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.

tmp_tt		95.75 325	95.42 323	96.76 331	89.90 363	94.42 340	98.86 210	97.87 337	78.01 357	99.30 220	99.69 112	97.70 210	95.89 359	99.29 59	98.14 339	99.95 1

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 40	99.68 31	99.85 23	99.95 3	99.98 3	99.92 16	99.28 40	99.98 6	99.75 13	100.00 1	99.94 2

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 35	99.72 19	99.88 15	99.92 6	99.98 3	99.93 13	99.94 1	99.98 6	99.77 12	100.00 1	99.92 3

UA-Net		99.78 13	99.76 14	99.86 16	99.72 106	99.71 63	99.91 3	99.95 4	99.96 2	99.71 98	99.91 19	99.15 52	99.97 16	99.50 31	100.00 1	99.90 4

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 38	99.70 22	99.86 19	99.89 11	99.98 3	99.90 21	99.94 1	99.98 6	99.75 13	100.00 1	99.90 4

EU-MVSNet		99.39 77	99.62 25	98.72 281	99.88 23	96.44 320	99.56 61	99.85 23	99.90 7	99.90 22	99.85 36	98.09 182	99.83 231	99.58 22	99.95 48	99.90 4

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 17	99.77 11	99.80 46	99.73 38	99.97 6	99.92 16	99.77 7	99.98 6	99.43 36	100.00 1	99.90 4

RRT_test8_iter05		97.35 294	97.25 291	97.63 317	98.81 329	93.13 347	99.26 117	99.89 12	99.51 82	99.83 48	99.68 123	79.03 364	99.88 153	99.53 27	99.72 194	99.89 8

CVMVSNet		98.61 224	98.88 183	97.80 312	99.58 151	93.60 345	99.26 117	99.64 130	99.66 56	99.72 93	99.67 129	93.26 297	99.93 67	99.30 56	99.81 148	99.87 9

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 8	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

FC-MVSNet-test		99.70 19	99.65 22	99.86 16	99.88 23	99.86 11	99.72 19	99.78 57	99.90 7	99.82 50	99.83 42	98.45 147	99.87 166	99.51 29	99.97 29	99.86 11

PS-CasMVS		99.66 24	99.58 35	99.89 7	99.80 55	99.85 12	99.66 39	99.73 79	99.62 64	99.84 43	99.71 99	98.62 121	99.96 33	99.30 56	99.96 41	99.86 11

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 7	99.73 16	99.85 23	99.70 46	99.92 18	99.93 13	99.45 21	99.97 16	99.36 46	100.00 1	99.85 13

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 91	99.93 4	99.95 10	99.89 25	99.71 9	99.96 33	99.51 29	99.97 29	99.84 14

CP-MVSNet		99.54 45	99.43 60	99.87 14	99.76 83	99.82 25	99.57 59	99.61 142	99.54 78	99.80 59	99.64 140	97.79 207	99.95 43	99.21 64	99.94 61	99.84 14

Test_1112_low_res		98.95 187	98.73 197	99.63 110	99.68 128	99.15 200	98.09 288	99.80 46	97.14 303	99.46 180	99.40 237	96.11 270	99.89 138	99.01 93	99.84 121	99.84 14

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 44	100.00 1	99.90 7	100.00 1	99.97 9	99.61 16	99.97 16	99.75 13	100.00 1	99.84 14

nrg030		99.70 19	99.66 21	99.82 23	99.76 83	99.84 17	99.61 51	99.70 95	99.93 4	99.78 67	99.68 123	99.10 57	99.78 268	99.45 34	99.96 41	99.83 18

FIs		99.65 29	99.58 35	99.84 19	99.84 33	99.85 12	99.66 39	99.75 71	99.86 16	99.74 87	99.79 59	98.27 167	99.85 204	99.37 45	99.93 69	99.83 18

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 17	99.82 8	99.82 36	99.84 22	99.94 11	99.91 19	99.13 56	99.96 33	99.83 9	99.99 12	99.83 18

PEN-MVS		99.66 24	99.59 32	99.89 7	99.83 37	99.87 8	99.66 39	99.73 79	99.70 46	99.84 43	99.73 86	98.56 129	99.96 33	99.29 59	99.94 61	99.83 18

WR-MVS_H		99.61 35	99.53 47	99.87 14	99.80 55	99.83 21	99.67 35	99.75 71	99.58 77	99.85 40	99.69 112	98.18 178	99.94 54	99.28 61	99.95 48	99.83 18

test_part1		98.63 222	98.26 243	99.75 55	99.40 232	99.49 119	99.67 35	99.68 104	99.86 16	99.88 32	99.86 35	86.73 347	99.93 67	99.34 48	99.97 29	99.81 23

Anonymous20231211		99.62 33	99.57 38	99.76 45	99.61 144	99.60 102	99.81 9	99.73 79	99.82 27	99.90 22	99.90 21	97.97 193	99.86 186	99.42 40	99.96 41	99.80 24

APDe-MVS		99.48 52	99.36 72	99.85 18	99.55 172	99.81 28	99.50 65	99.69 101	98.99 159	99.75 79	99.71 99	98.79 98	99.93 67	98.46 135	99.85 117	99.80 24

DTE-MVSNet		99.68 22	99.61 29	99.88 11	99.80 55	99.87 8	99.67 35	99.71 91	99.72 41	99.84 43	99.78 65	98.67 115	99.97 16	99.30 56	99.95 48	99.80 24

XXY-MVS		99.71 18	99.67 20	99.81 26	99.89 21	99.72 61	99.59 56	99.82 36	99.39 104	99.82 50	99.84 41	99.38 27	99.91 104	99.38 43	99.93 69	99.80 24

1112_ss		99.05 165	98.84 188	99.67 87	99.66 134	99.29 170	98.52 252	99.82 36	97.65 277	99.43 186	99.16 288	96.42 261	99.91 104	99.07 89	99.84 121	99.80 24

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 15	99.90 4	99.96 1	99.92 5	99.90 7	99.97 6	99.87 30	99.81 5	99.95 43	99.54 25	99.99 12	99.80 24
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
bset_n11_16_dypcd		98.69 218	98.45 225	99.42 177	99.69 119	98.52 250	96.06 353	96.80 347	99.71 42	99.73 91	99.54 202	95.14 280	99.96 33	99.39 42	99.95 48	99.79 30

PMMVS2		99.48 52	99.45 55	99.57 135	99.76 83	98.99 215	98.09 288	99.90 11	98.95 165	99.78 67	99.58 183	99.57 19	99.93 67	99.48 32	99.95 48	99.79 30

CHOSEN 1792x2688		99.39 77	99.30 85	99.65 99	99.88 23	99.25 180	98.78 227	99.88 15	98.66 198	99.96 8	99.79 59	97.45 226	99.93 67	99.34 48	99.99 12	99.78 32

IU-MVS					99.69 119	99.77 40		99.22 285	97.50 285	99.69 103				97.75 196	99.70 200	99.77 33

test_0728_THIRD									99.18 133	99.62 130	99.61 168	98.58 126	99.91 104	97.72 198	99.80 153	99.77 33

test_0728_SECOND				99.83 21	99.70 116	99.79 35	99.14 156	99.61 142					99.92 86	97.88 183	99.72 194	99.77 33

MSP-MVS		99.04 168	98.79 195	99.81 26	99.78 71	99.73 57	99.35 92	99.57 171	98.54 212	99.54 160	98.99 310	96.81 252	99.93 67	96.97 251	99.53 252	99.77 33
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		99.14 146	98.92 177	99.82 23	99.57 161	99.77 40	98.74 230	99.60 153	98.55 209	99.76 74	99.69 112	98.23 172	99.92 86	96.39 283	99.75 173	99.76 37

Baseline_NR-MVSNet		99.49 50	99.37 69	99.82 23	99.91 15	99.84 17	98.83 215	99.86 19	99.68 50	99.65 117	99.88 28	97.67 215	99.87 166	99.03 91	99.86 114	99.76 37

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 21	99.83 6	99.85 23	99.80 31	99.93 14	99.93 13	98.54 132	99.93 67	99.59 20	99.98 21	99.76 37

test_241102_TWO								99.54 186	99.13 144	99.76 74	99.63 150	98.32 164	99.92 86	97.85 189	99.69 203	99.75 40

DP-MVS		99.48 52	99.39 64	99.74 61	99.57 161	99.62 94	99.29 112	99.61 142	99.87 14	99.74 87	99.76 75	98.69 111	99.87 166	98.20 156	99.80 153	99.75 40

v10		99.69 21	99.69 18	99.66 94	99.81 50	99.39 148	99.66 39	99.75 71	99.60 74	99.92 18	99.87 30	98.75 106	99.86 186	99.90 2	99.99 12	99.73 42

EI-MVSNet-UG-set		99.48 52	99.50 49	99.42 177	99.57 161	98.65 245	99.24 125	99.46 225	99.68 50	99.80 59	99.66 133	98.99 71	99.89 138	99.19 69	99.90 83	99.72 43

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 23	99.66 81	99.69 28	99.92 5	99.67 52	99.77 72	99.75 79	99.61 16	99.98 6	99.35 47	99.98 21	99.72 43
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HyFIR lowres test		98.91 190	98.64 205	99.73 69	99.85 32	99.47 123	98.07 291	99.83 31	98.64 200	99.89 26	99.60 175	92.57 303	100.00 1	99.33 51	99.97 29	99.72 43

EI-MVSNet-Vis-set		99.47 58	99.49 50	99.42 177	99.57 161	98.66 242	99.24 125	99.46 225	99.67 52	99.79 64	99.65 138	98.97 74	99.89 138	99.15 78	99.89 91	99.71 46

v8		99.68 22	99.69 18	99.65 99	99.80 55	99.40 146	99.66 39	99.76 65	99.64 60	99.93 14	99.85 36	98.66 117	99.84 220	99.88 6	99.99 12	99.71 46

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 12	99.75 14	99.86 19	99.70 46	99.91 20	99.89 25	99.60 18	99.87 166	99.59 20	99.74 181	99.71 46

VPA-MVSNet		99.66 24	99.62 25	99.79 34	99.68 128	99.75 48	99.62 47	99.69 101	99.85 20	99.80 59	99.81 51	98.81 91	99.91 104	99.47 33	99.88 99	99.70 49

WR-MVS		99.11 154	98.93 173	99.66 94	99.30 265	99.42 141	98.42 262	99.37 253	99.04 157	99.57 146	99.20 285	96.89 250	99.86 186	98.66 127	99.87 107	99.70 49

ACMH	98.42 6	99.59 36	99.54 43	99.72 75	99.86 29	99.62 94	99.56 61	99.79 52	98.77 190	99.80 59	99.85 36	99.64 13	99.85 204	98.70 123	99.89 91	99.70 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 4	99.83 6	99.91 8	99.85 20	99.94 11	99.95 11	99.73 8	99.90 124	99.65 16	99.97 29	99.69 52

