High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 65	98.87 9	99.30 12	99.01 16	99.63 9	99.66 3	99.27 2	99.68 128	97.75 30	99.89 22	99.62 26

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 67	99.18 5	99.20 18	99.67 2	99.73 3	99.65 4	99.15 3	99.86 22	97.22 48	99.92 14	99.77 8

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 66	99.17 6	99.05 45	98.05 43	99.61 11	99.52 5	93.72 182	99.88 19	98.72 9	99.88 23	99.65 24

Gipuma		98.07 41	98.31 29	97.36 152	99.76 5	96.28 70	98.51 23	99.10 33	98.76 23	96.79 202	99.34 17	96.61 79	98.82 308	96.38 76	99.50 119	96.98 318
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 52	98.76 11	98.89 81	98.49 28	99.38 17	99.14 33	95.44 130	99.84 29	96.47 73	99.80 36	99.47 64

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 38	99.71 7	96.99 46	99.69 2	99.57 6	99.02 15	99.62 10	99.36 14	98.53 7	99.52 184	98.58 12	99.95 5	99.66 22
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 63	98.54 20	99.22 15	96.23 115	99.71 4	99.48 7	98.77 6	99.93 3	98.89 3	99.95 5	99.84 5

PS-MVSNAJss		98.53 19	98.63 19	98.21 80	99.68 9	94.82 131	98.10 50	99.21 16	96.91 88	99.75 2	99.45 9	95.82 109	99.92 5	98.80 4	99.96 4	99.89 1

jajsoiax		98.77 9	98.79 12	98.74 35	99.66 10	96.48 63	98.45 27	99.12 30	95.83 142	99.67 6	99.37 12	98.25 10	99.92 5	98.77 5	99.94 8	99.82 6

v7n		98.73 11	98.99 5	97.95 98	99.64 11	94.20 157	98.67 13	99.14 28	99.08 10	99.42 15	99.23 21	96.53 84	99.91 13	99.27 2	99.93 10	99.73 15

test_djsdf		98.73 11	98.74 16	98.69 40	99.63 12	96.30 69	98.67 13	99.02 54	96.50 103	99.32 20	99.44 10	97.43 31	99.92 5	98.73 7	99.95 5	99.86 2

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 13	97.29 39	98.65 16	99.19 20	95.62 150	99.35 19	99.37 12	97.38 33	99.90 14	98.59 11	99.91 17	99.77 8

FOURS1					99.59 14	98.20 4	99.03 7	99.25 14	98.96 18	98.87 41

PEN-MVS		98.75 10	98.85 10	98.44 56	99.58 15	95.67 91	98.45 27	99.15 26	99.33 5	99.30 21	99.00 41	97.27 38	99.92 5	97.64 34	99.92 14	99.75 13

EGC-MVSNET		83.08 340	77.93 343	98.53 51	99.57 16	97.55 27	98.33 33	98.57 163	4.71 376	10.38 377	98.90 50	95.60 122	99.50 189	95.69 108	99.61 79	98.55 233

Baseline_NR-MVSNet		97.72 77	97.79 57	97.50 134	99.56 17	93.29 187	95.44 195	98.86 92	98.20 38	98.37 77	99.24 20	94.69 152	99.55 175	95.98 94	99.79 38	99.65 24

SixPastTwentyTwo		97.49 94	97.57 83	97.26 158	99.56 17	92.33 205	98.28 37	96.97 282	98.30 34	99.45 14	99.35 16	88.43 269	99.89 17	98.01 20	99.76 44	99.54 40

PS-CasMVS		98.73 11	98.85 10	98.39 60	99.55 19	95.47 103	98.49 24	99.13 29	99.22 8	99.22 27	98.96 45	97.35 34	99.92 5	97.79 28	99.93 10	99.79 7

DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 19	96.12 74	98.48 26	99.10 33	99.36 4	99.29 23	99.06 39	97.27 38	99.93 3	97.71 32	99.91 17	99.70 18

HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 21	97.48 32	98.35 30	99.03 52	95.88 137	97.88 139	98.22 112	98.15 12	99.74 79	96.50 72	99.62 73	99.42 83

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 21	98.06 8	99.34 4	99.44 10	98.85 20	99.00 37	99.20 23	97.42 32	99.59 162	97.21 50	99.76 44	99.40 86

pm-mvs1		98.47 21	98.67 17	97.86 105	99.52 23	94.58 141	98.28 37	99.00 62	97.57 63	99.27 24	99.22 22	98.32 9	99.50 189	97.09 56	99.75 48	99.50 47

TransMVSNet (Re)		98.38 25	98.67 17	97.51 131	99.51 24	93.39 186	98.20 45	98.87 89	98.23 36	99.48 12	99.27 19	98.47 8	99.55 175	96.52 70	99.53 105	99.60 27

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 24	96.61 57	98.55 19	99.17 21	99.05 13	99.17 29	98.79 55	95.47 128	99.89 17	97.95 21	99.91 17	99.75 13

PMVS	89.60 17	96.71 145	96.97 122	95.95 226	99.51 24	97.81 17	97.42 94	97.49 263	97.93 46	95.95 246	98.58 69	96.88 66	96.91 364	89.59 293	99.36 162	93.12 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		97.69 79	97.36 97	98.70 39	99.50 27	96.84 49	95.38 202	98.99 65	92.45 254	98.11 111	98.31 92	97.25 41	99.77 57	96.60 66	99.62 73	99.48 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		98.16 33	98.37 27	97.56 126	99.49 28	93.10 192	98.35 30	99.21 16	98.43 29	98.89 40	98.83 54	94.30 167	99.81 36	97.87 24	99.91 17	99.77 8

VPNet		97.26 110	97.49 90	96.59 193	99.47 29	90.58 240	96.27 148	98.53 166	97.77 49	98.46 70	98.41 83	94.59 158	99.68 128	94.61 171	99.29 185	99.52 44

CP-MVSNet		98.42 23	98.46 24	98.30 69	99.46 30	95.22 119	98.27 39	98.84 101	99.05 13	99.01 36	98.65 67	95.37 131	99.90 14	97.57 36	99.91 17	99.77 8

XXY-MVS		97.54 90	97.70 64	97.07 167	99.46 30	92.21 209	97.22 103	99.00 62	94.93 180	98.58 59	98.92 48	97.31 36	99.41 219	94.44 178	99.43 145	99.59 28

zzz-MVS		98.01 45	97.66 69	99.06 4	99.44 32	97.90 12	95.66 185	98.73 131	97.69 59	97.90 136	97.96 142	95.81 113	99.82 33	96.13 83	99.61 79	99.45 71

MTAPA		98.14 34	97.84 53	99.06 4	99.44 32	97.90 12	97.25 100	98.73 131	97.69 59	97.90 136	97.96 142	95.81 113	99.82 33	96.13 83	99.61 79	99.45 71

SteuartSystems-ACMMP		98.02 44	97.76 61	98.79 31	99.43 34	97.21 43	97.15 106	98.90 80	96.58 99	98.08 117	97.87 156	97.02 53	99.76 62	95.25 139	99.59 85	99.40 86
Skip Steuart: Steuart Systems R&D Blog.
ACMH	93.61 9	98.44 22	98.76 13	97.51 131	99.43 34	93.54 182	98.23 40	99.05 45	97.40 75	99.37 18	99.08 37	98.79 5	99.47 197	97.74 31	99.71 58	99.50 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 38	97.83 55	98.92 22	99.42 36	97.46 33	98.57 17	99.05 45	95.43 159	97.41 167	97.50 190	97.98 15	99.79 43	95.58 119	99.57 90	99.50 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v3		96.44 159	96.28 160	96.95 172	99.41 37	91.53 225	97.65 76	90.31 360	98.89 19	98.93 39	99.36 14	84.57 298	99.92 5	97.81 26	99.56 93	99.39 88

VDDNet		96.98 123	96.84 130	97.41 148	99.40 38	93.26 188	97.94 57	95.31 317	99.26 7	98.39 76	99.18 27	87.85 278	99.62 154	95.13 151	99.09 213	99.35 100

ACMH+	93.58 10	98.23 32	98.31 29	97.98 97	99.39 39	95.22 119	97.55 83	99.20 18	98.21 37	99.25 25	98.51 76	98.21 11	99.40 221	94.79 165	99.72 55	99.32 103

TSAR-MVS + MP.		97.42 99	97.23 107	98.00 96	99.38 40	95.00 126	97.63 78	98.20 206	93.00 242	98.16 105	98.06 132	95.89 104	99.72 90	95.67 110	99.10 212	99.28 117
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
FIs		97.93 56	98.07 36	97.48 138	99.38 40	92.95 195	98.03 55	99.11 31	98.04 44	98.62 53	98.66 65	93.75 181	99.78 47	97.23 47	99.84 29	99.73 15

lessismore_v0				97.05 168	99.36 42	92.12 213		84.07 372		98.77 48	98.98 43	85.36 292	99.74 79	97.34 46	99.37 159	99.30 109

Anonymous20240521		97.07 117	97.51 87	95.76 234	99.35 43	88.18 277	97.78 66	98.40 183	97.11 83	98.34 83	99.04 40	89.58 256	99.79 43	98.09 18	99.93 10	99.30 109

ACMMP_NAP		97.89 62	97.63 76	98.67 41	99.35 43	96.84 49	96.36 144	98.79 118	95.07 173	97.88 139	98.35 88	97.24 42	99.72 90	96.05 87	99.58 87	99.45 71

Vis-MVSNet		98.27 29	98.34 28	98.07 89	99.33 45	95.21 121	98.04 53	99.46 9	97.32 78	97.82 147	99.11 34	96.75 73	99.86 22	97.84 25	99.36 162	99.15 142
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high		98.31 28	98.94 6	96.41 206	99.33 45	89.64 251	97.92 60	99.56 7	99.27 6	99.66 8	99.50 6	97.67 25	99.83 32	97.55 37	99.98 2	99.77 8

ZNCC-MVS		97.92 57	97.62 78	98.83 26	99.32 47	97.24 41	97.45 90	98.84 101	95.76 144	96.93 197	97.43 196	97.26 40	99.79 43	96.06 85	99.53 105	99.45 71

MP-MVS		97.64 82	97.18 110	99.00 12	99.32 47	97.77 18	97.49 89	98.73 131	96.27 112	95.59 261	97.75 168	96.30 97	99.78 47	93.70 212	99.48 127	99.45 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu		95.95 178	95.80 180	96.42 204	99.28 49	90.62 239	95.31 208	99.08 39	88.40 300	96.97 195	98.17 117	92.11 219	99.78 47	93.64 213	99.21 194	98.86 201

tfpnnormal		97.72 77	97.97 43	96.94 173	99.26 50	92.23 208	97.83 65	98.45 173	98.25 35	99.13 31	98.66 65	96.65 76	99.69 121	93.92 204	99.62 73	98.91 190

MSP-MVS		97.45 97	96.92 127	99.03 8	99.26 50	97.70 19	97.66 75	98.89 81	95.65 148	98.51 63	96.46 265	92.15 217	99.81 36	95.14 149	98.58 266	99.58 29
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
testgi		96.07 172	96.50 153	94.80 274	99.26 50	87.69 290	95.96 169	98.58 162	95.08 172	98.02 125	96.25 276	97.92 16	97.60 361	88.68 307	98.74 251	99.11 157

