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 1

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 84	99.51 2	87.38 129	97.70 6	97.54 105	98.16 2	98.94 2	99.33 2	97.84 4	99.08 94	90.73 124	99.73 14	99.59 12

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 10	91.85 57	97.98 5	98.01 64	94.15 48	98.93 3	99.07 5	88.07 178	99.57 13	95.86 9	99.69 15	99.46 18

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 23	93.86 30	99.07 2	98.98 3	97.01 12	98.92 4	98.78 14	95.22 37	98.61 171	96.85 2	99.77 10	99.31 27
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
LCM-MVSNet-Re		94.20 119	94.58 104	93.04 170	95.91 207	83.13 201	93.79 132	99.19 2	92.00 87	98.84 5	98.04 35	93.64 70	99.02 105	81.28 267	98.54 149	96.96 214

PS-MVSNAJss		96.01 51	96.04 51	95.89 63	98.82 22	88.51 111	95.57 68	97.88 77	88.72 171	98.81 6	98.86 10	90.77 140	99.60 8	95.43 11	99.53 35	99.57 13

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 21	92.51 48	96.58 26	97.61 100	87.57 197	98.80 7	98.90 9	96.50 10	99.59 12	96.15 7	99.47 39	99.40 21

Anonymous20231211		96.60 25	97.13 12	95.00 100	97.46 117	86.35 156	97.11 14	98.24 27	97.58 7	98.72 8	98.97 7	93.15 84	99.15 83	93.18 64	99.74 13	99.50 16

ACMH	88.36 12	96.59 27	97.43 5	94.07 137	98.56 36	85.33 172	96.33 39	98.30 20	94.66 39	98.72 8	98.30 30	97.51 5	98.00 225	94.87 14	99.59 27	98.86 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		96.59 27	96.42 29	97.12 29	98.76 26	92.49 49	96.44 33	97.42 113	86.96 206	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 43	99.32 26

wuyk23d		87.83 264	90.79 203	78.96 341	90.46 328	88.63 105	92.72 156	90.67 304	91.65 109	98.68 11	97.64 53	96.06 16	77.53 361	59.84 356	99.41 52	70.73 359

DTE-MVSNet		96.74 17	97.43 5	94.67 111	99.13 5	84.68 178	96.51 28	97.94 76	98.14 3	98.67 12	98.32 29	95.04 44	99.69 2	93.27 61	99.82 8	99.62 10

PS-CasMVS		96.69 20	97.43 5	94.49 124	99.13 5	84.09 188	96.61 24	97.97 70	97.91 5	98.64 13	98.13 32	95.24 36	99.65 3	93.39 55	99.84 3	99.72 2

PEN-MVS		96.69 20	97.39 8	94.61 113	99.16 3	84.50 179	96.54 27	98.05 55	98.06 4	98.64 13	98.25 31	95.01 47	99.65 3	92.95 74	99.83 6	99.68 4

SixPastTwentyTwo		94.91 86	95.21 81	93.98 139	98.52 44	83.19 199	95.93 55	94.84 235	94.86 38	98.49 15	98.74 16	81.45 249	99.60 8	94.69 16	99.39 54	99.15 37

WR-MVS_H		96.60 25	97.05 14	95.24 92	99.02 11	86.44 152	96.78 21	98.08 48	97.42 8	98.48 16	97.86 45	91.76 116	99.63 6	94.23 26	99.84 3	99.66 6

v7n		96.82 10	97.31 10	95.33 86	98.54 41	86.81 142	96.83 18	98.07 51	96.59 19	98.46 17	98.43 27	92.91 90	99.52 17	96.25 6	99.76 11	99.65 8

anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 84	94.03 24	96.97 15	97.61 100	87.68 194	98.45 18	98.77 15	94.20 66	99.50 19	96.70 3	99.40 53	99.53 14

CP-MVSNet		96.19 46	96.80 17	94.38 130	98.99 13	83.82 191	96.31 41	97.53 107	97.60 6	98.34 19	97.52 59	91.98 112	99.63 6	93.08 70	99.81 9	99.70 3

test_part1		94.39 108	94.55 105	93.92 144	96.14 189	82.86 204	95.54 69	98.09 47	95.36 35	98.27 20	98.36 28	75.91 290	99.44 23	93.41 54	99.84 3	99.47 17

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 39	91.77 59	97.15 11	97.37 115	88.98 165	98.26 21	98.86 10	93.35 78	99.60 8	96.41 4	99.45 43	99.66 6

ACMH+	88.43 11	96.48 30	96.82 16	95.47 81	98.54 41	89.06 96	95.65 65	98.61 7	96.10 25	98.16 22	97.52 59	96.90 7	98.62 170	90.30 136	99.60 25	98.72 90

pmmvs6		96.80 13	97.36 9	95.15 96	99.12 7	87.82 125	96.68 22	97.86 78	96.10 25	98.14 23	99.28 3	97.94 3	98.21 208	91.38 115	99.69 15	99.42 19

ANet_high		94.83 93	96.28 36	90.47 255	96.65 151	73.16 321	94.33 115	98.74 6	96.39 22	98.09 24	98.93 8	93.37 77	98.70 161	90.38 131	99.68 18	99.53 14

nrg030		96.32 41	96.55 26	95.62 75	97.83 92	88.55 109	95.77 61	98.29 23	92.68 69	98.03 25	97.91 42	95.13 40	98.95 117	93.85 33	99.49 38	99.36 24

MIMVSNet1		95.52 65	95.45 71	95.72 72	99.14 4	89.02 97	96.23 46	96.87 159	93.73 56	97.87 26	98.49 24	90.73 144	99.05 99	86.43 215	99.60 25	99.10 44

TransMVSNet (Re)		95.27 78	96.04 51	92.97 173	98.37 59	81.92 212	95.07 87	96.76 166	93.97 52	97.77 27	98.57 19	95.72 18	97.90 231	88.89 173	99.23 76	99.08 45

DPE-MVS		95.89 53	95.88 57	95.92 62	97.93 89	89.83 84	93.46 140	98.30 20	92.37 76	97.75 28	96.95 93	95.14 39	99.51 18	91.74 104	99.28 70	98.41 117
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_0402		95.73 59	96.22 39	94.26 132	98.19 69	85.77 167	93.24 145	97.24 132	96.88 15	97.69 29	97.77 48	94.12 67	99.13 87	91.54 112	99.29 65	97.88 161

NR-MVSNet		95.28 76	95.28 79	95.26 91	97.75 95	87.21 133	95.08 86	97.37 115	93.92 54	97.65 30	95.90 159	90.10 158	99.33 64	90.11 144	99.66 21	99.26 29

SED-MVS		96.00 52	96.41 32	94.76 108	98.51 45	86.97 138	95.21 79	98.10 44	91.95 88	97.63 31	97.25 77	96.48 11	99.35 56	93.29 59	99.29 65	97.95 153

test_241102_ONE					98.51 45	86.97 138		98.10 44	91.85 94	97.63 31	97.03 90	96.48 11	98.95 117

test0726					98.51 45	86.69 145	95.34 74	98.18 32	91.85 94	97.63 31	97.37 68	95.58 22

Anonymous20240529		95.50 66	95.83 61	94.50 122	97.33 123	85.93 164	95.19 83	96.77 165	96.64 18	97.61 34	98.05 34	93.23 81	98.79 141	88.60 180	99.04 99	98.78 81

abl_6		97.31 5	97.12 13	97.86 3	98.54 41	95.32 7	96.61 24	98.35 16	95.81 30	97.55 35	97.44 64	96.51 9	99.40 40	94.06 30	99.23 76	98.85 75

test_241102_TWO								98.10 44	91.95 88	97.54 36	97.25 77	95.37 28	99.35 56	93.29 59	99.25 73	98.49 110

FC-MVSNet-test		95.32 73	95.88 57	93.62 153	98.49 53	81.77 213	95.90 57	98.32 17	93.93 53	97.53 37	97.56 56	88.48 171	99.40 40	92.91 75	99.83 6	99.68 4

K. test v3		93.37 136	93.27 146	93.66 152	98.05 78	82.62 206	94.35 114	86.62 327	96.05 27	97.51 38	98.85 12	76.59 288	99.65 3	93.21 63	98.20 190	98.73 89

TranMVSNet+NR-MVSNet		96.07 50	96.26 37	95.50 80	98.26 65	87.69 126	93.75 133	97.86 78	95.96 29	97.48 39	97.14 84	95.33 32	99.44 23	90.79 123	99.76 11	99.38 22

v8		94.65 100	95.29 78	92.74 184	96.65 151	79.77 246	94.59 104	97.17 136	91.86 93	97.47 40	97.93 40	88.16 176	99.08 94	94.32 22	99.47 39	99.38 22

v10		94.68 99	95.27 80	92.90 179	96.57 157	80.15 232	94.65 103	97.57 103	90.68 132	97.43 41	98.00 37	88.18 175	99.15 83	94.84 15	99.55 34	99.41 20

APDe-MVS		96.46 32	96.64 22	95.93 60	97.68 103	89.38 93	96.90 17	98.41 13	92.52 73	97.43 41	97.92 41	95.11 41	99.50 19	94.45 19	99.30 64	98.92 67

SMA-MVS		95.77 58	95.54 68	96.47 50	98.27 64	91.19 65	95.09 85	97.79 89	86.48 210	97.42 43	97.51 61	94.47 62	99.29 68	93.55 42	99.29 65	98.93 63
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
DVP-MVS		95.82 57	96.18 41	94.72 110	98.51 45	86.69 145	95.20 81	97.00 146	91.85 94	97.40 44	97.35 72	95.58 22	99.34 59	93.44 51	99.31 62	98.13 136
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									93.26 65	97.40 44	97.35 72	94.69 54	99.34 59	93.88 32	99.42 47	98.89 69

pm-mvs1		95.43 68	95.94 54	93.93 143	98.38 57	85.08 175	95.46 72	97.12 140	91.84 97	97.28 46	98.46 25	95.30 34	97.71 251	90.17 142	99.42 47	98.99 53

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 11	95.95 5	98.61 3	98.81 5	97.41 9	97.28 46	98.46 25	94.62 57	98.84 132	94.64 17	99.53 35	98.99 53

SD-MVS		95.19 79	95.73 65	93.55 156	96.62 154	88.88 102	94.67 101	98.05 55	91.26 118	97.25 48	96.40 131	95.42 26	94.36 334	92.72 80	99.19 80	97.40 197
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
ACMM	88.83 9	96.30 43	96.07 49	96.97 35	98.39 56	92.95 44	94.74 99	98.03 60	90.82 128	97.15 49	96.85 101	96.25 15	99.00 109	93.10 68	99.33 60	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				93.87 148	98.05 78	83.77 192		80.32 359		97.13 50	97.91 42	77.49 276	99.11 91	92.62 82	98.08 201	98.74 87

