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		98.46 1	98.38 1	98.72 10	99.80 4	96.19 15	99.80 24	97.99 56	97.05 11	99.41 8	99.59 2	92.89 26	100.00 1	98.99 36	99.90 7	99.96 10

DVP-MVS++		98.18 2	98.09 5	98.44 16	99.61 24	95.38 24	99.55 57	97.68 103	93.01 89	99.23 17	99.45 14	95.12 8	99.98 9	99.25 23	99.92 3	99.97 7

SED-MVS		98.18 2	98.10 4	98.41 18	99.63 18	95.24 27	99.77 27	97.72 92	94.17 57	99.30 14	99.54 3	93.32 20	99.98 9	99.70 5	99.81 23	99.99 1

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 10	99.70 37	97.98 57	97.18 9	95.96 115	99.33 22	92.62 27	100.00 1	98.99 36	99.93 1	99.98 6

NCCC		98.12 5	98.11 3	98.13 25	99.76 6	94.46 53	99.81 18	97.88 63	96.54 20	98.84 32	99.46 10	92.55 28	99.98 9	98.25 61	99.93 1	99.94 18

DPE-MVS		98.11 6	98.00 6	98.44 16	99.50 42	95.39 23	99.29 95	97.72 92	94.50 50	98.64 40	99.54 3	93.32 20	99.97 21	99.58 12	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.07 7	98.00 6	98.29 19	99.66 12	95.20 32	99.72 34	97.47 157	93.95 62	99.07 23	99.46 10	93.18 23	99.97 21	99.64 8	99.82 19	99.69 60
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
DPM-MVS		97.86 8	97.25 23	99.68 1	98.25 100	99.10 1	99.76 30	97.78 84	96.61 19	98.15 55	99.53 7	93.62 17	100.00 1	91.79 201	99.80 26	99.94 18

MVS_0304		97.81 9	97.51 15	98.74 9	98.97 75	96.57 11	99.91 3	98.17 39	97.45 4	98.76 35	98.97 76	86.69 119	99.96 28	99.72 3	98.92 92	99.69 60

MSP-MVS		97.77 10	98.18 2	96.53 106	99.54 36	90.14 167	99.41 82	97.70 97	95.46 37	98.60 42	99.19 38	95.71 5	99.49 127	98.15 63	99.85 13	99.95 15
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
MM		97.76 11	97.39 20	98.86 5	98.30 99	96.83 7	99.81 18	99.13 9	97.66 2	98.29 53	98.96 81	85.84 140	99.90 55	99.72 3	98.80 100	99.85 30

HPM-MVS++		97.72 12	97.59 13	98.14 24	99.53 40	94.76 45	99.19 106	97.75 87	95.66 33	98.21 54	99.29 23	91.10 36	99.99 5	97.68 72	99.87 9	99.68 62

fmvsm_l_conf0.5_n_a		97.70 13	97.80 11	97.42 51	97.59 128	92.91 93	99.86 7	98.04 52	96.70 17	99.58 5	99.26 24	90.90 41	99.94 35	99.57 13	98.66 108	99.40 98

fmvsm_l_conf0.5_n		97.65 14	97.72 12	97.41 52	97.51 133	92.78 96	99.85 10	98.05 50	96.78 15	99.60 4	99.23 29	90.42 52	99.92 44	99.55 15	98.50 117	99.55 82

APDe-MVS		97.53 15	97.47 16	97.70 40	99.58 30	93.63 70	99.56 56	97.52 147	93.59 79	98.01 64	99.12 56	90.80 45	99.55 121	99.26 21	99.79 27	99.93 20
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD-MVS		97.51 16	97.40 19	97.81 36	99.01 74	93.79 69	99.33 93	97.38 172	93.73 74	98.83 33	99.02 72	90.87 44	99.88 64	98.69 41	99.74 29	99.77 46
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
MSLP-MVS++		97.50 17	97.45 18	97.63 42	99.65 16	93.21 81	99.70 37	98.13 45	94.61 48	97.78 70	99.46 10	89.85 61	99.81 90	97.97 65	99.91 6	99.88 26

TSAR-MVS + MP.		97.44 18	97.46 17	97.39 54	99.12 67	93.49 76	98.52 202	97.50 152	94.46 52	98.99 25	98.64 114	91.58 33	99.08 165	98.49 51	99.83 15	99.60 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_l_conf0.5_n_9		97.33 19	97.32 22	97.37 55	97.64 123	92.45 106	99.93 1	97.85 66	97.39 5	99.84 1	99.09 62	85.42 149	99.92 44	99.52 18	99.20 78	99.73 53

SteuartSystems-ACMMP		97.25 20	97.34 21	97.01 71	97.38 139	91.46 127	99.75 32	97.66 109	94.14 61	98.13 56	99.26 24	92.16 32	99.66 109	97.91 67	99.64 42	99.90 22
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		97.24 21	96.99 25	98.00 31	99.30 54	94.20 61	99.16 112	97.65 116	89.55 193	99.22 19	99.52 8	90.34 55	99.99 5	98.32 58	99.83 15	99.82 32
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
MG-MVS		97.24 21	96.83 35	98.47 15	99.79 5	95.71 19	99.07 131	99.06 10	94.45 54	96.42 106	98.70 110	88.81 75	99.74 103	95.35 131	99.86 12	99.97 7

SF-MVS		97.22 23	96.92 27	98.12 27	99.11 68	94.88 38	99.44 75	97.45 160	89.60 189	98.70 37	99.42 17	90.42 52	99.72 104	98.47 52	99.65 40	99.77 46

train_agg		97.20 24	97.08 24	97.57 46	99.57 33	93.17 83	99.38 85	97.66 109	90.18 167	98.39 49	99.18 41	90.94 39	99.66 109	98.58 47	99.85 13	99.88 26

DeepC-MVS_fast	93.52 2	97.16 25	96.84 33	98.13 25	99.61 24	94.45 54	98.85 154	97.64 118	96.51 23	95.88 118	99.39 18	87.35 103	99.99 5	96.61 98	99.69 38	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_l_conf0.5_n_3		97.12 26	96.89 30	97.79 39	97.39 138	93.84 68	99.87 6	97.70 97	97.34 7	99.39 10	99.20 35	82.86 189	99.94 35	99.21 26	99.07 81	99.58 81

DELS-MVS		97.12 26	96.60 44	98.68 11	98.03 110	96.57 11	99.84 12	97.84 68	96.36 25	95.20 136	98.24 140	88.17 84	99.83 84	96.11 112	99.60 50	99.64 71
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
patch_mono-2		97.10 28	97.97 8	94.49 215	99.21 63	83.73 338	99.62 51	98.25 34	95.28 39	99.38 11	98.91 89	92.28 31	99.94 35	99.61 11	99.22 74	99.78 41

test_fmvsm_n_1920		97.08 29	97.55 14	95.67 157	97.94 113	89.61 192	99.93 1	98.48 25	97.08 10	99.08 22	99.13 53	88.17 84	99.93 41	99.11 31	99.06 82	97.47 241

fmvsm_s_conf0.5_n_8		97.06 30	96.94 26	97.44 48	97.78 117	92.77 97	99.83 13	97.83 72	97.58 3	99.25 16	99.20 35	82.71 197	99.92 44	99.64 8	98.61 110	99.64 71

CANet		97.00 31	96.49 47	98.55 12	98.86 86	96.10 16	99.83 13	97.52 147	95.90 27	97.21 81	98.90 91	82.66 199	99.93 41	98.71 40	98.80 100	99.63 74

TSAR-MVS + GP.		96.95 32	96.91 29	97.07 68	98.88 85	91.62 123	99.58 54	96.54 243	95.09 42	96.84 92	98.63 116	91.16 34	99.77 100	99.04 33	96.42 167	99.81 35

APD-MVS		96.95 32	96.72 40	97.63 42	99.51 41	93.58 71	99.16 112	97.44 164	90.08 173	98.59 43	99.07 63	89.06 69	99.42 138	97.92 66	99.66 39	99.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-MVSNAJ		96.87 34	96.40 51	98.29 19	97.35 141	97.29 5	99.03 137	97.11 201	95.83 28	98.97 27	99.14 51	82.48 203	99.60 118	98.60 44	99.08 79	98.00 222

balanced_conf03		96.83 35	96.51 46	97.81 36	97.60 127	95.15 34	98.40 220	96.77 226	93.00 91	98.69 38	96.19 252	89.75 63	98.76 181	98.45 53	99.72 32	99.51 87

EPNet		96.82 36	96.68 42	97.25 62	98.65 92	93.10 85	99.48 66	98.76 14	96.54 20	97.84 68	98.22 141	87.49 96	99.66 109	95.35 131	97.78 136	99.00 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		96.80 37	96.85 32	96.66 97	97.85 116	94.42 56	94.76 381	98.36 31	92.50 102	95.62 129	97.52 173	97.92 1	97.38 285	98.31 59	98.80 100	98.20 215

fmvsm_s_conf0.5_n_6		96.78 38	96.64 43	97.20 64	96.03 214	93.20 82	99.82 17	97.68 103	95.20 40	99.61 3	99.11 60	84.52 163	99.90 55	99.04 33	98.77 104	98.50 189

test_fmvsmconf_n		96.78 38	96.84 33	96.61 99	95.99 215	90.25 161	99.90 4	98.13 45	96.68 18	98.42 48	98.92 88	85.34 151	99.88 64	99.12 30	99.08 79	99.70 57

fmvsm_s_conf0.5_n_9		96.76 40	96.92 27	96.29 122	97.95 112	89.21 198	99.81 18	97.55 138	97.04 12	99.68 2	99.22 31	82.84 191	99.94 35	99.56 14	98.61 110	99.71 55

MVS_111021_HR		96.69 41	96.69 41	96.72 92	98.58 94	91.00 141	99.14 120	99.45 1	93.86 69	95.15 137	98.73 104	88.48 79	99.76 101	97.23 82	99.56 52	99.40 98

lecture		96.67 42	96.77 38	96.39 114	99.27 57	89.71 188	99.65 47	98.62 22	92.28 109	98.62 41	99.07 63	86.74 116	99.79 96	97.83 71	98.82 97	99.66 66

reproduce-ours		96.66 43	96.80 36	96.22 124	98.95 79	89.03 207	98.62 185	97.38 172	93.42 81	96.80 97	99.36 19	88.92 72	99.80 92	98.51 49	99.26 71	99.82 32

our_new_method		96.66 43	96.80 36	96.22 124	98.95 79	89.03 207	98.62 185	97.38 172	93.42 81	96.80 97	99.36 19	88.92 72	99.80 92	98.51 49	99.26 71	99.82 32

xiu_mvs_v2_base		96.66 43	96.17 63	98.11 28	97.11 161	96.96 6	99.01 140	97.04 208	95.51 36	98.86 31	99.11 60	82.19 211	99.36 145	98.59 46	98.14 128	98.00 222

PHI-MVS		96.65 46	96.46 50	97.21 63	99.34 50	91.77 119	99.70 37	98.05 50	86.48 292	98.05 61	99.20 35	89.33 67	99.96 28	98.38 54	99.62 46	99.90 22

BP-MVS1		96.59 47	96.36 53	97.29 58	95.05 269	94.72 47	99.44 75	97.45 160	92.71 98	96.41 107	98.50 124	94.11 16	98.50 194	95.61 126	97.97 130	98.66 183

ACMMP_NAP		96.59 47	96.18 60	97.81 36	98.82 87	93.55 73	98.88 153	97.59 131	90.66 147	97.98 65	99.14 51	86.59 122	100.00 1	96.47 102	99.46 57	99.89 25

fmvsm_s_conf0.5_n_3		96.58 49	96.55 45	96.66 97	97.23 148	92.59 103	99.81 18	97.82 73	97.35 6	99.42 7	99.16 44	80.27 232	99.93 41	99.26 21	98.60 112	97.45 242

reproduce_model		96.57 50	96.75 39	96.02 138	98.93 82	88.46 230	98.56 198	97.34 178	93.18 87	96.96 88	99.35 21	88.69 77	99.80 92	98.53 48	99.21 77	99.79 38

CDPH-MVS		96.56 51	96.18 60	97.70 40	99.59 28	93.92 65	99.13 125	97.44 164	89.02 207	97.90 67	99.22 31	88.90 74	99.49 127	94.63 152	99.79 27	99.68 62

DeepPCF-MVS	93.56 1	96.55 52	97.84 10	92.68 277	98.71 91	78.11 401	99.70 37	97.71 96	98.18 1	97.36 77	99.76 1	90.37 54	99.94 35	99.27 20	99.54 54	99.99 1

XVS		96.47 53	96.37 52	96.77 86	99.62 22	90.66 151	99.43 79	97.58 133	92.41 106	96.86 90	98.96 81	87.37 99	99.87 68	95.65 121	99.43 61	99.78 41

fmvsm_s_conf0.5_n_5		96.46 54	96.23 57	97.15 67	96.42 189	92.80 95	99.83 13	97.39 171	94.50 50	98.71 36	99.13 53	82.52 200	99.90 55	99.24 25	98.38 122	98.74 168

HFP-MVS		96.42 55	96.26 55	96.90 81	99.69 8	90.96 142	99.47 68	97.81 77	90.54 156	96.88 89	99.05 68	87.57 94	99.96 28	95.65 121	99.72 32	99.78 41

PAPR		96.35 56	95.82 75	97.94 33	99.63 18	94.19 62	99.42 81	97.55 138	92.43 103	93.82 166	99.12 56	87.30 104	99.91 51	94.02 163	99.06 82	99.74 50

PAPM		96.35 56	95.94 69	97.58 44	94.10 305	95.25 26	98.93 147	98.17 39	94.26 56	93.94 161	98.72 106	89.68 64	97.88 245	96.36 103	99.29 69	99.62 76

lupinMVS		96.32 58	95.94 69	97.44 48	95.05 269	94.87 39	99.86 7	96.50 245	93.82 72	98.04 62	98.77 100	85.52 142	98.09 224	96.98 87	98.97 88	99.37 101

region2R		96.30 59	96.17 63	96.70 93	99.70 7	90.31 160	99.46 72	97.66 109	90.55 155	97.07 85	99.07 63	86.85 113	99.97 21	95.43 129	99.74 29	99.81 35

