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 21	97.12 31	94.66 9	96.79 25	98.78 9	86.42 30	99.95 3	97.59 34	99.18 7	99.00 31

DPM-MVS		96.21 2	95.53 14	98.26 1	96.26 107	95.09 1	99.15 10	96.98 42	93.39 21	96.45 33	98.79 8	90.17 9	99.99 1	89.33 161	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 29	97.10 33	95.17 4	92.11 100	98.46 33	87.33 25	99.97 2	97.21 40	99.31 4	99.63 7

DVP-MVS++		96.05 4	96.41 3	94.96 24	99.05 9	85.34 61	98.13 62	96.77 67	88.38 90	97.70 12	98.77 10	92.06 3	99.84 13	97.47 35	99.37 1	99.70 3

SED-MVS		95.88 5	96.22 4	94.87 25	99.03 15	85.03 74	99.12 14	96.78 61	88.72 82	97.79 9	98.91 2	88.48 17	99.82 19	98.15 20	98.97 17	99.74 1

MM		95.85 6	95.74 10	96.15 8	96.34 104	89.50 9	99.18 8	98.10 8	95.68 1	96.64 29	97.92 73	80.72 72	99.80 26	99.16 2	97.96 58	99.15 27

NCCC		95.63 7	95.94 8	94.69 32	99.21 6	85.15 71	99.16 9	96.96 46	94.11 14	95.59 44	98.64 19	85.07 36	99.91 4	95.61 57	99.10 9	99.00 31

MSP-MVS		95.62 8	96.54 1	92.86 103	98.31 48	80.10 209	97.42 120	96.78 61	92.20 34	97.11 21	98.29 46	93.46 1	99.10 115	96.01 50	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 9	85.18 66	99.06 21	96.46 113	88.75 80	96.69 26	98.76 12	87.69 23	99.76 38	97.90 28	98.85 21	98.77 41
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		95.58 9	95.44 16	96.01 10	97.63 72	89.26 12	99.27 5	96.59 96	94.71 8	97.08 22	97.99 67	78.69 105	99.86 10	99.15 3	97.85 62	98.91 35

DPE-MVS		95.32 11	95.55 13	94.64 33	98.79 23	84.87 79	97.77 88	96.74 72	86.11 151	96.54 32	98.89 6	88.39 19	99.74 46	97.67 33	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 34	86.04 40	97.81 85	96.93 49	92.45 28	95.69 42	98.50 28	85.38 34	99.85 11	94.75 70	99.18 7	98.65 51

patch_mono-2		95.14 13	96.08 7	92.33 137	98.44 43	77.84 286	98.43 49	97.21 24	92.58 27	97.68 14	97.65 91	86.88 27	99.83 17	98.25 16	97.60 70	99.33 18

DELS-MVS		94.98 14	94.49 30	96.44 6	96.42 103	90.59 7	99.21 7	97.02 39	94.40 13	91.46 109	97.08 122	83.32 56	99.69 58	92.83 101	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	92.84 106	95.20 147	80.55 191	99.45 1	96.36 129	95.17 4	98.48 3	98.55 22	80.53 76	99.78 33	98.87 7	97.79 65	98.19 79

fmvsm_l_conf0.5_n_a		94.91 15	95.30 17	93.72 62	94.50 176	84.30 87	99.14 12	96.00 159	91.94 40	97.91 7	98.60 20	84.78 38	99.77 36	98.84 8	96.03 120	97.08 180

fmvsm_l_conf0.5_n		94.89 17	95.24 18	93.86 53	94.42 179	84.61 82	99.13 13	96.15 147	92.06 37	97.92 5	98.52 27	84.52 41	99.74 46	98.76 9	95.67 127	97.22 167

CANet		94.89 17	94.64 27	95.63 13	97.55 78	88.12 18	99.06 21	96.39 123	94.07 16	95.34 46	97.80 82	76.83 143	99.87 8	97.08 42	97.64 69	98.89 36

SD-MVS		94.84 19	95.02 22	94.29 40	97.87 64	84.61 82	97.76 90	96.19 145	89.59 72	96.66 28	98.17 54	84.33 43	99.60 69	96.09 49	98.50 38	98.66 50
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 20	95.60 11	92.45 128	95.29 143	80.96 178	99.29 4	97.21 24	94.50 12	97.29 20	98.44 34	82.15 64	99.78 33	98.56 10	97.68 68	96.61 204

TSAR-MVS + MP.		94.79 21	95.17 20	93.64 68	97.66 71	84.10 90	95.85 245	96.42 118	91.26 46	97.49 18	96.80 135	86.50 29	98.49 148	95.54 59	99.03 13	98.33 67
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 22	94.68 26	94.76 29	98.02 59	85.94 44	97.47 113	96.77 67	85.32 171	97.92 5	98.70 17	83.09 59	99.84 13	95.79 54	99.08 10	98.49 58
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 23	94.92 23	93.68 66	94.52 171	82.80 118	99.33 2	96.37 127	95.08 6	97.59 17	98.48 31	77.40 128	99.79 30	98.28 14	97.21 84	98.44 62

DeepPCF-MVS	89.82 1	94.61 23	96.17 5	89.91 247	97.09 96	70.21 388	98.99 27	96.69 80	95.57 2	95.08 52	99.23 1	86.40 31	99.87 8	97.84 31	98.66 32	99.65 6

balanced_conf03		94.60 25	94.30 36	95.48 16	96.45 102	88.82 14	96.33 212	95.58 191	91.12 48	95.84 41	93.87 238	83.47 55	98.37 158	97.26 38	98.81 24	99.24 23

APDe-MVS		94.56 26	94.75 24	93.96 51	98.84 22	83.40 105	98.04 70	96.41 119	85.79 160	95.00 54	98.28 47	84.32 46	99.18 108	97.35 37	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 27	95.22 19	92.41 133	95.79 127	78.61 257	98.73 36	96.00 159	94.91 7	97.73 11	98.73 15	79.09 97	99.79 30	99.14 4	96.86 100	98.83 38

fmvsm_s_conf0.5_n_8		94.52 27	95.04 21	92.96 98	95.15 151	81.14 170	99.09 18	96.66 85	95.53 3	97.84 8	98.71 16	76.33 154	99.81 22	99.24 1	96.85 102	97.92 102

DeepC-MVS_fast	89.06 2	94.48 29	94.30 36	95.02 22	98.86 21	85.68 51	98.06 68	96.64 89	93.64 19	91.74 107	98.54 24	80.17 82	99.90 5	92.28 108	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
TSAR-MVS + GP.		94.35 30	94.50 29	93.89 52	97.38 90	83.04 113	98.10 64	95.29 214	91.57 42	93.81 71	97.45 100	86.64 28	99.43 86	96.28 48	94.01 147	99.20 25

train_agg		94.28 31	94.45 31	93.74 59	98.64 31	83.71 97	97.82 83	96.65 86	84.50 199	95.16 48	98.09 60	84.33 43	99.36 91	95.91 53	98.96 19	98.16 82

MSLP-MVS++		94.28 31	94.39 33	93.97 50	98.30 49	84.06 91	98.64 42	96.93 49	90.71 55	93.08 82	98.70 17	79.98 86	99.21 101	94.12 79	99.07 11	98.63 52

MG-MVS		94.25 33	93.72 44	95.85 12	99.38 3	89.35 11	97.98 72	98.09 9	89.99 66	92.34 94	96.97 127	81.30 70	98.99 121	88.54 171	98.88 20	99.20 25

fmvsm_s_conf0.5_n_6		94.17 34	94.70 25	92.58 123	93.50 213	81.20 168	99.08 19	96.48 112	92.24 33	98.62 2	98.39 39	78.58 107	99.72 51	98.08 24	97.36 79	96.81 194

SF-MVS		94.17 34	94.05 41	94.55 35	97.56 77	85.95 42	97.73 92	96.43 117	84.02 216	95.07 53	98.74 14	82.93 60	99.38 88	95.42 61	98.51 36	98.32 68

PS-MVSNAJ		94.17 34	93.52 51	96.10 9	95.65 131	92.35 2	98.21 57	95.79 180	92.42 29	96.24 35	98.18 51	71.04 241	99.17 109	96.77 45	97.39 78	96.79 195

