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

UA-Net		98.88 7	98.76 13	99.22 2	99.11 87	97.89 14	99.47 3	99.32 11	99.08 10	97.87 144	99.67 2	96.47 90	99.92 5	97.88 25	99.98 2	99.85 3

abl_6		98.42 23	98.19 32	99.09 3	99.16 74	98.10 6	97.73 80	99.11 31	97.76 53	98.62 56	98.27 109	97.88 19	99.80 44	95.67 112	99.50 122	99.38 92

zzz-MVS		98.01 46	97.66 72	99.06 4	99.44 35	97.90 12	95.66 191	98.73 132	97.69 61	97.90 138	97.96 147	95.81 114	99.82 35	96.13 85	99.61 82	99.45 73

MTAPA		98.14 35	97.84 54	99.06 4	99.44 35	97.90 12	97.25 106	98.73 132	97.69 61	97.90 138	97.96 147	95.81 114	99.82 35	96.13 85	99.61 82	99.45 73

test1172		98.08 41	97.76 64	99.05 6	98.78 119	98.07 7	97.41 101	98.85 97	97.57 65	98.15 109	97.96 147	96.60 82	99.76 63	95.30 138	99.18 202	99.33 104

mPP-MVS		97.91 62	97.53 89	99.04 7	99.22 62	97.87 15	97.74 78	98.78 123	96.04 129	97.10 181	97.73 177	96.53 85	99.78 49	95.16 148	99.50 122	99.46 68

MSP-MVS		97.45 100	96.92 130	99.03 8	99.26 53	97.70 19	97.66 81	98.89 82	95.65 151	98.51 66	96.46 271	92.15 217	99.81 38	95.14 151	98.58 268	99.58 31
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
SR-MVS-dyc-post		98.14 35	97.84 54	99.02 9	98.81 114	98.05 9	97.55 89	98.86 93	97.77 50	98.20 102	98.07 132	96.60 82	99.76 63	95.49 123	99.20 198	99.26 124

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 22	98.06 8	99.34 4	99.44 10	98.85 20	99.00 38	99.20 24	97.42 32	99.59 164	97.21 50	99.76 47	99.40 88

SR-MVS		98.00 47	97.66 72	99.01 11	98.77 121	97.93 11	97.38 102	98.83 109	97.32 80	98.06 121	97.85 164	96.65 77	99.77 58	95.00 160	99.11 212	99.32 105

MP-MVS		97.64 85	97.18 113	99.00 12	99.32 50	97.77 18	97.49 95	98.73 132	96.27 115	95.59 264	97.75 174	96.30 98	99.78 49	93.70 214	99.48 130	99.45 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Effi-MVS+-dtu		96.81 139	96.09 171	98.99 13	96.90 307	98.69 2	96.42 146	98.09 226	95.86 142	95.15 273	95.54 309	94.26 168	99.81 38	94.06 198	98.51 272	98.47 241

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 14	97.29 39	98.65 18	99.19 20	95.62 153	99.35 19	99.37 12	97.38 33	99.90 14	98.59 12	99.91 17	99.77 9

CP-MVS		97.92 59	97.56 88	98.99 13	98.99 101	97.82 16	97.93 64	98.96 73	96.11 124	96.89 201	97.45 199	96.85 69	99.78 49	95.19 144	99.63 75	99.38 92

PGM-MVS		97.88 66	97.52 90	98.96 16	99.20 70	97.62 22	97.09 116	99.06 43	95.45 160	97.55 154	97.94 152	97.11 44	99.78 49	94.77 170	99.46 135	99.48 63

RPSCF		97.87 67	97.51 91	98.95 17	99.15 77	98.43 3	97.56 88	99.06 43	96.19 121	98.48 70	98.70 65	94.72 151	99.24 265	94.37 185	99.33 180	99.17 142

XVS		97.96 48	97.63 79	98.94 18	99.15 77	97.66 20	97.77 73	98.83 109	97.42 73	96.32 230	97.64 183	96.49 88	99.72 90	95.66 114	99.37 162	99.45 73

X-MVStestdata		92.86 287	90.83 311	98.94 18	99.15 77	97.66 20	97.77 73	98.83 109	97.42 73	96.32 230	36.50 377	96.49 88	99.72 90	95.66 114	99.37 162	99.45 73

ACMMPR		97.95 52	97.62 81	98.94 18	99.20 70	97.56 26	97.59 86	98.83 109	96.05 127	97.46 165	97.63 184	96.77 73	99.76 63	95.61 118	99.46 135	99.49 57

ACMMP		98.05 43	97.75 66	98.93 21	99.23 59	97.60 23	98.09 56	98.96 73	95.75 149	97.91 137	98.06 137	96.89 64	99.76 63	95.32 137	99.57 93	99.43 84
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
region2R		97.92 59	97.59 85	98.92 22	99.22 62	97.55 27	97.60 85	98.84 102	96.00 132	97.22 171	97.62 185	96.87 68	99.76 63	95.48 126	99.43 148	99.46 68

HPM-MVS		98.11 39	97.83 57	98.92 22	99.42 39	97.46 33	98.57 19	99.05 45	95.43 162	97.41 167	97.50 195	97.98 15	99.79 45	95.58 121	99.57 93	99.50 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 22	97.48 32	98.35 34	99.03 52	95.88 140	97.88 141	98.22 116	98.15 12	99.74 80	96.50 73	99.62 76	99.42 85

ACMM	93.33 11	98.05 43	97.79 59	98.85 25	99.15 77	97.55 27	96.68 139	98.83 109	95.21 168	98.36 83	98.13 124	98.13 14	99.62 156	96.04 90	99.54 105	99.39 90
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		97.92 59	97.62 81	98.83 26	99.32 50	97.24 41	97.45 96	98.84 102	95.76 147	96.93 198	97.43 201	97.26 40	99.79 45	96.06 87	99.53 108	99.45 73

HFP-MVS		97.94 55	97.64 77	98.83 26	99.15 77	97.50 30	97.59 86	98.84 102	96.05 127	97.49 159	97.54 190	97.07 48	99.70 115	95.61 118	99.46 135	99.30 111

#test#		97.62 87	97.22 111	98.83 26	99.15 77	97.50 30	96.81 128	98.84 102	94.25 203	97.49 159	97.54 190	97.07 48	99.70 115	94.37 185	99.46 135	99.30 111

GST-MVS		97.82 73	97.49 94	98.81 29	99.23 59	97.25 40	97.16 110	98.79 119	95.96 134	97.53 155	97.40 203	96.93 60	99.77 58	95.04 157	99.35 170	99.42 85

HPM-MVS++		96.99 123	96.38 159	98.81 29	98.64 135	97.59 24	95.97 174	98.20 208	95.51 158	95.06 274	96.53 267	94.10 172	99.70 115	94.29 189	99.15 204	99.13 151

APD-MVS_3200maxsize		98.13 38	97.90 48	98.79 31	98.79 117	97.31 38	97.55 89	98.92 79	97.72 57	98.25 98	98.13 124	97.10 45	99.75 70	95.44 130	99.24 196	99.32 105

SteuartSystems-ACMMP		98.02 45	97.76 64	98.79 31	99.43 37	97.21 43	97.15 111	98.90 81	96.58 102	98.08 119	97.87 163	97.02 53	99.76 63	95.25 141	99.59 88	99.40 88
Skip Steuart: Steuart Systems R&D Blog.
mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 62	98.54 22	99.22 15	96.23 118	99.71 4	99.48 7	98.77 6	99.93 3	98.89 3	99.95 5	99.84 5

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 27	96.61 58	98.55 21	99.17 21	99.05 13	99.17 29	98.79 58	95.47 128	99.89 18	97.95 24	99.91 17	99.75 14

jajsoiax		98.77 9	98.79 12	98.74 35	99.66 11	96.48 62	98.45 30	99.12 30	95.83 145	99.67 6	99.37 12	98.25 10	99.92 5	98.77 5	99.94 8	99.82 6

LPG-MVS_test		97.94 55	97.67 71	98.74 35	99.15 77	97.02 44	97.09 116	99.02 54	95.15 172	98.34 86	98.23 113	97.91 17	99.70 115	94.41 182	99.73 54	99.50 49

LGP-MVS_train				98.74 35	99.15 77	97.02 44		99.02 54	95.15 172	98.34 86	98.23 113	97.91 17	99.70 115	94.41 182	99.73 54	99.50 49

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 7	99.02 15	99.62 10	99.36 14	98.53 7	99.52 186	98.58 13	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
MP-MVS-pluss		97.69 82	97.36 100	98.70 39	99.50 30	96.84 49	95.38 208	98.99 65	92.45 256	98.11 113	98.31 96	97.25 41	99.77 58	96.60 67	99.62 76	99.48 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_djsdf		98.73 11	98.74 16	98.69 40	99.63 13	96.30 68	98.67 15	99.02 54	96.50 106	99.32 20	99.44 10	97.43 31	99.92 5	98.73 7	99.95 5	99.86 2

ACMMP_NAP		97.89 65	97.63 79	98.67 41	99.35 46	96.84 49	96.36 150	98.79 119	95.07 176	97.88 141	98.35 92	97.24 42	99.72 90	96.05 89	99.58 90	99.45 73

MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 52	98.76 12	98.89 82	98.49 28	99.38 17	99.14 34	95.44 130	99.84 30	96.47 74	99.80 40	99.47 66

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 66	99.18 5	99.20 18	99.67 2	99.73 3	99.65 4	99.15 3	99.86 24	97.22 49	99.92 14	99.77 9

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 69	97.35 37	97.96 62	99.16 22	98.34 32	98.78 49	98.52 79	97.32 35	99.45 207	94.08 197	99.67 68	99.13 151
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 65	99.17 6	99.05 45	98.05 43	99.61 11	99.52 5	93.72 182	99.88 20	98.72 9	99.88 26	99.65 24

SMA-MVS		97.48 98	97.11 116	98.60 46	98.83 113	96.67 55	96.74 133	98.73 132	91.61 268	98.48 70	98.36 91	96.53 85	99.68 130	95.17 146	99.54 105	99.45 73
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
DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 20	96.12 73	98.48 29	99.10 33	99.36 4	99.29 23	99.06 41	97.27 38	99.93 3	97.71 34	99.91 17	99.70 19

LS3D		97.77 77	97.50 93	98.57 48	96.24 320	97.58 25	98.45 30	98.85 97	98.58 27	97.51 157	97.94 152	95.74 118	99.63 148	95.19 144	98.97 226	98.51 237

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 64	98.87 9	99.30 12	99.01 16	99.63 9	99.66 3	99.27 2	99.68 130	97.75 32	99.89 25	99.62 27

ACMP	92.54 13	97.47 99	97.10 117	98.55 50	99.04 98	96.70 53	96.24 158	98.89 82	93.71 219	97.97 132	97.75 174	97.44 30	99.63 148	93.22 224	99.70 64	99.32 105
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EGC-MVSNET		83.08 343	77.93 346	98.53 51	99.57 17	97.55 27	98.33 37	98.57 165	4.71 379	10.38 380	98.90 53	95.60 123	99.50 191	95.69 110	99.61 82	98.55 235