ACMMPR		96.28 60	96.14 67	96.73 90	99.68 9	90.47 156	99.47 68	97.80 79	90.54 156	96.83 94	99.03 70	86.51 127	99.95 32	95.65 121	99.72 32	99.75 49

CP-MVS		96.22 61	96.15 66	96.42 111	99.67 10	89.62 191	99.70 37	97.61 125	90.07 174	96.00 114	99.16 44	87.43 97	99.92 44	96.03 115	99.72 32	99.70 57

fmvsm_s_conf0.5_n		96.19 62	96.49 47	95.30 180	97.37 140	89.16 201	99.86 7	98.47 26	95.68 32	98.87 30	99.15 48	82.44 207	99.92 44	99.14 29	97.43 146	96.83 264

fmvsm_s_conf0.5_n_4		96.17 63	96.49 47	95.21 183	97.06 164	89.26 196	99.76 30	98.07 48	95.99 26	99.35 12	99.22 31	82.19 211	99.89 62	99.06 32	97.68 138	96.49 278

SR-MVS		96.13 64	96.16 65	96.07 135	99.42 47	89.04 205	98.59 193	97.33 179	90.44 159	96.84 92	99.12 56	86.75 115	99.41 141	97.47 75	99.44 60	99.76 48

ZNCC-MVS		96.09 65	95.81 77	96.95 79	99.42 47	91.19 131	99.55 57	97.53 143	89.72 183	95.86 120	98.94 87	86.59 122	99.97 21	95.13 137	99.56 52	99.68 62

MTAPA		96.09 65	95.80 78	96.96 78	99.29 55	91.19 131	97.23 309	97.45 160	92.58 100	94.39 151	99.24 28	86.43 129	99.99 5	96.22 105	99.40 64	99.71 55

GDP-MVS		96.05 67	95.63 87	97.31 57	95.37 243	94.65 50	99.36 89	96.42 250	92.14 114	97.07 85	98.53 120	93.33 19	98.50 194	91.76 202	96.66 164	98.78 162

ETV-MVS		96.00 68	96.00 68	96.00 140	96.56 181	91.05 139	99.63 50	96.61 235	93.26 86	97.39 76	98.30 138	86.62 121	98.13 221	98.07 64	97.57 140	98.82 157

MP-MVS		96.00 68	95.82 75	96.54 105	99.47 46	90.13 169	99.36 89	97.41 168	90.64 150	95.49 131	98.95 84	85.51 144	99.98 9	96.00 116	99.59 51	99.52 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SPE-MVS-test		95.98 70	96.34 54	94.90 198	98.06 109	87.66 246	99.69 44	96.10 278	93.66 76	98.35 52	99.05 68	86.28 131	97.66 267	96.96 88	98.90 94	99.37 101

fmvsm_s_conf0.5_n_a		95.97 71	96.19 58	95.31 178	96.51 185	89.01 209	99.81 18	98.39 29	95.46 37	99.19 21	99.16 44	81.44 223	99.91 51	98.83 39	96.97 156	97.01 260

GST-MVS		95.97 71	95.66 83	96.90 81	99.49 45	91.22 129	99.45 74	97.48 155	89.69 184	95.89 117	98.72 106	86.37 130	99.95 32	94.62 153	99.22 74	99.52 85

WTY-MVS		95.97 71	95.11 101	98.54 13	97.62 124	96.65 9	99.44 75	98.74 15	92.25 110	95.21 135	98.46 133	86.56 124	99.46 133	95.00 142	92.69 228	99.50 89

test_fmvsmconf0.1_n		95.94 74	95.79 79	96.40 113	92.42 349	89.92 178	99.79 25	96.85 220	96.53 22	97.22 80	98.67 112	82.71 197	99.84 80	98.92 38	98.98 87	99.43 97

PVSNet_Blended		95.94 74	95.66 83	96.75 88	98.77 89	91.61 124	99.88 5	98.04 52	93.64 78	94.21 154	97.76 157	83.50 174	99.87 68	97.41 76	97.75 137	98.79 160

mPP-MVS		95.90 76	95.75 80	96.38 115	99.58 30	89.41 195	99.26 101	97.41 168	90.66 147	94.82 141	98.95 84	86.15 135	99.98 9	95.24 136	99.64 42	99.74 50

NormalMVS		95.87 77	95.83 73	95.99 141	99.27 57	90.37 157	99.14 120	96.39 252	94.92 43	96.30 109	97.98 148	85.33 152	99.23 153	94.35 157	98.82 97	98.37 201

fmvsm_s_conf0.5_n_7		95.87 77	96.25 56	94.72 207	96.19 204	87.74 242	99.66 45	97.94 59	95.78 29	98.44 47	99.23 29	81.26 226	99.90 55	99.17 28	98.57 114	96.52 277

fmvsm_s_conf0.5_n_2		95.85 79	95.83 73	95.91 146	97.19 152	91.79 117	99.78 26	97.65 116	97.23 8	99.22 19	99.06 66	75.93 274	99.90 55	99.30 19	97.09 155	96.02 288

PGM-MVS		95.85 79	95.65 85	96.45 109	99.50 42	89.77 186	98.22 240	98.90 13	89.19 202	96.74 99	98.95 84	85.91 139	99.92 44	93.94 164	99.46 57	99.66 66

DP-MVS Recon		95.85 79	95.15 98	97.95 32	99.87 2	94.38 57	99.60 52	97.48 155	86.58 287	94.42 149	99.13 53	87.36 102	99.98 9	93.64 171	98.33 124	99.48 91

MP-MVS-pluss		95.80 82	95.30 92	97.29 58	98.95 79	92.66 98	98.59 193	97.14 197	88.95 210	93.12 178	99.25 26	85.62 141	99.94 35	96.56 100	99.48 56	99.28 111
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVS_111021_LR		95.78 83	95.94 69	95.28 181	98.19 105	87.69 243	98.80 160	99.26 7	93.39 83	95.04 139	98.69 111	84.09 168	99.76 101	96.96 88	99.06 82	98.38 197

alignmvs		95.77 84	95.00 105	98.06 29	97.35 141	95.68 20	99.71 36	97.50 152	91.50 125	96.16 113	98.61 118	86.28 131	99.00 168	96.19 106	91.74 253	99.51 87

EI-MVSNet-Vis-set		95.76 85	95.63 87	96.17 130	99.14 66	90.33 159	98.49 208	97.82 73	91.92 116	94.75 143	98.88 95	87.06 109	99.48 131	95.40 130	97.17 153	98.70 176

SR-MVS-dyc-post		95.75 86	95.86 72	95.41 171	99.22 61	87.26 262	98.40 220	97.21 188	89.63 186	96.67 102	98.97 76	86.73 118	99.36 145	96.62 96	99.31 67	99.60 77

CS-MVS		95.75 86	96.19 58	94.40 219	97.88 115	86.22 284	99.66 45	96.12 277	92.69 99	98.07 60	98.89 93	87.09 107	97.59 273	96.71 93	98.62 109	99.39 100

myMVS_eth3d28		95.74 88	95.34 91	96.92 80	97.41 136	93.58 71	99.28 98	97.70 97	90.97 140	93.91 162	97.25 191	90.59 48	98.75 182	96.85 92	94.14 208	98.44 192

MVSMamba_PlusPlus		95.73 89	95.15 98	97.44 48	97.28 147	94.35 59	98.26 237	96.75 227	83.09 352	97.84 68	95.97 260	89.59 65	98.48 199	97.86 68	99.73 31	99.49 90

UBG		95.73 89	95.41 89	96.69 94	96.97 168	93.23 80	99.13 125	97.79 81	91.28 133	94.38 152	96.78 231	92.37 30	98.56 193	96.17 108	93.84 212	98.26 208

dcpmvs_2		95.67 91	96.18 60	94.12 234	98.82 87	84.22 331	97.37 302	95.45 346	90.70 146	95.77 124	98.63 116	90.47 50	98.68 188	99.20 27	99.22 74	99.45 94

APD-MVS_3200maxsize		95.64 92	95.65 85	95.62 163	99.24 60	87.80 241	98.42 215	97.22 187	88.93 212	96.64 104	98.98 75	85.49 145	99.36 145	96.68 95	99.27 70	99.70 57

fmvsm_s_conf0.1_n		95.56 93	95.68 82	95.20 185	94.35 296	89.10 203	99.50 64	97.67 108	94.76 47	98.68 39	99.03 70	81.13 227	99.86 74	98.63 43	97.36 148	96.63 270

SymmetryMVS		95.49 94	95.27 94	96.17 130	97.13 158	90.37 157	99.14 120	98.59 23	94.92 43	96.30 109	97.98 148	85.33 152	99.23 153	94.35 157	93.67 218	98.92 146

test_fmvsmvis_n_1920		95.47 95	95.40 90	95.70 155	94.33 298	90.22 164	99.70 37	96.98 215	96.80 14	92.75 187	98.89 93	82.46 206	99.92 44	98.36 55	98.33 124	96.97 261

EI-MVSNet-UG-set		95.43 96	95.29 93	95.86 148	99.07 72	89.87 180	98.43 214	97.80 79	91.78 118	94.11 156	98.77 100	86.25 133	99.48 131	94.95 144	96.45 166	98.22 213

PAPM_NR		95.43 96	95.05 103	96.57 104	99.42 47	90.14 167	98.58 196	97.51 149	90.65 149	92.44 193	98.90 91	87.77 93	99.90 55	90.88 210	99.32 66	99.68 62

HPM-MVS		95.41 98	95.22 96	95.99 141	99.29 55	89.14 202	99.17 111	97.09 205	87.28 270	95.40 132	98.48 130	84.93 157	99.38 143	95.64 125	99.65 40	99.47 93
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
jason		95.40 99	94.86 107	97.03 70	92.91 343	94.23 60	99.70 37	96.30 260	93.56 80	96.73 100	98.52 122	81.46 222	97.91 241	96.08 113	98.47 120	98.96 138
jason: jason.
testing11		95.33 100	94.98 106	96.37 116	97.20 150	92.31 108	99.29 95	97.68 103	90.59 152	94.43 148	97.20 195	90.79 46	98.60 191	95.25 135	92.38 238	98.18 216

HY-MVS	88.56 7	95.29 101	94.23 118	98.48 14	97.72 119	96.41 13	94.03 393	98.74 15	92.42 105	95.65 128	94.76 286	86.52 126	99.49 127	95.29 134	92.97 224	99.53 84

test_yl		95.27 102	94.60 111	97.28 60	98.53 95	92.98 89	99.05 135	98.70 18	86.76 284	94.65 146	97.74 159	87.78 91	99.44 134	95.57 127	92.61 229	99.44 95

DCV-MVSNet		95.27 102	94.60 111	97.28 60	98.53 95	92.98 89	99.05 135	98.70 18	86.76 284	94.65 146	97.74 159	87.78 91	99.44 134	95.57 127	92.61 229	99.44 95

fmvsm_s_conf0.1_n_2		95.24 104	95.04 104	95.83 149	95.60 228	91.71 122	99.65 47	96.18 272	96.99 13	98.79 34	98.91 89	73.91 297	99.87 68	99.00 35	96.30 171	95.91 290

testing3-2		95.17 105	94.78 108	96.33 120	97.35 141	92.35 107	99.85 10	98.43 28	90.60 151	92.84 186	97.00 211	90.89 42	98.89 173	95.95 117	90.12 279	97.76 227

fmvsm_s_conf0.1_n_a		95.16 106	95.15 98	95.18 186	92.06 356	88.94 213	99.29 95	97.53 143	94.46 52	98.98 26	98.99 74	79.99 235	99.85 78	98.24 62	96.86 160	96.73 268

EIA-MVS		95.11 107	95.27 94	94.64 211	96.34 195	86.51 273	99.59 53	96.62 234	92.51 101	94.08 157	98.64 114	86.05 136	98.24 210	95.07 139	98.50 117	99.18 119

EC-MVSNet		95.09 108	95.17 97	94.84 201	95.42 238	88.17 233	99.48 66	95.92 300	91.47 126	97.34 78	98.36 135	82.77 193	97.41 284	97.24 81	98.58 113	98.94 143

VNet		95.08 109	94.26 117	97.55 47	98.07 108	93.88 66	98.68 176	98.73 17	90.33 162	97.16 84	97.43 179	79.19 246	99.53 124	96.91 90	91.85 251	99.24 114

sasdasda		95.02 110	93.96 131	98.20 21	97.53 131	95.92 17	98.71 171	96.19 270	91.78 118	95.86 120	98.49 127	79.53 241	99.03 166	96.12 110	91.42 265	99.66 66

canonicalmvs		95.02 110	93.96 131	98.20 21	97.53 131	95.92 17	98.71 171	96.19 270	91.78 118	95.86 120	98.49 127	79.53 241	99.03 166	96.12 110	91.42 265	99.66 66

MGCFI-Net		94.89 112	93.84 139	98.06 29	97.49 134	95.55 21	98.64 182	96.10 278	91.60 123	95.75 125	98.46 133	79.31 245	98.98 170	95.95 117	91.24 270	99.65 70

HPM-MVS_fast		94.89 112	94.62 110	95.70 155	99.11 68	88.44 231	99.14 120	97.11 201	85.82 304	95.69 127	98.47 131	83.46 176	99.32 150	93.16 183	99.63 45	99.35 104

testing91		94.88 114	94.44 114	96.21 126	97.19 152	91.90 116	99.23 103	97.66 109	89.91 177	93.66 168	97.05 209	90.21 57	98.50 194	93.52 173	91.53 262	98.25 209

testing99		94.88 114	94.45 113	96.17 130	97.20 150	91.91 115	99.20 105	97.66 109	89.95 176	93.68 167	97.06 207	90.28 56	98.50 194	93.52 173	91.54 259	98.12 219

CSCG		94.87 116	94.71 109	95.36 172	99.54 36	86.49 274	99.34 92	98.15 43	82.71 362	90.15 235	99.25 26	89.48 66	99.86 74	94.97 143	98.82 97	99.72 54

sss		94.85 117	93.94 133	97.58 44	96.43 188	94.09 64	98.93 147	99.16 8	89.50 194	95.27 134	97.85 150	81.50 220	99.65 113	92.79 190	94.02 210	98.99 135

test2506		94.80 118	94.21 119	96.58 102	96.41 191	92.18 111	98.01 262	98.96 11	90.82 144	93.46 173	97.28 187	85.92 137	98.45 200	89.82 223	97.19 151	99.12 125