SteuartSystems-ACMMP		94.13 37	94.44 32	93.20 87	95.41 138	81.35 166	99.02 25	96.59 96	89.50 74	94.18 67	98.36 43	83.68 54	99.45 85	94.77 69	98.45 41	98.81 40
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 38	94.15 39	93.76 57	97.27 93	84.35 85	98.29 54	97.64 14	94.57 10	95.36 45	96.88 130	79.96 87	99.12 114	91.30 121	96.11 117	97.82 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 39	94.36 34	92.86 103	92.82 243	81.12 171	99.26 6	96.37 127	93.47 20	95.16 48	98.21 49	79.00 98	99.64 64	98.21 18	96.73 106	97.83 111

fmvsm_s_conf0.5_n_3		93.95 40	94.53 28	92.20 148	94.41 180	80.04 210	98.90 31	95.96 164	94.53 11	97.63 16	98.58 21	75.95 161	99.79 30	98.25 16	96.60 108	96.77 197

xiu_mvs_v2_base		93.92 41	93.26 57	95.91 11	95.07 154	92.02 6	98.19 58	95.68 186	92.06 37	96.01 40	98.14 56	70.83 246	98.96 123	96.74 47	96.57 109	96.76 199

lupinMVS		93.87 42	93.58 49	94.75 30	93.00 230	88.08 19	99.15 10	95.50 198	91.03 51	94.90 55	97.66 87	78.84 101	97.56 203	94.64 73	97.46 73	98.62 53

fmvsm_s_conf0.5_n		93.69 43	94.13 40	92.34 135	94.56 168	82.01 142	99.07 20	97.13 29	92.09 35	96.25 34	98.53 26	76.47 149	99.80 26	98.39 12	94.71 137	95.22 251

APD-MVS		93.61 44	93.59 48	93.69 65	98.76 24	83.26 108	97.21 132	96.09 151	82.41 262	94.65 61	98.21 49	81.96 67	98.81 133	94.65 72	98.36 47	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 45	94.32 35	91.41 191	93.89 198	79.24 231	98.89 32	96.53 104	92.82 25	97.37 19	98.47 32	77.21 135	99.78 33	98.11 23	95.59 129	95.21 252

PHI-MVS		93.59 45	93.63 47	93.48 79	98.05 58	81.76 155	98.64 42	97.13 29	82.60 258	94.09 68	98.49 29	80.35 77	99.85 11	94.74 71	98.62 33	98.83 38

fmvsm_s_conf0.5_n_5		93.57 47	93.75 43	93.01 95	92.87 242	82.73 119	98.93 30	95.90 172	90.96 53	95.61 43	98.39 39	76.57 147	99.63 66	98.32 13	96.24 113	96.68 203

BP-MVS1		93.55 48	93.50 52	93.71 63	92.64 251	85.39 60	97.78 87	96.84 57	89.52 73	92.00 101	97.06 124	88.21 20	98.03 173	91.45 120	96.00 122	97.70 124

ACMMP_NAP		93.46 49	93.23 58	94.17 46	97.16 94	84.28 88	96.82 174	96.65 86	86.24 148	94.27 65	97.99 67	77.94 117	99.83 17	93.39 87	98.57 34	98.39 65

MVS_111021_HR		93.41 50	93.39 55	93.47 81	97.34 91	82.83 117	97.56 105	98.27 6	89.16 78	89.71 135	97.14 117	79.77 88	99.56 76	93.65 85	97.94 59	98.02 91

fmvsm_s_conf0.5_n_a		93.34 51	93.71 45	92.22 146	93.38 216	81.71 158	98.86 33	96.98 42	91.64 41	96.85 24	98.55 22	75.58 169	99.77 36	97.88 30	93.68 156	95.18 253

lecture		93.17 52	93.57 50	91.96 160	97.80 65	78.79 252	98.50 48	96.98 42	86.61 144	94.75 60	98.16 55	78.36 111	99.35 93	93.89 81	97.12 89	97.75 118

PVSNet_Blended		93.13 53	92.98 63	93.57 73	97.47 79	83.86 93	99.32 3	96.73 74	91.02 52	89.53 140	96.21 148	76.42 151	99.57 74	94.29 76	95.81 126	97.29 165

CDPH-MVS		93.12 54	92.91 65	93.74 59	98.65 30	83.88 92	97.67 96	96.26 137	83.00 248	93.22 79	98.24 48	81.31 69	99.21 101	89.12 162	98.74 30	98.14 84

dcpmvs_2		93.10 55	93.46 54	92.02 158	97.77 67	79.73 220	94.82 293	93.86 308	86.91 135	91.33 113	96.76 136	85.20 35	98.06 171	96.90 44	97.60 70	98.27 74

test_fmvsmconf0.1_n		93.08 56	93.22 59	92.65 116	88.45 362	80.81 183	99.00 26	95.11 220	93.21 22	94.00 69	97.91 75	76.84 141	99.59 70	97.91 27	96.55 110	97.54 137

SPE-MVS-test		92.98 57	93.67 46	90.90 213	96.52 101	76.87 309	98.68 39	94.73 241	90.36 63	94.84 57	97.89 77	77.94 117	97.15 245	94.28 78	97.80 64	98.70 49

fmvsm_s_conf0.5_n_2		92.97 58	93.38 56	91.73 174	94.10 192	80.64 188	98.96 28	95.89 173	94.09 15	97.05 23	98.40 38	68.92 258	99.80 26	98.53 11	94.50 141	94.74 264

alignmvs		92.97 58	92.26 84	95.12 21	95.54 135	87.77 22	98.67 40	96.38 124	88.04 101	93.01 83	97.45 100	79.20 95	98.60 139	93.25 93	88.76 218	98.99 33

fmvsm_s_conf0.1_n		92.93 60	93.16 60	92.24 143	90.52 321	81.92 146	98.42 50	96.24 139	91.17 47	96.02 39	98.35 44	75.34 180	99.74 46	97.84 31	94.58 139	95.05 256

HFP-MVS		92.89 61	92.86 68	92.98 97	98.71 25	81.12 171	97.58 103	96.70 78	85.20 176	91.75 106	97.97 72	78.47 108	99.71 54	90.95 126	98.41 43	98.12 87

NormalMVS		92.88 62	92.97 64	92.59 122	97.80 65	82.02 140	97.94 75	94.70 242	92.34 30	92.15 98	96.53 143	77.03 136	98.57 141	91.13 124	97.12 89	97.19 173

fmvsm_s_conf0.5_n_7		92.88 62	93.82 42	90.08 238	92.79 246	76.45 317	98.54 46	96.74 72	92.28 32	95.22 47	98.49 29	74.91 187	98.15 169	98.28 14	97.13 88	95.63 236

PAPM		92.87 64	92.40 78	94.30 39	92.25 271	87.85 21	96.40 206	96.38 124	91.07 50	88.72 157	96.90 128	82.11 65	97.37 228	90.05 149	97.70 67	97.67 126

GDP-MVS		92.85 65	92.55 75	93.75 58	92.82 243	85.76 47	97.63 97	95.05 224	88.34 92	93.15 80	97.10 121	86.92 26	98.01 175	87.95 179	94.00 148	97.47 146

ZNCC-MVS		92.75 66	92.60 73	93.23 86	98.24 51	81.82 153	97.63 97	96.50 108	85.00 186	91.05 118	97.74 84	78.38 109	99.80 26	90.48 139	98.34 48	98.07 89

PAPR		92.74 67	92.17 88	94.45 36	98.89 20	84.87 79	97.20 134	96.20 143	87.73 110	88.40 161	98.12 57	78.71 104	99.76 38	87.99 178	96.28 112	98.74 43

CS-MVS		92.73 68	93.48 53	90.48 226	96.27 106	75.93 330	98.55 45	94.93 228	89.32 75	94.54 63	97.67 86	78.91 100	97.02 250	93.80 82	97.32 81	98.49 58

jason		92.73 68	92.23 85	94.21 44	90.50 322	87.30 30	98.65 41	95.09 221	90.61 57	92.76 88	97.13 118	75.28 181	97.30 231	93.32 91	96.75 105	98.02 91
jason: jason.
myMVS_eth3d28		92.72 70	92.23 85	94.21 44	96.16 110	87.46 29	97.37 124	96.99 41	88.13 99	88.18 167	95.47 172	84.12 48	98.04 172	92.46 107	91.17 193	97.14 176

ETV-MVS		92.72 70	92.87 66	92.28 141	94.54 170	81.89 149	97.98 72	95.21 218	89.77 70	93.11 81	96.83 132	77.23 134	97.50 212	95.74 55	95.38 131	97.44 152

region2R		92.72 70	92.70 70	92.79 108	98.68 26	80.53 196	97.53 108	96.51 106	85.22 174	91.94 104	97.98 70	77.26 130	99.67 62	90.83 133	98.37 46	98.18 80

reproduce-ours		92.70 73	93.02 61	91.75 171	97.45 81	77.77 290	96.16 224	95.94 168	84.12 212	92.45 89	98.43 35	80.06 84	99.24 97	95.35 62	97.18 85	98.24 76

our_new_method		92.70 73	93.02 61	91.75 171	97.45 81	77.77 290	96.16 224	95.94 168	84.12 212	92.45 89	98.43 35	80.06 84	99.24 97	95.35 62	97.18 85	98.24 76

XVS		92.69 75	92.71 69	92.63 119	98.52 37	80.29 199	97.37 124	96.44 115	87.04 132	91.38 110	97.83 81	77.24 132	99.59 70	90.46 141	98.07 54	98.02 91

