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.

TestfortrainingZip a		99.73 1	99.67 1	99.92 1	99.88 13	99.91 2	99.69 62	99.87 6	99.34 26	99.90 34	99.83 104	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 148	99.63 46	99.48 3	99.98 13	99.83 104	98.75 60	99.99 4	99.97 2	99.96 17	99.94 17

fmvsm_l_conf0.5_n		99.71 2	99.67 1	99.85 43	99.84 38	99.63 82	99.56 148	99.63 46	99.47 4	99.98 13	99.82 117	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

test_fmvsmconf_n		99.70 4	99.64 5	99.87 21	99.80 63	99.66 71	99.48 225	99.64 42	99.45 11	99.92 30	99.92 18	98.62 76	99.99 4	99.96 13	99.99 1	99.96 7

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 132	99.69 22	99.43 17	99.98 13	99.91 26	98.62 76	100.00 1	99.97 2	99.95 23	99.90 25

MED-MVS		99.66 6	99.60 8	99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.18 34	99.90 34	99.83 104	99.30 4	99.95 76	98.83 170	99.89 68	99.83 63

APDe-MVS		99.66 6	99.57 10	99.92 1	99.77 78	99.89 6	99.75 42	99.56 90	99.02 62	99.88 43	99.85 83	99.18 12	99.96 41	99.22 102	99.92 39	99.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmvis_n_1920		99.65 8	99.61 7	99.77 74	99.38 276	99.37 123	99.58 132	99.62 51	99.41 21	99.87 49	99.92 18	98.81 49	100.00 1	99.97 2	99.93 33	99.94 17

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 70	99.88 10	99.56 148	99.55 100	99.15 38	99.90 34	99.90 35	99.00 24	99.97 29	99.11 120	99.91 46	99.86 42

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 168	99.66 32	99.46 7	99.98 13	99.89 44	97.27 133	99.99 4	99.97 2	99.95 23	99.95 11

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 179	99.54 109	99.13 41	99.89 40	99.89 44	98.96 27	99.96 41	99.04 130	99.90 57	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 179	99.54 109	99.13 41	99.89 40	99.89 44	98.96 27	99.96 41	99.04 130	99.90 57	99.85 46

SED-MVS		99.61 10	99.52 14	99.88 15	99.84 38	99.90 3	99.60 113	99.48 202	99.08 56	99.91 31	99.81 132	99.20 9	99.96 41	98.91 151	99.85 94	99.79 92

lecture		99.60 14	99.50 19	99.89 11	99.89 8	99.90 3	99.75 42	99.59 73	99.06 61	99.88 43	99.85 83	98.41 93	99.96 41	99.28 94	99.84 102	99.83 63

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 227	99.88 10	99.87 8	99.51 154	98.99 69	99.88 43	99.81 132	99.27 7	99.96 41	98.85 164	99.80 125	99.81 79

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 235	99.63 46	99.45 11	99.98 13	99.89 44	97.02 148	99.99 4	99.98 1	99.96 17	99.95 11

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 71	99.63 101	99.39 282	98.91 82	99.78 81	99.85 83	99.36 2	99.94 92	98.84 167	99.88 76	99.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 101	99.78 70	99.14 160	99.60 113	99.45 247	99.01 64	99.90 34	99.83 104	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 101	99.78 70	99.15 159	99.61 112	99.45 247	99.01 64	99.89 40	99.82 117	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 140	99.37 298	99.10 48	99.81 69	99.80 150	98.94 34	99.96 41	98.93 148	99.86 87	99.81 79
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
ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 287	99.70 18	99.18 34	99.83 64	99.83 104	98.74 65	99.93 110	98.83 170	99.89 68	99.83 63

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 81	99.52 179	99.65 39	99.10 48	99.98 13	99.92 18	97.35 129	99.96 41	99.94 21	99.92 39	99.95 11

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 258	99.65 75	99.50 200	99.61 60	99.45 11	99.87 49	99.92 18	97.31 130	99.97 29	99.95 16	99.99 1	99.97 4

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 189	99.62 51	99.46 7	99.99 2	99.90 35	96.60 172	99.98 20	99.95 16	99.95 23	99.96 7

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 227	99.67 68	99.50 200	99.64 42	99.43 17	99.98 13	99.78 174	97.26 136	99.95 76	99.95 16	99.93 33	99.92 23

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 122	99.51 154	98.62 112	99.79 76	99.83 104	99.28 6	99.97 29	98.48 221	99.90 57	99.84 53
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XVS		99.53 27	99.42 32	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 214	99.74 197	98.81 49	99.94 92	98.79 178	99.86 87	99.84 53

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 224	98.79 95	99.68 117	99.81 132	98.43 89	99.97 29	98.88 154	99.90 57	99.83 63

fmvsm_s_conf0.5_n		99.51 29	99.40 38	99.85 43	99.84 38	99.65 75	99.51 189	99.67 27	99.13 41	99.98 13	99.92 18	96.60 172	99.96 41	99.95 16	99.96 17	99.95 11

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 254	99.76 91	99.75 192	99.13 14	99.92 123	99.07 127	99.92 39	99.85 46

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 187	99.09 165	98.94 414	99.48 202	99.10 48	99.96 27	99.91 26	98.85 44	99.96 41	99.72 32	99.58 169	99.82 72

CS-MVS		99.50 31	99.48 22	99.54 125	99.76 82	99.42 118	99.90 1	99.55 100	98.56 118	99.78 81	99.70 214	98.65 74	99.79 243	99.65 41	99.78 134	99.41 262

SPE-MVS-test		99.49 33	99.48 22	99.54 125	99.78 70	99.30 138	99.89 2	99.58 78	98.56 118	99.73 97	99.69 225	98.55 81	99.82 225	99.69 35	99.85 94	99.48 241

HFP-MVS		99.49 33	99.37 44	99.86 34	99.87 20	99.80 38	99.66 82	99.67 27	98.15 175	99.68 117	99.69 225	99.06 18	99.96 41	98.69 190	99.87 79	99.84 53

ACMMPR		99.49 33	99.36 46	99.86 34	99.87 20	99.79 41	99.66 82	99.67 27	98.15 175	99.67 123	99.69 225	98.95 32	99.96 41	98.69 190	99.87 79	99.84 53

DeepC-MVS_fast	98.69 1	99.49 33	99.39 40	99.77 74	99.63 167	99.59 88	99.36 293	99.46 236	99.07 58	99.79 76	99.82 117	98.85 44	99.92 123	98.68 192	99.87 79	99.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R		99.48 37	99.35 48	99.87 21	99.88 13	99.80 38	99.65 88	99.66 32	98.13 183	99.66 128	99.68 233	98.96 27	99.96 41	98.62 199	99.87 79	99.84 53

APD-MVS_3200maxsize		99.48 37	99.35 48	99.85 43	99.76 82	99.83 22	99.63 101	99.54 109	98.36 141	99.79 76	99.82 117	98.86 43	99.95 76	98.62 199	99.81 120	99.78 98

DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 386	99.66 32	99.14 40	99.57 164	99.80 150	98.46 87	99.94 92	99.57 48	99.84 102	99.60 193
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
ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 199	99.55 171	99.64 252	98.91 39	99.96 41	98.72 185	99.90 57	99.82 72

ACMMP_NAP		99.47 40	99.34 50	99.88 15	99.87 20	99.86 18	99.47 235	99.48 202	98.05 207	99.76 91	99.86 76	98.82 48	99.93 110	98.82 177	99.91 46	99.84 53

MVSMamba_PlusPlus		99.46 42	99.41 37	99.64 101	99.68 133	99.50 108	99.75 42	99.50 177	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 247

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 210	99.58 93	99.74 47	99.51 154	98.42 134	99.87 49	99.84 98	98.05 111	99.91 135	99.58 47	99.94 31	99.52 224

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 293	99.51 154	98.73 102	99.88 43	99.84 98	98.72 67	99.96 41	98.16 254	99.87 79	99.88 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.46 42	99.47 24	99.44 172	99.60 193	99.16 155	99.41 268	99.71 16	98.98 72	99.45 187	99.78 174	99.19 11	99.54 321	99.28 94	99.84 102	99.63 185

SR-MVS-dyc-post		99.45 46	99.31 60	99.85 43	99.76 82	99.82 28	99.63 101	99.52 132	98.38 137	99.76 91	99.82 117	98.53 82	99.95 76	98.61 202	99.81 120	99.77 100

PGM-MVS		99.45 46	99.31 60	99.86 34	99.87 20	99.78 47	99.58 132	99.65 39	97.84 238	99.71 110	99.80 150	99.12 15	99.97 29	98.33 239	99.87 79	99.83 63

CP-MVS		99.45 46	99.32 54	99.85 43	99.83 47	99.75 51	99.69 62	99.52 132	98.07 200	99.53 174	99.63 258	98.93 38	99.97 29	98.74 182	99.91 46	99.83 63

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 189	99.63 145	99.84 98	98.73 66	99.96 41	98.55 217	99.83 113	99.81 79
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
SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 148	99.47 224	97.45 288	99.78 81	99.82 117	99.18 12	99.91 135	98.79 178	99.89 68	99.81 79
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
mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 202	98.12 189	99.50 179	99.75 192	98.78 53	99.97 29	98.57 211	99.89 68	99.83 63

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 206	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 225	98.20 103	99.70 284	99.64 43	99.82 117	99.54 217

SR-MVS		99.43 53	99.29 66	99.86 34	99.75 92	99.83 22	99.59 122	99.62 51	98.21 168	99.73 97	99.79 167	98.68 70	99.96 41	98.44 227	99.77 137	99.79 92

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 317	99.40 279	98.79 95	99.52 176	99.62 263	98.91 39	99.90 148	98.64 196	99.75 142	99.82 72

MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 177	98.70 106	99.77 85	99.49 310	98.21 102	99.95 76	98.46 225	99.77 137	99.88 35
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
UA-Net		99.42 55	99.29 66	99.80 64	99.62 176	99.55 96	99.50 200	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 150	99.90 57	99.89 29

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 269	99.68 117	99.63 258	98.91 39	99.94 92	98.58 208	99.91 46	99.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 176	99.71 58	99.26 336	99.52 132	98.82 89	99.39 210	99.71 210	98.96 27	99.85 187	98.59 207	99.80 125	99.77 100

fmvsm_s_conf0.5_n_10		99.41 59	99.24 78	99.92 1	99.83 47	99.84 20	99.53 177	99.56 90	99.45 11	99.99 2	99.92 18	94.92 256	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 225	99.62 51	99.46 7	99.99 2	99.92 18	95.24 243	99.96 41	99.97 2	99.97 9	99.96 7

SD-MVS		99.41 59	99.52 14	99.05 235	99.74 100	99.68 64	99.46 239	99.52 132	99.11 47	99.88 43	99.91 26	99.43 1	97.70 461	98.72 185	99.93 33	99.77 100
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
MVS_111021_LR		99.41 59	99.33 52	99.65 95	99.77 78	99.51 107	98.94 414	99.85 9	98.82 89	99.65 137	99.74 197	98.51 84	99.80 237	98.83 170	99.89 68	99.64 180

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 412	99.85 9	98.82 89	99.54 172	99.73 203	98.51 84	99.74 261	98.91 151	99.88 76	99.77 100

MM		99.40 64	99.28 69	99.74 80	99.67 136	99.31 135	99.52 179	98.87 411	99.55 1	99.74 95	99.80 150	96.47 180	99.98 20	99.97 2	99.97 9	99.94 17

GST-MVS		99.40 64	99.24 78	99.85 43	99.86 25	99.79 41	99.60 113	99.67 27	97.97 222	99.63 145	99.68 233	98.52 83	99.95 76	98.38 232	99.86 87	99.81 79

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 256	99.51 154	98.68 109	99.27 244	99.53 296	98.64 75	99.96 41	98.44 227	99.80 125	99.79 92

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 140	99.54 109	97.82 244	99.71 110	99.80 150	98.95 32	99.93 110	98.19 250	99.84 102	99.74 116

fmvsm_s_conf0.5_n_5		99.37 68	99.21 84	99.86 34	99.80 63	99.68 64	99.42 263	99.61 60	99.37 24	99.97 25	99.86 76	94.96 251	99.99 4	99.97 2	99.93 33	99.92 23

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 225	99.66 32	99.45 11	99.99 2	99.93 10	94.64 281	99.97 29	99.94 21	99.97 9	99.95 11

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 239	99.60 67	99.47 4	99.98 13	99.94 6	94.98 250	99.95 76	99.97 2	99.79 132	99.73 125

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 312	99.52 132	97.18 314	99.60 157	99.79 167	98.79 52	99.95 76	98.83 170	99.91 46	99.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 217	99.60 67	99.42 20	99.99 2	99.86 76	95.15 246	99.95 76	99.95 16	99.89 68	99.73 125

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 186	99.67 136	98.61 249	99.07 380	99.33 320	99.00 67	99.82 68	99.81 132	99.06 18	99.84 196	99.09 125	99.42 181	99.65 173

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 235	99.93 2	97.66 263	99.71 110	99.86 76	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 158	99.70 122	98.63 245	99.42 263	99.63 46	99.46 7	99.98 13	99.88 55	95.59 226	99.96 41	99.97 2	99.98 4	99.85 46

NCCC		99.34 75	99.19 88	99.79 68	99.61 187	99.65 75	99.30 312	99.48 202	98.86 84	99.21 259	99.63 258	98.72 67	99.90 148	98.25 246	99.63 164	99.80 88

mamv4		99.33 77	99.42 32	99.07 231	99.67 136	97.73 308	99.42 263	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 217