IS-MVSNet		96.93 125	96.68 139	97.70 117	99.25 53	94.00 163	98.57 17	96.74 291	98.36 31	98.14 109	97.98 141	88.23 271	99.71 104	93.10 224	99.72 55	99.38 90

DVP-MVS		97.78 73	97.65 71	98.16 81	99.24 54	95.51 98	96.74 127	98.23 202	95.92 134	98.40 74	98.28 101	97.06 50	99.71 104	95.48 124	99.52 110	99.26 122
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
test0726					99.24 54	95.51 98	96.89 120	98.89 81	95.92 134	98.64 52	98.31 92	97.06 50

test_0728_SECOND				98.25 75	99.23 56	95.49 102	96.74 127	98.89 81					99.75 69	95.48 124	99.52 110	99.53 43

GST-MVS		97.82 70	97.49 90	98.81 29	99.23 56	97.25 40	97.16 105	98.79 118	95.96 131	97.53 153	97.40 198	96.93 60	99.77 57	95.04 155	99.35 167	99.42 83

ACMMP		98.05 42	97.75 63	98.93 21	99.23 56	97.60 23	98.09 51	98.96 73	95.75 146	97.91 135	98.06 132	96.89 64	99.76 62	95.32 135	99.57 90	99.43 82
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
KD-MVS_self_test		97.86 66	98.07 36	97.25 159	99.22 59	92.81 198	97.55 83	98.94 76	97.10 84	98.85 42	98.88 51	95.03 143	99.67 133	97.39 45	99.65 68	99.26 122

SED-MVS		97.94 53	97.90 47	98.07 89	99.22 59	95.35 109	96.79 124	98.83 108	96.11 121	99.08 32	98.24 107	97.87 20	99.72 90	95.44 128	99.51 115	99.14 145

IU-MVS					99.22 59	95.40 104		98.14 218	85.77 325	98.36 80				95.23 141	99.51 115	99.49 55

test_241102_ONE					99.22 59	95.35 109		98.83 108	96.04 126	99.08 32	98.13 119	97.87 20	99.33 242

nrg030		98.54 18	98.62 21	98.32 65	99.22 59	95.66 92	97.90 61	99.08 39	98.31 33	99.02 35	98.74 59	97.68 24	99.61 160	97.77 29	99.85 28	99.70 18

region2R		97.92 57	97.59 81	98.92 22	99.22 59	97.55 27	97.60 79	98.84 101	96.00 129	97.22 171	97.62 180	96.87 68	99.76 62	95.48 124	99.43 145	99.46 66

mPP-MVS		97.91 60	97.53 85	99.04 7	99.22 59	97.87 15	97.74 72	98.78 122	96.04 126	97.10 181	97.73 171	96.53 84	99.78 47	95.16 146	99.50 119	99.46 66

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 66	97.35 37	97.96 56	99.16 22	98.34 32	98.78 46	98.52 75	97.32 35	99.45 204	94.08 195	99.67 65	99.13 148
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		97.95 51	97.62 78	98.94 18	99.20 67	97.56 26	97.59 80	98.83 108	96.05 124	97.46 164	97.63 179	96.77 72	99.76 62	95.61 116	99.46 132	99.49 55

PGM-MVS		97.88 63	97.52 86	98.96 16	99.20 67	97.62 22	97.09 111	99.06 43	95.45 157	97.55 152	97.94 147	97.11 44	99.78 47	94.77 168	99.46 132	99.48 61

test_0402		97.84 67	97.97 43	97.47 139	99.19 69	94.07 160	96.71 132	98.73 131	98.66 25	98.56 60	98.41 83	96.84 70	99.69 121	94.82 163	99.81 33	98.64 223

EPP-MVSNet		96.84 131	96.58 143	97.65 121	99.18 70	93.78 173	98.68 12	96.34 295	97.91 47	97.30 169	98.06 132	88.46 268	99.85 26	93.85 206	99.40 155	99.32 103

abl_6		98.42 23	98.19 32	99.09 3	99.16 71	98.10 6	97.73 74	99.11 31	97.76 52	98.62 53	98.27 105	97.88 19	99.80 42	95.67 110	99.50 119	99.38 90

XVG-ACMP-BASELINE		97.58 88	97.28 103	98.49 53	99.16 71	96.90 48	96.39 141	98.98 68	95.05 174	98.06 119	98.02 136	95.86 105	99.56 171	94.37 183	99.64 70	99.00 173

CHOSEN 1792x2688		94.10 256	93.41 263	96.18 217	99.16 71	90.04 245	92.15 320	98.68 146	79.90 357	96.22 234	97.83 159	87.92 277	99.42 210	89.18 299	99.65 68	99.08 162

HFP-MVS		97.94 53	97.64 74	98.83 26	99.15 74	97.50 30	97.59 80	98.84 101	96.05 124	97.49 158	97.54 185	97.07 48	99.70 113	95.61 116	99.46 132	99.30 109

#test#		97.62 84	97.22 108	98.83 26	99.15 74	97.50 30	96.81 123	98.84 101	94.25 202	97.49 158	97.54 185	97.07 48	99.70 113	94.37 183	99.46 132	99.30 109

XVS		97.96 47	97.63 76	98.94 18	99.15 74	97.66 20	97.77 67	98.83 108	97.42 71	96.32 227	97.64 178	96.49 87	99.72 90	95.66 112	99.37 159	99.45 71

X-MVStestdata		92.86 282	90.83 307	98.94 18	99.15 74	97.66 20	97.77 67	98.83 108	97.42 71	96.32 227	36.50 374	96.49 87	99.72 90	95.66 112	99.37 159	99.45 71

LPG-MVS_test		97.94 53	97.67 68	98.74 35	99.15 74	97.02 44	97.09 111	99.02 54	95.15 169	98.34 83	98.23 109	97.91 17	99.70 113	94.41 180	99.73 52	99.50 47

LGP-MVS_train				98.74 35	99.15 74	97.02 44		99.02 54	95.15 169	98.34 83	98.23 109	97.91 17	99.70 113	94.41 180	99.73 52	99.50 47

RPSCF		97.87 64	97.51 87	98.95 17	99.15 74	98.43 3	97.56 82	99.06 43	96.19 118	98.48 67	98.70 62	94.72 151	99.24 261	94.37 183	99.33 177	99.17 138

ACMM	93.33 11	98.05 42	97.79 57	98.85 25	99.15 74	97.55 27	96.68 133	98.83 108	95.21 165	98.36 80	98.13 119	98.13 14	99.62 154	96.04 88	99.54 102	99.39 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		97.95 51	98.08 35	97.56 126	99.14 82	93.67 176	98.23 40	98.66 151	97.41 74	99.00 37	99.19 24	95.47 128	99.73 85	95.83 103	99.76 44	99.30 109

Vis-MVSNet (Re-imp)		95.11 211	94.85 212	95.87 231	99.12 83	89.17 259	97.54 88	94.92 319	96.50 103	96.58 214	97.27 213	83.64 303	99.48 194	88.42 310	99.67 65	98.97 177

dcpmvs_2		97.12 115	97.99 42	94.51 287	99.11 84	84.00 338	97.75 70	99.65 4	97.38 76	99.14 30	98.42 82	95.16 138	99.96 2	95.52 120	99.78 41	99.58 29

OPM-MVS		97.54 90	97.25 104	98.41 58	99.11 84	96.61 57	95.24 215	98.46 172	94.58 192	98.10 114	98.07 127	97.09 47	99.39 226	95.16 146	99.44 137	99.21 131
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UA-Net		98.88 7	98.76 13	99.22 2	99.11 84	97.89 14	99.47 3	99.32 11	99.08 10	97.87 142	99.67 2	96.47 89	99.92 5	97.88 23	99.98 2	99.85 3

AllTest		97.20 114	96.92 127	98.06 91	99.08 87	96.16 72	97.14 108	99.16 22	94.35 198	97.78 148	98.07 127	95.84 106	99.12 276	91.41 249	99.42 148	98.91 190

TestCases				98.06 91	99.08 87	96.16 72		99.16 22	94.35 198	97.78 148	98.07 127	95.84 106	99.12 276	91.41 249	99.42 148	98.91 190

TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 57	99.07 89	95.87 82	96.73 131	99.05 45	98.67 24	98.84 43	98.45 80	97.58 28	99.88 19	96.45 74	99.86 25	99.54 40

test1111		94.53 241	94.81 216	93.72 301	99.06 90	81.94 350	98.31 34	83.87 373	96.37 108	98.49 66	99.17 29	81.49 309	99.73 85	96.64 64	99.86 25	99.49 55

VPA-MVSNet		98.27 29	98.46 24	97.70 117	99.06 90	93.80 171	97.76 69	99.00 62	98.40 30	99.07 34	98.98 43	96.89 64	99.75 69	97.19 53	99.79 38	99.55 39

114514_t		93.96 260	93.22 267	96.19 216	99.06 90	90.97 233	95.99 166	98.94 76	73.88 370	93.43 320	96.93 236	92.38 215	99.37 232	89.09 300	99.28 186	98.25 264

EG-PatchMatch MVS		97.69 79	97.79 57	97.40 149	99.06 90	93.52 183	95.96 169	98.97 72	94.55 193	98.82 44	98.76 58	97.31 36	99.29 253	97.20 52	99.44 137	99.38 90

test_one_0601					99.05 94	95.50 101		98.87 89	97.21 82	98.03 123	98.30 96	96.93 60

ACMP	92.54 13	97.47 96	97.10 114	98.55 50	99.04 95	96.70 53	96.24 152	98.89 81	93.71 218	97.97 130	97.75 168	97.44 30	99.63 146	93.22 221	99.70 61	99.32 103
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part2					99.03 96	96.07 76				98.08 117

XVG-OURS-SEG-HR		97.38 102	97.07 117	98.30 69	99.01 97	97.41 36	94.66 245	99.02 54	95.20 166	98.15 107	97.52 188	98.83 4	98.43 341	94.87 161	96.41 333	99.07 164

XVG-OURS		97.12 115	96.74 136	98.26 72	98.99 98	97.45 34	93.82 281	99.05 45	95.19 167	98.32 88	97.70 174	95.22 137	98.41 342	94.27 188	98.13 281	98.93 185

CP-MVS		97.92 57	97.56 84	98.99 13	98.99 98	97.82 16	97.93 58	98.96 73	96.11 121	96.89 200	97.45 194	96.85 69	99.78 47	95.19 142	99.63 72	99.38 90

test2506		89.86 320	89.16 325	91.97 334	98.95 100	76.83 367	98.54 20	61.07 381	96.20 116	97.07 186	99.16 30	55.19 380	99.69 121	96.43 75	99.83 31	99.38 90

ECVR-MVS		94.37 247	94.48 234	94.05 298	98.95 100	83.10 342	98.31 34	82.48 374	96.20 116	98.23 98	99.16 30	81.18 312	99.66 139	95.95 95	99.83 31	99.38 90

CSCG		97.40 101	97.30 100	97.69 119	98.95 100	94.83 130	97.28 99	98.99 65	96.35 111	98.13 110	95.95 293	95.99 102	99.66 139	94.36 186	99.73 52	98.59 229