ACMMPR		92.69 75	92.67 71	92.75 110	98.66 28	80.57 190	97.58 103	96.69 80	85.20 176	91.57 108	97.92 73	77.01 138	99.67 62	90.95 126	98.41 43	98.00 96

UBG		92.68 77	92.35 79	93.70 64	95.61 132	85.65 54	97.25 130	97.06 36	87.92 104	89.28 144	95.03 195	86.06 33	98.07 170	92.24 109	90.69 198	97.37 158

WTY-MVS		92.65 78	91.68 97	95.56 14	96.00 115	88.90 13	98.23 56	97.65 13	88.57 85	89.82 134	97.22 115	79.29 92	99.06 118	89.57 157	88.73 219	98.73 47

MP-MVS		92.61 79	92.67 71	92.42 132	98.13 56	79.73 220	97.33 127	96.20 143	85.63 162	90.53 125	97.66 87	78.14 115	99.70 57	92.12 111	98.30 50	97.85 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 80	92.35 79	93.29 83	97.30 92	82.53 123	96.44 202	96.04 157	84.68 194	89.12 147	98.37 42	77.48 127	99.74 46	93.31 92	98.38 45	97.59 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 81	92.60 73	92.34 135	98.50 40	79.90 213	98.40 51	96.40 121	84.75 190	90.48 127	98.09 60	77.40 128	99.21 101	91.15 123	98.23 52	97.92 102

reproduce_model		92.53 82	92.87 66	91.50 187	97.41 85	77.14 307	96.02 231	95.91 171	83.65 234	92.45 89	98.39 39	79.75 89	99.21 101	95.27 65	96.98 94	98.14 84

testing11		92.48 83	92.04 92	93.78 56	95.94 119	86.00 41	97.56 105	97.08 34	87.52 115	89.32 143	95.40 174	84.60 39	98.02 174	91.93 117	89.04 214	97.32 161

SymmetryMVS		92.45 84	92.33 81	92.82 107	95.19 148	82.02 140	97.94 75	97.43 17	92.34 30	92.15 98	96.53 143	77.03 136	98.57 141	91.13 124	91.19 191	97.87 106

MTAPA		92.45 84	92.31 82	92.86 103	97.90 61	80.85 182	92.88 348	96.33 131	87.92 104	90.20 131	98.18 51	76.71 146	99.76 38	92.57 105	98.09 53	97.96 101

GST-MVS		92.43 86	92.22 87	93.04 94	98.17 54	81.64 161	97.40 122	96.38 124	84.71 193	90.90 121	97.40 105	77.55 126	99.76 38	89.75 154	97.74 66	97.72 121

fmvsm_s_conf0.1_n_a		92.38 87	92.49 76	92.06 155	88.08 367	81.62 162	97.97 74	96.01 158	90.62 56	96.58 30	98.33 45	74.09 200	99.71 54	97.23 39	93.46 161	94.86 260

MVSMamba_PlusPlus		92.37 88	91.55 100	94.83 27	95.37 140	87.69 24	95.60 257	95.42 207	74.65 375	93.95 70	92.81 257	83.11 58	97.70 192	94.49 74	98.53 35	99.11 28

sasdasda		92.27 89	91.22 106	95.41 17	95.80 125	88.31 15	97.09 150	94.64 252	88.49 87	92.99 84	97.31 107	72.68 217	98.57 141	93.38 89	88.58 225	99.36 16

canonicalmvs		92.27 89	91.22 106	95.41 17	95.80 125	88.31 15	97.09 150	94.64 252	88.49 87	92.99 84	97.31 107	72.68 217	98.57 141	93.38 89	88.58 225	99.36 16

fmvsm_s_conf0.1_n_2		92.26 91	92.48 77	91.60 182	92.29 267	80.55 191	98.73 36	94.33 279	93.80 18	96.18 36	98.11 58	66.93 274	99.75 43	98.19 19	93.74 155	94.50 271

SR-MVS		92.16 92	92.27 83	91.83 169	98.37 45	78.41 263	96.67 188	95.76 181	82.19 266	91.97 102	98.07 64	76.44 150	98.64 137	93.71 84	97.27 82	98.45 61

test_fmvsmvis_n_1920		92.12 93	92.10 90	92.17 150	90.87 313	81.04 174	98.34 53	93.90 305	92.71 26	87.24 180	97.90 76	74.83 188	99.72 51	96.96 43	96.20 114	95.76 234

VNet		92.11 94	91.22 106	94.79 28	96.91 97	86.98 31	97.91 78	97.96 10	86.38 147	93.65 73	95.74 158	70.16 251	98.95 125	93.39 87	88.87 217	98.43 63

CSCG		92.02 95	91.65 98	93.12 90	98.53 36	80.59 189	97.47 113	97.18 27	77.06 355	84.64 215	97.98 70	83.98 50	99.52 79	90.72 135	97.33 80	99.23 24

MGCFI-Net		91.95 96	91.03 112	94.72 31	95.68 130	86.38 36	96.93 166	94.48 261	88.25 95	92.78 87	97.24 113	72.34 222	98.46 151	93.13 98	88.43 232	99.32 19

PGM-MVS		91.93 97	91.80 95	92.32 139	98.27 50	79.74 219	95.28 268	97.27 22	83.83 226	90.89 122	97.78 83	76.12 158	99.56 76	88.82 166	97.93 61	97.66 127

testing99		91.91 98	91.35 103	93.60 71	95.98 117	85.70 49	97.31 128	96.92 51	86.82 138	88.91 151	95.25 178	84.26 47	97.89 185	88.80 167	87.94 238	97.21 170

testing91		91.90 99	91.31 105	93.66 67	95.99 116	85.68 51	97.39 123	96.89 52	86.75 142	88.85 153	95.23 181	83.93 51	97.90 184	88.91 164	87.89 239	97.41 154

mPP-MVS		91.88 100	91.82 94	92.07 154	98.38 44	78.63 256	97.29 129	96.09 151	85.12 182	88.45 160	97.66 87	75.53 170	99.68 60	89.83 150	98.02 57	97.88 104

EI-MVSNet-Vis-set		91.84 101	91.77 96	92.04 157	97.60 74	81.17 169	96.61 189	96.87 54	88.20 97	89.19 145	97.55 99	78.69 105	99.14 111	90.29 146	90.94 195	95.80 228

EIA-MVS		91.73 102	92.05 91	90.78 218	94.52 171	76.40 319	98.06 68	95.34 212	89.19 77	88.90 152	97.28 112	77.56 125	97.73 191	90.77 134	96.86 100	98.20 78

EC-MVSNet		91.73 102	92.11 89	90.58 222	93.54 207	77.77 290	98.07 67	94.40 273	87.44 117	92.99 84	97.11 120	74.59 194	96.87 264	93.75 83	97.08 91	97.11 177

DP-MVS Recon		91.72 104	90.85 114	94.34 38	99.50 1	85.00 76	98.51 47	95.96 164	80.57 290	88.08 170	97.63 93	76.84 141	99.89 7	85.67 199	94.88 134	98.13 86

CHOSEN 280x420		91.71 105	91.85 93	91.29 196	94.94 158	82.69 120	87.89 403	96.17 146	85.94 157	87.27 179	94.31 220	90.27 8	95.65 322	94.04 80	95.86 124	95.53 241

HY-MVS	84.06 6	91.63 106	90.37 128	95.39 19	96.12 112	88.25 17	90.22 379	97.58 15	88.33 93	90.50 126	91.96 275	79.26 93	99.06 118	90.29 146	89.07 213	98.88 37

HPM-MVS		91.62 107	91.53 101	91.89 164	97.88 63	79.22 233	96.99 156	95.73 184	82.07 268	89.50 142	97.19 116	75.59 168	98.93 128	90.91 128	97.94 59	97.54 137
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 108	91.64 99	91.47 189	95.74 128	78.79 252	96.15 226	96.77 67	88.49 87	88.64 158	97.07 123	72.33 223	99.19 107	93.13 98	96.48 111	96.43 209

DeepC-MVS	86.58 3	91.53 109	91.06 111	92.94 100	94.52 171	81.89 149	95.95 235	95.98 162	90.76 54	83.76 231	96.76 136	73.24 211	99.71 54	91.67 119	96.96 95	97.22 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 110	90.53 121	94.24 42	97.41 85	85.18 66	98.08 65	97.72 11	80.94 281	89.85 132	96.14 149	75.61 166	98.81 133	90.42 144	88.56 227	98.74 43

DCV-MVSNet		91.46 110	90.53 121	94.24 42	97.41 85	85.18 66	98.08 65	97.72 11	80.94 281	89.85 132	96.14 149	75.61 166	98.81 133	90.42 144	88.56 227	98.74 43