DPE-MVS		97.64 85	97.35 101	98.50 52	98.85 112	96.18 70	95.21 222	98.99 65	95.84 144	98.78 49	98.08 130	96.84 70	99.81 38	93.98 204	99.57 93	99.52 46
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVG-ACMP-BASELINE		97.58 91	97.28 106	98.49 53	99.16 74	96.90 48	96.39 147	98.98 68	95.05 177	98.06 121	98.02 141	95.86 106	99.56 173	94.37 185	99.64 73	99.00 175

CPTT-MVS		96.69 149	96.08 172	98.49 53	98.89 109	96.64 57	97.25 106	98.77 124	92.89 249	96.01 248	97.13 225	92.23 216	99.67 135	92.24 236	99.34 173	99.17 142

APDe-MVS		98.14 35	98.03 41	98.47 55	98.72 125	96.04 76	98.07 57	99.10 33	95.96 134	98.59 61	98.69 66	96.94 58	99.81 38	96.64 65	99.58 90	99.57 36

PEN-MVS		98.75 10	98.85 10	98.44 56	99.58 16	95.67 92	98.45 30	99.15 26	99.33 5	99.30 21	99.00 43	97.27 38	99.92 5	97.64 36	99.92 14	99.75 14

mvsmamba		98.16 33	98.06 38	98.44 56	99.53 25	95.87 82	98.70 13	98.94 76	97.71 59	98.85 43	99.10 36	91.35 234	99.83 33	98.47 14	99.90 23	99.64 26

RRT_MVS		97.95 52	97.79 59	98.43 58	99.67 10	95.56 96	98.86 10	96.73 295	97.99 45	99.15 30	99.35 16	89.84 256	99.90 14	98.64 10	99.90 23	99.82 6

TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 58	99.07 92	95.87 82	96.73 137	99.05 45	98.67 24	98.84 45	98.45 84	97.58 28	99.88 20	96.45 75	99.86 28	99.54 42

OPM-MVS		97.54 93	97.25 107	98.41 60	99.11 87	96.61 58	95.24 220	98.46 173	94.58 194	98.10 116	98.07 132	97.09 47	99.39 229	95.16 148	99.44 140	99.21 133
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
APD-MVS		97.00 122	96.53 152	98.41 60	98.55 149	96.31 67	96.32 153	98.77 124	92.96 248	97.44 166	97.58 189	95.84 107	99.74 80	91.96 239	99.35 170	99.19 138
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		98.73 11	98.85 10	98.39 62	99.55 20	95.47 105	98.49 27	99.13 29	99.22 8	99.22 27	98.96 47	97.35 34	99.92 5	97.79 30	99.93 10	99.79 8

UniMVSNet_NR-MVSNet		97.83 71	97.65 74	98.37 63	98.72 125	95.78 85	95.66 191	99.02 54	98.11 40	98.31 93	97.69 181	94.65 156	99.85 27	97.02 59	99.71 61	99.48 63

testtj		96.69 149	96.13 168	98.36 64	98.46 164	96.02 79	96.44 145	98.70 142	94.26 202	96.79 204	97.13 225	94.07 173	99.75 70	90.53 279	98.80 247	99.31 110

DU-MVS		97.79 75	97.60 84	98.36 64	98.73 123	95.78 85	95.65 194	98.87 90	97.57 65	98.31 93	97.83 165	94.69 152	99.85 27	97.02 59	99.71 61	99.46 68

UniMVSNet (Re)		97.83 71	97.65 74	98.35 66	98.80 116	95.86 84	95.92 180	99.04 51	97.51 70	98.22 101	97.81 169	94.68 154	99.78 49	97.14 55	99.75 52	99.41 87

CS-MVS		98.09 40	98.01 42	98.32 67	98.45 165	96.69 54	98.52 25	99.69 2	98.07 42	96.07 244	97.19 223	96.88 66	99.86 24	97.50 41	99.73 54	98.41 244

mvs-test1		96.20 170	95.50 194	98.32 67	96.90 307	98.16 5	95.07 229	98.09 226	95.86 142	93.63 314	94.32 333	94.26 168	99.71 106	94.06 198	97.27 322	97.07 319

nrg030		98.54 18	98.62 21	98.32 67	99.22 62	95.66 93	97.90 67	99.08 39	98.31 33	99.02 36	98.74 62	97.68 24	99.61 162	97.77 31	99.85 31	99.70 19

DeepPCF-MVS	94.58 5	96.90 131	96.43 158	98.31 70	97.48 271	97.23 42	92.56 317	98.60 160	92.84 250	98.54 64	97.40 203	96.64 79	98.78 315	94.40 184	99.41 157	98.93 187

CP-MVSNet		98.42 23	98.46 24	98.30 71	99.46 33	95.22 121	98.27 43	98.84 102	99.05 13	99.01 37	98.65 70	95.37 131	99.90 14	97.57 38	99.91 17	99.77 9

XVG-OURS-SEG-HR		97.38 105	97.07 120	98.30 71	99.01 100	97.41 36	94.66 249	99.02 54	95.20 169	98.15 109	97.52 193	98.83 4	98.43 344	94.87 163	96.41 337	99.07 166

h-mvs33		96.29 166	95.63 189	98.26 73	98.50 157	96.11 74	96.90 124	97.09 279	96.58 102	97.21 173	98.19 118	84.14 302	99.78 49	95.89 101	96.17 341	98.89 196

NR-MVSNet		97.96 48	97.86 53	98.26 73	98.73 123	95.54 98	98.14 53	98.73 132	97.79 49	99.42 15	97.83 165	94.40 165	99.78 49	95.91 100	99.76 47	99.46 68

XVG-OURS		97.12 118	96.74 139	98.26 73	98.99 101	97.45 34	93.82 285	99.05 45	95.19 170	98.32 91	97.70 179	95.22 137	98.41 345	94.27 190	98.13 285	98.93 187

test_0728_SECOND				98.25 76	99.23 59	95.49 104	96.74 133	98.89 82					99.75 70	95.48 126	99.52 113	99.53 45

PHI-MVS		96.96 127	96.53 152	98.25 76	97.48 271	96.50 61	96.76 132	98.85 97	93.52 222	96.19 240	96.85 246	95.94 104	99.42 213	93.79 210	99.43 148	98.83 205

MSC_two_6792asdad				98.22 78	97.75 249	95.34 113		98.16 217					99.75 70	95.87 103	99.51 118	99.57 36

No_MVS				98.22 78	97.75 249	95.34 113		98.16 217					99.75 70	95.87 103	99.51 118	99.57 36

SF-MVS		97.60 89	97.39 98	98.22 78	98.93 106	95.69 89	97.05 118	99.10 33	95.32 165	97.83 147	97.88 161	96.44 92	99.72 90	94.59 177	99.39 159	99.25 128

PS-MVSNAJss		98.53 19	98.63 19	98.21 81	99.68 9	94.82 133	98.10 55	99.21 16	96.91 90	99.75 2	99.45 9	95.82 110	99.92 5	98.80 4	99.96 4	99.89 1

ETH3D-3000-0.1		96.89 133	96.46 157	98.16 82	98.62 140	95.69 89	95.96 175	98.98 68	93.36 227	97.04 188	97.31 216	94.93 148	99.63 148	92.60 231	99.34 173	99.17 142

DVP-MVS		97.78 76	97.65 74	98.16 82	99.24 57	95.51 100	96.74 133	98.23 203	95.92 137	98.40 77	98.28 105	97.06 50	99.71 106	95.48 126	99.52 113	99.26 124
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
DeepC-MVS	95.41 4	97.82 73	97.70 67	98.16 82	98.78 119	95.72 87	96.23 159	99.02 54	93.92 214	98.62 56	98.99 44	97.69 23	99.62 156	96.18 84	99.87 27	99.15 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	96.13 3	97.73 79	97.59 85	98.15 85	98.11 203	95.60 95	98.04 59	98.70 142	98.13 39	96.93 198	98.45 84	95.30 135	99.62 156	95.64 116	98.96 227	99.24 130

CS-MVS-test		97.91 62	97.84 54	98.14 86	98.52 152	96.03 78	98.38 33	99.67 3	98.11 40	95.50 266	96.92 243	96.81 72	99.87 22	96.87 63	99.76 47	98.51 237

PM-MVS		97.36 108	97.10 117	98.14 86	98.91 108	96.77 51	96.20 160	98.63 158	93.82 216	98.54 64	98.33 94	93.98 175	99.05 290	95.99 95	99.45 139	98.61 230

DVP-MVS++		97.96 48	97.90 48	98.12 88	97.75 249	95.40 106	99.03 7	98.89 82	96.62 97	98.62 56	98.30 100	96.97 56	99.75 70	95.70 108	99.25 193	99.21 133

NCCC		96.52 158	95.99 176	98.10 89	97.81 231	95.68 91	95.00 235	98.20 208	95.39 163	95.40 269	96.36 277	93.81 179	99.45 207	93.55 217	98.42 274	99.17 142

ETH3D cwj APD-0.16		96.23 169	95.61 191	98.09 90	97.91 219	95.65 94	94.94 237	98.74 130	91.31 274	96.02 247	97.08 230	94.05 174	99.69 123	91.51 251	98.94 231	98.93 187

SED-MVS		97.94 55	97.90 48	98.07 91	99.22 62	95.35 111	96.79 130	98.83 109	96.11 124	99.08 33	98.24 111	97.87 20	99.72 90	95.44 130	99.51 118	99.14 149

Vis-MVSNet		98.27 29	98.34 28	98.07 91	99.33 48	95.21 123	98.04 59	99.46 9	97.32 80	97.82 149	99.11 35	96.75 74	99.86 24	97.84 27	99.36 165	99.15 146
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AllTest		97.20 117	96.92 130	98.06 93	99.08 90	96.16 71	97.14 113	99.16 22	94.35 199	97.78 150	98.07 132	95.84 107	99.12 280	91.41 252	99.42 151	98.91 192

TestCases				98.06 93	99.08 90	96.16 71		99.16 22	94.35 199	97.78 150	98.07 132	95.84 107	99.12 280	91.41 252	99.42 151	98.91 192

N_pmnet		95.18 211	94.23 246	98.06 93	97.85 223	96.55 60	92.49 318	91.63 353	89.34 293	98.09 117	97.41 202	90.33 246	99.06 289	91.58 250	99.31 184	98.56 233

F-COLMAP		95.30 207	94.38 243	98.05 96	98.64 135	96.04 76	95.61 197	98.66 152	89.00 297	93.22 328	96.40 275	92.90 198	99.35 240	87.45 327	97.53 312	98.77 214

CNVR-MVS		96.92 129	96.55 149	98.03 97	98.00 213	95.54 98	94.87 240	98.17 214	94.60 191	96.38 227	97.05 233	95.67 120	99.36 237	95.12 154	99.08 216	99.19 138

TSAR-MVS + MP.		97.42 102	97.23 110	98.00 98	99.38 43	95.00 128	97.63 84	98.20 208	93.00 243	98.16 107	98.06 137	95.89 105	99.72 90	95.67 112	99.10 214	99.28 119
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMH+	93.58 10	98.23 32	98.31 29	97.98 99	99.39 42	95.22 121	97.55 89	99.20 18	98.21 37	99.25 25	98.51 80	98.21 11	99.40 224	94.79 167	99.72 58	99.32 105

v7n		98.73 11	98.99 5	97.95 100	99.64 12	94.20 160	98.67 15	99.14 28	99.08 10	99.42 15	99.23 22	96.53 85	99.91 13	99.27 2	99.93 10	99.73 16