HPM-MVS_fast		99.43 63	99.30 85	99.80 29	99.83 37	99.81 28	99.52 63	99.70 95	98.35 234	99.51 172	99.50 213	99.31 35	99.88 153	98.18 160	99.84 121	99.69 52

LPG-MVS_test		99.22 123	99.05 143	99.74 61	99.82 43	99.63 92	99.16 152	99.73 79	97.56 280	99.64 119	99.69 112	99.37 29	99.89 138	96.66 270	99.87 107	99.69 52

LGP-MVS_train				99.74 61	99.82 43	99.63 92		99.73 79	97.56 280	99.64 119	99.69 112	99.37 29	99.89 138	96.66 270	99.87 107	99.69 52

SteuartSystems-ACMMP		99.30 100	99.14 112	99.76 45	99.87 27	99.66 81	99.18 141	99.60 153	98.55 209	99.57 146	99.67 129	99.03 69	99.94 54	97.01 249	99.80 153	99.69 52
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		98.52 237	98.39 232	98.94 255	99.15 290	97.39 302	98.18 277	99.21 289	98.89 176	99.23 228	99.63 150	97.37 232	99.74 284	94.22 333	99.61 232	99.69 52

ACMMP_NAP		99.28 103	99.11 122	99.79 34	99.75 93	99.81 28	98.95 201	99.53 195	98.27 243	99.53 165	99.73 86	98.75 106	99.87 166	97.70 201	99.83 131	99.68 58

HFP-MVS		99.25 110	99.08 133	99.76 45	99.73 102	99.70 70	99.31 102	99.59 160	98.36 229	99.36 205	99.37 243	98.80 95	99.91 104	97.43 222	99.75 173	99.68 58

#test#		99.12 150	98.90 181	99.76 45	99.73 102	99.70 70	99.10 169	99.59 160	97.60 279	99.36 205	99.37 243	98.80 95	99.91 104	96.84 260	99.75 173	99.68 58

EI-MVSNet		99.38 79	99.44 57	99.21 229	99.58 151	98.09 277	99.26 117	99.46 225	99.62 64	99.75 79	99.67 129	98.54 132	99.85 204	99.15 78	99.92 73	99.68 58

TranMVSNet+NR-MVSNet		99.54 45	99.47 51	99.76 45	99.58 151	99.64 88	99.30 105	99.63 132	99.61 68	99.71 98	99.56 194	98.76 104	99.96 33	99.14 84	99.92 73	99.68 58

PVSNet_Blended_VisFu		99.40 73	99.38 66	99.44 172	99.90 19	98.66 242	98.94 203	99.91 8	97.97 260	99.79 64	99.73 86	99.05 67	99.97 16	99.15 78	99.99 12	99.68 58

IterMVS-LS		99.41 70	99.47 51	99.25 223	99.81 50	98.09 277	98.85 212	99.76 65	99.62 64	99.83 48	99.64 140	98.54 132	99.97 16	99.15 78	99.99 12	99.68 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MP-MVS-pluss		99.14 146	98.92 177	99.80 29	99.83 37	99.83 21	98.61 237	99.63 132	96.84 311	99.44 182	99.58 183	98.81 91	99.91 104	97.70 201	99.82 140	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		99.23 114	99.05 143	99.77 39	99.76 83	99.70 70	99.31 102	99.59 160	98.41 223	99.32 214	99.36 248	98.73 109	99.93 67	97.29 229	99.74 181	99.67 65

Regformer-4		99.45 61	99.44 57	99.50 155	99.52 183	98.94 221	99.17 146	99.53 195	99.64 60	99.76 74	99.60 175	98.96 77	99.90 124	98.91 107	99.84 121	99.67 65

XVS		99.27 107	99.11 122	99.75 55	99.71 109	99.71 63	99.37 88	99.61 142	99.29 115	98.76 287	99.47 225	98.47 143	99.88 153	97.62 209	99.73 188	99.67 65

v1240		99.56 40	99.58 35	99.51 152	99.80 55	99.00 214	99.00 188	99.65 124	99.15 142	99.90 22	99.75 79	99.09 59	99.88 153	99.90 2	99.96 41	99.67 65

X-MVStestdata		96.09 318	94.87 328	99.75 55	99.71 109	99.71 63	99.37 88	99.61 142	99.29 115	98.76 287	61.30 365	98.47 143	99.88 153	97.62 209	99.73 188	99.67 65

VPNet		99.46 59	99.37 69	99.71 79	99.82 43	99.59 105	99.48 69	99.70 95	99.81 28	99.69 103	99.58 183	97.66 219	99.86 186	99.17 74	99.44 265	99.67 65

ACMMPR		99.23 114	99.06 139	99.76 45	99.74 99	99.69 74	99.31 102	99.59 160	98.36 229	99.35 207	99.38 242	98.61 123	99.93 67	97.43 222	99.75 173	99.67 65

SixPastTwentyTwo		99.42 66	99.30 85	99.76 45	99.92 14	99.67 79	99.70 22	99.14 295	99.65 58	99.89 26	99.90 21	96.20 268	99.94 54	99.42 40	99.92 73	99.67 65

HPM-MVS		99.25 110	99.07 137	99.78 37	99.81 50	99.75 48	99.61 51	99.67 109	97.72 274	99.35 207	99.25 274	99.23 45	99.92 86	97.21 240	99.82 140	99.67 65
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v144192		99.55 43	99.54 43	99.58 130	99.78 71	99.20 195	99.11 168	99.62 135	99.18 133	99.89 26	99.72 92	98.66 117	99.87 166	99.88 6	99.97 29	99.66 75

v1921920		99.56 40	99.57 38	99.55 142	99.75 93	99.11 203	99.05 179	99.61 142	99.15 142	99.88 32	99.71 99	99.08 62	99.87 166	99.90 2	99.97 29	99.66 75

v1192		99.57 37	99.57 38	99.57 135	99.77 79	99.22 189	99.04 181	99.60 153	99.18 133	99.87 38	99.72 92	99.08 62	99.85 204	99.89 5	99.98 21	99.66 75

PGM-MVS		99.20 130	99.01 156	99.77 39	99.75 93	99.71 63	99.16 152	99.72 88	97.99 258	99.42 188	99.60 175	98.81 91	99.93 67	96.91 254	99.74 181	99.66 75

mPP-MVS		99.19 133	99.00 159	99.76 45	99.76 83	99.68 77	99.38 84	99.54 186	98.34 238	99.01 258	99.50 213	98.53 136	99.93 67	97.18 242	99.78 164	99.66 75

CP-MVS		99.23 114	99.05 143	99.75 55	99.66 134	99.66 81	99.38 84	99.62 135	98.38 227	99.06 256	99.27 269	98.79 98	99.94 54	97.51 218	99.82 140	99.66 75

EG-PatchMatch MVS		99.57 37	99.56 42	99.62 119	99.77 79	99.33 164	99.26 117	99.76 65	99.32 113	99.80 59	99.78 65	99.29 38	99.87 166	99.15 78	99.91 82	99.66 75

UGNet		99.38 79	99.34 74	99.49 158	98.90 316	98.90 229	99.70 22	99.35 257	99.86 16	98.57 301	99.81 51	98.50 142	99.93 67	99.38 43	99.98 21	99.66 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
TSAR-MVS + MP.		99.34 91	99.24 100	99.63 110	99.82 43	99.37 154	99.26 117	99.35 257	98.77 190	99.57 146	99.70 106	99.27 43	99.88 153	97.71 199	99.75 173	99.65 83
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		99.30 100	99.14 112	99.80 29	99.81 50	99.81 28	98.73 232	99.53 195	99.27 119	99.42 188	99.63 150	98.21 173	99.95 43	97.83 192	99.79 158	99.65 83

MTAPA		99.35 86	99.20 104	99.80 29	99.81 50	99.81 28	99.33 95	99.53 195	99.27 119	99.42 188	99.63 150	98.21 173	99.95 43	97.83 192	99.79 158	99.65 83

Regformer-3		99.41 70	99.41 62	99.40 187	99.52 183	98.70 239	99.17 146	99.44 230	99.62 64	99.75 79	99.60 175	98.90 84	99.85 204	98.89 108	99.84 121	99.65 83

MCST-MVS		99.02 171	98.81 192	99.65 99	99.58 151	99.49 119	98.58 241	99.07 298	98.40 225	99.04 257	99.25 274	98.51 141	99.80 262	97.31 228	99.51 255	99.65 83

UniMVSNet_NR-MVSNet		99.37 81	99.25 99	99.72 75	99.47 210	99.56 111	98.97 199	99.61 142	99.43 101	99.67 109	99.28 267	97.85 203	99.95 43	99.17 74	99.81 148	99.65 83

ZNCC-MVS		99.22 123	99.04 149	99.77 39	99.76 83	99.73 57	99.28 113	99.56 176	98.19 248	99.14 245	99.29 265	98.84 90	99.92 86	97.53 217	99.80 153	99.64 89

v1144		99.54 45	99.53 47	99.59 126	99.79 65	99.28 172	99.10 169	99.61 142	99.20 131	99.84 43	99.73 86	98.67 115	99.84 220	99.86 8	99.98 21	99.64 89

v2v482		99.50 48	99.47 51	99.58 130	99.78 71	99.25 180	99.14 156	99.58 169	99.25 123	99.81 56	99.62 159	98.24 169	99.84 220	99.83 9	99.97 29	99.64 89

K. test v3		98.87 198	98.60 208	99.69 84	99.93 13	99.46 127	99.74 15	94.97 354	99.78 34	99.88 32	99.88 28	93.66 295	99.97 16	99.61 18	99.95 48	99.64 89

DeepC-MVS	98.90 4	99.62 33	99.61 29	99.67 87	99.72 106	99.44 134	99.24 125	99.71 91	99.27 119	99.93 14	99.90 21	99.70 11	99.93 67	98.99 94	99.99 12	99.64 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		98.56 231	98.17 253	99.72 75	99.45 218	99.60 102	98.88 206	99.50 210	96.88 308	99.18 240	99.48 220	97.08 245	99.92 86	93.69 340	99.38 275	99.63 94

SMA-MVS		99.19 133	99.00 159	99.73 69	99.46 215	99.73 57	99.13 162	99.52 203	97.40 290	99.57 146	99.64 140	98.93 78	99.83 231	97.61 211	99.79 158	99.63 94
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
IterMVS-SCA-FT		99.00 177	99.16 108	98.51 288	99.75 93	95.90 328	98.07 291	99.84 29	99.84 22	99.89 26	99.73 86	96.01 272	99.99 4	99.33 51	100.00 1	99.63 94

