High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 39	99.71 8	96.99 44	99.69 2	99.57 4	99.02 16	99.62 11	99.36 15	98.53 8	99.52 168	98.58 13	99.95 6	99.66 22
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_part1		99.41 2	99.62 2	98.80 31	99.76 5	96.58 57	99.49 3	99.65 2	99.89 2	99.94 2	99.77 2	99.03 4	99.92 4	99.05 3	99.99 2	99.90 1

UA-Net		98.88 8	98.76 14	99.22 2	99.11 81	97.89 13	99.47 4	99.32 8	99.08 11	97.87 134	99.67 3	96.47 84	99.92 4	97.88 23	99.98 3	99.85 4

TDRefinement		98.90 6	98.86 9	99.02 9	99.54 20	98.06 7	99.34 5	99.44 7	98.85 20	99.00 36	99.20 24	97.42 32	99.59 147	97.21 45	99.76 38	99.40 81

UniMVSNet_ETH3D		99.12 4	99.28 4	98.65 44	99.77 3	96.34 64	99.18 6	99.20 14	99.67 3	99.73 4	99.65 5	99.15 3	99.86 21	97.22 44	99.92 13	99.77 9

OurMVSNet-221017-0		98.61 17	98.61 24	98.63 46	99.77 3	96.35 63	99.17 7	99.05 41	98.05 40	99.61 12	99.52 6	93.72 174	99.88 19	98.72 10	99.88 22	99.65 23

pmmvs6		99.07 5	99.24 5	98.56 50	99.81 2	96.38 62	98.87 8	99.30 9	99.01 17	99.63 10	99.66 4	99.27 2	99.68 115	97.75 30	99.89 21	99.62 25

Anonymous20231211		98.55 18	98.76 14	97.94 93	98.79 105	94.37 137	98.84 9	99.15 22	99.37 4	99.67 7	99.43 12	95.61 118	99.72 77	98.12 17	99.86 24	99.73 16

MIMVSNet1		98.51 21	98.45 27	98.67 42	99.72 7	96.71 50	98.76 10	98.89 75	98.49 27	99.38 18	99.14 31	95.44 125	99.84 26	96.47 67	99.80 33	99.47 59

EPP-MVSNet		96.84 125	96.58 136	97.65 111	99.18 67	93.78 161	98.68 11	96.34 281	97.91 44	97.30 159	98.06 122	88.46 258	99.85 23	93.85 189	99.40 141	99.32 96

v7n		98.73 12	98.99 6	97.95 92	99.64 12	94.20 145	98.67 12	99.14 24	99.08 11	99.42 16	99.23 22	96.53 79	99.91 13	99.27 2	99.93 11	99.73 16

MVSFormer		96.14 161	96.36 149	95.49 233	97.68 235	87.81 272	98.67 12	99.02 50	96.50 92	94.48 267	96.15 264	86.90 273	99.92 4	98.73 8	99.13 190	98.74 201

test_djsdf		98.73 12	98.74 17	98.69 41	99.63 13	96.30 66	98.67 12	99.02 50	96.50 92	99.32 21	99.44 11	97.43 31	99.92 4	98.73 8	99.95 6	99.86 3

anonymousdsp		98.72 15	98.63 20	98.99 13	99.62 14	97.29 37	98.65 15	99.19 16	95.62 135	99.35 20	99.37 13	97.38 33	99.90 14	98.59 12	99.91 16	99.77 9

HPM-MVS		98.11 39	97.83 51	98.92 22	99.42 35	97.46 31	98.57 16	99.05 41	95.43 144	97.41 157	97.50 179	97.98 16	99.79 39	95.58 103	99.57 79	99.50 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
IS-MVSNet		96.93 119	96.68 132	97.70 107	99.25 51	94.00 151	98.57 16	96.74 277	98.36 30	98.14 102	97.98 131	88.23 261	99.71 92	93.10 207	99.72 47	99.38 86

WR-MVS_H		98.65 16	98.62 22	98.75 34	99.51 23	96.61 55	98.55 18	99.17 17	99.05 14	99.17 29	98.79 50	95.47 123	99.89 17	97.95 21	99.91 16	99.75 14

mvs_tets		98.90 6	98.94 7	98.75 34	99.69 9	96.48 60	98.54 19	99.22 11	96.23 103	99.71 5	99.48 8	98.77 7	99.93 2	98.89 4	99.95 6	99.84 6

Gipuma		98.07 41	98.31 30	97.36 141	99.76 5	96.28 67	98.51 20	99.10 29	98.76 22	96.79 189	99.34 18	96.61 74	98.82 290	96.38 69	99.50 106	96.98 299
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PS-CasMVS		98.73 12	98.85 11	98.39 60	99.55 18	95.47 97	98.49 21	99.13 25	99.22 9	99.22 27	98.96 41	97.35 34	99.92 4	97.79 28	99.93 11	99.79 8

3Dnovator	96.53 2	97.61 80	97.64 70	97.50 124	97.74 231	93.65 168	98.49 21	98.88 80	96.86 82	97.11 168	98.55 68	95.82 106	99.73 73	95.94 87	99.42 134	99.13 137

DTE-MVSNet		98.79 9	98.86 9	98.59 48	99.55 18	96.12 71	98.48 23	99.10 29	99.36 5	99.29 23	99.06 36	97.27 38	99.93 2	97.71 32	99.91 16	99.70 19

jajsoiax		98.77 10	98.79 13	98.74 36	99.66 11	96.48 60	98.45 24	99.12 26	95.83 128	99.67 7	99.37 13	98.25 11	99.92 4	98.77 6	99.94 9	99.82 7

PEN-MVS		98.75 11	98.85 11	98.44 56	99.58 15	95.67 87	98.45 24	99.15 22	99.33 6	99.30 22	99.00 37	97.27 38	99.92 4	97.64 33	99.92 13	99.75 14

LS3D		97.77 71	97.50 84	98.57 49	96.24 295	97.58 24	98.45 24	98.85 88	98.58 26	97.51 146	97.94 137	95.74 114	99.63 131	95.19 125	98.97 208	98.51 221

FC-MVSNet-test		98.16 34	98.37 28	97.56 116	99.49 27	93.10 181	98.35 27	99.21 12	98.43 28	98.89 39	98.83 49	94.30 159	99.81 32	97.87 24	99.91 16	99.77 9

HPM-MVS_fast		98.32 28	98.13 34	98.88 24	99.54 20	97.48 30	98.35 27	99.03 48	95.88 123	97.88 131	98.22 103	98.15 13	99.74 67	96.50 66	99.62 63	99.42 78

ab-mvs		96.59 144	96.59 135	96.60 181	98.64 122	92.21 196	98.35 27	97.67 239	94.45 179	96.99 179	98.79 50	94.96 139	99.49 174	90.39 263	99.07 200	98.08 253

pm-mvs1		98.47 22	98.67 18	97.86 98	99.52 22	94.58 130	98.28 30	99.00 58	97.57 60	99.27 24	99.22 23	98.32 10	99.50 173	97.09 51	99.75 42	99.50 43

SixPastTwentyTwo		97.49 89	97.57 79	97.26 147	99.56 16	92.33 192	98.28 30	96.97 269	98.30 33	99.45 15	99.35 17	88.43 259	99.89 17	98.01 20	99.76 38	99.54 36

CP-MVSNet		98.42 24	98.46 25	98.30 68	99.46 29	95.22 109	98.27 32	98.84 92	99.05 14	99.01 35	98.65 62	95.37 126	99.90 14	97.57 34	99.91 16	99.77 9

GG-mvs-BLEND				90.60 322	91.00 354	84.21 321	98.23 33	72.63 358		82.76 351	84.11 351	56.14 358	96.79 346	72.20 348	92.09 340	90.78 347

GBi-Net		96.99 114	96.80 126	97.56 116	97.96 198	93.67 164	98.23 33	98.66 142	95.59 137	97.99 118	99.19 25	89.51 250	99.73 73	94.60 155	99.44 124	99.30 102

test1		96.99 114	96.80 126	97.56 116	97.96 198	93.67 164	98.23 33	98.66 142	95.59 137	97.99 118	99.19 25	89.51 250	99.73 73	94.60 155	99.44 124	99.30 102

FMVSNet1		97.95 50	98.08 36	97.56 116	99.14 79	93.67 164	98.23 33	98.66 142	97.41 70	99.00 36	99.19 25	95.47 123	99.73 73	95.83 90	99.76 38	99.30 102

ACMH	93.61 9	98.44 23	98.76 14	97.51 121	99.43 33	93.54 170	98.23 33	99.05 41	97.40 71	99.37 19	99.08 35	98.79 6	99.47 180	97.74 31	99.71 50	99.50 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		98.38 26	98.67 18	97.51 121	99.51 23	93.39 174	98.20 38	98.87 82	98.23 35	99.48 13	99.27 20	98.47 9	99.55 160	96.52 64	99.53 94	99.60 26

gg-mvs-nofinetune		88.28 314	86.96 319	92.23 314	92.84 350	84.44 318	98.19 39	74.60 355	99.08 11	87.01 348	99.47 9	56.93 357	98.23 332	78.91 340	95.61 325	94.01 338

QAPM		95.88 172	95.57 181	96.80 170	97.90 204	91.84 208	98.18 40	98.73 122	88.41 280	96.42 207	98.13 109	94.73 142	99.75 60	88.72 286	98.94 213	98.81 192

NR-MVSNet		97.96 47	97.86 48	98.26 70	98.73 111	95.54 92	98.14 41	98.73 122	97.79 45	99.42 16	97.83 149	94.40 157	99.78 41	95.91 89	99.76 38	99.46 61

MIMVSNet		93.42 259	92.86 259	95.10 246	98.17 177	88.19 262	98.13 42	93.69 308	92.07 241	95.04 253	98.21 104	80.95 300	99.03 272	81.42 335	98.06 268	98.07 255

PS-MVSNAJss		98.53 20	98.63 20	98.21 75	99.68 10	94.82 120	98.10 43	99.21 12	96.91 80	99.75 3	99.45 10	95.82 106	99.92 4	98.80 5	99.96 5	99.89 2

ACMMP		98.05 42	97.75 59	98.93 21	99.23 54	97.60 22	98.09 44	98.96 68	95.75 132	97.91 127	98.06 122	96.89 60	99.76 53	95.32 118	99.57 79	99.43 77
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
APDe-MVS		98.14 35	98.03 40	98.47 55	98.72 113	96.04 73	98.07 45	99.10 29	95.96 117	98.59 55	98.69 58	96.94 55	99.81 32	96.64 59	99.58 76	99.57 32