MP-MVS		99.33 77	99.15 93	99.87 21	99.88 13	99.82 28	99.66 82	99.46 236	98.09 195	99.48 183	99.74 197	98.29 99	99.96 41	97.93 276	99.87 79	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_11		99.32 79	99.16 92	99.80 64	99.83 47	99.70 60	99.57 140	99.56 90	99.45 11	99.99 2	99.93 10	94.18 304	99.99 4	99.96 13	99.98 4	99.73 125

fmvsm_s_conf0.5_n_2		99.32 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 250	99.58 78	99.47 4	99.99 2	99.93 10	94.04 309	99.96 41	99.96 13	99.93 33	99.93 22

PS-MVSNAJ		99.32 79	99.32 54	99.30 202	99.57 202	98.94 197	98.97 408	99.46 236	98.92 81	99.71 110	99.24 380	99.01 20	99.98 20	99.35 76	99.66 159	98.97 313

CSCG		99.32 79	99.32 54	99.32 195	99.85 31	98.29 275	99.71 57	99.66 32	98.11 191	99.41 203	99.80 150	98.37 96	99.96 41	98.99 136	99.96 17	99.72 135

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 206	99.52 105	99.58 132	99.80 11	97.12 320	99.62 149	99.73 203	98.58 78	99.90 148	98.61 202	99.91 46	99.68 158

DeepC-MVS	98.35 2	99.30 83	99.19 88	99.64 101	99.82 53	99.23 148	99.62 106	99.55 100	98.94 78	99.63 145	99.95 3	95.82 215	99.94 92	99.37 75	99.97 9	99.73 125
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n		99.29 85	99.10 99	99.86 34	99.70 122	99.65 75	99.53 177	99.62 51	98.74 101	99.99 2	99.95 3	94.53 289	99.94 92	99.89 25	99.96 17	99.97 4

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 189	99.63 167	98.97 183	99.12 370	99.51 154	98.86 84	99.84 56	99.47 319	98.18 104	99.99 4	99.50 57	99.31 191	99.08 298

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 189	99.63 167	98.97 183	99.12 370	99.51 154	98.86 84	99.84 56	99.47 319	98.18 104	99.99 4	99.50 57	99.31 191	99.08 298

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 189	99.63 167	98.97 183	99.12 370	99.51 154	98.86 84	99.84 56	99.47 319	98.18 104	99.99 4	99.50 57	99.31 191	99.08 298

NormalMVS		99.27 89	99.19 88	99.52 139	99.89 8	98.83 224	99.65 88	99.52 132	99.10 48	99.84 56	99.76 187	95.80 217	99.99 4	99.30 89	99.84 102	99.74 116

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 200	99.50 177	97.16 316	99.77 85	99.82 117	98.78 53	99.94 92	97.56 317	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D		99.27 89	99.12 97	99.74 80	99.18 331	99.75 51	99.56 148	99.57 85	98.45 130	99.49 182	99.85 83	97.77 118	99.94 92	98.33 239	99.84 102	99.52 224

fmvsm_s_conf0.1_n_a		99.26 92	99.06 110	99.85 43	99.52 224	99.62 83	99.54 168	99.62 51	98.69 107	99.99 2	99.96 1	94.47 291	99.94 92	99.88 26	99.92 39	99.98 2

patch_mono-2		99.26 92	99.62 6	98.16 360	99.81 57	94.59 435	99.52 179	99.64 42	99.33 28	99.73 97	99.90 35	99.00 24	99.99 4	99.69 35	99.98 4	99.89 29

ETV-MVS		99.26 92	99.21 84	99.40 179	99.46 251	99.30 138	99.56 148	99.52 132	98.52 122	99.44 192	99.27 376	98.41 93	99.86 181	99.10 123	99.59 168	99.04 305

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 205	99.53 218	98.91 204	99.02 394	99.45 247	98.80 94	99.71 110	99.26 378	98.94 34	99.98 20	99.34 81	99.23 200	98.98 312

CANet		99.25 96	99.14 94	99.59 113	99.41 266	99.16 155	99.35 298	99.57 85	98.82 89	99.51 178	99.61 267	96.46 181	99.95 76	99.59 45	99.98 4	99.65 173

3Dnovator	97.25 9	99.24 97	99.05 112	99.81 60	99.12 347	99.66 71	99.84 12	99.74 13	99.09 55	98.92 315	99.90 35	95.94 208	99.98 20	98.95 144	99.92 39	99.79 92

LuminaMVS		99.23 98	99.10 99	99.61 109	99.35 283	99.31 135	99.46 239	99.13 371	98.61 113	99.86 53	99.89 44	96.41 186	99.91 135	99.67 37	99.51 174	99.63 185

dcpmvs_2		99.23 98	99.58 9	98.16 360	99.83 47	94.68 432	99.76 37	99.52 132	99.07 58	99.98 13	99.88 55	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

test_fmvsmconf0.01_n		99.22 100	99.03 117	99.79 68	98.42 439	99.48 111	99.55 163	99.51 154	99.39 22	99.78 81	99.93 10	94.80 263	99.95 76	99.93 23	99.95 23	99.94 17

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 158	99.64 163	98.85 219	99.32 306	99.48 202	98.50 124	99.81 69	99.81 132	96.82 160	99.88 168	99.40 71	99.12 216	99.71 146

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 167	99.89 8	98.52 259	99.39 280	99.94 1	98.73 102	99.11 278	99.89 44	95.50 229	99.94 92	99.50 57	99.97 9	99.89 29

F-COLMAP		99.19 101	99.04 114	99.64 101	99.78 70	99.27 143	99.42 263	99.54 109	97.29 305	99.41 203	99.59 272	98.42 91	99.93 110	98.19 250	99.69 153	99.73 125

E3new		99.18 104	99.08 105	99.48 158	99.63 167	98.94 197	99.46 239	99.50 177	98.06 204	99.72 102	99.84 98	97.27 133	99.84 196	99.10 123	99.13 211	99.67 162

viewcassd2359sk11		99.18 104	99.08 105	99.49 154	99.65 158	98.95 193	99.48 225	99.51 154	98.10 194	99.72 102	99.87 68	97.13 139	99.84 196	99.13 117	99.14 208	99.69 152

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 149	99.62 176	99.01 177	99.50 200	99.52 132	98.25 160	99.68 117	99.82 117	96.93 153	99.80 237	99.15 116	99.11 218	99.70 149

EIA-MVS		99.18 104	99.09 104	99.45 167	99.49 241	99.18 152	99.67 75	99.53 125	97.66 263	99.40 208	99.44 326	98.10 107	99.81 230	98.94 145	99.62 165	99.35 271

3Dnovator+	97.12 13	99.18 104	98.97 140	99.82 57	99.17 339	99.68 64	99.81 20	99.51 154	99.20 33	98.72 344	99.89 44	95.68 223	99.97 29	98.86 162	99.86 87	99.81 79

MVSFormer		99.17 109	99.12 97	99.29 205	99.51 227	98.94 197	99.88 4	99.46 236	97.55 275	99.80 74	99.65 246	97.39 125	99.28 364	99.03 132	99.85 94	99.65 173

sss		99.17 109	99.05 112	99.53 133	99.62 176	98.97 183	99.36 293	99.62 51	97.83 239	99.67 123	99.65 246	97.37 128	99.95 76	99.19 106	99.19 203	99.68 158

SSM_0404		99.16 111	99.06 110	99.44 172	99.65 158	98.96 187	99.49 217	99.50 177	98.14 180	99.62 149	99.85 83	96.85 155	99.85 187	99.19 106	99.26 196	99.52 224

guyue		99.16 111	99.04 114	99.52 139	99.69 127	98.92 203	99.59 122	98.81 418	98.73 102	99.90 34	99.87 68	95.34 236	99.88 168	99.66 40	99.81 120	99.74 116

test_cas_vis1_n_1920		99.16 111	99.01 132	99.61 109	99.81 57	98.86 218	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 333	99.98 20	99.55 50	99.91 46	99.99 1

DP-MVS		99.16 111	98.95 148	99.78 71	99.77 78	99.53 101	99.41 268	99.50 177	97.03 332	99.04 295	99.88 55	97.39 125	99.92 123	98.66 194	99.90 57	99.87 40

E6		99.15 115	99.03 117	99.50 149	99.66 149	98.90 208	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.31 190	99.84 196	99.30 89	99.10 225	99.76 107

E2		99.15 115	99.03 117	99.49 154	99.65 158	98.93 202	99.49 217	99.52 132	98.14 180	99.72 102	99.88 55	96.57 176	99.84 196	99.17 112	99.13 211	99.72 135

E3		99.15 115	99.03 117	99.49 154	99.62 176	98.91 204	99.49 217	99.52 132	98.13 183	99.72 102	99.88 55	96.61 171	99.84 196	99.17 112	99.13 211	99.72 135

SymmetryMVS		99.15 115	99.02 126	99.52 139	99.72 111	98.83 224	99.65 88	99.34 312	99.10 48	99.84 56	99.76 187	95.80 217	99.99 4	99.30 89	98.72 260	99.73 125

MGCNet		99.15 115	98.96 144	99.73 83	98.92 384	99.37 123	99.37 287	96.92 468	99.51 2	99.66 128	99.78 174	96.69 167	99.97 29	99.84 28	99.97 9	99.84 53

casdiffmvs_mvg		99.15 115	99.02 126	99.55 124	99.66 149	99.09 165	99.64 95	99.56 90	98.26 155	99.45 187	99.87 68	96.03 202	99.81 230	99.54 51	99.15 207	99.73 125
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		99.15 115	99.02 126	99.53 133	99.66 149	99.14 160	99.72 53	99.48 202	98.35 142	99.42 198	99.84 98	96.07 199	99.79 243	99.51 56	99.14 208	99.67 162

E5		99.14 122	99.02 126	99.50 149	99.69 127	98.91 204	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.26 194	99.84 196	99.30 89	99.10 225	99.76 107

diffmvs		99.14 122	99.02 126	99.51 144	99.61 187	98.96 187	99.28 322	99.49 190	98.46 128	99.72 102	99.71 210	96.50 179	99.88 168	99.31 86	99.11 218	99.67 162
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		99.14 122	98.99 136	99.59 113	99.58 197	99.41 120	99.16 361	99.44 256	98.45 130	99.19 265	99.49 310	98.08 109	99.89 163	97.73 300	99.75 142	99.48 241

E4		99.13 125	99.01 132	99.49 154	99.68 133	98.90 208	99.52 179	99.52 132	98.13 183	99.71 110	99.90 35	96.32 188	99.84 196	99.21 104	99.11 218	99.75 111

SSM_0407		99.13 125	99.03 117	99.43 175	99.62 176	98.88 211	99.51 189	99.50 177	98.14 180	99.37 214	99.85 83	96.85 155	99.83 216	99.19 106	99.25 197	99.60 193

CDPH-MVS		99.13 125	98.91 156	99.80 64	99.75 92	99.71 58	99.15 364	99.41 272	96.60 365	99.60 157	99.55 287	98.83 47	99.90 148	97.48 325	99.83 113	99.78 98

casdiffmvs		99.13 125	98.98 139	99.56 122	99.65 158	99.16 155	99.56 148	99.50 177	98.33 145	99.41 203	99.86 76	95.92 209	99.83 216	99.45 68	99.16 204	99.70 149
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jason		99.13 125	99.03 117	99.45 167	99.46 251	98.87 215	99.12 370	99.26 350	98.03 216	99.79 76	99.65 246	97.02 148	99.85 187	99.02 134	99.90 57	99.65 173
jason: jason.
lupinMVS		99.13 125	99.01 132	99.46 166	99.51 227	98.94 197	99.05 386	99.16 367	97.86 232	99.80 74	99.56 284	97.39 125	99.86 181	98.94 145	99.85 94	99.58 208

EPP-MVSNet		99.13 125	98.99 136	99.53 133	99.65 158	99.06 171	99.81 20	99.33 320	97.43 292	99.60 157	99.88 55	97.14 138	99.84 196	99.13 117	98.94 239	99.69 152

MG-MVS		99.13 125	99.02 126	99.45 167	99.57 202	98.63 245	99.07 380	99.34 312	98.99 69	99.61 154	99.82 117	97.98 113	99.87 175	97.00 357	99.80 125	99.85 46

KinetiMVS		99.12 133	98.92 153	99.70 87	99.67 136	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 132	94.54 287	99.96 41	98.40 230	99.93 33	99.74 116

BP-MVS1		99.12 133	98.94 150	99.65 95	99.51 227	99.30 138	99.67 75	98.92 399	98.48 126	99.84 56	99.69 225	94.96 251	99.92 123	99.62 44	99.79 132	99.71 146

CHOSEN 280x420		99.12 133	99.13 95	99.08 230	99.66 149	97.89 301	98.43 457	99.71 16	98.88 83	99.62 149	99.76 187	96.63 170	99.70 284	99.46 67	99.99 1	99.66 167

DP-MVS Recon		99.12 133	98.95 148	99.65 95	99.74 100	99.70 60	99.27 327	99.57 85	96.40 381	99.42 198	99.68 233	98.75 60	99.80 237	97.98 273	99.72 148	99.44 257

Vis-MVSNet		99.12 133	98.97 140	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 68	94.77 268	99.84 196	99.19 106	99.41 182	99.74 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		99.12 133	99.08 105	99.24 215	99.46 251	98.55 253	99.51 189	99.46 236	98.09 195	99.45 187	99.82 117	98.34 97	99.51 323	98.70 187	98.93 240	99.67 162

viewdifsd2359ckpt07		99.11 139	99.00 135	99.43 175	99.63 167	98.73 235	99.45 243	99.54 109	98.33 145	99.62 149	99.81 132	96.17 196	99.87 175	99.27 97	99.14 208	99.69 152

SDMVSNet		99.11 139	98.90 158	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 142	99.88 55	94.56 284	99.93 110	99.67 37	98.26 290	99.72 135

VNet		99.11 139	98.90 158	99.73 83	99.52 224	99.56 94	99.41 268	99.39 282	99.01 64	99.74 95	99.78 174	95.56 227	99.92 123	99.52 55	98.18 298	99.72 135

