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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 7

UniMVSNet_ETH3D		99.69 3	99.69 5	99.69 3	99.84 15	99.34 11	99.69 5	99.58 27	99.90 2	99.86 8	99.78 6	99.58 4	99.95 13	99.00 32	99.95 16	99.78 13

UA-Net		99.47 12	99.40 15	99.70 2	99.49 83	99.29 14	99.80 3	99.72 8	99.82 3	99.04 104	99.81 4	98.05 65	99.96 8	98.85 38	99.99 5	99.86 6

ANet_high		99.57 8	99.67 6	99.28 73	99.89 7	98.09 111	99.14 41	99.93 1	99.82 3	99.93 2	99.81 4	99.17 13	99.94 21	99.31 17	100.00 1	99.82 8

test_normal		99.74 2	99.80 2	99.57 18	99.92 3	99.13 44	99.80 3	99.66 16	99.78 5	99.88 7	99.88 2	99.64 3	99.82 132	99.66 4	99.99 5	99.77 15

gg-mvs-nofinetune		92.37 301	91.20 305	95.85 288	95.80 332	92.38 290	99.31 19	81.84 337	99.75 6	91.83 328	99.74 9	68.29 336	99.02 320	87.15 317	97.12 306	96.16 321

LFMVS		97.20 212	96.72 220	98.64 158	98.72 226	96.95 193	98.93 57	94.14 319	99.74 7	98.78 144	99.01 116	84.45 300	99.73 203	97.44 109	99.27 211	99.25 181

Anonymous20231211		99.27 26	99.27 25	99.26 78	99.29 116	98.18 105	99.49 9	99.51 53	99.70 8	99.80 10	99.68 15	96.84 139	99.83 121	99.21 22	99.91 39	99.77 15

nrg030		99.40 19	99.35 18	99.54 28	99.58 50	99.13 44	98.98 54	99.48 65	99.68 9	99.46 40	99.26 68	98.62 29	99.73 203	99.17 25	99.92 34	99.76 20

VDDNet		98.21 139	97.95 151	99.01 115	99.58 50	97.74 153	99.01 49	97.29 284	99.67 10	98.97 116	99.50 35	90.45 267	99.80 153	97.88 88	99.20 221	99.48 106

v7n		99.53 9	99.57 9	99.41 56	99.88 8	98.54 84	99.45 10	99.61 22	99.66 11	99.68 20	99.66 18	98.44 39	99.95 13	99.73 2	99.96 15	99.75 22

pmmvs6		99.67 4	99.70 4	99.60 13	99.90 5	99.27 17	99.53 8	99.76 6	99.64 12	99.84 9	99.83 3	99.50 6	99.87 75	99.36 15	99.92 34	99.64 36

DTE-MVSNet		99.43 16	99.35 18	99.66 4	99.71 30	99.30 13	99.31 19	99.51 53	99.64 12	99.56 25	99.46 41	98.23 50	99.97 3	98.78 41	99.93 25	99.72 24

VPA-MVSNet		99.30 25	99.30 24	99.28 73	99.49 83	98.36 95	99.00 51	99.45 76	99.63 14	99.52 32	99.44 46	98.25 48	99.88 61	99.09 27	99.84 54	99.62 40

DP-MVS		98.93 48	98.81 50	99.28 73	99.21 130	98.45 90	98.46 93	99.33 117	99.63 14	99.48 37	99.15 89	97.23 121	99.75 194	97.17 121	99.66 136	99.63 39

LTVRE_ROB	98.40 1	99.67 4	99.71 3	99.56 23	99.85 14	99.11 49	99.90 1	99.78 4	99.63 14	99.78 11	99.67 17	99.48 7	99.81 144	99.30 18	99.97 12	99.77 15
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
PEN-MVS		99.41 18	99.34 20	99.62 6	99.73 24	99.14 41	99.29 24	99.54 48	99.62 17	99.56 25	99.42 48	98.16 58	99.96 8	98.78 41	99.93 25	99.77 15

K. test v3		98.00 153	97.66 172	99.03 111	99.79 20	97.56 162	99.19 37	92.47 324	99.62 17	99.52 32	99.66 18	89.61 272	99.96 8	99.25 21	99.81 67	99.56 67

FC-MVSNet-test		99.27 26	99.25 26	99.34 68	99.77 21	98.37 94	99.30 23	99.57 34	99.61 19	99.40 49	99.50 35	97.12 124	99.85 90	99.02 31	99.94 20	99.80 11

VDD-MVS		98.56 98	98.39 105	99.07 102	99.13 151	98.07 117	98.59 77	97.01 288	99.59 20	99.11 89	99.27 66	94.82 217	99.79 166	98.34 65	99.63 141	99.34 157

MIMVSNet1		99.38 21	99.32 22	99.55 25	99.86 12	99.19 30	99.41 11	99.59 25	99.59 20	99.71 15	99.57 28	97.12 124	99.90 44	99.21 22	99.87 50	99.54 78

Gipuma		99.03 35	99.16 30	98.64 158	99.94 2	98.51 86	99.32 16	99.75 7	99.58 22	98.60 162	99.62 22	98.22 53	99.51 281	97.70 99	99.73 100	97.89 284
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PS-CasMVS		99.40 19	99.33 21	99.62 6	99.71 30	99.10 50	99.29 24	99.53 49	99.53 23	99.46 40	99.41 50	98.23 50	99.95 13	98.89 37	99.95 16	99.81 10

FIs		99.14 32	99.09 34	99.29 72	99.70 36	98.28 96	99.13 42	99.52 52	99.48 24	99.24 75	99.41 50	96.79 145	99.82 132	98.69 48	99.88 47	99.76 20

PS-MVSNAJss		99.46 13	99.49 11	99.35 65	99.90 5	98.15 107	99.20 33	99.65 18	99.48 24	99.92 3	99.71 13	98.07 62	99.96 8	99.53 10	100.00 1	99.93 1

VPNet		98.87 54	98.83 47	99.01 115	99.70 36	97.62 161	98.43 95	99.35 106	99.47 26	99.28 67	99.05 106	96.72 152	99.82 132	98.09 75	99.36 196	99.59 51

WR-MVS_H		99.33 24	99.22 28	99.65 5	99.71 30	99.24 20	99.32 16	99.55 44	99.46 27	99.50 36	99.34 59	97.30 113	99.93 25	98.90 35	99.93 25	99.77 15

tfpnnormal		98.90 52	98.90 43	98.91 126	99.67 40	97.82 145	99.00 51	99.44 79	99.45 28	99.51 35	99.24 71	98.20 56	99.86 80	95.92 200	99.69 120	99.04 213

OurMVSNet-221017-0		99.37 22	99.31 23	99.53 34	99.91 4	98.98 54	99.63 7	99.58 27	99.44 29	99.78 11	99.76 7	96.39 166	99.92 31	99.44 14	99.92 34	99.68 30

CP-MVSNet		99.21 29	99.09 34	99.56 23	99.65 43	98.96 58	99.13 42	99.34 112	99.42 30	99.33 59	99.26 68	97.01 131	99.94 21	98.74 45	99.93 25	99.79 12

TranMVSNet+NR-MVSNet		99.17 30	99.07 36	99.46 52	99.37 105	98.87 60	98.39 98	99.42 87	99.42 30	99.36 55	99.06 99	98.38 42	99.95 13	98.34 65	99.90 43	99.57 62

TransMVSNet (Re)		99.44 14	99.47 13	99.36 60	99.80 18	98.58 79	99.27 30	99.57 34	99.39 32	99.75 13	99.62 22	99.17 13	99.83 121	99.06 29	99.62 144	99.66 33

TDRefinement		99.42 17	99.38 16	99.55 25	99.76 22	99.33 12	99.68 6	99.71 9	99.38 33	99.53 30	99.61 24	98.64 28	99.80 153	98.24 68	99.84 54	99.52 88

Baseline_NR-MVSNet		98.98 42	98.86 45	99.36 60	99.82 17	98.55 81	97.47 191	99.57 34	99.37 34	99.21 79	99.61 24	96.76 149	99.83 121	98.06 77	99.83 60	99.71 25

SixPastTwentyTwo		98.75 68	98.62 71	99.16 88	99.83 16	97.96 132	99.28 28	98.20 262	99.37 34	99.70 16	99.65 20	92.65 256	99.93 25	99.04 30	99.84 54	99.60 45

RPMNet		96.82 231	96.66 226	97.28 253	97.71 296	94.22 256	98.11 119	96.90 294	99.37 34	96.91 258	99.34 59	86.72 282	99.81 144	97.53 105	97.36 302	97.81 290

PatchT		96.65 238	96.35 237	97.54 243	97.40 309	95.32 233	97.98 136	96.64 298	99.33 37	96.89 262	99.42 48	84.32 302	99.81 144	97.69 101	97.49 297	97.48 305

VNet		98.42 116	98.30 117	98.79 142	98.79 219	97.29 173	98.23 107	98.66 244	99.31 38	98.85 136	98.80 161	94.80 220	99.78 176	98.13 73	99.13 235	99.31 169

pm-mvs1		99.44 14	99.48 12	99.33 70	99.80 18	98.63 73	99.29 24	99.63 19	99.30 39	99.65 21	99.60 26	99.16 15	99.82 132	99.07 28	99.83 60	99.56 67

test_0402		98.76 67	98.71 59	98.93 123	99.56 61	98.14 109	98.45 94	99.34 112	99.28 40	98.95 119	98.91 135	98.34 45	99.79 166	95.63 215	99.91 39	98.86 238

mvs_tets		99.63 6	99.67 6	99.49 46	99.88 8	98.61 76	99.34 14	99.71 9	99.27 41	99.90 4	99.74 9	99.68 2	99.97 3	99.55 9	99.99 5	99.88 3

Anonymous20240529		98.93 48	98.87 44	99.12 93	99.19 134	98.22 104	99.01 49	98.99 202	99.25 42	99.54 27	99.37 53	97.04 127	99.80 153	97.89 85	99.52 177	99.35 154

Regformer-4		98.73 71	98.68 64	98.89 129	99.02 174	97.22 179	97.17 211	99.06 182	99.21 43	99.17 86	98.85 150	97.45 106	99.86 80	98.48 58	99.70 114	99.60 45