Vis-MVSNet		98.27 30	98.34 29	98.07 83	99.33 43	95.21 111	98.04 46	99.46 6	97.32 73	97.82 139	99.11 32	96.75 68	99.86 21	97.84 25	99.36 148	99.15 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	96.13 3	97.73 72	97.59 77	98.15 79	98.11 187	95.60 90	98.04 46	98.70 132	98.13 38	96.93 184	98.45 75	95.30 130	99.62 139	95.64 98	98.96 209	99.24 119

FIs		97.93 55	98.07 37	97.48 128	99.38 39	92.95 184	98.03 48	99.11 27	98.04 41	98.62 51	98.66 60	93.75 173	99.78 41	97.23 43	99.84 27	99.73 16

COLMAP_ROB	94.48 6	98.25 32	98.11 35	98.64 45	99.21 63	97.35 35	97.96 49	99.16 18	98.34 31	98.78 43	98.52 70	97.32 35	99.45 187	94.08 178	99.67 57	99.13 137
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		96.98 117	96.84 123	97.41 137	99.40 37	93.26 176	97.94 50	95.31 298	99.26 8	98.39 72	99.18 28	87.85 268	99.62 139	95.13 134	99.09 197	99.35 93

CP-MVS		97.92 56	97.56 80	98.99 13	98.99 92	97.82 15	97.93 51	98.96 68	96.11 107	96.89 187	97.45 183	96.85 64	99.78 41	95.19 125	99.63 62	99.38 86

ANet_high		98.31 29	98.94 7	96.41 196	99.33 43	89.64 237	97.92 52	99.56 5	99.27 7	99.66 9	99.50 7	97.67 26	99.83 29	97.55 35	99.98 3	99.77 9

nrg030		98.54 19	98.62 22	98.32 65	99.22 57	95.66 88	97.90 53	99.08 35	98.31 32	99.02 34	98.74 54	97.68 25	99.61 145	97.77 29	99.85 26	99.70 19

ambc				96.56 187	98.23 169	91.68 211	97.88 54	98.13 208		98.42 69	98.56 67	94.22 162	99.04 269	94.05 182	99.35 153	98.95 167

Anonymous20240529		97.96 47	98.04 39	97.71 105	98.69 120	94.28 142	97.86 55	98.31 186	98.79 21	99.23 26	98.86 48	95.76 113	99.61 145	95.49 104	99.36 148	99.23 120

canonicalmvs		97.23 109	97.21 103	97.30 144	97.65 239	94.39 135	97.84 56	99.05 41	97.42 67	96.68 195	93.85 314	97.63 27	99.33 225	96.29 71	98.47 254	98.18 250

tfpnnormal		97.72 73	97.97 41	96.94 161	99.26 48	92.23 195	97.83 57	98.45 164	98.25 34	99.13 30	98.66 60	96.65 71	99.69 109	93.92 187	99.62 63	98.91 178

XVS		97.96 47	97.63 72	98.94 18	99.15 71	97.66 19	97.77 58	98.83 99	97.42 67	96.32 212	97.64 168	96.49 82	99.72 77	95.66 96	99.37 145	99.45 66

X-MVStestdata		92.86 268	90.83 293	98.94 18	99.15 71	97.66 19	97.77 58	98.83 99	97.42 67	96.32 212	36.50 353	96.49 82	99.72 77	95.66 96	99.37 145	99.45 66

VPA-MVSNet		98.27 30	98.46 25	97.70 107	99.06 86	93.80 159	97.76 60	99.00 58	98.40 29	99.07 33	98.98 39	96.89 60	99.75 60	97.19 48	99.79 34	99.55 35

UGNet		96.81 130	96.56 138	97.58 115	96.64 285	93.84 158	97.75 61	97.12 264	96.47 95	93.62 291	98.88 47	93.22 183	99.53 164	95.61 100	99.69 54	99.36 92
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
mPP-MVS		97.91 59	97.53 81	99.04 7	99.22 57	97.87 14	97.74 62	98.78 113	96.04 112	97.10 169	97.73 161	96.53 79	99.78 41	95.16 129	99.50 106	99.46 61

OpenMVS	94.22 8	95.48 186	95.20 187	96.32 199	97.16 272	91.96 205	97.74 62	98.84 92	87.26 290	94.36 269	98.01 128	93.95 168	99.67 120	90.70 253	98.75 235	97.35 292

abl_6		98.42 24	98.19 33	99.09 3	99.16 68	98.10 5	97.73 64	99.11 27	97.76 49	98.62 51	98.27 96	97.88 20	99.80 38	95.67 94	99.50 106	99.38 86

MSP-MVS		97.45 92	96.92 120	99.03 8	99.26 48	97.70 18	97.66 65	98.89 75	95.65 133	98.51 60	96.46 251	92.15 209	99.81 32	95.14 132	98.58 250	99.58 28

LFMVS		95.32 194	94.88 202	96.62 180	98.03 189	91.47 214	97.65 66	90.72 337	99.11 10	97.89 130	98.31 85	79.20 305	99.48 177	93.91 188	99.12 193	98.93 173

K. test v3		96.44 151	96.28 152	96.95 160	99.41 36	91.53 212	97.65 66	90.31 340	98.89 19	98.93 38	99.36 15	84.57 288	99.92 4	97.81 26	99.56 82	99.39 84

TSAR-MVS + MP.		97.42 95	97.23 101	98.00 90	99.38 39	95.00 115	97.63 68	98.20 196	93.00 226	98.16 98	98.06 122	95.89 101	99.72 77	95.67 94	99.10 196	99.28 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
region2R		97.92 56	97.59 77	98.92 22	99.22 57	97.55 26	97.60 69	98.84 92	96.00 115	97.22 161	97.62 170	96.87 63	99.76 53	95.48 107	99.43 131	99.46 61

HFP-MVS		97.94 52	97.64 70	98.83 26	99.15 71	97.50 28	97.59 70	98.84 92	96.05 110	97.49 148	97.54 174	97.07 48	99.70 101	95.61 100	99.46 119	99.30 102

ACMMPR		97.95 50	97.62 74	98.94 18	99.20 64	97.56 25	97.59 70	98.83 99	96.05 110	97.46 154	97.63 169	96.77 67	99.76 53	95.61 100	99.46 119	99.49 51

RPSCF		97.87 62	97.51 83	98.95 17	99.15 71	98.43 3	97.56 72	99.06 39	96.19 104	98.48 63	98.70 57	94.72 143	99.24 244	94.37 166	99.33 163	99.17 127

SR-MVS-dyc-post		98.14 35	97.84 49	99.02 9	98.81 102	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.60 76	99.76 53	95.49 104	99.20 179	99.26 114

RE-MVS-def			97.88 47		98.81 102	98.05 8	97.55 73	98.86 84	97.77 46	98.20 93	98.07 117	96.94 55		95.49 104	99.20 179	99.26 114

APD-MVS_3200maxsize		98.13 38	97.90 44	98.79 32	98.79 105	97.31 36	97.55 73	98.92 72	97.72 53	98.25 89	98.13 109	97.10 45	99.75 60	95.44 111	99.24 177	99.32 96

ACMH+	93.58 10	98.23 33	98.31 30	97.98 91	99.39 38	95.22 109	97.55 73	99.20 14	98.21 36	99.25 25	98.51 71	98.21 12	99.40 204	94.79 148	99.72 47	99.32 96

Vis-MVSNet (Re-imp)		95.11 202	94.85 203	95.87 220	99.12 80	89.17 245	97.54 77	94.92 300	96.50 92	96.58 199	97.27 201	83.64 291	99.48 177	88.42 291	99.67 57	98.97 165

MP-MVS		97.64 77	97.18 104	99.00 12	99.32 45	97.77 17	97.49 78	98.73 122	96.27 100	95.59 243	97.75 158	96.30 93	99.78 41	93.70 194	99.48 114	99.45 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		97.92 56	97.62 74	98.83 26	99.32 45	97.24 39	97.45 79	98.84 92	95.76 130	96.93 184	97.43 184	97.26 40	99.79 39	96.06 77	99.53 94	99.45 66

tttt0517		93.31 262	92.56 269	95.57 228	98.71 116	87.86 269	97.44 80	87.17 348	95.79 129	97.47 153	96.84 227	64.12 351	99.81 32	96.20 73	99.32 165	99.02 160

v10		97.55 84	97.97 41	96.31 200	98.60 130	89.64 237	97.44 80	99.02 50	96.60 88	98.72 49	99.16 30	93.48 178	99.72 77	98.76 7	99.92 13	99.58 28

v8		97.60 81	98.06 38	96.23 202	98.71 116	89.44 241	97.43 82	98.82 107	97.29 75	98.74 47	99.10 33	93.86 169	99.68 115	98.61 11	99.94 9	99.56 33

PMVS	89.60 17	96.71 138	96.97 116	95.95 215	99.51 23	97.81 16	97.42 83	97.49 251	97.93 43	95.95 229	98.58 64	96.88 62	96.91 344	89.59 274	99.36 148	93.12 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test1172		98.08 40	97.76 57	99.05 6	98.78 107	98.07 6	97.41 84	98.85 88	97.57 60	98.15 100	97.96 132	96.60 76	99.76 53	95.30 119	99.18 183	99.33 95

SR-MVS		98.00 46	97.66 65	99.01 11	98.77 109	97.93 10	97.38 85	98.83 99	97.32 73	98.06 112	97.85 147	96.65 71	99.77 49	95.00 141	99.11 194	99.32 96

FMVSNet5		93.39 260	92.35 271	96.50 189	95.83 310	90.81 225	97.31 86	98.27 187	92.74 234	96.27 216	98.28 92	62.23 353	99.67 120	90.86 243	99.36 148	99.03 158

HY-MVS	91.43 15	92.58 272	91.81 278	94.90 254	96.49 289	88.87 250	97.31 86	94.62 302	85.92 302	90.50 331	96.84 227	85.05 283	99.40 204	83.77 330	95.78 323	96.43 318

CSCG		97.40 97	97.30 94	97.69 109	98.95 94	94.83 119	97.28 88	98.99 61	96.35 99	98.13 103	95.95 275	95.99 99	99.66 125	94.36 169	99.73 44	98.59 216

MTAPA		98.14 35	97.84 49	99.06 4	99.44 31	97.90 11	97.25 89	98.73 122	97.69 56	97.90 128	97.96 132	95.81 110	99.82 30	96.13 75	99.61 69	99.45 66

CPTT-MVS		96.69 139	96.08 161	98.49 53	98.89 99	96.64 54	97.25 89	98.77 114	92.89 232	96.01 228	97.13 207	92.23 208	99.67 120	92.24 216	99.34 156	99.17 127

EU-MVSNet		94.25 236	94.47 223	93.60 287	98.14 182	82.60 327	97.24 91	92.72 321	85.08 313	98.48 63	98.94 43	82.59 294	98.76 297	97.47 38	99.53 94	99.44 76