CPTT-MVS		99.11 139	98.90 158	99.74 80	99.80 63	99.46 114	99.59 122	99.49 190	97.03 332	99.63 145	99.69 225	97.27 133	99.96 41	97.82 287	99.84 102	99.81 79

HyFIR lowres test		99.11 139	98.92 153	99.65 95	99.90 4	99.37 123	99.02 394	99.91 3	97.67 262	99.59 160	99.75 192	95.90 211	99.73 267	99.53 53	99.02 235	99.86 42

MVS_Test		99.10 144	98.97 140	99.48 158	99.49 241	99.14 160	99.67 75	99.34 312	97.31 303	99.58 161	99.76 187	97.65 121	99.82 225	98.87 157	99.07 230	99.46 252

AstraMVS		99.09 145	99.03 117	99.25 212	99.66 149	98.13 284	99.57 140	98.24 451	98.82 89	99.91 31	99.88 55	95.81 216	99.90 148	99.72 32	99.67 158	99.74 116

CDS-MVSNet		99.09 145	99.03 117	99.25 212	99.42 261	98.73 235	99.45 243	99.46 236	98.11 191	99.46 186	99.77 183	98.01 112	99.37 347	98.70 187	98.92 242	99.66 167
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewmacassd2359aftdt		99.08 147	98.94 150	99.50 149	99.66 149	98.96 187	99.51 189	99.54 109	98.27 152	99.42 198	99.89 44	95.88 213	99.80 237	99.20 105	99.11 218	99.76 107

mamba_0408		99.08 147	98.96 144	99.44 172	99.62 176	98.88 211	99.25 338	99.47 224	98.05 207	99.37 214	99.81 132	96.85 155	99.85 187	98.98 137	99.25 197	99.60 193

GDP-MVS		99.08 147	98.89 162	99.64 101	99.53 218	99.34 127	99.64 95	99.48 202	98.32 147	99.77 85	99.66 244	95.14 247	99.93 110	98.97 142	99.50 176	99.64 180

PVSNet_Blended		99.08 147	98.97 140	99.42 177	99.76 82	98.79 230	98.78 431	99.91 3	96.74 350	99.67 123	99.49 310	97.53 122	99.88 168	98.98 137	99.85 94	99.60 193

OMC-MVS		99.08 147	99.04 114	99.20 219	99.67 136	98.22 279	99.28 322	99.52 132	98.07 200	99.66 128	99.81 132	97.79 117	99.78 249	97.79 291	99.81 120	99.60 193

viewdifsd2359ckpt13		99.06 152	98.93 152	99.45 167	99.63 167	98.96 187	99.50 200	99.51 154	97.83 239	99.28 238	99.80 150	96.68 169	99.71 277	99.05 129	99.12 216	99.68 158

SSM_04072		99.06 152	98.96 144	99.35 188	99.62 176	98.88 211	99.25 338	99.47 224	98.05 207	99.37 214	99.81 132	96.85 155	99.58 315	98.98 137	99.25 197	99.60 193

mvsmamba		99.06 152	98.96 144	99.36 186	99.47 249	98.64 244	99.70 58	99.05 383	97.61 268	99.65 137	99.83 104	96.54 177	99.92 123	99.19 106	99.62 165	99.51 233

WTY-MVS		99.06 152	98.88 165	99.61 109	99.62 176	99.16 155	99.37 287	99.56 90	98.04 214	99.53 174	99.62 263	96.84 159	99.94 92	98.85 164	98.49 275	99.72 135

IS-MVSNet		99.05 156	98.87 166	99.57 120	99.73 107	99.32 131	99.75 42	99.20 362	98.02 219	99.56 165	99.86 76	96.54 177	99.67 292	98.09 261	99.13 211	99.73 125

PAPM_NR		99.04 157	98.84 174	99.66 91	99.74 100	99.44 116	99.39 280	99.38 290	97.70 258	99.28 238	99.28 373	98.34 97	99.85 187	96.96 361	99.45 179	99.69 152

API-MVS		99.04 157	99.03 117	99.06 233	99.40 271	99.31 135	99.55 163	99.56 90	98.54 120	99.33 228	99.39 342	98.76 57	99.78 249	96.98 359	99.78 134	98.07 438

mvs_anonymous		99.03 159	98.99 136	99.16 223	99.38 276	98.52 259	99.51 189	99.38 290	97.79 245	99.38 212	99.81 132	97.30 131	99.45 329	99.35 76	98.99 237	99.51 233

sasdasda		99.02 160	98.86 169	99.51 144	99.42 261	99.32 131	99.80 25	99.48 202	98.63 110	99.31 230	98.81 424	97.09 143	99.75 258	99.27 97	97.90 309	99.47 247

train_agg		99.02 160	98.77 181	99.77 74	99.67 136	99.65 75	99.05 386	99.41 272	96.28 385	98.95 311	99.49 310	98.76 57	99.91 135	97.63 308	99.72 148	99.75 111

canonicalmvs		99.02 160	98.86 169	99.51 144	99.42 261	99.32 131	99.80 25	99.48 202	98.63 110	99.31 230	98.81 424	97.09 143	99.75 258	99.27 97	97.90 309	99.47 247

PLC	97.94 4	99.02 160	98.85 172	99.53 133	99.66 149	99.01 177	99.24 343	99.52 132	96.85 344	99.27 244	99.48 316	98.25 101	99.91 135	97.76 296	99.62 165	99.65 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
viewdifsd2359ckpt09		99.01 164	98.87 166	99.40 179	99.62 176	98.79 230	99.44 250	99.51 154	97.76 249	99.35 223	99.69 225	96.42 185	99.75 258	98.97 142	99.11 218	99.66 167

viewmambaseed2359dif		99.01 164	98.90 158	99.32 195	99.58 197	98.51 261	99.33 303	99.54 109	97.85 235	99.44 192	99.85 83	96.01 203	99.79 243	99.41 70	99.13 211	99.67 162

MGCFI-Net		99.01 164	98.85 172	99.50 149	99.42 261	99.26 144	99.82 16	99.48 202	98.60 115	99.28 238	98.81 424	97.04 147	99.76 255	99.29 93	97.87 312	99.47 247

AdaColmap		99.01 164	98.80 177	99.66 91	99.56 206	99.54 98	99.18 359	99.70 18	98.18 173	99.35 223	99.63 258	96.32 188	99.90 148	97.48 325	99.77 137	99.55 215

1112_ss		98.98 168	98.77 181	99.59 113	99.68 133	99.02 175	99.25 338	99.48 202	97.23 311	99.13 274	99.58 276	96.93 153	99.90 148	98.87 157	98.78 257	99.84 53

MSDG		98.98 168	98.80 177	99.53 133	99.76 82	99.19 150	98.75 434	99.55 100	97.25 308	99.47 184	99.77 183	97.82 116	99.87 175	96.93 364	99.90 57	99.54 217

CANet_DTU		98.97 170	98.87 166	99.25 212	99.33 289	98.42 272	99.08 379	99.30 339	99.16 37	99.43 195	99.75 192	95.27 239	99.97 29	98.56 214	99.95 23	99.36 270

DPM-MVS		98.95 171	98.71 189	99.66 91	99.63 167	99.55 96	98.64 445	99.10 374	97.93 225	99.42 198	99.55 287	98.67 72	99.80 237	95.80 398	99.68 156	99.61 190

114514_t		98.93 172	98.67 193	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 452	99.71 110	99.78 174	98.06 110	99.90 148	98.84 167	99.91 46	99.74 116

PS-MVSNAJss		98.92 173	98.92 153	98.90 260	98.78 405	98.53 255	99.78 32	99.54 109	98.07 200	99.00 302	99.76 187	99.01 20	99.37 347	99.13 117	97.23 352	98.81 322

RRT-MVS		98.91 174	98.75 183	99.39 184	99.46 251	98.61 249	99.76 37	99.50 177	98.06 204	99.81 69	99.88 55	93.91 316	99.94 92	99.11 120	99.27 194	99.61 190

Test_1112_low_res		98.89 175	98.66 196	99.57 120	99.69 127	98.95 193	99.03 391	99.47 224	96.98 334	99.15 272	99.23 381	96.77 164	99.89 163	98.83 170	98.78 257	99.86 42

Elysia		98.88 176	98.65 198	99.58 116	99.58 197	99.34 127	99.65 88	99.52 132	98.26 155	99.83 64	99.87 68	93.37 327	99.90 148	97.81 289	99.91 46	99.49 238

StellarMVS		98.88 176	98.65 198	99.58 116	99.58 197	99.34 127	99.65 88	99.52 132	98.26 155	99.83 64	99.87 68	93.37 327	99.90 148	97.81 289	99.91 46	99.49 238

test_fmvs1		98.88 176	98.79 180	99.16 223	99.69 127	97.61 317	99.55 163	99.49 190	99.32 29	99.98 13	99.91 26	91.41 381	99.96 41	99.82 29	99.92 39	99.90 25

AllTest		98.87 179	98.72 187	99.31 197	99.86 25	98.48 266	99.56 148	99.61 60	97.85 235	99.36 220	99.85 83	95.95 206	99.85 187	96.66 377	99.83 113	99.59 204

UGNet		98.87 179	98.69 191	99.40 179	99.22 322	98.72 237	99.44 250	99.68 24	99.24 32	99.18 269	99.42 330	92.74 343	99.96 41	99.34 81	99.94 31	99.53 223
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
Vis-MVSNet (Re-imp)		98.87 179	98.72 187	99.31 197	99.71 117	98.88 211	99.80 25	99.44 256	97.91 227	99.36 220	99.78 174	95.49 230	99.43 338	97.91 277	99.11 218	99.62 188

IMVS_0407		98.86 182	98.91 156	98.72 294	99.55 210	96.93 357	99.50 200	99.44 256	98.05 207	99.66 128	99.80 150	97.13 139	99.65 300	98.15 256	98.92 242	99.60 193

IMVS_0403		98.86 182	98.89 162	98.78 289	99.55 210	96.93 357	99.58 132	99.44 256	98.05 207	99.68 117	99.80 150	96.81 161	99.80 237	98.15 256	98.92 242	99.60 193

test_yl		98.86 182	98.63 201	99.54 125	99.49 241	99.18 152	99.50 200	99.07 380	98.22 166	99.61 154	99.51 304	95.37 234	99.84 196	98.60 205	98.33 282	99.59 204

DCV-MVSNet		98.86 182	98.63 201	99.54 125	99.49 241	99.18 152	99.50 200	99.07 380	98.22 166	99.61 154	99.51 304	95.37 234	99.84 196	98.60 205	98.33 282	99.59 204

EPNet		98.86 182	98.71 189	99.30 202	97.20 459	98.18 280	99.62 106	98.91 404	99.28 31	98.63 363	99.81 132	95.96 205	99.99 4	99.24 101	99.72 148	99.73 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		98.86 182	98.80 177	99.03 237	99.76 82	98.79 230	99.28 322	99.91 3	97.42 294	99.67 123	99.37 348	97.53 122	99.88 168	98.98 137	97.29 350	98.42 416

ab-mvs		98.86 182	98.63 201	99.54 125	99.64 163	99.19 150	99.44 250	99.54 109	97.77 248	99.30 234	99.81 132	94.20 301	99.93 110	99.17 112	98.82 254	99.49 238

MAR-MVS		98.86 182	98.63 201	99.54 125	99.37 279	99.66 71	99.45 243	99.54 109	96.61 362	99.01 298	99.40 338	97.09 143	99.86 181	97.68 307	99.53 173	99.10 293
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
COLMAP_ROB	97.56 6	98.86 182	98.75 183	99.17 222	99.88 13	98.53 255	99.34 301	99.59 73	97.55 275	98.70 351	99.89 44	95.83 214	99.90 148	98.10 260	99.90 57	99.08 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		98.85 191	98.62 206	99.53 133	99.61 187	99.08 168	99.80 25	99.51 154	97.10 324	99.31 230	99.78 174	95.23 244	99.77 251	98.21 248	99.03 233	99.75 111

HY-MVS	97.30 7	98.85 191	98.64 200	99.47 164	99.42 261	99.08 168	99.62 106	99.36 300	97.39 297	99.28 238	99.68 233	96.44 183	99.92 123	98.37 234	98.22 293	99.40 264

PVSNet	96.02 17	98.85 191	98.84 174	98.89 264	99.73 107	97.28 327	98.32 463	99.60 67	97.86 232	99.50 179	99.57 281	96.75 165	99.86 181	98.56 214	99.70 152	99.54 217

PatchMatch-RL		98.84 194	98.62 206	99.52 139	99.71 117	99.28 141	99.06 384	99.77 12	97.74 253	99.50 179	99.53 296	95.41 232	99.84 196	97.17 350	99.64 162	99.44 257

Effi-MVS+		98.81 195	98.59 212	99.48 158	99.46 251	99.12 163	98.08 470	99.50 177	97.50 283	99.38 212	99.41 334	96.37 187	99.81 230	99.11 120	98.54 272	99.51 233

alignmvs		98.81 195	98.56 215	99.58 116	99.43 259	99.42 118	99.51 189	98.96 394	98.61 113	99.35 223	98.92 419	94.78 265	99.77 251	99.35 76	98.11 303	99.54 217

DeepPCF-MVS	98.18 3	98.81 195	99.37 44	97.12 419	99.60 193	91.75 461	98.61 446	99.44 256	99.35 25	99.83 64	99.85 83	98.70 69	99.81 230	99.02 134	99.91 46	99.81 79