FMVSNet1		99.17 30	99.17 29	99.17 85	99.55 64	98.24 99	99.20 33	99.44 79	99.21 43	99.43 45	99.55 30	97.82 80	99.86 80	98.42 62	99.89 46	99.41 130

LS3D		98.63 88	98.38 107	99.36 60	97.25 313	99.38 5	99.12 44	99.32 119	99.21 43	98.44 176	98.88 144	97.31 112	99.80 153	96.58 162	99.34 200	98.92 231

alignmvs		97.35 200	96.88 213	98.78 145	98.54 256	98.09 111	97.71 163	97.69 276	99.20 46	97.59 226	95.90 304	88.12 279	99.55 268	98.18 72	98.96 253	98.70 255

EI-MVSNet-UG-set		98.69 78	98.71 59	98.62 162	99.10 154	96.37 207	97.23 203	98.87 217	99.20 46	99.19 81	98.99 119	97.30 113	99.85 90	98.77 44	99.79 77	99.65 35

EI-MVSNet-Vis-set		98.68 81	98.70 62	98.63 160	99.09 157	96.40 206	97.23 203	98.86 221	99.20 46	99.18 85	98.97 125	97.29 115	99.85 90	98.72 46	99.78 81	99.64 36

JIA-IIPM		95.52 263	95.03 268	97.00 261	96.85 320	94.03 262	96.93 224	95.82 307	99.20 46	94.63 312	99.71 13	83.09 309	99.60 252	94.42 243	94.64 323	97.36 307

canonicalmvs		98.34 125	98.26 120	98.58 166	98.46 262	97.82 145	98.96 55	99.46 73	99.19 50	97.46 238	95.46 312	98.59 31	99.46 289	98.08 76	98.71 264	98.46 264

casdiffmvs		98.95 46	99.00 39	98.81 138	99.38 103	97.33 172	97.82 152	99.57 34	99.17 51	99.35 56	99.17 83	98.35 44	99.69 218	98.46 59	99.73 100	99.41 130

UniMVSNet_NR-MVSNet		98.86 56	98.68 64	99.40 58	99.17 142	98.74 66	97.68 166	99.40 89	99.14 52	99.06 97	98.59 196	96.71 153	99.93 25	98.57 53	99.77 85	99.53 84

Regformer-3		98.61 91	98.61 74	98.63 160	99.02 174	96.53 204	97.17 211	98.84 223	99.13 53	99.10 92	98.85 150	97.24 120	99.79 166	98.41 63	99.70 114	99.57 62

MVSFormer		98.26 134	98.43 99	97.77 227	98.88 202	93.89 270	99.39 12	99.56 41	99.11 54	98.16 191	98.13 232	93.81 239	99.97 3	99.26 19	99.57 161	99.43 125

test_djsdf		99.52 10	99.51 10	99.53 34	99.86 12	98.74 66	99.39 12	99.56 41	99.11 54	99.70 16	99.73 11	99.00 16	99.97 3	99.26 19	99.98 10	99.89 2

Vis-MVSNet		99.34 23	99.36 17	99.27 76	99.73 24	98.26 97	99.17 38	99.78 4	99.11 54	99.27 69	99.48 39	98.82 21	99.95 13	98.94 34	99.93 25	99.59 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	96.62 9	99.08 34	99.00 39	99.33 70	99.71 30	98.83 62	98.60 75	99.58 27	99.11 54	99.53 30	99.18 79	98.81 22	99.67 228	96.71 155	99.77 85	99.50 96

IterMVS-SCA-FT		97.85 169	98.18 128	96.87 268	99.27 118	91.16 307	95.53 290	99.25 143	99.10 58	99.41 47	99.35 57	93.10 249	99.96 8	98.65 49	99.94 20	99.49 100

NR-MVSNet		98.95 46	98.82 48	99.36 60	99.16 144	98.72 71	99.22 32	99.20 154	99.10 58	99.72 14	98.76 168	96.38 168	99.86 80	98.00 82	99.82 63	99.50 96

UGNet		98.53 107	98.45 95	98.79 142	97.94 289	96.96 192	99.08 45	98.54 249	99.10 58	96.82 266	99.47 40	96.55 158	99.84 106	98.56 56	99.94 20	99.55 75
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
jajsoiax		99.58 7	99.61 8	99.48 47	99.87 11	98.61 76	99.28 28	99.66 16	99.09 61	99.89 6	99.68 15	99.53 5	99.97 3	99.50 11	99.99 5	99.87 4

COLMAP_ROB	96.50 10	98.99 38	98.85 46	99.41 56	99.58 50	99.10 50	98.74 66	99.56 41	99.09 61	99.33 59	99.19 77	98.40 41	99.72 211	95.98 198	99.76 94	99.42 128
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		98.78 64	98.77 54	98.78 145	99.46 91	97.20 181	97.78 154	99.24 148	99.04 63	99.41 47	98.90 138	97.65 87	99.76 187	97.70 99	99.79 77	99.39 137

v8		99.01 36	99.16 30	98.57 168	99.47 90	96.31 209	98.90 58	99.47 71	99.03 64	99.52 32	99.57 28	96.93 135	99.81 144	99.60 5	99.98 10	99.60 45

EPP-MVSNet		98.30 128	98.04 145	99.07 102	99.56 61	97.83 142	99.29 24	98.07 266	99.03 64	98.59 163	99.13 92	92.16 260	99.90 44	96.87 142	99.68 125	99.49 100

IS-MVSNet		98.19 141	97.90 156	99.08 100	99.57 54	97.97 129	99.31 19	98.32 257	99.01 66	98.98 113	99.03 111	91.59 263	99.79 166	95.49 220	99.80 72	99.48 106

3Dnovator+	97.89 3	98.69 78	98.51 83	99.24 81	98.81 216	98.40 91	99.02 48	99.19 159	98.99 67	98.07 198	99.28 64	97.11 126	99.84 106	96.84 144	99.32 202	99.47 113

PMVS	91.26 20	97.86 164	97.94 153	97.65 234	99.71 30	97.94 135	98.52 84	98.68 243	98.99 67	97.52 233	99.35 57	97.41 108	98.18 330	91.59 299	99.67 131	96.82 314
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Regformer-2		98.60 93	98.46 93	99.02 114	98.85 206	97.71 155	96.91 227	99.09 180	98.98 69	99.01 108	98.64 186	97.37 111	99.84 106	97.75 98	99.57 161	99.52 88

Regformer-1		98.55 102	98.44 97	98.87 131	98.85 206	97.29 173	96.91 227	98.99 202	98.97 70	98.99 111	98.64 186	97.26 119	99.81 144	97.79 91	99.57 161	99.51 91

EI-MVSNet		98.40 119	98.51 83	98.04 216	99.10 154	94.73 246	97.20 207	98.87 217	98.97 70	99.06 97	99.02 112	96.00 179	99.80 153	98.58 51	99.82 63	99.60 45

EPNet		96.14 251	95.44 258	98.25 202	90.76 336	95.50 228	97.92 140	94.65 312	98.97 70	92.98 324	98.85 150	89.12 276	99.87 75	95.99 197	99.68 125	99.39 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-LS		98.55 102	98.70 62	98.09 209	99.48 88	94.73 246	97.22 206	99.39 91	98.97 70	99.38 51	99.31 63	96.00 179	99.93 25	98.58 51	99.97 12	99.60 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		97.35 200	96.97 208	98.50 182	97.31 312	96.47 205	98.18 112	98.92 210	98.95 74	98.78 144	99.37 53	85.44 295	99.85 90	95.96 199	99.83 60	99.17 201

anonymousdsp		99.51 11	99.47 13	99.62 6	99.88 8	99.08 53	99.34 14	99.69 12	98.93 75	99.65 21	99.72 12	98.93 19	99.95 13	99.11 26	100.00 1	99.82 8

UniMVSNet (Re)		98.87 54	98.71 59	99.35 65	99.24 123	98.73 69	97.73 162	99.38 93	98.93 75	99.12 88	98.73 170	96.77 147	99.86 80	98.63 50	99.80 72	99.46 115

Anonymous202405211		97.90 158	97.50 182	99.08 100	98.90 196	98.25 98	98.53 83	96.16 303	98.87 77	99.11 89	98.86 147	90.40 268	99.78 176	97.36 113	99.31 204	99.19 195

baseline		98.96 45	99.02 37	98.76 148	99.38 103	97.26 176	98.49 90	99.50 55	98.86 78	99.19 81	99.06 99	98.23 50	99.69 218	98.71 47	99.76 94	99.33 163

IterMVS		97.73 176	98.11 138	96.57 274	99.24 123	90.28 308	95.52 292	99.21 152	98.86 78	99.33 59	99.33 61	93.11 248	99.94 21	98.49 57	99.94 20	99.48 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DU-MVS		98.82 58	98.63 70	99.39 59	99.16 144	98.74 66	97.54 183	99.25 143	98.84 80	99.06 97	98.76 168	96.76 149	99.93 25	98.57 53	99.77 85	99.50 96

zzz-MVS		98.79 61	98.52 81	99.61 9	99.67 40	99.36 6	97.33 196	99.20 154	98.83 81	98.89 128	98.90 138	96.98 133	99.92 31	97.16 122	99.70 114	99.56 67

MTAPA		98.88 53	98.64 69	99.61 9	99.67 40	99.36 6	98.43 95	99.20 154	98.83 81	98.89 128	98.90 138	96.98 133	99.92 31	97.16 122	99.70 114	99.56 67

v10		98.97 43	99.11 33	98.55 173	99.44 96	96.21 211	98.90 58	99.55 44	98.73 83	99.48 37	99.60 26	96.63 156	99.83 121	99.70 3	99.99 5	99.61 44

UnsupCasMVSNet_eth		97.89 160	97.60 177	98.75 151	99.31 113	97.17 184	97.62 172	99.35 106	98.72 84	98.76 147	98.68 176	92.57 257	99.74 198	97.76 97	95.60 319	99.34 157

Fast-Effi-MVS+-dtu		98.27 132	98.09 139	98.81 138	98.43 265	98.11 110	97.61 174	99.50 55	98.64 85	97.39 243	97.52 269	98.12 61	99.95 13	96.90 139	98.71 264	98.38 270