API-MVS		94.78 119	94.18 122	96.59 101	99.21 63	90.06 174	98.80 160	97.78 84	83.59 344	93.85 164	99.21 34	83.79 171	99.97 21	92.37 195	99.00 86	99.74 50

thisisatest0515		94.75 120	94.19 120	96.43 110	96.13 211	92.64 101	99.47 68	97.60 127	87.55 265	93.17 177	97.59 169	94.71 12	98.42 201	88.28 244	93.20 221	98.24 212

xiu_mvs_v1_base_debu		94.73 121	93.98 128	96.99 73	95.19 251	95.24 27	98.62 185	96.50 245	92.99 92	97.52 72	98.83 97	72.37 312	99.15 158	97.03 84	96.74 161	96.58 273

xiu_mvs_v1_base		94.73 121	93.98 128	96.99 73	95.19 251	95.24 27	98.62 185	96.50 245	92.99 92	97.52 72	98.83 97	72.37 312	99.15 158	97.03 84	96.74 161	96.58 273

xiu_mvs_v1_base_debi		94.73 121	93.98 128	96.99 73	95.19 251	95.24 27	98.62 185	96.50 245	92.99 92	97.52 72	98.83 97	72.37 312	99.15 158	97.03 84	96.74 161	96.58 273

MVSFormer		94.71 124	94.08 125	96.61 99	95.05 269	94.87 39	97.77 277	96.17 274	86.84 280	98.04 62	98.52 122	85.52 142	95.99 356	89.83 221	98.97 88	98.96 138

PVSNet_Blended_VisFu		94.67 125	94.11 123	96.34 118	97.14 157	91.10 136	99.32 94	97.43 166	92.10 115	91.53 209	96.38 248	83.29 180	99.68 107	93.42 179	96.37 168	98.25 209

ACMMP		94.67 125	94.30 116	95.79 151	99.25 59	88.13 235	98.41 217	98.67 21	90.38 161	91.43 210	98.72 106	82.22 210	99.95 32	93.83 168	95.76 183	99.29 110
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
CPTT-MVS		94.60 127	94.43 115	95.09 190	99.66 12	86.85 267	99.44 75	97.47 157	83.22 349	94.34 153	98.96 81	82.50 201	99.55 121	94.81 146	99.50 55	98.88 149

diffmvs		94.59 128	94.19 120	95.81 150	95.54 232	90.69 149	98.70 174	95.68 330	91.61 121	95.96 115	97.81 152	80.11 233	98.06 229	96.52 101	95.76 183	98.67 180
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mvsany_test1		94.57 129	95.09 102	92.98 264	95.84 220	82.07 362	98.76 166	95.24 361	92.87 97	96.45 105	98.71 109	84.81 160	99.15 158	97.68 72	95.49 189	97.73 229

DeepC-MVS	91.02 4	94.56 130	93.92 134	96.46 108	97.16 156	90.76 147	98.39 225	97.11 201	93.92 64	88.66 258	98.33 136	78.14 260	99.85 78	95.02 140	98.57 114	98.78 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETVMVS		94.50 131	93.90 137	96.31 121	97.48 135	92.98 89	99.07 131	97.86 65	88.09 245	94.40 150	96.90 221	88.35 81	97.28 289	90.72 215	92.25 244	98.66 183

testing222		94.48 132	94.00 127	95.95 144	97.30 144	92.27 109	98.82 157	97.92 61	89.20 201	94.82 141	97.26 189	87.13 106	97.32 288	91.95 199	91.56 257	98.25 209

MAR-MVS		94.43 133	94.09 124	95.45 168	99.10 70	87.47 252	98.39 225	97.79 81	88.37 234	94.02 159	99.17 43	78.64 256	99.91 51	92.48 192	98.85 96	98.96 138
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
CHOSEN 1792x2688		94.35 134	93.82 140	95.95 144	97.40 137	88.74 223	98.41 217	98.27 33	92.18 112	91.43 210	96.40 245	78.88 247	99.81 90	93.59 172	97.81 133	99.30 109

CANet_DTU		94.31 135	93.35 153	97.20 64	97.03 167	94.71 48	98.62 185	95.54 339	95.61 34	97.21 81	98.47 131	71.88 317	99.84 80	88.38 243	97.46 145	97.04 258

diffmvs_AUTHOR		94.30 136	93.92 134	95.45 168	94.77 283	89.92 178	98.55 201	95.68 330	91.33 131	95.83 123	97.64 166	79.58 238	98.05 232	96.19 106	95.66 186	98.37 201

mvsmamba		94.27 137	93.91 136	95.35 174	96.42 189	88.61 225	97.77 277	96.38 255	91.17 137	94.05 158	95.27 278	78.41 258	97.96 240	97.36 78	98.40 121	99.48 91

PLC	91.07 3	94.23 138	94.01 126	94.87 199	99.17 65	87.49 251	99.25 102	96.55 242	88.43 232	91.26 214	98.21 143	85.92 137	99.86 74	89.77 225	97.57 140	97.24 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
guyue		94.21 139	93.72 143	95.66 158	95.22 248	90.17 166	98.74 167	96.85 220	93.67 75	93.01 182	96.72 235	78.83 250	98.06 229	96.04 114	94.44 203	98.77 164

test_fmvsmconf0.01_n		94.14 140	93.51 148	96.04 136	86.79 426	89.19 199	99.28 98	95.94 294	95.70 30	95.50 130	98.49 127	73.27 303	99.79 96	98.28 60	98.32 126	99.15 121

114514_t		94.06 141	93.05 162	97.06 69	99.08 71	92.26 110	98.97 145	97.01 213	82.58 364	92.57 191	98.22 141	80.68 230	99.30 151	89.34 231	99.02 85	99.63 74

baseline2		94.04 142	93.80 141	94.74 205	93.07 342	90.25 161	98.12 250	98.16 42	89.86 178	86.53 279	96.95 214	95.56 6	98.05 232	91.44 204	94.53 202	95.93 289

thisisatest0530		94.00 143	93.52 147	95.43 170	95.76 223	90.02 176	98.99 142	97.60 127	86.58 287	91.74 201	97.36 184	94.78 11	98.34 203	86.37 272	92.48 236	97.94 225

casdiffmvs_mvg		94.00 143	93.33 155	96.03 137	95.22 248	90.90 145	99.09 129	95.99 287	90.58 153	91.55 208	97.37 183	79.91 236	98.06 229	95.01 141	95.22 193	99.13 124
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		93.98 145	93.43 150	95.61 164	95.07 268	89.86 181	98.80 160	95.84 315	90.98 139	92.74 188	97.66 165	79.71 237	98.10 223	94.72 149	95.37 190	98.87 152
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		93.95 146	93.48 149	95.36 172	95.48 235	89.25 197	98.74 167	96.10 278	90.10 171	93.48 172	97.55 172	80.05 234	98.14 219	94.66 151	95.16 194	98.69 177

MVS		93.92 147	92.28 183	98.83 7	95.69 225	96.82 8	96.22 351	98.17 39	84.89 322	84.34 297	98.61 118	79.32 244	99.83 84	93.88 166	99.43 61	99.86 29

baseline		93.91 148	93.30 156	95.72 154	95.10 266	90.07 171	97.48 296	95.91 306	91.03 138	93.54 171	97.68 163	79.58 238	98.02 236	94.27 160	95.14 195	99.08 130

viewmanbaseed2359cas		93.90 149	93.34 154	95.56 166	95.39 241	89.72 187	98.58 196	96.00 286	90.32 163	93.58 170	97.78 155	78.71 254	98.07 227	94.43 156	95.29 191	98.88 149

OMC-MVS		93.90 149	93.62 145	94.73 206	98.63 93	87.00 265	98.04 261	96.56 241	92.19 111	92.46 192	98.73 104	79.49 243	99.14 162	92.16 197	94.34 207	98.03 221

Effi-MVS+		93.87 151	93.15 160	96.02 138	95.79 221	90.76 147	96.70 333	95.78 317	86.98 277	95.71 126	97.17 199	79.58 238	98.01 237	94.57 154	96.09 178	99.31 108

test_cas_vis1_n_1920		93.86 152	93.74 142	94.22 230	95.39 241	86.08 294	99.73 33	96.07 283	96.38 24	97.19 83	97.78 155	65.46 371	99.86 74	96.71 93	98.92 92	96.73 268

TESTMET0.1,1		93.82 153	93.26 158	95.49 167	95.21 250	90.25 161	99.15 117	97.54 142	89.18 203	91.79 200	94.87 284	89.13 68	97.63 270	86.21 274	96.29 173	98.60 185

AdaColmap		93.82 153	93.06 161	96.10 134	99.88 1	89.07 204	98.33 231	97.55 138	86.81 282	90.39 231	98.65 113	75.09 284	99.98 9	93.32 180	97.53 143	99.26 113

EPP-MVSNet		93.75 155	93.67 144	94.01 240	95.86 219	85.70 306	98.67 178	97.66 109	84.46 329	91.36 213	97.18 198	91.16 34	97.79 253	92.93 186	93.75 216	98.53 187

thres200		93.69 156	92.59 177	96.97 77	97.76 118	94.74 46	99.35 91	99.36 2	89.23 200	91.21 216	96.97 213	83.42 177	98.77 179	85.08 286	90.96 271	97.39 244

PVSNet	87.13 12	93.69 156	92.83 170	96.28 123	97.99 111	90.22 164	99.38 85	98.93 12	91.42 129	93.66 168	97.68 163	71.29 324	99.64 115	87.94 249	97.20 150	98.98 136

HyFIR lowres test		93.68 158	93.29 157	94.87 199	97.57 130	88.04 237	98.18 244	98.47 26	87.57 264	91.24 215	95.05 282	85.49 145	97.46 280	93.22 182	92.82 225	99.10 128

MVS_Test		93.67 159	92.67 173	96.69 94	96.72 178	92.66 98	97.22 310	96.03 285	87.69 262	95.12 138	94.03 294	81.55 218	98.28 207	89.17 237	96.46 165	99.14 122

CNLPA		93.64 160	92.74 171	96.36 117	98.96 78	90.01 177	99.19 106	95.89 309	86.22 295	89.40 252	98.85 96	80.66 231	99.84 80	88.57 241	96.92 158	99.24 114

PMMVS		93.62 161	93.90 137	92.79 270	96.79 176	81.40 368	98.85 154	96.81 222	91.25 134	96.82 95	98.15 145	77.02 270	98.13 221	93.15 184	96.30 171	98.83 156

CDS-MVSNet		93.47 162	93.04 163	94.76 203	94.75 284	89.45 194	98.82 157	97.03 210	87.91 252	90.97 217	96.48 243	89.06 69	96.36 330	89.50 227	92.81 227	98.49 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt13		93.45 163	92.86 169	95.21 183	95.45 236	88.91 217	98.59 193	95.92 300	89.39 199	92.67 190	97.33 185	78.02 262	98.03 235	93.27 181	95.12 196	98.69 177

1314		93.44 164	91.98 191	97.84 34	95.24 246	94.38 57	96.22 351	97.92 61	90.18 167	82.28 325	97.71 162	77.63 265	99.80 92	91.94 200	98.67 107	99.34 106

tfpn200view9		93.43 165	92.27 184	96.90 81	97.68 121	94.84 41	99.18 108	99.36 2	88.45 229	90.79 219	96.90 221	83.31 178	98.75 182	84.11 303	90.69 273	97.12 253

3Dnovator+	87.72 8	93.43 165	91.84 196	98.17 23	95.73 224	95.08 35	98.92 149	97.04 208	91.42 129	81.48 345	97.60 168	74.60 287	99.79 96	90.84 211	98.97 88	99.64 71

RRT-MVS		93.39 167	92.64 174	95.64 159	96.11 212	88.75 222	97.40 298	95.77 319	89.46 196	92.70 189	95.42 275	72.98 306	98.81 177	96.91 90	96.97 156	99.37 101

thres400		93.39 167	92.27 184	96.73 90	97.68 121	94.84 41	99.18 108	99.36 2	88.45 229	90.79 219	96.90 221	83.31 178	98.75 182	84.11 303	90.69 273	96.61 271

AstraMVS		93.38 169	93.01 164	94.50 214	93.94 313	86.55 271	98.91 150	95.86 313	93.88 68	92.88 184	97.49 175	75.61 282	98.21 214	96.15 109	92.39 237	98.73 173

PVSNet_BlendedMVS		93.36 170	93.20 159	93.84 246	98.77 89	91.61 124	99.47 68	98.04 52	91.44 127	94.21 154	92.63 328	83.50 174	99.87 68	97.41 76	83.37 323	90.05 393

thres100view900		93.34 171	92.15 187	96.90 81	97.62 124	94.84 41	99.06 134	99.36 2	87.96 250	90.47 229	96.78 231	83.29 180	98.75 182	84.11 303	90.69 273	97.12 253

tttt0517		93.30 172	93.01 164	94.17 232	95.57 230	86.47 275	98.51 205	97.60 127	85.99 300	90.55 226	97.19 197	94.80 10	98.31 204	85.06 287	91.86 250	97.74 228

UA-Net		93.30 172	92.62 176	95.34 175	96.27 198	88.53 229	95.88 362	96.97 216	90.90 141	95.37 133	97.07 206	82.38 208	99.10 164	83.91 307	94.86 199	98.38 197

test-mter		93.27 174	92.89 168	94.40 219	94.94 275	87.27 260	99.15 117	97.25 182	88.95 210	91.57 205	94.04 292	88.03 89	97.58 274	85.94 278	96.13 176	98.36 204

Vis-MVSNet (Re-imp)		93.26 175	93.00 166	94.06 237	96.14 208	86.71 270	98.68 176	96.70 229	88.30 238	89.71 248	97.64 166	85.43 148	96.39 328	88.06 248	96.32 169	99.08 130