FIs		94.90 87	95.35 74	93.55 156	98.28 63	81.76 214	95.33 75	98.14 39	93.05 67	97.07 51	97.18 82	87.65 185	99.29 68	91.72 105	99.69 15	99.61 11

LPG-MVS_test		96.38 40	96.23 38	96.84 40	98.36 60	92.13 52	95.33 75	98.25 24	91.78 101	97.07 51	97.22 80	96.38 13	99.28 70	92.07 93	99.59 27	99.11 41

LGP-MVS_train				96.84 40	98.36 60	92.13 52		98.25 24	91.78 101	97.07 51	97.22 80	96.38 13	99.28 70	92.07 93	99.59 27	99.11 41

VPA-MVSNet		95.14 80	95.67 67	93.58 155	97.76 94	83.15 200	94.58 106	97.58 102	93.39 63	97.05 54	98.04 35	93.25 80	98.51 184	89.75 154	99.59 27	99.08 45

FMVSNet1		94.84 92	95.13 84	93.97 140	97.60 108	84.29 181	95.99 51	96.56 175	92.38 75	97.03 55	98.53 21	90.12 155	98.98 110	88.78 175	99.16 83	98.65 92

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 90	94.85 51	99.42 28	93.49 44	98.84 118	98.00 145

RE-MVS-def			96.66 20		98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 90	95.40 27		93.49 44	98.84 118	98.00 145

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 88	93.82 32	96.31 41	98.25 24	95.51 34	96.99 58	97.05 89	95.63 21	99.39 45	93.31 58	98.88 113	98.75 84

EG-PatchMatch MVS		94.54 105	94.67 101	94.14 135	97.87 91	86.50 148	92.00 192	96.74 167	88.16 183	96.93 59	97.61 54	93.04 88	97.90 231	91.60 109	98.12 197	98.03 143

test1172		96.79 15	96.52 27	97.60 9	98.03 81	94.87 10	96.07 50	98.06 54	95.76 31	96.89 60	96.85 101	94.85 51	99.42 28	93.35 57	98.81 126	98.53 107

DIV-MVS_2432*1600		94.10 121	94.73 97	92.19 202	97.66 105	79.49 251	94.86 95	97.12 140	89.59 154	96.87 61	97.65 52	90.40 152	98.34 198	89.08 169	99.35 57	98.75 84

MP-MVS-pluss		96.08 49	95.92 56	96.57 45	99.06 9	91.21 64	93.25 144	98.32 17	87.89 188	96.86 62	97.38 67	95.55 24	99.39 45	95.47 10	99.47 39	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 57	94.31 15	96.79 20	98.32 17	96.69 16	96.86 62	97.56 56	95.48 25	98.77 149	90.11 144	99.44 45	98.31 122
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS		96.70 19	96.42 29	97.54 11	98.05 78	94.69 11	96.13 47	98.07 51	95.17 36	96.82 64	96.73 112	95.09 43	99.43 27	92.99 73	98.71 136	98.50 109

UniMVSNet_NR-MVSNet		95.35 71	95.21 81	95.76 70	97.69 102	88.59 107	92.26 181	97.84 82	94.91 37	96.80 65	95.78 169	90.42 149	99.41 35	91.60 109	99.58 31	99.29 28

DU-MVS		95.28 76	95.12 85	95.75 71	97.75 95	88.59 107	92.58 161	97.81 85	93.99 50	96.80 65	95.90 159	90.10 158	99.41 35	91.60 109	99.58 31	99.26 29

OPM-MVS		95.61 63	95.45 71	96.08 53	98.49 53	91.00 67	92.65 160	97.33 124	90.05 144	96.77 67	96.85 101	95.04 44	98.56 179	92.77 76	99.06 91	98.70 91

test_part2					98.21 68	89.41 91				96.72 68

v1240		93.29 138	93.71 130	92.06 209	96.01 201	77.89 275	91.81 207	97.37 115	85.12 235	96.69 69	96.40 131	86.67 205	99.07 98	94.51 18	98.76 132	99.22 32

tfpnnormal		94.27 115	94.87 91	92.48 196	97.71 99	80.88 227	94.55 110	95.41 222	93.70 57	96.67 70	97.72 49	91.40 124	98.18 212	87.45 198	99.18 82	98.36 118

SteuartSystems-ACMMP		96.40 38	96.30 35	96.71 42	98.63 29	91.96 55	95.70 62	98.01 64	93.34 64	96.64 71	96.57 122	94.99 48	99.36 55	93.48 47	99.34 58	98.82 77
Skip Steuart: Steuart Systems R&D Blog.
WR-MVS		93.49 133	93.72 129	92.80 183	97.57 110	80.03 238	90.14 250	95.68 209	93.70 57	96.62 72	95.39 191	87.21 193	99.04 102	87.50 197	99.64 23	99.33 25

ACMP	88.15 13	95.71 60	95.43 73	96.54 46	98.17 70	91.73 60	94.24 117	98.08 48	89.46 155	96.61 73	96.47 125	95.85 17	99.12 89	90.45 128	99.56 33	98.77 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DP-MVS		95.62 62	95.84 60	94.97 101	97.16 130	88.62 106	94.54 111	97.64 96	96.94 14	96.58 74	97.32 75	93.07 87	98.72 155	90.45 128	98.84 118	97.57 185

IterMVS-LS		93.78 128	94.28 115	92.27 199	96.27 178	79.21 258	91.87 201	96.78 163	91.77 103	96.57 75	97.07 87	87.15 194	98.74 153	91.99 95	99.03 100	98.86 72
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 7	93.92 27	97.16 10	98.17 35	93.11 66	96.48 76	97.36 71	96.92 6	99.34 59	94.31 23	99.38 55	98.92 67

ambc				92.98 172	96.88 142	83.01 203	95.92 56	96.38 185		96.41 77	97.48 62	88.26 174	97.80 242	89.96 149	98.93 110	98.12 137

Regformer-4		94.90 87	94.67 101	95.59 76	92.78 294	89.02 97	92.39 173	95.91 202	94.50 42	96.41 77	95.56 180	92.10 108	99.01 107	94.23 26	98.14 194	98.74 87

ACMMP_NAP		96.21 45	96.12 46	96.49 49	98.90 17	91.42 62	94.57 107	98.03 60	90.42 139	96.37 79	97.35 72	95.68 19	99.25 74	94.44 20	99.34 58	98.80 79

xxxxxxxxxxxxxcwj		95.03 81	94.93 88	95.33 86	97.46 117	88.05 119	92.04 189	98.42 12	87.63 195	96.36 80	96.68 115	94.37 63	99.32 65	92.41 87	99.05 94	98.64 96

SF-MVS		95.88 55	95.88 57	95.87 64	98.12 72	89.65 87	95.58 67	98.56 8	91.84 97	96.36 80	96.68 115	94.37 63	99.32 65	92.41 87	99.05 94	98.64 96

ACMMP		96.61 24	96.34 34	97.43 19	98.61 32	93.88 28	96.95 16	98.18 32	92.26 81	96.33 82	96.84 104	95.10 42	99.40 40	93.47 48	99.33 60	99.02 50
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
VDDNet		94.03 123	94.27 117	93.31 165	98.87 19	82.36 208	95.51 71	91.78 296	97.19 11	96.32 83	98.60 18	84.24 224	98.75 150	87.09 203	98.83 123	98.81 78

UniMVSNet (Re)		95.32 73	95.15 83	95.80 67	97.79 93	88.91 99	92.91 152	98.07 51	93.46 62	96.31 84	95.97 158	90.14 154	99.34 59	92.11 90	99.64 23	99.16 36

XVG-ACMP-BASELINE		95.68 61	95.34 75	96.69 43	98.40 55	93.04 41	94.54 111	98.05 55	90.45 138	96.31 84	96.76 108	92.91 90	98.72 155	91.19 116	99.42 47	98.32 120

zzz-MVS		96.47 31	96.14 44	97.47 15	98.95 15	94.05 21	93.69 135	97.62 97	94.46 44	96.29 86	96.94 94	93.56 71	99.37 52	94.29 24	99.42 47	98.99 53

MTAPA		96.65 22	96.38 33	97.47 15	98.95 15	94.05 21	95.88 58	97.62 97	94.46 44	96.29 86	96.94 94	93.56 71	99.37 52	94.29 24	99.42 47	98.99 53

Baseline_NR-MVSNet		94.47 107	95.09 86	92.60 191	98.50 52	80.82 228	92.08 187	96.68 169	93.82 55	96.29 86	98.56 20	90.10 158	97.75 249	90.10 146	99.66 21	99.24 31

IS-MVSNet		94.49 106	94.35 112	94.92 102	98.25 66	86.46 151	97.13 13	94.31 249	96.24 23	96.28 89	96.36 138	82.88 233	99.35 56	88.19 184	99.52 37	98.96 60

VDD-MVS		94.37 109	94.37 111	94.40 129	97.49 114	86.07 162	93.97 128	93.28 266	94.49 43	96.24 90	97.78 46	87.99 181	98.79 141	88.92 171	99.14 85	98.34 119

DeepPCF-MVS	90.46 6	94.20 119	93.56 137	96.14 51	95.96 203	92.96 43	89.48 268	97.46 111	85.14 233	96.23 91	95.42 188	93.19 82	98.08 218	90.37 132	98.76 132	97.38 200

PM-MVS		93.33 137	92.67 159	95.33 86	96.58 156	94.06 19	92.26 181	92.18 287	85.92 221	96.22 92	96.61 120	85.64 218	95.99 310	90.35 133	98.23 185	95.93 254

DeepC-MVS	91.39 4	95.43 68	95.33 76	95.71 73	97.67 104	90.17 78	93.86 131	98.02 62	87.35 199	96.22 92	97.99 38	94.48 61	99.05 99	92.73 79	99.68 18	97.93 155
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
V42		93.43 135	93.58 135	92.97 173	95.34 235	81.22 222	92.67 159	96.49 180	87.25 201	96.20 94	96.37 137	87.32 191	98.85 131	92.39 89	98.21 188	98.85 75

CSCG		94.69 98	94.75 95	94.52 121	97.55 111	87.87 123	95.01 90	97.57 103	92.68 69	96.20 94	93.44 257	91.92 113	98.78 145	89.11 168	99.24 75	96.92 215

v1921920		93.26 141	93.61 134	92.19 202	96.04 200	78.31 269	91.88 200	97.24 132	85.17 232	96.19 96	96.19 148	86.76 204	99.05 99	94.18 28	98.84 118	99.22 32

Regformer-2		94.86 90	94.55 105	95.77 69	92.83 292	89.98 80	91.87 201	96.40 183	94.38 46	96.19 96	95.04 203	92.47 103	99.04 102	93.49 44	98.31 174	98.28 124

EI-MVSNet-UG-set		94.35 111	94.27 117	94.59 118	92.46 297	85.87 165	92.42 171	94.69 242	93.67 61	96.13 98	95.84 164	91.20 133	98.86 129	93.78 35	98.23 185	99.03 49

