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		96.30 1	96.54 1	95.55 15	99.31 5	87.69 24	99.06 16	97.12 30	94.66 5	96.79 16	98.78 9	86.42 27	99.95 3	97.59 23	99.18 7	99.00 30

DPM-MVS		96.21 2	95.53 13	98.26 1	96.26 100	95.09 1	99.15 7	96.98 38	93.39 14	96.45 25	98.79 8	90.17 9	99.99 1	89.33 129	99.25 6	99.70 3

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 23	97.10 32	95.17 3	92.11 82	98.46 26	87.33 23	99.97 2	97.21 28	99.31 4	99.63 7

DVP-MVS++		96.05 4	96.41 3	94.96 23	99.05 9	85.34 57	98.13 49	96.77 61	88.38 73	97.70 8	98.77 10	92.06 3	99.84 12	97.47 24	99.37 1	99.70 3

SED-MVS		95.88 5	96.22 4	94.87 25	99.03 15	85.03 70	99.12 11	96.78 55	88.72 65	97.79 6	98.91 2	88.48 16	99.82 18	98.15 11	98.97 17	99.74 1

MM		95.85 6	95.74 10	96.15 8	96.34 96	89.50 9	99.18 5	98.10 8	95.68 1	96.64 21	97.92 58	80.72 65	99.80 25	99.16 1	97.96 58	99.15 26

NCCC		95.63 7	95.94 8	94.69 31	99.21 6	85.15 67	99.16 6	96.96 41	94.11 9	95.59 33	98.64 17	85.07 31	99.91 4	95.61 45	99.10 9	99.00 30

MSP-MVS		95.62 8	96.54 1	92.86 97	98.31 48	80.10 178	97.42 103	96.78 55	92.20 23	97.11 14	98.29 33	93.46 1	99.10 101	96.01 38	99.30 5	99.38 14
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
DVP-MVS		95.58 9	95.91 9	94.57 33	99.05 9	85.18 62	99.06 16	96.46 102	88.75 63	96.69 18	98.76 12	87.69 21	99.76 31	97.90 17	98.85 21	98.77 38
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
MVS_0304		95.36 10	95.20 17	95.85 11	94.89 147	89.22 12	98.83 25	97.88 11	94.68 4	95.14 39	97.99 52	80.80 64	99.81 21	98.60 6	97.95 59	98.50 54

DPE-MVS		95.32 11	95.55 12	94.64 32	98.79 23	84.87 75	97.77 73	96.74 66	86.11 122	96.54 24	98.89 6	88.39 18	99.74 38	97.67 22	99.05 12	99.31 20
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		95.32 11	95.48 14	94.85 26	98.62 34	86.04 39	97.81 71	96.93 44	92.45 21	95.69 32	98.50 24	85.38 29	99.85 10	94.75 55	99.18 7	98.65 47

patch_mono-2		95.14 13	96.08 7	92.33 120	98.44 43	77.84 244	98.43 36	97.21 23	92.58 20	97.68 10	97.65 76	86.88 24	99.83 16	98.25 9	97.60 69	99.33 18

DELS-MVS		94.98 14	94.49 24	96.44 6	96.42 95	90.59 7	99.21 4	97.02 36	94.40 8	91.46 90	97.08 106	83.32 48	99.69 49	92.83 83	98.70 30	99.04 28
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
fmvsm_l_conf0.5_n_a		94.91 15	95.30 15	93.72 60	94.50 161	84.30 83	99.14 9	96.00 144	91.94 29	97.91 5	98.60 18	84.78 33	99.77 29	98.84 4	96.03 107	97.08 152

fmvsm_l_conf0.5_n		94.89 16	95.24 16	93.86 51	94.42 163	84.61 78	99.13 10	96.15 133	92.06 26	97.92 3	98.52 23	84.52 35	99.74 38	98.76 5	95.67 113	97.22 144

CANet		94.89 16	94.64 22	95.63 13	97.55 75	88.12 17	99.06 16	96.39 112	94.07 10	95.34 35	97.80 67	76.83 126	99.87 8	97.08 30	97.64 68	98.89 33

SD-MVS		94.84 18	95.02 19	94.29 39	97.87 64	84.61 78	97.76 75	96.19 131	89.59 56	96.66 20	98.17 41	84.33 37	99.60 59	96.09 37	98.50 36	98.66 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
test_fmvsm_n_1920		94.81 19	95.60 11	92.45 113	95.29 132	80.96 153	99.29 2	97.21 23	94.50 7	97.29 13	98.44 27	82.15 56	99.78 28	98.56 7	97.68 67	96.61 170

TSAR-MVS + MP.		94.79 20	95.17 18	93.64 63	97.66 69	84.10 86	95.85 213	96.42 107	91.26 34	97.49 12	96.80 118	86.50 26	98.49 132	95.54 48	99.03 13	98.33 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SMA-MVS		94.70 21	94.68 21	94.76 28	98.02 59	85.94 43	97.47 96	96.77 61	85.32 139	97.92 3	98.70 15	83.09 51	99.84 12	95.79 42	99.08 10	98.49 55
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
DeepPCF-MVS	89.82 1	94.61 22	96.17 5	89.91 203	97.09 90	70.21 335	98.99 22	96.69 73	95.57 2	95.08 41	99.23 1	86.40 28	99.87 8	97.84 20	98.66 31	99.65 6

APDe-MVS		94.56 23	94.75 20	93.96 49	98.84 22	83.40 100	98.04 57	96.41 108	85.79 130	95.00 43	98.28 34	84.32 40	99.18 94	97.35 26	98.77 27	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS_fast	89.06 2	94.48 24	94.30 29	95.02 21	98.86 21	85.68 49	98.06 55	96.64 81	93.64 12	91.74 88	98.54 20	80.17 74	99.90 5	92.28 88	98.75 28	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		94.35 25	94.50 23	93.89 50	97.38 84	83.04 107	98.10 51	95.29 189	91.57 31	93.81 59	97.45 85	86.64 25	99.43 76	96.28 36	94.01 132	99.20 24

MVSMamba_pp		94.31 26	94.11 33	94.91 24	96.34 96	87.41 28	97.90 65	95.44 178	93.29 15	96.74 17	93.94 194	79.27 83	98.22 146	95.61 45	98.42 40	99.11 27

train_agg		94.28 27	94.45 25	93.74 57	98.64 31	83.71 92	97.82 69	96.65 78	84.50 163	95.16 36	98.09 45	84.33 37	99.36 81	95.91 41	98.96 19	98.16 75

MSLP-MVS++		94.28 27	94.39 27	93.97 48	98.30 49	84.06 87	98.64 31	96.93 44	90.71 41	93.08 69	98.70 15	79.98 76	99.21 88	94.12 64	99.07 11	98.63 48

MG-MVS		94.25 29	93.72 35	95.85 11	99.38 3	89.35 11	97.98 59	98.09 9	89.99 52	92.34 78	96.97 110	81.30 62	98.99 107	88.54 136	98.88 20	99.20 24

SF-MVS		94.17 30	94.05 34	94.55 34	97.56 74	85.95 41	97.73 77	96.43 106	84.02 177	95.07 42	98.74 14	82.93 52	99.38 78	95.42 50	98.51 34	98.32 64

PS-MVSNAJ		94.17 30	93.52 41	96.10 9	95.65 122	92.35 2	98.21 44	95.79 158	92.42 22	96.24 27	98.18 38	71.04 216	99.17 95	96.77 33	97.39 78	96.79 163

SteuartSystems-ACMMP		94.13 32	94.44 26	93.20 82	95.41 127	81.35 144	99.02 20	96.59 88	89.50 57	94.18 55	98.36 30	83.68 47	99.45 75	94.77 54	98.45 39	98.81 37
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 33	94.15 31	93.76 56	97.27 87	84.35 81	98.29 41	97.64 15	94.57 6	95.36 34	96.88 113	79.96 77	99.12 100	91.30 98	96.11 104	97.82 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 34	94.36 28	92.86 97	92.82 215	81.12 147	99.26 3	96.37 116	93.47 13	95.16 36	98.21 36	79.00 89	99.64 55	98.21 10	96.73 95	97.83 101

xiu_mvs_v2_base		93.92 35	93.26 45	95.91 10	95.07 140	92.02 6	98.19 45	95.68 164	92.06 26	96.01 31	98.14 42	70.83 220	98.96 109	96.74 35	96.57 97	96.76 166

lupinMVS		93.87 36	93.58 40	94.75 29	93.00 207	88.08 18	99.15 7	95.50 173	91.03 38	94.90 44	97.66 72	78.84 92	97.56 177	94.64 58	97.46 73	98.62 49

fmvsm_s_conf0.5_n		93.69 37	94.13 32	92.34 118	94.56 154	82.01 121	99.07 15	97.13 28	92.09 24	96.25 26	98.53 22	76.47 131	99.80 25	98.39 8	94.71 122	95.22 209

APD-MVS		93.61 38	93.59 39	93.69 61	98.76 24	83.26 103	97.21 113	96.09 137	82.41 216	94.65 49	98.21 36	81.96 59	98.81 119	94.65 57	98.36 46	99.01 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		93.59 39	93.63 38	93.48 74	98.05 58	81.76 134	98.64 31	97.13 28	82.60 212	94.09 56	98.49 25	80.35 69	99.85 10	94.74 56	98.62 32	98.83 35

ACMMP_NAP		93.46 40	93.23 46	94.17 44	97.16 88	84.28 84	96.82 154	96.65 78	86.24 120	94.27 53	97.99 52	77.94 105	99.83 16	93.39 71	98.57 33	98.39 61

MVS_111021_HR		93.41 41	93.39 44	93.47 76	97.34 85	82.83 109	97.56 88	98.27 6	89.16 61	89.71 115	97.14 102	79.77 78	99.56 66	93.65 69	97.94 60	98.02 83

fmvsm_s_conf0.5_n_a		93.34 42	93.71 36	92.22 127	93.38 196	81.71 137	98.86 24	96.98 38	91.64 30	96.85 15	98.55 19	75.58 149	99.77 29	97.88 19	93.68 137	95.18 210

PVSNet_Blended		93.13 43	92.98 49	93.57 68	97.47 76	83.86 89	99.32 1	96.73 67	91.02 39	89.53 120	96.21 129	76.42 133	99.57 64	94.29 61	95.81 112	97.29 142

CDPH-MVS		93.12 44	92.91 50	93.74 57	98.65 30	83.88 88	97.67 81	96.26 123	83.00 202	93.22 67	98.24 35	81.31 61	99.21 88	89.12 130	98.74 29	98.14 77

dcpmvs_2		93.10 45	93.46 43	92.02 138	97.77 65	79.73 188	94.82 254	93.86 269	86.91 110	91.33 94	96.76 119	85.20 30	98.06 152	96.90 32	97.60 69	98.27 70

test_fmvsmconf0.1_n		93.08 46	93.22 47	92.65 106	88.45 310	80.81 157	99.00 21	95.11 194	93.21 16	94.00 57	97.91 60	76.84 124	99.59 60	97.91 16	96.55 98	97.54 122

CS-MVS-test		92.98 47	93.67 37	90.90 174	96.52 94	76.87 263	98.68 28	94.73 215	90.36 49	94.84 46	97.89 62	77.94 105	97.15 209	94.28 63	97.80 64	98.70 45

alignmvs		92.97 48	92.26 64	95.12 20	95.54 124	87.77 22	98.67 29	96.38 113	88.04 81	93.01 70	97.45 85	79.20 86	98.60 125	93.25 77	88.76 184	98.99 32

fmvsm_s_conf0.1_n		92.93 49	93.16 48	92.24 125	90.52 277	81.92 125	98.42 37	96.24 125	91.17 35	96.02 30	98.35 31	75.34 160	99.74 38	97.84 20	94.58 124	95.05 211

HFP-MVS		92.89 50	92.86 52	92.98 91	98.71 25	81.12 147	97.58 86	96.70 71	85.20 144	91.75 87	97.97 57	78.47 97	99.71 45	90.95 101	98.41 42	98.12 79

PAPM		92.87 51	92.40 60	94.30 38	92.25 233	87.85 21	96.40 182	96.38 113	91.07 37	88.72 135	96.90 111	82.11 57	97.37 194	90.05 120	97.70 66	97.67 113

ZNCC-MVS		92.75 52	92.60 57	93.23 81	98.24 51	81.82 131	97.63 82	96.50 98	85.00 150	91.05 99	97.74 69	78.38 98	99.80 25	90.48 111	98.34 47	98.07 81