PMMVS		98.80 198	98.62 206	99.34 189	99.27 307	98.70 238	98.76 433	99.31 334	97.34 300	99.21 259	99.07 397	97.20 137	99.82 225	98.56 214	98.87 249	99.52 224

icg_test_0407_2		98.79 199	98.86 169	98.57 310	99.55 210	96.93 357	99.07 380	99.44 256	98.05 207	99.66 128	99.80 150	97.13 139	99.18 387	98.15 256	98.92 242	99.60 193

viewdifsd2359ckpt11		98.78 200	98.74 185	98.89 264	99.67 136	97.04 346	99.50 200	99.58 78	98.26 155	99.56 165	99.90 35	94.36 294	99.87 175	99.49 61	98.32 286	99.77 100

viewmsd2359difaftdt		98.78 200	98.74 185	98.90 260	99.67 136	97.04 346	99.50 200	99.58 78	98.26 155	99.56 165	99.90 35	94.36 294	99.87 175	99.49 61	98.32 286	99.77 100

Effi-MVS+-dtu		98.78 200	98.89 162	98.47 328	99.33 289	96.91 362	99.57 140	99.30 339	98.47 127	99.41 203	98.99 409	96.78 163	99.74 261	98.73 184	99.38 183	98.74 337

FIs		98.78 200	98.63 201	99.23 217	99.18 331	99.54 98	99.83 15	99.59 73	98.28 150	98.79 338	99.81 132	96.75 165	99.37 347	99.08 126	96.38 369	98.78 325

Fast-Effi-MVS+-dtu		98.77 204	98.83 176	98.60 305	99.41 266	96.99 352	99.52 179	99.49 190	98.11 191	99.24 251	99.34 358	96.96 152	99.79 243	97.95 275	99.45 179	99.02 308

sd_testset		98.75 205	98.57 213	99.29 205	99.81 57	98.26 277	99.56 148	99.62 51	98.78 98	99.64 142	99.88 55	92.02 365	99.88 168	99.54 51	98.26 290	99.72 135

FA-MVS(test-final)		98.75 205	98.53 217	99.41 178	99.55 210	99.05 173	99.80 25	99.01 388	96.59 367	99.58 161	99.59 272	95.39 233	99.90 148	97.78 292	99.49 177	99.28 279

FC-MVSNet-test		98.75 205	98.62 206	99.15 227	99.08 358	99.45 115	99.86 11	99.60 67	98.23 165	98.70 351	99.82 117	96.80 162	99.22 379	99.07 127	96.38 369	98.79 323

XVG-OURS		98.73 208	98.68 192	98.88 268	99.70 122	97.73 308	98.92 416	99.55 100	98.52 122	99.45 187	99.84 98	95.27 239	99.91 135	98.08 265	98.84 252	99.00 309

Fast-Effi-MVS+		98.70 209	98.43 222	99.51 144	99.51 227	99.28 141	99.52 179	99.47 224	96.11 401	99.01 298	99.34 358	96.20 195	99.84 196	97.88 279	98.82 254	99.39 265

XVG-OURS-SEG-HR		98.69 210	98.62 206	98.89 264	99.71 117	97.74 307	99.12 370	99.54 109	98.44 133	99.42 198	99.71 210	94.20 301	99.92 123	98.54 218	98.90 248	99.00 309

1314		98.68 211	98.54 216	99.11 229	98.89 388	98.65 242	99.27 327	99.49 190	96.89 342	97.99 404	99.56 284	97.72 120	99.83 216	97.74 299	99.27 194	98.84 321

VortexMVS		98.67 212	98.66 196	98.68 300	99.62 176	97.96 295	99.59 122	99.41 272	98.13 183	99.31 230	99.70 214	95.48 231	99.27 367	99.40 71	97.32 349	98.79 323

EI-MVSNet		98.67 212	98.67 193	98.68 300	99.35 283	97.97 293	99.50 200	99.38 290	96.93 341	99.20 262	99.83 104	97.87 114	99.36 351	98.38 232	97.56 328	98.71 341

test_djsdf		98.67 212	98.57 213	98.98 243	98.70 419	98.91 204	99.88 4	99.46 236	97.55 275	99.22 256	99.88 55	95.73 221	99.28 364	99.03 132	97.62 323	98.75 333

QAPM		98.67 212	98.30 232	99.80 64	99.20 325	99.67 68	99.77 34	99.72 14	94.74 429	98.73 343	99.90 35	95.78 219	99.98 20	96.96 361	99.88 76	99.76 107

nrg030		98.64 216	98.42 223	99.28 209	99.05 364	99.69 63	99.81 20	99.46 236	98.04 214	99.01 298	99.82 117	96.69 167	99.38 344	99.34 81	94.59 414	98.78 325

test_vis1_n_1920		98.63 217	98.40 225	99.31 197	99.86 25	97.94 300	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 432	100.00 1	99.92 24	99.92 39	99.98 2

PAPR		98.63 217	98.34 228	99.51 144	99.40 271	99.03 174	98.80 429	99.36 300	96.33 382	99.00 302	99.12 395	98.46 87	99.84 196	95.23 413	99.37 190	99.66 167

CVMVSNet		98.57 219	98.67 193	98.30 348	99.35 283	95.59 406	99.50 200	99.55 100	98.60 115	99.39 210	99.83 104	94.48 290	99.45 329	98.75 181	98.56 270	99.85 46

IMVS_0404		98.53 220	98.52 218	98.55 316	99.55 210	96.93 357	99.20 355	99.44 256	98.05 207	98.96 309	99.80 150	94.66 279	99.13 395	98.15 256	98.92 242	99.60 193

MVSTER		98.49 221	98.32 230	99.00 241	99.35 283	99.02 175	99.54 168	99.38 290	97.41 295	99.20 262	99.73 203	93.86 318	99.36 351	98.87 157	97.56 328	98.62 385

FE-MVS		98.48 222	98.17 237	99.40 179	99.54 217	98.96 187	99.68 72	98.81 418	95.54 412	99.62 149	99.70 214	93.82 319	99.93 110	97.35 337	99.46 178	99.32 276

OpenMVS	96.50 16	98.47 223	98.12 244	99.52 139	99.04 366	99.53 101	99.82 16	99.72 14	94.56 432	98.08 399	99.88 55	94.73 272	99.98 20	97.47 327	99.76 140	99.06 304

IterMVS-LS		98.46 224	98.42 223	98.58 309	99.59 195	98.00 291	99.37 287	99.43 267	96.94 340	99.07 287	99.59 272	97.87 114	99.03 411	98.32 241	95.62 392	98.71 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		98.44 225	98.28 233	98.94 250	98.50 436	98.96 187	99.77 34	99.50 177	97.07 326	98.87 324	99.77 183	94.76 269	99.28 364	98.66 194	97.60 324	98.57 400

jajsoiax		98.43 226	98.28 233	98.88 268	98.60 430	98.43 270	99.82 16	99.53 125	98.19 170	98.63 363	99.80 150	93.22 332	99.44 334	99.22 102	97.50 335	98.77 329

tttt0517		98.42 227	98.14 241	99.28 209	99.66 149	98.38 273	99.74 47	96.85 469	97.68 260	99.79 76	99.74 197	91.39 382	99.89 163	98.83 170	99.56 170	99.57 211

BH-untuned		98.42 227	98.36 226	98.59 306	99.49 241	96.70 370	99.27 327	99.13 371	97.24 310	98.80 336	99.38 345	95.75 220	99.74 261	97.07 355	99.16 204	99.33 275

test_fmvs1_n		98.41 229	98.14 241	99.21 218	99.82 53	97.71 313	99.74 47	99.49 190	99.32 29	99.99 2	99.95 3	85.32 448	99.97 29	99.82 29	99.84 102	99.96 7

D2MVS		98.41 229	98.50 219	98.15 363	99.26 310	96.62 376	99.40 276	99.61 60	97.71 255	98.98 305	99.36 351	96.04 201	99.67 292	98.70 187	97.41 345	98.15 434

BH-RMVSNet		98.41 229	98.08 250	99.40 179	99.41 266	98.83 224	99.30 312	98.77 424	97.70 258	98.94 313	99.65 246	92.91 339	99.74 261	96.52 381	99.55 172	99.64 180

mvs_tets		98.40 232	98.23 235	98.91 258	98.67 423	98.51 261	99.66 82	99.53 125	98.19 170	98.65 360	99.81 132	92.75 341	99.44 334	99.31 86	97.48 339	98.77 329

MonoMVSNet		98.38 233	98.47 221	98.12 365	98.59 432	96.19 393	99.72 53	98.79 422	97.89 229	99.44 192	99.52 300	96.13 197	98.90 433	98.64 196	97.54 330	99.28 279

XXY-MVS		98.38 233	98.09 249	99.24 215	99.26 310	99.32 131	99.56 148	99.55 100	97.45 288	98.71 345	99.83 104	93.23 330	99.63 310	98.88 154	96.32 371	98.76 331

ACMM	97.58 5	98.37 235	98.34 228	98.48 323	99.41 266	97.10 337	99.56 148	99.45 247	98.53 121	99.04 295	99.85 83	93.00 335	99.71 277	98.74 182	97.45 340	98.64 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest0530		98.35 236	98.03 256	99.31 197	99.63 167	98.56 252	99.54 168	96.75 471	97.53 279	99.73 97	99.65 246	91.25 386	99.89 163	98.62 199	99.56 170	99.48 241

tpmrst		98.33 237	98.48 220	97.90 382	99.16 341	94.78 428	99.31 310	99.11 373	97.27 306	99.45 187	99.59 272	95.33 237	99.84 196	98.48 221	98.61 264	99.09 297

baseline1		98.31 238	97.95 265	99.38 185	99.50 239	98.74 234	99.59 122	98.93 396	98.41 135	99.14 273	99.60 270	94.59 282	99.79 243	98.48 221	93.29 434	99.61 190

PatchmatchNet		98.31 238	98.36 226	98.19 358	99.16 341	95.32 417	99.27 327	98.92 399	97.37 298	99.37 214	99.58 276	94.90 258	99.70 284	97.43 332	99.21 201	99.54 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		98.30 240	97.98 261	99.26 211	99.57 202	98.16 281	99.41 268	98.55 443	96.03 406	99.19 265	99.74 197	91.87 368	99.92 123	99.16 115	98.29 289	99.70 149

VPA-MVSNet		98.29 241	97.95 265	99.30 202	99.16 341	99.54 98	99.50 200	99.58 78	98.27 152	99.35 223	99.37 348	92.53 353	99.65 300	99.35 76	94.46 415	98.72 339

UniMVSNet (Re)		98.29 241	98.00 259	99.13 228	99.00 371	99.36 126	99.49 217	99.51 154	97.95 223	98.97 307	99.13 392	96.30 191	99.38 344	98.36 236	93.34 433	98.66 372

HQP_MVS		98.27 243	98.22 236	98.44 334	99.29 302	96.97 354	99.39 280	99.47 224	98.97 75	99.11 278	99.61 267	92.71 346	99.69 289	97.78 292	97.63 321	98.67 363

UniMVSNet_NR-MVSNet		98.22 244	97.97 262	98.96 246	98.92 384	98.98 180	99.48 225	99.53 125	97.76 249	98.71 345	99.46 323	96.43 184	99.22 379	98.57 211	92.87 441	98.69 350

LPG-MVS_test		98.22 244	98.13 243	98.49 321	99.33 289	97.05 343	99.58 132	99.55 100	97.46 285	99.24 251	99.83 104	92.58 351	99.72 271	98.09 261	97.51 333	98.68 355

RPSCF		98.22 244	98.62 206	96.99 422	99.82 53	91.58 462	99.72 53	99.44 256	96.61 362	99.66 128	99.89 44	95.92 209	99.82 225	97.46 328	99.10 225	99.57 211

ADS-MVSNet		98.20 247	98.08 250	98.56 314	99.33 289	96.48 381	99.23 346	99.15 368	96.24 389	99.10 281	99.67 239	94.11 306	99.71 277	96.81 369	99.05 231	99.48 241

OPM-MVS		98.19 248	98.10 246	98.45 331	98.88 389	97.07 341	99.28 322	99.38 290	98.57 117	99.22 256	99.81 132	92.12 363	99.66 295	98.08 265	97.54 330	98.61 394
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA		98.19 248	98.16 238	98.27 354	99.30 298	95.55 407	99.07 380	98.97 392	97.57 272	99.43 195	99.57 281	92.72 344	99.74 261	97.58 312	99.20 202	99.52 224

miper_ehance_all_eth		98.18 250	98.10 246	98.41 337	99.23 318	97.72 310	98.72 437	99.31 334	96.60 365	98.88 321	99.29 371	97.29 132	99.13 395	97.60 310	95.99 380	98.38 421

CR-MVSNet		98.17 251	97.93 268	98.87 272	99.18 331	98.49 264	99.22 350	99.33 320	96.96 336	99.56 165	99.38 345	94.33 297	99.00 416	94.83 420	98.58 267	99.14 290

miper_enhance_ethall		98.16 252	98.08 250	98.41 337	98.96 380	97.72 310	98.45 456	99.32 330	96.95 338	98.97 307	99.17 387	97.06 146	99.22 379	97.86 282	95.99 380	98.29 425

CLD-MVS		98.16 252	98.10 246	98.33 344	99.29 302	96.82 367	98.75 434	99.44 256	97.83 239	99.13 274	99.55 287	92.92 337	99.67 292	98.32 241	97.69 319	98.48 408
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		98.14 254	97.79 281	99.19 220	99.50 239	98.50 263	98.61 446	96.82 470	96.95 338	99.54 172	99.43 328	91.66 377	99.86 181	98.08 265	99.51 174	99.22 287

pmmvs4		98.13 255	97.90 270	98.81 284	98.61 429	98.87 215	98.99 402	99.21 361	96.44 377	99.06 292	99.58 276	95.90 211	99.11 401	97.18 349	96.11 376	98.46 413

WR-MVS_H		98.13 255	97.87 275	98.90 260	99.02 368	98.84 221	99.70 58	99.59 73	97.27 306	98.40 380	99.19 386	95.53 228	99.23 374	98.34 238	93.78 429	98.61 394

c3_l		98.12 257	98.04 255	98.38 341	99.30 298	97.69 314	98.81 428	99.33 320	96.67 355	98.83 331	99.34 358	97.11 142	98.99 417	97.58 312	95.34 399	98.48 408