EI-MVSNet-Vis-set		94.36 110	94.28 115	94.61 113	92.55 296	85.98 163	92.44 169	94.69 242	93.70 57	96.12 99	95.81 165	91.24 130	98.86 129	93.76 38	98.22 187	98.98 58

v1192		93.49 133	93.78 127	92.62 190	96.16 187	79.62 248	91.83 206	97.22 134	86.07 218	96.10 100	96.38 136	87.22 192	99.02 105	94.14 29	98.88 113	99.22 32

FMVSNet2		92.78 159	92.73 158	92.95 175	95.40 231	81.98 211	94.18 119	95.53 219	88.63 173	96.05 101	97.37 68	81.31 251	98.81 139	87.38 201	98.67 140	98.06 139

Regformer-3		94.28 114	94.23 119	94.46 126	92.78 294	86.28 158	92.39 173	94.70 241	93.69 60	95.97 102	95.56 180	91.34 125	98.48 189	93.45 49	98.14 194	98.62 100

v144192		93.20 146	93.54 138	92.16 206	96.05 196	78.26 270	91.95 193	97.14 137	84.98 239	95.96 103	96.11 152	87.08 196	99.04 102	93.79 34	98.84 118	99.17 35

Regformer-1		94.55 103	94.33 113	95.19 94	92.83 292	88.54 110	91.87 201	95.84 206	93.99 50	95.95 104	95.04 203	92.00 110	98.79 141	93.14 67	98.31 174	98.23 127

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 18	93.53 37	97.51 7	98.44 9	92.35 78	95.95 104	96.41 130	96.71 8	99.42 28	93.99 31	99.36 56	99.13 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v148		92.87 156	93.29 143	91.62 220	96.25 181	77.72 278	91.28 219	95.05 228	89.69 150	95.93 106	96.04 154	87.34 190	98.38 194	90.05 147	97.99 208	98.78 81

v1144		93.50 132	93.81 125	92.57 192	96.28 177	79.61 249	91.86 205	96.96 149	86.95 207	95.91 107	96.32 140	87.65 185	98.96 115	93.51 43	98.88 113	99.13 39

Anonymous20240521		92.86 157	93.57 136	90.74 249	96.57 157	75.50 305	94.15 120	95.60 211	89.38 156	95.90 108	97.90 44	80.39 258	97.96 229	92.60 83	99.68 18	98.75 84

IU-MVS					98.51 45	86.66 147		96.83 160	72.74 325	95.83 109				93.00 72	99.29 65	98.64 96

Patchmatch-RL test		88.81 249	88.52 242	89.69 273	95.33 236	79.94 240	86.22 323	92.71 277	78.46 296	95.80 110	94.18 234	66.25 322	95.33 323	89.22 166	98.53 150	93.78 309

PGM-MVS		96.32 41	95.94 54	97.43 19	98.59 35	93.84 31	95.33 75	98.30 20	91.40 114	95.76 111	96.87 100	95.26 35	99.45 22	92.77 76	99.21 78	99.00 51

casdiffmvs		94.32 113	94.80 93	92.85 181	96.05 196	81.44 219	92.35 176	98.05 55	91.53 112	95.75 112	96.80 105	93.35 78	98.49 185	91.01 120	98.32 173	98.64 96

GST-MVS		96.24 44	95.99 53	97.00 34	98.65 28	92.71 47	95.69 64	98.01 64	92.08 86	95.74 113	96.28 143	95.22 37	99.42 28	93.17 65	99.06 91	98.88 71

VPNet		93.08 147	93.76 128	91.03 238	98.60 33	75.83 303	91.51 213	95.62 210	91.84 97	95.74 113	97.10 86	89.31 165	98.32 199	85.07 232	99.06 91	98.93 63

EU-MVSNet		87.39 275	86.71 278	89.44 275	93.40 280	76.11 298	94.93 93	90.00 307	57.17 359	95.71 115	97.37 68	64.77 330	97.68 253	92.67 81	94.37 303	94.52 292

v2v482		93.29 138	93.63 133	92.29 198	96.35 171	78.82 263	91.77 209	96.28 187	88.45 177	95.70 116	96.26 145	86.02 213	98.90 121	93.02 71	98.81 126	99.14 38

HFP-MVS		96.39 39	96.17 43	97.04 31	98.51 45	93.37 38	96.30 43	97.98 67	92.35 78	95.63 117	96.47 125	95.37 28	99.27 72	93.78 35	99.14 85	98.48 111

#test#		95.89 53	95.51 69	97.04 31	98.51 45	93.37 38	95.14 84	97.98 67	89.34 158	95.63 117	96.47 125	95.37 28	99.27 72	91.99 95	99.14 85	98.48 111

Anonymous202405211		92.58 166	92.50 163	92.83 182	96.55 159	83.22 198	92.43 170	91.64 297	94.10 49	95.59 119	96.64 118	81.88 248	97.50 259	85.12 229	98.52 151	97.77 172

ACMMPR		96.46 32	96.14 44	97.41 21	98.60 33	93.82 32	96.30 43	97.96 71	92.35 78	95.57 120	96.61 120	94.93 50	99.41 35	93.78 35	99.15 84	99.00 51

RRT_MVS		91.36 193	90.05 218	95.29 90	89.21 341	88.15 116	92.51 167	94.89 233	86.73 209	95.54 121	95.68 172	61.82 343	99.30 67	94.91 13	99.13 88	98.43 115

XXY-MVS		92.58 166	93.16 148	90.84 247	97.75 95	79.84 242	91.87 201	96.22 193	85.94 220	95.53 122	97.68 50	92.69 96	94.48 330	83.21 248	97.51 229	98.21 130

new-patchmatchnet		88.97 245	90.79 203	83.50 332	94.28 265	55.83 364	85.34 327	93.56 262	86.18 216	95.47 123	95.73 170	83.10 231	96.51 295	85.40 224	98.06 202	98.16 132

mPP-MVS		96.46 32	96.05 50	97.69 5	98.62 30	94.65 12	96.45 31	97.74 91	92.59 72	95.47 123	96.68 115	94.50 60	99.42 28	93.10 68	99.26 72	98.99 53

UA-Net		97.35 4	97.24 11	97.69 5	98.22 67	93.87 29	98.42 4	98.19 31	96.95 13	95.46 125	99.23 4	93.45 73	99.57 13	95.34 12	99.89 2	99.63 9

APD-MVS		95.00 83	94.69 98	95.93 60	97.38 120	90.88 70	94.59 104	97.81 85	89.22 163	95.46 125	96.17 151	93.42 76	99.34 59	89.30 160	98.87 116	97.56 187
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			94.81 92		97.49 114		94.11 122	98.37 14	87.56 198	95.38 127	96.03 155	94.66 55	99.08 94	90.70 125	98.97 106

IterMVS-SCA-FT		91.65 185	91.55 182	91.94 211	93.89 274	79.22 257	87.56 298	93.51 263	91.53 112	95.37 128	96.62 119	78.65 267	98.90 121	91.89 101	94.95 291	97.70 177

ZNCC-MVS		96.42 36	96.20 40	97.07 30	98.80 25	92.79 46	96.08 49	98.16 38	91.74 105	95.34 129	96.36 138	95.68 19	99.44 23	94.41 21	99.28 70	98.97 59

LS3D		96.11 48	95.83 61	96.95 37	94.75 249	94.20 17	97.34 9	97.98 67	97.31 10	95.32 130	96.77 106	93.08 86	99.20 79	91.79 102	98.16 192	97.44 193

tttt0517		89.81 232	88.90 238	92.55 193	97.00 136	79.73 247	95.03 89	83.65 350	89.88 148	95.30 131	94.79 217	53.64 358	99.39 45	91.99 95	98.79 129	98.54 106

XVG-OURS		94.72 97	94.12 121	96.50 48	98.00 84	94.23 16	91.48 214	98.17 35	90.72 130	95.30 131	96.47 125	87.94 182	96.98 280	91.41 114	97.61 227	98.30 123

region2R		96.41 37	96.09 47	97.38 23	98.62 30	93.81 34	96.32 40	97.96 71	92.26 81	95.28 133	96.57 122	95.02 46	99.41 35	93.63 39	99.11 89	98.94 62

GeoE		94.55 103	94.68 100	94.15 134	97.23 125	85.11 174	94.14 121	97.34 123	88.71 172	95.26 134	95.50 183	94.65 56	99.12 89	90.94 121	98.40 159	98.23 127

TinyColmap		92.00 180	92.76 155	89.71 272	95.62 225	77.02 286	90.72 231	96.17 196	87.70 193	95.26 134	96.29 142	92.54 100	96.45 297	81.77 262	98.77 131	95.66 267

alignmvs		93.26 141	92.85 152	94.50 122	95.70 217	87.45 127	93.45 141	95.76 207	91.58 110	95.25 136	92.42 285	81.96 246	98.72 155	91.61 108	97.87 214	97.33 202

EI-MVSNet		92.99 151	93.26 147	92.19 202	92.12 304	79.21 258	92.32 178	94.67 244	91.77 103	95.24 137	95.85 161	87.14 195	98.49 185	91.99 95	98.26 179	98.86 72

MVSTER		89.32 238	88.75 240	91.03 238	90.10 331	76.62 293	90.85 227	94.67 244	82.27 265	95.24 137	95.79 166	61.09 346	98.49 185	90.49 127	98.26 179	97.97 152

canonicalmvs		94.59 101	94.69 98	94.30 131	95.60 226	87.03 137	95.59 66	98.24 27	91.56 111	95.21 139	92.04 291	94.95 49	98.66 167	91.45 113	97.57 228	97.20 207

MSP-MVS		95.34 72	94.63 103	97.48 14	98.67 27	94.05 21	96.41 35	98.18 32	91.26 118	95.12 140	95.15 196	86.60 207	99.50 19	93.43 53	96.81 251	98.89 69
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
GBi-Net		93.21 144	92.96 149	93.97 140	95.40 231	84.29 181	95.99 51	96.56 175	88.63 173	95.10 141	98.53 21	81.31 251	98.98 110	86.74 206	98.38 164	98.65 92

test1		93.21 144	92.96 149	93.97 140	95.40 231	84.29 181	95.99 51	96.56 175	88.63 173	95.10 141	98.53 21	81.31 251	98.98 110	86.74 206	98.38 164	98.65 92

FMVSNet3		90.78 202	90.32 213	92.16 206	93.03 289	79.92 241	92.54 162	94.95 231	86.17 217	95.10 141	96.01 156	69.97 308	98.75 150	86.74 206	98.38 164	97.82 168

CP-MVS		96.44 35	96.08 48	97.54 11	98.29 62	94.62 13	96.80 19	98.08 48	92.67 71	95.08 144	96.39 135	94.77 53	99.42 28	93.17 65	99.44 45	98.58 105