PAPR		92.74 53	92.17 67	94.45 35	98.89 20	84.87 75	97.20 115	96.20 129	87.73 89	88.40 139	98.12 43	78.71 95	99.76 31	87.99 143	96.28 100	98.74 39

CS-MVS		92.73 54	93.48 42	90.48 186	96.27 99	75.93 283	98.55 34	94.93 201	89.32 58	94.54 51	97.67 71	78.91 91	97.02 213	93.80 66	97.32 80	98.49 55

jason		92.73 54	92.23 65	94.21 43	90.50 278	87.30 29	98.65 30	95.09 195	90.61 43	92.76 75	97.13 103	75.28 161	97.30 197	93.32 75	96.75 94	98.02 83
jason: jason.
ETV-MVS		92.72 56	92.87 51	92.28 124	94.54 156	81.89 127	97.98 59	95.21 192	89.77 55	93.11 68	96.83 115	77.23 120	97.50 185	95.74 43	95.38 116	97.44 131

region2R		92.72 56	92.70 54	92.79 100	98.68 26	80.53 167	97.53 91	96.51 96	85.22 142	91.94 85	97.98 55	77.26 116	99.67 53	90.83 106	98.37 45	98.18 73

XVS		92.69 58	92.71 53	92.63 108	98.52 37	80.29 170	97.37 107	96.44 104	87.04 107	91.38 91	97.83 66	77.24 118	99.59 60	90.46 112	98.07 54	98.02 83

ACMMPR		92.69 58	92.67 55	92.75 101	98.66 28	80.57 163	97.58 86	96.69 73	85.20 144	91.57 89	97.92 58	77.01 121	99.67 53	90.95 101	98.41 42	98.00 88

WTY-MVS		92.65 60	91.68 77	95.56 14	96.00 107	88.90 13	98.23 43	97.65 14	88.57 68	89.82 114	97.22 100	79.29 82	99.06 104	89.57 125	88.73 185	98.73 43

MP-MVS		92.61 61	92.67 55	92.42 116	98.13 56	79.73 188	97.33 109	96.20 129	85.63 132	90.53 106	97.66 72	78.14 103	99.70 48	92.12 90	98.30 49	97.85 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 62	92.35 61	93.29 78	97.30 86	82.53 113	96.44 178	96.04 142	84.68 158	89.12 126	98.37 29	77.48 114	99.74 38	93.31 76	98.38 44	97.59 120
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 63	92.60 57	92.34 118	98.50 40	79.90 181	98.40 38	96.40 110	84.75 154	90.48 108	98.09 45	77.40 115	99.21 88	91.15 100	98.23 51	97.92 94

testing11		92.48 64	92.04 72	93.78 55	95.94 112	86.00 40	97.56 88	97.08 33	87.52 94	89.32 123	95.40 150	84.60 34	98.02 153	91.93 95	89.04 180	97.32 138

MTAPA		92.45 65	92.31 62	92.86 97	97.90 61	80.85 156	92.88 300	96.33 118	87.92 84	90.20 111	98.18 38	76.71 129	99.76 31	92.57 87	98.09 53	97.96 93

GST-MVS		92.43 66	92.22 66	93.04 89	98.17 54	81.64 139	97.40 105	96.38 113	84.71 157	90.90 102	97.40 90	77.55 113	99.76 31	89.75 123	97.74 65	97.72 109

fmvsm_s_conf0.1_n_a		92.38 67	92.49 59	92.06 135	88.08 314	81.62 140	97.97 61	96.01 143	90.62 42	96.58 22	98.33 32	74.09 179	99.71 45	97.23 27	93.46 142	94.86 215

iter_conf05_11		92.35 68	92.15 68	92.94 93	95.96 111	81.81 132	95.60 224	94.81 210	86.97 109	93.95 58	93.63 203	79.44 79	98.19 149	90.92 103	98.10 52	98.82 36

sasdasda		92.27 69	91.22 85	95.41 16	95.80 117	88.31 14	97.09 131	94.64 223	88.49 70	92.99 71	97.31 92	72.68 194	98.57 127	93.38 73	88.58 187	99.36 16

canonicalmvs		92.27 69	91.22 85	95.41 16	95.80 117	88.31 14	97.09 131	94.64 223	88.49 70	92.99 71	97.31 92	72.68 194	98.57 127	93.38 73	88.58 187	99.36 16

SR-MVS		92.16 71	92.27 63	91.83 146	98.37 45	78.41 222	96.67 165	95.76 159	82.19 220	91.97 83	98.07 49	76.44 132	98.64 123	93.71 68	97.27 81	98.45 58

test_fmvsmvis_n_1920		92.12 72	92.10 70	92.17 130	90.87 270	81.04 149	98.34 40	93.90 266	92.71 19	87.24 152	97.90 61	74.83 167	99.72 43	96.96 31	96.20 101	95.76 194

VNet		92.11 73	91.22 85	94.79 27	96.91 91	86.98 30	97.91 63	97.96 10	86.38 119	93.65 61	95.74 138	70.16 225	98.95 111	93.39 71	88.87 183	98.43 59

CSCG		92.02 74	91.65 78	93.12 85	98.53 36	80.59 162	97.47 96	97.18 26	77.06 305	84.64 178	97.98 55	83.98 43	99.52 69	90.72 108	97.33 79	99.23 23

MGCFI-Net		91.95 75	91.03 91	94.72 30	95.68 121	86.38 35	96.93 146	94.48 232	88.25 77	92.78 74	97.24 98	72.34 199	98.46 135	93.13 80	88.43 191	99.32 19

PGM-MVS		91.93 76	91.80 75	92.32 122	98.27 50	79.74 187	95.28 234	97.27 21	83.83 185	90.89 103	97.78 68	76.12 139	99.56 66	88.82 133	97.93 62	97.66 114

testing99		91.91 77	91.35 82	93.60 66	95.98 109	85.70 47	97.31 110	96.92 46	86.82 113	88.91 129	95.25 153	84.26 41	97.89 163	88.80 134	87.94 197	97.21 146

testing91		91.90 78	91.31 84	93.66 62	95.99 108	85.68 49	97.39 106	96.89 47	86.75 117	88.85 131	95.23 156	83.93 44	97.90 162	88.91 131	87.89 198	97.41 133

mPP-MVS		91.88 79	91.82 74	92.07 134	98.38 44	78.63 216	97.29 111	96.09 137	85.12 146	88.45 138	97.66 72	75.53 150	99.68 51	89.83 121	98.02 57	97.88 95

EI-MVSNet-Vis-set		91.84 80	91.77 76	92.04 137	97.60 71	81.17 146	96.61 166	96.87 49	88.20 78	89.19 124	97.55 84	78.69 96	99.14 97	90.29 117	90.94 167	95.80 192

EIA-MVS		91.73 81	92.05 71	90.78 179	94.52 157	76.40 272	98.06 55	95.34 187	89.19 60	88.90 130	97.28 97	77.56 112	97.73 168	90.77 107	96.86 91	98.20 72

EC-MVSNet		91.73 81	92.11 69	90.58 183	93.54 188	77.77 247	98.07 54	94.40 240	87.44 96	92.99 71	97.11 105	74.59 173	96.87 223	93.75 67	97.08 84	97.11 150

DP-MVS Recon		91.72 83	90.85 92	94.34 37	99.50 1	85.00 72	98.51 35	95.96 148	80.57 244	88.08 144	97.63 78	76.84 124	99.89 7	85.67 162	94.88 119	98.13 78

CHOSEN 280x420		91.71 84	91.85 73	91.29 161	94.94 144	82.69 110	87.89 347	96.17 132	85.94 127	87.27 151	94.31 183	90.27 8	95.65 278	94.04 65	95.86 110	95.53 200

HY-MVS	84.06 6	91.63 85	90.37 105	95.39 18	96.12 104	88.25 16	90.22 328	97.58 16	88.33 75	90.50 107	91.96 228	79.26 84	99.06 104	90.29 117	89.07 179	98.88 34

HPM-MVS		91.62 86	91.53 80	91.89 142	97.88 63	79.22 200	96.99 136	95.73 162	82.07 222	89.50 122	97.19 101	75.59 148	98.93 114	90.91 104	97.94 60	97.54 122
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 87	91.64 79	91.47 157	95.74 119	78.79 213	96.15 197	96.77 61	88.49 70	88.64 136	97.07 107	72.33 200	99.19 93	93.13 80	96.48 99	96.43 175

DeepC-MVS	86.58 3	91.53 88	91.06 90	92.94 93	94.52 157	81.89 127	95.95 205	95.98 146	90.76 40	83.76 189	96.76 119	73.24 190	99.71 45	91.67 97	96.96 86	97.22 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 89	90.53 99	94.24 41	97.41 80	85.18 62	98.08 52	97.72 12	80.94 235	89.85 112	96.14 130	75.61 146	98.81 119	90.42 115	88.56 189	98.74 39

DCV-MVSNet		91.46 89	90.53 99	94.24 41	97.41 80	85.18 62	98.08 52	97.72 12	80.94 235	89.85 112	96.14 130	75.61 146	98.81 119	90.42 115	88.56 189	98.74 39

PAPM_NR		91.46 89	90.82 93	93.37 77	98.50 40	81.81 132	95.03 250	96.13 134	84.65 159	86.10 162	97.65 76	79.24 85	99.75 36	83.20 190	96.88 89	98.56 51

MVSFormer		91.36 92	90.57 98	93.73 59	93.00 207	88.08 18	94.80 256	94.48 232	80.74 240	94.90 44	97.13 103	78.84 92	95.10 306	83.77 179	97.46 73	98.02 83

EI-MVSNet-UG-set		91.35 93	91.22 85	91.73 148	97.39 82	80.68 160	96.47 175	96.83 52	87.92 84	88.30 142	97.36 91	77.84 108	99.13 99	89.43 128	89.45 175	95.37 204

SR-MVS-dyc-post		91.29 94	91.45 81	90.80 177	97.76 67	76.03 278	96.20 195	95.44 178	80.56 245	90.72 104	97.84 64	75.76 145	98.61 124	91.99 93	96.79 92	97.75 107

PVSNet_Blended_VisFu		91.24 95	90.77 94	92.66 105	95.09 138	82.40 117	97.77 73	95.87 155	88.26 76	86.39 158	93.94 194	76.77 127	99.27 84	88.80 134	94.00 133	96.31 181

APD-MVS_3200maxsize		91.23 96	91.35 82	90.89 175	97.89 62	76.35 273	96.30 188	95.52 172	79.82 263	91.03 100	97.88 63	74.70 169	98.54 129	92.11 91	96.89 88	97.77 106

diffmvs		91.17 97	90.74 95	92.44 115	93.11 206	82.50 115	96.25 191	93.62 284	87.79 87	90.40 109	95.93 134	73.44 188	97.42 189	93.62 70	92.55 152	97.41 133
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_mvg		91.13 98	90.45 102	93.17 84	92.99 210	83.58 96	97.46 98	94.56 229	87.69 90	87.19 153	94.98 171	74.50 174	97.60 174	91.88 96	92.79 149	98.34 62
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		91.09 99	90.49 101	92.87 96	95.82 115	85.04 69	96.51 173	97.28 20	86.05 125	89.13 125	95.34 152	80.16 75	96.62 235	85.82 160	88.31 193	96.96 155

test_fmvsmconf0.01_n		91.08 100	90.68 96	92.29 123	82.43 369	80.12 177	97.94 62	93.93 262	92.07 25	91.97 83	97.60 79	67.56 233	99.53 68	97.09 29	95.56 115	97.21 146

CHOSEN 1792x2688		91.07 101	90.21 109	93.64 63	95.18 136	83.53 97	96.26 190	96.13 134	88.92 62	84.90 172	93.10 212	72.86 192	99.62 58	88.86 132	95.67 113	97.79 105

ETVMVS		90.99 102	90.26 106	93.19 83	95.81 116	85.64 51	96.97 141	97.18 26	85.43 136	88.77 134	94.86 173	82.00 58	96.37 242	82.70 195	88.60 186	97.57 121

