVS		94.54 136	94.67 146	94.14 166	97.87 113	86.50 206	92.00 273	96.74 245	88.16 251	96.93 92	97.61 91	93.04 127	97.90 304	91.60 160	98.12 278	98.03 211

fmvsm_l_conf0.5_n_9		94.51 137	95.11 121	92.72 245	96.70 205	83.14 284	91.91 280	97.89 122	88.44 239	97.30 68	97.57 93	91.60 164	97.54 343	95.82 28	98.74 190	97.47 285

E5new		94.50 138	95.15 114	92.55 258	97.04 172	80.27 340	92.96 216	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 201	91.16 173	98.73 192	98.79 106

E6new		94.50 138	95.15 114	92.55 258	97.04 172	80.28 338	92.96 216	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 201	91.16 173	98.73 192	98.79 106

E6		94.50 138	95.15 114	92.55 258	97.04 172	80.28 338	92.96 216	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 201	91.16 173	98.73 192	98.79 106

E5		94.50 138	95.15 114	92.55 258	97.04 172	80.27 340	92.96 216	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 201	91.16 173	98.73 192	98.79 106

fmvsm_s_conf0.5_n_5		94.50 138	94.80 133	93.60 194	96.80 195	84.93 248	92.81 228	97.59 160	85.27 337	96.85 98	97.29 129	91.48 172	98.05 288	96.67 15	98.47 231	97.83 247

IS-MVSNet		94.49 143	94.35 163	94.92 121	98.25 82	86.46 209	97.13 17	94.31 355	96.24 34	96.28 138	96.36 223	82.88 326	99.35 67	88.19 287	99.52 41	98.96 77

Baseline_NR-MVSNet		94.47 144	95.09 124	92.60 256	98.50 64	80.82 335	92.08 269	96.68 252	93.82 75	96.29 136	98.56 29	90.10 217	97.75 327	90.10 222	99.66 23	99.24 41

MGCFI-Net		94.44 145	94.67 146	93.75 186	95.56 324	85.47 240	95.25 113	98.24 54	91.53 145	95.04 231	92.21 432	94.94 63	98.54 214	91.56 163	97.66 324	97.24 304

SDMVSNet		94.43 146	95.02 125	92.69 247	97.93 108	82.88 291	91.92 279	95.99 294	93.65 81	95.51 189	98.63 25	94.60 78	96.48 410	87.57 304	99.35 67	98.70 125

MM		94.41 147	94.14 173	95.22 109	95.84 300	87.21 185	94.31 152	90.92 436	94.48 58	92.80 330	97.52 100	85.27 303	99.49 29	96.58 17	99.57 35	98.97 73

SSM_0404		94.38 148	94.69 141	93.43 207	97.16 163	83.23 279	93.95 172	97.84 130	91.46 149	95.70 179	96.56 204	92.50 144	99.08 111	88.83 264	98.23 264	97.98 217

fmvsm_s_conf0.1_n_2		94.38 148	94.78 136	93.19 220	97.07 171	81.72 315	91.97 274	97.51 171	87.05 287	97.31 67	97.92 67	88.29 246	98.15 272	97.10 6	98.81 172	99.70 5

VDD-MVS		94.37 150	94.37 160	94.40 157	97.49 141	86.07 223	93.97 170	93.28 390	94.49 57	96.24 140	97.78 75	87.99 255	98.79 160	88.92 261	99.14 112	98.34 175

EI-MVSNet-Vis-set		94.36 151	94.28 167	94.61 142	92.55 425	85.98 226	92.44 250	94.69 346	93.70 77	96.12 149	95.81 265	91.24 179	98.86 146	93.76 79	98.22 268	98.98 70

EI-MVSNet-UG-set		94.35 152	94.27 169	94.59 146	92.46 428	85.87 231	92.42 252	94.69 346	93.67 80	96.13 148	95.84 263	91.20 182	98.86 146	93.78 76	98.23 264	99.03 62

PHI-MVS		94.34 153	93.80 186	95.95 63	95.65 316	91.67 88	94.82 129	97.86 126	87.86 259	93.04 321	94.16 359	91.58 165	98.78 164	90.27 211	98.96 143	97.41 291

casdiffmvs		94.32 154	94.80 133	92.85 238	96.05 284	81.44 322	92.35 256	98.05 92	91.53 145	95.75 174	96.80 180	93.35 112	98.49 224	91.01 182	98.32 252	98.64 138
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpnnormal		94.27 155	94.87 131	92.48 265	97.71 125	80.88 334	94.55 145	95.41 317	93.70 77	96.67 109	97.72 81	91.40 175	98.18 266	87.45 306	99.18 106	98.36 171

fmvsm_s_conf0.5_n_4		94.26 156	94.58 150	93.31 213	96.40 241	82.73 299	92.59 241	97.41 178	86.60 292	96.33 130	97.07 156	89.91 221	98.07 285	96.88 10	98.01 293	99.13 50

fmvsm_s_conf0.1_n_a		94.26 156	94.37 160	93.95 176	97.36 151	85.72 235	94.15 160	95.44 314	83.25 380	95.51 189	98.05 50	92.54 140	97.19 376	95.55 36	97.46 338	98.94 81

HQP_MVS		94.26 156	93.93 182	95.23 107	97.71 125	88.12 164	94.56 143	97.81 135	91.74 136	93.31 299	95.59 280	86.93 280	98.95 135	89.26 248	98.51 227	98.60 144

baseline		94.26 156	94.80 133	92.64 249	96.08 281	80.99 331	93.69 183	98.04 98	90.80 169	94.89 238	96.32 225	93.19 118	98.48 229	91.68 158	98.51 227	98.43 160

fmvsm_s_conf0.5_n_2		94.25 160	94.63 148	93.10 223	96.65 209	81.75 314	91.72 292	97.25 196	86.93 291	97.20 76	97.67 86	88.44 244	98.14 275	97.06 9	98.77 182	99.42 24

SSM_0407		94.23 161	94.56 152	93.24 218	96.65 209	82.79 294	93.66 185	97.84 130	91.46 149	95.19 218	96.56 204	92.50 144	98.99 126	88.83 264	98.32 252	97.93 228

OMC-MVS		94.22 162	93.69 193	95.81 73	97.25 156	91.27 93	92.27 264	97.40 179	87.10 286	94.56 250	95.42 292	93.74 99	98.11 276	86.62 321	98.85 163	98.06 204

LCM-MVSNet-Re		94.20 163	94.58 150	93.04 224	95.91 295	83.13 285	93.79 179	99.19 5	92.00 117	98.84 8	98.04 52	93.64 101	99.02 123	81.28 404	98.54 221	96.96 324

DeepPCF-MVS	90.46 6	94.20 163	93.56 200	96.14 56	95.96 291	92.96 60	89.48 385	97.46 175	85.14 343	96.23 141	95.42 292	93.19 118	98.08 281	90.37 204	98.76 184	97.38 298

fmvsm_s_conf0.1_n		94.19 165	94.41 157	93.52 203	97.22 160	84.37 254	93.73 181	95.26 323	84.45 360	95.76 170	98.00 55	91.85 158	97.21 373	95.62 31	97.82 310	98.98 70

fmvsm_s_conf0.5_n_6		94.14 166	94.54 153	92.95 229	96.51 230	82.74 298	92.71 234	98.13 73	86.56 294	96.44 121	96.85 176	88.51 241	98.05 288	96.03 23	99.09 117	98.06 204

NormalMVS		94.10 167	93.36 207	96.31 55	99.01 15	90.84 104	94.70 134	97.90 118	90.98 162	93.22 309	95.73 273	78.94 368	99.12 105	90.38 201	99.42 54	98.97 73

KD-MVS_self_test		94.10 167	94.73 140	92.19 277	97.66 131	79.49 374	94.86 128	97.12 208	89.59 205	96.87 94	97.65 88	90.40 208	98.34 247	89.08 257	99.35 67	98.75 115

NCCC		94.08 169	93.54 201	95.70 80	96.49 232	89.90 120	92.39 254	96.91 225	90.64 174	92.33 354	94.60 337	90.58 204	98.96 133	90.21 215	97.70 321	98.23 186

FE-MVSNET2		94.07 170	94.47 156	92.90 234	97.45 147	81.26 325	93.58 188	97.54 165	88.28 245	96.46 120	97.92 67	91.41 174	98.74 171	88.12 291	99.44 51	98.69 128

VDDNet		94.03 171	94.27 169	93.31 213	98.87 26	82.36 304	95.51 101	91.78 426	97.19 15	96.32 132	98.60 27	84.24 311	98.75 168	87.09 313	98.83 169	98.81 102

fmvsm_s_conf0.5_n_a		94.02 172	94.08 176	93.84 182	96.72 204	85.73 234	93.65 187	95.23 325	83.30 378	95.13 223	97.56 95	92.22 150	97.17 377	95.51 37	97.41 341	98.64 138

E4		94.00 173	94.53 154	92.42 268	96.78 198	79.99 352	91.33 304	98.16 70	89.69 201	95.27 208	97.16 144	93.94 98	98.64 192	89.99 224	98.42 237	98.61 143

fmvsm_s_conf0.5_n		94.00 173	94.20 171	93.42 208	96.69 206	84.37 254	93.38 198	95.13 329	84.50 359	95.40 196	97.55 99	91.77 160	97.20 374	95.59 33	97.79 311	98.69 128

dcpmvs_2		93.96 175	95.01 126	90.82 359	97.60 134	74.04 473	93.68 184	98.85 9	89.80 199	97.82 36	97.01 163	91.14 186	99.21 92	90.56 193	98.59 214	99.19 45

sd_testset		93.94 176	94.39 158	92.61 255	97.93 108	83.24 278	93.17 206	95.04 331	93.65 81	95.51 189	98.63 25	94.49 84	95.89 430	81.72 397	99.35 67	98.70 125

EPP-MVSNet		93.91 177	93.68 194	94.59 146	98.08 91	85.55 239	97.44 11	94.03 364	94.22 63	94.94 235	96.19 239	82.07 338	99.57 14	87.28 310	98.89 157	98.65 132

Effi-MVS+-dtu		93.90 178	92.60 238	97.77 3	94.74 362	96.67 5	94.00 168	95.41 317	89.94 195	91.93 368	92.13 435	90.12 215	98.97 132	87.68 303	97.48 336	97.67 268

viewmacassd2359aftdt		93.83 179	94.36 162	92.24 273	96.45 235	79.58 370	91.60 294	97.96 109	89.14 216	95.05 230	97.09 155	93.69 100	98.48 229	89.79 229	98.43 235	98.65 132

fmvsm_l_conf0.5_n		93.79 180	93.81 184	93.73 188	96.16 272	86.26 216	92.46 248	96.72 246	81.69 410	95.77 167	97.11 152	90.83 195	97.82 315	95.58 34	97.99 297	97.11 309

IterMVS-LS		93.78 181	94.28 167	92.27 270	96.27 261	79.21 385	91.87 284	96.78 240	91.77 134	96.57 117	97.07 156	87.15 273	98.74 171	91.99 145	99.03 129	98.86 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 182	93.44 204	94.60 145	96.14 275	87.90 172	93.36 199	97.14 204	85.53 330	93.90 275	95.45 289	91.30 178	98.59 201	89.51 238	98.62 210	97.31 301
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_LR		93.66 183	93.28 210	94.80 130	96.25 264	90.95 100	90.21 353	95.43 316	87.91 256	93.74 281	94.40 347	92.88 133	96.38 416	90.39 200	98.28 257	97.07 314

viewdifsd2359ckpt07		93.63 184	94.33 164	91.55 310	96.19 270	77.86 414	90.11 360	97.74 143	90.76 170	96.11 150	96.61 199	94.37 87	98.27 254	88.82 266	98.23 264	98.51 152

MVS_111021_HR		93.63 184	93.42 206	94.26 161	96.65 209	86.96 194	89.30 392	96.23 280	88.36 244	93.57 287	94.60 337	93.45 107	97.77 323	90.23 214	98.38 243	98.03 211

fmvsm_s_conf0.5_n_7		93.61 186	93.94 181	92.63 252	96.11 278	82.76 297	90.81 323	97.55 164	86.57 293	93.14 315	97.69 83	90.17 213	96.83 398	94.46 56	98.93 148	98.31 178

mamba_0408		93.60 187	93.72 189	93.27 216	96.65 209	82.79 294	88.81 408	97.68 148	90.62 177	95.19 218	96.01 254	91.54 170	99.08 111	88.63 273	98.32 252	97.93 228

fmvsm_l_conf0.5_n_a		93.59 188	93.63 195	93.49 205	96.10 279	85.66 237	92.32 259	96.57 260	81.32 417	95.63 184	97.14 148	90.19 211	97.73 330	95.37 44	98.03 290	97.07 314