XXY-MVS		97.54 85	97.70 61	97.07 155	99.46 29	92.21 196	97.22 92	99.00 58	94.93 165	98.58 56	98.92 45	97.31 36	99.41 202	94.44 161	99.43 131	99.59 27

GST-MVS		97.82 67	97.49 85	98.81 29	99.23 54	97.25 38	97.16 93	98.79 109	95.96 117	97.53 144	97.40 186	96.93 57	99.77 49	95.04 138	99.35 153	99.42 78

SteuartSystems-ACMMP		98.02 44	97.76 57	98.79 32	99.43 33	97.21 41	97.15 94	98.90 74	96.58 90	98.08 110	97.87 146	97.02 53	99.76 53	95.25 122	99.59 74	99.40 81
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FMVSNet2		96.72 136	96.67 133	96.87 166	97.96 198	91.88 206	97.15 94	98.06 218	95.59 137	98.50 62	98.62 63	89.51 250	99.65 126	94.99 142	99.60 72	99.07 152

AllTest		97.20 110	96.92 120	98.06 85	99.08 83	96.16 69	97.14 96	99.16 18	94.35 183	97.78 140	98.07 117	95.84 103	99.12 258	91.41 230	99.42 134	98.91 178

DP-MVS		97.87 62	97.89 46	97.81 101	98.62 127	94.82 120	97.13 97	98.79 109	98.98 18	98.74 47	98.49 72	95.80 112	99.49 174	95.04 138	99.44 124	99.11 145

PGM-MVS		97.88 61	97.52 82	98.96 16	99.20 64	97.62 21	97.09 98	99.06 39	95.45 142	97.55 143	97.94 137	97.11 44	99.78 41	94.77 151	99.46 119	99.48 56

LPG-MVS_test		97.94 52	97.67 64	98.74 36	99.15 71	97.02 42	97.09 98	99.02 50	95.15 154	98.34 78	98.23 100	97.91 18	99.70 101	94.41 163	99.73 44	99.50 43

SF-MVS		97.60 81	97.39 89	98.22 74	98.93 95	95.69 84	97.05 100	99.10 29	95.32 147	97.83 137	97.88 144	96.44 86	99.72 77	94.59 158	99.39 142	99.25 117

VDD-MVS		97.37 99	97.25 98	97.74 104	98.69 120	94.50 133	97.04 101	95.61 294	98.59 25	98.51 60	98.72 55	92.54 202	99.58 149	96.02 82	99.49 110	99.12 142

wuyk23d		93.25 264	95.20 187	87.40 334	96.07 305	95.38 99	97.04 101	94.97 299	95.33 146	99.70 6	98.11 113	98.14 14	91.94 351	77.76 344	99.68 56	74.89 350

MVS_0304		95.50 183	95.05 195	96.84 168	96.28 294	93.12 180	97.00 103	96.16 283	95.03 160	89.22 339	97.70 164	90.16 242	99.48 177	94.51 160	99.34 156	97.93 269

LCM-MVSNet-Re		97.33 102	97.33 93	97.32 143	98.13 185	93.79 160	96.99 104	99.65 2	96.74 85	99.47 14	98.93 44	96.91 59	99.84 26	90.11 266	99.06 203	98.32 236

MAR-MVS		94.21 239	93.03 256	97.76 102	96.94 280	97.44 33	96.97 105	97.15 262	87.89 288	92.00 322	92.73 326	92.14 210	99.12 258	83.92 327	97.51 293	96.73 311
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
test0726					99.24 52	95.51 94	96.89 106	98.89 75	95.92 120	98.64 50	98.31 85	97.06 50

baseline		97.44 93	97.78 56	96.43 193	98.52 139	90.75 226	96.84 107	99.03 48	96.51 91	97.86 135	98.02 126	96.67 70	99.36 217	97.09 51	99.47 116	99.19 124

API-MVS		95.09 204	95.01 196	95.31 239	96.61 286	94.02 150	96.83 108	97.18 261	95.60 136	95.79 235	94.33 309	94.54 153	98.37 327	85.70 315	98.52 251	93.52 339

#test#		97.62 79	97.22 102	98.83 26	99.15 71	97.50 28	96.81 109	98.84 92	94.25 187	97.49 148	97.54 174	97.07 48	99.70 101	94.37 166	99.46 119	99.30 102

SED-MVS		97.94 52	97.90 44	98.07 83	99.22 57	95.35 101	96.79 110	98.83 99	96.11 107	99.08 31	98.24 98	97.87 21	99.72 77	95.44 111	99.51 104	99.14 134

OPU-MVS				97.64 112	98.01 192	95.27 104	96.79 110				97.35 195	96.97 54	98.51 318	91.21 236	99.25 176	99.14 134

PHI-MVS		96.96 118	96.53 142	98.25 72	97.48 248	96.50 59	96.76 112	98.85 88	93.52 206	96.19 221	96.85 226	95.94 100	99.42 193	93.79 191	99.43 131	98.83 190

DVP-MVS		97.78 70	97.65 67	98.16 76	99.24 52	95.51 94	96.74 113	98.23 192	95.92 120	98.40 70	98.28 92	97.06 50	99.71 92	95.48 107	99.52 99	99.26 114

test_0728_SECOND				98.25 72	99.23 54	95.49 96	96.74 113	98.89 75					99.75 60	95.48 107	99.52 99	99.53 39

Anonymous202405211		96.34 154	95.98 166	97.43 135	98.25 166	93.85 157	96.74 113	94.41 305	97.72 53	98.37 73	98.03 125	87.15 272	99.53 164	94.06 179	99.07 200	98.92 177

SMA-MVS		97.48 90	97.11 107	98.60 47	98.83 101	96.67 52	96.74 113	98.73 122	91.61 249	98.48 63	98.36 80	96.53 79	99.68 115	95.17 127	99.54 91	99.45 66
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
TranMVSNet+NR-MVSNet		98.33 27	98.30 32	98.43 57	99.07 85	95.87 78	96.73 117	99.05 41	98.67 23	98.84 40	98.45 75	97.58 28	99.88 19	96.45 68	99.86 24	99.54 36

test_0402		97.84 64	97.97 41	97.47 129	99.19 66	94.07 148	96.71 118	98.73 122	98.66 24	98.56 57	98.41 77	96.84 65	99.69 109	94.82 146	99.81 30	98.64 210

ACMM	93.33 11	98.05 42	97.79 53	98.85 25	99.15 71	97.55 26	96.68 119	98.83 99	95.21 150	98.36 75	98.13 109	98.13 15	99.62 139	96.04 80	99.54 91	99.39 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		93.14 266	92.64 267	94.62 266	97.34 262	87.20 284	96.67 120	93.02 316	94.71 171	96.51 204	95.83 278	81.64 295	98.60 311	90.00 269	88.06 346	98.07 255

MTMP							96.55 121	74.60 355

SD-MVS		97.37 99	97.70 61	96.35 197	98.14 182	95.13 112	96.54 122	98.92 72	95.94 119	99.19 28	98.08 115	97.74 23	95.06 349	95.24 123	99.54 91	98.87 187
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
HQP_MVS		96.66 142	96.33 151	97.68 110	98.70 118	94.29 139	96.50 123	98.75 118	96.36 97	96.16 222	96.77 233	91.91 220	99.46 183	92.59 213	99.20 179	99.28 109

plane_prior2							96.50 123		96.36 97

testtj		96.69 139	96.13 157	98.36 62	98.46 149	96.02 75	96.44 125	98.70 132	94.26 186	96.79 189	97.13 207	94.07 165	99.75 60	90.53 258	98.80 230	99.31 101

CS-MVS		95.86 173	95.59 180	96.69 177	97.85 206	93.14 179	96.42 126	99.25 10	94.17 191	93.56 295	90.76 345	96.05 98	99.72 77	93.28 201	98.91 217	97.21 293

Effi-MVS+-dtu		96.81 130	96.09 160	98.99 13	96.90 282	98.69 2	96.42 126	98.09 211	95.86 125	95.15 251	95.54 286	94.26 160	99.81 32	94.06 179	98.51 253	98.47 223

thres100view900		91.76 287	91.26 286	93.26 293	98.21 170	84.50 317	96.39 128	90.39 338	96.87 81	96.33 211	93.08 319	73.44 335	99.42 193	78.85 341	97.74 279	95.85 323

XVG-ACMP-BASELINE		97.58 83	97.28 97	98.49 53	99.16 68	96.90 46	96.39 128	98.98 64	95.05 159	98.06 112	98.02 126	95.86 102	99.56 156	94.37 166	99.64 61	99.00 161

Patchmtry		95.03 206	94.59 218	96.33 198	94.83 327	90.82 223	96.38 130	97.20 259	96.59 89	97.49 148	98.57 65	77.67 312	99.38 212	92.95 210	99.62 63	98.80 193

ACMMP_NAP		97.89 60	97.63 72	98.67 42	99.35 42	96.84 47	96.36 131	98.79 109	95.07 158	97.88 131	98.35 81	97.24 42	99.72 77	96.05 79	99.58 76	99.45 66

VNet		96.84 125	96.83 124	96.88 165	98.06 188	92.02 203	96.35 132	97.57 250	97.70 55	97.88 131	97.80 154	92.40 206	99.54 162	94.73 153	98.96 209	99.08 150

V42		97.04 112	97.16 105	96.68 179	98.59 132	91.05 217	96.33 133	98.36 178	94.60 174	97.99 118	98.30 89	93.32 180	99.62 139	97.40 40	99.53 94	99.38 86

APD-MVS		97.00 113	96.53 142	98.41 58	98.55 136	96.31 65	96.32 134	98.77 114	92.96 231	97.44 156	97.58 173	95.84 103	99.74 67	91.96 218	99.35 153	99.19 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		97.26 106	97.49 85	96.59 183	99.47 28	90.58 228	96.27 135	98.53 157	97.77 46	98.46 66	98.41 77	94.59 150	99.68 115	94.61 154	99.29 171	99.52 40

thres600view7		92.03 283	91.43 281	93.82 283	98.19 172	84.61 316	96.27 135	90.39 338	96.81 83	96.37 210	93.11 317	73.44 335	99.49 174	80.32 337	97.95 271	97.36 290

EPNet		93.72 251	92.62 268	97.03 158	87.61 357	92.25 194	96.27 135	91.28 331	96.74 85	87.65 345	97.39 190	85.00 284	99.64 129	92.14 217	99.48 114	99.20 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DSMNet-mixed		92.19 280	91.83 277	93.25 294	96.18 300	83.68 324	96.27 135	93.68 310	76.97 347	92.54 318	99.18 28	89.20 255	98.55 315	83.88 328	98.60 249	97.51 286