PAPM_NR		91.46 110	90.82 115	93.37 82	98.50 40	81.81 154	95.03 288	96.13 148	84.65 195	86.10 195	97.65 91	79.24 94	99.75 43	83.20 227	96.88 98	98.56 55

testing3-2		91.37 113	91.01 113	92.44 130	95.93 120	83.77 96	98.83 34	97.45 16	86.88 136	86.63 187	94.69 210	84.57 40	97.75 190	89.65 155	84.44 272	95.80 228

MVSFormer		91.36 114	90.57 120	93.73 61	93.00 230	88.08 19	94.80 295	94.48 261	80.74 286	94.90 55	97.13 118	78.84 101	95.10 351	83.77 216	97.46 73	98.02 91

EI-MVSNet-UG-set		91.35 115	91.22 106	91.73 174	97.39 88	80.68 186	96.47 199	96.83 58	87.92 104	88.30 164	97.36 106	77.84 120	99.13 113	89.43 160	89.45 208	95.37 245

SR-MVS-dyc-post		91.29 116	91.45 102	90.80 216	97.76 69	76.03 325	96.20 221	95.44 203	80.56 291	90.72 123	97.84 79	75.76 165	98.61 138	91.99 114	96.79 103	97.75 118

PVSNet_Blended_VisFu		91.24 117	90.77 116	92.66 115	95.09 152	82.40 131	97.77 88	95.87 177	88.26 94	86.39 190	93.94 236	76.77 144	99.27 95	88.80 167	94.00 148	96.31 215

APD-MVS_3200maxsize		91.23 118	91.35 103	90.89 214	97.89 62	76.35 320	96.30 215	95.52 196	79.82 313	91.03 119	97.88 78	74.70 190	98.54 145	92.11 112	96.89 97	97.77 116

diffmvs		91.17 119	90.74 117	92.44 130	93.11 228	82.50 128	96.25 218	93.62 330	87.79 108	90.40 129	95.93 153	73.44 209	97.42 218	93.62 86	92.55 171	97.41 154
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		91.13 120	90.45 124	93.17 89	92.99 233	83.58 101	97.46 115	94.56 258	87.69 111	87.19 181	94.98 200	74.50 195	97.60 198	91.88 118	92.79 168	98.34 66
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		91.09 121	90.49 123	92.87 102	95.82 123	85.04 73	96.51 197	97.28 21	86.05 154	89.13 146	95.34 176	80.16 83	96.62 277	85.82 197	88.31 234	96.96 184

test_fmvsmconf0.01_n		91.08 122	90.68 118	92.29 140	82.43 424	80.12 208	97.94 75	93.93 301	92.07 36	91.97 102	97.60 94	67.56 266	99.53 78	97.09 41	95.56 130	97.21 170

CHOSEN 1792x2688		91.07 123	90.21 132	93.64 68	95.18 149	83.53 102	96.26 217	96.13 148	88.92 79	84.90 208	93.10 254	72.86 214	99.62 68	88.86 165	95.67 127	97.79 115

ETVMVS		90.99 124	90.26 129	93.19 88	95.81 124	85.64 55	96.97 161	97.18 27	85.43 168	88.77 156	94.86 202	82.00 66	96.37 284	82.70 232	88.60 224	97.57 136

CANet_DTU		90.98 125	90.04 139	93.83 54	94.76 164	86.23 38	96.32 213	93.12 354	93.11 23	93.71 72	96.82 134	63.08 305	99.48 83	84.29 209	95.12 133	95.77 233

test2506		90.96 126	90.39 126	92.65 116	93.54 207	82.46 129	96.37 207	97.35 19	86.78 140	87.55 174	95.25 178	77.83 121	97.50 212	84.07 211	94.80 135	97.98 98

thisisatest0515		90.95 127	90.26 129	93.01 95	94.03 197	84.27 89	97.91 78	96.67 82	83.18 241	86.87 185	95.51 170	88.66 15	97.85 186	80.46 252	89.01 215	96.92 188

casdiffmvs		90.95 127	90.39 126	92.63 119	92.82 243	82.53 123	96.83 172	94.47 264	87.69 111	88.47 159	95.56 169	74.04 201	97.54 208	90.90 129	92.74 169	97.83 111
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		90.87 129	89.96 142	93.60 71	94.15 188	83.84 95	97.14 143	98.13 7	85.93 158	89.68 136	96.09 151	71.67 233	99.30 94	87.69 184	89.16 212	97.66 127

diffmvs_AUTHOR		90.86 130	90.41 125	92.24 143	92.01 287	82.22 136	96.18 223	93.64 329	87.28 122	90.46 128	95.64 164	72.82 215	97.39 223	93.17 95	92.46 174	97.11 177

baseline		90.76 131	90.10 135	92.74 111	92.90 241	82.56 122	94.60 298	94.56 258	87.69 111	89.06 149	95.67 162	73.76 204	97.51 211	90.43 143	92.23 181	98.16 82

viewmanbaseed2359cas		90.74 132	90.07 137	92.76 109	92.98 234	82.93 116	96.53 194	94.28 282	87.08 131	88.96 150	95.64 164	72.03 230	97.58 201	90.85 131	92.26 179	97.76 117

Effi-MVS+		90.70 133	89.90 145	93.09 92	93.61 204	83.48 103	95.20 276	92.79 360	83.22 240	91.82 105	95.70 160	71.82 232	97.48 214	91.25 122	93.67 157	98.32 68

viewcassd2359sk11		90.66 134	90.06 138	92.47 126	93.22 220	82.21 137	96.70 186	94.47 264	86.94 134	88.22 166	95.50 171	73.15 212	97.59 199	90.86 130	91.48 188	97.60 134

MAR-MVS		90.63 135	90.22 131	91.86 166	98.47 42	78.20 274	97.18 136	96.61 92	83.87 223	88.18 167	98.18 51	68.71 259	99.75 43	83.66 221	97.15 87	97.63 130
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		90.60 136	88.64 166	96.50 5	94.25 184	90.53 8	93.33 336	97.21 24	77.59 346	78.88 290	97.31 107	71.52 236	99.69 58	89.60 156	98.03 56	99.27 22

xiu_mvs_v1_base_debu		90.54 137	89.54 148	93.55 74	92.31 259	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 352	98.32 160	92.72 102	93.46 161	94.74 264

xiu_mvs_v1_base		90.54 137	89.54 148	93.55 74	92.31 259	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 352	98.32 160	92.72 102	93.46 161	94.74 264

xiu_mvs_v1_base_debi		90.54 137	89.54 148	93.55 74	92.31 259	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 352	98.32 160	92.72 102	93.46 161	94.74 264

mvsmamba		90.53 140	90.08 136	91.88 165	94.81 162	80.93 179	93.94 319	94.45 267	88.24 96	87.02 184	92.35 264	68.04 261	95.80 310	94.86 68	97.03 93	98.92 34

baseline2		90.39 141	90.21 132	90.93 210	90.86 314	80.99 176	95.20 276	97.41 18	86.03 156	80.07 280	94.61 211	90.58 6	97.47 215	87.29 188	89.86 205	94.35 272

ACMMP		90.39 141	89.97 141	91.64 179	97.58 76	78.21 273	96.78 178	96.72 76	84.73 192	84.72 212	97.23 114	71.22 238	99.63 66	88.37 176	92.41 177	97.08 180
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		90.38 143	90.17 134	91.03 207	97.61 73	77.35 301	97.15 142	95.48 199	79.51 319	88.79 154	96.90 128	71.64 235	98.81 133	87.01 192	97.44 75	96.94 185

MVS_Test		90.29 144	89.18 154	93.62 70	95.23 144	84.93 77	94.41 301	94.66 249	84.31 205	90.37 130	91.02 289	75.13 183	97.82 187	83.11 229	94.42 142	98.12 87

API-MVS		90.18 145	88.97 159	93.80 55	98.66 28	82.95 115	97.50 112	95.63 190	75.16 370	86.31 191	97.69 85	72.49 220	99.90 5	81.26 248	96.07 118	98.56 55

viewdifsd2359ckpt13		90.08 146	89.36 151	92.26 142	93.03 229	81.90 148	96.37 207	94.34 276	86.16 149	87.44 175	95.30 177	70.93 245	97.55 205	89.05 163	91.59 187	97.35 160

PVSNet_BlendedMVS		90.05 147	89.96 142	90.33 231	97.47 79	83.86 93	98.02 71	96.73 74	87.98 102	89.53 140	89.61 310	76.42 151	99.57 74	94.29 76	79.59 307	87.57 380

