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 14	98.69 68	98.20 7	99.93 1	99.98 2	96.82 21	100.00 1	99.75 28	100.00 1	99.99 23

NCCC		99.37 2	99.25 2	99.71 14	99.96 8	99.15 21	99.97 27	98.62 81	98.02 13	99.90 3	99.95 3	97.33 15	100.00 1	99.54 39	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 27	98.64 76	98.47 2	99.13 87	99.92 13	96.38 29	100.00 1	99.74 30	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 52	98.32 174	97.28 32	99.83 13	99.91 14	97.22 17	100.00 1	99.99 5	100.00 1	99.89 84
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 34	98.43 133	97.27 34	99.80 17	99.94 4	96.71 22	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 52	98.43 133	96.48 59	99.80 17	99.93 11	97.44 12	100.00 1	99.92 12	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 11	99.89 45	99.24 19	99.87 104	98.44 125	97.48 27	99.64 43	99.94 4	96.68 24	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 61	99.98 14	98.86 53	97.10 40	99.80 17	99.94 4	95.92 35	100.00 1	99.51 40	100.00 1	100.00 1

MSP-MVS		99.09 9	99.12 5	98.98 75	99.93 24	97.24 101	99.95 52	98.42 145	97.50 26	99.52 60	99.88 21	97.43 14	99.71 138	99.50 41	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 52	98.56 92	97.56 25	99.44 66	99.85 30	95.38 46	100.00 1	99.31 51	99.99 21	99.87 87

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 45	99.91 84	98.39 157	97.20 38	99.46 64	99.85 30	95.53 43	99.79 123	99.86 21	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 12	98.97 13	99.18 52	98.72 142	97.71 82	99.98 14	98.44 125	96.85 46	99.80 17	99.91 14	97.57 6	99.85 108	99.44 46	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
CHOSEN 280x420		99.01 13	99.03 10	98.95 78	99.38 96	98.87 33	98.46 311	99.42 22	97.03 42	99.02 91	99.09 147	99.35 1	98.21 246	99.73 32	99.78 82	99.77 101

fmvsm_l_conf0.5_n_a		99.00 14	98.91 14	99.28 45	99.21 102	97.91 77	99.98 14	98.85 56	98.25 4	99.92 2	99.75 69	94.72 64	99.97 53	99.87 19	99.64 90	99.95 71

fmvsm_l_conf0.5_n		98.94 15	98.84 17	99.25 46	99.17 106	97.81 80	99.98 14	98.86 53	98.25 4	99.90 3	99.76 63	94.21 83	99.97 53	99.87 19	99.52 104	99.98 48

TSAR-MVS + MP.		98.93 16	98.77 18	99.41 38	99.74 69	98.67 49	99.77 146	98.38 161	96.73 53	99.88 6	99.74 76	94.89 59	99.59 149	99.80 25	99.98 32	99.97 58
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 17	98.70 19	99.56 25	99.70 76	98.73 46	99.94 68	98.34 171	96.38 65	99.81 15	99.76 63	94.59 67	99.98 43	99.84 22	99.96 46	99.97 58
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 18	98.65 20	99.68 15	99.94 13	99.07 24	99.64 184	99.44 20	97.33 31	99.00 92	99.72 81	94.03 88	99.98 43	98.73 86	100.00 1	100.00 1

train_agg		98.88 19	98.65 20	99.59 23	99.92 31	98.92 29	99.96 34	98.43 133	94.35 124	99.71 34	99.86 26	95.94 33	99.85 108	99.69 35	99.98 32	99.99 23

MVS_0304		98.87 20	98.61 23	99.67 16	99.18 103	99.13 22	99.87 104	99.65 12	98.17 8	98.75 107	99.75 69	92.76 124	99.94 77	99.88 18	99.44 113	99.94 74

MM		98.83 21	98.53 27	99.76 10	99.59 81	99.33 8	99.99 4	99.76 6	98.39 3	99.39 73	99.80 51	90.49 175	99.96 61	99.89 16	99.43 115	99.98 48

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 98	99.92 1	99.96 34	98.44 125	97.96 14	99.55 55	99.94 4	97.18 19	100.00 1	93.81 217	99.94 54	99.98 48

DeepC-MVS_fast	96.59 1	98.81 23	98.54 26	99.62 20	99.90 42	98.85 35	99.24 242	98.47 117	98.14 10	99.08 88	99.91 14	93.09 114	100.00 1	99.04 63	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
SMA-MVS		98.76 24	98.48 29	99.62 20	99.87 51	98.87 33	99.86 116	98.38 161	93.19 171	99.77 27	99.94 4	95.54 41	100.00 1	99.74 30	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
MVS_111021_HR		98.72 25	98.62 22	99.01 73	99.36 97	97.18 104	99.93 75	99.90 1	96.81 51	98.67 110	99.77 61	93.92 90	99.89 96	99.27 53	99.94 54	99.96 64

XVS		98.70 26	98.55 25	99.15 59	99.94 13	97.50 93	99.94 68	98.42 145	96.22 72	99.41 69	99.78 59	94.34 76	99.96 61	98.92 73	99.95 49	99.99 23

SF-MVS		98.67 27	98.40 32	99.50 30	99.77 65	98.67 49	99.90 90	98.21 188	93.53 160	99.81 15	99.89 19	94.70 66	99.86 107	99.84 22	99.93 60	99.96 64

CDPH-MVS		98.65 28	98.36 38	99.49 32	99.94 13	98.73 46	99.87 104	98.33 172	93.97 144	99.76 28	99.87 24	94.99 57	99.75 132	98.55 98	100.00 1	99.98 48

APD-MVS		98.62 29	98.35 39	99.41 38	99.90 42	98.51 59	99.87 104	98.36 165	94.08 137	99.74 31	99.73 78	94.08 86	99.74 134	99.42 47	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 30	98.51 28	98.86 83	99.73 72	96.63 123	99.97 27	97.92 221	98.07 11	98.76 105	99.55 108	95.00 56	99.94 77	99.91 15	97.68 167	99.99 23

PAPM		98.60 30	98.42 31	99.14 61	96.05 280	98.96 26	99.90 90	99.35 25	96.68 55	98.35 125	99.66 96	96.45 28	98.51 213	99.45 45	99.89 66	99.96 64

HFP-MVS		98.56 32	98.37 36	99.14 61	99.96 8	97.43 97	99.95 52	98.61 82	94.77 106	99.31 77	99.85 30	94.22 81	100.00 1	98.70 87	99.98 32	99.98 48

region2R		98.54 33	98.37 36	99.05 68	99.96 8	97.18 104	99.96 34	98.55 98	94.87 104	99.45 65	99.85 30	94.07 87	100.00 1	98.67 89	100.00 1	99.98 48

DELS-MVS		98.54 33	98.22 44	99.50 30	99.15 108	98.65 53	100.00 1	98.58 87	97.70 20	98.21 132	99.24 139	92.58 130	99.94 77	98.63 94	99.94 54	99.92 81
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 35	98.16 49	99.58 24	99.97 3	98.77 42	99.95 52	98.43 133	95.35 92	98.03 136	99.75 69	94.03 88	99.98 43	98.11 117	99.83 74	99.99 23

ACMMPR		98.50 36	98.32 40	99.05 68	99.96 8	97.18 104	99.95 52	98.60 84	94.77 106	99.31 77	99.84 41	93.73 97	100.00 1	98.70 87	99.98 32	99.98 48

ACMMP_NAP		98.49 37	98.14 50	99.54 27	99.66 78	98.62 55	99.85 119	98.37 164	94.68 111	99.53 58	99.83 43	92.87 120	100.00 1	98.66 91	99.84 73	99.99 23

EPNet		98.49 37	98.40 32	98.77 87	99.62 80	96.80 120	99.90 90	99.51 17	97.60 22	99.20 83	99.36 128	93.71 98	99.91 89	97.99 124	98.71 142	99.61 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SR-MVS		98.46 39	98.30 43	98.93 79	99.88 49	97.04 110	99.84 124	98.35 167	94.92 102	99.32 76	99.80 51	93.35 104	99.78 125	99.30 52	99.95 49	99.96 64

CP-MVS		98.45 40	98.32 40	98.87 82	99.96 8	96.62 124	99.97 27	98.39 157	94.43 119	98.90 96	99.87 24	94.30 78	100.00 1	99.04 63	99.99 21	99.99 23

test_fmvsm_n_1920		98.44 41	98.61 23	97.92 144	99.27 101	95.18 186	100.00 1	98.90 47	98.05 12	99.80 17	99.73 78	92.64 127	99.99 36	99.58 38	99.51 107	98.59 225

PS-MVSNAJ		98.44 41	98.20 46	99.16 57	98.80 138	98.92 29	99.54 201	98.17 193	97.34 29	99.85 9	99.85 30	91.20 155	99.89 96	99.41 48	99.67 88	98.69 222

test_fmvsmconf_n		98.43 43	98.32 40	98.78 85	98.12 189	96.41 131	99.99 4	98.83 59	98.22 6	99.67 39	99.64 99	91.11 159	99.94 77	99.67 36	99.62 93	99.98 48

MVS_111021_LR		98.42 44	98.38 34	98.53 109	99.39 95	95.79 156	99.87 104	99.86 2	96.70 54	98.78 102	99.79 55	92.03 145	99.90 91	99.17 57	99.86 72	99.88 85

DP-MVS Recon		98.41 45	98.02 57	99.56 25	99.97 3	98.70 48	99.92 78	98.44 125	92.06 217	98.40 123	99.84 41	95.68 39	100.00 1	98.19 112	99.71 86	99.97 58

PHI-MVS		98.41 45	98.21 45	99.03 70	99.86 53	97.10 109	99.98 14	98.80 62	90.78 258	99.62 47	99.78 59	95.30 47	100.00 1	99.80 25	99.93 60	99.99 23

mPP-MVS		98.39 47	98.20 46	98.97 76	99.97 3	96.92 115	99.95 52	98.38 161	95.04 98	98.61 114	99.80 51	93.39 102	100.00 1	98.64 92	100.00 1	99.98 48

PGM-MVS		98.34 48	98.13 51	98.99 74	99.92 31	97.00 111	99.75 154	99.50 18	93.90 149	99.37 74	99.76 63	93.24 111	100.00 1	97.75 140	99.96 46	99.98 48

SR-MVS-dyc-post		98.31 49	98.17 48	98.71 90	99.79 62	96.37 135	99.76 151	98.31 176	94.43 119	99.40 71	99.75 69	93.28 109	99.78 125	98.90 76	99.92 63	99.97 58

ZNCC-MVS		98.31 49	98.03 56	99.17 55	99.88 49	97.59 87	99.94 68	98.44 125	94.31 127	98.50 118	99.82 46	93.06 115	99.99 36	98.30 110	99.99 21	99.93 76

MTAPA		98.29 51	97.96 62	99.30 44	99.85 54	97.93 76	99.39 223	98.28 181	95.76 81	97.18 160	99.88 21	92.74 125	100.00 1	98.67 89	99.88 68	99.99 23

GST-MVS		98.27 52	97.97 59	99.17 55	99.92 31	97.57 89	99.93 75	98.39 157	94.04 142	98.80 101	99.74 76	92.98 117	100.00 1	98.16 114	99.76 83	99.93 76

CANet		98.27 52	97.82 69	99.63 17	99.72 74	99.10 23	99.98 14	98.51 108	97.00 43	98.52 116	99.71 83	87.80 206	99.95 69	99.75 28	99.38 117	99.83 91

EI-MVSNet-Vis-set		98.27 52	98.11 53	98.75 88	99.83 57	96.59 126	99.40 219	98.51 108	95.29 94	98.51 117	99.76 63	93.60 101	99.71 138	98.53 99	99.52 104	99.95 71

APD-MVS_3200maxsize		98.25 55	98.08 55	98.78 85	99.81 60	96.60 125	99.82 134	98.30 179	93.95 146	99.37 74	99.77 61	92.84 121	99.76 131	98.95 70	99.92 63	99.97 58

patch_mono-2		98.24 56	99.12 5	95.59 227	99.67 77	86.91 346	99.95 52	98.89 49	97.60 22	99.90 3	99.76 63	96.54 27	99.98 43	99.94 11	99.82 78	99.88 85

xiu_mvs_v2_base		98.23 57	97.97 59	99.02 72	98.69 143	98.66 51	99.52 203	98.08 205	97.05 41	99.86 7	99.86 26	90.65 168	99.71 138	99.39 50	98.63 143	98.69 222