SF-MVS		97.60 86	97.39 95	98.22 77	98.93 103	95.69 88	97.05 113	99.10 33	95.32 162	97.83 145	97.88 154	96.44 91	99.72 90	94.59 175	99.39 156	99.25 126

HyFIR lowres test		93.72 265	92.65 280	96.91 176	98.93 103	91.81 222	91.23 337	98.52 167	82.69 345	96.46 221	96.52 263	80.38 317	99.90 14	90.36 283	98.79 246	99.03 170

PM-MVS		97.36 105	97.10 114	98.14 85	98.91 105	96.77 51	96.20 154	98.63 157	93.82 215	98.54 61	98.33 90	93.98 175	99.05 286	95.99 93	99.45 136	98.61 228

CPTT-MVS		96.69 146	96.08 169	98.49 53	98.89 106	96.64 56	97.25 100	98.77 123	92.89 248	96.01 245	97.13 220	92.23 216	99.67 133	92.24 233	99.34 170	99.17 138

patch_mono-2		96.59 151	96.93 125	95.55 244	98.88 107	87.12 300	94.47 252	99.30 12	94.12 207	96.65 212	98.41 83	94.98 146	99.87 21	95.81 105	99.78 41	99.66 22

GeoE		97.75 75	97.70 64	97.89 102	98.88 107	94.53 142	97.10 110	98.98 68	95.75 146	97.62 150	97.59 182	97.61 27	99.77 57	96.34 78	99.44 137	99.36 98

DPE-MVS		97.64 82	97.35 98	98.50 52	98.85 109	96.18 71	95.21 217	98.99 65	95.84 141	98.78 46	98.08 125	96.84 70	99.81 36	93.98 202	99.57 90	99.52 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		97.48 95	97.11 113	98.60 46	98.83 110	96.67 54	96.74 127	98.73 131	91.61 265	98.48 67	98.36 87	96.53 84	99.68 128	95.17 144	99.54 102	99.45 71
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
SR-MVS-dyc-post		98.14 34	97.84 53	99.02 9	98.81 111	98.05 9	97.55 83	98.86 92	97.77 49	98.20 100	98.07 127	96.60 81	99.76 62	95.49 121	99.20 195	99.26 122

RE-MVS-def			97.88 51		98.81 111	98.05 9	97.55 83	98.86 92	97.77 49	98.20 100	98.07 127	96.94 58		95.49 121	99.20 195	99.26 122

UniMVSNet (Re)		97.83 68	97.65 71	98.35 64	98.80 113	95.86 83	95.92 173	99.04 51	97.51 68	98.22 99	97.81 163	94.68 154	99.78 47	97.14 55	99.75 48	99.41 85

Anonymous20231211		98.55 17	98.76 13	97.94 99	98.79 114	94.37 149	98.84 10	99.15 26	99.37 3	99.67 6	99.43 11	95.61 121	99.72 90	98.12 16	99.86 25	99.73 15

APD-MVS_3200maxsize		98.13 37	97.90 47	98.79 31	98.79 114	97.31 38	97.55 83	98.92 78	97.72 56	98.25 95	98.13 119	97.10 45	99.75 69	95.44 128	99.24 193	99.32 103

test1172		98.08 39	97.76 61	99.05 6	98.78 116	98.07 7	97.41 95	98.85 96	97.57 63	98.15 107	97.96 142	96.60 81	99.76 62	95.30 136	99.18 199	99.33 102

DeepC-MVS	95.41 4	97.82 70	97.70 64	98.16 81	98.78 116	95.72 86	96.23 153	99.02 54	93.92 214	98.62 53	98.99 42	97.69 23	99.62 154	96.18 82	99.87 24	99.15 142
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		98.00 46	97.66 69	99.01 11	98.77 118	97.93 11	97.38 96	98.83 108	97.32 78	98.06 119	97.85 157	96.65 76	99.77 57	95.00 158	99.11 210	99.32 103

MCST-MVS		96.24 165	95.80 180	97.56 126	98.75 119	94.13 159	94.66 245	98.17 212	90.17 283	96.21 235	96.10 286	95.14 139	99.43 209	94.13 194	98.85 241	99.13 148

DU-MVS		97.79 72	97.60 80	98.36 62	98.73 120	95.78 84	95.65 188	98.87 89	97.57 63	98.31 90	97.83 159	94.69 152	99.85 26	97.02 59	99.71 58	99.46 66

NR-MVSNet		97.96 47	97.86 52	98.26 72	98.73 120	95.54 96	98.14 48	98.73 131	97.79 48	99.42 15	97.83 159	94.40 165	99.78 47	95.91 98	99.76 44	99.46 66

Anonymous20231206		95.27 205	95.06 203	95.88 230	98.72 122	89.37 256	95.70 181	97.85 239	88.00 305	96.98 194	97.62 180	91.95 224	99.34 239	89.21 298	99.53 105	98.94 181

APDe-MVS		98.14 34	98.03 40	98.47 55	98.72 122	96.04 77	98.07 52	99.10 33	95.96 131	98.59 58	98.69 63	96.94 58	99.81 36	96.64 64	99.58 87	99.57 34

UniMVSNet_NR-MVSNet		97.83 68	97.65 71	98.37 61	98.72 122	95.78 84	95.66 185	99.02 54	98.11 40	98.31 90	97.69 176	94.65 156	99.85 26	97.02 59	99.71 58	99.48 61

tttt0517		93.31 276	92.56 283	95.57 241	98.71 125	87.86 284	97.44 91	87.17 368	95.79 143	97.47 163	96.84 241	64.12 369	99.81 36	96.20 81	99.32 179	99.02 172

v8		97.60 86	98.06 38	96.23 213	98.71 125	89.44 255	97.43 93	98.82 116	97.29 80	98.74 49	99.10 35	93.86 177	99.68 128	98.61 10	99.94 8	99.56 37

HQP_MVS		96.66 149	96.33 159	97.68 120	98.70 127	94.29 151	96.50 137	98.75 127	96.36 109	96.16 237	96.77 247	91.91 228	99.46 200	92.59 230	99.20 195	99.28 117

plane_prior7					98.70 127	94.67 139

Anonymous20240529		97.96 47	98.04 39	97.71 115	98.69 129	94.28 154	97.86 63	98.31 196	98.79 22	99.23 26	98.86 53	95.76 116	99.61 160	95.49 121	99.36 162	99.23 129

VDD-MVS		97.37 103	97.25 104	97.74 113	98.69 129	94.50 145	97.04 114	95.61 311	98.59 26	98.51 63	98.72 60	92.54 210	99.58 164	96.02 90	99.49 123	99.12 153

DROMVSNet		97.90 61	97.94 46	97.79 109	98.66 131	95.14 122	98.31 34	99.66 3	97.57 63	95.95 246	97.01 232	96.99 55	99.82 33	97.66 33	99.64 70	98.39 245

HPM-MVS++		96.99 120	96.38 156	98.81 29	98.64 132	97.59 24	95.97 168	98.20 206	95.51 155	95.06 270	96.53 261	94.10 172	99.70 113	94.29 187	99.15 201	99.13 148

ab-mvs		96.59 151	96.59 142	96.60 192	98.64 132	92.21 209	98.35 30	97.67 251	94.45 194	96.99 192	98.79 55	94.96 147	99.49 191	90.39 282	99.07 216	98.08 273

F-COLMAP		95.30 204	94.38 239	98.05 94	98.64 132	96.04 77	95.61 191	98.66 151	89.00 293	93.22 324	96.40 269	92.90 198	99.35 237	87.45 324	97.53 308	98.77 212

ITE_SJBPF				97.85 106	98.64 132	96.66 55		98.51 169	95.63 149	97.22 171	97.30 212	95.52 124	98.55 335	90.97 259	98.90 233	98.34 253

v148		96.58 153	96.97 122	95.42 250	98.63 136	87.57 291	95.09 222	97.90 236	95.91 136	98.24 97	97.96 142	93.42 187	99.39 226	96.04 88	99.52 110	99.29 116

ETH3D-3000-0.1		96.89 130	96.46 154	98.16 81	98.62 137	95.69 88	95.96 169	98.98 68	93.36 226	97.04 188	97.31 211	94.93 148	99.63 146	92.60 228	99.34 170	99.17 138

UnsupCasMVSNet_bld		94.72 230	94.26 241	96.08 220	98.62 137	90.54 243	93.38 296	98.05 231	90.30 281	97.02 190	96.80 246	89.54 257	99.16 272	88.44 309	96.18 336	98.56 231

DP-MVS		97.87 64	97.89 50	97.81 108	98.62 137	94.82 131	97.13 109	98.79 118	98.98 17	98.74 49	98.49 77	95.80 115	99.49 191	95.04 155	99.44 137	99.11 157

v10		97.55 89	97.97 43	96.31 210	98.60 140	89.64 251	97.44 91	99.02 54	96.60 97	98.72 51	99.16 30	93.48 186	99.72 90	98.76 6	99.92 14	99.58 29

Test_1112_low_res		93.53 272	92.86 272	95.54 245	98.60 140	88.86 265	92.75 308	98.69 144	82.66 346	92.65 334	96.92 238	84.75 296	99.56 171	90.94 260	97.76 294	98.19 269

V42		97.04 118	97.16 111	96.68 190	98.59 142	91.05 230	96.33 146	98.36 188	94.60 189	97.99 126	98.30 96	93.32 188	99.62 154	97.40 44	99.53 105	99.38 90

1112_ss		94.12 255	93.42 262	96.23 213	98.59 142	90.85 234	94.24 260	98.85 96	85.49 327	92.97 327	94.94 315	86.01 288	99.64 144	91.78 243	97.92 288	98.20 268

v2v482		96.78 138	97.06 118	95.95 226	98.57 144	88.77 268	95.36 203	98.26 199	95.18 168	97.85 144	98.23 109	92.58 207	99.63 146	97.80 27	99.69 62	99.45 71

WR-MVS		96.90 128	96.81 132	97.16 161	98.56 145	92.20 211	94.33 255	98.12 221	97.34 77	98.20 100	97.33 209	92.81 199	99.75 69	94.79 165	99.81 33	99.54 40

APD-MVS		97.00 119	96.53 149	98.41 58	98.55 146	96.31 68	96.32 147	98.77 123	92.96 247	97.44 166	97.58 184	95.84 106	99.74 79	91.96 236	99.35 167	99.19 135
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test		94.66 234	94.49 233	95.19 257	98.54 147	88.91 263	92.57 312	98.74 129	91.46 268	98.32 88	97.75 168	77.31 333	98.81 310	96.06 85	99.61 79	97.85 292

9.14			96.69 138		98.53 148		96.02 164	98.98 68	93.23 231	97.18 175	97.46 193	96.47 89	99.62 154	92.99 225	99.32 179

baseline		97.44 98	97.78 60	96.43 203	98.52 149	90.75 238	96.84 121	99.03 52	96.51 102	97.86 143	98.02 136	96.67 75	99.36 234	97.09 56	99.47 129	99.19 135

casdiffmvs		97.50 93	97.81 56	96.56 197	98.51 150	91.04 231	95.83 177	99.09 38	97.23 81	98.33 87	98.30 96	97.03 52	99.37 232	96.58 68	99.38 158	99.28 117