E2		93.53 189	93.96 179	92.25 271	96.39 242	79.76 362	91.06 314	98.05 92	88.58 234	94.71 247	96.64 195	93.08 123	98.57 207	89.16 252	97.97 299	98.42 161

E3		93.53 189	93.96 179	92.25 271	96.39 242	79.76 362	91.06 314	98.05 92	88.58 234	94.71 247	96.64 195	93.07 125	98.57 207	89.16 252	97.97 299	98.42 161

v1144		93.50 191	93.81 184	92.57 257	96.28 258	79.61 366	91.86 286	96.96 218	86.95 289	95.91 160	96.32 225	87.65 262	98.96 133	93.51 86	98.88 159	99.13 50

v1192		93.49 192	93.78 187	92.62 254	96.16 272	79.62 365	91.83 287	97.22 200	86.07 310	96.10 151	96.38 221	87.22 270	99.02 123	94.14 65	98.88 159	99.22 42

WR-MVS		93.49 192	93.72 189	92.80 241	97.57 137	80.03 350	90.14 357	95.68 301	93.70 77	96.62 113	95.39 297	87.21 271	99.04 121	87.50 305	99.64 25	99.33 31

RoMa-SfM		93.45 194	92.92 223	95.03 115	96.77 199	94.01 31	93.01 211	95.19 327	83.99 368	97.28 71	95.33 300	87.17 272	93.66 472	88.55 278	99.00 132	97.42 290

BridgeMVS		93.45 194	94.17 172	91.28 330	95.81 304	78.40 400	96.20 69	97.48 174	88.56 237	95.29 205	97.20 142	85.56 302	99.21 92	92.52 131	98.91 153	96.24 368

LuminaMVS		93.43 196	93.18 213	94.16 163	97.32 154	85.29 244	93.36 199	93.94 370	88.09 252	97.12 81	96.43 211	80.11 356	98.98 127	93.53 85	98.76 184	98.21 189

V42		93.43 196	93.58 198	92.97 227	95.34 336	81.22 327	92.67 236	96.49 266	87.25 276	96.20 144	96.37 222	87.32 268	98.85 148	92.39 135	98.21 269	98.85 97

K. test v3		93.37 198	93.27 211	93.66 191	98.05 94	82.62 300	94.35 149	86.62 473	96.05 38	97.51 54	98.85 17	76.59 418	99.65 4	93.21 105	98.20 271	98.73 120

viewdifsd2359ckpt11		93.36 199	93.99 177	91.48 315	95.50 328	78.39 402	90.47 338	96.69 249	88.59 232	96.03 154	96.88 173	93.48 105	97.63 338	90.20 216	98.07 285	98.41 164

viewmsd2359difaftdt		93.36 199	93.99 177	91.48 315	95.50 328	78.39 402	90.47 338	96.69 249	88.59 232	96.03 154	96.88 173	93.48 105	97.63 338	90.20 216	98.07 285	98.41 164

PM-MVS		93.33 201	92.67 235	95.33 99	96.58 221	94.06 25	92.26 265	92.18 414	85.92 315	96.22 142	96.61 199	85.64 300	95.99 428	90.35 205	98.23 264	95.93 384

v1240		93.29 202	93.71 192	92.06 285	96.01 289	77.89 413	91.81 288	97.37 180	85.12 344	96.69 108	96.40 215	86.67 285	99.07 117	94.51 54	98.76 184	99.22 42

v2v482		93.29 202	93.63 195	92.29 269	96.35 250	78.82 394	91.77 291	96.28 276	88.45 238	95.70 179	96.26 232	86.02 294	98.90 139	93.02 112	98.81 172	99.14 49

SymmetryMVS		93.26 204	92.36 247	95.97 61	97.13 167	90.84 104	94.70 134	91.61 429	90.98 162	93.22 309	95.73 273	78.94 368	99.12 105	90.38 201	98.53 222	97.97 221

alignmvs		93.26 204	92.85 224	94.50 151	95.70 311	87.45 180	93.45 195	95.76 298	91.58 141	95.25 213	92.42 425	81.96 341	98.72 174	91.61 159	97.87 308	97.33 300

v1921920		93.26 204	93.61 197	92.19 277	96.04 288	78.31 406	91.88 283	97.24 198	85.17 341	96.19 147	96.19 239	86.76 284	99.05 118	94.18 64	98.84 164	99.22 42

SSM_04072		93.25 207	93.72 189	91.84 294	96.65 209	82.79 294	88.81 408	97.68 148	90.62 177	95.19 218	96.01 254	91.54 170	94.81 457	88.63 273	98.32 252	97.93 228

MSLP-MVS++		93.25 207	93.88 183	91.37 323	96.34 251	82.81 293	93.11 208	97.74 143	89.37 210	94.08 265	95.29 302	90.40 208	96.35 418	90.35 205	98.25 261	94.96 423

GBi-Net		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 261	88.63 229	95.10 226	98.53 30	81.31 346	98.98 127	86.74 316	98.38 243	98.65 132

test1		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 261	88.63 229	95.10 226	98.53 30	81.31 346	98.98 127	86.74 316	98.38 243	98.65 132

v144192		93.20 211	93.54 201	92.16 281	96.05 284	78.26 407	91.95 275	97.14 204	84.98 350	95.96 156	96.11 248	87.08 276	99.04 121	93.79 75	98.84 164	99.17 46

viewcassd2359sk11		93.16 212	93.51 203	92.13 283	96.07 282	79.59 367	90.88 320	97.97 107	87.82 260	94.23 259	96.19 239	92.31 147	98.53 218	88.58 276	97.51 333	98.28 181

usedtu_dtu_shiyan2		93.15 213	92.40 245	95.41 95	98.56 49	90.53 111	94.71 133	94.14 362	92.10 115	93.73 282	96.94 167	89.66 225	97.77 323	72.97 496	98.81 172	97.92 233

viewmanbaseed2359cas		93.08 214	93.43 205	92.01 289	95.69 312	79.29 381	91.15 308	97.70 147	87.45 272	94.18 262	96.12 246	92.31 147	98.37 244	88.58 276	97.73 316	98.38 170

VPNet		93.08 214	93.76 188	91.03 343	98.60 46	75.83 455	91.51 297	95.62 302	91.84 128	95.74 175	97.10 154	89.31 228	98.32 248	85.07 352	99.06 119	98.93 83

UGNet		93.08 214	92.50 241	94.79 131	93.87 392	87.99 168	95.07 121	94.26 359	90.64 174	87.33 477	97.67 86	86.89 282	98.49 224	88.10 292	98.71 198	97.91 235
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
TSAR-MVS + GP.		93.07 217	92.41 244	95.06 114	95.82 302	90.87 103	90.97 316	92.61 407	88.04 254	94.61 249	93.79 375	88.08 250	97.81 317	89.41 241	98.39 242	96.50 349

ETV-MVS		92.99 218	92.74 228	93.72 189	95.86 299	86.30 215	92.33 258	97.84 130	91.70 139	92.81 329	86.17 507	92.22 150	99.19 96	88.03 297	97.73 316	95.66 399

EI-MVSNet		92.99 218	93.26 212	92.19 277	92.12 442	79.21 385	92.32 259	94.67 348	91.77 134	95.24 214	95.85 261	87.14 274	98.49 224	91.99 145	98.26 259	98.86 94

DKM		92.97 220	92.35 248	94.81 129	96.53 228	93.72 46	90.94 317	94.88 336	85.21 339	96.42 123	95.18 305	83.11 322	93.06 479	89.66 236	99.24 93	97.64 270

MCST-MVS		92.91 221	92.51 240	94.10 168	97.52 139	85.72 235	91.36 303	97.13 206	80.33 426	92.91 328	94.24 354	91.23 180	98.72 174	89.99 224	97.93 304	97.86 243

h-mvs33		92.89 222	91.99 260	95.58 86	96.97 179	90.55 110	93.94 173	94.01 368	89.23 212	93.95 272	96.19 239	76.88 413	99.14 101	91.02 180	95.71 416	97.04 318

MGCNet		92.88 223	92.27 250	94.69 136	92.35 431	86.03 224	92.88 225	89.68 444	90.53 180	91.52 377	96.43 211	82.52 334	99.32 77	95.01 48	99.54 38	98.71 124

QAPM		92.88 223	92.77 226	93.22 219	95.82 302	83.31 276	96.45 46	97.35 186	83.91 370	93.75 279	96.77 182	89.25 229	98.88 142	84.56 359	97.02 362	97.49 284

DKM-HiRes		92.87 225	91.94 262	95.65 82	97.16 163	93.66 47	90.90 319	94.27 358	87.11 285	95.29 205	95.39 297	77.59 394	95.36 442	90.86 185	98.92 152	97.94 225

v148		92.87 225	93.29 208	91.62 307	96.25 264	77.72 419	91.28 305	95.05 330	89.69 201	95.93 159	96.04 251	87.34 267	98.38 240	90.05 223	97.99 297	98.78 111

Anonymous20240521		92.86 227	93.57 199	90.74 362	96.57 222	75.50 457	94.15 160	95.60 303	89.38 209	95.90 161	97.90 72	80.39 355	97.96 301	92.60 128	99.68 20	98.75 115

E3new		92.83 228	93.10 216	92.04 286	95.78 306	79.45 375	90.76 325	97.90 118	87.23 277	93.79 278	95.70 276	91.55 166	98.49 224	88.17 289	96.99 367	98.16 196

Effi-MVS+		92.79 229	92.74 228	92.94 231	95.10 347	83.30 277	94.00 168	97.53 168	91.36 154	89.35 436	90.65 467	94.01 96	98.66 188	87.40 308	95.30 436	96.88 330

FMVSNet2		92.78 230	92.73 230	92.95 229	95.40 332	81.98 310	94.18 159	95.53 312	88.63 229	96.05 152	97.37 115	81.31 346	98.81 156	87.38 309	98.67 205	98.06 204

Fast-Effi-MVS+-dtu		92.77 231	92.16 253	94.58 149	94.66 367	88.25 159	92.05 270	96.65 254	89.62 204	90.08 419	91.23 453	92.56 139	98.60 199	86.30 330	96.27 397	96.90 326

AstraMVS		92.75 232	92.73 230	92.79 242	97.02 176	81.48 321	92.88 225	90.62 440	87.99 255	96.48 118	96.71 191	82.02 339	98.48 229	92.44 133	98.46 232	98.40 168

LF4IMVS		92.72 233	92.02 259	94.84 128	95.65 316	91.99 79	92.92 222	96.60 257	85.08 346	92.44 345	93.62 381	86.80 283	96.35 418	86.81 315	98.25 261	96.18 372

train_agg		92.71 234	91.83 267	95.35 97	96.45 235	89.46 126	90.60 334	96.92 222	79.37 438	90.49 404	94.39 348	91.20 182	98.88 142	88.66 272	98.43 235	97.72 264

viewmamba		92.69 235	93.03 217	91.69 304	93.92 390	79.50 373	89.92 365	97.33 188	88.86 224	93.13 317	95.79 266	90.97 191	97.65 336	90.86 185	96.45 391	97.94 225

VNet		92.67 236	92.96 219	91.79 297	96.27 261	80.15 344	91.95 275	94.98 333	92.19 111	94.52 252	96.07 250	87.43 266	97.39 358	84.83 355	98.38 243	97.83 247

CDPH-MVS		92.67 236	91.83 267	95.18 111	96.94 181	88.46 156	90.70 330	97.07 211	77.38 456	92.34 353	95.08 312	92.67 138	98.88 142	85.74 337	98.57 216	98.20 191

balanced_ft_v1		92.65 238	93.17 214	91.10 340	94.47 372	77.32 426	96.67 34	96.70 248	88.23 247	93.70 283	97.16 144	83.33 319	99.41 43	90.51 196	97.76 313	96.57 341

viewdifsd2359ckpt09		92.60 239	92.34 249	93.36 210	95.94 294	83.36 274	92.35 256	97.93 117	83.17 384	92.92 327	94.66 334	89.87 222	98.57 207	86.51 326	97.71 320	98.15 198

guyue		92.60 239	92.62 236	92.52 264	96.73 202	81.00 330	93.00 213	91.83 425	88.28 245	96.38 125	96.23 234	80.71 352	98.37 244	92.06 144	98.37 248	98.20 191