ETH3D-3000-0.1		94.86 90	94.55 105	95.81 65	97.61 107	89.72 85	94.05 124	98.37 14	88.09 184	95.06 145	95.85 161	92.58 98	99.10 93	90.33 135	98.99 101	98.62 100

AllTest		94.88 89	94.51 108	96.00 55	98.02 82	92.17 50	95.26 78	98.43 10	90.48 136	95.04 146	96.74 110	92.54 100	97.86 237	85.11 230	98.98 102	97.98 149

TestCases				96.00 55	98.02 82	92.17 50		98.43 10	90.48 136	95.04 146	96.74 110	92.54 100	97.86 237	85.11 230	98.98 102	97.98 149

YYNet1		88.17 259	88.24 250	87.93 300	92.21 301	73.62 318	80.75 350	88.77 310	82.51 263	94.99 148	95.11 199	82.70 237	93.70 339	83.33 246	93.83 310	96.48 232

EPP-MVSNet		93.91 125	93.68 132	94.59 118	98.08 75	85.55 170	97.44 8	94.03 254	94.22 47	94.94 149	96.19 148	82.07 244	99.57 13	87.28 202	98.89 111	98.65 92

MDA-MVSNet-bldmvs		91.04 197	90.88 199	91.55 222	94.68 256	80.16 231	85.49 326	92.14 290	90.41 140	94.93 150	95.79 166	85.10 219	96.93 283	85.15 227	94.19 308	97.57 185

testtj		94.81 94	94.42 109	96.01 54	97.23 125	90.51 76	94.77 98	97.85 81	91.29 117	94.92 151	95.66 173	91.71 117	99.40 40	88.07 188	98.25 182	98.11 138

baseline		94.26 116	94.80 93	92.64 187	96.08 194	80.99 225	93.69 135	98.04 59	90.80 129	94.89 152	96.32 140	93.19 82	98.48 189	91.68 107	98.51 153	98.43 115

MDA-MVSNet_test_wron		88.16 260	88.23 251	87.93 300	92.22 300	73.71 317	80.71 351	88.84 309	82.52 262	94.88 153	95.14 197	82.70 237	93.61 340	83.28 247	93.80 311	96.46 233

LFMVS		91.33 194	91.16 196	91.82 213	96.27 178	79.36 253	95.01 90	85.61 338	96.04 28	94.82 154	97.06 88	72.03 304	98.46 191	84.96 233	98.70 138	97.65 181

ITE_SJBPF				95.95 57	97.34 122	93.36 40		96.55 178	91.93 90	94.82 154	95.39 191	91.99 111	97.08 277	85.53 223	97.96 209	97.41 194

ZD-MVS					97.23 125	90.32 77		97.54 105	84.40 245	94.78 156	95.79 166	92.76 95	99.39 45	88.72 178	98.40 159

TSAR-MVS + MP.		94.96 85	94.75 95	95.57 78	98.86 20	88.69 103	96.37 36	96.81 161	85.23 230	94.75 157	97.12 85	91.85 114	99.40 40	93.45 49	98.33 171	98.62 100
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Patchmtry		90.11 222	89.92 220	90.66 251	90.35 329	77.00 287	92.96 150	92.81 273	90.25 142	94.74 158	96.93 96	67.11 314	97.52 258	85.17 225	98.98 102	97.46 191

3Dnovator+	92.74 2	95.86 56	95.77 64	96.13 52	96.81 147	90.79 72	96.30 43	97.82 84	96.13 24	94.74 158	97.23 79	91.33 126	99.16 82	93.25 62	98.30 176	98.46 113

cl_fuxian		91.32 195	91.42 187	91.00 241	92.29 299	76.79 292	87.52 301	96.42 182	85.76 224	94.72 160	93.89 246	82.73 236	98.16 214	90.93 122	98.55 146	98.04 142

TSAR-MVS + GP.		93.07 149	92.41 165	95.06 99	95.82 210	90.87 71	90.97 225	92.61 281	88.04 185	94.61 161	93.79 249	88.08 177	97.81 241	89.41 159	98.39 162	96.50 231

OMC-MVS		94.22 118	93.69 131	95.81 65	97.25 124	91.27 63	92.27 180	97.40 114	87.10 205	94.56 162	95.42 188	93.74 69	98.11 217	86.62 210	98.85 117	98.06 139

testgi		90.38 213	91.34 190	87.50 305	97.49 114	71.54 330	89.43 269	95.16 227	88.38 179	94.54 163	94.68 220	92.88 92	93.09 344	71.60 336	97.85 215	97.88 161

VNet		92.67 163	92.96 149	91.79 214	96.27 178	80.15 232	91.95 193	94.98 230	92.19 84	94.52 164	96.07 153	87.43 189	97.39 268	84.83 234	98.38 164	97.83 166

eth_miper_zixun_eth		90.72 203	90.61 207	91.05 237	92.04 306	76.84 291	86.91 310	96.67 170	85.21 231	94.41 165	93.92 244	79.53 262	98.26 205	89.76 153	97.02 243	98.06 139

test20.03		90.80 201	90.85 201	90.63 252	95.63 224	79.24 256	89.81 262	92.87 272	89.90 147	94.39 166	96.40 131	85.77 214	95.27 325	73.86 323	99.05 94	97.39 198

XVS		96.49 29	96.18 41	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 167	96.49 124	94.56 58	99.39 45	93.57 40	99.05 94	98.93 63

X-MVStestdata		90.70 204	88.45 244	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 167	26.89 363	94.56 58	99.39 45	93.57 40	99.05 94	98.93 63

3Dnovator	92.54 3	94.80 95	94.90 89	94.47 125	95.47 229	87.06 135	96.63 23	97.28 130	91.82 100	94.34 169	97.41 65	90.60 147	98.65 169	92.47 85	98.11 198	97.70 177

RRT_test8_iter05		88.21 258	88.17 253	88.33 296	91.62 313	66.82 349	91.73 210	96.60 173	86.34 213	94.14 170	95.38 193	47.72 364	99.11 91	91.78 103	98.26 179	99.06 47

Vis-MVSNet		95.50 66	95.48 70	95.56 79	98.11 73	89.40 92	95.35 73	98.22 29	92.36 77	94.11 171	98.07 33	92.02 109	99.44 23	93.38 56	97.67 224	97.85 165
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS		90.18 220	90.16 214	90.21 264	93.15 285	75.98 300	87.56 298	92.97 271	86.43 212	94.09 172	96.40 131	78.32 271	97.43 264	87.87 192	94.69 298	97.23 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSLP-MVS++		93.25 143	93.88 124	91.37 226	96.34 172	82.81 205	93.11 146	97.74 91	89.37 157	94.08 173	95.29 194	90.40 152	96.35 302	90.35 133	98.25 182	94.96 282

BH-untuned		90.68 205	90.90 198	90.05 269	95.98 202	79.57 250	90.04 253	94.94 232	87.91 186	94.07 174	93.00 267	87.76 184	97.78 245	79.19 291	95.17 288	92.80 326

miper_ehance_all_eth		90.48 209	90.42 211	90.69 250	91.62 313	76.57 294	86.83 313	96.18 195	83.38 250	94.06 175	92.66 278	82.20 242	98.04 220	89.79 152	97.02 243	97.45 192

cl-mvsnet____		90.65 206	90.56 208	90.91 245	91.85 308	76.98 289	86.75 315	95.36 225	85.53 227	94.06 175	94.89 210	77.36 280	97.98 228	90.27 138	98.98 102	97.76 173

cl-mvsnet1		90.65 206	90.56 208	90.91 245	91.85 308	76.99 288	86.75 315	95.36 225	85.52 229	94.06 175	94.89 210	77.37 279	97.99 227	90.28 137	98.97 106	97.76 173

pmmvs-eth3d		91.54 188	90.73 205	93.99 138	95.76 215	87.86 124	90.83 228	93.98 258	78.23 298	94.02 178	96.22 147	82.62 239	96.83 286	86.57 211	98.33 171	97.29 204

hse-mvs3		92.89 154	91.99 172	95.58 77	96.97 137	90.55 74	93.94 129	94.01 257	89.23 161	93.95 179	96.19 148	76.88 285	99.14 85	91.02 118	95.71 274	97.04 211

hse-mvs2		92.24 176	91.20 193	95.38 83	96.16 187	90.65 73	92.52 163	92.01 294	89.23 161	93.95 179	92.99 268	76.88 285	98.69 163	91.02 118	96.03 266	96.81 220

UnsupCasMVSNet_eth		90.33 216	90.34 212	90.28 260	94.64 258	80.24 230	89.69 264	95.88 203	85.77 223	93.94 181	95.69 171	81.99 245	92.98 345	84.21 241	91.30 336	97.62 183

CNVR-MVS		94.58 102	94.29 114	95.46 82	96.94 139	89.35 94	91.81 207	96.80 162	89.66 151	93.90 182	95.44 187	92.80 94	98.72 155	92.74 78	98.52 151	98.32 120

DeepC-MVS_fast	89.96 7	93.73 129	93.44 140	94.60 117	96.14 189	87.90 122	93.36 143	97.14 137	85.53 227	93.90 182	95.45 186	91.30 128	98.59 175	89.51 157	98.62 142	97.31 203
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
bset_n11_16_dypcd		89.99 228	89.15 231	92.53 194	94.75 249	81.34 220	84.19 338	87.56 321	85.13 234	93.77 184	92.46 280	72.82 299	99.01 107	92.46 86	99.21 78	97.23 205

XVG-OURS-SEG-HR		95.38 70	95.00 87	96.51 47	98.10 74	94.07 18	92.46 168	98.13 40	90.69 131	93.75 185	96.25 146	98.03 2	97.02 279	92.08 92	95.55 277	98.45 114

QAPM		92.88 155	92.77 154	93.22 168	95.82 210	83.31 195	96.45 31	97.35 122	83.91 248	93.75 185	96.77 106	89.25 166	98.88 124	84.56 238	97.02 243	97.49 190

MVS_111021_LR		93.66 130	93.28 145	94.80 106	96.25 181	90.95 68	90.21 246	95.43 221	87.91 186	93.74 187	94.40 226	92.88 92	96.38 300	90.39 130	98.28 177	97.07 208

thisisatest0530		88.69 252	87.52 263	92.20 201	96.33 173	79.36 253	92.81 154	84.01 349	86.44 211	93.67 188	92.68 277	53.62 359	99.25 74	89.65 156	98.45 157	98.00 145

ETH3D cwj APD-0.16		93.99 124	93.38 142	95.80 67	96.82 145	89.92 81	92.72 156	98.02 62	84.73 243	93.65 189	95.54 182	91.68 118	99.22 77	88.78 175	98.49 156	98.26 126

PCF-MVS	84.52 17	89.12 241	87.71 260	93.34 163	96.06 195	85.84 166	86.58 322	97.31 125	68.46 343	93.61 190	93.89 246	87.51 188	98.52 183	67.85 347	98.11 198	95.66 267
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		93.63 131	93.42 141	94.26 132	96.65 151	86.96 140	89.30 274	96.23 191	88.36 180	93.57 191	94.60 221	93.45 73	97.77 246	90.23 140	98.38 164	98.03 143