APD-MVS_3200maxsize		98.84 57	98.61 74	99.53 34	99.19 134	99.27 17	98.49 90	99.33 117	98.64 85	99.03 107	98.98 123	97.89 74	99.85 90	96.54 169	99.42 191	99.46 115

XVS		98.72 72	98.45 95	99.53 34	99.46 91	99.21 23	98.65 70	99.34 112	98.62 87	97.54 231	98.63 190	97.50 102	99.83 121	96.79 146	99.53 173	99.56 67

X-MVStestdata		94.32 279	92.59 297	99.53 34	99.46 91	99.21 23	98.65 70	99.34 112	98.62 87	97.54 231	45.85 333	97.50 102	99.83 121	96.79 146	99.53 173	99.56 67

abl_6		98.99 38	98.78 52	99.61 9	99.45 94	99.46 3	98.60 75	99.50 55	98.59 89	99.24 75	99.04 108	98.54 34	99.89 53	96.45 175	99.62 144	99.50 96

GBi-Net		98.65 84	98.47 91	99.17 85	98.90 196	98.24 99	99.20 33	99.44 79	98.59 89	98.95 119	99.55 30	94.14 233	99.86 80	97.77 93	99.69 120	99.41 130

test1		98.65 84	98.47 91	99.17 85	98.90 196	98.24 99	99.20 33	99.44 79	98.59 89	98.95 119	99.55 30	94.14 233	99.86 80	97.77 93	99.69 120	99.41 130

FMVSNet2		98.49 110	98.40 103	98.75 151	98.90 196	97.14 187	98.61 74	99.13 174	98.59 89	99.19 81	99.28 64	94.14 233	99.82 132	97.97 83	99.80 72	99.29 175

WR-MVS		98.40 119	98.19 127	99.03 111	99.00 176	97.65 158	96.85 230	98.94 205	98.57 93	98.89 128	98.50 205	95.60 195	99.85 90	97.54 104	99.85 52	99.59 51

3Dnovator	98.27 2	98.81 60	98.73 56	99.05 108	98.76 220	97.81 147	99.25 31	99.30 129	98.57 93	98.55 168	99.33 61	97.95 73	99.90 44	97.16 122	99.67 131	99.44 121

XXY-MVS		99.14 32	99.15 32	99.10 97	99.76 22	97.74 153	98.85 63	99.62 20	98.48 95	99.37 53	99.49 38	98.75 24	99.86 80	98.20 71	99.80 72	99.71 25

testing_2		98.93 48	98.99 41	98.76 148	99.57 54	97.03 189	97.85 149	99.13 174	98.46 96	99.44 43	99.44 46	98.22 53	99.74 198	98.85 38	99.94 20	99.51 91

LCM-MVSNet-Re		98.64 86	98.48 89	99.11 95	98.85 206	98.51 86	98.49 90	99.83 3	98.37 97	99.69 18	99.46 41	98.21 55	99.92 31	94.13 254	99.30 207	98.91 233

MDA-MVSNet-bldmvs		97.94 157	97.91 155	98.06 214	99.44 96	94.96 242	96.63 242	99.15 173	98.35 98	98.83 139	99.11 94	94.31 230	99.85 90	96.60 161	98.72 262	99.37 144

thres600view7		94.45 277	93.83 283	96.29 279	99.06 165	91.53 297	97.99 135	94.24 317	98.34 99	97.44 240	95.01 316	79.84 318	99.67 228	84.33 322	98.23 278	97.66 299

thres100view900		94.19 282	93.67 286	95.75 290	99.06 165	91.35 301	98.03 129	94.24 317	98.33 100	97.40 242	94.98 318	79.84 318	99.62 246	83.05 324	98.08 288	96.29 318

Vis-MVSNet (Re-imp)		97.46 195	97.16 201	98.34 195	99.55 64	96.10 212	98.94 56	98.44 253	98.32 101	98.16 191	98.62 192	88.76 277	99.73 203	93.88 261	99.79 77	99.18 197

new-patchmatchnet		98.35 124	98.74 55	97.18 257	99.24 123	92.23 292	96.42 252	99.48 65	98.30 102	99.69 18	99.53 33	97.44 107	99.82 132	98.84 40	99.77 85	99.49 100

v148		98.45 114	98.60 76	98.00 218	99.44 96	94.98 241	97.44 192	99.06 182	98.30 102	99.32 64	98.97 125	96.65 155	99.62 246	98.37 64	99.85 52	99.39 137

ACMH	96.65 7	99.25 28	99.24 27	99.26 78	99.72 29	98.38 93	99.07 46	99.55 44	98.30 102	99.65 21	99.45 45	99.22 10	99.76 187	98.44 60	99.77 85	99.64 36
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS		98.71 73	98.43 99	99.57 18	99.18 141	99.35 8	98.36 100	99.29 132	98.29 105	98.88 132	98.85 150	97.53 99	99.87 75	96.14 193	99.31 204	99.48 106

Effi-MVS+-dtu		98.26 134	97.90 156	99.35 65	98.02 286	99.49 2	98.02 131	99.16 170	98.29 105	97.64 222	97.99 244	96.44 164	99.95 13	96.66 158	98.93 255	98.60 260

mvs-test1		97.83 172	97.48 186	98.89 129	98.02 286	99.20 28	97.20 207	99.16 170	98.29 105	96.46 281	97.17 283	96.44 164	99.92 31	96.66 158	97.90 293	97.54 304

save fliter					99.11 152	97.97 129	96.53 246	99.02 195	98.24 108

EU-MVSNet		97.66 181	98.50 85	95.13 298	99.63 48	85.84 322	98.35 101	98.21 261	98.23 109	99.54 27	99.46 41	95.02 211	99.68 224	98.24 68	99.87 50	99.87 4

test_yl		96.69 235	96.29 240	97.90 220	98.28 272	95.24 234	97.29 199	97.36 280	98.21 110	98.17 189	97.86 250	86.27 285	99.55 268	94.87 230	98.32 276	98.89 234

DCV-MVSNet		96.69 235	96.29 240	97.90 220	98.28 272	95.24 234	97.29 199	97.36 280	98.21 110	98.17 189	97.86 250	86.27 285	99.55 268	94.87 230	98.32 276	98.89 234

baseline1		95.96 254	95.44 258	97.52 245	98.51 259	93.99 264	98.39 98	96.09 305	98.21 110	98.40 183	97.76 256	86.88 281	99.63 244	95.42 221	89.27 332	98.95 225

SD-MVS		98.40 119	98.68 64	97.54 243	98.96 183	97.99 124	97.88 144	99.36 101	98.20 113	99.63 24	99.04 108	98.76 23	95.33 334	96.56 167	99.74 97	99.31 169
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		97.99 156	97.67 169	98.93 123	99.19 134	97.65 158	97.77 157	99.27 137	98.20 113	97.79 214	97.98 245	94.90 213	99.70 214	94.42 243	99.51 178	99.45 119

plane_prior2							97.77 157		98.20 113

test_0728_THIRD									98.17 116	99.08 95	99.02 112	97.89 74	99.88 61	97.07 129	99.71 110	99.70 28

E-PMN		94.17 283	94.37 277	93.58 312	96.86 319	85.71 324	90.11 332	97.07 287	98.17 116	97.82 212	97.19 282	84.62 299	98.94 323	89.77 311	97.68 296	96.09 324

EG-PatchMatch MVS		98.99 38	99.01 38	98.94 122	99.50 76	97.47 166	98.04 128	99.59 25	98.15 118	99.40 49	99.36 56	98.58 32	99.76 187	98.78 41	99.68 125	99.59 51

EIA-MVS		98.03 150	97.86 159	98.56 172	98.69 236	98.07 117	97.51 187	99.50 55	98.10 119	97.50 235	95.51 310	98.41 40	99.88 61	96.27 184	99.24 216	97.71 297

tttt0517		95.64 260	94.98 269	97.64 236	99.36 106	93.81 272	98.72 68	90.47 330	98.08 120	98.67 154	98.34 219	73.88 332	99.92 31	97.77 93	99.51 178	99.20 190

DVP-MVS		98.77 66	98.52 81	99.52 39	99.50 76	99.21 23	98.02 131	98.84 223	97.97 121	99.08 95	99.02 112	97.61 92	99.88 61	96.99 131	99.63 141	99.48 106

test0726					99.50 76	99.21 23	98.17 115	99.35 106	97.97 121	99.26 73	99.06 99	97.61 92

tfpn200view9		94.03 287	93.44 288	95.78 289	98.93 188	91.44 299	97.60 175	94.29 315	97.94 123	97.10 248	94.31 325	79.67 320	99.62 246	83.05 324	98.08 288	96.29 318

thres400		94.14 284	93.44 288	96.24 281	98.93 188	91.44 299	97.60 175	94.29 315	97.94 123	97.10 248	94.31 325	79.67 320	99.62 246	83.05 324	98.08 288	97.66 299

EMVS		93.83 290	94.02 280	93.23 316	96.83 321	84.96 325	89.77 333	96.32 302	97.92 125	97.43 241	96.36 299	86.17 287	98.93 324	87.68 316	97.73 295	95.81 325

SteuartSystems-ACMMP		98.79 61	98.54 79	99.54 28	99.73 24	99.16 34	98.23 107	99.31 123	97.92 125	98.90 126	98.90 138	98.00 68	99.88 61	96.15 192	99.72 106	99.58 57
Skip Steuart: Steuart Systems R&D Blog.
v2v482		98.56 98	98.62 71	98.37 193	99.42 100	95.81 221	97.58 178	99.16 170	97.90 127	99.28 67	99.01 116	95.98 183	99.79 166	99.33 16	99.90 43	99.51 91

FMVSNet3		97.50 190	97.24 198	98.29 200	98.08 284	95.83 220	97.86 147	98.91 212	97.89 128	98.95 119	98.95 130	87.06 280	99.81 144	97.77 93	99.69 120	99.23 185

V42		98.78 64	98.78 52	98.76 148	99.44 96	97.04 188	98.27 104	99.19 159	97.87 129	99.25 74	99.16 85	96.84 139	99.78 176	99.21 22	99.84 54	99.46 115