MP-MVS		98.23 57	97.97 59	99.03 70	99.94 13	97.17 107	99.95 52	98.39 157	94.70 110	98.26 130	99.81 50	91.84 149	100.00 1	98.85 79	99.97 42	99.93 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		98.14 59	97.99 58	98.60 99	99.80 61	96.27 137	99.36 228	98.50 114	95.21 96	98.30 127	99.75 69	93.29 108	99.73 137	98.37 106	99.30 121	99.81 94

PAPM_NR		98.12 60	97.93 64	98.70 91	99.94 13	96.13 147	99.82 134	98.43 133	94.56 114	97.52 150	99.70 85	94.40 71	99.98 43	97.00 155	99.98 32	99.99 23

WTY-MVS		98.10 61	97.60 77	99.60 22	98.92 126	99.28 17	99.89 98	99.52 15	95.58 86	98.24 131	99.39 125	93.33 105	99.74 134	97.98 126	95.58 214	99.78 100

MP-MVS-pluss		98.07 62	97.64 75	99.38 42	99.74 69	98.41 62	99.74 157	98.18 192	93.35 165	96.45 179	99.85 30	92.64 127	99.97 53	98.91 75	99.89 66	99.77 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVSMamba_pp		98.05 63	97.76 71	98.92 80	98.56 151	98.06 68	99.92 78	97.75 234	96.28 70	99.71 34	98.43 215	90.37 177	99.11 177	98.99 69	99.88 68	99.58 138

HPM-MVS		97.96 64	97.72 72	98.68 92	99.84 56	96.39 134	99.90 90	98.17 193	92.61 195	98.62 113	99.57 107	91.87 148	99.67 145	98.87 78	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
PVSNet_Blended		97.94 65	97.64 75	98.83 84	99.59 81	96.99 112	100.00 1	99.10 31	95.38 91	98.27 128	99.08 148	89.00 198	99.95 69	99.12 58	99.25 123	99.57 140

PLC	95.54 3	97.93 66	97.89 67	98.05 137	99.82 58	94.77 198	99.92 78	98.46 119	93.93 147	97.20 159	99.27 134	95.44 45	99.97 53	97.41 145	99.51 107	99.41 167
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETV-MVS		97.92 67	97.80 70	98.25 125	98.14 187	96.48 128	99.98 14	97.63 242	95.61 85	99.29 80	99.46 116	92.55 131	98.82 192	99.02 67	98.54 144	99.46 160

CS-MVS-test		97.88 68	97.94 63	97.70 159	99.28 100	95.20 185	99.98 14	97.15 299	95.53 88	99.62 47	99.79 55	92.08 144	98.38 229	98.75 85	99.28 122	99.52 152

API-MVS		97.86 69	97.66 74	98.47 112	99.52 88	95.41 175	99.47 212	98.87 52	91.68 228	98.84 98	99.85 30	92.34 138	99.99 36	98.44 102	99.96 46	100.00 1

lupinMVS		97.85 70	97.60 77	98.62 97	97.28 242	97.70 84	99.99 4	97.55 253	95.50 90	99.43 67	99.67 94	90.92 163	98.71 202	98.40 103	99.62 93	99.45 162

test_yl		97.83 71	97.37 85	99.21 49	99.18 103	97.98 73	99.64 184	99.27 27	91.43 237	97.88 142	98.99 157	95.84 37	99.84 116	98.82 80	95.32 220	99.79 97

DCV-MVSNet		97.83 71	97.37 85	99.21 49	99.18 103	97.98 73	99.64 184	99.27 27	91.43 237	97.88 142	98.99 157	95.84 37	99.84 116	98.82 80	95.32 220	99.79 97

mvsany_test1		97.82 73	97.90 66	97.55 167	98.77 140	93.04 242	99.80 140	97.93 218	96.95 45	99.61 53	99.68 93	90.92 163	99.83 118	99.18 56	98.29 153	99.80 96

alignmvs		97.81 74	97.33 88	99.25 46	98.77 140	98.66 51	99.99 4	98.44 125	94.40 123	98.41 121	99.47 114	93.65 99	99.42 164	98.57 97	94.26 234	99.67 115

fmvsm_s_conf0.5_n		97.80 75	97.85 68	97.67 160	99.06 111	94.41 204	99.98 14	98.97 40	97.34 29	99.63 44	99.69 87	87.27 213	99.97 53	99.62 37	99.06 132	98.62 224

HPM-MVS_fast		97.80 75	97.50 80	98.68 92	99.79 62	96.42 130	99.88 101	98.16 197	91.75 227	98.94 94	99.54 110	91.82 150	99.65 147	97.62 143	99.99 21	99.99 23

CS-MVS		97.79 77	97.91 65	97.43 175	99.10 109	94.42 203	99.99 4	97.10 304	95.07 97	99.68 38	99.75 69	92.95 118	98.34 233	98.38 104	99.14 128	99.54 146

HY-MVS	92.50 7	97.79 77	97.17 96	99.63 17	98.98 118	99.32 9	97.49 343	99.52 15	95.69 83	98.32 126	97.41 247	93.32 106	99.77 128	98.08 120	95.75 211	99.81 94

CNLPA		97.76 79	97.38 84	98.92 80	99.53 87	96.84 117	99.87 104	98.14 201	93.78 152	96.55 177	99.69 87	92.28 139	99.98 43	97.13 151	99.44 113	99.93 76

test_fmvsmconf0.1_n		97.74 80	97.44 82	98.64 96	95.76 291	96.20 143	99.94 68	98.05 208	98.17 8	98.89 97	99.42 118	87.65 208	99.90 91	99.50 41	99.60 99	99.82 92

ACMMP		97.74 80	97.44 82	98.66 94	99.92 31	96.13 147	99.18 247	99.45 19	94.84 105	96.41 182	99.71 83	91.40 152	99.99 36	97.99 124	98.03 162	99.87 87
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 82	97.72 72	97.77 154	98.63 149	94.26 209	99.96 34	98.92 46	97.18 39	99.75 29	99.69 87	87.00 218	99.97 53	99.46 44	98.89 135	99.08 201

DeepPCF-MVS	95.94 2	97.71 83	98.98 12	93.92 291	99.63 79	81.76 374	99.96 34	98.56 92	99.47 1	99.19 85	99.99 1	94.16 85	100.00 1	99.92 12	99.93 60	100.00 1

test_fmvsmvis_n_1920		97.67 84	97.59 79	97.91 146	97.02 249	95.34 177	99.95 52	98.45 120	97.87 15	97.02 164	99.59 104	89.64 186	99.98 43	99.41 48	99.34 120	98.42 228

CPTT-MVS		97.64 85	97.32 89	98.58 102	99.97 3	95.77 157	99.96 34	98.35 167	89.90 274	98.36 124	99.79 55	91.18 158	99.99 36	98.37 106	99.99 21	99.99 23

sss		97.57 86	97.03 101	99.18 52	98.37 166	98.04 70	99.73 162	99.38 23	93.46 162	98.76 105	99.06 150	91.21 154	99.89 96	96.33 167	97.01 184	99.62 126

test2506		97.53 87	97.19 94	98.58 102	98.66 146	96.90 116	98.81 289	99.77 5	94.93 100	97.95 138	98.96 163	92.51 132	99.20 172	94.93 188	98.15 155	99.64 121

EIA-MVS		97.53 87	97.46 81	97.76 156	98.04 192	94.84 194	99.98 14	97.61 247	94.41 122	97.90 140	99.59 104	92.40 136	98.87 189	98.04 121	99.13 129	99.59 132

iter_conf05_11		97.49 89	97.34 87	97.93 143	98.51 159	95.15 188	99.58 192	97.64 240	93.76 154	99.26 82	98.38 217	90.51 174	99.10 180	98.58 96	99.88 68	99.54 146

testing11		97.48 90	97.27 90	98.10 133	98.36 167	96.02 150	99.92 78	98.45 120	93.45 164	98.15 134	98.70 189	95.48 44	99.22 168	97.85 132	95.05 224	99.07 202

xiu_mvs_v1_base_debu		97.43 91	97.06 97	98.55 104	97.74 209	98.14 64	99.31 233	97.86 227	96.43 62	99.62 47	99.69 87	85.56 232	99.68 142	99.05 60	98.31 150	97.83 239

xiu_mvs_v1_base		97.43 91	97.06 97	98.55 104	97.74 209	98.14 64	99.31 233	97.86 227	96.43 62	99.62 47	99.69 87	85.56 232	99.68 142	99.05 60	98.31 150	97.83 239

xiu_mvs_v1_base_debi		97.43 91	97.06 97	98.55 104	97.74 209	98.14 64	99.31 233	97.86 227	96.43 62	99.62 47	99.69 87	85.56 232	99.68 142	99.05 60	98.31 150	97.83 239

MAR-MVS		97.43 91	97.19 94	98.15 131	99.47 92	94.79 197	99.05 263	98.76 63	92.65 193	98.66 111	99.82 46	88.52 203	99.98 43	98.12 116	99.63 92	99.67 115
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 95	98.09 54	95.42 232	99.58 85	87.24 342	99.23 243	96.95 320	94.28 130	98.93 95	99.73 78	94.39 74	99.16 176	99.89 16	99.82 78	99.86 89

thisisatest0515		97.41 96	97.02 102	98.59 101	97.71 216	97.52 91	99.97 27	98.54 101	91.83 223	97.45 153	99.04 151	97.50 7	99.10 180	94.75 196	96.37 196	99.16 193

114514_t		97.41 96	96.83 110	99.14 61	99.51 90	97.83 78	99.89 98	98.27 183	88.48 301	99.06 89	99.66 96	90.30 179	99.64 148	96.32 168	99.97 42	99.96 64

EC-MVSNet		97.38 98	97.24 91	97.80 149	97.41 231	95.64 166	99.99 4	97.06 309	94.59 113	99.63 44	99.32 130	89.20 196	98.14 249	98.76 84	99.23 125	99.62 126

iter_conf05		97.35 99	96.89 108	98.73 89	98.60 150	97.59 87	98.26 323	97.46 264	90.34 266	95.94 192	98.32 220	94.29 79	99.23 167	99.03 66	99.82 78	99.36 172

fmvsm_s_conf0.1_n		97.30 100	97.21 93	97.60 166	97.38 233	94.40 206	99.90 90	98.64 76	96.47 61	99.51 62	99.65 98	84.99 240	99.93 85	99.22 55	99.09 131	98.46 226

OMC-MVS		97.28 101	97.23 92	97.41 176	99.76 66	93.36 237	99.65 180	97.95 216	96.03 76	97.41 154	99.70 85	89.61 187	99.51 153	96.73 164	98.25 154	99.38 169

PVSNet_Blended_VisFu		97.27 102	96.81 111	98.66 94	98.81 137	96.67 122	99.92 78	98.64 76	94.51 115	96.38 183	98.49 208	89.05 197	99.88 102	97.10 153	98.34 148	99.43 165

jason		97.24 103	96.86 109	98.38 120	95.73 294	97.32 100	99.97 27	97.40 273	95.34 93	98.60 115	99.54 110	87.70 207	98.56 210	97.94 127	99.47 109	99.25 188
jason: jason.
AdaColmap		97.23 104	96.80 112	98.51 110	99.99 1	95.60 168	99.09 252	98.84 58	93.32 167	96.74 172	99.72 81	86.04 229	100.00 1	98.01 122	99.43 115	99.94 74

VNet		97.21 105	96.57 122	99.13 65	98.97 119	97.82 79	99.03 266	99.21 29	94.31 127	99.18 86	98.88 174	86.26 228	99.89 96	98.93 72	94.32 232	99.69 112

testing99		97.17 106	96.91 104	97.95 140	98.35 169	95.70 162	99.91 84	98.43 133	92.94 177	97.36 155	98.72 187	94.83 60	99.21 169	97.00 155	94.64 226	98.95 207

testing91		97.16 107	96.90 105	97.97 139	98.35 169	95.67 165	99.91 84	98.42 145	92.91 179	97.33 156	98.72 187	94.81 61	99.21 169	96.98 157	94.63 227	99.03 204