ET-MVSNet_ETH3D		90.01 148	89.03 155	92.95 99	94.38 181	86.77 33	98.14 59	96.31 134	89.30 76	63.33 414	96.72 139	90.09 10	93.63 389	90.70 137	82.29 294	98.46 60

test_vis1_n_1920		89.95 149	90.59 119	88.03 291	92.36 257	68.98 397	99.12 14	94.34 276	93.86 17	93.64 74	97.01 126	51.54 384	99.59 70	96.76 46	96.71 107	95.53 241

test_cas_vis1_n_1920		89.90 150	90.02 140	89.54 257	90.14 333	74.63 342	98.71 38	94.43 270	93.04 24	92.40 92	96.35 146	53.41 380	99.08 117	95.59 58	96.16 115	94.90 258

viewmacassd2359aftdt		89.89 151	89.01 158	92.52 125	91.56 295	82.46 129	96.32 213	94.06 297	86.41 146	88.11 169	95.01 197	69.68 254	97.47 215	88.73 170	91.19 191	97.63 130

guyue		89.85 152	89.33 153	91.40 192	92.53 255	80.15 207	96.82 174	95.68 186	89.66 71	86.43 189	94.23 223	67.00 272	97.16 241	91.96 116	89.65 206	96.89 189

TESTMET0.1,1		89.83 153	89.34 152	91.31 194	92.54 254	80.19 205	97.11 146	96.57 99	86.15 150	86.85 186	91.83 280	79.32 91	96.95 255	81.30 246	92.35 178	96.77 197

EPP-MVSNet		89.76 154	89.72 147	89.87 248	93.78 200	76.02 327	97.22 131	96.51 106	79.35 321	85.11 204	95.01 197	84.82 37	97.10 248	87.46 187	88.21 236	96.50 207

CPTT-MVS		89.72 155	89.87 146	89.29 260	98.33 47	73.30 353	97.70 94	95.35 211	75.68 366	87.40 176	97.44 103	70.43 248	98.25 163	89.56 158	96.90 96	96.33 214

RRT-MVS		89.67 156	88.67 165	92.67 114	94.44 178	81.08 173	94.34 305	94.45 267	86.05 154	85.79 197	92.39 263	63.39 303	98.16 168	93.22 94	93.95 151	98.76 42

thisisatest0530		89.65 157	89.02 156	91.53 184	93.46 214	80.78 184	96.52 195	96.67 82	81.69 274	83.79 230	94.90 201	88.85 14	97.68 194	77.80 280	87.49 246	96.14 218

3Dnovator+	82.88 8	89.63 158	87.85 182	94.99 23	94.49 177	86.76 34	97.84 82	95.74 183	86.10 152	75.47 336	96.02 152	65.00 290	99.51 81	82.91 231	97.07 92	98.72 48

viewmambaseed2359dif		89.52 159	89.02 156	91.03 207	92.24 272	78.83 244	95.89 240	93.77 322	83.04 245	88.28 165	95.80 157	72.08 228	97.40 221	89.76 153	90.32 200	96.87 192

CDS-MVSNet		89.50 160	88.96 160	91.14 204	91.94 291	80.93 179	97.09 150	95.81 179	84.26 210	84.72 212	94.20 226	80.31 78	95.64 323	83.37 226	88.96 216	96.85 193
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 161	89.92 144	88.06 289	94.64 165	69.57 394	96.22 219	94.95 227	87.27 124	91.37 112	96.54 142	65.88 282	97.39 223	88.54 171	93.89 152	97.23 166

HyFIR lowres test		89.36 162	88.60 167	91.63 181	94.91 160	80.76 185	95.60 257	95.53 194	82.56 259	84.03 224	91.24 286	78.03 116	96.81 268	87.07 191	88.41 233	97.32 161

3Dnovator	82.32 10	89.33 163	87.64 187	94.42 37	93.73 203	85.70 49	97.73 92	96.75 71	86.73 143	76.21 325	95.93 153	62.17 309	99.68 60	81.67 241	97.81 63	97.88 104

h-mvs33		89.30 164	88.95 161	90.36 230	95.07 154	76.04 324	96.96 163	97.11 32	90.39 61	92.22 96	95.10 192	74.70 190	98.86 130	93.14 96	65.89 404	96.16 217

LFMVS		89.27 165	87.64 187	94.16 48	97.16 94	85.52 58	97.18 136	94.66 249	79.17 327	89.63 138	96.57 141	55.35 369	98.22 164	89.52 159	89.54 207	98.74 43

MVSTER		89.25 166	88.92 162	90.24 234	95.98 117	84.66 81	96.79 177	95.36 209	87.19 128	80.33 275	90.61 296	90.02 11	95.97 299	85.38 202	78.64 316	90.09 320

KinetiMVS		89.13 167	87.95 180	92.65 116	92.16 277	82.39 132	97.04 154	96.05 155	86.59 145	88.08 170	94.85 203	61.54 321	98.38 157	81.28 247	93.99 150	97.19 173

CostFormer		89.08 168	88.39 171	91.15 203	93.13 226	79.15 236	88.61 395	96.11 150	83.14 242	89.58 139	86.93 351	83.83 53	96.87 264	88.22 177	85.92 261	97.42 153

viewdifsd2359ckpt07		89.04 169	88.30 173	91.27 197	92.32 258	78.90 242	95.89 240	93.77 322	84.48 201	85.18 203	95.16 187	69.83 252	97.70 192	88.75 169	89.29 210	97.22 167

PVSNet	82.34 9	89.02 170	87.79 184	92.71 113	95.49 136	81.50 164	97.70 94	97.29 20	87.76 109	85.47 201	95.12 191	56.90 358	98.90 129	80.33 253	94.02 146	97.71 123

AstraMVS		88.99 171	88.35 172	90.92 211	90.81 317	78.29 266	96.73 181	94.24 284	89.96 67	86.13 194	95.04 194	62.12 314	97.41 219	92.54 106	87.57 245	97.06 182

test-mter		88.95 172	88.60 167	89.98 243	92.26 269	77.23 303	97.11 146	95.96 164	85.32 171	86.30 192	91.38 283	76.37 153	96.78 271	80.82 249	91.92 183	95.94 224

1314		88.94 173	87.20 201	94.17 46	93.21 221	85.73 48	93.33 336	96.64 89	82.89 250	75.98 328	96.36 145	66.83 276	99.39 87	83.52 225	96.02 121	97.39 157

UA-Net		88.92 174	88.48 170	90.24 234	94.06 194	77.18 305	93.04 344	94.66 249	87.39 119	91.09 117	93.89 237	74.92 186	98.18 167	75.83 307	91.43 189	95.35 246

thres200		88.92 174	87.65 186	92.73 112	96.30 105	85.62 56	97.85 81	98.86 1	84.38 204	84.82 209	93.99 235	75.12 184	98.01 175	70.86 348	86.67 250	94.56 270

Vis-MVSNet (Re-imp)		88.88 176	88.87 164	88.91 268	93.89 198	74.43 345	96.93 166	94.19 289	84.39 203	83.22 241	95.67 162	78.24 112	94.70 365	78.88 273	94.40 143	97.61 133

baseline1		88.85 177	87.49 194	92.93 101	95.21 146	86.85 32	95.47 262	94.61 255	87.29 121	83.11 243	94.99 199	80.70 73	96.89 261	82.28 237	73.72 342	95.05 256

AdaColmap		88.81 178	87.61 190	92.39 134	99.33 4	79.95 211	96.70 186	95.58 191	77.51 347	83.05 244	96.69 140	61.90 319	99.72 51	84.29 209	93.47 160	97.50 143

OMC-MVS		88.80 179	88.16 177	90.72 219	95.30 142	77.92 283	94.81 294	94.51 260	86.80 139	84.97 207	96.85 131	67.53 267	98.60 139	85.08 203	87.62 242	95.63 236

114514_t		88.79 180	87.57 192	92.45 128	98.21 53	81.74 156	96.99 156	95.45 202	75.16 370	82.48 247	95.69 161	68.59 260	98.50 147	80.33 253	95.18 132	97.10 179

mvs_anonymous		88.68 181	87.62 189	91.86 166	94.80 163	81.69 159	93.53 331	94.92 229	82.03 269	78.87 291	90.43 299	75.77 164	95.34 336	85.04 204	93.16 165	98.55 57

Vis-MVSNet		88.67 182	87.82 183	91.24 199	92.68 247	78.82 245	96.95 164	93.85 309	87.55 114	87.07 183	95.13 190	63.43 302	97.21 238	77.58 287	96.15 116	97.70 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 182	88.16 177	90.20 236	93.61 204	76.86 310	96.77 180	93.07 355	84.02 216	83.62 234	95.60 167	74.69 193	96.24 291	78.43 277	93.66 158	97.49 144

IB-MVS	85.34 4	88.67 182	87.14 204	93.26 84	93.12 227	84.32 86	98.76 35	97.27 22	87.19 128	79.36 286	90.45 298	83.92 52	98.53 146	84.41 208	69.79 371	96.93 186
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		88.60 185	87.47 196	92.00 159	93.21 221	80.97 177	96.47 199	92.46 363	83.64 235	80.86 268	97.30 110	80.24 80	97.62 197	77.60 286	85.49 266	97.40 156

tttt0517		88.57 186	88.19 176	89.71 254	93.00 230	75.99 328	95.67 252	96.67 82	80.78 285	81.82 260	94.40 219	88.97 13	97.58 201	76.05 305	86.31 254	95.57 239