ACMP	92.54 13	97.47 91	97.10 108	98.55 51	99.04 89	96.70 51	96.24 139	98.89 75	93.71 203	97.97 122	97.75 158	97.44 30	99.63 131	93.22 204	99.70 53	99.32 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS	95.41 4	97.82 67	97.70 61	98.16 76	98.78 107	95.72 82	96.23 140	99.02 50	93.92 199	98.62 51	98.99 38	97.69 24	99.62 139	96.18 74	99.87 23	99.15 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PM-MVS		97.36 101	97.10 108	98.14 80	98.91 97	96.77 49	96.20 141	98.63 148	93.82 200	98.54 58	98.33 83	93.98 167	99.05 268	95.99 85	99.45 123	98.61 215

MVS_Test		96.27 155	96.79 128	94.73 263	96.94 280	86.63 291	96.18 142	98.33 183	94.94 163	96.07 225	98.28 92	95.25 131	99.26 241	97.21 45	97.90 274	98.30 239

CR-MVSNet		93.29 263	92.79 262	94.78 261	95.44 319	88.15 263	96.18 142	97.20 259	84.94 317	94.10 274	98.57 65	77.67 312	99.39 209	95.17 127	95.81 320	96.81 308

RPMNet		94.68 223	94.60 216	94.90 254	95.44 319	88.15 263	96.18 142	98.86 84	97.43 66	94.10 274	98.49 72	79.40 304	99.76 53	95.69 93	95.81 320	96.81 308

EIA-MVS		96.04 165	95.77 174	96.85 167	97.80 218	92.98 183	96.12 145	99.16 18	94.65 172	93.77 285	91.69 337	95.68 115	99.67 120	94.18 174	98.85 226	97.91 270

Effi-MVS+		96.19 159	96.01 163	96.71 175	97.43 254	92.19 199	96.12 145	99.10 29	95.45 142	93.33 304	94.71 301	97.23 43	99.56 156	93.21 205	97.54 291	98.37 229

alignmvs		96.01 167	95.52 182	97.50 124	97.77 228	94.71 124	96.07 147	96.84 272	97.48 65	96.78 193	94.28 311	85.50 281	99.40 204	96.22 72	98.73 239	98.40 226

PatchT		93.75 250	93.57 247	94.29 279	95.05 325	87.32 282	96.05 148	92.98 317	97.54 63	94.25 270	98.72 55	75.79 325	99.24 244	95.92 88	95.81 320	96.32 319

Patchmatch-test		93.60 256	93.25 253	94.63 265	96.14 304	87.47 278	96.04 149	94.50 304	93.57 205	96.47 205	96.97 219	76.50 320	98.61 309	90.67 254	98.41 256	97.81 276

thisisatest0530		92.71 271	91.76 279	95.56 230	98.42 151	88.23 261	96.03 150	87.35 347	94.04 195	96.56 201	95.47 288	64.03 352	99.77 49	94.78 150	99.11 194	98.68 209

9.14			96.69 131		98.53 138		96.02 151	98.98 64	93.23 216	97.18 163	97.46 182	96.47 84	99.62 139	92.99 208	99.32 165

DeepC-MVS_fast	94.34 7	96.74 133	96.51 144	97.44 134	97.69 234	94.15 146	96.02 151	98.43 167	93.17 222	97.30 159	97.38 192	95.48 122	99.28 238	93.74 192	99.34 156	98.88 185
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing_2		97.43 94	97.71 60	96.60 181	98.91 97	90.85 221	96.01 153	98.54 156	94.78 168	98.78 43	98.96 41	96.35 92	99.54 162	97.25 42	99.82 29	99.40 81

114514_t		93.96 247	93.22 254	96.19 205	99.06 86	90.97 220	95.99 154	98.94 71	73.88 350	93.43 301	96.93 222	92.38 207	99.37 215	89.09 281	99.28 172	98.25 244

FMVSNet3		95.26 197	94.94 197	96.22 204	96.53 288	90.06 232	95.99 154	97.66 241	94.11 193	97.99 118	97.91 141	80.22 303	99.63 131	94.60 155	99.44 124	98.96 166

HPM-MVS++		96.99 114	96.38 148	98.81 29	98.64 122	97.59 23	95.97 156	98.20 196	95.51 140	95.06 252	96.53 247	94.10 164	99.70 101	94.29 170	99.15 185	99.13 137

ETH3D-3000-0.1		96.89 124	96.46 146	98.16 76	98.62 127	95.69 84	95.96 157	98.98 64	93.36 211	97.04 175	97.31 199	94.93 140	99.63 131	92.60 211	99.34 156	99.17 127

testgi		96.07 163	96.50 145	94.80 260	99.26 48	87.69 275	95.96 157	98.58 153	95.08 157	98.02 117	96.25 260	97.92 17	97.60 341	88.68 288	98.74 236	99.11 145

EG-PatchMatch MVS		97.69 75	97.79 53	97.40 138	99.06 86	93.52 171	95.96 157	98.97 67	94.55 178	98.82 41	98.76 53	97.31 36	99.29 236	97.20 47	99.44 124	99.38 86

PAPM_NR		94.61 227	94.17 234	95.96 213	98.36 155	91.23 215	95.93 160	97.95 221	92.98 227	93.42 302	94.43 308	90.53 233	98.38 325	87.60 301	96.29 317	98.27 242

UniMVSNet (Re)		97.83 65	97.65 67	98.35 64	98.80 104	95.86 79	95.92 161	99.04 47	97.51 64	98.22 92	97.81 153	94.68 146	99.78 41	97.14 50	99.75 42	99.41 80

1314		92.38 276	92.30 272	92.64 308	95.42 321	85.15 309	95.86 162	96.97 269	85.40 311	90.62 328	93.06 320	91.12 227	97.80 339	86.74 309	95.49 327	94.97 334

1121		94.26 235	93.26 252	97.27 145	98.26 165	94.73 122	95.86 162	97.71 237	77.96 344	94.53 264	96.71 237	91.93 218	99.40 204	87.71 297	98.64 245	97.69 280

MVS		90.02 301	89.20 308	92.47 309	94.71 328	86.90 288	95.86 162	96.74 277	64.72 352	90.62 328	92.77 324	92.54 202	98.39 324	79.30 339	95.56 326	92.12 343

casdiffmvs		97.50 88	97.81 52	96.56 187	98.51 140	91.04 218	95.83 165	99.09 34	97.23 76	98.33 81	98.30 89	97.03 52	99.37 215	96.58 62	99.38 144	99.28 109

tpmvs		90.79 297	90.87 291	90.57 323	92.75 351	76.30 346	95.79 166	93.64 311	91.04 256	91.91 323	96.26 259	77.19 318	98.86 289	89.38 278	89.85 344	96.56 316

RRT_test8_iter05		92.46 274	92.52 270	92.29 313	95.33 322	77.43 343	95.73 167	98.55 155	94.41 180	97.46 154	97.72 163	57.44 356	99.74 67	96.92 56	99.14 186	99.69 21

MSLP-MVS++		96.42 153	96.71 130	95.57 228	97.82 213	90.56 230	95.71 168	98.84 92	94.72 170	96.71 194	97.39 190	94.91 141	98.10 336	95.28 120	99.02 205	98.05 262

tfpn200view9		91.55 289	91.00 288	93.21 296	98.02 190	84.35 319	95.70 169	90.79 335	96.26 101	95.90 233	92.13 332	73.62 333	99.42 193	78.85 341	97.74 279	95.85 323

Anonymous20231206		95.27 196	95.06 194	95.88 219	98.72 113	89.37 242	95.70 169	97.85 227	88.00 286	96.98 181	97.62 170	91.95 216	99.34 222	89.21 279	99.53 94	98.94 169

thres400		91.68 288	91.00 288	93.71 285	98.02 190	84.35 319	95.70 169	90.79 335	96.26 101	95.90 233	92.13 332	73.62 333	99.42 193	78.85 341	97.74 279	97.36 290

test20.03		96.58 145	96.61 134	96.48 191	98.49 143	91.72 210	95.68 172	97.69 238	96.81 83	98.27 88	97.92 140	94.18 163	98.71 301	90.78 247	99.66 59	99.00 161

zzz-MVS		98.01 45	97.66 65	99.06 4	99.44 31	97.90 11	95.66 173	98.73 122	97.69 56	97.90 128	97.96 132	95.81 110	99.82 30	96.13 75	99.61 69	99.45 66

UniMVSNet_NR-MVSNet		97.83 65	97.65 67	98.37 61	98.72 113	95.78 80	95.66 173	99.02 50	98.11 39	98.31 84	97.69 166	94.65 148	99.85 23	97.02 54	99.71 50	99.48 56

DU-MVS		97.79 69	97.60 76	98.36 62	98.73 111	95.78 80	95.65 175	98.87 82	97.57 60	98.31 84	97.83 149	94.69 144	99.85 23	97.02 54	99.71 50	99.46 61

EPMVS		89.26 309	88.55 313	91.39 317	92.36 352	79.11 337	95.65 175	79.86 353	88.60 279	93.12 306	96.53 247	70.73 344	98.10 336	90.75 248	89.32 345	96.98 299

MVP-Stereo		95.69 176	95.28 186	96.92 162	98.15 181	93.03 182	95.64 177	98.20 196	90.39 261	96.63 198	97.73 161	91.63 223	99.10 263	91.84 223	97.31 300	98.63 212
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		95.30 195	94.38 227	98.05 88	98.64 122	96.04 73	95.61 178	98.66 142	89.00 274	93.22 305	96.40 255	92.90 190	99.35 220	87.45 305	97.53 292	98.77 199

v144192		96.69 139	96.90 122	96.03 210	98.25 166	88.92 248	95.49 179	98.77 114	93.05 225	98.09 108	98.29 91	92.51 204	99.70 101	98.11 18	99.56 82	99.47 59

Fast-Effi-MVS+-dtu		96.44 151	96.12 158	97.39 139	97.18 271	94.39 135	95.46 180	98.73 122	96.03 114	94.72 258	94.92 298	96.28 95	99.69 109	93.81 190	97.98 270	98.09 252

Baseline_NR-MVSNet		97.72 73	97.79 53	97.50 124	99.56 16	93.29 175	95.44 181	98.86 84	98.20 37	98.37 73	99.24 21	94.69 144	99.55 160	95.98 86	99.79 34	99.65 23

LF4IMVS		96.07 163	95.63 178	97.36 141	98.19 172	95.55 91	95.44 181	98.82 107	92.29 240	95.70 241	96.55 245	92.63 198	98.69 303	91.75 226	99.33 163	97.85 272

v1921920		96.72 136	96.96 118	95.99 211	98.21 170	88.79 253	95.42 183	98.79 109	93.22 217	98.19 96	98.26 97	92.68 195	99.70 101	98.34 16	99.55 88	99.49 51

plane_prior						94.29 139	95.42 183		94.31 185						98.93 215

v1144		96.84 125	97.08 110	96.13 208	98.42 151	89.28 244	95.41 185	98.67 140	94.21 188	97.97 122	98.31 85	93.06 185	99.65 126	98.06 19	99.62 63	99.45 66

