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 22	97.12 32	94.66 9	96.79 26	98.78 10	86.42 30	99.95 3	97.59 36	99.18 7	99.00 31

DPM-MVS		96.21 2	95.53 14	98.26 1	96.26 108	95.09 1	99.15 11	96.98 43	93.39 22	96.45 34	98.79 9	90.17 9	99.99 1	89.33 163	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 30	97.10 34	95.17 4	92.11 102	98.46 35	87.33 25	99.97 2	97.21 42	99.31 4	99.63 7

DVP-MVS++		96.05 4	96.41 3	94.96 24	99.05 10	85.34 61	98.13 64	96.77 68	88.38 91	97.70 13	98.77 11	92.06 3	99.84 14	97.47 37	99.37 1	99.70 3

SED-MVS		95.88 5	96.22 4	94.87 25	99.03 16	85.03 74	99.12 15	96.78 62	88.72 83	97.79 10	98.91 2	88.48 17	99.82 20	98.15 21	98.97 17	99.74 1

MM		95.85 6	95.74 10	96.15 8	96.34 105	89.50 9	99.18 8	98.10 8	95.68 1	96.64 30	97.92 75	80.72 73	99.80 28	99.16 2	97.96 59	99.15 27

NCCC		95.63 7	95.94 8	94.69 32	99.21 6	85.15 71	99.16 10	96.96 47	94.11 14	95.59 45	98.64 20	85.07 36	99.91 4	95.61 59	99.10 9	99.00 31

MSP-MVS		95.62 8	96.54 1	92.86 105	98.31 49	80.10 212	97.42 123	96.78 62	92.20 35	97.11 22	98.29 48	93.46 1	99.10 117	96.01 52	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 34	99.05 10	85.18 66	99.06 22	96.46 114	88.75 81	96.69 27	98.76 13	87.69 23	99.76 40	97.90 29	98.85 21	98.77 42
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
MGCNet		95.58 9	95.44 16	96.01 10	97.63 73	89.26 12	99.27 5	96.59 97	94.71 8	97.08 23	97.99 69	78.69 106	99.86 10	99.15 3	97.85 63	98.91 36

DPE-MVS		95.32 11	95.55 13	94.64 33	98.79 24	84.87 79	97.77 90	96.74 73	86.11 153	96.54 33	98.89 7	88.39 19	99.74 48	97.67 35	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 15	94.85 26	98.62 35	86.04 40	97.81 87	96.93 50	92.45 29	95.69 43	98.50 30	85.38 34	99.85 12	94.75 72	99.18 7	98.65 52

patch_mono-2		95.14 13	96.08 7	92.33 139	98.44 44	77.84 289	98.43 50	97.21 25	92.58 28	97.68 15	97.65 93	86.88 27	99.83 18	98.25 17	97.60 71	99.33 18

DELS-MVS		94.98 14	94.49 32	96.44 6	96.42 104	90.59 7	99.21 7	97.02 40	94.40 13	91.46 111	97.08 124	83.32 57	99.69 60	92.83 103	98.70 31	99.04 29
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_9		94.91 15	95.60 11	92.84 108	95.20 148	80.55 194	99.45 1	96.36 131	95.17 4	98.48 3	98.55 23	80.53 77	99.78 35	98.87 7	97.79 66	98.19 80

fmvsm_l_conf0.5_n_a		94.91 15	95.30 17	93.72 63	94.50 178	84.30 87	99.14 13	96.00 161	91.94 41	97.91 7	98.60 21	84.78 39	99.77 38	98.84 8	96.03 122	97.08 182

fmvsm_l_conf0.5_n		94.89 17	95.24 18	93.86 54	94.42 181	84.61 82	99.13 14	96.15 149	92.06 38	97.92 5	98.52 29	84.52 42	99.74 48	98.76 9	95.67 129	97.22 169

CANet		94.89 17	94.64 29	95.63 13	97.55 79	88.12 18	99.06 22	96.39 124	94.07 16	95.34 47	97.80 84	76.83 144	99.87 8	97.08 44	97.64 70	98.89 37

SD-MVS		94.84 19	95.02 23	94.29 40	97.87 65	84.61 82	97.76 92	96.19 147	89.59 73	96.66 29	98.17 56	84.33 44	99.60 71	96.09 51	98.50 39	98.66 51
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
MED-MVS		94.82 20	95.04 21	94.17 46	99.17 8	83.70 99	97.66 99	97.22 24	85.79 162	95.34 47	98.90 5	84.89 37	99.86 10	97.78 34	98.60 34	98.94 34

test_fmvsm_n_1920		94.81 21	95.60 11	92.45 130	95.29 144	80.96 181	99.29 4	97.21 25	94.50 12	97.29 21	98.44 36	82.15 65	99.78 35	98.56 11	97.68 69	96.61 207

TSAR-MVS + MP.		94.79 22	95.17 20	93.64 69	97.66 72	84.10 90	95.85 249	96.42 119	91.26 47	97.49 19	96.80 137	86.50 29	98.49 150	95.54 61	99.03 13	98.33 68
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 23	94.68 28	94.76 29	98.02 60	85.94 44	97.47 116	96.77 68	85.32 174	97.92 5	98.70 18	83.09 60	99.84 14	95.79 56	99.08 10	98.49 59
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
fmvsm_l_conf0.5_n_3		94.61 24	94.92 24	93.68 67	94.52 173	82.80 120	99.33 2	96.37 129	95.08 6	97.59 18	98.48 33	77.40 129	99.79 32	98.28 15	97.21 85	98.44 63

DeepPCF-MVS	89.82 1	94.61 24	96.17 5	89.91 250	97.09 97	70.21 391	98.99 28	96.69 81	95.57 2	95.08 54	99.23 1	86.40 31	99.87 8	97.84 32	98.66 32	99.65 6

balanced_conf03		94.60 26	94.30 38	95.48 16	96.45 103	88.82 14	96.33 216	95.58 193	91.12 49	95.84 42	93.87 241	83.47 56	98.37 160	97.26 40	98.81 24	99.24 23

APDe-MVS		94.56 27	94.75 25	93.96 52	98.84 23	83.40 106	98.04 72	96.41 120	85.79 162	95.00 56	98.28 49	84.32 47	99.18 110	97.35 39	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_9		94.52 28	95.22 19	92.41 135	95.79 128	78.61 260	98.73 37	96.00 161	94.91 7	97.73 12	98.73 16	79.09 98	99.79 32	99.14 4	96.86 102	98.83 39

fmvsm_s_conf0.5_n_8		94.52 28	95.04 21	92.96 100	95.15 153	81.14 173	99.09 19	96.66 86	95.53 3	97.84 9	98.71 17	76.33 155	99.81 24	99.24 1	96.85 104	97.92 103

DeepC-MVS_fast	89.06 2	94.48 30	94.30 38	95.02 22	98.86 22	85.68 51	98.06 70	96.64 90	93.64 20	91.74 109	98.54 25	80.17 83	99.90 5	92.28 110	98.75 29	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
fmvsm_s_conf0.5_n_10		94.36 31	94.73 26	93.23 87	95.19 149	82.87 118	99.18 8	96.39 124	93.97 17	97.91 7	98.53 27	75.88 165	99.82 20	98.58 10	96.95 97	97.00 185

TSAR-MVS + GP.		94.35 32	94.50 31	93.89 53	97.38 91	83.04 114	98.10 66	95.29 216	91.57 43	93.81 73	97.45 102	86.64 28	99.43 88	96.28 50	94.01 149	99.20 25

train_agg		94.28 33	94.45 33	93.74 60	98.64 32	83.71 97	97.82 85	96.65 87	84.50 202	95.16 50	98.09 62	84.33 44	99.36 93	95.91 55	98.96 19	98.16 83

MSLP-MVS++		94.28 33	94.39 35	93.97 51	98.30 50	84.06 91	98.64 43	96.93 50	90.71 56	93.08 84	98.70 18	79.98 87	99.21 103	94.12 81	99.07 11	98.63 53

MG-MVS		94.25 35	93.72 46	95.85 12	99.38 3	89.35 11	97.98 74	98.09 9	89.99 67	92.34 96	96.97 129	81.30 71	98.99 123	88.54 174	98.88 20	99.20 25

fmvsm_s_conf0.5_n_6		94.17 36	94.70 27	92.58 125	93.50 215	81.20 171	99.08 20	96.48 113	92.24 34	98.62 2	98.39 41	78.58 108	99.72 53	98.08 25	97.36 80	96.81 197

SF-MVS		94.17 36	94.05 43	94.55 35	97.56 78	85.95 42	97.73 94	96.43 118	84.02 219	95.07 55	98.74 15	82.93 61	99.38 90	95.42 63	98.51 37	98.32 69

PS-MVSNAJ		94.17 36	93.52 53	96.10 9	95.65 132	92.35 2	98.21 59	95.79 182	92.42 30	96.24 36	98.18 53	71.04 244	99.17 111	96.77 47	97.39 79	96.79 198

SteuartSystems-ACMMP		94.13 39	94.44 34	93.20 89	95.41 139	81.35 169	99.02 26	96.59 97	89.50 75	94.18 69	98.36 45	83.68 55	99.45 87	94.77 71	98.45 42	98.81 41
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 40	94.15 41	93.76 58	97.27 94	84.35 85	98.29 56	97.64 14	94.57 10	95.36 46	96.88 132	79.96 88	99.12 116	91.30 123	96.11 119	97.82 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 41	94.36 36	92.86 105	92.82 246	81.12 174	99.26 6	96.37 129	93.47 21	95.16 50	98.21 51	79.00 99	99.64 66	98.21 19	96.73 108	97.83 112

fmvsm_s_conf0.5_n_3		93.95 42	94.53 30	92.20 150	94.41 182	80.04 213	98.90 32	95.96 166	94.53 11	97.63 17	98.58 22	75.95 162	99.79 32	98.25 17	96.60 110	96.77 200

xiu_mvs_v2_base		93.92 43	93.26 59	95.91 11	95.07 156	92.02 6	98.19 60	95.68 188	92.06 38	96.01 41	98.14 58	70.83 249	98.96 125	96.74 49	96.57 111	96.76 202

lupinMVS		93.87 44	93.58 51	94.75 30	93.00 233	88.08 19	99.15 11	95.50 200	91.03 52	94.90 57	97.66 89	78.84 102	97.56 205	94.64 75	97.46 74	98.62 54

fmvsm_s_conf0.5_n		93.69 45	94.13 42	92.34 137	94.56 170	82.01 144	99.07 21	97.13 30	92.09 36	96.25 35	98.53 27	76.47 150	99.80 28	98.39 13	94.71 139	95.22 254

APD-MVS		93.61 46	93.59 50	93.69 66	98.76 25	83.26 109	97.21 135	96.09 153	82.41 265	94.65 63	98.21 51	81.96 68	98.81 135	94.65 74	98.36 48	99.01 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.5_n_4		93.59 47	94.32 37	91.41 194	93.89 200	79.24 234	98.89 33	96.53 105	92.82 26	97.37 20	98.47 34	77.21 136	99.78 35	98.11 24	95.59 131	95.21 255

PHI-MVS		93.59 47	93.63 49	93.48 80	98.05 59	81.76 157	98.64 43	97.13 30	82.60 261	94.09 70	98.49 31	80.35 78	99.85 12	94.74 73	98.62 33	98.83 39

fmvsm_s_conf0.5_n_5		93.57 49	93.75 45	93.01 97	92.87 245	82.73 121	98.93 31	95.90 174	90.96 54	95.61 44	98.39 41	76.57 148	99.63 68	98.32 14	96.24 115	96.68 206

BP-MVS1		93.55 50	93.50 54	93.71 64	92.64 254	85.39 60	97.78 89	96.84 58	89.52 74	92.00 103	97.06 126	88.21 20	98.03 175	91.45 122	96.00 124	97.70 125

ACMMP_NAP		93.46 51	93.23 60	94.17 46	97.16 95	84.28 88	96.82 177	96.65 87	86.24 150	94.27 67	97.99 69	77.94 118	99.83 18	93.39 89	98.57 35	98.39 66

MVS_111021_HR		93.41 52	93.39 57	93.47 82	97.34 92	82.83 119	97.56 108	98.27 6	89.16 79	89.71 137	97.14 119	79.77 89	99.56 78	93.65 87	97.94 60	98.02 92

fmvsm_s_conf0.5_n_a		93.34 53	93.71 47	92.22 148	93.38 218	81.71 160	98.86 34	96.98 43	91.64 42	96.85 25	98.55 23	75.58 171	99.77 38	97.88 31	93.68 158	95.18 256

lecture		93.17 54	93.57 52	91.96 163	97.80 66	78.79 255	98.50 49	96.98 43	86.61 145	94.75 62	98.16 57	78.36 112	99.35 95	93.89 83	97.12 90	97.75 119

PVSNet_Blended		93.13 55	92.98 65	93.57 74	97.47 80	83.86 93	99.32 3	96.73 75	91.02 53	89.53 142	96.21 150	76.42 152	99.57 76	94.29 78	95.81 128	97.29 167

CDPH-MVS		93.12 56	92.91 67	93.74 60	98.65 31	83.88 92	97.67 98	96.26 139	83.00 251	93.22 81	98.24 50	81.31 70	99.21 103	89.12 164	98.74 30	98.14 85

dcpmvs_2		93.10 57	93.46 56	92.02 161	97.77 68	79.73 223	94.82 297	93.86 310	86.91 136	91.33 115	96.76 138	85.20 35	98.06 173	96.90 46	97.60 71	98.27 75

test_fmvsmconf0.1_n		93.08 58	93.22 61	92.65 118	88.45 365	80.81 186	99.00 27	95.11 222	93.21 23	94.00 71	97.91 77	76.84 142	99.59 72	97.91 28	96.55 112	97.54 138