PVSNet	91.05 13	97.13 108	96.69 117	98.45 114	99.52 88	95.81 155	99.95 52	99.65 12	94.73 108	99.04 90	99.21 141	84.48 244	99.95 69	94.92 189	98.74 141	99.58 138

thisisatest0530		97.10 109	96.72 115	98.22 126	97.60 222	96.70 121	99.92 78	98.54 101	91.11 247	97.07 163	98.97 161	97.47 10	99.03 182	93.73 222	96.09 199	98.92 208

CSCG		97.10 109	97.04 100	97.27 185	99.89 45	91.92 268	99.90 90	99.07 34	88.67 297	95.26 206	99.82 46	93.17 113	99.98 43	98.15 115	99.47 109	99.90 83

sasdasda		97.09 111	96.32 128	99.39 40	98.93 123	98.95 27	99.72 165	97.35 276	94.45 116	97.88 142	99.42 118	86.71 220	99.52 151	98.48 100	93.97 238	99.72 107

fmvsm_s_conf0.1_n_a		97.09 111	96.90 105	97.63 164	95.65 300	94.21 211	99.83 131	98.50 114	96.27 71	99.65 41	99.64 99	84.72 241	99.93 85	99.04 63	98.84 138	98.74 219

canonicalmvs		97.09 111	96.32 128	99.39 40	98.93 123	98.95 27	99.72 165	97.35 276	94.45 116	97.88 142	99.42 118	86.71 220	99.52 151	98.48 100	93.97 238	99.72 107

testing222		97.08 114	96.75 114	98.06 136	98.56 151	96.82 118	99.85 119	98.61 82	92.53 201	98.84 98	98.84 183	93.36 103	98.30 237	95.84 176	94.30 233	99.05 203

ETVMVS		97.03 115	96.64 118	98.20 127	98.67 145	97.12 108	99.89 98	98.57 89	91.10 248	98.17 133	98.59 199	93.86 94	98.19 247	95.64 179	95.24 222	99.28 185

MGCFI-Net		97.00 116	96.22 132	99.34 43	98.86 134	98.80 39	99.67 177	97.30 283	94.31 127	97.77 146	99.41 122	86.36 226	99.50 155	98.38 104	93.90 240	99.72 107

diffmvs		97.00 116	96.64 118	98.09 134	97.64 220	96.17 146	99.81 136	97.19 293	94.67 112	98.95 93	99.28 131	86.43 224	98.76 197	98.37 106	97.42 173	99.33 178
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		96.96 118	96.21 133	99.22 48	98.97 119	98.84 36	99.85 119	99.71 7	93.17 172	96.26 185	98.88 174	89.87 184	99.51 153	94.26 207	94.91 225	99.31 180

MVSFormer		96.94 119	96.60 120	97.95 140	97.28 242	97.70 84	99.55 199	97.27 288	91.17 244	99.43 67	99.54 110	90.92 163	96.89 313	94.67 199	99.62 93	99.25 188

F-COLMAP		96.93 120	96.95 103	96.87 195	99.71 75	91.74 273	99.85 119	97.95 216	93.11 174	95.72 199	99.16 145	92.35 137	99.94 77	95.32 182	99.35 119	98.92 208

DeepC-MVS	94.51 4	96.92 121	96.40 127	98.45 114	99.16 107	95.90 153	99.66 179	98.06 206	96.37 68	94.37 215	99.49 113	83.29 253	99.90 91	97.63 142	99.61 97	99.55 142
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tttt0517		96.85 122	96.49 124	97.92 144	97.48 229	95.89 154	99.85 119	98.54 101	90.72 259	96.63 174	98.93 172	97.47 10	99.02 183	93.03 234	95.76 210	98.85 212

1314		96.84 123	95.96 144	99.48 34	96.74 267	98.52 58	98.31 320	98.86 53	95.82 79	89.91 267	98.98 159	87.49 210	99.96 61	97.80 133	99.73 85	99.96 64

CHOSEN 1792x2688		96.81 124	96.53 123	97.64 162	98.91 130	93.07 239	99.65 180	99.80 3	95.64 84	95.39 203	98.86 179	84.35 246	99.90 91	96.98 157	99.16 127	99.95 71

UWE-MVS		96.79 125	96.72 115	97.00 190	98.51 159	93.70 225	99.71 168	98.60 84	92.96 176	97.09 161	98.34 219	96.67 26	98.85 191	92.11 243	96.50 192	98.44 227

tfpn200view9		96.79 125	95.99 138	99.19 51	98.94 121	98.82 37	99.78 143	99.71 7	92.86 180	96.02 190	98.87 177	89.33 191	99.50 155	93.84 214	94.57 228	99.27 186

thres400		96.78 127	95.99 138	99.16 57	98.94 121	98.82 37	99.78 143	99.71 7	92.86 180	96.02 190	98.87 177	89.33 191	99.50 155	93.84 214	94.57 228	99.16 193

CANet_DTU		96.76 128	96.15 134	98.60 99	98.78 139	97.53 90	99.84 124	97.63 242	97.25 37	99.20 83	99.64 99	81.36 266	99.98 43	92.77 237	98.89 135	98.28 231

PMMVS		96.76 128	96.76 113	96.76 198	98.28 174	92.10 263	99.91 84	97.98 213	94.12 135	99.53 58	99.39 125	86.93 219	98.73 199	96.95 160	97.73 165	99.45 162

thres100view900		96.74 130	95.92 148	99.18 52	98.90 131	98.77 42	99.74 157	99.71 7	92.59 197	95.84 195	98.86 179	89.25 193	99.50 155	93.84 214	94.57 228	99.27 186

TESTMET0.1,1		96.74 130	96.26 130	98.16 128	97.36 235	96.48 128	99.96 34	98.29 180	91.93 220	95.77 198	98.07 228	95.54 41	98.29 238	90.55 269	98.89 135	99.70 110

baseline2		96.71 132	96.49 124	97.37 179	95.63 302	95.96 152	99.74 157	98.88 51	92.94 177	91.61 247	98.97 161	97.72 5	98.62 208	94.83 193	98.08 161	97.53 249

thres600view7		96.69 133	95.87 151	99.14 61	98.90 131	98.78 41	99.74 157	99.71 7	92.59 197	95.84 195	98.86 179	89.25 193	99.50 155	93.44 226	94.50 231	99.16 193

EPP-MVSNet		96.69 133	96.60 120	96.96 192	97.74 209	93.05 241	99.37 226	98.56 92	88.75 295	95.83 197	99.01 154	96.01 31	98.56 210	96.92 161	97.20 178	99.25 188

HyFIR lowres test		96.66 135	96.43 126	97.36 181	99.05 112	93.91 220	99.70 172	99.80 3	90.54 261	96.26 185	98.08 227	92.15 142	98.23 245	96.84 163	95.46 215	99.93 76

MVS		96.60 136	95.56 159	99.72 13	96.85 260	99.22 20	98.31 320	98.94 41	91.57 230	90.90 256	99.61 103	86.66 222	99.96 61	97.36 146	99.88 68	99.99 23

test_cas_vis1_n_1920		96.59 137	96.23 131	97.65 161	98.22 178	94.23 210	99.99 4	97.25 290	97.77 17	99.58 54	99.08 148	77.10 303	99.97 53	97.64 141	99.45 112	98.74 219

UA-Net		96.54 138	95.96 144	98.27 124	98.23 177	95.71 161	98.00 336	98.45 120	93.72 156	98.41 121	99.27 134	88.71 202	99.66 146	91.19 254	97.69 166	99.44 164

EPMVS		96.53 139	96.01 137	98.09 134	98.43 164	96.12 149	96.36 365	99.43 21	93.53 160	97.64 148	95.04 331	94.41 70	98.38 229	91.13 255	98.11 158	99.75 103

test-LLR		96.47 140	96.04 136	97.78 152	97.02 249	95.44 172	99.96 34	98.21 188	94.07 138	95.55 200	96.38 281	93.90 92	98.27 242	90.42 272	98.83 139	99.64 121

MVS_Test		96.46 141	95.74 153	98.61 98	98.18 182	97.23 102	99.31 233	97.15 299	91.07 249	98.84 98	97.05 260	88.17 205	98.97 185	94.39 203	97.50 170	99.61 129

casdiffmvs_mvg		96.43 142	95.94 146	97.89 148	97.44 230	95.47 171	99.86 116	97.29 286	93.35 165	96.03 189	99.19 142	85.39 235	98.72 201	97.89 131	97.04 182	99.49 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		96.43 142	95.98 140	97.76 156	97.34 236	95.17 187	99.51 205	97.17 296	93.92 148	96.90 167	99.28 131	85.37 236	98.64 207	97.50 144	96.86 188	99.46 160

casdiffmvs		96.42 144	95.97 143	97.77 154	97.30 240	94.98 190	99.84 124	97.09 306	93.75 155	96.58 176	99.26 137	85.07 238	98.78 195	97.77 138	97.04 182	99.54 146
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		96.39 145	95.74 153	98.32 122	91.47 369	95.56 169	99.84 124	97.30 283	97.74 18	97.89 141	99.35 129	79.62 285	99.85 108	99.25 54	99.24 124	99.55 142

test-mter		96.39 145	95.93 147	97.78 152	97.02 249	95.44 172	99.96 34	98.21 188	91.81 225	95.55 200	96.38 281	95.17 48	98.27 242	90.42 272	98.83 139	99.64 121

CDS-MVSNet		96.34 147	96.07 135	97.13 187	97.37 234	94.96 191	99.53 202	97.91 222	91.55 231	95.37 204	98.32 220	95.05 53	97.13 296	93.80 218	95.75 211	99.30 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		96.32 148	95.98 140	97.35 182	97.93 197	94.82 195	99.47 212	98.15 200	91.83 223	95.09 207	99.11 146	91.37 153	97.47 278	93.47 225	97.43 171	99.74 104

3Dnovator+	91.53 11	96.31 149	95.24 167	99.52 28	96.88 259	98.64 54	99.72 165	98.24 185	95.27 95	88.42 306	98.98 159	82.76 255	99.94 77	97.10 153	99.83 74	99.96 64

Effi-MVS+		96.30 150	95.69 155	98.16 128	97.85 202	96.26 138	97.41 345	97.21 292	90.37 264	98.65 112	98.58 202	86.61 223	98.70 203	97.11 152	97.37 175	99.52 152

IS-MVSNet		96.29 151	95.90 149	97.45 172	98.13 188	94.80 196	99.08 254	97.61 247	92.02 219	95.54 202	98.96 163	90.64 169	98.08 252	93.73 222	97.41 174	99.47 159

3Dnovator	91.47 12	96.28 152	95.34 164	99.08 67	96.82 262	97.47 96	99.45 216	98.81 60	95.52 89	89.39 281	99.00 156	81.97 259	99.95 69	97.27 148	99.83 74	99.84 90

tpmrst		96.27 153	95.98 140	97.13 187	97.96 195	93.15 238	96.34 366	98.17 193	92.07 215	98.71 109	95.12 329	93.91 91	98.73 199	94.91 191	96.62 189	99.50 156

CostFormer		96.10 154	95.88 150	96.78 197	97.03 248	92.55 255	97.08 353	97.83 230	90.04 272	98.72 108	94.89 338	95.01 55	98.29 238	96.54 166	95.77 209	99.50 156

PVSNet_BlendedMVS		96.05 155	95.82 152	96.72 200	99.59 81	96.99 112	99.95 52	99.10 31	94.06 140	98.27 128	95.80 296	89.00 198	99.95 69	99.12 58	87.53 290	93.24 347

PatchMatch-RL		96.04 156	95.40 161	97.95 140	99.59 81	95.22 184	99.52 203	99.07 34	93.96 145	96.49 178	98.35 218	82.28 257	99.82 120	90.15 277	99.22 126	98.81 215

1112_ss		96.01 157	95.20 169	98.42 117	97.80 205	96.41 131	99.65 180	96.66 342	92.71 188	92.88 234	99.40 123	92.16 141	99.30 165	91.92 246	93.66 241	99.55 142