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 21	99.76 13	99.87 17	99.73 38	99.89 26	99.87 30	99.63 14	99.87 166	99.54 25	99.92 73	99.63 94

MP-MVS		99.06 162	98.83 190	99.76 45	99.76 83	99.71 63	99.32 98	99.50 210	98.35 234	98.97 260	99.48 220	98.37 157	99.92 86	95.95 303	99.75 173	99.63 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DU-MVS		99.33 95	99.21 103	99.71 79	99.43 223	99.56 111	98.83 215	99.53 195	99.38 105	99.67 109	99.36 248	97.67 215	99.95 43	99.17 74	99.81 148	99.63 94

NR-MVSNet		99.40 73	99.31 80	99.68 85	99.43 223	99.55 114	99.73 16	99.50 210	99.46 94	99.88 32	99.36 248	97.54 223	99.87 166	98.97 98	99.87 107	99.63 94

IterMVS		98.97 181	99.16 108	98.42 292	99.74 99	95.64 331	98.06 293	99.83 31	99.83 25	99.85 40	99.74 82	96.10 271	99.99 4	99.27 62	100.00 1	99.63 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPP-MVSNet		99.17 141	99.00 159	99.66 94	99.80 55	99.43 138	99.70 22	99.24 283	99.48 85	99.56 153	99.77 72	94.89 282	99.93 67	98.72 122	99.89 91	99.63 94

ACMMP		99.25 110	99.08 133	99.74 61	99.79 65	99.68 77	99.50 65	99.65 124	98.07 254	99.52 167	99.69 112	98.57 127	99.92 86	97.18 242	99.79 158	99.63 94
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
DeepC-MVS_fast	98.47 5	99.23 114	99.12 119	99.56 139	99.28 269	99.22 189	98.99 193	99.40 243	99.08 150	99.58 143	99.64 140	98.90 84	99.83 231	97.44 221	99.75 173	99.63 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		99.16 142	98.96 170	99.75 55	99.73 102	99.73 57	99.20 135	99.55 181	98.22 245	99.32 214	99.35 253	98.65 119	99.91 104	96.86 257	99.74 181	99.62 105

new-patchmatchnet		99.35 86	99.57 38	98.71 283	99.82 43	96.62 318	98.55 247	99.75 71	99.50 83	99.88 32	99.87 30	99.31 35	99.88 153	99.43 36	100.00 1	99.62 105

RRT_MVS		98.75 210	98.54 218	99.41 185	98.14 354	98.61 246	98.98 197	99.66 113	99.31 114	99.84 43	99.75 79	91.98 307	99.98 6	99.20 67	99.95 48	99.62 105

CPTT-MVS		98.74 212	98.44 227	99.64 106	99.61 144	99.38 151	99.18 141	99.55 181	96.49 316	99.27 222	99.37 243	97.11 244	99.92 86	95.74 310	99.67 213	99.62 105

MIMVSNet1		99.66 24	99.62 25	99.80 29	99.94 10	99.87 8	99.69 28	99.77 60	99.78 34	99.93 14	99.89 25	97.94 194	99.92 86	99.65 16	99.98 21	99.62 105

DeepPCF-MVS	98.42 6	99.18 137	99.02 153	99.67 87	99.22 278	99.75 48	97.25 339	99.47 221	98.72 195	99.66 113	99.70 106	99.29 38	99.63 331	98.07 169	99.81 148	99.62 105

3Dnovator+	98.92 3	99.35 86	99.24 100	99.67 87	99.35 244	99.47 123	99.62 47	99.50 210	99.44 96	99.12 248	99.78 65	98.77 103	99.94 54	97.87 186	99.72 194	99.62 105

DVP-MVS		99.32 97	99.17 107	99.77 39	99.69 119	99.80 33	99.14 156	99.31 266	99.16 138	99.62 130	99.61 168	98.35 159	99.91 104	97.88 183	99.72 194	99.61 112
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
APD-MVS		98.87 198	98.59 210	99.71 79	99.50 194	99.62 94	99.01 186	99.57 171	96.80 313	99.54 160	99.63 150	98.29 165	99.91 104	95.24 320	99.71 198	99.61 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.82 203	98.57 214	99.58 130	99.21 280	99.31 167	98.61 237	99.25 280	98.65 199	98.43 309	99.26 272	97.86 201	99.81 257	96.55 274	99.27 292	99.61 112

TAMVS		99.49 50	99.45 55	99.63 110	99.48 205	99.42 141	99.45 72	99.57 171	99.66 56	99.78 67	99.83 42	97.85 203	99.86 186	99.44 35	99.96 41	99.61 112

Regformer-1		99.32 97	99.27 95	99.47 163	99.41 229	98.95 220	98.99 193	99.48 217	99.48 85	99.66 113	99.52 207	98.78 100	99.87 166	98.36 140	99.74 181	99.60 116

Regformer-2		99.34 91	99.27 95	99.53 148	99.41 229	99.10 207	98.99 193	99.53 195	99.47 90	99.66 113	99.52 207	98.80 95	99.89 138	98.31 146	99.74 181	99.60 116

HPM-MVS++		98.96 184	98.70 202	99.74 61	99.52 183	99.71 63	98.86 210	99.19 290	98.47 219	98.59 299	99.06 300	98.08 184	99.91 104	96.94 252	99.60 235	99.60 116

V42		99.56 40	99.54 43	99.63 110	99.79 65	99.46 127	99.39 82	99.59 160	99.24 125	99.86 39	99.70 106	98.55 130	99.82 241	99.79 11	99.95 48	99.60 116

HQP_MVS		98.90 192	98.68 204	99.55 142	99.58 151	99.24 185	98.80 223	99.54 186	98.94 166	99.14 245	99.25 274	97.24 236	99.82 241	95.84 306	99.78 164	99.60 116

plane_prior5								99.54 186					99.82 241	95.84 306	99.78 164	99.60 116

TDRefinement		99.72 17	99.70 17	99.77 39	99.90 19	99.85 12	99.86 5	99.92 5	99.69 49	99.78 67	99.92 16	99.37 29	99.88 153	98.93 106	99.95 48	99.60 116

ACMH+	98.40 8	99.50 48	99.43 60	99.71 79	99.86 29	99.76 46	99.32 98	99.77 60	99.53 80	99.77 72	99.76 75	99.26 44	99.78 268	97.77 194	99.88 99	99.60 116

ACMM	98.09 11	99.46 59	99.38 66	99.72 75	99.80 55	99.69 74	99.13 162	99.65 124	98.99 159	99.64 119	99.72 92	99.39 23	99.86 186	98.23 153	99.81 148	99.60 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		98.97 181	98.82 191	99.42 177	99.71 109	98.81 233	99.62 47	98.68 314	99.81 28	99.38 203	99.80 53	94.25 289	99.85 204	98.79 115	99.32 285	99.59 125

casdiffmvs		99.63 30	99.61 29	99.67 87	99.79 65	99.59 105	99.13 162	99.85 23	99.79 33	99.76 74	99.72 92	99.33 34	99.82 241	99.21 64	99.94 61	99.59 125

UniMVSNet (Re)		99.37 81	99.26 97	99.68 85	99.51 188	99.58 108	98.98 197	99.60 153	99.43 101	99.70 100	99.36 248	97.70 210	99.88 153	99.20 67	99.87 107	99.59 125

DSMNet-mixed		99.48 52	99.65 22	98.95 254	99.71 109	97.27 304	99.50 65	99.82 36	99.59 76	99.41 196	99.85 36	99.62 15	100.00 1	99.53 27	99.89 91	99.59 125

3Dnovator	99.15 2	99.43 63	99.36 72	99.65 99	99.39 234	99.42 141	99.70 22	99.56 176	99.23 127	99.35 207	99.80 53	99.17 50	99.95 43	98.21 155	99.84 121	99.59 125

SED-MVS		99.40 73	99.28 92	99.77 39	99.69 119	99.82 25	99.20 135	99.54 186	99.13 144	99.82 50	99.63 150	98.91 81	99.92 86	97.85 189	99.70 200	99.58 130

OPU-MVS				99.29 214	99.12 295	99.44 134	99.20 135				99.40 237	99.00 70	98.84 356	96.54 275	99.60 235	99.58 130

abl_6		99.36 84	99.23 102	99.75 55	99.71 109	99.74 54	99.33 95	99.76 65	99.07 152	99.65 117	99.63 150	99.09 59	99.92 86	97.13 245	99.76 170	99.58 130

EPNet		98.13 266	97.77 279	99.18 234	94.57 362	97.99 281	99.24 125	97.96 334	99.74 37	97.29 345	99.62 159	93.13 299	99.97 16	98.59 129	99.83 131	99.58 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.03 169	98.85 186	99.55 142	99.80 55	99.25 180	99.73 16	99.15 294	99.37 106	99.61 136	99.71 99	94.73 285	99.81 257	97.70 201	99.88 99	99.58 130

ACMP	97.51 14	99.05 165	98.84 188	99.67 87	99.78 71	99.55 114	98.88 206	99.66 113	97.11 305	99.47 177	99.60 175	99.07 64	99.89 138	96.18 292	99.85 117	99.58 130
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		99.23 114	99.05 143	99.74 61	99.52 183	99.75 48	99.20 135	99.61 142	98.97 161	99.48 175	99.58 183	98.41 151	99.91 104	97.15 244	99.55 244	99.57 136

SR-MVS		99.19 133	99.00 159	99.74 61	99.51 188	99.72 61	99.18 141	99.60 153	98.85 179	99.47 177	99.58 183	98.38 156	99.92 86	96.92 253	99.54 250	99.57 136

lessismore_v0				99.64 106	99.86 29	99.38 151		90.66 361		99.89 26	99.83 42	94.56 287	99.97 16	99.56 24	99.92 73	99.57 136

pmmvs5		99.19 133	99.11 122	99.42 177	99.76 83	98.88 230	98.55 247	99.73 79	98.82 183	99.72 93	99.62 159	96.56 255	99.82 241	99.32 53	99.95 48	99.56 139

APD-MVS_3200maxsize		99.31 99	99.16 108	99.74 61	99.53 178	99.75 48	99.27 116	99.61 142	99.19 132	99.57 146	99.64 140	98.76 104	99.90 124	97.29 229	99.62 225	99.56 139

CDPH-MVS		98.56 231	98.20 248	99.61 122	99.50 194	99.46 127	98.32 268	99.41 236	95.22 334	99.21 234	99.10 297	98.34 161	99.82 241	95.09 323	99.66 217	99.56 139

our_test_3		98.85 200	99.09 131	98.13 304	99.66 134	94.90 338	97.72 317	99.58 169	99.07 152	99.64 119	99.62 159	98.19 176	99.93 67	98.41 137	99.95 48	99.55 142

