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		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 5	99.99 1	100.00 1	99.98 10	99.78 8	100.00 1	99.92 4	100.00 1	99.87 12

RRT_MVS		99.67 32	99.59 44	99.91 2	99.94 14	99.88 11	99.78 11	99.27 294	99.87 24	99.91 24	99.87 36	98.04 202	99.96 38	99.68 20	99.99 12	99.90 7

test_djsdf		99.84 9	99.81 12	99.91 2	99.94 14	99.84 22	99.77 14	99.80 56	99.73 54	99.97 9	99.92 21	99.77 9	99.98 10	99.43 48	100.00 1	99.90 7

ANet_high		99.88 4	99.87 7	99.91 2	99.99 1	99.91 4	99.65 58	100.00 1	99.90 13	100.00 1	99.97 11	99.61 19	99.97 20	99.75 16	100.00 1	99.84 17

UniMVSNet_ETH3D		99.85 7	99.83 10	99.90 5	99.89 29	99.91 4	99.89 4	99.71 102	99.93 9	99.95 14	99.89 30	99.71 11	99.96 38	99.51 40	99.97 39	99.84 17

anonymousdsp		99.80 13	99.77 15	99.90 5	99.96 4	99.88 11	99.73 26	99.85 32	99.70 62	99.92 22	99.93 17	99.45 28	99.97 20	99.36 60	100.00 1	99.85 16

mvs_tets		99.90 2	99.90 3	99.90 5	99.96 4	99.79 43	99.72 29	99.88 23	99.92 11	99.98 6	99.93 17	99.94 1	99.98 10	99.77 15	100.00 1	99.92 6

PS-MVSNAJss		99.84 9	99.82 11	99.89 8	99.96 4	99.77 49	99.68 43	99.85 32	99.95 4	99.98 6	99.92 21	99.28 46	99.98 10	99.75 16	100.00 1	99.94 4

jajsoiax		99.89 3	99.89 4	99.89 8	99.96 4	99.78 46	99.70 34	99.86 28	99.89 18	99.98 6	99.90 26	99.94 1	99.98 10	99.75 16	100.00 1	99.90 7

PS-CasMVS		99.66 34	99.58 48	99.89 8	99.80 66	99.85 17	99.66 52	99.73 90	99.62 83	99.84 54	99.71 117	98.62 132	99.96 38	99.30 72	99.96 53	99.86 14

PEN-MVS		99.66 34	99.59 44	99.89 8	99.83 47	99.87 13	99.66 52	99.73 90	99.70 62	99.84 54	99.73 104	98.56 142	99.96 38	99.29 75	99.94 73	99.83 21

v7n		99.82 12	99.80 13	99.88 12	99.96 4	99.84 22	99.82 8	99.82 45	99.84 35	99.94 15	99.91 24	99.13 65	99.96 38	99.83 12	99.99 12	99.83 21

DTE-MVSNet		99.68 29	99.61 39	99.88 12	99.80 66	99.87 13	99.67 47	99.71 102	99.72 57	99.84 54	99.78 82	98.67 126	99.97 20	99.30 72	99.95 62	99.80 28

LTVRE_ROB	99.19 1	99.88 4	99.87 7	99.88 12	99.91 23	99.90 7	99.96 1	99.92 12	99.90 13	99.97 9	99.87 36	99.81 7	99.95 48	99.54 35	99.99 12	99.80 28
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
CP-MVSNet		99.54 58	99.43 74	99.87 15	99.76 95	99.82 33	99.57 76	99.61 154	99.54 97	99.80 69	99.64 157	97.79 224	99.95 48	99.21 82	99.94 73	99.84 17

WR-MVS_H		99.61 47	99.53 60	99.87 15	99.80 66	99.83 27	99.67 47	99.75 82	99.58 96	99.85 51	99.69 130	98.18 194	99.94 62	99.28 77	99.95 62	99.83 21

UA-Net		99.78 15	99.76 17	99.86 17	99.72 119	99.71 76	99.91 3	99.95 11	99.96 2	99.71 111	99.91 24	99.15 60	99.97 20	99.50 42	100.00 1	99.90 7

FC-MVSNet-test		99.70 23	99.65 30	99.86 17	99.88 33	99.86 16	99.72 29	99.78 67	99.90 13	99.82 59	99.83 52	98.45 160	99.87 189	99.51 40	99.97 39	99.86 14

bld_raw_dy_0_64		99.70 23	99.65 30	99.85 19	99.95 12	99.77 49	99.66 52	99.71 102	99.95 4	99.91 24	99.77 89	98.35 173	100.00 1	99.54 35	99.99 12	99.79 34

APDe-MVS		99.48 66	99.36 87	99.85 19	99.55 192	99.81 36	99.50 85	99.69 114	98.99 180	99.75 92	99.71 117	98.79 109	99.93 79	98.46 155	99.85 135	99.80 28

mvsmamba		99.74 20	99.70 20	99.85 19	99.93 20	99.83 27	99.76 18	99.81 54	99.96 2	99.91 24	99.81 62	98.60 136	99.94 62	99.58 30	99.98 28	99.77 40

FIs		99.65 39	99.58 48	99.84 22	99.84 43	99.85 17	99.66 52	99.75 82	99.86 27	99.74 101	99.79 75	98.27 182	99.85 227	99.37 58	99.93 81	99.83 21

OurMVSNet-221017-0		99.75 17	99.71 19	99.84 22	99.96 4	99.83 27	99.83 6	99.85 32	99.80 44	99.93 18	99.93 17	98.54 145	99.93 79	99.59 27	99.98 28	99.76 45

test_0728_SECOND				99.83 24	99.70 130	99.79 43	99.14 177	99.61 154					99.92 99	97.88 203	99.72 213	99.77 40

pmmvs6		99.86 6	99.86 9	99.83 24	99.94 14	99.90 7	99.83 6	99.91 15	99.85 32	99.94 15	99.95 13	99.73 10	99.90 143	99.65 22	99.97 39	99.69 61

DPE-MVS		99.14 162	98.92 192	99.82 26	99.57 180	99.77 49	98.74 253	99.60 167	98.55 230	99.76 84	99.69 130	98.23 188	99.92 99	96.39 308	99.75 192	99.76 45
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
nrg030		99.70 23	99.66 28	99.82 26	99.76 95	99.84 22	99.61 66	99.70 108	99.93 9	99.78 77	99.68 141	99.10 66	99.78 293	99.45 46	99.96 53	99.83 21

Baseline_NR-MVSNet		99.49 64	99.37 84	99.82 26	99.91 23	99.84 22	98.83 237	99.86 28	99.68 67	99.65 132	99.88 33	97.67 232	99.87 189	99.03 108	99.86 131	99.76 45

MSP-MVS		99.04 183	98.79 210	99.81 29	99.78 83	99.73 70	99.35 113	99.57 186	98.54 233	99.54 178	98.99 338	96.81 269	99.93 79	96.97 274	99.53 274	99.77 40
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
TransMVSNet (Re)		99.78 15	99.77 15	99.81 29	99.91 23	99.85 17	99.75 21	99.86 28	99.70 62	99.91 24	99.89 30	99.60 21	99.87 189	99.59 27	99.74 200	99.71 54

XXY-MVS		99.71 22	99.67 27	99.81 29	99.89 29	99.72 74	99.59 72	99.82 45	99.39 125	99.82 59	99.84 51	99.38 34	99.91 123	99.38 55	99.93 81	99.80 28

MP-MVS-pluss		99.14 162	98.92 192	99.80 32	99.83 47	99.83 27	98.61 260	99.63 144	96.84 334	99.44 203	99.58 202	98.81 102	99.91 123	97.70 225	99.82 159	99.67 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		99.30 116	99.14 128	99.80 32	99.81 61	99.81 36	98.73 255	99.53 212	99.27 139	99.42 209	99.63 167	98.21 189	99.95 48	97.83 212	99.79 177	99.65 92

MTAPA		99.35 102	99.20 120	99.80 32	99.81 61	99.81 36	99.33 116	99.53 212	99.27 139	99.42 209	99.63 167	98.21 189	99.95 48	97.83 212	99.79 177	99.65 92

HPM-MVS_fast		99.43 78	99.30 100	99.80 32	99.83 47	99.81 36	99.52 82	99.70 108	98.35 255	99.51 190	99.50 235	99.31 42	99.88 176	98.18 179	99.84 140	99.69 61

MIMVSNet1		99.66 34	99.62 35	99.80 32	99.94 14	99.87 13	99.69 40	99.77 70	99.78 49	99.93 18	99.89 30	97.94 211	99.92 99	99.65 22	99.98 28	99.62 117

ACMMP_NAP		99.28 119	99.11 138	99.79 37	99.75 106	99.81 36	98.95 223	99.53 212	98.27 264	99.53 183	99.73 104	98.75 117	99.87 189	97.70 225	99.83 150	99.68 67

VPA-MVSNet		99.66 34	99.62 35	99.79 37	99.68 142	99.75 61	99.62 61	99.69 114	99.85 32	99.80 69	99.81 62	98.81 102	99.91 123	99.47 44	99.88 113	99.70 57

Vis-MVSNet		99.75 17	99.74 18	99.79 37	99.88 33	99.66 94	99.69 40	99.92 12	99.67 71	99.77 82	99.75 98	99.61 19	99.98 10	99.35 61	99.98 28	99.72 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		99.69 26	99.66 28	99.78 40	99.76 95	99.76 57	99.60 71	99.82 45	99.46 112	99.75 92	99.56 215	99.63 16	99.95 48	99.43 48	99.88 113	99.62 117

pm-mvs1		99.79 14	99.79 14	99.78 40	99.91 23	99.83 27	99.76 18	99.87 25	99.73 54	99.89 33	99.87 36	99.63 16	99.87 189	99.54 35	99.92 85	99.63 106

HPM-MVS		99.25 126	99.07 153	99.78 40	99.81 61	99.75 61	99.61 66	99.67 122	97.72 295	99.35 228	99.25 301	99.23 53	99.92 99	97.21 264	99.82 159	99.67 74
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DVP-MVS++		99.38 94	99.25 114	99.77 43	99.03 334	99.77 49	99.74 23	99.61 154	99.18 154	99.76 84	99.61 185	99.00 80	99.92 99	97.72 220	99.60 255	99.62 117

SED-MVS		99.40 88	99.28 107	99.77 43	99.69 134	99.82 33	99.20 156	99.54 203	99.13 166	99.82 59	99.63 167	98.91 92	99.92 99	97.85 209	99.70 218	99.58 145

ZNCC-MVS		99.22 139	99.04 165	99.77 43	99.76 95	99.73 70	99.28 134	99.56 191	98.19 269	99.14 270	99.29 292	98.84 101	99.92 99	97.53 241	99.80 172	99.64 101

DVP-MVS		99.32 113	99.17 123	99.77 43	99.69 134	99.80 41	99.14 177	99.31 285	99.16 160	99.62 147	99.61 185	98.35 173	99.91 123	97.88 203	99.72 213	99.61 127
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
region2R		99.23 130	99.05 159	99.77 43	99.76 95	99.70 83	99.31 123	99.59 174	98.41 244	99.32 236	99.36 275	98.73 120	99.93 79	97.29 253	99.74 200	99.67 74

PGM-MVS		99.20 146	99.01 171	99.77 43	99.75 106	99.71 76	99.16 173	99.72 99	97.99 279	99.42 209	99.60 194	98.81 102	99.93 79	96.91 277	99.74 200	99.66 84

TDRefinement		99.72 21	99.70 20	99.77 43	99.90 27	99.85 17	99.86 5	99.92 12	99.69 65	99.78 77	99.92 21	99.37 36	99.88 176	98.93 123	99.95 62	99.60 131