ACMH	97.28 8	98.10 258	97.99 260	98.44 334	99.41 266	96.96 356	99.60 113	99.56 90	98.09 195	98.15 397	99.91 26	90.87 390	99.70 284	98.88 154	97.45 340	98.67 363
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVSNET3		98.09 259	97.82 279	98.89 264	98.70 419	98.90 208	98.57 449	99.47 224	96.78 348	98.87 324	99.05 400	94.75 270	99.23 374	97.45 330	96.74 360	98.53 403

Anonymous20240529		98.09 259	97.68 298	99.34 189	99.66 149	98.44 269	99.40 276	99.43 267	93.67 439	99.22 256	99.89 44	90.23 398	99.93 110	99.26 100	98.33 282	99.66 167

CP-MVSNet		98.09 259	97.78 284	99.01 239	98.97 379	99.24 147	99.67 75	99.46 236	97.25 308	98.48 377	99.64 252	93.79 320	99.06 407	98.63 198	94.10 423	98.74 337

dmvs_re		98.08 262	98.16 238	97.85 386	99.55 210	94.67 433	99.70 58	98.92 399	98.15 175	99.06 292	99.35 354	93.67 324	99.25 371	97.77 295	97.25 351	99.64 180

DU-MVS		98.08 262	97.79 281	98.96 246	98.87 392	98.98 180	99.41 268	99.45 247	97.87 231	98.71 345	99.50 307	94.82 261	99.22 379	98.57 211	92.87 441	98.68 355

v2v482		98.06 264	97.77 286	98.92 254	98.90 387	98.82 227	99.57 140	99.36 300	96.65 357	99.19 265	99.35 354	94.20 301	99.25 371	97.72 302	94.97 407	98.69 350

V42		98.06 264	97.79 281	98.86 275	98.98 377	98.84 221	99.69 62	99.34 312	96.53 369	99.30 234	99.37 348	94.67 277	99.32 359	97.57 316	94.66 412	98.42 416

test-LLR		98.06 264	97.90 270	98.55 316	98.79 402	97.10 337	98.67 440	97.75 460	97.34 300	98.61 367	98.85 421	94.45 292	99.45 329	97.25 341	99.38 183	99.10 293

WR-MVS		98.06 264	97.73 293	99.06 233	98.86 395	99.25 146	99.19 357	99.35 307	97.30 304	98.66 354	99.43 328	93.94 313	99.21 384	98.58 208	94.28 419	98.71 341

ACMP	97.20 11	98.06 264	97.94 267	98.45 331	99.37 279	97.01 350	99.44 250	99.49 190	97.54 278	98.45 378	99.79 167	91.95 367	99.72 271	97.91 277	97.49 338	98.62 385
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		98.05 269	97.96 263	98.33 344	99.26 310	97.38 324	98.56 452	99.31 334	96.65 357	98.88 321	99.52 300	96.58 174	99.12 400	97.39 334	95.53 396	98.47 410

test1111		98.04 270	98.11 245	97.83 390	99.74 100	93.82 444	99.58 132	95.40 478	99.12 46	99.65 137	99.93 10	90.73 391	99.84 196	99.43 69	99.38 183	99.82 72

ECVR-MVS		98.04 270	98.05 254	98.00 373	99.74 100	94.37 439	99.59 122	94.98 479	99.13 41	99.66 128	99.93 10	90.67 392	99.84 196	99.40 71	99.38 183	99.80 88

EPNet_dtu		98.03 272	97.96 263	98.23 356	98.27 441	95.54 409	99.23 346	98.75 425	99.02 62	97.82 413	99.71 210	96.11 198	99.48 324	93.04 442	99.65 161	99.69 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		98.03 272	97.76 290	98.84 279	99.39 274	98.98 180	99.40 276	99.38 290	96.67 355	99.07 287	99.28 373	92.93 336	98.98 418	97.10 351	96.65 362	98.56 401

ADS-MVSNet2		98.02 274	98.07 253	97.87 384	99.33 289	95.19 420	99.23 346	99.08 377	96.24 389	99.10 281	99.67 239	94.11 306	98.93 430	96.81 369	99.05 231	99.48 241

HQP-MVS		98.02 274	97.90 270	98.37 342	99.19 328	96.83 365	98.98 405	99.39 282	98.24 162	98.66 354	99.40 338	92.47 355	99.64 304	97.19 347	97.58 326	98.64 376

LTVRE_ROB	97.16 12	98.02 274	97.90 270	98.40 339	99.23 318	96.80 368	99.70 58	99.60 67	97.12 320	98.18 395	99.70 214	91.73 373	99.72 271	98.39 231	97.45 340	98.68 355
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
cl____		98.01 277	97.84 278	98.55 316	99.25 314	97.97 293	98.71 438	99.34 312	96.47 376	98.59 370	99.54 292	95.65 224	99.21 384	97.21 343	95.77 386	98.46 413

DIV-MVS_self_test		98.01 277	97.85 277	98.48 323	99.24 316	97.95 298	98.71 438	99.35 307	96.50 370	98.60 369	99.54 292	95.72 222	99.03 411	97.21 343	95.77 386	98.46 413

miper_lstm_enhance		98.00 279	97.91 269	98.28 353	99.34 288	97.43 322	98.88 420	99.36 300	96.48 374	98.80 336	99.55 287	95.98 204	98.91 431	97.27 340	95.50 397	98.51 406

BH-w/o		98.00 279	97.89 274	98.32 346	99.35 283	96.20 392	99.01 399	98.90 406	96.42 379	98.38 381	99.00 407	95.26 241	99.72 271	96.06 391	98.61 264	99.03 306

v1144		97.98 281	97.69 297	98.85 278	98.87 392	98.66 241	99.54 168	99.35 307	96.27 387	99.23 255	99.35 354	94.67 277	99.23 374	96.73 372	95.16 403	98.68 355

EU-MVSNet		97.98 281	98.03 256	97.81 393	98.72 416	96.65 375	99.66 82	99.66 32	98.09 195	98.35 383	99.82 117	95.25 242	98.01 454	97.41 333	95.30 400	98.78 325

tpmvs		97.98 281	98.02 258	97.84 388	99.04 366	94.73 429	99.31 310	99.20 362	96.10 405	98.76 341	99.42 330	94.94 253	99.81 230	96.97 360	98.45 276	98.97 313

tt0805		97.97 284	97.77 286	98.57 310	99.59 195	96.61 377	99.45 243	99.08 377	98.21 168	98.88 321	99.80 150	88.66 416	99.70 284	98.58 208	97.72 318	99.39 265

NR-MVSNet		97.97 284	97.61 307	99.02 238	98.87 392	99.26 144	99.47 235	99.42 269	97.63 265	97.08 432	99.50 307	95.07 249	99.13 395	97.86 282	93.59 430	98.68 355

v8		97.95 286	97.63 305	98.93 252	98.95 381	98.81 229	99.80 25	99.41 272	96.03 406	99.10 281	99.42 330	94.92 256	99.30 362	96.94 363	94.08 424	98.66 372

Patchmatch-test		97.93 287	97.65 301	98.77 290	99.18 331	97.07 341	99.03 391	99.14 370	96.16 396	98.74 342	99.57 281	94.56 284	99.72 271	93.36 437	99.11 218	99.52 224

PS-CasMVS		97.93 287	97.59 309	98.95 248	98.99 374	99.06 171	99.68 72	99.52 132	97.13 318	98.31 385	99.68 233	92.44 359	99.05 408	98.51 219	94.08 424	98.75 333

TranMVSNet+NR-MVSNet		97.93 287	97.66 300	98.76 291	98.78 405	98.62 247	99.65 88	99.49 190	97.76 249	98.49 376	99.60 270	94.23 300	98.97 425	98.00 272	92.90 439	98.70 346

test_vis1_n		97.92 290	97.44 331	99.34 189	99.53 218	98.08 287	99.74 47	99.49 190	99.15 38	100.00 1	99.94 6	79.51 470	99.98 20	99.88 26	99.76 140	99.97 4

v144192		97.92 290	97.60 308	98.87 272	98.83 399	98.65 242	99.55 163	99.34 312	96.20 392	99.32 229	99.40 338	94.36 294	99.26 370	96.37 388	95.03 406	98.70 346

ACMH+	97.24 10	97.92 290	97.78 284	98.32 346	99.46 251	96.68 374	99.56 148	99.54 109	98.41 135	97.79 415	99.87 68	90.18 399	99.66 295	98.05 269	97.18 355	98.62 385

LFMVS		97.90 293	97.35 343	99.54 125	99.52 224	99.01 177	99.39 280	98.24 451	97.10 324	99.65 137	99.79 167	84.79 451	99.91 135	99.28 94	98.38 279	99.69 152

reproduce_monomvs		97.89 294	97.87 275	97.96 377	99.51 227	95.45 412	99.60 113	99.25 352	99.17 36	98.85 330	99.49 310	89.29 408	99.64 304	99.35 76	96.31 372	98.78 325

Anonymous20231211		97.88 295	97.54 313	98.90 260	99.71 117	98.53 255	99.48 225	99.57 85	94.16 435	98.81 334	99.68 233	93.23 330	99.42 340	98.84 167	94.42 417	98.76 331

OurMVSNet-221017-0		97.88 295	97.77 286	98.19 358	98.71 418	96.53 379	99.88 4	99.00 389	97.79 245	98.78 339	99.94 6	91.68 374	99.35 354	97.21 343	96.99 359	98.69 350

v7n		97.87 297	97.52 315	98.92 254	98.76 412	98.58 251	99.84 12	99.46 236	96.20 392	98.91 316	99.70 214	94.89 259	99.44 334	96.03 392	93.89 427	98.75 333

baseline2		97.87 297	97.55 310	98.82 281	99.18 331	98.02 290	99.41 268	96.58 475	96.97 335	96.51 439	99.17 387	93.43 325	99.57 316	97.71 303	99.03 233	98.86 319

thres600view7		97.86 299	97.51 317	98.92 254	99.72 111	97.95 298	99.59 122	98.74 428	97.94 224	99.27 244	98.62 432	91.75 371	99.86 181	93.73 433	98.19 297	98.96 315

UBG		97.85 300	97.48 320	98.95 248	99.25 314	97.64 315	99.24 343	98.74 428	97.90 228	98.64 361	98.20 449	88.65 417	99.81 230	98.27 244	98.40 277	99.42 259

cl22		97.85 300	97.64 304	98.48 323	99.09 355	97.87 302	98.60 448	99.33 320	97.11 323	98.87 324	99.22 382	92.38 360	99.17 389	98.21 248	95.99 380	98.42 416

v10		97.85 300	97.52 315	98.86 275	98.99 374	98.67 240	99.75 42	99.41 272	95.70 410	98.98 305	99.41 334	94.75 270	99.23 374	96.01 394	94.63 413	98.67 363

GA-MVS		97.85 300	97.47 323	99.00 241	99.38 276	97.99 292	98.57 449	99.15 368	97.04 331	98.90 318	99.30 369	89.83 402	99.38 344	96.70 374	98.33 282	99.62 188

testing3-2		97.84 304	97.70 296	98.24 355	99.53 218	95.37 416	99.55 163	98.67 438	98.46 128	99.27 244	99.34 358	86.58 437	99.83 216	99.32 84	98.63 263	99.52 224

tfpnnormal		97.84 304	97.47 323	98.98 243	99.20 325	99.22 149	99.64 95	99.61 60	96.32 383	98.27 389	99.70 214	93.35 329	99.44 334	95.69 401	95.40 398	98.27 426

VPNet		97.84 304	97.44 331	99.01 239	99.21 323	98.94 197	99.48 225	99.57 85	98.38 137	99.28 238	99.73 203	88.89 411	99.39 342	99.19 106	93.27 435	98.71 341

LCM-MVSNet-Re		97.83 307	98.15 240	96.87 428	99.30 298	92.25 459	99.59 122	98.26 449	97.43 292	96.20 443	99.13 392	96.27 192	98.73 440	98.17 253	98.99 237	99.64 180

XVG-ACMP-BASELINE		97.83 307	97.71 295	98.20 357	99.11 349	96.33 386	99.41 268	99.52 132	98.06 204	99.05 294	99.50 307	89.64 405	99.73 267	97.73 300	97.38 347	98.53 403

IterMVS		97.83 307	97.77 286	98.02 370	99.58 197	96.27 389	99.02 394	99.48 202	97.22 312	98.71 345	99.70 214	92.75 341	99.13 395	97.46 328	96.00 379	98.67 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		97.82 310	97.75 291	98.06 367	99.57 202	96.36 385	99.02 394	99.49 190	97.18 314	98.71 345	99.72 207	92.72 344	99.14 392	97.44 331	95.86 385	98.67 363

EPMVS		97.82 310	97.65 301	98.35 343	98.88 389	95.98 396	99.49 217	94.71 481	97.57 272	99.26 249	99.48 316	92.46 358	99.71 277	97.87 281	99.08 229	99.35 271