UWE-MVS		93.18 176	93.40 152	92.50 280	96.56 181	83.55 340	98.09 256	97.84 68	89.50 194	91.72 202	96.23 251	91.08 37	96.70 311	86.28 273	93.33 220	97.26 250

thres600view7		93.18 176	92.00 190	96.75 88	97.62 124	94.92 36	99.07 131	99.36 2	87.96 250	90.47 229	96.78 231	83.29 180	98.71 187	82.93 319	90.47 277	96.61 271

3Dnovator	87.35 11	93.17 178	91.77 199	97.37 55	95.41 239	93.07 86	98.82 157	97.85 66	91.53 124	82.56 318	97.58 170	71.97 316	99.82 87	91.01 208	99.23 73	99.22 117

viewmacassd2359aftdt		93.16 179	92.44 180	95.31 178	94.34 297	89.19 199	98.40 220	95.84 315	89.62 188	92.87 185	97.31 186	76.07 272	98.00 238	92.93 186	94.58 201	98.75 167

LuminaMVS		93.16 179	92.30 182	95.76 152	92.26 351	92.64 101	97.60 294	96.21 267	90.30 164	93.06 180	95.59 270	76.00 273	97.89 243	94.93 145	94.70 200	96.76 265

test-LLR		93.11 181	92.68 172	94.40 219	94.94 275	87.27 260	99.15 117	97.25 182	90.21 165	91.57 205	94.04 292	84.89 158	97.58 274	85.94 278	96.13 176	98.36 204

test_vis1_n_1920		93.08 182	93.42 151	92.04 290	96.31 196	79.36 387	99.83 13	96.06 284	96.72 16	98.53 45	98.10 146	58.57 399	99.91 51	97.86 68	98.79 103	96.85 263

KinetiMVS		93.07 183	91.98 191	96.34 118	94.84 280	91.78 118	98.73 170	97.18 193	91.25 134	94.01 160	97.09 205	71.02 325	98.86 174	86.77 265	96.89 159	98.37 201

viewmambaseed2359dif		93.05 184	92.64 174	94.25 227	94.94 275	86.53 272	98.38 227	95.69 329	87.03 273	93.38 174	97.74 159	78.79 252	98.08 226	93.49 176	94.35 206	98.15 218

IS-MVSNet		93.00 185	92.51 178	94.49 215	96.14 208	87.36 256	98.31 234	95.70 327	88.58 225	90.17 234	97.50 174	83.02 187	97.22 290	87.06 256	96.07 180	98.90 148

CostFormer		92.89 186	92.48 179	94.12 234	94.99 272	85.89 301	92.89 405	97.00 214	86.98 277	95.00 140	90.78 365	90.05 60	97.51 278	92.92 188	91.73 254	98.96 138

tpmrst		92.78 187	92.16 186	94.65 209	96.27 198	87.45 253	91.83 415	97.10 204	89.10 206	94.68 145	90.69 369	88.22 83	97.73 264	89.78 224	91.80 252	98.77 164

MVSTER		92.71 188	92.32 181	93.86 245	97.29 145	92.95 92	99.01 140	96.59 237	90.09 172	85.51 287	94.00 296	94.61 15	96.56 317	90.77 214	83.03 325	92.08 330

1112_ss		92.71 188	91.55 203	96.20 127	95.56 231	91.12 134	98.48 210	94.69 382	88.29 239	86.89 276	98.50 124	87.02 110	98.66 189	84.75 291	89.77 282	98.81 158

Vis-MVSNet		92.64 190	91.85 195	95.03 195	95.12 258	88.23 232	98.48 210	96.81 222	91.61 121	92.16 198	97.22 194	71.58 322	98.00 238	85.85 281	97.81 133	98.88 149
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		92.62 191	92.09 189	94.20 231	94.10 305	87.68 244	98.41 217	96.97 216	87.53 266	89.74 246	96.04 258	84.77 162	96.49 323	88.97 239	92.31 241	98.42 193

baseline1		92.61 192	91.28 209	96.58 102	97.05 166	94.63 51	97.72 282	96.20 268	89.82 179	88.56 259	96.85 225	86.85 113	97.82 249	88.42 242	80.10 342	97.30 248

EPMVS		92.59 193	91.59 202	95.59 165	97.22 149	90.03 175	91.78 416	98.04 52	90.42 160	91.66 204	90.65 372	86.49 128	97.46 280	81.78 330	96.31 170	99.28 111

ET-MVSNet_ETH3D		92.56 194	91.45 205	95.88 147	96.39 193	94.13 63	99.46 72	96.97 216	92.18 112	66.94 437	98.29 139	94.65 14	94.28 402	94.34 159	83.82 318	99.24 114

mvs_anonymous		92.50 195	91.65 201	95.06 192	96.60 180	89.64 190	97.06 317	96.44 249	86.64 286	84.14 298	93.93 299	82.49 202	96.17 348	91.47 203	96.08 179	99.35 104

h-mvs33		92.47 196	91.95 193	94.05 238	97.13 158	85.01 320	98.36 229	98.08 47	93.85 70	96.27 111	96.73 234	83.19 183	99.43 137	95.81 119	68.09 416	97.70 233

test_fmvs1		92.35 197	92.94 167	90.57 322	97.19 152	75.43 417	99.55 57	94.97 371	95.20 40	96.82 95	97.57 171	59.59 397	99.84 80	97.30 79	98.29 127	96.46 280

SSM_0404		92.33 198	91.33 207	95.33 177	95.35 244	90.54 154	97.45 297	95.49 342	86.17 296	90.26 233	97.13 201	75.65 279	97.82 249	89.26 235	95.26 192	97.63 237

BH-w/o		92.32 199	91.79 198	93.91 244	96.85 171	86.18 290	99.11 128	95.74 321	88.13 243	84.81 291	97.00 211	77.26 267	97.91 241	89.16 238	98.03 129	97.64 234

ECVR-MVS		92.29 200	91.33 207	95.15 187	96.41 191	87.84 240	98.10 253	94.84 375	90.82 144	91.42 212	97.28 187	65.61 368	98.49 198	90.33 217	97.19 151	99.12 125

EPNet_dtu		92.28 201	92.15 187	92.70 276	97.29 145	84.84 323	98.64 182	97.82 73	92.91 95	93.02 181	97.02 210	85.48 147	95.70 371	72.25 400	94.89 198	97.55 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		92.27 202	90.97 218	96.18 128	95.53 233	91.10 136	98.47 212	94.66 383	88.28 240	86.83 277	93.50 312	87.00 111	98.65 190	84.69 292	89.74 283	98.80 159

LFMVS		92.23 203	90.84 223	96.42 111	98.24 102	91.08 138	98.24 239	96.22 266	83.39 347	94.74 144	98.31 137	61.12 392	98.85 175	94.45 155	92.82 225	99.32 107

FA-MVS(test-final)		92.22 204	91.08 214	95.64 159	96.05 213	88.98 210	91.60 419	97.25 182	86.99 274	91.84 199	92.12 332	83.03 186	99.00 168	86.91 261	93.91 211	98.93 144

test1111		92.12 205	91.19 211	94.94 197	96.15 206	87.36 256	98.12 250	94.84 375	90.85 143	90.97 217	97.26 189	65.60 369	98.37 202	89.74 226	97.14 154	99.07 132

IB-MVS	89.43 6	92.12 205	90.83 225	95.98 143	95.40 240	90.78 146	99.81 18	98.06 49	91.23 136	85.63 286	93.66 307	90.63 47	98.78 178	91.22 205	71.85 405	98.36 204
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
reproduce_monomvs		92.11 207	91.82 197	92.98 264	98.25 100	90.55 153	98.38 227	97.93 60	94.81 45	80.46 355	92.37 330	96.46 3	97.17 291	94.06 162	73.61 387	91.23 361

F-COLMAP		92.07 208	91.75 200	93.02 263	98.16 106	82.89 350	98.79 164	95.97 289	86.54 289	87.92 263	97.80 153	78.69 255	99.65 113	85.97 276	95.93 182	96.53 276

PatchmatchNet		92.05 209	91.04 215	95.06 192	96.17 205	89.04 205	91.26 424	97.26 181	89.56 192	90.64 223	90.56 378	88.35 81	97.11 294	79.53 343	96.07 180	99.03 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SSM_0407		92.04 210	91.03 216	95.07 191	95.12 258	89.81 183	97.18 313	95.49 342	86.17 296	89.50 249	97.13 201	75.65 279	97.68 265	89.26 235	93.79 213	97.73 229

IMVS_0403		91.93 211	91.13 212	94.34 222	94.61 289	86.22 284	96.70 333	95.72 322	88.78 216	90.00 240	96.93 217	78.07 261	98.07 227	86.73 266	92.59 231	98.74 168

UGNet		91.91 212	90.85 222	95.10 189	97.06 164	88.69 224	98.01 262	98.24 36	92.41 106	92.39 195	93.61 308	60.52 394	99.68 107	88.14 246	97.25 149	96.92 262
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_0407		91.79 213	90.98 217	94.24 229	94.61 289	86.22 284	96.45 340	95.72 322	88.78 216	89.76 244	96.93 217	77.24 268	97.77 255	86.73 266	92.59 231	98.74 168

tpm2		91.77 214	91.09 213	93.82 247	94.83 281	85.56 309	92.51 410	97.16 196	84.00 335	93.83 165	90.66 371	87.54 95	97.17 291	87.73 251	91.55 258	98.72 174

Fast-Effi-MVS+		91.72 215	90.79 226	94.49 215	95.89 217	87.40 255	99.54 62	95.70 327	85.01 320	89.28 254	95.68 269	77.75 264	97.57 277	83.22 314	95.06 197	98.51 188

hse-mvs2		91.67 216	91.51 204	92.15 287	96.22 200	82.61 358	97.74 281	97.53 143	93.85 70	96.27 111	96.15 253	83.19 183	97.44 282	95.81 119	66.86 423	96.40 282

icg_test_0407_2		91.56 217	90.90 221	93.54 252	94.61 289	86.22 284	95.72 369	95.72 322	88.78 216	89.76 244	96.93 217	77.24 268	95.65 372	86.73 266	92.59 231	98.74 168

HQP-MVS		91.50 218	91.23 210	92.29 282	93.95 310	86.39 278	99.16 112	96.37 256	93.92 64	87.57 266	96.67 238	73.34 300	97.77 255	93.82 169	86.29 295	92.72 310

PatchMatch-RL		91.47 219	90.54 230	94.26 226	98.20 103	86.36 280	96.94 321	97.14 197	87.75 258	88.98 255	95.75 267	71.80 319	99.40 142	80.92 335	97.39 147	97.02 259

BH-untuned		91.46 220	90.84 223	93.33 258	96.51 185	84.83 324	98.84 156	95.50 341	86.44 294	83.50 302	96.70 236	75.49 283	97.77 255	86.78 264	97.81 133	97.40 243

mamv4		91.41 221	93.57 146	84.91 405	97.11 161	58.11 453	95.68 371	95.93 298	82.09 374	89.78 243	95.71 268	90.09 59	98.24 210	97.26 80	98.50 117	98.38 197

QAPM		91.41 221	89.49 247	97.17 66	95.66 227	93.42 77	98.60 191	97.51 149	80.92 388	81.39 346	97.41 180	72.89 309	99.87 68	82.33 324	98.68 106	98.21 214

FE-MVS		91.38 223	90.16 236	95.05 194	96.46 187	87.53 250	89.69 433	97.84 68	82.97 355	92.18 197	92.00 338	84.07 169	98.93 172	80.71 337	95.52 188	98.68 179

WBMVS		91.35 224	90.49 231	93.94 242	96.97 168	93.40 78	99.27 100	96.71 228	87.40 268	83.10 310	91.76 344	92.38 29	96.23 344	88.95 240	77.89 352	92.17 326

HQP_MVS		91.26 225	90.95 219	92.16 286	93.84 318	86.07 296	99.02 138	96.30 260	93.38 84	86.99 273	96.52 240	72.92 307	97.75 262	93.46 177	86.17 298	92.67 312

PCF-MVS	89.78 5	91.26 225	89.63 244	96.16 133	95.44 237	91.58 126	95.29 375	96.10 278	85.07 317	82.75 312	97.45 178	78.28 259	99.78 99	80.60 339	95.65 187	97.12 253
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 227	89.99 237	95.03 195	96.75 177	88.55 227	98.65 180	94.95 372	87.74 259	87.74 265	97.80 153	68.27 345	98.14 219	80.53 340	97.49 144	98.41 194

VDD-MVS		91.24 228	90.18 235	94.45 218	97.08 163	85.84 304	98.40 220	96.10 278	86.99 274	93.36 175	98.16 144	54.27 418	99.20 155	96.59 99	90.63 276	98.31 207

SDMVSNet		91.09 229	89.91 238	94.65 209	96.80 174	90.54 154	97.78 275	97.81 77	88.34 236	85.73 283	95.26 279	66.44 363	98.26 208	94.25 161	86.75 292	95.14 294

test_fmvs1_n		91.07 230	91.41 206	90.06 336	94.10 305	74.31 421	99.18 108	94.84 375	94.81 45	96.37 108	97.46 177	50.86 431	99.82 87	97.14 83	97.90 131	96.04 287

CLD-MVS		91.06 231	90.71 227	92.10 288	94.05 309	86.10 293	99.55 57	96.29 263	94.16 59	84.70 292	97.17 199	69.62 334	97.82 249	94.74 148	86.08 300	92.39 315
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ab-mvs		91.05 232	89.17 254	96.69 94	95.96 216	91.72 121	92.62 409	97.23 186	85.61 308	89.74 246	93.89 301	68.55 342	99.42 138	91.09 206	87.84 287	98.92 146

UWE-MVS-28		90.99 233	91.93 194	88.15 372	95.12 258	77.87 404	97.18 313	97.79 81	88.72 221	88.69 257	96.52 240	86.54 125	90.75 435	84.64 294	92.16 248	95.83 291

XVG-OURS-SEG-HR		90.95 234	90.66 229	91.83 293	95.18 254	81.14 375	95.92 359	95.92 300	88.40 233	90.33 232	97.85 150	70.66 328	99.38 143	92.83 189	88.83 284	94.98 297

cascas		90.93 235	89.33 251	95.76 152	95.69 225	93.03 88	98.99 142	96.59 237	80.49 390	86.79 278	94.45 289	65.23 373	98.60 191	93.52 173	92.18 245	95.66 293