YYNet1		98.95 187	98.99 164	98.84 271	99.64 138	97.14 308	98.22 276	99.32 262	98.92 171	99.59 141	99.66 133	97.40 228	99.83 231	98.27 150	99.90 83	99.55 142

MDA-MVSNet_test_wron		98.95 187	98.99 164	98.85 269	99.64 138	97.16 307	98.23 275	99.33 260	98.93 169	99.56 153	99.66 133	97.39 230	99.83 231	98.29 148	99.88 99	99.55 142

MVSFormer		99.41 70	99.44 57	99.31 211	99.57 161	98.40 258	99.77 11	99.80 46	99.73 38	99.63 123	99.30 262	98.02 188	99.98 6	99.43 36	99.69 203	99.55 142

jason		99.16 142	99.11 122	99.32 208	99.75 93	98.44 255	98.26 273	99.39 246	98.70 196	99.74 87	99.30 262	98.54 132	99.97 16	98.48 134	99.82 140	99.55 142
jason: jason.
CDS-MVSNet		99.22 123	99.13 115	99.50 155	99.35 244	99.11 203	98.96 200	99.54 186	99.46 94	99.61 136	99.70 106	96.31 265	99.83 231	99.34 48	99.88 99	99.55 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
COLMAP_ROB	98.06 12	99.45 61	99.37 69	99.70 83	99.83 37	99.70 70	99.38 84	99.78 57	99.53 80	99.67 109	99.78 65	99.19 48	99.86 186	97.32 227	99.87 107	99.55 142
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS-dyc-post		99.27 107	99.11 122	99.73 69	99.54 173	99.74 54	99.26 117	99.62 135	99.16 138	99.52 167	99.64 140	98.41 151	99.91 104	97.27 232	99.61 232	99.54 149

RE-MVS-def			99.13 115		99.54 173	99.74 54	99.26 117	99.62 135	99.16 138	99.52 167	99.64 140	98.57 127		97.27 232	99.61 232	99.54 149

SD-MVS		99.01 175	99.30 85	98.15 303	99.50 194	99.40 146	98.94 203	99.61 142	99.22 130	99.75 79	99.82 48	99.54 20	95.51 360	97.48 219	99.87 107	99.54 149
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
CNVR-MVS		98.99 180	98.80 194	99.56 139	99.25 274	99.43 138	98.54 250	99.27 275	98.58 206	98.80 282	99.43 233	98.53 136	99.70 295	97.22 239	99.59 237	99.54 149

MVS_111021_HR		99.12 150	99.02 153	99.40 187	99.50 194	99.11 203	97.92 309	99.71 91	98.76 193	99.08 252	99.47 225	99.17 50	99.54 341	97.85 189	99.76 170	99.54 149

v148		99.40 73	99.41 62	99.39 190	99.76 83	98.94 221	99.09 173	99.59 160	99.17 136	99.81 56	99.61 168	98.41 151	99.69 301	99.32 53	99.94 61	99.53 154

diffmvs		99.34 91	99.32 79	99.39 190	99.67 133	98.77 236	98.57 245	99.81 45	99.61 68	99.48 175	99.41 235	98.47 143	99.86 186	98.97 98	99.90 83	99.53 154

baseline		99.63 30	99.62 25	99.66 94	99.80 55	99.62 94	99.44 75	99.80 46	99.71 42	99.72 93	99.69 112	99.15 52	99.83 231	99.32 53	99.94 61	99.53 154

HQP4-MVS										98.15 319			99.70 295			99.53 154

GBi-Net		99.42 66	99.31 80	99.73 69	99.49 199	99.77 40	99.68 31	99.70 95	99.44 96	99.62 130	99.83 42	97.21 238	99.90 124	98.96 100	99.90 83	99.53 154

test1		99.42 66	99.31 80	99.73 69	99.49 199	99.77 40	99.68 31	99.70 95	99.44 96	99.62 130	99.83 42	97.21 238	99.90 124	98.96 100	99.90 83	99.53 154

FMVSNet1		99.66 24	99.63 24	99.73 69	99.78 71	99.77 40	99.68 31	99.70 95	99.67 52	99.82 50	99.83 42	98.98 72	99.90 124	99.24 63	99.97 29	99.53 154

HQP-MVS		98.36 252	98.02 260	99.39 190	99.31 261	98.94 221	97.98 301	99.37 253	97.45 287	98.15 319	98.83 328	96.67 253	99.70 295	94.73 326	99.67 213	99.53 154

QAPM		98.40 250	97.99 261	99.65 99	99.39 234	99.47 123	99.67 35	99.52 203	91.70 350	98.78 285	99.80 53	98.55 130	99.95 43	94.71 328	99.75 173	99.53 154

F-COLMAP		98.74 212	98.45 225	99.62 119	99.57 161	99.47 123	98.84 213	99.65 124	96.31 320	98.93 264	99.19 287	97.68 214	99.87 166	96.52 276	99.37 279	99.53 154

MVSTER		98.47 243	98.22 246	99.24 226	99.06 305	98.35 263	99.08 176	99.46 225	99.27 119	99.75 79	99.66 133	88.61 337	99.85 204	99.14 84	99.92 73	99.52 164

PVSNet_BlendedMVS		99.03 169	99.01 156	99.09 242	99.54 173	97.99 281	98.58 241	99.82 36	97.62 278	99.34 210	99.71 99	98.52 139	99.77 276	97.98 175	99.97 29	99.52 164

OPM-MVS		99.26 109	99.13 115	99.63 110	99.70 116	99.61 100	98.58 241	99.48 217	98.50 215	99.52 167	99.63 150	99.14 54	99.76 278	97.89 182	99.77 168	99.51 166

agg_prior1		98.33 257	97.92 271	99.57 135	99.35 244	99.36 157	97.99 300	99.39 246	94.85 341	97.76 340	98.98 313	98.03 186	99.85 204	95.49 314	99.44 265	99.51 166

AllTest		99.21 128	99.07 137	99.63 110	99.78 71	99.64 88	99.12 166	99.83 31	98.63 201	99.63 123	99.72 92	98.68 112	99.75 282	96.38 284	99.83 131	99.51 166

TestCases				99.63 110	99.78 71	99.64 88		99.83 31	98.63 201	99.63 123	99.72 92	98.68 112	99.75 282	96.38 284	99.83 131	99.51 166

BH-RMVSNet		98.41 248	98.14 255	99.21 229	99.21 280	98.47 252	98.60 239	98.26 331	98.35 234	98.93 264	99.31 260	97.20 241	99.66 321	94.32 331	99.10 300	99.51 166

USDC		98.96 184	98.93 173	99.05 248	99.54 173	97.99 281	97.07 345	99.80 46	98.21 246	99.75 79	99.77 72	98.43 148	99.64 330	97.90 181	99.88 99	99.51 166

test9_res														95.10 322	99.44 265	99.50 172

train_agg		98.35 255	97.95 265	99.57 135	99.35 244	99.35 161	98.11 286	99.41 236	94.90 338	97.92 331	98.99 310	98.02 188	99.85 204	95.38 318	99.44 265	99.50 172

agg_prior2														94.58 330	99.46 264	99.50 172

VDD-MVS		99.20 130	99.11 122	99.44 172	99.43 223	98.98 216	99.50 65	98.32 330	99.80 31	99.56 153	99.69 112	96.99 248	99.85 204	98.99 94	99.73 188	99.50 172

MDA-MVSNet-bldmvs		99.06 162	99.05 143	99.07 246	99.80 55	97.83 288	98.89 205	99.72 88	99.29 115	99.63 123	99.70 106	96.47 259	99.89 138	98.17 162	99.82 140	99.50 172

DIV-MVS_2432*1600		99.63 30	99.59 32	99.76 45	99.84 33	99.90 4	99.37 88	99.79 52	99.83 25	99.88 32	99.85 36	98.42 150	99.90 124	99.60 19	99.73 188	99.49 177

xxxxxxxxxxxxxcwj		99.11 154	98.96 170	99.54 146	99.53 178	99.25 180	98.29 270	99.76 65	99.07 152	99.42 188	99.61 168	98.86 87	99.87 166	96.45 281	99.68 206	99.49 177

SF-MVS		99.10 158	98.93 173	99.62 119	99.58 151	99.51 117	99.13 162	99.65 124	97.97 260	99.42 188	99.61 168	98.86 87	99.87 166	96.45 281	99.68 206	99.49 177

Anonymous20240529		99.42 66	99.34 74	99.65 99	99.53 178	99.60 102	99.63 46	99.39 246	99.47 90	99.76 74	99.78 65	98.13 180	99.86 186	98.70 123	99.68 206	99.49 177

WTY-MVS		98.59 228	98.37 234	99.26 220	99.43 223	98.40 258	98.74 230	99.13 297	98.10 251	99.21 234	99.24 279	94.82 283	99.90 124	97.86 187	98.77 317	99.49 177

ppachtmachnet_test		98.89 195	99.12 119	98.20 302	99.66 134	95.24 335	97.63 321	99.68 104	99.08 150	99.78 67	99.62 159	98.65 119	99.88 153	98.02 170	99.96 41	99.48 182

Anonymous20231206		99.35 86	99.31 80	99.47 163	99.74 99	99.06 213	99.28 113	99.74 76	99.23 127	99.72 93	99.53 205	97.63 221	99.88 153	99.11 86	99.84 121	99.48 182

test_prior3		98.62 223	98.34 238	99.46 166	99.35 244	99.22 189	97.95 305	99.39 246	97.87 267	98.05 325	99.05 301	97.90 197	99.69 301	95.99 299	99.49 259	99.48 182

test_prior				99.46 166	99.35 244	99.22 189		99.39 246					99.69 301			99.48 182

test12				99.54 146	99.29 267	99.33 164		99.16 293		98.43 309		97.54 223	99.82 241		99.47 262	99.48 182

VNet		99.18 137	99.06 139	99.56 139	99.24 276	99.36 157	99.33 95	99.31 266	99.67 52	99.47 177	99.57 191	96.48 258	99.84 220	99.15 78	99.30 287	99.47 187

test20.03		99.55 43	99.54 43	99.58 130	99.79 65	99.37 154	99.02 184	99.89 12	99.60 74	99.82 50	99.62 159	98.81 91	99.89 138	99.43 36	99.86 114	99.47 187

114514_t		98.49 241	98.11 256	99.64 106	99.73 102	99.58 108	99.24 125	99.76 65	89.94 353	99.42 188	99.56 194	97.76 209	99.86 186	97.74 197	99.82 140	99.47 187

sss		98.90 192	98.77 196	99.27 218	99.48 205	98.44 255	98.72 233	99.32 262	97.94 264	99.37 204	99.35 253	96.31 265	99.91 104	98.85 110	99.63 224	99.47 187

