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.

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 19	98.69 76	98.20 9	99.93 2	99.98 2	96.82 24	100.00 1	99.75 37	100.00 1	99.99 23

NCCC		99.37 2	99.25 2	99.71 15	99.96 8	99.15 22	99.97 37	98.62 92	98.02 20	99.90 5	99.95 3	97.33 17	100.00 1	99.54 53	100.00 1	100.00 1

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 37	98.64 85	98.47 3	99.13 101	99.92 13	96.38 34	100.00 1	99.74 39	100.00 1	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 66	98.32 191	97.28 43	99.83 20	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 91
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
SED-MVS		99.28 5	99.11 7	99.77 8	99.93 24	99.30 12	99.96 47	98.43 150	97.27 45	99.80 24	99.94 4	96.71 27	100.00 1	100.00 1	100.00 1	100.00 1

DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 39	99.31 10	99.95 66	98.43 150	96.48 76	99.80 24	99.93 11	97.44 14	100.00 1	99.92 13	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 11	99.89 45	99.24 19	99.87 123	98.44 142	97.48 37	99.64 54	99.94 4	96.68 29	99.99 36	99.99 5	100.00 1	99.99 23
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 62	99.98 19	98.86 56	97.10 51	99.80 24	99.94 4	95.92 40	100.00 1	99.51 54	100.00 1	100.00 1

MSP-MVS		99.09 9	99.12 5	98.98 86	99.93 24	97.24 115	99.95 66	98.42 162	97.50 36	99.52 72	99.88 24	97.43 16	99.71 153	99.50 56	99.98 32	100.00 1
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
HPM-MVS++		99.07 10	98.88 16	99.63 17	99.90 42	99.02 25	99.95 66	98.56 107	97.56 35	99.44 78	99.85 33	95.38 52	100.00 1	99.31 66	99.99 21	99.87 94

MVS_0304		99.06 11	98.84 17	99.72 13	99.76 68	99.21 21	99.99 5	99.34 25	98.70 2	99.44 78	99.75 75	93.24 123	99.99 36	99.94 11	99.41 125	99.95 77

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 45	99.91 101	98.39 174	97.20 49	99.46 76	99.85 33	95.53 48	99.79 138	99.86 23	100.00 1	99.99 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SteuartSystems-ACMMP		99.02 13	98.97 13	99.18 57	98.72 156	97.71 93	99.98 19	98.44 142	96.85 60	99.80 24	99.91 14	97.57 8	99.85 123	99.44 61	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
CHOSEN 280x420		99.01 14	99.03 10	98.95 89	99.38 102	98.87 33	98.46 359	99.42 21	97.03 55	99.02 108	99.09 172	99.35 2	98.21 285	99.73 41	99.78 84	99.77 110

fmvsm_l_conf0.5_n_a		99.00 15	98.91 14	99.28 48	99.21 110	97.91 86	99.98 19	98.85 59	98.25 5	99.92 4	99.75 75	94.72 71	99.97 59	99.87 21	99.64 92	99.95 77

fmvsm_l_conf0.5_n		98.94 16	98.84 17	99.25 50	99.17 114	97.81 90	99.98 19	98.86 56	98.25 5	99.90 5	99.76 67	94.21 94	99.97 59	99.87 21	99.52 108	99.98 52

TSAR-MVS + MP.		98.93 17	98.77 19	99.41 39	99.74 72	98.67 49	99.77 165	98.38 178	96.73 67	99.88 10	99.74 82	94.89 66	99.59 167	99.80 28	99.98 32	99.97 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		98.92 18	98.70 20	99.56 25	99.70 80	98.73 46	99.94 83	98.34 188	96.38 82	99.81 22	99.76 67	94.59 74	99.98 47	99.84 25	99.96 46	99.97 62
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
MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 209	99.44 19	97.33 42	99.00 109	99.72 88	94.03 99	99.98 47	98.73 103	100.00 1	100.00 1

train_agg		98.88 20	98.65 24	99.59 23	99.92 31	98.92 29	99.96 47	98.43 150	94.35 149	99.71 45	99.86 29	95.94 38	99.85 123	99.69 45	99.98 32	99.99 23

MM		98.83 21	98.53 30	99.76 10	99.59 87	99.33 8	99.99 5	99.76 6	98.39 4	99.39 86	99.80 54	90.49 188	99.96 71	99.89 18	99.43 123	99.98 52

DPM-MVS		98.83 21	98.46 33	99.97 1	99.33 104	99.92 1	99.96 47	98.44 142	97.96 21	99.55 67	99.94 4	97.18 21	100.00 1	93.81 255	99.94 55	99.98 52

DeepC-MVS_fast	96.59 1	98.81 23	98.54 29	99.62 20	99.90 42	98.85 35	99.24 278	98.47 134	98.14 14	99.08 104	99.91 14	93.09 127	100.00 1	99.04 79	99.99 21	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
reproduce-ours		98.78 24	98.67 21	99.09 74	99.70 80	97.30 112	99.74 177	98.25 202	97.10 51	99.10 102	99.90 18	94.59 74	99.99 36	99.77 33	99.91 67	99.99 23

our_new_method		98.78 24	98.67 21	99.09 74	99.70 80	97.30 112	99.74 177	98.25 202	97.10 51	99.10 102	99.90 18	94.59 74	99.99 36	99.77 33	99.91 67	99.99 23

SMA-MVS		98.76 26	98.48 32	99.62 20	99.87 51	98.87 33	99.86 134	98.38 178	93.19 199	99.77 36	99.94 4	95.54 46	100.00 1	99.74 39	99.99 21	100.00 1
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
reproduce_model		98.75 27	98.66 23	99.03 79	99.71 78	97.10 125	99.73 184	98.23 206	97.02 56	99.18 99	99.90 18	94.54 78	99.99 36	99.77 33	99.90 69	99.99 23

MVS_111021_HR		98.72 28	98.62 26	99.01 83	99.36 103	97.18 118	99.93 90	99.90 1	96.81 65	98.67 128	99.77 65	93.92 101	99.89 111	99.27 68	99.94 55	99.96 70

XVS		98.70 29	98.55 28	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	99.78 62	94.34 86	99.96 71	98.92 89	99.95 50	99.99 23

lecture		98.67 30	98.46 33	99.28 48	99.86 53	97.88 87	99.97 37	99.25 30	96.07 92	99.79 33	99.70 94	92.53 146	99.98 47	99.51 54	99.48 115	99.97 62

SF-MVS		98.67 30	98.40 36	99.50 30	99.77 67	98.67 49	99.90 107	98.21 208	93.53 187	99.81 22	99.89 22	94.70 73	99.86 122	99.84 25	99.93 61	99.96 70

CDPH-MVS		98.65 32	98.36 42	99.49 32	99.94 13	98.73 46	99.87 123	98.33 189	93.97 169	99.76 37	99.87 27	94.99 64	99.75 147	98.55 113	100.00 1	99.98 52

APD-MVS		98.62 33	98.35 43	99.41 39	99.90 42	98.51 59	99.87 123	98.36 182	94.08 162	99.74 41	99.73 85	94.08 97	99.74 149	99.42 62	99.99 21	99.99 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + GP.		98.60 34	98.51 31	98.86 93	99.73 75	96.63 144	99.97 37	97.92 244	98.07 17	98.76 124	99.55 125	95.00 63	99.94 88	99.91 16	97.68 190	99.99 23

PAPM		98.60 34	98.42 35	99.14 67	96.05 325	98.96 26	99.90 107	99.35 24	96.68 69	98.35 148	99.66 109	96.45 33	98.51 251	99.45 60	99.89 70	99.96 70

HFP-MVS		98.56 36	98.37 40	99.14 67	99.96 8	97.43 108	99.95 66	98.61 93	94.77 127	99.31 90	99.85 33	94.22 92	100.00 1	98.70 104	99.98 32	99.98 52

fmvsm_l_conf0.5_n_9		98.55 37	98.23 48	99.49 32	99.10 118	98.50 60	99.99 5	98.70 74	98.14 14	99.94 1	99.68 105	89.02 210	99.98 47	99.89 18	99.61 99	99.99 23

region2R		98.54 38	98.37 40	99.05 77	99.96 8	97.18 118	99.96 47	98.55 113	94.87 124	99.45 77	99.85 33	94.07 98	100.00 1	98.67 106	100.00 1	99.98 52

DELS-MVS		98.54 38	98.22 49	99.50 30	99.15 116	98.65 53	100.00 1	98.58 99	97.70 30	98.21 156	99.24 161	92.58 144	99.94 88	98.63 111	99.94 55	99.92 87
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
PAPR		98.52 40	98.16 55	99.58 24	99.97 3	98.77 42	99.95 66	98.43 150	95.35 111	98.03 161	99.75 75	94.03 99	99.98 47	98.11 140	99.83 77	99.99 23

ACMMPR		98.50 41	98.32 44	99.05 77	99.96 8	97.18 118	99.95 66	98.60 95	94.77 127	99.31 90	99.84 44	93.73 108	100.00 1	98.70 104	99.98 32	99.98 52

ACMMP_NAP		98.49 42	98.14 56	99.54 27	99.66 84	98.62 55	99.85 137	98.37 181	94.68 132	99.53 70	99.83 46	92.87 133	100.00 1	98.66 108	99.84 76	99.99 23

EPNet		98.49 42	98.40 36	98.77 99	99.62 86	96.80 138	99.90 107	99.51 16	97.60 32	99.20 96	99.36 145	93.71 109	99.91 104	97.99 148	98.71 158	99.61 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SR-MVS		98.46 44	98.30 47	98.93 90	99.88 49	97.04 127	99.84 142	98.35 184	94.92 121	99.32 89	99.80 54	93.35 116	99.78 140	99.30 67	99.95 50	99.96 70

CP-MVS		98.45 45	98.32 44	98.87 92	99.96 8	96.62 145	99.97 37	98.39 174	94.43 144	98.90 113	99.87 27	94.30 89	100.00 1	99.04 79	99.99 21	99.99 23

test_fmvsm_n_1920		98.44 46	98.61 27	97.92 166	99.27 109	95.18 218	100.00 1	98.90 50	98.05 18	99.80 24	99.73 85	92.64 141	99.99 36	99.58 52	99.51 111	98.59 259

PS-MVSNAJ		98.44 46	98.20 51	99.16 63	98.80 151	98.92 29	99.54 232	98.17 213	97.34 40	99.85 16	99.85 33	91.20 170	99.89 111	99.41 63	99.67 90	98.69 256

test_fmvsmconf_n		98.43 48	98.32 44	98.78 97	98.12 208	96.41 154	99.99 5	98.83 63	98.22 7	99.67 49	99.64 112	91.11 174	99.94 88	99.67 47	99.62 95	99.98 52

MVS_111021_LR		98.42 49	98.38 38	98.53 124	99.39 101	95.79 181	99.87 123	99.86 2	96.70 68	98.78 119	99.79 58	92.03 160	99.90 106	99.17 72	99.86 75	99.88 92

fmvsm_l_conf0.5_n_3		98.41 50	98.08 61	99.39 41	99.12 117	98.29 65	99.98 19	98.64 85	98.14 14	99.86 13	99.76 67	87.99 222	99.97 59	99.72 42	99.54 106	99.91 89

DP-MVS Recon		98.41 50	98.02 65	99.56 25	99.97 3	98.70 48	99.92 93	98.44 142	92.06 255	98.40 146	99.84 44	95.68 44	100.00 1	98.19 135	99.71 88	99.97 62

PHI-MVS		98.41 50	98.21 50	99.03 79	99.86 53	97.10 125	99.98 19	98.80 68	90.78 302	99.62 58	99.78 62	95.30 53	100.00 1	99.80 28	99.93 61	99.99 23

mPP-MVS		98.39 53	98.20 51	98.97 87	99.97 3	96.92 132	99.95 66	98.38 178	95.04 117	98.61 132	99.80 54	93.39 114	100.00 1	98.64 109	100.00 1	99.98 52

fmvsm_s_conf0.5_n_8		98.38 54	98.05 63	99.35 45	99.20 111	98.12 72	99.98 19	98.81 64	98.22 7	99.80 24	99.71 91	87.37 233	99.97 59	99.91 16	99.48 115	99.97 62

PGM-MVS		98.34 55	98.13 57	98.99 84	99.92 31	97.00 128	99.75 174	99.50 17	93.90 175	99.37 87	99.76 67	93.24 123	100.00 1	97.75 165	99.96 46	99.98 52

BP-MVS1		98.33 56	98.18 53	98.81 95	97.44 258	97.98 81	99.96 47	98.17 213	94.88 123	98.77 121	99.59 118	97.59 7	99.08 203	98.24 133	98.93 148	99.36 189

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 102	99.79 64	96.37 158	99.76 170	98.31 193	94.43 144	99.40 84	99.75 75	93.28 121	99.78 140	98.90 92	99.92 64	99.97 62

ZNCC-MVS		98.31 57	98.03 64	99.17 60	99.88 49	97.59 99	99.94 83	98.44 142	94.31 152	98.50 139	99.82 49	93.06 128	99.99 36	98.30 129	99.99 21	99.93 82

MTAPA		98.29 59	97.96 72	99.30 47	99.85 56	97.93 85	99.39 256	98.28 198	95.76 99	97.18 191	99.88 24	92.74 137	100.00 1	98.67 106	99.88 73	99.99 23

fmvsm_s_conf0.5_n_6		98.27 60	97.96 72	99.23 52	97.66 242	98.11 73	99.98 19	98.64 85	97.85 25	99.87 11	99.72 88	88.86 213	99.93 97	99.64 49	99.36 129	99.63 136

balanced_conf03		98.27 60	97.99 67	99.11 72	98.64 164	98.43 63	99.47 244	97.79 256	94.56 135	99.74 41	98.35 256	94.33 88	99.25 188	99.12 73	99.96 46	99.64 130