KD-MVS_self_test		99.63 40	99.59 44	99.76 50	99.84 43	99.90 7	99.37 109	99.79 62	99.83 38	99.88 39	99.85 46	98.42 164	99.90 143	99.60 26	99.73 207	99.49 195

Anonymous20231211		99.62 45	99.57 51	99.76 50	99.61 159	99.60 115	99.81 9	99.73 90	99.82 40	99.90 29	99.90 26	97.97 210	99.86 209	99.42 53	99.96 53	99.80 28

HFP-MVS		99.25 126	99.08 149	99.76 50	99.73 115	99.70 83	99.31 123	99.59 174	98.36 250	99.36 226	99.37 270	98.80 106	99.91 123	97.43 246	99.75 192	99.68 67

#test#		99.12 166	98.90 196	99.76 50	99.73 115	99.70 83	99.10 190	99.59 174	97.60 300	99.36 226	99.37 270	98.80 106	99.91 123	96.84 283	99.75 192	99.68 67

ACMMPR		99.23 130	99.06 155	99.76 50	99.74 112	99.69 87	99.31 123	99.59 174	98.36 250	99.35 228	99.38 268	98.61 134	99.93 79	97.43 246	99.75 192	99.67 74

MP-MVS		99.06 177	98.83 205	99.76 50	99.76 95	99.71 76	99.32 119	99.50 228	98.35 255	98.97 285	99.48 243	98.37 171	99.92 99	95.95 328	99.75 192	99.63 106
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 58	99.47 64	99.76 50	99.58 170	99.64 101	99.30 126	99.63 144	99.61 87	99.71 111	99.56 215	98.76 115	99.96 38	99.14 101	99.92 85	99.68 67

mPP-MVS		99.19 149	99.00 174	99.76 50	99.76 95	99.68 90	99.38 105	99.54 203	98.34 259	99.01 283	99.50 235	98.53 149	99.93 79	97.18 265	99.78 183	99.66 84

SixPastTwentyTwo		99.42 81	99.30 100	99.76 50	99.92 22	99.67 92	99.70 34	99.14 317	99.65 77	99.89 33	99.90 26	96.20 286	99.94 62	99.42 53	99.92 85	99.67 74

SteuartSystems-ACMMP		99.30 116	99.14 128	99.76 50	99.87 37	99.66 94	99.18 162	99.60 167	98.55 230	99.57 164	99.67 146	99.03 79	99.94 62	97.01 272	99.80 172	99.69 61
Skip Steuart: Steuart Systems R&D Blog.
mvsany_test		99.85 7	99.88 5	99.75 60	99.95 12	99.37 168	99.53 81	99.98 4	99.77 52	99.99 5	99.95 13	99.85 3	99.94 62	99.95 3	99.98 28	99.94 4

GST-MVS		99.16 158	98.96 185	99.75 60	99.73 115	99.73 70	99.20 156	99.55 197	98.22 266	99.32 236	99.35 280	98.65 130	99.91 123	96.86 280	99.74 200	99.62 117

test_part1		98.63 236	98.26 260	99.75 60	99.40 254	99.49 134	99.67 47	99.68 117	99.86 27	99.88 39	99.86 43	86.73 372	99.93 79	99.34 62	99.97 39	99.81 27

XVS		99.27 123	99.11 138	99.75 60	99.71 122	99.71 76	99.37 109	99.61 154	99.29 135	98.76 312	99.47 248	98.47 156	99.88 176	97.62 233	99.73 207	99.67 74

X-MVStestdata		96.09 338	94.87 347	99.75 60	99.71 122	99.71 76	99.37 109	99.61 154	99.29 135	98.76 312	61.30 391	98.47 156	99.88 176	97.62 233	99.73 207	99.67 74

abl_6		99.36 100	99.23 118	99.75 60	99.71 122	99.74 67	99.33 116	99.76 75	99.07 173	99.65 132	99.63 167	99.09 68	99.92 99	97.13 268	99.76 189	99.58 145

CP-MVS		99.23 130	99.05 159	99.75 60	99.66 148	99.66 94	99.38 105	99.62 147	98.38 248	99.06 281	99.27 296	98.79 109	99.94 62	97.51 242	99.82 159	99.66 84

MSC_two_6792asdad				99.74 67	99.03 334	99.53 129		99.23 304					99.92 99	97.77 214	99.69 221	99.78 36

No_MVS				99.74 67	99.03 334	99.53 129		99.23 304					99.92 99	97.77 214	99.69 221	99.78 36

test1172		99.23 130	99.05 159	99.74 67	99.52 204	99.75 61	99.20 156	99.61 154	98.97 182	99.48 195	99.58 202	98.41 165	99.91 123	97.15 267	99.55 266	99.57 151

SR-MVS		99.19 149	99.00 174	99.74 67	99.51 209	99.72 74	99.18 162	99.60 167	98.85 200	99.47 197	99.58 202	98.38 170	99.92 99	96.92 276	99.54 272	99.57 151

HPM-MVS++		98.96 199	98.70 217	99.74 67	99.52 204	99.71 76	98.86 232	99.19 312	98.47 240	98.59 324	99.06 327	98.08 200	99.91 123	96.94 275	99.60 255	99.60 131

APD-MVS_3200maxsize		99.31 115	99.16 124	99.74 67	99.53 198	99.75 61	99.27 137	99.61 154	99.19 153	99.57 164	99.64 157	98.76 115	99.90 143	97.29 253	99.62 245	99.56 154

LPG-MVS_test		99.22 139	99.05 159	99.74 67	99.82 54	99.63 105	99.16 173	99.73 90	97.56 301	99.64 134	99.69 130	99.37 36	99.89 161	96.66 293	99.87 124	99.69 61

LGP-MVS_train				99.74 67	99.82 54	99.63 105		99.73 90	97.56 301	99.64 134	99.69 130	99.37 36	99.89 161	96.66 293	99.87 124	99.69 61

DP-MVS		99.48 66	99.39 79	99.74 67	99.57 180	99.62 107	99.29 133	99.61 154	99.87 24	99.74 101	99.76 93	98.69 122	99.87 189	98.20 175	99.80 172	99.75 48

ACMMP		99.25 126	99.08 149	99.74 67	99.79 76	99.68 90	99.50 85	99.65 136	98.07 275	99.52 185	99.69 130	98.57 140	99.92 99	97.18 265	99.79 177	99.63 106
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
SR-MVS-dyc-post		99.27 123	99.11 138	99.73 77	99.54 193	99.74 67	99.26 139	99.62 147	99.16 160	99.52 185	99.64 157	98.41 165	99.91 123	97.27 256	99.61 252	99.54 165

SMA-MVS		99.19 149	99.00 174	99.73 77	99.46 237	99.73 70	99.13 183	99.52 220	97.40 312	99.57 164	99.64 157	98.93 89	99.83 256	97.61 235	99.79 177	99.63 106
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
GBi-Net		99.42 81	99.31 95	99.73 77	99.49 220	99.77 49	99.68 43	99.70 108	99.44 115	99.62 147	99.83 52	97.21 255	99.90 143	98.96 117	99.90 95	99.53 171

test1		99.42 81	99.31 95	99.73 77	99.49 220	99.77 49	99.68 43	99.70 108	99.44 115	99.62 147	99.83 52	97.21 255	99.90 143	98.96 117	99.90 95	99.53 171

FMVSNet1		99.66 34	99.63 34	99.73 77	99.78 83	99.77 49	99.68 43	99.70 108	99.67 71	99.82 59	99.83 52	98.98 83	99.90 143	99.24 79	99.97 39	99.53 171

HyFIR lowres test		98.91 205	98.64 220	99.73 77	99.85 42	99.47 137	98.07 314	99.83 40	98.64 221	99.89 33	99.60 194	92.57 324	100.00 1	99.33 66	99.97 39	99.72 51

FMVS1		99.63 40	99.60 42	99.72 83	99.94 14	99.95 2	99.47 91	99.89 19	99.43 120	99.88 39	99.80 65	99.26 50	99.90 143	98.81 132	99.88 113	99.32 248

APD_test		99.63 40	99.60 42	99.72 83	99.94 14	99.95 2	99.47 91	99.89 19	99.43 120	99.88 39	99.80 65	99.26 50	99.90 143	98.81 132	99.88 113	99.32 248

testtj		98.56 246	98.17 271	99.72 83	99.45 240	99.60 115	98.88 228	99.50 228	96.88 331	99.18 265	99.48 243	97.08 262	99.92 99	93.69 365	99.38 296	99.63 106

UniMVSNet_NR-MVSNet		99.37 97	99.25 114	99.72 83	99.47 232	99.56 124	98.97 221	99.61 154	99.43 120	99.67 124	99.28 294	97.85 220	99.95 48	99.17 91	99.81 167	99.65 92

ACMM	98.09 11	99.46 73	99.38 81	99.72 83	99.80 66	99.69 87	99.13 183	99.65 136	98.99 180	99.64 134	99.72 110	99.39 30	99.86 209	98.23 172	99.81 167	99.60 131
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	98.42 6	99.59 49	99.54 56	99.72 83	99.86 39	99.62 107	99.56 78	99.79 62	98.77 211	99.80 69	99.85 46	99.64 15	99.85 227	98.70 143	99.89 104	99.70 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		99.46 73	99.37 84	99.71 89	99.82 54	99.59 118	99.48 89	99.70 108	99.81 41	99.69 117	99.58 202	97.66 236	99.86 209	99.17 91	99.44 287	99.67 74

DU-MVS		99.33 111	99.21 119	99.71 89	99.43 245	99.56 124	98.83 237	99.53 212	99.38 126	99.67 124	99.36 275	97.67 232	99.95 48	99.17 91	99.81 167	99.63 106

APD-MVS		98.87 213	98.59 225	99.71 89	99.50 215	99.62 107	99.01 209	99.57 186	96.80 336	99.54 178	99.63 167	98.29 180	99.91 123	95.24 345	99.71 216	99.61 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH+	98.40 8	99.50 62	99.43 74	99.71 89	99.86 39	99.76 57	99.32 119	99.77 70	99.53 99	99.77 82	99.76 93	99.26 50	99.78 293	97.77 214	99.88 113	99.60 131

COLMAP_ROB	98.06 12	99.45 75	99.37 84	99.70 93	99.83 47	99.70 83	99.38 105	99.78 67	99.53 99	99.67 124	99.78 82	99.19 56	99.86 209	97.32 251	99.87 124	99.55 157
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v3		98.87 213	98.60 223	99.69 94	99.93 20	99.46 141	99.74 23	94.97 377	99.78 49	99.88 39	99.88 33	93.66 314	99.97 20	99.61 25	99.95 62	99.64 101

UniMVSNet (Re)		99.37 97	99.26 112	99.68 95	99.51 209	99.58 121	98.98 220	99.60 167	99.43 120	99.70 114	99.36 275	97.70 227	99.88 176	99.20 85	99.87 124	99.59 140

NR-MVSNet		99.40 88	99.31 95	99.68 95	99.43 245	99.55 127	99.73 26	99.50 228	99.46 112	99.88 39	99.36 275	97.54 240	99.87 189	98.97 115	99.87 124	99.63 106

DROMVSNet		99.69 26	99.69 24	99.68 95	99.71 122	99.91 4	99.76 18	99.96 9	99.86 27	99.51 190	99.39 266	99.57 23	99.93 79	99.64 24	99.86 131	99.20 273