MP-MVS		96.14 47	95.68 66	97.51 13	98.81 23	94.06 19	96.10 48	97.78 90	92.73 68	93.48 192	96.72 113	94.23 65	99.42 28	91.99 95	99.29 65	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
RPSCF		95.58 64	94.89 90	97.62 8	97.58 109	96.30 4	95.97 54	97.53 107	92.42 74	93.41 193	97.78 46	91.21 132	97.77 246	91.06 117	97.06 241	98.80 79

OpenMVS_ROB	85.12 16	89.52 236	89.05 233	90.92 243	94.58 259	81.21 223	91.10 223	93.41 265	77.03 305	93.41 193	93.99 242	83.23 230	97.80 242	79.93 282	94.80 295	93.74 311

PMVS	87.21 14	94.97 84	95.33 76	93.91 145	98.97 14	97.16 2	95.54 69	95.85 205	96.47 20	93.40 195	97.46 63	95.31 33	95.47 318	86.18 219	98.78 130	89.11 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
HQP_MVS		94.26 116	93.93 123	95.23 93	97.71 99	88.12 117	94.56 108	97.81 85	91.74 105	93.31 196	95.59 175	86.93 199	98.95 117	89.26 164	98.51 153	98.60 103

plane_prior3						88.43 113			90.35 141	93.31 196

thres600view7		87.66 268	87.10 272	89.36 278	96.05 196	73.17 320	92.72 156	85.31 341	91.89 92	93.29 198	90.97 305	63.42 336	98.39 192	73.23 326	96.99 248	96.51 228

CPTT-MVS		94.74 96	94.12 121	96.60 44	98.15 71	93.01 42	95.84 59	97.66 95	89.21 164	93.28 199	95.46 185	88.89 168	98.98 110	89.80 151	98.82 124	97.80 170

USDC		89.02 242	89.08 232	88.84 286	95.07 240	74.50 312	88.97 280	96.39 184	73.21 322	93.27 200	96.28 143	82.16 243	96.39 299	77.55 301	98.80 128	95.62 270

thres100view900		87.35 276	86.89 274	88.72 288	96.14 189	73.09 322	93.00 149	85.31 341	92.13 85	93.26 201	90.96 306	63.42 336	98.28 201	71.27 338	96.54 258	94.79 285

N_pmnet		88.90 247	87.25 267	93.83 149	94.40 263	93.81 34	84.73 331	87.09 324	79.36 287	93.26 201	92.43 284	79.29 263	91.68 349	77.50 303	97.22 238	96.00 251

CL-MVSNet_2432*1600		90.04 227	89.90 221	90.47 255	95.24 237	77.81 276	86.60 321	92.62 280	85.64 226	93.25 203	93.92 244	83.84 226	96.06 308	79.93 282	98.03 205	97.53 189

mvs_anonymous		90.37 214	91.30 191	87.58 304	92.17 303	68.00 343	89.84 261	94.73 240	83.82 249	93.22 204	97.40 66	87.54 187	97.40 267	87.94 191	95.05 290	97.34 201

test_yl		90.11 222	89.73 225	91.26 230	94.09 269	79.82 243	90.44 238	92.65 278	90.90 124	93.19 205	93.30 260	73.90 295	98.03 221	82.23 258	96.87 249	95.93 254

DCV-MVSNet		90.11 222	89.73 225	91.26 230	94.09 269	79.82 243	90.44 238	92.65 278	90.90 124	93.19 205	93.30 260	73.90 295	98.03 221	82.23 258	96.87 249	95.93 254

D2MVS		89.93 229	89.60 227	90.92 243	94.03 271	78.40 268	88.69 287	94.85 234	78.96 292	93.08 207	95.09 200	74.57 293	96.94 281	88.19 184	98.96 108	97.41 194

UnsupCasMVSNet_bld		88.50 254	88.03 256	89.90 270	95.52 228	78.88 262	87.39 302	94.02 256	79.32 288	93.06 208	94.02 240	80.72 256	94.27 335	75.16 317	93.08 321	96.54 226

miper_lstm_enhance		89.90 230	89.80 222	90.19 266	91.37 317	77.50 280	83.82 342	95.00 229	84.84 241	93.05 209	94.96 207	76.53 289	95.20 326	89.96 149	98.67 140	97.86 163

PHI-MVS		94.34 112	93.80 126	95.95 57	95.65 221	91.67 61	94.82 96	97.86 78	87.86 189	93.04 210	94.16 235	91.58 120	98.78 145	90.27 138	98.96 108	97.41 194

TAMVS		90.16 221	89.05 233	93.49 161	96.49 162	86.37 154	90.34 243	92.55 282	80.84 274	92.99 211	94.57 223	81.94 247	98.20 209	73.51 324	98.21 188	95.90 257

Vis-MVSNet (Re-imp)		90.42 211	90.16 214	91.20 234	97.66 105	77.32 283	94.33 115	87.66 320	91.20 120	92.99 211	95.13 198	75.40 292	98.28 201	77.86 297	99.19 80	97.99 148

ab-mvs		92.40 170	92.62 160	91.74 216	97.02 135	81.65 215	95.84 59	95.50 220	86.95 207	92.95 213	97.56 56	90.70 145	97.50 259	79.63 285	97.43 232	96.06 249

MCST-MVS		92.91 153	92.51 162	94.10 136	97.52 112	85.72 168	91.36 218	97.13 139	80.33 276	92.91 214	94.24 231	91.23 131	98.72 155	89.99 148	97.93 211	97.86 163

ETV-MVS		92.99 151	92.74 156	93.72 151	95.86 209	86.30 157	92.33 177	97.84 82	91.70 108	92.81 215	86.17 347	92.22 105	99.19 80	88.03 189	97.73 218	95.66 267

TAPA-MVS	88.58 10	92.49 169	91.75 180	94.73 109	96.50 161	89.69 86	92.91 152	97.68 94	78.02 299	92.79 216	94.10 236	90.85 139	97.96 229	84.76 236	98.16 192	96.54 226
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
BH-RMVSNet		90.47 210	90.44 210	90.56 254	95.21 238	78.65 267	89.15 278	93.94 259	88.21 181	92.74 217	94.22 232	86.38 208	97.88 233	78.67 294	95.39 283	95.14 278

旧先验2							90.00 255		68.65 342	92.71 218			96.52 294	85.15 227

cl-mvsnet2		89.02 242	88.50 243	90.59 253	89.76 333	76.45 295	86.62 320	94.03 254	82.98 258	92.65 219	92.49 279	72.05 303	97.53 257	88.93 170	97.02 243	97.78 171

tfpn200view9		87.05 284	86.52 282	88.67 289	95.77 213	72.94 323	91.89 198	86.00 333	90.84 126	92.61 220	89.80 318	63.93 333	98.28 201	71.27 338	96.54 258	94.79 285

thres400		87.20 280	86.52 282	89.24 282	95.77 213	72.94 323	91.89 198	86.00 333	90.84 126	92.61 220	89.80 318	63.93 333	98.28 201	71.27 338	96.54 258	96.51 228

MS-PatchMatch		88.05 261	87.75 259	88.95 283	93.28 281	77.93 273	87.88 294	92.49 283	75.42 311	92.57 222	93.59 254	80.44 257	94.24 337	81.28 267	92.75 324	94.69 290

miper_enhance_ethall		88.42 255	87.87 258	90.07 267	88.67 346	75.52 304	85.10 328	95.59 215	75.68 308	92.49 223	89.45 326	78.96 264	97.88 233	87.86 193	97.02 243	96.81 220

testdata				91.03 238	96.87 143	82.01 210		94.28 250	71.55 329	92.46 224	95.42 188	85.65 217	97.38 270	82.64 253	97.27 236	93.70 312

LF4IMVS		92.72 161	92.02 171	94.84 105	95.65 221	91.99 54	92.92 151	96.60 173	85.08 237	92.44 225	93.62 252	86.80 203	96.35 302	86.81 205	98.25 182	96.18 245

diffmvs		91.74 183	91.93 174	91.15 236	93.06 287	78.17 271	88.77 285	97.51 110	86.28 214	92.42 226	93.96 243	88.04 179	97.46 262	90.69 126	96.67 256	97.82 168

HPM-MVS++		95.02 82	94.39 110	96.91 38	97.88 90	93.58 36	94.09 123	96.99 148	91.05 123	92.40 227	95.22 195	91.03 138	99.25 74	92.11 90	98.69 139	97.90 159

ppachtmachnet_test		88.61 253	88.64 241	88.50 292	91.76 310	70.99 333	84.59 334	92.98 270	79.30 289	92.38 228	93.53 256	79.57 261	97.45 263	86.50 214	97.17 239	97.07 208

Anonymous20231206		88.77 250	88.29 248	90.20 265	96.31 175	78.81 264	89.56 267	93.49 264	74.26 316	92.38 228	95.58 178	82.21 241	95.43 320	72.07 332	98.75 134	96.34 237

MVS_Test		92.57 168	93.29 143	90.40 258	93.53 279	75.85 301	92.52 163	96.96 149	88.73 170	92.35 230	96.70 114	90.77 140	98.37 197	92.53 84	95.49 279	96.99 213

PVSNet_Blended_VisFu		91.63 186	91.20 193	92.94 177	97.73 98	83.95 190	92.14 185	97.46 111	78.85 294	92.35 230	94.98 206	84.16 225	99.08 94	86.36 216	96.77 253	95.79 261

CDPH-MVS		92.67 163	91.83 176	95.18 95	96.94 139	88.46 112	90.70 232	97.07 143	77.38 301	92.34 232	95.08 201	92.67 97	98.88 124	85.74 221	98.57 145	98.20 131

NCCC		94.08 122	93.54 138	95.70 74	96.49 162	89.90 83	92.39 173	96.91 155	90.64 133	92.33 233	94.60 221	90.58 148	98.96 115	90.21 141	97.70 222	98.23 127

CLD-MVS		91.82 182	91.41 188	93.04 170	96.37 166	83.65 193	86.82 314	97.29 128	84.65 244	92.27 234	89.67 323	92.20 106	97.85 239	83.95 242	99.47 39	97.62 183
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DELS-MVS		92.05 179	92.16 167	91.72 217	94.44 261	80.13 234	87.62 295	97.25 131	87.34 200	92.22 235	93.18 264	89.54 164	98.73 154	89.67 155	98.20 190	96.30 239
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
baseline1		87.62 270	87.31 265	88.54 291	94.71 255	74.27 315	93.10 147	88.20 316	86.20 215	92.18 236	93.04 266	73.21 298	95.52 315	79.32 289	85.82 349	95.83 259

API-MVS		91.52 189	91.61 181	91.26 230	94.16 266	86.26 159	94.66 102	94.82 236	91.17 121	92.13 237	91.08 304	90.03 161	97.06 278	79.09 292	97.35 235	90.45 343