SPE-MVS-test		92.98 59	93.67 48	90.90 216	96.52 102	76.87 312	98.68 40	94.73 243	90.36 64	94.84 59	97.89 79	77.94 118	97.15 248	94.28 80	97.80 65	98.70 50

fmvsm_s_conf0.5_n_2		92.97 60	93.38 58	91.73 177	94.10 194	80.64 191	98.96 29	95.89 175	94.09 15	97.05 24	98.40 40	68.92 261	99.80 28	98.53 12	94.50 143	94.74 267

alignmvs		92.97 60	92.26 86	95.12 21	95.54 136	87.77 22	98.67 41	96.38 126	88.04 102	93.01 85	97.45 102	79.20 96	98.60 141	93.25 95	88.76 221	98.99 33

fmvsm_s_conf0.1_n		92.93 62	93.16 62	92.24 145	90.52 324	81.92 148	98.42 51	96.24 141	91.17 48	96.02 40	98.35 46	75.34 182	99.74 48	97.84 32	94.58 141	95.05 259

HFP-MVS		92.89 63	92.86 70	92.98 99	98.71 26	81.12 174	97.58 106	96.70 79	85.20 179	91.75 108	97.97 74	78.47 109	99.71 56	90.95 128	98.41 44	98.12 88

NormalMVS		92.88 64	92.97 66	92.59 124	97.80 66	82.02 142	97.94 77	94.70 244	92.34 31	92.15 100	96.53 145	77.03 137	98.57 143	91.13 126	97.12 90	97.19 175

fmvsm_s_conf0.5_n_7		92.88 64	93.82 44	90.08 241	92.79 249	76.45 320	98.54 47	96.74 73	92.28 33	95.22 49	98.49 31	74.91 189	98.15 171	98.28 15	97.13 89	95.63 239

PAPM		92.87 66	92.40 80	94.30 39	92.25 274	87.85 21	96.40 209	96.38 126	91.07 51	88.72 159	96.90 130	82.11 66	97.37 231	90.05 151	97.70 68	97.67 127

GDP-MVS		92.85 67	92.55 77	93.75 59	92.82 246	85.76 47	97.63 100	95.05 226	88.34 93	93.15 82	97.10 123	86.92 26	98.01 177	87.95 182	94.00 150	97.47 148

ZNCC-MVS		92.75 68	92.60 75	93.23 87	98.24 52	81.82 155	97.63 100	96.50 109	85.00 189	91.05 120	97.74 86	78.38 110	99.80 28	90.48 141	98.34 49	98.07 90

PAPR		92.74 69	92.17 90	94.45 36	98.89 21	84.87 79	97.20 137	96.20 145	87.73 111	88.40 163	98.12 59	78.71 105	99.76 40	87.99 181	96.28 114	98.74 44

CS-MVS		92.73 70	93.48 55	90.48 229	96.27 107	75.93 333	98.55 46	94.93 230	89.32 76	94.54 65	97.67 88	78.91 101	97.02 253	93.80 84	97.32 82	98.49 59

jason		92.73 70	92.23 87	94.21 44	90.50 325	87.30 30	98.65 42	95.09 223	90.61 58	92.76 90	97.13 120	75.28 183	97.30 234	93.32 93	96.75 107	98.02 92
jason: jason.
myMVS_eth3d28		92.72 72	92.23 87	94.21 44	96.16 111	87.46 29	97.37 127	96.99 42	88.13 100	88.18 169	95.47 175	84.12 49	98.04 174	92.46 109	91.17 195	97.14 178

ETV-MVS		92.72 72	92.87 68	92.28 143	94.54 172	81.89 151	97.98 74	95.21 220	89.77 71	93.11 83	96.83 134	77.23 135	97.50 215	95.74 57	95.38 133	97.44 154

region2R		92.72 72	92.70 72	92.79 110	98.68 27	80.53 199	97.53 111	96.51 107	85.22 177	91.94 106	97.98 72	77.26 131	99.67 64	90.83 135	98.37 47	98.18 81

reproduce-ours		92.70 75	93.02 63	91.75 174	97.45 82	77.77 293	96.16 228	95.94 170	84.12 215	92.45 91	98.43 37	80.06 85	99.24 99	95.35 64	97.18 86	98.24 77

our_new_method		92.70 75	93.02 63	91.75 174	97.45 82	77.77 293	96.16 228	95.94 170	84.12 215	92.45 91	98.43 37	80.06 85	99.24 99	95.35 64	97.18 86	98.24 77

XVS		92.69 77	92.71 71	92.63 121	98.52 38	80.29 202	97.37 127	96.44 116	87.04 133	91.38 112	97.83 83	77.24 133	99.59 72	90.46 143	98.07 55	98.02 92

ACMMPR		92.69 77	92.67 73	92.75 112	98.66 29	80.57 193	97.58 106	96.69 81	85.20 179	91.57 110	97.92 75	77.01 139	99.67 64	90.95 128	98.41 44	98.00 97

UBG		92.68 79	92.35 81	93.70 65	95.61 133	85.65 54	97.25 133	97.06 37	87.92 105	89.28 146	95.03 198	86.06 33	98.07 172	92.24 111	90.69 201	97.37 160

WTY-MVS		92.65 80	91.68 99	95.56 14	96.00 116	88.90 13	98.23 58	97.65 13	88.57 86	89.82 136	97.22 117	79.29 93	99.06 120	89.57 159	88.73 222	98.73 48

MP-MVS		92.61 81	92.67 73	92.42 134	98.13 57	79.73 223	97.33 130	96.20 145	85.63 165	90.53 127	97.66 89	78.14 116	99.70 59	92.12 113	98.30 51	97.85 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 82	92.35 81	93.29 84	97.30 93	82.53 125	96.44 205	96.04 159	84.68 197	89.12 149	98.37 44	77.48 128	99.74 48	93.31 94	98.38 46	97.59 136
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 83	92.60 75	92.34 137	98.50 41	79.90 216	98.40 52	96.40 122	84.75 193	90.48 129	98.09 62	77.40 129	99.21 103	91.15 125	98.23 53	97.92 103

reproduce_model		92.53 84	92.87 68	91.50 190	97.41 86	77.14 310	96.02 235	95.91 173	83.65 237	92.45 91	98.39 41	79.75 90	99.21 103	95.27 67	96.98 95	98.14 85

testing11		92.48 85	92.04 94	93.78 57	95.94 120	86.00 41	97.56 108	97.08 35	87.52 116	89.32 145	95.40 177	84.60 40	98.02 176	91.93 119	89.04 217	97.32 163

SymmetryMVS		92.45 86	92.33 83	92.82 109	95.19 149	82.02 142	97.94 77	97.43 17	92.34 31	92.15 100	96.53 145	77.03 137	98.57 143	91.13 126	91.19 193	97.87 107

MTAPA		92.45 86	92.31 84	92.86 105	97.90 62	80.85 185	92.88 352	96.33 133	87.92 105	90.20 133	98.18 53	76.71 147	99.76 40	92.57 107	98.09 54	97.96 102

GST-MVS		92.43 88	92.22 89	93.04 96	98.17 55	81.64 163	97.40 125	96.38 126	84.71 196	90.90 123	97.40 107	77.55 127	99.76 40	89.75 156	97.74 67	97.72 122

fmvsm_s_conf0.1_n_a		92.38 89	92.49 78	92.06 158	88.08 370	81.62 164	97.97 76	96.01 160	90.62 57	96.58 31	98.33 47	74.09 202	99.71 56	97.23 41	93.46 163	94.86 263

MVSMamba_PlusPlus		92.37 90	91.55 102	94.83 27	95.37 141	87.69 24	95.60 261	95.42 209	74.65 378	93.95 72	92.81 260	83.11 59	97.70 194	94.49 76	98.53 36	99.11 28

sasdasda		92.27 91	91.22 108	95.41 17	95.80 126	88.31 15	97.09 153	94.64 254	88.49 88	92.99 86	97.31 109	72.68 219	98.57 143	93.38 91	88.58 228	99.36 16

canonicalmvs		92.27 91	91.22 108	95.41 17	95.80 126	88.31 15	97.09 153	94.64 254	88.49 88	92.99 86	97.31 109	72.68 219	98.57 143	93.38 91	88.58 228	99.36 16

fmvsm_s_conf0.1_n_2		92.26 93	92.48 79	91.60 185	92.29 270	80.55 194	98.73 37	94.33 281	93.80 19	96.18 37	98.11 60	66.93 277	99.75 45	98.19 20	93.74 157	94.50 274

SR-MVS		92.16 94	92.27 85	91.83 172	98.37 46	78.41 266	96.67 191	95.76 183	82.19 269	91.97 104	98.07 66	76.44 151	98.64 139	93.71 86	97.27 83	98.45 62

test_fmvsmvis_n_1920		92.12 95	92.10 92	92.17 152	90.87 316	81.04 177	98.34 55	93.90 307	92.71 27	87.24 182	97.90 78	74.83 190	99.72 53	96.96 45	96.20 116	95.76 237

VNet		92.11 96	91.22 108	94.79 28	96.91 98	86.98 31	97.91 80	97.96 10	86.38 148	93.65 75	95.74 160	70.16 254	98.95 127	93.39 89	88.87 220	98.43 64

CSCG		92.02 97	91.65 100	93.12 92	98.53 37	80.59 192	97.47 116	97.18 28	77.06 358	84.64 218	97.98 72	83.98 51	99.52 81	90.72 137	97.33 81	99.23 24

MGCFI-Net		91.95 98	91.03 114	94.72 31	95.68 131	86.38 36	96.93 169	94.48 263	88.25 96	92.78 89	97.24 115	72.34 224	98.46 153	93.13 100	88.43 235	99.32 19

PGM-MVS		91.93 99	91.80 97	92.32 141	98.27 51	79.74 222	95.28 272	97.27 22	83.83 229	90.89 124	97.78 85	76.12 159	99.56 78	88.82 169	97.93 62	97.66 128

testing99		91.91 100	91.35 105	93.60 72	95.98 118	85.70 49	97.31 131	96.92 52	86.82 139	88.91 153	95.25 181	84.26 48	97.89 187	88.80 170	87.94 241	97.21 172

testing91		91.90 101	91.31 107	93.66 68	95.99 117	85.68 51	97.39 126	96.89 53	86.75 143	88.85 155	95.23 184	83.93 52	97.90 186	88.91 167	87.89 242	97.41 156

mPP-MVS		91.88 102	91.82 96	92.07 157	98.38 45	78.63 259	97.29 132	96.09 153	85.12 185	88.45 162	97.66 89	75.53 172	99.68 62	89.83 152	98.02 58	97.88 105

EI-MVSNet-Vis-set		91.84 103	91.77 98	92.04 160	97.60 75	81.17 172	96.61 192	96.87 55	88.20 98	89.19 147	97.55 101	78.69 106	99.14 113	90.29 148	90.94 198	95.80 231

EIA-MVS		91.73 104	92.05 93	90.78 221	94.52 173	76.40 322	98.06 70	95.34 214	89.19 78	88.90 154	97.28 114	77.56 126	97.73 193	90.77 136	96.86 102	98.20 79

EC-MVSNet		91.73 104	92.11 91	90.58 225	93.54 209	77.77 293	98.07 69	94.40 275	87.44 118	92.99 86	97.11 122	74.59 196	96.87 267	93.75 85	97.08 92	97.11 179

DP-MVS Recon		91.72 106	90.85 116	94.34 38	99.50 1	85.00 76	98.51 48	95.96 166	80.57 293	88.08 172	97.63 95	76.84 142	99.89 7	85.67 202	94.88 136	98.13 87

CHOSEN 280x420		91.71 107	91.85 95	91.29 199	94.94 160	82.69 122	87.89 407	96.17 148	85.94 159	87.27 181	94.31 223	90.27 8	95.65 325	94.04 82	95.86 126	95.53 244

HY-MVS	84.06 6	91.63 108	90.37 130	95.39 19	96.12 113	88.25 17	90.22 383	97.58 15	88.33 94	90.50 128	91.96 278	79.26 94	99.06 120	90.29 148	89.07 216	98.88 38

HPM-MVS		91.62 109	91.53 103	91.89 167	97.88 64	79.22 236	96.99 159	95.73 186	82.07 271	89.50 144	97.19 118	75.59 170	98.93 130	90.91 130	97.94 60	97.54 138
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 110	91.64 101	91.47 192	95.74 129	78.79 255	96.15 230	96.77 68	88.49 88	88.64 160	97.07 125	72.33 225	99.19 109	93.13 100	96.48 113	96.43 212

DeepC-MVS	86.58 3	91.53 111	91.06 113	92.94 102	94.52 173	81.89 151	95.95 239	95.98 164	90.76 55	83.76 234	96.76 138	73.24 213	99.71 56	91.67 121	96.96 96	97.22 169
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 112	90.53 123	94.24 42	97.41 86	85.18 66	98.08 67	97.72 11	80.94 284	89.85 134	96.14 151	75.61 168	98.81 135	90.42 146	88.56 230	98.74 44

DCV-MVSNet		91.46 112	90.53 123	94.24 42	97.41 86	85.18 66	98.08 67	97.72 11	80.94 284	89.85 134	96.14 151	75.61 168	98.81 135	90.42 146	88.56 230	98.74 44

PAPM_NR		91.46 112	90.82 117	93.37 83	98.50 41	81.81 156	95.03 292	96.13 150	84.65 198	86.10 198	97.65 93	79.24 95	99.75 45	83.20 230	96.88 100	98.56 56

testing3-2		91.37 115	91.01 115	92.44 132	95.93 121	83.77 96	98.83 35	97.45 16	86.88 137	86.63 189	94.69 213	84.57 41	97.75 192	89.65 157	84.44 275	95.80 231

MVSFormer		91.36 116	90.57 122	93.73 62	93.00 233	88.08 19	94.80 299	94.48 263	80.74 289	94.90 57	97.13 120	78.84 102	95.10 354	83.77 219	97.46 74	98.02 92

EI-MVSNet-UG-set		91.35 117	91.22 108	91.73 177	97.39 89	80.68 189	96.47 202	96.83 59	87.92 105	88.30 166	97.36 108	77.84 121	99.13 115	89.43 162	89.45 211	95.37 248

SR-MVS-dyc-post		91.29 118	91.45 104	90.80 219	97.76 70	76.03 328	96.20 225	95.44 205	80.56 294	90.72 125	97.84 81	75.76 167	98.61 140	91.99 116	96.79 105	97.75 119

PVSNet_Blended_VisFu		91.24 119	90.77 118	92.66 117	95.09 154	82.40 133	97.77 90	95.87 179	88.26 95	86.39 193	93.94 239	76.77 145	99.27 97	88.80 170	94.00 150	96.31 218