UWE-MVS		88.56 187	88.91 163	87.50 305	94.17 187	72.19 365	95.82 247	97.05 37	84.96 187	84.78 210	93.51 248	81.33 68	94.75 363	79.43 264	89.17 211	95.57 239

tfpn200view9		88.48 188	87.15 202	92.47 126	96.21 108	85.30 64	97.44 116	98.85 2	83.37 238	83.99 225	93.82 240	75.36 177	97.93 178	69.04 356	86.24 257	94.17 274

test-LLR		88.48 188	87.98 179	89.98 243	92.26 269	77.23 303	97.11 146	95.96 164	83.76 229	86.30 192	91.38 283	72.30 224	96.78 271	80.82 249	91.92 183	95.94 224

TAMVS		88.48 188	87.79 184	90.56 223	91.09 308	79.18 234	96.45 201	95.88 175	83.64 235	83.12 242	93.33 249	75.94 162	95.74 318	82.40 234	88.27 235	96.75 200

thres400		88.42 191	87.15 202	92.23 145	96.21 108	85.30 64	97.44 116	98.85 2	83.37 238	83.99 225	93.82 240	75.36 177	97.93 178	69.04 356	86.24 257	93.45 290

tpmrst		88.36 192	87.38 198	91.31 194	94.36 182	79.92 212	87.32 407	95.26 216	85.32 171	88.34 162	86.13 368	80.60 75	96.70 273	83.78 215	85.34 269	97.30 164

ECVR-MVS		88.35 193	87.25 200	91.65 178	93.54 207	79.40 227	96.56 193	90.78 399	86.78 140	85.57 199	95.25 178	57.25 356	97.56 203	84.73 207	94.80 135	97.98 98

thres100view900		88.30 194	86.95 209	92.33 137	96.10 113	84.90 78	97.14 143	98.85 2	82.69 256	83.41 238	93.66 244	75.43 174	97.93 178	69.04 356	86.24 257	94.17 274

VDD-MVS		88.28 195	87.02 207	92.06 155	95.09 152	80.18 206	97.55 107	94.45 267	83.09 243	89.10 148	95.92 155	47.97 401	98.49 148	93.08 100	86.91 249	97.52 142

BH-w/o		88.24 196	87.47 196	90.54 225	95.03 157	78.54 258	97.41 121	93.82 314	84.08 214	78.23 297	94.51 214	69.34 257	97.21 238	80.21 257	94.58 139	95.87 227

hse-mvs2		88.22 197	88.21 175	88.25 285	93.54 207	73.41 350	95.41 265	95.89 173	90.39 61	92.22 96	94.22 224	74.70 190	96.66 276	93.14 96	64.37 409	94.69 269

test1111		88.11 198	87.04 206	91.35 193	93.15 224	78.79 252	96.57 191	90.78 399	86.88 136	85.04 205	95.20 184	57.23 357	97.39 223	83.88 213	94.59 138	97.87 106

IMVS_0403		88.07 199	87.02 207	91.24 199	92.30 262	78.81 247	93.62 327	93.84 310	85.14 178	84.36 217	94.49 215	69.49 255	97.46 217	81.33 242	88.61 220	97.46 147

thres600view7		88.06 200	86.70 217	92.15 152	96.10 113	85.17 70	97.14 143	98.85 2	82.70 255	83.41 238	93.66 244	75.43 174	97.82 187	67.13 365	85.88 262	93.45 290

Test_1112_low_res		88.03 201	86.73 214	91.94 163	93.15 224	80.88 181	96.44 202	92.41 367	83.59 237	80.74 270	91.16 287	80.18 81	97.59 199	77.48 289	85.40 267	97.36 159

LuminaMVS		88.02 202	86.89 211	91.43 190	88.65 360	83.16 110	94.84 292	94.41 272	83.67 233	86.56 188	91.95 277	62.04 315	96.88 263	89.78 152	90.06 202	94.24 273

PLC	83.97 7	88.00 203	87.38 198	89.83 250	98.02 59	76.46 316	97.16 140	94.43 270	79.26 326	81.98 257	96.28 147	69.36 256	99.27 95	77.71 284	92.25 180	93.77 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 204	87.48 195	89.44 258	92.16 277	80.54 195	98.14 59	94.92 229	91.41 44	79.43 285	95.40 174	62.34 308	97.27 234	90.60 138	82.90 286	90.50 310
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+		87.93 205	86.94 210	90.92 211	94.04 195	79.16 235	98.26 55	93.72 325	81.29 277	83.94 228	92.90 256	69.83 252	96.68 274	76.70 297	91.74 185	96.93 186

HQP-MVS		87.91 206	87.55 193	88.98 267	92.08 282	78.48 259	97.63 97	94.80 237	90.52 58	82.30 250	94.56 212	65.40 286	97.32 229	87.67 185	83.01 283	91.13 302

IMVS_0407		87.82 207	86.72 215	91.14 204	92.30 262	78.81 247	93.34 335	93.84 310	85.14 178	83.68 232	94.49 215	67.75 262	97.14 246	81.33 242	88.61 220	97.46 147

reproduce_monomvs		87.80 208	87.60 191	88.40 279	96.56 100	80.26 202	95.80 248	96.32 133	91.56 43	73.60 348	88.36 326	88.53 16	96.25 290	90.47 140	67.23 397	88.67 355

test_fmvs1		87.79 209	88.52 169	85.62 341	92.98 234	64.31 418	97.88 80	92.42 366	87.95 103	92.24 95	95.82 156	47.94 402	98.44 155	95.31 64	94.09 144	94.09 278

WBMVS		87.73 210	86.79 212	90.56 223	95.61 132	85.68 51	97.63 97	95.52 196	83.77 228	78.30 296	88.44 325	86.14 32	95.78 312	82.54 233	73.15 349	90.21 315

UGNet		87.73 210	86.55 219	91.27 197	95.16 150	79.11 237	96.35 210	96.23 140	88.14 98	87.83 173	90.48 297	50.65 389	99.09 116	80.13 258	94.03 145	95.60 238
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)		87.71 212	86.23 223	92.17 150	94.19 186	80.55 191	87.16 409	96.07 154	82.12 267	85.98 196	88.35 327	72.04 229	98.49 148	80.26 255	89.87 204	97.48 145

SSM_0404		87.69 213	86.26 221	91.95 161	92.94 236	83.02 114	94.69 297	92.33 369	80.11 306	84.65 214	94.18 227	64.68 295	96.90 259	82.34 235	90.44 199	95.94 224

EPNet_dtu		87.65 214	87.89 181	86.93 318	94.57 167	71.37 380	96.72 182	96.50 108	88.56 86	87.12 182	95.02 196	75.91 163	94.01 381	66.62 369	90.00 203	95.42 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 215	88.22 174	85.67 339	89.78 337	67.18 405	95.25 273	87.93 420	83.96 219	88.79 154	97.06 124	72.52 219	94.53 371	92.21 110	86.45 253	95.30 248

icg_test_0407_2		87.55 216	86.59 218	90.43 227	92.30 262	78.81 247	92.17 357	93.84 310	85.14 178	83.68 232	94.49 215	67.75 262	95.02 355	81.33 242	88.61 220	97.46 147

HQP_MVS		87.50 217	87.09 205	88.74 272	91.86 292	77.96 280	97.18 136	94.69 245	89.89 68	81.33 263	94.15 229	64.77 293	97.30 231	87.08 189	82.82 287	90.96 304

EPMVS		87.47 218	85.90 226	92.18 149	95.41 138	82.26 135	87.00 410	96.28 135	85.88 159	84.23 220	85.57 375	75.07 185	96.26 288	71.14 346	92.50 172	98.03 90

tpm2		87.35 219	86.26 221	90.62 221	92.93 240	78.67 255	88.06 402	95.99 161	79.33 322	87.40 176	86.43 362	80.28 79	96.40 282	80.23 256	85.73 265	96.79 195

SSM_0407		87.33 220	85.87 227	91.71 177	92.94 236	82.53 123	94.30 308	92.33 369	80.11 306	83.50 235	94.18 227	64.68 295	96.80 270	82.34 235	88.51 229	95.79 230

ab-mvs		87.08 221	84.94 245	93.48 79	93.34 217	83.67 99	88.82 392	95.70 185	81.18 278	84.55 216	90.14 305	62.72 306	98.94 127	85.49 201	82.54 291	97.85 109

SDMVSNet		87.02 222	85.61 229	91.24 199	94.14 189	83.30 107	93.88 321	95.98 162	84.30 207	79.63 283	92.01 271	58.23 342	97.68 194	90.28 148	82.02 295	92.75 293