Anonymous202405211		92.58 241	92.50 241	92.83 239	96.55 224	83.22 281	92.43 251	91.64 428	94.10 65	95.59 186	96.64 195	81.88 343	97.50 346	85.12 349	98.52 225	97.77 257

XXY-MVS		92.58 241	93.16 215	90.84 357	97.75 120	79.84 356	91.87 284	96.22 283	85.94 314	95.53 188	97.68 84	92.69 137	94.48 461	83.21 376	97.51 333	98.21 189

viewdifsd2359ckpt13		92.57 243	92.48 243	92.83 239	95.60 321	82.35 306	91.80 290	97.49 173	85.04 348	93.14 315	95.41 295	90.94 192	98.25 256	86.68 319	96.24 400	97.87 242

MVS_Test		92.57 243	93.29 208	90.40 375	93.53 400	75.85 452	92.52 244	96.96 218	88.73 226	92.35 351	96.70 192	90.77 196	98.37 244	92.53 130	95.49 423	96.99 320

TAPA-MVS	88.58 10	92.49 245	91.75 269	94.73 133	96.50 231	89.69 122	92.91 223	97.68 148	78.02 453	92.79 331	94.10 360	90.85 194	97.96 301	84.76 357	98.16 273	96.54 342
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 246	92.72 232	91.71 302	96.65 209	78.91 391	88.85 405	97.17 202	83.89 371	92.45 344	96.76 184	89.86 223	97.09 382	90.24 213	98.59 214	99.12 53

test_fmvs3		92.42 247	92.40 245	92.46 267	93.80 396	87.28 183	93.86 176	97.05 212	76.86 462	96.25 139	98.66 23	82.87 327	91.26 492	95.44 39	96.83 373	98.82 99

ab-mvs		92.40 248	92.62 236	91.74 300	97.02 176	81.65 316	95.84 84	95.50 313	86.95 289	92.95 326	97.56 95	90.70 201	97.50 346	79.63 424	97.43 340	96.06 378

PMatch-Up-SfM		92.38 249	91.36 279	95.46 93	96.22 267	92.32 73	89.61 378	95.31 321	85.08 346	96.71 106	96.12 246	75.90 421	97.27 366	89.73 233	97.54 332	96.78 335

CANet		92.38 249	91.99 260	93.52 203	93.82 395	83.46 272	91.14 309	97.00 215	89.81 198	86.47 481	94.04 362	87.90 257	99.21 92	89.50 239	98.27 258	97.90 236

EIA-MVS		92.35 251	92.03 258	93.30 215	95.81 304	83.97 265	92.80 230	98.17 67	87.71 264	89.79 427	87.56 496	91.17 185	99.18 97	87.97 298	97.27 347	96.77 336

diffmvs_AUTHOR		92.34 252	92.70 233	91.26 331	94.20 379	78.42 399	89.12 397	97.60 158	87.16 281	93.17 314	95.50 286	88.66 237	97.57 342	91.30 170	97.61 327	97.79 253

DP-MVS Recon		92.31 253	91.88 265	93.60 194	97.18 162	86.87 196	91.10 311	97.37 180	84.92 351	92.08 365	94.08 361	88.59 238	98.20 263	83.50 373	98.14 276	95.73 394

IMVS_0407		92.28 254	92.83 225	90.63 368	95.19 342	76.72 438	92.79 231	96.89 226	85.92 315	93.55 288	94.50 342	91.06 187	98.07 285	88.49 280	97.07 356	97.10 310

RRT-MVS		92.28 254	93.01 218	90.07 385	94.06 385	73.01 481	95.36 103	97.88 123	92.24 109	95.16 221	97.52 100	78.51 378	99.29 81	90.55 194	95.83 413	97.92 233

F-COLMAP		92.28 254	91.06 290	95.95 63	97.52 139	91.90 81	93.53 191	97.18 201	83.98 369	88.70 452	94.04 362	88.41 245	98.55 213	80.17 416	95.99 408	97.39 296

OpenMVS	89.45 8	92.27 257	92.13 256	92.68 248	94.53 371	84.10 263	95.70 88	97.03 213	82.44 399	91.14 391	96.42 213	88.47 243	98.38 240	85.95 335	97.47 337	95.55 404

hse-mvs2		92.24 258	91.20 284	95.38 96	96.16 272	90.65 109	92.52 244	92.01 422	89.23 212	93.95 272	92.99 397	76.88 413	98.69 184	91.02 180	96.03 405	96.81 333

IMVS_0403		92.20 259	92.70 233	90.69 364	95.19 342	76.72 438	92.39 254	96.89 226	85.92 315	93.66 285	94.50 342	90.18 212	98.24 258	88.49 280	97.07 356	97.10 310

MVSFormer		92.18 260	92.23 251	92.04 286	94.74 362	80.06 348	97.15 15	97.37 180	88.98 219	88.83 444	92.79 408	77.02 408	99.60 9	96.41 18	96.75 377	96.46 353

VortexMVS		92.13 261	92.56 239	90.85 356	94.54 370	76.17 448	92.30 262	96.63 256	86.20 306	96.66 111	96.79 181	79.87 359	98.16 270	91.27 171	98.76 184	98.24 185

HQP-MVS		92.09 262	91.49 276	93.88 179	96.36 247	84.89 249	91.37 300	97.31 190	87.16 281	88.81 446	93.40 387	84.76 308	98.60 199	86.55 324	97.73 316	98.14 200

onestephybrid01		92.06 263	92.07 257	92.04 286	93.45 403	80.93 333	89.82 371	96.78 240	87.60 268	91.68 373	95.43 291	88.73 236	97.43 353	88.32 284	96.85 372	97.76 258

DELS-MVS		92.05 264	92.16 253	91.72 301	94.44 373	80.13 346	87.62 428	97.25 196	87.34 274	92.22 357	93.18 394	89.54 227	98.73 173	89.67 235	98.20 271	96.30 363
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
FE-MVSNET		92.02 265	92.22 252	91.41 320	96.63 217	79.08 387	91.53 296	96.84 236	85.52 333	95.16 221	96.14 244	83.97 314	97.50 346	85.48 341	98.75 188	97.64 270

TinyColmap		92.00 266	92.76 227	89.71 399	95.62 320	77.02 431	90.72 328	96.17 286	87.70 265	95.26 210	96.29 227	92.54 140	96.45 413	81.77 395	98.77 182	95.66 399

DenseAffine		91.92 267	90.90 293	94.97 118	96.37 244	93.07 56	90.35 346	93.65 378	84.62 357	95.66 183	94.39 348	78.19 384	94.97 455	86.02 334	98.90 154	96.87 331

CLD-MVS		91.82 268	91.41 278	93.04 224	96.37 244	83.65 269	86.82 451	97.29 193	84.65 356	92.27 355	89.67 477	92.20 152	97.85 314	83.95 370	99.47 44	97.62 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FA-MVS(test-final)		91.81 269	91.85 266	91.68 305	94.95 350	79.99 352	96.00 74	93.44 388	87.80 261	94.02 270	97.29 129	77.60 393	98.45 233	88.04 296	97.49 335	96.61 340

BP-MVS1		91.77 270	91.10 289	93.75 186	96.42 239	83.40 273	94.10 164	91.89 423	91.27 155	93.36 297	94.85 323	64.43 489	99.29 81	94.88 49	98.74 190	98.56 148

PMatch-SfM		91.76 271	90.58 309	95.30 103	95.64 318	91.67 88	89.49 384	94.79 343	84.45 360	96.31 133	96.02 253	71.68 451	97.26 368	89.13 255	97.75 314	96.98 321

diffmvs		91.74 272	91.93 263	91.15 339	93.06 413	78.17 408	88.77 411	97.51 171	86.28 303	92.42 346	93.96 367	88.04 253	97.46 350	90.69 191	96.67 381	97.82 250
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		91.72 273	91.20 284	93.26 217	96.17 271	91.02 96	91.14 309	95.55 311	90.16 192	90.87 397	93.56 384	86.31 290	94.40 464	79.92 422	97.12 354	94.37 443

IterMVS-SCA-FT		91.65 274	91.55 272	91.94 291	93.89 391	79.22 384	87.56 431	93.51 385	91.53 145	95.37 199	96.62 198	78.65 374	98.90 139	91.89 149	94.95 449	97.70 265

PVSNet_Blended_VisFu		91.63 275	91.20 284	92.94 231	97.73 123	83.95 266	92.14 268	97.46 175	78.85 448	92.35 351	94.98 315	84.16 312	99.08 111	86.36 329	96.77 376	95.79 392

AdaColmap		91.63 275	91.36 279	92.47 266	95.56 324	86.36 213	92.24 267	96.27 277	88.88 223	89.90 424	92.69 412	91.65 163	98.32 248	77.38 447	97.64 325	92.72 480

GDP-MVS		91.56 277	90.83 298	93.77 185	96.34 251	83.65 269	93.66 185	98.12 76	87.32 275	92.98 324	94.71 331	63.58 495	99.30 80	92.61 127	98.14 276	98.35 174

pmmvs-eth3d		91.54 278	90.73 303	93.99 171	95.76 309	87.86 174	90.83 322	93.98 369	78.23 452	94.02 270	96.22 235	82.62 333	96.83 398	86.57 322	98.33 250	97.29 302

API-MVS		91.52 279	91.61 271	91.26 331	94.16 380	86.26 216	94.66 137	94.82 339	91.17 159	92.13 363	91.08 457	90.03 220	97.06 386	79.09 433	97.35 344	90.45 502

hybridnocas07		91.51 280	91.66 270	91.04 342	93.14 411	78.03 409	88.75 413	96.92 222	85.97 313	91.63 376	95.31 301	87.67 260	97.31 361	88.97 259	96.61 385	97.79 253

xiu_mvs_v1_base_debu		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 365	96.05 291	83.22 381	91.26 383	90.74 462	91.55 166	98.82 151	89.29 245	95.91 409	93.62 464

xiu_mvs_v1_base		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 365	96.05 291	83.22 381	91.26 383	90.74 462	91.55 166	98.82 151	89.29 245	95.91 409	93.62 464

xiu_mvs_v1_base_debi		91.47 281	91.52 273	91.33 326	95.69 312	81.56 317	89.92 365	96.05 291	83.22 381	91.26 383	90.74 462	91.55 166	98.82 151	89.29 245	95.91 409	93.62 464

LFMVS		91.33 284	91.16 287	91.82 296	96.27 261	79.36 379	95.01 124	85.61 486	96.04 39	94.82 240	97.06 158	72.03 450	98.46 232	84.96 354	98.70 201	97.65 269

c3_l		91.32 285	91.42 277	91.00 346	92.29 433	76.79 437	87.52 434	96.42 270	85.76 323	94.72 246	93.89 370	82.73 330	98.16 270	90.93 184	98.55 218	98.04 208

SP-SuperGlue		91.30 286	91.15 288	91.75 299	91.06 474	90.99 99	90.32 349	93.55 384	90.63 176	91.17 388	93.82 374	79.84 360	88.92 511	93.30 100	96.63 383	95.34 411

ArgMatch-SfM		91.28 287	90.08 323	94.88 125	95.22 340	92.66 68	89.81 372	94.51 352	79.15 443	95.27 208	93.71 378	78.33 379	95.52 435	86.11 333	98.63 208	96.46 353

Fast-Effi-MVS+		91.28 287	90.86 296	92.53 263	95.45 331	82.53 301	89.25 395	96.52 265	85.00 349	89.91 423	88.55 489	92.94 129	98.84 149	84.72 358	95.44 425	96.22 370

icg_test_0407_2		91.18 289	91.92 264	88.94 420	95.19 342	76.72 438	84.66 496	96.89 226	85.92 315	93.55 288	94.50 342	91.06 187	92.99 480	88.49 280	97.07 356	97.10 310

hybrid		91.14 290	91.24 283	90.83 358	93.15 409	77.49 422	88.76 412	96.87 232	84.51 358	91.25 386	95.23 303	87.14 274	97.25 369	88.05 294	96.24 400	97.76 258

MDA-MVSNet-bldmvs		91.04 291	90.88 295	91.55 310	94.68 366	80.16 343	85.49 482	92.14 417	90.41 185	94.93 236	95.79 266	85.10 305	96.93 393	85.15 347	94.19 472	97.57 277

PAPM_NR		91.03 292	90.81 299	91.68 305	96.73 202	81.10 329	93.72 182	96.35 274	88.19 249	88.77 450	92.12 436	85.09 306	97.25 369	82.40 389	93.90 477	96.68 339