LCM-MVSNet-Re		99.28 119	99.15 127	99.67 98	99.33 281	99.76 57	99.34 114	99.97 5	98.93 190	99.91 24	99.79 75	98.68 123	99.93 79	96.80 285	99.56 262	99.30 253

casdiffmvs		99.63 40	99.61 39	99.67 98	99.79 76	99.59 118	99.13 183	99.85 32	99.79 47	99.76 84	99.72 110	99.33 41	99.82 266	99.21 82	99.94 73	99.59 140

1112_ss		99.05 180	98.84 203	99.67 98	99.66 148	99.29 185	98.52 275	99.82 45	97.65 298	99.43 207	99.16 315	96.42 278	99.91 123	99.07 106	99.84 140	99.80 28

DeepPCF-MVS	98.42 6	99.18 153	99.02 168	99.67 98	99.22 302	99.75 61	97.25 362	99.47 239	98.72 216	99.66 128	99.70 124	99.29 44	99.63 355	98.07 188	99.81 167	99.62 117

DeepC-MVS	98.90 4	99.62 45	99.61 39	99.67 98	99.72 119	99.44 148	99.24 146	99.71 102	99.27 139	99.93 18	99.90 26	99.70 13	99.93 79	98.99 111	99.99 12	99.64 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	97.51 14	99.05 180	98.84 203	99.67 98	99.78 83	99.55 127	98.88 228	99.66 126	97.11 328	99.47 197	99.60 194	99.07 74	99.89 161	96.18 317	99.85 135	99.58 145
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	98.92 3	99.35 102	99.24 116	99.67 98	99.35 266	99.47 137	99.62 61	99.50 228	99.44 115	99.12 273	99.78 82	98.77 114	99.94 62	97.87 206	99.72 213	99.62 117

v10		99.69 26	99.69 24	99.66 105	99.81 61	99.39 162	99.66 52	99.75 82	99.60 93	99.92 22	99.87 36	98.75 117	99.86 209	99.90 5	99.99 12	99.73 50

WR-MVS		99.11 170	98.93 188	99.66 105	99.30 288	99.42 155	98.42 285	99.37 272	99.04 178	99.57 164	99.20 312	96.89 267	99.86 209	98.66 147	99.87 124	99.70 57

XVG-OURS-SEG-HR		99.16 158	98.99 179	99.66 105	99.84 43	99.64 101	98.25 297	99.73 90	98.39 247	99.63 138	99.43 256	99.70 13	99.90 143	97.34 250	98.64 348	99.44 216

baseline		99.63 40	99.62 35	99.66 105	99.80 66	99.62 107	99.44 97	99.80 56	99.71 58	99.72 106	99.69 130	99.15 60	99.83 256	99.32 68	99.94 73	99.53 171

EPP-MVSNet		99.17 157	99.00 174	99.66 105	99.80 66	99.43 152	99.70 34	99.24 303	99.48 103	99.56 171	99.77 89	94.89 299	99.93 79	98.72 142	99.89 104	99.63 106

Anonymous20240529		99.42 81	99.34 89	99.65 110	99.53 198	99.60 115	99.63 60	99.39 265	99.47 108	99.76 84	99.78 82	98.13 196	99.86 209	98.70 143	99.68 226	99.49 195

v8		99.68 29	99.69 24	99.65 110	99.80 66	99.40 160	99.66 52	99.76 75	99.64 79	99.93 18	99.85 46	98.66 128	99.84 244	99.88 9	99.99 12	99.71 54

MCST-MVS		99.02 186	98.81 207	99.65 110	99.58 170	99.49 134	98.58 264	99.07 320	98.40 246	99.04 282	99.25 301	98.51 154	99.80 287	97.31 252	99.51 277	99.65 92

XVG-OURS		99.21 144	99.06 155	99.65 110	99.82 54	99.62 107	97.87 335	99.74 87	98.36 250	99.66 128	99.68 141	99.71 11	99.90 143	96.84 283	99.88 113	99.43 222

CHOSEN 1792x2688		99.39 92	99.30 100	99.65 110	99.88 33	99.25 195	98.78 249	99.88 23	98.66 219	99.96 11	99.79 75	97.45 243	99.93 79	99.34 62	99.99 12	99.78 36

QAPM		98.40 268	97.99 281	99.65 110	99.39 256	99.47 137	99.67 47	99.52 220	91.70 373	98.78 310	99.80 65	98.55 143	99.95 48	94.71 353	99.75 192	99.53 171

3Dnovator	99.15 2	99.43 78	99.36 87	99.65 110	99.39 256	99.42 155	99.70 34	99.56 191	99.23 147	99.35 228	99.80 65	99.17 58	99.95 48	98.21 174	99.84 140	99.59 140

patch_mono-2		99.51 61	99.46 68	99.64 117	99.70 130	99.11 218	99.04 203	99.87 25	99.71 58	99.47 197	99.79 75	98.24 184	99.98 10	99.38 55	99.96 53	99.83 21

EGC-MVSNET		89.05 350	85.52 353	99.64 117	99.89 29	99.78 46	99.56 78	99.52 220	24.19 384	49.96 385	99.83 52	99.15 60	99.92 99	97.71 222	99.85 135	99.21 269

CS-MVS-test		99.68 29	99.70 20	99.64 117	99.57 180	99.83 27	99.78 11	99.97 5	99.92 11	99.50 192	99.38 268	99.57 23	99.95 48	99.69 19	99.90 95	99.15 284

lessismore_v0				99.64 117	99.86 39	99.38 165		90.66 385		99.89 33	99.83 52	94.56 304	99.97 20	99.56 33	99.92 85	99.57 151

114514_t		98.49 258	98.11 275	99.64 117	99.73 115	99.58 121	99.24 146	99.76 75	89.94 376	99.42 209	99.56 215	97.76 226	99.86 209	97.74 219	99.82 159	99.47 205

CPTT-MVS		98.74 227	98.44 241	99.64 117	99.61 159	99.38 165	99.18 162	99.55 197	96.49 339	99.27 247	99.37 270	97.11 261	99.92 99	95.74 335	99.67 233	99.62 117

RPSCF		99.18 153	99.02 168	99.64 117	99.83 47	99.85 17	99.44 97	99.82 45	98.33 260	99.50 192	99.78 82	97.90 214	99.65 352	96.78 286	99.83 150	99.44 216

Anonymous202405211		98.75 225	98.46 239	99.63 124	99.34 276	99.66 94	99.47 91	97.65 363	99.28 138	99.56 171	99.50 235	93.15 318	99.84 244	98.62 148	99.58 260	99.40 228

TSAR-MVS + MP.		99.34 107	99.24 116	99.63 124	99.82 54	99.37 168	99.26 139	99.35 276	98.77 211	99.57 164	99.70 124	99.27 49	99.88 176	97.71 222	99.75 192	99.65 92
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		99.26 125	99.13 131	99.63 124	99.70 130	99.61 113	98.58 264	99.48 235	98.50 236	99.52 185	99.63 167	99.14 63	99.76 303	97.89 202	99.77 187	99.51 184
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AllTest		99.21 144	99.07 153	99.63 124	99.78 83	99.64 101	99.12 187	99.83 40	98.63 222	99.63 138	99.72 110	98.68 123	99.75 307	96.38 309	99.83 150	99.51 184

TestCases				99.63 124	99.78 83	99.64 101		99.83 40	98.63 222	99.63 138	99.72 110	98.68 123	99.75 307	96.38 309	99.83 150	99.51 184

V42		99.56 53	99.54 56	99.63 124	99.79 76	99.46 141	99.39 103	99.59 174	99.24 145	99.86 49	99.70 124	98.55 143	99.82 266	99.79 14	99.95 62	99.60 131

XVG-ACMP-BASELINE		99.23 130	99.10 146	99.63 124	99.82 54	99.58 121	98.83 237	99.72 99	98.36 250	99.60 156	99.71 117	98.92 90	99.91 123	97.08 270	99.84 140	99.40 228

Test_1112_low_res		98.95 202	98.73 212	99.63 124	99.68 142	99.15 215	98.09 311	99.80 56	97.14 326	99.46 201	99.40 262	96.11 288	99.89 161	99.01 110	99.84 140	99.84 17

TAMVS		99.49 64	99.45 69	99.63 124	99.48 226	99.42 155	99.45 94	99.57 186	99.66 75	99.78 77	99.83 52	97.85 220	99.86 209	99.44 47	99.96 53	99.61 127

SF-MVS		99.10 174	98.93 188	99.62 133	99.58 170	99.51 132	99.13 183	99.65 136	97.97 281	99.42 209	99.61 185	98.86 98	99.87 189	96.45 305	99.68 226	99.49 195

EG-PatchMatch MVS		99.57 50	99.56 55	99.62 133	99.77 91	99.33 179	99.26 139	99.76 75	99.32 134	99.80 69	99.78 82	99.29 44	99.87 189	99.15 95	99.91 94	99.66 84

F-COLMAP		98.74 227	98.45 240	99.62 133	99.57 180	99.47 137	98.84 235	99.65 136	96.31 343	98.93 289	99.19 314	97.68 231	99.87 189	96.52 300	99.37 300	99.53 171

CDPH-MVS		98.56 246	98.20 266	99.61 136	99.50 215	99.46 141	98.32 291	99.41 255	95.22 357	99.21 259	99.10 324	98.34 176	99.82 266	95.09 348	99.66 237	99.56 154

LS3D		99.24 129	99.11 138	99.61 136	98.38 371	99.79 43	99.57 76	99.68 117	99.61 87	99.15 268	99.71 117	98.70 121	99.91 123	97.54 239	99.68 226	99.13 292

tfpnnormal		99.43 78	99.38 81	99.60 138	99.87 37	99.75 61	99.59 72	99.78 67	99.71 58	99.90 29	99.69 130	98.85 100	99.90 143	97.25 261	99.78 183	99.15 284

CSCG		99.37 97	99.29 105	99.60 138	99.71 122	99.46 141	99.43 99	99.85 32	98.79 208	99.41 217	99.60 194	98.92 90	99.92 99	98.02 189	99.92 85	99.43 222

ETH3D-3000-0.1		98.77 222	98.50 237	99.59 140	99.47 232	99.53 129	98.77 250	99.60 167	97.33 317	99.23 253	99.50 235	97.91 213	99.83 256	95.02 349	99.67 233	99.41 226

v1144		99.54 58	99.53 60	99.59 140	99.79 76	99.28 187	99.10 190	99.61 154	99.20 152	99.84 54	99.73 104	98.67 126	99.84 244	99.86 11	99.98 28	99.64 101

UnsupCasMVSNet_eth		98.83 216	98.57 229	99.59 140	99.68 142	99.45 146	98.99 216	99.67 122	99.48 103	99.55 176	99.36 275	94.92 298	99.86 209	98.95 121	96.57 376	99.45 211

PHI-MVS		99.11 170	98.95 187	99.59 140	99.13 317	99.59 118	99.17 167	99.65 136	97.88 287	99.25 249	99.46 251	98.97 85	99.80 287	97.26 258	99.82 159	99.37 236

CS-MVS		99.67 32	99.70 20	99.58 144	99.53 198	99.84 22	99.79 10	99.96 9	99.90 13	99.61 153	99.41 258	99.51 27	99.95 48	99.66 21	99.89 104	98.96 317

v144192		99.55 56	99.54 56	99.58 144	99.78 83	99.20 210	99.11 189	99.62 147	99.18 154	99.89 33	99.72 110	98.66 128	99.87 189	99.88 9	99.97 39	99.66 84

v2v482		99.50 62	99.47 64	99.58 144	99.78 83	99.25 195	99.14 177	99.58 184	99.25 143	99.81 66	99.62 176	98.24 184	99.84 244	99.83 12	99.97 39	99.64 101

test20.03		99.55 56	99.54 56	99.58 144	99.79 76	99.37 168	99.02 207	99.89 19	99.60 93	99.82 59	99.62 176	98.81 102	99.89 161	99.43 48	99.86 131	99.47 205