旧先验1					99.49 199	99.29 170		99.26 277			99.39 241	97.67 215			99.36 280	99.46 191

1121		98.56 231	98.24 244	99.52 149	99.49 199	99.24 185	99.30 105	99.22 285	95.77 327	98.52 304	99.29 265	97.39 230	99.85 204	95.79 308	99.34 282	99.46 191

MVP-Stereo		99.16 142	99.08 133	99.43 175	99.48 205	99.07 211	99.08 176	99.55 181	98.63 201	99.31 216	99.68 123	98.19 176	99.78 268	98.18 160	99.58 238	99.45 193
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				99.52 149	99.50 194	99.22 189		99.26 277	95.66 330	98.60 298	99.28 267	97.67 215	99.89 138	95.95 303	99.32 285	99.45 193

LFMVS		98.46 244	98.19 251	99.26 220	99.24 276	98.52 250	99.62 47	96.94 346	99.87 14	99.31 216	99.58 183	91.04 318	99.81 257	98.68 126	99.42 270	99.45 193

testgi		99.29 102	99.26 97	99.37 197	99.75 93	98.81 233	98.84 213	99.89 12	98.38 227	99.75 79	99.04 304	99.36 32	99.86 186	99.08 88	99.25 293	99.45 193

UnsupCasMVSNet_eth		98.83 201	98.57 214	99.59 126	99.68 128	99.45 132	98.99 193	99.67 109	99.48 85	99.55 158	99.36 248	94.92 281	99.86 186	98.95 104	96.57 352	99.45 193

无先验							98.01 296	99.23 284	95.83 326				99.85 204	95.79 308		99.44 198

testdata				99.42 177	99.51 188	98.93 225		99.30 269	96.20 321	98.87 274	99.40 237	98.33 163	99.89 138	96.29 287	99.28 289	99.44 198

XVG-OURS-SEG-HR		99.16 142	98.99 164	99.66 94	99.84 33	99.64 88	98.25 274	99.73 79	98.39 226	99.63 123	99.43 233	99.70 11	99.90 124	97.34 226	98.64 325	99.44 198

FMVSNet2		99.35 86	99.28 92	99.55 142	99.49 199	99.35 161	99.45 72	99.57 171	99.44 96	99.70 100	99.74 82	97.21 238	99.87 166	99.03 91	99.94 61	99.44 198

N_pmnet		98.73 214	98.53 220	99.35 201	99.72 106	98.67 241	98.34 265	94.65 355	98.35 234	99.79 64	99.68 123	98.03 186	99.93 67	98.28 149	99.92 73	99.44 198

RPSCF		99.18 137	99.02 153	99.64 106	99.83 37	99.85 12	99.44 75	99.82 36	98.33 239	99.50 173	99.78 65	97.90 197	99.65 328	96.78 263	99.83 131	99.44 198

原ACMM1				99.37 197	99.47 210	98.87 232		99.27 275	96.74 314	98.26 314	99.32 258	97.93 195	99.82 241	95.96 302	99.38 275	99.43 204

test222					99.51 188	99.08 210	97.83 314	99.29 271	95.21 335	98.68 293	99.31 260	97.28 235			99.38 275	99.43 204

XVG-OURS		99.21 128	99.06 139	99.65 99	99.82 43	99.62 94	97.87 312	99.74 76	98.36 229	99.66 113	99.68 123	99.71 9	99.90 124	96.84 260	99.88 99	99.43 204

CSCG		99.37 81	99.29 90	99.60 124	99.71 109	99.46 127	99.43 77	99.85 23	98.79 187	99.41 196	99.60 175	98.92 79	99.92 86	98.02 170	99.92 73	99.43 204

ETH3D-3000-0.1		98.77 207	98.50 222	99.59 126	99.47 210	99.53 116	98.77 228	99.60 153	97.33 294	99.23 228	99.50 213	97.91 196	99.83 231	95.02 324	99.67 213	99.41 208

TinyColmap		98.97 181	98.93 173	99.07 246	99.46 215	98.19 269	97.75 316	99.75 71	98.79 187	99.54 160	99.70 106	98.97 74	99.62 332	96.63 272	99.83 131	99.41 208

ETH3 D test6400		97.76 279	97.19 294	99.50 155	99.38 237	99.26 176	98.34 265	99.49 215	92.99 347	98.54 303	99.20 285	95.92 274	99.82 241	91.14 347	99.66 217	99.40 210

Anonymous202405211		98.75 210	98.46 224	99.63 110	99.34 254	99.66 81	99.47 71	97.65 339	99.28 118	99.56 153	99.50 213	93.15 298	99.84 220	98.62 128	99.58 238	99.40 210

XVG-ACMP-BASELINE		99.23 114	99.10 130	99.63 110	99.82 43	99.58 108	98.83 215	99.72 88	98.36 229	99.60 138	99.71 99	98.92 79	99.91 104	97.08 247	99.84 121	99.40 210

MS-PatchMatch		99.00 177	98.97 168	99.09 242	99.11 300	98.19 269	98.76 229	99.33 260	98.49 217	99.44 182	99.58 183	98.21 173	99.69 301	98.20 156	99.62 225	99.39 213

FMVSNet3		98.80 205	98.63 207	99.32 208	99.13 293	98.72 238	99.10 169	99.48 217	99.23 127	99.62 130	99.64 140	92.57 303	99.86 186	98.96 100	99.90 83	99.39 213

ambc				99.20 231	99.35 244	98.53 248	99.17 146	99.46 225		99.67 109	99.80 53	98.46 146	99.70 295	97.92 180	99.70 200	99.38 215

FMVSNet5		97.80 277	97.25 291	99.42 177	98.83 325	98.97 218	99.38 84	99.80 46	98.87 177	99.25 224	99.69 112	80.60 359	99.91 104	98.96 100	99.90 83	99.38 215

PAPM_NR		98.36 252	98.04 259	99.33 204	99.48 205	98.93 225	98.79 226	99.28 274	97.54 282	98.56 302	98.57 338	97.12 243	99.69 301	94.09 335	98.90 312	99.38 215

EPNet_dtu		97.62 284	97.79 278	97.11 330	96.67 359	92.31 351	98.51 253	98.04 332	99.24 125	95.77 355	99.47 225	93.78 294	99.66 321	98.98 96	99.62 225	99.37 218
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		99.11 154	98.95 172	99.59 126	99.13 293	99.59 105	99.17 146	99.65 124	97.88 266	99.25 224	99.46 228	98.97 74	99.80 262	97.26 234	99.82 140	99.37 218

PLC	97.35 16	98.36 252	97.99 261	99.48 161	99.32 260	99.24 185	98.50 254	99.51 206	95.19 336	98.58 300	98.96 318	96.95 249	99.83 231	95.63 311	99.25 293	99.37 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		97.62 284	97.20 293	98.90 267	99.76 83	97.40 301	99.48 69	94.36 356	99.06 156	99.70 100	99.49 218	84.55 353	99.94 54	98.73 121	99.65 220	99.36 221

pmmvs-eth3d		99.48 52	99.47 51	99.51 152	99.77 79	99.41 145	98.81 220	99.66 113	99.42 103	99.75 79	99.66 133	99.20 47	99.76 278	98.98 96	99.99 12	99.36 221

PVSNet_0	95.53 19	95.85 324	95.31 326	97.47 320	98.78 333	93.48 346	95.72 354	99.40 243	96.18 322	97.37 343	97.73 354	95.73 275	99.58 339	95.49 314	81.40 358	99.36 221

lupinMVS		98.96 184	98.87 184	99.24 226	99.57 161	98.40 258	98.12 284	99.18 291	98.28 242	99.63 123	99.13 290	98.02 188	99.97 16	98.22 154	99.69 203	99.35 224

Vis-MVSNet (Re-imp)		98.77 207	98.58 213	99.34 202	99.78 71	98.88 230	99.61 51	99.56 176	99.11 148	99.24 227	99.56 194	93.00 301	99.78 268	97.43 222	99.89 91	99.35 224

GA-MVS		97.99 274	97.68 282	98.93 258	99.52 183	98.04 280	97.19 341	99.05 301	98.32 240	98.81 280	98.97 316	89.89 334	99.41 351	98.33 144	99.05 302	99.34 226

CANet		99.11 154	99.05 143	99.28 216	98.83 325	98.56 247	98.71 235	99.41 236	99.25 123	99.23 228	99.22 281	97.66 219	99.94 54	99.19 69	99.97 29	99.33 227

Patchmtry		98.78 206	98.54 218	99.49 158	98.89 319	99.19 196	99.32 98	99.67 109	99.65 58	99.72 93	99.79 59	91.87 310	99.95 43	98.00 174	99.97 29	99.33 227

PAPR		97.56 287	97.07 296	99.04 249	98.80 330	98.11 275	97.63 321	99.25 280	94.56 344	98.02 329	98.25 349	97.43 227	99.68 312	90.90 348	98.74 321	99.33 227

CHOSEN 280x420		98.41 248	98.41 230	98.40 293	99.34 254	95.89 329	96.94 347	99.44 230	98.80 186	99.25 224	99.52 207	93.51 296	99.98 6	98.94 105	99.98 21	99.32 230

baseline1		97.73 280	97.33 288	98.96 253	99.30 265	97.73 292	99.40 80	98.42 326	99.33 112	99.46 180	99.21 283	91.18 316	99.82 241	98.35 142	91.26 357	99.32 230

TAPA-MVS	97.92 13	98.03 271	97.55 285	99.46 166	99.47 210	99.44 134	98.50 254	99.62 135	86.79 354	99.07 255	99.26 272	98.26 168	99.62 332	97.28 231	99.73 188	99.31 232
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re		99.28 103	99.15 111	99.67 87	99.33 259	99.76 46	99.34 93	99.97 2	98.93 169	99.91 20	99.79 59	98.68 112	99.93 67	96.80 262	99.56 240	99.30 233

TSAR-MVS + GP.		99.12 150	99.04 149	99.38 194	99.34 254	99.16 198	98.15 280	99.29 271	98.18 249	99.63 123	99.62 159	99.18 49	99.68 312	98.20 156	99.74 181	99.30 233

PVSNet_Blended		98.70 217	98.59 210	99.02 250	99.54 173	97.99 281	97.58 324	99.82 36	95.70 329	99.34 210	98.98 313	98.52 139	99.77 276	97.98 175	99.83 131	99.30 233

MVS_0304		98.88 196	98.71 199	99.39 190	98.85 323	98.91 228	99.45 72	99.30 269	98.56 207	97.26 346	99.68 123	96.18 269	99.96 33	99.17 74	99.94 61	99.29 236

MVS_111021_LR		99.13 148	99.03 151	99.42 177	99.58 151	99.32 166	97.91 311	99.73 79	98.68 197	99.31 216	99.48 220	99.09 59	99.66 321	97.70 201	99.77 168	99.29 236