CSCG		98.68 81	98.50 85	99.20 84	99.45 94	98.63 73	98.56 80	99.57 34	97.87 129	98.85 136	98.04 242	97.66 86	99.84 106	96.72 153	99.81 67	99.13 205

xiu_mvs_v1_base_debu		97.86 164	98.17 129	96.92 265	98.98 180	93.91 267	96.45 249	99.17 167	97.85 131	98.41 179	97.14 286	98.47 36	99.92 31	98.02 79	99.05 242	96.92 311

xiu_mvs_v1_base		97.86 164	98.17 129	96.92 265	98.98 180	93.91 267	96.45 249	99.17 167	97.85 131	98.41 179	97.14 286	98.47 36	99.92 31	98.02 79	99.05 242	96.92 311

xiu_mvs_v1_base_debi		97.86 164	98.17 129	96.92 265	98.98 180	93.91 267	96.45 249	99.17 167	97.85 131	98.41 179	97.14 286	98.47 36	99.92 31	98.02 79	99.05 242	96.92 311

diffmvs		98.22 138	98.24 122	98.17 207	99.00 176	95.44 230	96.38 254	99.58 27	97.79 134	98.53 171	98.50 205	96.76 149	99.74 198	97.95 84	99.64 139	99.34 157

CANet		97.87 163	97.76 163	98.19 206	97.75 294	95.51 227	96.76 235	99.05 186	97.74 135	96.93 255	98.21 230	95.59 196	99.89 53	97.86 90	99.93 25	99.19 195

DELS-MVS		98.27 132	98.20 125	98.48 183	98.86 204	96.70 201	95.60 288	99.20 154	97.73 136	98.45 175	98.71 172	97.50 102	99.82 132	98.21 70	99.59 151	98.93 230
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
RPSCF		98.62 90	98.36 109	99.42 53	99.65 43	99.42 4	98.55 81	99.57 34	97.72 137	98.90 126	99.26 68	96.12 174	99.52 277	95.72 211	99.71 110	99.32 165

MVS_Test		98.18 142	98.36 109	97.67 232	98.48 260	94.73 246	98.18 112	99.02 195	97.69 138	98.04 201	99.11 94	97.22 122	99.56 265	98.57 53	98.90 256	98.71 253

DPE-MVS		98.59 96	98.26 120	99.57 18	99.27 118	99.15 39	97.01 218	99.39 91	97.67 139	99.44 43	98.99 119	97.53 99	99.89 53	95.40 222	99.68 125	99.66 33

ab-mvs		98.41 117	98.36 109	98.59 165	99.19 134	97.23 177	99.32 16	98.81 229	97.66 140	98.62 158	99.40 52	96.82 142	99.80 153	95.88 201	99.51 178	98.75 251

MSDG		97.71 177	97.52 181	98.28 201	98.91 195	96.82 196	94.42 316	99.37 97	97.65 141	98.37 184	98.29 225	97.40 109	99.33 304	94.09 255	99.22 218	98.68 259

NCCC		97.86 164	97.47 187	99.05 108	98.61 248	98.07 117	96.98 220	98.90 213	97.63 142	97.04 252	97.93 248	95.99 182	99.66 236	95.31 223	98.82 258	99.43 125

PM-MVS		98.82 58	98.72 58	99.12 93	99.64 46	98.54 84	97.98 136	99.68 13	97.62 143	99.34 58	99.18 79	97.54 97	99.77 182	97.79 91	99.74 97	99.04 213

ACMM	96.08 12	98.91 51	98.73 56	99.48 47	99.55 64	99.14 41	98.07 122	99.37 97	97.62 143	99.04 104	98.96 128	98.84 20	99.79 166	97.43 110	99.65 137	99.49 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS		98.46 113	98.09 139	99.54 28	99.57 54	99.22 22	98.50 89	99.19 159	97.61 145	97.58 227	98.66 181	97.40 109	99.88 61	94.72 235	99.60 150	99.54 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_HR		98.25 136	98.08 142	98.75 151	99.09 157	97.46 167	95.97 269	99.27 137	97.60 146	97.99 203	98.25 226	98.15 60	99.38 299	96.87 142	99.57 161	99.42 128

MVS_111021_LR		98.30 128	98.12 137	98.83 136	99.16 144	98.03 122	96.09 266	99.30 129	97.58 147	98.10 196	98.24 227	98.25 48	99.34 302	96.69 156	99.65 137	99.12 206

APDe-MVS		98.99 38	98.79 51	99.60 13	99.21 130	99.15 39	98.87 60	99.48 65	97.57 148	99.35 56	99.24 71	97.83 77	99.89 53	97.88 88	99.70 114	99.75 22

API-MVS		97.04 223	96.91 212	97.42 250	97.88 292	98.23 103	98.18 112	98.50 251	97.57 148	97.39 243	96.75 291	96.77 147	99.15 317	90.16 310	99.02 246	94.88 328

DeepC-MVS	97.60 4	98.97 43	98.93 42	99.10 97	99.35 110	97.98 128	98.01 134	99.46 73	97.56 150	99.54 27	99.50 35	98.97 17	99.84 106	98.06 77	99.92 34	99.49 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSP-MVS		98.40 119	98.00 148	99.61 9	99.57 54	99.25 19	98.57 79	99.35 106	97.55 151	99.31 66	97.71 258	94.61 223	99.88 61	96.14 193	99.19 225	99.70 28

CP-MVS		98.70 76	98.42 101	99.52 39	99.36 106	99.12 47	98.72 68	99.36 101	97.54 152	98.30 185	98.40 213	97.86 76	99.89 53	96.53 170	99.72 106	99.56 67

v1144		98.60 93	98.66 67	98.41 189	99.36 106	95.90 217	97.58 178	99.34 112	97.51 153	99.27 69	99.15 89	96.34 170	99.80 153	99.47 13	99.93 25	99.51 91

PMMVS2		98.07 149	98.08 142	98.04 216	99.41 101	94.59 252	94.59 313	99.40 89	97.50 154	98.82 141	98.83 156	96.83 141	99.84 106	97.50 107	99.81 67	99.71 25

ITE_SJBPF				98.87 131	99.22 128	98.48 88		99.35 106	97.50 154	98.28 186	98.60 195	97.64 90	99.35 301	93.86 262	99.27 211	98.79 247

MVSTER		96.86 228	96.55 232	97.79 226	97.91 291	94.21 258	97.56 180	98.87 217	97.49 156	99.06 97	99.05 106	80.72 315	99.80 153	98.44 60	99.82 63	99.37 144

Patchmatch-RL test		97.26 207	97.02 206	97.99 219	99.52 71	95.53 226	96.13 265	99.71 9	97.47 157	99.27 69	99.16 85	84.30 303	99.62 246	97.89 85	99.77 85	98.81 242

HFP-MVS		98.71 73	98.44 97	99.51 43	99.49 83	99.16 34	98.52 84	99.31 123	97.47 157	98.58 165	98.50 205	97.97 71	99.85 90	96.57 164	99.59 151	99.53 84

MSLP-MVS++		98.02 151	98.14 136	97.64 236	98.58 252	95.19 237	97.48 189	99.23 150	97.47 157	97.90 206	98.62 192	97.04 127	98.81 327	97.55 102	99.41 192	98.94 229

ACMMPR		98.70 76	98.42 101	99.54 28	99.52 71	99.14 41	98.52 84	99.31 123	97.47 157	98.56 167	98.54 200	97.75 82	99.88 61	96.57 164	99.59 151	99.58 57

mPP-MVS		98.64 86	98.34 112	99.54 28	99.54 67	99.17 32	98.63 72	99.24 148	97.47 157	98.09 197	98.68 176	97.62 91	99.89 53	96.22 186	99.62 144	99.57 62

region2R		98.69 78	98.40 103	99.54 28	99.53 69	99.17 32	98.52 84	99.31 123	97.46 162	98.44 176	98.51 202	97.83 77	99.88 61	96.46 174	99.58 157	99.58 57

HPM-MVS++		98.10 146	97.64 174	99.48 47	99.09 157	99.13 44	97.52 185	98.75 237	97.46 162	96.90 261	97.83 253	96.01 178	99.84 106	95.82 208	99.35 198	99.46 115

TinyColmap		97.89 160	97.98 149	97.60 238	98.86 204	94.35 255	96.21 262	99.44 79	97.45 164	99.06 97	98.88 144	97.99 70	99.28 311	94.38 247	99.58 157	99.18 197

GST-MVS		98.61 91	98.30 117	99.52 39	99.51 73	99.20 28	98.26 105	99.25 143	97.44 165	98.67 154	98.39 214	97.68 84	99.85 90	96.00 196	99.51 178	99.52 88

MVS_0304		97.64 182	97.35 193	98.52 177	97.87 293	96.69 202	98.59 77	98.05 268	97.44 165	93.74 323	98.85 150	93.69 244	99.88 61	98.11 74	99.81 67	98.98 220

v1192		98.60 93	98.66 67	98.41 189	99.27 118	95.88 218	97.52 185	99.36 101	97.41 167	99.33 59	99.20 76	96.37 169	99.82 132	99.57 7	99.92 34	99.55 75

plane_prior3						97.78 149			97.41 167	97.79 214

ETV-MVS		98.00 153	97.74 165	98.80 140	98.72 226	98.09 111	98.05 126	99.60 24	97.39 169	96.63 271	95.55 309	97.68 84	99.80 153	96.73 152	99.27 211	98.52 262

thres200		93.72 292	93.14 293	95.46 295	98.66 246	91.29 303	96.61 243	94.63 313	97.39 169	96.83 265	93.71 328	79.88 317	99.56 265	82.40 327	98.13 285	95.54 327

testgi		98.32 126	98.39 105	98.13 208	99.57 54	95.54 225	97.78 154	99.49 63	97.37 171	99.19 81	97.65 261	98.96 18	99.49 283	96.50 172	98.99 250	99.34 157

mvs_anonymous		97.83 172	98.16 132	96.87 268	98.18 279	91.89 294	97.31 198	98.90 213	97.37 171	98.83 139	99.46 41	96.28 171	99.79 166	98.90 35	98.16 283	98.95 225