ArgMatch-Sym		90.98 293	89.75 332	94.68 137	95.17 346	92.64 69	89.09 398	93.46 387	78.60 449	95.11 225	92.37 426	80.44 353	95.24 448	85.04 353	98.44 234	96.18 372

SP-LightGlue		90.98 293	90.67 304	91.92 292	91.04 475	91.02 96	90.68 331	94.22 360	89.56 206	90.35 412	92.90 403	77.08 404	89.38 507	93.92 71	96.27 397	95.35 410

MSDG		90.82 295	90.67 304	91.26 331	94.16 380	83.08 287	86.63 457	96.19 284	90.60 179	91.94 367	91.89 441	89.16 230	95.75 432	80.96 409	94.51 460	94.95 424

test20.03		90.80 296	90.85 297	90.63 368	95.63 319	79.24 383	89.81 372	92.87 397	89.90 196	94.39 255	96.40 215	85.77 295	95.27 447	73.86 490	99.05 122	97.39 296

FMVSNet3		90.78 297	90.32 317	92.16 281	93.03 415	79.92 355	92.54 243	94.95 334	86.17 309	95.10 226	96.01 254	69.97 460	98.75 168	86.74 316	98.38 243	97.82 250

viewmambaseed2359dif		90.77 298	90.81 299	90.64 367	93.46 402	77.04 430	88.83 406	96.29 275	80.79 424	92.21 359	95.11 309	88.99 231	97.28 363	85.39 344	96.20 403	97.59 275

eth_miper_zixun_eth		90.72 299	90.61 306	91.05 341	92.04 445	76.84 436	86.91 447	96.67 253	85.21 339	94.41 254	93.92 368	79.53 363	98.26 255	89.76 231	97.02 362	98.06 204

X-MVStestdata		90.70 300	88.45 360	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	26.89 547	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

BH-untuned		90.68 301	90.90 293	90.05 389	95.98 290	79.57 371	90.04 361	94.94 335	87.91 256	94.07 266	93.00 396	87.76 258	97.78 322	79.19 431	95.17 442	92.80 479

IMVS_0404		90.67 302	91.06 290	89.50 402	95.19 342	76.72 438	86.58 460	96.89 226	85.92 315	89.17 438	94.50 342	85.77 295	94.67 458	88.49 280	97.07 356	97.10 310

cl____		90.65 303	90.56 310	90.91 354	91.85 451	76.98 434	86.75 452	95.36 319	85.53 330	94.06 267	94.89 319	77.36 401	97.98 300	90.27 211	98.98 135	97.76 258

DIV-MVS_self_test		90.65 303	90.56 310	90.91 354	91.85 451	76.99 433	86.75 452	95.36 319	85.52 333	94.06 267	94.89 319	77.37 400	97.99 299	90.28 210	98.97 141	97.76 258

dtuplus		90.63 305	90.59 308	90.74 362	93.85 394	77.43 424	89.01 400	96.16 287	81.42 414	92.77 332	95.54 285	88.59 238	97.28 363	81.99 393	96.00 406	97.50 283

test_fmvs2		90.62 306	90.40 314	91.29 329	91.93 449	85.46 241	92.70 235	96.48 267	74.44 478	94.91 237	97.59 92	75.52 423	90.57 495	93.44 93	96.56 386	97.84 246

114514_t		90.51 307	89.80 329	92.63 252	98.00 102	82.24 307	93.40 197	97.29 193	65.84 530	89.40 435	94.80 327	86.99 278	98.75 168	83.88 371	98.61 211	96.89 328

miper_ehance_all_eth		90.48 308	90.42 313	90.69 364	91.62 461	76.57 444	86.83 450	96.18 285	83.38 377	94.06 267	92.66 414	82.20 336	98.04 290	89.79 229	97.02 362	97.45 287

BH-RMVSNet		90.47 309	90.44 312	90.56 371	95.21 341	78.65 398	89.15 396	93.94 370	88.21 248	92.74 334	94.22 355	86.38 288	97.88 308	78.67 436	95.39 428	95.14 416

Vis-MVSNet (Re-imp)		90.42 310	90.16 319	91.20 336	97.66 131	77.32 426	94.33 150	87.66 465	91.20 158	92.99 322	95.13 308	75.40 424	98.28 250	77.86 440	99.19 102	97.99 216

test_vis3_rt		90.40 311	90.03 324	91.52 313	92.58 423	88.95 140	90.38 344	97.72 146	73.30 488	97.79 37	97.51 104	77.05 405	87.10 525	89.03 258	94.89 450	98.50 153

PLC	85.34 15	90.40 311	88.92 347	94.85 127	96.53 228	90.02 118	91.58 295	96.48 267	80.16 427	86.14 484	92.18 433	85.73 297	98.25 256	76.87 452	94.61 459	96.30 363
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 313	90.61 306	89.74 398	98.04 97	71.50 493	95.59 93	79.72 535	89.41 208	95.94 158	98.14 44	70.79 455	98.81 156	88.52 279	99.32 77	98.90 90

testgi		90.38 314	91.34 281	87.50 454	97.49 141	71.54 492	89.43 387	95.16 328	88.38 241	94.54 251	94.68 333	92.88 133	93.09 478	71.60 505	97.85 309	97.88 239

mvs_anonymous		90.37 315	91.30 282	87.58 453	92.17 440	68.00 509	89.84 370	94.73 345	83.82 372	93.22 309	97.40 113	87.54 264	97.40 357	87.94 299	95.05 446	97.34 299

PVSNet_BlendedMVS		90.35 316	89.96 325	91.54 312	94.81 355	78.80 396	90.14 357	96.93 220	79.43 437	88.68 454	95.06 313	86.27 291	98.15 272	80.27 412	98.04 289	97.68 267

SP-DiffGlue		90.34 317	90.20 318	90.76 361	90.52 489	90.29 114	90.37 345	94.02 366	87.19 279	93.85 277	92.55 417	78.24 382	87.50 518	89.68 234	95.41 426	94.49 440

UnsupCasMVSNet_eth		90.33 318	90.34 316	90.28 377	94.64 368	80.24 342	89.69 377	95.88 295	85.77 322	93.94 274	95.69 277	81.99 340	92.98 481	84.21 365	91.30 511	97.62 272

MAR-MVS		90.32 319	88.87 351	94.66 141	94.82 354	91.85 82	94.22 157	94.75 344	80.91 420	87.52 475	88.07 494	86.63 286	97.87 311	76.67 454	96.21 402	94.25 446
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
RPMNet		90.31 320	90.14 322	90.81 360	91.01 477	78.93 388	92.52 244	98.12 76	91.91 121	89.10 439	96.89 172	68.84 464	99.41 43	90.17 218	92.70 499	94.08 449

mvsmamba		90.24 321	89.43 338	92.64 249	95.52 326	82.36 304	96.64 35	92.29 412	81.77 407	92.14 362	96.28 229	70.59 456	99.10 110	84.44 361	95.22 441	96.47 352

IterMVS		90.18 322	90.16 319	90.21 381	93.15 409	75.98 451	87.56 431	92.97 396	86.43 299	94.09 264	96.40 215	78.32 380	97.43 353	87.87 300	94.69 457	97.23 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS		90.16 323	92.96 219	81.78 513	97.88 111	48.48 549	90.75 326	87.69 464	96.02 40	96.70 107	97.63 90	85.60 301	97.80 318	85.73 338	98.60 213	99.06 60

TAMVS		90.16 323	89.05 343	93.49 205	96.49 232	86.37 212	90.34 348	92.55 408	80.84 423	92.99 322	94.57 340	81.94 342	98.20 263	73.51 491	98.21 269	95.90 387

ECVR-MVS		90.12 325	90.16 319	90.00 390	97.81 116	72.68 485	95.76 87	78.54 539	89.04 217	95.36 200	98.10 47	70.51 457	98.64 192	87.10 312	99.18 106	98.67 130

dtuonlycased		90.11 326	90.39 315	89.28 411	97.09 170	72.61 486	85.75 476	95.27 322	81.57 413	94.42 253	94.89 319	90.47 205	96.81 400	78.74 434	95.27 438	98.41 164

test_yl		90.11 326	89.73 333	91.26 331	94.09 383	79.82 357	90.44 340	92.65 404	90.90 164	93.19 312	93.30 389	73.90 433	98.03 291	82.23 390	96.87 370	95.93 384

DCV-MVSNet		90.11 326	89.73 333	91.26 331	94.09 383	79.82 357	90.44 340	92.65 404	90.90 164	93.19 312	93.30 389	73.90 433	98.03 291	82.23 390	96.87 370	95.93 384

Patchmtry		90.11 326	89.92 326	90.66 366	90.35 495	77.00 432	92.96 216	92.81 398	90.25 187	94.74 244	96.93 169	67.11 471	97.52 345	85.17 345	98.98 135	97.46 286

MVP-Stereo		90.07 330	88.92 347	93.54 199	96.31 255	86.49 207	90.93 318	95.59 307	79.80 430	91.48 378	95.59 280	80.79 350	97.39 358	78.57 438	91.19 512	96.76 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LoFTR		90.05 331	89.57 336	91.50 314	93.73 397	91.47 90	90.72 328	89.37 448	81.71 409	97.13 79	96.40 215	74.09 432	92.38 484	84.18 366	98.79 179	90.63 501

AUN-MVS		90.05 331	88.30 365	95.32 101	96.09 280	90.52 112	92.42 252	92.05 421	82.08 403	88.45 457	92.86 404	65.76 481	98.69 184	88.91 262	96.07 404	96.75 338

CL-MVSNet_self_test		90.04 333	89.90 327	90.47 372	95.24 339	77.81 415	86.60 459	92.62 406	85.64 326	93.25 307	93.92 368	83.84 315	96.06 425	79.93 420	98.03 290	97.53 281

D2MVS		89.93 334	89.60 335	90.92 352	94.03 386	78.40 400	88.69 415	94.85 337	78.96 446	93.08 318	95.09 311	74.57 428	96.94 391	88.19 287	98.96 143	97.41 291

miper_lstm_enhance		89.90 335	89.80 329	90.19 383	91.37 467	77.50 421	83.82 510	95.00 332	84.84 353	93.05 320	94.96 316	76.53 419	95.20 449	89.96 226	98.67 205	97.86 243

SSC-MVS3.2		89.88 336	91.06 290	86.31 477	95.90 296	63.76 530	82.68 515	92.43 411	91.42 152	92.37 350	94.58 339	86.34 289	96.60 406	84.35 364	99.50 42	98.57 147

CANet_DTU		89.85 337	89.17 341	91.87 293	92.20 437	80.02 351	90.79 324	95.87 296	86.02 311	82.53 519	91.77 444	80.01 357	98.57 207	85.66 339	97.70 321	97.01 319

tttt0517		89.81 338	88.90 349	92.55 258	97.00 178	79.73 364	95.03 123	83.65 505	89.88 197	95.30 203	94.79 328	53.64 519	99.39 54	91.99 145	98.79 179	98.54 149

EPNet		89.80 339	88.25 369	94.45 155	83.91 539	86.18 220	93.87 175	87.07 471	91.16 160	80.64 531	94.72 330	78.83 370	98.89 141	85.17 345	98.89 157	98.28 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ALIKED-LG		89.78 340	88.57 357	93.39 209	93.97 387	95.11 11	94.30 153	95.57 310	79.81 429	93.27 303	94.93 318	72.44 442	92.52 483	75.11 473	97.77 312	92.53 483

SP-MNN		89.68 341	89.55 337	90.06 388	90.43 494	88.06 166	89.60 379	92.13 418	86.42 300	89.57 432	92.55 417	78.14 386	87.91 517	90.35 205	96.74 379	94.22 447

CDS-MVSNet		89.55 342	88.22 372	93.53 201	95.37 335	86.49 207	89.26 393	93.59 381	79.76 432	91.15 390	92.31 428	77.12 403	98.38 240	77.51 445	97.92 305	95.71 395
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 343	89.80 329	88.76 424	94.88 351	72.47 489	89.60 379	92.44 410	85.82 321	89.48 433	95.98 257	82.85 328	97.74 329	81.87 394	95.27 438	96.08 377

OpenMVS_ROB	85.12 16	89.52 344	89.05 343	90.92 352	94.58 369	81.21 328	91.10 311	93.41 389	77.03 461	93.41 293	93.99 366	83.23 321	97.80 318	79.93 420	94.80 454	93.74 460

test_vis1_n_1920		89.45 345	89.85 328	88.28 438	93.59 399	76.71 442	90.67 332	97.78 141	79.67 434	90.30 413	96.11 248	76.62 417	92.17 486	90.31 208	93.57 482	95.96 382