PatchmatchNet		95.94 158	95.45 160	97.39 178	97.83 203	94.41 204	96.05 372	98.40 154	92.86 180	97.09 161	95.28 326	94.21 83	98.07 254	89.26 285	98.11 158	99.70 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FA-MVS(test-final)		95.86 159	95.09 173	98.15 131	97.74 209	95.62 167	96.31 367	98.17 193	91.42 239	96.26 185	96.13 290	90.56 172	99.47 162	92.18 242	97.07 180	99.35 175

TAMVS		95.85 160	95.58 158	96.65 203	97.07 246	93.50 231	99.17 248	97.82 231	91.39 241	95.02 208	98.01 230	92.20 140	97.30 285	93.75 221	95.83 208	99.14 196

LS3D		95.84 161	95.11 172	98.02 138	99.85 54	95.10 189	98.74 294	98.50 114	87.22 318	93.66 224	99.86 26	87.45 211	99.95 69	90.94 261	99.81 81	99.02 205

baseline1		95.78 162	94.86 179	98.54 107	98.47 163	98.07 67	99.06 259	97.99 211	92.68 191	94.13 220	98.62 198	93.28 109	98.69 204	93.79 219	85.76 298	98.84 213

Test_1112_low_res		95.72 163	94.83 180	98.42 117	97.79 206	96.41 131	99.65 180	96.65 343	92.70 189	92.86 235	96.13 290	92.15 142	99.30 165	91.88 247	93.64 242	99.55 142

Vis-MVSNet		95.72 163	95.15 171	97.45 172	97.62 221	94.28 208	99.28 239	98.24 185	94.27 132	96.84 169	98.94 170	79.39 287	98.76 197	93.25 227	98.49 145	99.30 182
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet_dtu		95.71 165	95.39 162	96.66 202	98.92 126	93.41 234	99.57 195	98.90 47	96.19 74	97.52 150	98.56 204	92.65 126	97.36 280	77.89 367	98.33 149	99.20 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		95.71 165	95.38 163	96.68 201	98.49 162	92.28 259	99.84 124	97.50 261	92.12 214	92.06 244	98.79 184	84.69 242	98.67 206	95.29 183	99.66 89	99.09 199

FE-MVS		95.70 167	95.01 176	97.79 151	98.21 179	94.57 199	95.03 379	98.69 68	88.90 292	97.50 152	96.19 287	92.60 129	99.49 160	89.99 279	97.94 164	99.31 180

ECVR-MVS		95.66 168	95.05 174	97.51 170	98.66 146	93.71 224	98.85 286	98.45 120	94.93 100	96.86 168	98.96 163	75.22 326	99.20 172	95.34 181	98.15 155	99.64 121

mvs_anonymous		95.65 169	95.03 175	97.53 168	98.19 181	95.74 159	99.33 230	97.49 262	90.87 253	90.47 260	97.10 256	88.23 204	97.16 293	95.92 174	97.66 168	99.68 113

test1111		95.57 170	94.98 177	97.37 179	98.56 151	93.37 236	98.86 284	98.45 120	94.95 99	96.63 174	98.95 168	75.21 327	99.11 177	95.02 186	98.14 157	99.64 121

MVSTER		95.53 171	95.22 168	96.45 207	98.56 151	97.72 81	99.91 84	97.67 238	92.38 208	91.39 249	97.14 254	97.24 16	97.30 285	94.80 194	87.85 285	94.34 285

tpm2		95.47 172	95.18 170	96.35 212	96.91 255	91.70 277	96.96 356	97.93 218	88.04 308	98.44 120	95.40 315	93.32 106	97.97 258	94.00 210	95.61 213	99.38 169

test_vis1_n_1920		95.44 173	95.31 165	95.82 223	98.50 161	88.74 325	99.98 14	97.30 283	97.84 16	99.85 9	99.19 142	66.82 363	99.97 53	98.82 80	99.46 111	98.76 217

QAPM		95.40 174	94.17 195	99.10 66	96.92 254	97.71 82	99.40 219	98.68 70	89.31 280	88.94 294	98.89 173	82.48 256	99.96 61	93.12 233	99.83 74	99.62 126

test_fmvs1		95.35 175	95.68 157	94.36 276	98.99 117	84.98 356	99.96 34	96.65 343	97.60 22	99.73 32	98.96 163	71.58 343	99.93 85	98.31 109	99.37 118	98.17 232

UGNet		95.33 176	94.57 185	97.62 165	98.55 155	94.85 193	98.67 302	99.32 26	95.75 82	96.80 171	96.27 285	72.18 340	99.96 61	94.58 201	99.05 133	98.04 236
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
bld_raw_dy_0_64		95.27 177	94.55 186	97.45 172	98.17 183	96.49 127	96.67 360	97.42 269	79.97 375	91.83 245	98.04 229	90.64 169	99.01 184	98.60 95	99.57 101	96.85 253

mamv4		95.24 178	96.90 105	90.25 344	98.65 148	72.11 391	98.28 322	97.64 240	89.99 273	95.93 193	98.25 222	94.74 63	99.11 177	99.01 68	99.64 90	99.53 151

BH-untuned		95.18 179	94.83 180	96.22 214	98.36 167	91.22 285	99.80 140	97.32 281	90.91 252	91.08 253	98.67 191	83.51 250	98.54 212	94.23 208	99.61 97	98.92 208

BH-RMVSNet		95.18 179	94.31 192	97.80 149	98.17 183	95.23 183	99.76 151	97.53 257	92.52 202	94.27 218	99.25 138	76.84 308	98.80 193	90.89 263	99.54 103	99.35 175

PCF-MVS	94.20 5	95.18 179	94.10 196	98.43 116	98.55 155	95.99 151	97.91 338	97.31 282	90.35 265	89.48 280	99.22 140	85.19 237	99.89 96	90.40 274	98.47 146	99.41 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
dp		95.05 182	94.43 188	96.91 193	97.99 194	92.73 249	96.29 368	97.98 213	89.70 277	95.93 193	94.67 344	93.83 96	98.45 218	86.91 317	96.53 191	99.54 146

Fast-Effi-MVS+		95.02 183	94.19 194	97.52 169	97.88 199	94.55 200	99.97 27	97.08 307	88.85 294	94.47 214	97.96 234	84.59 243	98.41 221	89.84 281	97.10 179	99.59 132

IB-MVS	92.85 6	94.99 184	93.94 201	98.16 128	97.72 214	95.69 164	99.99 4	98.81 60	94.28 130	92.70 236	96.90 264	95.08 51	99.17 175	96.07 171	73.88 374	99.60 131
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
h-mvs33		94.92 185	94.36 189	96.59 204	98.85 135	91.29 284	98.93 275	98.94 41	95.90 77	98.77 103	98.42 216	90.89 166	99.77 128	97.80 133	70.76 379	98.72 221

XVG-OURS		94.82 186	94.74 183	95.06 244	98.00 193	89.19 320	99.08 254	97.55 253	94.10 136	94.71 210	99.62 102	80.51 278	99.74 134	96.04 172	93.06 250	96.25 259

SDMVSNet		94.80 187	93.96 200	97.33 183	98.92 126	95.42 174	99.59 190	98.99 37	92.41 206	92.55 238	97.85 237	75.81 320	98.93 188	97.90 130	91.62 253	97.64 244

ADS-MVSNet		94.79 188	94.02 198	97.11 189	97.87 200	93.79 221	94.24 380	98.16 197	90.07 270	96.43 180	94.48 349	90.29 180	98.19 247	87.44 304	97.23 176	99.36 172

XVG-OURS-SEG-HR		94.79 188	94.70 184	95.08 243	98.05 191	89.19 320	99.08 254	97.54 255	93.66 157	94.87 209	99.58 106	78.78 294	99.79 123	97.31 147	93.40 245	96.25 259

OpenMVS	90.15 15	94.77 190	93.59 210	98.33 121	96.07 279	97.48 95	99.56 197	98.57 89	90.46 262	86.51 329	98.95 168	78.57 297	99.94 77	93.86 213	99.74 84	97.57 248

LFMVS		94.75 191	93.56 212	98.30 123	99.03 113	95.70 162	98.74 294	97.98 213	87.81 311	98.47 119	99.39 125	67.43 361	99.53 150	98.01 122	95.20 223	99.67 115

SCA		94.69 192	93.81 205	97.33 183	97.10 245	94.44 201	98.86 284	98.32 174	93.30 168	96.17 188	95.59 305	76.48 313	97.95 261	91.06 257	97.43 171	99.59 132

ab-mvs		94.69 192	93.42 216	98.51 110	98.07 190	96.26 138	96.49 363	98.68 70	90.31 267	94.54 211	97.00 262	76.30 315	99.71 138	95.98 173	93.38 246	99.56 141

CVMVSNet		94.68 194	94.94 178	93.89 294	96.80 263	86.92 345	99.06 259	98.98 38	94.45 116	94.23 219	99.02 152	85.60 231	95.31 360	90.91 262	95.39 218	99.43 165

cascas		94.64 195	93.61 207	97.74 158	97.82 204	96.26 138	99.96 34	97.78 233	85.76 336	94.00 221	97.54 244	76.95 307	99.21 169	97.23 149	95.43 217	97.76 243

HQP-MVS		94.61 196	94.50 187	94.92 249	95.78 287	91.85 269	99.87 104	97.89 223	96.82 48	93.37 226	98.65 194	80.65 276	98.39 225	97.92 128	89.60 257	94.53 267

TR-MVS		94.54 197	93.56 212	97.49 171	97.96 195	94.34 207	98.71 297	97.51 260	90.30 268	94.51 213	98.69 190	75.56 321	98.77 196	92.82 236	95.99 201	99.35 175

DP-MVS		94.54 197	93.42 216	97.91 146	99.46 94	94.04 215	98.93 275	97.48 263	81.15 370	90.04 264	99.55 108	87.02 217	99.95 69	88.97 287	98.11 158	99.73 105

Effi-MVS+-dtu		94.53 199	95.30 166	92.22 327	97.77 207	82.54 367	99.59 190	97.06 309	94.92 102	95.29 205	95.37 319	85.81 230	97.89 264	94.80 194	97.07 180	96.23 261

HQP_MVS		94.49 200	94.36 189	94.87 250	95.71 297	91.74 273	99.84 124	97.87 225	96.38 65	93.01 230	98.59 199	80.47 280	98.37 231	97.79 136	89.55 260	94.52 269

myMVS_eth3d		94.46 201	94.76 182	93.55 304	97.68 217	90.97 287	99.71 168	98.35 167	90.79 256	92.10 242	98.67 191	92.46 135	93.09 382	87.13 310	95.95 204	96.59 257

TAPA-MVS	92.12 8	94.42 202	93.60 209	96.90 194	99.33 98	91.78 272	99.78 143	98.00 210	89.89 275	94.52 212	99.47 114	91.97 146	99.18 174	69.90 385	99.52 104	99.73 105
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
hse-mvs2		94.38 203	94.08 197	95.31 237	98.27 175	90.02 310	99.29 238	98.56 92	95.90 77	98.77 103	98.00 231	90.89 166	98.26 244	97.80 133	69.20 385	97.64 244

ET-MVSNet_ETH3D		94.37 204	93.28 222	97.64 162	98.30 171	97.99 72	99.99 4	97.61 247	94.35 124	71.57 390	99.45 117	96.23 30	95.34 359	96.91 162	85.14 305	99.59 132

MSDG		94.37 204	93.36 220	97.40 177	98.88 133	93.95 219	99.37 226	97.38 274	85.75 338	90.80 257	99.17 144	84.11 248	99.88 102	86.35 318	98.43 147	98.36 230

GeoE		94.36 206	93.48 214	96.99 191	97.29 241	93.54 230	99.96 34	96.72 340	88.35 304	93.43 225	98.94 170	82.05 258	98.05 255	88.12 299	96.48 194	99.37 171

miper_enhance_ethall		94.36 206	93.98 199	95.49 228	98.68 144	95.24 182	99.73 162	97.29 286	93.28 169	89.86 269	95.97 294	94.37 75	97.05 302	92.20 241	84.45 310	94.19 294

tpmvs		94.28 208	93.57 211	96.40 209	98.55 155	91.50 282	95.70 378	98.55 98	87.47 313	92.15 241	94.26 353	91.42 151	98.95 187	88.15 297	95.85 207	98.76 217

test_fmvs1_n		94.25 209	94.36 189	93.92 291	97.68 217	83.70 362	99.90 90	96.57 346	97.40 28	99.67 39	98.88 174	61.82 379	99.92 88	98.23 111	99.13 129	98.14 235