APD-MVS_3200maxsize		91.23 120	91.35 105	90.89 217	97.89 63	76.35 323	96.30 219	95.52 198	79.82 316	91.03 121	97.88 80	74.70 192	98.54 147	92.11 114	96.89 99	97.77 117

diffmvs		91.17 121	90.74 119	92.44 132	93.11 231	82.50 130	96.25 222	93.62 333	87.79 109	90.40 131	95.93 155	73.44 211	97.42 221	93.62 88	92.55 173	97.41 156
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		91.13 122	90.45 126	93.17 91	92.99 236	83.58 102	97.46 118	94.56 260	87.69 112	87.19 183	94.98 203	74.50 197	97.60 200	91.88 120	92.79 170	98.34 67
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		91.09 123	90.49 125	92.87 104	95.82 124	85.04 73	96.51 200	97.28 21	86.05 156	89.13 148	95.34 179	80.16 84	96.62 280	85.82 200	88.31 237	96.96 187

test_fmvsmconf0.01_n		91.08 124	90.68 120	92.29 142	82.43 427	80.12 211	97.94 77	93.93 303	92.07 37	91.97 104	97.60 96	67.56 269	99.53 80	97.09 43	95.56 132	97.21 172

CHOSEN 1792x2688		91.07 125	90.21 134	93.64 69	95.18 151	83.53 103	96.26 221	96.13 150	88.92 80	84.90 211	93.10 257	72.86 216	99.62 70	88.86 168	95.67 129	97.79 116

ETVMVS		90.99 126	90.26 131	93.19 90	95.81 125	85.64 55	96.97 164	97.18 28	85.43 171	88.77 158	94.86 205	82.00 67	96.37 287	82.70 235	88.60 227	97.57 137

CANet_DTU		90.98 127	90.04 141	93.83 55	94.76 166	86.23 38	96.32 217	93.12 357	93.11 24	93.71 74	96.82 136	63.08 308	99.48 85	84.29 212	95.12 135	95.77 236

test2506		90.96 128	90.39 128	92.65 118	93.54 209	82.46 131	96.37 210	97.35 19	86.78 141	87.55 176	95.25 181	77.83 122	97.50 215	84.07 214	94.80 137	97.98 99

thisisatest0515		90.95 129	90.26 131	93.01 97	94.03 199	84.27 89	97.91 80	96.67 83	83.18 244	86.87 187	95.51 173	88.66 15	97.85 188	80.46 255	89.01 218	96.92 191

casdiffmvs		90.95 129	90.39 128	92.63 121	92.82 246	82.53 125	96.83 175	94.47 266	87.69 112	88.47 161	95.56 172	74.04 203	97.54 210	90.90 131	92.74 171	97.83 112
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		90.87 131	89.96 144	93.60 72	94.15 190	83.84 95	97.14 146	98.13 7	85.93 160	89.68 138	96.09 153	71.67 235	99.30 96	87.69 187	89.16 215	97.66 128

diffmvs_AUTHOR		90.86 132	90.41 127	92.24 145	92.01 290	82.22 138	96.18 227	93.64 331	87.28 123	90.46 130	95.64 166	72.82 217	97.39 226	93.17 97	92.46 176	97.11 179

baseline		90.76 133	90.10 137	92.74 113	92.90 244	82.56 124	94.60 302	94.56 260	87.69 112	89.06 151	95.67 164	73.76 206	97.51 214	90.43 145	92.23 183	98.16 83

viewmanbaseed2359cas		90.74 134	90.07 139	92.76 111	92.98 237	82.93 117	96.53 197	94.28 284	87.08 132	88.96 152	95.64 166	72.03 232	97.58 203	90.85 133	92.26 181	97.76 118

Effi-MVS+		90.70 135	89.90 147	93.09 94	93.61 206	83.48 104	95.20 280	92.79 363	83.22 243	91.82 107	95.70 162	71.82 234	97.48 217	91.25 124	93.67 159	98.32 69

viewcassd2359sk11		90.66 136	90.06 140	92.47 128	93.22 223	82.21 139	96.70 189	94.47 266	86.94 135	88.22 168	95.50 174	73.15 214	97.59 201	90.86 132	91.48 190	97.60 135

MAR-MVS		90.63 137	90.22 133	91.86 169	98.47 43	78.20 277	97.18 139	96.61 93	83.87 226	88.18 169	98.18 53	68.71 262	99.75 45	83.66 224	97.15 88	97.63 131
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		90.60 138	88.64 169	96.50 5	94.25 186	90.53 8	93.33 340	97.21 25	77.59 349	78.88 293	97.31 109	71.52 239	99.69 60	89.60 158	98.03 57	99.27 22

xiu_mvs_v1_base_debu		90.54 139	89.54 150	93.55 75	92.31 262	87.58 26	96.99 159	94.87 234	87.23 126	93.27 78	97.56 98	57.43 355	98.32 162	92.72 104	93.46 163	94.74 267

xiu_mvs_v1_base		90.54 139	89.54 150	93.55 75	92.31 262	87.58 26	96.99 159	94.87 234	87.23 126	93.27 78	97.56 98	57.43 355	98.32 162	92.72 104	93.46 163	94.74 267

xiu_mvs_v1_base_debi		90.54 139	89.54 150	93.55 75	92.31 262	87.58 26	96.99 159	94.87 234	87.23 126	93.27 78	97.56 98	57.43 355	98.32 162	92.72 104	93.46 163	94.74 267

mvsmamba		90.53 142	90.08 138	91.88 168	94.81 164	80.93 182	93.94 323	94.45 269	88.24 97	87.02 186	92.35 267	68.04 264	95.80 313	94.86 70	97.03 94	98.92 35

baseline2		90.39 143	90.21 134	90.93 213	90.86 317	80.99 179	95.20 280	97.41 18	86.03 158	80.07 283	94.61 214	90.58 6	97.47 218	87.29 191	89.86 208	94.35 275

ACMMP		90.39 143	89.97 143	91.64 182	97.58 77	78.21 276	96.78 181	96.72 77	84.73 195	84.72 215	97.23 116	71.22 241	99.63 68	88.37 179	92.41 179	97.08 182
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		90.38 145	90.17 136	91.03 210	97.61 74	77.35 304	97.15 145	95.48 201	79.51 322	88.79 156	96.90 130	71.64 237	98.81 135	87.01 195	97.44 76	96.94 188

MVS_Test		90.29 146	89.18 157	93.62 71	95.23 145	84.93 77	94.41 305	94.66 251	84.31 208	90.37 132	91.02 292	75.13 185	97.82 189	83.11 232	94.42 144	98.12 88

API-MVS		90.18 147	88.97 162	93.80 56	98.66 29	82.95 116	97.50 115	95.63 192	75.16 373	86.31 194	97.69 87	72.49 222	99.90 5	81.26 251	96.07 120	98.56 56

viewdifsd2359ckpt13		90.08 148	89.36 153	92.26 144	93.03 232	81.90 150	96.37 210	94.34 278	86.16 151	87.44 177	95.30 180	70.93 248	97.55 207	89.05 165	91.59 189	97.35 162

PVSNet_BlendedMVS		90.05 149	89.96 144	90.33 234	97.47 80	83.86 93	98.02 73	96.73 75	87.98 103	89.53 142	89.61 313	76.42 152	99.57 76	94.29 78	79.59 310	87.57 383

ET-MVSNet_ETH3D		90.01 150	89.03 158	92.95 101	94.38 183	86.77 33	98.14 61	96.31 136	89.30 77	63.33 417	96.72 141	90.09 10	93.63 392	90.70 139	82.29 297	98.46 61

viewdifsd2359ckpt09		90.00 151	89.28 156	92.15 154	93.31 220	81.38 167	96.37 210	93.64 331	86.34 149	86.62 190	95.64 166	71.58 238	97.52 213	88.93 166	91.06 196	97.54 138

test_vis1_n_1920		89.95 152	90.59 121	88.03 294	92.36 260	68.98 400	99.12 15	94.34 278	93.86 18	93.64 76	97.01 128	51.54 387	99.59 72	96.76 48	96.71 109	95.53 244

test_cas_vis1_n_1920		89.90 153	90.02 142	89.54 260	90.14 336	74.63 345	98.71 39	94.43 272	93.04 25	92.40 94	96.35 148	53.41 383	99.08 119	95.59 60	96.16 117	94.90 261

viewmacassd2359aftdt		89.89 154	89.01 161	92.52 127	91.56 298	82.46 131	96.32 217	94.06 299	86.41 147	88.11 171	95.01 200	69.68 257	97.47 218	88.73 173	91.19 193	97.63 131

guyue		89.85 155	89.33 155	91.40 195	92.53 258	80.15 210	96.82 177	95.68 188	89.66 72	86.43 192	94.23 226	67.00 275	97.16 244	91.96 118	89.65 209	96.89 192

TESTMET0.1,1		89.83 156	89.34 154	91.31 197	92.54 257	80.19 208	97.11 149	96.57 100	86.15 152	86.85 188	91.83 283	79.32 92	96.95 258	81.30 249	92.35 180	96.77 200

EPP-MVSNet		89.76 157	89.72 149	89.87 251	93.78 202	76.02 330	97.22 134	96.51 107	79.35 324	85.11 207	95.01 200	84.82 38	97.10 251	87.46 190	88.21 239	96.50 210

CPTT-MVS		89.72 158	89.87 148	89.29 263	98.33 48	73.30 356	97.70 96	95.35 213	75.68 369	87.40 178	97.44 105	70.43 251	98.25 165	89.56 160	96.90 98	96.33 217

RRT-MVS		89.67 159	88.67 168	92.67 116	94.44 180	81.08 176	94.34 309	94.45 269	86.05 156	85.79 200	92.39 266	63.39 306	98.16 170	93.22 96	93.95 153	98.76 43

thisisatest0530		89.65 160	89.02 159	91.53 187	93.46 216	80.78 187	96.52 198	96.67 83	81.69 277	83.79 233	94.90 204	88.85 14	97.68 196	77.80 283	87.49 249	96.14 221

3Dnovator+	82.88 8	89.63 161	87.85 185	94.99 23	94.49 179	86.76 34	97.84 84	95.74 185	86.10 154	75.47 339	96.02 154	65.00 293	99.51 83	82.91 234	97.07 93	98.72 49

viewmambaseed2359dif		89.52 162	89.02 159	91.03 210	92.24 275	78.83 247	95.89 244	93.77 324	83.04 248	88.28 167	95.80 159	72.08 230	97.40 224	89.76 155	90.32 203	96.87 195

CDS-MVSNet		89.50 163	88.96 163	91.14 207	91.94 294	80.93 182	97.09 153	95.81 181	84.26 213	84.72 215	94.20 229	80.31 79	95.64 326	83.37 229	88.96 219	96.85 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 164	89.92 146	88.06 292	94.64 167	69.57 397	96.22 223	94.95 229	87.27 125	91.37 114	96.54 144	65.88 285	97.39 226	88.54 174	93.89 154	97.23 168

HyFIR lowres test		89.36 165	88.60 170	91.63 184	94.91 162	80.76 188	95.60 261	95.53 196	82.56 262	84.03 227	91.24 289	78.03 117	96.81 271	87.07 194	88.41 236	97.32 163

3Dnovator	82.32 10	89.33 166	87.64 190	94.42 37	93.73 205	85.70 49	97.73 94	96.75 72	86.73 144	76.21 328	95.93 155	62.17 312	99.68 62	81.67 244	97.81 64	97.88 105

h-mvs33		89.30 167	88.95 164	90.36 233	95.07 156	76.04 327	96.96 166	97.11 33	90.39 62	92.22 98	95.10 195	74.70 192	98.86 132	93.14 98	65.89 407	96.16 220

LFMVS		89.27 168	87.64 190	94.16 49	97.16 95	85.52 58	97.18 139	94.66 251	79.17 330	89.63 140	96.57 143	55.35 372	98.22 166	89.52 161	89.54 210	98.74 44

MVSTER		89.25 169	88.92 165	90.24 237	95.98 118	84.66 81	96.79 180	95.36 211	87.19 129	80.33 278	90.61 299	90.02 11	95.97 302	85.38 205	78.64 319	90.09 323

KinetiMVS		89.13 170	87.95 183	92.65 118	92.16 280	82.39 134	97.04 157	96.05 157	86.59 146	88.08 172	94.85 206	61.54 324	98.38 159	81.28 250	93.99 152	97.19 175

CostFormer		89.08 171	88.39 174	91.15 206	93.13 229	79.15 239	88.61 399	96.11 152	83.14 245	89.58 141	86.93 354	83.83 54	96.87 267	88.22 180	85.92 264	97.42 155

viewdifsd2359ckpt07		89.04 172	88.30 176	91.27 200	92.32 261	78.90 245	95.89 244	93.77 324	84.48 204	85.18 206	95.16 190	69.83 255	97.70 194	88.75 172	89.29 213	97.22 169

PVSNet	82.34 9	89.02 173	87.79 187	92.71 115	95.49 137	81.50 166	97.70 96	97.29 20	87.76 110	85.47 204	95.12 194	56.90 361	98.90 131	80.33 256	94.02 148	97.71 124

AstraMVS		88.99 174	88.35 175	90.92 214	90.81 320	78.29 269	96.73 184	94.24 286	89.96 68	86.13 197	95.04 197	62.12 317	97.41 222	92.54 108	87.57 248	97.06 184

test-mter		88.95 175	88.60 170	89.98 246	92.26 272	77.23 306	97.11 149	95.96 166	85.32 174	86.30 195	91.38 286	76.37 154	96.78 274	80.82 252	91.92 185	95.94 227

1314		88.94 176	87.20 204	94.17 46	93.21 224	85.73 48	93.33 340	96.64 90	82.89 253	75.98 331	96.36 147	66.83 279	99.39 89	83.52 228	96.02 123	97.39 159

UA-Net		88.92 177	88.48 173	90.24 237	94.06 196	77.18 308	93.04 348	94.66 251	87.39 120	91.09 119	93.89 240	74.92 188	98.18 169	75.83 310	91.43 191	95.35 249

thres200		88.92 177	87.65 189	92.73 114	96.30 106	85.62 56	97.85 83	98.86 1	84.38 207	84.82 212	93.99 238	75.12 186	98.01 177	70.86 351	86.67 253	94.56 273