DP-MVS Recon		92.31 173	91.88 175	93.60 154	97.18 129	86.87 141	91.10 223	97.37 115	84.92 240	92.08 238	94.08 237	88.59 170	98.20 209	83.50 245	98.14 194	95.73 263

our_test_3		87.55 271	87.59 262	87.44 306	91.76 310	70.48 334	83.83 341	90.55 305	79.79 279	92.06 239	92.17 288	78.63 269	95.63 313	84.77 235	94.73 296	96.22 242

MSDG		90.82 200	90.67 206	91.26 230	94.16 266	83.08 202	86.63 319	96.19 194	90.60 135	91.94 240	91.89 292	89.16 167	95.75 312	80.96 273	94.51 301	94.95 283

Effi-MVS+-dtu		93.90 127	92.60 161	97.77 4	94.74 251	96.67 3	94.00 126	95.41 222	89.94 145	91.93 241	92.13 289	90.12 155	98.97 114	87.68 195	97.48 230	97.67 180

ETH3 D test6400		91.91 181	91.25 192	93.89 146	96.59 155	84.41 180	92.10 186	97.72 93	78.52 295	91.82 242	93.78 250	88.70 169	99.13 87	83.61 244	98.39 162	98.14 134

Gipuma		95.31 75	95.80 63	93.81 150	97.99 87	90.91 69	96.42 34	97.95 73	96.69 16	91.78 243	98.85 12	91.77 115	95.49 317	91.72 105	99.08 90	95.02 281
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
HyFIR lowres test		87.19 281	85.51 291	92.24 200	97.12 134	80.51 229	85.03 329	96.06 198	66.11 349	91.66 244	92.98 269	70.12 307	99.14 85	75.29 316	95.23 287	97.07 208

MVP-Stereo		90.07 225	88.92 236	93.54 158	96.31 175	86.49 149	90.93 226	95.59 215	79.80 278	91.48 245	95.59 175	80.79 255	97.39 268	78.57 295	91.19 337	96.76 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres200		85.85 291	85.18 292	87.88 302	94.44 261	72.52 326	89.08 279	86.21 329	88.57 176	91.44 246	88.40 334	64.22 331	98.00 225	68.35 346	95.88 272	93.12 320

FMVSNet5		87.82 265	86.56 280	91.62 220	92.31 298	79.81 245	93.49 139	94.81 238	83.26 251	91.36 247	96.93 96	52.77 360	97.49 261	76.07 312	98.03 205	97.55 188

CS-MVS		93.91 125	94.22 120	92.95 175	95.65 221	83.25 197	94.91 94	98.87 4	91.32 116	91.32 248	93.07 265	92.24 104	99.37 52	91.90 100	98.73 135	96.21 244

新几何1				93.17 169	97.16 130	87.29 130		94.43 246	67.95 344	91.29 249	94.94 208	86.97 198	98.23 207	81.06 272	97.75 217	93.98 305

xiu_mvs_v1_base_debu		91.47 190	91.52 183	91.33 227	95.69 218	81.56 216	89.92 257	96.05 199	83.22 252	91.26 250	90.74 308	91.55 121	98.82 134	89.29 161	95.91 269	93.62 314

xiu_mvs_v1_base		91.47 190	91.52 183	91.33 227	95.69 218	81.56 216	89.92 257	96.05 199	83.22 252	91.26 250	90.74 308	91.55 121	98.82 134	89.29 161	95.91 269	93.62 314

xiu_mvs_v1_base_debi		91.47 190	91.52 183	91.33 227	95.69 218	81.56 216	89.92 257	96.05 199	83.22 252	91.26 250	90.74 308	91.55 121	98.82 134	89.29 161	95.91 269	93.62 314

CDS-MVSNet		89.55 234	88.22 252	93.53 159	95.37 234	86.49 149	89.26 275	93.59 261	79.76 280	91.15 253	92.31 286	77.12 281	98.38 194	77.51 302	97.92 212	95.71 264
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	89.45 8	92.27 175	92.13 169	92.68 186	94.53 260	84.10 187	95.70 62	97.03 144	82.44 264	91.14 254	96.42 129	88.47 172	98.38 194	85.95 220	97.47 231	95.55 271

1121		90.26 219	89.23 228	93.34 163	97.15 132	87.40 128	91.94 195	94.39 247	67.88 345	91.02 255	94.91 209	86.91 201	98.59 175	81.17 270	97.71 221	94.02 304

CNLPA		91.72 184	91.20 193	93.26 167	96.17 186	91.02 66	91.14 221	95.55 218	90.16 143	90.87 256	93.56 255	86.31 209	94.40 333	79.92 284	97.12 240	94.37 295

test_prior3		93.29 138	92.85 152	94.61 113	95.95 204	87.23 131	90.21 246	97.36 120	89.33 159	90.77 257	94.81 213	90.41 150	98.68 165	88.21 182	98.55 146	97.93 155

test_prior2							90.21 246		89.33 159	90.77 257	94.81 213	90.41 150		88.21 182	98.55 146

test222					96.95 138	85.27 173	88.83 283	93.61 260	65.09 352	90.74 259	94.85 212	84.62 223			97.36 234	93.91 306

TR-MVS		87.70 266	87.17 269	89.27 280	94.11 268	79.26 255	88.69 287	91.86 295	81.94 268	90.69 260	89.79 320	82.82 235	97.42 265	72.65 330	91.98 333	91.14 339

CVMVSNet		85.16 294	84.72 293	86.48 311	92.12 304	70.19 335	92.32 178	88.17 317	56.15 360	90.64 261	95.85 161	67.97 312	96.69 290	88.78 175	90.52 340	92.56 329

TEST9					96.45 164	89.46 88	90.60 234	96.92 153	79.09 290	90.49 262	94.39 227	91.31 127	98.88 124

train_agg		92.71 162	91.83 176	95.35 84	96.45 164	89.46 88	90.60 234	96.92 153	79.37 285	90.49 262	94.39 227	91.20 133	98.88 124	88.66 179	98.43 158	97.72 176

test_8					96.37 166	89.14 95	90.51 237	96.89 156	79.37 285	90.42 264	94.36 229	91.20 133	98.82 134

KD-MVS_2432*1600		82.17 312	80.75 319	86.42 313	82.04 364	70.09 337	81.75 348	90.80 302	82.56 260	90.37 265	89.30 327	42.90 369	96.11 306	74.47 319	92.55 327	93.06 321

miper_refine_blended		82.17 312	80.75 319	86.42 313	82.04 364	70.09 337	81.75 348	90.80 302	82.56 260	90.37 265	89.30 327	42.90 369	96.11 306	74.47 319	92.55 327	93.06 321

agg_prior1		92.60 165	91.76 179	95.10 98	96.20 183	88.89 100	90.37 241	96.88 157	79.67 282	90.21 267	94.41 225	91.30 128	98.78 145	88.46 181	98.37 169	97.64 182

agg_prior					96.20 183	88.89 100		96.88 157		90.21 267			98.78 145

jason		89.17 240	88.32 246	91.70 218	95.73 216	80.07 235	88.10 292	93.22 267	71.98 328	90.09 269	92.79 273	78.53 270	98.56 179	87.43 199	97.06 241	96.46 233
jason: jason.
Fast-Effi-MVS+-dtu		92.77 160	92.16 167	94.58 120	94.66 257	88.25 114	92.05 188	96.65 171	89.62 152	90.08 270	91.23 301	92.56 99	98.60 173	86.30 217	96.27 263	96.90 216

CHOSEN 1792x2688		87.19 281	85.92 289	91.00 241	97.13 133	79.41 252	84.51 335	95.60 211	64.14 353	90.07 271	94.81 213	78.26 272	97.14 276	73.34 325	95.38 284	96.46 233

PatchMatch-RL		89.18 239	88.02 257	92.64 187	95.90 208	92.87 45	88.67 289	91.06 300	80.34 275	90.03 272	91.67 296	83.34 228	94.42 332	76.35 311	94.84 294	90.64 342

BH-w/o		87.21 279	87.02 273	87.79 303	94.77 248	77.27 284	87.90 293	93.21 269	81.74 269	89.99 273	88.39 335	83.47 227	96.93 283	71.29 337	92.43 329	89.15 344

Fast-Effi-MVS+		91.28 196	90.86 200	92.53 194	95.45 230	82.53 207	89.25 277	96.52 179	85.00 238	89.91 274	88.55 333	92.94 89	98.84 132	84.72 237	95.44 281	96.22 242

AdaColmap		91.63 186	91.36 189	92.47 197	95.56 227	86.36 155	92.24 183	96.27 188	88.88 169	89.90 275	92.69 276	91.65 119	98.32 199	77.38 304	97.64 225	92.72 328

mvs-test1		93.07 149	91.80 178	96.89 39	94.74 251	95.83 6	92.17 184	95.41 222	89.94 145	89.85 276	90.59 314	90.12 155	98.88 124	87.68 195	95.66 275	95.97 252

GA-MVS		87.70 266	86.82 275	90.31 259	93.27 282	77.22 285	84.72 333	92.79 275	85.11 236	89.82 277	90.07 315	66.80 317	97.76 248	84.56 238	94.27 306	95.96 253

Patchmatch-test		86.10 290	86.01 287	86.38 315	90.63 324	74.22 316	89.57 266	86.69 326	85.73 225	89.81 278	92.83 271	65.24 328	91.04 351	77.82 300	95.78 273	93.88 308

EIA-MVS		92.35 172	92.03 170	93.30 166	95.81 212	83.97 189	92.80 155	98.17 35	87.71 192	89.79 279	87.56 337	91.17 136	99.18 81	87.97 190	97.27 236	96.77 222

test12				94.43 128	95.95 204	86.75 143		96.24 190		89.76 280		89.79 162	98.79 141		97.95 210	97.75 175

pmmvs4		88.95 246	87.70 261	92.70 185	94.30 264	85.60 169	87.22 304	92.16 289	74.62 314	89.75 281	94.19 233	77.97 274	96.41 298	82.71 252	96.36 262	96.09 247

原ACMM1				92.87 180	96.91 141	84.22 184		97.01 145	76.84 306	89.64 282	94.46 224	88.00 180	98.70 161	81.53 265	98.01 207	95.70 265

MG-MVS		89.54 235	89.80 222	88.76 287	94.88 242	72.47 327	89.60 265	92.44 284	85.82 222	89.48 283	95.98 157	82.85 234	97.74 250	81.87 261	95.27 286	96.08 248

114514_t		90.51 208	89.80 222	92.63 189	98.00 84	82.24 209	93.40 142	97.29 128	65.84 350	89.40 284	94.80 216	86.99 197	98.75 150	83.88 243	98.61 143	96.89 217

Effi-MVS+		92.79 158	92.74 156	92.94 177	95.10 239	83.30 196	94.00 126	97.53 107	91.36 115	89.35 285	90.65 313	94.01 68	98.66 167	87.40 200	95.30 285	96.88 218