Anonymous20231211		98.55 17	98.76 13	97.94 101	98.79 117	94.37 151	98.84 11	99.15 26	99.37 3	99.67 6	99.43 11	95.61 122	99.72 90	98.12 19	99.86 28	99.73 16

Regformer-2		97.41 103	97.24 109	97.93 102	97.21 293	94.72 136	94.85 242	98.27 198	97.74 54	98.11 113	97.50 195	95.58 124	99.69 123	96.57 70	99.31 184	99.37 99

bld_raw_dy_0_64		97.69 82	97.61 83	97.91 103	99.54 22	94.27 157	98.06 58	98.60 160	96.60 99	98.79 48	98.95 48	89.62 257	99.84 30	98.43 16	99.91 17	99.62 27

OMC-MVS		96.48 160	96.00 175	97.91 103	98.30 174	96.01 80	94.86 241	98.60 160	91.88 265	97.18 175	97.21 222	96.11 101	99.04 291	90.49 283	99.34 173	98.69 222

GeoE		97.75 78	97.70 67	97.89 105	98.88 110	94.53 144	97.10 115	98.98 68	95.75 149	97.62 152	97.59 187	97.61 27	99.77 58	96.34 79	99.44 140	99.36 100

xxxxxxxxxxxxxcwj		97.24 115	97.03 123	97.89 105	98.48 160	94.71 137	94.53 254	99.07 42	95.02 179	97.83 147	97.88 161	96.44 92	99.72 90	94.59 177	99.39 159	99.25 128

train_agg		95.46 200	94.66 225	97.88 107	97.84 227	95.23 118	93.62 291	98.39 185	87.04 317	93.78 307	95.99 293	94.58 159	99.52 186	91.76 247	98.90 235	98.89 196

pm-mvs1		98.47 21	98.67 17	97.86 108	99.52 26	94.58 143	98.28 41	99.00 62	97.57 65	99.27 24	99.22 23	98.32 9	99.50 191	97.09 56	99.75 52	99.50 49

ITE_SJBPF				97.85 109	98.64 135	96.66 56		98.51 170	95.63 152	97.22 171	97.30 217	95.52 125	98.55 338	90.97 262	98.90 235	98.34 255

CDPH-MVS		95.45 201	94.65 226	97.84 110	98.28 177	94.96 129	93.73 289	98.33 194	85.03 338	95.44 267	96.60 263	95.31 134	99.44 210	90.01 289	99.13 208	99.11 159

DP-MVS		97.87 67	97.89 51	97.81 111	98.62 140	94.82 133	97.13 114	98.79 119	98.98 17	98.74 52	98.49 81	95.80 116	99.49 194	95.04 157	99.44 140	99.11 159

hse-mvs2		95.77 187	95.09 203	97.79 112	97.84 227	95.51 100	95.66 191	95.43 319	96.58 102	97.21 173	96.16 285	84.14 302	99.54 180	95.89 101	96.92 324	98.32 256

DROMVSNet		97.90 64	97.94 47	97.79 112	98.66 134	95.14 124	98.31 38	99.66 4	97.57 65	95.95 249	97.01 237	96.99 55	99.82 35	97.66 35	99.64 73	98.39 247

MAR-MVS		94.21 255	93.03 273	97.76 114	96.94 305	97.44 35	96.97 123	97.15 276	87.89 311	92.00 346	92.73 351	92.14 218	99.12 280	83.92 351	97.51 313	96.73 336
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
agg_prior1		95.39 203	94.60 231	97.75 115	97.80 235	94.96 129	93.39 299	98.36 189	87.20 315	93.49 320	95.97 296	94.65 156	99.53 182	91.69 249	98.86 241	98.77 214

AUN-MVS		93.95 265	92.69 283	97.74 116	97.80 235	95.38 108	95.57 198	95.46 318	91.26 275	92.64 339	96.10 291	74.67 347	99.55 177	93.72 213	96.97 323	98.30 260

VDD-MVS		97.37 106	97.25 107	97.74 116	98.69 132	94.50 147	97.04 119	95.61 314	98.59 26	98.51 66	98.72 63	92.54 210	99.58 166	96.02 92	99.49 126	99.12 156

Anonymous20240529		97.96 48	98.04 40	97.71 118	98.69 132	94.28 156	97.86 69	98.31 197	98.79 22	99.23 26	98.86 56	95.76 117	99.61 162	95.49 123	99.36 165	99.23 131

Regformer-4		97.53 95	97.47 96	97.71 118	97.35 281	93.91 168	95.26 218	98.14 220	97.97 46	98.34 86	97.89 157	95.49 126	99.71 106	97.41 44	99.42 151	99.51 48

VPA-MVSNet		98.27 29	98.46 24	97.70 120	99.06 93	93.80 174	97.76 75	99.00 62	98.40 30	99.07 35	98.98 45	96.89 64	99.75 70	97.19 53	99.79 41	99.55 41

IS-MVSNet		96.93 128	96.68 142	97.70 120	99.25 56	94.00 166	98.57 19	96.74 293	98.36 31	98.14 111	97.98 146	88.23 274	99.71 106	93.10 227	99.72 58	99.38 92

CSCG		97.40 104	97.30 103	97.69 122	98.95 103	94.83 132	97.28 105	98.99 65	96.35 114	98.13 112	95.95 298	95.99 103	99.66 141	94.36 188	99.73 54	98.59 231

HQP_MVS		96.66 152	96.33 162	97.68 123	98.70 130	94.29 153	96.50 143	98.75 128	96.36 112	96.16 241	96.77 253	91.91 228	99.46 203	92.59 233	99.20 198	99.28 119

EPP-MVSNet		96.84 134	96.58 146	97.65 124	99.18 73	93.78 176	98.68 14	96.34 298	97.91 48	97.30 169	98.06 137	88.46 271	99.85 27	93.85 208	99.40 158	99.32 105

OPU-MVS				97.64 125	98.01 209	95.27 116	96.79 130				97.35 212	96.97 56	98.51 341	91.21 258	99.25 193	99.14 149

MVS_111021_LR		96.82 138	96.55 149	97.62 126	98.27 179	95.34 113	93.81 287	98.33 194	94.59 193	96.56 219	96.63 262	96.61 80	98.73 320	94.80 166	99.34 173	98.78 211

Regformer-1		97.27 112	97.16 114	97.61 127	97.21 293	93.86 171	94.85 242	98.04 235	97.62 64	98.03 125	97.50 195	95.34 132	99.63 148	96.52 71	99.31 184	99.35 102

UGNet		96.81 139	96.56 148	97.58 128	96.64 310	93.84 173	97.75 76	97.12 278	96.47 109	93.62 315	98.88 54	93.22 191	99.53 182	95.61 118	99.69 65	99.36 100
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
FC-MVSNet-test		98.16 33	98.37 27	97.56 129	99.49 31	93.10 194	98.35 34	99.21 16	98.43 29	98.89 41	98.83 57	94.30 167	99.81 38	97.87 26	99.91 17	99.77 9

MCST-MVS		96.24 168	95.80 183	97.56 129	98.75 122	94.13 162	94.66 249	98.17 214	90.17 286	96.21 238	96.10 291	95.14 139	99.43 212	94.13 196	98.85 243	99.13 151

GBi-Net		96.99 123	96.80 136	97.56 129	97.96 215	93.67 179	98.23 45	98.66 152	95.59 155	97.99 128	99.19 25	89.51 263	99.73 85	94.60 174	99.44 140	99.30 111

test1		96.99 123	96.80 136	97.56 129	97.96 215	93.67 179	98.23 45	98.66 152	95.59 155	97.99 128	99.19 25	89.51 263	99.73 85	94.60 174	99.44 140	99.30 111

FMVSNet1		97.95 52	98.08 35	97.56 129	99.14 85	93.67 179	98.23 45	98.66 152	97.41 76	99.00 38	99.19 25	95.47 128	99.73 85	95.83 105	99.76 47	99.30 111

TransMVSNet (Re)		98.38 25	98.67 17	97.51 134	99.51 27	93.39 189	98.20 50	98.87 90	98.23 36	99.48 12	99.27 20	98.47 8	99.55 177	96.52 71	99.53 108	99.60 29

PLC	91.02 16	94.05 262	92.90 275	97.51 134	98.00 213	95.12 126	94.25 263	98.25 201	86.17 323	91.48 349	95.25 313	91.01 237	99.19 270	85.02 346	96.69 332	98.22 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	93.61 9	98.44 22	98.76 13	97.51 134	99.43 37	93.54 185	98.23 45	99.05 45	97.40 77	99.37 18	99.08 39	98.79 5	99.47 200	97.74 33	99.71 61	99.50 49
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
alignmvs		96.01 179	95.52 193	97.50 137	97.77 246	94.71 137	96.07 166	96.84 287	97.48 71	96.78 208	94.28 334	85.50 294	99.40 224	96.22 81	98.73 256	98.40 245

Baseline_NR-MVSNet		97.72 80	97.79 59	97.50 137	99.56 18	93.29 190	95.44 201	98.86 93	98.20 38	98.37 80	99.24 21	94.69 152	99.55 177	95.98 96	99.79 41	99.65 24

3Dnovator	96.53 2	97.61 88	97.64 77	97.50 137	97.74 252	93.65 183	98.49 27	98.88 88	96.86 92	97.11 180	98.55 77	95.82 110	99.73 85	95.94 98	99.42 151	99.13 151

TSAR-MVS + GP.		96.47 161	96.12 169	97.49 140	97.74 252	95.23 118	94.15 270	96.90 286	93.26 231	98.04 124	96.70 258	94.41 164	98.89 306	94.77 170	99.14 205	98.37 249

FIs		97.93 58	98.07 36	97.48 141	99.38 43	92.95 197	98.03 61	99.11 31	98.04 44	98.62 56	98.66 68	93.75 181	99.78 49	97.23 48	99.84 32	99.73 16

ETH3 D test6400		94.77 228	93.87 259	97.47 142	98.12 202	93.73 177	94.56 253	98.70 142	85.45 333	94.70 284	95.93 300	91.77 230	99.63 148	86.45 333	99.14 205	99.05 170

test_part1		96.77 142	96.53 152	97.47 142	98.04 205	92.92 198	97.93 64	98.85 97	98.83 21	99.30 21	99.07 40	79.25 323	99.79 45	97.59 37	99.93 10	99.69 21

test_0402		97.84 70	97.97 44	97.47 142	99.19 72	94.07 163	96.71 138	98.73 132	98.66 25	98.56 63	98.41 87	96.84 70	99.69 123	94.82 165	99.81 37	98.64 225

test_prior3		95.91 182	95.39 195	97.46 145	97.79 241	94.26 158	93.33 302	98.42 180	94.21 204	94.02 302	96.25 281	93.64 183	99.34 242	91.90 241	98.96 227	98.79 209

test_prior				97.46 145	97.79 241	94.26 158		98.42 180					99.34 242			98.79 209

test12				97.46 145	97.61 263	94.07 163		97.78 248		93.57 318		93.31 189	99.42 213		98.78 249	98.89 196