PM-MVS		99.36 100	99.29 105	99.58 144	99.83 47	99.66 94	98.95 223	99.86 28	98.85 200	99.81 66	99.73 104	98.40 169	99.92 99	98.36 160	99.83 150	99.17 280

NCCC		98.82 218	98.57 229	99.58 144	99.21 304	99.31 182	98.61 260	99.25 300	98.65 220	98.43 334	99.26 299	97.86 218	99.81 282	96.55 298	99.27 314	99.61 127

train_agg		98.35 273	97.95 285	99.57 150	99.35 266	99.35 176	98.11 309	99.41 255	94.90 361	97.92 355	98.99 338	98.02 205	99.85 227	95.38 343	99.44 287	99.50 190

agg_prior1		98.33 275	97.92 291	99.57 150	99.35 266	99.36 172	97.99 323	99.39 265	94.85 364	97.76 364	98.98 341	98.03 203	99.85 227	95.49 339	99.44 287	99.51 184

v1192		99.57 50	99.57 51	99.57 150	99.77 91	99.22 204	99.04 203	99.60 167	99.18 154	99.87 48	99.72 110	99.08 72	99.85 227	99.89 8	99.98 28	99.66 84

PMMVS2		99.48 66	99.45 69	99.57 150	99.76 95	98.99 231	98.09 311	99.90 18	98.95 186	99.78 77	99.58 202	99.57 23	99.93 79	99.48 43	99.95 62	99.79 34

VNet		99.18 153	99.06 155	99.56 154	99.24 300	99.36 172	99.33 116	99.31 285	99.67 71	99.47 197	99.57 212	96.48 275	99.84 244	99.15 95	99.30 308	99.47 205

CNVR-MVS		98.99 195	98.80 209	99.56 154	99.25 298	99.43 152	98.54 273	99.27 294	98.58 227	98.80 307	99.43 256	98.53 149	99.70 320	97.22 263	99.59 259	99.54 165

DeepC-MVS_fast	98.47 5	99.23 130	99.12 135	99.56 154	99.28 293	99.22 204	98.99 216	99.40 262	99.08 171	99.58 161	99.64 157	98.90 95	99.83 256	97.44 245	99.75 192	99.63 106
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1921920		99.56 53	99.57 51	99.55 157	99.75 106	99.11 218	99.05 201	99.61 154	99.15 164	99.88 39	99.71 117	99.08 72	99.87 189	99.90 5	99.97 39	99.66 84

HQP_MVS		98.90 207	98.68 219	99.55 157	99.58 170	99.24 200	98.80 245	99.54 203	98.94 187	99.14 270	99.25 301	97.24 253	99.82 266	95.84 331	99.78 183	99.60 131

FMVSNet2		99.35 102	99.28 107	99.55 157	99.49 220	99.35 176	99.45 94	99.57 186	99.44 115	99.70 114	99.74 100	97.21 255	99.87 189	99.03 108	99.94 73	99.44 216

IS-MVSNet		99.03 184	98.85 201	99.55 157	99.80 66	99.25 195	99.73 26	99.15 316	99.37 127	99.61 153	99.71 117	94.73 302	99.81 282	97.70 225	99.88 113	99.58 145

xxxxxxxxxxxxxcwj		99.11 170	98.96 185	99.54 161	99.53 198	99.25 195	98.29 293	99.76 75	99.07 173	99.42 209	99.61 185	98.86 98	99.87 189	96.45 305	99.68 226	99.49 195

test12				99.54 161	99.29 290	99.33 179		99.16 315		98.43 334		97.54 240	99.82 266		99.47 284	99.48 200

FMVS2		99.83 11	99.93 2	99.53 163	99.96 4	98.62 268	99.67 47	100.00 1	99.95 4	100.00 1	99.95 13	99.85 3	99.99 6	99.98 1	99.99 12	99.98 1

dcpmvs_2		99.61 47	99.64 33	99.53 163	99.79 76	98.82 250	99.58 74	99.97 5	99.95 4	99.96 11	99.76 93	98.44 161	99.99 6	99.34 62	99.96 53	99.78 36

Regformer-2		99.34 107	99.27 110	99.53 163	99.41 251	99.10 223	98.99 216	99.53 212	99.47 108	99.66 128	99.52 228	98.80 106	99.89 161	98.31 166	99.74 200	99.60 131

Effi-MVS+-dtu		99.07 176	98.92 192	99.52 166	98.89 347	99.78 46	99.15 175	99.66 126	99.34 130	98.92 292	99.24 306	97.69 229	99.98 10	98.11 185	99.28 311	98.81 331

新几何1				99.52 166	99.50 215	99.22 204		99.26 297	95.66 353	98.60 323	99.28 294	97.67 232	99.89 161	95.95 328	99.32 306	99.45 211

1121		98.56 246	98.24 261	99.52 166	99.49 220	99.24 200	99.30 126	99.22 307	95.77 350	98.52 329	99.29 292	97.39 247	99.85 227	95.79 333	99.34 303	99.46 209

ETH3D cwj APD-0.16		98.50 255	98.16 272	99.51 169	99.04 332	99.39 162	98.47 279	99.47 239	96.70 338	98.78 310	99.33 284	97.62 239	99.86 209	94.69 354	99.38 296	99.28 258

pmmvs-eth3d		99.48 66	99.47 64	99.51 169	99.77 91	99.41 159	98.81 242	99.66 126	99.42 124	99.75 92	99.66 150	99.20 55	99.76 303	98.98 113	99.99 12	99.36 239

v1240		99.56 53	99.58 48	99.51 169	99.80 66	99.00 230	99.00 211	99.65 136	99.15 164	99.90 29	99.75 98	99.09 68	99.88 176	99.90 5	99.96 53	99.67 74

ETH3 D test6400		97.76 298	97.19 314	99.50 172	99.38 259	99.26 191	98.34 288	99.49 233	92.99 370	98.54 328	99.20 312	95.92 292	99.82 266	91.14 372	99.66 237	99.40 228

Regformer-4		99.45 75	99.44 71	99.50 172	99.52 204	98.94 238	99.17 167	99.53 212	99.64 79	99.76 84	99.60 194	98.96 88	99.90 143	98.91 124	99.84 140	99.67 74

CDS-MVSNet		99.22 139	99.13 131	99.50 172	99.35 266	99.11 218	98.96 222	99.54 203	99.46 112	99.61 153	99.70 124	96.31 283	99.83 256	99.34 62	99.88 113	99.55 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20240521		99.44 77	99.42 76	99.49 175	99.89 29	98.96 236	99.62 61	99.76 75	99.85 32	99.82 59	99.88 33	96.39 281	99.97 20	99.59 27	99.98 28	99.55 157

Patchmtry		98.78 221	98.54 233	99.49 175	98.89 347	99.19 211	99.32 119	99.67 122	99.65 77	99.72 106	99.79 75	91.87 332	99.95 48	98.00 193	99.97 39	99.33 245

UGNet		99.38 94	99.34 89	99.49 175	98.90 344	98.90 246	99.70 34	99.35 276	99.86 27	98.57 326	99.81 62	98.50 155	99.93 79	99.38 55	99.98 28	99.66 84
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
Gipuma		99.57 50	99.59 44	99.49 175	99.98 3	99.71 76	99.72 29	99.84 38	99.81 41	99.94 15	99.78 82	98.91 92	99.71 318	98.41 157	99.95 62	99.05 308
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DELS-MVS		99.34 107	99.30 100	99.48 179	99.51 209	99.36 172	98.12 307	99.53 212	99.36 129	99.41 217	99.61 185	99.22 54	99.87 189	99.21 82	99.68 226	99.20 273
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
PLC	97.35 16	98.36 270	97.99 281	99.48 179	99.32 283	99.24 200	98.50 277	99.51 224	95.19 359	98.58 325	98.96 346	96.95 266	99.83 256	95.63 336	99.25 315	99.37 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231206		99.35 102	99.31 95	99.47 181	99.74 112	99.06 229	99.28 134	99.74 87	99.23 147	99.72 106	99.53 226	97.63 238	99.88 176	99.11 103	99.84 140	99.48 200

Regformer-1		99.32 113	99.27 110	99.47 181	99.41 251	98.95 237	98.99 216	99.48 235	99.48 103	99.66 128	99.52 228	98.78 111	99.87 189	98.36 160	99.74 200	99.60 131

ab-mvs		99.33 111	99.28 107	99.47 181	99.57 180	99.39 162	99.78 11	99.43 252	98.87 198	99.57 164	99.82 59	98.06 201	99.87 189	98.69 145	99.73 207	99.15 284

Fast-Effi-MVS+		99.02 186	98.87 199	99.46 184	99.38 259	99.50 133	99.04 203	99.79 62	97.17 324	98.62 321	98.74 361	99.34 40	99.95 48	98.32 165	99.41 293	98.92 322

test_prior3		98.62 237	98.34 252	99.46 184	99.35 266	99.22 204	97.95 328	99.39 265	97.87 288	98.05 350	99.05 328	97.90 214	99.69 326	95.99 324	99.49 281	99.48 200

test_prior				99.46 184	99.35 266	99.22 204		99.39 265					99.69 326			99.48 200

TAPA-MVS	97.92 13	98.03 289	97.55 305	99.46 184	99.47 232	99.44 148	98.50 277	99.62 147	86.79 377	99.07 280	99.26 299	98.26 183	99.62 356	97.28 255	99.73 207	99.31 252
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EIA-MVS		99.12 166	99.01 171	99.45 188	99.36 264	99.62 107	99.34 114	99.79 62	98.41 244	98.84 302	98.89 353	98.75 117	99.84 244	98.15 183	99.51 277	98.89 324

test_0402		99.22 139	99.14 128	99.45 188	99.79 76	99.43 152	99.28 134	99.68 117	99.54 97	99.40 222	99.56 215	99.07 74	99.82 266	96.01 322	99.96 53	99.11 293

h-mvs33		98.61 238	98.34 252	99.44 190	99.60 161	98.67 261	99.27 137	99.44 248	99.68 67	99.32 236	99.49 240	92.50 327	100.00 1	99.24 79	96.51 377	99.65 92

VDD-MVS		99.20 146	99.11 138	99.44 190	99.43 245	98.98 232	99.50 85	98.32 354	99.80 44	99.56 171	99.69 130	96.99 265	99.85 227	98.99 111	99.73 207	99.50 190

PVSNet_Blended_VisFu		99.40 88	99.38 81	99.44 190	99.90 27	98.66 264	98.94 225	99.91 15	97.97 281	99.79 74	99.73 104	99.05 77	99.97 20	99.15 95	99.99 12	99.68 67

OMC-MVS		98.90 207	98.72 213	99.44 190	99.39 256	99.42 155	98.58 264	99.64 142	97.31 318	99.44 203	99.62 176	98.59 137	99.69 326	96.17 318	99.79 177	99.22 267

Fast-Effi-MVS+-dtu		99.20 146	99.12 135	99.43 194	99.25 298	99.69 87	99.05 201	99.82 45	99.50 101	98.97 285	99.05 328	98.98 83	99.98 10	98.20 175	99.24 317	98.62 339

MVP-Stereo		99.16 158	99.08 149	99.43 194	99.48 226	99.07 227	99.08 198	99.55 197	98.63 222	99.31 240	99.68 141	98.19 192	99.78 293	98.18 179	99.58 260	99.45 211
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs5		99.19 149	99.11 138	99.42 196	99.76 95	98.88 247	98.55 270	99.73 90	98.82 204	99.72 106	99.62 176	96.56 272	99.82 266	99.32 68	99.95 62	99.56 154