IterMVS-LS		96.92 126	97.29 101	95.79 233	98.51 150	88.13 280	95.10 220	98.66 151	96.99 85	98.46 70	98.68 64	92.55 208	99.74 79	96.91 62	99.79 38	99.50 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		95.55 191	95.13 198	96.80 182	98.51 150	93.99 164	94.60 247	98.69 144	90.20 282	95.78 255	96.21 279	92.73 202	98.98 295	90.58 276	98.86 239	97.42 309

h-mvs33		96.29 163	95.63 186	98.26 72	98.50 153	96.11 75	96.90 119	97.09 277	96.58 99	97.21 173	98.19 114	84.14 299	99.78 47	95.89 99	96.17 337	98.89 194

test20.03		96.58 153	96.61 141	96.48 201	98.49 154	91.72 223	95.68 184	97.69 250	96.81 91	98.27 94	97.92 150	94.18 171	98.71 319	90.78 266	99.66 67	99.00 173

plane_prior1					98.49 154

xxxxxxxxxxxxxcwj		97.24 112	97.03 120	97.89 102	98.48 156	94.71 135	94.53 250	99.07 42	95.02 176	97.83 145	97.88 154	96.44 91	99.72 90	94.59 175	99.39 156	99.25 126

save fliter					98.48 156	94.71 135	94.53 250	98.41 181	95.02 176

CS-MVS-test		97.69 79	97.49 90	98.31 67	98.48 156	96.61 57	97.21 104	99.53 8	98.10 41	96.05 241	95.33 308	95.49 125	99.86 22	97.49 40	99.74 50	98.45 241

MDA-MVSNet-bldmvs		95.69 185	95.67 184	95.74 235	98.48 156	88.76 269	92.84 305	97.25 269	96.00 129	97.59 151	97.95 146	91.38 233	99.46 200	93.16 223	96.35 334	98.99 176

UnsupCasMVSNet_eth		95.91 179	95.73 183	96.44 202	98.48 156	91.52 226	95.31 208	98.45 173	95.76 144	97.48 161	97.54 185	89.53 259	98.69 321	94.43 179	94.61 352	99.13 148

testtj		96.69 146	96.13 165	98.36 62	98.46 161	96.02 79	96.44 139	98.70 141	94.26 201	96.79 202	97.13 220	94.07 173	99.75 69	90.53 277	98.80 245	99.31 108

CS-MVS		98.08 39	98.01 41	98.29 71	98.46 161	96.58 60	98.53 22	99.69 2	98.07 42	96.04 242	97.18 218	96.88 66	99.86 22	97.48 41	99.74 50	98.43 242

ZD-MVS					98.43 163	95.94 81		98.56 164	90.72 277	96.66 210	97.07 226	95.02 144	99.74 79	91.08 256	98.93 231

thisisatest0530		92.71 285	91.76 293	95.56 243	98.42 164	88.23 275	96.03 163	87.35 367	94.04 210	96.56 216	95.47 306	64.03 370	99.77 57	94.78 167	99.11 210	98.68 222

v1144		96.84 131	97.08 116	96.13 219	98.42 164	89.28 258	95.41 199	98.67 149	94.21 203	97.97 130	98.31 92	93.06 193	99.65 141	98.06 19	99.62 73	99.45 71

plane_prior6					98.38 166	94.37 149						91.91 228

FPMVS		89.92 319	88.63 327	93.82 299	98.37 167	96.94 47	91.58 328	93.34 333	88.00 305	90.32 352	97.10 224	70.87 361	91.13 373	71.91 371	96.16 338	93.39 363

PAPM_NR		94.61 237	94.17 246	95.96 224	98.36 168	91.23 228	95.93 172	97.95 233	92.98 243	93.42 321	94.43 327	90.53 242	98.38 345	87.60 320	96.29 335	98.27 262

MVS_111021_HR		96.73 142	96.54 148	97.27 156	98.35 169	93.66 179	93.42 293	98.36 188	94.74 184	96.58 214	96.76 249	96.54 83	98.99 293	94.87 161	99.27 188	99.15 142

TAMVS		95.49 193	94.94 206	97.16 161	98.31 170	93.41 185	95.07 225	96.82 287	91.09 274	97.51 155	97.82 162	89.96 252	99.42 210	88.42 310	99.44 137	98.64 223

OMC-MVS		96.48 157	96.00 172	97.91 101	98.30 171	96.01 80	94.86 237	98.60 159	91.88 262	97.18 175	97.21 217	96.11 100	99.04 287	90.49 281	99.34 170	98.69 220

新几何1				97.25 159	98.29 172	94.70 138		97.73 247	77.98 363	94.83 277	96.67 254	92.08 221	99.45 204	88.17 314	98.65 260	97.61 303

jason		94.39 246	94.04 250	95.41 252	98.29 172	87.85 286	92.74 310	96.75 290	85.38 332	95.29 266	96.15 281	88.21 272	99.65 141	94.24 189	99.34 170	98.74 214
jason: jason.
v1192		96.83 134	97.06 118	96.15 218	98.28 174	89.29 257	95.36 203	98.77 123	93.73 217	98.11 111	98.34 89	93.02 197	99.67 133	98.35 14	99.58 87	99.50 47

CDPH-MVS		95.45 198	94.65 222	97.84 107	98.28 174	94.96 127	93.73 285	98.33 193	85.03 335	95.44 263	96.60 257	95.31 134	99.44 207	90.01 287	99.13 206	99.11 157

MVS_111021_LR		96.82 135	96.55 146	97.62 123	98.27 176	95.34 111	93.81 283	98.33 193	94.59 191	96.56 216	96.63 256	96.61 79	98.73 317	94.80 164	99.34 170	98.78 209

CLD-MVS		95.47 196	95.07 201	96.69 189	98.27 176	92.53 202	91.36 331	98.67 149	91.22 273	95.78 255	94.12 331	95.65 120	98.98 295	90.81 264	99.72 55	98.57 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
1121		94.26 248	93.26 265	97.27 156	98.26 178	94.73 133	95.86 174	97.71 249	77.96 364	94.53 284	96.71 251	91.93 226	99.40 221	87.71 316	98.64 261	97.69 300

Anonymous202405211		96.34 162	95.98 174	97.43 146	98.25 179	93.85 169	96.74 127	94.41 324	97.72 56	98.37 77	98.03 135	87.15 282	99.53 180	94.06 196	99.07 216	98.92 189

pmmvs-eth3d		96.49 156	96.18 164	97.42 147	98.25 179	94.29 151	94.77 242	98.07 229	89.81 286	97.97 130	98.33 90	93.11 192	99.08 283	95.46 127	99.84 29	98.89 194

v144192		96.69 146	96.90 129	96.03 221	98.25 179	88.92 262	95.49 193	98.77 123	93.05 240	98.09 115	98.29 100	92.51 212	99.70 113	98.11 17	99.56 93	99.47 64

ambc				96.56 197	98.23 182	91.68 224	97.88 62	98.13 220		98.42 73	98.56 72	94.22 170	99.04 287	94.05 199	99.35 167	98.95 179

thres100view900		91.76 301	91.26 300	93.26 310	98.21 183	84.50 333	96.39 141	90.39 358	96.87 89	96.33 226	93.08 340	73.44 353	99.42 210	78.85 362	97.74 295	95.85 344

v1921920		96.72 143	96.96 124	95.99 222	98.21 183	88.79 267	95.42 197	98.79 118	93.22 232	98.19 103	98.26 106	92.68 203	99.70 113	98.34 15	99.55 99	99.49 55

thres600view7		92.03 297	91.43 295	93.82 299	98.19 185	84.61 332	96.27 148	90.39 358	96.81 91	96.37 225	93.11 336	73.44 353	99.49 191	80.32 358	97.95 287	97.36 310

PatchMatch-RL		94.61 237	93.81 257	97.02 171	98.19 185	95.72 86	93.66 286	97.23 270	88.17 303	94.94 275	95.62 302	91.43 232	98.57 332	87.36 325	97.68 301	96.76 331

LF4IMVS		96.07 172	95.63 186	97.36 152	98.19 185	95.55 95	95.44 195	98.82 116	92.29 256	95.70 259	96.55 259	92.63 206	98.69 321	91.75 245	99.33 177	97.85 292

v1240		96.74 140	97.02 121	95.91 229	98.18 188	88.52 270	95.39 201	98.88 87	93.15 238	98.46 70	98.40 86	92.80 200	99.71 104	98.45 13	99.49 123	99.49 55

TAPA-MVS	93.32 12	94.93 218	94.23 242	97.04 169	98.18 188	94.51 143	95.22 216	98.73 131	81.22 352	96.25 233	95.95 293	93.80 180	98.98 295	89.89 289	98.87 237	97.62 302
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.17 190	93.24 189	92.74 310	97.61 261	75.17 368	94.65 281	96.69 253	90.96 238			98.66 258	97.66 301

MIMVSNet		93.42 273	92.86 272	95.10 260	98.17 190	88.19 276	98.13 49	93.69 327	92.07 257	95.04 273	98.21 113	80.95 315	99.03 290	81.42 356	98.06 284	98.07 275

原ACMM1				96.58 194	98.16 192	92.12 213		98.15 217	85.90 323	93.49 316	96.43 266	92.47 213	99.38 229	87.66 319	98.62 262	98.23 265

testdata				95.70 238	98.16 192	90.58 240		97.72 248	80.38 355	95.62 260	97.02 230	92.06 222	98.98 295	89.06 302	98.52 267	97.54 305

MVP-Stereo		95.69 185	95.28 194	96.92 174	98.15 194	93.03 193	95.64 190	98.20 206	90.39 280	96.63 213	97.73 171	91.63 231	99.10 281	91.84 242	97.31 316	98.63 225
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		97.37 103	97.70 64	96.35 207	98.14 195	95.13 123	96.54 136	98.92 78	95.94 133	99.19 28	98.08 125	97.74 22	95.06 370	95.24 140	99.54 102	98.87 200
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
EU-MVSNet		94.25 249	94.47 235	93.60 304	98.14 195	82.60 345	97.24 102	92.72 340	85.08 333	98.48 67	98.94 46	82.59 306	98.76 315	97.47 42	99.53 105	99.44 81

NP-MVS					98.14 195	93.72 175					95.08 311

LCM-MVSNet-Re		97.33 106	97.33 99	97.32 154	98.13 198	93.79 172	96.99 117	99.65 4	96.74 93	99.47 13	98.93 47	96.91 63	99.84 29	90.11 285	99.06 219	98.32 254

ETH3 D test6400		94.77 225	93.87 256	97.47 139	98.12 199	93.73 174	94.56 249	98.70 141	85.45 330	94.70 280	95.93 295	91.77 230	99.63 146	86.45 330	99.14 202	99.05 168

3Dnovator+	96.13 3	97.73 76	97.59 81	98.15 84	98.11 200	95.60 94	98.04 53	98.70 141	98.13 39	96.93 197	98.45 80	95.30 135	99.62 154	95.64 114	98.96 225	99.24 128

VNet		96.84 131	96.83 131	96.88 177	98.06 201	92.02 216	96.35 145	97.57 262	97.70 58	97.88 139	97.80 164	92.40 214	99.54 178	94.73 170	98.96 225	99.08 162

test_part1		96.77 139	96.53 149	97.47 139	98.04 202	92.92 196	97.93 58	98.85 96	98.83 21	99.30 21	99.07 38	79.25 320	99.79 43	97.59 35	99.93 10	99.69 20