DeepC-MVS_fast	94.34 7	96.74 143	96.51 155	97.44 148	97.69 255	94.15 161	96.02 170	98.43 177	93.17 238	97.30 169	97.38 209	95.48 127	99.28 258	93.74 211	99.34 173	98.88 200
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous202405211		96.34 165	95.98 177	97.43 149	98.25 182	93.85 172	96.74 133	94.41 328	97.72 57	98.37 80	98.03 140	87.15 285	99.53 182	94.06 198	99.07 218	98.92 191

pmmvs-eth3d		96.49 159	96.18 167	97.42 150	98.25 182	94.29 153	94.77 246	98.07 232	89.81 290	97.97 132	98.33 94	93.11 192	99.08 287	95.46 129	99.84 32	98.89 196

VDDNet		96.98 126	96.84 133	97.41 151	99.40 41	93.26 191	97.94 63	95.31 320	99.26 7	98.39 79	99.18 28	87.85 281	99.62 156	95.13 153	99.09 215	99.35 102

EG-PatchMatch MVS		97.69 82	97.79 59	97.40 152	99.06 93	93.52 186	95.96 175	98.97 72	94.55 195	98.82 46	98.76 61	97.31 36	99.29 256	97.20 52	99.44 140	99.38 92

Fast-Effi-MVS+-dtu		96.44 162	96.12 169	97.39 153	97.18 296	94.39 149	95.46 200	98.73 132	96.03 131	94.72 282	94.92 321	96.28 100	99.69 123	93.81 209	97.98 290	98.09 274

LF4IMVS		96.07 175	95.63 189	97.36 154	98.19 188	95.55 97	95.44 201	98.82 117	92.29 259	95.70 262	96.55 265	92.63 206	98.69 324	91.75 248	99.33 180	97.85 294

Gipuma		98.07 42	98.31 29	97.36 154	99.76 5	96.28 69	98.51 26	99.10 33	98.76 23	96.79 204	99.34 18	96.61 80	98.82 311	96.38 77	99.50 122	96.98 322
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		97.33 109	97.33 102	97.32 156	98.13 201	93.79 175	96.99 122	99.65 5	96.74 95	99.47 13	98.93 50	96.91 63	99.84 30	90.11 287	99.06 221	98.32 256

canonicalmvs		97.23 116	97.21 112	97.30 157	97.65 260	94.39 149	97.84 70	99.05 45	97.42 73	96.68 211	93.85 337	97.63 26	99.33 245	96.29 80	98.47 273	98.18 272

1121		94.26 251	93.26 268	97.27 158	98.26 181	94.73 135	95.86 181	97.71 252	77.96 367	94.53 288	96.71 257	91.93 226	99.40 224	87.71 319	98.64 263	97.69 302

MVS_111021_HR		96.73 145	96.54 151	97.27 158	98.35 172	93.66 182	93.42 297	98.36 189	94.74 186	96.58 217	96.76 255	96.54 84	98.99 297	94.87 163	99.27 191	99.15 146

SixPastTwentyTwo		97.49 97	97.57 87	97.26 160	99.56 18	92.33 207	98.28 41	96.97 284	98.30 34	99.45 14	99.35 16	88.43 272	99.89 18	98.01 23	99.76 47	99.54 42

KD-MVS_self_test		97.86 69	98.07 36	97.25 161	99.22 62	92.81 200	97.55 89	98.94 76	97.10 86	98.85 43	98.88 54	95.03 143	99.67 135	97.39 46	99.65 71	99.26 124

新几何1				97.25 161	98.29 175	94.70 140		97.73 250	77.98 366	94.83 281	96.67 260	92.08 221	99.45 207	88.17 316	98.65 262	97.61 305

WR-MVS		96.90 131	96.81 135	97.16 163	98.56 148	92.20 213	94.33 259	98.12 223	97.34 79	98.20 102	97.33 214	92.81 199	99.75 70	94.79 167	99.81 37	99.54 42

TAMVS		95.49 196	94.94 209	97.16 163	98.31 173	93.41 188	95.07 229	96.82 289	91.09 277	97.51 157	97.82 168	89.96 253	99.42 213	88.42 312	99.44 140	98.64 225

CDS-MVSNet		94.88 224	94.12 251	97.14 165	97.64 261	93.57 184	93.96 281	97.06 281	90.05 287	96.30 233	96.55 265	86.10 290	99.47 200	90.10 288	99.31 184	98.40 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		97.32 110	97.39 98	97.11 166	97.36 280	92.08 217	95.34 211	97.65 258	97.74 54	98.29 96	98.11 128	95.05 140	99.68 130	97.50 41	99.50 122	99.56 39

Regformer-3		97.25 114	97.29 104	97.11 166	97.35 281	92.32 208	95.26 218	97.62 263	97.67 63	98.17 106	97.89 157	95.05 140	99.56 173	97.16 54	99.42 151	99.46 68

EI-MVSNet-UG-set		97.32 110	97.40 97	97.09 168	97.34 285	92.01 219	95.33 212	97.65 258	97.74 54	98.30 95	98.14 122	95.04 142	99.69 123	97.55 39	99.52 113	99.58 31

XXY-MVS		97.54 93	97.70 67	97.07 169	99.46 33	92.21 211	97.22 109	99.00 62	94.93 183	98.58 62	98.92 51	97.31 36	99.41 222	94.44 180	99.43 148	99.59 30

lessismore_v0				97.05 170	99.36 45	92.12 215		84.07 375		98.77 51	98.98 45	85.36 295	99.74 80	97.34 47	99.37 162	99.30 111

TAPA-MVS	93.32 12	94.93 221	94.23 246	97.04 171	98.18 191	94.51 145	95.22 221	98.73 132	81.22 355	96.25 236	95.95 298	93.80 180	98.98 299	89.89 291	98.87 239	97.62 304
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		93.72 268	92.62 286	97.03 172	87.61 382	92.25 209	96.27 154	91.28 354	96.74 95	87.65 368	97.39 207	85.00 297	99.64 146	92.14 237	99.48 130	99.20 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchMatch-RL		94.61 240	93.81 260	97.02 173	98.19 188	95.72 87	93.66 290	97.23 272	88.17 307	94.94 279	95.62 307	91.43 232	98.57 335	87.36 328	97.68 305	96.76 335

K. test v3		96.44 162	96.28 163	96.95 174	99.41 40	91.53 227	97.65 82	90.31 363	98.89 19	98.93 40	99.36 14	84.57 301	99.92 5	97.81 28	99.56 96	99.39 90

tfpnnormal		97.72 80	97.97 44	96.94 175	99.26 53	92.23 210	97.83 71	98.45 174	98.25 35	99.13 32	98.66 68	96.65 77	99.69 123	93.92 206	99.62 76	98.91 192

MVP-Stereo		95.69 188	95.28 197	96.92 176	98.15 197	93.03 195	95.64 196	98.20 208	90.39 283	96.63 216	97.73 177	91.63 231	99.10 285	91.84 245	97.31 320	98.63 227
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP-MVS		95.17 213	94.58 234	96.92 176	97.85 223	92.47 205	94.26 260	98.43 177	93.18 235	92.86 333	95.08 315	90.33 246	99.23 267	90.51 281	98.74 253	99.05 170

HyFIR lowres test		93.72 268	92.65 284	96.91 178	98.93 106	91.81 224	91.23 341	98.52 168	82.69 348	96.46 224	96.52 269	80.38 320	99.90 14	90.36 285	98.79 248	99.03 172

VNet		96.84 134	96.83 134	96.88 179	98.06 204	92.02 218	96.35 151	97.57 265	97.70 60	97.88 141	97.80 170	92.40 214	99.54 180	94.73 172	98.96 227	99.08 164

FMVSNet2		96.72 146	96.67 143	96.87 180	97.96 215	91.88 221	97.15 111	98.06 233	95.59 155	98.50 68	98.62 71	89.51 263	99.65 143	94.99 161	99.60 86	99.07 166

EIA-MVS		96.04 177	95.77 185	96.85 181	97.80 235	92.98 196	96.12 164	99.16 22	94.65 189	93.77 309	91.69 362	95.68 119	99.67 135	94.18 193	98.85 243	97.91 292

MVS_0304		95.50 195	95.05 207	96.84 182	96.28 319	93.12 193	97.00 121	96.16 300	95.03 178	89.22 363	97.70 179	90.16 252	99.48 197	94.51 179	99.34 173	97.93 291

ETV-MVS		96.13 174	95.90 181	96.82 183	97.76 247	93.89 169	95.40 206	98.95 75	95.87 141	95.58 265	91.00 368	96.36 97	99.72 90	93.36 218	98.83 245	96.85 329

DP-MVS Recon		95.55 194	95.13 201	96.80 184	98.51 154	93.99 167	94.60 251	98.69 145	90.20 285	95.78 258	96.21 284	92.73 202	98.98 299	90.58 278	98.86 241	97.42 313

QAPM		95.88 184	95.57 192	96.80 184	97.90 221	91.84 223	98.18 52	98.73 132	88.41 303	96.42 225	98.13 124	94.73 150	99.75 70	88.72 307	98.94 231	98.81 207

CMPMVS	73.10 23	92.74 289	91.39 300	96.77 186	93.57 368	94.67 141	94.21 267	97.67 254	80.36 359	93.61 316	96.60 263	82.85 309	97.35 365	84.86 347	98.78 249	98.29 263
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+		95.49 196	95.07 204	96.75 187	97.67 259	92.82 199	94.22 266	98.60 160	91.61 268	93.42 325	92.90 347	96.73 75	99.70 115	92.60 231	97.89 295	97.74 299

CNLPA		95.04 217	94.47 239	96.75 187	97.81 231	95.25 117	94.12 274	97.89 240	94.41 197	94.57 286	95.69 303	90.30 249	98.35 351	86.72 332	98.76 251	96.64 337

Effi-MVS+		96.19 171	96.01 174	96.71 189	97.43 277	92.19 214	96.12 164	99.10 33	95.45 160	93.33 327	94.71 324	97.23 43	99.56 173	93.21 225	97.54 311	98.37 249

pmmvs4		94.82 226	94.19 249	96.70 190	97.42 278	92.75 202	92.09 327	96.76 291	86.80 320	95.73 261	97.22 221	89.28 266	98.89 306	93.28 222	99.14 205	98.46 243

CLD-MVS		95.47 199	95.07 204	96.69 191	98.27 179	92.53 204	91.36 335	98.67 150	91.22 276	95.78 258	94.12 335	95.65 121	98.98 299	90.81 267	99.72 58	98.57 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		97.04 121	97.16 114	96.68 192	98.59 145	91.05 232	96.33 152	98.36 189	94.60 191	97.99 128	98.30 100	93.32 188	99.62 156	97.40 45	99.53 108	99.38 92

LFMVS		95.32 206	94.88 215	96.62 193	98.03 206	91.47 229	97.65 82	90.72 360	99.11 9	97.89 140	98.31 96	79.20 324	99.48 197	93.91 207	99.12 211	98.93 187

ab-mvs		96.59 154	96.59 145	96.60 194	98.64 135	92.21 211	98.35 34	97.67 254	94.45 196	96.99 193	98.79 58	94.96 147	99.49 194	90.39 284	99.07 218	98.08 275

VPNet		97.26 113	97.49 94	96.59 195	99.47 32	90.58 242	96.27 154	98.53 167	97.77 50	98.46 73	98.41 87	94.59 158	99.68 130	94.61 173	99.29 188	99.52 46

原ACMM1				96.58 196	98.16 195	92.12 215		98.15 219	85.90 327	93.49 320	96.43 272	92.47 213	99.38 232	87.66 322	98.62 264	98.23 267