FIs		94.10 210	93.43 215	96.11 216	94.70 315	96.82 118	99.58 192	98.93 45	92.54 200	89.34 283	97.31 250	87.62 209	97.10 299	94.22 209	86.58 294	94.40 278

mvsmamba		94.10 210	93.72 206	95.25 239	93.57 333	94.13 213	99.67 177	96.45 351	93.63 159	91.34 251	97.77 240	86.29 227	97.22 291	96.65 165	88.10 282	94.40 278

CLD-MVS		94.06 212	93.90 202	94.55 265	96.02 281	90.69 294	99.98 14	97.72 235	96.62 58	91.05 255	98.85 182	77.21 302	98.47 214	98.11 117	89.51 262	94.48 271
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
testing3		93.92 213	94.23 193	92.99 318	97.54 224	90.23 305	99.99 4	99.16 30	90.57 260	91.33 252	98.63 197	92.99 116	92.52 386	82.46 344	95.39 218	96.22 262

test0.0.03 1		93.86 214	93.61 207	94.64 259	95.02 311	92.18 262	99.93 75	98.58 87	94.07 138	87.96 310	98.50 207	93.90 92	94.96 364	81.33 351	93.17 247	96.78 254

X-MVStestdata		93.83 215	92.06 248	99.15 59	99.94 13	97.50 93	99.94 68	98.42 145	96.22 72	99.41 69	41.37 412	94.34 76	99.96 61	98.92 73	99.95 49	99.99 23

GA-MVS		93.83 215	92.84 228	96.80 196	95.73 294	93.57 228	99.88 101	97.24 291	92.57 199	92.92 232	96.66 273	78.73 295	97.67 272	87.75 302	94.06 237	99.17 192

FC-MVSNet-test		93.81 217	93.15 224	95.80 224	94.30 322	96.20 143	99.42 218	98.89 49	92.33 210	89.03 293	97.27 252	87.39 212	96.83 317	93.20 228	86.48 295	94.36 281

ADS-MVSNet2		93.80 218	93.88 203	93.55 304	97.87 200	85.94 350	94.24 380	96.84 331	90.07 270	96.43 180	94.48 349	90.29 180	95.37 358	87.44 304	97.23 176	99.36 172

cl22		93.77 219	93.25 223	95.33 236	99.49 91	94.43 202	99.61 188	98.09 203	90.38 263	89.16 291	95.61 303	90.56 172	97.34 282	91.93 245	84.45 310	94.21 293

VDD-MVS		93.77 219	92.94 227	96.27 213	98.55 155	90.22 306	98.77 293	97.79 232	90.85 254	96.82 170	99.42 118	61.18 382	99.77 128	98.95 70	94.13 235	98.82 214

EI-MVSNet		93.73 221	93.40 219	94.74 255	96.80 263	92.69 250	99.06 259	97.67 238	88.96 289	91.39 249	99.02 152	88.75 201	97.30 285	91.07 256	87.85 285	94.22 291

Fast-Effi-MVS+-dtu		93.72 222	93.86 204	93.29 309	97.06 247	86.16 348	99.80 140	96.83 332	92.66 192	92.58 237	97.83 239	81.39 265	97.67 272	89.75 282	96.87 187	96.05 264

tpm		93.70 223	93.41 218	94.58 263	95.36 306	87.41 341	97.01 354	96.90 327	90.85 254	96.72 173	94.14 354	90.40 176	96.84 316	90.75 266	88.54 276	99.51 154

PS-MVSNAJss		93.64 224	93.31 221	94.61 260	92.11 360	92.19 261	99.12 250	97.38 274	92.51 203	88.45 301	96.99 263	91.20 155	97.29 288	94.36 204	87.71 287	94.36 281

test_vis1_n		93.61 225	93.03 226	95.35 234	95.86 286	86.94 344	99.87 104	96.36 353	96.85 46	99.54 57	98.79 184	52.41 392	99.83 118	98.64 92	98.97 134	99.29 184

sd_testset		93.55 226	92.83 229	95.74 225	98.92 126	90.89 292	98.24 325	98.85 56	92.41 206	92.55 238	97.85 237	71.07 348	98.68 205	93.93 211	91.62 253	97.64 244

gg-mvs-nofinetune		93.51 227	91.86 253	98.47 112	97.72 214	97.96 75	92.62 388	98.51 108	74.70 390	97.33 156	69.59 403	98.91 3	97.79 267	97.77 138	99.56 102	99.67 115

nrg030		93.51 227	92.53 240	96.45 207	94.36 320	97.20 103	99.81 136	97.16 298	91.60 229	89.86 269	97.46 245	86.37 225	97.68 271	95.88 175	80.31 344	94.46 272

tpm cat1		93.51 227	92.52 241	96.47 205	97.77 207	91.47 283	96.13 370	98.06 206	80.98 371	92.91 233	93.78 357	89.66 185	98.87 189	87.03 313	96.39 195	99.09 199

CR-MVSNet		93.45 230	92.62 234	95.94 219	96.29 273	92.66 251	92.01 391	96.23 355	92.62 194	96.94 165	93.31 362	91.04 160	96.03 349	79.23 360	95.96 202	99.13 197

AUN-MVS		93.28 231	92.60 235	95.34 235	98.29 172	90.09 309	99.31 233	98.56 92	91.80 226	96.35 184	98.00 231	89.38 190	98.28 240	92.46 238	69.22 384	97.64 244

OPM-MVS		93.21 232	92.80 230	94.44 272	93.12 343	90.85 293	99.77 146	97.61 247	96.19 74	91.56 248	98.65 194	75.16 328	98.47 214	93.78 220	89.39 263	93.99 316
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
dmvs_re		93.20 233	93.15 224	93.34 307	96.54 271	83.81 361	98.71 297	98.51 108	91.39 241	92.37 240	98.56 204	78.66 296	97.83 266	93.89 212	89.74 256	98.38 229

kuosan		93.17 234	92.60 235	94.86 253	98.40 165	89.54 318	98.44 313	98.53 104	84.46 351	88.49 300	97.92 235	90.57 171	97.05 302	83.10 340	93.49 243	97.99 237

miper_ehance_all_eth		93.16 235	92.60 235	94.82 254	97.57 223	93.56 229	99.50 207	97.07 308	88.75 295	88.85 295	95.52 309	90.97 162	96.74 320	90.77 265	84.45 310	94.17 295

VDDNet		93.12 236	91.91 251	96.76 198	96.67 270	92.65 253	98.69 300	98.21 188	82.81 363	97.75 147	99.28 131	61.57 380	99.48 161	98.09 119	94.09 236	98.15 233

Anonymous202405211		93.10 237	91.99 249	96.40 209	99.10 109	89.65 316	98.88 280	97.93 218	83.71 356	94.00 221	98.75 186	68.79 353	99.88 102	95.08 185	91.71 252	99.68 113

UniMVSNet (Re)		93.07 238	92.13 245	95.88 220	94.84 312	96.24 142	99.88 101	98.98 38	92.49 204	89.25 285	95.40 315	87.09 216	97.14 295	93.13 232	78.16 355	94.26 288

LPG-MVS_test		92.96 239	92.71 233	93.71 298	95.43 304	88.67 327	99.75 154	97.62 244	92.81 183	90.05 262	98.49 208	75.24 324	98.40 223	95.84 176	89.12 264	94.07 308

UniMVSNet_NR-MVSNet		92.95 240	92.11 246	95.49 228	94.61 317	95.28 180	99.83 131	99.08 33	91.49 232	89.21 288	96.86 267	87.14 215	96.73 321	93.20 228	77.52 360	94.46 272

WB-MVSnew		92.90 241	92.77 232	93.26 311	96.95 253	93.63 227	99.71 168	98.16 197	91.49 232	94.28 217	98.14 225	81.33 267	96.48 330	79.47 359	95.46 215	89.68 383

ACMM	91.95 10	92.88 242	92.52 241	93.98 290	95.75 293	89.08 323	99.77 146	97.52 259	93.00 175	89.95 266	97.99 233	76.17 317	98.46 217	93.63 224	88.87 268	94.39 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		92.83 243	92.29 244	94.47 270	91.90 363	92.46 256	99.55 199	97.27 288	91.17 244	89.96 265	96.07 293	81.10 269	96.89 313	94.67 199	88.91 266	94.05 310

D2MVS		92.76 244	92.59 239	93.27 310	95.13 307	89.54 318	99.69 173	99.38 23	92.26 211	87.59 314	94.61 346	85.05 239	97.79 267	91.59 250	88.01 283	92.47 360

ACMP	92.05 9	92.74 245	92.42 243	93.73 296	95.91 285	88.72 326	99.81 136	97.53 257	94.13 134	87.00 323	98.23 223	74.07 334	98.47 214	96.22 170	88.86 269	93.99 316
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		92.70 246	91.55 258	96.16 215	95.09 308	96.20 143	98.88 280	99.00 36	91.02 251	91.82 246	95.29 325	76.05 319	97.96 260	95.62 180	81.19 332	94.30 286

FMVSNet3		92.69 247	91.58 256	95.99 218	98.29 172	97.42 98	99.26 241	97.62 244	89.80 276	89.68 273	95.32 321	81.62 264	96.27 339	87.01 314	85.65 299	94.29 287

IterMVS-LS		92.69 247	92.11 246	94.43 274	96.80 263	92.74 247	99.45 216	96.89 328	88.98 287	89.65 276	95.38 318	88.77 200	96.34 336	90.98 260	82.04 326	94.22 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		92.65 249	91.50 259	96.10 217	96.85 260	90.49 300	91.50 393	97.19 293	82.76 364	90.23 261	95.59 305	95.02 54	98.00 257	77.41 369	96.98 185	99.82 92

c3_l		92.53 250	91.87 252	94.52 266	97.40 232	92.99 243	99.40 219	96.93 325	87.86 309	88.69 298	95.44 313	89.95 183	96.44 332	90.45 271	80.69 341	94.14 304

AllTest		92.48 251	91.64 254	95.00 246	99.01 114	88.43 331	98.94 274	96.82 334	86.50 327	88.71 296	98.47 212	74.73 330	99.88 102	85.39 325	96.18 197	96.71 255

DU-MVS		92.46 252	91.45 261	95.49 228	94.05 325	95.28 180	99.81 136	98.74 64	92.25 212	89.21 288	96.64 275	81.66 262	96.73 321	93.20 228	77.52 360	94.46 272

eth_miper_zixun_eth		92.41 253	91.93 250	93.84 295	97.28 242	90.68 295	98.83 287	96.97 319	88.57 300	89.19 290	95.73 300	89.24 195	96.69 323	89.97 280	81.55 329	94.15 301

DIV-MVS_self_test		92.32 254	91.60 255	94.47 270	97.31 239	92.74 247	99.58 192	96.75 338	86.99 322	87.64 313	95.54 307	89.55 188	96.50 329	88.58 291	82.44 323	94.17 295

cl____		92.31 255	91.58 256	94.52 266	97.33 238	92.77 245	99.57 195	96.78 337	86.97 323	87.56 315	95.51 310	89.43 189	96.62 325	88.60 290	82.44 323	94.16 300

LCM-MVSNet-Re		92.31 255	92.60 235	91.43 334	97.53 225	79.27 384	99.02 267	91.83 399	92.07 215	80.31 365	94.38 352	83.50 251	95.48 356	97.22 150	97.58 169	99.54 146

WR-MVS		92.31 255	91.25 263	95.48 231	94.45 319	95.29 179	99.60 189	98.68 70	90.10 269	88.07 309	96.89 265	80.68 275	96.80 319	93.14 231	79.67 348	94.36 281