EI-MVSNet-UG-set		99.48 66	99.50 62	99.42 196	99.57 180	98.65 267	99.24 146	99.46 243	99.68 67	99.80 69	99.66 150	98.99 82	99.89 161	99.19 86	99.90 95	99.72 51

EI-MVSNet-Vis-set		99.47 72	99.49 63	99.42 196	99.57 180	98.66 264	99.24 146	99.46 243	99.67 71	99.79 74	99.65 155	98.97 85	99.89 161	99.15 95	99.89 104	99.71 54

testdata				99.42 196	99.51 209	98.93 242		99.30 288	96.20 344	98.87 299	99.40 262	98.33 178	99.89 161	96.29 312	99.28 311	99.44 216

VDDNet		98.97 196	98.82 206	99.42 196	99.71 122	98.81 251	99.62 61	98.68 337	99.81 41	99.38 224	99.80 65	94.25 306	99.85 227	98.79 134	99.32 306	99.59 140

FMVSNet5		97.80 296	97.25 312	99.42 196	98.83 353	98.97 234	99.38 105	99.80 56	98.87 198	99.25 249	99.69 130	80.60 383	99.91 123	98.96 117	99.90 95	99.38 233

MVS_111021_LR		99.13 164	99.03 167	99.42 196	99.58 170	99.32 181	97.91 334	99.73 90	98.68 218	99.31 240	99.48 243	99.09 68	99.66 346	97.70 225	99.77 187	99.29 256

CMPMVS	77.52 23	98.50 255	98.19 269	99.41 203	98.33 373	99.56 124	99.01 209	99.59 174	95.44 354	99.57 164	99.80 65	95.64 294	99.46 374	96.47 304	99.92 85	99.21 269
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
iter_conf_final		98.75 225	98.54 233	99.40 204	99.33 281	98.75 255	99.26 139	99.59 174	99.80 44	99.76 84	99.58 202	90.17 353	99.92 99	99.37 58	99.97 39	99.54 165

Regformer-3		99.41 85	99.41 77	99.40 204	99.52 204	98.70 259	99.17 167	99.44 248	99.62 83	99.75 92	99.60 194	98.90 95	99.85 227	98.89 125	99.84 140	99.65 92

UnsupCasMVSNet_bld		98.55 249	98.27 259	99.40 204	99.56 191	99.37 168	97.97 327	99.68 117	97.49 308	99.08 277	99.35 280	95.41 297	99.82 266	97.70 225	98.19 361	99.01 315

MVS_111021_HR		99.12 166	99.02 168	99.40 204	99.50 215	99.11 218	97.92 332	99.71 102	98.76 214	99.08 277	99.47 248	99.17 58	99.54 365	97.85 209	99.76 189	99.54 165

MVS_0304		98.88 211	98.71 214	99.39 208	98.85 351	98.91 245	99.45 94	99.30 288	98.56 228	97.26 370	99.68 141	96.18 287	99.96 38	99.17 91	99.94 73	99.29 256

v148		99.40 88	99.41 77	99.39 208	99.76 95	98.94 238	99.09 195	99.59 174	99.17 158	99.81 66	99.61 185	98.41 165	99.69 326	99.32 68	99.94 73	99.53 171

diffmvs		99.34 107	99.32 94	99.39 208	99.67 147	98.77 254	98.57 268	99.81 54	99.61 87	99.48 195	99.41 258	98.47 156	99.86 209	98.97 115	99.90 95	99.53 171

HQP-MVS		98.36 270	98.02 280	99.39 208	99.31 284	98.94 238	97.98 324	99.37 272	97.45 309	98.15 344	98.83 356	96.67 270	99.70 320	94.73 351	99.67 233	99.53 171

TSAR-MVS + GP.		99.12 166	99.04 165	99.38 212	99.34 276	99.16 213	98.15 303	99.29 290	98.18 270	99.63 138	99.62 176	99.18 57	99.68 337	98.20 175	99.74 200	99.30 253

AdaColmap		98.60 240	98.35 251	99.38 212	99.12 319	99.22 204	98.67 259	99.42 254	97.84 292	98.81 305	99.27 296	97.32 251	99.81 282	95.14 346	99.53 274	99.10 295

ITE_SJBPF				99.38 212	99.63 154	99.44 148		99.73 90	98.56 228	99.33 233	99.53 226	98.88 97	99.68 337	96.01 322	99.65 240	99.02 314

FMVS		99.75 17	99.88 5	99.37 215	99.96 4	98.21 291	99.51 84	100.00 1	99.94 8	100.00 1	99.93 17	99.58 22	99.94 62	99.97 2	99.99 12	99.97 2

原ACMM1				99.37 215	99.47 232	98.87 249		99.27 294	96.74 337	98.26 339	99.32 285	97.93 212	99.82 266	95.96 327	99.38 296	99.43 222

testgi		99.29 118	99.26 112	99.37 215	99.75 106	98.81 251	98.84 235	99.89 19	98.38 248	99.75 92	99.04 331	99.36 39	99.86 209	99.08 105	99.25 315	99.45 211

MSDG		99.08 175	98.98 182	99.37 215	99.60 161	99.13 216	97.54 348	99.74 87	98.84 203	99.53 183	99.55 222	99.10 66	99.79 290	97.07 271	99.86 131	99.18 278

pmmvs4		99.13 164	99.06 155	99.36 219	99.57 180	99.10 223	98.01 319	99.25 300	98.78 210	99.58 161	99.44 255	98.24 184	99.76 303	98.74 140	99.93 81	99.22 267

N_pmnet		98.73 229	98.53 235	99.35 220	99.72 119	98.67 261	98.34 288	94.65 378	98.35 255	99.79 74	99.68 141	98.03 203	99.93 79	98.28 168	99.92 85	99.44 216

Effi-MVS+		99.06 177	98.97 183	99.34 221	99.31 284	98.98 232	98.31 292	99.91 15	98.81 205	98.79 308	98.94 348	99.14 63	99.84 244	98.79 134	98.74 344	99.20 273

Vis-MVSNet (Re-imp)		98.77 222	98.58 228	99.34 221	99.78 83	98.88 247	99.61 66	99.56 191	99.11 170	99.24 252	99.56 215	93.00 322	99.78 293	97.43 246	99.89 104	99.35 242

Patchmatch-RL test		98.60 240	98.36 249	99.33 223	99.77 91	99.07 227	98.27 295	99.87 25	98.91 193	99.74 101	99.72 110	90.57 349	99.79 290	98.55 151	99.85 135	99.11 293

PAPM_NR		98.36 270	98.04 278	99.33 223	99.48 226	98.93 242	98.79 248	99.28 293	97.54 304	98.56 327	98.57 366	97.12 260	99.69 326	94.09 360	98.90 335	99.38 233

PCF-MVS	96.03 18	96.73 326	95.86 337	99.33 223	99.44 242	99.16 213	96.87 371	99.44 248	86.58 378	98.95 287	99.40 262	94.38 305	99.88 176	87.93 377	99.80 172	98.95 319
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		98.76 224	98.57 229	99.33 223	99.57 180	98.97 234	97.53 350	99.55 197	96.41 340	99.27 247	99.13 317	99.07 74	99.78 293	96.73 289	99.89 104	99.23 265
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DPM-MVS		98.28 276	97.94 289	99.32 227	99.36 264	99.11 218	97.31 360	98.78 334	96.88 331	98.84 302	99.11 323	97.77 225	99.61 360	94.03 362	99.36 301	99.23 265

jason		99.16 158	99.11 138	99.32 227	99.75 106	98.44 277	98.26 296	99.39 265	98.70 217	99.74 101	99.30 289	98.54 145	99.97 20	98.48 154	99.82 159	99.55 157
jason: jason.
FMVSNet3		98.80 220	98.63 222	99.32 227	99.13 317	98.72 258	99.10 190	99.48 235	99.23 147	99.62 147	99.64 157	92.57 324	99.86 209	98.96 117	99.90 95	99.39 231

MVSFormer		99.41 85	99.44 71	99.31 230	99.57 180	98.40 280	99.77 14	99.80 56	99.73 54	99.63 138	99.30 289	98.02 205	99.98 10	99.43 48	99.69 221	99.55 157

DP-MVS Recon		98.50 255	98.23 262	99.31 230	99.49 220	99.46 141	98.56 269	99.63 144	94.86 363	98.85 301	99.37 270	97.81 222	99.59 362	96.08 319	99.44 287	98.88 325

PatchMatch-RL		98.68 233	98.47 238	99.30 232	99.44 242	99.28 187	98.14 305	99.54 203	97.12 327	99.11 274	99.25 301	97.80 223	99.70 320	96.51 301	99.30 308	98.93 321

OPU-MVS				99.29 233	99.12 319	99.44 148	99.20 156				99.40 262	99.00 80	98.84 380	96.54 299	99.60 255	99.58 145

D2MVS		99.22 139	99.19 121	99.29 233	99.69 134	98.74 257	98.81 242	99.41 255	98.55 230	99.68 119	99.69 130	98.13 196	99.87 189	98.82 130	99.98 28	99.24 262

CANet		99.11 170	99.05 159	99.28 235	98.83 353	98.56 270	98.71 258	99.41 255	99.25 143	99.23 253	99.22 308	97.66 236	99.94 62	99.19 86	99.97 39	99.33 245

CNLPA		98.57 245	98.34 252	99.28 235	99.18 311	99.10 223	98.34 288	99.41 255	98.48 239	98.52 329	98.98 341	97.05 263	99.78 293	95.59 337	99.50 279	98.96 317

sss		98.90 207	98.77 211	99.27 237	99.48 226	98.44 277	98.72 256	99.32 281	97.94 285	99.37 225	99.35 280	96.31 283	99.91 123	98.85 127	99.63 244	99.47 205

LF4IMVS		99.01 190	98.92 192	99.27 237	99.71 122	99.28 187	98.59 263	99.77 70	98.32 261	99.39 223	99.41 258	98.62 132	99.84 244	96.62 297	99.84 140	98.69 337

LFMVS		98.46 261	98.19 269	99.26 239	99.24 300	98.52 273	99.62 61	96.94 370	99.87 24	99.31 240	99.58 202	91.04 340	99.81 282	98.68 146	99.42 292	99.45 211

WTY-MVS		98.59 243	98.37 248	99.26 239	99.43 245	98.40 280	98.74 253	99.13 319	98.10 272	99.21 259	99.24 306	94.82 300	99.90 143	97.86 207	98.77 340	99.49 195

OpenMVS	98.12 10	98.23 281	97.89 295	99.26 239	99.19 309	99.26 191	99.65 58	99.69 114	91.33 374	98.14 348	99.77 89	98.28 181	99.96 38	95.41 342	99.55 266	98.58 343

alignmvs		98.28 276	97.96 284	99.25 242	99.12 319	98.93 242	99.03 206	98.42 350	99.64 79	98.72 315	97.85 380	90.86 345	99.62 356	98.88 126	99.13 320	99.19 276

IterMVS-LS		99.41 85	99.47 64	99.25 242	99.81 61	98.09 300	98.85 234	99.76 75	99.62 83	99.83 58	99.64 157	98.54 145	99.97 20	99.15 95	99.99 12	99.68 67
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lupinMVS		98.96 199	98.87 199	99.24 244	99.57 180	98.40 280	98.12 307	99.18 313	98.28 263	99.63 138	99.13 317	98.02 205	99.97 20	98.22 173	99.69 221	99.35 242