XVG-OURS		90.83 236	90.49 231	91.86 292	95.23 247	81.25 372	95.79 367	95.92 300	88.96 209	90.02 239	98.03 147	71.60 321	99.35 148	91.06 207	87.78 288	94.98 297

TR-MVS		90.77 237	89.44 248	94.76 203	96.31 196	88.02 238	97.92 266	95.96 291	85.52 309	88.22 262	97.23 193	66.80 359	98.09 224	84.58 295	92.38 238	98.17 217

OpenMVS	85.28 14	90.75 238	88.84 265	96.48 107	93.58 327	93.51 75	98.80 160	97.41 168	82.59 363	78.62 376	97.49 175	68.00 349	99.82 87	84.52 297	98.55 116	96.11 286

FIs		90.70 239	89.87 239	93.18 260	92.29 350	91.12 134	98.17 246	98.25 34	89.11 205	83.44 303	94.82 285	82.26 209	96.17 348	87.76 250	82.76 327	92.25 320

MonoMVSNet		90.69 240	89.78 240	93.45 255	91.78 364	84.97 322	96.51 338	94.44 387	90.56 154	85.96 282	90.97 361	78.61 257	96.27 343	95.35 131	83.79 319	99.11 127

X-MVStestdata		90.69 240	88.66 270	96.77 86	99.62 22	90.66 151	99.43 79	97.58 133	92.41 106	96.86 90	29.59 470	87.37 99	99.87 68	95.65 121	99.43 61	99.78 41

mamba_0408		90.65 242	89.16 255	95.12 188	95.12 258	89.81 183	83.02 452	95.17 368	85.95 301	89.50 249	96.85 225	75.85 275	97.82 249	87.19 254	93.79 213	97.73 229

SCA		90.64 243	89.25 253	94.83 202	94.95 274	88.83 218	96.26 348	97.21 188	90.06 175	90.03 238	90.62 374	66.61 360	96.81 307	83.16 315	94.36 205	98.84 153

Elysia		90.62 244	88.95 261	95.64 159	93.08 340	91.94 113	97.65 289	96.39 252	84.72 325	90.59 224	95.95 261	62.22 385	98.23 212	83.69 310	96.23 174	96.74 266

StellarMVS		90.62 244	88.95 261	95.64 159	93.08 340	91.94 113	97.65 289	96.39 252	84.72 325	90.59 224	95.95 261	62.22 385	98.23 212	83.69 310	96.23 174	96.74 266

GeoE		90.60 246	89.56 245	93.72 251	95.10 266	85.43 310	99.41 82	94.94 373	83.96 337	87.21 272	96.83 230	74.37 291	97.05 298	80.50 341	93.73 217	98.67 180

viewmsd2359difaftdt		90.43 247	89.65 242	92.74 273	93.72 324	82.67 354	98.09 256	95.27 356	89.80 181	90.12 236	97.40 181	69.43 336	98.20 215	92.45 194	80.62 337	97.34 245

viewdifsd2359ckpt11		90.42 248	89.65 242	92.73 275	93.71 325	82.67 354	98.09 256	95.27 356	89.80 181	90.10 237	97.40 181	69.43 336	98.18 217	92.46 193	80.61 338	97.34 245

test_vis1_n		90.40 249	90.27 234	90.79 317	91.55 368	76.48 411	99.12 127	94.44 387	94.31 55	97.34 78	96.95 214	43.60 443	99.42 138	97.57 74	97.60 139	96.47 279

TAPA-MVS	87.50 9	90.35 250	89.05 259	94.25 227	98.48 97	85.17 317	98.42 215	96.58 240	82.44 369	87.24 271	98.53 120	82.77 193	98.84 176	59.09 443	97.88 132	98.72 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
miper_enhance_ethall		90.33 251	89.70 241	92.22 283	97.12 160	88.93 215	98.35 230	95.96 291	88.60 224	83.14 309	92.33 331	87.38 98	96.18 346	86.49 271	77.89 352	91.55 347

SSM_04072		90.31 252	89.16 255	93.74 249	95.12 258	89.81 183	83.02 452	95.17 368	85.95 301	89.50 249	96.85 225	75.85 275	93.69 408	87.19 254	93.79 213	97.73 229

CVMVSNet		90.30 253	90.91 220	88.46 371	94.32 299	73.58 425	97.61 292	97.59 131	90.16 170	88.43 261	97.10 203	76.83 271	92.86 416	82.64 321	93.54 219	98.93 144

nrg030		90.23 254	88.87 264	94.32 224	91.53 369	93.54 74	98.79 164	95.89 309	88.12 244	84.55 294	94.61 288	78.80 251	96.88 304	92.35 196	75.21 369	92.53 314

FC-MVSNet-test		90.22 255	89.40 249	92.67 278	91.78 364	89.86 181	97.89 267	98.22 37	88.81 215	82.96 311	94.66 287	81.90 216	95.96 358	85.89 280	82.52 330	92.20 325

LS3D		90.19 256	88.72 268	94.59 213	98.97 75	86.33 281	96.90 323	96.60 236	74.96 419	84.06 300	98.74 103	75.78 278	99.83 84	74.93 377	97.57 140	97.62 238

VortexMVS		90.18 257	89.28 252	92.89 268	95.58 229	90.94 144	97.82 272	95.94 294	90.90 141	82.11 332	91.48 350	78.75 253	96.08 352	91.99 198	78.97 346	91.65 338

AUN-MVS		90.17 258	89.50 246	92.19 285	96.21 201	82.67 354	97.76 280	97.53 143	88.05 246	91.67 203	96.15 253	83.10 185	97.47 279	88.11 247	66.91 422	96.43 281

dp		90.16 259	88.83 266	94.14 233	96.38 194	86.42 276	91.57 420	97.06 207	84.76 324	88.81 256	90.19 390	84.29 166	97.43 283	75.05 376	91.35 268	98.56 186

GA-MVS		90.10 260	88.69 269	94.33 223	92.44 348	87.97 239	99.08 130	96.26 264	89.65 185	86.92 275	93.11 320	68.09 347	96.96 300	82.54 323	90.15 278	98.05 220

VDDNet		90.08 261	88.54 276	94.69 208	94.41 295	87.68 244	98.21 242	96.40 251	76.21 412	93.33 176	97.75 158	54.93 416	98.77 179	94.71 150	90.96 271	97.61 239

gg-mvs-nofinetune		90.00 262	87.71 288	96.89 85	96.15 206	94.69 49	85.15 443	97.74 88	68.32 441	92.97 183	60.16 458	96.10 4	96.84 305	93.89 165	98.87 95	99.14 122

Effi-MVS+-dtu		89.97 263	90.68 228	87.81 376	95.15 255	71.98 432	97.87 270	95.40 350	91.92 116	87.57 266	91.44 351	74.27 293	96.84 305	89.45 228	93.10 223	94.60 300

EI-MVSNet		89.87 264	89.38 250	91.36 304	94.32 299	85.87 302	97.61 292	96.59 237	85.10 315	85.51 287	97.10 203	81.30 225	96.56 317	83.85 309	83.03 325	91.64 339

IMVS_0404		89.79 265	88.57 274	93.47 254	94.61 289	86.22 284	94.45 383	95.72 322	88.78 216	81.88 337	96.93 217	65.39 372	95.47 378	86.73 266	92.59 231	98.74 168

OPM-MVS		89.76 266	89.15 257	91.57 301	90.53 381	85.58 308	98.11 252	95.93 298	92.88 96	86.05 280	96.47 244	67.06 358	97.87 246	89.29 234	86.08 300	91.26 360
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpm		89.67 267	88.95 261	91.82 294	92.54 347	81.43 367	92.95 404	95.92 300	87.81 255	90.50 228	89.44 399	84.99 156	95.65 372	83.67 312	82.71 328	98.38 197

UniMVSNet_NR-MVSNet		89.60 268	88.55 275	92.75 272	92.17 354	90.07 171	98.74 167	98.15 43	88.37 234	83.21 305	93.98 297	82.86 189	95.93 360	86.95 259	72.47 399	92.25 320

cl22		89.57 269	88.79 267	91.91 291	97.94 113	87.62 247	97.98 264	96.51 244	85.03 318	82.37 324	91.79 341	83.65 172	96.50 321	85.96 277	77.89 352	91.61 344

PS-MVSNAJss		89.54 270	89.05 259	91.00 310	88.77 403	84.36 329	97.39 299	95.97 289	88.47 226	81.88 337	93.80 303	82.48 203	96.50 321	89.34 231	83.34 324	92.15 327

UniMVSNet (Re)		89.50 271	88.32 279	93.03 262	92.21 353	90.96 142	98.90 152	98.39 29	89.13 204	83.22 304	92.03 334	81.69 217	96.34 336	86.79 263	72.53 398	91.81 335

sd_testset		89.23 272	88.05 285	92.74 273	96.80 174	85.33 313	95.85 365	97.03 210	88.34 236	85.73 283	95.26 279	61.12 392	97.76 261	85.61 282	86.75 292	95.14 294

tpmvs		89.16 273	87.76 286	93.35 257	97.19 152	84.75 325	90.58 431	97.36 176	81.99 375	84.56 293	89.31 402	83.98 170	98.17 218	74.85 379	90.00 281	97.12 253

VPA-MVSNet		89.10 274	87.66 289	93.45 255	92.56 346	91.02 140	97.97 265	98.32 32	86.92 279	86.03 281	92.01 336	68.84 341	97.10 296	90.92 209	75.34 368	92.23 322

ADS-MVSNet		88.99 275	87.30 294	94.07 236	96.21 201	87.56 249	87.15 437	96.78 225	83.01 353	89.91 241	87.27 416	78.87 248	97.01 299	74.20 384	92.27 242	97.64 234

test0.0.03 1		88.96 276	88.61 271	90.03 340	91.09 375	84.43 328	98.97 145	97.02 212	90.21 165	80.29 357	96.31 250	84.89 158	91.93 430	72.98 394	85.70 303	93.73 302

miper_ehance_all_eth		88.94 277	88.12 283	91.40 302	95.32 245	86.93 266	97.85 271	95.55 338	84.19 332	81.97 335	91.50 349	84.16 167	95.91 363	84.69 292	77.89 352	91.36 355

tpm cat1		88.89 278	87.27 295	93.76 248	95.79 221	85.32 314	90.76 429	97.09 205	76.14 413	85.72 285	88.59 405	82.92 188	98.04 234	76.96 362	91.43 264	97.90 226

LPG-MVS_test		88.86 279	88.47 277	90.06 336	93.35 335	80.95 377	98.22 240	95.94 294	87.73 260	83.17 307	96.11 255	66.28 364	97.77 255	90.19 219	85.19 305	91.46 350

Anonymous202405211		88.84 280	87.03 300	94.27 225	98.14 107	84.18 332	98.44 213	95.58 337	76.79 410	89.34 253	96.88 224	53.42 422	99.54 123	87.53 253	87.12 291	99.09 129

Fast-Effi-MVS+-dtu		88.84 280	88.59 273	89.58 351	93.44 333	78.18 398	98.65 180	94.62 384	88.46 228	84.12 299	95.37 277	68.91 339	96.52 320	82.06 327	91.70 255	94.06 301

DU-MVS		88.83 282	87.51 290	92.79 270	91.46 370	90.07 171	98.71 171	97.62 124	88.87 214	83.21 305	93.68 305	74.63 285	95.93 360	86.95 259	72.47 399	92.36 316

CR-MVSNet		88.83 282	87.38 293	93.16 261	93.47 330	86.24 282	84.97 445	94.20 396	88.92 213	90.76 221	86.88 420	84.43 164	94.82 394	70.64 404	92.17 246	98.41 194

FMVSNet3		88.81 284	87.08 298	93.99 241	96.52 184	94.59 52	98.08 259	96.20 268	85.85 303	82.12 328	91.60 347	74.05 295	95.40 382	79.04 347	80.24 339	91.99 333

ACMM	86.95 13	88.77 285	88.22 281	90.43 327	93.61 326	81.34 370	98.50 206	95.92 300	87.88 253	83.85 301	95.20 281	67.20 356	97.89 243	86.90 262	84.90 307	92.06 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		88.75 286	86.56 306	95.34 175	98.92 83	87.45 253	97.64 291	93.52 407	70.55 432	81.49 344	97.25 191	74.43 290	99.88 64	71.14 403	94.09 209	98.67 180

ACMP	87.39 10	88.71 287	88.24 280	90.12 335	93.91 316	81.06 376	98.50 206	95.67 332	89.43 197	80.37 356	95.55 271	65.67 366	97.83 248	90.55 216	84.51 309	91.47 349
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
WB-MVSnew		88.69 288	88.34 278	89.77 346	94.30 303	85.99 299	98.14 247	97.31 180	87.15 272	87.85 264	96.07 257	69.91 329	95.52 376	72.83 396	91.47 263	87.80 419

dmvs_re		88.69 288	88.06 284	90.59 321	93.83 320	78.68 394	95.75 368	96.18 272	87.99 249	84.48 296	96.32 249	67.52 353	96.94 302	84.98 289	85.49 304	96.14 285

myMVS_eth3d		88.68 290	89.07 258	87.50 380	95.14 256	79.74 385	97.68 285	96.66 231	86.52 290	82.63 315	96.84 228	85.22 155	89.89 440	69.43 410	91.54 259	92.87 308

LCM-MVSNet-Re		88.59 291	88.61 271	88.51 370	95.53 233	72.68 430	96.85 325	88.43 450	88.45 229	73.14 411	90.63 373	75.82 277	94.38 401	92.95 185	95.71 185	98.48 191

WR-MVS		88.54 292	87.22 297	92.52 279	91.93 361	89.50 193	98.56 198	97.84 68	86.99 274	81.87 339	93.81 302	74.25 294	95.92 362	85.29 284	74.43 378	92.12 328

IterMVS-LS		88.34 293	87.44 291	91.04 309	94.10 305	85.85 303	98.10 253	95.48 344	85.12 314	82.03 333	91.21 357	81.35 224	95.63 374	83.86 308	75.73 366	91.63 340
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPNet		88.30 294	86.57 305	93.49 253	91.95 359	91.35 128	98.18 244	97.20 192	88.61 223	84.52 295	94.89 283	62.21 387	96.76 310	89.34 231	72.26 402	92.36 316