MVP-Stereo		97.81 312	97.75 291	97.99 374	97.53 452	96.60 378	98.96 409	98.85 413	97.22 312	97.23 426	99.36 351	95.28 238	99.46 327	95.51 405	99.78 134	97.92 451
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		97.81 312	97.44 331	98.91 258	98.88 389	98.68 239	99.51 189	99.34 312	96.18 394	99.20 262	99.34 358	94.03 310	99.36 351	95.32 411	95.18 402	98.69 350

ttmdpeth		97.80 314	97.63 305	98.29 349	98.77 410	97.38 324	99.64 95	99.36 300	98.78 98	96.30 442	99.58 276	92.34 362	99.39 342	98.36 236	95.58 393	98.10 436

v1921920		97.80 314	97.45 326	98.84 279	98.80 401	98.53 255	99.52 179	99.34 312	96.15 398	99.24 251	99.47 319	93.98 312	99.29 363	95.40 409	95.13 404	98.69 350

v148		97.79 316	97.55 310	98.50 320	98.74 413	97.72 310	99.54 168	99.33 320	96.26 388	98.90 318	99.51 304	94.68 276	99.14 392	97.83 286	93.15 438	98.63 383

thres400		97.77 317	97.38 339	98.92 254	99.69 127	97.96 295	99.50 200	98.73 434	97.83 239	99.17 270	98.45 439	91.67 375	99.83 216	93.22 439	98.18 298	98.96 315

thres100view900		97.76 318	97.45 326	98.69 299	99.72 111	97.86 304	99.59 122	98.74 428	97.93 225	99.26 249	98.62 432	91.75 371	99.83 216	93.22 439	98.18 298	98.37 422

PEN-MVS		97.76 318	97.44 331	98.72 294	98.77 410	98.54 254	99.78 32	99.51 154	97.06 328	98.29 388	99.64 252	92.63 350	98.89 434	98.09 261	93.16 437	98.72 339

Baseline_NR-MVSNet		97.76 318	97.45 326	98.68 300	99.09 355	98.29 275	99.41 268	98.85 413	95.65 411	98.63 363	99.67 239	94.82 261	99.10 403	98.07 268	92.89 440	98.64 376

TR-MVS		97.76 318	97.41 337	98.82 281	99.06 361	97.87 302	98.87 422	98.56 442	96.63 361	98.68 353	99.22 382	92.49 354	99.65 300	95.40 409	97.79 316	98.95 317

Patchmtry		97.75 322	97.40 338	98.81 284	99.10 352	98.87 215	99.11 376	99.33 320	94.83 427	98.81 334	99.38 345	94.33 297	99.02 413	96.10 390	95.57 394	98.53 403

dp		97.75 322	97.80 280	97.59 406	99.10 352	93.71 447	99.32 306	98.88 409	96.48 374	99.08 286	99.55 287	92.67 349	99.82 225	96.52 381	98.58 267	99.24 285

WBMVS		97.74 324	97.50 318	98.46 329	99.24 316	97.43 322	99.21 352	99.42 269	97.45 288	98.96 309	99.41 334	88.83 412	99.23 374	98.94 145	96.02 377	98.71 341

TAPA-MVS	97.07 15	97.74 324	97.34 346	98.94 250	99.70 122	97.53 318	99.25 338	99.51 154	91.90 455	99.30 234	99.63 258	98.78 53	99.64 304	88.09 465	99.87 79	99.65 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS		97.73 326	97.35 343	98.88 268	99.47 249	97.12 336	99.34 301	98.85 413	98.19 170	99.67 123	99.85 83	82.98 459	99.92 123	99.49 61	98.32 286	99.60 193

MIMVSNet		97.73 326	97.45 326	98.57 310	99.45 257	97.50 320	99.02 394	98.98 391	96.11 401	99.41 203	99.14 391	90.28 394	98.74 439	95.74 399	98.93 240	99.47 247

tfpn200view9		97.72 328	97.38 339	98.72 294	99.69 127	97.96 295	99.50 200	98.73 434	97.83 239	99.17 270	98.45 439	91.67 375	99.83 216	93.22 439	98.18 298	98.37 422

CostFormer		97.72 328	97.73 293	97.71 398	99.15 345	94.02 443	99.54 168	99.02 387	94.67 430	99.04 295	99.35 354	92.35 361	99.77 251	98.50 220	97.94 308	99.34 274

FMVSNet2		97.72 328	97.36 341	98.80 286	99.51 227	98.84 221	99.45 243	99.42 269	96.49 371	98.86 329	99.29 371	90.26 395	98.98 418	96.44 383	96.56 365	98.58 399

test0.0.03 1		97.71 331	97.42 336	98.56 314	98.41 440	97.82 305	98.78 431	98.63 440	97.34 300	98.05 403	98.98 411	94.45 292	98.98 418	95.04 416	97.15 356	98.89 318

h-mvs33		97.70 332	97.28 355	98.97 245	99.70 122	97.27 328	99.36 293	99.45 247	98.94 78	99.66 128	99.64 252	94.93 254	99.99 4	99.48 64	84.36 469	99.65 173

myMVS_eth3d28		97.69 333	97.34 346	98.73 292	99.27 307	97.52 319	99.33 303	98.78 423	98.03 216	98.82 333	98.49 437	86.64 436	99.46 327	98.44 227	98.24 292	99.23 286

v1240		97.69 333	97.32 350	98.79 287	98.85 396	98.43 270	99.48 225	99.36 300	96.11 401	99.27 244	99.36 351	93.76 322	99.24 373	94.46 423	95.23 401	98.70 346

cascas		97.69 333	97.43 335	98.48 323	98.60 430	97.30 326	98.18 468	99.39 282	92.96 448	98.41 379	98.78 428	93.77 321	99.27 367	98.16 254	98.61 264	98.86 319

pm-mvs1		97.68 336	97.28 355	98.88 268	99.06 361	98.62 247	99.50 200	99.45 247	96.32 383	97.87 411	99.79 167	92.47 355	99.35 354	97.54 319	93.54 431	98.67 363

GBi-Net		97.68 336	97.48 320	98.29 349	99.51 227	97.26 330	99.43 256	99.48 202	96.49 371	99.07 287	99.32 366	90.26 395	98.98 418	97.10 351	96.65 362	98.62 385

test1		97.68 336	97.48 320	98.29 349	99.51 227	97.26 330	99.43 256	99.48 202	96.49 371	99.07 287	99.32 366	90.26 395	98.98 418	97.10 351	96.65 362	98.62 385

tpm		97.67 339	97.55 310	98.03 368	99.02 368	95.01 424	99.43 256	98.54 444	96.44 377	99.12 276	99.34 358	91.83 370	99.60 313	97.75 298	96.46 367	99.48 241

PCF-MVS	97.08 14	97.66 340	97.06 368	99.47 164	99.61 187	99.09 165	98.04 471	99.25 352	91.24 458	98.51 374	99.70 214	94.55 286	99.91 135	92.76 447	99.85 94	99.42 259
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew		97.65 341	97.65 301	97.63 401	98.78 405	97.62 316	99.13 367	98.33 448	97.36 299	99.07 287	98.94 415	95.64 225	99.15 390	92.95 443	98.68 262	96.12 472

our_test_3		97.65 341	97.68 298	97.55 407	98.62 427	94.97 425	98.84 424	99.30 339	96.83 347	98.19 394	99.34 358	97.01 150	99.02 413	95.00 417	96.01 378	98.64 376

testgi		97.65 341	97.50 318	98.13 364	99.36 282	96.45 382	99.42 263	99.48 202	97.76 249	97.87 411	99.45 325	91.09 387	98.81 436	94.53 422	98.52 273	99.13 292

thres200		97.61 344	97.28 355	98.62 304	99.64 163	98.03 289	99.26 336	98.74 428	97.68 260	99.09 284	98.32 445	91.66 377	99.81 230	92.88 444	98.22 293	98.03 441

PAPM		97.59 345	97.09 367	99.07 231	99.06 361	98.26 277	98.30 464	99.10 374	94.88 425	98.08 399	99.34 358	96.27 192	99.64 304	89.87 458	98.92 242	99.31 277

UWE-MVS		97.58 346	97.29 354	98.48 323	99.09 355	96.25 390	99.01 399	96.61 474	97.86 232	99.19 265	99.01 406	88.72 413	99.90 148	97.38 335	98.69 261	99.28 279

SD_0403		97.55 347	97.53 314	97.62 402	99.61 187	93.64 450	99.72 53	99.44 256	98.03 216	98.62 366	99.39 342	96.06 200	99.57 316	87.88 467	99.01 236	99.66 167

VDDNet		97.55 347	97.02 369	99.16 223	99.49 241	98.12 286	99.38 285	99.30 339	95.35 414	99.68 117	99.90 35	82.62 461	99.93 110	99.31 86	98.13 302	99.42 259

TESTMET0.1,1		97.55 347	97.27 358	98.40 339	98.93 382	96.53 379	98.67 440	97.61 463	96.96 336	98.64 361	99.28 373	88.63 419	99.45 329	97.30 339	99.38 183	99.21 288

pmmvs5		97.52 350	97.30 352	98.16 360	98.57 433	96.73 369	99.27 327	98.90 406	96.14 399	98.37 382	99.53 296	91.54 380	99.14 392	97.51 322	95.87 384	98.63 383

LF4IMVS		97.52 350	97.46 325	97.70 399	98.98 377	95.55 407	99.29 317	98.82 416	98.07 200	98.66 354	99.64 252	89.97 400	99.61 312	97.01 356	96.68 361	97.94 449

DTE-MVSNet		97.51 352	97.19 361	98.46 329	98.63 426	98.13 284	99.84 12	99.48 202	96.68 354	97.97 406	99.67 239	92.92 337	98.56 443	96.88 368	92.60 445	98.70 346

testing11		97.50 353	97.10 366	98.71 297	99.20 325	96.91 362	99.29 317	98.82 416	97.89 229	98.21 393	98.40 441	85.63 445	99.83 216	98.45 226	98.04 305	99.37 269

ETVMVS		97.50 353	96.90 373	99.29 205	99.23 318	98.78 233	99.32 306	98.90 406	97.52 281	98.56 371	98.09 455	84.72 452	99.69 289	97.86 282	97.88 311	99.39 265

hse-mvs2		97.50 353	97.14 363	98.59 306	99.49 241	97.05 343	99.28 322	99.22 358	98.94 78	99.66 128	99.42 330	94.93 254	99.65 300	99.48 64	83.80 471	99.08 298

SixPastTwentyTwo		97.50 353	97.33 349	98.03 368	98.65 424	96.23 391	99.77 34	98.68 437	97.14 317	97.90 409	99.93 10	90.45 393	99.18 387	97.00 357	96.43 368	98.67 363

JIA-IIPM		97.50 353	97.02 369	98.93 252	98.73 414	97.80 306	99.30 312	98.97 392	91.73 456	98.91 316	94.86 474	95.10 248	99.71 277	97.58 312	97.98 306	99.28 279

ppachtmachnet_test		97.49 358	97.45 326	97.61 405	98.62 427	95.24 418	98.80 429	99.46 236	96.11 401	98.22 392	99.62 263	96.45 182	98.97 425	93.77 431	95.97 383	98.61 394

test-mter		97.49 358	97.13 365	98.55 316	98.79 402	97.10 337	98.67 440	97.75 460	96.65 357	98.61 367	98.85 421	88.23 423	99.45 329	97.25 341	99.38 183	99.10 293

testing91		97.44 360	97.02 369	98.71 297	99.18 331	96.89 364	99.19 357	99.04 384	97.78 247	98.31 385	98.29 446	85.41 447	99.85 187	98.01 271	97.95 307	99.39 265

tpm2		97.44 360	97.34 346	97.74 397	99.15 345	94.36 440	99.45 243	98.94 395	93.45 444	98.90 318	99.44 326	91.35 383	99.59 314	97.31 338	98.07 304	99.29 278

tpm cat1		97.39 362	97.36 341	97.50 409	99.17 339	93.73 446	99.43 256	99.31 334	91.27 457	98.71 345	99.08 396	94.31 299	99.77 251	96.41 386	98.50 274	99.00 309

UWE-MVS-28		97.36 363	97.24 359	97.75 395	98.84 398	94.44 437	99.24 343	97.58 464	97.98 221	99.00 302	99.00 407	91.35 383	99.53 322	93.75 432	98.39 278	99.27 283

testing99		97.36 363	96.94 372	98.63 303	99.18 331	96.70 370	99.30 312	98.93 396	97.71 255	98.23 390	98.26 447	84.92 450	99.84 196	98.04 270	97.85 314	99.35 271

SSC-MVS3.2		97.34 365	97.15 362	97.93 379	99.02 368	95.76 403	99.48 225	99.58 78	97.62 267	99.09 284	99.53 296	87.95 426	99.27 367	96.42 384	95.66 391	98.75 333

USDC		97.34 365	97.20 360	97.75 395	99.07 359	95.20 419	98.51 454	99.04 384	97.99 220	98.31 385	99.86 76	89.02 409	99.55 320	95.67 403	97.36 348	98.49 407

UniMVSNet_ETH3D		97.32 367	96.81 375	98.87 272	99.40 271	97.46 321	99.51 189	99.53 125	95.86 409	98.54 373	99.77 183	82.44 462	99.66 295	98.68 192	97.52 332	99.50 237

testing3		97.28 368	96.76 377	98.82 281	99.37 279	98.07 288	99.45 243	99.36 300	97.56 274	97.89 410	98.95 414	83.70 456	98.82 435	96.03 392	98.56 270	99.58 208

MVS		97.28 368	96.55 381	99.48 158	98.78 405	98.95 193	99.27 327	99.39 282	83.53 474	98.08 399	99.54 292	96.97 151	99.87 175	94.23 427	99.16 204	99.63 185

test_fmvs2		97.25 370	97.30 352	97.09 420	99.43 259	93.31 453	99.73 51	98.87 411	98.83 88	99.28 238	99.80 150	84.45 453	99.66 295	97.88 279	97.45 340	98.30 424

DSMNet-mixed		97.25 370	97.35 343	96.95 425	97.84 447	93.61 451	99.57 140	96.63 473	96.13 400	98.87 324	98.61 434	94.59 282	97.70 461	95.08 415	98.86 250	99.55 215

MS-PatchMatch		97.24 372	97.32 350	96.99 422	98.45 438	93.51 452	98.82 427	99.32 330	97.41 295	98.13 398	99.30 369	88.99 410	99.56 318	95.68 402	99.80 125	97.90 452

testing222		97.16 373	96.50 382	99.16 223	99.16 341	98.47 268	99.27 327	98.66 439	97.71 255	98.23 390	98.15 450	82.28 464	99.84 196	97.36 336	97.66 320	99.18 289