MVSTER		98.47 260	98.22 264	99.24 244	99.06 329	98.35 285	99.08 198	99.46 243	99.27 139	99.75 92	99.66 150	88.61 361	99.85 227	99.14 101	99.92 85	99.52 182

mvs-test1		98.83 216	98.70 217	99.22 246	98.89 347	99.65 99	98.88 228	99.66 126	99.34 130	98.29 337	98.94 348	97.69 229	99.96 38	98.11 185	98.54 352	98.04 366

EI-MVSNet		99.38 94	99.44 71	99.21 247	99.58 170	98.09 300	99.26 139	99.46 243	99.62 83	99.75 92	99.67 146	98.54 145	99.85 227	99.15 95	99.92 85	99.68 67

BH-RMVSNet		98.41 266	98.14 274	99.21 247	99.21 304	98.47 274	98.60 262	98.26 355	98.35 255	98.93 289	99.31 287	97.20 258	99.66 346	94.32 356	99.10 323	99.51 184

ambc				99.20 249	99.35 266	98.53 271	99.17 167	99.46 243		99.67 124	99.80 65	98.46 159	99.70 320	97.92 199	99.70 218	99.38 233

MVS_Test		99.28 119	99.31 95	99.19 250	99.35 266	98.79 253	99.36 112	99.49 233	99.17 158	99.21 259	99.67 146	98.78 111	99.66 346	99.09 104	99.66 237	99.10 295

MAR-MVS		98.24 280	97.92 291	99.19 250	98.78 360	99.65 99	99.17 167	99.14 317	95.36 355	98.04 352	98.81 358	97.47 242	99.72 314	95.47 341	99.06 324	98.21 360
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
EPNet		98.13 284	97.77 299	99.18 252	94.57 387	97.99 304	99.24 146	97.96 359	99.74 53	97.29 369	99.62 176	93.13 319	99.97 20	98.59 149	99.83 150	99.58 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
hse-mvs2		98.52 252	98.30 257	99.16 253	99.29 290	98.60 269	98.77 250	99.02 324	99.68 67	99.32 236	99.04 331	92.50 327	99.85 227	99.24 79	97.87 368	99.03 310

ETV-MVS		99.18 153	99.18 122	99.16 253	99.34 276	99.28 187	99.12 187	99.79 62	99.48 103	98.93 289	98.55 368	99.40 29	99.93 79	98.51 153	99.52 276	98.28 356

FE-MVS		97.85 294	97.42 307	99.15 255	99.44 242	98.75 255	99.77 14	98.20 356	95.85 348	99.33 233	99.80 65	88.86 360	99.88 176	96.40 307	99.12 321	98.81 331

CL-MVSNet_self_test		98.71 231	98.56 232	99.15 255	99.22 302	98.66 264	97.14 365	99.51 224	98.09 274	99.54 178	99.27 296	96.87 268	99.74 309	98.43 156	98.96 330	99.03 310

iter_conf05		98.46 261	98.23 262	99.15 255	99.04 332	97.99 304	99.10 190	99.61 154	99.79 47	99.76 84	99.58 202	87.88 363	99.92 99	99.31 71	99.97 39	99.53 171

AUN-MVS		97.82 295	97.38 308	99.14 258	99.27 295	98.53 271	98.72 256	99.02 324	98.10 272	97.18 372	99.03 335	89.26 359	99.85 227	97.94 198	97.91 366	99.03 310

test_yl		98.25 278	97.95 285	99.13 259	99.17 312	98.47 274	99.00 211	98.67 339	98.97 182	99.22 257	99.02 336	91.31 336	99.69 326	97.26 258	98.93 331	99.24 262

DCV-MVSNet		98.25 278	97.95 285	99.13 259	99.17 312	98.47 274	99.00 211	98.67 339	98.97 182	99.22 257	99.02 336	91.31 336	99.69 326	97.26 258	98.93 331	99.24 262

MIMVSNet		98.43 264	98.20 266	99.11 261	99.53 198	98.38 283	99.58 74	98.61 341	98.96 185	99.33 233	99.76 93	90.92 342	99.81 282	97.38 249	99.76 189	99.15 284

PMMVS		98.49 258	98.29 258	99.11 261	98.96 341	98.42 279	97.54 348	99.32 281	97.53 305	98.47 333	98.15 377	97.88 217	99.82 266	97.46 244	99.24 317	99.09 298

FA-MVS(test-final)		98.52 252	98.32 255	99.10 263	99.48 226	98.67 261	99.77 14	98.60 343	97.35 315	99.63 138	99.80 65	93.07 320	99.84 244	97.92 199	99.30 308	98.78 334

CANet_DTU		98.91 205	98.85 201	99.09 264	98.79 358	98.13 295	98.18 300	99.31 285	99.48 103	98.86 300	99.51 232	96.56 272	99.95 48	99.05 107	99.95 62	99.19 276

MS-PatchMatch		99.00 192	98.97 183	99.09 264	99.11 324	98.19 292	98.76 252	99.33 279	98.49 238	99.44 203	99.58 202	98.21 189	99.69 326	98.20 175	99.62 245	99.39 231

canonicalmvs		99.02 186	99.00 174	99.09 264	99.10 325	98.70 259	99.61 66	99.66 126	99.63 82	98.64 320	97.65 382	99.04 78	99.54 365	98.79 134	98.92 333	99.04 309

PVSNet_BlendedMVS		99.03 184	99.01 171	99.09 264	99.54 193	97.99 304	98.58 264	99.82 45	97.62 299	99.34 231	99.71 117	98.52 152	99.77 301	97.98 194	99.97 39	99.52 182

MDA-MVSNet-bldmvs		99.06 177	99.05 159	99.07 268	99.80 66	97.83 312	98.89 227	99.72 99	99.29 135	99.63 138	99.70 124	96.47 276	99.89 161	98.17 181	99.82 159	99.50 190

TinyColmap		98.97 196	98.93 188	99.07 268	99.46 237	98.19 292	97.75 339	99.75 82	98.79 208	99.54 178	99.70 124	98.97 85	99.62 356	96.63 296	99.83 150	99.41 226

USDC		98.96 199	98.93 188	99.05 270	99.54 193	97.99 304	97.07 368	99.80 56	98.21 267	99.75 92	99.77 89	98.43 162	99.64 354	97.90 201	99.88 113	99.51 184

PAPR		97.56 308	97.07 316	99.04 271	98.80 357	98.11 298	97.63 344	99.25 300	94.56 367	98.02 353	98.25 376	97.43 244	99.68 337	90.90 373	98.74 344	99.33 245

PVSNet_Blended		98.70 232	98.59 225	99.02 272	99.54 193	97.99 304	97.58 347	99.82 45	95.70 352	99.34 231	98.98 341	98.52 152	99.77 301	97.98 194	99.83 150	99.30 253

MVS		95.72 345	94.63 349	98.99 273	98.56 368	97.98 310	99.30 126	98.86 329	72.71 382	97.30 368	99.08 325	98.34 176	99.74 309	89.21 374	98.33 356	99.26 259

HY-MVS	98.23 9	98.21 283	97.95 285	98.99 273	99.03 334	98.24 287	99.61 66	98.72 336	96.81 335	98.73 314	99.51 232	94.06 307	99.86 209	96.91 277	98.20 359	98.86 327

baseline1		97.73 300	97.33 309	98.96 275	99.30 288	97.73 316	99.40 101	98.42 350	99.33 133	99.46 201	99.21 310	91.18 338	99.82 266	98.35 162	91.26 382	99.32 248

DSMNet-mixed		99.48 66	99.65 30	98.95 276	99.71 122	97.27 328	99.50 85	99.82 45	99.59 95	99.41 217	99.85 46	99.62 18	100.00 1	99.53 38	99.89 104	99.59 140

thisisatest0530		97.45 310	96.95 320	98.94 277	99.68 142	97.73 316	99.09 195	94.19 381	98.61 225	99.56 171	99.30 289	84.30 379	99.93 79	98.27 169	99.54 272	99.16 282

mvs_anonymous		99.28 119	99.39 79	98.94 277	99.19 309	97.81 313	99.02 207	99.55 197	99.78 49	99.85 51	99.80 65	98.24 184	99.86 209	99.57 32	99.50 279	99.15 284

MG-MVS		98.52 252	98.39 246	98.94 277	99.15 314	97.39 326	98.18 300	99.21 311	98.89 197	99.23 253	99.63 167	97.37 249	99.74 309	94.22 358	99.61 252	99.69 61

GA-MVS		97.99 292	97.68 302	98.93 280	99.52 204	98.04 303	97.19 364	99.05 323	98.32 261	98.81 305	98.97 344	89.89 357	99.41 375	98.33 164	99.05 325	99.34 244

cl____		98.54 250	98.41 244	98.92 281	99.03 334	97.80 314	97.46 354	99.59 174	98.90 194	99.60 156	99.46 251	93.85 310	99.78 293	97.97 196	99.89 104	99.17 280

DIV-MVS_self_test		98.54 250	98.42 243	98.92 281	99.03 334	97.80 314	97.46 354	99.59 174	98.90 194	99.60 156	99.46 251	93.87 309	99.78 293	97.97 196	99.89 104	99.18 278

ET-MVSNet_ETH3D		96.78 324	96.07 333	98.91 283	99.26 297	97.92 311	97.70 342	96.05 374	97.96 284	92.37 383	98.43 372	87.06 366	99.90 143	98.27 169	97.56 371	98.91 323

xiu_mvs_v1_base_debu		99.23 130	99.34 89	98.91 283	99.59 165	98.23 288	98.47 279	99.66 126	99.61 87	99.68 119	98.94 348	99.39 30	99.97 20	99.18 88	99.55 266	98.51 346

xiu_mvs_v1_base		99.23 130	99.34 89	98.91 283	99.59 165	98.23 288	98.47 279	99.66 126	99.61 87	99.68 119	98.94 348	99.39 30	99.97 20	99.18 88	99.55 266	98.51 346

xiu_mvs_v1_base_debi		99.23 130	99.34 89	98.91 283	99.59 165	98.23 288	98.47 279	99.66 126	99.61 87	99.68 119	98.94 348	99.39 30	99.97 20	99.18 88	99.55 266	98.51 346

MSLP-MVS++		99.05 180	99.09 147	98.91 283	99.21 304	98.36 284	98.82 241	99.47 239	98.85 200	98.90 295	99.56 215	98.78 111	99.09 378	98.57 150	99.68 226	99.26 259

pmmvs3		98.08 287	97.80 296	98.91 283	99.41 251	97.69 318	97.87 335	99.66 126	95.87 347	99.50 192	99.51 232	90.35 351	99.97 20	98.55 151	99.47 284	99.08 301

tttt0517		97.62 305	97.20 313	98.90 289	99.76 95	97.40 325	99.48 89	94.36 379	99.06 177	99.70 114	99.49 240	84.55 378	99.94 62	98.73 141	99.65 240	99.36 239

OpenMVS_ROB	97.31 17	97.36 314	96.84 324	98.89 290	99.29 290	99.45 146	98.87 231	99.48 235	86.54 379	99.44 203	99.74 100	97.34 250	99.86 209	91.61 369	99.28 311	97.37 374

MDA-MVSNet_test_wron		98.95 202	98.99 179	98.85 291	99.64 152	97.16 331	98.23 298	99.33 279	98.93 190	99.56 171	99.66 150	97.39 247	99.83 256	98.29 167	99.88 113	99.55 157

PMVS	92.94 21	98.82 218	98.81 207	98.85 291	99.84 43	97.99 304	99.20 156	99.47 239	99.71 58	99.42 209	99.82 59	98.09 198	99.47 372	93.88 364	99.85 135	99.07 306
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
YYNet1		98.95 202	98.99 179	98.84 293	99.64 152	97.14 332	98.22 299	99.32 281	98.92 192	99.59 159	99.66 150	97.40 245	99.83 256	98.27 169	99.90 95	99.55 157

new_pmnet		98.88 211	98.89 197	98.84 293	99.70 130	97.62 319	98.15 303	99.50 228	97.98 280	99.62 147	99.54 224	98.15 195	99.94 62	97.55 238	99.84 140	98.95 319