AdaColmap		95.11 214	94.62 230	96.58 196	97.33 287	94.45 148	94.92 238	98.08 228	93.15 239	93.98 305	95.53 310	94.34 166	99.10 285	85.69 338	98.61 265	96.20 345

PCF-MVS	89.43 18	92.12 300	90.64 314	96.57 198	97.80 235	93.48 187	89.88 359	98.45 174	74.46 372	96.04 246	95.68 304	90.71 242	99.31 249	73.73 371	99.01 225	96.91 326
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ambc				96.56 199	98.23 185	91.68 226	97.88 68	98.13 222		98.42 76	98.56 76	94.22 170	99.04 291	94.05 201	99.35 170	98.95 181

casdiffmvs		97.50 96	97.81 58	96.56 199	98.51 154	91.04 233	95.83 184	99.09 38	97.23 83	98.33 90	98.30 100	97.03 52	99.37 235	96.58 69	99.38 161	99.28 119

FMVSNet5		93.39 278	92.35 288	96.50 201	95.83 336	90.81 239	97.31 103	98.27 198	92.74 251	96.27 234	98.28 105	62.23 376	99.67 135	90.86 265	99.36 165	99.03 172

CANet		95.86 185	95.65 188	96.49 202	96.41 316	90.82 237	94.36 258	98.41 182	94.94 181	92.62 341	96.73 256	92.68 203	99.71 106	95.12 154	99.60 86	98.94 183

test20.03		96.58 156	96.61 144	96.48 203	98.49 158	91.72 225	95.68 190	97.69 253	96.81 93	98.27 97	97.92 155	94.18 171	98.71 322	90.78 269	99.66 70	99.00 175

UnsupCasMVSNet_eth		95.91 182	95.73 186	96.44 204	98.48 160	91.52 228	95.31 214	98.45 174	95.76 147	97.48 162	97.54 190	89.53 262	98.69 324	94.43 181	94.61 356	99.13 151

iter_conf_final		94.54 244	93.91 258	96.43 205	97.23 292	90.41 246	96.81 128	98.10 224	93.87 215	96.80 203	97.89 157	68.02 369	99.72 90	96.73 64	99.77 46	99.18 141

baseline		97.44 101	97.78 63	96.43 205	98.52 152	90.75 240	96.84 126	99.03 52	96.51 105	97.86 145	98.02 141	96.67 76	99.36 237	97.09 56	99.47 132	99.19 138

DPM-MVS		93.68 270	92.77 282	96.42 207	97.91 219	92.54 203	91.17 342	97.47 268	84.99 339	93.08 330	94.74 323	89.90 254	99.00 295	87.54 325	98.09 287	97.72 300

PVSNet_Blended_VisFu		95.95 181	95.80 183	96.42 207	99.28 52	90.62 241	95.31 214	99.08 39	88.40 304	96.97 196	98.17 121	92.11 219	99.78 49	93.64 215	99.21 197	98.86 203

ANet_high		98.31 28	98.94 6	96.41 209	99.33 48	89.64 253	97.92 66	99.56 8	99.27 6	99.66 8	99.50 6	97.67 25	99.83 33	97.55 39	99.98 2	99.77 9

SD-MVS		97.37 106	97.70 67	96.35 210	98.14 198	95.13 125	96.54 142	98.92 79	95.94 136	99.19 28	98.08 130	97.74 22	95.06 373	95.24 142	99.54 105	98.87 202
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
Patchmtry		95.03 219	94.59 233	96.33 211	94.83 352	90.82 237	96.38 149	97.20 273	96.59 101	97.49 159	98.57 74	77.67 331	99.38 232	92.95 230	99.62 76	98.80 208

OpenMVS	94.22 8	95.48 198	95.20 198	96.32 212	97.16 297	91.96 220	97.74 78	98.84 102	87.26 313	94.36 293	98.01 143	93.95 176	99.67 135	90.70 275	98.75 252	97.35 316

v10		97.55 92	97.97 44	96.31 213	98.60 143	89.64 253	97.44 97	99.02 54	96.60 99	98.72 54	99.16 31	93.48 186	99.72 90	98.76 6	99.92 14	99.58 31

PMMVS		92.39 293	91.08 305	96.30 214	93.12 370	92.81 200	90.58 350	95.96 306	79.17 363	91.85 348	92.27 355	90.29 250	98.66 329	89.85 292	96.68 333	97.43 312

v8		97.60 89	98.06 38	96.23 215	98.71 128	89.44 257	97.43 99	98.82 117	97.29 82	98.74 52	99.10 36	93.86 177	99.68 130	98.61 11	99.94 8	99.56 39

1112_ss		94.12 258	93.42 265	96.23 215	98.59 145	90.85 236	94.24 264	98.85 97	85.49 330	92.97 331	94.94 319	86.01 291	99.64 146	91.78 246	97.92 292	98.20 270

FMVSNet3		95.26 209	94.94 209	96.22 217	96.53 313	90.06 247	95.99 172	97.66 256	94.11 209	97.99 128	97.91 156	80.22 321	99.63 148	94.60 174	99.44 140	98.96 180

114514_t		93.96 263	93.22 270	96.19 218	99.06 93	90.97 235	95.99 172	98.94 76	73.88 373	93.43 324	96.93 241	92.38 215	99.37 235	89.09 302	99.28 189	98.25 266

CHOSEN 1792x2688		94.10 259	93.41 266	96.18 219	99.16 74	90.04 248	92.15 324	98.68 147	79.90 360	96.22 237	97.83 165	87.92 280	99.42 213	89.18 301	99.65 71	99.08 164

v1192		96.83 137	97.06 121	96.15 220	98.28 177	89.29 259	95.36 209	98.77 124	93.73 218	98.11 113	98.34 93	93.02 197	99.67 135	98.35 17	99.58 90	99.50 49

v1144		96.84 134	97.08 119	96.13 221	98.42 167	89.28 260	95.41 205	98.67 150	94.21 204	97.97 132	98.31 96	93.06 193	99.65 143	98.06 22	99.62 76	99.45 73

UnsupCasMVSNet_bld		94.72 233	94.26 245	96.08 222	98.62 140	90.54 245	93.38 300	98.05 234	90.30 284	97.02 191	96.80 252	89.54 260	99.16 276	88.44 311	96.18 340	98.56 233

v144192		96.69 149	96.90 132	96.03 223	98.25 182	88.92 264	95.49 199	98.77 124	93.05 241	98.09 117	98.29 104	92.51 212	99.70 115	98.11 20	99.56 96	99.47 66

v1921920		96.72 146	96.96 127	95.99 224	98.21 186	88.79 269	95.42 203	98.79 119	93.22 233	98.19 105	98.26 110	92.68 203	99.70 115	98.34 18	99.55 102	99.49 57

DELS-MVS		96.17 172	96.23 164	95.99 224	97.55 267	90.04 248	92.38 322	98.52 168	94.13 207	96.55 221	97.06 232	94.99 145	99.58 166	95.62 117	99.28 189	98.37 249
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CANet_DTU		94.65 238	94.21 248	95.96 226	95.90 333	89.68 252	93.92 282	97.83 246	93.19 234	90.12 358	95.64 306	88.52 270	99.57 172	93.27 223	99.47 132	98.62 228

PAPM_NR		94.61 240	94.17 250	95.96 226	98.36 171	91.23 230	95.93 179	97.95 236	92.98 244	93.42 325	94.43 331	90.53 243	98.38 348	87.60 323	96.29 339	98.27 264

v2v482		96.78 141	97.06 121	95.95 228	98.57 147	88.77 270	95.36 209	98.26 200	95.18 171	97.85 146	98.23 113	92.58 207	99.63 148	97.80 29	99.69 65	99.45 73

PMVS	89.60 17	96.71 148	96.97 125	95.95 228	99.51 27	97.81 17	97.42 100	97.49 266	97.93 47	95.95 249	98.58 73	96.88 66	96.91 367	89.59 295	99.36 165	93.12 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSDG		95.33 205	95.13 201	95.94 230	97.40 279	91.85 222	91.02 346	98.37 188	95.30 166	96.31 232	95.99 293	94.51 162	98.38 348	89.59 295	97.65 308	97.60 306

v1240		96.74 143	97.02 124	95.91 231	98.18 191	88.52 272	95.39 207	98.88 88	93.15 239	98.46 73	98.40 90	92.80 200	99.71 106	98.45 15	99.49 126	99.49 57

Anonymous20231206		95.27 208	95.06 206	95.88 232	98.72 125	89.37 258	95.70 187	97.85 242	88.00 309	96.98 195	97.62 185	91.95 224	99.34 242	89.21 300	99.53 108	98.94 183

Vis-MVSNet (Re-imp)		95.11 214	94.85 216	95.87 233	99.12 86	89.17 261	97.54 94	94.92 323	96.50 106	96.58 217	97.27 218	83.64 306	99.48 197	88.42 312	99.67 68	98.97 179

CL-MVSNet_self_test		95.04 217	94.79 222	95.82 234	97.51 269	89.79 251	91.14 343	96.82 289	93.05 241	96.72 209	96.40 275	90.82 240	99.16 276	91.95 240	98.66 260	98.50 239

IterMVS-LS		96.92 129	97.29 104	95.79 235	98.51 154	88.13 283	95.10 225	98.66 152	96.99 87	98.46 73	98.68 67	92.55 208	99.74 80	96.91 61	99.79 41	99.50 49
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240521		97.07 120	97.51 91	95.76 236	99.35 46	88.18 280	97.78 72	98.40 184	97.11 85	98.34 86	99.04 42	89.58 259	99.79 45	98.09 21	99.93 10	99.30 111

EI-MVSNet		96.63 153	96.93 128	95.74 237	97.26 290	88.13 283	95.29 216	97.65 258	96.99 87	97.94 135	98.19 118	92.55 208	99.58 166	96.91 61	99.56 96	99.50 49

MDA-MVSNet-bldmvs		95.69 188	95.67 187	95.74 237	98.48 160	88.76 271	92.84 309	97.25 271	96.00 132	97.59 153	97.95 151	91.38 233	99.46 203	93.16 226	96.35 338	98.99 178

sss		94.22 253	93.72 261	95.74 237	97.71 254	89.95 250	93.84 284	96.98 283	88.38 305	93.75 310	95.74 302	87.94 276	98.89 306	91.02 261	98.10 286	98.37 249

testdata				95.70 240	98.16 195	90.58 242		97.72 251	80.38 358	95.62 263	97.02 235	92.06 222	98.98 299	89.06 304	98.52 270	97.54 308

test_yl		94.40 247	94.00 254	95.59 241	96.95 303	89.52 255	94.75 247	95.55 316	96.18 122	96.79 204	96.14 288	81.09 316	99.18 271	90.75 270	97.77 296	98.07 277

DCV-MVSNet		94.40 247	94.00 254	95.59 241	96.95 303	89.52 255	94.75 247	95.55 316	96.18 122	96.79 204	96.14 288	81.09 316	99.18 271	90.75 270	97.77 296	98.07 277

tttt0517		93.31 280	92.56 287	95.57 243	98.71 128	87.86 288	97.44 97	87.17 371	95.79 146	97.47 164	96.84 247	64.12 373	99.81 38	96.20 82	99.32 182	99.02 174