GST-MVS		98.27 60	97.97 69	99.17 60	99.92 31	97.57 100	99.93 90	98.39 174	94.04 167	98.80 118	99.74 82	92.98 130	100.00 1	98.16 137	99.76 85	99.93 82

CANet		98.27 60	97.82 82	99.63 17	99.72 77	99.10 23	99.98 19	98.51 125	97.00 57	98.52 136	99.71 91	87.80 223	99.95 80	99.75 37	99.38 127	99.83 99

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 100	99.83 59	96.59 149	99.40 252	98.51 125	95.29 113	98.51 138	99.76 67	93.60 112	99.71 153	98.53 116	99.52 108	99.95 77

APD-MVS_3200maxsize		98.25 65	98.08 61	98.78 97	99.81 62	96.60 147	99.82 152	98.30 196	93.95 171	99.37 87	99.77 65	92.84 134	99.76 146	98.95 85	99.92 64	99.97 62

patch_mono-2		98.24 66	99.12 5	95.59 271	99.67 83	86.91 393	99.95 66	98.89 52	97.60 32	99.90 5	99.76 67	96.54 32	99.98 47	99.94 11	99.82 81	99.88 92

xiu_mvs_v2_base		98.23 67	97.97 69	99.02 82	98.69 157	98.66 51	99.52 234	98.08 227	97.05 54	99.86 13	99.86 29	90.65 183	99.71 153	99.39 65	98.63 159	98.69 256

MP-MVS		98.23 67	97.97 69	99.03 79	99.94 13	97.17 121	99.95 66	98.39 174	94.70 131	98.26 153	99.81 53	91.84 164	100.00 1	98.85 95	99.97 42	99.93 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_9		98.15 69	98.02 65	98.55 118	99.28 107	95.84 179	99.99 5	98.57 101	98.17 11	99.93 2	99.74 82	87.04 238	99.97 59	99.86 23	99.59 103	99.83 99

EI-MVSNet-UG-set		98.14 70	97.99 67	98.60 112	99.80 63	96.27 160	99.36 262	98.50 131	95.21 115	98.30 150	99.75 75	93.29 120	99.73 152	98.37 125	99.30 132	99.81 103

PAPM_NR		98.12 71	97.93 75	98.70 103	99.94 13	96.13 171	99.82 152	98.43 150	94.56 135	97.52 178	99.70 94	94.40 81	99.98 47	97.00 181	99.98 32	99.99 23

WTY-MVS		98.10 72	97.60 93	99.60 22	98.92 139	99.28 17	99.89 117	99.52 14	95.58 105	98.24 155	99.39 142	93.33 117	99.74 149	97.98 150	95.58 255	99.78 109

fmvsm_s_conf0.5_n_5		98.08 73	97.71 87	99.17 60	98.67 159	97.69 97	99.99 5	98.57 101	97.40 38	99.89 8	99.69 98	85.99 254	99.96 71	99.80 28	99.40 126	99.85 97

MP-MVS-pluss		98.07 74	97.64 91	99.38 44	99.74 72	98.41 64	99.74 177	98.18 212	93.35 193	96.45 213	99.85 33	92.64 141	99.97 59	98.91 91	99.89 70	99.77 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		97.96 75	97.72 85	98.68 104	99.84 58	96.39 157	99.90 107	98.17 213	92.61 229	98.62 131	99.57 124	91.87 163	99.67 161	98.87 94	99.99 21	99.99 23
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_3		97.95 76	97.66 89	98.81 95	98.99 129	98.07 75	99.98 19	98.81 64	98.18 10	99.89 8	99.70 94	84.15 282	99.97 59	99.76 36	99.50 113	98.39 265

PVSNet_Blended		97.94 77	97.64 91	98.83 94	99.59 87	96.99 129	100.00 1	99.10 34	95.38 110	98.27 151	99.08 173	89.00 211	99.95 80	99.12 73	99.25 134	99.57 152

PLC	95.54 3	97.93 78	97.89 78	98.05 158	99.82 60	94.77 231	99.92 93	98.46 136	93.93 172	97.20 189	99.27 155	95.44 51	99.97 59	97.41 170	99.51 111	99.41 183
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETV-MVS		97.92 79	97.80 83	98.25 144	98.14 206	96.48 151	99.98 19	97.63 273	95.61 104	99.29 93	99.46 133	92.55 145	98.82 219	99.02 83	98.54 163	99.46 175

NormalMVS		97.90 80	97.85 80	98.04 159	99.86 53	95.39 203	99.61 215	97.78 258	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 191	99.48 115	99.06 230

GDP-MVS		97.88 81	97.59 95	98.75 100	97.59 247	97.81 90	99.95 66	97.37 307	94.44 143	99.08 104	99.58 121	97.13 23	99.08 203	94.99 221	98.17 174	99.37 187

SPE-MVS-test		97.88 81	97.94 74	97.70 185	99.28 107	95.20 217	99.98 19	97.15 336	95.53 107	99.62 58	99.79 58	92.08 159	98.38 268	98.75 102	99.28 133	99.52 164

myMVS_eth3d28		97.86 83	97.59 95	98.68 104	98.50 177	97.26 114	99.92 93	98.55 113	93.79 178	98.26 153	98.75 218	95.20 54	99.48 179	98.93 87	96.40 226	99.29 206

API-MVS		97.86 83	97.66 89	98.47 129	99.52 94	95.41 201	99.47 244	98.87 55	91.68 267	98.84 115	99.85 33	92.34 153	99.99 36	98.44 121	99.96 46	100.00 1

lupinMVS		97.85 85	97.60 93	98.62 110	97.28 273	97.70 95	99.99 5	97.55 285	95.50 109	99.43 80	99.67 107	90.92 178	98.71 234	98.40 122	99.62 95	99.45 177

UBG		97.84 86	97.69 88	98.29 142	98.38 184	96.59 149	99.90 107	98.53 120	93.91 174	98.52 136	98.42 254	96.77 25	99.17 197	98.54 114	96.20 230	99.11 225

MVSMamba_PlusPlus		97.83 87	97.45 101	98.99 84	98.60 166	98.15 67	99.58 221	97.74 263	90.34 313	99.26 95	98.32 259	94.29 90	99.23 189	99.03 82	99.89 70	99.58 150

test_yl		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 209	99.27 27	91.43 276	97.88 168	98.99 182	95.84 42	99.84 131	98.82 96	95.32 261	99.79 106

DCV-MVSNet		97.83 87	97.37 106	99.21 54	99.18 112	97.98 81	99.64 209	99.27 27	91.43 276	97.88 168	98.99 182	95.84 42	99.84 131	98.82 96	95.32 261	99.79 106

mvsany_test1		97.82 90	97.90 77	97.55 197	98.77 153	93.04 279	99.80 158	97.93 241	96.95 59	99.61 65	99.68 105	90.92 178	99.83 133	99.18 71	98.29 172	99.80 105

alignmvs		97.81 91	97.33 108	99.25 50	98.77 153	98.66 51	99.99 5	98.44 142	94.40 148	98.41 144	99.47 131	93.65 110	99.42 183	98.57 112	94.26 276	99.67 124

fmvsm_s_conf0.5_n		97.80 92	97.85 80	97.67 186	99.06 121	94.41 239	99.98 19	98.97 43	97.34 40	99.63 55	99.69 98	87.27 234	99.97 59	99.62 50	99.06 144	98.62 258

HPM-MVS_fast		97.80 92	97.50 98	98.68 104	99.79 64	96.42 153	99.88 120	98.16 218	91.75 266	98.94 111	99.54 127	91.82 165	99.65 165	97.62 168	99.99 21	99.99 23

CS-MVS		97.79 94	97.91 76	97.43 206	99.10 118	94.42 238	99.99 5	97.10 347	95.07 116	99.68 48	99.75 75	92.95 131	98.34 272	98.38 123	99.14 139	99.54 158

HY-MVS	92.50 7	97.79 94	97.17 117	99.63 17	98.98 131	99.32 9	97.49 392	99.52 14	95.69 102	98.32 149	97.41 289	93.32 118	99.77 143	98.08 143	95.75 246	99.81 103

CNLPA		97.76 96	97.38 105	98.92 91	99.53 93	96.84 134	99.87 123	98.14 222	93.78 179	96.55 210	99.69 98	92.28 154	99.98 47	97.13 177	99.44 122	99.93 82

fmvsm_s_conf0.5_n_4		97.75 97	97.86 79	97.42 207	99.01 124	94.69 232	99.97 37	98.76 69	97.91 23	99.87 11	99.76 67	86.70 244	99.93 97	99.67 47	99.12 142	97.64 286

test_fmvsmconf0.1_n		97.74 98	97.44 102	98.64 109	95.76 336	96.20 167	99.94 83	98.05 230	98.17 11	98.89 114	99.42 135	87.65 225	99.90 106	99.50 56	99.60 102	99.82 101

ACMMP		97.74 98	97.44 102	98.66 107	99.92 31	96.13 171	99.18 283	99.45 18	94.84 125	96.41 216	99.71 91	91.40 167	99.99 36	97.99 148	98.03 183	99.87 94
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
fmvsm_s_conf0.5_n_a		97.73 100	97.72 85	97.77 180	98.63 165	94.26 245	99.96 47	98.92 49	97.18 50	99.75 38	99.69 98	87.00 240	99.97 59	99.46 59	98.89 149	99.08 228

testing3-2		97.72 101	97.43 104	98.60 112	98.55 170	97.11 124	100.00 1	99.23 31	93.78 179	97.90 165	98.73 220	95.50 49	99.69 157	98.53 116	94.63 268	98.99 236

DeepPCF-MVS	95.94 2	97.71 102	98.98 12	93.92 336	99.63 85	81.76 427	99.96 47	98.56 107	99.47 1	99.19 98	99.99 1	94.16 96	100.00 1	99.92 13	99.93 61	100.00 1

fmvsm_s_conf0.5_n_7		97.70 103	97.74 84	97.59 195	98.44 181	95.16 220	99.97 37	98.65 82	97.95 22	99.62 58	99.78 62	86.09 252	99.94 88	99.69 45	99.50 113	97.66 285

test_fmvsmvis_n_1920		97.67 104	97.59 95	97.91 168	97.02 286	95.34 206	99.95 66	98.45 137	97.87 24	97.02 195	99.59 118	89.64 198	99.98 47	99.41 63	99.34 131	98.42 264

SymmetryMVS		97.64 105	97.46 99	98.17 147	98.74 155	95.39 203	99.61 215	99.26 29	96.52 74	98.61 132	99.31 150	92.73 138	99.67 161	96.77 191	95.63 253	99.45 177

CPTT-MVS		97.64 105	97.32 109	98.58 116	99.97 3	95.77 182	99.96 47	98.35 184	89.90 322	98.36 147	99.79 58	91.18 173	99.99 36	98.37 125	99.99 21	99.99 23

fmvsm_s_conf0.5_n_2		97.59 107	97.28 110	98.53 124	99.01 124	98.15 67	99.98 19	98.59 97	98.17 11	99.75 38	99.63 115	81.83 301	99.94 88	99.78 31	98.79 155	97.51 294

sss		97.57 108	97.03 122	99.18 57	98.37 186	98.04 78	99.73 184	99.38 22	93.46 190	98.76 124	99.06 175	91.21 169	99.89 111	96.33 198	97.01 215	99.62 137

test2506		97.53 109	97.19 115	98.58 116	98.66 161	96.90 133	98.81 334	99.77 5	94.93 119	97.95 163	98.96 188	92.51 147	99.20 194	94.93 223	98.15 176	99.64 130

EIA-MVS		97.53 109	97.46 99	97.76 182	98.04 212	94.84 227	99.98 19	97.61 279	94.41 147	97.90 165	99.59 118	92.40 151	98.87 216	98.04 145	99.13 140	99.59 144

testing11		97.48 111	97.27 111	98.10 154	98.36 187	96.02 174	99.92 93	98.45 137	93.45 192	98.15 158	98.70 223	95.48 50	99.22 190	97.85 156	95.05 265	99.07 229

xiu_mvs_v1_base_debu		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 268	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 280

xiu_mvs_v1_base		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 268	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 280

xiu_mvs_v1_base_debi		97.43 112	97.06 118	98.55 118	97.74 230	98.14 69	99.31 268	97.86 250	96.43 79	99.62 58	99.69 98	85.56 262	99.68 158	99.05 76	98.31 169	97.83 280

MAR-MVS		97.43 112	97.19 115	98.15 151	99.47 98	94.79 230	99.05 301	98.76 69	92.65 227	98.66 129	99.82 49	88.52 217	99.98 47	98.12 139	99.63 94	99.67 124
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
dcpmvs_2		97.42 116	98.09 60	95.42 277	99.58 91	87.24 389	99.23 279	96.95 368	94.28 155	98.93 112	99.73 85	94.39 84	99.16 199	99.89 18	99.82 81	99.86 96

thisisatest0515		97.41 117	97.02 123	98.59 115	97.71 237	97.52 102	99.97 37	98.54 117	91.83 262	97.45 181	99.04 176	97.50 9	99.10 202	94.75 231	96.37 228	99.16 218

114514_t		97.41 117	96.83 131	99.14 67	99.51 96	97.83 88	99.89 117	98.27 200	88.48 350	99.06 106	99.66 109	90.30 191	99.64 166	96.32 199	99.97 42	99.96 70

EC-MVSNet		97.38 119	97.24 112	97.80 174	97.41 260	95.64 191	99.99 5	97.06 355	94.59 134	99.63 55	99.32 147	89.20 208	98.14 288	98.76 101	99.23 136	99.62 137

fmvsm_s_conf0.1_n		97.30 120	97.21 114	97.60 194	97.38 262	94.40 241	99.90 107	98.64 85	96.47 78	99.51 74	99.65 111	84.99 270	99.93 97	99.22 70	99.09 143	98.46 261