Vis-MVSNet (Re-imp)		88.88 179	88.87 167	88.91 271	93.89 200	74.43 348	96.93 169	94.19 291	84.39 206	83.22 244	95.67 164	78.24 113	94.70 368	78.88 276	94.40 145	97.61 134

baseline1		88.85 180	87.49 197	92.93 103	95.21 147	86.85 32	95.47 266	94.61 257	87.29 122	83.11 246	94.99 202	80.70 74	96.89 264	82.28 240	73.72 345	95.05 259

AdaColmap		88.81 181	87.61 193	92.39 136	99.33 4	79.95 214	96.70 189	95.58 193	77.51 350	83.05 247	96.69 142	61.90 322	99.72 53	84.29 212	93.47 162	97.50 145

OMC-MVS		88.80 182	88.16 180	90.72 222	95.30 143	77.92 286	94.81 298	94.51 262	86.80 140	84.97 210	96.85 133	67.53 270	98.60 141	85.08 206	87.62 245	95.63 239

114514_t		88.79 183	87.57 195	92.45 130	98.21 54	81.74 158	96.99 159	95.45 204	75.16 373	82.48 250	95.69 163	68.59 263	98.50 149	80.33 256	95.18 134	97.10 181

mvs_anonymous		88.68 184	87.62 192	91.86 169	94.80 165	81.69 161	93.53 335	94.92 231	82.03 272	78.87 294	90.43 302	75.77 166	95.34 339	85.04 207	93.16 167	98.55 58

Vis-MVSNet		88.67 185	87.82 186	91.24 202	92.68 250	78.82 248	96.95 167	93.85 311	87.55 115	87.07 185	95.13 193	63.43 305	97.21 241	77.58 290	96.15 118	97.70 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 185	88.16 180	90.20 239	93.61 206	76.86 313	96.77 183	93.07 358	84.02 219	83.62 237	95.60 170	74.69 195	96.24 294	78.43 280	93.66 160	97.49 146

IB-MVS	85.34 4	88.67 185	87.14 207	93.26 85	93.12 230	84.32 86	98.76 36	97.27 22	87.19 129	79.36 289	90.45 301	83.92 53	98.53 148	84.41 211	69.79 374	96.93 189
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		88.60 188	87.47 199	92.00 162	93.21 224	80.97 180	96.47 202	92.46 366	83.64 238	80.86 271	97.30 112	80.24 81	97.62 199	77.60 289	85.49 269	97.40 158

tttt0517		88.57 189	88.19 179	89.71 257	93.00 233	75.99 331	95.67 256	96.67 83	80.78 288	81.82 263	94.40 222	88.97 13	97.58 203	76.05 308	86.31 257	95.57 242

UWE-MVS		88.56 190	88.91 166	87.50 308	94.17 189	72.19 368	95.82 251	97.05 38	84.96 190	84.78 213	93.51 251	81.33 69	94.75 366	79.43 267	89.17 214	95.57 242

tfpn200view9		88.48 191	87.15 205	92.47 128	96.21 109	85.30 64	97.44 119	98.85 2	83.37 241	83.99 228	93.82 243	75.36 179	97.93 180	69.04 359	86.24 260	94.17 277

test-LLR		88.48 191	87.98 182	89.98 246	92.26 272	77.23 306	97.11 149	95.96 166	83.76 232	86.30 195	91.38 286	72.30 226	96.78 274	80.82 252	91.92 185	95.94 227

TAMVS		88.48 191	87.79 187	90.56 226	91.09 311	79.18 237	96.45 204	95.88 177	83.64 238	83.12 245	93.33 252	75.94 163	95.74 321	82.40 237	88.27 238	96.75 203

thres400		88.42 194	87.15 205	92.23 147	96.21 109	85.30 64	97.44 119	98.85 2	83.37 241	83.99 228	93.82 243	75.36 179	97.93 180	69.04 359	86.24 260	93.45 293

tpmrst		88.36 195	87.38 201	91.31 197	94.36 184	79.92 215	87.32 411	95.26 218	85.32 174	88.34 164	86.13 371	80.60 76	96.70 276	83.78 218	85.34 272	97.30 166

ECVR-MVS		88.35 196	87.25 203	91.65 181	93.54 209	79.40 230	96.56 196	90.78 402	86.78 141	85.57 202	95.25 181	57.25 359	97.56 205	84.73 210	94.80 137	97.98 99

thres100view900		88.30 197	86.95 212	92.33 139	96.10 114	84.90 78	97.14 146	98.85 2	82.69 259	83.41 241	93.66 247	75.43 176	97.93 180	69.04 359	86.24 260	94.17 277

VDD-MVS		88.28 198	87.02 210	92.06 158	95.09 154	80.18 209	97.55 110	94.45 269	83.09 246	89.10 150	95.92 157	47.97 404	98.49 150	93.08 102	86.91 252	97.52 144

BH-w/o		88.24 199	87.47 199	90.54 228	95.03 159	78.54 261	97.41 124	93.82 316	84.08 217	78.23 300	94.51 217	69.34 260	97.21 241	80.21 260	94.58 141	95.87 230

hse-mvs2		88.22 200	88.21 178	88.25 288	93.54 209	73.41 353	95.41 269	95.89 175	90.39 62	92.22 98	94.22 227	74.70 192	96.66 279	93.14 98	64.37 412	94.69 272

test1111		88.11 201	87.04 209	91.35 196	93.15 227	78.79 255	96.57 194	90.78 402	86.88 137	85.04 208	95.20 187	57.23 360	97.39 226	83.88 216	94.59 140	97.87 107

IMVS_0403		88.07 202	87.02 210	91.24 202	92.30 265	78.81 250	93.62 331	93.84 312	85.14 181	84.36 220	94.49 218	69.49 258	97.46 220	81.33 245	88.61 223	97.46 149

thres600view7		88.06 203	86.70 220	92.15 154	96.10 114	85.17 70	97.14 146	98.85 2	82.70 258	83.41 241	93.66 247	75.43 176	97.82 189	67.13 368	85.88 265	93.45 293

Test_1112_low_res		88.03 204	86.73 217	91.94 166	93.15 227	80.88 184	96.44 205	92.41 370	83.59 240	80.74 273	91.16 290	80.18 82	97.59 201	77.48 292	85.40 270	97.36 161

LuminaMVS		88.02 205	86.89 214	91.43 193	88.65 363	83.16 111	94.84 296	94.41 274	83.67 236	86.56 191	91.95 280	62.04 318	96.88 266	89.78 154	90.06 205	94.24 276

PLC	83.97 7	88.00 206	87.38 201	89.83 253	98.02 60	76.46 319	97.16 143	94.43 272	79.26 329	81.98 260	96.28 149	69.36 259	99.27 97	77.71 287	92.25 182	93.77 287
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 207	87.48 198	89.44 261	92.16 280	80.54 198	98.14 61	94.92 231	91.41 45	79.43 288	95.40 177	62.34 311	97.27 237	90.60 140	82.90 289	90.50 313
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+		87.93 208	86.94 213	90.92 214	94.04 197	79.16 238	98.26 57	93.72 327	81.29 280	83.94 231	92.90 259	69.83 255	96.68 277	76.70 300	91.74 187	96.93 189

HQP-MVS		87.91 209	87.55 196	88.98 270	92.08 285	78.48 262	97.63 100	94.80 239	90.52 59	82.30 253	94.56 215	65.40 289	97.32 232	87.67 188	83.01 286	91.13 305

IMVS_0407		87.82 210	86.72 218	91.14 207	92.30 265	78.81 250	93.34 339	93.84 312	85.14 181	83.68 235	94.49 218	67.75 265	97.14 249	81.33 245	88.61 223	97.46 149

reproduce_monomvs		87.80 211	87.60 194	88.40 282	96.56 101	80.26 205	95.80 252	96.32 135	91.56 44	73.60 351	88.36 329	88.53 16	96.25 293	90.47 142	67.23 400	88.67 358

test_fmvs1		87.79 212	88.52 172	85.62 344	92.98 237	64.31 421	97.88 82	92.42 369	87.95 104	92.24 97	95.82 158	47.94 405	98.44 157	95.31 66	94.09 146	94.09 281

WBMVS		87.73 213	86.79 215	90.56 226	95.61 133	85.68 51	97.63 100	95.52 198	83.77 231	78.30 299	88.44 328	86.14 32	95.78 315	82.54 236	73.15 352	90.21 318

UGNet		87.73 213	86.55 222	91.27 200	95.16 152	79.11 240	96.35 214	96.23 142	88.14 99	87.83 175	90.48 300	50.65 392	99.09 118	80.13 261	94.03 147	95.60 241
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)		87.71 215	86.23 226	92.17 152	94.19 188	80.55 194	87.16 413	96.07 156	82.12 270	85.98 199	88.35 330	72.04 231	98.49 150	80.26 258	89.87 207	97.48 147

SSM_0404		87.69 216	86.26 224	91.95 164	92.94 239	83.02 115	94.69 301	92.33 372	80.11 309	84.65 217	94.18 230	64.68 298	96.90 262	82.34 238	90.44 202	95.94 227

EPNet_dtu		87.65 217	87.89 184	86.93 321	94.57 169	71.37 383	96.72 185	96.50 109	88.56 87	87.12 184	95.02 199	75.91 164	94.01 384	66.62 372	90.00 206	95.42 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 218	88.22 177	85.67 342	89.78 340	67.18 408	95.25 277	87.93 423	83.96 222	88.79 156	97.06 126	72.52 221	94.53 374	92.21 112	86.45 256	95.30 251

icg_test_0407_2		87.55 219	86.59 221	90.43 230	92.30 265	78.81 250	92.17 361	93.84 312	85.14 181	83.68 235	94.49 218	67.75 265	95.02 358	81.33 245	88.61 223	97.46 149

HQP_MVS		87.50 220	87.09 208	88.74 275	91.86 295	77.96 283	97.18 139	94.69 247	89.89 69	81.33 266	94.15 232	64.77 296	97.30 234	87.08 192	82.82 290	90.96 307

EPMVS		87.47 221	85.90 229	92.18 151	95.41 139	82.26 137	87.00 414	96.28 137	85.88 161	84.23 223	85.57 378	75.07 187	96.26 291	71.14 349	92.50 174	98.03 91

tpm2		87.35 222	86.26 224	90.62 224	92.93 243	78.67 258	88.06 406	95.99 163	79.33 325	87.40 178	86.43 365	80.28 80	96.40 285	80.23 259	85.73 268	96.79 198

SSM_0407		87.33 223	85.87 230	91.71 180	92.94 239	82.53 125	94.30 312	92.33 372	80.11 309	83.50 238	94.18 230	64.68 298	96.80 273	82.34 238	88.51 232	95.79 233

ab-mvs		87.08 224	84.94 248	93.48 80	93.34 219	83.67 100	88.82 396	95.70 187	81.18 281	84.55 219	90.14 308	62.72 309	98.94 129	85.49 204	82.54 294	97.85 110

SDMVSNet		87.02 225	85.61 232	91.24 202	94.14 191	83.30 108	93.88 325	95.98 164	84.30 210	79.63 286	92.01 274	58.23 345	97.68 196	90.28 150	82.02 298	92.75 296

CNLPA		86.96 226	85.37 237	91.72 179	97.59 76	79.34 233	97.21 135	91.05 397	74.22 380	78.90 292	96.75 140	67.21 274	98.95 127	74.68 320	90.77 199	96.88 194

BH-untuned		86.95 227	85.94 228	89.99 245	94.52 173	77.46 301	96.78 181	93.37 346	81.80 274	76.62 318	93.81 245	66.64 280	97.02 253	76.06 307	93.88 155	95.48 246

QAPM		86.88 228	84.51 252	93.98 50	94.04 197	85.89 45	97.19 138	96.05 157	73.62 385	75.12 342	95.62 169	62.02 319	99.74 48	70.88 350	96.06 121	96.30 219

BH-RMVSNet		86.84 229	85.28 240	91.49 191	95.35 142	80.26 205	96.95 167	92.21 374	82.86 255	81.77 265	95.46 176	59.34 337	97.64 198	69.79 357	93.81 156	96.57 209

PatchmatchNet		86.83 230	85.12 245	91.95 164	94.12 193	82.27 136	86.55 418	95.64 191	84.59 200	82.98 248	84.99 390	77.26 131	95.96 305	68.61 362	91.34 192	97.64 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 231	85.43 235	90.87 218	88.76 356	85.34 61	97.06 156	94.33 281	84.31 208	80.45 276	91.98 277	72.36 223	96.36 288	88.48 177	71.13 361	90.93 309

PCF-MVS	84.09 5	86.77 232	85.00 247	92.08 156	92.06 288	83.07 113	92.14 362	94.47 266	79.63 320	76.90 314	94.78 208	71.15 242	99.20 108	72.87 335	91.05 197	93.98 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 233	86.10 227	88.61 278	90.05 337	80.21 207	96.14 231	96.95 48	85.56 168	78.37 298	92.30 268	76.73 146	95.28 343	79.51 265	79.27 313	90.35 315

cascas		86.50 234	84.48 254	92.55 126	92.64 254	85.95 42	97.04 157	95.07 225	75.32 371	80.50 274	91.02 292	54.33 380	97.98 179	86.79 197	87.62 245	93.71 288

VDDNet		86.44 235	84.51 252	92.22 148	91.56 298	81.83 154	97.10 152	94.64 254	69.50 413	87.84 174	95.19 188	48.01 403	97.92 185	89.82 153	86.92 251	96.89 192

viewdifsd2359ckpt11		86.38 236	85.29 238	89.66 259	90.42 327	75.65 337	95.27 275	92.45 367	85.54 169	84.27 222	94.73 209	62.16 313	97.39 226	87.78 184	74.97 339	95.96 224

viewmsd2359difaftdt		86.38 236	85.29 238	89.67 258	90.42 327	75.65 337	95.27 275	92.45 367	85.54 169	84.28 221	94.73 209	62.16 313	97.39 226	87.78 184	74.97 339	95.96 224

GeoE		86.36 238	85.20 241	89.83 253	93.17 226	76.13 325	97.53 111	92.11 375	79.58 321	80.99 269	94.01 235	66.60 281	96.17 297	73.48 332	89.30 212	97.20 174

test_fmvs1_n		86.34 239	86.72 218	85.17 352	87.54 377	63.64 426	96.91 171	92.37 371	87.49 117	91.33 115	95.58 171	40.81 433	98.46 153	95.00 69	93.49 161	93.41 295

