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.

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 69	99.02 38	99.88 20	99.85 54	99.18 10	99.96 30	99.22 69	99.92 24	99.90 17
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 116	99.51 115					99.96 30	98.93 96	99.86 62	99.88 26

DPE-MVS		99.46 31	99.32 40	99.91 2	99.78 56	99.88 8	99.36 230	99.51 115	98.73 76	99.88 20	99.84 64	98.72 61	99.96 30	98.16 192	99.87 54	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MTAPA		99.52 17	99.39 27	99.89 4	99.90 4	99.86 13	99.66 69	99.47 173	98.79 70	99.68 74	99.81 90	98.43 83	99.97 21	98.88 102	99.90 39	99.83 49

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 169	99.88 8	99.87 9	99.51 115	98.99 45	99.88 20	99.81 90	99.27 5	99.96 30	98.85 112	99.80 97	99.81 61

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 95	99.48 155	99.08 33	99.91 16	99.81 90	99.20 7	99.96 30	98.91 99	99.85 69	99.79 74

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 116	99.37 239	99.10 27	99.81 37	99.80 103	98.94 29	99.96 30	98.93 96	99.86 62	99.81 61
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
MP-MVS-pluss		99.37 53	99.20 66	99.88 5	99.90 4	99.87 12	99.30 245	99.52 101	97.18 238	99.60 106	99.79 115	98.79 47	99.95 59	98.83 118	99.91 31	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 42	99.27 57	99.88 5	99.89 8	99.80 27	99.67 64	99.50 135	98.70 78	99.77 51	99.49 247	98.21 94	99.95 59	98.46 169	99.77 107	99.88 26
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
ACMMP_NAP		99.47 29	99.34 36	99.88 5	99.87 15	99.86 13	99.47 184	99.48 155	98.05 153	99.76 56	99.86 49	98.82 43	99.93 84	98.82 122	99.91 31	99.84 40

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 178	99.64 36	99.45 5	99.92 15	99.92 14	98.62 70	99.99 4	99.96 7	99.99 1	99.96 7

MSC_two_6792asdad				99.87 11	99.51 169	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

No_MVS				99.87 11	99.51 169	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

ZNCC-MVS		99.47 29	99.33 38	99.87 11	99.87 15	99.81 25	99.64 78	99.67 23	98.08 147	99.55 118	99.64 192	98.91 34	99.96 30	98.72 130	99.90 39	99.82 54

region2R		99.48 26	99.35 34	99.87 11	99.88 11	99.80 27	99.65 75	99.66 28	98.13 137	99.66 83	99.68 174	98.96 24	99.96 30	98.62 143	99.87 54	99.84 40

HPM-MVS++		99.39 51	99.23 64	99.87 11	99.75 73	99.84 15	99.43 198	99.51 115	98.68 81	99.27 184	99.53 235	98.64 69	99.96 30	98.44 170	99.80 97	99.79 74

XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	99.74 143	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

X-MVStestdata		96.55 309	95.45 327	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	64.01 400	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

MP-MVS		99.33 57	99.15 70	99.87 11	99.88 11	99.82 22	99.66 69	99.46 182	98.09 143	99.48 130	99.74 143	98.29 91	99.96 30	97.93 208	99.87 54	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 101	99.51 115	98.62 83	99.79 42	99.83 68	99.28 4	99.97 21	98.48 165	99.90 39	99.84 40
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.1_n		99.29 62	99.10 75	99.86 21	99.70 101	99.65 57	99.53 147	99.62 41	98.74 75	99.99 2	99.95 3	94.53 234	99.94 69	99.89 13	99.96 12	99.97 4

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 196	99.65 57	99.50 163	99.61 48	99.45 5	99.87 25	99.92 14	97.31 121	99.97 21	99.95 8	99.99 1	99.97 4

SR-MVS		99.43 40	99.29 53	99.86 21	99.75 73	99.83 16	99.59 101	99.62 41	98.21 124	99.73 62	99.79 115	98.68 64	99.96 30	98.44 170	99.77 107	99.79 74

HFP-MVS		99.49 22	99.37 30	99.86 21	99.87 15	99.80 27	99.66 69	99.67 23	98.15 133	99.68 74	99.69 168	99.06 16	99.96 30	98.69 135	99.87 54	99.84 40

ACMMPR		99.49 22	99.36 32	99.86 21	99.87 15	99.79 30	99.66 69	99.67 23	98.15 133	99.67 78	99.69 168	98.95 27	99.96 30	98.69 135	99.87 54	99.84 40

PGM-MVS		99.45 33	99.31 47	99.86 21	99.87 15	99.78 36	99.58 109	99.65 33	97.84 171	99.71 68	99.80 103	99.12 13	99.97 21	98.33 179	99.87 54	99.83 49

mPP-MVS		99.44 37	99.30 49	99.86 21	99.88 11	99.79 30	99.69 55	99.48 155	98.12 138	99.50 126	99.75 138	98.78 48	99.97 21	98.57 155	99.89 48	99.83 49

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 122	99.63 39	99.48 3	99.98 6	99.83 68	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 122	99.63 39	99.47 4	99.98 6	99.82 76	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

fmvsm_s_conf0.1_n_a		99.26 68	99.06 81	99.85 28	99.52 166	99.62 65	99.54 139	99.62 41	98.69 79	99.99 2	99.96 1	94.47 236	99.94 69	99.88 14	99.92 24	99.98 2

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 148	99.65 33	99.10 27	99.98 6	99.92 14	97.35 120	99.96 30	99.94 10	99.92 24	99.95 9

fmvsm_s_conf0.5_n		99.51 18	99.40 25	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 146	99.96 30	99.95 8	99.96 12	99.95 9

SR-MVS-dyc-post		99.45 33	99.31 47	99.85 28	99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.53 76	99.95 59	98.61 146	99.81 93	99.77 82

GST-MVS		99.40 50	99.24 62	99.85 28	99.86 20	99.79 30	99.60 95	99.67 23	97.97 159	99.63 96	99.68 174	98.52 77	99.95 59	98.38 173	99.86 62	99.81 61

SMA-MVS		99.44 37	99.30 49	99.85 28	99.73 87	99.83 16	99.56 122	99.47 173	97.45 214	99.78 47	99.82 76	99.18 10	99.91 105	98.79 123	99.89 48	99.81 61
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
APD-MVS_3200maxsize		99.48 26	99.35 34	99.85 28	99.76 65	99.83 16	99.63 82	99.54 85	98.36 106	99.79 42	99.82 76	98.86 38	99.95 59	98.62 143	99.81 93	99.78 80

HPM-MVS_fast		99.51 18	99.40 25	99.85 28	99.91 1	99.79 30	99.76 37	99.56 69	97.72 185	99.76 56	99.75 138	99.13 12	99.92 95	99.07 83	99.92 24	99.85 36

CP-MVS		99.45 33	99.32 40	99.85 28	99.83 39	99.75 39	99.69 55	99.52 101	98.07 148	99.53 121	99.63 198	98.93 33	99.97 21	98.74 127	99.91 31	99.83 49

APD-MVS		99.27 66	99.08 79	99.84 39	99.75 73	99.79 30	99.50 163	99.50 135	97.16 240	99.77 51	99.82 76	98.78 48	99.94 69	97.56 246	99.86 62	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS		99.42 42	99.28 55	99.83 40	99.90 4	99.72 42	99.81 20	99.54 85	97.59 196	99.68 74	99.63 198	98.91 34	99.94 69	98.58 152	99.91 31	99.84 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MCST-MVS		99.43 40	99.30 49	99.82 41	99.79 54	99.74 41	99.29 249	99.40 220	98.79 70	99.52 123	99.62 203	98.91 34	99.90 116	98.64 141	99.75 112	99.82 54

ACMMP		99.45 33	99.32 40	99.82 41	99.89 8	99.67 51	99.62 88	99.69 18	98.12 138	99.63 96	99.84 64	98.73 60	99.96 30	98.55 161	99.83 86	99.81 61
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
3Dnovator+	97.12 13	99.18 78	98.97 100	99.82 41	99.17 266	99.68 48	99.81 20	99.51 115	99.20 18	98.72 275	99.89 30	95.68 182	99.97 21	98.86 110	99.86 62	99.81 61

TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 82	99.39 223	98.91 58	99.78 47	99.85 54	99.36 2	99.94 69	98.84 115	99.88 51	99.82 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator	97.25 9	99.24 73	99.05 82	99.81 44	99.12 273	99.66 53	99.84 13	99.74 10	99.09 32	98.92 249	99.90 26	95.94 170	99.98 13	98.95 93	99.92 24	99.79 74

UA-Net		99.42 42	99.29 53	99.80 46	99.62 136	99.55 77	99.50 163	99.70 15	98.79 70	99.77 51	99.96 1	97.45 115	99.96 30	98.92 98	99.90 39	99.89 20

CDPH-MVS		99.13 88	98.91 108	99.80 46	99.75 73	99.71 44	99.15 284	99.41 212	96.60 287	99.60 106	99.55 226	98.83 42	99.90 116	97.48 253	99.83 86	99.78 80

QAPM		98.67 155	98.30 173	99.80 46	99.20 255	99.67 51	99.77 34	99.72 11	94.74 350	98.73 274	99.90 26	95.78 177	99.98 13	96.96 285	99.88 51	99.76 87

test_fmvsmconf0.01_n		99.22 75	99.03 86	99.79 49	98.42 355	99.48 89	99.55 134	99.51 115	99.39 10	99.78 47	99.93 9	94.80 212	99.95 59	99.93 11	99.95 16	99.94 11

SF-MVS		99.38 52	99.24 62	99.79 49	99.79 54	99.68 48	99.57 116	99.54 85	97.82 176	99.71 68	99.80 103	98.95 27	99.93 84	98.19 188	99.84 77	99.74 92

NCCC		99.34 56	99.19 67	99.79 49	99.61 140	99.65 57	99.30 245	99.48 155	98.86 60	99.21 198	99.63 198	98.72 61	99.90 116	98.25 184	99.63 133	99.80 70

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 94	99.58 109	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 16	99.90 17

CNVR-MVS		99.42 42	99.30 49	99.78 52	99.62 136	99.71 44	99.26 266	99.52 101	98.82 65	99.39 155	99.71 154	98.96 24	99.85 145	98.59 151	99.80 97	99.77 82

DP-MVS		99.16 82	98.95 104	99.78 52	99.77 62	99.53 82	99.41 207	99.50 135	97.03 256	99.04 231	99.88 36	97.39 116	99.92 95	98.66 139	99.90 39	99.87 31

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 55	99.38 211	99.37 100	99.58 109	99.62 41	99.41 9	99.87 25	99.92 14	98.81 44	100.00 1	99.97 1	99.93 22	99.94 11

train_agg		99.02 111	98.77 126	99.77 55	99.67 111	99.65 57	99.05 304	99.41 212	96.28 307	98.95 244	99.49 247	98.76 52	99.91 105	97.63 237	99.72 118	99.75 88

DeepC-MVS_fast	98.69 1	99.49 22	99.39 27	99.77 55	99.63 130	99.59 70	99.36 230	99.46 182	99.07 35	99.79 42	99.82 76	98.85 39	99.92 95	98.68 137	99.87 54	99.82 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SDMVSNet		99.11 98	98.90 109	99.75 58	99.81 46	99.59 70	99.81 20	99.65 33	98.78 73	99.64 93	99.88 36	94.56 230	99.93 84	99.67 21	98.26 224	99.72 103

新几何1				99.75 58	99.75 73	99.59 70		99.54 85	96.76 272	99.29 179	99.64 192	98.43 83	99.94 69	96.92 290	99.66 128	99.72 103

test12				99.75 58	99.64 127	99.61 67		99.29 279		99.21 198		98.38 87	99.89 126		99.74 115	99.74 92