ETV-MVS		96.13 162	95.90 170	96.82 169	97.76 229	93.89 154	95.40 186	98.95 70	95.87 124	95.58 244	91.00 342	96.36 91	99.72 77	93.36 198	98.83 228	96.85 306

v1240		96.74 133	97.02 115	95.91 218	98.18 175	88.52 256	95.39 187	98.88 80	93.15 223	98.46 66	98.40 79	92.80 192	99.71 92	98.45 14	99.49 110	99.49 51

MP-MVS-pluss		97.69 75	97.36 91	98.70 40	99.50 26	96.84 47	95.38 188	98.99 61	92.45 238	98.11 104	98.31 85	97.25 41	99.77 49	96.60 60	99.62 63	99.48 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1192		96.83 128	97.06 112	96.15 207	98.28 161	89.29 243	95.36 189	98.77 114	93.73 202	98.11 104	98.34 82	93.02 189	99.67 120	98.35 15	99.58 76	99.50 43

v2v482		96.78 132	97.06 112	95.95 215	98.57 134	88.77 254	95.36 189	98.26 189	95.18 153	97.85 136	98.23 100	92.58 199	99.63 131	97.80 27	99.69 54	99.45 66

EI-MVSNet-Vis-set		97.32 103	97.39 89	97.11 152	97.36 257	92.08 202	95.34 191	97.65 243	97.74 50	98.29 87	98.11 113	95.05 134	99.68 115	97.50 37	99.50 106	99.56 33

EI-MVSNet-UG-set		97.32 103	97.40 88	97.09 154	97.34 262	92.01 204	95.33 192	97.65 243	97.74 50	98.30 86	98.14 108	95.04 136	99.69 109	97.55 35	99.52 99	99.58 28

CostFormer		89.75 306	89.25 305	91.26 319	94.69 329	78.00 341	95.32 193	91.98 326	81.50 330	90.55 330	96.96 221	71.06 342	98.89 285	88.59 289	92.63 338	96.87 304

PVSNet_Blended_VisFu		95.95 169	95.80 172	96.42 194	99.28 47	90.62 227	95.31 194	99.08 35	88.40 281	96.97 182	98.17 107	92.11 211	99.78 41	93.64 195	99.21 178	98.86 188

UnsupCasMVSNet_eth		95.91 170	95.73 175	96.44 192	98.48 145	91.52 213	95.31 194	98.45 164	95.76 130	97.48 151	97.54 174	89.53 249	98.69 303	94.43 162	94.61 331	99.13 137

EI-MVSNet		96.63 143	96.93 119	95.74 222	97.26 267	88.13 265	95.29 196	97.65 243	96.99 77	97.94 125	98.19 105	92.55 200	99.58 149	96.91 57	99.56 82	99.50 43

CVMVSNet		92.33 278	92.79 262	90.95 320	97.26 267	75.84 348	95.29 196	92.33 324	81.86 327	96.27 216	98.19 105	81.44 296	98.46 320	94.23 173	98.29 260	98.55 220

RRT_MVS		94.90 209	94.07 236	97.39 139	93.18 344	93.21 178	95.26 198	97.49 251	93.94 198	98.25 89	97.85 147	72.96 337	99.84 26	97.90 22	99.78 37	99.14 134

Regformer-3		97.25 107	97.29 95	97.11 152	97.35 258	92.32 193	95.26 198	97.62 248	97.67 58	98.17 97	97.89 142	95.05 134	99.56 156	97.16 49	99.42 134	99.46 61

Regformer-4		97.53 87	97.47 87	97.71 105	97.35 258	93.91 153	95.26 198	98.14 206	97.97 42	98.34 78	97.89 142	95.49 121	99.71 92	97.41 39	99.42 134	99.51 42

OPM-MVS		97.54 85	97.25 98	98.41 58	99.11 81	96.61 55	95.24 201	98.46 163	94.58 177	98.10 107	98.07 117	97.09 47	99.39 209	95.16 129	99.44 124	99.21 122

TAPA-MVS	93.32 12	94.93 208	94.23 230	97.04 157	98.18 175	94.51 131	95.22 202	98.73 122	81.22 332	96.25 218	95.95 275	93.80 172	98.98 277	89.89 270	98.87 222	97.62 282
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPE-MVS		97.64 77	97.35 92	98.50 52	98.85 100	96.18 68	95.21 203	98.99 61	95.84 127	98.78 43	98.08 115	96.84 65	99.81 32	93.98 185	99.57 79	99.52 40

MVSTER		94.21 239	93.93 242	95.05 248	95.83 310	86.46 292	95.18 204	97.65 243	92.41 239	97.94 125	98.00 130	72.39 338	99.58 149	96.36 70	99.56 82	99.12 142

PatchmatchNet		91.98 284	91.87 276	92.30 312	94.60 330	79.71 336	95.12 205	93.59 312	89.52 269	93.61 292	97.02 217	77.94 310	99.18 250	90.84 244	94.57 333	98.01 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DWT-MVSNet_test		87.92 317	86.77 321	91.39 317	93.18 344	78.62 338	95.10 206	91.42 330	85.58 306	88.00 343	88.73 348	60.60 354	98.90 283	90.60 255	87.70 347	96.65 312

IterMVS-LS		96.92 120	97.29 95	95.79 221	98.51 140	88.13 265	95.10 206	98.66 142	96.99 77	98.46 66	98.68 59	92.55 200	99.74 67	96.91 57	99.79 34	99.50 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		96.58 145	96.97 116	95.42 236	98.63 126	87.57 276	95.09 208	97.90 224	95.91 122	98.24 91	97.96 132	93.42 179	99.39 209	96.04 80	99.52 99	99.29 108

tpm2		88.47 312	87.69 316	90.79 321	94.98 326	77.34 344	95.09 208	91.83 327	77.51 346	89.40 337	96.41 253	67.83 348	98.73 299	83.58 332	92.60 339	96.29 320

OpenMVS_ROB	91.80 14	93.64 255	93.05 255	95.42 236	97.31 266	91.21 216	95.08 210	96.68 279	81.56 329	96.88 188	96.41 253	90.44 235	99.25 243	85.39 320	97.67 286	95.80 325

mvs-test1		96.20 158	95.50 183	98.32 65	96.90 282	98.16 4	95.07 211	98.09 211	95.86 125	93.63 290	94.32 310	94.26 160	99.71 92	94.06 179	97.27 302	97.07 296

TAMVS		95.49 184	94.94 197	97.16 149	98.31 157	93.41 173	95.07 211	96.82 274	91.09 255	97.51 146	97.82 152	89.96 243	99.42 193	88.42 291	99.44 124	98.64 210

tpmrst		90.31 299	90.61 297	89.41 327	94.06 338	72.37 354	95.06 213	93.69 308	88.01 285	92.32 320	96.86 225	77.45 314	98.82 290	91.04 238	87.01 348	97.04 298

ADS-MVSNet2		91.47 290	90.51 298	94.36 276	95.51 317	85.63 300	95.05 214	95.70 292	83.46 323	92.69 313	96.84 227	79.15 306	99.41 202	85.66 317	90.52 341	98.04 263

ADS-MVSNet		90.95 296	90.26 300	93.04 299	95.51 317	82.37 328	95.05 214	93.41 313	83.46 323	92.69 313	96.84 227	79.15 306	98.70 302	85.66 317	90.52 341	98.04 263

tpm		91.08 294	90.85 292	91.75 315	95.33 322	78.09 339	95.03 216	91.27 332	88.75 277	93.53 296	97.40 186	71.24 340	99.30 232	91.25 235	93.87 334	97.87 271

NCCC		96.52 147	95.99 165	98.10 81	97.81 214	95.68 86	95.00 217	98.20 196	95.39 145	95.40 247	96.36 256	93.81 171	99.45 187	93.55 197	98.42 255	99.17 127

test_post1							94.98 218				10.37 357	76.21 323	99.04 269	89.47 276

ETH3D cwj APD-0.16		96.23 157	95.61 179	98.09 82	97.91 202	95.65 89	94.94 219	98.74 120	91.31 253	96.02 227	97.08 212	94.05 166	99.69 109	91.51 229	98.94 213	98.93 173

AdaColmap		95.11 202	94.62 215	96.58 184	97.33 264	94.45 134	94.92 220	98.08 213	93.15 223	93.98 281	95.53 287	94.34 158	99.10 263	85.69 316	98.61 247	96.20 321

MDTV_nov1_ep13_2view						57.28 358	94.89 221		80.59 334	94.02 278		78.66 308		85.50 319		97.82 274

CNVR-MVS		96.92 120	96.55 139	98.03 89	98.00 196	95.54 92	94.87 222	98.17 202	94.60 174	96.38 209	97.05 215	95.67 116	99.36 217	95.12 135	99.08 198	99.19 124

OMC-MVS		96.48 149	96.00 164	97.91 95	98.30 158	96.01 76	94.86 223	98.60 150	91.88 246	97.18 163	97.21 205	96.11 96	99.04 269	90.49 262	99.34 156	98.69 207

Regformer-1		97.27 105	97.16 105	97.61 114	97.21 269	93.86 156	94.85 224	98.04 220	97.62 59	98.03 116	97.50 179	95.34 127	99.63 131	96.52 64	99.31 167	99.35 93

Regformer-2		97.41 96	97.24 100	97.93 94	97.21 269	94.72 123	94.85 224	98.27 187	97.74 50	98.11 104	97.50 179	95.58 119	99.69 109	96.57 63	99.31 167	99.37 91

EPNet_dtu		91.39 291	90.75 294	93.31 292	90.48 356	82.61 326	94.80 226	92.88 318	93.39 210	81.74 353	94.90 299	81.36 297	99.11 261	88.28 293	98.87 222	98.21 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			91.28 284		94.31 332	73.51 352	94.80 226	93.16 315	86.75 297	93.45 300	97.40 186	76.37 321	98.55 315	88.85 284	96.43 314

pmmvs-eth3d		96.49 148	96.18 156	97.42 136	98.25 166	94.29 139	94.77 228	98.07 217	89.81 267	97.97 122	98.33 83	93.11 184	99.08 265	95.46 110	99.84 27	98.89 182

test_yl		94.40 232	94.00 239	95.59 226	96.95 278	89.52 239	94.75 229	95.55 296	96.18 105	96.79 189	96.14 266	81.09 298	99.18 250	90.75 248	97.77 276	98.07 255