LFMVS		95.32 203	94.88 211	96.62 191	98.03 203	91.47 227	97.65 76	90.72 357	99.11 9	97.89 138	98.31 92	79.20 321	99.48 194	93.91 205	99.12 209	98.93 185

tfpn200view9		91.55 303	91.00 302	93.21 313	98.02 204	84.35 335	95.70 181	90.79 355	96.26 113	95.90 251	92.13 353	73.62 350	99.42 210	78.85 362	97.74 295	95.85 344

thres400		91.68 302	91.00 302	93.71 302	98.02 204	84.35 335	95.70 181	90.79 355	96.26 113	95.90 251	92.13 353	73.62 350	99.42 210	78.85 362	97.74 295	97.36 310

OPU-MVS				97.64 122	98.01 206	95.27 114	96.79 124				97.35 207	96.97 56	98.51 338	91.21 255	99.25 190	99.14 145

xiu_mvs_v1_base_debu		95.62 188	95.96 175	94.60 281	98.01 206	88.42 271	93.99 274	98.21 203	92.98 243	95.91 248	94.53 323	96.39 93	99.72 90	95.43 131	98.19 278	95.64 348

xiu_mvs_v1_base		95.62 188	95.96 175	94.60 281	98.01 206	88.42 271	93.99 274	98.21 203	92.98 243	95.91 248	94.53 323	96.39 93	99.72 90	95.43 131	98.19 278	95.64 348

xiu_mvs_v1_base_debi		95.62 188	95.96 175	94.60 281	98.01 206	88.42 271	93.99 274	98.21 203	92.98 243	95.91 248	94.53 323	96.39 93	99.72 90	95.43 131	98.19 278	95.64 348

CNVR-MVS		96.92 126	96.55 146	98.03 95	98.00 210	95.54 96	94.87 236	98.17 212	94.60 189	96.38 224	97.05 228	95.67 119	99.36 234	95.12 152	99.08 214	99.19 135

PLC	91.02 16	94.05 259	92.90 271	97.51 131	98.00 210	95.12 124	94.25 259	98.25 200	86.17 319	91.48 345	95.25 309	91.01 236	99.19 266	85.02 343	96.69 328	98.22 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		96.99 120	96.80 133	97.56 126	97.96 212	93.67 176	98.23 40	98.66 151	95.59 152	97.99 126	99.19 24	89.51 260	99.73 85	94.60 172	99.44 137	99.30 109

test1		96.99 120	96.80 133	97.56 126	97.96 212	93.67 176	98.23 40	98.66 151	95.59 152	97.99 126	99.19 24	89.51 260	99.73 85	94.60 172	99.44 137	99.30 109

FMVSNet2		96.72 143	96.67 140	96.87 178	97.96 212	91.88 219	97.15 106	98.06 230	95.59 152	98.50 65	98.62 68	89.51 260	99.65 141	94.99 159	99.60 83	99.07 164

BH-untuned		94.69 231	94.75 219	94.52 286	97.95 215	87.53 292	94.07 271	97.01 280	93.99 211	97.10 181	95.65 300	92.65 205	98.95 300	87.60 320	96.74 327	97.09 314

ETH3D cwj APD-0.16		96.23 166	95.61 188	98.09 88	97.91 216	95.65 93	94.94 233	98.74 129	91.31 271	96.02 244	97.08 225	94.05 174	99.69 121	91.51 248	98.94 229	98.93 185

DPM-MVS		93.68 267	92.77 278	96.42 204	97.91 216	92.54 201	91.17 338	97.47 266	84.99 336	93.08 326	94.74 319	89.90 253	99.00 291	87.54 322	98.09 283	97.72 298

QAPM		95.88 181	95.57 189	96.80 182	97.90 218	91.84 221	98.18 47	98.73 131	88.41 299	96.42 222	98.13 119	94.73 150	99.75 69	88.72 305	98.94 229	98.81 205

TinyColmap		96.00 177	96.34 158	94.96 265	97.90 218	87.91 283	94.13 269	98.49 170	94.41 195	98.16 105	97.76 165	96.29 98	98.68 324	90.52 278	99.42 148	98.30 258

HQP-NCC					97.85 220		94.26 256		93.18 234	92.86 329

ACMP_Plane					97.85 220		94.26 256		93.18 234	92.86 329

N_pmnet		95.18 208	94.23 242	98.06 91	97.85 220	96.55 61	92.49 314	91.63 348	89.34 289	98.09 115	97.41 197	90.33 245	99.06 285	91.58 247	99.31 181	98.56 231

HQP-MVS		95.17 210	94.58 230	96.92 174	97.85 220	92.47 203	94.26 256	98.43 176	93.18 234	92.86 329	95.08 311	90.33 245	99.23 263	90.51 279	98.74 251	99.05 168

hse-mvs2		95.77 184	95.09 200	97.79 109	97.84 224	95.51 98	95.66 185	95.43 316	96.58 99	97.21 173	96.16 280	84.14 299	99.54 178	95.89 99	96.92 320	98.32 254

TEST9					97.84 224	95.23 116	93.62 287	98.39 184	86.81 315	93.78 303	95.99 288	94.68 154	99.52 184

train_agg		95.46 197	94.66 221	97.88 104	97.84 224	95.23 116	93.62 287	98.39 184	87.04 313	93.78 303	95.99 288	94.58 159	99.52 184	91.76 244	98.90 233	98.89 194

MSLP-MVS++		96.42 161	96.71 137	95.57 241	97.82 227	90.56 242	95.71 180	98.84 101	94.72 185	96.71 208	97.39 202	94.91 149	98.10 356	95.28 137	99.02 221	98.05 282

test_8					97.81 228	95.07 125	93.54 290	98.38 186	87.04 313	93.71 307	95.96 292	94.58 159	99.52 184

NCCC		96.52 155	95.99 173	98.10 87	97.81 228	95.68 90	95.00 231	98.20 206	95.39 160	95.40 265	96.36 272	93.81 179	99.45 204	93.55 215	98.42 271	99.17 138

WTY-MVS		93.55 271	93.00 270	95.19 257	97.81 228	87.86 284	93.89 279	96.00 301	89.02 292	94.07 296	95.44 307	86.27 286	99.33 242	87.69 318	96.82 324	98.39 245

CNLPA		95.04 214	94.47 235	96.75 185	97.81 228	95.25 115	94.12 270	97.89 237	94.41 195	94.57 282	95.69 298	90.30 248	98.35 348	86.72 329	98.76 249	96.64 334

AUN-MVS		93.95 262	92.69 279	97.74 113	97.80 232	95.38 106	95.57 192	95.46 315	91.26 272	92.64 335	96.10 286	74.67 344	99.55 175	93.72 211	96.97 319	98.30 258

EIA-MVS		96.04 174	95.77 182	96.85 179	97.80 232	92.98 194	96.12 158	99.16 22	94.65 187	93.77 305	91.69 358	95.68 118	99.67 133	94.18 191	98.85 241	97.91 290

agg_prior1		95.39 200	94.60 227	97.75 112	97.80 232	94.96 127	93.39 295	98.36 188	87.20 311	93.49 316	95.97 291	94.65 156	99.53 180	91.69 246	98.86 239	98.77 212

agg_prior					97.80 232	94.96 127		98.36 188		93.49 316			99.53 180

旧先验1					97.80 232	93.87 167		97.75 246			97.04 229	93.57 185			98.68 255	98.72 217

PCF-MVS	89.43 18	92.12 296	90.64 310	96.57 196	97.80 232	93.48 184	89.88 355	98.45 173	74.46 369	96.04 242	95.68 299	90.71 241	99.31 246	73.73 368	99.01 223	96.91 322
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior3		95.91 179	95.39 192	97.46 142	97.79 238	94.26 155	93.33 298	98.42 179	94.21 203	94.02 298	96.25 276	93.64 183	99.34 239	91.90 238	98.96 225	98.79 207

test_prior				97.46 142	97.79 238	94.26 155		98.42 179					99.34 239			98.79 207

PVSNet_BlendedMVS		95.02 217	94.93 208	95.27 254	97.79 238	87.40 295	94.14 268	98.68 146	88.94 294	94.51 285	98.01 138	93.04 194	99.30 249	89.77 291	99.49 123	99.11 157

PVSNet_Blended		93.96 260	93.65 259	94.91 266	97.79 238	87.40 295	91.43 330	98.68 146	84.50 340	94.51 285	94.48 326	93.04 194	99.30 249	89.77 291	98.61 263	98.02 285

USDC		94.56 239	94.57 232	94.55 285	97.78 242	86.43 310	92.75 308	98.65 156	85.96 321	96.91 199	97.93 149	90.82 239	98.74 316	90.71 271	99.59 85	98.47 238

alignmvs		96.01 176	95.52 190	97.50 134	97.77 243	94.71 135	96.07 160	96.84 285	97.48 69	96.78 206	94.28 330	85.50 291	99.40 221	96.22 80	98.73 254	98.40 243

ETV-MVS		96.13 171	95.90 178	96.82 181	97.76 244	93.89 166	95.40 200	98.95 75	95.87 138	95.58 262	91.00 364	96.36 96	99.72 90	93.36 216	98.83 243	96.85 325

D2MVS		95.18 208	95.17 197	95.21 256	97.76 244	87.76 289	94.15 266	97.94 234	89.77 287	96.99 192	97.68 177	87.45 280	99.14 274	95.03 157	99.81 33	98.74 214

DVP-MVS++		97.96 47	97.90 47	98.12 86	97.75 246	95.40 104	99.03 7	98.89 81	96.62 95	98.62 53	98.30 96	96.97 56	99.75 69	95.70 106	99.25 190	99.21 131

MSC_two_6792asdad				98.22 77	97.75 246	95.34 111		98.16 215					99.75 69	95.87 101	99.51 115	99.57 34

No_MVS				98.22 77	97.75 246	95.34 111		98.16 215					99.75 69	95.87 101	99.51 115	99.57 34

TSAR-MVS + GP.		96.47 158	96.12 166	97.49 137	97.74 249	95.23 116	94.15 266	96.90 284	93.26 230	98.04 122	96.70 252	94.41 164	98.89 303	94.77 168	99.14 202	98.37 247

3Dnovator	96.53 2	97.61 85	97.64 74	97.50 134	97.74 249	93.65 180	98.49 24	98.88 87	96.86 90	97.11 180	98.55 73	95.82 109	99.73 85	95.94 96	99.42 148	99.13 148

sss		94.22 250	93.72 258	95.74 235	97.71 251	89.95 247	93.84 280	96.98 281	88.38 301	93.75 306	95.74 297	87.94 273	98.89 303	91.02 258	98.10 282	98.37 247

DeepC-MVS_fast	94.34 7	96.74 140	96.51 152	97.44 145	97.69 252	94.15 158	96.02 164	98.43 176	93.17 237	97.30 169	97.38 204	95.48 127	99.28 255	93.74 209	99.34 170	98.88 198
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-SCA-FT		95.86 182	96.19 163	94.85 271	97.68 253	85.53 318	92.42 316	97.63 259	96.99 85	98.36 80	98.54 74	87.94 273	99.75 69	97.07 58	99.08 214	99.27 121