MSLP-MVS++		96.42 164	96.71 140	95.57 243	97.82 230	90.56 244	95.71 186	98.84 102	94.72 187	96.71 210	97.39 207	94.91 149	98.10 359	95.28 139	99.02 223	98.05 284

thisisatest0530		92.71 290	91.76 297	95.56 245	98.42 167	88.23 278	96.03 169	87.35 370	94.04 211	96.56 219	95.47 311	64.03 374	99.77 58	94.78 169	99.11 212	98.68 224

patch_mono-2		96.59 154	96.93 128	95.55 246	98.88 110	87.12 305	94.47 256	99.30 12	94.12 208	96.65 215	98.41 87	94.98 146	99.87 22	95.81 107	99.78 44	99.66 22

Test_1112_low_res		93.53 276	92.86 276	95.54 247	98.60 143	88.86 267	92.75 312	98.69 145	82.66 349	92.65 338	96.92 243	84.75 299	99.56 173	90.94 263	97.76 298	98.19 271

pmmvs5		94.63 239	94.34 244	95.50 248	97.63 262	88.34 276	94.02 276	97.13 277	87.15 316	95.22 272	97.15 224	87.50 282	99.27 261	93.99 203	99.26 192	98.88 200

MVSFormer		96.14 173	96.36 160	95.49 249	97.68 256	87.81 291	98.67 15	99.02 54	96.50 106	94.48 291	96.15 286	86.90 286	99.92 5	98.73 7	99.13 208	98.74 216

ET-MVSNet_ETH3D		91.12 310	89.67 322	95.47 250	96.41 316	89.15 263	91.54 333	90.23 364	89.07 295	86.78 372	92.84 348	69.39 367	99.44 210	94.16 194	96.61 334	97.82 296

iter_conf05		93.65 272	93.05 271	95.46 251	96.13 330	87.45 298	95.95 178	98.22 204	92.66 252	97.04 188	97.89 157	63.52 375	99.72 90	96.19 83	99.82 36	99.21 133

diffmvs		96.04 177	96.23 164	95.46 251	97.35 281	88.03 286	93.42 297	99.08 39	94.09 210	96.66 213	96.93 241	93.85 178	99.29 256	96.01 94	98.67 258	99.06 168

v148		96.58 156	96.97 125	95.42 253	98.63 139	87.57 295	95.09 226	97.90 239	95.91 139	98.24 99	97.96 147	93.42 187	99.39 229	96.04 90	99.52 113	99.29 118

OpenMVS_ROB	91.80 14	93.64 273	93.05 271	95.42 253	97.31 289	91.21 231	95.08 228	96.68 296	81.56 352	96.88 202	96.41 273	90.44 245	99.25 264	85.39 342	97.67 306	95.80 349

jason		94.39 249	94.04 253	95.41 255	98.29 175	87.85 290	92.74 314	96.75 292	85.38 335	95.29 270	96.15 286	88.21 275	99.65 143	94.24 191	99.34 173	98.74 216
jason: jason.
API-MVS		95.09 216	95.01 208	95.31 256	96.61 311	94.02 165	96.83 127	97.18 275	95.60 154	95.79 256	94.33 332	94.54 161	98.37 350	85.70 337	98.52 270	93.52 364

PVSNet_BlendedMVS		95.02 220	94.93 211	95.27 257	97.79 241	87.40 300	94.14 272	98.68 147	88.94 298	94.51 289	98.01 143	93.04 194	99.30 252	89.77 293	99.49 126	99.11 159

lupinMVS		93.77 266	93.28 267	95.24 258	97.68 256	87.81 291	92.12 325	96.05 302	84.52 342	94.48 291	95.06 317	86.90 286	99.63 148	93.62 216	99.13 208	98.27 264

D2MVS		95.18 211	95.17 200	95.21 259	97.76 247	87.76 293	94.15 270	97.94 237	89.77 291	96.99 193	97.68 182	87.45 283	99.14 278	95.03 159	99.81 37	98.74 216

Patchmatch-RL test		94.66 237	94.49 237	95.19 260	98.54 150	88.91 265	92.57 316	98.74 130	91.46 271	98.32 91	97.75 174	77.31 336	98.81 313	96.06 87	99.61 82	97.85 294

WTY-MVS		93.55 275	93.00 274	95.19 260	97.81 231	87.86 288	93.89 283	96.00 304	89.02 296	94.07 300	95.44 312	86.27 289	99.33 245	87.69 321	96.82 328	98.39 247

FE-MVS		92.95 286	92.22 290	95.11 262	97.21 293	88.33 277	98.54 22	93.66 334	89.91 289	96.21 238	98.14 122	70.33 365	99.50 191	87.79 318	98.24 281	97.51 309

JIA-IIPM		91.79 304	90.69 313	95.11 262	93.80 365	90.98 234	94.16 269	91.78 352	96.38 110	90.30 357	99.30 19	72.02 359	98.90 305	88.28 314	90.17 368	95.45 355

MIMVSNet		93.42 277	92.86 276	95.10 264	98.17 193	88.19 279	98.13 54	93.69 331	92.07 260	95.04 277	98.21 117	80.95 318	99.03 294	81.42 359	98.06 288	98.07 277

PAPR		92.22 297	91.27 303	95.07 265	95.73 340	88.81 268	91.97 328	97.87 241	85.80 328	90.91 351	92.73 351	91.16 235	98.33 352	79.48 362	95.76 347	98.08 275

MVSTER		94.21 255	93.93 257	95.05 266	95.83 336	86.46 313	95.18 223	97.65 258	92.41 257	97.94 135	98.00 145	72.39 358	99.58 166	96.36 78	99.56 96	99.12 156

cl____		94.73 229	94.64 227	95.01 267	95.85 335	87.00 307	91.33 337	98.08 228	93.34 228	97.10 181	97.33 214	84.01 305	99.30 252	95.14 151	99.56 96	98.71 221

DIV-MVS_self_test		94.73 229	94.64 227	95.01 267	95.86 334	87.00 307	91.33 337	98.08 228	93.34 228	97.10 181	97.34 213	84.02 304	99.31 249	95.15 150	99.55 102	98.72 219

FA-MVS(test-final)		94.91 222	94.89 214	94.99 269	97.51 269	88.11 285	98.27 43	95.20 321	92.40 258	96.68 211	98.60 72	83.44 307	99.28 258	93.34 219	98.53 269	97.59 307

TinyColmap		96.00 180	96.34 161	94.96 270	97.90 221	87.91 287	94.13 273	98.49 171	94.41 197	98.16 107	97.76 171	96.29 99	98.68 327	90.52 280	99.42 151	98.30 260

PVSNet_Blended		93.96 263	93.65 262	94.91 271	97.79 241	87.40 300	91.43 334	98.68 147	84.50 343	94.51 289	94.48 330	93.04 194	99.30 252	89.77 293	98.61 265	98.02 287

BH-RMVSNet		94.56 242	94.44 242	94.91 271	97.57 264	87.44 299	93.78 288	96.26 299	93.69 220	96.41 226	96.50 270	92.10 220	99.00 295	85.96 335	97.71 302	98.31 258

RPMNet		94.68 236	94.60 231	94.90 273	95.44 345	88.15 281	96.18 161	98.86 93	97.43 72	94.10 298	98.49 81	79.40 322	99.76 63	95.69 110	95.81 343	96.81 333

HY-MVS	91.43 15	92.58 291	91.81 296	94.90 273	96.49 314	88.87 266	97.31 103	94.62 325	85.92 326	90.50 355	96.84 247	85.05 296	99.40 224	83.77 354	95.78 346	96.43 342

GA-MVS		92.83 288	92.15 292	94.87 275	96.97 302	87.27 303	90.03 354	96.12 301	91.83 266	94.05 301	94.57 325	76.01 343	98.97 303	92.46 235	97.34 319	98.36 254

miper_lstm_enhance		94.81 227	94.80 221	94.85 276	96.16 326	86.45 314	91.14 343	98.20 208	93.49 223	97.03 190	97.37 211	84.97 298	99.26 262	95.28 139	99.56 96	98.83 205

IterMVS-SCA-FT		95.86 185	96.19 166	94.85 276	97.68 256	85.53 323	92.42 320	97.63 262	96.99 87	98.36 83	98.54 78	87.94 276	99.75 70	97.07 58	99.08 216	99.27 123

c3_l		95.20 210	95.32 196	94.83 278	96.19 324	86.43 315	91.83 330	98.35 193	93.47 224	97.36 168	97.26 219	88.69 269	99.28 258	95.41 136	99.36 165	98.78 211

testgi		96.07 175	96.50 156	94.80 279	99.26 53	87.69 294	95.96 175	98.58 164	95.08 175	98.02 127	96.25 281	97.92 16	97.60 364	88.68 309	98.74 253	99.11 159

CR-MVSNet		93.29 281	92.79 279	94.78 280	95.44 345	88.15 281	96.18 161	97.20 273	84.94 340	94.10 298	98.57 74	77.67 331	99.39 229	95.17 146	95.81 343	96.81 333

eth_miper_zixun_eth		94.89 223	94.93 211	94.75 281	95.99 332	86.12 318	91.35 336	98.49 171	93.40 225	97.12 179	97.25 220	86.87 288	99.35 240	95.08 156	98.82 246	98.78 211

MVS_Test		96.27 167	96.79 138	94.73 282	96.94 305	86.63 312	96.18 161	98.33 194	94.94 181	96.07 244	98.28 105	95.25 136	99.26 262	97.21 50	97.90 294	98.30 260

miper_ehance_all_eth		94.69 234	94.70 224	94.64 283	95.77 338	86.22 317	91.32 339	98.24 202	91.67 267	97.05 187	96.65 261	88.39 273	99.22 269	94.88 162	98.34 276	98.49 240

Patchmatch-test		93.60 274	93.25 269	94.63 284	96.14 329	87.47 297	96.04 168	94.50 327	93.57 221	96.47 223	96.97 238	76.50 339	98.61 332	90.67 276	98.41 275	97.81 298

baseline1		93.14 284	92.64 285	94.62 285	97.34 285	87.20 304	96.67 140	93.02 340	94.71 188	96.51 222	95.83 301	81.64 311	98.60 334	90.00 290	88.06 371	98.07 277

xiu_mvs_v1_base_debu		95.62 191	95.96 178	94.60 286	98.01 209	88.42 273	93.99 278	98.21 205	92.98 244	95.91 251	94.53 327	96.39 94	99.72 90	95.43 133	98.19 282	95.64 351

xiu_mvs_v1_base		95.62 191	95.96 178	94.60 286	98.01 209	88.42 273	93.99 278	98.21 205	92.98 244	95.91 251	94.53 327	96.39 94	99.72 90	95.43 133	98.19 282	95.64 351

xiu_mvs_v1_base_debi		95.62 191	95.96 178	94.60 286	98.01 209	88.42 273	93.99 278	98.21 205	92.98 244	95.91 251	94.53 327	96.39 94	99.72 90	95.43 133	98.19 282	95.64 351

MS-PatchMatch		94.83 225	94.91 213	94.57 289	96.81 309	87.10 306	94.23 265	97.34 270	88.74 301	97.14 177	97.11 228	91.94 225	98.23 355	92.99 228	97.92 292	98.37 249

USDC		94.56 242	94.57 236	94.55 290	97.78 245	86.43 315	92.75 312	98.65 157	85.96 325	96.91 200	97.93 154	90.82 240	98.74 319	90.71 274	99.59 88	98.47 241