MM				99.74 61		99.31 107	99.52 148	98.87 338	99.55 1	99.74 60	99.80 103	96.47 151	99.98 13	99.97 1	99.97 7	99.94 11

CPTT-MVS		99.11 98	98.90 109	99.74 61	99.80 52	99.46 92	99.59 101	99.49 143	97.03 256	99.63 96	99.69 168	97.27 124	99.96 30	97.82 218	99.84 77	99.81 61

LS3D		99.27 66	99.12 73	99.74 61	99.18 260	99.75 39	99.56 122	99.57 64	98.45 96	99.49 129	99.85 54	97.77 109	99.94 69	98.33 179	99.84 77	99.52 167

VNet		99.11 98	98.90 109	99.73 64	99.52 166	99.56 75	99.41 207	99.39 223	99.01 40	99.74 60	99.78 121	95.56 184	99.92 95	99.52 34	98.18 231	99.72 103

MVS_0304		99.42 42	99.32 40	99.72 65	99.70 101	99.27 113	99.52 148	97.57 381	99.51 2	99.82 35	99.78 121	98.09 100	99.96 30	99.97 1	99.97 7	99.94 11

114514_t		98.93 119	98.67 135	99.72 65	99.85 26	99.53 82	99.62 88	99.59 57	92.65 370	99.71 68	99.78 121	98.06 102	99.90 116	98.84 115	99.91 31	99.74 92

PHI-MVS		99.30 60	99.17 69	99.70 67	99.56 155	99.52 85	99.58 109	99.80 8	97.12 244	99.62 100	99.73 149	98.58 72	99.90 116	98.61 146	99.91 31	99.68 119

test_prior				99.68 68	99.67 111	99.48 89		99.56 69					99.83 162			99.74 92

DPM-MVS		98.95 118	98.71 131	99.66 69	99.63 130	99.55 77	98.64 360	99.10 307	97.93 162	99.42 143	99.55 226	98.67 66	99.80 179	95.80 319	99.68 126	99.61 144

PAPM_NR		99.04 108	98.84 119	99.66 69	99.74 80	99.44 94	99.39 219	99.38 231	97.70 187	99.28 180	99.28 304	98.34 89	99.85 145	96.96 285	99.45 145	99.69 115

MVS_111021_HR		99.41 47	99.32 40	99.66 69	99.72 91	99.47 91	98.95 329	99.85 6	98.82 65	99.54 119	99.73 149	98.51 78	99.74 196	98.91 99	99.88 51	99.77 82

AdaColmap		99.01 114	98.80 122	99.66 69	99.56 155	99.54 79	99.18 279	99.70 15	98.18 131	99.35 167	99.63 198	96.32 157	99.90 116	97.48 253	99.77 107	99.55 159

原ACMM1				99.65 73	99.73 87	99.33 103		99.47 173	97.46 211	99.12 214	99.66 185	98.67 66	99.91 105	97.70 234	99.69 123	99.71 112

DELS-MVS		99.48 26	99.42 22	99.65 73	99.72 91	99.40 99	99.05 304	99.66 28	99.14 21	99.57 113	99.80 103	98.46 81	99.94 69	99.57 27	99.84 77	99.60 146
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
DP-MVS Recon		99.12 94	98.95 104	99.65 73	99.74 80	99.70 46	99.27 258	99.57 64	96.40 303	99.42 143	99.68 174	98.75 55	99.80 179	97.98 205	99.72 118	99.44 191

MVS_111021_LR		99.41 47	99.33 38	99.65 73	99.77 62	99.51 86	98.94 331	99.85 6	98.82 65	99.65 89	99.74 143	98.51 78	99.80 179	98.83 118	99.89 48	99.64 136

HyFIR lowres test		99.11 98	98.92 106	99.65 73	99.90 4	99.37 100	99.02 312	99.91 3	97.67 191	99.59 109	99.75 138	95.90 173	99.73 202	99.53 32	99.02 182	99.86 33

OPU-MVS				99.64 78	99.56 155	99.72 42	99.60 95				99.70 158	99.27 5	99.42 265	98.24 185	99.80 97	99.79 74

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 78	99.78 56	99.14 131	99.60 95	99.45 193	99.01 40	99.90 18	99.83 68	98.98 23	99.93 84	99.59 25	99.95 16	99.86 33

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 78	99.78 56	99.15 130	99.61 94	99.45 193	99.01 40	99.89 19	99.82 76	99.01 18	99.92 95	99.56 28	99.95 16	99.85 36

F-COLMAP		99.19 76	99.04 84	99.64 78	99.78 56	99.27 113	99.42 205	99.54 85	97.29 229	99.41 147	99.59 212	98.42 85	99.93 84	98.19 188	99.69 123	99.73 97

DeepC-MVS	98.35 2	99.30 60	99.19 67	99.64 78	99.82 42	99.23 118	99.62 88	99.55 77	98.94 54	99.63 96	99.95 3	95.82 176	99.94 69	99.37 50	99.97 7	99.73 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvsany_test1		99.50 20	99.46 20	99.62 83	99.61 140	99.09 136	98.94 331	99.48 155	99.10 27	99.96 14	99.91 20	98.85 39	99.96 30	99.72 18	99.58 137	99.82 54

test_cas_vis1_n_1920		99.16 82	99.01 94	99.61 84	99.81 46	98.86 175	99.65 75	99.64 36	99.39 10	99.97 13	99.94 6	93.20 272	99.98 13	99.55 29	99.91 31	99.99 1

PVSNet_Blended_VisFu		99.36 54	99.28 55	99.61 84	99.86 20	99.07 141	99.47 184	99.93 2	97.66 192	99.71 68	99.86 49	97.73 110	99.96 30	99.47 43	99.82 90	99.79 74

WTY-MVS		99.06 106	98.88 113	99.61 84	99.62 136	99.16 125	99.37 226	99.56 69	98.04 154	99.53 121	99.62 203	96.84 138	99.94 69	98.85 112	98.49 213	99.72 103

CANet		99.25 72	99.14 71	99.59 87	99.41 202	99.16 125	99.35 235	99.57 64	98.82 65	99.51 125	99.61 207	96.46 152	99.95 59	99.59 25	99.98 4	99.65 129

1112_ss		98.98 115	98.77 126	99.59 87	99.68 110	99.02 146	99.25 268	99.48 155	97.23 235	99.13 212	99.58 216	96.93 137	99.90 116	98.87 105	98.78 199	99.84 40

CNLPA		99.14 86	98.99 96	99.59 87	99.58 149	99.41 98	99.16 281	99.44 201	98.45 96	99.19 204	99.49 247	98.08 101	99.89 126	97.73 229	99.75 112	99.48 178

alignmvs		98.81 138	98.56 157	99.58 90	99.43 197	99.42 96	99.51 156	98.96 324	98.61 84	99.35 167	98.92 346	94.78 214	99.77 189	99.35 51	98.11 236	99.54 161

EC-MVSNet		99.44 37	99.39 27	99.58 90	99.56 155	99.49 87	99.88 4	99.58 61	98.38 102	99.73 62	99.69 168	98.20 95	99.70 218	99.64 24	99.82 90	99.54 161

Test_1112_low_res		98.89 122	98.66 138	99.57 92	99.69 106	98.95 162	99.03 309	99.47 173	96.98 258	99.15 210	99.23 312	96.77 141	99.89 126	98.83 118	98.78 199	99.86 33

IS-MVSNet		99.05 107	98.87 114	99.57 92	99.73 87	99.32 104	99.75 41	99.20 296	98.02 157	99.56 114	99.86 49	96.54 149	99.67 225	98.09 195	99.13 170	99.73 97

casdiffmvs		99.13 88	98.98 99	99.56 94	99.65 125	99.16 125	99.56 122	99.50 135	98.33 110	99.41 147	99.86 49	95.92 171	99.83 162	99.45 45	99.16 165	99.70 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		99.12 94	98.97 100	99.56 94	99.78 56	99.10 135	99.68 61	99.66 28	98.49 93	99.86 27	99.87 44	94.77 217	99.84 151	99.19 71	99.41 148	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
casdiffmvs_mvg		99.15 84	99.02 90	99.55 96	99.66 119	99.09 136	99.64 78	99.56 69	98.26 116	99.45 134	99.87 44	96.03 165	99.81 173	99.54 30	99.15 168	99.73 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		99.50 20	99.48 15	99.54 97	99.76 65	99.42 96	99.90 1	99.55 77	98.56 87	99.78 47	99.70 158	98.65 68	99.79 182	99.65 23	99.78 104	99.41 195

test_yl		98.86 127	98.63 141	99.54 97	99.49 180	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 190	99.84 151	98.60 149	98.33 218	99.59 150

DCV-MVSNet		98.86 127	98.63 141	99.54 97	99.49 180	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 190	99.84 151	98.60 149	98.33 218	99.59 150

CS-MVS-test		99.49 22	99.48 15	99.54 97	99.78 56	99.30 109	99.89 2	99.58 61	98.56 87	99.73 62	99.69 168	98.55 75	99.82 168	99.69 19	99.85 69	99.48 178

testdata				99.54 97	99.75 73	98.95 162		99.51 115	97.07 250	99.43 140	99.70 158	98.87 37	99.94 69	97.76 225	99.64 131	99.72 103

LFMVS		97.90 233	97.35 276	99.54 97	99.52 166	99.01 148	99.39 219	98.24 369	97.10 248	99.65 89	99.79 115	84.79 372	99.91 105	99.28 63	98.38 215	99.69 115

ab-mvs		98.86 127	98.63 141	99.54 97	99.64 127	99.19 120	99.44 194	99.54 85	97.77 179	99.30 176	99.81 90	94.20 244	99.93 84	99.17 74	98.82 196	99.49 177

MAR-MVS		98.86 127	98.63 141	99.54 97	99.37 214	99.66 53	99.45 188	99.54 85	96.61 285	99.01 234	99.40 272	97.09 129	99.86 139	97.68 236	99.53 141	99.10 216
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
GeoE		98.85 134	98.62 146	99.53 105	99.61 140	99.08 139	99.80 25	99.51 115	97.10 248	99.31 174	99.78 121	95.23 198	99.77 189	98.21 186	99.03 180	99.75 88

baseline		99.15 84	99.02 90	99.53 105	99.66 119	99.14 131	99.72 49	99.48 155	98.35 107	99.42 143	99.84 64	96.07 163	99.79 182	99.51 35	99.14 169	99.67 122

sss		99.17 80	99.05 82	99.53 105	99.62 136	98.97 153	99.36 230	99.62 41	97.83 172	99.67 78	99.65 186	97.37 119	99.95 59	99.19 71	99.19 164	99.68 119

EPP-MVSNet		99.13 88	98.99 96	99.53 105	99.65 125	99.06 142	99.81 20	99.33 257	97.43 217	99.60 106	99.88 36	97.14 126	99.84 151	99.13 76	98.94 185	99.69 115

PLC	97.94 4	99.02 111	98.85 118	99.53 105	99.66 119	99.01 148	99.24 270	99.52 101	96.85 268	99.27 184	99.48 252	98.25 93	99.91 105	97.76 225	99.62 134	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		98.98 115	98.80 122	99.53 105	99.76 65	99.19 120	98.75 350	99.55 77	97.25 232	99.47 131	99.77 129	97.82 107	99.87 136	96.93 288	99.90 39	99.54 161

PatchMatch-RL		98.84 137	98.62 146	99.52 111	99.71 96	99.28 111	99.06 302	99.77 9	97.74 184	99.50 126	99.53 235	95.41 188	99.84 151	97.17 275	99.64 131	99.44 191

OpenMVS	96.50 16	98.47 165	98.12 185	99.52 111	99.04 291	99.53 82	99.82 17	99.72 11	94.56 353	98.08 322	99.88 36	94.73 220	99.98 13	97.47 255	99.76 110	99.06 227