MVSFormer		96.14 170	96.36 157	95.49 247	97.68 253	87.81 287	98.67 13	99.02 54	96.50 103	94.48 287	96.15 281	86.90 283	99.92 5	98.73 7	99.13 206	98.74 214

lupinMVS		93.77 263	93.28 264	95.24 255	97.68 253	87.81 287	92.12 321	96.05 299	84.52 339	94.48 287	95.06 313	86.90 283	99.63 146	93.62 214	99.13 206	98.27 262

Fast-Effi-MVS+		95.49 193	95.07 201	96.75 185	97.67 256	92.82 197	94.22 262	98.60 159	91.61 265	93.42 321	92.90 343	96.73 74	99.70 113	92.60 228	97.89 291	97.74 297

canonicalmvs		97.23 113	97.21 109	97.30 155	97.65 257	94.39 147	97.84 64	99.05 45	97.42 71	96.68 209	93.85 333	97.63 26	99.33 242	96.29 79	98.47 270	98.18 270

CDS-MVSNet		94.88 221	94.12 247	97.14 163	97.64 258	93.57 181	93.96 277	97.06 279	90.05 284	96.30 230	96.55 259	86.10 287	99.47 197	90.10 286	99.31 181	98.40 243
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs5		94.63 236	94.34 240	95.50 246	97.63 259	88.34 274	94.02 272	97.13 275	87.15 312	95.22 268	97.15 219	87.50 279	99.27 257	93.99 201	99.26 189	98.88 198

test12				97.46 142	97.61 260	94.07 160		97.78 245		93.57 314		93.31 189	99.42 210		98.78 247	98.89 194

PMMVS2		93.66 268	94.07 248	92.45 329	97.57 261	80.67 355	86.46 364	96.00 301	93.99 211	97.10 181	97.38 204	89.90 253	97.82 358	88.76 304	99.47 129	98.86 201

BH-RMVSNet		94.56 239	94.44 238	94.91 266	97.57 261	87.44 294	93.78 284	96.26 296	93.69 219	96.41 223	96.50 264	92.10 220	99.00 291	85.96 332	97.71 298	98.31 256

bset_n11_16_dypcd		94.53 241	93.95 254	96.25 212	97.56 263	89.85 248	88.52 361	91.32 350	94.90 181	97.51 155	96.38 271	82.34 307	99.78 47	97.22 48	99.80 36	99.12 153

PVSNet	86.72 19	91.10 307	90.97 304	91.49 336	97.56 263	78.04 361	87.17 363	94.60 322	84.65 338	92.34 339	92.20 352	87.37 281	98.47 339	85.17 342	97.69 300	97.96 287

DELS-MVS		96.17 169	96.23 161	95.99 222	97.55 265	90.04 245	92.38 318	98.52 167	94.13 206	96.55 218	97.06 227	94.99 145	99.58 164	95.62 115	99.28 186	98.37 247
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
IterMVS		95.42 199	95.83 179	94.20 295	97.52 266	83.78 340	92.41 317	97.47 266	95.49 156	98.06 119	98.49 77	87.94 273	99.58 164	96.02 90	99.02 221	99.23 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CL-MVSNet_self_test		95.04 214	94.79 218	95.82 232	97.51 267	89.79 249	91.14 339	96.82 287	93.05 240	96.72 207	96.40 269	90.82 239	99.16 272	91.95 237	98.66 258	98.50 236

new-patchmatchnet		95.67 187	96.58 143	92.94 321	97.48 268	80.21 356	92.96 304	98.19 211	94.83 182	98.82 44	98.79 55	93.31 189	99.51 188	95.83 103	99.04 220	99.12 153

MDA-MVSNet_test_wron		94.73 226	94.83 215	94.42 289	97.48 268	85.15 325	90.28 349	95.87 305	92.52 251	97.48 161	97.76 165	91.92 227	99.17 271	93.32 217	96.80 326	98.94 181

PHI-MVS		96.96 124	96.53 149	98.25 75	97.48 268	96.50 62	96.76 126	98.85 96	93.52 221	96.19 236	96.85 240	95.94 103	99.42 210	93.79 208	99.43 145	98.83 203

DeepPCF-MVS	94.58 5	96.90 128	96.43 155	98.31 67	97.48 268	97.23 42	92.56 313	98.60 159	92.84 249	98.54 61	97.40 198	96.64 78	98.78 312	94.40 182	99.41 154	98.93 185

thres200		91.00 309	90.42 313	92.77 323	97.47 272	83.98 339	94.01 273	91.18 353	95.12 171	95.44 263	91.21 362	73.93 346	99.31 246	77.76 365	97.63 305	95.01 354

YYNet1		94.73 226	94.84 213	94.41 290	97.47 272	85.09 327	90.29 348	95.85 306	92.52 251	97.53 153	97.76 165	91.97 223	99.18 267	93.31 218	96.86 323	98.95 179

Effi-MVS+		96.19 168	96.01 171	96.71 187	97.43 274	92.19 212	96.12 158	99.10 33	95.45 157	93.33 323	94.71 320	97.23 43	99.56 171	93.21 222	97.54 307	98.37 247

pmmvs4		94.82 223	94.19 245	96.70 188	97.42 275	92.75 200	92.09 323	96.76 289	86.80 316	95.73 258	97.22 216	89.28 263	98.89 303	93.28 219	99.14 202	98.46 240

MSDG		95.33 202	95.13 198	95.94 228	97.40 276	91.85 220	91.02 342	98.37 187	95.30 163	96.31 229	95.99 288	94.51 162	98.38 345	89.59 293	97.65 304	97.60 304

EI-MVSNet-Vis-set		97.32 107	97.39 95	97.11 164	97.36 277	92.08 215	95.34 205	97.65 255	97.74 53	98.29 93	98.11 123	95.05 140	99.68 128	97.50 39	99.50 119	99.56 37

PS-MVSNAJ		94.10 256	94.47 235	93.00 318	97.35 278	84.88 329	91.86 325	97.84 241	91.96 260	94.17 292	92.50 350	95.82 109	99.71 104	91.27 252	97.48 310	94.40 358

Regformer-3		97.25 111	97.29 101	97.11 164	97.35 278	92.32 206	95.26 212	97.62 260	97.67 61	98.17 104	97.89 152	95.05 140	99.56 171	97.16 54	99.42 148	99.46 66

Regformer-4		97.53 92	97.47 93	97.71 115	97.35 278	93.91 165	95.26 212	98.14 218	97.97 45	98.34 83	97.89 152	95.49 125	99.71 104	97.41 43	99.42 148	99.51 46

diffmvs		96.04 174	96.23 161	95.46 249	97.35 278	88.03 282	93.42 293	99.08 39	94.09 209	96.66 210	96.93 236	93.85 178	99.29 253	96.01 92	98.67 256	99.06 166

EI-MVSNet-UG-set		97.32 107	97.40 94	97.09 166	97.34 282	92.01 217	95.33 206	97.65 255	97.74 53	98.30 92	98.14 118	95.04 142	99.69 121	97.55 37	99.52 110	99.58 29

baseline1		93.14 280	92.64 281	94.62 280	97.34 282	87.20 299	96.67 134	93.02 335	94.71 186	96.51 219	95.83 296	81.64 308	98.60 331	90.00 288	88.06 367	98.07 275

AdaColmap		95.11 211	94.62 226	96.58 194	97.33 284	94.45 146	94.92 234	98.08 225	93.15 238	93.98 301	95.53 305	94.34 166	99.10 281	85.69 335	98.61 263	96.20 342

xiu_mvs_v2_base		94.22 250	94.63 225	92.99 319	97.32 285	84.84 330	92.12 321	97.84 241	91.96 260	94.17 292	93.43 334	96.07 101	99.71 104	91.27 252	97.48 310	94.42 357

OpenMVS_ROB	91.80 14	93.64 269	93.05 268	95.42 250	97.31 286	91.21 229	95.08 224	96.68 293	81.56 349	96.88 201	96.41 267	90.44 244	99.25 260	85.39 339	97.67 302	95.80 346

EI-MVSNet		96.63 150	96.93 125	95.74 235	97.26 287	88.13 280	95.29 210	97.65 255	96.99 85	97.94 133	98.19 114	92.55 208	99.58 164	96.91 62	99.56 93	99.50 47

CVMVSNet		92.33 292	92.79 275	90.95 340	97.26 287	75.84 370	95.29 210	92.33 343	81.86 347	96.27 231	98.19 114	81.44 310	98.46 340	94.23 190	98.29 276	98.55 233

Regformer-1		97.27 109	97.16 111	97.61 124	97.21 289	93.86 168	94.85 238	98.04 232	97.62 62	98.03 123	97.50 190	95.34 132	99.63 146	96.52 70	99.31 181	99.35 100

Regformer-2		97.41 100	97.24 106	97.93 100	97.21 289	94.72 134	94.85 238	98.27 197	97.74 53	98.11 111	97.50 190	95.58 123	99.69 121	96.57 69	99.31 181	99.37 97

Fast-Effi-MVS+-dtu		96.44 159	96.12 166	97.39 150	97.18 291	94.39 147	95.46 194	98.73 131	96.03 128	94.72 278	94.92 317	96.28 99	99.69 121	93.81 207	97.98 286	98.09 272

OpenMVS	94.22 8	95.48 195	95.20 195	96.32 209	97.16 292	91.96 218	97.74 72	98.84 101	87.26 309	94.36 289	98.01 138	93.95 176	99.67 133	90.70 272	98.75 250	97.35 312

BH-w/o		92.14 295	91.94 289	92.73 324	97.13 293	85.30 321	92.46 315	95.64 308	89.33 290	94.21 291	92.74 346	89.60 255	98.24 351	81.68 355	94.66 351	94.66 356

MG-MVS		94.08 258	94.00 251	94.32 292	97.09 294	85.89 315	93.19 302	95.96 303	92.52 251	94.93 276	97.51 189	89.54 257	98.77 313	87.52 323	97.71 298	98.31 256

thisisatest0515		90.43 312	89.18 324	94.17 297	97.07 295	85.44 319	89.75 356	87.58 366	88.28 302	93.69 309	91.72 357	65.27 368	99.58 164	90.59 275	98.67 256	97.50 307

MVS-HIRNet		88.40 330	90.20 315	82.99 355	97.01 296	60.04 379	93.11 303	85.61 371	84.45 341	88.72 361	99.09 36	84.72 297	98.23 352	82.52 354	96.59 331	90.69 370

GA-MVS		92.83 283	92.15 288	94.87 270	96.97 297	87.27 298	90.03 350	96.12 298	91.83 263	94.05 297	94.57 321	76.01 340	98.97 299	92.46 232	97.34 315	98.36 252

test_yl		94.40 244	94.00 251	95.59 239	96.95 298	89.52 253	94.75 243	95.55 313	96.18 119	96.79 202	96.14 283	81.09 313	99.18 267	90.75 267	97.77 292	98.07 275

DCV-MVSNet		94.40 244	94.00 251	95.59 239	96.95 298	89.52 253	94.75 243	95.55 313	96.18 119	96.79 202	96.14 283	81.09 313	99.18 267	90.75 267	97.77 292	98.07 275

MVS_Test		96.27 164	96.79 135	94.73 277	96.94 300	86.63 307	96.18 155	98.33 193	94.94 178	96.07 240	98.28 101	95.25 136	99.26 258	97.21 50	97.90 290	98.30 258