OMC-MVS		97.28 121	97.23 113	97.41 208	99.76 68	93.36 274	99.65 205	97.95 239	96.03 93	97.41 183	99.70 94	89.61 199	99.51 171	96.73 193	98.25 173	99.38 185

PVSNet_Blended_VisFu		97.27 122	96.81 133	98.66 107	98.81 150	96.67 143	99.92 93	98.64 85	94.51 137	96.38 217	98.49 247	89.05 209	99.88 117	97.10 179	98.34 167	99.43 181

fmvsm_s_conf0.1_n_2		97.25 123	96.85 130	98.43 133	98.08 209	98.08 74	99.92 93	97.76 262	98.05 18	99.65 51	99.58 121	80.88 314	99.93 97	99.59 51	98.17 174	97.29 295

jason		97.24 124	96.86 129	98.38 138	95.73 339	97.32 111	99.97 37	97.40 303	95.34 112	98.60 135	99.54 127	87.70 224	98.56 248	97.94 151	99.47 118	99.25 212
jason: jason.
AdaColmap		97.23 125	96.80 134	98.51 127	99.99 1	95.60 193	99.09 290	98.84 62	93.32 195	96.74 204	99.72 88	86.04 253	100.00 1	98.01 146	99.43 123	99.94 81

VNet		97.21 126	96.57 145	99.13 71	98.97 132	97.82 89	99.03 304	99.21 32	94.31 152	99.18 99	98.88 200	86.26 251	99.89 111	98.93 87	94.32 274	99.69 121

testing99		97.17 127	96.91 125	97.95 162	98.35 189	95.70 187	99.91 101	98.43 150	92.94 210	97.36 184	98.72 221	94.83 67	99.21 191	97.00 181	94.64 267	98.95 238

testing91		97.16 128	96.90 126	97.97 161	98.35 189	95.67 190	99.91 101	98.42 162	92.91 212	97.33 185	98.72 221	94.81 68	99.21 191	96.98 183	94.63 268	99.03 233

guyue		97.15 129	96.82 132	98.15 151	97.56 249	96.25 165	99.71 191	97.84 253	95.75 100	98.13 159	98.65 228	87.58 227	98.82 219	98.29 130	97.91 186	99.36 189

PVSNet	91.05 13	97.13 130	96.69 140	98.45 131	99.52 94	95.81 180	99.95 66	99.65 12	94.73 129	99.04 107	99.21 164	84.48 279	99.95 80	94.92 224	98.74 157	99.58 150

thisisatest0530		97.10 131	96.72 138	98.22 145	97.60 246	96.70 139	99.92 93	98.54 117	91.11 287	97.07 194	98.97 186	97.47 12	99.03 205	93.73 260	96.09 233	98.92 242

CSCG		97.10 131	97.04 121	97.27 218	99.89 45	91.92 306	99.90 107	99.07 37	88.67 346	95.26 246	99.82 49	93.17 126	99.98 47	98.15 138	99.47 118	99.90 90

sasdasda		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 140	97.88 168	99.42 135	86.71 242	99.52 169	98.48 118	93.97 280	99.72 116

fmvsm_s_conf0.1_n_a		97.09 133	96.90 126	97.63 191	95.65 346	94.21 247	99.83 149	98.50 131	96.27 87	99.65 51	99.64 112	84.72 274	99.93 97	99.04 79	98.84 152	98.74 253

canonicalmvs		97.09 133	96.32 154	99.39 41	98.93 136	98.95 27	99.72 188	97.35 308	94.45 140	97.88 168	99.42 135	86.71 242	99.52 169	98.48 118	93.97 280	99.72 116

testing222		97.08 136	96.75 136	98.06 157	98.56 167	96.82 135	99.85 137	98.61 93	92.53 235	98.84 115	98.84 213	93.36 115	98.30 276	95.84 207	94.30 275	99.05 232

ETVMVS		97.03 137	96.64 141	98.20 146	98.67 159	97.12 122	99.89 117	98.57 101	91.10 288	98.17 157	98.59 236	93.86 105	98.19 286	95.64 211	95.24 263	99.28 208

MGCFI-Net		97.00 138	96.22 158	99.34 46	98.86 147	98.80 39	99.67 203	97.30 316	94.31 152	97.77 174	99.41 139	86.36 249	99.50 173	98.38 123	93.90 282	99.72 116

diffmvs		97.00 138	96.64 141	98.09 155	97.64 244	96.17 170	99.81 154	97.19 329	94.67 133	98.95 110	99.28 152	86.43 247	98.76 227	98.37 125	97.42 196	99.33 196
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
thres200		96.96 140	96.21 159	99.22 53	98.97 132	98.84 36	99.85 137	99.71 7	93.17 200	96.26 219	98.88 200	89.87 196	99.51 171	94.26 243	94.91 266	99.31 202

mvsmamba		96.94 141	96.73 137	97.55 197	97.99 214	94.37 242	99.62 212	97.70 265	93.13 203	98.42 143	97.92 277	88.02 221	98.75 229	98.78 99	99.01 146	99.52 164

MVSFormer		96.94 141	96.60 143	97.95 162	97.28 273	97.70 95	99.55 230	97.27 321	91.17 283	99.43 80	99.54 127	90.92 178	96.89 357	94.67 234	99.62 95	99.25 212

F-COLMAP		96.93 143	96.95 124	96.87 230	99.71 78	91.74 311	99.85 137	97.95 239	93.11 205	95.72 235	99.16 170	92.35 152	99.94 88	95.32 214	99.35 130	98.92 242

DeepC-MVS	94.51 4	96.92 144	96.40 153	98.45 131	99.16 115	95.90 177	99.66 204	98.06 228	96.37 85	94.37 258	99.49 130	83.29 289	99.90 106	97.63 167	99.61 99	99.55 154
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tttt0517		96.85 145	96.49 147	97.92 166	97.48 257	95.89 178	99.85 137	98.54 117	90.72 304	96.63 206	98.93 198	97.47 12	99.02 206	93.03 273	95.76 245	98.85 246

1314		96.84 146	95.96 172	99.48 35	96.74 309	98.52 58	98.31 368	98.86 56	95.82 97	89.91 311	98.98 184	87.49 230	99.96 71	97.80 158	99.73 87	99.96 70

CHOSEN 1792x2688		96.81 147	96.53 146	97.64 188	98.91 143	93.07 276	99.65 205	99.80 3	95.64 103	95.39 242	98.86 209	84.35 281	99.90 106	96.98 183	99.16 138	99.95 77

UWE-MVS		96.79 148	96.72 138	97.00 224	98.51 175	93.70 261	99.71 191	98.60 95	92.96 209	97.09 192	98.34 258	96.67 31	98.85 218	92.11 286	96.50 223	98.44 263

tfpn200view9		96.79 148	95.99 166	99.19 56	98.94 134	98.82 37	99.78 161	99.71 7	92.86 213	96.02 226	98.87 207	89.33 203	99.50 173	93.84 252	94.57 270	99.27 210

thres400		96.78 150	95.99 166	99.16 63	98.94 134	98.82 37	99.78 161	99.71 7	92.86 213	96.02 226	98.87 207	89.33 203	99.50 173	93.84 252	94.57 270	99.16 218

CANet_DTU		96.76 151	96.15 161	98.60 112	98.78 152	97.53 101	99.84 142	97.63 273	97.25 48	99.20 96	99.64 112	81.36 307	99.98 47	92.77 276	98.89 149	98.28 269

PMMVS		96.76 151	96.76 135	96.76 234	98.28 194	92.10 301	99.91 101	97.98 236	94.12 160	99.53 70	99.39 142	86.93 241	98.73 231	96.95 186	97.73 187	99.45 177

diffmvs_AUTHOR		96.75 153	96.41 152	97.79 176	97.20 276	95.46 197	99.69 198	97.15 336	94.46 139	98.78 119	99.21 164	85.64 259	98.77 225	98.27 131	97.31 200	99.13 222

thres100view900		96.74 154	95.92 176	99.18 57	98.90 144	98.77 42	99.74 177	99.71 7	92.59 231	95.84 230	98.86 209	89.25 205	99.50 173	93.84 252	94.57 270	99.27 210

TESTMET0.1,1		96.74 154	96.26 156	98.16 148	97.36 265	96.48 151	99.96 47	98.29 197	91.93 258	95.77 233	98.07 270	95.54 46	98.29 277	90.55 313	98.89 149	99.70 119

baseline2		96.71 156	96.49 147	97.37 211	95.63 348	95.96 176	99.74 177	98.88 54	92.94 210	91.61 290	98.97 186	97.72 6	98.62 245	94.83 228	98.08 182	97.53 293

thres600view7		96.69 157	95.87 179	99.14 67	98.90 144	98.78 41	99.74 177	99.71 7	92.59 231	95.84 230	98.86 209	89.25 205	99.50 173	93.44 265	94.50 273	99.16 218

EPP-MVSNet		96.69 157	96.60 143	96.96 226	97.74 230	93.05 278	99.37 260	98.56 107	88.75 344	95.83 232	99.01 179	96.01 36	98.56 248	96.92 187	97.20 204	99.25 212

HyFIR lowres test		96.66 159	96.43 151	97.36 213	99.05 122	93.91 256	99.70 196	99.80 3	90.54 306	96.26 219	98.08 269	92.15 157	98.23 284	96.84 190	95.46 256	99.93 82

LuminaMVS		96.63 160	96.21 159	97.87 171	95.58 350	96.82 135	99.12 286	97.67 268	94.47 138	97.88 168	98.31 261	87.50 229	98.71 234	98.07 144	97.29 201	98.10 274

MVS		96.60 161	95.56 188	99.72 13	96.85 301	99.22 20	98.31 368	98.94 44	91.57 269	90.90 298	99.61 117	86.66 245	99.96 71	97.36 171	99.88 73	99.99 23

test_cas_vis1_n_1920		96.59 162	96.23 157	97.65 187	98.22 198	94.23 246	99.99 5	97.25 324	97.77 27	99.58 66	99.08 173	77.10 346	99.97 59	97.64 166	99.45 121	98.74 253

AstraMVS		96.57 163	96.46 150	96.91 227	96.79 307	92.50 293	99.90 107	97.38 304	96.02 94	97.79 173	99.32 147	86.36 249	98.99 207	98.26 132	96.33 229	99.23 215

UA-Net		96.54 164	95.96 172	98.27 143	98.23 197	95.71 186	98.00 383	98.45 137	93.72 183	98.41 144	99.27 155	88.71 216	99.66 164	91.19 298	97.69 188	99.44 180

EPMVS		96.53 165	96.01 165	98.09 155	98.43 182	96.12 173	96.36 416	99.43 20	93.53 187	97.64 176	95.04 381	94.41 80	98.38 268	91.13 299	98.11 179	99.75 112

test-LLR		96.47 166	96.04 164	97.78 178	97.02 286	95.44 198	99.96 47	98.21 208	94.07 163	95.55 238	96.38 323	93.90 103	98.27 281	90.42 316	98.83 153	99.64 130

MVS_Test		96.46 167	95.74 181	98.61 111	98.18 202	97.23 116	99.31 268	97.15 336	91.07 289	98.84 115	97.05 302	88.17 220	98.97 210	94.39 238	97.50 193	99.61 141

viewmanbaseed2359cas		96.45 168	96.07 162	97.59 195	97.55 250	94.59 233	99.70 196	97.33 312	93.62 186	97.00 196	99.32 147	85.57 261	98.71 234	97.26 174	97.33 199	99.47 173

casdiffmvs_mvg		96.43 169	95.94 174	97.89 170	97.44 258	95.47 196	99.86 134	97.29 319	93.35 193	96.03 225	99.19 166	85.39 265	98.72 233	97.89 155	97.04 212	99.49 172
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		96.43 169	95.98 168	97.76 182	97.34 266	95.17 219	99.51 236	97.17 333	93.92 173	96.90 199	99.28 152	85.37 266	98.64 244	97.50 169	96.86 219	99.46 175

casdiffmvs		96.42 171	95.97 171	97.77 180	97.30 271	94.98 222	99.84 142	97.09 350	93.75 182	96.58 208	99.26 159	85.07 268	98.78 224	97.77 163	97.04 212	99.54 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		96.39 172	95.74 181	98.32 140	91.47 421	95.56 194	99.84 142	97.30 316	97.74 28	97.89 167	99.35 146	79.62 328	99.85 123	99.25 69	99.24 135	99.55 154

test-mter		96.39 172	95.93 175	97.78 178	97.02 286	95.44 198	99.96 47	98.21 208	91.81 264	95.55 238	96.38 323	95.17 55	98.27 281	90.42 316	98.83 153	99.64 130

CDS-MVSNet		96.34 174	96.07 162	97.13 220	97.37 264	94.96 223	99.53 233	97.91 245	91.55 270	95.37 243	98.32 259	95.05 60	97.13 338	93.80 256	95.75 246	99.30 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		96.32 175	95.98 168	97.35 215	97.93 218	94.82 228	99.47 244	98.15 221	91.83 262	95.09 247	99.11 171	91.37 168	97.47 319	93.47 264	97.43 194	99.74 113

3Dnovator+	91.53 11	96.31 176	95.24 199	99.52 28	96.88 300	98.64 54	99.72 188	98.24 204	95.27 114	88.42 353	98.98 184	82.76 292	99.94 88	97.10 179	99.83 77	99.96 70

Effi-MVS+		96.30 177	95.69 183	98.16 148	97.85 223	96.26 161	97.41 394	97.21 328	90.37 311	98.65 130	98.58 239	86.61 246	98.70 237	97.11 178	97.37 198	99.52 164

IS-MVSNet		96.29 178	95.90 177	97.45 204	98.13 207	94.80 229	99.08 292	97.61 279	92.02 257	95.54 240	98.96 188	90.64 184	98.08 292	93.73 260	97.41 197	99.47 173