TR-MVS		86.30 240	84.93 249	90.42 231	94.63 168	77.58 299	96.57 194	93.82 316	80.30 304	82.42 252	95.16 190	58.74 341	97.55 207	74.88 318	87.82 243	96.13 222

X-MVStestdata		86.26 241	84.14 263	92.63 121	98.52 38	80.29 202	97.37 127	96.44 116	87.04 133	91.38 112	20.73 472	77.24 133	99.59 72	90.46 143	98.07 55	98.02 92

AUN-MVS		86.25 242	85.57 233	88.26 287	93.57 208	73.38 354	95.45 267	95.88 177	83.94 223	85.47 204	94.21 228	73.70 209	96.67 278	83.54 226	64.41 411	94.73 271

OpenMVS	79.58 14	86.09 243	83.62 273	93.50 78	90.95 313	86.71 35	97.44 119	95.83 180	75.35 370	72.64 365	95.72 161	57.42 358	99.64 66	71.41 344	95.85 127	94.13 280

FE-MVS		86.06 244	84.15 262	91.78 173	94.33 185	79.81 217	84.58 431	96.61 93	76.69 363	85.00 209	87.38 345	70.71 250	98.37 160	70.39 354	91.70 188	97.17 177

FC-MVSNet-test		85.96 245	85.39 236	87.66 301	89.38 353	78.02 280	95.65 258	96.87 55	85.12 185	77.34 307	91.94 281	76.28 157	94.74 367	77.09 295	78.82 317	90.21 318

miper_enhance_ethall		85.95 246	85.20 241	88.19 291	94.85 163	79.76 219	96.00 236	94.06 299	82.98 252	77.74 305	88.76 321	79.42 91	95.46 335	80.58 254	72.42 354	89.36 337

OPM-MVS		85.84 247	85.10 246	88.06 292	88.34 367	77.83 290	95.72 254	94.20 290	87.89 108	80.45 276	94.05 234	58.57 342	97.26 238	83.88 216	82.76 292	89.09 344
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 248	85.20 241	87.59 304	91.55 300	77.41 302	95.13 286	95.36 211	80.43 299	80.33 278	94.71 211	73.72 207	95.97 302	76.96 298	78.64 319	89.39 331

GA-MVS		85.79 249	84.04 264	91.02 212	89.47 351	80.27 204	96.90 172	94.84 237	85.57 166	80.88 270	89.08 316	56.56 365	96.47 284	77.72 286	85.35 271	96.34 215

XVG-OURS-SEG-HR		85.74 250	85.16 244	87.49 310	90.22 331	71.45 381	91.29 373	94.09 297	81.37 279	83.90 232	95.22 185	60.30 330	97.53 212	85.58 203	84.42 277	93.50 291

MonoMVSNet		85.68 251	84.22 260	90.03 243	88.43 366	77.83 290	92.95 351	91.46 387	87.28 123	78.11 301	85.96 373	66.31 284	94.81 364	90.71 138	76.81 330	97.46 149

SCA		85.63 252	83.64 272	91.60 185	92.30 265	81.86 153	92.88 352	95.56 195	84.85 191	82.52 249	85.12 388	58.04 348	95.39 336	73.89 328	87.58 247	97.54 138

Elysia		85.62 253	83.66 269	91.51 188	88.76 356	82.21 139	95.15 284	94.70 244	76.96 360	84.13 224	92.20 270	50.81 390	97.26 238	77.81 281	92.42 177	95.06 257

StellarMVS		85.62 253	83.66 269	91.51 188	88.76 356	82.21 139	95.15 284	94.70 244	76.96 360	84.13 224	92.20 270	50.81 390	97.26 238	77.81 281	92.42 177	95.06 257

test_vis1_n		85.60 255	85.70 231	85.33 349	84.79 408	64.98 419	96.83 175	91.61 386	87.36 121	91.00 122	94.84 207	36.14 440	97.18 243	95.66 58	93.03 168	93.82 286

tpm		85.55 256	84.47 255	88.80 274	90.19 333	75.39 340	88.79 397	94.69 247	84.83 192	83.96 230	85.21 384	78.22 114	94.68 370	76.32 306	78.02 327	96.34 215

mamv4		85.50 257	86.76 216	81.72 395	93.23 222	54.93 453	89.95 386	92.94 360	69.96 410	79.00 291	92.20 270	80.69 75	94.22 380	92.06 115	90.77 199	96.01 223

UniMVSNet_NR-MVSNet		85.49 258	84.59 251	88.21 290	89.44 352	79.36 231	96.71 187	96.41 120	85.22 177	78.11 301	90.98 294	76.97 141	95.14 351	79.14 273	68.30 388	90.12 321

gg-mvs-nofinetune		85.48 259	82.90 286	93.24 86	94.51 177	85.82 46	79.22 444	96.97 46	61.19 439	87.33 180	53.01 461	90.58 6	96.07 298	86.07 199	97.23 84	97.81 115

VortexMVS		85.45 260	84.40 256	88.63 277	93.25 221	81.66 162	95.39 271	94.34 278	87.15 131	75.10 343	87.65 341	66.58 282	95.19 347	86.89 196	73.21 351	89.03 348

UWE-MVS-28		85.41 261	86.36 223	82.59 387	91.12 310	66.81 413	93.88 325	97.03 39	83.86 228	78.55 295	93.84 242	77.76 124	88.55 434	73.47 333	87.69 244	92.41 300

IMVS_0404		85.34 262	83.69 266	90.29 235	92.30 265	78.81 250	90.62 380	93.84 312	85.14 181	72.51 368	94.49 218	54.36 379	94.61 371	81.33 245	88.61 223	97.46 149

VPA-MVSNet		85.32 263	83.83 265	89.77 256	90.25 330	82.63 123	96.36 213	97.07 36	83.03 250	81.21 268	89.02 318	61.58 323	96.31 290	85.02 208	70.95 363	90.36 314

UniMVSNet (Re)		85.31 264	84.23 259	88.55 279	89.75 342	80.55 194	96.72 185	96.89 53	85.42 172	78.40 297	88.93 319	75.38 178	95.52 333	78.58 278	68.02 391	89.57 330

mamba_0408		85.26 265	83.10 282	91.74 176	92.94 239	82.53 125	72.52 459	91.77 381	80.36 301	83.50 238	94.01 235	64.97 294	96.90 262	79.37 268	88.51 232	95.79 233

XVG-OURS		85.18 266	84.38 257	87.59 304	90.42 327	71.73 378	91.06 377	94.07 298	82.00 273	83.29 243	95.08 196	56.42 366	97.55 207	83.70 223	83.42 282	93.49 292

cl22		85.11 267	84.17 261	87.92 295	95.06 158	78.82 248	95.51 264	94.22 289	79.74 318	76.77 315	87.92 337	75.96 161	95.68 322	79.93 263	72.42 354	89.27 339

TAPA-MVS	81.61 12	85.02 268	83.67 268	89.06 267	96.79 99	73.27 359	95.92 241	94.79 241	74.81 376	80.47 275	96.83 134	71.07 243	98.19 168	49.82 440	92.57 172	95.71 238
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 269	83.66 269	89.02 269	95.86 123	74.55 347	92.49 356	93.60 334	79.30 327	79.29 290	91.47 284	58.53 343	98.45 155	70.22 355	92.17 184	94.07 282

PS-MVSNAJss		84.91 270	84.30 258	86.74 322	85.89 396	74.40 349	94.95 293	94.16 293	83.93 224	76.45 321	90.11 309	71.04 244	95.77 316	83.16 231	79.02 316	90.06 325

CVMVSNet		84.83 271	85.57 233	82.63 386	91.55 300	60.38 439	95.13 286	95.03 227	80.60 292	82.10 259	94.71 211	66.40 283	90.19 427	74.30 325	90.32 203	97.31 165

FMVSNet3		84.71 272	82.71 290	90.70 223	94.55 171	87.71 23	95.92 241	94.67 250	81.73 276	75.82 334	88.08 335	66.99 276	94.47 375	71.23 346	75.38 336	89.91 327

VPNet		84.69 273	82.92 285	90.01 244	89.01 355	83.45 105	96.71 187	95.46 203	85.71 164	79.65 285	92.18 273	56.66 364	96.01 301	83.05 233	67.84 394	90.56 312

SSM_04072		84.64 274	83.10 282	89.25 264	92.94 239	82.53 125	72.52 459	91.77 381	80.36 301	83.50 238	94.01 235	64.97 294	89.41 430	79.37 268	88.51 232	95.79 233

sd_testset		84.62 275	83.11 281	89.17 265	94.14 191	77.78 292	91.54 372	94.38 276	84.30 210	79.63 286	92.01 274	52.28 385	96.98 256	77.67 288	82.02 298	92.75 296

Effi-MVS+-dtu		84.61 276	84.90 250	83.72 374	91.96 292	63.14 429	94.95 293	93.34 347	85.57 166	79.79 284	87.12 351	61.99 320	95.61 329	83.55 225	85.83 266	92.41 300

miper_ehance_all_eth		84.57 277	83.60 274	87.50 308	92.64 254	78.25 272	95.40 270	93.47 338	79.28 328	76.41 322	87.64 342	76.53 149	95.24 345	78.58 278	72.42 354	89.01 350

DU-MVS		84.57 277	83.33 279	88.28 286	88.76 356	79.36 231	96.43 207	95.41 210	85.42 172	78.11 301	90.82 295	67.61 267	95.14 351	79.14 273	68.30 388	90.33 316

F-COLMAP		84.50 279	83.44 278	87.67 300	95.22 146	72.22 366	95.95 239	93.78 321	75.74 368	76.30 325	95.18 189	59.50 335	98.45 155	72.67 337	86.59 255	92.35 302

Anonymous202405211		84.41 280	81.93 301	91.85 171	96.78 100	78.41 266	97.44 119	91.34 391	70.29 407	84.06 226	94.26 225	41.09 430	98.96 125	79.46 266	82.65 293	98.17 82

WR-MVS		84.32 281	82.96 284	88.41 281	89.38 353	80.32 201	96.59 193	96.25 140	83.97 221	76.63 317	90.36 303	67.53 270	94.86 362	75.82 311	70.09 372	90.06 325

dp		84.30 282	82.31 295	90.28 236	94.24 187	77.97 282	86.57 417	95.53 196	79.94 315	80.75 272	85.16 386	71.49 240	96.39 286	63.73 388	83.36 283	96.48 211

LPG-MVS_test		84.20 283	83.49 277	86.33 328	90.88 314	73.06 360	95.28 272	94.13 294	82.20 267	76.31 323	93.20 253	54.83 377	96.95 258	83.72 221	80.83 303	88.98 351

dmvs_re		84.10 284	82.90 286	87.70 299	91.41 304	73.28 357	90.59 381	93.19 351	85.02 187	77.96 304	93.68 246	57.92 353	96.18 296	75.50 313	80.87 302	93.63 289

WB-MVSnew		84.08 285	83.51 276	85.80 337	91.34 305	76.69 317	95.62 260	96.27 138	81.77 275	81.81 264	92.81 260	58.23 345	94.70 368	66.66 371	87.06 250	85.99 407

ACMP	81.66 11	84.00 286	83.22 280	86.33 328	91.53 302	72.95 364	95.91 243	93.79 320	83.70 235	73.79 350	92.22 269	54.31 381	96.89 264	83.98 215	79.74 308	89.16 342
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 287	82.80 289	87.31 314	91.46 303	77.39 303	95.66 257	93.43 341	80.44 297	75.51 338	87.26 348	73.72 207	95.16 350	76.99 296	70.72 365	89.39 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 288	82.00 300	89.35 262	87.13 379	81.38 167	95.72 254	94.26 285	80.15 308	75.92 333	90.63 298	61.96 321	96.52 282	78.98 275	73.28 350	90.14 320

c3_l		83.80 289	82.65 291	87.25 316	92.10 284	77.74 297	95.25 277	93.04 359	78.58 339	76.01 330	87.21 350	75.25 184	95.11 353	77.54 291	68.89 382	88.91 356

LCM-MVSNet-Re		83.75 290	83.54 275	84.39 367	93.54 209	64.14 423	92.51 355	84.03 445	83.90 225	66.14 405	86.59 359	67.36 272	92.68 399	84.89 209	92.87 169	96.35 214

ACMM	80.70 13	83.72 291	82.85 288	86.31 331	91.19 307	72.12 370	95.88 246	94.29 283	80.44 297	77.02 312	91.96 278	55.24 373	97.14 249	79.30 271	80.38 305	89.67 329
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 292	81.38 309	90.39 232	93.53 214	78.19 278	85.56 425	95.09 223	70.78 405	78.51 296	83.28 405	74.80 191	97.03 252	66.77 370	84.05 278	95.95 226

CR-MVSNet		83.53 293	81.36 310	90.06 242	90.16 334	79.75 220	79.02 446	91.12 394	84.24 214	82.27 257	80.35 421	75.45 174	93.67 391	63.37 391	86.25 258	96.75 203

v2v482		83.46 294	81.86 302	88.25 288	86.19 390	79.65 225	96.34 215	94.02 301	81.56 278	77.32 308	88.23 332	65.62 286	96.03 299	77.77 284	69.72 376	89.09 344

NR-MVSNet		83.35 295	81.52 308	88.84 272	88.76 356	81.31 170	94.45 304	95.16 221	84.65 198	67.81 394	90.82 295	70.36 252	94.87 361	74.75 319	66.89 404	90.33 316

Fast-Effi-MVS+-dtu		83.33 296	82.60 292	85.50 346	89.55 349	69.38 398	96.09 234	91.38 388	82.30 266	75.96 332	91.41 285	56.71 362	95.58 331	75.13 317	84.90 274	91.54 303

cl____		83.27 297	82.12 297	86.74 322	92.20 276	75.95 332	95.11 288	93.27 349	78.44 342	74.82 345	87.02 353	74.19 200	95.19 347	74.67 321	69.32 378	89.09 344