CANet_DTU		90.98 103	90.04 113	93.83 52	94.76 150	86.23 37	96.32 187	93.12 310	93.11 17	93.71 60	96.82 117	63.08 263	99.48 73	84.29 172	95.12 118	95.77 193

test2506		90.96 104	90.39 103	92.65 106	93.54 188	82.46 116	96.37 183	97.35 18	86.78 115	87.55 147	95.25 153	77.83 109	97.50 185	84.07 174	94.80 120	97.98 90

thisisatest0515		90.95 105	90.26 106	93.01 90	94.03 179	84.27 85	97.91 63	96.67 75	83.18 196	86.87 156	95.51 148	88.66 15	97.85 164	80.46 207	89.01 181	96.92 159

casdiffmvs		90.95 105	90.39 103	92.63 108	92.82 215	82.53 113	96.83 152	94.47 235	87.69 90	88.47 137	95.56 147	74.04 180	97.54 181	90.90 105	92.74 150	97.83 101
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sss		90.87 107	89.96 116	93.60 66	94.15 171	83.84 91	97.14 124	98.13 7	85.93 128	89.68 116	96.09 132	71.67 208	99.30 83	87.69 146	89.16 178	97.66 114

baseline		90.76 108	90.10 112	92.74 102	92.90 213	82.56 112	94.60 258	94.56 229	87.69 90	89.06 128	95.67 142	73.76 183	97.51 184	90.43 114	92.23 158	98.16 75

Effi-MVS+		90.70 109	89.90 119	93.09 87	93.61 185	83.48 98	95.20 240	92.79 316	83.22 195	91.82 86	95.70 140	71.82 207	97.48 187	91.25 99	93.67 138	98.32 64

iter_conf05		90.65 110	89.59 122	93.82 53	95.37 129	87.90 20	91.32 319	93.55 288	74.65 322	83.45 191	92.81 214	83.11 50	97.70 169	94.49 59	97.57 71	95.85 191

MAR-MVS		90.63 111	90.22 108	91.86 143	98.47 42	78.20 232	97.18 117	96.61 84	83.87 184	88.18 143	98.18 38	68.71 229	99.75 36	83.66 184	97.15 83	97.63 117
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
MVS		90.60 112	88.64 136	96.50 5	94.25 167	90.53 8	93.33 289	97.21 23	77.59 296	78.88 244	97.31 92	71.52 211	99.69 49	89.60 124	98.03 56	99.27 22

xiu_mvs_v1_base_debu		90.54 113	89.54 123	93.55 69	92.31 226	87.58 25	96.99 136	94.87 205	87.23 102	93.27 64	97.56 81	57.43 305	98.32 142	92.72 84	93.46 142	94.74 219

xiu_mvs_v1_base		90.54 113	89.54 123	93.55 69	92.31 226	87.58 25	96.99 136	94.87 205	87.23 102	93.27 64	97.56 81	57.43 305	98.32 142	92.72 84	93.46 142	94.74 219

xiu_mvs_v1_base_debi		90.54 113	89.54 123	93.55 69	92.31 226	87.58 25	96.99 136	94.87 205	87.23 102	93.27 64	97.56 81	57.43 305	98.32 142	92.72 84	93.46 142	94.74 219

baseline2		90.39 116	90.21 109	90.93 172	90.86 271	80.99 151	95.20 240	97.41 17	86.03 126	80.07 234	94.61 178	90.58 6	97.47 188	87.29 150	89.86 173	94.35 225

ACMMP		90.39 116	89.97 115	91.64 151	97.58 73	78.21 231	96.78 157	96.72 69	84.73 156	84.72 176	97.23 99	71.22 213	99.63 57	88.37 141	92.41 155	97.08 152
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
HPM-MVS_fast		90.38 118	90.17 111	91.03 170	97.61 70	77.35 256	97.15 123	95.48 174	79.51 269	88.79 132	96.90 111	71.64 210	98.81 119	87.01 154	97.44 75	96.94 156

MVS_Test		90.29 119	89.18 127	93.62 65	95.23 133	84.93 73	94.41 261	94.66 220	84.31 168	90.37 110	91.02 242	75.13 163	97.82 165	83.11 192	94.42 126	98.12 79

API-MVS		90.18 120	88.97 130	93.80 54	98.66 28	82.95 108	97.50 95	95.63 167	75.16 317	86.31 159	97.69 70	72.49 197	99.90 5	81.26 203	96.07 105	98.56 51

PVSNet_BlendedMVS		90.05 121	89.96 116	90.33 190	97.47 76	83.86 89	98.02 58	96.73 67	87.98 82	89.53 120	89.61 264	76.42 133	99.57 64	94.29 61	79.59 263	87.57 328

ET-MVSNet_ETH3D		90.01 122	89.03 128	92.95 92	94.38 164	86.77 32	98.14 46	96.31 120	89.30 59	63.33 359	96.72 122	90.09 10	93.63 338	90.70 109	82.29 249	98.46 57

test_vis1_n_1920		89.95 123	90.59 97	88.03 242	92.36 225	68.98 344	99.12 11	94.34 243	93.86 11	93.64 62	97.01 109	51.54 335	99.59 60	96.76 34	96.71 96	95.53 200

test_cas_vis1_n_1920		89.90 124	90.02 114	89.54 211	90.14 286	74.63 294	98.71 27	94.43 238	93.04 18	92.40 76	96.35 127	53.41 331	99.08 103	95.59 47	96.16 102	94.90 213

TESTMET0.1,1		89.83 125	89.34 126	91.31 159	92.54 223	80.19 175	97.11 127	96.57 90	86.15 121	86.85 157	91.83 232	79.32 81	96.95 217	81.30 202	92.35 156	96.77 165

EPP-MVSNet		89.76 126	89.72 121	89.87 204	93.78 181	76.02 280	97.22 112	96.51 96	79.35 271	85.11 168	95.01 169	84.82 32	97.10 211	87.46 149	88.21 195	96.50 173

CPTT-MVS		89.72 127	89.87 120	89.29 214	98.33 47	73.30 305	97.70 79	95.35 186	75.68 313	87.40 148	97.44 88	70.43 222	98.25 145	89.56 126	96.90 87	96.33 180

thisisatest0530		89.65 128	89.02 129	91.53 155	93.46 194	80.78 158	96.52 171	96.67 75	81.69 228	83.79 188	94.90 172	88.85 14	97.68 170	77.80 231	87.49 203	96.14 184

3Dnovator+	82.88 8	89.63 129	87.85 149	94.99 22	94.49 162	86.76 33	97.84 68	95.74 161	86.10 123	75.47 287	96.02 133	65.00 253	99.51 71	82.91 194	97.07 85	98.72 44

CDS-MVSNet		89.50 130	88.96 131	91.14 167	91.94 250	80.93 154	97.09 131	95.81 157	84.26 173	84.72 176	94.20 188	80.31 70	95.64 279	83.37 189	88.96 182	96.85 162
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 131	89.92 118	88.06 240	94.64 151	69.57 341	96.22 192	94.95 200	87.27 101	91.37 93	96.54 125	65.88 245	97.39 192	88.54 136	93.89 134	97.23 143

HyFIR lowres test		89.36 132	88.60 137	91.63 153	94.91 146	80.76 159	95.60 224	95.53 170	82.56 213	84.03 182	91.24 239	78.03 104	96.81 227	87.07 153	88.41 192	97.32 138

3Dnovator	82.32 10	89.33 133	87.64 154	94.42 36	93.73 184	85.70 47	97.73 77	96.75 65	86.73 118	76.21 276	95.93 134	62.17 267	99.68 51	81.67 201	97.81 63	97.88 95

h-mvs33		89.30 134	88.95 132	90.36 189	95.07 140	76.04 277	96.96 143	97.11 31	90.39 47	92.22 80	95.10 166	74.70 169	98.86 116	93.14 78	65.89 353	96.16 183

LFMVS		89.27 135	87.64 154	94.16 46	97.16 88	85.52 54	97.18 117	94.66 220	79.17 277	89.63 118	96.57 124	55.35 322	98.22 146	89.52 127	89.54 174	98.74 39

MVSTER		89.25 136	88.92 133	90.24 192	95.98 109	84.66 77	96.79 156	95.36 184	87.19 105	80.33 229	90.61 249	90.02 11	95.97 256	85.38 165	78.64 272	90.09 271

CostFormer		89.08 137	88.39 141	91.15 166	93.13 204	79.15 203	88.61 340	96.11 136	83.14 197	89.58 119	86.93 301	83.83 46	96.87 223	88.22 142	85.92 217	97.42 132

PVSNet	82.34 9	89.02 138	87.79 151	92.71 104	95.49 125	81.50 142	97.70 79	97.29 19	87.76 88	85.47 166	95.12 165	56.90 311	98.90 115	80.33 208	94.02 131	97.71 111

test-mter		88.95 139	88.60 137	89.98 199	92.26 231	77.23 258	97.11 127	95.96 148	85.32 139	86.30 160	91.38 236	76.37 135	96.78 229	80.82 204	91.92 160	95.94 188

1314		88.94 140	87.20 167	94.17 44	93.21 199	85.73 46	93.33 289	96.64 81	82.89 204	75.98 279	96.36 126	66.83 241	99.39 77	83.52 188	96.02 108	97.39 136

UA-Net		88.92 141	88.48 140	90.24 192	94.06 176	77.18 260	93.04 297	94.66 220	87.39 98	91.09 98	93.89 196	74.92 166	98.18 151	75.83 258	91.43 164	95.35 205

thres200		88.92 141	87.65 153	92.73 103	96.30 98	85.62 52	97.85 67	98.86 1	84.38 167	84.82 173	93.99 193	75.12 164	98.01 154	70.86 298	86.67 207	94.56 224

Vis-MVSNet (Re-imp)		88.88 143	88.87 135	88.91 221	93.89 180	74.43 297	96.93 146	94.19 251	84.39 166	83.22 195	95.67 142	78.24 100	94.70 316	78.88 226	94.40 127	97.61 119

baseline1		88.85 144	87.49 160	92.93 95	95.21 135	86.85 31	95.47 229	94.61 226	87.29 100	83.11 197	94.99 170	80.70 66	96.89 221	82.28 197	73.72 295	95.05 211

AdaColmap		88.81 145	87.61 157	92.39 117	99.33 4	79.95 179	96.70 164	95.58 168	77.51 297	83.05 198	96.69 123	61.90 273	99.72 43	84.29 172	93.47 141	97.50 128

OMC-MVS		88.80 146	88.16 145	90.72 180	95.30 131	77.92 241	94.81 255	94.51 231	86.80 114	84.97 171	96.85 114	67.53 234	98.60 125	85.08 166	87.62 200	95.63 196

114514_t		88.79 147	87.57 158	92.45 113	98.21 53	81.74 135	96.99 136	95.45 177	75.16 317	82.48 201	95.69 141	68.59 230	98.50 131	80.33 208	95.18 117	97.10 151

mvs_anonymous		88.68 148	87.62 156	91.86 143	94.80 149	81.69 138	93.53 285	94.92 202	82.03 223	78.87 245	90.43 252	75.77 144	95.34 292	85.04 167	93.16 146	98.55 53

Vis-MVSNet		88.67 149	87.82 150	91.24 163	92.68 217	78.82 210	96.95 144	93.85 270	87.55 93	87.07 155	95.13 164	63.43 261	97.21 203	77.58 238	96.15 103	97.70 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 149	88.16 145	90.20 194	93.61 185	76.86 264	96.77 159	93.07 311	84.02 177	83.62 190	95.60 145	74.69 172	96.24 248	78.43 230	93.66 139	97.49 129

IB-MVS	85.34 4	88.67 149	87.14 170	93.26 79	93.12 205	84.32 82	98.76 26	97.27 21	87.19 105	79.36 240	90.45 251	83.92 45	98.53 130	84.41 171	69.79 321	96.93 157
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
1112_ss		88.60 152	87.47 162	92.00 139	93.21 199	80.97 152	96.47 175	92.46 319	83.64 190	80.86 222	97.30 95	80.24 72	97.62 173	77.60 237	85.49 222	97.40 135

tttt0517		88.57 153	88.19 144	89.71 210	93.00 207	75.99 281	95.67 219	96.67 75	80.78 239	81.82 214	94.40 182	88.97 13	97.58 176	76.05 256	86.31 211	95.57 198