CR-MVSNet		98.35 273	98.20 266	98.83 295	99.05 330	98.12 296	99.30 126	99.67 122	97.39 313	99.16 266	99.79 75	91.87 332	99.91 123	98.78 137	98.77 340	98.44 351

PatchT		98.45 263	98.32 255	98.83 295	98.94 342	98.29 286	99.24 146	98.82 332	99.84 35	99.08 277	99.76 93	91.37 335	99.94 62	98.82 130	99.00 329	98.26 357

RPMNet		98.60 240	98.53 235	98.83 295	99.05 330	98.12 296	99.30 126	99.62 147	99.86 27	99.16 266	99.74 100	92.53 326	99.92 99	98.75 139	98.77 340	98.44 351

miper_lstm_enhance		98.65 235	98.60 223	98.82 298	99.20 307	97.33 327	97.78 338	99.66 126	99.01 179	99.59 159	99.50 235	94.62 303	99.85 227	98.12 184	99.90 95	99.26 259

FPMVS		96.32 334	95.50 341	98.79 299	99.60 161	98.17 294	98.46 284	98.80 333	97.16 325	96.28 374	99.63 167	82.19 380	99.09 378	88.45 376	98.89 336	99.10 295

xiu_mvs_v2_base		99.02 186	99.11 138	98.77 300	99.37 262	98.09 300	98.13 306	99.51 224	99.47 108	99.42 209	98.54 369	99.38 34	99.97 20	98.83 128	99.33 305	98.24 358

PS-MVSNAJ		99.00 192	99.08 149	98.76 301	99.37 262	98.10 299	98.00 321	99.51 224	99.47 108	99.41 217	98.50 371	99.28 46	99.97 20	98.83 128	99.34 303	98.20 362

test0.0.03 1		97.37 313	96.91 323	98.74 302	97.72 380	97.57 320	97.60 346	97.36 369	98.00 277	99.21 259	98.02 378	90.04 355	99.79 290	98.37 159	95.89 380	98.86 327

c3_l		98.72 230	98.71 214	98.72 303	99.12 319	97.22 330	97.68 343	99.56 191	98.90 194	99.54 178	99.48 243	96.37 282	99.73 312	97.88 203	99.88 113	99.21 269

EU-MVSNet		99.39 92	99.62 35	98.72 303	99.88 33	96.44 344	99.56 78	99.85 32	99.90 13	99.90 29	99.85 46	98.09 198	99.83 256	99.58 30	99.95 62	99.90 7

new-patchmatchnet		99.35 102	99.57 51	98.71 305	99.82 54	96.62 342	98.55 270	99.75 82	99.50 101	99.88 39	99.87 36	99.31 42	99.88 176	99.43 48	100.00 1	99.62 117

thisisatest0515		96.98 320	96.42 327	98.66 306	99.42 250	97.47 322	97.27 361	94.30 380	97.24 320	99.15 268	98.86 355	85.01 376	99.87 189	97.10 269	99.39 295	98.63 338

eth_miper_zixun_eth		98.68 233	98.71 214	98.60 307	99.10 325	96.84 339	97.52 352	99.54 203	98.94 187	99.58 161	99.48 243	96.25 285	99.76 303	98.01 192	99.93 81	99.21 269

miper_ehance_all_eth		98.59 243	98.59 225	98.59 308	98.98 340	97.07 333	97.49 353	99.52 220	98.50 236	99.52 185	99.37 270	96.41 280	99.71 318	97.86 207	99.62 245	99.00 316

BH-untuned		98.22 282	98.09 276	98.58 309	99.38 259	97.24 329	98.55 270	98.98 327	97.81 293	99.20 264	98.76 360	97.01 264	99.65 352	94.83 350	98.33 356	98.86 327

IterMVS-SCA-FT		99.00 192	99.16 124	98.51 310	99.75 106	95.90 352	98.07 314	99.84 38	99.84 35	99.89 33	99.73 104	96.01 290	99.99 6	99.33 66	100.00 1	99.63 106

JIA-IIPM		98.06 288	97.92 291	98.50 311	98.59 367	97.02 334	98.80 245	98.51 346	99.88 23	97.89 357	99.87 36	91.89 331	99.90 143	98.16 182	97.68 370	98.59 341

Patchmatch-test		98.10 286	97.98 283	98.48 312	99.27 295	96.48 343	99.40 101	99.07 320	98.81 205	99.23 253	99.57 212	90.11 354	99.87 189	96.69 290	99.64 242	99.09 298

baseline2		96.83 323	96.28 329	98.46 313	99.09 327	96.91 337	98.83 237	93.87 382	97.23 321	96.23 377	98.36 373	88.12 362	99.90 143	96.68 291	98.14 363	98.57 344

IterMVS		98.97 196	99.16 124	98.42 314	99.74 112	95.64 355	98.06 316	99.83 40	99.83 38	99.85 51	99.74 100	96.10 289	99.99 6	99.27 78	100.00 1	99.63 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl22		97.56 308	97.28 310	98.40 315	98.37 372	96.75 340	97.24 363	99.37 272	97.31 318	99.41 217	99.22 308	87.30 364	99.37 376	97.70 225	99.62 245	99.08 301

CHOSEN 280x420		98.41 266	98.41 244	98.40 315	99.34 276	95.89 353	96.94 370	99.44 248	98.80 207	99.25 249	99.52 228	93.51 316	99.98 10	98.94 122	99.98 28	99.32 248

API-MVS		98.38 269	98.39 246	98.35 317	98.83 353	99.26 191	99.14 177	99.18 313	98.59 226	98.66 319	98.78 359	98.61 134	99.57 364	94.14 359	99.56 262	96.21 378

PVSNet	97.47 15	98.42 265	98.44 241	98.35 317	99.46 237	96.26 346	96.70 373	99.34 278	97.68 297	99.00 284	99.13 317	97.40 245	99.72 314	97.59 237	99.68 226	99.08 301

miper_enhance_ethall		98.03 289	97.94 289	98.32 319	98.27 374	96.43 345	96.95 369	99.41 255	96.37 342	99.43 207	98.96 346	94.74 301	99.69 326	97.71 222	99.62 245	98.83 330

TR-MVS		97.44 311	97.15 315	98.32 319	98.53 369	97.46 323	98.47 279	97.91 361	96.85 333	98.21 343	98.51 370	96.42 278	99.51 370	92.16 368	97.29 372	97.98 367

PAPM		95.61 346	94.71 348	98.31 321	99.12 319	96.63 341	96.66 374	98.46 349	90.77 375	96.25 375	98.68 363	93.01 321	99.69 326	81.60 383	97.86 369	98.62 339

MVE	92.54 22	96.66 328	96.11 332	98.31 321	99.68 142	97.55 321	97.94 330	95.60 376	99.37 127	90.68 384	98.70 362	96.56 272	98.61 382	86.94 382	99.55 266	98.77 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
1314		98.00 291	97.90 294	98.27 323	98.90 344	97.45 324	99.30 126	99.06 322	94.98 360	97.21 371	99.12 321	98.43 162	99.67 342	95.58 338	98.56 351	97.71 370

ppachtmachnet_test		98.89 210	99.12 135	98.20 324	99.66 148	95.24 359	97.63 344	99.68 117	99.08 171	99.78 77	99.62 176	98.65 130	99.88 176	98.02 189	99.96 53	99.48 200

SD-MVS		99.01 190	99.30 100	98.15 325	99.50 215	99.40 160	98.94 225	99.61 154	99.22 151	99.75 92	99.82 59	99.54 26	95.51 385	97.48 243	99.87 124	99.54 165
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
our_test_3		98.85 215	99.09 147	98.13 326	99.66 148	94.90 362	97.72 340	99.58 184	99.07 173	99.64 134	99.62 176	98.19 192	99.93 79	98.41 157	99.95 62	99.55 157

ADS-MVSNet2		97.78 297	97.66 304	98.12 327	99.14 315	95.36 357	99.22 153	98.75 335	96.97 329	98.25 340	99.64 157	90.90 343	99.94 62	96.51 301	99.56 262	99.08 301

DeepMVS_CX				97.98 328	99.69 134	96.95 335		99.26 297	75.51 381	95.74 379	98.28 375	96.47 276	99.62 356	91.23 371	97.89 367	97.38 373

gg-mvs-nofinetune		95.87 342	95.17 346	97.97 329	98.19 376	96.95 335	99.69 40	89.23 388	99.89 18	96.24 376	99.94 16	81.19 381	99.51 370	93.99 363	98.20 359	97.44 372

thres600view7		96.60 329	96.16 331	97.93 330	99.63 154	96.09 350	99.18 162	97.57 364	98.77 211	98.72 315	97.32 385	87.04 367	99.72 314	88.57 375	98.62 349	97.98 367

thres400		96.40 331	95.89 335	97.92 331	99.58 170	96.11 348	99.00 211	97.54 367	98.43 241	98.52 329	96.98 388	86.85 369	99.67 342	87.62 378	98.51 353	97.98 367

ADS-MVSNet		97.72 303	97.67 303	97.86 332	99.14 315	94.65 363	99.22 153	98.86 329	96.97 329	98.25 340	99.64 157	90.90 343	99.84 244	96.51 301	99.56 262	99.08 301

IB-MVS	95.41 20	95.30 347	94.46 351	97.84 333	98.76 362	95.33 358	97.33 359	96.07 373	96.02 346	95.37 381	97.41 384	76.17 389	99.96 38	97.54 239	95.44 381	98.22 359
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
CVMVSNet		98.61 238	98.88 198	97.80 334	99.58 170	93.60 369	99.26 139	99.64 142	99.66 75	99.72 106	99.67 146	93.26 317	99.93 79	99.30 72	99.81 167	99.87 12

BH-w/o		97.20 315	97.01 318	97.76 335	99.08 328	95.69 354	98.03 318	98.52 345	95.76 351	97.96 354	98.02 378	95.62 295	99.47 372	92.82 367	97.25 373	98.12 364

tpm		97.15 316	96.95 320	97.75 336	98.91 343	94.24 365	99.32 119	97.96 359	97.71 296	98.29 337	99.32 285	86.72 373	99.92 99	98.10 187	96.24 379	99.09 298

test-LLR		97.15 316	96.95 320	97.74 337	98.18 377	95.02 360	97.38 356	96.10 371	98.00 277	97.81 361	98.58 364	90.04 355	99.91 123	97.69 231	98.78 338	98.31 354

test-mter		96.23 337	95.73 339	97.74 337	98.18 377	95.02 360	97.38 356	96.10 371	97.90 286	97.81 361	98.58 364	79.12 387	99.91 123	97.69 231	98.78 338	98.31 354

tfpn200view9		96.30 335	95.89 335	97.53 339	99.58 170	96.11 348	99.00 211	97.54 367	98.43 241	98.52 329	96.98 388	86.85 369	99.67 342	87.62 378	98.51 353	96.81 376

cascas		96.99 319	96.82 325	97.48 340	97.57 383	95.64 355	96.43 375	99.56 191	91.75 372	97.13 373	97.61 383	95.58 296	98.63 381	96.68 291	99.11 322	98.18 363

thres100view900		96.39 332	96.03 334	97.47 341	99.63 154	95.93 351	99.18 162	97.57 364	98.75 215	98.70 317	97.31 386	87.04 367	99.67 342	87.62 378	98.51 353	96.81 376