MSDG		88.29 295	86.37 308	94.04 239	96.90 170	86.15 292	96.52 337	94.36 393	77.89 405	79.22 371	96.95 214	69.72 332	99.59 119	73.20 393	92.58 235	96.37 283

test_djsdf		88.26 296	87.73 287	89.84 343	88.05 413	82.21 360	97.77 277	96.17 274	86.84 280	82.41 323	91.95 340	72.07 315	95.99 356	89.83 221	84.50 310	91.32 357

c3_l		88.19 297	87.23 296	91.06 308	94.97 273	86.17 291	97.72 282	95.38 351	83.43 346	81.68 343	91.37 352	82.81 192	95.72 370	84.04 306	73.70 386	91.29 359

D2MVS		87.96 298	87.39 292	89.70 348	91.84 363	83.40 342	98.31 234	98.49 24	88.04 247	78.23 382	90.26 384	73.57 298	96.79 309	84.21 300	83.53 321	88.90 411

cl____		87.82 299	86.79 304	90.89 314	94.88 278	85.43 310	97.81 273	95.24 361	82.91 360	80.71 351	91.22 356	81.97 215	95.84 365	81.34 332	75.06 370	91.40 354

DIV-MVS_self_test		87.82 299	86.81 303	90.87 315	94.87 279	85.39 312	97.81 273	95.22 366	82.92 359	80.76 350	91.31 355	81.99 213	95.81 367	81.36 331	75.04 371	91.42 353

eth_miper_zixun_eth		87.76 301	87.00 301	90.06 336	94.67 286	82.65 357	97.02 320	95.37 352	84.19 332	81.86 341	91.58 348	81.47 221	95.90 364	83.24 313	73.61 387	91.61 344

testing3		87.75 302	88.22 281	86.36 391	94.66 287	77.41 406	99.52 63	97.95 58	86.05 299	81.12 347	96.69 237	86.18 134	89.31 444	61.65 437	90.12 279	92.35 319

TranMVSNet+NR-MVSNet		87.75 302	86.31 309	92.07 289	90.81 378	88.56 226	98.33 231	97.18 193	87.76 257	81.87 339	93.90 300	72.45 311	95.43 380	83.13 317	71.30 409	92.23 322

XXY-MVS		87.75 302	86.02 313	92.95 267	90.46 382	89.70 189	97.71 284	95.90 307	84.02 334	80.95 348	94.05 291	67.51 354	97.10 296	85.16 285	78.41 349	92.04 332

NR-MVSNet		87.74 305	86.00 314	92.96 266	91.46 370	90.68 150	96.65 335	97.42 167	88.02 248	73.42 408	93.68 305	77.31 266	95.83 366	84.26 299	71.82 406	92.36 316

Anonymous20240529		87.66 306	85.58 320	93.92 243	97.59 128	85.01 320	98.13 248	97.13 199	66.69 446	88.47 260	96.01 259	55.09 414	99.51 125	87.00 258	84.12 314	97.23 252

ADS-MVSNet2		87.62 307	86.88 302	89.86 342	96.21 201	79.14 390	87.15 437	92.99 410	83.01 353	89.91 241	87.27 416	78.87 248	92.80 419	74.20 384	92.27 242	97.64 234

pmmvs4		87.58 308	86.17 312	91.80 295	89.58 393	88.92 216	97.25 307	95.28 355	82.54 365	80.49 353	93.17 319	75.62 281	96.05 354	82.75 320	78.90 347	90.42 384

jajsoiax		87.35 309	86.51 307	89.87 341	87.75 420	81.74 364	97.03 318	95.98 288	88.47 226	80.15 359	93.80 303	61.47 389	96.36 330	89.44 229	84.47 311	91.50 348

PVSNet_0	83.28 16	87.31 310	85.16 326	93.74 249	94.78 282	84.59 326	98.91 150	98.69 20	89.81 180	78.59 378	93.23 317	61.95 388	99.34 149	94.75 147	55.72 449	97.30 248

v2v482		87.27 311	85.76 317	91.78 299	89.59 392	87.58 248	98.56 198	95.54 339	84.53 328	82.51 319	91.78 342	73.11 304	96.47 324	82.07 326	74.14 384	91.30 358

mvs_tets		87.09 312	86.22 310	89.71 347	87.87 416	81.39 369	96.73 332	95.90 307	88.19 242	79.99 361	93.61 308	59.96 396	96.31 338	89.40 230	84.34 312	91.43 352

V42		87.00 313	85.68 319	90.98 311	89.91 386	86.08 294	98.32 233	95.61 335	83.67 343	82.72 313	90.67 370	74.00 296	96.53 319	81.94 329	74.28 381	90.32 386

miper_lstm_enhance		86.90 314	86.20 311	89.00 365	94.53 293	81.19 373	96.74 331	95.24 361	82.33 370	80.15 359	90.51 381	81.99 213	94.68 398	80.71 337	73.58 389	91.12 364

FMVSNet2		86.90 314	84.79 334	93.24 259	95.11 263	92.54 104	97.67 287	95.86 313	82.94 356	80.55 352	91.17 358	62.89 382	95.29 384	77.23 359	79.71 345	91.90 334

v1144		86.83 316	85.31 325	91.40 302	89.75 390	87.21 264	98.31 234	95.45 346	83.22 349	82.70 314	90.78 365	73.36 299	96.36 330	79.49 344	74.69 375	90.63 381

SD_0403		86.82 317	87.08 298	86.04 395	93.55 328	69.09 441	94.11 392	95.02 370	87.84 254	80.48 354	95.86 265	73.05 305	91.04 434	72.53 398	91.26 269	97.99 224

MS-PatchMatch		86.75 318	85.92 315	89.22 359	91.97 357	82.47 359	96.91 322	96.14 276	83.74 340	77.73 384	93.53 311	58.19 401	97.37 287	76.75 365	98.35 123	87.84 417

anonymousdsp		86.69 319	85.75 318	89.53 352	86.46 428	82.94 347	96.39 342	95.71 326	83.97 336	79.63 366	90.70 368	68.85 340	95.94 359	86.01 275	84.02 315	89.72 399

GBi-Net		86.67 320	84.96 328	91.80 295	95.11 263	88.81 219	96.77 327	95.25 358	82.94 356	82.12 328	90.25 385	62.89 382	94.97 389	79.04 347	80.24 339	91.62 341

test1		86.67 320	84.96 328	91.80 295	95.11 263	88.81 219	96.77 327	95.25 358	82.94 356	82.12 328	90.25 385	62.89 382	94.97 389	79.04 347	80.24 339	91.62 341

MVP-Stereo		86.61 322	85.83 316	88.93 367	88.70 405	83.85 337	96.07 356	94.41 392	82.15 373	75.64 396	91.96 339	67.65 352	96.45 326	77.20 361	98.72 105	86.51 429
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CP-MVSNet		86.54 323	85.45 323	89.79 345	91.02 377	82.78 353	97.38 301	97.56 137	85.37 311	79.53 368	93.03 321	71.86 318	95.25 385	79.92 342	73.43 393	91.34 356

WR-MVS_H		86.53 324	85.49 322	89.66 350	91.04 376	83.31 344	97.53 295	98.20 38	84.95 321	79.64 365	90.90 363	78.01 263	95.33 383	76.29 369	72.81 395	90.35 385

tt0805		86.50 325	84.79 334	91.63 300	91.97 357	81.49 366	96.49 339	97.38 172	82.24 371	82.44 320	95.82 266	51.22 428	98.25 209	84.55 296	80.96 336	95.13 296

v144192		86.40 326	84.89 331	90.91 312	89.48 396	85.59 307	98.21 242	95.43 349	82.45 368	82.62 317	90.58 377	72.79 310	96.36 330	78.45 354	74.04 385	90.79 373

v148		86.38 327	85.06 327	90.37 331	89.47 397	84.10 333	98.52 202	95.48 344	83.80 339	80.93 349	90.22 388	74.60 287	96.31 338	80.92 335	71.55 407	90.69 379

v1192		86.32 328	84.71 336	91.17 306	89.53 395	86.40 277	98.13 248	95.44 348	82.52 366	82.42 322	90.62 374	71.58 322	96.33 337	77.23 359	74.88 372	90.79 373

Patchmatch-test		86.25 329	84.06 346	92.82 269	94.42 294	82.88 351	82.88 454	94.23 395	71.58 428	79.39 369	90.62 374	89.00 71	96.42 327	63.03 433	91.37 267	99.16 120

v8		86.11 330	84.45 341	91.10 307	89.99 385	86.85 267	97.24 308	95.36 353	81.99 375	79.89 363	89.86 394	74.53 289	96.39 328	78.83 351	72.32 401	90.05 393

v1921920		86.02 331	84.44 342	90.77 318	89.32 398	85.20 315	98.10 253	95.35 354	82.19 372	82.25 326	90.71 367	70.73 326	96.30 341	76.85 364	74.49 377	90.80 372

JIA-IIPM		85.97 332	84.85 332	89.33 358	93.23 337	73.68 424	85.05 444	97.13 199	69.62 437	91.56 207	68.03 456	88.03 89	96.96 300	77.89 357	93.12 222	97.34 245

pmmvs5		85.87 333	84.40 344	90.30 332	88.53 407	84.23 330	98.60 191	93.71 403	81.53 380	80.29 357	92.02 335	64.51 375	95.52 376	82.04 328	78.34 350	91.15 363

XVG-ACMP-BASELINE		85.86 334	84.95 330	88.57 369	89.90 387	77.12 408	94.30 387	95.60 336	87.40 268	82.12 328	92.99 323	53.42 422	97.66 267	85.02 288	83.83 316	90.92 369

Baseline_NR-MVSNet		85.83 335	84.82 333	88.87 368	88.73 404	83.34 343	98.63 184	91.66 428	80.41 393	82.44 320	91.35 353	74.63 285	95.42 381	84.13 302	71.39 408	87.84 417

PS-CasMVS		85.81 336	84.58 339	89.49 355	90.77 379	82.11 361	97.20 311	97.36 176	84.83 323	79.12 373	92.84 324	67.42 355	95.16 387	78.39 355	73.25 394	91.21 362

IterMVS		85.81 336	84.67 337	89.22 359	93.51 329	83.67 339	96.32 345	94.80 378	85.09 316	78.69 374	90.17 391	66.57 362	93.17 415	79.48 345	77.42 359	90.81 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		85.77 338	84.11 345	90.73 319	89.26 399	85.15 318	97.88 269	95.23 365	81.89 378	82.16 327	90.55 379	69.60 335	96.31 338	75.59 374	74.87 373	90.72 378

IterMVS-SCA-FT		85.73 339	84.64 338	89.00 365	93.46 332	82.90 349	96.27 346	94.70 381	85.02 319	78.62 376	90.35 383	66.61 360	93.33 412	79.38 346	77.36 360	90.76 375

v10		85.73 339	84.01 347	90.87 315	90.03 384	86.73 269	97.20 311	95.22 366	81.25 383	79.85 364	89.75 395	73.30 302	96.28 342	76.87 363	72.64 397	89.61 401

UniMVSNet_ETH3D		85.65 341	83.79 350	91.21 305	90.41 383	80.75 380	95.36 373	95.78 317	78.76 399	81.83 342	94.33 290	49.86 434	96.66 312	84.30 298	83.52 322	96.22 284

PatchT		85.44 342	83.19 353	92.22 283	93.13 339	83.00 346	83.80 451	96.37 256	70.62 431	90.55 226	79.63 448	84.81 160	94.87 392	58.18 445	91.59 256	98.79 160

RPSCF		85.33 343	85.55 321	84.67 408	94.63 288	62.28 448	93.73 395	93.76 401	74.38 422	85.23 290	97.06 207	64.09 376	98.31 204	80.98 333	86.08 300	93.41 306

SSC-MVS3.2		85.22 344	83.90 349	89.17 361	91.87 362	79.84 384	97.66 288	96.63 233	86.81 282	81.99 334	91.35 353	55.80 407	96.00 355	76.52 368	76.53 363	91.67 337

PEN-MVS		85.21 345	83.93 348	89.07 364	89.89 388	81.31 371	97.09 316	97.24 185	84.45 330	78.66 375	92.68 327	68.44 344	94.87 392	75.98 371	70.92 410	91.04 366

test_fmvs2		85.10 346	85.45 323	84.02 411	89.85 389	65.63 446	98.49 208	92.59 415	90.45 158	85.43 289	93.32 313	43.94 441	96.59 315	90.81 212	84.19 313	89.85 397

RPMNet		85.07 347	81.88 366	94.64 211	93.47 330	86.24 282	84.97 445	97.21 188	64.85 448	90.76 221	78.80 449	80.95 229	99.27 152	53.76 450	92.17 246	98.41 194

AllTest		84.97 348	83.12 354	90.52 325	96.82 172	78.84 392	95.89 360	92.17 420	77.96 403	75.94 392	95.50 272	55.48 410	99.18 156	71.15 401	87.14 289	93.55 304

USDC		84.74 349	82.93 355	90.16 334	91.73 366	83.54 341	95.00 378	93.30 409	88.77 220	73.19 410	93.30 315	53.62 421	97.65 269	75.88 372	81.54 334	89.30 404

Anonymous20231211		84.72 350	82.65 362	90.91 312	97.71 120	84.55 327	97.28 305	96.67 230	66.88 445	79.18 372	90.87 364	58.47 400	96.60 314	82.61 322	74.20 382	91.59 346

pm-mvs1		84.68 351	82.78 359	90.40 328	89.58 393	85.18 316	97.31 303	94.73 380	81.93 377	76.05 391	92.01 336	65.48 370	96.11 351	78.75 352	69.14 413	89.91 396