DCV-MVSNet		94.40 232	94.00 239	95.59 226	96.95 278	89.52 239	94.75 229	95.55 296	96.18 105	96.79 189	96.14 266	81.09 298	99.18 250	90.75 248	97.77 276	98.07 255

MCST-MVS		96.24 156	95.80 172	97.56 116	98.75 110	94.13 147	94.66 231	98.17 202	90.17 264	96.21 220	96.10 269	95.14 133	99.43 192	94.13 177	98.85 226	99.13 137

XVG-OURS-SEG-HR		97.38 98	97.07 111	98.30 68	99.01 91	97.41 34	94.66 231	99.02 50	95.20 151	98.15 100	97.52 177	98.83 5	98.43 321	94.87 144	96.41 315	99.07 152

mvs_anonymous		95.36 192	96.07 162	93.21 296	96.29 293	81.56 330	94.60 233	97.66 241	93.30 214	96.95 183	98.91 46	93.03 188	99.38 212	96.60 60	97.30 301	98.69 207

DP-MVS Recon		95.55 182	95.13 190	96.80 170	98.51 140	93.99 152	94.60 233	98.69 135	90.20 263	95.78 237	96.21 263	92.73 194	98.98 277	90.58 257	98.86 224	97.42 289

ETH3 D test6400		94.77 215	93.87 243	97.47 129	98.12 186	93.73 162	94.56 235	98.70 132	85.45 310	94.70 260	95.93 277	91.77 222	99.63 131	86.45 311	99.14 186	99.05 156

xxxxxxxxxxxxxcwj		97.24 108	97.03 114	97.89 96	98.48 145	94.71 124	94.53 236	99.07 38	95.02 161	97.83 137	97.88 144	96.44 86	99.72 77	94.59 158	99.39 142	99.25 117

save fliter					98.48 145	94.71 124	94.53 236	98.41 172	95.02 161

tpm cat1		88.01 316	87.33 317	90.05 326	94.48 331	76.28 347	94.47 238	94.35 306	73.84 351	89.26 338	95.61 285	73.64 332	98.30 330	84.13 326	86.20 349	95.57 330

CANet		95.86 173	95.65 177	96.49 190	96.41 291	90.82 223	94.36 239	98.41 172	94.94 163	92.62 317	96.73 236	92.68 195	99.71 92	95.12 135	99.60 72	98.94 169

WR-MVS		96.90 122	96.81 125	97.16 149	98.56 135	92.20 198	94.33 240	98.12 209	97.34 72	98.20 93	97.33 197	92.81 191	99.75 60	94.79 148	99.81 30	99.54 36

HQP-NCC					97.85 206		94.26 241		93.18 219	92.86 310

ACMP_Plane					97.85 206		94.26 241		93.18 219	92.86 310

HQP-MVS		95.17 201	94.58 219	96.92 162	97.85 206	92.47 190	94.26 241	98.43 167	93.18 219	92.86 310	95.08 292	90.33 236	99.23 246	90.51 260	98.74 236	99.05 156

PLC	91.02 16	94.05 246	92.90 258	97.51 121	98.00 196	95.12 113	94.25 244	98.25 190	86.17 299	91.48 325	95.25 290	91.01 228	99.19 249	85.02 322	96.69 310	98.22 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1112_ss		94.12 242	93.42 249	96.23 202	98.59 132	90.85 221	94.24 245	98.85 88	85.49 307	92.97 308	94.94 296	86.01 278	99.64 129	91.78 224	97.92 272	98.20 248

MS-PatchMatch		94.83 212	94.91 201	94.57 270	96.81 284	87.10 285	94.23 246	97.34 256	88.74 278	97.14 165	97.11 210	91.94 217	98.23 332	92.99 208	97.92 272	98.37 229

Fast-Effi-MVS+		95.49 184	95.07 192	96.75 173	97.67 238	92.82 185	94.22 247	98.60 150	91.61 249	93.42 302	92.90 322	96.73 69	99.70 101	92.60 211	97.89 275	97.74 277

CMPMVS	73.10 23	92.74 270	91.39 282	96.77 172	93.57 343	94.67 128	94.21 248	97.67 239	80.36 336	93.61 292	96.60 243	82.85 293	97.35 342	84.86 323	98.78 232	98.29 241
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		88.08 315	88.05 315	88.16 333	92.85 349	68.81 356	94.17 249	92.88 318	85.47 308	91.38 326	96.14 266	68.87 347	98.81 292	86.88 308	83.80 351	96.87 304

JIA-IIPM		91.79 286	90.69 295	95.11 245	93.80 340	90.98 219	94.16 250	91.78 328	96.38 96	90.30 333	99.30 19	72.02 339	98.90 283	88.28 293	90.17 343	95.45 331

D2MVS		95.18 199	95.17 189	95.21 242	97.76 229	87.76 274	94.15 251	97.94 222	89.77 268	96.99 179	97.68 167	87.45 270	99.14 256	95.03 140	99.81 30	98.74 201

TSAR-MVS + GP.		96.47 150	96.12 158	97.49 127	97.74 231	95.23 106	94.15 251	96.90 271	93.26 215	98.04 115	96.70 238	94.41 156	98.89 285	94.77 151	99.14 186	98.37 229

PVSNet_BlendedMVS		95.02 207	94.93 199	95.27 240	97.79 223	87.40 280	94.14 253	98.68 137	88.94 275	94.51 265	98.01 128	93.04 186	99.30 232	89.77 272	99.49 110	99.11 145

TinyColmap		96.00 168	96.34 150	94.96 251	97.90 204	87.91 268	94.13 254	98.49 161	94.41 180	98.16 98	97.76 155	96.29 94	98.68 306	90.52 259	99.42 134	98.30 239

CNLPA		95.04 205	94.47 223	96.75 173	97.81 214	95.25 105	94.12 255	97.89 225	94.41 180	94.57 262	95.69 280	90.30 239	98.35 328	86.72 310	98.76 234	96.64 313

BH-untuned		94.69 221	94.75 208	94.52 272	97.95 201	87.53 277	94.07 256	97.01 267	93.99 196	97.10 169	95.65 282	92.65 197	98.95 282	87.60 301	96.74 309	97.09 295

pmmvs5		94.63 226	94.34 228	95.50 232	97.63 241	88.34 260	94.02 257	97.13 263	87.15 292	95.22 250	97.15 206	87.50 269	99.27 240	93.99 184	99.26 175	98.88 185

thres200		91.00 295	90.42 299	92.77 306	97.47 252	83.98 322	94.01 258	91.18 333	95.12 156	95.44 245	91.21 340	73.93 329	99.31 229	77.76 344	97.63 289	95.01 333

xiu_mvs_v1_base_debu		95.62 179	95.96 167	94.60 267	98.01 192	88.42 257	93.99 259	98.21 193	92.98 227	95.91 230	94.53 304	96.39 88	99.72 77	95.43 114	98.19 262	95.64 327

xiu_mvs_v1_base		95.62 179	95.96 167	94.60 267	98.01 192	88.42 257	93.99 259	98.21 193	92.98 227	95.91 230	94.53 304	96.39 88	99.72 77	95.43 114	98.19 262	95.64 327

xiu_mvs_v1_base_debi		95.62 179	95.96 167	94.60 267	98.01 192	88.42 257	93.99 259	98.21 193	92.98 227	95.91 230	94.53 304	96.39 88	99.72 77	95.43 114	98.19 262	95.64 327

CDS-MVSNet		94.88 211	94.12 235	97.14 151	97.64 240	93.57 169	93.96 262	97.06 266	90.05 265	96.30 215	96.55 245	86.10 277	99.47 180	90.10 267	99.31 167	98.40 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		94.65 225	94.21 232	95.96 213	95.90 307	89.68 236	93.92 263	97.83 231	93.19 218	90.12 334	95.64 283	88.52 257	99.57 155	93.27 203	99.47 116	98.62 213

WTY-MVS		93.55 257	93.00 257	95.19 243	97.81 214	87.86 269	93.89 264	96.00 286	89.02 273	94.07 276	95.44 289	86.27 276	99.33 225	87.69 299	96.82 306	98.39 228

sss		94.22 237	93.72 245	95.74 222	97.71 233	89.95 235	93.84 265	96.98 268	88.38 282	93.75 286	95.74 279	87.94 263	98.89 285	91.02 239	98.10 266	98.37 229

baseline2		89.65 307	88.44 314	93.25 294	95.62 315	82.71 325	93.82 266	85.94 350	88.89 276	87.35 347	92.54 328	71.23 341	99.33 225	86.01 312	94.60 332	97.72 278

XVG-OURS		97.12 111	96.74 129	98.26 70	98.99 92	97.45 32	93.82 266	99.05 41	95.19 152	98.32 82	97.70 164	95.22 132	98.41 322	94.27 171	98.13 265	98.93 173

MVS_111021_LR		96.82 129	96.55 139	97.62 113	98.27 163	95.34 103	93.81 268	98.33 183	94.59 176	96.56 201	96.63 242	96.61 74	98.73 299	94.80 147	99.34 156	98.78 196

BH-RMVSNet		94.56 229	94.44 226	94.91 252	97.57 243	87.44 279	93.78 269	96.26 282	93.69 204	96.41 208	96.50 250	92.10 212	99.00 273	85.96 313	97.71 282	98.31 237

CDPH-MVS		95.45 189	94.65 211	97.84 100	98.28 161	94.96 116	93.73 270	98.33 183	85.03 315	95.44 245	96.60 243	95.31 129	99.44 190	90.01 268	99.13 190	99.11 145

PatchMatch-RL		94.61 227	93.81 244	97.02 159	98.19 172	95.72 82	93.66 271	97.23 258	88.17 284	94.94 255	95.62 284	91.43 224	98.57 312	87.36 306	97.68 285	96.76 310

TEST9					97.84 211	95.23 106	93.62 272	98.39 174	86.81 295	93.78 283	95.99 270	94.68 146	99.52 168

train_agg		95.46 188	94.66 210	97.88 97	97.84 211	95.23 106	93.62 272	98.39 174	87.04 293	93.78 283	95.99 270	94.58 151	99.52 168	91.76 225	98.90 218	98.89 182

test_prior4						95.38 99	93.61 274

test_8					97.81 214	95.07 114	93.54 275	98.38 176	87.04 293	93.71 287	95.96 274	94.58 151	99.52 168

TR-MVS		92.54 273	92.20 273	93.57 288	96.49 289	86.66 290	93.51 276	94.73 301	89.96 266	94.95 254	93.87 313	90.24 241	98.61 309	81.18 336	94.88 328	95.45 331

新几何2							93.43 277

diffmvs		96.04 165	96.23 153	95.46 235	97.35 258	88.03 267	93.42 278	99.08 35	94.09 194	96.66 196	96.93 222	93.85 170	99.29 236	96.01 84	98.67 241	99.06 154