Fast-Effi-MVS+		98.70 150	98.43 163	99.51 113	99.51 169	99.28 111	99.52 148	99.47 173	96.11 323	99.01 234	99.34 290	96.20 161	99.84 151	97.88 211	98.82 196	99.39 198

canonicalmvs		99.02 111	98.86 117	99.51 113	99.42 199	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 351	97.09 129	99.75 195	99.27 66	97.90 240	99.47 184

diffmvs		99.14 86	99.02 90	99.51 113	99.61 140	98.96 157	99.28 253	99.49 143	98.46 95	99.72 67	99.71 154	96.50 150	99.88 131	99.31 58	99.11 171	99.67 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPR		98.63 159	98.34 169	99.51 113	99.40 207	99.03 145	98.80 345	99.36 240	96.33 304	99.00 238	99.12 326	98.46 81	99.84 151	95.23 334	99.37 156	99.66 125

Effi-MVS+		98.81 138	98.59 153	99.48 117	99.46 190	99.12 134	98.08 383	99.50 135	97.50 209	99.38 158	99.41 269	96.37 156	99.81 173	99.11 78	98.54 210	99.51 173

MVS		97.28 293	96.55 305	99.48 117	98.78 326	98.95 162	99.27 258	99.39 223	83.53 387	98.08 322	99.54 231	96.97 135	99.87 136	94.23 347	99.16 165	99.63 140

MVS_Test		99.10 102	98.97 100	99.48 117	99.49 180	99.14 131	99.67 64	99.34 250	97.31 227	99.58 110	99.76 135	97.65 112	99.82 168	98.87 105	99.07 177	99.46 186

HY-MVS	97.30 7	98.85 134	98.64 140	99.47 120	99.42 199	99.08 139	99.62 88	99.36 240	97.39 222	99.28 180	99.68 174	96.44 154	99.92 95	98.37 175	98.22 226	99.40 197

PCF-MVS	97.08 14	97.66 274	97.06 296	99.47 120	99.61 140	99.09 136	98.04 384	99.25 287	91.24 375	98.51 302	99.70 158	94.55 232	99.91 105	92.76 364	99.85 69	99.42 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
lupinMVS		99.13 88	99.01 94	99.46 122	99.51 169	98.94 165	99.05 304	99.16 301	97.86 167	99.80 40	99.56 223	97.39 116	99.86 139	98.94 94	99.85 69	99.58 154

EIA-MVS		99.18 78	99.09 78	99.45 123	99.49 180	99.18 122	99.67 64	99.53 96	97.66 192	99.40 152	99.44 261	98.10 99	99.81 173	98.94 94	99.62 134	99.35 201

jason		99.13 88	99.03 86	99.45 123	99.46 190	98.87 172	99.12 289	99.26 285	98.03 156	99.79 42	99.65 186	97.02 132	99.85 145	99.02 87	99.90 39	99.65 129
jason: jason.
CHOSEN 1792x2688		99.19 76	99.10 75	99.45 123	99.89 8	98.52 208	99.39 219	99.94 1	98.73 76	99.11 216	99.89 30	95.50 186	99.94 69	99.50 36	99.97 7	99.89 20

MG-MVS		99.13 88	99.02 90	99.45 123	99.57 151	98.63 195	99.07 299	99.34 250	98.99 45	99.61 103	99.82 76	97.98 104	99.87 136	97.00 281	99.80 97	99.85 36

MSLP-MVS++		99.46 31	99.47 17	99.44 127	99.60 145	99.16 125	99.41 207	99.71 13	98.98 48	99.45 134	99.78 121	99.19 9	99.54 250	99.28 63	99.84 77	99.63 140

PVSNet_Blended		99.08 104	98.97 100	99.42 128	99.76 65	98.79 184	98.78 347	99.91 3	96.74 273	99.67 78	99.49 247	97.53 113	99.88 131	98.98 90	99.85 69	99.60 146

FA-MVS(test-final)		98.75 145	98.53 159	99.41 129	99.55 159	99.05 144	99.80 25	99.01 318	96.59 289	99.58 110	99.59 212	95.39 189	99.90 116	97.78 221	99.49 143	99.28 208

FE-MVS		98.48 164	98.17 178	99.40 130	99.54 161	98.96 157	99.68 61	98.81 344	95.54 334	99.62 100	99.70 158	93.82 259	99.93 84	97.35 262	99.46 144	99.32 205

ETV-MVS		99.26 68	99.21 65	99.40 130	99.46 190	99.30 109	99.56 122	99.52 101	98.52 91	99.44 139	99.27 307	98.41 86	99.86 139	99.10 79	99.59 136	99.04 228

BH-RMVSNet		98.41 171	98.08 191	99.40 130	99.41 202	98.83 180	99.30 245	98.77 347	97.70 187	98.94 246	99.65 186	92.91 278	99.74 196	96.52 304	99.55 140	99.64 136

UGNet		98.87 124	98.69 133	99.40 130	99.22 252	98.72 188	99.44 194	99.68 20	99.24 17	99.18 207	99.42 265	92.74 282	99.96 30	99.34 55	99.94 21	99.53 166
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
baseline1		98.31 179	97.95 206	99.38 134	99.50 178	98.74 186	99.59 101	98.93 326	98.41 100	99.14 211	99.60 210	94.59 228	99.79 182	98.48 165	93.29 354	99.61 144

TSAR-MVS + GP.		99.36 54	99.36 32	99.36 135	99.67 111	98.61 198	99.07 299	99.33 257	99.00 43	99.82 35	99.81 90	99.06 16	99.84 151	99.09 80	99.42 147	99.65 129

test_vis1_n		97.92 230	97.44 264	99.34 136	99.53 162	98.08 234	99.74 44	99.49 143	99.15 20	100.00 1	99.94 6	79.51 384	99.98 13	99.88 14	99.76 110	99.97 4

Anonymous20240529		98.09 200	97.68 236	99.34 136	99.66 119	98.44 217	99.40 215	99.43 207	93.67 360	99.22 195	99.89 30	90.23 335	99.93 84	99.26 67	98.33 218	99.66 125

xiu_mvs_v1_base_debu		99.29 62	99.27 57	99.34 136	99.63 130	98.97 153	99.12 289	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 221

xiu_mvs_v1_base		99.29 62	99.27 57	99.34 136	99.63 130	98.97 153	99.12 289	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 221

xiu_mvs_v1_base_debi		99.29 62	99.27 57	99.34 136	99.63 130	98.97 153	99.12 289	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 221

PMMVS		98.80 141	98.62 146	99.34 136	99.27 241	98.70 189	98.76 349	99.31 271	97.34 224	99.21 198	99.07 328	97.20 125	99.82 168	98.56 158	98.87 191	99.52 167

CSCG		99.32 58	99.32 40	99.32 142	99.85 26	98.29 223	99.71 51	99.66 28	98.11 140	99.41 147	99.80 103	98.37 88	99.96 30	98.99 89	99.96 12	99.72 103

test_vis1_n_1920		98.63 159	98.40 166	99.31 143	99.86 20	97.94 246	99.67 64	99.62 41	99.43 7	99.99 2	99.91 20	87.29 363	100.00 1	99.92 12	99.92 24	99.98 2

thisisatest0530		98.35 177	98.03 197	99.31 143	99.63 130	98.56 201	99.54 139	96.75 387	97.53 206	99.73 62	99.65 186	91.25 323	99.89 126	98.62 143	99.56 138	99.48 178

AllTest		98.87 124	98.72 129	99.31 143	99.86 20	98.48 214	99.56 122	99.61 48	97.85 169	99.36 164	99.85 54	95.95 168	99.85 145	96.66 301	99.83 86	99.59 150

TestCases				99.31 143	99.86 20	98.48 214		99.61 48	97.85 169	99.36 164	99.85 54	95.95 168	99.85 145	96.66 301	99.83 86	99.59 150

Vis-MVSNet (Re-imp)		98.87 124	98.72 129	99.31 143	99.71 96	98.88 171	99.80 25	99.44 201	97.91 164	99.36 164	99.78 121	95.49 187	99.43 264	97.91 209	99.11 171	99.62 142

PS-MVSNAJ		99.32 58	99.32 40	99.30 148	99.57 151	98.94 165	98.97 325	99.46 182	98.92 57	99.71 68	99.24 311	99.01 18	99.98 13	99.35 51	99.66 128	98.97 236

VPA-MVSNet		98.29 182	97.95 206	99.30 148	99.16 268	99.54 79	99.50 163	99.58 61	98.27 115	99.35 167	99.37 280	92.53 292	99.65 233	99.35 51	94.46 337	98.72 261

EPNet		98.86 127	98.71 131	99.30 148	97.20 375	98.18 228	99.62 88	98.91 332	99.28 16	98.63 293	99.81 90	95.96 167	99.99 4	99.24 68	99.72 118	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sd_testset		98.75 145	98.57 155	99.29 151	99.81 46	98.26 225	99.56 122	99.62 41	98.78 73	99.64 93	99.88 36	92.02 303	99.88 131	99.54 30	98.26 224	99.72 103

xiu_mvs_v2_base		99.26 68	99.25 61	99.29 151	99.53 162	98.91 169	99.02 312	99.45 193	98.80 69	99.71 68	99.26 309	98.94 29	99.98 13	99.34 55	99.23 161	98.98 235

MVSFormer		99.17 80	99.12 73	99.29 151	99.51 169	98.94 165	99.88 4	99.46 182	97.55 202	99.80 40	99.65 186	97.39 116	99.28 292	99.03 85	99.85 69	99.65 129

tttt0517		98.42 169	98.14 182	99.28 154	99.66 119	98.38 221	99.74 44	96.85 385	97.68 189	99.79 42	99.74 143	91.39 320	99.89 126	98.83 118	99.56 138	99.57 156

nrg030		98.64 158	98.42 164	99.28 154	99.05 290	99.69 47	99.81 20	99.46 182	98.04 154	99.01 234	99.82 76	96.69 144	99.38 268	99.34 55	94.59 336	98.78 248

Anonymous202405211		98.30 181	97.98 202	99.26 156	99.57 151	98.16 229	99.41 207	98.55 363	96.03 328	99.19 204	99.74 143	91.87 306	99.92 95	99.16 75	98.29 223	99.70 113

CANet_DTU		98.97 117	98.87 114	99.25 157	99.33 224	98.42 220	99.08 298	99.30 275	99.16 19	99.43 140	99.75 138	95.27 194	99.97 21	98.56 158	99.95 16	99.36 200

CDS-MVSNet		99.09 103	99.03 86	99.25 157	99.42 199	98.73 187	99.45 188	99.46 182	98.11 140	99.46 133	99.77 129	98.01 103	99.37 273	98.70 132	98.92 188	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		98.38 175	98.09 190	99.24 159	99.26 243	99.32 104	99.56 122	99.55 77	97.45 214	98.71 276	99.83 68	93.23 269	99.63 241	98.88 102	96.32 297	98.76 253

TAMVS		99.12 94	99.08 79	99.24 159	99.46 190	98.55 202	99.51 156	99.46 182	98.09 143	99.45 134	99.82 76	98.34 89	99.51 251	98.70 132	98.93 186	99.67 122

FIs		98.78 142	98.63 141	99.23 161	99.18 260	99.54 79	99.83 16	99.59 57	98.28 113	98.79 269	99.81 90	96.75 142	99.37 273	99.08 82	96.38 295	98.78 248

test_fmvs1_n		98.41 171	98.14 182	99.21 162	99.82 42	97.71 258	99.74 44	99.49 143	99.32 14	99.99 2	99.95 3	85.32 370	99.97 21	99.82 16	99.84 77	99.96 7