COLMAP_ROB	90.47 14	92.18 258	91.49 260	94.25 279	99.00 116	88.04 337	98.42 317	96.70 341	82.30 366	88.43 304	99.01 154	76.97 306	99.85 108	86.11 321	96.50 192	94.86 266
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240529		92.10 259	90.65 271	96.47 205	98.82 136	90.61 297	98.72 296	98.67 73	75.54 387	93.90 223	98.58 202	66.23 365	99.90 91	94.70 198	90.67 255	98.90 211

pmmvs4		92.10 259	91.07 267	95.18 241	92.82 351	94.96 191	99.48 211	96.83 332	87.45 314	88.66 299	96.56 279	83.78 249	96.83 317	89.29 284	84.77 308	93.75 332

jajsoiax		91.92 261	91.18 264	94.15 280	91.35 370	90.95 290	99.00 268	97.42 269	92.61 195	87.38 319	97.08 257	72.46 339	97.36 280	94.53 202	88.77 270	94.13 305

XXY-MVS		91.82 262	90.46 274	95.88 220	93.91 328	95.40 176	98.87 283	97.69 237	88.63 299	87.87 311	97.08 257	74.38 333	97.89 264	91.66 249	84.07 314	94.35 284

miper_lstm_enhance		91.81 263	91.39 262	93.06 317	97.34 236	89.18 322	99.38 224	96.79 336	86.70 326	87.47 317	95.22 327	90.00 182	95.86 353	88.26 295	81.37 331	94.15 301

mvs_tets		91.81 263	91.08 266	94.00 288	91.63 367	90.58 298	98.67 302	97.43 267	92.43 205	87.37 320	97.05 260	71.76 341	97.32 284	94.75 196	88.68 272	94.11 306

VPNet		91.81 263	90.46 274	95.85 222	94.74 314	95.54 170	98.98 269	98.59 86	92.14 213	90.77 258	97.44 246	68.73 355	97.54 276	94.89 192	77.89 357	94.46 272

RPSCF		91.80 266	92.79 231	88.83 355	98.15 186	69.87 393	98.11 332	96.60 345	83.93 354	94.33 216	99.27 134	79.60 286	99.46 163	91.99 244	93.16 248	97.18 251

PVSNet_0	88.03 19	91.80 266	90.27 280	96.38 211	98.27 175	90.46 301	99.94 68	99.61 14	93.99 143	86.26 335	97.39 249	71.13 347	99.89 96	98.77 83	67.05 390	98.79 216

anonymousdsp		91.79 268	90.92 268	94.41 275	90.76 375	92.93 244	98.93 275	97.17 296	89.08 282	87.46 318	95.30 322	78.43 300	96.92 312	92.38 239	88.73 271	93.39 343

JIA-IIPM		91.76 269	90.70 270	94.94 248	96.11 278	87.51 340	93.16 387	98.13 202	75.79 386	97.58 149	77.68 400	92.84 121	97.97 258	88.47 294	96.54 190	99.33 178

TranMVSNet+NR-MVSNet		91.68 270	90.61 273	94.87 250	93.69 332	93.98 218	99.69 173	98.65 74	91.03 250	88.44 302	96.83 271	80.05 283	96.18 342	90.26 276	76.89 368	94.45 277

NR-MVSNet		91.56 271	90.22 281	95.60 226	94.05 325	95.76 158	98.25 324	98.70 67	91.16 246	80.78 364	96.64 275	83.23 254	96.57 327	91.41 251	77.73 359	94.46 272

dongtai		91.55 272	91.13 265	92.82 321	98.16 185	86.35 347	99.47 212	98.51 108	83.24 359	85.07 344	97.56 243	90.33 178	94.94 365	76.09 375	91.73 251	97.18 251

v2v482		91.30 273	90.07 287	95.01 245	93.13 341	93.79 221	99.77 146	97.02 312	88.05 307	89.25 285	95.37 319	80.73 274	97.15 294	87.28 308	80.04 347	94.09 307

WR-MVS_H		91.30 273	90.35 277	94.15 280	94.17 324	92.62 254	99.17 248	98.94 41	88.87 293	86.48 331	94.46 351	84.36 245	96.61 326	88.19 296	78.51 353	93.21 348

tt0805		91.28 275	90.18 283	94.60 261	96.26 275	87.55 339	98.39 318	98.72 65	89.00 286	89.22 287	98.47 212	62.98 376	98.96 186	90.57 268	88.00 284	97.28 250

V42		91.28 275	90.12 286	94.74 255	93.42 338	93.46 232	99.68 175	97.02 312	87.36 315	89.85 271	95.05 330	81.31 268	97.34 282	87.34 307	80.07 346	93.40 342

CP-MVSNet		91.23 277	90.22 281	94.26 278	93.96 327	92.39 258	99.09 252	98.57 89	88.95 290	86.42 332	96.57 278	79.19 290	96.37 334	90.29 275	78.95 350	94.02 311

XVG-ACMP-BASELINE		91.22 278	90.75 269	92.63 324	93.73 331	85.61 351	98.52 310	97.44 266	92.77 186	89.90 268	96.85 268	66.64 364	98.39 225	92.29 240	88.61 273	93.89 324

v1144		91.09 279	89.83 288	94.87 250	93.25 340	93.69 226	99.62 187	96.98 317	86.83 325	89.64 277	94.99 335	80.94 271	97.05 302	85.08 328	81.16 333	93.87 326

FMVSNet2		91.02 280	89.56 294	95.41 233	97.53 225	95.74 159	98.98 269	97.41 272	87.05 319	88.43 304	95.00 334	71.34 344	96.24 341	85.12 327	85.21 304	94.25 290

MVP-Stereo		90.93 281	90.45 276	92.37 326	91.25 372	88.76 324	98.05 335	96.17 357	87.27 317	84.04 347	95.30 322	78.46 299	97.27 290	83.78 336	99.70 87	91.09 371
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		90.91 282	90.17 284	93.12 314	96.78 266	90.42 303	98.89 278	97.05 311	89.03 284	86.49 330	95.42 314	76.59 311	95.02 362	87.22 309	84.09 313	93.93 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		90.88 283	89.82 289	94.08 283	97.53 225	91.97 264	98.43 314	96.95 320	87.05 319	89.68 273	94.72 340	71.34 344	96.11 344	87.01 314	85.65 299	94.17 295

test1		90.88 283	89.82 289	94.08 283	97.53 225	91.97 264	98.43 314	96.95 320	87.05 319	89.68 273	94.72 340	71.34 344	96.11 344	87.01 314	85.65 299	94.17 295

IterMVS-SCA-FT		90.85 285	90.16 285	92.93 319	96.72 268	89.96 311	98.89 278	96.99 315	88.95 290	86.63 327	95.67 301	76.48 313	95.00 363	87.04 312	84.04 316	93.84 328

v144192		90.79 286	89.52 296	94.59 262	93.11 344	92.77 245	99.56 197	96.99 315	86.38 329	89.82 272	94.95 337	80.50 279	97.10 299	83.98 334	80.41 342	93.90 323

v148		90.70 287	89.63 292	93.92 291	92.97 347	90.97 287	99.75 154	96.89 328	87.51 312	88.27 307	95.01 332	81.67 261	97.04 305	87.40 306	77.17 365	93.75 332

MS-PatchMatch		90.65 288	90.30 279	91.71 333	94.22 323	85.50 353	98.24 325	97.70 236	88.67 297	86.42 332	96.37 283	67.82 359	98.03 256	83.62 337	99.62 93	91.60 368

ACMH	89.72 17	90.64 289	89.63 292	93.66 302	95.64 301	88.64 329	98.55 306	97.45 265	89.03 284	81.62 359	97.61 242	69.75 351	98.41 221	89.37 283	87.62 289	93.92 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		90.63 290	89.51 297	93.99 289	93.83 329	91.70 277	98.98 269	98.52 105	88.48 301	86.15 336	96.53 280	75.46 322	96.31 338	88.83 288	78.86 352	93.95 319

v1192		90.62 291	89.25 301	94.72 257	93.13 341	93.07 239	99.50 207	97.02 312	86.33 330	89.56 279	95.01 332	79.22 289	97.09 301	82.34 346	81.16 333	94.01 313

v8		90.54 292	89.17 302	94.66 258	93.43 337	93.40 235	99.20 245	96.94 324	85.76 336	87.56 315	94.51 347	81.96 260	97.19 292	84.94 329	78.25 354	93.38 344

v1921920		90.46 293	89.12 303	94.50 268	92.96 348	92.46 256	99.49 209	96.98 317	86.10 332	89.61 278	95.30 322	78.55 298	97.03 307	82.17 347	80.89 340	94.01 313

our_test_3		90.39 294	89.48 299	93.12 314	92.40 356	89.57 317	99.33 230	96.35 354	87.84 310	85.30 341	94.99 335	84.14 247	96.09 347	80.38 355	84.56 309	93.71 337

PatchT		90.38 295	88.75 311	95.25 239	95.99 282	90.16 307	91.22 395	97.54 255	76.80 382	97.26 158	86.01 394	91.88 147	96.07 348	66.16 393	95.91 206	99.51 154

ACMH+	89.98 16	90.35 296	89.54 295	92.78 323	95.99 282	86.12 349	98.81 289	97.18 295	89.38 279	83.14 352	97.76 241	68.42 357	98.43 219	89.11 286	86.05 297	93.78 331

Baseline_NR-MVSNet		90.33 297	89.51 297	92.81 322	92.84 349	89.95 312	99.77 146	93.94 390	84.69 350	89.04 292	95.66 302	81.66 262	96.52 328	90.99 259	76.98 366	91.97 366

MIMVSNet		90.30 298	88.67 312	95.17 242	96.45 272	91.64 279	92.39 389	97.15 299	85.99 333	90.50 259	93.19 364	66.95 362	94.86 367	82.01 348	93.43 244	99.01 206

LTVRE_ROB	88.28 18	90.29 299	89.05 306	94.02 286	95.08 309	90.15 308	97.19 349	97.43 267	84.91 348	83.99 348	97.06 259	74.00 335	98.28 240	84.08 332	87.71 287	93.62 338
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		90.25 300	88.82 309	94.57 264	93.53 335	93.43 233	99.08 254	96.87 330	85.00 345	87.34 321	94.51 347	80.93 272	97.02 309	82.85 342	79.23 349	93.26 346

v1240		90.20 301	88.79 310	94.44 272	93.05 346	92.27 260	99.38 224	96.92 326	85.89 334	89.36 282	94.87 339	77.89 301	97.03 307	80.66 354	81.08 336	94.01 313

PEN-MVS		90.19 302	89.06 305	93.57 303	93.06 345	90.90 291	99.06 259	98.47 117	88.11 306	85.91 338	96.30 284	76.67 309	95.94 352	87.07 311	76.91 367	93.89 324

pmmvs5		90.17 303	89.09 304	93.40 306	92.10 361	89.77 315	99.74 157	95.58 369	85.88 335	87.24 322	95.74 298	73.41 337	96.48 330	88.54 292	83.56 317	93.95 319

EU-MVSNet		90.14 304	90.34 278	89.54 350	92.55 354	81.06 378	98.69 300	98.04 209	91.41 240	86.59 328	96.84 270	80.83 273	93.31 381	86.20 319	81.91 327	94.26 288

UniMVSNet_ETH3D		90.06 305	88.58 313	94.49 269	94.67 316	88.09 336	97.81 341	97.57 252	83.91 355	88.44 302	97.41 247	57.44 386	97.62 274	91.41 251	88.59 275	97.77 242

Syy-MVS		90.00 306	90.63 272	88.11 362	97.68 217	74.66 389	99.71 168	98.35 167	90.79 256	92.10 242	98.67 191	79.10 292	93.09 382	63.35 396	95.95 204	96.59 257

USDC		90.00 306	88.96 307	93.10 316	94.81 313	88.16 335	98.71 297	95.54 370	93.66 157	83.75 350	97.20 253	65.58 367	98.31 236	83.96 335	87.49 291	92.85 354

Anonymous20231211		89.86 308	88.44 315	94.13 282	98.93 123	90.68 295	98.54 308	98.26 184	76.28 383	86.73 325	95.54 307	70.60 349	97.56 275	90.82 264	80.27 345	94.15 301

OurMVSNet-221017-0		89.81 309	89.48 299	90.83 339	91.64 366	81.21 376	98.17 330	95.38 373	91.48 234	85.65 340	97.31 250	72.66 338	97.29 288	88.15 297	84.83 307	93.97 318