ACMH	83.09 17	84.60 352	82.61 363	90.57 322	93.18 338	82.94 347	96.27 346	94.92 374	81.01 386	72.61 417	93.61 308	56.54 405	97.79 253	74.31 382	81.07 335	90.99 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	81.71 19	84.59 353	82.72 361	90.18 333	92.89 344	83.18 345	93.15 402	94.74 379	78.99 396	75.14 399	92.69 326	65.64 367	97.63 270	69.46 409	81.82 333	89.74 398
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
COLMAP_ROB	82.69 18	84.54 354	82.82 356	89.70 348	96.72 178	78.85 391	95.89 360	92.83 413	71.55 429	77.54 386	95.89 264	59.40 398	99.14 162	67.26 420	88.26 285	91.11 365
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		84.48 355	81.83 367	92.42 281	91.73 366	87.36 256	85.52 440	94.42 391	81.40 381	81.91 336	87.58 410	51.92 425	92.81 418	73.84 388	88.15 286	97.08 257

our_test_3		84.47 356	82.80 357	89.50 353	89.01 400	83.90 336	97.03 318	94.56 385	81.33 382	75.36 398	90.52 380	71.69 320	94.54 400	68.81 414	76.84 361	90.07 391

v7n		84.42 357	82.75 360	89.43 357	88.15 411	81.86 363	96.75 330	95.67 332	80.53 389	78.38 380	89.43 400	69.89 330	96.35 335	73.83 389	72.13 403	90.07 391

kuosan		84.40 358	83.34 352	87.60 378	95.87 218	79.21 388	92.39 411	96.87 219	76.12 414	73.79 405	93.98 297	81.51 219	90.63 436	64.13 429	75.42 367	92.95 307

ACMH+	83.78 15	84.21 359	82.56 365	89.15 362	93.73 323	79.16 389	96.43 341	94.28 394	81.09 385	74.00 404	94.03 294	54.58 417	97.67 266	76.10 370	78.81 348	90.63 381

EU-MVSNet		84.19 360	84.42 343	83.52 416	88.64 406	67.37 444	96.04 357	95.76 320	85.29 312	78.44 379	93.18 318	70.67 327	91.48 432	75.79 373	75.98 364	91.70 336

DTE-MVSNet		84.14 361	82.80 357	88.14 373	88.95 402	79.87 383	96.81 326	96.24 265	83.50 345	77.60 385	92.52 329	67.89 351	94.24 403	72.64 397	69.05 414	90.32 386

OurMVSNet-221017-0		84.13 362	83.59 351	85.77 399	87.81 417	70.24 437	94.89 379	93.65 405	86.08 298	76.53 387	93.28 316	61.41 390	96.14 350	80.95 334	77.69 358	90.93 368

Syy-MVS		84.10 363	84.53 340	82.83 418	95.14 256	65.71 445	97.68 285	96.66 231	86.52 290	82.63 315	96.84 228	68.15 346	89.89 440	45.62 456	91.54 259	92.87 308

FMVSNet1		83.94 364	81.32 373	91.80 295	91.94 360	88.81 219	96.77 327	95.25 358	77.98 401	78.25 381	90.25 385	50.37 433	94.97 389	73.27 392	77.81 357	91.62 341

mmtdpeth		83.69 365	82.59 364	86.99 386	92.82 345	76.98 409	96.16 354	91.63 429	82.89 361	92.41 194	82.90 433	54.95 415	98.19 216	96.27 104	53.27 452	85.81 433

tfpnnormal		83.65 366	81.35 372	90.56 324	91.37 372	88.06 236	97.29 304	97.87 64	78.51 400	76.20 389	90.91 362	64.78 374	96.47 324	61.71 436	73.50 390	87.13 426

ppachtmachnet_test		83.63 367	81.57 370	89.80 344	89.01 400	85.09 319	97.13 315	94.50 386	78.84 397	76.14 390	91.00 360	69.78 331	94.61 399	63.40 431	74.36 379	89.71 400

Patchmtry		83.61 368	81.64 368	89.50 353	93.36 334	82.84 352	84.10 448	94.20 396	69.47 438	79.57 367	86.88 420	84.43 164	94.78 395	68.48 416	74.30 380	90.88 370

KD-MVS_2432*1600		82.98 369	80.52 378	90.38 329	94.32 299	88.98 210	92.87 406	95.87 311	80.46 391	73.79 405	87.49 413	82.76 195	93.29 413	70.56 405	46.53 460	88.87 412

miper_refine_blended		82.98 369	80.52 378	90.38 329	94.32 299	88.98 210	92.87 406	95.87 311	80.46 391	73.79 405	87.49 413	82.76 195	93.29 413	70.56 405	46.53 460	88.87 412

SixPastTwentyTwo		82.63 371	81.58 369	85.79 398	88.12 412	71.01 435	95.17 376	92.54 416	84.33 331	72.93 415	92.08 333	60.41 395	95.61 375	74.47 381	74.15 383	90.75 376

testgi		82.29 372	81.00 375	86.17 393	87.24 423	74.84 420	97.39 299	91.62 430	88.63 222	75.85 395	95.42 275	46.07 440	91.55 431	66.87 423	79.94 343	92.12 328

FMVSNet5		82.29 372	80.54 377	87.52 379	93.79 322	84.01 334	93.73 395	92.47 417	76.92 408	74.27 402	86.15 424	63.69 380	89.24 445	69.07 412	74.79 374	89.29 405

TransMVSNet (Re)		81.97 374	79.61 384	89.08 363	89.70 391	84.01 334	97.26 306	91.85 426	78.84 397	73.07 414	91.62 346	67.17 357	95.21 386	67.50 419	59.46 443	88.02 416

LF4IMVS		81.94 375	81.17 374	84.25 410	87.23 424	68.87 443	93.35 401	91.93 425	83.35 348	75.40 397	93.00 322	49.25 437	96.65 313	78.88 350	78.11 351	87.22 425

Patchmatch-RL test		81.90 376	80.13 380	87.23 383	80.71 446	70.12 439	84.07 449	88.19 451	83.16 351	70.57 420	82.18 438	87.18 105	92.59 421	82.28 325	62.78 432	98.98 136

DSMNet-mixed		81.60 377	81.43 371	82.10 421	84.36 435	60.79 449	93.63 397	86.74 454	79.00 395	79.32 370	87.15 418	63.87 378	89.78 442	66.89 422	91.92 249	95.73 292

dongtai		81.36 378	80.61 376	83.62 414	94.25 304	73.32 426	95.15 377	96.81 222	73.56 425	69.79 423	92.81 325	81.00 228	86.80 452	52.08 453	70.06 412	90.75 376

test_vis1_rt		81.31 379	80.05 382	85.11 402	91.29 373	70.66 436	98.98 144	77.39 466	85.76 306	68.80 428	82.40 436	36.56 453	99.44 134	92.67 191	86.55 294	85.24 440

K. test v3		81.04 380	79.77 383	84.83 406	87.41 421	70.23 438	95.60 372	93.93 400	83.70 342	67.51 435	89.35 401	55.76 408	93.58 411	76.67 366	68.03 417	90.67 380

Anonymous20231206		80.76 381	79.42 385	84.79 407	84.78 434	72.98 427	96.53 336	92.97 411	79.56 394	74.33 401	88.83 403	61.27 391	92.15 427	60.59 439	75.92 365	89.24 406

CMPMVS	58.40 21	80.48 382	80.11 381	81.59 424	85.10 433	59.56 451	94.14 391	95.95 293	68.54 440	60.71 447	93.31 314	55.35 413	97.87 246	83.06 318	84.85 308	87.33 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		80.42 383	77.94 388	87.85 375	92.09 355	78.58 395	93.74 394	89.94 442	74.99 418	69.77 424	91.78 342	46.09 439	97.58 274	65.17 428	77.89 352	87.38 421

EG-PatchMatch MVS		79.92 384	77.59 390	86.90 387	87.06 425	77.90 403	96.20 353	94.06 398	74.61 420	66.53 439	88.76 404	40.40 449	96.20 345	67.02 421	83.66 320	86.61 427

pmmvs6		79.90 385	77.31 392	87.67 377	84.17 436	78.13 400	95.86 364	93.68 404	67.94 442	72.67 416	89.62 397	50.98 430	95.75 368	74.80 380	66.04 424	89.14 407

CL-MVSNet_self_test		79.89 386	78.34 387	84.54 409	81.56 444	75.01 418	96.88 324	95.62 334	81.10 384	75.86 394	85.81 425	68.49 343	90.26 438	63.21 432	56.51 447	88.35 414

ttmdpeth		79.80 387	77.91 389	85.47 401	83.34 439	75.75 414	95.32 374	91.45 433	76.84 409	74.81 400	91.71 345	53.98 420	94.13 404	72.42 399	61.29 436	86.51 429

MDA-MVSNet_test_wron		79.65 388	77.05 393	87.45 381	87.79 419	80.13 381	96.25 349	94.44 387	73.87 423	51.80 454	87.47 415	68.04 348	92.12 428	66.02 424	67.79 419	90.09 389

YYNet1		79.64 389	77.04 394	87.43 382	87.80 418	79.98 382	96.23 350	94.44 387	73.83 424	51.83 453	87.53 411	67.96 350	92.07 429	66.00 425	67.75 420	90.23 388

MVS-HIRNet		79.01 390	75.13 403	90.66 320	93.82 321	81.69 365	85.16 442	93.75 402	54.54 454	74.17 403	59.15 460	57.46 403	96.58 316	63.74 430	94.38 204	93.72 303

UnsupCasMVSNet_eth		78.90 391	76.67 396	85.58 400	82.81 442	74.94 419	91.98 414	96.31 259	84.64 327	65.84 442	87.71 409	51.33 427	92.23 426	72.89 395	56.50 448	89.56 402

test_0402		78.81 392	76.33 397	86.26 392	91.18 374	78.44 397	95.88 362	91.34 434	68.55 439	70.51 422	89.91 393	52.65 424	94.99 388	47.14 455	79.78 344	85.34 439

pmmvs-eth3d		78.71 393	76.16 398	86.38 390	80.25 449	81.19 373	94.17 390	92.13 422	77.97 402	66.90 438	82.31 437	55.76 408	92.56 422	73.63 391	62.31 435	85.38 437

Anonymous20240521		78.63 394	76.90 395	83.82 412	82.82 441	72.86 428	95.72 369	93.57 406	73.55 426	72.17 418	84.79 429	49.69 435	92.51 423	65.29 427	74.50 376	86.09 432

sc_t1		78.53 395	74.87 405	89.48 356	87.92 415	77.36 407	94.80 380	90.61 439	57.65 451	76.28 388	89.59 398	38.25 450	96.18 346	74.04 386	64.72 429	94.91 299

test20.03		78.51 396	77.48 391	81.62 423	83.07 440	71.03 434	96.11 355	92.83 413	81.66 379	69.31 427	89.68 396	57.53 402	87.29 451	58.65 444	68.47 415	86.53 428

mvs5depth		78.17 397	75.56 400	85.97 396	80.43 448	76.44 412	85.46 441	89.24 447	76.39 411	78.17 383	88.26 406	51.73 426	95.73 369	69.31 411	61.09 437	85.73 434

TDRefinement		78.01 398	75.31 401	86.10 394	70.06 461	73.84 423	93.59 398	91.58 431	74.51 421	73.08 413	91.04 359	49.63 436	97.12 293	74.88 378	59.47 442	87.33 423

OpenMVS_ROB	73.86 20	77.99 399	75.06 404	86.77 389	83.81 438	77.94 402	96.38 343	91.53 432	67.54 443	68.38 430	87.13 419	43.94 441	96.08 352	55.03 449	81.83 332	86.29 431

MDA-MVSNet-bldmvs		77.82 400	74.75 406	87.03 384	88.33 409	78.52 396	96.34 344	92.85 412	75.57 416	48.87 456	87.89 408	57.32 404	92.49 424	60.79 438	64.80 428	90.08 390

KD-MVS_self_test		77.47 401	75.88 399	82.24 419	81.59 443	68.93 442	92.83 408	94.02 399	77.03 407	73.14 411	83.39 432	55.44 412	90.42 437	67.95 417	57.53 446	87.38 421

dmvs_testset		77.17 402	78.99 386	71.71 434	87.25 422	38.55 471	91.44 421	81.76 462	85.77 305	69.49 426	95.94 263	69.71 333	84.37 454	52.71 452	76.82 362	92.21 324

tt0320		76.58 403	73.16 411	86.86 388	88.03 414	77.60 405	93.55 400	90.63 438	55.37 453	70.93 419	84.98 427	41.57 445	94.01 405	69.02 413	64.32 430	88.97 409

MVStest1		76.56 404	73.43 409	85.96 397	86.30 430	80.88 379	94.26 388	91.74 427	61.98 450	58.53 449	89.96 392	69.30 338	91.47 433	59.26 442	49.56 458	85.52 436

new_pmnet		76.02 405	73.71 408	82.95 417	83.88 437	72.85 429	91.26 424	92.26 419	70.44 433	62.60 445	81.37 441	47.64 438	92.32 425	61.85 435	72.10 404	83.68 446

tt0320-xc		75.92 406	72.23 415	87.01 385	88.40 408	78.15 399	93.57 399	89.15 448	55.46 452	69.66 425	85.79 426	38.20 451	93.85 406	69.72 408	60.08 441	89.03 408

MIMVSNet1		75.92 406	73.30 410	83.81 413	81.29 445	75.57 416	92.26 412	92.05 423	73.09 427	67.48 436	86.18 423	40.87 448	87.64 450	55.78 447	70.68 411	88.21 415

mvsany_test3		75.85 408	74.52 407	79.83 426	73.53 458	60.64 450	91.73 417	87.87 453	83.91 338	70.55 421	82.52 435	31.12 455	93.66 409	86.66 270	62.83 431	85.19 441

test_fmvs3		75.09 409	75.19 402	74.81 431	77.45 454	54.08 457	95.93 358	90.64 437	82.51 367	73.29 409	81.19 442	22.29 460	86.29 453	85.50 283	67.89 418	84.06 444

FE-MVSNET		75.08 410	72.25 414	83.56 415	77.93 453	76.96 410	94.36 385	87.96 452	75.72 415	66.01 441	81.60 440	50.48 432	88.85 446	55.38 448	60.82 438	84.86 443

PM-MVS		74.88 411	72.85 412	80.98 425	78.98 451	64.75 447	90.81 428	85.77 455	80.95 387	68.23 432	82.81 434	29.08 457	92.84 417	76.54 367	62.46 434	85.36 438

new-patchmatchnet		74.80 412	72.40 413	81.99 422	78.36 452	72.20 431	94.44 384	92.36 418	77.06 406	63.47 444	79.98 447	51.04 429	88.85 446	60.53 440	54.35 450	84.92 442