OMC-MVS		99.08 104	99.04 84	99.20 163	99.67 111	98.22 227	99.28 253	99.52 101	98.07 148	99.66 83	99.81 90	97.79 108	99.78 187	97.79 220	99.81 93	99.60 146

thisisatest0515		98.14 195	97.79 220	99.19 164	99.50 178	98.50 211	98.61 361	96.82 386	96.95 262	99.54 119	99.43 263	91.66 315	99.86 139	98.08 199	99.51 142	99.22 211

RPMNet		96.72 307	95.90 319	99.19 164	99.18 260	98.49 212	99.22 275	99.52 101	88.72 383	99.56 114	97.38 377	94.08 250	99.95 59	86.87 387	98.58 205	99.14 213

COLMAP_ROB	97.56 6	98.86 127	98.75 128	99.17 166	99.88 11	98.53 204	99.34 238	99.59 57	97.55 202	98.70 282	99.89 30	95.83 175	99.90 116	98.10 194	99.90 39	99.08 221
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvs1		98.88 123	98.79 125	99.16 167	99.69 106	97.61 260	99.55 134	99.49 143	99.32 14	99.98 6	99.91 20	91.41 319	99.96 30	99.82 16	99.92 24	99.90 17

VDDNet		97.55 279	97.02 297	99.16 167	99.49 180	98.12 233	99.38 224	99.30 275	95.35 336	99.68 74	99.90 26	82.62 380	99.93 84	99.31 58	98.13 235	99.42 193

mvs_anonymous		99.03 110	98.99 96	99.16 167	99.38 211	98.52 208	99.51 156	99.38 231	97.79 177	99.38 158	99.81 90	97.30 122	99.45 255	99.35 51	98.99 183	99.51 173

FC-MVSNet-test		98.75 145	98.62 146	99.15 170	99.08 283	99.45 93	99.86 12	99.60 54	98.23 121	98.70 282	99.82 76	96.80 139	99.22 304	99.07 83	96.38 295	98.79 247

UniMVSNet (Re)		98.29 182	98.00 200	99.13 171	99.00 295	99.36 102	99.49 174	99.51 115	97.95 160	98.97 242	99.13 323	96.30 158	99.38 268	98.36 177	93.34 353	98.66 292

1314		98.68 154	98.54 158	99.11 172	98.89 310	98.65 193	99.27 258	99.49 143	96.89 266	97.99 327	99.56 223	97.72 111	99.83 162	97.74 228	99.27 160	98.84 244

CHOSEN 280x420		99.12 94	99.13 72	99.08 173	99.66 119	97.89 247	98.43 371	99.71 13	98.88 59	99.62 100	99.76 135	96.63 145	99.70 218	99.46 44	99.99 1	99.66 125

mvsmamba		98.92 120	98.87 114	99.08 173	99.07 284	99.16 125	99.88 4	99.51 115	98.15 133	99.40 152	99.89 30	97.12 127	99.33 283	99.38 48	97.40 272	98.73 260

PAPM		97.59 278	97.09 295	99.07 175	99.06 287	98.26 225	98.30 378	99.10 307	94.88 346	98.08 322	99.34 290	96.27 159	99.64 236	89.87 375	98.92 188	99.31 206

WR-MVS		98.06 204	97.73 232	99.06 176	98.86 318	99.25 116	99.19 278	99.35 246	97.30 228	98.66 285	99.43 263	93.94 254	99.21 309	98.58 152	94.28 341	98.71 263

API-MVS		99.04 108	99.03 86	99.06 176	99.40 207	99.31 107	99.55 134	99.56 69	98.54 89	99.33 171	99.39 276	98.76 52	99.78 187	96.98 283	99.78 104	98.07 355

ET-MVSNet_ETH3D		96.49 311	95.64 325	99.05 178	99.53 162	98.82 181	98.84 341	97.51 382	97.63 194	84.77 387	99.21 316	92.09 302	98.91 349	98.98 90	92.21 364	99.41 195

SD-MVS		99.41 47	99.52 11	99.05 178	99.74 80	99.68 48	99.46 187	99.52 101	99.11 26	99.88 20	99.91 20	99.43 1	97.70 378	98.72 130	99.93 22	99.77 82
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
PVSNet_BlendedMVS		98.86 127	98.80 122	99.03 180	99.76 65	98.79 184	99.28 253	99.91 3	97.42 219	99.67 78	99.37 280	97.53 113	99.88 131	98.98 90	97.29 276	98.42 336

NR-MVSNet		97.97 224	97.61 243	99.02 181	98.87 315	99.26 115	99.47 184	99.42 209	97.63 194	97.08 352	99.50 244	95.07 201	99.13 317	97.86 214	93.59 351	98.68 277

VPNet		97.84 242	97.44 264	99.01 182	99.21 253	98.94 165	99.48 178	99.57 64	98.38 102	99.28 180	99.73 149	88.89 346	99.39 267	99.19 71	93.27 355	98.71 263

CP-MVSNet		98.09 200	97.78 223	99.01 182	98.97 303	99.24 117	99.67 64	99.46 182	97.25 232	98.48 305	99.64 192	93.79 260	99.06 327	98.63 142	94.10 344	98.74 258

GA-MVS		97.85 239	97.47 256	99.00 184	99.38 211	97.99 239	98.57 364	99.15 302	97.04 255	98.90 252	99.30 300	89.83 338	99.38 268	96.70 298	98.33 218	99.62 142

MVSTER		98.49 163	98.32 171	99.00 184	99.35 218	99.02 146	99.54 139	99.38 231	97.41 220	99.20 201	99.73 149	93.86 258	99.36 277	98.87 105	97.56 253	98.62 307

iter_conf_final		98.71 149	98.61 152	98.99 186	99.49 180	98.96 157	99.63 82	99.41 212	98.19 127	99.39 155	99.77 129	94.82 209	99.38 268	99.30 61	97.52 256	98.64 296

bld_raw_dy_0_64		98.69 152	98.58 154	98.99 186	98.88 311	98.96 157	99.80 25	99.41 212	97.91 164	99.32 172	99.87 44	95.70 181	99.31 289	99.09 80	97.27 277	98.71 263

tfpnnormal		97.84 242	97.47 256	98.98 188	99.20 255	99.22 119	99.64 78	99.61 48	96.32 305	98.27 316	99.70 158	93.35 268	99.44 260	95.69 322	95.40 320	98.27 346

test_djsdf		98.67 155	98.57 155	98.98 188	98.70 337	98.91 169	99.88 4	99.46 182	97.55 202	99.22 195	99.88 36	95.73 179	99.28 292	99.03 85	97.62 248	98.75 255

h-mvs33		97.70 267	97.28 286	98.97 190	99.70 101	97.27 268	99.36 230	99.45 193	98.94 54	99.66 83	99.64 192	94.93 203	99.99 4	99.48 41	84.36 383	99.65 129

UniMVSNet_NR-MVSNet		98.22 185	97.97 203	98.96 191	98.92 307	98.98 150	99.48 178	99.53 96	97.76 180	98.71 276	99.46 259	96.43 155	99.22 304	98.57 155	92.87 360	98.69 272

DU-MVS		98.08 202	97.79 220	98.96 191	98.87 315	98.98 150	99.41 207	99.45 193	97.87 166	98.71 276	99.50 244	94.82 209	99.22 304	98.57 155	92.87 360	98.68 277

PS-CasMVS		97.93 227	97.59 245	98.95 193	98.99 298	99.06 142	99.68 61	99.52 101	97.13 242	98.31 313	99.68 174	92.44 298	99.05 328	98.51 163	94.08 345	98.75 255

anonymousdsp		98.44 167	98.28 174	98.94 194	98.50 352	98.96 157	99.77 34	99.50 135	97.07 250	98.87 258	99.77 129	94.76 218	99.28 292	98.66 139	97.60 249	98.57 322

TAPA-MVS	97.07 15	97.74 260	97.34 279	98.94 194	99.70 101	97.53 261	99.25 268	99.51 115	91.90 372	99.30 176	99.63 198	98.78 48	99.64 236	88.09 382	99.87 54	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v8		97.95 226	97.63 242	98.93 196	98.95 305	98.81 183	99.80 25	99.41 212	96.03 328	99.10 219	99.42 265	94.92 205	99.30 290	96.94 287	94.08 345	98.66 292

JIA-IIPM		97.50 284	97.02 297	98.93 196	98.73 332	97.80 252	99.30 245	98.97 322	91.73 373	98.91 250	94.86 387	95.10 200	99.71 212	97.58 241	97.98 238	99.28 208

v7n		97.87 236	97.52 250	98.92 198	98.76 330	98.58 200	99.84 13	99.46 182	96.20 314	98.91 250	99.70 158	94.89 207	99.44 260	96.03 313	93.89 348	98.75 255

v2v482		98.06 204	97.77 225	98.92 198	98.90 308	98.82 181	99.57 116	99.36 240	96.65 280	99.19 204	99.35 286	94.20 244	99.25 297	97.72 231	94.97 329	98.69 272

thres600view7		97.86 238	97.51 252	98.92 198	99.72 91	97.95 244	99.59 101	98.74 351	97.94 161	99.27 184	98.62 357	91.75 309	99.86 139	93.73 352	98.19 230	98.96 238

thres400		97.77 253	97.38 272	98.92 198	99.69 106	97.96 242	99.50 163	98.73 356	97.83 172	99.17 208	98.45 362	91.67 313	99.83 162	93.22 357	98.18 231	98.96 238

v1192		97.81 249	97.44 264	98.91 202	98.88 311	98.68 190	99.51 156	99.34 250	96.18 316	99.20 201	99.34 290	94.03 251	99.36 277	95.32 332	95.18 324	98.69 272

mvs_tets		98.40 174	98.23 176	98.91 202	98.67 340	98.51 210	99.66 69	99.53 96	98.19 127	98.65 291	99.81 90	92.75 280	99.44 260	99.31 58	97.48 264	98.77 251

Anonymous20231211		97.88 234	97.54 249	98.90 204	99.71 96	98.53 204	99.48 178	99.57 64	94.16 356	98.81 265	99.68 174	93.23 269	99.42 265	98.84 115	94.42 339	98.76 253

PS-MVSNAJss		98.92 120	98.92 106	98.90 204	98.78 326	98.53 204	99.78 32	99.54 85	98.07 148	99.00 238	99.76 135	99.01 18	99.37 273	99.13 76	97.23 279	98.81 245

WR-MVS_H		98.13 196	97.87 216	98.90 204	99.02 293	98.84 177	99.70 52	99.59 57	97.27 230	98.40 308	99.19 317	95.53 185	99.23 301	98.34 178	93.78 350	98.61 316

XVG-OURS-SEG-HR		98.69 152	98.62 146	98.89 207	99.71 96	97.74 253	99.12 289	99.54 85	98.44 99	99.42 143	99.71 154	94.20 244	99.92 95	98.54 162	98.90 190	99.00 232

PVSNet	96.02 17	98.85 134	98.84 119	98.89 207	99.73 87	97.28 267	98.32 377	99.60 54	97.86 167	99.50 126	99.57 220	96.75 142	99.86 139	98.56 158	99.70 122	99.54 161

jajsoiax		98.43 168	98.28 174	98.88 209	98.60 347	98.43 218	99.82 17	99.53 96	98.19 127	98.63 293	99.80 103	93.22 271	99.44 260	99.22 69	97.50 260	98.77 251

pm-mvs1		97.68 270	97.28 286	98.88 209	99.06 287	98.62 196	99.50 163	99.45 193	96.32 305	97.87 332	99.79 115	92.47 294	99.35 280	97.54 248	93.54 352	98.67 284