PVSNet_0	95.53 19	95.85 343	95.31 345	97.47 341	98.78 360	93.48 370	95.72 376	99.40 262	96.18 345	97.37 367	97.73 381	95.73 293	99.58 363	95.49 339	81.40 383	99.36 239

TESTMET0.1,1		96.24 336	95.84 338	97.41 343	98.24 375	93.84 368	97.38 356	95.84 375	98.43 241	97.81 361	98.56 367	79.77 384	99.89 161	97.77 214	98.77 340	98.52 345

GG-mvs-BLEND				97.36 344	97.59 381	96.87 338	99.70 34	88.49 389		94.64 382	97.26 387	80.66 382	99.12 377	91.50 370	96.50 378	96.08 380

SCA		98.11 285	98.36 249	97.36 344	99.20 307	92.99 371	98.17 302	98.49 348	98.24 265	99.10 276	99.57 212	96.01 290	99.94 62	96.86 280	99.62 245	99.14 289

thres200		96.09 338	95.68 340	97.33 346	99.48 226	96.22 347	98.53 274	97.57 364	98.06 276	98.37 336	96.73 390	86.84 371	99.61 360	86.99 381	98.57 350	96.16 379

KD-MVS_2432*1600		95.89 340	95.41 343	97.31 347	94.96 385	93.89 366	97.09 366	99.22 307	97.23 321	98.88 296	99.04 331	79.23 385	99.54 365	96.24 315	96.81 374	98.50 349

miper_refine_blended		95.89 340	95.41 343	97.31 347	94.96 385	93.89 366	97.09 366	99.22 307	97.23 321	98.88 296	99.04 331	79.23 385	99.54 365	96.24 315	96.81 374	98.50 349

PatchmatchNet		97.65 304	97.80 296	97.18 349	98.82 356	92.49 373	99.17 167	98.39 352	98.12 271	98.79 308	99.58 202	90.71 347	99.89 161	97.23 262	99.41 293	99.16 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.53 330	96.32 328	97.17 350	98.18 377	92.97 372	99.39 103	89.95 387	98.21 267	98.61 322	99.59 200	86.69 374	99.72 314	96.99 273	99.23 319	98.81 331

EPNet_dtu		97.62 305	97.79 298	97.11 351	96.67 384	92.31 374	98.51 276	98.04 357	99.24 145	95.77 378	99.47 248	93.78 312	99.66 346	98.98 113	99.62 245	99.37 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ECVR-MVS		97.73 300	98.04 278	96.78 352	99.59 165	90.81 383	99.72 29	90.43 386	99.89 18	99.86 49	99.86 43	93.60 315	99.89 161	99.46 45	99.99 12	99.65 92

tmp_tt		95.75 344	95.42 342	96.76 353	89.90 389	94.42 364	98.86 232	97.87 362	78.01 380	99.30 245	99.69 130	97.70 227	95.89 384	99.29 75	98.14 363	99.95 3

MVS-HIRNet		97.86 293	98.22 264	96.76 353	99.28 293	91.53 379	98.38 287	92.60 383	99.13 166	99.31 240	99.96 12	97.18 259	99.68 337	98.34 163	99.83 150	99.07 306

tpm2		96.35 333	96.22 330	96.73 355	98.88 350	91.75 377	99.21 155	98.51 346	93.27 369	97.89 357	99.21 310	84.83 377	99.70 320	96.04 321	98.18 362	98.75 336

tpmrst		97.73 300	98.07 277	96.73 355	98.71 364	92.00 375	99.10 190	98.86 329	98.52 234	98.92 292	99.54 224	91.90 330	99.82 266	98.02 189	99.03 327	98.37 353

tpmvs		97.39 312	97.69 301	96.52 357	98.41 370	91.76 376	99.30 126	98.94 328	97.74 294	97.85 360	99.55 222	92.40 329	99.73 312	96.25 314	98.73 346	98.06 365

test1111		97.74 299	98.16 272	96.49 358	99.60 161	89.86 387	99.71 33	91.21 384	99.89 18	99.88 39	99.87 36	93.73 313	99.90 143	99.56 33	99.99 12	99.70 57

CostFormer		96.71 327	96.79 326	96.46 359	98.90 344	90.71 384	99.41 100	98.68 337	94.69 366	98.14 348	99.34 283	86.32 375	99.80 287	97.60 236	98.07 365	98.88 325

E-PMN		97.14 318	97.43 306	96.27 360	98.79 358	91.62 378	95.54 377	99.01 326	99.44 115	98.88 296	99.12 321	92.78 323	99.68 337	94.30 357	99.03 327	97.50 371

dp		96.86 322	97.07 316	96.24 361	98.68 366	90.30 386	99.19 161	98.38 353	97.35 315	98.23 342	99.59 200	87.23 365	99.82 266	96.27 313	98.73 346	98.59 341

tpm cat1		96.78 324	96.98 319	96.16 362	98.85 351	90.59 385	99.08 198	99.32 281	92.37 371	97.73 366	99.46 251	91.15 339	99.69 326	96.07 320	98.80 337	98.21 360

EMVS		96.96 321	97.28 310	95.99 363	98.76 362	91.03 381	95.26 378	98.61 341	99.34 130	98.92 292	98.88 354	93.79 311	99.66 346	92.87 366	99.05 325	97.30 375

test2506		94.73 348	94.59 350	95.15 364	99.59 165	85.90 389	99.75 21	74.01 390	99.89 18	99.71 111	99.86 43	79.00 388	99.90 143	99.52 39	99.99 12	99.65 92

wuyk23d		97.58 307	99.13 131	92.93 365	99.69 134	99.49 134	99.52 82	99.77 70	97.97 281	99.96 11	99.79 75	99.84 5	99.94 62	95.85 330	99.82 159	79.36 381

test_method		91.72 349	92.32 352	89.91 366	93.49 388	70.18 390	90.28 379	99.56 191	61.71 383	95.39 380	99.52 228	93.90 308	99.94 62	98.76 138	98.27 358	99.62 117

test123		29.31 351	33.05 356	18.08 367	25.93 391	12.24 391	97.53 350	10.93 392	11.78 385	24.21 386	50.08 395	21.04 390	8.60 386	23.51 384	32.43 385	33.39 382

testmvs		28.94 352	33.33 354	15.79 368	26.03 390	9.81 392	96.77 372	15.67 391	11.55 386	23.87 387	50.74 394	19.03 391	8.53 387	23.21 385	33.07 384	29.03 383

test_blank		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		24.88 353	33.17 355	0.00 369	0.00 392	0.00 393	0.00 380	99.62 147	0.00 387	0.00 388	99.13 317	99.82 6	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		16.61 354	22.14 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	99.28 46	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		8.26 363	11.02 366	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	99.16 315	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		8.33 355	11.11 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	100.00 1	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

FOURS1					99.83 47	99.89 10	99.74 23	99.71 102	99.69 65	99.63 138

PC_three_1452									97.56 301	99.68 119	99.41 258	99.09 68	97.09 383	96.66 293	99.60 255	99.62 117

test_one_0601					99.63 154	99.76 57		99.55 197	99.23 147	99.31 240	99.61 185	98.59 137

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					99.43 245	99.61 113		99.43 252	96.38 341	99.11 274	99.07 326	97.86 218	99.92 99	94.04 361	99.49 281

RE-MVS-def			99.13 131		99.54 193	99.74 67	99.26 139	99.62 147	99.16 160	99.52 185	99.64 157	98.57 140		97.27 256	99.61 252	99.54 165

IU-MVS					99.69 134	99.77 49		99.22 307	97.50 307	99.69 117				97.75 218	99.70 218	99.77 40

test_241102_TWO								99.54 203	99.13 166	99.76 84	99.63 167	98.32 179	99.92 99	97.85 209	99.69 221	99.75 48

test_241102_ONE					99.69 134	99.82 33		99.54 203	99.12 169	99.82 59	99.49 240	98.91 92	99.52 369

9.14			98.64 220		99.45 240		98.81 242	99.60 167	97.52 306	99.28 246	99.56 215	98.53 149	99.83 256	95.36 344	99.64 242

save fliter					99.53 198	99.25 195	98.29 293	99.38 271	99.07 173

test_0728_THIRD									99.18 154	99.62 147	99.61 185	98.58 139	99.91 123	97.72 220	99.80 172	99.77 40

test0726					99.69 134	99.80 41	99.24 146	99.57 186	99.16 160	99.73 105	99.65 155	98.35 173

GSMVS																99.14 289

test_part2					99.62 158	99.67 92				99.55 176

sam_mvs1												90.81 346				99.14 289

sam_mvs												90.52 350

MTGPA								99.53 212

test_post1							99.14 177				51.63 393	89.54 358	99.82 266	96.86 280

test_post											52.41 392	90.25 352	99.86 209

patchmatchnet-post											99.62 176	90.58 348	99.94 62

MTMP							99.09 195	98.59 344

gm-plane-assit					97.59 381	89.02 388			93.47 368		98.30 374		99.84 244	96.38 309

test9_res														95.10 347	99.44 287	99.50 190

TEST9					99.35 266	99.35 176	98.11 309	99.41 255	94.83 365	97.92 355	98.99 338	98.02 205	99.85 227

test_8					99.34 276	99.31 182	98.08 313	99.40 262	94.90 361	97.87 359	98.97 344	98.02 205	99.84 244

agg_prior2														94.58 355	99.46 286	99.50 190

agg_prior					99.35 266	99.36 172		99.39 265		97.76 364			99.85 227

test_prior4						99.19 211	98.00 321

test_prior2							97.95 328		97.87 288	98.05 350	99.05 328	97.90 214		95.99 324	99.49 281

旧先验2							97.94 330		95.33 356	98.94 288			99.88 176	96.75 287

新几何2							98.04 317

旧先验1					99.49 220	99.29 185		99.26 297			99.39 266	97.67 232			99.36 301	99.46 209

无先验							98.01 319	99.23 304	95.83 349				99.85 227	95.79 333		99.44 216

原ACMM2							97.92 332

test222					99.51 209	99.08 226	97.83 337	99.29 290	95.21 358	98.68 318	99.31 287	97.28 252			99.38 296	99.43 222

testdata2													99.89 161	95.99 324

segment_acmp												98.37 171

testdata1							97.72 340		97.86 291

plane_prior7					99.58 170	99.38 165

plane_prior6					99.47 232	99.26 191						97.24 253

plane_prior5								99.54 203					99.82 266	95.84 331	99.78 183	99.60 131

plane_prior4											99.25 301

plane_prior3						99.31 182			98.36 250	99.14 270

plane_prior2							98.80 245		98.94 187

plane_prior1					99.51 209

plane_prior						99.24 200	98.42 285		97.87 288						99.71 216

n2								0.00 393

nn								0.00 393

door-mid								99.83 40

test11								99.29 290

door								99.77 70

HQP5-MVS						98.94 238

HQP-NCC					99.31 284		97.98 324		97.45 309	98.15 344

ACMP_Plane					99.31 284		97.98 324		97.45 309	98.15 344

BP-MVS														94.73 351

HQP4-MVS										98.15 344			99.70 320			99.53 171

HQP3-MVS								99.37 272							99.67 233

HQP2-MVS												96.67 270

NP-MVS					99.40 254	99.13 216					98.83 356

MDTV_nov1_ep13_2view						91.44 380	99.14 177		97.37 314	99.21 259		91.78 334		96.75 287		99.03 310

MDTV_nov1_ep13			97.73 300		98.70 365	90.83 382	99.15 175	98.02 358	98.51 235	98.82 304	99.61 185	90.98 341	99.66 346	96.89 279	98.92 333

ACMMP++_ref															99.94 73

ACMMP++															99.79 177

Test By Simon												98.41 165

High-resolution multi-view benchmark