WB-MVS		89.44 346	92.15 255	81.32 514	97.73 123	48.22 550	89.73 375	87.98 462	95.24 47	96.05 152	96.99 164	85.18 304	96.95 390	82.45 388	97.97 299	98.78 111

DPM-MVS		89.35 347	88.40 361	92.18 280	96.13 277	84.20 261	86.96 446	96.15 288	75.40 472	87.36 476	91.55 451	83.30 320	98.01 295	82.17 392	96.62 384	94.32 445

MVSTER		89.32 348	88.75 352	91.03 343	90.10 500	76.62 443	90.85 321	94.67 348	82.27 400	95.24 214	95.79 266	61.09 505	98.49 224	90.49 197	98.26 259	97.97 221

usedtu_dtu_shiyan1		89.18 349	88.59 355	90.95 350	94.75 359	77.79 416	86.25 466	94.63 350	81.61 411	90.88 395	92.24 430	77.03 406	98.08 281	82.62 382	97.27 347	96.97 322

FE-MVSNET3		89.18 349	88.59 355	90.95 350	94.75 359	77.79 416	86.25 466	94.63 350	81.61 411	90.88 395	92.25 429	77.03 406	98.08 281	82.62 382	97.27 347	96.97 322

PatchMatch-RL		89.18 349	88.02 377	92.64 249	95.90 296	92.87 62	88.67 417	91.06 432	80.34 425	90.03 421	91.67 447	83.34 318	94.42 463	76.35 459	94.84 453	90.64 500

jason		89.17 352	88.32 364	91.70 303	95.73 310	80.07 347	88.10 422	93.22 391	71.98 498	90.09 415	92.79 408	78.53 377	98.56 211	87.43 307	97.06 360	96.46 353
jason: jason.
PCF-MVS	84.52 17	89.12 353	87.71 381	93.34 211	96.06 283	85.84 232	86.58 460	97.31 190	68.46 521	93.61 286	93.89 370	87.51 265	98.52 221	67.85 520	98.11 279	95.66 399
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		89.11 354	88.21 373	91.83 295	91.30 468	90.25 115	88.09 423	78.76 537	76.37 465	96.43 122	98.39 38	83.79 316	90.43 498	86.57 322	94.20 470	94.80 431

usedtu_blend_shiyan5		89.08 355	88.33 363	91.34 325	91.29 469	79.59 367	94.02 166	97.13 206	90.07 193	90.09 415	83.30 525	72.25 445	98.10 279	81.45 401	95.32 432	96.33 359

FE-MVS		89.06 356	88.29 366	91.36 324	94.78 357	79.57 371	96.77 29	90.99 433	84.87 352	92.96 325	96.29 227	60.69 507	98.80 159	80.18 415	97.11 355	95.71 395

ELoFTR		89.04 357	88.72 353	89.99 391	94.38 376	89.08 137	90.15 356	89.10 449	75.60 469	95.85 164	96.52 206	75.00 426	89.26 508	83.82 372	98.08 283	91.61 491

cl22		89.02 358	88.50 359	90.59 370	89.76 504	76.45 445	86.62 458	94.03 364	82.98 389	92.65 336	92.49 420	72.05 449	97.53 344	88.93 260	97.02 362	97.78 256

USDC		89.02 358	89.08 342	88.84 423	95.07 348	74.50 466	88.97 401	96.39 271	73.21 489	93.27 303	96.28 229	82.16 337	96.39 415	77.55 444	98.80 176	95.62 402

test_vis1_n		89.01 360	89.01 345	89.03 416	92.57 424	82.46 303	92.62 240	96.06 289	73.02 491	90.40 408	95.77 271	74.86 427	89.68 502	90.78 188	94.98 447	94.95 424

xiu_mvs_v2_base		89.00 361	89.19 340	88.46 436	94.86 353	74.63 463	86.97 445	95.60 303	80.88 421	87.83 468	88.62 488	91.04 189	98.81 156	82.51 387	94.38 464	91.93 487

new-patchmatchnet		88.97 362	90.79 301	83.50 505	94.28 378	55.83 545	85.34 485	93.56 383	86.18 308	95.47 192	95.73 273	83.10 323	96.51 409	85.40 342	98.06 287	98.16 196

pmmvs4		88.95 363	87.70 382	92.70 246	94.30 377	85.60 238	87.22 440	92.16 416	74.62 477	89.75 429	94.19 357	77.97 390	96.41 414	82.71 380	96.36 393	96.09 376

N_pmnet		88.90 364	87.25 394	93.83 183	94.40 375	93.81 44	84.73 491	87.09 469	79.36 440	93.26 305	92.43 424	79.29 365	91.68 489	77.50 446	97.22 351	96.00 380

PS-MVSNAJ		88.86 365	88.99 346	88.48 435	94.88 351	74.71 461	86.69 455	95.60 303	80.88 421	87.83 468	87.37 500	90.77 196	98.82 151	82.52 386	94.37 465	91.93 487

Patchmatch-RL test		88.81 366	88.52 358	89.69 400	95.33 337	79.94 354	86.22 469	92.71 402	78.46 450	95.80 166	94.18 358	66.25 479	95.33 445	89.22 250	98.53 222	93.78 458

SD_0403		88.79 367	88.88 350	88.51 433	95.89 298	72.58 487	94.27 154	95.24 324	83.77 374	87.92 467	94.38 351	87.70 259	96.47 412	66.36 525	94.40 462	96.49 350

Anonymous20231206		88.77 368	88.29 366	90.20 382	96.31 255	78.81 395	89.56 382	93.49 386	74.26 482	92.38 348	95.58 283	82.21 335	95.43 441	72.07 500	98.75 188	96.34 358

PVSNet_Blended		88.74 369	88.16 375	90.46 374	94.81 355	78.80 396	86.64 456	96.93 220	74.67 476	88.68 454	89.18 484	86.27 291	98.15 272	80.27 412	96.00 406	94.44 442

test_fmvs1_n		88.73 370	88.38 362	89.76 396	92.06 444	82.53 301	92.30 262	96.59 259	71.14 504	92.58 339	95.41 295	68.55 465	89.57 504	91.12 178	95.66 418	97.18 308

thisisatest0530		88.69 371	87.52 384	92.20 276	96.33 253	79.36 379	92.81 228	84.01 502	86.44 298	93.67 284	92.68 413	53.62 520	99.25 89	89.65 237	98.45 233	98.00 213

ppachtmachnet_test		88.61 372	88.64 354	88.50 434	91.76 454	70.99 496	84.59 498	92.98 395	79.30 442	92.38 348	93.53 385	79.57 362	97.45 351	86.50 327	97.17 353	97.07 314

UnsupCasMVSNet_bld		88.50 373	88.03 376	89.90 392	95.52 326	78.88 392	87.39 437	94.02 366	79.32 441	93.06 319	94.02 364	80.72 351	94.27 466	75.16 472	93.08 495	96.54 342

MonoMVSNet		88.46 374	89.28 339	85.98 479	90.52 489	70.07 502	95.31 109	94.81 341	88.38 241	93.47 292	96.13 245	73.21 437	95.07 450	82.61 384	89.12 520	92.81 478

blended_shiyan8		88.43 375	87.44 386	91.40 321	92.37 429	79.45 375	87.43 435	93.92 372	82.51 396	91.24 387	85.42 513	74.35 429	98.23 260	84.43 362	95.28 437	96.52 345

ALIKED-MNN		88.42 376	87.16 398	92.21 275	93.47 401	93.93 35	92.87 227	95.20 326	71.10 505	87.62 472	93.76 376	77.41 397	91.34 491	74.50 480	98.53 222	91.36 492

blended_shiyan6		88.42 376	87.43 387	91.40 321	92.37 429	79.43 377	87.41 436	93.91 373	82.51 396	91.17 388	85.44 512	74.34 430	98.24 258	84.38 363	95.32 432	96.53 344

miper_enhance_ethall		88.42 376	87.87 379	90.07 385	88.67 518	75.52 456	85.10 486	95.59 307	75.68 467	92.49 341	89.45 480	78.96 367	97.88 308	87.86 301	97.02 362	96.81 333

1112_ss		88.42 376	87.41 389	91.45 317	96.69 206	80.99 331	89.72 376	96.72 246	73.37 487	87.00 479	90.69 465	77.38 399	98.20 263	81.38 403	93.72 480	95.15 415

lupinMVS		88.34 380	87.31 391	91.45 317	94.74 362	80.06 348	87.23 439	92.27 413	71.10 505	88.83 444	91.15 454	77.02 408	98.53 218	86.67 320	96.75 377	95.76 393

test_cas_vis1_n_1920		88.25 381	88.27 368	88.20 441	92.19 438	78.92 390	89.45 386	95.44 314	75.29 475	93.23 308	95.65 279	71.58 452	90.23 499	88.05 294	93.55 484	95.44 407

SP-NN		88.21 382	87.96 378	88.97 418	89.33 512	87.99 168	88.06 424	90.93 435	85.48 335	84.50 498	91.11 456	77.25 402	84.79 535	90.55 194	94.42 461	94.14 448

YYNet1		88.17 383	88.24 370	87.93 447	92.21 436	73.62 476	80.75 524	88.77 451	82.51 396	94.99 234	95.11 309	82.70 331	93.70 471	83.33 374	93.83 478	96.48 351

MDA-MVSNet_test_wron		88.16 384	88.23 371	87.93 447	92.22 435	73.71 475	80.71 525	88.84 450	82.52 395	94.88 239	95.14 307	82.70 331	93.61 473	83.28 375	93.80 479	96.46 353

gbinet_0.2-2-1-0.02		88.14 385	86.86 408	91.99 290	90.70 484	80.51 336	87.36 438	93.01 394	83.45 376	90.38 409	82.42 531	72.73 440	98.54 214	85.40 342	96.27 397	96.90 326

MS-PatchMatch		88.05 386	87.75 380	88.95 419	93.28 406	77.93 411	87.88 426	92.49 409	75.42 471	92.57 340	93.59 383	80.44 353	94.24 468	81.28 404	92.75 498	94.69 437

SIFT-NCM-Cal		87.99 387	87.39 390	89.77 395	92.16 441	93.98 34	86.51 463	82.96 514	85.99 312	91.10 392	92.99 397	80.00 358	87.11 524	77.21 448	97.60 329	88.22 512

SIFT-UMatch		87.96 388	87.52 384	89.29 409	91.48 464	92.84 63	85.46 483	83.94 503	87.47 271	91.86 369	92.92 401	76.78 416	87.35 521	79.73 423	98.00 296	87.69 516

SIFT-ConvMatch		87.94 389	87.21 395	90.11 384	91.67 459	93.60 49	85.55 481	83.12 512	86.48 296	92.15 361	92.98 399	78.11 387	88.58 513	76.60 455	98.25 261	88.14 514

SIFT-UM-Cal		87.93 390	87.42 388	89.44 404	90.95 479	92.71 66	84.33 502	88.32 455	86.32 301	90.41 407	92.73 411	78.78 371	88.31 514	76.83 453	98.16 273	87.31 520

CR-MVSNet		87.89 391	87.12 401	90.22 380	91.01 477	78.93 388	92.52 244	92.81 398	73.08 490	89.10 439	96.93 169	67.11 471	97.64 337	88.80 267	92.70 499	94.08 449

pmmvs5		87.87 392	87.14 399	90.07 385	93.26 408	76.97 435	88.89 403	92.18 414	73.71 485	88.36 458	93.89 370	76.86 415	96.73 403	80.32 411	96.81 374	96.51 346

wuyk23d		87.83 393	90.79 301	78.96 521	90.46 493	88.63 147	92.72 232	90.67 439	91.65 140	98.68 14	97.64 89	96.06 19	77.53 543	59.84 536	99.41 60	70.73 541

FMVSNet5		87.82 394	86.56 417	91.62 307	92.31 432	79.81 359	93.49 193	94.81 341	83.26 379	91.36 380	96.93 169	52.77 522	97.49 349	76.07 462	98.03 290	97.55 280

SIFT-MNN		87.81 395	87.11 402	89.90 392	92.19 438	93.62 48	86.73 454	84.68 496	87.19 279	90.95 394	92.80 407	73.54 436	87.09 527	78.62 437	97.32 345	88.98 508