VDD-MVS		97.73 261	97.35 276	98.88 209	99.47 189	97.12 276	99.34 238	98.85 340	98.19 127	99.67 78	99.85 54	82.98 378	99.92 95	99.49 40	98.32 222	99.60 146

XVG-OURS		98.73 148	98.68 134	98.88 209	99.70 101	97.73 254	98.92 333	99.55 77	98.52 91	99.45 134	99.84 64	95.27 194	99.91 105	98.08 199	98.84 194	99.00 232

UniMVSNet_ETH3D		97.32 292	96.81 300	98.87 213	99.40 207	97.46 263	99.51 156	99.53 96	95.86 331	98.54 301	99.77 129	82.44 381	99.66 228	98.68 137	97.52 256	99.50 176

v144192		97.92 230	97.60 244	98.87 213	98.83 321	98.65 193	99.55 134	99.34 250	96.20 314	99.32 172	99.40 272	94.36 239	99.26 296	96.37 309	95.03 328	98.70 268

iter_conf05		98.55 162	98.44 162	98.87 213	99.34 222	98.60 199	99.55 134	99.42 209	98.21 124	99.37 160	99.77 129	93.55 265	99.38 268	99.30 61	97.48 264	98.63 304

CR-MVSNet		98.17 192	97.93 209	98.87 213	99.18 260	98.49 212	99.22 275	99.33 257	96.96 260	99.56 114	99.38 277	94.33 240	99.00 336	94.83 340	98.58 205	99.14 213

v10		97.85 239	97.52 250	98.86 217	98.99 298	98.67 191	99.75 41	99.41 212	95.70 332	98.98 240	99.41 269	94.75 219	99.23 301	96.01 315	94.63 335	98.67 284

V42		98.06 204	97.79 220	98.86 217	98.98 301	98.84 177	99.69 55	99.34 250	96.53 291	99.30 176	99.37 280	94.67 225	99.32 286	97.57 245	94.66 334	98.42 336

TransMVSNet (Re)		97.15 298	96.58 304	98.86 217	99.12 273	98.85 176	99.49 174	98.91 332	95.48 335	97.16 350	99.80 103	93.38 267	99.11 322	94.16 349	91.73 365	98.62 307

v1144		97.98 221	97.69 235	98.85 220	98.87 315	98.66 192	99.54 139	99.35 246	96.27 309	99.23 194	99.35 286	94.67 225	99.23 301	96.73 296	95.16 325	98.68 277

v1921920		97.80 251	97.45 259	98.84 221	98.80 322	98.53 204	99.52 148	99.34 250	96.15 320	99.24 190	99.47 255	93.98 253	99.29 291	95.40 330	95.13 326	98.69 272

FMVSNet3		98.03 212	97.76 229	98.84 221	99.39 210	98.98 150	99.40 215	99.38 231	96.67 278	99.07 224	99.28 304	92.93 275	98.98 338	97.10 276	96.65 288	98.56 323

RRT_MVS		98.70 150	98.66 138	98.83 223	98.90 308	98.45 216	99.89 2	99.28 281	97.76 180	98.94 246	99.92 14	96.98 134	99.25 297	99.28 63	97.00 285	98.80 246

testing3		97.28 293	96.76 302	98.82 224	99.37 214	98.07 235	99.45 188	99.36 240	97.56 201	97.89 331	98.95 342	83.70 376	98.82 352	96.03 313	98.56 208	99.58 154

baseline2		97.87 236	97.55 246	98.82 224	99.18 260	98.02 237	99.41 207	96.58 390	96.97 259	96.51 357	99.17 318	93.43 266	99.57 246	97.71 232	99.03 180	98.86 242

TR-MVS		97.76 254	97.41 270	98.82 224	99.06 287	97.87 248	98.87 339	98.56 362	96.63 284	98.68 284	99.22 313	92.49 293	99.65 233	95.40 330	97.79 242	98.95 240

pmmvs4		98.13 196	97.90 211	98.81 227	98.61 346	98.87 172	98.99 319	99.21 295	96.44 299	99.06 228	99.58 216	95.90 173	99.11 322	97.18 274	96.11 301	98.46 333

Patchmtry		97.75 258	97.40 271	98.81 227	99.10 278	98.87 172	99.11 295	99.33 257	94.83 348	98.81 265	99.38 277	94.33 240	99.02 333	96.10 311	95.57 316	98.53 324

FMVSNet2		97.72 263	97.36 274	98.80 229	99.51 169	98.84 177	99.45 188	99.42 209	96.49 293	98.86 262	99.29 302	90.26 332	98.98 338	96.44 306	96.56 291	98.58 321

v1240		97.69 268	97.32 282	98.79 230	98.85 319	98.43 218	99.48 178	99.36 240	96.11 323	99.27 184	99.36 283	93.76 262	99.24 300	94.46 343	95.23 323	98.70 268

PatchT		97.03 302	96.44 307	98.79 230	98.99 298	98.34 222	99.16 281	99.07 313	92.13 371	99.52 123	97.31 380	94.54 233	98.98 338	88.54 380	98.73 201	99.03 229

Patchmatch-test		97.93 227	97.65 239	98.77 232	99.18 260	97.07 281	99.03 309	99.14 304	96.16 318	98.74 273	99.57 220	94.56 230	99.72 206	93.36 356	99.11 171	99.52 167

TranMVSNet+NR-MVSNet		97.93 227	97.66 238	98.76 233	98.78 326	98.62 196	99.65 75	99.49 143	97.76 180	98.49 304	99.60 210	94.23 243	98.97 345	98.00 204	92.90 358	98.70 268

gg-mvs-nofinetune		96.17 318	95.32 329	98.73 234	98.79 323	98.14 231	99.38 224	94.09 397	91.07 377	98.07 325	91.04 393	89.62 342	99.35 280	96.75 295	99.09 175	98.68 277

tfpn200view9		97.72 263	97.38 272	98.72 235	99.69 106	97.96 242	99.50 163	98.73 356	97.83 172	99.17 208	98.45 362	91.67 313	99.83 162	93.22 357	98.18 231	98.37 342

PEN-MVS		97.76 254	97.44 264	98.72 235	98.77 329	98.54 203	99.78 32	99.51 115	97.06 252	98.29 315	99.64 192	92.63 289	98.89 351	98.09 195	93.16 356	98.72 261

thres100view900		97.76 254	97.45 259	98.69 237	99.72 91	97.86 250	99.59 101	98.74 351	97.93 162	99.26 188	98.62 357	91.75 309	99.83 162	93.22 357	98.18 231	98.37 342

EI-MVSNet		98.67 155	98.67 135	98.68 238	99.35 218	97.97 240	99.50 163	99.38 231	96.93 265	99.20 201	99.83 68	97.87 105	99.36 277	98.38 173	97.56 253	98.71 263

Baseline_NR-MVSNet		97.76 254	97.45 259	98.68 238	99.09 281	98.29 223	99.41 207	98.85 340	95.65 333	98.63 293	99.67 180	94.82 209	99.10 324	98.07 202	92.89 359	98.64 296

thres200		97.61 277	97.28 286	98.62 240	99.64 127	98.03 236	99.26 266	98.74 351	97.68 189	99.09 222	98.32 366	91.66 315	99.81 173	92.88 361	98.22 226	98.03 358

Fast-Effi-MVS+-dtu		98.77 144	98.83 121	98.60 241	99.41 202	96.99 290	99.52 148	99.49 143	98.11 140	99.24 190	99.34 290	96.96 136	99.79 182	97.95 207	99.45 145	99.02 231

hse-mvs2		97.50 284	97.14 292	98.59 242	99.49 180	97.05 283	99.28 253	99.22 292	98.94 54	99.66 83	99.42 265	94.93 203	99.65 233	99.48 41	83.80 385	99.08 221

AUN-MVS		96.88 304	96.31 310	98.59 242	99.48 188	97.04 286	99.27 258	99.22 292	97.44 216	98.51 302	99.41 269	91.97 304	99.66 228	97.71 232	83.83 384	99.07 226

BH-untuned		98.42 169	98.36 167	98.59 242	99.49 180	96.70 302	99.27 258	99.13 305	97.24 234	98.80 267	99.38 277	95.75 178	99.74 196	97.07 279	99.16 165	99.33 204

IterMVS-LS		98.46 166	98.42 164	98.58 245	99.59 147	98.00 238	99.37 226	99.43 207	96.94 264	99.07 224	99.59 212	97.87 105	99.03 331	98.32 181	95.62 315	98.71 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		97.97 224	97.77 225	98.57 246	99.59 147	96.61 307	99.45 188	99.08 310	98.21 124	98.88 255	99.80 103	88.66 349	99.70 218	98.58 152	97.72 244	99.39 198

MIMVSNet		97.73 261	97.45 259	98.57 246	99.45 195	97.50 262	99.02 312	98.98 321	96.11 323	99.41 147	99.14 322	90.28 331	98.74 356	95.74 320	98.93 186	99.47 184

IB-MVS	95.67 18	96.22 315	95.44 328	98.57 246	99.21 253	96.70 302	98.65 359	97.74 379	96.71 275	97.27 346	98.54 360	86.03 366	99.92 95	98.47 168	86.30 381	99.10 216
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
ADS-MVSNet		98.20 188	98.08 191	98.56 249	99.33 224	96.48 311	99.23 271	99.15 302	96.24 311	99.10 219	99.67 180	94.11 248	99.71 212	96.81 293	99.05 178	99.48 178

test0.0.03 1		97.71 266	97.42 269	98.56 249	98.41 356	97.82 251	98.78 347	98.63 360	97.34 224	98.05 326	98.98 339	94.45 237	98.98 338	95.04 337	97.15 283	98.89 241

cl____		98.01 217	97.84 218	98.55 251	99.25 247	97.97 240	98.71 354	99.34 250	96.47 298	98.59 299	99.54 231	95.65 183	99.21 309	97.21 268	95.77 310	98.46 333

test-LLR		98.06 204	97.90 211	98.55 251	98.79 323	97.10 277	98.67 356	97.75 377	97.34 224	98.61 296	98.85 348	94.45 237	99.45 255	97.25 266	99.38 149	99.10 216

test-mter		97.49 287	97.13 294	98.55 251	98.79 323	97.10 277	98.67 356	97.75 377	96.65 280	98.61 296	98.85 348	88.23 355	99.45 255	97.25 266	99.38 149	99.10 216

v148		97.79 252	97.55 246	98.50 254	98.74 331	97.72 255	99.54 139	99.33 257	96.26 310	98.90 252	99.51 241	94.68 224	99.14 314	97.83 217	93.15 357	98.63 304

LPG-MVS_test		98.22 185	98.13 184	98.49 255	99.33 224	97.05 283	99.58 109	99.55 77	97.46 211	99.24 190	99.83 68	92.58 290	99.72 206	98.09 195	97.51 258	98.68 277

LGP-MVS_train				98.49 255	99.33 224	97.05 283		99.55 77	97.46 211	99.24 190	99.83 68	92.58 290	99.72 206	98.09 195	97.51 258	98.68 277

cl22		97.85 239	97.64 241	98.48 257	99.09 281	97.87 248	98.60 363	99.33 257	97.11 247	98.87 258	99.22 313	92.38 299	99.17 313	98.21 186	95.99 304	98.42 336

DIV-MVS_self_test		98.01 217	97.85 217	98.48 257	99.24 248	97.95 244	98.71 354	99.35 246	96.50 292	98.60 298	99.54 231	95.72 180	99.03 331	97.21 268	95.77 310	98.46 333

cascas		97.69 268	97.43 268	98.48 257	98.60 347	97.30 266	98.18 382	99.39 223	92.96 368	98.41 307	98.78 353	93.77 261	99.27 295	98.16 192	98.61 202	98.86 242