UWE-MVS		88.56 154	88.91 134	87.50 256	94.17 170	72.19 316	95.82 215	97.05 35	84.96 151	84.78 174	93.51 206	81.33 60	94.75 314	79.43 219	89.17 177	95.57 198

tfpn200view9		88.48 155	87.15 168	92.47 112	96.21 101	85.30 60	97.44 99	98.85 2	83.37 193	83.99 183	93.82 197	75.36 157	97.93 156	69.04 306	86.24 214	94.17 226

test-LLR		88.48 155	87.98 147	89.98 199	92.26 231	77.23 258	97.11 127	95.96 148	83.76 187	86.30 160	91.38 236	72.30 201	96.78 229	80.82 204	91.92 160	95.94 188

TAMVS		88.48 155	87.79 151	90.56 184	91.09 265	79.18 201	96.45 177	95.88 153	83.64 190	83.12 196	93.33 207	75.94 142	95.74 274	82.40 196	88.27 194	96.75 167

thres400		88.42 158	87.15 168	92.23 126	96.21 101	85.30 60	97.44 99	98.85 2	83.37 193	83.99 183	93.82 197	75.36 157	97.93 156	69.04 306	86.24 214	93.45 242

tpmrst		88.36 159	87.38 164	91.31 159	94.36 165	79.92 180	87.32 351	95.26 191	85.32 139	88.34 140	86.13 317	80.60 68	96.70 231	83.78 178	85.34 225	97.30 141

ECVR-MVS		88.35 160	87.25 166	91.65 150	93.54 188	79.40 195	96.56 170	90.78 347	86.78 115	85.57 165	95.25 153	57.25 309	97.56 177	84.73 170	94.80 120	97.98 90

thres100view900		88.30 161	86.95 174	92.33 120	96.10 105	84.90 74	97.14 124	98.85 2	82.69 210	83.41 192	93.66 201	75.43 154	97.93 156	69.04 306	86.24 214	94.17 226

VDD-MVS		88.28 162	87.02 173	92.06 135	95.09 138	80.18 176	97.55 90	94.45 237	83.09 198	89.10 127	95.92 136	47.97 349	98.49 132	93.08 82	86.91 206	97.52 127

BH-w/o		88.24 163	87.47 162	90.54 185	95.03 143	78.54 217	97.41 104	93.82 271	84.08 175	78.23 250	94.51 181	69.34 228	97.21 203	80.21 212	94.58 124	95.87 190

hse-mvs2		88.22 164	88.21 143	88.25 236	93.54 188	73.41 302	95.41 232	95.89 152	90.39 47	92.22 80	94.22 186	74.70 169	96.66 234	93.14 78	64.37 358	94.69 223

test1111		88.11 165	87.04 172	91.35 158	93.15 202	78.79 213	96.57 168	90.78 347	86.88 112	85.04 169	95.20 159	57.23 310	97.39 192	83.88 176	94.59 123	97.87 97

thres600view7		88.06 166	86.70 179	92.15 132	96.10 105	85.17 66	97.14 124	98.85 2	82.70 209	83.41 192	93.66 201	75.43 154	97.82 165	67.13 315	85.88 218	93.45 242

Test_1112_low_res		88.03 167	86.73 177	91.94 141	93.15 202	80.88 155	96.44 178	92.41 321	83.59 192	80.74 224	91.16 240	80.18 73	97.59 175	77.48 240	85.40 223	97.36 137

PLC	83.97 7	88.00 168	87.38 164	89.83 206	98.02 59	76.46 270	97.16 121	94.43 238	79.26 276	81.98 211	96.28 128	69.36 227	99.27 84	77.71 235	92.25 157	93.77 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 169	87.48 161	89.44 212	92.16 238	80.54 166	98.14 46	94.92 202	91.41 32	79.43 239	95.40 150	62.34 266	97.27 200	90.60 110	82.90 241	90.50 261
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Fast-Effi-MVS+		87.93 170	86.94 175	90.92 173	94.04 177	79.16 202	98.26 42	93.72 280	81.29 231	83.94 186	92.90 213	69.83 226	96.68 232	76.70 248	91.74 162	96.93 157

HQP-MVS		87.91 171	87.55 159	88.98 220	92.08 242	78.48 218	97.63 82	94.80 211	90.52 44	82.30 204	94.56 179	65.40 249	97.32 195	87.67 147	83.01 238	91.13 253

test_fmvs1		87.79 172	88.52 139	85.62 292	92.98 211	64.31 361	97.88 66	92.42 320	87.95 83	92.24 79	95.82 137	47.94 350	98.44 139	95.31 51	94.09 129	94.09 230

UGNet		87.73 173	86.55 180	91.27 162	95.16 137	79.11 204	96.35 185	96.23 126	88.14 79	87.83 146	90.48 250	50.65 338	99.09 102	80.13 213	94.03 130	95.60 197
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
FA-MVS(test-final)		87.71 174	86.23 182	92.17 130	94.19 169	80.55 164	87.16 353	96.07 140	82.12 221	85.98 163	88.35 279	72.04 205	98.49 132	80.26 210	89.87 172	97.48 130

EPNet_dtu		87.65 175	87.89 148	86.93 270	94.57 153	71.37 329	96.72 160	96.50 98	88.56 69	87.12 154	95.02 168	75.91 143	94.01 331	66.62 318	90.00 171	95.42 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 176	88.22 142	85.67 290	89.78 290	67.18 351	95.25 237	87.93 368	83.96 180	88.79 132	97.06 108	72.52 196	94.53 321	92.21 89	86.45 210	95.30 207

HQP_MVS		87.50 177	87.09 171	88.74 225	91.86 251	77.96 238	97.18 117	94.69 216	89.89 53	81.33 217	94.15 189	64.77 255	97.30 197	87.08 151	82.82 242	90.96 255

EPMVS		87.47 178	85.90 185	92.18 129	95.41 127	82.26 120	87.00 354	96.28 121	85.88 129	84.23 180	85.57 323	75.07 165	96.26 246	71.14 296	92.50 153	98.03 82

tpm2		87.35 179	86.26 181	90.62 182	92.93 212	78.67 215	88.06 346	95.99 145	79.33 272	87.40 148	86.43 312	80.28 71	96.40 240	80.23 211	85.73 221	96.79 163

ab-mvs		87.08 180	84.94 201	93.48 74	93.34 197	83.67 94	88.82 337	95.70 163	81.18 232	84.55 179	90.14 258	62.72 264	98.94 113	85.49 164	82.54 246	97.85 99

SDMVSNet		87.02 181	85.61 187	91.24 163	94.14 172	83.30 102	93.88 277	95.98 146	84.30 170	79.63 237	92.01 224	58.23 295	97.68 170	90.28 119	82.02 250	92.75 245

CNLPA		86.96 182	85.37 192	91.72 149	97.59 72	79.34 198	97.21 113	91.05 342	74.22 324	78.90 243	96.75 121	67.21 238	98.95 111	74.68 268	90.77 168	96.88 161

BH-untuned		86.95 183	85.94 184	89.99 198	94.52 157	77.46 253	96.78 157	93.37 299	81.80 225	76.62 267	93.81 199	66.64 242	97.02 213	76.06 255	93.88 135	95.48 202

QAPM		86.88 184	84.51 206	93.98 47	94.04 177	85.89 44	97.19 116	96.05 141	73.62 329	75.12 290	95.62 144	62.02 270	99.74 38	70.88 297	96.06 106	96.30 182

BH-RMVSNet		86.84 185	85.28 193	91.49 156	95.35 130	80.26 173	96.95 144	92.21 323	82.86 206	81.77 216	95.46 149	59.34 287	97.64 172	69.79 304	93.81 136	96.57 172

PatchmatchNet		86.83 186	85.12 198	91.95 140	94.12 174	82.27 119	86.55 358	95.64 166	84.59 161	82.98 199	84.99 335	77.26 116	95.96 259	68.61 309	91.34 165	97.64 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 187	85.43 190	90.87 176	88.76 304	85.34 57	97.06 134	94.33 244	84.31 168	80.45 227	91.98 227	72.36 198	96.36 243	88.48 139	71.13 308	90.93 257

PCF-MVS	84.09 5	86.77 188	85.00 200	92.08 133	92.06 245	83.07 106	92.14 308	94.47 235	79.63 267	76.90 263	94.78 175	71.15 214	99.20 92	72.87 282	91.05 166	93.98 232
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 189	86.10 183	88.61 227	90.05 287	80.21 174	96.14 198	96.95 42	85.56 135	78.37 249	92.30 220	76.73 128	95.28 296	79.51 217	79.27 266	90.35 263

cascas		86.50 190	84.48 208	92.55 111	92.64 221	85.95 41	97.04 135	95.07 197	75.32 315	80.50 225	91.02 242	54.33 329	97.98 155	86.79 156	87.62 200	93.71 237

VDDNet		86.44 191	84.51 206	92.22 127	91.56 254	81.83 130	97.10 130	94.64 223	69.50 356	87.84 145	95.19 160	48.01 348	97.92 161	89.82 122	86.92 205	96.89 160

GeoE		86.36 192	85.20 194	89.83 206	93.17 201	76.13 275	97.53 91	92.11 324	79.58 268	80.99 220	94.01 192	66.60 243	96.17 251	73.48 280	89.30 176	97.20 148

test_fmvs1_n		86.34 193	86.72 178	85.17 299	87.54 322	63.64 366	96.91 148	92.37 322	87.49 95	91.33 94	95.58 146	40.81 375	98.46 135	95.00 53	93.49 140	93.41 244

TR-MVS		86.30 194	84.93 202	90.42 187	94.63 152	77.58 251	96.57 168	93.82 271	80.30 253	82.42 203	95.16 162	58.74 291	97.55 179	74.88 266	87.82 199	96.13 185

X-MVStestdata		86.26 195	84.14 215	92.63 108	98.52 37	80.29 170	97.37 107	96.44 104	87.04 107	91.38 91	20.73 410	77.24 118	99.59 60	90.46 112	98.07 54	98.02 83

AUN-MVS		86.25 196	85.57 188	88.26 235	93.57 187	73.38 303	95.45 230	95.88 153	83.94 181	85.47 166	94.21 187	73.70 186	96.67 233	83.54 186	64.41 357	94.73 222

OpenMVS	79.58 14	86.09 197	83.62 223	93.50 72	90.95 267	86.71 34	97.44 99	95.83 156	75.35 314	72.64 312	95.72 139	57.42 308	99.64 55	71.41 291	95.85 111	94.13 229

FE-MVS		86.06 198	84.15 214	91.78 147	94.33 166	79.81 182	84.58 370	96.61 84	76.69 307	85.00 170	87.38 292	70.71 221	98.37 141	70.39 301	91.70 163	97.17 149

FC-MVSNet-test		85.96 199	85.39 191	87.66 249	89.38 301	78.02 235	95.65 221	96.87 49	85.12 146	77.34 256	91.94 230	76.28 137	94.74 315	77.09 243	78.82 270	90.21 266

miper_enhance_ethall		85.95 200	85.20 194	88.19 239	94.85 148	79.76 184	96.00 202	94.06 259	82.98 203	77.74 254	88.76 272	79.42 80	95.46 288	80.58 206	72.42 302	89.36 284

OPM-MVS		85.84 201	85.10 199	88.06 240	88.34 311	77.83 245	95.72 217	94.20 250	87.89 86	80.45 227	94.05 191	58.57 292	97.26 201	83.88 176	82.76 244	89.09 291
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
bld_raw_dy_0_64		85.80 202	84.02 217	91.13 168	92.90 213	85.52 54	84.17 371	93.47 290	61.49 378	74.88 292	90.07 260	79.05 88	97.26 201	86.07 158	94.11 128	90.62 258

EI-MVSNet		85.80 202	85.20 194	87.59 252	91.55 255	77.41 254	95.13 244	95.36 184	80.43 250	80.33 229	94.71 176	73.72 184	95.97 256	76.96 246	78.64 272	89.39 279

GA-MVS		85.79 204	84.04 216	91.02 171	89.47 299	80.27 172	96.90 149	94.84 208	85.57 133	80.88 221	89.08 267	56.56 315	96.47 239	77.72 234	85.35 224	96.34 178

XVG-OURS-SEG-HR		85.74 205	85.16 197	87.49 258	90.22 282	71.45 328	91.29 320	94.09 257	81.37 230	83.90 187	95.22 157	60.30 280	97.53 183	85.58 163	84.42 229	93.50 240