ETH3D cwj APD-0.16		98.50 238	98.16 254	99.51 152	99.04 308	99.39 148	98.47 256	99.47 221	96.70 315	98.78 285	99.33 257	97.62 222	99.86 186	94.69 329	99.38 275	99.28 238

miper_lstm_enhance		98.65 221	98.60 208	98.82 276	99.20 283	97.33 303	97.78 315	99.66 113	99.01 158	99.59 141	99.50 213	94.62 286	99.85 204	98.12 165	99.90 83	99.26 239

MVS		95.72 326	94.63 330	98.99 251	98.56 341	97.98 286	99.30 105	98.86 306	72.71 359	97.30 344	99.08 298	98.34 161	99.74 284	89.21 349	98.33 333	99.26 239

MSLP-MVS++		99.05 165	99.09 131	98.91 261	99.21 280	98.36 262	98.82 219	99.47 221	98.85 179	98.90 270	99.56 194	98.78 100	99.09 354	98.57 130	99.68 206	99.26 239

D2MVS		99.22 123	99.19 105	99.29 214	99.69 119	98.74 237	98.81 220	99.41 236	98.55 209	99.68 105	99.69 112	98.13 180	99.87 166	98.82 113	99.98 21	99.24 242

test_yl		98.25 260	97.95 265	99.13 238	99.17 288	98.47 252	99.00 188	98.67 316	98.97 161	99.22 232	99.02 308	91.31 314	99.69 301	97.26 234	98.93 308	99.24 242

DCV-MVSNet		98.25 260	97.95 265	99.13 238	99.17 288	98.47 252	99.00 188	98.67 316	98.97 161	99.22 232	99.02 308	91.31 314	99.69 301	97.26 234	98.93 308	99.24 242

DPM-MVS		98.28 258	97.94 269	99.32 208	99.36 242	99.11 203	97.31 337	98.78 311	96.88 308	98.84 277	99.11 296	97.77 208	99.61 336	94.03 337	99.36 280	99.23 245

CLD-MVS		98.76 209	98.57 214	99.33 204	99.57 161	98.97 218	97.53 327	99.55 181	96.41 317	99.27 222	99.13 290	99.07 64	99.78 268	96.73 266	99.89 91	99.23 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		99.13 148	99.06 139	99.36 200	99.57 161	99.10 207	98.01 296	99.25 280	98.78 189	99.58 143	99.44 232	98.24 169	99.76 278	98.74 120	99.93 69	99.22 247

OMC-MVS		98.90 192	98.72 198	99.44 172	99.39 234	99.42 141	98.58 241	99.64 130	97.31 295	99.44 182	99.62 159	98.59 125	99.69 301	96.17 293	99.79 158	99.22 247

eth_miper_zixun_eth		98.68 219	98.71 199	98.60 285	99.10 301	96.84 315	97.52 329	99.54 186	98.94 166	99.58 143	99.48 220	96.25 267	99.76 278	98.01 173	99.93 69	99.21 249

cl_fuxian		98.72 215	98.71 199	98.72 281	99.12 295	97.22 306	97.68 320	99.56 176	98.90 173	99.54 160	99.48 220	96.37 264	99.73 287	97.88 183	99.88 99	99.21 249

CMPMVS	77.52 23	98.50 238	98.19 251	99.41 185	98.33 347	99.56 111	99.01 186	99.59 160	95.44 331	99.57 146	99.80 53	95.64 276	99.46 350	96.47 280	99.92 73	99.21 249
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		99.06 162	98.97 168	99.34 202	99.31 261	98.98 216	98.31 269	99.91 8	98.81 184	98.79 283	98.94 320	99.14 54	99.84 220	98.79 115	98.74 321	99.20 252

DELS-MVS		99.34 91	99.30 85	99.48 161	99.51 188	99.36 157	98.12 284	99.53 195	99.36 108	99.41 196	99.61 168	99.22 46	99.87 166	99.21 64	99.68 206	99.20 252
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CANet_DTU		98.91 190	98.85 186	99.09 242	98.79 331	98.13 272	98.18 277	99.31 266	99.48 85	98.86 275	99.51 210	96.56 255	99.95 43	99.05 90	99.95 48	99.19 254

alignmvs		98.28 258	97.96 264	99.25 223	99.12 295	98.93 225	99.03 183	98.42 326	99.64 60	98.72 290	97.85 353	90.86 323	99.62 332	98.88 109	99.13 298	99.19 254

cl-mvsnet1		98.54 235	98.42 229	98.92 259	99.03 309	97.80 290	97.46 331	99.59 160	98.90 173	99.60 138	99.46 228	93.87 291	99.78 268	97.97 177	99.89 91	99.18 256

MSDG		99.08 160	98.98 167	99.37 197	99.60 146	99.13 201	97.54 325	99.74 76	98.84 182	99.53 165	99.55 200	99.10 57	99.79 265	97.07 248	99.86 114	99.18 256

cl-mvsnet_		98.54 235	98.41 230	98.92 259	99.03 309	97.80 290	97.46 331	99.59 160	98.90 173	99.60 138	99.46 228	93.85 292	99.78 268	97.97 177	99.89 91	99.17 258

PM-MVS		99.36 84	99.29 90	99.58 130	99.83 37	99.66 81	98.95 201	99.86 19	98.85 179	99.81 56	99.73 86	98.40 155	99.92 86	98.36 140	99.83 131	99.17 258

thisisatest0530		97.45 289	96.95 300	98.94 255	99.68 128	97.73 292	99.09 173	94.19 358	98.61 204	99.56 153	99.30 262	84.30 354	99.93 67	98.27 150	99.54 250	99.16 260

PatchmatchNet		97.65 283	97.80 276	97.18 328	98.82 328	92.49 350	99.17 146	98.39 328	98.12 250	98.79 283	99.58 183	90.71 325	99.89 138	97.23 238	99.41 271	99.16 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpnnormal		99.43 63	99.38 66	99.60 124	99.87 27	99.75 48	99.59 56	99.78 57	99.71 42	99.90 22	99.69 112	98.85 89	99.90 124	97.25 237	99.78 164	99.15 262

mvs_anonymous		99.28 103	99.39 64	98.94 255	99.19 285	97.81 289	99.02 184	99.55 181	99.78 34	99.85 40	99.80 53	98.24 169	99.86 186	99.57 23	99.50 257	99.15 262

ab-mvs		99.33 95	99.28 92	99.47 163	99.57 161	99.39 148	99.78 10	99.43 233	98.87 177	99.57 146	99.82 48	98.06 185	99.87 166	98.69 125	99.73 188	99.15 262

MIMVSNet		98.43 246	98.20 248	99.11 240	99.53 178	98.38 261	99.58 58	98.61 318	98.96 164	99.33 212	99.76 75	90.92 320	99.81 257	97.38 225	99.76 170	99.15 262

GSMVS																99.14 266

sam_mvs1												90.81 324				99.14 266

SCA		98.11 267	98.36 235	97.36 323	99.20 283	92.99 348	98.17 279	98.49 324	98.24 244	99.10 251	99.57 191	96.01 272	99.94 54	96.86 257	99.62 225	99.14 266

LS3D		99.24 113	99.11 122	99.61 122	98.38 345	99.79 35	99.57 59	99.68 104	99.61 68	99.15 243	99.71 99	98.70 110	99.91 104	97.54 215	99.68 206	99.13 269

Patchmatch-RL test		98.60 225	98.36 235	99.33 204	99.77 79	99.07 211	98.27 272	99.87 17	98.91 172	99.74 87	99.72 92	90.57 327	99.79 265	98.55 131	99.85 117	99.11 270

test_0402		99.22 123	99.14 112	99.45 170	99.79 65	99.43 138	99.28 113	99.68 104	99.54 78	99.40 201	99.56 194	99.07 64	99.82 241	96.01 297	99.96 41	99.11 270

MVS_Test		99.28 103	99.31 80	99.19 232	99.35 244	98.79 235	99.36 91	99.49 215	99.17 136	99.21 234	99.67 129	98.78 100	99.66 321	99.09 87	99.66 217	99.10 272

AdaColmap		98.60 225	98.35 237	99.38 194	99.12 295	99.22 189	98.67 236	99.42 235	97.84 271	98.81 280	99.27 269	97.32 234	99.81 257	95.14 321	99.53 252	99.10 272

FPMVS		96.32 314	95.50 322	98.79 277	99.60 146	98.17 271	98.46 261	98.80 310	97.16 302	96.28 351	99.63 150	82.19 355	99.09 354	88.45 351	98.89 313	99.10 272

Patchmatch-test		98.10 268	97.98 263	98.48 290	99.27 271	96.48 319	99.40 80	99.07 298	98.81 184	99.23 228	99.57 191	90.11 331	99.87 166	96.69 267	99.64 222	99.09 275

tpm		97.15 296	96.95 300	97.75 314	98.91 315	94.24 341	99.32 98	97.96 334	97.71 275	98.29 312	99.32 258	86.72 348	99.92 86	98.10 168	96.24 354	99.09 275

PMMVS		98.49 241	98.29 241	99.11 240	98.96 313	98.42 257	97.54 325	99.32 262	97.53 283	98.47 308	98.15 350	97.88 200	99.82 241	97.46 220	99.24 295	99.09 275

cl-mvsnet2		97.56 287	97.28 289	98.40 293	98.37 346	96.75 316	97.24 340	99.37 253	97.31 295	99.41 196	99.22 281	87.30 339	99.37 352	97.70 201	99.62 225	99.08 278

ADS-MVSNet2		97.78 278	97.66 284	98.12 305	99.14 291	95.36 333	99.22 132	98.75 312	96.97 306	98.25 315	99.64 140	90.90 321	99.94 54	96.51 277	99.56 240	99.08 278

ADS-MVSNet		97.72 282	97.67 283	97.86 310	99.14 291	94.65 339	99.22 132	98.86 306	96.97 306	98.25 315	99.64 140	90.90 321	99.84 220	96.51 277	99.56 240	99.08 278

pmmvs3		98.08 269	97.80 276	98.91 261	99.41 229	97.69 294	97.87 312	99.66 113	95.87 325	99.50 173	99.51 210	90.35 329	99.97 16	98.55 131	99.47 262	99.08 278

PVSNet	97.47 15	98.42 247	98.44 227	98.35 295	99.46 215	96.26 322	96.70 350	99.34 259	97.68 276	99.00 259	99.13 290	97.40 228	99.72 289	97.59 213	99.68 206	99.08 278

MVS-HIRNet		97.86 275	98.22 246	96.76 331	99.28 269	91.53 357	98.38 264	92.60 360	99.13 144	99.31 216	99.96 10	97.18 242	99.68 312	98.34 143	99.83 131	99.07 283