MAR-MVS		94.21 252	93.03 269	97.76 111	96.94 300	97.44 35	96.97 118	97.15 274	87.89 307	92.00 342	92.73 347	92.14 218	99.12 276	83.92 348	97.51 309	96.73 332
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
Effi-MVS+-dtu		96.81 136	96.09 168	98.99 13	96.90 302	98.69 2	96.42 140	98.09 223	95.86 139	95.15 269	95.54 304	94.26 168	99.81 36	94.06 196	98.51 269	98.47 238

mvs-test1		96.20 167	95.50 191	98.32 65	96.90 302	98.16 5	95.07 225	98.09 223	95.86 139	93.63 310	94.32 329	94.26 168	99.71 104	94.06 196	97.27 318	97.07 315

MS-PatchMatch		94.83 222	94.91 210	94.57 284	96.81 304	87.10 301	94.23 261	97.34 268	88.74 297	97.14 177	97.11 223	91.94 225	98.23 352	92.99 225	97.92 288	98.37 247

UGNet		96.81 136	96.56 145	97.58 125	96.64 305	93.84 170	97.75 70	97.12 276	96.47 106	93.62 311	98.88 51	93.22 191	99.53 180	95.61 116	99.69 62	99.36 98
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
API-MVS		95.09 213	95.01 205	95.31 253	96.61 306	94.02 162	96.83 122	97.18 273	95.60 151	95.79 253	94.33 328	94.54 161	98.37 347	85.70 334	98.52 267	93.52 361

PAPM		87.64 336	85.84 341	93.04 316	96.54 307	84.99 328	88.42 362	95.57 312	79.52 358	83.82 370	93.05 342	80.57 316	98.41 342	62.29 374	92.79 358	95.71 347

FMVSNet3		95.26 206	94.94 206	96.22 215	96.53 308	90.06 244	95.99 166	97.66 253	94.11 208	97.99 126	97.91 151	80.22 318	99.63 146	94.60 172	99.44 137	98.96 178

HY-MVS	91.43 15	92.58 286	91.81 292	94.90 268	96.49 309	88.87 264	97.31 97	94.62 321	85.92 322	90.50 351	96.84 241	85.05 293	99.40 221	83.77 351	95.78 342	96.43 339

TR-MVS		92.54 287	92.20 287	93.57 305	96.49 309	86.66 306	93.51 291	94.73 320	89.96 285	94.95 274	93.87 332	90.24 250	98.61 329	81.18 357	94.88 349	95.45 352

ET-MVSNet_ETH3D		91.12 306	89.67 318	95.47 248	96.41 311	89.15 261	91.54 329	90.23 361	89.07 291	86.78 369	92.84 344	69.39 364	99.44 207	94.16 192	96.61 330	97.82 294

CANet		95.86 182	95.65 185	96.49 200	96.41 311	90.82 235	94.36 254	98.41 181	94.94 178	92.62 337	96.73 250	92.68 203	99.71 104	95.12 152	99.60 83	98.94 181

mvs_anonymous		95.36 201	96.07 170	93.21 313	96.29 313	81.56 351	94.60 247	97.66 253	93.30 229	96.95 196	98.91 49	93.03 196	99.38 229	96.60 66	97.30 317	98.69 220

MVS_0304		95.50 192	95.05 204	96.84 180	96.28 314	93.12 191	97.00 116	96.16 297	95.03 175	89.22 359	97.70 174	90.16 251	99.48 194	94.51 177	99.34 170	97.93 289

SCA		93.38 275	93.52 261	92.96 320	96.24 315	81.40 352	93.24 300	94.00 326	91.58 267	94.57 282	96.97 233	87.94 273	99.42 210	89.47 295	97.66 303	98.06 279

LS3D		97.77 74	97.50 89	98.57 48	96.24 315	97.58 25	98.45 27	98.85 96	98.58 27	97.51 155	97.94 147	95.74 117	99.63 146	95.19 142	98.97 224	98.51 235

new_pmnet		92.34 291	91.69 294	94.32 292	96.23 317	89.16 260	92.27 319	92.88 337	84.39 342	95.29 266	96.35 273	85.66 290	96.74 368	84.53 346	97.56 306	97.05 316

MVE	73.61 22	86.48 338	85.92 340	88.18 352	96.23 317	85.28 323	81.78 370	75.79 376	86.01 320	82.53 372	91.88 355	92.74 201	87.47 375	71.42 372	94.86 350	91.78 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
c3_l		95.20 207	95.32 193	94.83 273	96.19 319	86.43 310	91.83 326	98.35 192	93.47 223	97.36 168	97.26 214	88.69 266	99.28 255	95.41 134	99.36 162	98.78 209

DSMNet-mixed		92.19 294	91.83 291	93.25 311	96.18 320	83.68 341	96.27 148	93.68 329	76.97 367	92.54 338	99.18 27	89.20 265	98.55 335	83.88 349	98.60 265	97.51 306

miper_lstm_enhance		94.81 224	94.80 217	94.85 271	96.16 321	86.45 309	91.14 339	98.20 206	93.49 222	97.03 189	97.37 206	84.97 295	99.26 258	95.28 137	99.56 93	98.83 203

our_test_3		94.20 254	94.58 230	93.07 315	96.16 321	81.20 353	90.42 347	96.84 285	90.72 277	97.14 177	97.13 220	90.47 243	99.11 279	94.04 200	98.25 277	98.91 190

ppachtmachnet_test		94.49 243	94.84 213	93.46 307	96.16 321	82.10 347	90.59 345	97.48 265	90.53 279	97.01 191	97.59 182	91.01 236	99.36 234	93.97 203	99.18 199	98.94 181

Patchmatch-test		93.60 270	93.25 266	94.63 279	96.14 324	87.47 293	96.04 162	94.50 323	93.57 220	96.47 220	96.97 233	76.50 336	98.61 329	90.67 273	98.41 272	97.81 296

wuyk23d		93.25 278	95.20 195	87.40 354	96.07 325	95.38 106	97.04 114	94.97 318	95.33 161	99.70 5	98.11 123	98.14 13	91.94 372	77.76 365	99.68 64	74.89 372

eth_miper_zixun_eth		94.89 220	94.93 208	94.75 276	95.99 326	86.12 313	91.35 332	98.49 170	93.40 224	97.12 179	97.25 215	86.87 285	99.35 237	95.08 154	98.82 244	98.78 209

CANet_DTU		94.65 235	94.21 244	95.96 224	95.90 327	89.68 250	93.92 278	97.83 243	93.19 233	90.12 354	95.64 301	88.52 267	99.57 170	93.27 220	99.47 129	98.62 226

DIV-MVS_self_test		94.73 226	94.64 223	95.01 263	95.86 328	87.00 302	91.33 333	98.08 225	93.34 227	97.10 181	97.34 208	84.02 301	99.31 246	95.15 148	99.55 99	98.72 217

cl____		94.73 226	94.64 223	95.01 263	95.85 329	87.00 302	91.33 333	98.08 225	93.34 227	97.10 181	97.33 209	84.01 302	99.30 249	95.14 149	99.56 93	98.71 219

MVSTER		94.21 252	93.93 255	95.05 262	95.83 330	86.46 308	95.18 218	97.65 255	92.41 255	97.94 133	98.00 140	72.39 356	99.58 164	96.36 77	99.56 93	99.12 153

FMVSNet5		93.39 274	92.35 285	96.50 199	95.83 330	90.81 237	97.31 97	98.27 197	92.74 250	96.27 231	98.28 101	62.23 371	99.67 133	90.86 262	99.36 162	99.03 170

miper_ehance_all_eth		94.69 231	94.70 220	94.64 278	95.77 332	86.22 312	91.32 335	98.24 201	91.67 264	97.05 187	96.65 255	88.39 270	99.22 265	94.88 160	98.34 273	98.49 237

PVSNet_0	81.89 21	84.49 339	83.21 342	88.34 351	95.76 333	74.97 373	83.49 367	92.70 341	78.47 362	87.94 364	86.90 371	83.38 304	96.63 369	73.44 369	66.86 375	93.40 362

PAPR		92.22 293	91.27 299	95.07 261	95.73 334	88.81 266	91.97 324	97.87 238	85.80 324	90.91 347	92.73 347	91.16 234	98.33 349	79.48 359	95.76 343	98.08 273

baseline2		89.65 322	88.44 329	93.25 311	95.62 335	82.71 343	93.82 281	85.94 370	88.89 295	87.35 367	92.54 349	71.23 359	99.33 242	86.01 331	94.60 353	97.72 298

CHOSEN 280x420		89.98 317	89.19 323	92.37 330	95.60 336	81.13 354	86.22 365	97.09 277	81.44 351	87.44 366	93.15 335	73.99 345	99.47 197	88.69 306	99.07 216	96.52 338

ADS-MVSNet2		91.47 304	90.51 312	94.36 291	95.51 337	85.63 316	95.05 228	95.70 307	83.46 343	92.69 332	96.84 241	79.15 322	99.41 219	85.66 336	90.52 362	98.04 283

ADS-MVSNet		90.95 310	90.26 314	93.04 316	95.51 337	82.37 346	95.05 228	93.41 332	83.46 343	92.69 332	96.84 241	79.15 322	98.70 320	85.66 336	90.52 362	98.04 283

CR-MVSNet		93.29 277	92.79 275	94.78 275	95.44 339	88.15 278	96.18 155	97.20 271	84.94 337	94.10 294	98.57 70	77.67 328	99.39 226	95.17 144	95.81 339	96.81 329

RPMNet		94.68 233	94.60 227	94.90 268	95.44 339	88.15 278	96.18 155	98.86 92	97.43 70	94.10 294	98.49 77	79.40 319	99.76 62	95.69 108	95.81 339	96.81 329

1314		92.38 290	92.30 286	92.64 325	95.42 341	85.15 325	95.86 174	96.97 282	85.40 331	90.62 348	93.06 341	91.12 235	97.80 359	86.74 328	95.49 346	94.97 355

RRT_test8_iter05		92.46 288	92.52 284	92.29 332	95.33 342	77.43 364	95.73 179	98.55 165	94.41 195	97.46 164	97.72 173	57.44 374	99.74 79	96.92 61	99.14 202	99.69 20

tpm		91.08 308	90.85 306	91.75 335	95.33 342	78.09 360	95.03 230	91.27 352	88.75 296	93.53 315	97.40 198	71.24 358	99.30 249	91.25 254	93.87 355	97.87 291

IB-MVS	85.98 20	88.63 328	86.95 337	93.68 303	95.12 344	84.82 331	90.85 343	90.17 362	87.55 308	88.48 362	91.34 361	58.01 373	99.59 162	87.24 326	93.80 356	96.63 336
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
PatchT		93.75 264	93.57 260	94.29 294	95.05 345	87.32 297	96.05 161	92.98 336	97.54 67	94.25 290	98.72 60	75.79 341	99.24 261	95.92 97	95.81 339	96.32 340

tpm2		88.47 329	87.69 333	90.79 341	94.98 346	77.34 365	95.09 222	91.83 346	77.51 366	89.40 357	96.41 267	67.83 366	98.73 317	83.58 353	92.60 360	96.29 341