EPNet_dtu		94.93 273	94.78 273	95.38 296	93.58 335	87.68 316	96.78 233	95.69 309	97.35 173	89.14 332	98.09 239	88.15 278	99.49 283	94.95 229	99.30 207	98.98 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		96.55 241	96.34 238	97.17 258	98.35 268	93.06 280	98.40 97	97.79 272	97.33 174	98.41 179	98.67 178	83.68 307	99.69 218	95.16 224	99.31 204	98.77 249

HPM-MVS_fast		99.01 36	98.82 48	99.57 18	99.71 30	99.35 8	99.00 51	99.50 55	97.33 174	98.94 123	98.86 147	98.75 24	99.82 132	97.53 105	99.71 110	99.56 67

XVG-OURS-SEG-HR		98.49 110	98.28 119	99.14 91	99.49 83	98.83 62	96.54 245	99.48 65	97.32 176	99.11 89	98.61 194	99.33 9	99.30 308	96.23 185	98.38 275	99.28 176

DeepC-MVS_fast	96.85 6	98.30 128	98.15 134	98.75 151	98.61 248	97.23 177	97.76 159	99.09 180	97.31 177	98.75 148	98.66 181	97.56 96	99.64 243	96.10 195	99.55 168	99.39 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Effi-MVS+		98.02 151	97.82 161	98.62 162	98.53 258	97.19 182	97.33 196	99.68 13	97.30 178	96.68 269	97.46 274	98.56 33	99.80 153	96.63 160	98.20 280	98.86 238

XVG-OURS		98.53 107	98.34 112	99.11 95	99.50 76	98.82 64	95.97 269	99.50 55	97.30 178	99.05 102	98.98 123	99.35 8	99.32 305	95.72 211	99.68 125	99.18 197

MDA-MVSNet_test_wron		97.60 185	97.66 172	97.41 251	99.04 169	93.09 279	95.27 297	98.42 254	97.26 180	98.88 132	98.95 130	95.43 203	99.73 203	97.02 130	98.72 262	99.41 130

miper_lstm_enhance		97.18 214	97.16 201	97.25 256	98.16 280	92.85 284	95.15 302	99.31 123	97.25 181	98.74 150	98.78 164	90.07 269	99.78 176	97.19 120	99.80 72	99.11 207

xiu_mvs_v2_base		97.16 216	97.49 183	96.17 283	98.54 256	92.46 288	95.45 294	98.84 223	97.25 181	97.48 237	96.49 295	98.31 46	99.90 44	96.34 181	98.68 266	96.15 322

PS-MVSNAJ		97.08 220	97.39 190	96.16 285	98.56 254	92.46 288	95.24 299	98.85 222	97.25 181	97.49 236	95.99 302	98.07 62	99.90 44	96.37 179	98.67 267	96.12 323

YYNet1		97.60 185	97.67 169	97.39 252	99.04 169	93.04 282	95.27 297	98.38 256	97.25 181	98.92 125	98.95 130	95.48 202	99.73 203	96.99 131	98.74 260	99.41 130

XVG-ACMP-BASELINE		98.56 98	98.34 112	99.22 83	99.54 67	98.59 78	97.71 163	99.46 73	97.25 181	98.98 113	98.99 119	97.54 97	99.84 106	95.88 201	99.74 97	99.23 185

CNVR-MVS		98.17 144	97.87 158	99.07 102	98.67 241	98.24 99	97.01 218	98.93 207	97.25 181	97.62 223	98.34 219	97.27 116	99.57 262	96.42 178	99.33 201	99.39 137

CANet_DTU		97.26 207	97.06 205	97.84 223	97.57 301	94.65 250	96.19 264	98.79 232	97.23 187	95.14 308	98.24 227	93.22 246	99.84 106	97.34 114	99.84 54	99.04 213

v1921920		98.54 105	98.60 76	98.38 192	99.20 133	95.76 222	97.56 180	99.36 101	97.23 187	99.38 51	99.17 83	96.02 177	99.84 106	99.57 7	99.90 43	99.54 78

MIMVSNet		96.62 240	96.25 243	97.71 231	99.04 169	94.66 249	99.16 39	96.92 293	97.23 187	97.87 207	99.10 96	86.11 289	99.65 241	91.65 297	99.21 220	98.82 241

FMVSNet5		96.01 253	95.20 265	98.41 189	97.53 304	96.10 212	98.74 66	99.50 55	97.22 190	98.03 202	99.04 108	69.80 335	99.88 61	97.27 117	99.71 110	99.25 181

thisisatest0530		95.27 267	94.45 275	97.74 230	99.19 134	94.37 254	97.86 147	90.20 331	97.17 191	98.22 188	97.65 261	73.53 333	99.90 44	96.90 139	99.35 198	98.95 225

v1240		98.55 102	98.62 71	98.32 196	99.22 128	95.58 224	97.51 187	99.45 76	97.16 192	99.45 42	99.24 71	96.12 174	99.85 90	99.60 5	99.88 47	99.55 75

ACMMP		98.75 68	98.50 85	99.52 39	99.56 61	99.16 34	98.87 60	99.37 97	97.16 192	98.82 141	99.01 116	97.71 83	99.87 75	96.29 183	99.69 120	99.54 78
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
v144192		98.54 105	98.57 78	98.45 186	99.21 130	95.98 215	97.63 171	99.36 101	97.15 194	99.32 64	99.18 79	95.84 190	99.84 106	99.50 11	99.91 39	99.54 78

CS-MVS		97.82 174	97.59 179	98.52 177	98.76 220	98.04 121	98.20 111	99.61 22	97.10 195	96.02 292	94.87 322	98.27 47	99.84 106	96.31 182	99.17 228	97.69 298

OPM-MVS		98.56 98	98.32 116	99.25 80	99.41 101	98.73 69	97.13 215	99.18 163	97.10 195	98.75 148	98.92 134	98.18 57	99.65 241	96.68 157	99.56 166	99.37 144

PGM-MVS		98.66 83	98.37 108	99.55 25	99.53 69	99.18 31	98.23 107	99.49 63	97.01 197	98.69 152	98.88 144	98.00 68	99.89 53	95.87 204	99.59 151	99.58 57

TSAR-MVS + MP.		98.63 88	98.49 88	99.06 107	99.64 46	97.90 137	98.51 88	98.94 205	96.96 198	99.24 75	98.89 143	97.83 77	99.81 144	96.88 141	99.49 186	99.48 106
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		98.75 68	98.48 89	99.57 18	99.58 50	99.29 14	97.82 152	99.25 143	96.94 199	98.78 144	99.12 93	98.02 66	99.84 106	97.13 126	99.67 131	99.59 51

CVMVSNet		96.25 250	97.21 199	93.38 315	99.10 154	80.56 335	97.20 207	98.19 264	96.94 199	99.00 110	99.02 112	89.50 274	99.80 153	96.36 180	99.59 151	99.78 13

CNLPA		97.17 215	96.71 221	98.55 173	98.56 254	98.05 120	96.33 256	98.93 207	96.91 201	97.06 251	97.39 277	94.38 229	99.45 291	91.66 296	99.18 227	98.14 278

DeepPCF-MVS	96.93 5	98.32 126	98.01 147	99.23 82	98.39 267	98.97 55	95.03 304	99.18 163	96.88 202	99.33 59	98.78 164	98.16 58	99.28 311	96.74 150	99.62 144	99.44 121

wuyk23d		96.06 252	97.62 176	91.38 318	98.65 247	98.57 80	98.85 63	96.95 291	96.86 203	99.90 4	99.16 85	99.18 12	98.40 329	89.23 313	99.77 85	77.18 332

AllTest		98.44 115	98.20 125	99.16 88	99.50 76	98.55 81	98.25 106	99.58 27	96.80 204	98.88 132	99.06 99	97.65 87	99.57 262	94.45 241	99.61 148	99.37 144

TestCases				99.16 88	99.50 76	98.55 81		99.58 27	96.80 204	98.88 132	99.06 99	97.65 87	99.57 262	94.45 241	99.61 148	99.37 144

HPM-MVS		98.79 61	98.53 80	99.59 17	99.65 43	99.29 14	99.16 39	99.43 84	96.74 206	98.61 160	98.38 215	98.62 29	99.87 75	96.47 173	99.67 131	99.59 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
plane_prior						97.65 158	97.07 216		96.72 207						99.36 196

BH-untuned		96.83 229	96.75 219	97.08 259	98.74 224	93.33 278	96.71 238	98.26 259	96.72 207	98.44 176	97.37 279	95.20 207	99.47 287	91.89 294	97.43 299	98.44 267

DI_MVS_plusplus_test		97.57 188	97.40 188	98.07 213	99.06 165	95.71 223	96.58 244	96.96 289	96.71 209	98.69 152	98.13 232	93.81 239	99.68 224	97.45 108	99.19 225	98.80 246

BH-RMVSNet		96.83 229	96.58 231	97.58 240	98.47 261	94.05 261	96.67 240	97.36 280	96.70 210	97.87 207	97.98 245	95.14 209	99.44 292	90.47 309	98.58 272	99.25 181

TAMVS		98.24 137	98.05 144	98.80 140	99.07 161	97.18 183	97.88 144	98.81 229	96.66 211	99.17 86	99.21 74	94.81 219	99.77 182	96.96 135	99.88 47	99.44 121

LPG-MVS_test		98.71 73	98.46 93	99.47 50	99.57 54	98.97 55	98.23 107	99.48 65	96.60 212	99.10 92	99.06 99	98.71 26	99.83 121	95.58 218	99.78 81	99.62 40

LGP-MVS_train				99.47 50	99.57 54	98.97 55		99.48 65	96.60 212	99.10 92	99.06 99	98.71 26	99.83 121	95.58 218	99.78 81	99.62 40

our_test_3		97.39 199	97.73 167	96.34 278	98.70 233	89.78 310	94.61 312	98.97 204	96.50 214	99.04 104	98.85 150	95.98 183	99.84 106	97.26 118	99.67 131	99.41 130

test_prior3		97.48 194	97.00 207	98.95 120	98.69 236	97.95 133	95.74 283	99.03 191	96.48 215	96.11 286	97.63 263	95.92 187	99.59 256	94.16 249	99.20 221	99.30 172

test_prior2							95.74 283		96.48 215	96.11 286	97.63 263	95.92 187		94.16 249	99.20 221