BH-untuned		94.69 234	94.75 223	94.52 291	97.95 218	87.53 296	94.07 275	97.01 282	93.99 212	97.10 181	95.65 305	92.65 205	98.95 304	87.60 323	96.74 331	97.09 318

dcpmvs_2		97.12 118	97.99 43	94.51 292	99.11 87	84.00 343	97.75 76	99.65 5	97.38 78	99.14 31	98.42 86	95.16 138	99.96 2	95.52 122	99.78 44	99.58 31

cl22		93.25 282	92.84 278	94.46 293	94.30 358	86.00 319	91.09 345	96.64 297	90.74 279	95.79 256	96.31 279	78.24 328	98.77 316	94.15 195	98.34 276	98.62 228

MDA-MVSNet_test_wron		94.73 229	94.83 219	94.42 294	97.48 271	85.15 330	90.28 353	95.87 308	92.52 253	97.48 162	97.76 171	91.92 227	99.17 275	93.32 220	96.80 330	98.94 183

YYNet1		94.73 229	94.84 217	94.41 295	97.47 275	85.09 332	90.29 352	95.85 309	92.52 253	97.53 155	97.76 171	91.97 223	99.18 271	93.31 221	96.86 327	98.95 181

ADS-MVSNet2		91.47 308	90.51 316	94.36 296	95.51 343	85.63 321	95.05 232	95.70 310	83.46 346	92.69 336	96.84 247	79.15 325	99.41 222	85.66 339	90.52 366	98.04 285

new_pmnet		92.34 295	91.69 298	94.32 297	96.23 322	89.16 262	92.27 323	92.88 342	84.39 345	95.29 270	96.35 278	85.66 293	96.74 371	84.53 349	97.56 310	97.05 320

MG-MVS		94.08 261	94.00 254	94.32 297	97.09 299	85.89 320	93.19 306	95.96 306	92.52 253	94.93 280	97.51 194	89.54 260	98.77 316	87.52 326	97.71 302	98.31 258

PatchT		93.75 267	93.57 263	94.29 299	95.05 350	87.32 302	96.05 167	92.98 341	97.54 69	94.25 294	98.72 63	75.79 344	99.24 265	95.92 99	95.81 343	96.32 343

miper_enhance_ethall		93.14 284	92.78 281	94.20 300	93.65 366	85.29 327	89.97 355	97.85 242	85.05 337	96.15 243	94.56 326	85.74 292	99.14 278	93.74 211	98.34 276	98.17 273

IterMVS		95.42 202	95.83 182	94.20 300	97.52 268	83.78 345	92.41 321	97.47 268	95.49 159	98.06 121	98.49 81	87.94 276	99.58 166	96.02 92	99.02 223	99.23 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thisisatest0515		90.43 316	89.18 328	94.17 302	97.07 300	85.44 324	89.75 360	87.58 369	88.28 306	93.69 313	91.72 361	65.27 372	99.58 166	90.59 277	98.67 258	97.50 311

ECVR-MVS		94.37 250	94.48 238	94.05 303	98.95 103	83.10 347	98.31 38	82.48 377	96.20 119	98.23 100	99.16 31	81.18 315	99.66 141	95.95 97	99.83 34	99.38 92

thres600view7		92.03 301	91.43 299	93.82 304	98.19 188	84.61 337	96.27 154	90.39 361	96.81 93	96.37 228	93.11 340	73.44 356	99.49 194	80.32 361	97.95 291	97.36 314

FPMVS		89.92 323	88.63 331	93.82 304	98.37 170	96.94 47	91.58 332	93.34 338	88.00 309	90.32 356	97.10 229	70.87 363	91.13 376	71.91 374	96.16 342	93.39 366

test1111		94.53 245	94.81 220	93.72 306	99.06 93	81.94 355	98.31 38	83.87 376	96.37 111	98.49 69	99.17 30	81.49 312	99.73 85	96.64 65	99.86 28	99.49 57

thres400		91.68 306	91.00 306	93.71 307	98.02 207	84.35 340	95.70 187	90.79 358	96.26 116	95.90 254	92.13 357	73.62 353	99.42 213	78.85 365	97.74 299	97.36 314

IB-MVS	85.98 20	88.63 332	86.95 341	93.68 308	95.12 349	84.82 336	90.85 347	90.17 365	87.55 312	88.48 366	91.34 365	58.01 377	99.59 164	87.24 329	93.80 360	96.63 339
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
EU-MVSNet		94.25 252	94.47 239	93.60 309	98.14 198	82.60 350	97.24 108	92.72 345	85.08 336	98.48 70	98.94 49	82.59 310	98.76 318	97.47 43	99.53 108	99.44 83

TR-MVS		92.54 292	92.20 291	93.57 310	96.49 314	86.66 311	93.51 295	94.73 324	89.96 288	94.95 278	93.87 336	90.24 251	98.61 332	81.18 360	94.88 353	95.45 355

cascas		91.89 303	91.35 301	93.51 311	94.27 359	85.60 322	88.86 364	98.61 159	79.32 362	92.16 345	91.44 364	89.22 267	98.12 358	90.80 268	97.47 316	96.82 332

ppachtmachnet_test		94.49 246	94.84 217	93.46 312	96.16 326	82.10 352	90.59 349	97.48 267	90.53 282	97.01 192	97.59 187	91.01 237	99.36 237	93.97 205	99.18 202	98.94 183

pmmvs3		90.00 320	88.90 330	93.32 313	94.20 362	85.34 325	91.25 340	92.56 347	78.59 364	93.82 306	95.17 314	67.36 371	98.69 324	89.08 303	98.03 289	95.92 346

EPNet_dtu		91.39 309	90.75 312	93.31 314	90.48 379	82.61 349	94.80 244	92.88 342	93.39 226	81.74 376	94.90 322	81.36 314	99.11 283	88.28 314	98.87 239	98.21 269
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		91.76 305	91.26 304	93.26 315	98.21 186	84.50 338	96.39 147	90.39 361	96.87 91	96.33 229	93.08 344	73.44 356	99.42 213	78.85 365	97.74 299	95.85 347

baseline2		89.65 326	88.44 333	93.25 316	95.62 341	82.71 348	93.82 285	85.94 373	88.89 299	87.35 370	92.54 353	71.23 361	99.33 245	86.01 334	94.60 357	97.72 300

DSMNet-mixed		92.19 298	91.83 295	93.25 316	96.18 325	83.68 346	96.27 154	93.68 333	76.97 370	92.54 342	99.18 28	89.20 268	98.55 338	83.88 352	98.60 267	97.51 309

tfpn200view9		91.55 307	91.00 306	93.21 318	98.02 207	84.35 340	95.70 187	90.79 358	96.26 116	95.90 254	92.13 357	73.62 353	99.42 213	78.85 365	97.74 299	95.85 347

mvs_anonymous		95.36 204	96.07 173	93.21 318	96.29 318	81.56 356	94.60 251	97.66 256	93.30 230	96.95 197	98.91 52	93.03 196	99.38 232	96.60 67	97.30 321	98.69 222

our_test_3		94.20 257	94.58 234	93.07 320	96.16 326	81.20 358	90.42 351	96.84 287	90.72 280	97.14 177	97.13 225	90.47 244	99.11 283	94.04 202	98.25 280	98.91 192

ADS-MVSNet		90.95 314	90.26 318	93.04 321	95.51 343	82.37 351	95.05 232	93.41 337	83.46 346	92.69 336	96.84 247	79.15 325	98.70 323	85.66 339	90.52 366	98.04 285

PAPM		87.64 339	85.84 344	93.04 321	96.54 312	84.99 333	88.42 365	95.57 315	79.52 361	83.82 373	93.05 346	80.57 319	98.41 345	62.29 377	92.79 362	95.71 350

PS-MVSNAJ		94.10 259	94.47 239	93.00 323	97.35 281	84.88 334	91.86 329	97.84 244	91.96 263	94.17 296	92.50 354	95.82 110	99.71 106	91.27 255	97.48 314	94.40 361

xiu_mvs_v2_base		94.22 253	94.63 229	92.99 324	97.32 288	84.84 335	92.12 325	97.84 244	91.96 263	94.17 296	93.43 338	96.07 102	99.71 106	91.27 255	97.48 314	94.42 360

SCA		93.38 279	93.52 264	92.96 325	96.24 320	81.40 357	93.24 304	94.00 330	91.58 270	94.57 286	96.97 238	87.94 276	99.42 213	89.47 297	97.66 307	98.06 281

new-patchmatchnet		95.67 190	96.58 146	92.94 326	97.48 271	80.21 361	92.96 308	98.19 213	94.83 184	98.82 46	98.79 58	93.31 189	99.51 190	95.83 105	99.04 222	99.12 156

test0.0.03 1		90.11 318	89.21 325	92.83 327	93.89 364	86.87 310	91.74 331	88.74 368	92.02 261	94.71 283	91.14 367	73.92 350	94.48 374	83.75 355	92.94 361	97.16 317

thres200		91.00 313	90.42 317	92.77 328	97.47 275	83.98 344	94.01 277	91.18 356	95.12 174	95.44 267	91.21 366	73.93 349	99.31 249	77.76 368	97.63 309	95.01 357

BH-w/o		92.14 299	91.94 293	92.73 329	97.13 298	85.30 326	92.46 319	95.64 311	89.33 294	94.21 295	92.74 350	89.60 258	98.24 354	81.68 358	94.66 355	94.66 359

1314		92.38 294	92.30 289	92.64 330	95.42 347	85.15 330	95.86 181	96.97 284	85.40 334	90.62 352	93.06 345	91.12 236	97.80 362	86.74 331	95.49 350	94.97 358

KD-MVS_2432*1600		88.93 330	87.74 335	92.49 331	88.04 380	81.99 353	89.63 361	95.62 312	91.35 272	95.06 274	93.11 340	56.58 379	98.63 330	85.19 343	95.07 351	96.85 329

miper_refine_blended		88.93 330	87.74 335	92.49 331	88.04 380	81.99 353	89.63 361	95.62 312	91.35 272	95.06 274	93.11 340	56.58 379	98.63 330	85.19 343	95.07 351	96.85 329

MVS		90.02 319	89.20 326	92.47 333	94.71 353	86.90 309	95.86 181	96.74 293	64.72 375	90.62 352	92.77 349	92.54 210	98.39 347	79.30 363	95.56 349	92.12 368

PMMVS2		93.66 271	94.07 252	92.45 334	97.57 264	80.67 360	86.46 367	96.00 304	93.99 212	97.10 181	97.38 209	89.90 254	97.82 361	88.76 306	99.47 132	98.86 203

CHOSEN 280x420		89.98 321	89.19 327	92.37 335	95.60 342	81.13 359	86.22 368	97.09 279	81.44 354	87.44 369	93.15 339	73.99 348	99.47 200	88.69 308	99.07 218	96.52 341

PatchmatchNet		91.98 302	91.87 294	92.30 336	94.60 355	79.71 362	95.12 224	93.59 336	89.52 292	93.61 316	97.02 235	77.94 329	99.18 271	90.84 266	94.57 358	98.01 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
gg-mvs-nofinetune		88.28 335	86.96 340	92.23 337	92.84 373	84.44 339	98.19 51	74.60 380	99.08 10	87.01 371	99.47 8	56.93 378	98.23 355	78.91 364	95.61 348	94.01 362

test2506		89.86 324	89.16 329	91.97 338	98.95 103	76.83 370	98.54 22	61.07 384	96.20 119	97.07 186	99.16 31	55.19 383	99.69 123	96.43 76	99.83 34	99.38 92

tpm		91.08 312	90.85 310	91.75 339	95.33 348	78.09 364	95.03 234	91.27 355	88.75 300	93.53 319	97.40 203	71.24 360	99.30 252	91.25 257	93.87 359	97.87 293