SCA		85.63 206	83.64 222	91.60 154	92.30 229	81.86 129	92.88 300	95.56 169	84.85 152	82.52 200	85.12 333	58.04 298	95.39 289	73.89 276	87.58 202	97.54 122

test_vis1_n		85.60 207	85.70 186	85.33 296	84.79 353	64.98 359	96.83 152	91.61 333	87.36 99	91.00 101	94.84 174	36.14 381	97.18 205	95.66 44	93.03 147	93.82 235

tpm		85.55 208	84.47 209	88.80 224	90.19 283	75.39 288	88.79 338	94.69 216	84.83 153	83.96 185	85.21 329	78.22 101	94.68 318	76.32 254	78.02 280	96.34 178

mamv4		85.50 209	86.76 176	81.72 337	93.23 198	54.93 391	89.95 330	92.94 313	69.96 353	79.00 242	92.20 222	80.69 67	94.22 327	92.06 92	90.77 168	96.01 186

UniMVSNet_NR-MVSNet		85.49 210	84.59 204	88.21 238	89.44 300	79.36 196	96.71 162	96.41 108	85.22 142	78.11 251	90.98 244	76.97 123	95.14 303	79.14 223	68.30 335	90.12 268

gg-mvs-nofinetune		85.48 211	82.90 234	93.24 80	94.51 160	85.82 45	79.22 383	96.97 40	61.19 381	87.33 150	53.01 399	90.58 6	96.07 252	86.07 158	97.23 82	97.81 104

VPA-MVSNet		85.32 212	83.83 218	89.77 209	90.25 281	82.63 111	96.36 184	97.07 34	83.03 201	81.21 219	89.02 269	61.58 274	96.31 245	85.02 168	70.95 310	90.36 262

UniMVSNet (Re)		85.31 213	84.23 212	88.55 228	89.75 291	80.55 164	96.72 160	96.89 47	85.42 137	78.40 248	88.93 270	75.38 156	95.52 286	78.58 228	68.02 338	89.57 278

XVG-OURS		85.18 214	84.38 210	87.59 252	90.42 280	71.73 325	91.06 323	94.07 258	82.00 224	83.29 194	95.08 167	56.42 316	97.55 179	83.70 183	83.42 234	93.49 241

mvsmamba		85.17 215	84.54 205	87.05 268	87.94 316	75.11 291	96.22 192	87.79 370	86.91 110	78.55 246	91.77 233	64.93 254	95.91 262	86.94 155	79.80 258	90.12 268

cl22		85.11 216	84.17 213	87.92 243	95.06 142	78.82 210	95.51 227	94.22 249	79.74 265	76.77 264	87.92 286	75.96 141	95.68 275	79.93 215	72.42 302	89.27 286

TAPA-MVS	81.61 12	85.02 217	83.67 220	89.06 217	96.79 92	73.27 308	95.92 207	94.79 213	74.81 320	80.47 226	96.83 115	71.07 215	98.19 149	49.82 380	92.57 151	95.71 195
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 218	83.66 221	89.02 219	95.86 114	74.55 296	92.49 304	93.60 285	79.30 274	79.29 241	91.47 234	58.53 293	98.45 137	70.22 302	92.17 159	94.07 231

PS-MVSNAJss		84.91 219	84.30 211	86.74 271	85.89 341	74.40 298	94.95 251	94.16 253	83.93 182	76.45 269	90.11 259	71.04 216	95.77 269	83.16 191	79.02 269	90.06 273

CVMVSNet		84.83 220	85.57 188	82.63 331	91.55 255	60.38 377	95.13 244	95.03 198	80.60 243	82.10 210	94.71 176	66.40 244	90.19 371	74.30 273	90.32 170	97.31 140

FMVSNet3		84.71 221	82.71 238	90.70 181	94.55 155	87.71 23	95.92 207	94.67 219	81.73 227	75.82 282	88.08 284	66.99 239	94.47 322	71.23 293	75.38 288	89.91 275

VPNet		84.69 222	82.92 233	90.01 197	89.01 303	83.45 99	96.71 162	95.46 176	85.71 131	79.65 236	92.18 223	56.66 314	96.01 255	83.05 193	67.84 341	90.56 260

sd_testset		84.62 223	83.11 231	89.17 215	94.14 172	77.78 246	91.54 318	94.38 241	84.30 170	79.63 237	92.01 224	52.28 333	96.98 215	77.67 236	82.02 250	92.75 245

Effi-MVS+-dtu		84.61 224	84.90 203	83.72 321	91.96 248	63.14 369	94.95 251	93.34 300	85.57 133	79.79 235	87.12 298	61.99 271	95.61 282	83.55 185	85.83 219	92.41 249

miper_ehance_all_eth		84.57 225	83.60 224	87.50 256	92.64 221	78.25 227	95.40 233	93.47 290	79.28 275	76.41 270	87.64 289	76.53 130	95.24 298	78.58 228	72.42 302	89.01 296

DU-MVS		84.57 225	83.33 229	88.28 234	88.76 304	79.36 196	96.43 180	95.41 183	85.42 137	78.11 251	90.82 245	67.61 231	95.14 303	79.14 223	68.30 335	90.33 264

F-COLMAP		84.50 227	83.44 228	87.67 248	95.22 134	72.22 314	95.95 205	93.78 276	75.74 312	76.30 273	95.18 161	59.50 285	98.45 137	72.67 284	86.59 209	92.35 250

Anonymous202405211		84.41 228	81.93 249	91.85 145	96.78 93	78.41 222	97.44 99	91.34 337	70.29 351	84.06 181	94.26 185	41.09 373	98.96 109	79.46 218	82.65 245	98.17 74

WR-MVS		84.32 229	82.96 232	88.41 230	89.38 301	80.32 169	96.59 167	96.25 124	83.97 179	76.63 266	90.36 253	67.53 234	94.86 312	75.82 259	70.09 319	90.06 273

dp		84.30 230	82.31 243	90.28 191	94.24 168	77.97 237	86.57 357	95.53 170	79.94 262	80.75 223	85.16 331	71.49 212	96.39 241	63.73 333	83.36 235	96.48 174

LPG-MVS_test		84.20 231	83.49 227	86.33 277	90.88 268	73.06 309	95.28 234	94.13 254	82.20 218	76.31 271	93.20 208	54.83 327	96.95 217	83.72 181	80.83 255	88.98 297

dmvs_re		84.10 232	82.90 234	87.70 247	91.41 259	73.28 306	90.59 326	93.19 304	85.02 148	77.96 253	93.68 200	57.92 303	96.18 250	75.50 261	80.87 254	93.63 238

WB-MVSnew		84.08 233	83.51 226	85.80 286	91.34 260	76.69 268	95.62 223	96.27 122	81.77 226	81.81 215	92.81 214	58.23 295	94.70 316	66.66 317	87.06 204	85.99 352

ACMP	81.66 11	84.00 234	83.22 230	86.33 277	91.53 257	72.95 312	95.91 209	93.79 275	83.70 189	73.79 298	92.22 221	54.31 330	96.89 221	83.98 175	79.74 261	89.16 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 235	82.80 237	87.31 262	91.46 258	77.39 255	95.66 220	93.43 294	80.44 248	75.51 286	87.26 295	73.72 184	95.16 302	76.99 244	70.72 312	89.39 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 236	82.00 248	89.35 213	87.13 324	81.38 143	95.72 217	94.26 246	80.15 257	75.92 281	90.63 248	61.96 272	96.52 237	78.98 225	73.28 300	90.14 267

c3_l		83.80 237	82.65 239	87.25 264	92.10 241	77.74 249	95.25 237	93.04 312	78.58 286	76.01 278	87.21 297	75.25 162	95.11 305	77.54 239	68.89 329	88.91 302

LCM-MVSNet-Re		83.75 238	83.54 225	84.39 314	93.54 188	64.14 363	92.51 303	84.03 385	83.90 183	66.14 348	86.59 306	67.36 236	92.68 345	84.89 169	92.87 148	96.35 177

ACMM	80.70 13	83.72 239	82.85 236	86.31 280	91.19 262	72.12 318	95.88 210	94.29 245	80.44 248	77.02 261	91.96 228	55.24 323	97.14 210	79.30 221	80.38 257	89.67 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 240	81.38 257	90.39 188	93.53 193	78.19 233	85.56 365	95.09 195	70.78 349	78.51 247	83.28 349	74.80 168	97.03 212	66.77 316	84.05 230	95.95 187

CR-MVSNet		83.53 241	81.36 258	90.06 196	90.16 284	79.75 185	79.02 385	91.12 339	84.24 174	82.27 208	80.35 363	75.45 152	93.67 337	63.37 336	86.25 212	96.75 167

v2v482		83.46 242	81.86 250	88.25 236	86.19 335	79.65 190	96.34 186	94.02 260	81.56 229	77.32 257	88.23 281	65.62 246	96.03 253	77.77 232	69.72 323	89.09 291

NR-MVSNet		83.35 243	81.52 256	88.84 222	88.76 304	81.31 145	94.45 260	95.16 193	84.65 159	67.81 337	90.82 245	70.36 223	94.87 311	74.75 267	66.89 350	90.33 264

Fast-Effi-MVS+-dtu		83.33 244	82.60 240	85.50 294	89.55 297	69.38 342	96.09 201	91.38 334	82.30 217	75.96 280	91.41 235	56.71 312	95.58 284	75.13 265	84.90 227	91.54 251

cl____		83.27 245	82.12 245	86.74 271	92.20 234	75.95 282	95.11 246	93.27 302	78.44 289	74.82 293	87.02 300	74.19 177	95.19 300	74.67 269	69.32 325	89.09 291

DIV-MVS_self_test		83.27 245	82.12 245	86.74 271	92.19 235	75.92 284	95.11 246	93.26 303	78.44 289	74.81 294	87.08 299	74.19 177	95.19 300	74.66 270	69.30 326	89.11 290

TranMVSNet+NR-MVSNet		83.24 247	81.71 252	87.83 244	87.71 319	78.81 212	96.13 200	94.82 209	84.52 162	76.18 277	90.78 247	64.07 258	94.60 319	74.60 271	66.59 352	90.09 271

Anonymous20240529		83.15 248	80.60 268	90.80 177	95.74 119	78.27 226	96.81 155	94.92 202	60.10 386	81.89 213	92.54 218	45.82 357	98.82 118	79.25 222	78.32 278	95.31 206

eth_miper_zixun_eth		83.12 249	82.01 247	86.47 276	91.85 253	74.80 292	94.33 264	93.18 306	79.11 278	75.74 285	87.25 296	72.71 193	95.32 294	76.78 247	67.13 347	89.27 286

MS-PatchMatch		83.05 250	81.82 251	86.72 275	89.64 295	79.10 205	94.88 253	94.59 228	79.70 266	70.67 325	89.65 263	50.43 340	96.82 226	70.82 300	95.99 109	84.25 365

V42		83.04 251	81.53 255	87.57 254	86.27 334	79.09 206	95.87 211	94.11 256	80.35 252	77.22 259	86.79 304	65.32 251	96.02 254	77.74 233	70.14 315	87.61 327

tpmvs		83.04 251	80.77 264	89.84 205	95.43 126	77.96 238	85.59 364	95.32 188	75.31 316	76.27 274	83.70 345	73.89 181	97.41 190	59.53 347	81.93 252	94.14 228

test_djsdf		83.00 253	82.45 242	84.64 307	84.07 361	69.78 338	94.80 256	94.48 232	80.74 240	75.41 288	87.70 288	61.32 277	95.10 306	83.77 179	79.76 259	89.04 294

v1144		82.90 254	81.27 259	87.78 246	86.29 333	79.07 207	96.14 198	93.93 262	80.05 259	77.38 255	86.80 303	65.50 247	95.93 261	75.21 264	70.13 316	88.33 314

test0.0.03 1		82.79 255	82.48 241	83.74 320	86.81 327	72.22 314	96.52 171	95.03 198	83.76 187	73.00 308	93.20 208	72.30 201	88.88 374	64.15 331	77.52 281	90.12 268

FMVSNet2		82.79 255	80.44 270	89.83 206	92.66 218	85.43 56	95.42 231	94.35 242	79.06 280	74.46 295	87.28 293	56.38 317	94.31 325	69.72 305	74.68 292	89.76 276

D2MVS		82.67 257	81.55 254	86.04 284	87.77 318	76.47 269	95.21 239	96.58 89	82.66 211	70.26 328	85.46 326	60.39 279	95.80 268	76.40 252	79.18 267	85.83 355