CNLPA		86.96 223	85.37 234	91.72 176	97.59 75	79.34 230	97.21 132	91.05 394	74.22 377	78.90 289	96.75 138	67.21 271	98.95 125	74.68 317	90.77 196	96.88 191

BH-untuned		86.95 224	85.94 225	89.99 242	94.52 171	77.46 298	96.78 178	93.37 343	81.80 271	76.62 315	93.81 242	66.64 277	97.02 250	76.06 304	93.88 153	95.48 243

QAPM		86.88 225	84.51 249	93.98 49	94.04 195	85.89 45	97.19 135	96.05 155	73.62 382	75.12 339	95.62 166	62.02 316	99.74 46	70.88 347	96.06 119	96.30 216

BH-RMVSNet		86.84 226	85.28 237	91.49 188	95.35 141	80.26 202	96.95 164	92.21 371	82.86 252	81.77 262	95.46 173	59.34 334	97.64 196	69.79 354	93.81 154	96.57 206

PatchmatchNet		86.83 227	85.12 242	91.95 161	94.12 191	82.27 134	86.55 414	95.64 189	84.59 197	82.98 245	84.99 387	77.26 130	95.96 302	68.61 359	91.34 190	97.64 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 228	85.43 232	90.87 215	88.76 353	85.34 61	97.06 153	94.33 279	84.31 205	80.45 273	91.98 274	72.36 221	96.36 285	88.48 174	71.13 358	90.93 306

PCF-MVS	84.09 5	86.77 229	85.00 244	92.08 153	92.06 285	83.07 112	92.14 358	94.47 264	79.63 317	76.90 311	94.78 205	71.15 239	99.20 106	72.87 332	91.05 194	93.98 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 230	86.10 224	88.61 275	90.05 334	80.21 204	96.14 227	96.95 47	85.56 165	78.37 295	92.30 265	76.73 145	95.28 340	79.51 262	79.27 310	90.35 312

cascas		86.50 231	84.48 251	92.55 124	92.64 251	85.95 42	97.04 154	95.07 223	75.32 368	80.50 271	91.02 289	54.33 377	97.98 177	86.79 194	87.62 242	93.71 285

VDDNet		86.44 232	84.51 249	92.22 146	91.56 295	81.83 152	97.10 149	94.64 252	69.50 410	87.84 172	95.19 185	48.01 400	97.92 183	89.82 151	86.92 248	96.89 189

viewdifsd2359ckpt11		86.38 233	85.29 235	89.66 256	90.42 324	75.65 334	95.27 271	92.45 364	85.54 166	84.27 219	94.73 206	62.16 310	97.39 223	87.78 181	74.97 336	95.96 221

viewmsd2359difaftdt		86.38 233	85.29 235	89.67 255	90.42 324	75.65 334	95.27 271	92.45 364	85.54 166	84.28 218	94.73 206	62.16 310	97.39 223	87.78 181	74.97 336	95.96 221

GeoE		86.36 235	85.20 238	89.83 250	93.17 223	76.13 322	97.53 108	92.11 372	79.58 318	80.99 266	94.01 232	66.60 278	96.17 294	73.48 329	89.30 209	97.20 172

test_fmvs1_n		86.34 236	86.72 215	85.17 349	87.54 374	63.64 423	96.91 168	92.37 368	87.49 116	91.33 113	95.58 168	40.81 430	98.46 151	95.00 67	93.49 159	93.41 292

TR-MVS		86.30 237	84.93 246	90.42 228	94.63 166	77.58 296	96.57 191	93.82 314	80.30 301	82.42 249	95.16 187	58.74 338	97.55 205	74.88 315	87.82 240	96.13 219

X-MVStestdata		86.26 238	84.14 260	92.63 119	98.52 37	80.29 199	97.37 124	96.44 115	87.04 132	91.38 110	20.73 469	77.24 132	99.59 70	90.46 141	98.07 54	98.02 91

AUN-MVS		86.25 239	85.57 230	88.26 284	93.57 206	73.38 351	95.45 263	95.88 175	83.94 220	85.47 201	94.21 225	73.70 207	96.67 275	83.54 223	64.41 408	94.73 268

OpenMVS	79.58 14	86.09 240	83.62 270	93.50 77	90.95 310	86.71 35	97.44 116	95.83 178	75.35 367	72.64 362	95.72 159	57.42 355	99.64 64	71.41 341	95.85 125	94.13 277

FE-MVS		86.06 241	84.15 259	91.78 170	94.33 183	79.81 214	84.58 427	96.61 92	76.69 360	85.00 206	87.38 342	70.71 247	98.37 158	70.39 351	91.70 186	97.17 175

FC-MVSNet-test		85.96 242	85.39 233	87.66 298	89.38 350	78.02 277	95.65 254	96.87 54	85.12 182	77.34 304	91.94 278	76.28 156	94.74 364	77.09 292	78.82 314	90.21 315

miper_enhance_ethall		85.95 243	85.20 238	88.19 288	94.85 161	79.76 216	96.00 232	94.06 297	82.98 249	77.74 302	88.76 318	79.42 90	95.46 332	80.58 251	72.42 351	89.36 334

OPM-MVS		85.84 244	85.10 243	88.06 289	88.34 364	77.83 287	95.72 250	94.20 288	87.89 107	80.45 273	94.05 231	58.57 339	97.26 235	83.88 213	82.76 289	89.09 341
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 245	85.20 238	87.59 301	91.55 297	77.41 299	95.13 282	95.36 209	80.43 296	80.33 275	94.71 208	73.72 205	95.97 299	76.96 295	78.64 316	89.39 328

GA-MVS		85.79 246	84.04 261	91.02 209	89.47 348	80.27 201	96.90 169	94.84 235	85.57 163	80.88 267	89.08 313	56.56 362	96.47 281	77.72 283	85.35 268	96.34 212

XVG-OURS-SEG-HR		85.74 247	85.16 241	87.49 307	90.22 328	71.45 378	91.29 369	94.09 295	81.37 276	83.90 229	95.22 182	60.30 327	97.53 210	85.58 200	84.42 274	93.50 288

MonoMVSNet		85.68 248	84.22 257	90.03 240	88.43 363	77.83 287	92.95 347	91.46 384	87.28 122	78.11 298	85.96 370	66.31 281	94.81 361	90.71 136	76.81 327	97.46 147

SCA		85.63 249	83.64 269	91.60 182	92.30 262	81.86 151	92.88 348	95.56 193	84.85 188	82.52 246	85.12 385	58.04 345	95.39 333	73.89 325	87.58 244	97.54 137

Elysia		85.62 250	83.66 266	91.51 185	88.76 353	82.21 137	95.15 280	94.70 242	76.96 357	84.13 221	92.20 267	50.81 387	97.26 235	77.81 278	92.42 175	95.06 254

StellarMVS		85.62 250	83.66 266	91.51 185	88.76 353	82.21 137	95.15 280	94.70 242	76.96 357	84.13 221	92.20 267	50.81 387	97.26 235	77.81 278	92.42 175	95.06 254

test_vis1_n		85.60 252	85.70 228	85.33 346	84.79 405	64.98 416	96.83 172	91.61 383	87.36 120	91.00 120	94.84 204	36.14 437	97.18 240	95.66 56	93.03 166	93.82 283

tpm		85.55 253	84.47 252	88.80 271	90.19 330	75.39 337	88.79 393	94.69 245	84.83 189	83.96 227	85.21 381	78.22 113	94.68 367	76.32 303	78.02 324	96.34 212

mamv4		85.50 254	86.76 213	81.72 392	93.23 219	54.93 450	89.95 382	92.94 357	69.96 407	79.00 288	92.20 267	80.69 74	94.22 377	92.06 113	90.77 196	96.01 220

UniMVSNet_NR-MVSNet		85.49 255	84.59 248	88.21 287	89.44 349	79.36 228	96.71 184	96.41 119	85.22 174	78.11 298	90.98 291	76.97 140	95.14 348	79.14 270	68.30 385	90.12 318

gg-mvs-nofinetune		85.48 256	82.90 283	93.24 85	94.51 175	85.82 46	79.22 440	96.97 45	61.19 436	87.33 178	53.01 458	90.58 6	96.07 295	86.07 196	97.23 83	97.81 114

VortexMVS		85.45 257	84.40 253	88.63 274	93.25 218	81.66 160	95.39 267	94.34 276	87.15 130	75.10 340	87.65 338	66.58 279	95.19 344	86.89 193	73.21 348	89.03 345

UWE-MVS-28		85.41 258	86.36 220	82.59 384	91.12 307	66.81 410	93.88 321	97.03 38	83.86 225	78.55 292	93.84 239	77.76 123	88.55 431	73.47 330	87.69 241	92.41 297