PVSNet	86.72 19	91.10 311	90.97 308	91.49 340	97.56 266	78.04 365	87.17 366	94.60 326	84.65 341	92.34 343	92.20 356	87.37 284	98.47 342	85.17 345	97.69 304	97.96 289

EPMVS		89.26 328	88.55 332	91.39 341	92.36 375	79.11 363	95.65 194	79.86 378	88.60 302	93.12 329	96.53 267	70.73 364	98.10 359	90.75 270	89.32 370	96.98 322

CostFormer		89.75 325	89.25 323	91.26 342	94.69 354	78.00 366	95.32 213	91.98 350	81.50 353	90.55 354	96.96 240	71.06 362	98.89 306	88.59 310	92.63 363	96.87 327

CVMVSNet		92.33 296	92.79 279	90.95 343	97.26 290	75.84 373	95.29 216	92.33 348	81.86 350	96.27 234	98.19 118	81.44 313	98.46 343	94.23 192	98.29 279	98.55 235

tpm2		88.47 333	87.69 337	90.79 344	94.98 351	77.34 368	95.09 226	91.83 351	77.51 369	89.40 361	96.41 273	67.83 370	98.73 320	83.58 356	92.60 364	96.29 344

GG-mvs-BLEND				90.60 345	91.00 377	84.21 342	98.23 45	72.63 383		82.76 374	84.11 375	56.14 381	96.79 369	72.20 373	92.09 365	90.78 372

tpmvs		90.79 315	90.87 309	90.57 346	92.75 374	76.30 371	95.79 185	93.64 335	91.04 278	91.91 347	96.26 280	77.19 337	98.86 310	89.38 299	89.85 369	96.56 340

test-LLR		89.97 322	89.90 320	90.16 347	94.24 360	74.98 374	89.89 356	89.06 366	92.02 261	89.97 359	90.77 369	73.92 350	98.57 335	91.88 243	97.36 317	96.92 324

test-mter		87.92 338	87.17 339	90.16 347	94.24 360	74.98 374	89.89 356	89.06 366	86.44 322	89.97 359	90.77 369	54.96 384	98.57 335	91.88 243	97.36 317	96.92 324

tpm cat1		88.01 337	87.33 338	90.05 349	94.48 356	76.28 372	94.47 256	94.35 329	73.84 374	89.26 362	95.61 308	73.64 352	98.30 353	84.13 350	86.20 373	95.57 354

tpmrst		90.31 317	90.61 315	89.41 350	94.06 363	72.37 379	95.06 231	93.69 331	88.01 308	92.32 344	96.86 245	77.45 333	98.82 311	91.04 260	87.01 372	97.04 321

TESTMET0.1,1		87.20 340	86.57 342	89.07 351	93.62 367	72.84 378	89.89 356	87.01 372	85.46 332	89.12 364	90.20 371	56.00 382	97.72 363	90.91 264	96.92 324	96.64 337

E-PMN		89.52 327	89.78 321	88.73 352	93.14 369	77.61 367	83.26 371	92.02 349	94.82 185	93.71 311	93.11 340	75.31 345	96.81 368	85.81 336	96.81 329	91.77 370

EMVS		89.06 329	89.22 324	88.61 353	93.00 371	77.34 368	82.91 372	90.92 357	94.64 190	92.63 340	91.81 360	76.30 341	97.02 366	83.83 353	96.90 326	91.48 371

PVSNet_0	81.89 21	84.49 342	83.21 345	88.34 354	95.76 339	74.97 376	83.49 370	92.70 346	78.47 365	87.94 367	86.90 374	83.38 308	96.63 372	73.44 372	66.86 378	93.40 365

MVE	73.61 22	86.48 341	85.92 343	88.18 355	96.23 322	85.28 328	81.78 373	75.79 379	86.01 324	82.53 375	91.88 359	92.74 201	87.47 378	71.42 375	94.86 354	91.78 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		88.08 336	88.05 334	88.16 356	92.85 372	68.81 381	94.17 268	92.88 342	85.47 331	91.38 350	96.14 288	68.87 368	98.81 313	86.88 330	83.80 375	96.87 327

wuyk23d		93.25 282	95.20 198	87.40 357	96.07 331	95.38 108	97.04 119	94.97 322	95.33 164	99.70 5	98.11 128	98.14 13	91.94 375	77.76 368	99.68 67	74.89 375

MVS-HIRNet		88.40 334	90.20 319	82.99 358	97.01 301	60.04 382	93.11 307	85.61 374	84.45 344	88.72 365	99.09 38	84.72 300	98.23 355	82.52 357	96.59 335	90.69 373

DeepMVS_CX				77.17 359	90.94 378	85.28 328		74.08 382	52.51 376	80.87 377	88.03 373	75.25 346	70.63 379	59.23 378	84.94 374	75.62 374

test_method		66.88 344	66.13 347	69.11 360	62.68 383	25.73 385	49.76 374	96.04 303	14.32 378	64.27 379	91.69 362	73.45 355	88.05 377	76.06 370	66.94 377	93.54 363

tmp_tt		57.23 345	62.50 348	41.44 361	34.77 384	49.21 384	83.93 369	60.22 385	15.31 377	71.11 378	79.37 376	70.09 366	44.86 380	64.76 376	82.93 376	30.25 376

test123		12.59 347	15.49 350	3.87 362	6.07 385	2.55 386	90.75 348	2.59 387	2.52 380	5.20 382	13.02 379	4.96 385	1.85 382	5.20 379	9.09 379	7.23 377

testmvs		12.33 348	15.23 351	3.64 363	5.77 386	2.23 387	88.99 363	3.62 386	2.30 381	5.29 381	13.09 378	4.52 386	1.95 381	5.16 380	8.32 380	6.75 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		24.22 346	32.30 349	0.00 364	0.00 387	0.00 388	0.00 375	98.10 224	0.00 382	0.00 383	95.06 317	97.54 29	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		7.98 349	10.65 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	95.82 110	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		7.91 350	10.55 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	94.94 319	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.59 15	98.20 4	99.03 7	99.25 14	98.96 18	98.87 42

PC_three_1452									87.24 314	98.37 80	97.44 200	97.00 54	96.78 370	92.01 238	99.25 193	99.21 133

test_one_0601					99.05 97	95.50 103		98.87 90	97.21 84	98.03 125	98.30 100	96.93 60

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					98.43 166	95.94 81		98.56 166	90.72 280	96.66 213	97.07 231	95.02 144	99.74 80	91.08 259	98.93 233

RE-MVS-def			97.88 52		98.81 114	98.05 9	97.55 89	98.86 93	97.77 50	98.20 102	98.07 132	96.94 58		95.49 123	99.20 198	99.26 124

IU-MVS					99.22 62	95.40 106		98.14 220	85.77 329	98.36 83				95.23 143	99.51 118	99.49 57

test_241102_TWO								98.83 109	96.11 124	98.62 56	98.24 111	96.92 62	99.72 90	95.44 130	99.49 126	99.49 57

test_241102_ONE					99.22 62	95.35 111		98.83 109	96.04 129	99.08 33	98.13 124	97.87 20	99.33 245

9.14			96.69 141		98.53 151		96.02 170	98.98 68	93.23 232	97.18 175	97.46 198	96.47 90	99.62 156	92.99 228	99.32 182

save fliter					98.48 160	94.71 137	94.53 254	98.41 182	95.02 179

test_0728_THIRD									96.62 97	98.40 77	98.28 105	97.10 45	99.71 106	95.70 108	99.62 76	99.58 31

test0726					99.24 57	95.51 100	96.89 125	98.89 82	95.92 137	98.64 55	98.31 96	97.06 50

GSMVS																98.06 281

test_part2					99.03 99	96.07 75				98.08 119

sam_mvs1												77.80 330				98.06 281

sam_mvs												77.38 334

MTGPA								98.73 132

test_post1							94.98 236				10.37 381	76.21 342	99.04 291	89.47 297

test_post											10.87 380	76.83 338	99.07 288

patchmatchnet-post											96.84 247	77.36 335	99.42 213

MTMP							96.55 141	74.60 380

gm-plane-assit					91.79 376	71.40 380			81.67 351		90.11 372		98.99 297	84.86 347

test9_res														91.29 254	98.89 238	99.00 175

TEST9					97.84 227	95.23 118	93.62 291	98.39 185	86.81 319	93.78 307	95.99 293	94.68 154	99.52 186

test_8					97.81 231	95.07 127	93.54 294	98.38 187	87.04 317	93.71 311	95.96 297	94.58 159	99.52 186

agg_prior2														90.34 286	98.90 235	99.10 163

agg_prior					97.80 235	94.96 129		98.36 189		93.49 320			99.53 182

test_prior4						95.38 108	93.61 293

test_prior2							93.33 302		94.21 204	94.02 302	96.25 281	93.64 183		91.90 241	98.96 227

旧先验2							93.35 301		77.95 368	95.77 260			98.67 328	90.74 273

新几何2							93.43 296

旧先验1					97.80 235	93.87 170		97.75 249			97.04 234	93.57 185			98.68 257	98.72 219

无先验							93.20 305	97.91 238	80.78 356				99.40 224	87.71 319		97.94 290

原ACMM2							92.82 310

test222					98.17 193	93.24 192	92.74 314	97.61 264	75.17 371	94.65 285	96.69 259	90.96 239			98.66 260	97.66 303

testdata2													99.46 203	87.84 317

segment_acmp												95.34 132

testdata1							92.77 311		93.78 217

plane_prior7					98.70 130	94.67 141

plane_prior6					98.38 169	94.37 151						91.91 228

plane_prior5								98.75 128					99.46 203	92.59 233	99.20 198	99.28 119

plane_prior4											96.77 253

plane_prior3						94.51 145			95.29 167	96.16 241

plane_prior2							96.50 143		96.36 112

plane_prior1					98.49 158

plane_prior						94.29 153	95.42 203		94.31 201						98.93 233

n2								0.00 388

nn								0.00 388

door-mid								98.17 214

test11								98.08 228

door								97.81 247

HQP5-MVS						92.47 205

HQP-NCC					97.85 223		94.26 260		93.18 235	92.86 333

ACMP_Plane					97.85 223		94.26 260		93.18 235	92.86 333

BP-MVS														90.51 281

HQP4-MVS										92.87 332			99.23 267			99.06 168

HQP3-MVS								98.43 177							98.74 253

HQP2-MVS												90.33 246

NP-MVS					98.14 198	93.72 178					95.08 315

MDTV_nov1_ep13_2view						57.28 383	94.89 239		80.59 357	94.02 302		78.66 327		85.50 341		97.82 296

MDTV_nov1_ep13			91.28 302		94.31 357	73.51 377	94.80 244	93.16 339	86.75 321	93.45 323	97.40 203	76.37 340	98.55 338	88.85 305	96.43 336

ACMMP++_ref															99.52 113

ACMMP++															99.55 102

Test By Simon												94.51 162

High-resolution multi-view benchmark