MVP-Stereo		82.65 258	81.67 253	85.59 293	86.10 338	78.29 225	93.33 289	92.82 315	77.75 294	69.17 335	87.98 285	59.28 288	95.76 270	71.77 288	96.88 89	82.73 373
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 259	80.79 263	87.79 245	86.11 337	80.49 168	93.55 284	93.18 306	77.29 300	73.35 304	89.40 266	65.26 252	95.05 309	75.32 263	73.61 296	87.83 322

v144192		82.43 260	80.73 265	87.54 255	85.81 342	78.22 228	95.98 203	93.78 276	79.09 279	77.11 260	86.49 308	64.66 257	95.91 262	74.20 274	69.42 324	88.49 308

GBi-Net		82.42 261	80.43 271	88.39 231	92.66 218	81.95 122	94.30 266	93.38 296	79.06 280	75.82 282	85.66 319	56.38 317	93.84 333	71.23 293	75.38 288	89.38 281

test1		82.42 261	80.43 271	88.39 231	92.66 218	81.95 122	94.30 266	93.38 296	79.06 280	75.82 282	85.66 319	56.38 317	93.84 333	71.23 293	75.38 288	89.38 281

v148		82.41 263	80.89 262	86.99 269	86.18 336	76.81 265	96.27 189	93.82 271	80.49 247	75.28 289	86.11 318	67.32 237	95.75 271	75.48 262	67.03 349	88.42 312

v1192		82.31 264	80.55 269	87.60 251	85.94 339	78.47 221	95.85 213	93.80 274	79.33 272	76.97 262	86.51 307	63.33 262	95.87 264	73.11 281	70.13 316	88.46 310

LS3D		82.22 265	79.94 279	89.06 217	97.43 79	74.06 301	93.20 295	92.05 325	61.90 375	73.33 305	95.21 158	59.35 286	99.21 88	54.54 367	92.48 154	93.90 234

jajsoiax		82.12 266	81.15 261	85.03 301	84.19 359	70.70 331	94.22 270	93.95 261	83.07 199	73.48 300	89.75 262	49.66 344	95.37 291	82.24 198	79.76 259	89.02 295

v1921920		82.02 267	80.23 273	87.41 259	85.62 343	77.92 241	95.79 216	93.69 281	78.86 283	76.67 265	86.44 310	62.50 265	95.83 266	72.69 283	69.77 322	88.47 309

myMVS_eth3d		81.93 268	82.18 244	81.18 340	92.13 239	67.18 351	93.97 274	94.23 247	82.43 214	73.39 301	93.57 204	76.98 122	87.86 378	50.53 378	82.34 247	88.51 306

v8		81.88 269	80.06 277	87.32 261	86.63 328	79.04 208	94.41 261	93.65 283	78.77 284	73.19 307	85.57 323	66.87 240	95.81 267	73.84 278	67.61 343	87.11 336

mvs_tets		81.74 270	80.71 266	84.84 302	84.22 358	70.29 334	93.91 276	93.78 276	82.77 208	73.37 303	89.46 265	47.36 354	95.31 295	81.99 199	79.55 265	88.92 301

v1240		81.70 271	79.83 281	87.30 263	85.50 344	77.70 250	95.48 228	93.44 292	78.46 288	76.53 268	86.44 310	60.85 278	95.84 265	71.59 290	70.17 314	88.35 313

PVSNet_0	77.72 15	81.70 271	78.95 288	89.94 202	90.77 274	76.72 267	95.96 204	96.95 42	85.01 149	70.24 329	88.53 277	52.32 332	98.20 148	86.68 157	44.08 396	94.89 214

miper_lstm_enhance		81.66 273	80.66 267	84.67 306	91.19 262	71.97 321	91.94 310	93.19 304	77.86 293	72.27 315	85.26 327	73.46 187	93.42 341	73.71 279	67.05 348	88.61 304

DP-MVS		81.47 274	78.28 291	91.04 169	98.14 55	78.48 218	95.09 249	86.97 372	61.14 382	71.12 322	92.78 217	59.59 283	99.38 78	53.11 371	86.61 208	95.27 208

v10		81.43 275	79.53 283	87.11 266	86.38 330	78.87 209	94.31 265	93.43 294	77.88 292	73.24 306	85.26 327	65.44 248	95.75 271	72.14 287	67.71 342	86.72 340

pmmvs5		81.34 276	79.54 282	86.73 274	85.02 351	76.91 262	96.22 192	91.65 331	77.65 295	73.55 299	88.61 274	55.70 320	94.43 323	74.12 275	73.35 299	88.86 303

ADS-MVSNet		81.26 277	78.36 290	89.96 201	93.78 181	79.78 183	79.48 381	93.60 285	73.09 335	80.14 231	79.99 366	62.15 268	95.24 298	59.49 348	83.52 232	94.85 216

Baseline_NR-MVSNet		81.22 278	80.07 276	84.68 305	85.32 349	75.12 290	96.48 174	88.80 363	76.24 311	77.28 258	86.40 313	67.61 231	94.39 324	75.73 260	66.73 351	84.54 362

tt0805		81.20 279	79.06 287	87.61 250	86.50 329	72.97 311	93.66 280	95.48 174	74.11 325	76.23 275	91.99 226	41.36 372	97.40 191	77.44 241	74.78 291	92.45 248

WR-MVS_H		81.02 280	80.09 274	83.79 318	88.08 314	71.26 330	94.46 259	96.54 93	80.08 258	72.81 311	86.82 302	70.36 223	92.65 346	64.18 330	67.50 344	87.46 333

CP-MVSNet		81.01 281	80.08 275	83.79 318	87.91 317	70.51 332	94.29 269	95.65 165	80.83 237	72.54 314	88.84 271	63.71 259	92.32 349	68.58 310	68.36 334	88.55 305

anonymousdsp		80.98 282	79.97 278	84.01 315	81.73 371	70.44 333	92.49 304	93.58 287	77.10 304	72.98 309	86.31 314	57.58 304	94.90 310	79.32 220	78.63 274	86.69 341

UniMVSNet_ETH3D		80.86 283	78.75 289	87.22 265	86.31 332	72.02 319	91.95 309	93.76 279	73.51 330	75.06 291	90.16 257	43.04 366	95.66 276	76.37 253	78.55 275	93.98 232

testing3		80.74 284	81.17 260	79.44 349	91.15 264	63.48 367	97.16 121	95.76 159	80.83 237	71.36 319	93.15 211	78.22 101	87.30 383	43.19 391	79.67 262	87.55 331

IterMVS		80.67 285	79.16 285	85.20 298	89.79 289	76.08 276	92.97 299	91.86 327	80.28 254	71.20 321	85.14 332	57.93 302	91.34 361	72.52 285	70.74 311	88.18 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 286	77.77 296	89.14 216	93.43 195	77.24 257	91.89 311	90.18 351	69.86 355	68.02 336	91.94 230	52.21 334	98.84 117	59.32 350	83.12 236	91.35 252

IterMVS-SCA-FT		80.51 287	79.10 286	84.73 304	89.63 296	74.66 293	92.98 298	91.81 329	80.05 259	71.06 323	85.18 330	58.04 298	91.40 360	72.48 286	70.70 313	88.12 318

PS-CasMVS		80.27 288	79.18 284	83.52 324	87.56 321	69.88 337	94.08 272	95.29 189	80.27 255	72.08 316	88.51 278	59.22 289	92.23 351	67.49 312	68.15 337	88.45 311

pm-mvs1		80.05 289	78.02 294	86.15 282	85.42 345	75.81 285	95.11 246	92.69 318	77.13 302	70.36 327	87.43 291	58.44 294	95.27 297	71.36 292	64.25 359	87.36 334

RPMNet		79.85 290	75.92 309	91.64 151	90.16 284	79.75 185	79.02 385	95.44 178	58.43 391	82.27 208	72.55 388	73.03 191	98.41 140	46.10 387	86.25 212	96.75 167

PatchT		79.75 291	76.85 303	88.42 229	89.55 297	75.49 287	77.37 389	94.61 226	63.07 371	82.46 202	73.32 385	75.52 151	93.41 342	51.36 374	84.43 228	96.36 176

Anonymous20231211		79.72 292	77.19 300	87.33 260	95.59 123	77.16 261	95.18 243	94.18 252	59.31 389	72.57 313	86.20 316	47.89 351	95.66 276	74.53 272	69.24 327	89.18 288

test_fmvs2		79.59 293	79.90 280	78.67 352	82.86 368	55.82 388	95.20 240	89.55 355	81.09 233	80.12 233	89.80 261	34.31 386	93.51 340	87.82 144	78.36 277	86.69 341

ADS-MVSNet2		79.57 294	77.53 297	85.71 289	93.78 181	72.13 317	79.48 381	86.11 378	73.09 335	80.14 231	79.99 366	62.15 268	90.14 372	59.49 348	83.52 232	94.85 216

FMVSNet1		79.50 295	76.54 305	88.39 231	88.47 309	81.95 122	94.30 266	93.38 296	73.14 334	72.04 317	85.66 319	43.86 360	93.84 333	65.48 325	72.53 301	89.38 281

PEN-MVS		79.47 296	78.26 292	83.08 327	86.36 331	68.58 345	93.85 278	94.77 214	79.76 264	71.37 318	88.55 275	59.79 281	92.46 347	64.50 329	65.40 354	88.19 316

XVG-ACMP-BASELINE		79.38 297	77.90 295	83.81 317	84.98 352	67.14 355	89.03 336	93.18 306	80.26 256	72.87 310	88.15 283	38.55 377	96.26 246	76.05 256	78.05 279	88.02 319

v7n		79.32 298	77.34 298	85.28 297	84.05 362	72.89 313	93.38 287	93.87 268	75.02 319	70.68 324	84.37 339	59.58 284	95.62 281	67.60 311	67.50 344	87.32 335

MIMVSNet		79.18 299	75.99 308	88.72 226	87.37 323	80.66 161	79.96 380	91.82 328	77.38 299	74.33 296	81.87 355	41.78 369	90.74 367	66.36 323	83.10 237	94.76 218

JIA-IIPM		79.00 300	77.20 299	84.40 313	89.74 293	64.06 364	75.30 393	95.44 178	62.15 374	81.90 212	59.08 397	78.92 90	95.59 283	66.51 321	85.78 220	93.54 239

USDC		78.65 301	76.25 306	85.85 285	87.58 320	74.60 295	89.58 332	90.58 350	84.05 176	63.13 360	88.23 281	40.69 376	96.86 225	66.57 320	75.81 286	86.09 350

DTE-MVSNet		78.37 302	77.06 301	82.32 334	85.22 350	67.17 354	93.40 286	93.66 282	78.71 285	70.53 326	88.29 280	59.06 290	92.23 351	61.38 343	63.28 363	87.56 329

Patchmatch-test		78.25 303	74.72 317	88.83 223	91.20 261	74.10 300	73.91 396	88.70 366	59.89 387	66.82 343	85.12 333	78.38 98	94.54 320	48.84 383	79.58 264	97.86 98

tfpnnormal		78.14 304	75.42 311	86.31 280	88.33 312	79.24 199	94.41 261	96.22 127	73.51 330	69.81 331	85.52 325	55.43 321	95.75 271	47.65 385	67.86 340	83.95 368

ACMH	75.40 17	77.99 305	74.96 313	87.10 267	90.67 275	76.41 271	93.19 296	91.64 332	72.47 341	63.44 358	87.61 290	43.34 363	97.16 206	58.34 352	73.94 294	87.72 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 305	75.74 310	84.74 303	90.45 279	72.02 319	86.41 359	91.12 339	72.57 340	66.63 345	87.27 294	54.95 326	96.98 215	56.29 362	75.98 283	85.21 359
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
Syy-MVS		77.97 307	78.05 293	77.74 356	92.13 239	56.85 384	93.97 274	94.23 247	82.43 214	73.39 301	93.57 204	57.95 301	87.86 378	32.40 398	82.34 247	88.51 306

our_test_3		77.90 308	75.37 312	85.48 295	85.39 346	76.74 266	93.63 281	91.67 330	73.39 333	65.72 350	84.65 338	58.20 297	93.13 344	57.82 354	67.87 339	86.57 343

RPSCF		77.73 309	76.63 304	81.06 341	88.66 308	55.76 389	87.77 348	87.88 369	64.82 369	74.14 297	92.79 216	49.22 345	96.81 227	67.47 313	76.88 282	90.62 258