GA-MVS		87.70 396	86.82 409	90.31 376	93.27 407	77.22 429	84.72 494	92.79 400	85.11 345	89.82 425	90.07 468	66.80 474	97.76 326	84.56 359	94.27 468	95.96 382

TR-MVS		87.70 396	87.17 397	89.27 412	94.11 382	79.26 382	88.69 415	91.86 424	81.94 404	90.69 402	89.79 473	82.82 329	97.42 355	72.65 498	91.98 507	91.14 495

thres600view7		87.66 398	87.10 403	89.36 408	96.05 284	73.17 478	92.72 232	85.31 490	91.89 122	93.29 301	90.97 459	63.42 496	98.39 236	73.23 493	96.99 367	96.51 346

PAPR		87.65 399	86.77 411	90.27 378	92.85 420	77.38 425	88.56 418	96.23 280	76.82 464	84.98 495	89.75 475	86.08 293	97.16 379	72.33 499	93.35 487	96.26 367

baseline1		87.62 400	87.31 391	88.54 431	94.71 365	74.27 469	93.10 209	88.20 458	86.20 306	92.18 360	93.04 395	73.21 437	95.52 435	79.32 429	85.82 529	95.83 390

test_fmvs1		87.59 401	87.27 393	88.54 431	88.32 519	81.26 325	90.43 343	95.72 300	70.55 511	91.70 372	94.63 335	68.13 466	89.42 506	90.59 192	95.34 431	94.94 426

our_test_3		87.55 402	87.59 383	87.44 455	91.76 454	70.48 497	83.83 509	90.55 441	79.79 431	92.06 366	92.17 434	78.63 376	95.63 433	84.77 356	94.73 455	96.22 370

wanda-best-256-512		87.53 403	86.39 423	90.97 348	91.29 469	78.39 402	85.63 479	93.75 375	81.91 405	90.09 415	83.30 525	72.25 445	98.18 266	83.96 368	95.32 432	96.33 359

FE-blended-shiyan7		87.53 403	86.39 423	90.97 348	91.29 469	78.39 402	85.63 479	93.75 375	81.91 405	90.09 415	83.30 525	72.25 445	98.18 266	83.96 368	95.32 432	96.33 359

SIFT-CM-Cal		87.51 405	86.76 412	89.76 396	91.48 464	93.30 55	84.73 491	84.04 501	85.53 330	91.66 374	92.58 416	77.01 410	88.75 512	75.29 468	98.56 217	87.24 521

PatchT		87.51 405	88.17 374	85.55 483	90.64 485	66.91 513	92.02 272	86.09 477	92.20 110	89.05 443	97.16 144	64.15 491	96.37 417	89.21 251	92.98 497	93.37 469

Test_1112_low_res		87.50 407	86.58 415	90.25 379	96.80 195	77.75 418	87.53 433	96.25 278	69.73 517	86.47 481	93.61 382	75.67 422	97.88 308	79.95 418	93.20 490	95.11 419

SCA		87.43 408	87.21 395	88.10 443	92.01 446	71.98 491	89.43 387	88.11 460	82.26 401	88.71 451	92.83 405	78.65 374	97.59 340	79.61 426	93.30 488	94.75 434

EU-MVSNet		87.39 409	86.71 413	89.44 404	93.40 404	76.11 449	94.93 127	90.00 443	57.17 541	95.71 178	97.37 115	64.77 488	97.68 333	92.67 125	94.37 465	94.52 439

thres100view900		87.35 410	86.89 407	88.72 426	96.14 275	73.09 480	93.00 213	85.31 490	92.13 114	93.26 305	90.96 460	63.42 496	98.28 250	71.27 507	96.54 387	94.79 432

SIFT-NCMNet		87.31 411	87.07 404	88.02 444	90.01 502	91.85 82	82.65 516	89.57 446	86.52 295	93.34 298	92.51 419	78.05 389	86.22 532	71.95 501	98.98 135	86.01 529

CMPMVS	68.83 22	87.28 412	85.67 432	92.09 284	88.77 517	85.42 242	90.31 351	94.38 354	70.02 514	88.00 464	93.30 389	73.78 435	94.03 470	75.96 464	96.54 387	96.83 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 413	86.82 409	88.46 436	93.96 388	77.94 410	86.84 449	92.78 401	77.59 455	87.61 474	91.83 443	78.75 372	91.92 488	77.84 441	94.20 470	95.52 406

BH-w/o		87.21 414	87.02 405	87.79 452	94.77 358	77.27 428	87.90 425	93.21 393	81.74 408	89.99 422	88.39 491	83.47 317	96.93 393	71.29 506	92.43 503	89.15 506

thres400		87.20 415	86.52 419	89.24 414	95.77 307	72.94 482	91.89 281	86.00 478	90.84 166	92.61 337	89.80 471	63.93 492	98.28 250	71.27 507	96.54 387	96.51 346

CHOSEN 1792x2688		87.19 416	85.92 429	91.00 346	97.13 167	79.41 378	84.51 499	95.60 303	64.14 534	90.07 420	94.81 325	78.26 381	97.14 380	73.34 492	95.38 429	96.46 353

HyFIR lowres test		87.19 416	85.51 436	92.24 273	97.12 169	80.51 336	85.03 487	96.06 289	66.11 529	91.66 374	92.98 399	70.12 458	99.14 101	75.29 468	95.23 440	97.07 314

reproduce_monomvs		87.13 418	86.90 406	87.84 451	90.92 480	68.15 508	91.19 307	93.75 375	85.84 320	94.21 261	95.83 264	42.99 539	97.10 381	89.46 240	97.88 307	98.26 184

MIMVSNet		87.13 418	86.54 418	88.89 422	96.05 284	76.11 449	94.39 148	88.51 453	81.37 416	88.27 460	96.75 186	72.38 444	95.52 435	65.71 527	95.47 424	95.03 421

tfpn200view9		87.05 420	86.52 419	88.67 427	95.77 307	72.94 482	91.89 281	86.00 478	90.84 166	92.61 337	89.80 471	63.93 492	98.28 250	71.27 507	96.54 387	94.79 432

SIFT-PCN-Cal		87.04 421	86.65 414	88.22 440	90.09 501	90.20 116	83.84 508	85.36 488	85.16 342	91.83 370	91.84 442	78.22 383	87.02 528	74.79 476	98.71 198	87.44 518

SIFT-PointCN		87.02 422	86.47 422	88.65 429	90.27 497	91.47 90	83.91 506	84.08 500	84.84 353	91.35 381	92.24 430	75.25 425	87.29 523	77.11 451	99.20 101	87.20 523

cascas		87.02 422	86.28 426	89.25 413	91.56 463	76.45 445	84.33 502	96.78 240	71.01 507	86.89 480	85.91 508	81.35 345	96.94 391	83.09 377	95.60 420	94.35 444

WTY-MVS		86.93 424	86.50 421	88.24 439	94.96 349	74.64 462	87.19 441	92.07 420	78.29 451	88.32 459	91.59 449	78.06 388	94.27 466	74.88 475	93.15 492	95.80 391

ttmdpeth		86.91 425	86.57 416	87.91 449	89.68 506	74.24 470	91.49 298	87.09 469	79.84 428	89.46 434	97.86 73	65.42 483	91.04 493	81.57 399	96.74 379	98.44 159

HY-MVS	82.50 18	86.81 426	85.93 428	89.47 403	93.63 398	77.93 411	94.02 166	91.58 430	75.68 467	83.64 509	93.64 379	77.40 398	97.42 355	71.70 504	92.07 506	93.05 474

test_f		86.65 427	87.13 400	85.19 487	90.28 496	86.11 222	86.52 462	91.66 427	69.76 516	95.73 177	97.21 141	69.51 461	81.28 541	89.15 254	94.40 462	88.17 513

SIFT-NN-CMatch		86.64 428	85.79 430	89.18 415	91.21 472	93.07 56	84.60 497	80.33 532	84.07 367	89.10 439	91.58 450	78.69 373	87.33 522	75.28 470	97.28 346	87.13 524

SIFT-NN-PointCN		86.59 429	85.79 430	88.99 417	90.15 498	92.46 72	84.96 489	82.76 516	83.11 385	88.70 452	92.34 427	77.62 392	87.10 525	75.03 474	97.44 339	87.42 519

SIFT-NN-NCMNet		86.55 430	85.56 435	89.51 401	91.84 453	94.02 30	85.72 477	81.31 524	84.33 364	86.13 485	91.77 444	79.22 366	87.46 519	74.06 488	95.70 417	87.07 525

1314		86.46 431	86.33 425	86.87 466	91.65 460	74.54 464	91.94 277	94.10 363	74.28 481	84.78 497	87.33 501	83.03 325	95.00 451	78.72 435	91.16 513	91.06 496

SIFT-NN-UMatch		86.43 432	85.66 433	88.76 424	90.73 483	92.76 65	84.99 488	81.25 525	84.13 366	88.17 462	92.04 437	76.90 412	86.62 529	76.34 460	96.36 393	86.91 526

ET-MVSNet_ETH3D		86.15 433	84.27 446	91.79 297	93.04 414	81.28 324	87.17 442	86.14 476	79.57 435	83.65 508	88.66 486	57.10 512	98.18 266	87.74 302	95.40 427	95.90 387

Patchmatch-test		86.10 434	86.01 427	86.38 475	90.63 486	74.22 471	89.57 381	86.69 472	85.73 324	89.81 426	92.83 405	65.24 486	91.04 493	77.82 443	95.78 414	93.88 457

ALIKED-NN		85.96 435	84.14 448	91.44 319	91.73 456	93.37 52	90.32 349	93.65 378	67.84 523	82.08 521	92.92 401	72.88 439	90.01 500	69.17 516	96.64 382	90.93 497

thres200		85.85 436	85.18 438	87.88 450	94.44 373	72.52 488	89.08 399	86.21 475	88.57 236	91.44 379	88.40 490	64.22 490	98.00 297	68.35 518	95.88 412	93.12 471

MatchFormer		85.84 437	85.60 434	86.56 470	90.63 486	87.98 170	89.85 369	83.79 504	72.98 492	95.69 182	94.88 322	69.40 462	87.92 516	74.60 477	98.55 218	83.77 533

EPNet_dtu		85.63 438	84.37 444	89.40 407	86.30 530	74.33 468	91.64 293	88.26 456	84.84 353	72.96 542	89.85 469	71.27 454	97.69 332	76.60 455	97.62 326	96.18 372
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_vis1_rt		85.58 439	84.58 442	88.60 430	87.97 520	86.76 199	85.45 484	93.59 381	66.43 527	87.64 471	89.20 483	79.33 364	85.38 534	81.59 398	89.98 519	93.66 462

test2506		85.42 440	84.57 443	87.96 445	97.81 116	66.53 516	96.14 70	56.35 550	89.04 217	93.55 288	98.10 47	42.88 542	98.68 186	88.09 293	99.18 106	98.67 130

PatchmatchNet		85.22 441	84.64 441	86.98 462	89.51 510	69.83 504	90.52 336	87.34 468	78.87 447	87.22 478	92.74 410	66.91 473	96.53 407	81.77 395	86.88 527	94.58 438
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 442	84.72 440	86.48 471	92.12 442	70.19 498	92.32 259	88.17 459	56.15 542	90.64 403	95.85 261	67.97 469	96.69 404	88.78 268	90.52 516	92.56 481

JIA-IIPM		85.08 443	83.04 459	91.19 337	87.56 522	86.14 221	89.40 389	84.44 499	88.98 219	82.20 520	97.95 61	56.82 514	96.15 421	76.55 458	83.45 533	91.30 494

MVS		84.98 444	84.30 445	87.01 461	91.03 476	77.69 420	91.94 277	94.16 361	59.36 540	84.23 503	87.50 499	85.66 298	96.80 401	71.79 502	93.05 496	86.54 528

Syy-MVS		84.81 445	84.93 439	84.42 495	91.71 457	63.36 532	85.89 472	81.49 521	81.03 418	85.13 492	81.64 533	77.44 396	95.00 451	85.94 336	94.12 473	94.91 427

MVStest1		84.79 446	84.06 450	86.98 462	77.73 550	74.76 460	91.08 313	85.63 483	77.70 454	96.86 95	97.97 59	41.05 546	88.24 515	92.22 138	96.28 396	97.94 225

thisisatest0515		84.72 447	82.99 461	89.90 392	92.96 417	75.33 458	84.36 501	83.42 507	77.37 457	88.27 460	86.65 502	53.94 518	98.72 174	82.56 385	97.40 342	95.67 398

dmvs_re		84.69 448	83.94 452	86.95 464	92.24 434	82.93 290	89.51 383	87.37 467	84.38 363	85.37 489	85.08 517	72.44 442	86.59 530	68.05 519	91.03 515	91.33 493