TransMVSNet (Re)		97.15 374	96.58 380	98.86 275	99.12 347	98.85 219	99.49 217	98.91 404	95.48 413	97.16 430	99.80 150	93.38 326	99.11 401	94.16 429	91.73 448	98.62 385

TinyColmap		97.12 375	96.89 374	97.83 390	99.07 359	95.52 410	98.57 449	98.74 428	97.58 271	97.81 414	99.79 167	88.16 424	99.56 318	95.10 414	97.21 353	98.39 420

K. test v3		97.10 376	96.79 376	98.01 371	98.72 416	96.33 386	99.87 8	97.05 467	97.59 269	96.16 444	99.80 150	88.71 414	99.04 409	96.69 375	96.55 366	98.65 374

Syy-MVS		97.09 377	97.14 363	96.95 425	99.00 371	92.73 457	99.29 317	99.39 282	97.06 328	97.41 420	98.15 450	93.92 315	98.68 441	91.71 451	98.34 280	99.45 255

PatchT		97.03 378	96.44 384	98.79 287	98.99 374	98.34 274	99.16 361	99.07 380	92.13 454	99.52 176	97.31 467	94.54 287	98.98 418	88.54 463	98.73 259	99.03 306

mmtdpeth		96.95 379	96.71 378	97.67 400	99.33 289	94.90 427	99.89 2	99.28 345	98.15 175	99.72 102	98.57 435	86.56 438	99.90 148	99.82 29	89.02 462	98.20 431

myMVS_eth3d		96.89 380	96.37 385	98.43 336	99.00 371	97.16 334	99.29 317	99.39 282	97.06 328	97.41 420	98.15 450	83.46 458	98.68 441	95.27 412	98.34 280	99.45 255

AUN-MVS		96.88 381	96.31 387	98.59 306	99.48 248	97.04 346	99.27 327	99.22 358	97.44 291	98.51 374	99.41 334	91.97 366	99.66 295	97.71 303	83.83 470	99.07 303

FMVSNet1		96.84 382	96.36 386	98.29 349	99.32 296	97.26 330	99.43 256	99.48 202	95.11 418	98.55 372	99.32 366	83.95 455	98.98 418	95.81 397	96.26 373	98.62 385

test2506		96.81 383	96.65 379	97.29 415	99.74 100	92.21 460	99.60 113	85.06 491	99.13 41	99.77 85	99.93 10	87.82 430	99.85 187	99.38 74	99.38 183	99.80 88

RPMNet		96.72 384	95.90 397	99.19 220	99.18 331	98.49 264	99.22 350	99.52 132	88.72 467	99.56 165	97.38 464	94.08 308	99.95 76	86.87 472	98.58 267	99.14 290

mvs5depth		96.66 385	96.22 389	97.97 375	97.00 463	96.28 388	98.66 443	99.03 386	96.61 362	96.93 436	99.79 167	87.20 433	99.47 325	96.65 379	94.13 422	98.16 433

test_0402		96.64 386	96.24 388	97.85 386	98.85 396	96.43 383	99.44 250	99.26 350	93.52 441	96.98 434	99.52 300	88.52 420	99.20 386	92.58 449	97.50 335	97.93 450

X-MVStestdata		96.55 387	95.45 406	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 214	64.01 487	98.81 49	99.94 92	98.79 178	99.86 87	99.84 53

pmmvs6		96.53 388	96.09 393	97.82 392	98.69 421	95.47 411	99.37 287	99.47 224	93.46 443	97.41 420	99.78 174	87.06 435	99.33 357	96.92 366	92.70 443	98.65 374

ET-MVSNet_ETH3D		96.49 389	95.64 403	99.05 235	99.53 218	98.82 227	98.84 424	97.51 465	97.63 265	84.77 474	99.21 385	92.09 364	98.91 431	98.98 137	92.21 446	99.41 262

UnsupCasMVSNet_eth		96.44 390	96.12 391	97.40 412	98.65 424	95.65 404	99.36 293	99.51 154	97.13 318	96.04 446	98.99 409	88.40 421	98.17 450	96.71 373	90.27 456	98.40 419

FMVSNet5		96.43 391	96.19 390	97.15 416	99.11 349	95.89 400	99.32 306	99.52 132	94.47 434	98.34 384	99.07 397	87.54 431	97.07 467	92.61 448	95.72 389	98.47 410

new_pmnet		96.38 392	96.03 394	97.41 411	98.13 444	95.16 422	99.05 386	99.20 362	93.94 436	97.39 423	98.79 427	91.61 379	99.04 409	90.43 456	95.77 386	98.05 440

Anonymous20231206		96.22 393	96.03 394	96.79 430	97.31 457	94.14 442	99.63 101	99.08 377	96.17 395	97.04 433	99.06 399	93.94 313	97.76 460	86.96 471	95.06 405	98.47 410

IB-MVS	95.67 18	96.22 393	95.44 407	98.57 310	99.21 323	96.70 370	98.65 444	97.74 462	96.71 352	97.27 425	98.54 436	86.03 442	99.92 123	98.47 224	86.30 467	99.10 293
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
Anonymous20240521		96.20 395	95.89 398	97.13 418	97.72 451	94.96 426	99.79 31	99.29 343	93.01 447	97.20 429	99.03 403	89.69 404	98.36 447	91.16 454	96.13 375	98.07 438

gg-mvs-nofinetune		96.17 396	95.32 408	98.73 292	98.79 402	98.14 283	99.38 285	94.09 482	91.07 460	98.07 402	91.04 480	89.62 406	99.35 354	96.75 371	99.09 228	98.68 355

test20.03		96.12 397	95.96 396	96.63 431	97.44 453	95.45 412	99.51 189	99.38 290	96.55 368	96.16 444	99.25 379	93.76 322	96.17 473	87.35 470	94.22 420	98.27 426

PVSNet_0	94.43 19	96.09 398	95.47 405	97.94 378	99.31 297	94.34 441	97.81 472	99.70 18	97.12 320	97.46 419	98.75 429	89.71 403	99.79 243	97.69 306	81.69 474	99.68 158

MVStest1		96.08 399	95.48 404	97.89 383	98.93 382	96.70 370	99.56 148	99.35 307	92.69 451	91.81 469	99.46 323	89.90 401	98.96 427	95.00 417	92.61 444	98.00 445

EG-PatchMatch MVS		95.97 400	95.69 401	96.81 429	97.78 448	92.79 456	99.16 361	98.93 396	96.16 396	94.08 458	99.22 382	82.72 460	99.47 325	95.67 403	97.50 335	98.17 432

APD_test1		95.87 401	96.49 383	94.00 443	99.53 218	84.01 472	99.54 168	99.32 330	95.91 408	97.99 404	99.85 83	85.49 446	99.88 168	91.96 450	98.84 252	98.12 435

Patchmatch-RL test		95.84 402	95.81 400	95.95 438	95.61 469	90.57 464	98.24 465	98.39 446	95.10 420	95.20 451	98.67 431	94.78 265	97.77 459	96.28 389	90.02 457	99.51 233

test_vis1_rt		95.81 403	95.65 402	96.32 435	99.67 136	91.35 463	99.49 217	96.74 472	98.25 160	95.24 449	98.10 454	74.96 471	99.90 148	99.53 53	98.85 251	97.70 455

sc_t1		95.75 404	95.05 411	97.87 384	98.83 399	94.61 434	99.21 352	99.45 247	87.45 468	97.97 406	99.85 83	81.19 467	99.43 338	98.27 244	93.20 436	99.57 211

MVS-HIRNet		95.75 404	95.16 409	97.51 408	99.30 298	93.69 448	98.88 420	95.78 476	85.09 473	98.78 339	92.65 476	91.29 385	99.37 347	94.85 419	99.85 94	99.46 252

tt0320		95.71 406	95.07 410	97.62 402	99.05 364	95.02 423	99.25 338	99.52 132	86.81 469	97.97 406	99.72 207	83.58 457	99.15 390	96.38 387	93.35 432	98.68 355

MIMVSNet1		95.51 407	95.04 412	96.92 427	97.38 454	95.60 405	99.52 179	99.50 177	93.65 440	96.97 435	99.17 387	85.28 449	96.56 471	88.36 464	95.55 395	98.60 397

MDA-MVSNet_test_wron		95.45 408	94.60 415	98.01 371	98.16 443	97.21 333	99.11 376	99.24 355	93.49 442	80.73 480	98.98 411	93.02 334	98.18 449	94.22 428	94.45 416	98.64 376

TDRefinement		95.42 409	94.57 417	97.97 375	89.83 484	96.11 395	99.48 225	98.75 425	96.74 350	96.68 438	99.88 55	88.65 417	99.71 277	98.37 234	82.74 472	98.09 437

YYNet1		95.36 410	94.51 418	97.92 380	97.89 446	97.10 337	99.10 378	99.23 356	93.26 445	80.77 479	99.04 402	92.81 340	98.02 453	94.30 424	94.18 421	98.64 376

pmmvs-eth3d		95.34 411	94.73 414	97.15 416	95.53 471	95.94 398	99.35 298	99.10 374	95.13 416	93.55 461	97.54 462	88.15 425	97.91 456	94.58 421	89.69 461	97.61 456

tt0320-xc		95.31 412	94.59 416	97.45 410	98.92 384	94.73 429	99.20 355	99.31 334	86.74 470	97.23 426	99.72 207	81.14 468	98.95 428	97.08 354	91.98 447	98.67 363

blend_shiyan4		95.25 413	94.39 420	97.84 388	96.70 464	95.92 399	98.84 424	99.28 345	92.21 453	98.16 396	97.84 458	87.10 434	99.07 405	97.53 320	81.87 473	98.54 402

FE-MVSNET2		95.10 414	94.44 419	97.08 421	95.08 474	95.97 397	99.51 189	99.37 298	95.02 422	94.10 457	97.57 460	86.18 441	97.66 463	93.28 438	89.86 459	97.61 456

dmvs_testset		95.02 415	96.12 391	91.72 452	99.10 352	80.43 480	99.58 132	97.87 459	97.47 284	95.22 450	98.82 423	93.99 311	95.18 477	88.09 465	94.91 410	99.56 214

KD-MVS_self_test		95.00 416	94.34 421	96.96 424	97.07 462	95.39 415	99.56 148	99.44 256	95.11 418	97.13 431	97.32 466	91.86 369	97.27 466	90.35 457	81.23 475	98.23 430

MDA-MVSNet-bldmvs		94.96 417	93.98 424	97.92 380	98.24 442	97.27 328	99.15 364	99.33 320	93.80 438	80.09 481	99.03 403	88.31 422	97.86 458	93.49 436	94.36 418	98.62 385

N_pmnet		94.95 418	95.83 399	92.31 450	98.47 437	79.33 482	99.12 370	92.81 488	93.87 437	97.68 416	99.13 392	93.87 317	99.01 415	91.38 453	96.19 374	98.59 398

KD-MVS_2432*1600		94.62 419	93.72 427	97.31 413	97.19 460	95.82 401	98.34 460	99.20 362	95.00 423	97.57 417	98.35 443	87.95 426	98.10 451	92.87 445	77.00 478	98.01 442

miper_refine_blended		94.62 419	93.72 427	97.31 413	97.19 460	95.82 401	98.34 460	99.20 362	95.00 423	97.57 417	98.35 443	87.95 426	98.10 451	92.87 445	77.00 478	98.01 442

CL-MVSNet_self_test		94.49 421	93.97 425	96.08 437	96.16 466	93.67 449	98.33 462	99.38 290	95.13 416	97.33 424	98.15 450	92.69 348	96.57 470	88.67 462	79.87 476	97.99 446

new-patchmatchnet		94.48 422	94.08 423	95.67 439	95.08 474	92.41 458	99.18 359	99.28 345	94.55 433	93.49 462	97.37 465	87.86 429	97.01 468	91.57 452	88.36 463	97.61 456

OpenMVS_ROB	92.34 20	94.38 423	93.70 429	96.41 434	97.38 454	93.17 454	99.06 384	98.75 425	86.58 471	94.84 455	98.26 447	81.53 465	99.32 359	89.01 461	97.87 312	96.76 465

CMPMVS	69.68 23	94.13 424	94.90 413	91.84 451	97.24 458	80.01 481	98.52 453	99.48 202	89.01 465	91.99 468	99.67 239	85.67 444	99.13 395	95.44 407	97.03 358	96.39 469
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs3		94.09 425	93.25 432	96.60 432	94.76 477	94.49 436	98.92 416	98.18 455	89.66 461	96.48 440	98.06 456	86.28 440	97.33 465	89.68 459	87.20 466	97.97 448

FE-MVSNET		94.07 426	93.36 431	96.22 436	94.05 478	94.71 431	99.56 148	98.36 447	93.15 446	93.76 460	97.55 461	86.47 439	96.49 472	87.48 468	89.83 460	97.48 461

mvsany_test3		93.77 427	93.45 430	94.74 441	95.78 468	88.01 467	99.64 95	98.25 450	98.28 150	94.31 456	97.97 457	68.89 474	98.51 445	97.50 323	90.37 455	97.71 453

UnsupCasMVSNet_bld		93.53 428	92.51 434	96.58 433	97.38 454	93.82 444	98.24 465	99.48 202	91.10 459	93.10 463	96.66 469	74.89 472	98.37 446	94.03 430	87.71 465	97.56 459

dongtai		93.26 429	92.93 433	94.25 442	99.39 274	85.68 470	97.68 474	93.27 484	92.87 449	96.85 437	99.39 342	82.33 463	97.48 464	76.78 478	97.80 315	99.58 208

WB-MVS		93.10 430	94.10 422	90.12 457	95.51 473	81.88 477	99.73 51	99.27 349	95.05 421	93.09 464	98.91 420	94.70 275	91.89 481	76.62 479	94.02 426	96.58 467