MG-MVS		96.77 233	96.61 229	97.26 255	98.31 271	93.06 280	95.93 274	98.12 265	96.45 217	97.92 204	98.73 170	93.77 242	99.39 297	91.19 303	99.04 245	99.33 163

MVP-Stereo		98.08 148	97.92 154	98.57 168	98.96 183	96.79 197	97.90 143	99.18 163	96.41 218	98.46 174	98.95 130	95.93 186	99.60 252	96.51 171	98.98 252	99.31 169
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ppachtmachnet_test		97.50 190	97.74 165	96.78 272	98.70 233	91.23 306	94.55 314	99.05 186	96.36 219	99.21 79	98.79 163	96.39 166	99.78 176	96.74 150	99.82 63	99.34 157

TSAR-MVS + GP.		98.18 142	97.98 149	98.77 147	98.71 229	97.88 138	96.32 257	98.66 244	96.33 220	99.23 78	98.51 202	97.48 105	99.40 295	97.16 122	99.46 187	99.02 216

testdata1							95.44 295		96.32 221

LF4IMVS		97.90 158	97.69 168	98.52 177	99.17 142	97.66 157	97.19 210	99.47 71	96.31 222	97.85 209	98.20 231	96.71 153	99.52 277	94.62 236	99.72 106	98.38 270

#test#		98.50 109	98.16 132	99.51 43	99.49 83	99.16 34	98.03 129	99.31 123	96.30 223	98.58 165	98.50 205	97.97 71	99.85 90	95.68 214	99.59 151	99.53 84

test-LLR		93.90 289	93.85 282	94.04 307	96.53 323	84.62 327	94.05 319	92.39 325	96.17 224	94.12 317	95.07 314	82.30 312	99.67 228	95.87 204	98.18 281	97.82 288

test0.0.03 1		94.51 276	93.69 285	96.99 262	96.05 329	93.61 277	94.97 305	93.49 320	96.17 224	97.57 229	94.88 320	82.30 312	99.01 322	93.60 268	94.17 328	98.37 272

Anonymous20231206		98.21 139	98.21 124	98.20 205	99.51 73	95.43 231	98.13 116	99.32 119	96.16 226	98.93 124	98.82 159	96.00 179	99.83 121	97.32 115	99.73 100	99.36 150

SCA		96.41 246	96.66 226	95.67 291	98.24 275	88.35 313	95.85 279	96.88 295	96.11 227	97.67 221	98.67 178	93.10 249	99.85 90	94.16 249	99.22 218	98.81 242

MS-PatchMatch		97.68 179	97.75 164	97.45 248	98.23 277	93.78 273	97.29 199	98.84 223	96.10 228	98.64 157	98.65 183	96.04 176	99.36 300	96.84 144	99.14 232	99.20 190

HQP-NCC					98.67 241		96.29 258		96.05 229	95.55 300

ACMP_Plane					98.67 241		96.29 258		96.05 229	95.55 300

HQP-MVS		97.00 224	96.49 234	98.55 173	98.67 241	96.79 197	96.29 258	99.04 189	96.05 229	95.55 300	96.84 289	93.84 237	99.54 271	92.82 282	99.26 214	99.32 165

PHI-MVS		98.29 131	97.95 151	99.34 68	98.44 264	99.16 34	98.12 118	99.38 93	96.01 232	98.06 199	98.43 211	97.80 81	99.67 228	95.69 213	99.58 157	99.20 190

MVE	83.40 22	92.50 300	91.92 302	94.25 306	98.83 211	91.64 296	92.71 327	83.52 336	95.92 233	86.46 335	95.46 312	95.20 207	95.40 333	80.51 329	98.64 268	95.73 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CDS-MVSNet		97.69 178	97.35 193	98.69 155	98.73 225	97.02 191	96.92 226	98.75 237	95.89 234	98.59 163	98.67 178	92.08 262	99.74 198	96.72 153	99.81 67	99.32 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
D2MVS		97.84 170	97.84 160	97.83 224	99.14 149	94.74 245	96.94 222	98.88 215	95.84 235	98.89 128	98.96 128	94.40 228	99.69 218	97.55 102	99.95 16	99.05 211

testtj		97.79 175	97.25 197	99.42 53	99.03 172	98.85 61	97.78 154	99.18 163	95.83 236	98.12 195	98.50 205	95.50 200	99.86 80	92.23 292	99.07 241	99.54 78

PAPM_NR		96.82 231	96.32 239	98.30 199	99.07 161	96.69 202	97.48 189	98.76 234	95.81 237	96.61 273	96.47 297	94.12 236	99.17 315	90.82 308	97.78 294	99.06 210

ACMP	95.32 15	98.41 117	98.09 139	99.36 60	99.51 73	98.79 65	97.68 166	99.38 93	95.76 238	98.81 143	98.82 159	98.36 43	99.82 132	94.75 232	99.77 85	99.48 106
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 153	97.63 175	99.10 97	99.24 123	98.17 106	96.89 229	98.73 240	95.66 239	97.92 204	97.70 259	97.17 123	99.66 236	96.18 190	99.23 217	99.47 113

AdaColmap		97.14 217	96.71 221	98.46 185	98.34 269	97.80 148	96.95 221	98.93 207	95.58 240	96.92 256	97.66 260	95.87 189	99.53 273	90.97 304	99.14 232	98.04 281

pmmvs-eth3d		98.47 112	98.34 112	98.86 133	99.30 115	97.76 150	97.16 213	99.28 133	95.54 241	99.42 46	99.19 77	97.27 116	99.63 244	97.89 85	99.97 12	99.20 190

9.14			97.78 162		99.07 161		97.53 184	99.32 119	95.53 242	98.54 170	98.70 173	97.58 94	99.76 187	94.32 248	99.46 187

GA-MVS		95.86 256	95.32 262	97.49 246	98.60 250	94.15 260	93.83 322	97.93 270	95.49 243	96.68 269	97.42 276	83.21 308	99.30 308	96.22 186	98.55 273	99.01 217

tpmvs		95.02 272	95.25 263	94.33 305	96.39 327	85.87 321	98.08 121	96.83 296	95.46 244	95.51 304	98.69 174	85.91 290	99.53 273	94.16 249	96.23 316	97.58 302

UnsupCasMVSNet_bld		97.30 204	96.92 210	98.45 186	99.28 117	96.78 200	96.20 263	99.27 137	95.42 245	98.28 186	98.30 224	93.16 247	99.71 212	94.99 227	97.37 300	98.87 237

PatchmatchNet		95.58 261	95.67 252	95.30 297	97.34 311	87.32 317	97.65 170	96.65 297	95.30 246	97.07 250	98.69 174	84.77 297	99.75 194	94.97 228	98.64 268	98.83 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		97.63 184	97.17 200	98.99 117	99.27 118	97.86 140	95.98 268	93.41 321	95.25 247	99.47 39	98.90 138	95.63 194	99.85 90	96.91 136	99.73 100	99.27 177

MVS-HIRNet		94.32 279	95.62 253	90.42 319	98.46 262	75.36 336	96.29 258	89.13 333	95.25 247	95.38 305	99.75 8	92.88 254	99.19 314	94.07 256	99.39 194	96.72 316

OMC-MVS		97.88 162	97.49 183	99.04 110	98.89 201	98.63 73	96.94 222	99.25 143	95.02 249	98.53 171	98.51 202	97.27 116	99.47 287	93.50 272	99.51 178	99.01 217

tpmrst		95.07 270	95.46 257	93.91 309	97.11 315	84.36 329	97.62 172	96.96 289	94.98 250	96.35 283	98.80 161	85.46 294	99.59 256	95.60 216	96.23 316	97.79 293

APD-MVS		98.10 146	97.67 169	99.42 53	99.11 152	98.93 59	97.76 159	99.28 133	94.97 251	98.72 151	98.77 166	97.04 127	99.85 90	93.79 264	99.54 169	99.49 100
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WTY-MVS		96.67 237	96.27 242	97.87 222	98.81 216	94.61 251	96.77 234	97.92 271	94.94 252	97.12 247	97.74 257	91.11 265	99.82 132	93.89 260	98.15 284	99.18 197

CPTT-MVS		97.84 170	97.36 192	99.27 76	99.31 113	98.46 89	98.29 102	99.27 137	94.90 253	97.83 210	98.37 216	94.90 213	99.84 106	93.85 263	99.54 169	99.51 91

MP-MVS-pluss		98.57 97	98.23 123	99.60 13	99.69 38	99.35 8	97.16 213	99.38 93	94.87 254	98.97 116	98.99 119	98.01 67	99.88 61	97.29 116	99.70 114	99.58 57
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Fast-Effi-MVS+		97.67 180	97.38 191	98.57 168	98.71 229	97.43 169	97.23 203	99.45 76	94.82 255	96.13 285	96.51 294	98.52 35	99.91 41	96.19 188	98.83 257	98.37 272

ET-MVSNet_ETH3D		94.30 281	93.21 291	97.58 240	98.14 281	94.47 253	94.78 309	93.24 323	94.72 256	89.56 331	95.87 305	78.57 326	99.81 144	96.91 136	97.11 307	98.46 264

EPMVS		93.72 292	93.27 290	95.09 299	96.04 330	87.76 315	98.13 116	85.01 335	94.69 257	96.92 256	98.64 186	78.47 328	99.31 306	95.04 225	96.46 314	98.20 275

PVSNet_BlendedMVS		97.55 189	97.53 180	97.60 238	98.92 192	93.77 274	96.64 241	99.43 84	94.49 258	97.62 223	99.18 79	96.82 142	99.67 228	94.73 233	99.93 25	99.36 150

sss		97.21 211	96.93 209	98.06 214	98.83 211	95.22 236	96.75 236	98.48 252	94.49 258	97.27 246	97.90 249	92.77 255	99.80 153	96.57 164	99.32 202	99.16 204

tpm		94.67 275	94.34 278	95.66 292	97.68 300	88.42 312	97.88 144	94.90 311	94.46 260	96.03 291	98.56 199	78.66 324	99.79 166	95.88 201	95.01 322	98.78 248