PMVS	92.94 21	98.82 203	98.81 192	98.85 269	99.84 33	97.99 281	99.20 135	99.47 221	99.71 42	99.42 188	99.82 48	98.09 182	99.47 348	93.88 339	99.85 117	99.07 283
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		99.57 37	99.59 32	99.49 158	99.98 3	99.71 63	99.72 19	99.84 29	99.81 28	99.94 11	99.78 65	98.91 81	99.71 293	98.41 137	99.95 48	99.05 285
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
canonicalmvs		99.02 171	99.00 159	99.09 242	99.10 301	98.70 239	99.61 51	99.66 113	99.63 63	98.64 295	97.65 355	99.04 68	99.54 341	98.79 115	98.92 310	99.04 286

CL-MVSNet_2432*1600		98.71 216	98.56 217	99.15 236	99.22 278	98.66 242	97.14 342	99.51 206	98.09 253	99.54 160	99.27 269	96.87 251	99.74 284	98.43 136	98.96 307	99.03 287

AUN-MVS		97.82 276	97.38 287	99.14 237	99.27 271	98.53 248	98.72 233	99.02 302	98.10 251	97.18 348	99.03 307	89.26 336	99.85 204	97.94 179	97.91 343	99.03 287

MDTV_nov1_ep13_2view						91.44 358	99.14 156		97.37 292	99.21 234		91.78 312		96.75 264		99.03 287

ITE_SJBPF				99.38 194	99.63 140	99.44 134		99.73 79	98.56 207	99.33 212	99.53 205	98.88 86	99.68 312	96.01 297	99.65 220	99.02 290

UnsupCasMVSNet_bld		98.55 234	98.27 242	99.40 187	99.56 171	99.37 154	97.97 304	99.68 104	97.49 286	99.08 252	99.35 253	95.41 279	99.82 241	97.70 201	98.19 337	99.01 291

miper_ehance_all_eth		98.59 228	98.59 210	98.59 286	98.98 312	97.07 309	97.49 330	99.52 203	98.50 215	99.52 167	99.37 243	96.41 263	99.71 293	97.86 187	99.62 225	99.00 292

CNLPA		98.57 230	98.34 238	99.28 216	99.18 287	99.10 207	98.34 265	99.41 236	98.48 218	98.52 304	98.98 313	97.05 246	99.78 268	95.59 312	99.50 257	98.96 293

new_pmnet		98.88 196	98.89 182	98.84 271	99.70 116	97.62 295	98.15 280	99.50 210	97.98 259	99.62 130	99.54 202	98.15 179	99.94 54	97.55 214	99.84 121	98.95 294

PCF-MVS	96.03 18	96.73 306	95.86 317	99.33 204	99.44 221	99.16 198	96.87 348	99.44 230	86.58 355	98.95 262	99.40 237	94.38 288	99.88 153	87.93 352	99.80 153	98.95 294
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		98.68 219	98.47 223	99.30 213	99.44 221	99.28 172	98.14 282	99.54 186	97.12 304	99.11 249	99.25 274	97.80 206	99.70 295	96.51 277	99.30 287	98.93 296

Fast-Effi-MVS+		99.02 171	98.87 184	99.46 166	99.38 237	99.50 118	99.04 181	99.79 52	97.17 301	98.62 296	98.74 333	99.34 33	99.95 43	98.32 145	99.41 271	98.92 297

ET-MVSNet_ETH3D		96.78 304	96.07 313	98.91 261	99.26 273	97.92 287	97.70 319	96.05 351	97.96 263	92.37 359	98.43 345	87.06 341	99.90 124	98.27 150	97.56 347	98.91 298

EIA-MVS		99.12 150	99.01 156	99.45 170	99.36 242	99.62 94	99.34 93	99.79 52	98.41 223	98.84 277	98.89 325	98.75 106	99.84 220	98.15 164	99.51 255	98.89 299

CostFormer		96.71 307	96.79 306	96.46 337	98.90 316	90.71 361	99.41 78	98.68 314	94.69 343	98.14 323	99.34 256	86.32 350	99.80 262	97.60 212	98.07 341	98.88 300

DP-MVS Recon		98.50 238	98.23 245	99.31 211	99.49 199	99.46 127	98.56 246	99.63 132	94.86 340	98.85 276	99.37 243	97.81 205	99.59 338	96.08 294	99.44 265	98.88 300

test0.0.03 1		97.37 292	96.91 303	98.74 280	97.72 355	97.57 296	97.60 323	97.36 345	98.00 256	99.21 234	98.02 351	90.04 332	99.79 265	98.37 139	95.89 355	98.86 302

BH-untuned		98.22 264	98.09 257	98.58 287	99.38 237	97.24 305	98.55 247	98.98 304	97.81 272	99.20 239	98.76 332	97.01 247	99.65 328	94.83 325	98.33 333	98.86 302

HY-MVS	98.23 9	98.21 265	97.95 265	98.99 251	99.03 309	98.24 265	99.61 51	98.72 313	96.81 312	98.73 289	99.51 210	94.06 290	99.86 186	96.91 254	98.20 335	98.86 302

miper_enhance_ethall		98.03 271	97.94 269	98.32 297	98.27 348	96.43 321	96.95 346	99.41 236	96.37 319	99.43 186	98.96 318	94.74 284	99.69 301	97.71 199	99.62 225	98.83 305

Effi-MVS+-dtu		99.07 161	98.92 177	99.52 149	98.89 319	99.78 38	99.15 154	99.66 113	99.34 109	98.92 267	99.24 279	97.69 212	99.98 6	98.11 166	99.28 289	98.81 306

EPMVS		96.53 310	96.32 308	97.17 329	98.18 351	92.97 349	99.39 82	89.95 362	98.21 246	98.61 297	99.59 181	86.69 349	99.72 289	96.99 250	99.23 297	98.81 306

MVE	92.54 22	96.66 308	96.11 312	98.31 299	99.68 128	97.55 297	97.94 307	95.60 353	99.37 106	90.68 360	98.70 334	96.56 255	98.61 358	86.94 357	99.55 244	98.77 308
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tpm2		96.35 313	96.22 310	96.73 333	98.88 322	91.75 355	99.21 134	98.51 322	93.27 346	97.89 333	99.21 283	84.83 352	99.70 295	96.04 296	98.18 338	98.75 309

LF4IMVS		99.01 175	98.92 177	99.27 218	99.71 109	99.28 172	98.59 240	99.77 60	98.32 240	99.39 202	99.41 235	98.62 121	99.84 220	96.62 273	99.84 121	98.69 310

thisisatest0515		96.98 300	96.42 307	98.66 284	99.42 228	97.47 298	97.27 338	94.30 357	97.24 297	99.15 243	98.86 327	85.01 351	99.87 166	97.10 246	99.39 274	98.63 311

Fast-Effi-MVS+-dtu		99.20 130	99.12 119	99.43 175	99.25 274	99.69 74	99.05 179	99.82 36	99.50 83	98.97 260	99.05 301	98.98 72	99.98 6	98.20 156	99.24 295	98.62 312

PAPM		95.61 327	94.71 329	98.31 299	99.12 295	96.63 317	96.66 351	98.46 325	90.77 352	96.25 352	98.68 335	93.01 300	99.69 301	81.60 358	97.86 345	98.62 312

JIA-IIPM		98.06 270	97.92 271	98.50 289	98.59 340	97.02 310	98.80 223	98.51 322	99.88 13	97.89 333	99.87 30	91.89 309	99.90 124	98.16 163	97.68 346	98.59 314

dp		96.86 302	97.07 296	96.24 339	98.68 339	90.30 363	99.19 140	98.38 329	97.35 293	98.23 317	99.59 181	87.23 340	99.82 241	96.27 288	98.73 323	98.59 314

OpenMVS	98.12 10	98.23 263	97.89 275	99.26 220	99.19 285	99.26 176	99.65 44	99.69 101	91.33 351	98.14 323	99.77 72	98.28 166	99.96 33	95.41 317	99.55 244	98.58 316

baseline2		96.83 303	96.28 309	98.46 291	99.09 303	96.91 313	98.83 215	93.87 359	97.23 298	96.23 354	98.36 346	88.12 338	99.90 124	96.68 268	98.14 339	98.57 317

DWT-MVSNet_test		96.03 320	95.80 319	96.71 335	98.50 343	91.93 353	99.25 124	97.87 337	95.99 324	96.81 350	97.61 356	81.02 357	99.66 321	97.20 241	97.98 342	98.54 318

TESTMET0.1,1		96.24 316	95.84 318	97.41 322	98.24 349	93.84 344	97.38 333	95.84 352	98.43 220	97.81 337	98.56 339	79.77 360	99.89 138	97.77 194	98.77 317	98.52 319

xiu_mvs_v1_base_debu		99.23 114	99.34 74	98.91 261	99.59 148	98.23 266	98.47 256	99.66 113	99.61 68	99.68 105	98.94 320	99.39 23	99.97 16	99.18 71	99.55 244	98.51 320

xiu_mvs_v1_base		99.23 114	99.34 74	98.91 261	99.59 148	98.23 266	98.47 256	99.66 113	99.61 68	99.68 105	98.94 320	99.39 23	99.97 16	99.18 71	99.55 244	98.51 320

xiu_mvs_v1_base_debi		99.23 114	99.34 74	98.91 261	99.59 148	98.23 266	98.47 256	99.66 113	99.61 68	99.68 105	98.94 320	99.39 23	99.97 16	99.18 71	99.55 244	98.51 320

KD-MVS_2432*1600		95.89 321	95.41 324	97.31 326	94.96 360	93.89 342	97.09 343	99.22 285	97.23 298	98.88 271	99.04 304	79.23 361	99.54 341	96.24 290	96.81 350	98.50 323

miper_refine_blended		95.89 321	95.41 324	97.31 326	94.96 360	93.89 342	97.09 343	99.22 285	97.23 298	98.88 271	99.04 304	79.23 361	99.54 341	96.24 290	96.81 350	98.50 323

CR-MVSNet		98.35 255	98.20 248	98.83 273	99.05 306	98.12 273	99.30 105	99.67 109	97.39 291	99.16 241	99.79 59	91.87 310	99.91 104	98.78 118	98.77 317	98.44 325

RPMNet		98.60 225	98.53 220	98.83 273	99.05 306	98.12 273	99.30 105	99.62 135	99.86 16	99.16 241	99.74 82	92.53 305	99.92 86	98.75 119	98.77 317	98.44 325

tpmrst		97.73 280	98.07 258	96.73 333	98.71 337	92.00 352	99.10 169	98.86 306	98.52 213	98.92 267	99.54 202	91.90 308	99.82 241	98.02 170	99.03 304	98.37 327