3Dnovator	91.47 12	96.28 179	95.34 195	99.08 76	96.82 303	97.47 107	99.45 249	98.81 64	95.52 108	89.39 327	99.00 181	81.97 298	99.95 80	97.27 173	99.83 77	99.84 98

tpmrst		96.27 180	95.98 168	97.13 220	97.96 216	93.15 275	96.34 417	98.17 213	92.07 253	98.71 127	95.12 378	93.91 102	98.73 231	94.91 226	96.62 220	99.50 170

RRT-MVS		96.24 181	95.68 185	97.94 165	97.65 243	94.92 225	99.27 276	97.10 347	92.79 219	97.43 182	97.99 274	81.85 300	99.37 185	98.46 120	98.57 160	99.53 162

KinetiMVS		96.10 182	95.29 198	98.53 124	97.08 282	97.12 122	99.56 227	98.12 224	94.78 126	98.44 141	98.94 195	80.30 324	99.39 184	91.56 294	98.79 155	99.06 230

CostFormer		96.10 182	95.88 178	96.78 233	97.03 285	92.55 292	97.08 403	97.83 254	90.04 320	98.72 126	94.89 388	95.01 62	98.29 277	96.54 196	95.77 244	99.50 170

PVSNet_BlendedMVS		96.05 184	95.82 180	96.72 236	99.59 87	96.99 129	99.95 66	99.10 34	94.06 165	98.27 151	95.80 341	89.00 211	99.95 80	99.12 73	87.53 333	93.24 395

PatchMatch-RL		96.04 185	95.40 192	97.95 162	99.59 87	95.22 216	99.52 234	99.07 37	93.96 170	96.49 212	98.35 256	82.28 295	99.82 135	90.15 321	99.22 137	98.81 249

1112_ss		96.01 186	95.20 201	98.42 135	97.80 226	96.41 154	99.65 205	96.66 390	92.71 222	92.88 278	99.40 140	92.16 156	99.30 186	91.92 289	93.66 283	99.55 154

UWE-MVS-28		95.95 187	96.49 147	94.34 321	98.51 175	89.99 354	99.39 256	98.57 101	93.14 202	97.33 185	98.31 261	93.44 113	94.68 421	93.69 262	95.98 236	98.34 268

PatchmatchNet		95.94 188	95.45 190	97.39 210	97.83 224	94.41 239	96.05 423	98.40 171	92.86 213	97.09 192	95.28 373	94.21 94	98.07 294	89.26 331	98.11 179	99.70 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
viewmacassd2359aftdt		95.93 189	95.45 190	97.36 213	97.09 281	94.12 250	99.57 224	97.26 323	93.05 207	96.50 211	99.17 168	82.76 292	98.68 239	96.61 194	97.04 212	99.28 208

viewmambaseed2359dif		95.92 190	95.55 189	97.04 223	97.38 262	93.41 270	99.78 161	96.97 366	91.14 286	96.58 208	99.27 155	84.85 271	98.75 229	96.87 189	97.12 208	98.97 237

FA-MVS(test-final)		95.86 191	95.09 206	98.15 151	97.74 230	95.62 192	96.31 418	98.17 213	91.42 278	96.26 219	96.13 334	90.56 186	99.47 181	92.18 281	97.07 210	99.35 193

TAMVS		95.85 192	95.58 187	96.65 239	97.07 283	93.50 267	99.17 284	97.82 255	91.39 280	95.02 248	98.01 271	92.20 155	97.30 328	93.75 259	95.83 243	99.14 221

LS3D		95.84 193	95.11 205	98.02 160	99.85 56	95.10 221	98.74 340	98.50 131	87.22 368	93.66 267	99.86 29	87.45 231	99.95 80	90.94 305	99.81 83	99.02 234

baseline1		95.78 194	94.86 214	98.54 122	98.47 180	98.07 75	99.06 297	97.99 234	92.68 225	94.13 263	98.62 233	93.28 121	98.69 238	93.79 257	85.76 341	98.84 247

SSM_0404		95.75 195	95.16 203	97.50 202	97.53 252	95.39 203	99.11 288	97.25 324	90.81 296	95.27 245	98.83 214	84.74 272	98.67 241	95.24 216	97.69 188	98.45 262

Test_1112_low_res		95.72 196	94.83 215	98.42 135	97.79 227	96.41 154	99.65 205	96.65 391	92.70 223	92.86 279	96.13 334	92.15 157	99.30 186	91.88 290	93.64 284	99.55 154

Vis-MVSNet		95.72 196	95.15 204	97.45 204	97.62 245	94.28 244	99.28 274	98.24 204	94.27 157	96.84 201	98.94 195	79.39 330	98.76 227	93.25 266	98.49 164	99.30 204
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet_dtu		95.71 198	95.39 193	96.66 238	98.92 139	93.41 270	99.57 224	98.90 50	96.19 90	97.52 178	98.56 241	92.65 140	97.36 321	77.89 420	98.33 168	99.20 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 198	95.38 194	96.68 237	98.49 179	92.28 297	99.84 142	97.50 293	92.12 252	92.06 288	98.79 216	84.69 275	98.67 241	95.29 215	99.66 91	99.09 226

FE-MVS		95.70 200	95.01 210	97.79 176	98.21 199	94.57 234	95.03 430	98.69 76	88.90 340	97.50 180	96.19 330	92.60 143	99.49 178	89.99 323	97.94 185	99.31 202

ECVR-MVS		95.66 201	95.05 208	97.51 201	98.66 161	93.71 260	98.85 331	98.45 137	94.93 119	96.86 200	98.96 188	75.22 371	99.20 194	95.34 213	98.15 176	99.64 130

mvs_anonymous		95.65 202	95.03 209	97.53 199	98.19 201	95.74 184	99.33 265	97.49 294	90.87 293	90.47 304	97.10 298	88.23 219	97.16 335	95.92 205	97.66 191	99.68 122

SSM_0407		95.62 203	94.95 212	97.61 193	97.14 277	95.31 208	99.00 307	97.25 324	90.81 296	94.40 255	98.83 214	84.74 272	98.58 246	95.24 216	97.18 205	98.93 239

test1111		95.57 204	94.98 211	97.37 211	98.56 167	93.37 273	98.86 329	98.45 137	94.95 118	96.63 206	98.95 193	75.21 372	99.11 200	95.02 220	98.14 178	99.64 130

MVSTER		95.53 205	95.22 200	96.45 245	98.56 167	97.72 92	99.91 101	97.67 268	92.38 242	91.39 292	97.14 296	97.24 18	97.30 328	94.80 229	87.85 327	94.34 330

tpm2		95.47 206	95.18 202	96.35 250	96.91 296	91.70 315	96.96 406	97.93 241	88.04 357	98.44 141	95.40 362	93.32 118	97.97 298	94.00 246	95.61 254	99.38 185

test_vis1_n_1920		95.44 207	95.31 196	95.82 266	98.50 177	88.74 371	99.98 19	97.30 316	97.84 26	99.85 16	99.19 166	66.82 411	99.97 59	98.82 96	99.46 120	98.76 251

QAPM		95.40 208	94.17 233	99.10 73	96.92 295	97.71 93	99.40 252	98.68 78	89.31 328	88.94 340	98.89 199	82.48 294	99.96 71	93.12 272	99.83 77	99.62 137

reproduce_monomvs		95.38 209	95.07 207	96.32 251	99.32 106	96.60 147	99.76 170	98.85 59	96.65 70	87.83 359	96.05 338	99.52 1	98.11 290	96.58 195	81.07 383	94.25 335

test_fmvs1		95.35 210	95.68 185	94.36 320	98.99 129	84.98 404	99.96 47	96.65 391	97.60 32	99.73 43	98.96 188	71.58 390	99.93 97	98.31 128	99.37 128	98.17 270

UGNet		95.33 211	94.57 223	97.62 192	98.55 170	94.85 226	98.67 348	99.32 26	95.75 100	96.80 203	96.27 328	72.18 387	99.96 71	94.58 236	99.05 145	98.04 275
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
IMVS_0403		95.25 212	94.81 217	96.58 241	96.97 289	91.64 317	98.97 314	97.12 341	92.33 244	95.43 241	98.88 200	85.78 256	98.79 222	92.12 282	95.70 249	99.32 198

mamv4		95.24 213	96.90 126	90.25 398	98.65 163	72.11 445	98.28 370	97.64 272	89.99 321	95.93 228	98.25 264	94.74 70	99.11 200	99.01 84	99.64 92	99.53 162

IMVS_0407		95.21 214	94.80 218	96.46 244	96.97 289	91.64 317	98.81 334	97.12 341	92.33 244	95.60 236	98.88 200	85.65 257	98.42 258	92.12 282	95.70 249	99.32 198

BH-untuned		95.18 215	94.83 215	96.22 253	98.36 187	91.22 327	99.80 158	97.32 314	90.91 292	91.08 295	98.67 225	83.51 286	98.54 250	94.23 244	99.61 99	98.92 242

BH-RMVSNet		95.18 215	94.31 230	97.80 174	98.17 203	95.23 215	99.76 170	97.53 289	92.52 236	94.27 261	99.25 160	76.84 352	98.80 221	90.89 307	99.54 106	99.35 193

PCF-MVS	94.20 5	95.18 215	94.10 234	98.43 133	98.55 170	95.99 175	97.91 385	97.31 315	90.35 312	89.48 326	99.22 162	85.19 267	99.89 111	90.40 318	98.47 165	99.41 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
dp		95.05 218	94.43 225	96.91 227	97.99 214	92.73 286	96.29 419	97.98 236	89.70 325	95.93 228	94.67 394	93.83 107	98.45 256	86.91 364	96.53 222	99.54 158

icg_test_0407_2		95.04 219	94.78 219	95.84 265	96.97 289	91.64 317	98.63 351	97.12 341	92.33 244	95.60 236	98.88 200	85.65 257	96.56 374	92.12 282	95.70 249	99.32 198

Fast-Effi-MVS+		95.02 220	94.19 232	97.52 200	97.88 220	94.55 235	99.97 37	97.08 351	88.85 342	94.47 254	97.96 276	84.59 276	98.41 260	89.84 325	97.10 209	99.59 144

IB-MVS	92.85 6	94.99 221	93.94 242	98.16 148	97.72 235	95.69 189	99.99 5	98.81 64	94.28 155	92.70 280	96.90 306	95.08 58	99.17 197	96.07 202	73.88 424	99.60 143
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
mamba_0408		94.98 222	94.09 235	97.64 188	97.14 277	95.31 208	93.48 438	97.08 351	90.48 307	94.40 255	98.62 233	84.49 277	98.67 241	93.99 247	97.18 205	98.93 239

h-mvs33		94.92 223	94.36 227	96.59 240	98.85 148	91.29 326	98.93 319	98.94 44	95.90 95	98.77 121	98.42 254	90.89 181	99.77 143	97.80 158	70.76 431	98.72 255

MonoMVSNet		94.82 224	94.43 225	95.98 258	94.54 365	90.73 336	99.03 304	97.06 355	93.16 201	93.15 273	95.47 359	88.29 218	97.57 315	97.85 156	91.33 297	99.62 137

XVG-OURS		94.82 224	94.74 221	95.06 288	98.00 213	89.19 364	99.08 292	97.55 285	94.10 161	94.71 250	99.62 116	80.51 320	99.74 149	96.04 203	93.06 292	96.25 304

SDMVSNet		94.80 226	93.96 241	97.33 216	98.92 139	95.42 200	99.59 219	98.99 40	92.41 240	92.55 282	97.85 280	75.81 365	98.93 214	97.90 154	91.62 295	97.64 286

ADS-MVSNet		94.79 227	94.02 239	97.11 222	97.87 221	93.79 257	94.24 431	98.16 218	90.07 318	96.43 214	94.48 399	90.29 192	98.19 286	87.44 351	97.23 202	99.36 189

XVG-OURS-SEG-HR		94.79 227	94.70 222	95.08 287	98.05 211	89.19 364	99.08 292	97.54 287	93.66 184	94.87 249	99.58 121	78.78 337	99.79 138	97.31 172	93.40 287	96.25 304

SSM_04072		94.77 229	94.09 235	96.82 231	97.14 277	95.31 208	93.48 438	97.08 351	90.48 307	94.40 255	98.62 233	84.49 277	96.21 390	93.99 247	97.18 205	98.93 239

OpenMVS	90.15 15	94.77 229	93.59 251	98.33 139	96.07 324	97.48 106	99.56 227	98.57 101	90.46 309	86.51 377	98.95 193	78.57 340	99.94 88	93.86 251	99.74 86	97.57 291

LFMVS		94.75 231	93.56 253	98.30 141	99.03 123	95.70 187	98.74 340	97.98 236	87.81 361	98.47 140	99.39 142	67.43 409	99.53 168	98.01 146	95.20 264	99.67 124

SCA		94.69 232	93.81 246	97.33 216	97.10 280	94.44 236	98.86 329	98.32 191	93.30 196	96.17 224	95.59 351	76.48 358	97.95 301	91.06 301	97.43 194	99.59 144

ab-mvs		94.69 232	93.42 258	98.51 127	98.07 210	96.26 161	96.49 414	98.68 78	90.31 314	94.54 251	97.00 304	76.30 360	99.71 153	95.98 204	93.38 288	99.56 153

CVMVSNet		94.68 234	94.94 213	93.89 339	96.80 304	86.92 392	99.06 297	98.98 41	94.45 140	94.23 262	99.02 177	85.60 260	95.31 412	90.91 306	95.39 259	99.43 181

cascas		94.64 235	93.61 248	97.74 184	97.82 225	96.26 161	99.96 47	97.78 258	85.76 386	94.00 264	97.54 286	76.95 351	99.21 191	97.23 175	95.43 258	97.76 284