DIV-MVS_self_test		83.27 297	82.12 297	86.74 322	92.19 277	75.92 334	95.11 288	93.26 350	78.44 342	74.81 346	87.08 352	74.19 200	95.19 347	74.66 322	69.30 379	89.11 343

TranMVSNet+NR-MVSNet		83.24 299	81.71 304	87.83 296	87.71 374	78.81 250	96.13 233	94.82 238	84.52 201	76.18 329	90.78 297	64.07 301	94.60 372	74.60 323	66.59 406	90.09 323

Anonymous20240529		83.15 300	80.60 321	90.80 219	95.74 129	78.27 271	96.81 179	94.92 231	60.10 444	81.89 262	92.54 264	45.82 413	98.82 134	79.25 272	78.32 325	95.31 250

eth_miper_zixun_eth		83.12 301	82.01 299	86.47 327	91.85 297	74.80 343	94.33 310	93.18 353	79.11 331	75.74 337	87.25 349	72.71 218	95.32 341	76.78 299	67.13 401	89.27 339

MS-PatchMatch		83.05 302	81.82 303	86.72 326	89.64 346	79.10 241	94.88 295	94.59 259	79.70 319	70.67 381	89.65 312	50.43 394	96.82 270	70.82 353	95.99 125	84.25 422

V42		83.04 303	81.53 307	87.57 306	86.27 389	79.09 242	95.87 247	94.11 296	80.35 303	77.22 310	86.79 357	65.32 291	96.02 300	77.74 285	70.14 368	87.61 382

tpmvs		83.04 303	80.77 317	89.84 252	95.43 138	77.96 283	85.59 424	95.32 215	75.31 372	76.27 326	83.70 401	73.89 204	97.41 222	59.53 405	81.93 300	94.14 279

test_djsdf		83.00 305	82.45 294	84.64 360	84.07 417	69.78 394	94.80 299	94.48 263	80.74 289	75.41 340	87.70 340	61.32 327	95.10 354	83.77 219	79.76 306	89.04 347

v1144		82.90 306	81.27 311	87.78 298	86.29 388	79.07 243	96.14 231	93.93 303	80.05 312	77.38 306	86.80 356	65.50 287	95.93 307	75.21 316	70.13 369	88.33 369

test0.0.03 1		82.79 307	82.48 293	83.74 373	86.81 382	72.22 366	96.52 198	95.03 227	83.76 232	73.00 361	93.20 253	72.30 226	88.88 432	64.15 386	77.52 328	90.12 321

FMVSNet2		82.79 307	80.44 323	89.83 253	92.66 251	85.43 59	95.42 268	94.35 277	79.06 333	74.46 347	87.28 346	56.38 367	94.31 378	69.72 358	74.68 342	89.76 328

D2MVS		82.67 309	81.55 306	86.04 335	87.77 373	76.47 318	95.21 279	96.58 99	82.66 260	70.26 384	85.46 381	60.39 329	95.80 313	76.40 304	79.18 314	85.83 410

MVP-Stereo		82.65 310	81.67 305	85.59 345	86.10 393	78.29 269	93.33 340	92.82 362	77.75 347	69.17 391	87.98 336	59.28 338	95.76 317	71.77 341	96.88 100	82.73 430
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 311	80.79 316	87.79 297	86.11 392	80.49 200	93.55 334	93.18 353	77.29 353	73.35 357	89.40 315	65.26 292	95.05 357	75.32 315	73.61 346	87.83 377

v144192		82.43 312	80.73 318	87.54 307	85.81 397	78.22 273	95.98 237	93.78 321	79.09 332	77.11 311	86.49 361	64.66 300	95.91 308	74.20 326	69.42 377	88.49 363

GBi-Net		82.42 313	80.43 324	88.39 283	92.66 251	81.95 145	94.30 312	93.38 343	79.06 333	75.82 334	85.66 374	56.38 367	93.84 387	71.23 346	75.38 336	89.38 333

test1		82.42 313	80.43 324	88.39 283	92.66 251	81.95 145	94.30 312	93.38 343	79.06 333	75.82 334	85.66 374	56.38 367	93.84 387	71.23 346	75.38 336	89.38 333

v148		82.41 315	80.89 315	86.99 320	86.18 391	76.81 314	96.27 220	93.82 316	80.49 296	75.28 341	86.11 372	67.32 273	95.75 318	75.48 314	67.03 403	88.42 367

v1192		82.31 316	80.55 322	87.60 303	85.94 394	78.47 265	95.85 249	93.80 319	79.33 325	76.97 313	86.51 360	63.33 307	95.87 309	73.11 334	70.13 369	88.46 365

LS3D		82.22 317	79.94 332	89.06 267	97.43 85	74.06 352	93.20 346	92.05 376	61.90 434	73.33 358	95.21 186	59.35 336	99.21 103	54.54 427	92.48 175	93.90 285

jajsoiax		82.12 318	81.15 313	85.03 354	84.19 415	70.70 386	94.22 317	93.95 302	83.07 247	73.48 353	89.75 311	49.66 398	95.37 338	82.24 241	79.76 306	89.02 349

v1921920		82.02 319	80.23 326	87.41 311	85.62 398	77.92 286	95.79 253	93.69 328	78.86 336	76.67 316	86.44 363	62.50 310	95.83 311	72.69 336	69.77 375	88.47 364

myMVS_eth3d		81.93 320	82.18 296	81.18 398	92.13 282	67.18 408	93.97 321	94.23 287	82.43 263	73.39 354	93.57 249	76.98 140	87.86 438	50.53 438	82.34 295	88.51 361

v8		81.88 321	80.06 330	87.32 313	86.63 383	79.04 244	94.41 305	93.65 330	78.77 337	73.19 360	85.57 378	66.87 278	95.81 312	73.84 330	67.61 396	87.11 391

mvs_tets		81.74 322	80.71 319	84.84 355	84.22 414	70.29 390	93.91 324	93.78 321	82.77 257	73.37 356	89.46 314	47.36 409	95.31 342	81.99 242	79.55 312	88.92 355

v1240		81.70 323	79.83 334	87.30 315	85.50 399	77.70 298	95.48 265	93.44 339	78.46 341	76.53 320	86.44 363	60.85 328	95.84 310	71.59 343	70.17 367	88.35 368

PVSNet_0	77.72 15	81.70 323	78.95 342	89.94 249	90.77 321	76.72 316	95.96 238	96.95 48	85.01 188	70.24 385	88.53 326	52.32 384	98.20 167	86.68 198	44.08 457	94.89 262

miper_lstm_enhance		81.66 325	80.66 320	84.67 359	91.19 307	71.97 373	91.94 364	93.19 351	77.86 346	72.27 369	85.26 382	73.46 210	93.42 395	73.71 331	67.05 402	88.61 359

DP-MVS		81.47 326	78.28 345	91.04 209	98.14 56	78.48 262	95.09 291	86.97 427	61.14 440	71.12 378	92.78 263	59.59 333	99.38 90	53.11 431	86.61 254	95.27 253

v10		81.43 327	79.53 336	87.11 318	86.38 385	78.87 246	94.31 311	93.43 341	77.88 345	73.24 359	85.26 382	65.44 288	95.75 318	72.14 340	67.71 395	86.72 395

pmmvs5		81.34 328	79.54 335	86.73 325	85.02 406	76.91 311	96.22 223	91.65 384	77.65 348	73.55 352	88.61 323	55.70 370	94.43 376	74.12 327	73.35 349	88.86 357

SD_0403		81.29 329	81.13 314	81.78 394	90.20 332	60.43 438	89.97 385	91.31 393	83.87 226	71.78 372	93.08 258	63.86 302	89.61 429	60.00 404	86.07 263	95.30 251

ADS-MVSNet		81.26 330	78.36 344	89.96 248	93.78 202	79.78 218	79.48 442	93.60 334	73.09 391	80.14 280	79.99 424	62.15 315	95.24 345	59.49 406	83.52 280	94.85 264

Baseline_NR-MVSNet		81.22 331	80.07 329	84.68 358	85.32 404	75.12 342	96.48 201	88.80 418	76.24 367	77.28 309	86.40 366	67.61 267	94.39 377	75.73 312	66.73 405	84.54 419

tt0805		81.20 332	79.06 341	87.61 302	86.50 384	72.97 363	93.66 329	95.48 201	74.11 381	76.23 327	91.99 276	41.36 429	97.40 224	77.44 293	74.78 341	92.45 299

SSC-MVS3.2		81.06 333	79.49 337	85.75 340	89.78 340	73.00 362	94.40 308	95.23 219	83.76 232	76.61 319	87.82 339	49.48 399	94.88 360	66.80 369	71.56 359	89.38 333

WR-MVS_H		81.02 334	80.09 327	83.79 371	88.08 370	71.26 384	94.46 303	96.54 103	80.08 311	72.81 364	86.82 355	70.36 252	92.65 400	64.18 385	67.50 397	87.46 388

CP-MVSNet		81.01 335	80.08 328	83.79 371	87.91 372	70.51 387	94.29 316	95.65 190	80.83 286	72.54 367	88.84 320	63.71 303	92.32 405	68.58 363	68.36 387	88.55 360

anonymousdsp		80.98 336	79.97 331	84.01 368	81.73 429	70.44 389	92.49 356	93.58 336	77.10 357	72.98 362	86.31 367	57.58 354	94.90 359	79.32 270	78.63 321	86.69 396

UniMVSNet_ETH3D		80.86 337	78.75 343	87.22 317	86.31 387	72.02 371	91.95 363	93.76 326	73.51 386	75.06 344	90.16 307	43.04 422	95.66 323	76.37 305	78.55 322	93.98 283

testing3		80.74 338	81.17 312	79.44 408	91.15 309	63.48 427	97.16 143	95.76 183	80.83 286	71.36 375	93.15 256	78.22 114	87.30 443	43.19 452	79.67 309	87.55 386

IterMVS		80.67 339	79.16 339	85.20 351	89.79 339	76.08 326	92.97 350	91.86 378	80.28 305	71.20 377	85.14 387	57.93 352	91.34 417	72.52 338	70.74 364	88.18 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 340	77.77 350	89.14 266	93.43 217	77.24 305	91.89 365	90.18 406	69.86 412	68.02 393	91.94 281	52.21 386	98.84 133	59.32 408	83.12 284	91.35 304

IterMVS-SCA-FT		80.51 341	79.10 340	84.73 357	89.63 347	74.66 344	92.98 349	91.81 380	80.05 312	71.06 379	85.18 385	58.04 348	91.40 416	72.48 339	70.70 366	88.12 373

PS-CasMVS		80.27 342	79.18 338	83.52 377	87.56 376	69.88 393	94.08 319	95.29 216	80.27 306	72.08 370	88.51 327	59.22 339	92.23 407	67.49 365	68.15 390	88.45 366

pm-mvs1		80.05 343	78.02 348	86.15 333	85.42 400	75.81 335	95.11 288	92.69 365	77.13 355	70.36 383	87.43 344	58.44 344	95.27 344	71.36 345	64.25 413	87.36 389

RPMNet		79.85 344	75.92 364	91.64 182	90.16 334	79.75 220	79.02 446	95.44 205	58.43 449	82.27 257	72.55 450	73.03 215	98.41 158	46.10 447	86.25 258	96.75 203

PatchT		79.75 345	76.85 357	88.42 280	89.55 349	75.49 339	77.37 450	94.61 257	63.07 429	82.46 251	73.32 447	75.52 173	93.41 396	51.36 434	84.43 276	96.36 213

Anonymous20231211		79.72 346	77.19 354	87.33 312	95.59 135	77.16 309	95.18 283	94.18 292	59.31 447	72.57 366	86.20 370	47.89 406	95.66 323	74.53 324	69.24 380	89.18 341

test_fmvs2		79.59 347	79.90 333	78.67 413	82.86 426	55.82 450	95.20 280	89.55 410	81.09 282	80.12 282	89.80 310	34.31 445	93.51 394	87.82 183	78.36 324	86.69 396

ADS-MVSNet2		79.57 348	77.53 351	85.71 341	93.78 202	72.13 369	79.48 442	86.11 434	73.09 391	80.14 280	79.99 424	62.15 315	90.14 428	59.49 406	83.52 280	94.85 264

FMVSNet1		79.50 349	76.54 360	88.39 283	88.47 364	81.95 145	94.30 312	93.38 343	73.14 390	72.04 371	85.66 374	43.86 416	93.84 387	65.48 379	72.53 353	89.38 333

PEN-MVS		79.47 350	78.26 346	83.08 380	86.36 386	68.58 401	93.85 327	94.77 242	79.76 317	71.37 374	88.55 324	59.79 331	92.46 401	64.50 383	65.40 408	88.19 371

XVG-ACMP-BASELINE		79.38 351	77.90 349	83.81 370	84.98 407	67.14 412	89.03 395	93.18 353	80.26 307	72.87 363	88.15 334	38.55 435	96.26 291	76.05 308	78.05 326	88.02 374

v7n		79.32 352	77.34 352	85.28 350	84.05 418	72.89 365	93.38 337	93.87 309	75.02 375	70.68 380	84.37 394	59.58 334	95.62 328	67.60 364	67.50 397	87.32 390

MIMVSNet		79.18 353	75.99 363	88.72 276	87.37 378	80.66 190	79.96 440	91.82 379	77.38 352	74.33 348	81.87 412	41.78 425	90.74 423	66.36 377	83.10 285	94.76 266

JIA-IIPM		79.00 354	77.20 353	84.40 366	89.74 344	64.06 424	75.30 454	95.44 205	62.15 433	81.90 261	59.08 459	78.92 100	95.59 330	66.51 375	85.78 267	93.54 290

USDC		78.65 355	76.25 361	85.85 336	87.58 375	74.60 346	89.58 390	90.58 405	84.05 218	63.13 418	88.23 332	40.69 434	96.86 269	66.57 374	75.81 334	86.09 405

DTE-MVSNet		78.37 356	77.06 355	82.32 390	85.22 405	67.17 411	93.40 336	93.66 329	78.71 338	70.53 382	88.29 331	59.06 340	92.23 407	61.38 398	63.28 417	87.56 384