ACMM	97.58 5	98.37 176	98.34 169	98.48 257	99.41 202	97.10 277	99.56 122	99.45 193	98.53 90	99.04 231	99.85 54	93.00 274	99.71 212	98.74 127	97.45 266	98.64 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 142	98.89 112	98.47 261	99.33 224	96.91 296	99.57 116	99.30 275	98.47 94	99.41 147	98.99 337	96.78 140	99.74 196	98.73 129	99.38 149	98.74 258

DTE-MVSNet		97.51 283	97.19 291	98.46 262	98.63 343	98.13 232	99.84 13	99.48 155	96.68 277	97.97 329	99.67 180	92.92 276	98.56 360	96.88 292	92.60 363	98.70 268

OPM-MVS		98.19 189	98.10 187	98.45 263	98.88 311	97.07 281	99.28 253	99.38 231	98.57 86	99.22 195	99.81 90	92.12 301	99.66 228	98.08 199	97.54 255	98.61 316
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
GG-mvs-BLEND				98.45 263	98.55 350	98.16 229	99.43 198	93.68 398		97.23 347	98.46 361	89.30 343	99.22 304	95.43 329	98.22 226	97.98 363

ACMP	97.20 11	98.06 204	97.94 208	98.45 263	99.37 214	97.01 288	99.44 194	99.49 143	97.54 205	98.45 306	99.79 115	91.95 305	99.72 206	97.91 209	97.49 263	98.62 307
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		98.27 184	98.22 177	98.44 266	99.29 236	96.97 292	99.39 219	99.47 173	98.97 51	99.11 216	99.61 207	92.71 285	99.69 223	97.78 221	97.63 246	98.67 284

ACMH	97.28 8	98.10 199	97.99 201	98.44 266	99.41 202	96.96 294	99.60 95	99.56 69	98.09 143	98.15 320	99.91 20	90.87 327	99.70 218	98.88 102	97.45 266	98.67 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
myMVS_eth3d		96.89 303	96.37 308	98.43 268	99.00 295	97.16 274	99.29 249	99.39 223	97.06 252	97.41 341	98.15 368	83.46 377	98.68 358	95.27 333	98.34 216	99.45 189

miper_ehance_all_eth		98.18 191	98.10 187	98.41 269	99.23 249	97.72 255	98.72 353	99.31 271	96.60 287	98.88 255	99.29 302	97.29 123	99.13 317	97.60 239	95.99 304	98.38 341

miper_enhance_ethall		98.16 193	98.08 191	98.41 269	98.96 304	97.72 255	98.45 370	99.32 267	96.95 262	98.97 242	99.17 318	97.06 131	99.22 304	97.86 214	95.99 304	98.29 345

TESTMET0.1,1		97.55 279	97.27 289	98.40 271	98.93 306	96.53 309	98.67 356	97.61 380	96.96 260	98.64 292	99.28 304	88.63 351	99.45 255	97.30 264	99.38 149	99.21 212

LTVRE_ROB	97.16 12	98.02 214	97.90 211	98.40 271	99.23 249	96.80 300	99.70 52	99.60 54	97.12 244	98.18 319	99.70 158	91.73 311	99.72 206	98.39 172	97.45 266	98.68 277
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
c3_l		98.12 198	98.04 196	98.38 273	99.30 232	97.69 259	98.81 344	99.33 257	96.67 278	98.83 263	99.34 290	97.11 128	98.99 337	97.58 241	95.34 321	98.48 328

HQP-MVS		98.02 214	97.90 211	98.37 274	99.19 257	96.83 297	98.98 322	99.39 223	98.24 118	98.66 285	99.40 272	92.47 294	99.64 236	97.19 272	97.58 251	98.64 296

EPMVS		97.82 247	97.65 239	98.35 275	98.88 311	95.98 323	99.49 174	94.71 396	97.57 199	99.26 188	99.48 252	92.46 297	99.71 212	97.87 213	99.08 176	99.35 201

eth_miper_zixun_eth		98.05 209	97.96 204	98.33 276	99.26 243	97.38 265	98.56 366	99.31 271	96.65 280	98.88 255	99.52 238	96.58 147	99.12 321	97.39 261	95.53 318	98.47 330

CLD-MVS		98.16 193	98.10 187	98.33 276	99.29 236	96.82 299	98.75 350	99.44 201	97.83 172	99.13 212	99.55 226	92.92 276	99.67 225	98.32 181	97.69 245	98.48 328
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-w/o		98.00 219	97.89 215	98.32 278	99.35 218	96.20 320	99.01 317	98.90 334	96.42 301	98.38 309	99.00 336	95.26 196	99.72 206	96.06 312	98.61 202	99.03 229

ACMH+	97.24 10	97.92 230	97.78 223	98.32 278	99.46 190	96.68 304	99.56 122	99.54 85	98.41 100	97.79 336	99.87 44	90.18 336	99.66 228	98.05 203	97.18 282	98.62 307

CVMVSNet		98.57 161	98.67 135	98.30 280	99.35 218	95.59 330	99.50 163	99.55 77	98.60 85	99.39 155	99.83 68	94.48 235	99.45 255	98.75 126	98.56 208	99.85 36

GBi-Net		97.68 270	97.48 254	98.29 281	99.51 169	97.26 270	99.43 198	99.48 155	96.49 293	99.07 224	99.32 297	90.26 332	98.98 338	97.10 276	96.65 288	98.62 307

test1		97.68 270	97.48 254	98.29 281	99.51 169	97.26 270	99.43 198	99.48 155	96.49 293	99.07 224	99.32 297	90.26 332	98.98 338	97.10 276	96.65 288	98.62 307

FMVSNet1		96.84 305	96.36 309	98.29 281	99.32 230	97.26 270	99.43 198	99.48 155	95.11 340	98.55 300	99.32 297	83.95 375	98.98 338	95.81 318	96.26 298	98.62 307

miper_lstm_enhance		98.00 219	97.91 210	98.28 284	99.34 222	97.43 264	98.88 337	99.36 240	96.48 296	98.80 267	99.55 226	95.98 166	98.91 349	97.27 265	95.50 319	98.51 326

SCA		98.19 189	98.16 179	98.27 285	99.30 232	95.55 331	99.07 299	98.97 322	97.57 199	99.43 140	99.57 220	92.72 283	99.74 196	97.58 241	99.20 163	99.52 167

EPNet_dtu		98.03 212	97.96 204	98.23 286	98.27 357	95.54 333	99.23 271	98.75 348	99.02 38	97.82 334	99.71 154	96.11 162	99.48 252	93.04 360	99.65 130	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-ACMP-BASELINE		97.83 244	97.71 234	98.20 287	99.11 275	96.33 316	99.41 207	99.52 101	98.06 152	99.05 230	99.50 244	89.64 341	99.73 202	97.73 229	97.38 274	98.53 324

OurMVSNet-221017-0		97.88 234	97.77 225	98.19 288	98.71 336	96.53 309	99.88 4	99.00 319	97.79 177	98.78 270	99.94 6	91.68 312	99.35 280	97.21 268	96.99 286	98.69 272

PatchmatchNet		98.31 179	98.36 167	98.19 288	99.16 268	95.32 339	99.27 258	98.92 328	97.37 223	99.37 160	99.58 216	94.90 206	99.70 218	97.43 259	99.21 162	99.54 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patch_mono-2		99.26 68	99.62 5	98.16 290	99.81 46	94.59 352	99.52 148	99.64 36	99.33 13	99.73 62	99.90 26	99.00 22	99.99 4	99.69 19	99.98 4	99.89 20

dcpmvs_2		99.23 74	99.58 7	98.16 290	99.83 39	94.68 350	99.76 37	99.52 101	99.07 35	99.98 6	99.88 36	98.56 74	99.93 84	99.67 21	99.98 4	99.87 31

pmmvs5		97.52 281	97.30 284	98.16 290	98.57 349	96.73 301	99.27 258	98.90 334	96.14 321	98.37 310	99.53 235	91.54 318	99.14 314	97.51 250	95.87 308	98.63 304

D2MVS		98.41 171	98.50 160	98.15 293	99.26 243	96.62 306	99.40 215	99.61 48	97.71 186	98.98 240	99.36 283	96.04 164	99.67 225	98.70 132	97.41 271	98.15 352

testgi		97.65 275	97.50 253	98.13 294	99.36 217	96.45 312	99.42 205	99.48 155	97.76 180	97.87 332	99.45 260	91.09 324	98.81 353	94.53 342	98.52 211	99.13 215

ITE_SJBPF				98.08 295	99.29 236	96.37 314		98.92 328	98.34 108	98.83 263	99.75 138	91.09 324	99.62 242	95.82 317	97.40 272	98.25 348

IterMVS-SCA-FT		97.82 247	97.75 230	98.06 296	99.57 151	96.36 315	99.02 312	99.49 143	97.18 238	98.71 276	99.72 153	92.72 283	99.14 314	97.44 258	95.86 309	98.67 284

SixPastTwentyTwo		97.50 284	97.33 281	98.03 297	98.65 341	96.23 319	99.77 34	98.68 359	97.14 241	97.90 330	99.93 9	90.45 330	99.18 312	97.00 281	96.43 294	98.67 284

tpm		97.67 273	97.55 246	98.03 297	99.02 293	95.01 345	99.43 198	98.54 364	96.44 299	99.12 214	99.34 290	91.83 308	99.60 244	97.75 227	96.46 293	99.48 178

IterMVS		97.83 244	97.77 225	98.02 299	99.58 149	96.27 318	99.02 312	99.48 155	97.22 236	98.71 276	99.70 158	92.75 280	99.13 317	97.46 256	96.00 303	98.67 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDA-MVSNet_test_wron		95.45 327	94.60 334	98.01 300	98.16 359	97.21 273	99.11 295	99.24 289	93.49 363	80.73 393	98.98 339	93.02 273	98.18 366	94.22 348	94.45 338	98.64 296

K. test v3		97.10 300	96.79 301	98.01 300	98.72 334	96.33 316	99.87 9	97.05 384	97.59 196	96.16 361	99.80 103	88.71 347	99.04 329	96.69 299	96.55 292	98.65 294

ECVR-MVS		98.04 210	98.05 195	98.00 302	99.74 80	94.37 355	99.59 101	94.98 394	99.13 22	99.66 83	99.93 9	90.67 329	99.84 151	99.40 47	99.38 149	99.80 70

MVP-Stereo		97.81 249	97.75 230	97.99 303	97.53 368	96.60 308	98.96 326	98.85 340	97.22 236	97.23 347	99.36 283	95.28 193	99.46 254	95.51 326	99.78 104	97.92 367
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		95.42 328	94.57 335	97.97 304	89.83 396	96.11 322	99.48 178	98.75 348	96.74 273	96.68 356	99.88 36	88.65 350	99.71 212	98.37 175	82.74 386	98.09 354

PVSNet_0	94.43 19	96.09 320	95.47 326	97.94 305	99.31 231	94.34 357	97.81 385	99.70 15	97.12 244	97.46 340	98.75 354	89.71 339	99.79 182	97.69 235	81.69 387	99.68 119

MDA-MVSNet-bldmvs		94.96 333	93.98 340	97.92 306	98.24 358	97.27 268	99.15 284	99.33 257	93.80 359	80.09 394	99.03 333	88.31 354	97.86 375	93.49 355	94.36 340	98.62 307

YYNet1		95.36 329	94.51 336	97.92 306	97.89 362	97.10 277	99.10 297	99.23 290	93.26 366	80.77 392	99.04 332	92.81 279	98.02 370	94.30 344	94.18 343	98.64 296

tpmrst		98.33 178	98.48 161	97.90 308	99.16 268	94.78 348	99.31 243	99.11 306	97.27 230	99.45 134	99.59 212	95.33 192	99.84 151	98.48 165	98.61 202	99.09 220