CLD-MVS		97.49 192	97.16 201	98.48 183	99.07 161	97.03 189	94.71 310	99.21 152	94.46 260	98.06 199	97.16 284	97.57 95	99.48 286	94.46 240	99.78 81	98.95 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TESTMET0.1,1		92.19 304	91.77 303	93.46 313	96.48 325	82.80 332	94.05 319	91.52 328	94.45 262	94.00 320	94.88 320	66.65 338	99.56 265	95.78 209	98.11 286	98.02 282

PVSNet_Blended_VisFu		98.17 144	98.15 134	98.22 204	99.73 24	95.15 238	97.36 195	99.68 13	94.45 262	98.99 111	99.27 66	96.87 138	99.94 21	97.13 126	99.91 39	99.57 62

MDTV_nov1_ep13			95.22 264		97.06 316	83.20 331	97.74 161	96.16 303	94.37 264	96.99 254	98.83 156	83.95 305	99.53 273	93.90 259	97.95 292

TR-MVS		95.55 262	95.12 267	96.86 271	97.54 303	93.94 265	96.49 248	96.53 300	94.36 265	97.03 253	96.61 293	94.26 232	99.16 316	86.91 318	96.31 315	97.47 306

PatchFormer-LS_test		94.08 286	93.91 281	94.59 303	96.93 317	86.86 319	97.55 182	96.57 299	94.27 266	94.38 314	93.64 329	80.96 314	99.59 256	96.44 177	94.48 326	97.31 308

jason		97.45 196	97.35 193	97.76 228	99.24 123	93.93 266	95.86 277	98.42 254	94.24 267	98.50 173	98.13 232	94.82 217	99.91 41	97.22 119	99.73 100	99.43 125
jason: jason.
HyFIR lowres test		97.19 213	96.60 230	98.96 119	99.62 49	97.28 175	95.17 300	99.50 55	94.21 268	99.01 108	98.32 222	86.61 283	99.99 2	97.10 128	99.84 54	99.60 45

SMA-MVS		98.40 119	98.03 146	99.51 43	99.16 144	99.21 23	98.05 126	99.22 151	94.16 269	98.98 113	99.10 96	97.52 101	99.79 166	96.45 175	99.64 139	99.53 84

thisisatest0515		94.12 285	93.16 292	96.97 263	98.60 250	92.90 283	93.77 323	90.61 329	94.10 270	96.91 258	95.87 305	74.99 331	99.80 153	94.52 238	99.12 238	98.20 275

USDC		97.41 198	97.40 188	97.44 249	98.94 186	93.67 276	95.17 300	99.53 49	94.03 271	98.97 116	99.10 96	95.29 205	99.34 302	95.84 207	99.73 100	99.30 172

test-mter		92.33 302	91.76 304	94.04 307	96.53 323	84.62 327	94.05 319	92.39 325	94.00 272	94.12 317	95.07 314	65.63 340	99.67 228	95.87 204	98.18 281	97.82 288

baseline2		93.73 291	92.83 296	96.42 277	97.70 298	91.28 304	96.84 231	89.77 332	93.96 273	92.44 326	95.93 303	79.14 323	99.77 182	92.94 278	96.76 312	98.21 274

pmmvs5		97.64 182	97.49 183	98.08 212	99.14 149	95.12 240	96.70 239	99.05 186	93.77 274	98.62 158	98.83 156	93.23 245	99.75 194	98.33 67	99.76 94	99.36 150

BH-w/o		95.13 269	94.89 272	95.86 287	98.20 278	91.31 302	95.65 286	97.37 279	93.64 275	96.52 276	95.70 307	93.04 252	99.02 320	88.10 315	95.82 318	97.24 309

pmmvs4		97.58 187	97.28 196	98.51 180	98.84 209	96.93 194	95.40 296	98.52 250	93.60 276	98.61 160	98.65 183	95.10 210	99.60 252	96.97 134	99.79 77	98.99 219

CHOSEN 280x420		95.51 264	95.47 256	95.65 293	98.25 274	88.27 314	93.25 325	98.88 215	93.53 277	94.65 311	97.15 285	86.17 287	99.93 25	97.41 111	99.93 25	98.73 252

lupinMVS		97.06 221	96.86 214	97.65 234	98.88 202	93.89 270	95.48 293	97.97 269	93.53 277	98.16 191	97.58 265	93.81 239	99.91 41	96.77 148	99.57 161	99.17 201

PatchMatch-RL		97.24 210	96.78 217	98.61 164	99.03 172	97.83 142	96.36 255	99.06 182	93.49 279	97.36 245	97.78 254	95.75 191	99.49 283	93.44 273	98.77 259	98.52 262

DP-MVS Recon		97.33 202	96.92 210	98.57 168	99.09 157	97.99 124	96.79 232	99.35 106	93.18 280	97.71 218	98.07 241	95.00 212	99.31 306	93.97 257	99.13 235	98.42 269

1112_ss		97.29 206	96.86 214	98.58 166	99.34 112	96.32 208	96.75 236	99.58 27	93.14 281	96.89 262	97.48 272	92.11 261	99.86 80	96.91 136	99.54 169	99.57 62

F-COLMAP		97.30 204	96.68 223	99.14 91	99.19 134	98.39 92	97.27 202	99.30 129	92.93 282	96.62 272	98.00 243	95.73 192	99.68 224	92.62 287	98.46 274	99.35 154

FPMVS		93.44 295	92.23 299	97.08 259	99.25 122	97.86 140	95.61 287	97.16 286	92.90 283	93.76 322	98.65 183	75.94 330	95.66 332	79.30 331	97.49 297	97.73 295

DSMNet-mixed		97.42 197	97.60 177	96.87 268	99.15 148	91.46 298	98.54 82	99.12 176	92.87 284	97.58 227	99.63 21	96.21 172	99.90 44	95.74 210	99.54 169	99.27 177

dp		93.47 294	93.59 287	93.13 317	96.64 322	81.62 334	97.66 168	96.42 301	92.80 285	96.11 286	98.64 186	78.55 327	99.59 256	93.31 275	92.18 331	98.16 277

PVSNet	93.40 17	95.67 259	95.70 250	95.57 294	98.83 211	88.57 311	92.50 328	97.72 274	92.69 286	96.49 280	96.44 298	93.72 243	99.43 293	93.61 267	99.28 210	98.71 253

new_pmnet		96.99 225	96.76 218	97.67 232	98.72 226	94.89 243	95.95 273	98.20 262	92.62 287	98.55 168	98.54 200	94.88 216	99.52 277	93.96 258	99.44 190	98.59 261

原ACMM1				98.35 194	98.90 196	96.25 210		98.83 228	92.48 288	96.07 289	98.10 237	95.39 204	99.71 212	92.61 288	98.99 250	99.08 208

IB-MVS	91.63 19	92.24 303	90.90 306	96.27 280	97.22 314	91.24 305	94.36 317	93.33 322	92.37 289	92.24 327	94.58 324	66.20 339	99.89 53	93.16 277	94.63 324	97.66 299
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
CR-MVSNet		96.28 249	95.95 246	97.28 253	97.71 296	94.22 256	98.11 119	98.92 210	92.31 290	96.91 258	99.37 53	85.44 295	99.81 144	97.39 112	97.36 302	97.81 290

HY-MVS	95.94 13	95.90 255	95.35 261	97.55 242	97.95 288	94.79 244	98.81 65	96.94 292	92.28 291	95.17 307	98.57 198	89.90 271	99.75 194	91.20 302	97.33 304	98.10 279

DWT-MVSNet_test		92.75 299	92.05 301	94.85 300	96.48 325	87.21 318	97.83 151	94.99 310	92.22 292	92.72 325	94.11 327	70.75 334	99.46 289	95.01 226	94.33 327	97.87 286

MAR-MVS		96.47 245	95.70 250	98.79 142	97.92 290	99.12 47	98.28 103	98.60 248	92.16 293	95.54 303	96.17 300	94.77 222	99.52 277	89.62 312	98.23 278	97.72 296
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		96.32 247	95.59 255	98.51 180	98.76 220	97.21 180	94.54 315	98.26 259	91.94 294	96.37 282	97.25 281	93.06 251	99.43 293	91.42 301	98.74 260	98.89 234

agg_prior1		97.06 221	96.40 236	99.03 111	98.68 239	97.99 124	95.76 281	99.01 198	91.73 295	95.59 296	97.50 270	96.49 161	99.77 182	93.71 265	99.14 232	99.34 157

train_agg		97.10 218	96.45 235	99.07 102	98.71 229	98.08 115	95.96 271	99.03 191	91.64 296	95.85 293	97.53 267	96.47 162	99.76 187	93.67 266	99.16 229	99.36 150

test_8					98.67 241	98.01 123	95.91 276	99.02 195	91.64 296	95.79 295	97.50 270	96.47 162	99.76 187

CHOSEN 1792x2688		97.49 192	97.14 204	98.54 176	99.68 39	96.09 214	96.50 247	99.62 20	91.58 298	98.84 138	98.97 125	92.36 258	99.88 61	96.76 149	99.95 16	99.67 32

PMMVS		96.51 242	95.98 245	98.09 209	97.53 304	95.84 219	94.92 306	98.84 223	91.58 298	96.05 290	95.58 308	95.68 193	99.66 236	95.59 217	98.09 287	98.76 250

Test_1112_low_res		96.99 225	96.55 232	98.31 198	99.35 110	95.47 229	95.84 280	99.53 49	91.51 300	96.80 267	98.48 210	91.36 264	99.83 121	96.58 162	99.53 173	99.62 40

TEST9					98.71 229	98.08 115	95.96 271	99.03 191	91.40 301	95.85 293	97.53 267	96.52 159	99.76 187

PAPR		95.29 266	94.47 274	97.75 229	97.50 308	95.14 239	94.89 307	98.71 242	91.39 302	95.35 306	95.48 311	94.57 224	99.14 318	84.95 321	97.37 300	98.97 224

1314		95.74 258	95.60 254	96.17 283	97.53 304	92.75 285	98.07 122	98.31 258	91.22 303	94.25 315	96.68 292	95.53 197	99.03 319	91.64 298	97.18 305	96.74 315