Patchmatch-test		78.25 357	74.72 372	88.83 273	91.20 306	74.10 351	73.91 457	88.70 421	59.89 445	66.82 400	85.12 388	78.38 110	94.54 373	48.84 443	79.58 311	97.86 109

tfpnnormal		78.14 358	75.42 366	86.31 331	88.33 368	79.24 234	94.41 305	96.22 143	73.51 386	69.81 387	85.52 380	55.43 371	95.75 318	47.65 445	67.86 393	83.95 425

mmtdpeth		78.04 359	76.76 358	81.86 393	89.60 348	66.12 416	92.34 360	87.18 426	76.83 362	85.55 203	76.49 438	46.77 410	97.02 253	90.85 133	45.24 454	82.43 434

ACMH	75.40 17	77.99 360	74.96 368	87.10 319	90.67 322	76.41 321	93.19 347	91.64 385	72.47 397	63.44 416	87.61 343	43.34 419	97.16 244	58.34 411	73.94 344	87.72 378
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 360	75.74 365	84.74 356	90.45 326	72.02 371	86.41 419	91.12 394	72.57 396	66.63 402	87.27 347	54.95 376	96.98 256	56.29 421	75.98 331	85.21 414
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		77.97 362	78.05 347	77.74 417	92.13 282	56.85 446	93.97 321	94.23 287	82.43 263	73.39 354	93.57 249	57.95 351	87.86 438	32.40 460	82.34 295	88.51 361

our_test_3		77.90 363	75.37 367	85.48 347	85.39 401	76.74 315	93.63 330	91.67 383	73.39 389	65.72 407	84.65 393	58.20 347	93.13 398	57.82 413	67.87 392	86.57 398

RPSCF		77.73 364	76.63 359	81.06 399	88.66 362	55.76 451	87.77 408	87.88 424	64.82 427	74.14 349	92.79 262	49.22 400	96.81 271	67.47 366	76.88 329	90.62 311

KD-MVS_2432*1600		77.63 365	74.92 370	85.77 338	90.86 317	79.44 228	88.08 404	93.92 305	76.26 365	67.05 398	82.78 407	72.15 228	91.92 410	61.53 395	41.62 460	85.94 408

miper_refine_blended		77.63 365	74.92 370	85.77 338	90.86 317	79.44 228	88.08 404	93.92 305	76.26 365	67.05 398	82.78 407	72.15 228	91.92 410	61.53 395	41.62 460	85.94 408

ACMH+	76.62 16	77.47 367	74.94 369	85.05 353	91.07 312	71.58 380	93.26 344	90.01 407	71.80 400	64.76 411	88.55 324	41.62 426	96.48 283	62.35 394	71.00 362	87.09 392

Patchmtry		77.36 368	74.59 373	85.67 342	89.75 342	75.75 336	77.85 449	91.12 394	60.28 442	71.23 376	80.35 421	75.45 174	93.56 393	57.94 412	67.34 399	87.68 380

ppachtmachnet_test		77.19 369	74.22 377	86.13 334	85.39 401	78.22 273	93.98 320	91.36 390	71.74 401	67.11 397	84.87 391	56.67 363	93.37 397	52.21 432	64.59 410	86.80 394

OurMVSNet-221017-0		77.18 370	76.06 362	80.55 402	83.78 421	60.00 441	90.35 382	91.05 397	77.01 359	66.62 403	87.92 337	47.73 407	94.03 383	71.63 342	68.44 386	87.62 381

TransMVSNet (Re)		76.94 371	74.38 375	84.62 361	85.92 395	75.25 341	95.28 272	89.18 415	73.88 384	67.22 395	86.46 362	59.64 332	94.10 382	59.24 409	52.57 441	84.50 420

EU-MVSNet		76.92 372	76.95 356	76.83 422	84.10 416	54.73 454	91.77 367	92.71 364	72.74 394	69.57 388	88.69 322	58.03 350	87.43 442	64.91 382	70.00 373	88.33 369

Patchmatch-RL test		76.65 373	74.01 380	84.55 362	77.37 444	64.23 422	78.49 448	82.84 450	78.48 340	64.63 412	73.40 446	76.05 160	91.70 415	76.99 296	57.84 426	97.72 122

FMVSNet5		76.46 374	74.16 378	83.35 379	90.05 337	76.17 324	89.58 390	89.85 408	71.39 403	65.29 410	80.42 420	50.61 393	87.70 441	61.05 400	69.24 380	86.18 403

SixPastTwentyTwo		76.04 375	74.32 376	81.22 397	84.54 410	61.43 436	91.16 375	89.30 414	77.89 344	64.04 413	86.31 367	48.23 401	94.29 379	63.54 390	63.84 415	87.93 376

AllTest		75.92 376	73.06 384	84.47 363	92.18 278	67.29 406	91.07 376	84.43 440	67.63 418	63.48 414	90.18 305	38.20 436	97.16 244	57.04 417	73.37 347	88.97 353

CL-MVSNet_self_test		75.81 377	74.14 379	80.83 401	78.33 440	67.79 405	94.22 317	93.52 337	77.28 354	69.82 386	81.54 415	61.47 326	89.22 431	57.59 415	53.51 437	85.48 412

COLMAP_ROB	73.24 19	75.74 378	73.00 385	83.94 369	92.38 259	69.08 399	91.85 366	86.93 428	61.48 437	65.32 409	90.27 304	42.27 424	96.93 261	50.91 436	75.63 335	85.80 411
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	75.71 379	74.56 374	79.17 410	79.69 435	55.98 448	89.59 389	93.30 348	60.28 442	53.85 449	89.07 317	47.68 408	96.33 289	76.55 301	81.02 301	85.22 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 380	73.64 381	80.22 404	80.75 430	63.38 428	93.36 338	90.71 404	73.09 391	67.12 396	83.70 401	50.33 395	90.85 422	53.63 430	70.10 371	86.44 399

EG-PatchMatch MVS		74.92 381	72.02 389	83.62 375	83.76 423	73.28 357	93.62 331	92.04 377	68.57 416	58.88 437	83.80 400	31.87 450	95.57 332	56.97 419	78.67 318	82.00 438

testgi		74.88 382	73.40 382	79.32 409	80.13 434	61.75 433	93.21 345	86.64 432	79.49 323	66.56 404	91.06 291	35.51 443	88.67 433	56.79 420	71.25 360	87.56 384

pmmvs6		74.65 383	71.67 390	83.60 376	79.13 437	69.94 392	93.31 343	90.88 401	61.05 441	65.83 406	84.15 397	43.43 418	94.83 363	66.62 372	60.63 422	86.02 406

test_vis1_rt		73.96 384	72.40 387	78.64 414	83.91 419	61.16 437	95.63 259	68.18 467	76.32 364	60.09 434	74.77 441	29.01 456	97.54 210	87.74 186	75.94 332	77.22 450

K. test v3		73.62 385	71.59 391	79.69 406	82.98 425	59.85 442	90.85 379	88.83 417	77.13 355	58.90 436	82.11 409	43.62 417	91.72 414	65.83 378	54.10 436	87.50 387

pmmvs-eth3d		73.59 386	70.66 394	82.38 388	76.40 448	73.38 354	89.39 394	89.43 412	72.69 395	60.34 433	77.79 430	46.43 412	91.26 419	66.42 376	57.06 427	82.51 431

kuosan		73.55 387	72.39 388	77.01 420	89.68 345	66.72 414	85.24 428	93.44 339	67.76 417	60.04 435	83.40 404	71.90 233	84.25 451	45.34 449	54.75 431	80.06 446

MDA-MVSNet_test_wron		73.54 388	70.43 396	82.86 382	84.55 409	71.85 375	91.74 368	91.32 392	67.63 418	46.73 454	81.09 418	55.11 374	90.42 426	55.91 423	59.76 423	86.31 401

YYNet1		73.53 389	70.43 396	82.85 383	84.52 411	71.73 378	91.69 369	91.37 389	67.63 418	46.79 453	81.21 417	55.04 375	90.43 425	55.93 422	59.70 424	86.38 400

UnsupCasMVSNet_eth		73.25 390	70.57 395	81.30 396	77.53 442	66.33 415	87.24 412	93.89 308	80.38 300	57.90 441	81.59 413	42.91 423	90.56 424	65.18 381	48.51 448	87.01 393

DSMNet-mixed		73.13 391	72.45 386	75.19 428	77.51 443	46.82 459	85.09 429	82.01 452	67.61 422	69.27 390	81.33 416	50.89 389	86.28 446	54.54 427	83.80 279	92.46 298

OpenMVS_ROB	68.52 20	73.02 392	69.57 399	83.37 378	80.54 433	71.82 376	93.60 333	88.22 422	62.37 432	61.98 425	83.15 406	35.31 444	95.47 334	45.08 450	75.88 333	82.82 428

test_0402		72.68 393	69.54 400	82.09 391	88.67 361	71.81 377	92.72 354	86.77 431	61.52 436	62.21 424	83.91 399	43.22 420	93.76 390	34.60 458	72.23 357	80.72 445

TinyColmap		72.41 394	68.99 403	82.68 384	88.11 369	69.59 396	88.41 400	85.20 436	65.55 424	57.91 440	84.82 392	30.80 452	95.94 306	51.38 433	68.70 383	82.49 433

sc_t1		72.37 395	68.03 406	85.39 348	83.78 421	70.51 387	91.27 374	83.70 447	52.46 454	68.29 392	82.02 410	30.58 453	94.81 364	64.50 383	55.69 429	90.85 310

test20.03		72.36 396	71.15 392	75.98 426	77.79 441	59.16 443	92.40 358	89.35 413	74.09 382	61.50 428	84.32 395	48.09 402	85.54 449	50.63 437	62.15 420	83.24 426

LF4IMVS		72.36 396	70.82 393	76.95 421	79.18 436	56.33 447	86.12 421	86.11 434	69.30 414	63.06 419	86.66 358	33.03 448	92.25 406	65.33 380	68.64 384	82.28 435

Anonymous20240521		72.06 398	69.91 398	78.50 415	77.11 445	61.67 435	91.62 371	90.97 399	65.52 425	62.37 423	79.05 427	36.32 439	90.96 421	57.75 414	68.52 385	82.87 427

dmvs_testset		72.00 399	73.36 383	67.91 434	83.83 420	31.90 474	85.30 427	77.12 459	82.80 256	63.05 420	92.46 265	61.54 324	82.55 456	42.22 455	71.89 358	89.29 338

MDA-MVSNet-bldmvs		71.45 400	67.94 407	81.98 392	85.33 403	68.50 402	92.35 359	88.76 419	70.40 406	42.99 457	81.96 411	46.57 411	91.31 418	48.75 444	54.39 435	86.11 404

mvs5depth		71.40 401	68.36 405	80.54 403	75.31 452	65.56 418	79.94 441	85.14 437	69.11 415	71.75 373	81.59 413	41.02 431	93.94 385	60.90 401	50.46 444	82.10 436

MVS-HIRNet		71.36 402	67.00 408	84.46 365	90.58 323	69.74 395	79.15 445	87.74 425	46.09 458	61.96 426	50.50 462	45.14 414	95.64 326	53.74 429	88.11 240	88.00 375

KD-MVS_self_test		70.97 403	69.31 401	75.95 427	76.24 450	55.39 452	87.45 409	90.94 400	70.20 409	62.96 421	77.48 432	44.01 415	88.09 436	61.25 399	53.26 438	84.37 421

tt0320		70.21 404	66.07 412	82.64 385	83.42 424	70.82 385	89.63 388	84.10 443	49.75 457	62.71 422	77.28 433	33.35 446	92.45 403	58.78 410	55.62 430	84.64 418

tt0320-xc		69.70 405	65.27 417	82.99 381	84.33 412	71.92 374	89.56 392	82.08 451	50.11 455	61.87 427	77.50 431	30.48 454	92.34 404	60.30 402	51.20 443	84.71 417

ttmdpeth		69.58 406	66.92 410	77.54 419	75.95 451	62.40 431	88.09 403	84.32 442	62.87 431	65.70 408	86.25 369	36.53 438	88.53 435	55.65 425	46.96 453	81.70 441

test_fmvs3		69.56 407	69.19 402	70.67 432	69.01 458	47.05 458	90.87 378	86.81 429	71.31 404	66.79 401	77.15 434	16.40 463	83.17 454	81.84 243	62.51 419	81.79 440

dongtai		69.47 408	68.98 404	70.93 431	86.87 381	58.45 444	88.19 402	93.18 353	63.98 428	56.04 445	80.17 423	70.97 247	79.24 458	33.46 459	47.94 450	75.09 452

MIMVSNet1		69.44 409	66.65 411	77.84 416	76.48 447	62.84 430	87.42 410	88.97 416	66.96 423	57.75 443	79.72 426	32.77 449	85.83 448	46.32 446	63.42 416	84.85 416

PM-MVS		69.32 410	66.93 409	76.49 423	73.60 455	55.84 449	85.91 422	79.32 457	74.72 377	61.09 430	78.18 429	21.76 459	91.10 420	70.86 351	56.90 428	82.51 431

FE-MVSNET		69.26 411	66.03 413	78.93 411	73.82 454	68.33 403	89.65 387	84.06 444	70.21 408	57.79 442	76.94 437	41.48 428	86.98 445	45.85 448	54.51 434	81.48 443

TDRefinement		69.20 412	65.78 415	79.48 407	66.04 463	62.21 432	88.21 401	86.12 433	62.92 430	61.03 431	85.61 377	33.23 447	94.16 381	55.82 424	53.02 439	82.08 437

new-patchmatchnet		68.85 413	65.93 414	77.61 418	73.57 456	63.94 425	90.11 384	88.73 420	71.62 402	55.08 447	73.60 445	40.84 432	87.22 444	51.35 435	48.49 449	81.67 442

UnsupCasMVSNet_bld		68.60 414	64.50 418	80.92 400	74.63 453	67.80 404	83.97 433	92.94 360	65.12 426	54.63 448	68.23 455	35.97 441	92.17 409	60.13 403	44.83 455	82.78 429

mvsany_test3		67.19 415	65.34 416	72.72 430	63.08 464	48.57 457	83.12 436	78.09 458	72.07 398	61.21 429	77.11 435	22.94 458	87.78 440	78.59 277	51.88 442	81.80 439