RPMNet		89.76 310	87.28 325	97.19 186	96.29 273	92.66 251	92.01 391	98.31 176	70.19 396	96.94 165	85.87 395	87.25 214	99.78 125	62.69 397	95.96 202	99.13 197

Patchmtry		89.70 311	88.49 314	93.33 308	96.24 276	89.94 314	91.37 394	96.23 355	78.22 380	87.69 312	93.31 362	91.04 160	96.03 349	80.18 358	82.10 325	94.02 311

v7n		89.65 312	88.29 317	93.72 297	92.22 358	90.56 299	99.07 258	97.10 304	85.42 343	86.73 325	94.72 340	80.06 282	97.13 296	81.14 352	78.12 356	93.49 340

ppachtmachnet_test		89.58 313	88.35 316	93.25 312	92.40 356	90.44 302	99.33 230	96.73 339	85.49 341	85.90 339	95.77 297	81.09 270	96.00 351	76.00 376	82.49 322	93.30 345

test_fmvs2		89.47 314	89.70 291	88.77 358	94.54 318	75.74 386	99.83 131	94.70 383	94.71 109	91.08 253	96.82 272	54.46 389	97.78 269	92.87 235	88.27 279	92.80 355

DTE-MVSNet		89.40 315	88.24 318	92.88 320	92.66 353	89.95 312	99.10 251	98.22 187	87.29 316	85.12 343	96.22 286	76.27 316	95.30 361	83.56 338	75.74 371	93.41 341

pm-mvs1		89.36 316	87.81 322	94.01 287	93.40 339	91.93 267	98.62 305	96.48 350	86.25 331	83.86 349	96.14 289	73.68 336	97.04 305	86.16 320	75.73 372	93.04 351

tfpnnormal		89.29 317	87.61 323	94.34 277	94.35 321	94.13 213	98.95 273	98.94 41	83.94 353	84.47 346	95.51 310	74.84 329	97.39 279	77.05 372	80.41 342	91.48 370

LF4IMVS		89.25 318	88.85 308	90.45 343	92.81 352	81.19 377	98.12 331	94.79 380	91.44 236	86.29 334	97.11 255	65.30 370	98.11 251	88.53 293	85.25 303	92.07 363

testgi		89.01 319	88.04 320	91.90 331	93.49 336	84.89 357	99.73 162	95.66 367	93.89 151	85.14 342	98.17 224	59.68 383	94.66 369	77.73 368	88.88 267	96.16 263

SixPastTwentyTwo		88.73 320	88.01 321	90.88 337	91.85 364	82.24 369	98.22 328	95.18 378	88.97 288	82.26 355	96.89 265	71.75 342	96.67 324	84.00 333	82.98 318	93.72 336

FMVSNet1		88.50 321	86.64 327	94.08 283	95.62 303	91.97 264	98.43 314	96.95 320	83.00 361	86.08 337	94.72 340	59.09 384	96.11 344	81.82 350	84.07 314	94.17 295

FMVSNet5		88.32 322	87.47 324	90.88 337	96.90 258	88.39 333	97.28 347	95.68 366	82.60 365	84.67 345	92.40 370	79.83 284	91.16 391	76.39 374	81.51 330	93.09 349

DSMNet-mixed		88.28 323	88.24 318	88.42 360	89.64 382	75.38 388	98.06 334	89.86 403	85.59 340	88.20 308	92.14 372	76.15 318	91.95 389	78.46 365	96.05 200	97.92 238

K. test v3		88.05 324	87.24 326	90.47 342	91.82 365	82.23 370	98.96 272	97.42 269	89.05 283	76.93 380	95.60 304	68.49 356	95.42 357	85.87 324	81.01 338	93.75 332

KD-MVS_2432*1600		88.00 325	86.10 329	93.70 300	96.91 255	94.04 215	97.17 350	97.12 302	84.93 346	81.96 356	92.41 368	92.48 133	94.51 370	79.23 360	52.68 402	92.56 357

miper_refine_blended		88.00 325	86.10 329	93.70 300	96.91 255	94.04 215	97.17 350	97.12 302	84.93 346	81.96 356	92.41 368	92.48 133	94.51 370	79.23 360	52.68 402	92.56 357

TinyColmap		87.87 327	86.51 328	91.94 330	95.05 310	85.57 352	97.65 342	94.08 387	84.40 352	81.82 358	96.85 268	62.14 378	98.33 234	80.25 357	86.37 296	91.91 367

TransMVSNet (Re)		87.25 328	85.28 335	93.16 313	93.56 334	91.03 286	98.54 308	94.05 389	83.69 357	81.09 362	96.16 288	75.32 323	96.40 333	76.69 373	68.41 386	92.06 364

Patchmatch-RL test		86.90 329	85.98 333	89.67 349	84.45 392	75.59 387	89.71 398	92.43 396	86.89 324	77.83 377	90.94 376	94.22 81	93.63 378	87.75 302	69.61 381	99.79 97

test_vis1_rt		86.87 330	86.05 332	89.34 351	96.12 277	78.07 385	99.87 104	83.54 410	92.03 218	78.21 375	89.51 381	45.80 396	99.91 89	96.25 169	93.11 249	90.03 380

Anonymous20231206		86.32 331	85.42 334	89.02 354	89.11 384	80.53 382	99.05 263	95.28 374	85.43 342	82.82 353	93.92 355	74.40 332	93.44 380	66.99 390	81.83 328	93.08 350

MVS-HIRNet		86.22 332	83.19 345	95.31 237	96.71 269	90.29 304	92.12 390	97.33 280	62.85 397	86.82 324	70.37 402	69.37 352	97.49 277	75.12 377	97.99 163	98.15 233

pmmvs6		85.69 333	83.84 340	91.26 336	90.00 381	84.41 359	97.82 340	96.15 358	75.86 385	81.29 361	95.39 317	61.21 381	96.87 315	83.52 339	73.29 375	92.50 359

test_0402		85.58 334	83.94 339	90.50 341	93.81 330	85.04 355	98.55 306	95.20 377	76.01 384	79.72 369	95.13 328	64.15 373	96.26 340	66.04 394	86.88 293	90.21 379

UnsupCasMVSNet_eth		85.52 335	83.99 337	90.10 346	89.36 383	83.51 363	96.65 361	97.99 211	89.14 281	75.89 384	93.83 356	63.25 375	93.92 374	81.92 349	67.90 389	92.88 353

MDA-MVSNet_test_wron		85.51 336	83.32 344	92.10 328	90.96 373	88.58 330	99.20 245	96.52 348	79.70 377	57.12 402	92.69 366	79.11 291	93.86 376	77.10 371	77.46 362	93.86 327

YYNet1		85.50 337	83.33 343	92.00 329	90.89 374	88.38 334	99.22 244	96.55 347	79.60 378	57.26 401	92.72 365	79.09 293	93.78 377	77.25 370	77.37 363	93.84 328

EG-PatchMatch MVS		85.35 338	83.81 341	89.99 348	90.39 377	81.89 372	98.21 329	96.09 359	81.78 368	74.73 386	93.72 358	51.56 394	97.12 298	79.16 363	88.61 273	90.96 373

Anonymous20240521		85.15 339	83.81 341	89.16 353	88.32 385	82.69 365	98.80 291	95.74 364	79.72 376	81.53 360	90.99 375	65.38 369	94.16 372	72.69 380	81.11 335	90.63 376

TDRefinement		84.76 340	82.56 348	91.38 335	74.58 406	84.80 358	97.36 346	94.56 384	84.73 349	80.21 366	96.12 292	63.56 374	98.39 225	87.92 300	63.97 395	90.95 374

CMPMVS	61.59 21	84.75 341	85.14 336	83.57 370	90.32 378	62.54 398	96.98 355	97.59 251	74.33 391	69.95 392	96.66 273	64.17 372	98.32 235	87.88 301	88.41 278	89.84 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		84.72 342	83.99 337	86.91 364	88.19 387	80.62 381	98.88 280	95.94 361	88.36 303	78.87 370	94.62 345	68.75 354	89.11 395	66.52 392	75.82 370	91.00 372

CL-MVSNet_self_test		84.50 343	83.15 346	88.53 359	86.00 390	81.79 373	98.82 288	97.35 276	85.12 344	83.62 351	90.91 377	76.66 310	91.40 390	69.53 386	60.36 399	92.40 361

new_pmnet		84.49 344	82.92 347	89.21 352	90.03 380	82.60 366	96.89 358	95.62 368	80.59 372	75.77 385	89.17 382	65.04 371	94.79 368	72.12 382	81.02 337	90.23 378

MDA-MVSNet-bldmvs		84.09 345	81.52 352	91.81 332	91.32 371	88.00 338	98.67 302	95.92 362	80.22 374	55.60 403	93.32 361	68.29 358	93.60 379	73.76 378	76.61 369	93.82 330

pmmvs-eth3d		84.03 346	81.97 350	90.20 345	84.15 393	87.09 343	98.10 333	94.73 382	83.05 360	74.10 388	87.77 389	65.56 368	94.01 373	81.08 353	69.24 383	89.49 386

dmvs_testset		83.79 347	86.07 331	76.94 377	92.14 359	48.60 412	96.75 359	90.27 402	89.48 278	78.65 372	98.55 206	79.25 288	86.65 400	66.85 391	82.69 320	95.57 265

OpenMVS_ROB	79.82 20	83.77 348	81.68 351	90.03 347	88.30 386	82.82 364	98.46 311	95.22 376	73.92 392	76.00 383	91.29 374	55.00 388	96.94 311	68.40 388	88.51 277	90.34 377

KD-MVS_self_test		83.59 349	82.06 349	88.20 361	86.93 388	80.70 380	97.21 348	96.38 352	82.87 362	82.49 354	88.97 383	67.63 360	92.32 387	73.75 379	62.30 398	91.58 369

MIMVSNet1		82.58 350	80.51 356	88.78 356	86.68 389	84.20 360	96.65 361	95.41 372	78.75 379	78.59 373	92.44 367	51.88 393	89.76 394	65.26 395	78.95 350	92.38 362

mvsany_test3		82.12 351	81.14 353	85.06 368	81.87 397	70.41 392	97.09 352	92.14 397	91.27 243	77.84 376	88.73 384	39.31 399	95.49 355	90.75 266	71.24 378	89.29 388

new-patchmatchnet		81.19 352	79.34 359	86.76 365	82.86 396	80.36 383	97.92 337	95.27 375	82.09 367	72.02 389	86.87 391	62.81 377	90.74 393	71.10 383	63.08 396	89.19 389

APD_test1		81.15 353	80.92 354	81.86 373	92.45 355	59.76 402	96.04 373	93.61 393	73.29 393	77.06 378	96.64 275	44.28 398	96.16 343	72.35 381	82.52 321	89.67 384

test_method		80.79 354	79.70 358	84.08 369	92.83 350	67.06 395	99.51 205	95.42 371	54.34 401	81.07 363	93.53 359	44.48 397	92.22 388	78.90 364	77.23 364	92.94 352

PM-MVS		80.47 355	78.88 360	85.26 367	83.79 395	72.22 390	95.89 376	91.08 400	85.71 339	76.56 382	88.30 385	36.64 400	93.90 375	82.39 345	69.57 382	89.66 385

pmmvs3		80.27 356	77.77 361	87.76 363	80.32 401	82.43 368	98.23 327	91.97 398	72.74 394	78.75 371	87.97 388	57.30 387	90.99 392	70.31 384	62.37 397	89.87 381

N_pmnet		80.06 357	80.78 355	77.89 376	91.94 362	45.28 414	98.80 291	56.82 416	78.10 381	80.08 367	93.33 360	77.03 304	95.76 354	68.14 389	82.81 319	92.64 356

test_fmvs3		79.99 358	80.17 357	79.45 375	84.02 394	62.83 396	99.05 263	93.49 394	88.29 305	80.06 368	86.65 392	28.09 404	88.00 396	88.63 289	73.27 376	87.54 392

UnsupCasMVSNet_bld		79.97 359	77.03 364	88.78 356	85.62 391	81.98 371	93.66 385	97.35 276	75.51 388	70.79 391	83.05 397	48.70 395	94.91 366	78.31 366	60.29 400	89.46 387

test_f		78.40 360	77.59 362	80.81 374	80.82 399	62.48 399	96.96 356	93.08 395	83.44 358	74.57 387	84.57 396	27.95 405	92.63 385	84.15 331	72.79 377	87.32 393