UnsupCasMVSNet_bld		73.85 413	70.14 417	84.99 404	79.44 450	75.73 415	88.53 434	95.24 361	70.12 435	61.94 446	74.81 453	41.41 447	93.62 410	68.65 415	51.13 456	85.62 435

pmmvs3		72.86 414	69.76 419	82.17 420	73.86 457	74.19 422	94.20 389	89.01 449	64.23 449	67.72 433	80.91 445	41.48 446	88.65 448	62.40 434	54.02 451	83.68 446

test_f		71.94 415	70.82 416	75.30 430	72.77 459	53.28 458	91.62 418	89.66 445	75.44 417	64.47 443	78.31 450	20.48 461	89.56 443	78.63 353	66.02 425	83.05 449

N_pmnet		70.19 416	69.87 418	71.12 436	88.24 410	30.63 475	95.85 365	28.70 474	70.18 434	68.73 429	86.55 422	64.04 377	93.81 407	53.12 451	73.46 391	88.94 410

test_method		70.10 417	68.66 420	74.41 433	86.30 430	55.84 455	94.47 382	89.82 443	35.18 462	66.15 440	84.75 430	30.54 456	77.96 463	70.40 407	60.33 440	89.44 403

APD_test1		68.93 418	66.98 421	74.77 432	80.62 447	53.15 459	87.97 435	85.01 457	53.76 455	59.26 448	87.52 412	25.19 458	89.95 439	56.20 446	67.33 421	81.19 450

WB-MVS		66.44 419	66.29 422	66.89 439	74.84 455	44.93 466	93.00 403	84.09 460	71.15 430	55.82 451	81.63 439	63.79 379	80.31 461	21.85 465	50.47 457	75.43 452

SSC-MVS		65.42 420	65.20 423	66.06 440	73.96 456	43.83 467	92.08 413	83.54 461	69.77 436	54.73 452	80.92 444	63.30 381	79.92 462	20.48 466	48.02 459	74.44 453

FPMVS		61.57 421	60.32 424	65.34 441	60.14 468	42.44 469	91.02 427	89.72 444	44.15 457	42.63 460	80.93 443	19.02 462	80.59 460	42.50 457	72.76 396	73.00 454

test_vis3_rt		61.29 422	58.75 425	68.92 438	67.41 462	52.84 460	91.18 426	59.23 473	66.96 444	41.96 461	58.44 461	11.37 469	94.72 397	74.25 383	57.97 445	59.20 460

EGC-MVSNET		60.70 423	55.37 427	76.72 428	86.35 429	71.08 433	89.96 432	84.44 459	0.38 471	1.50 472	84.09 431	37.30 452	88.10 449	40.85 460	73.44 392	70.97 456

LCM-MVSNet		60.07 424	56.37 426	71.18 435	54.81 470	48.67 463	82.17 455	89.48 446	37.95 460	49.13 455	69.12 454	13.75 468	81.76 455	59.28 441	51.63 455	83.10 448

PMMVS2		58.97 425	55.07 428	70.69 437	62.72 465	55.37 456	85.97 439	80.52 463	49.48 456	45.94 457	68.31 455	15.73 466	80.78 459	49.79 454	37.12 462	75.91 451

testf1		56.38 426	53.73 429	64.31 443	64.84 463	45.11 464	80.50 456	75.94 468	38.87 458	42.74 458	75.07 451	11.26 470	81.19 457	41.11 458	53.27 452	66.63 457

APD_test2		56.38 426	53.73 429	64.31 443	64.84 463	45.11 464	80.50 456	75.94 468	38.87 458	42.74 458	75.07 451	11.26 470	81.19 457	41.11 458	53.27 452	66.63 457

Gipuma		54.77 428	52.22 432	62.40 445	86.50 427	59.37 452	50.20 463	90.35 441	36.52 461	41.20 462	49.49 463	18.33 464	81.29 456	32.10 462	65.34 426	46.54 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 429	52.86 431	56.05 446	32.75 474	41.97 470	73.42 460	76.12 467	21.91 467	39.68 463	96.39 247	42.59 444	65.10 466	78.00 356	14.92 467	61.08 459

ANet_high		50.71 430	46.17 433	64.33 442	44.27 472	52.30 461	76.13 459	78.73 464	64.95 447	27.37 465	55.23 462	14.61 467	67.74 465	36.01 461	18.23 465	72.95 455

PMVS	41.42 23	45.67 431	42.50 434	55.17 447	34.28 473	32.37 473	66.24 461	78.71 465	30.72 463	22.04 468	59.59 459	4.59 472	77.85 464	27.49 463	58.84 444	55.29 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 432	37.64 437	53.90 448	49.46 471	43.37 468	65.09 462	66.66 470	26.19 466	25.77 467	48.53 464	3.58 474	63.35 467	26.15 464	27.28 463	54.97 462
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 433	40.93 435	41.29 449	61.97 466	33.83 472	84.00 450	65.17 471	27.17 464	27.56 464	46.72 465	17.63 465	60.41 468	19.32 467	18.82 464	29.61 464

EMVS		39.96 434	39.88 436	40.18 450	59.57 469	32.12 474	84.79 447	64.57 472	26.27 465	26.14 466	44.18 468	18.73 463	59.29 469	17.03 468	17.67 466	29.12 465

cdsmvs_eth3d_5k		22.52 435	30.03 438	0.00 454	0.00 477	0.00 479	0.00 465	97.17 195	0.00 472	0.00 473	98.77 100	74.35 292	0.00 473	0.00 472	0.00 471	0.00 469

testmvs		18.81 436	23.05 439	6.10 453	4.48 475	2.29 478	97.78 275	3.00 476	3.27 469	18.60 469	62.71 457	1.53 476	2.49 472	14.26 470	1.80 469	13.50 467

wuyk23d		16.71 437	16.73 441	16.65 451	60.15 467	25.22 476	41.24 464	5.17 475	6.56 468	5.48 471	3.61 471	3.64 473	22.72 470	15.20 469	9.52 468	1.99 468

test123		16.58 438	19.47 440	7.91 452	3.59 476	5.37 477	94.32 386	1.39 477	2.49 470	13.98 470	44.60 467	2.91 475	2.65 471	11.35 471	0.57 470	15.70 466

ab-mvs-re		8.21 439	10.94 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	98.50 124	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		6.87 440	9.16 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	82.48 203	0.00 473	0.00 472	0.00 471	0.00 469

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						79.74 385								67.75 418

FOURS1					99.50 42	88.94 213	99.55 57	97.47 157	91.32 132	98.12 58

MSC_two_6792asdad				99.51 2	99.61 24	98.60 2		97.69 101					99.98 9	99.55 15	99.83 15	99.96 10

PC_three_1452									94.60 49	99.41 8	99.12 56	95.50 7	99.96 28	99.84 2	99.92 3	99.97 7

No_MVS				99.51 2	99.61 24	98.60 2		97.69 101					99.98 9	99.55 15	99.83 15	99.96 10

test_one_0601					99.59 28	94.89 37		97.64 118	93.14 88	98.93 29	99.45 14	93.45 18

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					99.67 10	93.28 79		97.61 125	87.78 256	97.41 75	99.16 44	90.15 58	99.56 120	98.35 56	99.70 37

RE-MVS-def			95.70 81		99.22 61	87.26 262	98.40 220	97.21 188	89.63 186	96.67 102	98.97 76	85.24 154		96.62 96	99.31 67	99.60 77

IU-MVS					99.63 18	95.38 24		97.73 91	95.54 35	99.54 6				99.69 7	99.81 23	99.99 1

OPU-MVS				99.49 4	99.64 17	98.51 4	99.77 27				99.19 38	95.12 8	99.97 21	99.90 1	99.92 3	99.99 1

test_241102_TWO								97.72 92	94.17 57	99.23 17	99.54 3	93.14 25	99.98 9	99.70 5	99.82 19	99.99 1

test_241102_ONE					99.63 18	95.24 27		97.72 92	94.16 59	99.30 14	99.49 9	93.32 20	99.98 9

9.14			96.87 31		99.34 50		99.50 64	97.49 154	89.41 198	98.59 43	99.43 16	89.78 62	99.69 106	98.69 41	99.62 46

save fliter					99.34 50	93.85 67	99.65 47	97.63 122	95.69 31

test_0728_THIRD									93.01 89	99.07 23	99.46 10	94.66 13	99.97 21	99.25 23	99.82 19	99.95 15

test_0728_SECOND				98.77 8	99.66 12	96.37 14	99.72 34	97.68 103					99.98 9	99.64 8	99.82 19	99.96 10

test0726					99.66 12	95.20 32	99.77 27	97.70 97	93.95 62	99.35 12	99.54 3	93.18 23

GSMVS																98.84 153

test_part2					99.54 36	95.42 22				98.13 56

sam_mvs1												88.39 80				98.84 153

sam_mvs												87.08 108

ambc				79.60 427	72.76 460	56.61 454	76.20 458	92.01 424		68.25 431	80.23 446	23.34 459	94.73 396	73.78 390	60.81 439	87.48 420

MTGPA								97.45 160

test_post1							90.74 430				41.37 469	85.38 150	96.36 330	83.16 315

test_post											46.00 466	87.37 99	97.11 294

patchmatchnet-post											84.86 428	88.73 76	96.81 307

GG-mvs-BLEND				96.98 76	96.53 183	94.81 44	87.20 436	97.74 88		93.91 162	96.40 245	96.56 2	96.94 302	95.08 138	98.95 91	99.20 118

MTMP							99.21 104	91.09 435

gm-plane-assit					94.69 285	88.14 234			88.22 241		97.20 195		98.29 206	90.79 213

test9_res														98.60 44	99.87 9	99.90 22

TEST9					99.57 33	93.17 83	99.38 85	97.66 109	89.57 191	98.39 49	99.18 41	90.88 43	99.66 109

test_8					99.55 35	93.07 86	99.37 88	97.64 118	90.18 167	98.36 51	99.19 38	90.94 39	99.64 115

agg_prior2														97.84 70	99.87 9	99.91 21

agg_prior					99.54 36	92.66 98		97.64 118		97.98 65			99.61 117

TestCases				90.52 325	96.82 172	78.84 392		92.17 420	77.96 403	75.94 392	95.50 272	55.48 410	99.18 156	71.15 401	87.14 289	93.55 304

test_prior4						92.00 112	99.41 82

test_prior2							99.57 55		91.43 128	98.12 58	98.97 76	90.43 51		98.33 57	99.81 23

test_prior				97.01 71	99.58 30	91.77 119		97.57 136					99.49 127			99.79 38

旧先验2							98.67 178		85.75 307	98.96 28			98.97 171	93.84 167

新几何2							98.26 237

新几何1				97.40 53	98.92 83	92.51 105		97.77 86	85.52 309	96.69 101	99.06 66	88.08 88	99.89 62	84.88 290	99.62 46	99.79 38

旧先验1					98.97 75	92.90 94		97.74 88			99.15 48	91.05 38			99.33 65	99.60 77

无先验							98.52 202	97.82 73	87.20 271				99.90 55	87.64 252		99.85 30

原ACMM2							98.69 175

原ACMM1				96.18 128	99.03 73	90.08 170		97.63 122	88.98 208	97.00 87	98.97 76	88.14 87	99.71 105	88.23 245	99.62 46	98.76 166

test222					98.32 98	91.21 130	98.08 259	97.58 133	83.74 340	95.87 119	99.02 72	86.74 116			99.64 42	99.81 35

testdata2													99.88 64	84.16 301

segment_acmp												90.56 49

testdata				95.26 182	98.20 103	87.28 259		97.60 127	85.21 313	98.48 46	99.15 48	88.15 86	98.72 186	90.29 218	99.45 59	99.78 41

testdata1							97.89 267		92.43 103

test12				97.83 35	99.33 53	94.45 54		97.55 138		97.56 71		88.60 78	99.50 126		99.71 36	99.55 82

plane_prior7					93.84 318	85.73 305

plane_prior6					93.92 315	86.02 298						72.92 307

plane_prior5								96.30 260					97.75 262	93.46 177	86.17 298	92.67 312

plane_prior4											96.52 240

plane_prior3						85.91 300			93.65 77	86.99 273

plane_prior2							99.02 138		93.38 84

plane_prior1					93.90 317

plane_prior						86.07 296	99.14 120		93.81 73						86.26 297

n2								0.00 478

nn								0.00 478

door-mid								84.90 458

lessismore_v0				85.08 403	85.59 432	69.28 440		90.56 440		67.68 434	90.21 389	54.21 419	95.46 379	73.88 387	62.64 433	90.50 383

LGP-MVS_train				90.06 336	93.35 335	80.95 377		95.94 294	87.73 260	83.17 307	96.11 255	66.28 364	97.77 255	90.19 219	85.19 305	91.46 350

test11								97.68 103

door								85.30 456

HQP5-MVS						86.39 278

HQP-NCC					93.95 310		99.16 112		93.92 64	87.57 266

ACMP_Plane					93.95 310		99.16 112		93.92 64	87.57 266

BP-MVS														93.82 169

HQP4-MVS										87.57 266			97.77 255			92.72 310

HQP3-MVS								96.37 256							86.29 295

HQP2-MVS												73.34 300

NP-MVS					93.94 313	86.22 284					96.67 238

MDTV_nov1_ep13_2view						91.17 133	91.38 422		87.45 267	93.08 179		86.67 120		87.02 257		98.95 142

MDTV_nov1_ep13			90.47 233		96.14 208	88.55 227	91.34 423	97.51 149	89.58 190	92.24 196	90.50 382	86.99 112	97.61 272	77.64 358	92.34 240

ACMMP++_ref															82.64 329

ACMMP++															83.83 316

Test By Simon												83.62 173

ITE_SJBPF				87.93 374	92.26 351	76.44 412		93.47 408	87.67 263	79.95 362	95.49 274	56.50 406	97.38 285	75.24 375	82.33 331	89.98 395

DeepMVS_CX				76.08 429	90.74 380	51.65 462		90.84 436	86.47 293	57.89 450	87.98 407	35.88 454	92.60 420	65.77 426	65.06 427	83.97 445

High-resolution multi-view benchmark