ADS-MVSNet2		98.02 214	98.07 194	97.87 309	99.33 224	95.19 342	99.23 271	99.08 310	96.24 311	99.10 219	99.67 180	94.11 248	98.93 348	96.81 293	99.05 178	99.48 178

dmvs_re		98.08 202	98.16 179	97.85 310	99.55 159	94.67 351	99.70 52	98.92 328	98.15 133	99.06 228	99.35 286	93.67 264	99.25 297	97.77 224	97.25 278	99.64 136

test_0402		96.64 308	96.24 311	97.85 310	98.85 319	96.43 313	99.44 194	99.26 285	93.52 362	96.98 354	99.52 238	88.52 352	99.20 311	92.58 366	97.50 260	97.93 366

tpmvs		97.98 221	98.02 199	97.84 312	99.04 291	94.73 349	99.31 243	99.20 296	96.10 327	98.76 272	99.42 265	94.94 202	99.81 173	96.97 284	98.45 214	98.97 236

test1111		98.04 210	98.11 186	97.83 313	99.74 80	93.82 360	99.58 109	95.40 393	99.12 25	99.65 89	99.93 9	90.73 328	99.84 151	99.43 46	99.38 149	99.82 54

TinyColmap		97.12 299	96.89 299	97.83 313	99.07 284	95.52 334	98.57 364	98.74 351	97.58 198	97.81 335	99.79 115	88.16 356	99.56 247	95.10 335	97.21 280	98.39 340

pmmvs6		96.53 310	96.09 315	97.82 315	98.69 338	95.47 335	99.37 226	99.47 173	93.46 364	97.41 341	99.78 121	87.06 364	99.33 283	96.92 290	92.70 362	98.65 294

EU-MVSNet		97.98 221	98.03 197	97.81 316	98.72 334	96.65 305	99.66 69	99.66 28	98.09 143	98.35 311	99.82 76	95.25 197	98.01 371	97.41 260	95.30 322	98.78 248

lessismore_v0				97.79 317	98.69 338	95.44 337		94.75 395		95.71 365	99.87 44	88.69 348	99.32 286	95.89 316	94.93 331	98.62 307

USDC		97.34 291	97.20 290	97.75 318	99.07 284	95.20 341	98.51 368	99.04 316	97.99 158	98.31 313	99.86 49	89.02 344	99.55 249	95.67 324	97.36 275	98.49 327

tpm2		97.44 289	97.34 279	97.74 319	99.15 271	94.36 356	99.45 188	98.94 325	93.45 365	98.90 252	99.44 261	91.35 321	99.59 245	97.31 263	98.07 237	99.29 207

CostFormer		97.72 263	97.73 232	97.71 320	99.15 271	94.02 359	99.54 139	99.02 317	94.67 351	99.04 231	99.35 286	92.35 300	99.77 189	98.50 164	97.94 239	99.34 203

LF4IMVS		97.52 281	97.46 258	97.70 321	98.98 301	95.55 331	99.29 249	98.82 343	98.07 148	98.66 285	99.64 192	89.97 337	99.61 243	97.01 280	96.68 287	97.94 365

EGC-MVSNET		82.80 357	77.86 363	97.62 322	97.91 361	96.12 321	99.33 240	99.28 281	8.40 401	25.05 402	99.27 307	84.11 374	99.33 283	89.20 377	98.22 226	97.42 376

ppachtmachnet_test		97.49 287	97.45 259	97.61 323	98.62 344	95.24 340	98.80 345	99.46 182	96.11 323	98.22 317	99.62 203	96.45 153	98.97 345	93.77 351	95.97 307	98.61 316

dp		97.75 258	97.80 219	97.59 324	99.10 278	93.71 363	99.32 241	98.88 336	96.48 296	99.08 223	99.55 226	92.67 288	99.82 168	96.52 304	98.58 205	99.24 210

our_test_3		97.65 275	97.68 236	97.55 325	98.62 344	94.97 346	98.84 341	99.30 275	96.83 271	98.19 318	99.34 290	97.01 133	99.02 333	95.00 338	96.01 302	98.64 296

MVS-HIRNet		95.75 325	95.16 330	97.51 326	99.30 232	93.69 364	98.88 337	95.78 391	85.09 386	98.78 270	92.65 389	91.29 322	99.37 273	94.85 339	99.85 69	99.46 186

tpm cat1		97.39 290	97.36 274	97.50 327	99.17 266	93.73 362	99.43 198	99.31 271	91.27 374	98.71 276	99.08 327	94.31 242	99.77 189	96.41 308	98.50 212	99.00 232

new_pmnet		96.38 314	96.03 316	97.41 328	98.13 360	95.16 344	99.05 304	99.20 296	93.94 357	97.39 344	98.79 352	91.61 317	99.04 329	90.43 373	95.77 310	98.05 357

UnsupCasMVSNet_eth		96.44 312	96.12 313	97.40 329	98.65 341	95.65 328	99.36 230	99.51 115	97.13 242	96.04 363	98.99 337	88.40 353	98.17 367	96.71 297	90.27 373	98.40 339

KD-MVS_2432*1600		94.62 335	93.72 343	97.31 330	97.19 376	95.82 326	98.34 374	99.20 296	95.00 344	97.57 338	98.35 364	87.95 358	98.10 368	92.87 362	77.00 391	98.01 359

miper_refine_blended		94.62 335	93.72 343	97.31 330	97.19 376	95.82 326	98.34 374	99.20 296	95.00 344	97.57 338	98.35 364	87.95 358	98.10 368	92.87 362	77.00 391	98.01 359

test2506		96.81 306	96.65 303	97.29 332	99.74 80	92.21 375	99.60 95	85.06 404	99.13 22	99.77 51	99.93 9	87.82 361	99.85 145	99.38 48	99.38 149	99.80 70

pmmvs-eth3d		95.34 330	94.73 333	97.15 333	95.53 385	95.94 324	99.35 235	99.10 307	95.13 338	93.55 376	97.54 375	88.15 357	97.91 373	94.58 341	89.69 376	97.61 372

FMVSNet5		96.43 313	96.19 312	97.15 333	99.11 275	95.89 325	99.32 241	99.52 101	94.47 355	98.34 312	99.07 328	87.54 362	97.07 382	92.61 365	95.72 313	98.47 330

Anonymous20240521		96.20 317	95.89 320	97.13 335	97.72 367	94.96 347	99.79 31	99.29 279	93.01 367	97.20 349	99.03 333	89.69 340	98.36 364	91.16 371	96.13 300	98.07 355

DeepPCF-MVS	98.18 3	98.81 138	99.37 30	97.12 336	99.60 145	91.75 376	98.61 361	99.44 201	99.35 12	99.83 34	99.85 54	98.70 63	99.81 173	99.02 87	99.91 31	99.81 61

test_fmvs2		97.25 295	97.30 284	97.09 337	99.43 197	93.31 368	99.73 47	98.87 338	98.83 64	99.28 180	99.80 103	84.45 373	99.66 228	97.88 211	97.45 266	98.30 344

MS-PatchMatch		97.24 297	97.32 282	96.99 338	98.45 354	93.51 367	98.82 343	99.32 267	97.41 220	98.13 321	99.30 300	88.99 345	99.56 247	95.68 323	99.80 97	97.90 368

RPSCF		98.22 185	98.62 146	96.99 338	99.82 42	91.58 377	99.72 49	99.44 201	96.61 285	99.66 83	99.89 30	95.92 171	99.82 168	97.46 256	99.10 174	99.57 156

KD-MVS_self_test		95.00 332	94.34 337	96.96 340	97.07 378	95.39 338	99.56 122	99.44 201	95.11 340	97.13 351	97.32 379	91.86 307	97.27 381	90.35 374	81.23 388	98.23 350

Syy-MVS		97.09 301	97.14 292	96.95 341	99.00 295	92.73 372	99.29 249	99.39 223	97.06 252	97.41 341	98.15 368	93.92 256	98.68 358	91.71 368	98.34 216	99.45 189

DSMNet-mixed		97.25 295	97.35 276	96.95 341	97.84 363	93.61 366	99.57 116	96.63 389	96.13 322	98.87 258	98.61 359	94.59 228	97.70 378	95.08 336	98.86 192	99.55 159

MIMVSNet1		95.51 326	95.04 331	96.92 343	97.38 370	95.60 329	99.52 148	99.50 135	93.65 361	96.97 355	99.17 318	85.28 371	96.56 386	88.36 381	95.55 317	98.60 319

LCM-MVSNet-Re		97.83 244	98.15 181	96.87 344	99.30 232	92.25 374	99.59 101	98.26 367	97.43 217	96.20 360	99.13 323	96.27 159	98.73 357	98.17 191	98.99 183	99.64 136

EG-PatchMatch MVS		95.97 321	95.69 323	96.81 345	97.78 364	92.79 371	99.16 281	98.93 326	96.16 318	94.08 374	99.22 313	82.72 379	99.47 253	95.67 324	97.50 260	98.17 351

Anonymous20231206		96.22 315	96.03 316	96.79 346	97.31 373	94.14 358	99.63 82	99.08 310	96.17 317	97.04 353	99.06 330	93.94 254	97.76 377	86.96 386	95.06 327	98.47 330

test20.03		96.12 319	95.96 318	96.63 347	97.44 369	95.45 336	99.51 156	99.38 231	96.55 290	96.16 361	99.25 310	93.76 262	96.17 387	87.35 385	94.22 342	98.27 346

pmmvs3		94.09 341	93.25 347	96.60 348	94.76 390	94.49 353	98.92 333	98.18 372	89.66 378	96.48 358	98.06 372	86.28 365	97.33 380	89.68 376	87.20 380	97.97 364

UnsupCasMVSNet_bld		93.53 343	92.51 348	96.58 349	97.38 370	93.82 360	98.24 379	99.48 155	91.10 376	93.10 378	96.66 382	74.89 386	98.37 363	94.03 350	87.71 379	97.56 374

OpenMVS_ROB	92.34 20	94.38 339	93.70 345	96.41 350	97.38 370	93.17 369	99.06 302	98.75 348	86.58 384	94.84 372	98.26 367	81.53 382	99.32 286	89.01 378	97.87 241	96.76 379

test_vis1_rt		95.81 324	95.65 324	96.32 351	99.67 111	91.35 378	99.49 174	96.74 388	98.25 117	95.24 366	98.10 371	74.96 385	99.90 116	99.53 32	98.85 193	97.70 371

CL-MVSNet_self_test		94.49 337	93.97 341	96.08 352	96.16 380	93.67 365	98.33 376	99.38 231	95.13 338	97.33 345	98.15 368	92.69 287	96.57 385	88.67 379	79.87 389	97.99 362

Patchmatch-RL test		95.84 323	95.81 322	95.95 353	95.61 383	90.57 379	98.24 379	98.39 366	95.10 342	95.20 368	98.67 356	94.78 214	97.77 376	96.28 310	90.02 374	99.51 173

new-patchmatchnet		94.48 338	94.08 339	95.67 354	95.08 388	92.41 373	99.18 279	99.28 281	94.55 354	93.49 377	97.37 378	87.86 360	97.01 383	91.57 369	88.36 377	97.61 372

PM-MVS		92.96 345	92.23 349	95.14 355	95.61 383	89.98 381	99.37 226	98.21 370	94.80 349	95.04 371	97.69 374	65.06 389	97.90 374	94.30 344	89.98 375	97.54 375

mvsany_test3		93.77 342	93.45 346	94.74 356	95.78 382	88.01 382	99.64 78	98.25 368	98.28 113	94.31 373	97.97 373	68.89 388	98.51 362	97.50 251	90.37 372	97.71 369