MVS_111021_HR		96.73 135	96.54 141	97.27 145	98.35 156	93.66 167	93.42 278	98.36 178	94.74 169	96.58 199	96.76 235	96.54 78	98.99 275	94.87 144	99.27 174	99.15 131

agg_prior1		95.39 191	94.60 216	97.75 103	97.80 218	94.96 116	93.39 280	98.36 178	87.20 291	93.49 297	95.97 273	94.65 148	99.53 164	91.69 227	98.86 224	98.77 199

UnsupCasMVSNet_bld		94.72 220	94.26 229	96.08 209	98.62 127	90.54 231	93.38 281	98.05 219	90.30 262	97.02 177	96.80 232	89.54 247	99.16 255	88.44 290	96.18 318	98.56 218

旧先验2							93.35 282		77.95 345	95.77 239			98.67 307	90.74 251

test_prior3		95.91 170	95.39 184	97.46 131	97.79 223	94.26 143	93.33 283	98.42 170	94.21 188	94.02 278	96.25 260	93.64 175	99.34 222	91.90 219	98.96 209	98.79 194

test_prior2							93.33 283		94.21 188	94.02 278	96.25 260	93.64 175		91.90 219	98.96 209

SCA		93.38 261	93.52 248	92.96 303	96.24 295	81.40 331	93.24 285	94.00 307	91.58 251	94.57 262	96.97 219	87.94 263	99.42 193	89.47 276	97.66 287	98.06 259

无先验							93.20 286	97.91 223	80.78 333				99.40 204	87.71 297		97.94 268

MG-MVS		94.08 245	94.00 239	94.32 277	97.09 274	85.89 299	93.19 287	95.96 288	92.52 235	94.93 256	97.51 178	89.54 247	98.77 295	87.52 304	97.71 282	98.31 237

MVS-HIRNet		88.40 313	90.20 301	82.99 335	97.01 276	60.04 357	93.11 288	85.61 351	84.45 321	88.72 341	99.09 34	84.72 287	98.23 332	82.52 333	96.59 313	90.69 348

new-patchmatchnet		95.67 178	96.58 136	92.94 304	97.48 248	80.21 335	92.96 289	98.19 201	94.83 166	98.82 41	98.79 50	93.31 181	99.51 172	95.83 90	99.04 204	99.12 142

MDA-MVSNet-bldmvs		95.69 176	95.67 176	95.74 222	98.48 145	88.76 255	92.84 290	97.25 257	96.00 115	97.59 142	97.95 136	91.38 225	99.46 183	93.16 206	96.35 316	98.99 164

原ACMM2							92.82 291

testdata1							92.77 292		93.78 201

Test_1112_low_res		93.53 258	92.86 259	95.54 231	98.60 130	88.86 251	92.75 293	98.69 135	82.66 326	92.65 315	96.92 224	84.75 286	99.56 156	90.94 241	97.76 278	98.19 249

USDC		94.56 229	94.57 221	94.55 271	97.78 227	86.43 294	92.75 293	98.65 147	85.96 301	96.91 186	97.93 139	90.82 231	98.74 298	90.71 252	99.59 74	98.47 223

test222					98.17 177	93.24 177	92.74 295	97.61 249	75.17 348	94.65 261	96.69 239	90.96 230			98.66 243	97.66 281

jason		94.39 234	94.04 238	95.41 238	98.29 159	87.85 271	92.74 295	96.75 276	85.38 312	95.29 248	96.15 264	88.21 262	99.65 126	94.24 172	99.34 156	98.74 201
jason: jason.
Patchmatch-RL test		94.66 224	94.49 222	95.19 243	98.54 137	88.91 249	92.57 297	98.74 120	91.46 252	98.32 82	97.75 158	77.31 317	98.81 292	96.06 77	99.61 69	97.85 272

DeepPCF-MVS	94.58 5	96.90 122	96.43 147	98.31 67	97.48 248	97.23 40	92.56 298	98.60 150	92.84 233	98.54 58	97.40 186	96.64 73	98.78 294	94.40 165	99.41 140	98.93 173

N_pmnet		95.18 199	94.23 230	98.06 85	97.85 206	96.55 58	92.49 299	91.63 329	89.34 270	98.09 108	97.41 185	90.33 236	99.06 267	91.58 228	99.31 167	98.56 218

BH-w/o		92.14 281	91.94 275	92.73 307	97.13 273	85.30 305	92.46 300	95.64 293	89.33 271	94.21 271	92.74 325	89.60 246	98.24 331	81.68 334	94.66 330	94.66 335

IterMVS-SCA-FT		95.86 173	96.19 155	94.85 257	97.68 235	85.53 302	92.42 301	97.63 247	96.99 77	98.36 75	98.54 69	87.94 263	99.75 60	97.07 53	99.08 198	99.27 113

IterMVS		95.42 190	95.83 171	94.20 280	97.52 247	83.78 323	92.41 302	97.47 254	95.49 141	98.06 112	98.49 72	87.94 263	99.58 149	96.02 82	99.02 205	99.23 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		96.17 160	96.23 153	95.99 211	97.55 246	90.04 233	92.38 303	98.52 158	94.13 192	96.55 203	97.06 214	94.99 138	99.58 149	95.62 99	99.28 172	98.37 229
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
new_pmnet		92.34 277	91.69 280	94.32 277	96.23 297	89.16 246	92.27 304	92.88 318	84.39 322	95.29 248	96.35 257	85.66 280	96.74 347	84.53 325	97.56 290	97.05 297

CHOSEN 1792x2688		94.10 243	93.41 250	96.18 206	99.16 68	90.04 233	92.15 305	98.68 137	79.90 337	96.22 219	97.83 149	87.92 267	99.42 193	89.18 280	99.65 60	99.08 150

xiu_mvs_v2_base		94.22 237	94.63 214	92.99 302	97.32 265	84.84 314	92.12 306	97.84 229	91.96 244	94.17 272	93.43 315	96.07 97	99.71 92	91.27 233	97.48 294	94.42 336

lupinMVS		93.77 249	93.28 251	95.24 241	97.68 235	87.81 272	92.12 306	96.05 285	84.52 319	94.48 267	95.06 294	86.90 273	99.63 131	93.62 196	99.13 190	98.27 242

pmmvs4		94.82 213	94.19 233	96.70 176	97.42 255	92.75 187	92.09 308	96.76 275	86.80 296	95.73 240	97.22 204	89.28 253	98.89 285	93.28 201	99.14 186	98.46 225

PAPR		92.22 279	91.27 285	95.07 247	95.73 314	88.81 252	91.97 309	97.87 226	85.80 304	90.91 327	92.73 326	91.16 226	98.33 329	79.48 338	95.76 324	98.08 253

PS-MVSNAJ		94.10 243	94.47 223	93.00 301	97.35 258	84.88 313	91.86 310	97.84 229	91.96 244	94.17 272	92.50 329	95.82 106	99.71 92	91.27 233	97.48 294	94.40 337

cl_fuxian		95.20 198	95.32 185	94.83 259	96.19 299	86.43 294	91.83 311	98.35 182	93.47 208	97.36 158	97.26 202	88.69 256	99.28 238	95.41 117	99.36 148	98.78 196

test0.0.03 1		90.11 300	89.21 307	92.83 305	93.89 339	86.87 289	91.74 312	88.74 345	92.02 242	94.71 259	91.14 341	73.92 330	94.48 350	83.75 331	92.94 336	97.16 294

FPMVS		89.92 305	88.63 312	93.82 283	98.37 154	96.94 45	91.58 313	93.34 314	88.00 286	90.32 332	97.10 211	70.87 343	91.13 352	71.91 349	96.16 319	93.39 341

ET-MVSNet_ETH3D		91.12 292	89.67 304	95.47 234	96.41 291	89.15 247	91.54 314	90.23 341	89.07 272	86.78 349	92.84 323	69.39 346	99.44 190	94.16 175	96.61 312	97.82 274

PVSNet_Blended		93.96 247	93.65 246	94.91 252	97.79 223	87.40 280	91.43 315	98.68 137	84.50 320	94.51 265	94.48 307	93.04 186	99.30 232	89.77 272	98.61 247	98.02 265

CLD-MVS		95.47 187	95.07 192	96.69 177	98.27 163	92.53 189	91.36 316	98.67 140	91.22 254	95.78 237	94.12 312	95.65 117	98.98 277	90.81 245	99.72 47	98.57 217
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		94.89 210	94.93 199	94.75 262	95.99 306	86.12 297	91.35 317	98.49 161	93.40 209	97.12 167	97.25 203	86.87 275	99.35 220	95.08 137	98.82 229	98.78 196

cl-mvsnet_		94.73 216	94.64 212	95.01 249	95.85 309	87.00 286	91.33 318	98.08 213	93.34 212	97.10 169	97.33 197	84.01 290	99.30 232	95.14 132	99.56 82	98.71 206

cl-mvsnet1		94.73 216	94.64 212	95.01 249	95.86 308	87.00 286	91.33 318	98.08 213	93.34 212	97.10 169	97.34 196	84.02 289	99.31 229	95.15 131	99.55 88	98.72 204

miper_ehance_all_eth		94.69 221	94.70 209	94.64 264	95.77 312	86.22 296	91.32 320	98.24 191	91.67 248	97.05 174	96.65 241	88.39 260	99.22 248	94.88 143	98.34 257	98.49 222

pmmvs3		90.00 302	88.90 311	93.32 291	94.20 337	85.34 304	91.25 321	92.56 323	78.59 341	93.82 282	95.17 291	67.36 349	98.69 303	89.08 282	98.03 269	95.92 322

HyFIR lowres test		93.72 251	92.65 266	96.91 164	98.93 95	91.81 209	91.23 322	98.52 158	82.69 325	96.46 206	96.52 249	80.38 302	99.90 14	90.36 264	98.79 231	99.03 158

DPM-MVS		93.68 253	92.77 265	96.42 194	97.91 202	92.54 188	91.17 323	97.47 254	84.99 316	93.08 307	94.74 300	89.90 244	99.00 273	87.54 303	98.09 267	97.72 278

miper_lstm_enhance		94.81 214	94.80 207	94.85 257	96.16 301	86.45 293	91.14 324	98.20 196	93.49 207	97.03 176	97.37 194	84.97 285	99.26 241	95.28 120	99.56 82	98.83 190

cl-mvsnet2		93.25 264	92.84 261	94.46 273	94.30 333	86.00 298	91.09 325	96.64 280	90.74 257	95.79 235	96.31 258	78.24 309	98.77 295	94.15 176	98.34 257	98.62 213