HQP-MVS		94.61 236	94.50 224	94.92 293	95.78 332	91.85 307	99.87 123	97.89 246	96.82 62	93.37 269	98.65 228	80.65 318	98.39 264	97.92 152	89.60 300	94.53 312

TR-MVS		94.54 237	93.56 253	97.49 203	97.96 216	94.34 243	98.71 343	97.51 292	90.30 315	94.51 253	98.69 224	75.56 366	98.77 225	92.82 275	95.99 235	99.35 193

DP-MVS		94.54 237	93.42 258	97.91 168	99.46 100	94.04 251	98.93 319	97.48 295	81.15 421	90.04 308	99.55 125	87.02 239	99.95 80	88.97 333	98.11 179	99.73 114

Effi-MVS+-dtu		94.53 239	95.30 197	92.22 374	97.77 228	82.54 420	99.59 219	97.06 355	94.92 121	95.29 244	95.37 366	85.81 255	97.89 304	94.80 229	97.07 210	96.23 306

WBMVS		94.52 240	94.03 238	95.98 258	98.38 184	96.68 142	99.92 93	97.63 273	90.75 303	89.64 321	95.25 374	96.77 25	96.90 356	94.35 241	83.57 361	94.35 328

Elysia		94.50 241	93.38 262	97.85 172	96.49 315	96.70 139	98.98 309	97.78 258	90.81 296	96.19 222	98.55 243	73.63 382	98.98 208	89.41 327	98.56 161	97.88 278

StellarMVS		94.50 241	93.38 262	97.85 172	96.49 315	96.70 139	98.98 309	97.78 258	90.81 296	96.19 222	98.55 243	73.63 382	98.98 208	89.41 327	98.56 161	97.88 278

HQP_MVS		94.49 243	94.36 227	94.87 294	95.71 342	91.74 311	99.84 142	97.87 248	96.38 82	93.01 274	98.59 236	80.47 322	98.37 270	97.79 161	89.55 303	94.52 314

myMVS_eth3d		94.46 244	94.76 220	93.55 349	97.68 239	90.97 329	99.71 191	98.35 184	90.79 300	92.10 286	98.67 225	92.46 150	93.09 436	87.13 357	95.95 239	96.59 302

TAPA-MVS	92.12 8	94.42 245	93.60 250	96.90 229	99.33 104	91.78 310	99.78 161	98.00 233	89.89 323	94.52 252	99.47 131	91.97 161	99.18 196	69.90 439	99.52 108	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
hse-mvs2		94.38 246	94.08 237	95.31 282	98.27 195	90.02 353	99.29 273	98.56 107	95.90 95	98.77 121	98.00 272	90.89 181	98.26 283	97.80 158	69.20 437	97.64 286

ET-MVSNet_ETH3D		94.37 247	93.28 267	97.64 188	98.30 191	97.99 80	99.99 5	97.61 279	94.35 149	71.57 443	99.45 134	96.23 35	95.34 411	96.91 188	85.14 348	99.59 144

MSDG		94.37 247	93.36 265	97.40 209	98.88 146	93.95 255	99.37 260	97.38 304	85.75 388	90.80 301	99.17 168	84.11 284	99.88 117	86.35 365	98.43 166	98.36 267

GeoE		94.36 249	93.48 256	96.99 225	97.29 272	93.54 266	99.96 47	96.72 388	88.35 353	93.43 268	98.94 195	82.05 296	98.05 295	88.12 346	96.48 225	99.37 187

miper_enhance_ethall		94.36 249	93.98 240	95.49 272	98.68 158	95.24 214	99.73 184	97.29 319	93.28 197	89.86 313	95.97 339	94.37 85	97.05 344	92.20 280	84.45 354	94.19 341

tpmvs		94.28 251	93.57 252	96.40 247	98.55 170	91.50 324	95.70 429	98.55 113	87.47 363	92.15 285	94.26 404	91.42 166	98.95 213	88.15 344	95.85 242	98.76 251

test_fmvs1_n		94.25 252	94.36 227	93.92 336	97.68 239	83.70 411	99.90 107	96.57 394	97.40 38	99.67 49	98.88 200	61.82 430	99.92 103	98.23 134	99.13 140	98.14 273

VortexMVS		94.11 253	93.50 255	95.94 260	97.70 238	96.61 146	99.35 263	97.18 331	93.52 189	89.57 324	95.74 343	87.55 228	96.97 352	95.76 210	85.13 349	94.23 337

FIs		94.10 254	93.43 257	96.11 255	94.70 362	96.82 135	99.58 221	98.93 48	92.54 234	89.34 329	97.31 292	87.62 226	97.10 341	94.22 245	86.58 337	94.40 323

viewmsd2359difaftdt		94.09 255	93.64 247	95.46 276	96.68 312	88.92 369	99.62 212	97.13 340	93.07 206	95.73 234	99.22 162	77.05 347	98.89 215	96.52 197	87.70 331	98.58 260

CLD-MVS		94.06 256	93.90 243	94.55 309	96.02 326	90.69 337	99.98 19	97.72 264	96.62 73	91.05 297	98.85 212	77.21 345	98.47 252	98.11 140	89.51 305	94.48 316
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
testing3		93.92 257	94.23 231	92.99 363	97.54 251	90.23 348	99.99 5	99.16 33	90.57 305	91.33 294	98.63 232	92.99 129	92.52 440	82.46 394	95.39 259	96.22 307

test0.0.03 1		93.86 258	93.61 248	94.64 303	95.02 358	92.18 300	99.93 90	98.58 99	94.07 163	87.96 357	98.50 246	93.90 103	94.96 416	81.33 401	93.17 289	96.78 299

IMVS_0404		93.83 259	93.17 269	95.80 267	96.97 289	91.64 317	97.78 389	97.12 341	92.33 244	90.87 299	98.88 200	76.78 353	96.43 380	92.12 282	95.70 249	99.32 198

X-MVStestdata		93.83 259	92.06 294	99.15 65	99.94 13	97.50 104	99.94 83	98.42 162	96.22 88	99.41 82	41.37 466	94.34 86	99.96 71	98.92 89	99.95 50	99.99 23

GA-MVS		93.83 259	92.84 274	96.80 232	95.73 339	93.57 264	99.88 120	97.24 327	92.57 233	92.92 276	96.66 315	78.73 338	97.67 312	87.75 349	94.06 279	99.17 217

FC-MVSNet-test		93.81 262	93.15 270	95.80 267	94.30 370	96.20 167	99.42 251	98.89 52	92.33 244	89.03 339	97.27 294	87.39 232	96.83 363	93.20 267	86.48 338	94.36 325

ADS-MVSNet2		93.80 263	93.88 244	93.55 349	97.87 221	85.94 398	94.24 431	96.84 379	90.07 318	96.43 214	94.48 399	90.29 192	95.37 410	87.44 351	97.23 202	99.36 189

cl22		93.77 264	93.25 268	95.33 281	99.49 97	94.43 237	99.61 215	98.09 225	90.38 310	89.16 337	95.61 349	90.56 186	97.34 323	91.93 288	84.45 354	94.21 340

VDD-MVS		93.77 264	92.94 273	96.27 252	98.55 170	90.22 349	98.77 339	97.79 256	90.85 294	96.82 202	99.42 135	61.18 433	99.77 143	98.95 85	94.13 277	98.82 248

EI-MVSNet		93.73 266	93.40 261	94.74 299	96.80 304	92.69 287	99.06 297	97.67 268	88.96 337	91.39 292	99.02 177	88.75 215	97.30 328	91.07 300	87.85 327	94.22 338

Fast-Effi-MVS+-dtu		93.72 267	93.86 245	93.29 354	97.06 284	86.16 395	99.80 158	96.83 380	92.66 226	92.58 281	97.83 282	81.39 306	97.67 312	89.75 326	96.87 218	96.05 309

tpm		93.70 268	93.41 260	94.58 307	95.36 353	87.41 387	97.01 404	96.90 375	90.85 294	96.72 205	94.14 405	90.40 189	96.84 361	90.75 310	88.54 319	99.51 168

PS-MVSNAJss		93.64 269	93.31 266	94.61 304	92.11 412	92.19 299	99.12 286	97.38 304	92.51 237	88.45 348	96.99 305	91.20 170	97.29 331	94.36 239	87.71 329	94.36 325

test_vis1_n		93.61 270	93.03 272	95.35 279	95.86 331	86.94 391	99.87 123	96.36 400	96.85 60	99.54 69	98.79 216	52.41 444	99.83 133	98.64 109	98.97 147	99.29 206

sd_testset		93.55 271	92.83 275	95.74 269	98.92 139	90.89 334	98.24 372	98.85 59	92.41 240	92.55 282	97.85 280	71.07 395	98.68 239	93.93 249	91.62 295	97.64 286

gg-mvs-nofinetune		93.51 272	91.86 299	98.47 129	97.72 235	97.96 84	92.62 441	98.51 125	74.70 440	97.33 185	69.59 457	98.91 4	97.79 307	97.77 163	99.56 105	99.67 124

nrg030		93.51 272	92.53 286	96.45 245	94.36 368	97.20 117	99.81 154	97.16 335	91.60 268	89.86 313	97.46 287	86.37 248	97.68 311	95.88 206	80.31 391	94.46 317

tpm cat1		93.51 272	92.52 287	96.47 242	97.77 228	91.47 325	96.13 421	98.06 228	80.98 422	92.91 277	93.78 408	89.66 197	98.87 216	87.03 360	96.39 227	99.09 226

CR-MVSNet		93.45 275	92.62 280	95.94 260	96.29 318	92.66 288	92.01 444	96.23 402	92.62 228	96.94 197	93.31 414	91.04 175	96.03 398	79.23 412	95.96 237	99.13 222

AUN-MVS		93.28 276	92.60 281	95.34 280	98.29 192	90.09 352	99.31 268	98.56 107	91.80 265	96.35 218	98.00 272	89.38 202	98.28 279	92.46 277	69.22 436	97.64 286

OPM-MVS		93.21 277	92.80 276	94.44 316	93.12 391	90.85 335	99.77 165	97.61 279	96.19 90	91.56 291	98.65 228	75.16 373	98.47 252	93.78 258	89.39 306	93.99 364
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
dmvs_re		93.20 278	93.15 270	93.34 352	96.54 314	83.81 410	98.71 343	98.51 125	91.39 280	92.37 284	98.56 241	78.66 339	97.83 306	93.89 250	89.74 299	98.38 266

kuosan		93.17 279	92.60 281	94.86 297	98.40 183	89.54 362	98.44 361	98.53 120	84.46 401	88.49 347	97.92 277	90.57 185	97.05 344	83.10 390	93.49 285	97.99 276

miper_ehance_all_eth		93.16 280	92.60 281	94.82 298	97.57 248	93.56 265	99.50 238	97.07 354	88.75 344	88.85 341	95.52 355	90.97 177	96.74 366	90.77 309	84.45 354	94.17 342

VDDNet		93.12 281	91.91 297	96.76 234	96.67 313	92.65 290	98.69 346	98.21 208	82.81 414	97.75 175	99.28 152	61.57 431	99.48 179	98.09 142	94.09 278	98.15 271

Anonymous202405211		93.10 282	91.99 295	96.40 247	99.10 118	89.65 360	98.88 325	97.93 241	83.71 406	94.00 264	98.75 218	68.79 400	99.88 117	95.08 219	91.71 294	99.68 122

UniMVSNet (Re)		93.07 283	92.13 291	95.88 262	94.84 359	96.24 166	99.88 120	98.98 41	92.49 238	89.25 331	95.40 362	87.09 237	97.14 337	93.13 271	78.16 402	94.26 333

LPG-MVS_test		92.96 284	92.71 279	93.71 343	95.43 351	88.67 373	99.75 174	97.62 276	92.81 216	90.05 306	98.49 247	75.24 369	98.40 262	95.84 207	89.12 307	94.07 356

UniMVSNet_NR-MVSNet		92.95 285	92.11 292	95.49 272	94.61 364	95.28 212	99.83 149	99.08 36	91.49 271	89.21 334	96.86 309	87.14 236	96.73 367	93.20 267	77.52 407	94.46 317

WB-MVSnew		92.90 286	92.77 278	93.26 356	96.95 294	93.63 263	99.71 191	98.16 218	91.49 271	94.28 260	98.14 267	81.33 308	96.48 377	79.47 411	95.46 256	89.68 437

ACMM	91.95 10	92.88 287	92.52 287	93.98 335	95.75 338	89.08 368	99.77 165	97.52 291	93.00 208	89.95 310	97.99 274	76.17 362	98.46 255	93.63 263	88.87 311	94.39 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		92.83 288	92.29 290	94.47 314	91.90 415	92.46 294	99.55 230	97.27 321	91.17 283	89.96 309	96.07 337	81.10 310	96.89 357	94.67 234	88.91 309	94.05 358

D2MVS		92.76 289	92.59 285	93.27 355	95.13 354	89.54 362	99.69 198	99.38 22	92.26 249	87.59 362	94.61 396	85.05 269	97.79 307	91.59 293	88.01 325	92.47 410

ACMP	92.05 9	92.74 290	92.42 289	93.73 341	95.91 330	88.72 372	99.81 154	97.53 289	94.13 159	87.00 371	98.23 265	74.07 379	98.47 252	96.22 201	88.86 312	93.99 364
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		92.70 291	91.55 304	96.16 254	95.09 355	96.20 167	98.88 325	99.00 39	91.02 291	91.82 289	95.29 372	76.05 364	97.96 300	95.62 212	81.19 378	94.30 331

FMVSNet3		92.69 292	91.58 302	95.99 257	98.29 192	97.42 109	99.26 277	97.62 276	89.80 324	89.68 317	95.32 368	81.62 305	96.27 387	87.01 361	85.65 342	94.29 332