KD-MVS_2432*1600		77.63 310	74.92 315	85.77 287	90.86 271	79.44 193	88.08 344	93.92 264	76.26 309	67.05 341	82.78 351	72.15 203	91.92 354	61.53 340	41.62 399	85.94 353

miper_refine_blended		77.63 310	74.92 315	85.77 287	90.86 271	79.44 193	88.08 344	93.92 264	76.26 309	67.05 341	82.78 351	72.15 203	91.92 354	61.53 340	41.62 399	85.94 353

ACMH+	76.62 16	77.47 312	74.94 314	85.05 300	91.07 266	71.58 327	93.26 293	90.01 352	71.80 344	64.76 353	88.55 275	41.62 370	96.48 238	62.35 339	71.00 309	87.09 337

Patchmtry		77.36 313	74.59 318	85.67 290	89.75 291	75.75 286	77.85 388	91.12 339	60.28 384	71.23 320	80.35 363	75.45 152	93.56 339	57.94 353	67.34 346	87.68 325

ppachtmachnet_test		77.19 314	74.22 322	86.13 283	85.39 346	78.22 228	93.98 273	91.36 336	71.74 345	67.11 340	84.87 336	56.67 313	93.37 343	52.21 372	64.59 356	86.80 339

OurMVSNet-221017-0		77.18 315	76.06 307	80.55 344	83.78 365	60.00 379	90.35 327	91.05 342	77.01 306	66.62 346	87.92 286	47.73 352	94.03 330	71.63 289	68.44 333	87.62 326

TransMVSNet (Re)		76.94 316	74.38 320	84.62 308	85.92 340	75.25 289	95.28 234	89.18 360	73.88 328	67.22 338	86.46 309	59.64 282	94.10 329	59.24 351	52.57 384	84.50 363

EU-MVSNet		76.92 317	76.95 302	76.83 360	84.10 360	54.73 392	91.77 313	92.71 317	72.74 338	69.57 332	88.69 273	58.03 300	87.43 382	64.91 328	70.00 320	88.33 314

Patchmatch-RL test		76.65 318	74.01 325	84.55 309	77.37 385	64.23 362	78.49 387	82.84 389	78.48 287	64.63 354	73.40 384	76.05 140	91.70 359	76.99 244	57.84 372	97.72 109

FMVSNet5		76.46 319	74.16 323	83.35 326	90.05 287	76.17 274	89.58 332	89.85 353	71.39 347	65.29 352	80.42 362	50.61 339	87.70 381	61.05 345	69.24 327	86.18 348

SixPastTwentyTwo		76.04 320	74.32 321	81.22 339	84.54 355	61.43 375	91.16 321	89.30 359	77.89 291	64.04 355	86.31 314	48.23 346	94.29 326	63.54 335	63.84 361	87.93 321

AllTest		75.92 321	73.06 329	84.47 310	92.18 236	67.29 349	91.07 322	84.43 383	67.63 360	63.48 356	90.18 255	38.20 378	97.16 206	57.04 358	73.37 297	88.97 299

CL-MVSNet_self_test		75.81 322	74.14 324	80.83 343	78.33 381	67.79 348	94.22 270	93.52 289	77.28 301	69.82 330	81.54 357	61.47 276	89.22 373	57.59 356	53.51 380	85.48 357

COLMAP_ROB	73.24 19	75.74 323	73.00 330	83.94 316	92.38 224	69.08 343	91.85 312	86.93 373	61.48 379	65.32 351	90.27 254	42.27 368	96.93 220	50.91 376	75.63 287	85.80 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 324	74.56 319	79.17 351	79.69 377	55.98 386	89.59 331	93.30 301	60.28 384	53.85 388	89.07 268	47.68 353	96.33 244	76.55 249	81.02 253	85.22 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 325	73.64 326	80.22 345	80.75 372	63.38 368	93.36 288	90.71 349	73.09 335	67.12 339	83.70 345	50.33 341	90.85 366	53.63 370	70.10 318	86.44 344

EG-PatchMatch MVS		74.92 326	72.02 334	83.62 322	83.76 366	73.28 306	93.62 282	92.04 326	68.57 358	58.88 377	83.80 344	31.87 390	95.57 285	56.97 360	78.67 271	82.00 379

testgi		74.88 327	73.40 327	79.32 350	80.13 376	61.75 372	93.21 294	86.64 376	79.49 270	66.56 347	91.06 241	35.51 384	88.67 375	56.79 361	71.25 307	87.56 329

pmmvs6		74.65 328	71.67 335	83.60 323	79.13 379	69.94 336	93.31 292	90.88 346	61.05 383	65.83 349	84.15 342	43.43 362	94.83 313	66.62 318	60.63 368	86.02 351

test_vis1_rt		73.96 329	72.40 332	78.64 353	83.91 363	61.16 376	95.63 222	68.18 405	76.32 308	60.09 374	74.77 379	29.01 394	97.54 181	87.74 145	75.94 284	77.22 388

K. test v3		73.62 330	71.59 336	79.69 347	82.98 367	59.85 380	90.85 325	88.83 362	77.13 302	58.90 376	82.11 353	43.62 361	91.72 358	65.83 324	54.10 379	87.50 332

pmmvs-eth3d		73.59 331	70.66 339	82.38 332	76.40 389	73.38 303	89.39 335	89.43 357	72.69 339	60.34 373	77.79 372	46.43 356	91.26 363	66.42 322	57.06 373	82.51 374

kuosan		73.55 332	72.39 333	77.01 358	89.68 294	66.72 356	85.24 367	93.44 292	67.76 359	60.04 375	83.40 348	71.90 206	84.25 390	45.34 388	54.75 375	80.06 384

MDA-MVSNet_test_wron		73.54 333	70.43 341	82.86 328	84.55 354	71.85 322	91.74 314	91.32 338	67.63 360	46.73 392	81.09 360	55.11 324	90.42 370	55.91 364	59.76 369	86.31 346

YYNet1		73.53 334	70.43 341	82.85 329	84.52 356	71.73 325	91.69 315	91.37 335	67.63 360	46.79 391	81.21 359	55.04 325	90.43 369	55.93 363	59.70 370	86.38 345

UnsupCasMVSNet_eth		73.25 335	70.57 340	81.30 338	77.53 383	66.33 357	87.24 352	93.89 267	80.38 251	57.90 381	81.59 356	42.91 367	90.56 368	65.18 327	48.51 389	87.01 338

DSMNet-mixed		73.13 336	72.45 331	75.19 366	77.51 384	46.82 397	85.09 368	82.01 390	67.61 364	69.27 334	81.33 358	50.89 337	86.28 385	54.54 367	83.80 231	92.46 247

OpenMVS_ROB	68.52 20	73.02 337	69.57 344	83.37 325	80.54 375	71.82 323	93.60 283	88.22 367	62.37 373	61.98 366	83.15 350	35.31 385	95.47 287	45.08 389	75.88 285	82.82 371

test_0402		72.68 338	69.54 345	82.09 335	88.67 307	71.81 324	92.72 302	86.77 375	61.52 377	62.21 365	83.91 343	43.22 364	93.76 336	34.60 396	72.23 305	80.72 383

TinyColmap		72.41 339	68.99 348	82.68 330	88.11 313	69.59 340	88.41 341	85.20 380	65.55 366	57.91 380	84.82 337	30.80 392	95.94 260	51.38 373	68.70 330	82.49 376

test20.03		72.36 340	71.15 337	75.98 364	77.79 382	59.16 381	92.40 306	89.35 358	74.09 326	61.50 368	84.32 340	48.09 347	85.54 388	50.63 377	62.15 366	83.24 369

LF4IMVS		72.36 340	70.82 338	76.95 359	79.18 378	56.33 385	86.12 361	86.11 378	69.30 357	63.06 361	86.66 305	33.03 388	92.25 350	65.33 326	68.64 331	82.28 377

Anonymous20240521		72.06 342	69.91 343	78.50 354	77.11 386	61.67 374	91.62 317	90.97 344	65.52 367	62.37 364	79.05 369	36.32 380	90.96 365	57.75 355	68.52 332	82.87 370

dmvs_testset		72.00 343	73.36 328	67.91 372	83.83 364	31.90 412	85.30 366	77.12 397	82.80 207	63.05 362	92.46 219	61.54 275	82.55 394	42.22 393	71.89 306	89.29 285

MDA-MVSNet-bldmvs		71.45 344	67.94 350	81.98 336	85.33 348	68.50 346	92.35 307	88.76 364	70.40 350	42.99 395	81.96 354	46.57 355	91.31 362	48.75 384	54.39 378	86.11 349

MVS-HIRNet		71.36 345	67.00 351	84.46 312	90.58 276	69.74 339	79.15 384	87.74 371	46.09 396	61.96 367	50.50 400	45.14 358	95.64 279	53.74 369	88.11 196	88.00 320

KD-MVS_self_test		70.97 346	69.31 346	75.95 365	76.24 391	55.39 390	87.45 349	90.94 345	70.20 352	62.96 363	77.48 373	44.01 359	88.09 376	61.25 344	53.26 381	84.37 364

test_fmvs3		69.56 347	69.19 347	70.67 370	69.01 396	47.05 396	90.87 324	86.81 374	71.31 348	66.79 344	77.15 374	16.40 401	83.17 392	81.84 200	62.51 365	81.79 381

dongtai		69.47 348	68.98 349	70.93 369	86.87 326	58.45 382	88.19 343	93.18 306	63.98 370	56.04 384	80.17 365	70.97 219	79.24 396	33.46 397	47.94 391	75.09 390

MIMVSNet1		69.44 349	66.65 353	77.84 355	76.48 388	62.84 370	87.42 350	88.97 361	66.96 365	57.75 382	79.72 368	32.77 389	85.83 387	46.32 386	63.42 362	84.85 361

PM-MVS		69.32 350	66.93 352	76.49 361	73.60 393	55.84 387	85.91 362	79.32 395	74.72 321	61.09 370	78.18 371	21.76 397	91.10 364	70.86 298	56.90 374	82.51 374

TDRefinement		69.20 351	65.78 355	79.48 348	66.04 401	62.21 371	88.21 342	86.12 377	62.92 372	61.03 371	85.61 322	33.23 387	94.16 328	55.82 365	53.02 382	82.08 378

new-patchmatchnet		68.85 352	65.93 354	77.61 357	73.57 394	63.94 365	90.11 329	88.73 365	71.62 346	55.08 386	73.60 383	40.84 374	87.22 384	51.35 375	48.49 390	81.67 382

UnsupCasMVSNet_bld		68.60 353	64.50 357	80.92 342	74.63 392	67.80 347	83.97 373	92.94 313	65.12 368	54.63 387	68.23 393	35.97 382	92.17 353	60.13 346	44.83 394	82.78 372

mvsany_test3		67.19 354	65.34 356	72.72 368	63.08 402	48.57 395	83.12 376	78.09 396	72.07 342	61.21 369	77.11 375	22.94 396	87.78 380	78.59 227	51.88 385	81.80 380

new_pmnet		66.18 355	63.18 358	75.18 367	76.27 390	61.74 373	83.79 374	84.66 382	56.64 393	51.57 389	71.85 391	31.29 391	87.93 377	49.98 379	62.55 364	75.86 389

pmmvs3		65.75 356	62.18 359	76.45 362	67.12 400	64.54 360	88.68 339	85.05 381	54.77 395	57.54 383	73.79 382	29.40 393	86.21 386	55.49 366	47.77 392	78.62 386

test_f		64.01 357	62.13 360	69.65 371	63.00 403	45.30 402	83.66 375	80.68 392	61.30 380	55.70 385	72.62 387	14.23 403	84.64 389	69.84 303	58.11 371	79.00 385

N_pmnet		61.30 358	60.20 361	64.60 377	84.32 357	17.00 418	91.67 316	10.98 416	61.77 376	58.45 379	78.55 370	49.89 343	91.83 357	42.27 392	63.94 360	84.97 360

WB-MVS		57.26 359	56.22 362	60.39 383	69.29 395	35.91 410	86.39 360	70.06 403	59.84 388	46.46 393	72.71 386	51.18 336	78.11 397	15.19 407	34.89 402	67.14 396

test_method		56.77 360	54.53 364	63.49 379	76.49 387	40.70 405	75.68 392	74.24 399	19.47 407	48.73 390	71.89 390	19.31 398	65.80 407	57.46 357	47.51 393	83.97 367