CDPH-MVS		97.26 207	96.66 226	99.07 102	99.00 176	98.15 107	96.03 267	99.01 198	91.21 304	97.79 214	97.85 252	96.89 137	99.69 218	92.75 285	99.38 195	99.39 137

PVSNet_Blended		96.88 227	96.68 223	97.47 247	98.92 192	93.77 274	94.71 310	99.43 84	90.98 305	97.62 223	97.36 280	96.82 142	99.67 228	94.73 233	99.56 166	98.98 220

PLC	94.65 16	96.51 242	95.73 249	98.85 134	98.75 223	97.91 136	96.42 252	99.06 182	90.94 306	95.59 296	97.38 278	94.41 227	99.59 256	90.93 305	98.04 291	99.05 211
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		95.43 265	94.98 269	96.76 273	98.14 281	91.74 295	97.92 140	97.76 273	90.23 307	96.51 277	98.91 135	85.61 292	99.85 90	92.88 280	96.90 308	98.69 256

ADS-MVSNet		95.24 268	94.93 271	96.18 282	98.14 281	90.10 309	97.92 140	97.32 283	90.23 307	96.51 277	98.91 135	85.61 292	99.74 198	92.88 280	96.90 308	98.69 256

QAPM		97.31 203	96.81 216	98.82 137	98.80 218	97.49 165	99.06 47	99.19 159	90.22 309	97.69 220	99.16 85	96.91 136	99.90 44	90.89 307	99.41 192	99.07 209

PVSNet_0	89.98 21	91.15 306	90.30 308	93.70 311	97.72 295	84.34 330	90.24 331	97.42 278	90.20 310	93.79 321	93.09 330	90.90 266	98.89 326	86.57 319	72.76 333	97.87 286

testdata				98.09 209	98.93 188	95.40 232		98.80 231	90.08 311	97.45 239	98.37 216	95.26 206	99.70 214	93.58 269	98.95 254	99.17 201

MDTV_nov1_ep13_2view						74.92 337	97.69 165		90.06 312	97.75 217		85.78 291		93.52 270		98.69 256

OpenMVS	96.65 7	97.09 219	96.68 223	98.32 196	98.32 270	97.16 185	98.86 62	99.37 97	89.48 313	96.29 284	99.15 89	96.56 157	99.90 44	92.90 279	99.20 221	97.89 284

无先验							95.74 283	98.74 239	89.38 314				99.73 203	92.38 290		99.22 189

CostFormer		93.97 288	93.78 284	94.51 304	97.53 304	85.83 323	97.98 136	95.96 306	89.29 315	94.99 310	98.63 190	78.63 325	99.62 246	94.54 237	96.50 313	98.09 280

CMPMVS	75.91 23	96.29 248	95.44 258	98.84 135	96.25 328	98.69 72	97.02 217	99.12 176	88.90 316	97.83 210	98.86 147	89.51 273	98.90 325	91.92 293	99.51 178	98.92 231
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		95.03 271	94.40 276	96.93 264	97.70 298	92.53 287	95.08 303	97.71 275	88.57 317	97.71 218	98.08 240	79.39 322	99.82 132	96.19 188	99.11 239	98.43 268

旧先验2							95.76 281		88.56 318	97.52 233			99.66 236	94.48 239

gm-plane-assit					94.83 333	81.97 333			88.07 319		94.99 317		99.60 252	91.76 295

1121		96.73 234	96.00 244	98.91 126	98.95 185	97.76 150	98.07 122	98.73 240	87.65 320	96.54 274	98.13 232	94.52 225	99.73 203	92.38 290	99.02 246	99.24 184

新几何1				98.91 126	98.94 186	97.76 150		98.76 234	87.58 321	96.75 268	98.10 237	94.80 220	99.78 176	92.73 286	99.00 249	99.20 190

PAPM		91.88 305	90.34 307	96.51 275	98.06 285	92.56 286	92.44 329	97.17 285	86.35 322	90.38 330	96.01 301	86.61 283	99.21 313	70.65 333	95.43 320	97.75 294

tpm2		93.09 298	92.58 298	94.62 302	97.56 302	86.53 320	97.66 168	95.79 308	86.15 323	94.07 319	98.23 229	75.95 329	99.53 273	90.91 306	96.86 311	97.81 290

test222					98.92 192	96.93 194	95.54 289	98.78 233	85.72 324	96.86 264	98.11 236	94.43 226			99.10 240	99.23 185

cascas		94.79 274	94.33 279	96.15 286	96.02 331	92.36 291	92.34 330	99.26 142	85.34 325	95.08 309	94.96 319	92.96 253	98.53 328	94.41 246	98.59 271	97.56 303

OpenMVS_ROB	95.38 14	95.84 257	95.18 266	97.81 225	98.41 266	97.15 186	97.37 194	98.62 247	83.86 326	98.65 156	98.37 216	94.29 231	99.68 224	88.41 314	98.62 270	96.60 317

TAPA-MVS	96.21 11	96.63 239	95.95 246	98.65 157	98.93 188	98.09 111	96.93 224	99.28 133	83.58 327	98.13 194	97.78 254	96.13 173	99.40 295	93.52 270	99.29 209	98.45 266
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm cat1		93.29 296	93.13 294	93.75 310	97.39 310	84.74 326	97.39 193	97.65 277	83.39 328	94.16 316	98.41 212	82.86 311	99.39 297	91.56 300	95.35 321	97.14 310

114514_t		96.50 244	95.77 248	98.69 155	99.48 88	97.43 169	97.84 150	99.55 44	81.42 329	96.51 277	98.58 197	95.53 197	99.67 228	93.41 274	99.58 157	98.98 220

PCF-MVS	92.86 18	94.36 278	93.00 295	98.42 188	98.70 233	97.56 162	93.16 326	99.11 178	79.59 330	97.55 230	97.43 275	92.19 259	99.73 203	79.85 330	99.45 189	97.97 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS		93.19 297	92.09 300	96.50 276	96.91 318	94.03 262	98.07 122	98.06 267	68.01 331	94.56 313	96.48 296	95.96 185	99.30 308	83.84 323	96.89 310	96.17 320

DeepMVS_CX				93.44 314	98.24 275	94.21 258		94.34 314	64.28 332	91.34 329	94.87 322	89.45 275	92.77 335	77.54 332	93.14 329	93.35 330

tmp_tt		78.77 307	78.73 309	78.90 320	58.45 337	74.76 338	94.20 318	78.26 339	39.16 333	86.71 334	92.82 331	80.50 316	75.19 336	86.16 320	92.29 330	86.74 331

test123		17.04 310	20.11 312	7.82 321	10.25 339	4.91 339	94.80 308	4.47 341	4.93 334	10.00 337	24.28 335	9.69 341	3.64 337	10.14 334	12.43 335	14.92 333

testmvs		17.12 309	20.53 311	6.87 322	12.05 338	4.20 340	93.62 324	6.73 340	4.62 335	10.41 336	24.33 334	8.28 342	3.56 338	9.69 335	15.07 334	12.86 334

cdsmvs_eth3d_5k		24.66 308	32.88 310	0.00 323	0.00 340	0.00 341	0.00 334	99.10 179	0.00 336	0.00 338	97.58 265	99.21 11	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		8.17 311	10.90 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	98.07 62	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		8.12 312	10.83 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	97.48 272	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save filter2										97.81 213	98.32 222	96.79 145	99.83 121	96.17 191	99.53 173	99.35 154

test_0728_SECOND				99.60 13	99.50 76	99.23 21	98.02 131	99.32 119					99.88 61	96.99 131	99.63 141	99.68 30

GSMVS																98.81 242

test_part2					99.36 106	99.10 50				99.05 102

test_part1				0.00 323		0.00 341	0.00 334	99.28 133				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												84.74 298				98.81 242

sam_mvs												84.29 304

ambc				98.24 203	98.82 214	95.97 216	98.62 73	99.00 201		99.27 69	99.21 74	96.99 132	99.50 282	96.55 168	99.50 185	99.26 180

MTGPA								99.20 154

test_post1							97.59 177				20.48 337	83.07 310	99.66 236	94.16 249

test_post											21.25 336	83.86 306	99.70 214

patchmatchnet-post											98.77 166	84.37 301	99.85 90

GG-mvs-BLEND				94.76 301	94.54 334	92.13 293	99.31 19	80.47 338		88.73 333	91.01 332	67.59 337	98.16 331	82.30 328	94.53 325	93.98 329

MTMP							97.93 139	91.91 327

test9_res														93.28 276	99.15 231	99.38 143

agg_prior2														92.50 289	99.16 229	99.37 144

agg_prior					98.68 239	97.99 124		99.01 198		95.59 296			99.77 182

test_prior4						97.97 129	95.86 277

test_prior				98.95 120	98.69 236	97.95 133		99.03 191					99.59 256			99.30 172

新几何2							95.93 274

旧先验1					98.82 214	97.45 168		98.76 234			98.34 219	95.50 200			99.01 248	99.23 185

原ACMM2							95.53 290

testdata2													99.79 166	92.80 284

segment_acmp												97.02 130

test12				98.93 123	98.58 252	97.83 142		98.66 244		96.53 275		95.51 199	99.69 218		99.13 235	99.27 177

plane_prior7					99.19 134	97.87 139

plane_prior6					98.99 179	97.70 156						94.90 213

plane_prior5								99.27 137					99.70 214	94.42 243	99.51 178	99.45 119

plane_prior4											97.98 245

plane_prior1					99.05 168

n2								0.00 342

nn								0.00 342

door-mid								99.57 34

lessismore_v0				98.97 118	99.73 24	97.53 164		86.71 334		99.37 53	99.52 34	89.93 270	99.92 31	98.99 33	99.72 106	99.44 121

test11								98.87 217

door								99.41 88

HQP5-MVS						96.79 197

BP-MVS														92.82 282

HQP4-MVS										95.56 299			99.54 271			99.32 165

HQP3-MVS								99.04 189							99.26 214

HQP2-MVS												93.84 237

NP-MVS					98.84 209	97.39 171					96.84 289

ACMMP++_ref															99.77 85

ACMMP++															99.68 125

Test By Simon												96.52 159

High-resolution multi-view benchmark