IterMVS-LS		92.69 292	92.11 292	94.43 318	96.80 304	92.74 284	99.45 249	96.89 376	88.98 335	89.65 320	95.38 365	88.77 214	96.34 384	90.98 304	82.04 372	94.22 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		92.65 294	91.50 305	96.10 256	96.85 301	90.49 343	91.50 446	97.19 329	82.76 415	90.23 305	95.59 351	95.02 61	98.00 297	77.41 422	96.98 216	99.82 101

SD_0403		92.63 295	93.38 262	90.40 397	97.32 269	77.91 439	97.75 390	98.03 232	91.89 259	90.83 300	98.29 263	82.00 297	93.79 430	88.51 340	95.75 246	99.52 164

c3_l		92.53 296	91.87 298	94.52 310	97.40 261	92.99 280	99.40 252	96.93 373	87.86 359	88.69 344	95.44 360	89.95 195	96.44 379	90.45 315	80.69 388	94.14 351

AllTest		92.48 297	91.64 300	95.00 290	99.01 124	88.43 377	98.94 317	96.82 382	86.50 377	88.71 342	98.47 251	74.73 375	99.88 117	85.39 373	96.18 231	96.71 300

DU-MVS		92.46 298	91.45 307	95.49 272	94.05 374	95.28 212	99.81 154	98.74 71	92.25 250	89.21 334	96.64 317	81.66 303	96.73 367	93.20 267	77.52 407	94.46 317

eth_miper_zixun_eth		92.41 299	91.93 296	93.84 340	97.28 273	90.68 338	98.83 332	96.97 366	88.57 349	89.19 336	95.73 346	89.24 207	96.69 369	89.97 324	81.55 375	94.15 348

DIV-MVS_self_test		92.32 300	91.60 301	94.47 314	97.31 270	92.74 284	99.58 221	96.75 386	86.99 372	87.64 361	95.54 353	89.55 200	96.50 376	88.58 337	82.44 369	94.17 342

cl____		92.31 301	91.58 302	94.52 310	97.33 268	92.77 282	99.57 224	96.78 385	86.97 373	87.56 363	95.51 356	89.43 201	96.62 371	88.60 336	82.44 369	94.16 347

LCM-MVSNet-Re		92.31 301	92.60 281	91.43 383	97.53 252	79.27 437	99.02 306	91.83 453	92.07 253	80.31 417	94.38 402	83.50 287	95.48 407	97.22 176	97.58 192	99.54 158

WR-MVS		92.31 301	91.25 309	95.48 275	94.45 367	95.29 211	99.60 218	98.68 78	90.10 317	88.07 356	96.89 307	80.68 317	96.80 365	93.14 270	79.67 395	94.36 325

COLMAP_ROB	90.47 14	92.18 304	91.49 306	94.25 324	99.00 128	88.04 383	98.42 365	96.70 389	82.30 417	88.43 351	99.01 179	76.97 350	99.85 123	86.11 369	96.50 223	94.86 311
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240529		92.10 305	90.65 317	96.47 242	98.82 149	90.61 340	98.72 342	98.67 81	75.54 437	93.90 266	98.58 239	66.23 413	99.90 106	94.70 233	90.67 298	98.90 245

pmmvs4		92.10 305	91.07 313	95.18 285	92.82 401	94.96 223	99.48 243	96.83 380	87.45 364	88.66 345	96.56 321	83.78 285	96.83 363	89.29 330	84.77 352	93.75 380

jajsoiax		91.92 307	91.18 310	94.15 325	91.35 422	90.95 332	99.00 307	97.42 300	92.61 229	87.38 367	97.08 299	72.46 386	97.36 321	94.53 237	88.77 313	94.13 353

XXY-MVS		91.82 308	90.46 320	95.88 262	93.91 377	95.40 202	98.87 328	97.69 267	88.63 348	87.87 358	97.08 299	74.38 378	97.89 304	91.66 292	84.07 358	94.35 328

miper_lstm_enhance		91.81 309	91.39 308	93.06 362	97.34 266	89.18 366	99.38 258	96.79 384	86.70 376	87.47 365	95.22 375	90.00 194	95.86 402	88.26 342	81.37 377	94.15 348

mvs_tets		91.81 309	91.08 312	94.00 333	91.63 419	90.58 341	98.67 348	97.43 298	92.43 239	87.37 368	97.05 302	71.76 388	97.32 326	94.75 231	88.68 315	94.11 354

VPNet		91.81 309	90.46 320	95.85 264	94.74 361	95.54 195	98.98 309	98.59 97	92.14 251	90.77 302	97.44 288	68.73 402	97.54 317	94.89 227	77.89 404	94.46 317

RPSCF		91.80 312	92.79 277	88.83 409	98.15 205	69.87 447	98.11 379	96.60 393	83.93 404	94.33 259	99.27 155	79.60 329	99.46 182	91.99 287	93.16 290	97.18 297

PVSNet_0	88.03 19	91.80 312	90.27 326	96.38 249	98.27 195	90.46 344	99.94 83	99.61 13	93.99 168	86.26 383	97.39 291	71.13 394	99.89 111	98.77 100	67.05 442	98.79 250

anonymousdsp		91.79 314	90.92 314	94.41 319	90.76 427	92.93 281	98.93 319	97.17 333	89.08 330	87.46 366	95.30 369	78.43 343	96.92 355	92.38 278	88.73 314	93.39 391

JIA-IIPM		91.76 315	90.70 316	94.94 292	96.11 323	87.51 386	93.16 440	98.13 223	75.79 436	97.58 177	77.68 454	92.84 134	97.97 298	88.47 341	96.54 221	99.33 196

TranMVSNet+NR-MVSNet		91.68 316	90.61 319	94.87 294	93.69 381	93.98 254	99.69 198	98.65 82	91.03 290	88.44 349	96.83 313	80.05 326	96.18 391	90.26 320	76.89 415	94.45 322

NR-MVSNet		91.56 317	90.22 327	95.60 270	94.05 374	95.76 183	98.25 371	98.70 74	91.16 285	80.78 416	96.64 317	83.23 290	96.57 373	91.41 295	77.73 406	94.46 317

dongtai		91.55 318	91.13 311	92.82 366	98.16 204	86.35 394	99.47 244	98.51 125	83.24 409	85.07 393	97.56 285	90.33 190	94.94 417	76.09 428	91.73 293	97.18 297

v2v482		91.30 319	90.07 333	95.01 289	93.13 389	93.79 257	99.77 165	97.02 359	88.05 356	89.25 331	95.37 366	80.73 316	97.15 336	87.28 355	80.04 394	94.09 355

WR-MVS_H		91.30 319	90.35 323	94.15 325	94.17 373	92.62 291	99.17 284	98.94 44	88.87 341	86.48 379	94.46 401	84.36 280	96.61 372	88.19 343	78.51 400	93.21 396

tt0805		91.28 321	90.18 329	94.60 305	96.26 320	87.55 385	98.39 366	98.72 72	89.00 334	89.22 333	98.47 251	62.98 426	98.96 212	90.57 312	88.00 326	97.28 296

V42		91.28 321	90.12 332	94.74 299	93.42 386	93.46 268	99.68 201	97.02 359	87.36 365	89.85 315	95.05 380	81.31 309	97.34 323	87.34 354	80.07 393	93.40 390

CP-MVSNet		91.23 323	90.22 327	94.26 323	93.96 376	92.39 296	99.09 290	98.57 101	88.95 338	86.42 380	96.57 320	79.19 333	96.37 382	90.29 319	78.95 397	94.02 359

XVG-ACMP-BASELINE		91.22 324	90.75 315	92.63 370	93.73 380	85.61 399	98.52 358	97.44 297	92.77 220	89.90 312	96.85 310	66.64 412	98.39 264	92.29 279	88.61 316	93.89 372

v1144		91.09 325	89.83 334	94.87 294	93.25 388	93.69 262	99.62 212	96.98 364	86.83 375	89.64 321	94.99 385	80.94 312	97.05 344	85.08 377	81.16 379	93.87 374

FMVSNet2		91.02 326	89.56 340	95.41 278	97.53 252	95.74 184	98.98 309	97.41 302	87.05 369	88.43 351	95.00 384	71.34 391	96.24 389	85.12 376	85.21 347	94.25 335

MVP-Stereo		90.93 327	90.45 322	92.37 373	91.25 424	88.76 370	98.05 382	96.17 404	87.27 367	84.04 397	95.30 369	78.46 342	97.27 333	83.78 386	99.70 89	91.09 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		90.91 328	90.17 330	93.12 359	96.78 308	90.42 346	98.89 323	97.05 358	89.03 332	86.49 378	95.42 361	76.59 356	95.02 414	87.22 356	84.09 357	93.93 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		90.88 329	89.82 335	94.08 328	97.53 252	91.97 302	98.43 362	96.95 368	87.05 369	89.68 317	94.72 390	71.34 391	96.11 393	87.01 361	85.65 342	94.17 342

test1		90.88 329	89.82 335	94.08 328	97.53 252	91.97 302	98.43 362	96.95 368	87.05 369	89.68 317	94.72 390	71.34 391	96.11 393	87.01 361	85.65 342	94.17 342

IterMVS-SCA-FT		90.85 331	90.16 331	92.93 364	96.72 310	89.96 355	98.89 323	96.99 362	88.95 338	86.63 375	95.67 347	76.48 358	95.00 415	87.04 359	84.04 360	93.84 376

v144192		90.79 332	89.52 342	94.59 306	93.11 392	92.77 282	99.56 227	96.99 362	86.38 379	89.82 316	94.95 387	80.50 321	97.10 341	83.98 384	80.41 389	93.90 371

v148		90.70 333	89.63 338	93.92 336	92.97 395	90.97 329	99.75 174	96.89 376	87.51 362	88.27 354	95.01 382	81.67 302	97.04 347	87.40 353	77.17 412	93.75 380

MS-PatchMatch		90.65 334	90.30 325	91.71 382	94.22 372	85.50 401	98.24 372	97.70 265	88.67 346	86.42 380	96.37 325	67.82 407	98.03 296	83.62 387	99.62 95	91.60 418

ACMH	89.72 17	90.64 335	89.63 338	93.66 347	95.64 347	88.64 375	98.55 354	97.45 296	89.03 332	81.62 410	97.61 284	69.75 398	98.41 260	89.37 329	87.62 332	93.92 370
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		90.63 336	89.51 343	93.99 334	93.83 378	91.70 315	98.98 309	98.52 122	88.48 350	86.15 384	96.53 322	75.46 367	96.31 386	88.83 334	78.86 399	93.95 367

v1192		90.62 337	89.25 347	94.72 301	93.13 389	93.07 276	99.50 238	97.02 359	86.33 380	89.56 325	95.01 382	79.22 332	97.09 343	82.34 396	81.16 379	94.01 361

v8		90.54 338	89.17 348	94.66 302	93.43 385	93.40 272	99.20 281	96.94 372	85.76 386	87.56 363	94.51 397	81.96 299	97.19 334	84.94 378	78.25 401	93.38 392

v1921920		90.46 339	89.12 349	94.50 312	92.96 396	92.46 294	99.49 240	96.98 364	86.10 382	89.61 323	95.30 369	78.55 341	97.03 349	82.17 397	80.89 387	94.01 361

our_test_3		90.39 340	89.48 345	93.12 359	92.40 408	89.57 361	99.33 265	96.35 401	87.84 360	85.30 390	94.99 385	84.14 283	96.09 396	80.38 407	84.56 353	93.71 385

PatchT		90.38 341	88.75 357	95.25 284	95.99 327	90.16 350	91.22 448	97.54 287	76.80 432	97.26 188	86.01 448	91.88 162	96.07 397	66.16 447	95.91 241	99.51 168

ACMH+	89.98 16	90.35 342	89.54 341	92.78 368	95.99 327	86.12 396	98.81 334	97.18 331	89.38 327	83.14 403	97.76 283	68.42 404	98.43 257	89.11 332	86.05 340	93.78 379

Baseline_NR-MVSNet		90.33 343	89.51 343	92.81 367	92.84 399	89.95 356	99.77 165	93.94 443	84.69 400	89.04 338	95.66 348	81.66 303	96.52 375	90.99 303	76.98 413	91.97 416

MIMVSNet		90.30 344	88.67 358	95.17 286	96.45 317	91.64 317	92.39 442	97.15 336	85.99 383	90.50 303	93.19 416	66.95 410	94.86 419	82.01 398	93.43 286	99.01 235

LTVRE_ROB	88.28 18	90.29 345	89.05 352	94.02 331	95.08 356	90.15 351	97.19 399	97.43 298	84.91 398	83.99 399	97.06 301	74.00 380	98.28 279	84.08 382	87.71 329	93.62 386
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
v10		90.25 346	88.82 355	94.57 308	93.53 383	93.43 269	99.08 292	96.87 378	85.00 395	87.34 369	94.51 397	80.93 313	97.02 351	82.85 392	79.23 396	93.26 394

v1240		90.20 347	88.79 356	94.44 316	93.05 394	92.27 298	99.38 258	96.92 374	85.89 384	89.36 328	94.87 389	77.89 344	97.03 349	80.66 405	81.08 382	94.01 361

PEN-MVS		90.19 348	89.06 351	93.57 348	93.06 393	90.90 333	99.06 297	98.47 134	88.11 355	85.91 386	96.30 327	76.67 354	95.94 401	87.07 358	76.91 414	93.89 372

pmmvs5		90.17 349	89.09 350	93.40 351	92.10 413	89.77 359	99.74 177	95.58 418	85.88 385	87.24 370	95.74 343	73.41 384	96.48 377	88.54 338	83.56 362	93.95 367

EU-MVSNet		90.14 350	90.34 324	89.54 404	92.55 405	81.06 431	98.69 346	98.04 231	91.41 279	86.59 376	96.84 312	80.83 315	93.31 435	86.20 367	81.91 373	94.26 333