MSDG		95.33 193	95.13 190	95.94 217	97.40 256	91.85 207	91.02 326	98.37 177	95.30 148	96.31 214	95.99 270	94.51 154	98.38 325	89.59 274	97.65 288	97.60 284

IB-MVS	85.98 20	88.63 311	86.95 320	93.68 286	95.12 324	84.82 315	90.85 327	90.17 342	87.55 289	88.48 342	91.34 339	58.01 355	99.59 147	87.24 307	93.80 335	96.63 315
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
test123		12.59 325	15.49 328	3.87 338	6.07 359	2.55 360	90.75 328	2.59 361	2.52 355	5.20 357	13.02 355	4.96 361	1.85 357	5.20 354	9.09 354	7.23 352

ppachtmachnet_test		94.49 231	94.84 204	93.46 290	96.16 301	82.10 329	90.59 329	97.48 253	90.53 260	97.01 178	97.59 172	91.01 228	99.36 217	93.97 186	99.18 183	98.94 169

PMMVS		92.39 275	91.08 287	96.30 201	93.12 347	92.81 186	90.58 330	95.96 288	79.17 340	91.85 324	92.27 330	90.29 240	98.66 308	89.85 271	96.68 311	97.43 288

our_test_3		94.20 241	94.58 219	93.07 298	96.16 301	81.20 332	90.42 331	96.84 272	90.72 258	97.14 165	97.13 207	90.47 234	99.11 261	94.04 183	98.25 261	98.91 178

YYNet1		94.73 216	94.84 204	94.41 275	97.47 252	85.09 311	90.29 332	95.85 291	92.52 235	97.53 144	97.76 155	91.97 215	99.18 250	93.31 200	96.86 305	98.95 167

MDA-MVSNet_test_wron		94.73 216	94.83 206	94.42 274	97.48 248	85.15 309	90.28 333	95.87 290	92.52 235	97.48 151	97.76 155	91.92 219	99.17 254	93.32 199	96.80 308	98.94 169

GA-MVS		92.83 269	92.15 274	94.87 256	96.97 277	87.27 283	90.03 334	96.12 284	91.83 247	94.05 277	94.57 302	76.01 324	98.97 281	92.46 215	97.34 299	98.36 234

miper_enhance_ethall		93.14 266	92.78 264	94.20 280	93.65 341	85.29 306	89.97 335	97.85 227	85.05 314	96.15 224	94.56 303	85.74 279	99.14 256	93.74 192	98.34 257	98.17 251

test-LLR		89.97 304	89.90 302	90.16 324	94.24 335	74.98 349	89.89 336	89.06 343	92.02 242	89.97 335	90.77 343	73.92 330	98.57 312	91.88 221	97.36 297	96.92 301

TESTMET0.1,1		87.20 320	86.57 322	89.07 328	93.62 342	72.84 353	89.89 336	87.01 349	85.46 309	89.12 340	90.20 346	56.00 359	97.72 340	90.91 242	96.92 303	96.64 313

test-mter		87.92 317	87.17 318	90.16 324	94.24 335	74.98 349	89.89 336	89.06 343	86.44 298	89.97 335	90.77 343	54.96 360	98.57 312	91.88 221	97.36 297	96.92 301

PCF-MVS	89.43 18	92.12 282	90.64 296	96.57 186	97.80 218	93.48 172	89.88 339	98.45 164	74.46 349	96.04 226	95.68 281	90.71 232	99.31 229	73.73 346	99.01 207	96.91 303
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thisisatest0515		90.43 298	89.18 310	94.17 282	97.07 275	85.44 303	89.75 340	87.58 346	88.28 283	93.69 289	91.72 336	65.27 350	99.58 149	90.59 256	98.67 241	97.50 287

testmvs		12.33 326	15.23 329	3.64 339	5.77 360	2.23 361	88.99 341	3.62 360	2.30 356	5.29 356	13.09 354	4.52 362	1.95 356	5.16 355	8.32 355	6.75 353

cascas		91.89 285	91.35 283	93.51 289	94.27 334	85.60 301	88.86 342	98.61 149	79.32 339	92.16 321	91.44 338	89.22 254	98.12 335	90.80 246	97.47 296	96.82 307

PAPM		87.64 319	85.84 324	93.04 299	96.54 287	84.99 312	88.42 343	95.57 295	79.52 338	83.82 350	93.05 321	80.57 301	98.41 322	62.29 352	92.79 337	95.71 326

PVSNet	86.72 19	91.10 293	90.97 290	91.49 316	97.56 245	78.04 340	87.17 344	94.60 303	84.65 318	92.34 319	92.20 331	87.37 271	98.47 319	85.17 321	97.69 284	97.96 267

PMMVS2		93.66 254	94.07 236	92.45 310	97.57 243	80.67 334	86.46 345	96.00 286	93.99 196	97.10 169	97.38 192	89.90 244	97.82 338	88.76 285	99.47 116	98.86 188

CHOSEN 280x420		89.98 303	89.19 309	92.37 311	95.60 316	81.13 333	86.22 346	97.09 265	81.44 331	87.44 346	93.15 316	73.99 328	99.47 180	88.69 287	99.07 200	96.52 317

tmp_tt		57.23 323	62.50 326	41.44 337	34.77 358	49.21 359	83.93 347	60.22 359	15.31 354	71.11 355	79.37 352	70.09 345	44.86 355	64.76 351	82.93 352	30.25 351

PVSNet_0	81.89 21	84.49 322	83.21 325	88.34 331	95.76 313	74.97 351	83.49 348	92.70 322	78.47 342	87.94 344	86.90 350	83.38 292	96.63 348	73.44 347	66.86 353	93.40 340

E-PMN		89.52 308	89.78 303	88.73 329	93.14 346	77.61 342	83.26 349	92.02 325	94.82 167	93.71 287	93.11 317	75.31 326	96.81 345	85.81 314	96.81 307	91.77 345

EMVS		89.06 310	89.22 306	88.61 330	93.00 348	77.34 344	82.91 350	90.92 334	94.64 173	92.63 316	91.81 335	76.30 322	97.02 343	83.83 329	96.90 304	91.48 346

MVE	73.61 22	86.48 321	85.92 323	88.18 332	96.23 297	85.28 307	81.78 351	75.79 354	86.01 300	82.53 352	91.88 334	92.74 193	87.47 353	71.42 350	94.86 329	91.78 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		24.22 324	32.30 327	0.00 340	0.00 361	0.00 362	0.00 352	98.10 210	0.00 357	0.00 358	95.06 294	97.54 29	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		7.98 327	10.65 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	95.82 106	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		7.91 328	10.55 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	94.94 296	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ZD-MVS					98.43 150	95.94 77		98.56 154	90.72 258	96.66 196	97.07 213	95.02 137	99.74 67	91.08 237	98.93 215

IU-MVS					99.22 57	95.40 98		98.14 206	85.77 305	98.36 75				95.23 124	99.51 104	99.49 51

test_241102_TWO								98.83 99	96.11 107	98.62 51	98.24 98	96.92 58	99.72 77	95.44 111	99.49 110	99.49 51

test_241102_ONE					99.22 57	95.35 101		98.83 99	96.04 112	99.08 31	98.13 109	97.87 21	99.33 225

test_0728_THIRD									96.62 87	98.40 70	98.28 92	97.10 45	99.71 92	95.70 92	99.62 63	99.58 28

GSMVS																98.06 259

test_part2					99.03 90	96.07 72				98.08 110

sam_mvs1												77.80 311				98.06 259

sam_mvs												77.38 315

MTGPA								98.73 122

test_post											10.87 356	76.83 319	99.07 266

patchmatchnet-post											96.84 227	77.36 316	99.42 193

gm-plane-assit					91.79 353	71.40 355			81.67 328		90.11 347		98.99 275	84.86 323

test9_res														91.29 232	98.89 221	99.00 161

agg_prior2														90.34 265	98.90 218	99.10 149

agg_prior					97.80 218	94.96 116		98.36 178		93.49 297			99.53 164

TestCases				98.06 85	99.08 83	96.16 69		99.16 18	94.35 183	97.78 140	98.07 117	95.84 103	99.12 258	91.41 230	99.42 134	98.91 178

test_prior				97.46 131	97.79 223	94.26 143		98.42 170					99.34 222			98.79 194

新几何1				97.25 148	98.29 159	94.70 127		97.73 235	77.98 343	94.83 257	96.67 240	92.08 213	99.45 187	88.17 295	98.65 244	97.61 283

旧先验1					97.80 218	93.87 155		97.75 234			97.04 216	93.57 177			98.68 240	98.72 204

原ACMM1				96.58 184	98.16 179	92.12 200		98.15 205	85.90 303	93.49 297	96.43 252	92.47 205	99.38 212	87.66 300	98.62 246	98.23 245

testdata2													99.46 183	87.84 296

segment_acmp												95.34 127

testdata				95.70 225	98.16 179	90.58 228		97.72 236	80.38 335	95.62 242	97.02 217	92.06 214	98.98 277	89.06 283	98.52 251	97.54 285

test12				97.46 131	97.61 242	94.07 148		97.78 233		93.57 294		93.31 181	99.42 193		98.78 232	98.89 182

plane_prior7					98.70 118	94.67 128

plane_prior6					98.38 153	94.37 137						91.91 220

plane_prior5								98.75 118					99.46 183	92.59 213	99.20 179	99.28 109

plane_prior4											96.77 233

plane_prior3						94.51 131			95.29 149	96.16 222

plane_prior1					98.49 143

n2								0.00 362

nn								0.00 362

door-mid								98.17 202

lessismore_v0				97.05 156	99.36 41	92.12 200		84.07 352		98.77 46	98.98 39	85.36 282	99.74 67	97.34 41	99.37 145	99.30 102

LGP-MVS_train				98.74 36	99.15 71	97.02 42		99.02 50	95.15 154	98.34 78	98.23 100	97.91 18	99.70 101	94.41 163	99.73 44	99.50 43

test11								98.08 213

door								97.81 232

HQP5-MVS						92.47 190

BP-MVS														90.51 260

HQP4-MVS										92.87 309			99.23 246			99.06 154

HQP3-MVS								98.43 167							98.74 236

HQP2-MVS												90.33 236

NP-MVS					98.14 182	93.72 163					95.08 292

ACMMP++_ref															99.52 99

ACMMP++															99.55 88

Test By Simon												94.51 154

ITE_SJBPF				97.85 99	98.64 122	96.66 53		98.51 160	95.63 134	97.22 161	97.30 200	95.52 120	98.55 315	90.97 240	98.90 218	98.34 235

DeepMVS_CX				77.17 336	90.94 355	85.28 307		74.08 357	52.51 353	80.87 354	88.03 349	75.25 327	70.63 354	59.23 353	84.94 350	75.62 349

High-resolution multi-view benchmark