CS-MVS		99.09 159	99.03 151	99.25 223	99.45 218	99.49 119	99.41 78	99.82 36	99.10 149	98.03 328	98.48 344	99.30 37	99.89 138	98.30 147	99.41 271	98.35 328

test-LLR		97.15 296	96.95 300	97.74 315	98.18 351	95.02 336	97.38 333	96.10 348	98.00 256	97.81 337	98.58 336	90.04 332	99.91 104	97.69 207	98.78 315	98.31 329

test-mter		96.23 317	95.73 320	97.74 315	98.18 351	95.02 336	97.38 333	96.10 348	97.90 265	97.81 337	98.58 336	79.12 363	99.91 104	97.69 207	98.78 315	98.31 329

ETV-MVS		99.18 137	99.18 106	99.16 235	99.34 254	99.28 172	99.12 166	99.79 52	99.48 85	98.93 264	98.55 340	99.40 22	99.93 67	98.51 133	99.52 254	98.28 331

PatchT		98.45 245	98.32 240	98.83 273	98.94 314	98.29 264	99.24 125	98.82 309	99.84 22	99.08 252	99.76 75	91.37 313	99.94 54	98.82 113	99.00 306	98.26 332

xiu_mvs_v2_base		99.02 171	99.11 122	98.77 278	99.37 240	98.09 277	98.13 283	99.51 206	99.47 90	99.42 188	98.54 341	99.38 27	99.97 16	98.83 111	99.33 284	98.24 333

IB-MVS	95.41 20	95.30 328	94.46 331	97.84 311	98.76 335	95.33 334	97.33 336	96.07 350	96.02 323	95.37 357	97.41 358	76.17 365	99.96 33	97.54 215	95.44 356	98.22 334
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpm cat1		96.78 304	96.98 299	96.16 340	98.85 323	90.59 362	99.08 176	99.32 262	92.37 348	97.73 342	99.46 228	91.15 317	99.69 301	96.07 295	98.80 314	98.21 335

MAR-MVS		98.24 262	97.92 271	99.19 232	98.78 333	99.65 86	99.17 146	99.14 295	95.36 332	98.04 327	98.81 330	97.47 225	99.72 289	95.47 316	99.06 301	98.21 335
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
PS-MVSNAJ		99.00 177	99.08 133	98.76 279	99.37 240	98.10 276	98.00 298	99.51 206	99.47 90	99.41 196	98.50 343	99.28 40	99.97 16	98.83 111	99.34 282	98.20 337

cascas		96.99 299	96.82 305	97.48 319	97.57 358	95.64 331	96.43 352	99.56 176	91.75 349	97.13 349	97.61 356	95.58 278	98.63 357	96.68 268	99.11 299	98.18 338

BH-w/o		97.20 295	97.01 298	97.76 313	99.08 304	95.69 330	98.03 295	98.52 321	95.76 328	97.96 330	98.02 351	95.62 277	99.47 348	92.82 342	97.25 349	98.12 339

tpmvs		97.39 291	97.69 281	96.52 336	98.41 344	91.76 354	99.30 105	98.94 305	97.74 273	97.85 336	99.55 200	92.40 306	99.73 287	96.25 289	98.73 323	98.06 340

mvs-test1		98.83 201	98.70 202	99.22 228	98.89 319	99.65 86	98.88 206	99.66 113	99.34 109	98.29 312	98.94 320	97.69 212	99.96 33	98.11 166	98.54 329	98.04 341

thres600view7		96.60 309	96.16 311	97.93 308	99.63 140	96.09 326	99.18 141	97.57 340	98.77 190	98.72 290	97.32 359	87.04 342	99.72 289	88.57 350	98.62 326	97.98 342

thres400		96.40 311	95.89 315	97.92 309	99.58 151	96.11 324	99.00 188	97.54 343	98.43 220	98.52 304	96.98 362	86.85 344	99.67 317	87.62 353	98.51 330	97.98 342

TR-MVS		97.44 290	97.15 295	98.32 297	98.53 342	97.46 299	98.47 256	97.91 336	96.85 310	98.21 318	98.51 342	96.42 261	99.51 346	92.16 343	97.29 348	97.98 342

1314		98.00 273	97.90 274	98.27 301	98.90 316	97.45 300	99.30 105	99.06 300	94.98 337	97.21 347	99.12 294	98.43 148	99.67 317	95.58 313	98.56 328	97.71 345

E-PMN		97.14 298	97.43 286	96.27 338	98.79 331	91.62 356	95.54 355	99.01 303	99.44 96	98.88 271	99.12 294	92.78 302	99.68 312	94.30 332	99.03 304	97.50 346

gg-mvs-nofinetune		95.87 323	95.17 327	97.97 307	98.19 350	96.95 311	99.69 28	89.23 363	99.89 11	96.24 353	99.94 12	81.19 356	99.51 346	93.99 338	98.20 335	97.44 347

DeepMVS_CX				97.98 306	99.69 119	96.95 311		99.26 277	75.51 358	95.74 356	98.28 348	96.47 259	99.62 332	91.23 346	97.89 344	97.38 348

OpenMVS_ROB	97.31 17	97.36 293	96.84 304	98.89 268	99.29 267	99.45 132	98.87 209	99.48 217	86.54 356	99.44 182	99.74 82	97.34 233	99.86 186	91.61 344	99.28 289	97.37 349

EMVS		96.96 301	97.28 289	95.99 341	98.76 335	91.03 359	95.26 356	98.61 318	99.34 109	98.92 267	98.88 326	93.79 293	99.66 321	92.87 341	99.05 302	97.30 350

thres100view900		96.39 312	96.03 314	97.47 320	99.63 140	95.93 327	99.18 141	97.57 340	98.75 194	98.70 292	97.31 360	87.04 342	99.67 317	87.62 353	98.51 330	96.81 351

tfpn200view9		96.30 315	95.89 315	97.53 318	99.58 151	96.11 324	99.00 188	97.54 343	98.43 220	98.52 304	96.98 362	86.85 344	99.67 317	87.62 353	98.51 330	96.81 351

API-MVS		98.38 251	98.39 232	98.35 295	98.83 325	99.26 176	99.14 156	99.18 291	98.59 205	98.66 294	98.78 331	98.61 123	99.57 340	94.14 334	99.56 240	96.21 353

thres200		96.09 318	95.68 321	97.33 325	99.48 205	96.22 323	98.53 251	97.57 340	98.06 255	98.37 311	96.73 364	86.84 346	99.61 336	86.99 356	98.57 327	96.16 354

GG-mvs-BLEND				97.36 323	97.59 356	96.87 314	99.70 22	88.49 364		94.64 358	97.26 361	80.66 358	99.12 353	91.50 345	96.50 353	96.08 355

wuyk23d		97.58 286	99.13 115	92.93 342	99.69 119	99.49 119	99.52 63	99.77 60	97.97 260	99.96 8	99.79 59	99.84 3	99.94 54	95.85 305	99.82 140	79.36 356

test123		29.31 329	33.05 334	18.08 343	25.93 365	12.24 365	97.53 327	10.93 366	11.78 360	24.21 361	50.08 369	21.04 366	8.60 361	23.51 359	32.43 360	33.39 357

testmvs		28.94 330	33.33 332	15.79 344	26.03 364	9.81 366	96.77 349	15.67 365	11.55 361	23.87 362	50.74 368	19.03 367	8.53 362	23.21 360	33.07 359	29.03 358

uanet_test		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		24.88 331	33.17 333	0.00 345	0.00 366	0.00 367	0.00 357	99.62 135	0.00 362	0.00 363	99.13 290	99.82 4	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		16.61 332	22.14 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	99.28 40	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.26 339	11.02 342	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	99.16 288	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		8.33 333	11.11 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	100.00 1	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					99.43 223	99.61 100		99.43 233	96.38 318	99.11 249	99.07 299	97.86 201	99.92 86	94.04 336	99.49 259

test_241102_ONE					99.69 119	99.82 25		99.54 186	99.12 147	99.82 50	99.49 218	98.91 81	99.52 345

9.14			98.64 205		99.45 218		98.81 220	99.60 153	97.52 284	99.28 221	99.56 194	98.53 136	99.83 231	95.36 319	99.64 222

save fliter					99.53 178	99.25 180	98.29 270	99.38 252	99.07 152

test0726					99.69 119	99.80 33	99.24 125	99.57 171	99.16 138	99.73 91	99.65 138	98.35 159

test_part2					99.62 143	99.67 79				99.55 158

sam_mvs												90.52 328

MTGPA								99.53 195

test_post1							99.14 156				51.63 367	89.54 335	99.82 241	96.86 257

test_post											52.41 366	90.25 330	99.86 186

patchmatchnet-post											99.62 159	90.58 326	99.94 54

MTMP							99.09 173	98.59 320

gm-plane-assit					97.59 356	89.02 364			93.47 345		98.30 347		99.84 220	96.38 284

TEST9					99.35 244	99.35 161	98.11 286	99.41 236	94.83 342	97.92 331	98.99 310	98.02 188	99.85 204

test_8					99.34 254	99.31 167	98.08 290	99.40 243	94.90 338	97.87 335	98.97 316	98.02 188	99.84 220

agg_prior					99.35 244	99.36 157		99.39 246		97.76 340			99.85 204

test_prior4						99.19 196	98.00 298

test_prior2							97.95 305		97.87 267	98.05 325	99.05 301	97.90 197		95.99 299	99.49 259

旧先验2							97.94 307		95.33 333	98.94 263			99.88 153	96.75 264

新几何2							98.04 294

原ACMM2							97.92 309

testdata2													99.89 138	95.99 299

segment_acmp												98.37 157

testdata1							97.72 317		97.86 270

plane_prior7					99.58 151	99.38 151

plane_prior6					99.47 210	99.26 176						97.24 236

plane_prior4											99.25 274

plane_prior3						99.31 167			98.36 229	99.14 245

plane_prior2							98.80 223		98.94 166

plane_prior1					99.51 188

plane_prior						99.24 185	98.42 262		97.87 267						99.71 198

n2								0.00 367

nn								0.00 367

door-mid								99.83 31

test11								99.29 271

door								99.77 60

HQP5-MVS						98.94 221

HQP-NCC					99.31 261		97.98 301		97.45 287	98.15 319

ACMP_Plane					99.31 261		97.98 301		97.45 287	98.15 319

BP-MVS														94.73 326

HQP3-MVS								99.37 253							99.67 213

HQP2-MVS												96.67 253

NP-MVS					99.40 232	99.13 201					98.83 328

MDTV_nov1_ep13			97.73 280		98.70 338	90.83 360	99.15 154	98.02 333	98.51 214	98.82 279	99.61 168	90.98 319	99.66 321	96.89 256	98.92 310

ACMMP++_ref															99.94 61

ACMMP++															99.79 158

Test By Simon												98.41 151

High-resolution multi-view benchmark