APD_test1		56.56 361	53.58 365	65.50 374	67.93 399	46.51 399	77.24 391	72.95 400	38.09 398	42.75 396	75.17 378	13.38 404	82.78 393	40.19 394	54.53 377	67.23 395

SSC-MVS		56.01 362	54.96 363	59.17 384	68.42 397	34.13 411	84.98 369	69.23 404	58.08 392	45.36 394	71.67 392	50.30 342	77.46 398	14.28 408	32.33 403	65.91 397

FPMVS		55.09 363	52.93 366	61.57 381	55.98 405	40.51 406	83.11 377	83.41 388	37.61 399	34.95 400	71.95 389	14.40 402	76.95 399	29.81 399	65.16 355	67.25 394

test_vis3_rt		54.10 364	51.04 367	63.27 380	58.16 404	46.08 401	84.17 371	49.32 415	56.48 394	36.56 399	49.48 402	8.03 411	91.91 356	67.29 314	49.87 386	51.82 401

LCM-MVSNet		52.52 365	48.24 368	65.35 375	47.63 412	41.45 404	72.55 397	83.62 387	31.75 400	37.66 398	57.92 398	9.19 410	76.76 400	49.26 381	44.60 395	77.84 387

EGC-MVSNET		52.46 366	47.56 369	67.15 373	81.98 370	60.11 378	82.54 378	72.44 401	0.11 413	0.70 414	74.59 380	25.11 395	83.26 391	29.04 400	61.51 367	58.09 398

PMMVS2		50.90 367	46.31 370	64.67 376	55.53 406	46.67 398	77.30 390	71.02 402	40.89 397	34.16 401	59.32 396	9.83 409	76.14 402	40.09 395	28.63 404	71.21 391

ANet_high		46.22 368	41.28 375	61.04 382	39.91 414	46.25 400	70.59 398	76.18 398	58.87 390	23.09 406	48.00 403	12.58 406	66.54 406	28.65 401	13.62 407	70.35 392

testf1		45.70 369	42.41 371	55.58 385	53.29 409	40.02 407	68.96 399	62.67 409	27.45 402	29.85 402	61.58 394	5.98 412	73.83 404	28.49 402	43.46 397	52.90 399

APD_test2		45.70 369	42.41 371	55.58 385	53.29 409	40.02 407	68.96 399	62.67 409	27.45 402	29.85 402	61.58 394	5.98 412	73.83 404	28.49 402	43.46 397	52.90 399

Gipuma		45.11 371	42.05 373	54.30 387	80.69 373	51.30 394	35.80 405	83.81 386	28.13 401	27.94 405	34.53 405	11.41 408	76.70 401	21.45 404	54.65 376	34.90 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 372	41.93 374	40.38 390	20.10 416	26.84 414	61.93 402	59.09 411	14.81 409	28.51 404	80.58 361	35.53 383	48.33 411	63.70 334	13.11 408	45.96 404

PMVS	34.80 23	39.19 373	35.53 376	50.18 388	29.72 415	30.30 413	59.60 403	66.20 408	26.06 404	17.91 408	49.53 401	3.12 414	74.09 403	18.19 406	49.40 387	46.14 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 374	29.49 379	46.92 389	41.86 413	36.28 409	50.45 404	56.52 412	18.75 408	18.28 407	37.84 404	2.41 415	58.41 408	18.71 405	20.62 405	46.06 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 375	32.39 377	33.65 391	53.35 408	25.70 415	74.07 395	53.33 413	21.08 405	17.17 409	33.63 407	11.85 407	54.84 409	12.98 409	14.04 406	20.42 406

EMVS		31.70 376	31.45 378	32.48 392	50.72 411	23.95 416	74.78 394	52.30 414	20.36 406	16.08 410	31.48 408	12.80 405	53.60 410	11.39 410	13.10 409	19.88 407

cdsmvs_eth3d_5k		21.43 377	28.57 380	0.00 396	0.00 419	0.00 421	0.00 407	95.93 151	0.00 414	0.00 415	97.66 72	63.57 260	0.00 415	0.00 414	0.00 413	0.00 411

wuyk23d		14.10 378	13.89 381	14.72 393	55.23 407	22.91 417	33.83 406	3.56 417	4.94 410	4.11 411	2.28 413	2.06 416	19.66 412	10.23 411	8.74 410	1.59 410

testmvs		9.92 379	12.94 382	0.84 395	0.65 417	0.29 420	93.78 279	0.39 418	0.42 411	2.85 412	15.84 411	0.17 418	0.30 414	2.18 412	0.21 411	1.91 409

test123		9.07 380	11.73 383	1.11 394	0.50 418	0.77 419	89.44 334	0.20 419	0.34 412	2.15 413	10.72 412	0.34 417	0.32 413	1.79 413	0.08 412	2.23 408

ab-mvs-re		8.11 381	10.81 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	97.30 95	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		5.92 382	7.89 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	71.04 216	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						67.18 351								49.00 382

FOURS1					98.51 39	78.01 236	98.13 49	96.21 128	83.04 200	94.39 52

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 52					99.81 21	98.08 14	98.81 24	99.43 11

PC_three_1452									91.12 36	98.33 2	98.42 28	92.51 2	99.81 21	98.96 3	99.37 1	99.70 3

No_MVS				97.14 3	99.05 9	92.19 4		96.83 52					99.81 21	98.08 14	98.81 24	99.43 11

test_one_0601					98.91 18	84.56 80		96.70 71	88.06 80	96.57 23	98.77 10	88.04 19

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					99.09 8	83.22 104		96.60 87	82.88 205	93.61 63	98.06 50	82.93 52	99.14 97	95.51 49	98.49 37

RE-MVS-def			91.18 89		97.76 67	76.03 278	96.20 195	95.44 178	80.56 245	90.72 104	97.84 64	73.36 189		91.99 93	96.79 92	97.75 107

IU-MVS					99.03 15	85.34 57		96.86 51	92.05 28	98.74 1				98.15 11	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 11				98.54 20	92.06 3	99.84 12	99.11 2	99.37 1	99.74 1

test_241102_TWO								96.78 55	88.72 65	97.70 8	98.91 2	87.86 20	99.82 18	98.15 11	99.00 15	99.47 9

test_241102_ONE					99.03 15	85.03 70		96.78 55	88.72 65	97.79 6	98.90 5	88.48 16	99.82 18

9.14			94.26 30		98.10 57		98.14 46	96.52 95	84.74 155	94.83 47	98.80 7	82.80 54	99.37 80	95.95 40	98.42 40

save fliter					98.24 51	83.34 101	98.61 33	96.57 90	91.32 33

test_0728_THIRD									88.38 73	96.69 18	98.76 12	89.64 12	99.76 31	97.47 24	98.84 23	99.38 14

test_0728_SECOND				95.14 19	99.04 14	86.14 38	99.06 16	96.77 61					99.84 12	97.90 17	98.85 21	99.45 10

test0726					99.05 9	85.18 62	99.11 14	96.78 55	88.75 63	97.65 11	98.91 2	87.69 21

GSMVS																97.54 122

test_part2					98.90 19	85.14 68				96.07 29

sam_mvs1												77.59 111				97.54 122

sam_mvs												75.35 159

ambc				76.02 363	68.11 398	51.43 393	64.97 401	89.59 354		60.49 372	74.49 381	17.17 400	92.46 347	61.50 342	52.85 383	84.17 366

MTGPA								96.33 118

test_post1							85.88 363				30.24 409	73.77 182	95.07 308	73.89 276

test_post											33.80 406	76.17 138	95.97 256

patchmatchnet-post											77.09 376	77.78 110	95.39 289

GG-mvs-BLEND				93.49 73	94.94 144	86.26 36	81.62 379	97.00 37		88.32 141	94.30 184	91.23 5	96.21 249	88.49 138	97.43 76	98.00 88

MTMP							97.53 91	68.16 406

gm-plane-assit					92.27 230	79.64 191			84.47 165		95.15 163		97.93 156	85.81 161

test9_res														96.00 39	99.03 13	98.31 66

TEST9					98.64 31	83.71 92	97.82 69	96.65 78	84.29 172	95.16 36	98.09 45	84.39 36	99.36 81

test_8					98.63 33	83.64 95	97.81 71	96.63 83	84.50 163	95.10 40	98.11 44	84.33 37	99.23 86

agg_prior2														94.30 60	99.00 15	98.57 50

agg_prior					98.59 35	83.13 105		96.56 92		94.19 54			99.16 96

TestCases				84.47 310	92.18 236	67.29 349		84.43 383	67.63 360	63.48 356	90.18 255	38.20 378	97.16 206	57.04 358	73.37 297	88.97 299

test_prior4						82.34 118	97.75 76

test_prior2							98.37 39		86.08 124	94.57 50	98.02 51	83.14 49		95.05 52	98.79 26

test_prior				93.09 87	98.68 26	81.91 126		96.40 110					99.06 104			98.29 68

旧先验2							96.97 141		74.06 327	96.10 28			97.76 167	88.38 140

新几何2							96.42 181

新几何1				93.12 85	97.44 78	81.60 141		96.71 70	74.54 323	91.22 97	97.57 80	79.13 87	99.51 71	77.40 242	98.46 38	98.26 71

旧先验1					97.39 82	79.58 192		96.54 93			98.08 48	84.00 42			97.42 77	97.62 118

无先验							96.87 150	96.78 55	77.39 298				99.52 69	79.95 214		98.43 59

原ACMM2							96.84 151

原ACMM1				91.22 165	97.77 65	78.10 234		96.61 84	81.05 234	91.28 96	97.42 89	77.92 107	98.98 108	79.85 216	98.51 34	96.59 171

test222					96.15 103	78.41 222	95.87 211	96.46 102	71.97 343	89.66 117	97.45 85	76.33 136			98.24 50	98.30 67

testdata2													99.48 73	76.45 251

segment_acmp												82.69 55

testdata				90.13 195	95.92 113	74.17 299		96.49 101	73.49 332	94.82 48	97.99 52	78.80 94	97.93 156	83.53 187	97.52 72	98.29 68

testdata1							95.57 226		87.44 96

test12				94.25 40	98.34 46	85.55 53		96.35 117		92.36 77		80.84 63	99.22 87		98.31 48	97.98 90

plane_prior7					91.86 251	77.55 252

plane_prior6					91.98 247	77.92 241						64.77 255

plane_prior5								94.69 216					97.30 197	87.08 151	82.82 242	90.96 255

plane_prior4											94.15 189

plane_prior3						77.75 248			90.17 51	81.33 217

plane_prior2							97.18 117		89.89 53

plane_prior1					91.95 249

plane_prior						77.96 238	97.52 94		90.36 49						82.96 240

n2								0.00 420

nn								0.00 420

door-mid								79.75 394

lessismore_v0				79.98 346	80.59 374	58.34 383		80.87 391		58.49 378	83.46 347	43.10 365	93.89 332	63.11 337	48.68 388	87.72 323

LGP-MVS_train				86.33 277	90.88 268	73.06 309		94.13 254	82.20 218	76.31 271	93.20 208	54.83 327	96.95 217	83.72 181	80.83 255	88.98 297

test11								96.50 98

door								80.13 393

HQP5-MVS						78.48 218

HQP-NCC					92.08 242		97.63 82		90.52 44	82.30 204

ACMP_Plane					92.08 242		97.63 82		90.52 44	82.30 204

BP-MVS														87.67 147

HQP4-MVS										82.30 204			97.32 195			91.13 253

HQP3-MVS								94.80 211							83.01 238

HQP2-MVS												65.40 249

NP-MVS					92.04 246	78.22 228					94.56 179

MDTV_nov1_ep13_2view						81.74 135	86.80 355		80.65 242	85.65 164		74.26 176		76.52 250		96.98 154

MDTV_nov1_ep13			83.69 219		94.09 175	81.01 150	86.78 356	96.09 137	83.81 186	84.75 175	84.32 340	74.44 175	96.54 236	63.88 332	85.07 226

ACMMP++_ref															78.45 276

ACMMP++															79.05 268

Test By Simon												71.65 209

ITE_SJBPF				82.38 332	87.00 325	65.59 358		89.55 355	79.99 261	69.37 333	91.30 238	41.60 371	95.33 293	62.86 338	74.63 293	86.24 347

DeepMVS_CX				64.06 378	78.53 380	43.26 403		68.11 407	69.94 354	38.55 397	76.14 377	18.53 399	79.34 395	43.72 390	41.62 399	69.57 393

High-resolution multi-view benchmark