CR-MVSNet		87.89 262	87.12 271	90.22 263	91.01 320	78.93 260	92.52 163	92.81 273	73.08 323	89.10 286	96.93 96	67.11 314	97.64 254	88.80 174	92.70 325	94.08 299

RPMNet		90.31 218	90.14 217	90.81 248	91.01 320	78.93 260	92.52 163	98.12 41	91.91 91	89.10 286	96.89 99	68.84 309	99.41 35	90.17 142	92.70 325	94.08 299

PatchT		87.51 272	88.17 253	85.55 318	90.64 323	66.91 345	92.02 191	86.09 331	92.20 83	89.05 288	97.16 83	64.15 332	96.37 301	89.21 167	92.98 323	93.37 318

MVSFormer		92.18 177	92.23 166	92.04 210	94.74 251	80.06 236	97.15 11	97.37 115	88.98 165	88.83 289	92.79 273	77.02 282	99.60 8	96.41 4	96.75 254	96.46 233

lupinMVS		88.34 257	87.31 265	91.45 224	94.74 251	80.06 236	87.23 303	92.27 286	71.10 332	88.83 289	91.15 302	77.02 282	98.53 182	86.67 209	96.75 254	95.76 262

HQP-NCC					96.36 168		91.37 215		87.16 202	88.81 291

ACMP_Plane					96.36 168		91.37 215		87.16 202	88.81 291

HQP4-MVS										88.81 291			98.61 171			98.15 133

HQP-MVS		92.09 178	91.49 186	93.88 147	96.36 168	84.89 176	91.37 215	97.31 125	87.16 202	88.81 291	93.40 258	84.76 221	98.60 173	86.55 212	97.73 218	98.14 134

PAPM_NR		91.03 198	90.81 202	91.68 219	96.73 149	81.10 224	93.72 134	96.35 186	88.19 182	88.77 295	92.12 290	85.09 220	97.25 272	82.40 257	93.90 309	96.68 225

SCA		87.43 274	87.21 268	88.10 299	92.01 307	71.98 329	89.43 269	88.11 318	82.26 266	88.71 296	92.83 271	78.65 267	97.59 255	79.61 286	93.30 316	94.75 287

F-COLMAP		92.28 174	91.06 197	95.95 57	97.52 112	91.90 56	93.53 138	97.18 135	83.98 247	88.70 297	94.04 238	88.41 173	98.55 181	80.17 278	95.99 268	97.39 198

PVSNet_BlendedMVS		90.35 215	89.96 219	91.54 223	94.81 246	78.80 265	90.14 250	96.93 151	79.43 284	88.68 298	95.06 202	86.27 210	98.15 215	80.27 275	98.04 204	97.68 179

PVSNet_Blended		88.74 251	88.16 255	90.46 257	94.81 246	78.80 265	86.64 318	96.93 151	74.67 313	88.68 298	89.18 329	86.27 210	98.15 215	80.27 275	96.00 267	94.44 294

AUN-MVS		90.05 226	88.30 247	95.32 89	96.09 193	90.52 75	92.42 171	92.05 293	82.08 267	88.45 300	92.86 270	65.76 324	98.69 163	88.91 172	96.07 265	96.75 224

pmmvs5		87.87 263	87.14 270	90.07 267	93.26 283	76.97 290	88.89 282	92.18 287	73.71 320	88.36 301	93.89 246	76.86 287	96.73 289	80.32 274	96.81 251	96.51 228

WTY-MVS		86.93 286	86.50 284	88.24 297	94.96 241	74.64 308	87.19 305	92.07 292	78.29 297	88.32 302	91.59 298	78.06 273	94.27 335	74.88 318	93.15 319	95.80 260

thisisatest0515		84.72 297	82.99 305	89.90 270	92.96 290	75.33 306	84.36 336	83.42 351	77.37 302	88.27 303	86.65 342	53.94 357	98.72 155	82.56 254	97.40 233	95.67 266

MIMVSNet		87.13 283	86.54 281	88.89 285	96.05 196	76.11 298	94.39 113	88.51 312	81.37 270	88.27 303	96.75 109	72.38 301	95.52 315	65.71 352	95.47 280	95.03 280

test0.0.03 1		82.48 309	81.47 313	85.48 319	89.70 334	73.57 319	84.73 331	81.64 355	83.07 256	88.13 305	86.61 343	62.86 339	89.10 357	66.24 351	90.29 341	93.77 310

CMPMVS	68.83 22	87.28 277	85.67 290	92.09 208	88.77 345	85.42 171	90.31 244	94.38 248	70.02 338	88.00 306	93.30 260	73.78 297	94.03 338	75.96 314	96.54 258	96.83 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS2		81.31 317	83.44 301	74.92 343	90.52 326	46.49 366	69.19 358	85.23 344	84.30 246	87.95 307	94.71 219	76.95 284	84.36 360	64.07 353	98.09 200	93.89 307

xiu_mvs_v2_base		89.00 244	89.19 229	88.46 294	94.86 244	74.63 309	86.97 308	95.60 211	80.88 272	87.83 308	88.62 332	91.04 137	98.81 139	82.51 256	94.38 302	91.93 334

PS-MVSNAJ		88.86 248	88.99 235	88.48 293	94.88 242	74.71 307	86.69 317	95.60 211	80.88 272	87.83 308	87.37 340	90.77 140	98.82 134	82.52 255	94.37 303	91.93 334

tpm		84.38 299	84.08 298	85.30 322	90.47 327	63.43 359	89.34 272	85.63 337	77.24 304	87.62 310	95.03 205	61.00 347	97.30 271	79.26 290	91.09 339	95.16 276

sss		87.23 278	86.82 275	88.46 294	93.96 272	77.94 272	86.84 312	92.78 276	77.59 300	87.61 311	91.83 293	78.75 266	91.92 348	77.84 298	94.20 307	95.52 272

MAR-MVS		90.32 217	88.87 239	94.66 112	94.82 245	91.85 57	94.22 118	94.75 239	80.91 271	87.52 312	88.07 336	86.63 206	97.87 236	76.67 308	96.21 264	94.25 298
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
DPM-MVS		89.35 237	88.40 245	92.18 205	96.13 192	84.20 185	86.96 309	96.15 197	75.40 312	87.36 313	91.55 299	83.30 229	98.01 224	82.17 260	96.62 257	94.32 297

UGNet		93.08 147	92.50 163	94.79 107	93.87 275	87.99 121	95.07 87	94.26 251	90.64 133	87.33 314	97.67 51	86.89 202	98.49 185	88.10 187	98.71 136	97.91 158
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
PatchmatchNet		85.22 293	84.64 294	86.98 309	89.51 338	69.83 340	90.52 236	87.34 323	78.87 293	87.22 315	92.74 275	66.91 316	96.53 293	81.77 262	86.88 348	94.58 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
1112_ss		88.42 255	87.41 264	91.45 224	96.69 150	80.99 225	89.72 263	96.72 168	73.37 321	87.00 316	90.69 311	77.38 278	98.20 209	81.38 266	93.72 312	95.15 277

cascas		87.02 285	86.28 286	89.25 281	91.56 315	76.45 295	84.33 337	96.78 163	71.01 333	86.89 317	85.91 348	81.35 250	96.94 281	83.09 249	95.60 276	94.35 296

CANet		92.38 171	91.99 172	93.52 160	93.82 277	83.46 194	91.14 221	97.00 146	89.81 149	86.47 318	94.04 238	87.90 183	99.21 78	89.50 158	98.27 178	97.90 159

Test_1112_low_res		87.50 273	86.58 279	90.25 262	96.80 148	77.75 277	87.53 300	96.25 189	69.73 339	86.47 318	93.61 253	75.67 291	97.88 233	79.95 280	93.20 317	95.11 279

PLC	85.34 15	90.40 212	88.92 236	94.85 104	96.53 160	90.02 79	91.58 212	96.48 181	80.16 277	86.14 320	92.18 287	85.73 215	98.25 206	76.87 307	94.61 300	96.30 239
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
new_pmnet		81.22 318	81.01 317	81.86 336	90.92 322	70.15 336	84.03 339	80.25 360	70.83 334	85.97 321	89.78 321	67.93 313	84.65 359	67.44 348	91.90 334	90.78 341

EPMVS		81.17 320	80.37 322	83.58 331	85.58 358	65.08 354	90.31 244	71.34 363	77.31 303	85.80 322	91.30 300	59.38 348	92.70 346	79.99 279	82.34 356	92.96 324

tpmvs		84.22 300	83.97 299	84.94 323	87.09 353	65.18 352	91.21 220	88.35 313	82.87 259	85.21 323	90.96 306	65.24 328	96.75 288	79.60 288	85.25 350	92.90 325

FPMVS		84.50 298	83.28 302	88.16 298	96.32 174	94.49 14	85.76 324	85.47 339	83.09 255	85.20 324	94.26 230	63.79 335	86.58 358	63.72 354	91.88 335	83.40 353

pmmvs3		80.83 321	78.96 329	86.45 312	87.23 352	77.48 281	84.87 330	82.31 353	63.83 354	85.03 325	89.50 325	49.66 361	93.10 343	73.12 328	95.10 289	88.78 348

PAPR		87.65 269	86.77 277	90.27 261	92.85 291	77.38 282	88.56 290	96.23 191	76.82 307	84.98 326	89.75 322	86.08 212	97.16 275	72.33 331	93.35 315	96.26 241

MDTV_nov1_ep13			83.88 300		89.42 339	61.52 360	88.74 286	87.41 322	73.99 318	84.96 327	94.01 241	65.25 327	95.53 314	78.02 296	93.16 318

1314		86.46 288	86.33 285	86.87 310	91.65 312	74.54 310	91.94 195	94.10 253	74.28 315	84.78 328	87.33 341	83.03 232	95.00 327	78.72 293	91.16 338	91.06 340

ADS-MVSNet2		84.01 301	82.20 309	89.41 276	89.04 342	76.37 297	87.57 296	90.98 301	72.71 326	84.46 329	92.45 281	68.08 310	96.48 296	70.58 342	83.97 351	95.38 273

ADS-MVSNet		82.25 310	81.55 311	84.34 328	89.04 342	65.30 351	87.57 296	85.13 345	72.71 326	84.46 329	92.45 281	68.08 310	92.33 347	70.58 342	83.97 351	95.38 273

PVSNet	76.22 20	82.89 307	82.37 307	84.48 327	93.96 272	64.38 357	78.60 353	88.61 311	71.50 330	84.43 331	86.36 346	74.27 294	94.60 329	69.87 344	93.69 313	94.46 293

MVS		84.98 296	84.30 296	87.01 308	91.03 319	77.69 279	91.94 195	94.16 252	59.36 358	84.23 332	87.50 339	85.66 216	96.80 287	71.79 333	93.05 322	86.54 350

tpmrst		82.85 308	82.93 306	82.64 334	87.65 347	58.99 362	90.14 250	87.90 319	75.54 310	83.93 333	91.63 297	66.79 319	95.36 321	81.21 269	81.54 357	93.57 317