Patchmtry		95.03 216	94.59 229	96.33 208	94.83 347	90.82 235	96.38 143	97.20 271	96.59 98	97.49 158	98.57 70	77.67 328	99.38 229	92.95 227	99.62 73	98.80 206

MVS		90.02 315	89.20 322	92.47 328	94.71 348	86.90 304	95.86 174	96.74 291	64.72 372	90.62 348	92.77 345	92.54 210	98.39 344	79.30 360	95.56 345	92.12 365

CostFormer		89.75 321	89.25 319	91.26 339	94.69 349	78.00 362	95.32 207	91.98 345	81.50 350	90.55 350	96.96 235	71.06 360	98.89 303	88.59 308	92.63 359	96.87 323

PatchmatchNet		91.98 298	91.87 290	92.30 331	94.60 350	79.71 357	95.12 219	93.59 331	89.52 288	93.61 312	97.02 230	77.94 326	99.18 267	90.84 263	94.57 354	98.01 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		88.01 333	87.33 334	90.05 346	94.48 351	76.28 369	94.47 252	94.35 325	73.84 371	89.26 358	95.61 303	73.64 349	98.30 350	84.13 347	86.20 370	95.57 351

MDTV_nov1_ep13			91.28 298		94.31 352	73.51 374	94.80 240	93.16 334	86.75 317	93.45 319	97.40 198	76.37 337	98.55 335	88.85 303	96.43 332

cl22		93.25 278	92.84 274	94.46 288	94.30 353	86.00 314	91.09 341	96.64 294	90.74 276	95.79 253	96.31 274	78.24 325	98.77 313	94.15 193	98.34 273	98.62 226

cascas		91.89 299	91.35 297	93.51 306	94.27 354	85.60 317	88.86 360	98.61 158	79.32 359	92.16 341	91.44 360	89.22 264	98.12 355	90.80 265	97.47 312	96.82 328

test-LLR		89.97 318	89.90 316	90.16 344	94.24 355	74.98 371	89.89 352	89.06 363	92.02 258	89.97 355	90.77 365	73.92 347	98.57 332	91.88 240	97.36 313	96.92 320

test-mter		87.92 334	87.17 335	90.16 344	94.24 355	74.98 371	89.89 352	89.06 363	86.44 318	89.97 355	90.77 365	54.96 381	98.57 332	91.88 240	97.36 313	96.92 320

pmmvs3		90.00 316	88.90 326	93.32 308	94.20 357	85.34 320	91.25 336	92.56 342	78.59 361	93.82 302	95.17 310	67.36 367	98.69 321	89.08 301	98.03 285	95.92 343

tpmrst		90.31 313	90.61 311	89.41 347	94.06 358	72.37 376	95.06 227	93.69 327	88.01 304	92.32 340	96.86 239	77.45 330	98.82 308	91.04 257	87.01 369	97.04 317

test0.0.03 1		90.11 314	89.21 321	92.83 322	93.89 359	86.87 305	91.74 327	88.74 365	92.02 258	94.71 279	91.14 363	73.92 347	94.48 371	83.75 352	92.94 357	97.16 313

JIA-IIPM		91.79 300	90.69 309	95.11 259	93.80 360	90.98 232	94.16 265	91.78 347	96.38 107	90.30 353	99.30 18	72.02 357	98.90 301	88.28 312	90.17 364	95.45 352

miper_enhance_ethall		93.14 280	92.78 277	94.20 295	93.65 361	85.29 322	89.97 351	97.85 239	85.05 334	96.15 239	94.56 322	85.74 289	99.14 274	93.74 209	98.34 273	98.17 271

TESTMET0.1,1		87.20 337	86.57 339	89.07 348	93.62 362	72.84 375	89.89 352	87.01 369	85.46 329	89.12 360	90.20 367	56.00 379	97.72 360	90.91 261	96.92 320	96.64 334

CMPMVS	73.10 23	92.74 284	91.39 296	96.77 184	93.57 363	94.67 139	94.21 263	97.67 251	80.36 356	93.61 312	96.60 257	82.85 305	97.35 362	84.86 344	98.78 247	98.29 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
RRT_MVS		94.90 219	94.07 248	97.39 150	93.18 364	93.21 190	95.26 212	97.49 263	93.94 213	98.25 95	97.85 157	72.96 355	99.84 29	97.90 22	99.78 41	99.14 145

DWT-MVSNet_test		87.92 334	86.77 338	91.39 337	93.18 364	78.62 359	95.10 220	91.42 349	85.58 326	88.00 363	88.73 369	60.60 372	98.90 301	90.60 274	87.70 368	96.65 333

E-PMN		89.52 323	89.78 317	88.73 349	93.14 366	77.61 363	83.26 368	92.02 344	94.82 183	93.71 307	93.11 336	75.31 342	96.81 365	85.81 333	96.81 325	91.77 367

PMMVS		92.39 289	91.08 301	96.30 211	93.12 367	92.81 198	90.58 346	95.96 303	79.17 360	91.85 344	92.27 351	90.29 249	98.66 326	89.85 290	96.68 329	97.43 308

EMVS		89.06 325	89.22 320	88.61 350	93.00 368	77.34 365	82.91 369	90.92 354	94.64 188	92.63 336	91.81 356	76.30 338	97.02 363	83.83 350	96.90 322	91.48 368

dp		88.08 332	88.05 330	88.16 353	92.85 369	68.81 378	94.17 264	92.88 337	85.47 328	91.38 346	96.14 283	68.87 365	98.81 310	86.88 327	83.80 372	96.87 323

gg-mvs-nofinetune		88.28 331	86.96 336	92.23 333	92.84 370	84.44 334	98.19 46	74.60 377	99.08 10	87.01 368	99.47 8	56.93 375	98.23 352	78.91 361	95.61 344	94.01 359

tpmvs		90.79 311	90.87 305	90.57 343	92.75 371	76.30 368	95.79 178	93.64 330	91.04 275	91.91 343	96.26 275	77.19 334	98.86 307	89.38 297	89.85 365	96.56 337

EPMVS		89.26 324	88.55 328	91.39 337	92.36 372	79.11 358	95.65 188	79.86 375	88.60 298	93.12 325	96.53 261	70.73 362	98.10 356	90.75 267	89.32 366	96.98 318

gm-plane-assit					91.79 373	71.40 377			81.67 348		90.11 368		98.99 293	84.86 344

GG-mvs-BLEND				90.60 342	91.00 374	84.21 337	98.23 40	72.63 380		82.76 371	84.11 372	56.14 378	96.79 366	72.20 370	92.09 361	90.78 369

DeepMVS_CX				77.17 356	90.94 375	85.28 323		74.08 379	52.51 373	80.87 374	88.03 370	75.25 343	70.63 376	59.23 375	84.94 371	75.62 371

EPNet_dtu		91.39 305	90.75 308	93.31 309	90.48 376	82.61 344	94.80 240	92.88 337	93.39 225	81.74 373	94.90 318	81.36 311	99.11 279	88.28 312	98.87 237	98.21 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		88.93 326	87.74 331	92.49 326	88.04 377	81.99 348	89.63 357	95.62 309	91.35 269	95.06 270	93.11 336	56.58 376	98.63 327	85.19 340	95.07 347	96.85 325

miper_refine_blended		88.93 326	87.74 331	92.49 326	88.04 377	81.99 348	89.63 357	95.62 309	91.35 269	95.06 270	93.11 336	56.58 376	98.63 327	85.19 340	95.07 347	96.85 325

EPNet		93.72 265	92.62 282	97.03 170	87.61 379	92.25 207	96.27 148	91.28 351	96.74 93	87.65 365	97.39 202	85.00 294	99.64 144	92.14 234	99.48 127	99.20 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_method		66.88 341	66.13 344	69.11 357	62.68 380	25.73 382	49.76 371	96.04 300	14.32 375	64.27 376	91.69 358	73.45 352	88.05 374	76.06 367	66.94 374	93.54 360

tmp_tt		57.23 342	62.50 345	41.44 358	34.77 381	49.21 381	83.93 366	60.22 382	15.31 374	71.11 375	79.37 373	70.09 363	44.86 377	64.76 373	82.93 373	30.25 373

test123		12.59 344	15.49 347	3.87 359	6.07 382	2.55 383	90.75 344	2.59 384	2.52 377	5.20 379	13.02 376	4.96 382	1.85 379	5.20 376	9.09 376	7.23 374

testmvs		12.33 345	15.23 348	3.64 360	5.77 383	2.23 384	88.99 359	3.62 383	2.30 378	5.29 378	13.09 375	4.52 383	1.95 378	5.16 377	8.32 377	6.75 375

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

eth-test2					0.00 384

eth-test					0.00 384

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		24.22 343	32.30 346	0.00 361	0.00 384	0.00 385	0.00 372	98.10 222	0.00 379	0.00 380	95.06 313	97.54 29	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		7.98 346	10.65 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	95.82 109	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		7.91 347	10.55 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	94.94 315	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

PC_three_1452									87.24 310	98.37 77	97.44 195	97.00 54	96.78 367	92.01 235	99.25 190	99.21 131

test_241102_TWO								98.83 108	96.11 121	98.62 53	98.24 107	96.92 62	99.72 90	95.44 128	99.49 123	99.49 55

test_0728_THIRD									96.62 95	98.40 74	98.28 101	97.10 45	99.71 104	95.70 106	99.62 73	99.58 29

GSMVS																98.06 279

sam_mvs1												77.80 327				98.06 279

sam_mvs												77.38 331

MTGPA								98.73 131

test_post1							94.98 232				10.37 378	76.21 339	99.04 287	89.47 295

test_post											10.87 377	76.83 335	99.07 284

patchmatchnet-post											96.84 241	77.36 332	99.42 210

MTMP							96.55 135	74.60 377

test9_res														91.29 251	98.89 236	99.00 173

agg_prior2														90.34 284	98.90 233	99.10 161

test_prior4						95.38 106	93.61 289

test_prior2							93.33 298		94.21 203	94.02 298	96.25 276	93.64 183		91.90 238	98.96 225

旧先验2							93.35 297		77.95 365	95.77 257			98.67 325	90.74 270

新几何2							93.43 292

无先验							93.20 301	97.91 235	80.78 353				99.40 221	87.71 316		97.94 288

原ACMM2							92.82 306

testdata2													99.46 200	87.84 315

segment_acmp												95.34 132

testdata1							92.77 307		93.78 216

plane_prior5								98.75 127					99.46 200	92.59 230	99.20 195	99.28 117

plane_prior4											96.77 247

plane_prior3						94.51 143			95.29 164	96.16 237

plane_prior2							96.50 137		96.36 109

plane_prior						94.29 151	95.42 197		94.31 200						98.93 231

n2								0.00 385

nn								0.00 385

door-mid								98.17 212

test11								98.08 225

door								97.81 244

HQP5-MVS						92.47 203

BP-MVS														90.51 279

HQP4-MVS										92.87 328			99.23 263			99.06 166

HQP3-MVS								98.43 176							98.74 251

HQP2-MVS												90.33 245

MDTV_nov1_ep13_2view						57.28 380	94.89 235		80.59 354	94.02 298		78.66 324		85.50 338		97.82 294

ACMMP++_ref															99.52 110

ACMMP++															99.55 99

Test By Simon												94.51 162

High-resolution multi-view benchmark