UniMVSNet_ETH3D		90.06 351	88.58 360	94.49 313	94.67 363	88.09 382	97.81 388	97.57 284	83.91 405	88.44 349	97.41 289	57.44 437	97.62 314	91.41 295	88.59 318	97.77 283

Syy-MVS		90.00 352	90.63 318	88.11 416	97.68 239	74.66 443	99.71 191	98.35 184	90.79 300	92.10 286	98.67 225	79.10 335	93.09 436	63.35 450	95.95 239	96.59 302

USDC		90.00 352	88.96 353	93.10 361	94.81 360	88.16 381	98.71 343	95.54 419	93.66 184	83.75 401	97.20 295	65.58 415	98.31 275	83.96 385	87.49 334	92.85 404

Anonymous20231211		89.86 354	88.44 362	94.13 327	98.93 136	90.68 338	98.54 356	98.26 201	76.28 433	86.73 373	95.54 353	70.60 396	97.56 316	90.82 308	80.27 392	94.15 348

OurMVSNet-221017-0		89.81 355	89.48 345	90.83 389	91.64 418	81.21 429	98.17 377	95.38 423	91.48 273	85.65 388	97.31 292	72.66 385	97.29 331	88.15 344	84.83 351	93.97 366

RPMNet		89.76 356	87.28 373	97.19 219	96.29 318	92.66 288	92.01 444	98.31 193	70.19 447	96.94 197	85.87 449	87.25 235	99.78 140	62.69 451	95.96 237	99.13 222

Patchmtry		89.70 357	88.49 361	93.33 353	96.24 321	89.94 358	91.37 447	96.23 402	78.22 430	87.69 360	93.31 414	91.04 175	96.03 398	80.18 410	82.10 371	94.02 359

v7n		89.65 358	88.29 364	93.72 342	92.22 410	90.56 342	99.07 296	97.10 347	85.42 393	86.73 373	94.72 390	80.06 325	97.13 338	81.14 402	78.12 403	93.49 388

SSC-MVS3.2		89.59 359	88.66 359	92.38 371	94.29 371	86.12 396	99.49 240	97.66 271	90.28 316	88.63 346	95.18 376	64.46 420	96.88 359	85.30 375	82.66 366	94.14 351

ppachtmachnet_test		89.58 360	88.35 363	93.25 357	92.40 408	90.44 345	99.33 265	96.73 387	85.49 391	85.90 387	95.77 342	81.09 311	96.00 400	76.00 429	82.49 368	93.30 393

test_fmvs2		89.47 361	89.70 337	88.77 412	94.54 365	75.74 440	99.83 149	94.70 436	94.71 130	91.08 295	96.82 314	54.46 440	97.78 309	92.87 274	88.27 322	92.80 405

DTE-MVSNet		89.40 362	88.24 365	92.88 365	92.66 404	89.95 356	99.10 289	98.22 207	87.29 366	85.12 392	96.22 329	76.27 361	95.30 413	83.56 388	75.74 419	93.41 389

pm-mvs1		89.36 363	87.81 369	94.01 332	93.40 387	91.93 305	98.62 352	96.48 398	86.25 381	83.86 400	96.14 333	73.68 381	97.04 347	86.16 368	75.73 420	93.04 400

tfpnnormal		89.29 364	87.61 371	94.34 321	94.35 369	94.13 249	98.95 316	98.94 44	83.94 403	84.47 396	95.51 356	74.84 374	97.39 320	77.05 425	80.41 389	91.48 420

LF4IMVS		89.25 365	88.85 354	90.45 396	92.81 402	81.19 430	98.12 378	94.79 432	91.44 275	86.29 382	97.11 297	65.30 418	98.11 290	88.53 339	85.25 346	92.07 413

testgi		89.01 366	88.04 367	91.90 378	93.49 384	84.89 405	99.73 184	95.66 416	93.89 177	85.14 391	98.17 266	59.68 434	94.66 422	77.73 421	88.88 310	96.16 308

SixPastTwentyTwo		88.73 367	88.01 368	90.88 386	91.85 416	82.24 422	98.22 375	95.18 428	88.97 336	82.26 406	96.89 307	71.75 389	96.67 370	84.00 383	82.98 363	93.72 384

mmtdpeth		88.52 368	87.75 370	90.85 388	95.71 342	83.47 415	98.94 317	94.85 430	88.78 343	97.19 190	89.58 434	63.29 424	98.97 210	98.54 114	62.86 450	90.10 433

FMVSNet1		88.50 369	86.64 376	94.08 328	95.62 349	91.97 302	98.43 362	96.95 368	83.00 412	86.08 385	94.72 390	59.09 435	96.11 393	81.82 400	84.07 358	94.17 342

FMVSNet5		88.32 370	87.47 372	90.88 386	96.90 299	88.39 379	97.28 397	95.68 415	82.60 416	84.67 395	92.40 423	79.83 327	91.16 445	76.39 427	81.51 376	93.09 398

DSMNet-mixed		88.28 371	88.24 365	88.42 414	89.64 435	75.38 442	98.06 381	89.86 457	85.59 390	88.20 355	92.14 425	76.15 363	91.95 443	78.46 418	96.05 234	97.92 277

ttmdpeth		88.23 372	87.06 375	91.75 381	89.91 434	87.35 388	98.92 322	95.73 412	87.92 358	84.02 398	96.31 326	68.23 406	96.84 361	86.33 366	76.12 417	91.06 422

K. test v3		88.05 373	87.24 374	90.47 395	91.82 417	82.23 423	98.96 315	97.42 300	89.05 331	76.93 433	95.60 350	68.49 403	95.42 409	85.87 372	81.01 385	93.75 380

KD-MVS_2432*1600		88.00 374	86.10 378	93.70 345	96.91 296	94.04 251	97.17 400	97.12 341	84.93 396	81.96 407	92.41 421	92.48 148	94.51 423	79.23 412	52.68 456	92.56 407

miper_refine_blended		88.00 374	86.10 378	93.70 345	96.91 296	94.04 251	97.17 400	97.12 341	84.93 396	81.96 407	92.41 421	92.48 148	94.51 423	79.23 412	52.68 456	92.56 407

TinyColmap		87.87 376	86.51 377	91.94 377	95.05 357	85.57 400	97.65 391	94.08 440	84.40 402	81.82 409	96.85 310	62.14 429	98.33 273	80.25 409	86.37 339	91.91 417

TransMVSNet (Re)		87.25 377	85.28 384	93.16 358	93.56 382	91.03 328	98.54 356	94.05 442	83.69 407	81.09 414	96.16 331	75.32 368	96.40 381	76.69 426	68.41 438	92.06 414

Patchmatch-RL test		86.90 378	85.98 382	89.67 403	84.45 446	75.59 441	89.71 452	92.43 450	86.89 374	77.83 430	90.94 429	94.22 92	93.63 432	87.75 349	69.61 433	99.79 106

test_vis1_rt		86.87 379	86.05 381	89.34 405	96.12 322	78.07 438	99.87 123	83.54 464	92.03 256	78.21 428	89.51 435	45.80 450	99.91 104	96.25 200	93.11 291	90.03 434

Anonymous20231206		86.32 380	85.42 383	89.02 408	89.11 437	80.53 435	99.05 301	95.28 424	85.43 392	82.82 404	93.92 406	74.40 377	93.44 434	66.99 444	81.83 374	93.08 399

MVS-HIRNet		86.22 381	83.19 394	95.31 282	96.71 311	90.29 347	92.12 443	97.33 312	62.85 451	86.82 372	70.37 456	69.37 399	97.49 318	75.12 430	97.99 184	98.15 271

pmmvs6		85.69 382	83.84 389	91.26 385	90.00 433	84.41 408	97.82 387	96.15 405	75.86 435	81.29 413	95.39 364	61.21 432	96.87 360	83.52 389	73.29 425	92.50 409

test_0402		85.58 383	83.94 388	90.50 394	93.81 379	85.04 403	98.55 354	95.20 427	76.01 434	79.72 422	95.13 377	64.15 422	96.26 388	66.04 448	86.88 336	90.21 431

UnsupCasMVSNet_eth		85.52 384	83.99 386	90.10 400	89.36 436	83.51 414	96.65 412	97.99 234	89.14 329	75.89 437	93.83 407	63.25 425	93.92 427	81.92 399	67.90 441	92.88 403

MDA-MVSNet_test_wron		85.51 385	83.32 393	92.10 375	90.96 425	88.58 376	99.20 281	96.52 396	79.70 427	57.12 456	92.69 418	79.11 334	93.86 429	77.10 424	77.46 409	93.86 375

YYNet1		85.50 386	83.33 392	92.00 376	90.89 426	88.38 380	99.22 280	96.55 395	79.60 428	57.26 455	92.72 417	79.09 336	93.78 431	77.25 423	77.37 410	93.84 376

EG-PatchMatch MVS		85.35 387	83.81 390	89.99 402	90.39 429	81.89 425	98.21 376	96.09 406	81.78 419	74.73 439	93.72 410	51.56 446	97.12 340	79.16 415	88.61 316	90.96 424

Anonymous20240521		85.15 388	83.81 390	89.16 407	88.32 438	82.69 418	98.80 337	95.74 411	79.72 426	81.53 411	90.99 428	65.38 417	94.16 425	72.69 434	81.11 381	90.63 428

MVStest1		85.03 389	82.76 398	91.83 379	92.95 397	89.16 367	98.57 353	94.82 431	71.68 445	68.54 448	95.11 379	83.17 291	95.66 405	74.69 431	65.32 445	90.65 427

sc_t1		85.01 390	82.46 400	92.67 369	92.44 407	83.09 416	97.39 395	95.72 413	65.06 448	85.64 389	96.16 331	49.50 447	97.34 323	84.86 379	75.39 421	97.57 291

mvs5depth		84.87 391	82.90 397	90.77 390	85.59 445	84.84 406	91.10 449	93.29 448	83.14 410	85.07 393	94.33 403	62.17 428	97.32 326	78.83 417	72.59 428	90.14 432

TDRefinement		84.76 392	82.56 399	91.38 384	74.58 460	84.80 407	97.36 396	94.56 437	84.73 399	80.21 418	96.12 336	63.56 423	98.39 264	87.92 347	63.97 448	90.95 425

CMPMVS	61.59 21	84.75 393	85.14 385	83.57 424	90.32 430	62.54 452	96.98 405	97.59 283	74.33 441	69.95 445	96.66 315	64.17 421	98.32 274	87.88 348	88.41 321	89.84 436
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 394	83.99 386	86.91 418	88.19 440	80.62 434	98.88 325	95.94 408	88.36 352	78.87 423	94.62 395	68.75 401	89.11 449	66.52 446	75.82 418	91.00 423

CL-MVSNet_self_test		84.50 395	83.15 395	88.53 413	86.00 443	81.79 426	98.82 333	97.35 308	85.12 394	83.62 402	90.91 430	76.66 355	91.40 444	69.53 440	60.36 453	92.40 411

new_pmnet		84.49 396	82.92 396	89.21 406	90.03 432	82.60 419	96.89 408	95.62 417	80.59 423	75.77 438	89.17 436	65.04 419	94.79 420	72.12 436	81.02 384	90.23 430

MDA-MVSNet-bldmvs		84.09 397	81.52 404	91.81 380	91.32 423	88.00 384	98.67 348	95.92 409	80.22 425	55.60 457	93.32 413	68.29 405	93.60 433	73.76 432	76.61 416	93.82 378

pmmvs-eth3d		84.03 398	81.97 402	90.20 399	84.15 447	87.09 390	98.10 380	94.73 434	83.05 411	74.10 441	87.77 443	65.56 416	94.01 426	81.08 403	69.24 435	89.49 440

dmvs_testset		83.79 399	86.07 380	76.94 431	92.14 411	48.60 466	96.75 411	90.27 456	89.48 326	78.65 425	98.55 243	79.25 331	86.65 454	66.85 445	82.69 365	95.57 310

OpenMVS_ROB	79.82 20	83.77 400	81.68 403	90.03 401	88.30 439	82.82 417	98.46 359	95.22 426	73.92 442	76.00 436	91.29 427	55.00 439	96.94 354	68.40 442	88.51 320	90.34 429

KD-MVS_self_test		83.59 401	82.06 401	88.20 415	86.93 441	80.70 433	97.21 398	96.38 399	82.87 413	82.49 405	88.97 437	67.63 408	92.32 441	73.75 433	62.30 452	91.58 419

tt0320		83.56 402	81.15 405	90.77 390	92.77 403	83.58 412	96.83 410	95.52 420	63.26 449	81.36 412	92.54 419	53.26 442	95.77 403	80.45 406	74.38 423	92.96 401

tt0320-xc		82.94 403	80.35 410	90.72 392	92.90 398	83.54 413	96.85 409	94.73 434	63.12 450	79.85 421	93.77 409	49.43 448	95.46 408	80.98 404	71.54 429	93.16 397

MIMVSNet1		82.58 404	80.51 409	88.78 410	86.68 442	84.20 409	96.65 412	95.41 422	78.75 429	78.59 426	92.44 420	51.88 445	89.76 448	65.26 449	78.95 397	92.38 412

mvsany_test3		82.12 405	81.14 406	85.06 422	81.87 451	70.41 446	97.09 402	92.14 451	91.27 282	77.84 429	88.73 438	39.31 453	95.49 406	90.75 310	71.24 430	89.29 442

new-patchmatchnet		81.19 406	79.34 413	86.76 419	82.86 450	80.36 436	97.92 384	95.27 425	82.09 418	72.02 442	86.87 445	62.81 427	90.74 447	71.10 437	63.08 449	89.19 443

APD_test1		81.15 407	80.92 407	81.86 427	92.45 406	59.76 456	96.04 424	93.61 446	73.29 443	77.06 431	96.64 317	44.28 452	96.16 392	72.35 435	82.52 367	89.67 438

test_method		80.79 408	79.70 412	84.08 423	92.83 400	67.06 449	99.51 236	95.42 421	54.34 455	81.07 415	93.53 411	44.48 451	92.22 442	78.90 416	77.23 411	92.94 402