MVStest1		66.93 416	63.01 420	78.69 412	78.56 438	71.43 382	85.51 426	86.81 429	49.79 456	48.57 452	84.15 397	53.46 382	83.31 452	43.14 453	37.15 463	81.34 444

new_pmnet		66.18 417	63.18 419	75.18 429	76.27 449	61.74 434	83.79 434	84.66 439	56.64 451	51.57 450	71.85 453	31.29 451	87.93 437	49.98 439	62.55 418	75.86 451

pmmvs3		65.75 418	62.18 421	76.45 424	67.12 462	64.54 420	88.68 398	85.05 438	54.77 453	57.54 444	73.79 444	29.40 455	86.21 447	55.49 426	47.77 451	78.62 448

test_f		64.01 419	62.13 422	69.65 433	63.00 465	45.30 464	83.66 435	80.68 454	61.30 438	55.70 446	72.62 449	14.23 465	84.64 450	69.84 356	58.11 425	79.00 447

N_pmnet		61.30 420	60.20 423	64.60 439	84.32 413	17.00 480	91.67 370	10.98 478	61.77 435	58.45 439	78.55 428	49.89 397	91.83 413	42.27 454	63.94 414	84.97 415

WB-MVS		57.26 421	56.22 424	60.39 445	69.29 457	35.91 472	86.39 420	70.06 465	59.84 446	46.46 455	72.71 448	51.18 388	78.11 459	15.19 469	34.89 464	67.14 458

test_method		56.77 422	54.53 426	63.49 441	76.49 446	40.70 467	75.68 453	74.24 461	19.47 469	48.73 451	71.89 452	19.31 460	65.80 469	57.46 416	47.51 452	83.97 424

APD_test1		56.56 423	53.58 427	65.50 436	67.93 461	46.51 461	77.24 452	72.95 462	38.09 460	42.75 458	75.17 440	13.38 466	82.78 455	40.19 456	54.53 433	67.23 457

SSC-MVS		56.01 424	54.96 425	59.17 446	68.42 459	34.13 473	84.98 430	69.23 466	58.08 450	45.36 456	71.67 454	50.30 396	77.46 460	14.28 470	32.33 465	65.91 459

FPMVS		55.09 425	52.93 428	61.57 443	55.98 467	40.51 468	83.11 437	83.41 449	37.61 461	34.95 462	71.95 451	14.40 464	76.95 461	29.81 461	65.16 409	67.25 456

test_vis3_rt		54.10 426	51.04 429	63.27 442	58.16 466	46.08 463	84.17 432	49.32 477	56.48 452	36.56 461	49.48 464	8.03 473	91.91 412	67.29 367	49.87 445	51.82 463

LCM-MVSNet		52.52 427	48.24 430	65.35 437	47.63 474	41.45 466	72.55 458	83.62 448	31.75 462	37.66 460	57.92 460	9.19 472	76.76 462	49.26 441	44.60 456	77.84 449

EGC-MVSNET		52.46 428	47.56 431	67.15 435	81.98 428	60.11 440	82.54 438	72.44 463	0.11 475	0.70 476	74.59 442	25.11 457	83.26 453	29.04 462	61.51 421	58.09 460

PMMVS2		50.90 429	46.31 432	64.67 438	55.53 468	46.67 460	77.30 451	71.02 464	40.89 459	34.16 463	59.32 458	9.83 471	76.14 464	40.09 457	28.63 466	71.21 453

ANet_high		46.22 430	41.28 437	61.04 444	39.91 476	46.25 462	70.59 461	76.18 460	58.87 448	23.09 468	48.00 465	12.58 468	66.54 468	28.65 463	13.62 469	70.35 454

testf1		45.70 431	42.41 433	55.58 447	53.29 471	40.02 469	68.96 462	62.67 471	27.45 464	29.85 464	61.58 456	5.98 474	73.83 466	28.49 464	43.46 458	52.90 461

APD_test2		45.70 431	42.41 433	55.58 447	53.29 471	40.02 469	68.96 462	62.67 471	27.45 464	29.85 464	61.58 456	5.98 474	73.83 466	28.49 464	43.46 458	52.90 461

Gipuma		45.11 433	42.05 435	54.30 449	80.69 431	51.30 456	35.80 468	83.81 446	28.13 463	27.94 467	34.53 467	11.41 470	76.70 463	21.45 466	54.65 432	34.90 467
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 434	41.93 436	40.38 452	20.10 478	26.84 476	61.93 465	59.09 473	14.81 471	28.51 466	80.58 419	35.53 442	48.33 473	63.70 389	13.11 470	45.96 466

PMVS	34.80 23	39.19 435	35.53 438	50.18 450	29.72 477	30.30 475	59.60 466	66.20 470	26.06 466	17.91 470	49.53 463	3.12 476	74.09 465	18.19 468	49.40 446	46.14 464
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 436	29.49 441	46.92 451	41.86 475	36.28 471	50.45 467	56.52 474	18.75 470	18.28 469	37.84 466	2.41 477	58.41 470	18.71 467	20.62 467	46.06 465
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 437	32.39 439	33.65 453	53.35 470	25.70 477	74.07 456	53.33 475	21.08 467	17.17 471	33.63 469	11.85 469	54.84 471	12.98 471	14.04 468	20.42 468

EMVS		31.70 438	31.45 440	32.48 454	50.72 473	23.95 478	74.78 455	52.30 476	20.36 468	16.08 472	31.48 470	12.80 467	53.60 472	11.39 472	13.10 471	19.88 469

cdsmvs_eth3d_5k		21.43 439	28.57 442	0.00 458	0.00 481	0.00 483	0.00 470	95.93 172	0.00 476	0.00 477	97.66 89	63.57 304	0.00 477	0.00 476	0.00 475	0.00 473

wuyk23d		14.10 440	13.89 443	14.72 455	55.23 469	22.91 479	33.83 469	3.56 479	4.94 472	4.11 473	2.28 475	2.06 478	19.66 474	10.23 473	8.74 472	1.59 472

testmvs		9.92 441	12.94 444	0.84 457	0.65 479	0.29 482	93.78 328	0.39 480	0.42 473	2.85 474	15.84 473	0.17 480	0.30 476	2.18 474	0.21 473	1.91 471

test123		9.07 442	11.73 445	1.11 456	0.50 480	0.77 481	89.44 393	0.20 481	0.34 474	2.15 475	10.72 474	0.34 479	0.32 475	1.79 475	0.08 474	2.23 470

ab-mvs-re		8.11 443	10.81 446	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	97.30 112	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

pcd_1.5k_mvsjas		5.92 444	7.89 447	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	71.04 244	0.00 477	0.00 476	0.00 475	0.00 473

mmdepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

monomultidepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

test_blank		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet_test		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

DCPMVS		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

sosnet-low-res		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

sosnet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uncertanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

Regformer		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

TestfortrainingZip							98.35 54

WAC-MVS						67.18 408								49.00 442

FOURS1					98.51 40	78.01 281	98.13 64	96.21 144	83.04 248	94.39 66

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 59					99.81 24	98.08 25	98.81 24	99.43 11

PC_three_1452									91.12 49	98.33 4	98.42 39	92.51 2	99.81 24	98.96 6	99.37 1	99.70 3

No_MVS				97.14 3	99.05 10	92.19 4		96.83 59					99.81 24	98.08 25	98.81 24	99.43 11

test_one_0601					98.91 19	84.56 84		96.70 79	88.06 101	96.57 32	98.77 11	88.04 21

eth-test2					0.00 481

eth-test					0.00 481

ZD-MVS					99.09 9	83.22 110		96.60 96	82.88 254	93.61 77	98.06 67	82.93 61	99.14 113	95.51 62	98.49 40

RE-MVS-def			91.18 112		97.76 70	76.03 328	96.20 225	95.44 205	80.56 294	90.72 125	97.84 81	73.36 212		91.99 116	96.79 105	97.75 119

IU-MVS					99.03 16	85.34 61		96.86 57	92.05 40	98.74 1				98.15 21	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 15				98.54 25	92.06 3	99.84 14	99.11 5	99.37 1	99.74 1

test_241102_TWO								96.78 62	88.72 83	97.70 13	98.91 2	87.86 22	99.82 20	98.15 21	99.00 15	99.47 9

test_241102_ONE					99.03 16	85.03 74		96.78 62	88.72 83	97.79 10	98.90 5	88.48 17	99.82 20

9.14			94.26 40		98.10 58		98.14 61	96.52 106	84.74 194	94.83 60	98.80 8	82.80 63	99.37 92	95.95 54	98.42 43

save fliter					98.24 52	83.34 107	98.61 45	96.57 100	91.32 46

test_0728_THIRD									88.38 91	96.69 27	98.76 13	89.64 12	99.76 40	97.47 37	98.84 23	99.38 14

test_0728_SECOND				95.14 20	99.04 15	86.14 39	99.06 22	96.77 68					99.84 14	97.90 29	98.85 21	99.45 10

test0726					99.05 10	85.18 66	99.11 18	96.78 62	88.75 81	97.65 16	98.91 2	87.69 23

GSMVS																97.54 138

test_part2					98.90 20	85.14 72				96.07 39

sam_mvs1												77.59 125				97.54 138

sam_mvs												75.35 181

ambc				76.02 425	68.11 460	51.43 455	64.97 464	89.59 409		60.49 432	74.49 443	17.17 462	92.46 401	61.50 397	52.85 440	84.17 423

MTGPA								96.33 133

test_post1							85.88 423				30.24 471	73.77 205	95.07 356	73.89 328

test_post											33.80 468	76.17 158	95.97 302

patchmatchnet-post											77.09 436	77.78 123	95.39 336

GG-mvs-BLEND				93.49 79	94.94 160	86.26 37	81.62 439	97.00 41		88.32 165	94.30 224	91.23 5	96.21 295	88.49 176	97.43 77	98.00 97

MTMP							97.53 111	68.16 468

gm-plane-assit					92.27 271	79.64 226			84.47 205		95.15 192		97.93 180	85.81 201

test9_res														96.00 53	99.03 13	98.31 71

TEST9					98.64 32	83.71 97	97.82 85	96.65 87	84.29 212	95.16 50	98.09 62	84.39 43	99.36 93

test_8					98.63 34	83.64 101	97.81 87	96.63 92	84.50 202	95.10 53	98.11 60	84.33 44	99.23 101

agg_prior2														94.30 77	99.00 15	98.57 55

agg_prior					98.59 36	83.13 112		96.56 102		94.19 68			99.16 112

TestCases				84.47 363	92.18 278	67.29 406		84.43 440	67.63 418	63.48 414	90.18 305	38.20 436	97.16 244	57.04 417	73.37 347	88.97 353

test_prior4						82.34 135	97.75 93

test_prior2							98.37 53		86.08 155	94.57 64	98.02 68	83.14 58		95.05 68	98.79 27

test_prior				93.09 94	98.68 27	81.91 149		96.40 122					99.06 120			98.29 73

旧先验2							96.97 164		74.06 383	96.10 38			97.76 191	88.38 178

新几何2							96.42 208

新几何1				93.12 92	97.44 84	81.60 165		96.71 78	74.54 379	91.22 118	97.57 97	79.13 97	99.51 83	77.40 294	98.46 41	98.26 76

旧先验1					97.39 89	79.58 227		96.54 103			98.08 65	84.00 50			97.42 78	97.62 133

无先验							96.87 173	96.78 62	77.39 351				99.52 81	79.95 262		98.43 64

原ACMM2							96.84 174

原ACMM1				91.22 205	97.77 68	78.10 279		96.61 93	81.05 283	91.28 117	97.42 106	77.92 120	98.98 124	79.85 264	98.51 37	96.59 208

test222					96.15 112	78.41 266	95.87 247	96.46 114	71.97 399	89.66 139	97.45 102	76.33 155			98.24 52	98.30 72

testdata2													99.48 85	76.45 303

segment_acmp												82.69 64

testdata				90.13 240	95.92 122	74.17 350		96.49 112	73.49 388	94.82 61	97.99 69	78.80 104	97.93 180	83.53 227	97.52 73	98.29 73

testdata1							95.57 263		87.44 118

test12				94.25 41	98.34 47	85.55 57		96.35 132		92.36 95		80.84 72	99.22 102		98.31 50	97.98 99

plane_prior7					91.86 295	77.55 300

plane_prior6					91.98 291	77.92 286						64.77 296

plane_prior5								94.69 247					97.30 234	87.08 192	82.82 290	90.96 307

plane_prior4											94.15 232

plane_prior3						77.75 296			90.17 66	81.33 266

plane_prior2							97.18 139		89.89 69

plane_prior1					91.95 293

plane_prior						77.96 283	97.52 114		90.36 64						82.96 288

n2								0.00 482

nn								0.00 482

door-mid								79.75 456

lessismore_v0				79.98 405	80.59 432	58.34 445		80.87 453		58.49 438	83.46 403	43.10 421	93.89 386	63.11 392	48.68 447	87.72 378

LGP-MVS_train				86.33 328	90.88 314	73.06 360		94.13 294	82.20 267	76.31 323	93.20 253	54.83 377	96.95 258	83.72 221	80.83 303	88.98 351

test11								96.50 109

door								80.13 455

HQP5-MVS						78.48 262

HQP-NCC					92.08 285		97.63 100		90.52 59	82.30 253

ACMP_Plane					92.08 285		97.63 100		90.52 59	82.30 253

BP-MVS														87.67 188

HQP4-MVS										82.30 253			97.32 232			91.13 305

HQP3-MVS								94.80 239							83.01 286

HQP2-MVS												65.40 289

NP-MVS					92.04 289	78.22 273					94.56 215

MDTV_nov1_ep13_2view						81.74 158	86.80 415		80.65 291	85.65 201		74.26 199		76.52 302		96.98 186

MDTV_nov1_ep13			83.69 266		94.09 195	81.01 178	86.78 416	96.09 153	83.81 230	84.75 214	84.32 395	74.44 198	96.54 281	63.88 387	85.07 273

ACMMP++_ref															78.45 323

ACMMP++															79.05 315

Test By Simon												71.65 236

ITE_SJBPF				82.38 388	87.00 380	65.59 417		89.55 410	79.99 314	69.37 389	91.30 288	41.60 427	95.33 340	62.86 393	74.63 343	86.24 402

DeepMVS_CX				64.06 440	78.53 439	43.26 465		68.11 469	69.94 411	38.55 459	76.14 439	18.53 461	79.34 457	43.72 451	41.62 460	69.57 455

High-resolution multi-view benchmark