WB-MVS		76.28 361	77.28 363	73.29 381	81.18 398	54.68 406	97.87 339	94.19 386	81.30 369	69.43 393	90.70 378	77.02 305	82.06 404	35.71 409	68.11 388	83.13 395

SSC-MVS		75.42 362	76.40 365	72.49 385	80.68 400	53.62 407	97.42 344	94.06 388	80.42 373	68.75 394	90.14 380	76.54 312	81.66 405	33.25 410	66.34 392	82.19 396

EGC-MVSNET		69.38 363	63.76 373	86.26 366	90.32 378	81.66 375	96.24 369	93.85 391	0.99 413	3.22 414	92.33 371	52.44 391	92.92 384	59.53 400	84.90 306	84.21 394

test_vis3_rt		68.82 364	66.69 369	75.21 380	76.24 405	60.41 401	96.44 364	68.71 415	75.13 389	50.54 406	69.52 404	16.42 414	96.32 337	80.27 356	66.92 391	68.89 402

FPMVS		68.72 365	68.72 366	68.71 387	65.95 410	44.27 416	95.97 375	94.74 381	51.13 402	53.26 404	90.50 379	25.11 407	83.00 403	60.80 398	80.97 339	78.87 400

testf1		68.38 366	66.92 367	72.78 383	78.80 402	50.36 409	90.95 396	87.35 408	55.47 399	58.95 398	88.14 386	20.64 409	87.60 397	57.28 401	64.69 393	80.39 398

APD_test2		68.38 366	66.92 367	72.78 383	78.80 402	50.36 409	90.95 396	87.35 408	55.47 399	58.95 398	88.14 386	20.64 409	87.60 397	57.28 401	64.69 393	80.39 398

LCM-MVSNet		67.77 368	64.73 371	76.87 378	62.95 412	56.25 405	89.37 399	93.74 392	44.53 404	61.99 396	80.74 398	20.42 411	86.53 401	69.37 387	59.50 401	87.84 390

PMMVS2		67.15 369	64.15 372	76.14 379	70.56 409	62.07 400	93.89 383	87.52 407	58.09 398	60.02 397	78.32 399	22.38 408	84.54 402	59.56 399	47.03 404	81.80 397

Gipuma		66.95 370	65.00 370	72.79 382	91.52 368	67.96 394	66.16 405	95.15 379	47.89 403	58.54 400	67.99 405	29.74 402	87.54 399	50.20 404	77.83 358	62.87 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		65.23 371	62.94 374	72.13 386	44.90 415	50.03 411	81.05 402	89.42 406	38.45 405	48.51 407	99.90 18	54.09 390	78.70 407	91.84 248	18.26 409	87.64 391

ANet_high		56.10 372	52.24 375	67.66 388	49.27 414	56.82 404	83.94 401	82.02 411	70.47 395	33.28 411	64.54 406	17.23 413	69.16 409	45.59 406	23.85 408	77.02 401

PMVS	49.05 23	53.75 373	51.34 377	60.97 390	40.80 416	34.68 417	74.82 404	89.62 405	37.55 406	28.67 412	72.12 401	7.09 416	81.63 406	43.17 407	68.21 387	66.59 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		52.30 374	52.18 376	52.67 391	71.51 407	45.40 413	93.62 386	76.60 413	36.01 407	43.50 408	64.13 407	27.11 406	67.31 410	31.06 411	26.06 406	45.30 409

MVE	53.74 22	51.54 375	47.86 379	62.60 389	59.56 413	50.93 408	79.41 403	77.69 412	35.69 408	36.27 410	61.76 409	5.79 418	69.63 408	37.97 408	36.61 405	67.24 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		51.44 376	51.22 378	52.11 392	70.71 408	44.97 415	94.04 382	75.66 414	35.34 409	42.40 409	61.56 410	28.93 403	65.87 411	27.64 412	24.73 407	45.49 408

testmvs		40.60 377	44.45 380	29.05 394	19.49 418	14.11 420	99.68 175	18.47 417	20.74 410	64.59 395	98.48 211	10.95 415	17.09 414	56.66 403	11.01 410	55.94 407

test123		37.68 378	39.14 381	33.31 393	19.94 417	24.83 419	98.36 319	9.75 418	15.53 411	51.31 405	87.14 390	19.62 412	17.74 413	47.10 405	3.47 412	57.36 406

cdsmvs_eth3d_5k		23.43 379	31.24 382	0.00 396	0.00 419	0.00 421	0.00 407	98.09 203	0.00 414	0.00 415	99.67 94	83.37 252	0.00 415	0.00 414	0.00 413	0.00 411

wuyk23d		20.37 380	20.84 383	18.99 395	65.34 411	27.73 418	50.43 406	7.67 419	9.50 412	8.01 413	6.34 413	6.13 417	26.24 412	23.40 413	10.69 411	2.99 410

ab-mvs-re		8.28 381	11.04 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	99.40 123	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		7.60 382	10.13 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	91.20 155	0.00 415	0.00 414	0.00 413	0.00 411

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.02 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						90.97 287								86.10 322

FOURS1					99.92 31	97.66 86	99.95 52	98.36 165	95.58 86	99.52 60

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 150					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 44	99.80 17	99.79 55	97.49 8	100.00 1	99.99 5	99.98 32	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 150					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 13	99.30 12		98.41 150	96.63 56	99.75 29	99.93 11	97.49 8

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					99.92 31	98.57 56		98.52 105	92.34 209	99.31 77	99.83 43	95.06 52	99.80 121	99.70 34	99.97 42

RE-MVS-def			98.13 51		99.79 62	96.37 135	99.76 151	98.31 176	94.43 119	99.40 71	99.75 69	92.95 118		98.90 76	99.92 63	99.97 58

IU-MVS					99.93 24	99.31 10		98.41 150	97.71 19	99.84 12				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 34				99.80 51	97.44 12	100.00 1	100.00 1	99.98 32	100.00 1

test_241102_TWO								98.43 133	97.27 34	99.80 17	99.94 4	97.18 19	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 133	97.26 36	99.80 17	99.88 21	96.71 22	100.00 1

9.14			98.38 34		99.87 51		99.91 84	98.33 172	93.22 170	99.78 26	99.89 19	94.57 68	99.85 108	99.84 22	99.97 42

save fliter					99.82 58	98.79 40	99.96 34	98.40 154	97.66 21

test_0728_THIRD									96.48 59	99.83 13	99.91 14	97.87 4	100.00 1	99.92 12	100.00 1	100.00 1

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 52	98.43 133					100.00 1	99.99 5	100.00 1	100.00 1

test0726					99.93 24	99.29 15	99.96 34	98.42 145	97.28 32	99.86 7	99.94 4	97.22 17

GSMVS																99.59 132

test_part2					99.89 45	99.25 18				99.49 63

sam_mvs1												94.72 64				99.59 132

sam_mvs												94.25 80

ambc				83.23 371	77.17 404	62.61 397	87.38 400	94.55 385		76.72 381	86.65 392	30.16 401	96.36 335	84.85 330	69.86 380	90.73 375

MTGPA								98.28 181

test_post1							95.78 377				59.23 411	93.20 112	97.74 270	91.06 257

test_post											63.35 408	94.43 69	98.13 250

patchmatchnet-post											91.70 373	95.12 49	97.95 261

GG-mvs-BLEND				98.54 107	98.21 179	98.01 71	93.87 384	98.52 105		97.92 139	97.92 235	99.02 2	97.94 263	98.17 113	99.58 100	99.67 115

MTMP							99.87 104	96.49 349

gm-plane-assit					96.97 252	93.76 223			91.47 235		98.96 163		98.79 194	94.92 189

test9_res														99.71 33	99.99 21	100.00 1

TEST9					99.92 31	98.92 29	99.96 34	98.43 133	93.90 149	99.71 34	99.86 26	95.88 36	99.85 108

test_8					99.92 31	98.88 32	99.96 34	98.43 133	94.35 124	99.69 37	99.85 30	95.94 33	99.85 108

agg_prior2														99.48 43	100.00 1	100.00 1

agg_prior					99.93 24	98.77 42		98.43 133		99.63 44			99.85 108

TestCases				95.00 246	99.01 114	88.43 331		96.82 334	86.50 327	88.71 296	98.47 212	74.73 330	99.88 102	85.39 325	96.18 197	96.71 255

test_prior4						98.05 69	99.94 68

test_prior2							99.95 52		95.78 80	99.73 32	99.76 63	96.00 32		99.78 27	100.00 1

test_prior				99.43 35	99.94 13	98.49 60		98.65 74					99.80 121			99.99 23

旧先验2							99.46 215		94.21 133	99.85 9			99.95 69	96.96 159

新几何2							99.40 219

新几何1				99.42 37	99.75 68	98.27 63		98.63 80	92.69 190	99.55 55	99.82 46	94.40 71	100.00 1	91.21 253	99.94 54	99.99 23

旧先验1					99.76 66	97.52 91		98.64 76			99.85 30	95.63 40			99.94 54	99.99 23

无先验							99.49 209	98.71 66	93.46 162				100.00 1	94.36 204		99.99 23

原ACMM2							99.90 90

原ACMM1				98.96 77	99.73 72	96.99 112		98.51 108	94.06 140	99.62 47	99.85 30	94.97 58	99.96 61	95.11 184	99.95 49	99.92 81

test222					99.55 86	97.41 99	99.34 229	98.55 98	91.86 222	99.27 81	99.83 43	93.84 95			99.95 49	99.99 23

testdata2													99.99 36	90.54 270

segment_acmp												96.68 24

testdata				98.42 117	99.47 92	95.33 178		98.56 92	93.78 152	99.79 25	99.85 30	93.64 100	99.94 77	94.97 187	99.94 54	100.00 1

testdata1							99.28 239		96.35 69

test12				99.43 35	99.74 69	98.56 57		98.40 154		99.65 41		94.76 62	99.75 132		99.98 32	99.99 23

plane_prior7					95.71 297	91.59 281

plane_prior6					95.76 291	91.72 276						80.47 280

plane_prior5								97.87 225					98.37 231	97.79 136	89.55 260	94.52 269

plane_prior4											98.59 199

plane_prior3						91.64 279			96.63 56	93.01 230

plane_prior2							99.84 124		96.38 65

plane_prior1					95.73 294

plane_prior						91.74 273	99.86 116		96.76 52						89.59 259

n2								0.00 420

nn								0.00 420

door-mid								89.69 404

lessismore_v0				90.53 340	90.58 376	80.90 379		95.80 363		77.01 379	95.84 295	66.15 366	96.95 310	83.03 341	75.05 373	93.74 335

LGP-MVS_train				93.71 298	95.43 304	88.67 327		97.62 244	92.81 183	90.05 262	98.49 208	75.24 324	98.40 223	95.84 176	89.12 264	94.07 308

test11								98.44 125

door								90.31 401

HQP5-MVS						91.85 269

HQP-NCC					95.78 287		99.87 104		96.82 48	93.37 226

ACMP_Plane					95.78 287		99.87 104		96.82 48	93.37 226

BP-MVS														97.92 128

HQP4-MVS										93.37 226			98.39 225			94.53 267

HQP3-MVS								97.89 223							89.60 257

HQP2-MVS												80.65 276

NP-MVS					95.77 290	91.79 271					98.65 194

MDTV_nov1_ep13_2view						96.26 138	96.11 371		91.89 221	98.06 135		94.40 71		94.30 206		99.67 115

MDTV_nov1_ep13			95.69 155		97.90 198	94.15 212	95.98 374	98.44 125	93.12 173	97.98 137	95.74 298	95.10 50	98.58 209	90.02 278	96.92 186

ACMMP++_ref															87.04 292

ACMMP++															88.23 280

Test By Simon												92.82 123

ITE_SJBPF				92.38 325	95.69 299	85.14 354		95.71 365	92.81 183	89.33 284	98.11 226	70.23 350	98.42 220	85.91 323	88.16 281	93.59 339

DeepMVS_CX				82.92 372	95.98 284	58.66 403		96.01 360	92.72 187	78.34 374	95.51 310	58.29 385	98.08 252	82.57 343	85.29 302	92.03 365

High-resolution multi-view benchmark