FPMVS		84.50 449	83.28 457	88.16 442	96.32 254	94.49 20	85.76 475	85.47 487	83.09 386	85.20 491	94.26 353	63.79 494	86.58 531	63.72 531	91.88 509	83.40 534

dtuonly		84.38 450	85.24 437	81.80 512	87.13 526	58.46 542	81.58 522	92.71 402	74.41 479	85.68 488	92.62 415	78.17 385	92.13 487	79.15 432	95.73 415	94.82 429

tpm		84.38 450	84.08 449	85.30 486	90.47 492	63.43 531	89.34 390	85.63 483	77.24 460	87.62 472	95.03 314	61.00 506	97.30 362	79.26 430	91.09 514	95.16 414

tpmvs		84.22 452	83.97 451	84.94 489	87.09 527	65.18 523	91.21 306	88.35 454	82.87 390	85.21 490	90.96 460	65.24 486	96.75 402	79.60 428	85.25 530	92.90 477

WB-MVSnew		84.20 453	83.89 453	85.16 488	91.62 461	66.15 520	88.44 421	81.00 527	76.23 466	87.98 465	87.77 495	84.98 307	93.35 476	62.85 534	94.10 475	95.98 381

SIFT-NN		84.10 454	83.04 459	87.28 458	90.76 482	92.16 76	84.45 500	81.34 523	83.54 375	83.80 507	89.75 475	70.08 459	82.09 540	68.68 517	94.96 448	87.60 517

ADS-MVSNet2		84.01 455	82.20 468	89.41 406	89.04 514	76.37 447	87.57 429	90.98 434	72.71 495	84.46 499	92.45 421	68.08 467	96.48 410	70.58 512	83.97 531	95.38 408

WBMVS		84.00 456	83.48 455	85.56 482	92.71 421	61.52 534	83.82 510	89.38 447	79.56 436	90.74 400	93.20 393	48.21 525	97.28 363	75.63 466	98.10 281	97.88 239

testing3-2		83.95 457	84.22 447	83.13 507	96.28 258	54.34 548	88.51 419	83.01 513	92.19 111	89.09 442	90.98 458	45.51 531	97.44 352	74.38 483	98.01 293	97.60 274

mvsany_test1		83.91 458	82.93 462	86.84 467	86.18 531	85.93 229	81.11 523	75.03 544	70.80 510	88.57 456	94.63 335	83.08 324	87.38 520	80.39 410	86.57 528	87.21 522

testing3		83.66 459	82.52 464	87.08 459	95.84 300	65.84 521	89.80 374	77.17 543	88.17 250	90.84 398	88.63 487	30.95 551	98.11 276	84.05 367	97.19 352	97.28 303

test-LLR		83.58 460	83.17 458	84.79 491	89.68 506	66.86 514	83.08 512	84.52 497	83.07 387	82.85 515	84.78 518	62.86 499	93.49 474	82.85 378	94.86 451	94.03 452

testing91		83.56 461	82.45 465	86.91 465	92.92 418	67.29 510	86.33 465	88.07 461	86.22 305	84.26 502	85.76 509	48.15 526	97.17 377	76.27 461	94.08 476	96.27 366

baseline2		83.38 462	81.54 473	88.90 421	91.38 466	72.84 484	88.78 410	81.22 526	78.97 445	79.82 533	87.56 496	61.73 503	97.80 318	74.30 485	90.05 518	96.05 379

blend_shiyan4		83.29 463	80.66 482	91.19 337	91.86 450	79.59 367	87.05 444	93.91 373	82.66 392	89.60 431	83.36 524	42.82 544	98.10 279	81.45 401	73.26 543	95.87 389

IB-MVS	77.21 19	83.11 464	81.05 476	89.29 409	91.15 473	75.85 452	85.66 478	86.00 478	79.70 433	82.02 524	86.61 503	48.26 524	98.39 236	77.84 441	92.22 504	93.63 463
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
CostFormer		83.09 465	82.21 467	85.73 480	89.27 513	67.01 512	90.35 346	86.47 474	70.42 512	83.52 511	93.23 392	61.18 504	96.85 397	77.21 448	88.26 524	93.34 470

PMMVS		83.00 466	81.11 475	88.66 428	83.81 540	86.44 210	82.24 518	85.65 482	61.75 539	82.07 522	85.64 511	79.75 361	91.59 490	75.99 463	93.09 494	87.94 515

testing99		82.94 467	81.72 470	86.59 468	92.55 425	66.53 516	86.08 471	85.70 481	85.47 336	83.95 505	85.70 510	45.87 530	97.07 385	76.58 457	93.56 483	96.17 375

PVSNet	76.22 20	82.89 468	82.37 466	84.48 494	93.96 388	64.38 528	78.60 529	88.61 452	71.50 502	84.43 501	86.36 506	74.27 431	94.60 460	69.87 514	93.69 481	94.46 441

tpmrst		82.85 469	82.93 462	82.64 508	87.65 521	58.99 541	90.14 357	87.90 463	75.54 470	83.93 506	91.63 448	66.79 476	95.36 442	81.21 406	81.54 537	93.57 468

MASt3R-SfM		82.76 470	82.17 469	84.53 493	83.29 542	86.01 225	82.08 519	80.49 531	63.10 537	92.22 357	94.20 356	69.18 463	77.62 542	79.63 424	95.37 430	89.94 505

test0.0.03 1		82.48 471	81.47 474	85.48 484	89.70 505	73.57 477	84.73 491	81.64 520	83.07 387	88.13 463	86.61 503	62.86 499	89.10 510	66.24 526	90.29 517	93.77 459

ADS-MVSNet		82.25 472	81.55 472	84.34 496	89.04 514	65.30 522	87.57 429	85.13 494	72.71 495	84.46 499	92.45 421	68.08 467	92.33 485	70.58 512	83.97 531	95.38 408

DSMNet-mixed		82.21 473	81.56 471	84.16 498	89.57 509	70.00 503	90.65 333	77.66 541	54.99 543	83.30 513	97.57 93	77.89 391	90.50 497	66.86 524	95.54 422	91.97 486

KD-MVS_2432*1600		82.17 474	80.75 480	86.42 473	82.04 544	70.09 500	81.75 520	90.80 437	82.56 393	90.37 410	89.30 481	42.90 540	96.11 423	74.47 481	92.55 501	93.06 472

miper_refine_blended		82.17 474	80.75 480	86.42 473	82.04 544	70.09 500	81.75 520	90.80 437	82.56 393	90.37 410	89.30 481	42.90 540	96.11 423	74.47 481	92.55 501	93.06 472

gg-mvs-nofinetune		82.10 476	81.02 477	85.34 485	87.46 524	71.04 494	94.74 131	67.56 546	96.44 28	79.43 534	98.99 11	45.24 532	96.15 421	67.18 522	92.17 505	88.85 509

testing11		81.98 477	80.52 484	86.38 475	92.69 422	67.13 511	85.79 474	84.80 495	82.16 402	81.19 530	85.41 514	45.24 532	96.88 396	74.14 487	93.24 489	95.14 416

PAPM		81.91 478	80.11 489	87.31 457	93.87 392	72.32 490	84.02 505	93.22 391	69.47 518	76.13 539	89.84 470	72.15 448	97.23 371	53.27 541	89.02 521	92.37 484

tpm2		81.46 479	80.35 487	84.80 490	89.90 503	65.14 524	90.44 340	85.36 488	65.82 531	82.05 523	92.44 423	57.94 510	96.69 404	70.71 511	88.49 523	92.56 481

PMMVS2		81.31 480	83.44 456	74.92 524	90.52 489	46.49 552	69.19 540	85.23 493	84.30 365	87.95 466	94.71 331	76.95 411	84.36 539	64.07 530	98.09 282	93.89 456

new_pmnet		81.22 481	81.01 478	81.86 511	90.92 480	70.15 499	84.03 504	80.25 534	70.83 508	85.97 486	89.78 474	67.93 470	84.65 536	67.44 521	91.90 508	90.78 499

test-mter		81.21 482	80.01 490	84.79 491	89.68 506	66.86 514	83.08 512	84.52 497	73.85 484	82.85 515	84.78 518	43.66 537	93.49 474	82.85 378	94.86 451	94.03 452

EPMVS		81.17 483	80.37 486	83.58 504	85.58 533	65.08 525	90.31 351	71.34 545	77.31 459	85.80 487	91.30 452	59.38 508	92.70 482	79.99 417	82.34 536	92.96 476

myMVS_eth3d28		80.97 484	80.42 485	82.62 509	93.35 405	58.25 543	84.70 495	85.62 485	86.31 302	84.04 504	85.20 516	46.00 529	94.07 469	62.93 533	95.65 419	95.53 405

EGC-MVSNET		80.97 484	75.73 504	96.67 45	98.85 28	94.55 19	96.83 24	96.60 257	2.44 549	5.32 551	98.25 42	92.24 149	98.02 294	91.85 150	99.21 99	97.45 287

pmmvs3		80.83 486	78.96 495	86.45 472	87.23 525	77.48 423	84.87 490	82.31 518	63.83 535	85.03 494	89.50 479	49.66 523	93.10 477	73.12 495	95.10 443	88.78 511

XFeat-MNN		80.76 487	79.73 491	83.85 502	79.29 548	82.86 292	76.90 532	83.32 510	69.86 515	92.27 355	87.53 498	57.82 511	84.65 536	74.17 486	96.44 392	84.03 532

E-PMN		80.72 488	80.86 479	80.29 517	85.11 536	68.77 506	72.96 536	81.97 519	87.76 263	83.25 514	83.01 529	62.22 502	89.17 509	77.15 450	94.31 467	82.93 535

tpm cat1		80.61 489	79.46 492	84.07 499	88.78 516	65.06 526	89.26 393	88.23 457	62.27 538	81.90 525	89.66 478	62.70 501	95.29 446	71.72 503	80.60 538	91.86 489

testing222		80.54 490	78.53 498	86.58 469	92.54 427	68.60 507	86.24 468	82.72 517	83.78 373	82.68 518	84.24 520	39.25 548	95.94 429	60.25 535	95.09 444	95.20 412

EMVS		80.35 491	80.28 488	80.54 516	84.73 538	69.07 505	72.54 538	80.73 529	87.80 261	81.66 526	81.73 532	62.89 498	89.84 501	75.79 465	94.65 458	82.71 536

UWE-MVS		80.29 492	79.10 493	83.87 501	91.97 448	59.56 539	86.50 464	77.43 542	75.40 472	87.79 470	88.10 493	44.08 536	96.90 395	64.23 529	96.36 393	95.14 416

UBG		80.28 493	78.94 496	84.31 497	92.86 419	61.77 533	83.87 507	83.31 511	77.33 458	82.78 517	83.72 522	47.60 528	96.06 425	65.47 528	93.48 485	95.11 419

CHOSEN 280x420		80.04 494	77.97 502	86.23 478	90.13 499	74.53 465	72.87 537	89.59 445	66.38 528	76.29 538	85.32 515	56.96 513	95.36 442	69.49 515	94.72 456	88.79 510

ETVMVS		79.85 495	77.94 503	85.59 481	92.97 416	66.20 519	86.13 470	80.99 528	81.41 415	83.52 511	83.89 521	41.81 545	94.98 454	56.47 539	94.25 469	95.61 403

PDCNetPlus		79.66 496	78.21 500	84.01 500	79.49 547	73.91 474	75.29 534	96.44 269	66.51 526	89.20 437	91.98 440	30.56 552	84.51 538	75.48 467	98.93 148	93.62 464

myMVS_eth3d		79.62 497	78.26 499	83.72 503	91.71 457	61.25 536	85.89 472	81.49 521	81.03 418	85.13 492	81.64 533	32.12 550	95.00 451	71.17 510	94.12 473	94.91 427

dp		79.28 498	78.62 497	81.24 515	85.97 532	56.45 544	86.91 447	85.26 492	72.97 493	81.45 528	89.17 485	56.01 516	95.45 440	73.19 494	76.68 542	91.82 490

TESTMET0.1,1		79.09 499	78.04 501	82.25 510	87.52 523	64.03 529	83.08 512	80.62 530	70.28 513	80.16 532	83.22 528	44.13 535	90.56 496	79.95 418	93.36 486	92.15 485