PM-MVS		80.47 409	78.88 414	85.26 421	83.79 449	72.22 444	95.89 427	91.08 454	85.71 389	76.56 435	88.30 439	36.64 454	93.90 428	82.39 395	69.57 434	89.66 439

pmmvs3		80.27 410	77.77 415	87.76 417	80.32 455	82.43 421	98.23 374	91.97 452	72.74 444	78.75 424	87.97 442	57.30 438	90.99 446	70.31 438	62.37 451	89.87 435

N_pmnet		80.06 411	80.78 408	77.89 430	91.94 414	45.28 468	98.80 337	56.82 470	78.10 431	80.08 419	93.33 412	77.03 348	95.76 404	68.14 443	82.81 364	92.64 406

test_fmvs3		79.99 412	80.17 411	79.45 429	84.02 448	62.83 450	99.05 301	93.49 447	88.29 354	80.06 420	86.65 446	28.09 458	88.00 450	88.63 335	73.27 426	87.54 446

UnsupCasMVSNet_bld		79.97 413	77.03 418	88.78 410	85.62 444	81.98 424	93.66 436	97.35 308	75.51 438	70.79 444	83.05 451	48.70 449	94.91 418	78.31 419	60.29 454	89.46 441

test_f		78.40 414	77.59 416	80.81 428	80.82 453	62.48 453	96.96 406	93.08 449	83.44 408	74.57 440	84.57 450	27.95 459	92.63 439	84.15 381	72.79 427	87.32 447

WB-MVS		76.28 415	77.28 417	73.29 435	81.18 452	54.68 460	97.87 386	94.19 439	81.30 420	69.43 446	90.70 431	77.02 349	82.06 458	35.71 463	68.11 440	83.13 449

SSC-MVS		75.42 416	76.40 419	72.49 439	80.68 454	53.62 461	97.42 393	94.06 441	80.42 424	68.75 447	90.14 433	76.54 357	81.66 459	33.25 464	66.34 444	82.19 450

EGC-MVSNET		69.38 417	63.76 427	86.26 420	90.32 430	81.66 428	96.24 420	93.85 444	0.99 467	3.22 468	92.33 424	52.44 443	92.92 438	59.53 454	84.90 350	84.21 448

test_vis3_rt		68.82 418	66.69 423	75.21 434	76.24 459	60.41 455	96.44 415	68.71 469	75.13 439	50.54 460	69.52 458	16.42 468	96.32 385	80.27 408	66.92 443	68.89 456

FPMVS		68.72 419	68.72 420	68.71 441	65.95 464	44.27 470	95.97 426	94.74 433	51.13 456	53.26 458	90.50 432	25.11 461	83.00 457	60.80 452	80.97 386	78.87 454

testf1		68.38 420	66.92 421	72.78 437	78.80 456	50.36 463	90.95 450	87.35 462	55.47 453	58.95 452	88.14 440	20.64 463	87.60 451	57.28 455	64.69 446	80.39 452

APD_test2		68.38 420	66.92 421	72.78 437	78.80 456	50.36 463	90.95 450	87.35 462	55.47 453	58.95 452	88.14 440	20.64 463	87.60 451	57.28 455	64.69 446	80.39 452

LCM-MVSNet		67.77 422	64.73 425	76.87 432	62.95 466	56.25 459	89.37 453	93.74 445	44.53 458	61.99 450	80.74 452	20.42 465	86.53 455	69.37 441	59.50 455	87.84 444

PMMVS2		67.15 423	64.15 426	76.14 433	70.56 463	62.07 454	93.89 434	87.52 461	58.09 452	60.02 451	78.32 453	22.38 462	84.54 456	59.56 453	47.03 458	81.80 451

Gipuma		66.95 424	65.00 424	72.79 436	91.52 420	67.96 448	66.16 459	95.15 429	47.89 457	58.54 454	67.99 459	29.74 456	87.54 453	50.20 458	77.83 405	62.87 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		65.23 425	62.94 428	72.13 440	44.90 469	50.03 465	81.05 456	89.42 460	38.45 459	48.51 461	99.90 18	54.09 441	78.70 461	91.84 291	18.26 463	87.64 445

ANet_high		56.10 426	52.24 429	67.66 442	49.27 468	56.82 458	83.94 455	82.02 465	70.47 446	33.28 465	64.54 460	17.23 467	69.16 463	45.59 460	23.85 462	77.02 455

PMVS	49.05 23	53.75 427	51.34 431	60.97 444	40.80 470	34.68 471	74.82 458	89.62 459	37.55 460	28.67 466	72.12 455	7.09 470	81.63 460	43.17 461	68.21 439	66.59 458
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 428	52.18 430	52.67 445	71.51 461	45.40 467	93.62 437	76.60 467	36.01 461	43.50 462	64.13 461	27.11 460	67.31 464	31.06 465	26.06 460	45.30 463

MVE	53.74 22	51.54 429	47.86 433	62.60 443	59.56 467	50.93 462	79.41 457	77.69 466	35.69 462	36.27 464	61.76 463	5.79 472	69.63 462	37.97 462	36.61 459	67.24 457
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 430	51.22 432	52.11 446	70.71 462	44.97 469	94.04 433	75.66 468	35.34 463	42.40 463	61.56 464	28.93 457	65.87 465	27.64 466	24.73 461	45.49 462

testmvs		40.60 431	44.45 434	29.05 448	19.49 472	14.11 474	99.68 201	18.47 471	20.74 464	64.59 449	98.48 250	10.95 469	17.09 468	56.66 457	11.01 464	55.94 461

test123		37.68 432	39.14 435	33.31 447	19.94 471	24.83 473	98.36 367	9.75 472	15.53 465	51.31 459	87.14 444	19.62 466	17.74 467	47.10 459	3.47 466	57.36 460

cdsmvs_eth3d_5k		23.43 433	31.24 436	0.00 450	0.00 473	0.00 475	0.00 461	98.09 225	0.00 468	0.00 469	99.67 107	83.37 288	0.00 469	0.00 468	0.00 467	0.00 465

wuyk23d		20.37 434	20.84 437	18.99 449	65.34 465	27.73 472	50.43 460	7.67 473	9.50 466	8.01 467	6.34 467	6.13 471	26.24 466	23.40 467	10.69 465	2.99 464

ab-mvs-re		8.28 435	11.04 438	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	99.40 140	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

pcd_1.5k_mvsjas		7.60 436	10.13 439	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	91.20 170	0.00 469	0.00 468	0.00 467	0.00 465

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.02 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 469	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

WAC-MVS						90.97 329								86.10 370

FOURS1					99.92 31	97.66 98	99.95 66	98.36 182	95.58 105	99.52 72

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 167					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 58	99.80 24	99.79 58	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 167					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 13	99.30 12		98.41 167	96.63 71	99.75 38	99.93 11	97.49 10

eth-test2					0.00 473

eth-test					0.00 473

ZD-MVS					99.92 31	98.57 56		98.52 122	92.34 243	99.31 90	99.83 46	95.06 59	99.80 136	99.70 44	99.97 42

RE-MVS-def			98.13 57		99.79 64	96.37 158	99.76 170	98.31 193	94.43 144	99.40 84	99.75 75	92.95 131		98.90 92	99.92 64	99.97 62

IU-MVS					99.93 24	99.31 10		98.41 167	97.71 29	99.84 19				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 47				99.80 54	97.44 14	100.00 1	100.00 1	99.98 32	100.00 1

test_241102_TWO								98.43 150	97.27 45	99.80 24	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 150	97.26 47	99.80 24	99.88 24	96.71 27	100.00 1

9.14			98.38 38		99.87 51		99.91 101	98.33 189	93.22 198	99.78 35	99.89 22	94.57 77	99.85 123	99.84 25	99.97 42

save fliter					99.82 60	98.79 40	99.96 47	98.40 171	97.66 31

test_0728_THIRD									96.48 76	99.83 20	99.91 14	97.87 5	100.00 1	99.92 13	100.00 1	100.00 1

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 66	98.43 150					100.00 1	99.99 5	100.00 1	100.00 1

test0726					99.93 24	99.29 15	99.96 47	98.42 162	97.28 43	99.86 13	99.94 4	97.22 19

GSMVS																99.59 144

test_part2					99.89 45	99.25 18				99.49 75

sam_mvs1												94.72 71				99.59 144

sam_mvs												94.25 91

ambc				83.23 425	77.17 458	62.61 451	87.38 454	94.55 438		76.72 434	86.65 446	30.16 455	96.36 383	84.85 380	69.86 432	90.73 426

MTGPA								98.28 198

test_post1							95.78 428				59.23 465	93.20 125	97.74 310	91.06 301

test_post											63.35 462	94.43 79	98.13 289

patchmatchnet-post											91.70 426	95.12 56	97.95 301

GG-mvs-BLEND				98.54 122	98.21 199	98.01 79	93.87 435	98.52 122		97.92 164	97.92 277	99.02 3	97.94 303	98.17 136	99.58 104	99.67 124

MTMP							99.87 123	96.49 397

gm-plane-assit					96.97 289	93.76 259			91.47 274		98.96 188		98.79 222	94.92 224

test9_res														99.71 43	99.99 21	100.00 1

TEST9					99.92 31	98.92 29	99.96 47	98.43 150	93.90 175	99.71 45	99.86 29	95.88 41	99.85 123

test_8					99.92 31	98.88 32	99.96 47	98.43 150	94.35 149	99.69 47	99.85 33	95.94 38	99.85 123

agg_prior2														99.48 58	100.00 1	100.00 1

agg_prior					99.93 24	98.77 42		98.43 150		99.63 55			99.85 123

TestCases				95.00 290	99.01 124	88.43 377		96.82 382	86.50 377	88.71 342	98.47 251	74.73 375	99.88 117	85.39 373	96.18 231	96.71 300

test_prior4						98.05 77	99.94 83

test_prior2							99.95 66		95.78 98	99.73 43	99.76 67	96.00 37		99.78 31	100.00 1

test_prior				99.43 36	99.94 13	98.49 61		98.65 82					99.80 136			99.99 23

旧先验2							99.46 248		94.21 158	99.85 16			99.95 80	96.96 185

新几何2							99.40 252

新几何1				99.42 38	99.75 71	98.27 66		98.63 91	92.69 224	99.55 67	99.82 49	94.40 81	100.00 1	91.21 297	99.94 55	99.99 23

旧先验1					99.76 68	97.52 102		98.64 85			99.85 33	95.63 45			99.94 55	99.99 23

无先验							99.49 240	98.71 73	93.46 190				100.00 1	94.36 239		99.99 23

原ACMM2							99.90 107

原ACMM1				98.96 88	99.73 75	96.99 129		98.51 125	94.06 165	99.62 58	99.85 33	94.97 65	99.96 71	95.11 218	99.95 50	99.92 87

test222					99.55 92	97.41 110	99.34 264	98.55 113	91.86 261	99.27 94	99.83 46	93.84 106			99.95 50	99.99 23

testdata2													99.99 36	90.54 314

segment_acmp												96.68 29

testdata				98.42 135	99.47 98	95.33 207		98.56 107	93.78 179	99.79 33	99.85 33	93.64 111	99.94 88	94.97 222	99.94 55	100.00 1

testdata1							99.28 274		96.35 86

test12				99.43 36	99.74 72	98.56 57		98.40 171		99.65 51		94.76 69	99.75 147		99.98 32	99.99 23

plane_prior7					95.71 342	91.59 323

plane_prior6					95.76 336	91.72 314						80.47 322

plane_prior5								97.87 248					98.37 270	97.79 161	89.55 303	94.52 314

plane_prior4											98.59 236

plane_prior3						91.64 317			96.63 71	93.01 274

plane_prior2							99.84 142		96.38 82

plane_prior1					95.73 339

plane_prior						91.74 311	99.86 134		96.76 66						89.59 302

n2								0.00 474

nn								0.00 474

door-mid								89.69 458

lessismore_v0				90.53 393	90.58 428	80.90 432		95.80 410		77.01 432	95.84 340	66.15 414	96.95 353	83.03 391	75.05 422	93.74 383

LGP-MVS_train				93.71 343	95.43 351	88.67 373		97.62 276	92.81 216	90.05 306	98.49 247	75.24 369	98.40 262	95.84 207	89.12 307	94.07 356

test11								98.44 142

door								90.31 455

HQP5-MVS						91.85 307

HQP-NCC					95.78 332		99.87 123		96.82 62	93.37 269

ACMP_Plane					95.78 332		99.87 123		96.82 62	93.37 269

BP-MVS														97.92 152

HQP4-MVS										93.37 269			98.39 264			94.53 312

HQP3-MVS								97.89 246							89.60 300

HQP2-MVS												80.65 318

NP-MVS					95.77 335	91.79 309					98.65 228

MDTV_nov1_ep13_2view						96.26 161	96.11 422		91.89 259	98.06 160		94.40 81		94.30 242		99.67 124

MDTV_nov1_ep13			95.69 183		97.90 219	94.15 248	95.98 425	98.44 142	93.12 204	97.98 162	95.74 343	95.10 57	98.58 246	90.02 322	96.92 217

ACMMP++_ref															87.04 335

ACMMP++															88.23 323

Test By Simon												92.82 136

ITE_SJBPF				92.38 371	95.69 345	85.14 402		95.71 414	92.81 216	89.33 330	98.11 268	70.23 397	98.42 258	85.91 371	88.16 324	93.59 387

DeepMVS_CX				82.92 426	95.98 329	58.66 457		96.01 407	92.72 221	78.34 427	95.51 356	58.29 436	98.08 292	82.57 393	85.29 345	92.03 415

High-resolution multi-view benchmark