PM-MVS		92.96 431	92.23 435	95.14 440	95.61 469	89.98 466	99.37 287	98.21 453	94.80 428	95.04 454	97.69 459	65.06 475	97.90 457	94.30 424	89.98 458	97.54 460

SSC-MVS		92.73 432	93.73 426	89.72 458	95.02 476	81.38 478	99.76 37	99.23 356	94.87 426	92.80 465	98.93 416	94.71 274	91.37 482	74.49 481	93.80 428	96.42 468

test_fmvs3		92.10 433	91.77 436	93.08 448	96.19 465	86.25 468	99.82 16	98.62 441	96.65 357	95.19 452	96.90 468	55.05 482	95.93 475	96.63 380	90.92 454	97.06 464

test_f		91.90 434	91.26 438	93.84 444	95.52 472	85.92 469	99.69 62	98.53 445	95.31 415	93.87 459	96.37 471	55.33 481	98.27 448	95.70 400	90.98 453	97.32 463

test_method		91.10 435	91.36 437	90.31 456	95.85 467	73.72 489	94.89 478	99.25 352	68.39 480	95.82 447	99.02 405	80.50 469	98.95 428	93.64 434	94.89 411	98.25 428

Gipuma		90.99 436	90.15 441	93.51 445	98.73 414	90.12 465	93.98 479	99.45 247	79.32 476	92.28 466	94.91 473	69.61 473	97.98 455	87.42 469	95.67 390	92.45 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		90.92 437	90.11 442	93.34 446	98.78 405	85.59 471	98.15 469	93.16 486	89.37 464	92.07 467	98.38 442	81.48 466	95.19 476	62.54 485	97.04 357	99.25 284

testf1		90.42 438	90.68 439	89.65 459	97.78 448	73.97 487	99.13 367	98.81 418	89.62 462	91.80 470	98.93 416	62.23 478	98.80 437	86.61 473	91.17 450	96.19 470

APD_test2		90.42 438	90.68 439	89.65 459	97.78 448	73.97 487	99.13 367	98.81 418	89.62 462	91.80 470	98.93 416	62.23 478	98.80 437	86.61 473	91.17 450	96.19 470

test_vis3_rt		87.04 440	85.81 443	90.73 455	93.99 479	81.96 476	99.76 37	90.23 490	92.81 450	81.35 478	91.56 478	40.06 486	99.07 405	94.27 426	88.23 464	91.15 478

PMMVS2		86.87 441	85.37 445	91.35 454	90.21 483	83.80 473	98.89 419	97.45 466	83.13 475	91.67 472	95.03 472	48.49 484	94.70 478	85.86 475	77.62 477	95.54 473

LCM-MVSNet		86.80 442	85.22 446	91.53 453	87.81 485	80.96 479	98.23 467	98.99 390	71.05 478	90.13 473	96.51 470	48.45 485	96.88 469	90.51 455	85.30 468	96.76 465

FPMVS		84.93 443	85.65 444	82.75 465	86.77 486	63.39 491	98.35 459	98.92 399	74.11 477	83.39 476	98.98 411	50.85 483	92.40 480	84.54 476	94.97 407	92.46 475

EGC-MVSNET		82.80 444	77.86 450	97.62 402	97.91 445	96.12 394	99.33 303	99.28 345	8.40 488	25.05 489	99.27 376	84.11 454	99.33 357	89.20 460	98.22 293	97.42 462

tmp_tt		82.80 444	81.52 447	86.66 461	66.61 491	68.44 490	92.79 481	97.92 457	68.96 479	80.04 482	99.85 83	85.77 443	96.15 474	97.86 282	43.89 484	95.39 474

E-PMN		80.61 446	79.88 448	82.81 464	90.75 482	76.38 485	97.69 473	95.76 477	66.44 482	83.52 475	92.25 477	62.54 477	87.16 484	68.53 483	61.40 481	84.89 482

EMVS		80.02 447	79.22 449	82.43 466	91.19 481	76.40 484	97.55 476	92.49 489	66.36 483	83.01 477	91.27 479	64.63 476	85.79 485	65.82 484	60.65 482	85.08 481

ANet_high		77.30 448	74.86 452	84.62 463	75.88 489	77.61 483	97.63 475	93.15 487	88.81 466	64.27 484	89.29 481	36.51 487	83.93 486	75.89 480	52.31 483	92.33 477

MVE	76.82 21	76.91 449	74.31 453	84.70 462	85.38 488	76.05 486	96.88 477	93.17 485	67.39 481	71.28 483	89.01 482	21.66 492	87.69 483	71.74 482	72.29 480	90.35 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	70.75 22	75.98 450	74.97 451	79.01 467	70.98 490	55.18 492	93.37 480	98.21 453	65.08 484	61.78 485	93.83 475	21.74 491	92.53 479	78.59 477	91.12 452	89.34 480
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 451	41.29 456	36.84 468	86.18 487	49.12 493	79.73 482	22.81 493	27.64 485	25.46 488	28.45 488	21.98 490	48.89 487	55.80 486	23.56 487	12.51 485

testmvs		39.17 452	43.78 454	25.37 470	36.04 493	16.84 495	98.36 458	26.56 492	20.06 486	38.51 487	67.32 483	29.64 489	15.30 489	37.59 487	39.90 485	43.98 484

test123		39.01 453	42.50 455	28.53 469	39.17 492	20.91 494	98.75 434	19.17 494	19.83 487	38.57 486	66.67 484	33.16 488	15.42 488	37.50 488	29.66 486	49.26 483

cdsmvs_eth3d_5k		24.64 454	32.85 457	0.00 471	0.00 494	0.00 496	0.00 483	99.51 154	0.00 489	0.00 490	99.56 284	96.58 174	0.00 490	0.00 489	0.00 488	0.00 486

ab-mvs-re		8.30 455	11.06 458	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	99.58 276	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

pcd_1.5k_mvsjas		8.27 456	11.03 459	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	99.01 20	0.00 490	0.00 489	0.00 488	0.00 486

test_blank		0.13 457	0.17 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	1.57 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

mmdepth		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

monomultidepth		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet_test		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

DCPMVS		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

sosnet-low-res		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

sosnet		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uncertanet		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

Regformer		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet		0.02 458	0.03 461	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.27 490	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

MED-MVS test				99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.34 26	99.90 34	99.83 104		99.95 76	98.83 170	99.89 68	99.83 63

TestfortrainingZip							99.69 62

WAC-MVS						97.16 334								95.47 406

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 69

MSC_two_6792asdad				99.87 21	99.51 227	99.76 49		99.33 320					99.96 41	98.87 157	99.84 102	99.89 29

PC_three_1452									98.18 173	99.84 56	99.70 214	99.31 3	98.52 444	98.30 243	99.80 125	99.81 79

No_MVS				99.87 21	99.51 227	99.76 49		99.33 320					99.96 41	98.87 157	99.84 102	99.89 29

test_one_0601					99.81 57	99.88 10		99.49 190	98.97 75	99.65 137	99.81 132	99.09 16

eth-test2					0.00 494

eth-test					0.00 494

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 345	99.56 165	99.54 292	98.58 78	99.96 41	96.93 364	99.75 142

RE-MVS-def			99.34 50		99.76 82	99.82 28	99.63 101	99.52 132	98.38 137	99.76 91	99.82 117	98.75 60		98.61 202	99.81 120	99.77 100

IU-MVS					99.84 38	99.88 10		99.32 330	98.30 149	99.84 56				98.86 162	99.85 94	99.89 29

OPU-MVS				99.64 101	99.56 206	99.72 56	99.60 113				99.70 214	99.27 7	99.42 340	98.24 247	99.80 125	99.79 92

test_241102_TWO								99.48 202	99.08 56	99.88 43	99.81 132	98.94 34	99.96 41	98.91 151	99.84 102	99.88 35

test_241102_ONE					99.84 38	99.90 3		99.48 202	99.07 58	99.91 31	99.74 197	99.20 9	99.76 255

9.14			99.10 99		99.72 111		99.40 276	99.51 154	97.53 279	99.64 142	99.78 174	98.84 46	99.91 135	97.63 308	99.82 117

save fliter					99.76 82	99.59 88	99.14 366	99.40 279	99.00 67

test_0728_THIRD									98.99 69	99.81 69	99.80 150	99.09 16	99.96 41	98.85 164	99.90 57	99.88 35

test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 140	99.51 154					99.96 41	98.93 148	99.86 87	99.88 35

test0726					99.85 31	99.89 6	99.62 106	99.50 177	99.10 48	99.86 53	99.82 117	98.94 34

GSMVS																99.52 224

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 260				99.52 224

sam_mvs												94.72 273

ambc				93.06 449	92.68 480	82.36 474	98.47 455	98.73 434		95.09 453	97.41 463	55.55 480	99.10 403	96.42 384	91.32 449	97.71 453

MTGPA								99.47 224

test_post1							99.23 346				65.14 486	94.18 304	99.71 277	97.58 312

test_post											65.99 485	94.65 280	99.73 267

patchmatchnet-post											98.70 430	94.79 264	99.74 261

GG-mvs-BLEND				98.45 331	98.55 434	98.16 281	99.43 256	93.68 483		97.23 426	98.46 438	89.30 407	99.22 379	95.43 408	98.22 293	97.98 447

MTMP							99.54 168	98.88 409

gm-plane-assit					98.54 435	92.96 455			94.65 431		99.15 390		99.64 304	97.56 317

test9_res														97.49 324	99.72 148	99.75 111

TEST9					99.67 136	99.65 75	99.05 386	99.41 272	96.22 391	98.95 311	99.49 310	98.77 56	99.91 135

test_8					99.67 136	99.61 85	99.03 391	99.41 272	96.28 385	98.93 314	99.48 316	98.76 57	99.91 135

agg_prior2														97.21 343	99.73 147	99.75 111

agg_prior					99.67 136	99.62 83		99.40 279		98.87 324			99.91 135

TestCases				99.31 197	99.86 25	98.48 266		99.61 60	97.85 235	99.36 220	99.85 83	95.95 206	99.85 187	96.66 377	99.83 113	99.59 204

test_prior4						99.56 94	98.99 402

test_prior2							98.96 409		98.34 143	99.01 298	99.52 300	98.68 70		97.96 274	99.74 145

test_prior				99.68 89	99.67 136	99.48 111		99.56 90					99.83 216			99.74 116

旧先验2							98.96 409		96.70 353	99.47 184			99.94 92	98.19 250

新几何2							99.01 399

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 349	99.29 237	99.64 252	98.43 89	99.94 92	96.92 366	99.66 159	99.72 135

旧先验1					99.74 100	99.59 88		99.54 109			99.69 225	98.47 86			99.68 156	99.73 125

无先验							98.99 402	99.51 154	96.89 342				99.93 110	97.53 320		99.72 135

原ACMM2							98.95 412

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 224	97.46 285	99.12 276	99.66 244	98.67 72	99.91 135	97.70 305	99.69 153	99.71 146

test222					99.75 92	99.49 109	98.91 418	99.49 190	96.42 379	99.34 227	99.65 246	98.28 100			99.69 153	99.72 135

testdata2													99.95 76	96.67 376

segment_acmp												98.96 27

testdata				99.54 125	99.75 92	98.95 193		99.51 154	97.07 326	99.43 195	99.70 214	98.87 42	99.94 92	97.76 296	99.64 162	99.72 135

testdata1							98.85 423		98.32 147

test12				99.75 77	99.64 163	99.61 85		99.29 343		99.21 259		98.38 95	99.89 163		99.74 145	99.74 116

plane_prior7					99.29 302	97.03 349

plane_prior6					99.27 307	96.98 353						92.71 346

plane_prior5								99.47 224					99.69 289	97.78 292	97.63 321	98.67 363

plane_prior4											99.61 267

plane_prior3						97.00 351			98.69 107	99.11 278

plane_prior2							99.39 280		98.97 75

plane_prior1					99.26 310

plane_prior						96.97 354	99.21 352		98.45 130						97.60 324

n2								0.00 495

nn								0.00 495

door-mid								98.05 456

lessismore_v0				97.79 394	98.69 421	95.44 414		94.75 480		95.71 448	99.87 68	88.69 415	99.32 359	95.89 395	94.93 409	98.62 385

LGP-MVS_train				98.49 321	99.33 289	97.05 343		99.55 100	97.46 285	99.24 251	99.83 104	92.58 351	99.72 271	98.09 261	97.51 333	98.68 355

test11								99.35 307

door								97.92 457

HQP5-MVS						96.83 365

HQP-NCC					99.19 328		98.98 405		98.24 162	98.66 354

ACMP_Plane					99.19 328		98.98 405		98.24 162	98.66 354

BP-MVS														97.19 347

HQP4-MVS										98.66 354			99.64 304			98.64 376

HQP3-MVS								99.39 282							97.58 326

HQP2-MVS												92.47 355

NP-MVS					99.23 318	96.92 361					99.40 338

MDTV_nov1_ep13_2view						95.18 421	99.35 298		96.84 345	99.58 161		95.19 245		97.82 287		99.46 252

MDTV_nov1_ep13			98.32 230		99.11 349	94.44 437	99.27 327	98.74 428	97.51 282	99.40 208	99.62 263	94.78 265	99.76 255	97.59 311	98.81 256

ACMMP++_ref															97.19 354

ACMMP++															97.43 344

Test By Simon												98.75 60

ITE_SJBPF				98.08 366	99.29 302	96.37 384		98.92 399	98.34 143	98.83 331	99.75 192	91.09 387	99.62 311	95.82 396	97.40 346	98.25 428

DeepMVS_CX				93.34 446	99.29 302	82.27 475		99.22 358	85.15 472	96.33 441	99.05 400	90.97 389	99.73 267	93.57 435	97.77 317	98.01 442

High-resolution multi-view benchmark