APD_test1		95.87 322	96.49 306	94.00 357	99.53 162	84.01 385	99.54 139	99.32 267	95.91 330	97.99 327	99.85 54	85.49 369	99.88 131	91.96 367	98.84 194	98.12 353

test_f		91.90 348	91.26 352	93.84 358	95.52 386	85.92 384	99.69 55	98.53 365	95.31 337	93.87 375	96.37 384	55.33 395	98.27 365	95.70 321	90.98 370	97.32 377

Gipuma		90.99 350	90.15 355	93.51 359	98.73 332	90.12 380	93.98 391	99.45 193	79.32 389	92.28 381	94.91 386	69.61 387	97.98 372	87.42 384	95.67 314	92.45 389
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				93.34 360	99.29 236	82.27 388		99.22 292	85.15 385	96.33 359	99.05 331	90.97 326	99.73 202	93.57 354	97.77 243	98.01 359

test_fmvs3		92.10 347	91.77 350	93.08 361	96.19 379	86.25 383	99.82 17	98.62 361	96.65 280	95.19 369	96.90 381	55.05 396	95.93 389	96.63 303	90.92 371	97.06 378

ambc				93.06 362	92.68 392	82.36 387	98.47 369	98.73 356		95.09 370	97.41 376	55.55 394	99.10 324	96.42 307	91.32 366	97.71 369

N_pmnet		94.95 334	95.83 321	92.31 363	98.47 353	79.33 395	99.12 289	92.81 401	93.87 358	97.68 337	99.13 323	93.87 257	99.01 335	91.38 370	96.19 299	98.59 320

CMPMVS	69.68 23	94.13 340	94.90 332	91.84 364	97.24 374	80.01 394	98.52 367	99.48 155	89.01 381	91.99 382	99.67 180	85.67 368	99.13 317	95.44 328	97.03 284	96.39 383
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dmvs_testset		95.02 331	96.12 313	91.72 365	99.10 278	80.43 393	99.58 109	97.87 376	97.47 210	95.22 367	98.82 350	93.99 252	95.18 390	88.09 382	94.91 332	99.56 158

LCM-MVSNet		86.80 355	85.22 359	91.53 366	87.81 397	80.96 392	98.23 381	98.99 320	71.05 391	90.13 386	96.51 383	48.45 399	96.88 384	90.51 372	85.30 382	96.76 379

PMMVS2		86.87 354	85.37 358	91.35 367	90.21 395	83.80 386	98.89 336	97.45 383	83.13 388	91.67 385	95.03 385	48.49 398	94.70 391	85.86 390	77.62 390	95.54 386

test_vis3_rt		87.04 353	85.81 356	90.73 368	93.99 391	81.96 389	99.76 37	90.23 403	92.81 369	81.35 391	91.56 391	40.06 400	99.07 326	94.27 346	88.23 378	91.15 391

test_method		91.10 349	91.36 351	90.31 369	95.85 381	73.72 402	94.89 390	99.25 287	68.39 393	95.82 364	99.02 335	80.50 383	98.95 347	93.64 353	94.89 333	98.25 348

WB-MVS		93.10 344	94.10 338	90.12 370	95.51 387	81.88 390	99.73 47	99.27 284	95.05 343	93.09 379	98.91 347	94.70 223	91.89 394	76.62 393	94.02 347	96.58 381

SSC-MVS		92.73 346	93.73 342	89.72 371	95.02 389	81.38 391	99.76 37	99.23 290	94.87 347	92.80 380	98.93 343	94.71 222	91.37 395	74.49 395	93.80 349	96.42 382

testf1		90.42 351	90.68 353	89.65 372	97.78 364	73.97 400	99.13 287	98.81 344	89.62 379	91.80 383	98.93 343	62.23 392	98.80 354	86.61 388	91.17 367	96.19 384

APD_test2		90.42 351	90.68 353	89.65 372	97.78 364	73.97 400	99.13 287	98.81 344	89.62 379	91.80 383	98.93 343	62.23 392	98.80 354	86.61 388	91.17 367	96.19 384

tmp_tt		82.80 357	81.52 360	86.66 374	66.61 403	68.44 403	92.79 393	97.92 374	68.96 392	80.04 395	99.85 54	85.77 367	96.15 388	97.86 214	43.89 397	95.39 387

MVE	76.82 21	76.91 362	74.31 366	84.70 375	85.38 400	76.05 399	96.88 389	93.17 399	67.39 394	71.28 396	89.01 395	21.66 406	87.69 396	71.74 396	72.29 393	90.35 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 361	74.86 365	84.62 376	75.88 401	77.61 396	97.63 387	93.15 400	88.81 382	64.27 397	89.29 394	36.51 401	83.93 399	75.89 394	52.31 396	92.33 390

E-PMN		80.61 359	79.88 361	82.81 377	90.75 394	76.38 398	97.69 386	95.76 392	66.44 395	83.52 388	92.25 390	62.54 391	87.16 397	68.53 397	61.40 394	84.89 395

FPMVS		84.93 356	85.65 357	82.75 378	86.77 398	63.39 404	98.35 373	98.92 328	74.11 390	83.39 389	98.98 339	50.85 397	92.40 393	84.54 391	94.97 329	92.46 388

EMVS		80.02 360	79.22 362	82.43 379	91.19 393	76.40 397	97.55 388	92.49 402	66.36 396	83.01 390	91.27 392	64.63 390	85.79 398	65.82 398	60.65 395	85.08 394

PMVS	70.75 22	75.98 363	74.97 364	79.01 380	70.98 402	55.18 405	93.37 392	98.21 370	65.08 397	61.78 398	93.83 388	21.74 405	92.53 392	78.59 392	91.12 369	89.34 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 364	41.29 369	36.84 381	86.18 399	49.12 406	79.73 394	22.81 406	27.64 398	25.46 401	28.45 401	21.98 404	48.89 400	55.80 399	23.56 400	12.51 398

test123		39.01 366	42.50 368	28.53 382	39.17 404	20.91 407	98.75 350	19.17 407	19.83 400	38.57 399	66.67 397	33.16 402	15.42 401	37.50 401	29.66 399	49.26 396

testmvs		39.17 365	43.78 367	25.37 383	36.04 405	16.84 408	98.36 372	26.56 405	20.06 399	38.51 400	67.32 396	29.64 403	15.30 402	37.59 400	39.90 398	43.98 397

test_blank		0.13 370	0.17 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	1.57 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet_test		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

DCPMVS		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

cdsmvs_eth3d_5k		24.64 367	32.85 370	0.00 384	0.00 406	0.00 409	0.00 395	99.51 115	0.00 402	0.00 403	99.56 223	96.58 147	0.00 403	0.00 402	0.00 401	0.00 399

pcd_1.5k_mvsjas		8.27 369	11.03 372	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	99.01 18	0.00 403	0.00 402	0.00 401	0.00 399

sosnet-low-res		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uncertanet		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

Regformer		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

ab-mvs-re		8.30 368	11.06 371	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	99.58 216	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet		0.02 371	0.03 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.27 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

WAC-MVS						97.16 274								95.47 327

FOURS1					99.91 1	99.93 1	99.87 9	99.56 69	99.10 27	99.81 37

PC_three_1452									98.18 131	99.84 29	99.70 158	99.31 3	98.52 361	98.30 183	99.80 97	99.81 61

test_one_0601					99.81 46	99.88 8		99.49 143	98.97 51	99.65 89	99.81 90	99.09 14

eth-test2					0.00 406

eth-test					0.00 406

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 269	99.56 114	99.54 231	98.58 72	99.96 30	96.93 288	99.75 112

RE-MVS-def			99.34 36		99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.75 55		98.61 146	99.81 93	99.77 82

IU-MVS					99.84 32	99.88 8		99.32 267	98.30 112	99.84 29				98.86 110	99.85 69	99.89 20

test_241102_TWO								99.48 155	99.08 33	99.88 20	99.81 90	98.94 29	99.96 30	98.91 99	99.84 77	99.88 26

test_241102_ONE					99.84 32	99.90 2		99.48 155	99.07 35	99.91 16	99.74 143	99.20 7	99.76 193

9.14			99.10 75		99.72 91		99.40 215	99.51 115	97.53 206	99.64 93	99.78 121	98.84 41	99.91 105	97.63 237	99.82 90

save fliter					99.76 65	99.59 70	99.14 286	99.40 220	99.00 43

test_0728_THIRD									98.99 45	99.81 37	99.80 103	99.09 14	99.96 30	98.85 112	99.90 39	99.88 26

test0726					99.85 26	99.89 4	99.62 88	99.50 135	99.10 27	99.86 27	99.82 76	98.94 29

GSMVS																99.52 167

test_part2					99.81 46	99.83 16				99.77 51

sam_mvs1												94.86 208				99.52 167

sam_mvs												94.72 221

MTGPA								99.47 173

test_post1							99.23 271				65.14 399	94.18 247	99.71 212	97.58 241

test_post											65.99 398	94.65 227	99.73 202

patchmatchnet-post											98.70 355	94.79 213	99.74 196

MTMP							99.54 139	98.88 336

gm-plane-assit					98.54 351	92.96 370			94.65 352		99.15 321		99.64 236	97.56 246

test9_res														97.49 252	99.72 118	99.75 88

TEST9					99.67 111	99.65 57	99.05 304	99.41 212	96.22 313	98.95 244	99.49 247	98.77 51	99.91 105

test_8					99.67 111	99.61 67	99.03 309	99.41 212	96.28 307	98.93 248	99.48 252	98.76 52	99.91 105

agg_prior2														97.21 268	99.73 117	99.75 88

agg_prior					99.67 111	99.62 65		99.40 220		98.87 258			99.91 105

test_prior4						99.56 75	98.99 319

test_prior2							98.96 326		98.34 108	99.01 234	99.52 238	98.68 64		97.96 206	99.74 115

旧先验2							98.96 326		96.70 276	99.47 131			99.94 69	98.19 188

新几何2							99.01 317

旧先验1					99.74 80	99.59 70		99.54 85			99.69 168	98.47 80			99.68 126	99.73 97

无先验							98.99 319	99.51 115	96.89 266				99.93 84	97.53 249		99.72 103

原ACMM2							98.95 329

test222					99.75 73	99.49 87	98.91 335	99.49 143	96.42 301	99.34 170	99.65 186	98.28 92			99.69 123	99.72 103

testdata2													99.95 59	96.67 300

segment_acmp												98.96 24

testdata1							98.85 340		98.32 111

plane_prior7					99.29 236	97.03 287

plane_prior6					99.27 241	96.98 291						92.71 285

plane_prior5								99.47 173					99.69 223	97.78 221	97.63 246	98.67 284

plane_prior4											99.61 207

plane_prior3						97.00 289			98.69 79	99.11 216

plane_prior2							99.39 219		98.97 51

plane_prior1					99.26 243

plane_prior						96.97 292	99.21 277		98.45 96						97.60 249

n2								0.00 408

nn								0.00 408

door-mid								98.05 373

test11								99.35 246

door								97.92 374

HQP5-MVS						96.83 297

HQP-NCC					99.19 257		98.98 322		98.24 118	98.66 285

ACMP_Plane					99.19 257		98.98 322		98.24 118	98.66 285

BP-MVS														97.19 272

HQP4-MVS										98.66 285			99.64 236			98.64 296

HQP3-MVS								99.39 223							97.58 251

HQP2-MVS												92.47 294

NP-MVS					99.23 249	96.92 295					99.40 272

MDTV_nov1_ep13_2view						95.18 343	99.35 235		96.84 269	99.58 110		95.19 199		97.82 218		99.46 186

MDTV_nov1_ep13			98.32 171		99.11 275	94.44 354	99.27 258	98.74 351	97.51 208	99.40 152	99.62 203	94.78 214	99.76 193	97.59 240	98.81 198

ACMMP++_ref															97.19 281

ACMMP++															97.43 270

Test By Simon												98.75 55

High-resolution multi-view benchmark