ET-MVSNet_ETH3D		86.15 289	84.27 297	91.79 214	93.04 288	81.28 221	87.17 306	86.14 330	79.57 283	83.65 334	88.66 331	57.10 351	98.18 212	87.74 194	95.40 282	95.90 257

HY-MVS	82.50 18	86.81 287	85.93 288	89.47 274	93.63 278	77.93 273	94.02 125	91.58 298	75.68 308	83.64 335	93.64 251	77.40 277	97.42 265	71.70 335	92.07 332	93.05 323

MDTV_nov1_ep13_2view						42.48 367	88.45 291		67.22 347	83.56 336		66.80 317		72.86 329		94.06 301

CostFormer		83.09 305	82.21 308	85.73 317	89.27 340	67.01 344	90.35 242	86.47 328	70.42 336	83.52 337	93.23 263	61.18 345	96.85 285	77.21 305	88.26 346	93.34 319

DSMNet-mixed		82.21 311	81.56 310	84.16 329	89.57 337	70.00 339	90.65 233	77.66 362	54.99 361	83.30 338	97.57 55	77.89 275	90.50 353	66.86 350	95.54 278	91.97 333

E-PMN		80.72 323	80.86 318	80.29 339	85.11 359	68.77 342	72.96 355	81.97 354	87.76 191	83.25 339	83.01 355	62.22 342	89.17 356	77.15 306	94.31 305	82.93 354

test-LLR		83.58 302	83.17 303	84.79 325	89.68 335	66.86 347	83.08 343	84.52 346	83.07 256	82.85 340	84.78 351	62.86 339	93.49 341	82.85 250	94.86 292	94.03 302

test-mter		81.21 319	80.01 326	84.79 325	89.68 335	66.86 347	83.08 343	84.52 346	73.85 319	82.85 340	84.78 351	43.66 368	93.49 341	82.85 250	94.86 292	94.03 302

CANet_DTU		89.85 231	89.17 230	91.87 212	92.20 302	80.02 239	90.79 229	95.87 204	86.02 219	82.53 342	91.77 294	80.01 259	98.57 178	85.66 222	97.70 222	97.01 212

MVS_0304		90.96 199	90.15 216	93.37 162	93.17 284	87.06 135	93.62 137	92.43 285	89.60 153	82.25 343	95.50 183	82.56 240	97.83 240	84.41 240	97.83 216	95.22 275

JIA-IIPM		85.08 295	83.04 304	91.19 235	87.56 348	86.14 161	89.40 271	84.44 348	88.98 165	82.20 344	97.95 39	56.82 353	96.15 304	76.55 310	83.45 353	91.30 338

PMMVS		83.00 306	81.11 314	88.66 290	83.81 363	86.44 152	82.24 347	85.65 336	61.75 357	82.07 345	85.64 349	79.75 260	91.59 350	75.99 313	93.09 320	87.94 349

tpm2		81.46 316	80.35 323	84.80 324	89.90 332	65.14 353	90.44 238	85.36 340	65.82 351	82.05 346	92.44 283	57.94 350	96.69 290	70.71 341	88.49 345	92.56 329

IB-MVS	77.21 19	83.11 304	81.05 315	89.29 279	91.15 318	75.85 301	85.66 325	86.00 333	79.70 281	82.02 347	86.61 343	48.26 363	98.39 192	77.84 298	92.22 330	93.63 313
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
tpm cat1		80.61 324	79.46 327	84.07 330	88.78 344	65.06 355	89.26 275	88.23 315	62.27 356	81.90 348	89.66 324	62.70 341	95.29 324	71.72 334	80.60 358	91.86 336

EMVS		80.35 325	80.28 324	80.54 338	84.73 361	69.07 341	72.54 357	80.73 357	87.80 190	81.66 349	81.73 356	62.89 338	89.84 354	75.79 315	94.65 299	82.71 355

dp		79.28 327	78.62 330	81.24 337	85.97 357	56.45 363	86.91 310	85.26 343	72.97 324	81.45 350	89.17 330	56.01 355	95.45 319	73.19 327	76.68 359	91.82 337

DWT-MVSNet_test		80.74 322	79.18 328	85.43 320	87.51 350	66.87 346	89.87 260	86.01 332	74.20 317	80.86 351	80.62 357	48.84 362	96.68 292	81.54 264	83.14 355	92.75 327

EPNet		89.80 233	88.25 249	94.45 127	83.91 362	86.18 160	93.87 130	87.07 325	91.16 122	80.64 352	94.72 218	78.83 265	98.89 123	85.17 225	98.89 111	98.28 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TESTMET0.1,1		79.09 328	78.04 331	82.25 335	87.52 349	64.03 358	83.08 343	80.62 358	70.28 337	80.16 353	83.22 354	44.13 367	90.56 352	79.95 280	93.36 314	92.15 332

baseline2		83.38 303	81.54 312	88.90 284	91.38 316	72.84 325	88.78 284	81.22 356	78.97 291	79.82 354	87.56 337	61.73 344	97.80 242	74.30 321	90.05 342	96.05 250

gg-mvs-nofinetune		82.10 314	81.02 316	85.34 321	87.46 351	71.04 331	94.74 99	67.56 364	96.44 21	79.43 355	98.99 6	45.24 365	96.15 304	67.18 349	92.17 331	88.85 346

PVSNet_0	70.34 21	74.58 330	72.96 333	79.47 340	90.63 324	66.24 350	73.26 354	83.40 352	63.67 355	78.02 356	78.35 359	72.53 300	89.59 355	56.68 358	60.05 362	82.57 356

MVS-HIRNet		78.83 329	80.60 321	73.51 344	93.07 286	47.37 365	87.10 307	78.00 361	68.94 341	77.53 357	97.26 76	71.45 305	94.62 328	63.28 355	88.74 344	78.55 358

CHOSEN 280x420		80.04 326	77.97 332	86.23 316	90.13 330	74.53 311	72.87 356	89.59 308	66.38 348	76.29 358	85.32 350	56.96 352	95.36 321	69.49 345	94.72 297	88.79 347

PAPM		81.91 315	80.11 325	87.31 307	93.87 275	72.32 328	84.02 340	93.22 267	69.47 340	76.13 359	89.84 317	72.15 302	97.23 273	53.27 360	89.02 343	92.37 331

GG-mvs-BLEND				83.24 333	85.06 360	71.03 332	94.99 92	65.55 365		74.09 360	75.51 360	44.57 366	94.46 331	59.57 357	87.54 347	84.24 352

EPNet_dtu		85.63 292	84.37 295	89.40 277	86.30 356	74.33 314	91.64 211	88.26 314	84.84 241	72.96 361	89.85 316	71.27 306	97.69 252	76.60 309	97.62 226	96.18 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVE	59.87 23	73.86 331	72.65 334	77.47 342	87.00 355	74.35 313	61.37 360	60.93 366	67.27 346	69.69 362	86.49 345	81.24 254	72.33 362	56.45 359	83.45 353	85.74 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				53.83 346	70.38 366	64.56 356		48.52 368	33.01 362	65.50 363	74.21 361	56.19 354	46.64 364	38.45 363	70.07 360	50.30 360

tmp_tt		37.97 333	44.33 336	18.88 347	11.80 368	21.54 368	63.51 359	45.66 369	4.23 364	51.34 364	50.48 362	59.08 349	22.11 365	44.50 362	68.35 361	13.00 361

test_method		50.44 332	48.94 335	54.93 345	39.68 367	12.38 369	28.59 361	90.09 306	6.82 363	41.10 365	78.41 358	54.41 356	70.69 363	50.12 361	51.26 363	81.72 357

test123		9.49 335	12.01 338	1.91 348	2.87 369	1.30 370	82.38 346	1.34 371	1.36 365	2.84 366	6.56 365	2.45 371	0.97 366	2.73 364	5.56 364	3.47 362

testmvs		9.02 336	11.42 339	1.81 349	2.77 370	1.13 371	79.44 352	1.90 370	1.18 366	2.65 367	6.80 364	1.95 372	0.87 367	2.62 365	3.45 365	3.44 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		23.35 334	31.13 337	0.00 350	0.00 371	0.00 372	0.00 362	95.58 217	0.00 367	0.00 368	91.15 302	93.43 75	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		7.56 337	10.09 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	90.77 140	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		7.56 337	10.08 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	90.69 311	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

OPU-MVS				95.15 96	96.84 144	89.43 90	95.21 79				95.66 173	93.12 85	98.06 219	86.28 218	98.61 143	97.95 153

save fliter					97.46 117	88.05 119	92.04 189	97.08 142	87.63 195

test_0728_SECOND				94.88 103	98.55 39	86.72 144	95.20 81	98.22 29					99.38 51	93.44 51	99.31 62	98.53 107

GSMVS																94.75 287

sam_mvs1												66.64 320				94.75 287

sam_mvs												66.41 321

MTGPA								97.62 97

test_post1							90.21 246				5.85 367	65.36 326	96.00 309	79.61 286

test_post											6.07 366	65.74 325	95.84 311

patchmatchnet-post											91.71 295	66.22 323	97.59 255

MTMP							94.82 96	54.62 367

gm-plane-assit					87.08 354	59.33 361			71.22 331		83.58 353		97.20 274	73.95 322

test9_res														88.16 186	98.40 159	97.83 166

agg_prior2														87.06 204	98.36 170	97.98 149

test_prior4						89.91 82	90.74 230

test_prior				94.61 113	95.95 204	87.23 131		97.36 120					98.68 165			97.93 155

新几何2							90.02 254

旧先验1					96.20 183	84.17 186		94.82 236			95.57 179	89.57 163			97.89 213	96.32 238

无先验							89.94 256	95.75 208	70.81 335				98.59 175	81.17 270		94.81 284

原ACMM2							89.34 272

testdata2													98.03 221	80.24 277

segment_acmp												92.14 107

testdata1							88.96 281		88.44 178

plane_prior7					97.71 99	88.68 104

plane_prior6					97.21 128	88.23 115						86.93 199

plane_prior5								97.81 85					98.95 117	89.26 164	98.51 153	98.60 103

plane_prior4											95.59 175

plane_prior2							94.56 108		91.74 105

plane_prior1					97.38 120

plane_prior						88.12 117	93.01 148		88.98 165						98.06 202

n2								0.00 372

nn								0.00 372

door-mid								92.13 291

test11								96.65 171

door								91.26 299

HQP5-MVS						84.89 176

BP-MVS														86.55 212

HQP3-MVS								97.31 125							97.73 218

HQP2-MVS												84.76 221

NP-MVS					96.82 145	87.10 134					93.40 258

ACMMP++_ref															98.82 124

ACMMP++															99.25 73

Test By Simon												90.61 146

High-resolution multi-view benchmark