MVS-HIRNet		78.83 500	80.60 483	73.51 525	93.07 412	47.37 551	87.10 443	78.00 540	68.94 519	77.53 536	97.26 133	71.45 453	94.62 459	63.28 532	88.74 522	78.55 540

dmvs_testset		78.23 501	78.99 494	75.94 523	91.99 447	55.34 547	88.86 404	78.70 538	82.69 391	81.64 527	79.46 535	75.93 420	85.74 533	48.78 543	82.85 535	86.76 527

0.4-1-1-0.1		77.15 502	73.55 506	87.95 446	85.49 534	75.84 454	80.59 526	82.87 515	73.51 486	73.61 541	68.65 541	42.84 543	97.22 372	75.20 471	79.18 539	90.80 498

XFeat-NN		75.97 503	74.88 505	79.25 520	77.98 549	79.81 359	70.81 539	79.50 536	64.75 533	86.32 483	82.83 530	53.44 521	76.70 544	66.89 523	91.40 510	81.23 539

0.4-1-1-0.2		75.80 504	72.05 510	87.04 460	82.70 543	74.17 472	77.51 530	83.48 506	71.80 499	71.57 543	65.16 543	43.07 538	96.96 389	74.34 484	78.78 540	90.00 504

0.3-1-1-0.015		75.73 505	71.83 511	87.44 455	83.47 541	74.98 459	78.69 528	83.38 509	72.24 497	70.43 544	65.81 542	39.55 547	97.08 383	74.57 478	78.30 541	90.28 503

UWE-MVS-28		74.73 506	73.18 507	79.35 519	85.42 535	55.55 546	87.63 427	65.92 547	74.39 480	77.33 537	88.19 492	47.63 527	89.48 505	39.01 545	93.14 493	93.03 475

PVSNet_0	70.34 21	74.58 507	72.96 508	79.47 518	90.63 486	66.24 518	73.26 535	83.40 508	63.67 536	78.02 535	78.35 537	72.53 441	89.59 503	56.68 538	60.05 546	82.57 537

MVE	59.87 23	73.86 508	72.65 509	77.47 522	87.00 529	74.35 467	61.37 542	60.93 549	67.27 524	69.69 545	86.49 505	81.24 349	72.33 546	56.45 540	83.45 533	85.74 530
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		58.71 509	56.43 512	65.55 526	45.28 553	59.80 538	54.31 543	55.90 551	37.80 545	81.24 529	73.75 540	38.27 549	70.23 548	34.22 547	87.09 526	66.64 542

dongtai		53.72 510	53.79 513	53.51 529	79.69 546	36.70 554	77.18 531	32.53 556	71.69 500	68.63 546	60.79 544	26.65 553	73.11 545	30.67 548	36.29 548	50.73 543

test_method		50.44 511	48.94 514	54.93 527	39.68 554	12.38 557	28.59 544	90.09 442	6.82 547	41.10 550	78.41 536	54.41 517	70.69 547	50.12 542	51.26 547	81.72 538

kuosan		43.63 512	44.25 516	41.78 530	66.04 552	34.37 555	75.56 533	32.62 555	53.25 544	50.46 549	51.18 545	25.28 554	49.13 549	13.44 549	30.41 549	41.84 545

tmp_tt		37.97 513	44.33 515	18.88 531	11.80 555	21.54 556	63.51 541	45.66 554	4.23 548	51.34 548	50.48 546	59.08 509	22.11 551	44.50 544	68.35 545	13.00 546

cdsmvs_eth3d_5k		23.35 514	31.13 517	0.00 534	0.00 558	0.00 560	0.00 545	95.58 309	0.00 552	0.00 554	91.15 454	93.43 109	0.00 554	0.00 552	0.00 552	0.00 549

test123		9.49 515	12.01 518	1.91 532	2.87 556	1.30 558	82.38 517	1.34 558	1.36 550	2.84 552	6.56 549	2.45 555	0.97 552	2.73 550	5.56 550	3.47 547

testmvs		9.02 516	11.42 519	1.81 533	2.77 557	1.13 559	79.44 527	1.90 557	1.18 551	2.65 553	6.80 548	1.95 556	0.87 553	2.62 551	3.45 551	3.44 548

pcd_1.5k_mvsjas		7.56 517	10.09 520	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	90.77 196	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		7.56 517	10.08 521	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	90.69 465	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test-260524					97.94 107	87.97 171		97.94 115		96.37 127		93.24 116	99.34 70	94.10 66	99.19 102

MED-MVS test				95.52 89	98.69 37	88.21 161	96.32 56	98.58 18	88.79 225	97.38 65	96.22 235		99.39 54	92.89 117	99.10 115	98.96 77

TestfortrainingZip				93.68 190	95.25 338	86.20 219	96.32 56	96.38 272	92.81 92	92.13 363	93.87 373	87.28 269	98.61 196		95.07 445	96.23 369

WAC-MVS						61.25 536								74.55 479

FOURS1					99.21 3	94.68 16	98.45 4	98.81 10	97.73 9	98.27 23

MSC_two_6792asdad				95.90 69	96.54 225	89.57 124		96.87 232					99.41 43	94.06 67	99.30 80	98.72 121

PC_three_1452									75.31 474	95.87 163	95.75 272	92.93 130	96.34 420	87.18 311	98.68 203	98.04 208

No_MVS				95.90 69	96.54 225	89.57 124		96.87 232					99.41 43	94.06 67	99.30 80	98.72 121

test_one_0601					98.26 80	87.14 187		98.18 63	94.25 61	96.99 90	97.36 120	95.13 50

eth-test2					0.00 558

eth-test					0.00 558

ZD-MVS					97.23 158	90.32 113		97.54 165	84.40 362	94.78 242	95.79 266	92.76 136	99.39 54	88.72 270	98.40 238

RE-MVS-def			96.66 27		98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 160	95.40 35		93.49 87	98.84 164	98.00 213

IU-MVS					98.51 58	86.66 204		96.83 237	72.74 494	95.83 165				93.00 113	99.29 83	98.64 138

OPU-MVS				95.15 112	96.84 191	89.43 128	95.21 114				95.66 278	93.12 121	98.06 287	86.28 331	98.61 211	97.95 223

test_241102_TWO								98.10 80	91.95 118	97.54 50	97.25 134	95.37 36	99.35 67	93.29 101	99.25 91	98.49 155

test_241102_ONE					98.51 58	86.97 192		98.10 80	91.85 125	97.63 45	97.03 160	96.48 13	98.95 135

9.14			94.81 132		97.49 141		94.11 163	98.37 34	87.56 270	95.38 197	96.03 252	94.66 75	99.08 111	90.70 190	98.97 141

save fliter					97.46 145	88.05 167	92.04 271	97.08 210	87.63 267

test_0728_THIRD									93.26 87	97.40 63	97.35 123	94.69 74	99.34 70	93.88 72	99.42 54	98.89 91

test_0728_SECOND				94.88 125	98.55 53	86.72 201	95.20 116	98.22 58					99.38 63	93.44 93	99.31 78	98.53 150

test0726					98.51 58	86.69 202	95.34 105	98.18 63	91.85 125	97.63 45	97.37 115	95.58 28

GSMVS																94.75 434

test_part2					98.21 84	89.41 129				96.72 105

sam_mvs1												66.64 477				94.75 434

sam_mvs												66.41 478

ambc				92.98 226	96.88 187	83.01 289	95.92 80	96.38 272		96.41 124	97.48 106	88.26 247	97.80 318	89.96 226	98.93 148	98.12 202

MTGPA								97.62 154

test_post1							90.21 353				5.85 551	65.36 484	96.00 427	79.61 426

test_post											6.07 550	65.74 482	95.84 431

patchmatchnet-post											91.71 446	66.22 480	97.59 340

GG-mvs-BLEND				83.24 506	85.06 537	71.03 495	94.99 126	65.55 548		74.09 540	75.51 538	44.57 534	94.46 462	59.57 537	87.54 525	84.24 531

MTMP							94.82 129	54.62 552

gm-plane-assit					87.08 528	59.33 540			71.22 503		83.58 523		97.20 374	73.95 489

test9_res														88.16 290	98.40 238	97.83 247

TEST9					96.45 235	89.46 126	90.60 334	96.92 222	79.09 444	90.49 404	94.39 348	91.31 177	98.88 142

test_8					96.37 244	89.14 136	90.51 337	96.89 226	79.37 438	90.42 406	94.36 352	91.20 182	98.82 151

agg_prior2														87.06 314	98.36 249	97.98 217

agg_prior					96.20 268	88.89 142		96.88 231		90.21 414			98.78 164

TestCases				96.00 59	98.02 98	92.17 74		98.43 27	90.48 181	95.04 231	96.74 187	92.54 140	97.86 312	85.11 350	98.98 135	97.98 217

test_prior4						89.91 119	90.74 327

test_prior2							90.21 353		89.33 211	90.77 399	94.81 325	90.41 207		88.21 285	98.55 218

test_prior				94.61 142	95.95 292	87.23 184		97.36 185					98.68 186			97.93 228

旧先验2							90.00 363		68.65 520	92.71 335			96.52 408	85.15 347

新几何2							90.02 362

新几何1				93.17 222	97.16 163	87.29 182		94.43 353	67.95 522	91.29 382	94.94 317	86.97 279	98.23 260	81.06 408	97.75 314	93.98 454

旧先验1					96.20 268	84.17 262		94.82 339			95.57 284	89.57 226			97.89 306	96.32 362

无先验							89.94 364	95.75 299	70.81 509				98.59 201	81.17 407		94.81 430

原ACMM2							89.34 390

原ACMM1				92.87 237	96.91 185	84.22 260		97.01 214	76.84 463	89.64 430	94.46 346	88.00 254	98.70 182	81.53 400	98.01 293	95.70 397

test222					96.95 180	85.27 245	88.83 406	93.61 380	65.09 532	90.74 400	94.85 323	84.62 310			97.36 343	93.91 455

testdata2													98.03 291	80.24 414

segment_acmp												92.14 153

testdata				91.03 343	96.87 188	82.01 309		94.28 357	71.55 501	92.46 343	95.42 292	85.65 299	97.38 360	82.64 381	97.27 347	93.70 461

testdata1							88.96 402		88.44 239

test12				94.43 156	95.95 292	86.75 200		96.24 279		89.76 428		89.79 224	98.79 160		97.95 303	97.75 262

plane_prior7					97.71 125	88.68 146

plane_prior6					97.21 161	88.23 160						86.93 280

plane_prior5								97.81 135					98.95 135	89.26 248	98.51 227	98.60 144

plane_prior4											95.59 280

plane_prior3						88.43 157			90.35 186	93.31 299

plane_prior2							94.56 143		91.74 136

plane_prior1					97.38 149

plane_prior						88.12 164	93.01 211		88.98 219						98.06 287

n2								0.00 559

nn								0.00 559

door-mid								92.13 418

lessismore_v0				93.87 180	98.05 94	83.77 268		80.32 533		97.13 79	97.91 70	77.49 395	99.11 109	92.62 126	98.08 283	98.74 119

LGP-MVS_train				96.84 41	98.36 75	92.13 77		98.25 46	91.78 132	97.07 83	97.22 139	96.38 16	99.28 85	92.07 142	99.59 29	99.11 54

test11								96.65 254

door								91.26 431

HQP5-MVS						84.89 249

HQP-NCC					96.36 247		91.37 300		87.16 281	88.81 446

ACMP_Plane					96.36 247		91.37 300		87.16 281	88.81 446

BP-MVS														86.55 324

HQP4-MVS										88.81 446			98.61 196			98.15 198

HQP3-MVS								97.31 190							97.73 316

HQP2-MVS												84.76 308

NP-MVS					96.82 193	87.10 188					93.40 387

MDTV_nov1_ep13_2view						42.48 553	88.45 420		67.22 525	83.56 510		66.80 474		72.86 497		94.06 451

MDTV_nov1_ep13			83.88 454		89.42 511	61.52 534	88.74 414	87.41 466	73.99 483	84.96 496	94.01 365	65.25 485	95.53 434	78.02 439	93.16 491

ACMMP++_ref															98.82 170

ACMMP++															99.25 91

Test By Simon												90.61 202

ITE_SJBPF				95.95 63	97.34 152	93.36 54		96.55 264	91.93 120	94.82 240	95.39 297	91.99 155	97.08 383	85.53 340	97.96 302	97.41 291

DeepMVS_CX				53.83 528	70.38 551	64.56 527		48.52 553	33.01 546	65.50 547	74.21 539	56.19 515	46.64 550	38.45 546	70.07 544	50.30 544

High-resolution multi-view benchmark