IMVS_0404		85.34 259	83.69 263	90.29 232	92.30 262	78.81 247	90.62 376	93.84 310	85.14 178	72.51 365	94.49 215	54.36 376	94.61 368	81.33 242	88.61 220	97.46 147

VPA-MVSNet		85.32 260	83.83 262	89.77 253	90.25 327	82.63 121	96.36 209	97.07 35	83.03 247	81.21 265	89.02 315	61.58 320	96.31 287	85.02 205	70.95 360	90.36 311

UniMVSNet (Re)		85.31 261	84.23 256	88.55 276	89.75 339	80.55 191	96.72 182	96.89 52	85.42 169	78.40 294	88.93 316	75.38 176	95.52 330	78.58 275	68.02 388	89.57 327

mamba_0408		85.26 262	83.10 279	91.74 173	92.94 236	82.53 123	72.52 455	91.77 378	80.36 298	83.50 235	94.01 232	64.97 291	96.90 259	79.37 265	88.51 229	95.79 230

XVG-OURS		85.18 263	84.38 254	87.59 301	90.42 324	71.73 375	91.06 373	94.07 296	82.00 270	83.29 240	95.08 193	56.42 363	97.55 205	83.70 220	83.42 279	93.49 289

cl22		85.11 264	84.17 258	87.92 292	95.06 156	78.82 245	95.51 260	94.22 287	79.74 315	76.77 312	87.92 334	75.96 160	95.68 319	79.93 260	72.42 351	89.27 336

TAPA-MVS	81.61 12	85.02 265	83.67 265	89.06 264	96.79 98	73.27 356	95.92 237	94.79 239	74.81 373	80.47 272	96.83 132	71.07 240	98.19 166	49.82 437	92.57 170	95.71 235
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 266	83.66 266	89.02 266	95.86 122	74.55 344	92.49 352	93.60 331	79.30 324	79.29 287	91.47 281	58.53 340	98.45 153	70.22 352	92.17 182	94.07 279

PS-MVSNAJss		84.91 267	84.30 255	86.74 319	85.89 393	74.40 346	94.95 289	94.16 291	83.93 221	76.45 318	90.11 306	71.04 241	95.77 313	83.16 228	79.02 313	90.06 322

CVMVSNet		84.83 268	85.57 230	82.63 383	91.55 297	60.38 436	95.13 282	95.03 225	80.60 289	82.10 256	94.71 208	66.40 280	90.19 424	74.30 322	90.32 200	97.31 163

FMVSNet3		84.71 269	82.71 287	90.70 220	94.55 169	87.71 23	95.92 237	94.67 248	81.73 273	75.82 331	88.08 332	66.99 273	94.47 372	71.23 343	75.38 333	89.91 324

VPNet		84.69 270	82.92 282	90.01 241	89.01 352	83.45 104	96.71 184	95.46 201	85.71 161	79.65 282	92.18 270	56.66 361	96.01 298	83.05 230	67.84 391	90.56 309

SSM_04072		84.64 271	83.10 279	89.25 261	92.94 236	82.53 123	72.52 455	91.77 378	80.36 298	83.50 235	94.01 232	64.97 291	89.41 427	79.37 265	88.51 229	95.79 230

sd_testset		84.62 272	83.11 278	89.17 262	94.14 189	77.78 289	91.54 368	94.38 274	84.30 207	79.63 283	92.01 271	52.28 382	96.98 253	77.67 285	82.02 295	92.75 293

Effi-MVS+-dtu		84.61 273	84.90 247	83.72 371	91.96 289	63.14 426	94.95 289	93.34 344	85.57 163	79.79 281	87.12 348	61.99 317	95.61 326	83.55 222	85.83 263	92.41 297

miper_ehance_all_eth		84.57 274	83.60 271	87.50 305	92.64 251	78.25 269	95.40 266	93.47 335	79.28 325	76.41 319	87.64 339	76.53 148	95.24 342	78.58 275	72.42 351	89.01 347

DU-MVS		84.57 274	83.33 276	88.28 283	88.76 353	79.36 228	96.43 204	95.41 208	85.42 169	78.11 298	90.82 292	67.61 264	95.14 348	79.14 270	68.30 385	90.33 313

F-COLMAP		84.50 276	83.44 275	87.67 297	95.22 145	72.22 363	95.95 235	93.78 319	75.74 365	76.30 322	95.18 186	59.50 332	98.45 153	72.67 334	86.59 252	92.35 299

Anonymous202405211		84.41 277	81.93 298	91.85 168	96.78 99	78.41 263	97.44 116	91.34 388	70.29 404	84.06 223	94.26 222	41.09 427	98.96 123	79.46 263	82.65 290	98.17 81

WR-MVS		84.32 278	82.96 281	88.41 278	89.38 350	80.32 198	96.59 190	96.25 138	83.97 218	76.63 314	90.36 300	67.53 267	94.86 359	75.82 308	70.09 369	90.06 322

dp		84.30 279	82.31 292	90.28 233	94.24 185	77.97 279	86.57 413	95.53 194	79.94 312	80.75 269	85.16 383	71.49 237	96.39 283	63.73 385	83.36 280	96.48 208

LPG-MVS_test		84.20 280	83.49 274	86.33 325	90.88 311	73.06 357	95.28 268	94.13 292	82.20 264	76.31 320	93.20 250	54.83 374	96.95 255	83.72 218	80.83 300	88.98 348

dmvs_re		84.10 281	82.90 283	87.70 296	91.41 301	73.28 354	90.59 377	93.19 348	85.02 184	77.96 301	93.68 243	57.92 350	96.18 293	75.50 310	80.87 299	93.63 286

WB-MVSnew		84.08 282	83.51 273	85.80 334	91.34 302	76.69 314	95.62 256	96.27 136	81.77 272	81.81 261	92.81 257	58.23 342	94.70 365	66.66 368	87.06 247	85.99 404

ACMP	81.66 11	84.00 283	83.22 277	86.33 325	91.53 299	72.95 361	95.91 239	93.79 318	83.70 232	73.79 347	92.22 266	54.31 378	96.89 261	83.98 212	79.74 305	89.16 339
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 284	82.80 286	87.31 311	91.46 300	77.39 300	95.66 253	93.43 338	80.44 294	75.51 335	87.26 345	73.72 205	95.16 347	76.99 293	70.72 362	89.39 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 285	82.00 297	89.35 259	87.13 376	81.38 165	95.72 250	94.26 283	80.15 305	75.92 330	90.63 295	61.96 318	96.52 279	78.98 272	73.28 347	90.14 317

c3_l		83.80 286	82.65 288	87.25 313	92.10 281	77.74 294	95.25 273	93.04 356	78.58 336	76.01 327	87.21 347	75.25 182	95.11 350	77.54 288	68.89 379	88.91 353

LCM-MVSNet-Re		83.75 287	83.54 272	84.39 364	93.54 207	64.14 420	92.51 351	84.03 442	83.90 222	66.14 402	86.59 356	67.36 269	92.68 396	84.89 206	92.87 167	96.35 211

ACMM	80.70 13	83.72 288	82.85 285	86.31 328	91.19 304	72.12 367	95.88 242	94.29 281	80.44 294	77.02 309	91.96 275	55.24 370	97.14 246	79.30 268	80.38 302	89.67 326
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 289	81.38 306	90.39 229	93.53 212	78.19 275	85.56 421	95.09 221	70.78 402	78.51 293	83.28 402	74.80 189	97.03 249	66.77 367	84.05 275	95.95 223

CR-MVSNet		83.53 290	81.36 307	90.06 239	90.16 331	79.75 217	79.02 442	91.12 391	84.24 211	82.27 254	80.35 418	75.45 172	93.67 388	63.37 388	86.25 255	96.75 200

v2v482		83.46 291	81.86 299	88.25 285	86.19 387	79.65 222	96.34 211	94.02 299	81.56 275	77.32 305	88.23 329	65.62 283	96.03 296	77.77 281	69.72 373	89.09 341

NR-MVSNet		83.35 292	81.52 305	88.84 269	88.76 353	81.31 167	94.45 300	95.16 219	84.65 195	67.81 391	90.82 292	70.36 249	94.87 358	74.75 316	66.89 401	90.33 313

Fast-Effi-MVS+-dtu		83.33 293	82.60 289	85.50 343	89.55 346	69.38 395	96.09 230	91.38 385	82.30 263	75.96 329	91.41 282	56.71 359	95.58 328	75.13 314	84.90 271	91.54 300

cl____		83.27 294	82.12 294	86.74 319	92.20 273	75.95 329	95.11 284	93.27 346	78.44 339	74.82 342	87.02 350	74.19 198	95.19 344	74.67 318	69.32 375	89.09 341

DIV-MVS_self_test		83.27 294	82.12 294	86.74 319	92.19 274	75.92 331	95.11 284	93.26 347	78.44 339	74.81 343	87.08 349	74.19 198	95.19 344	74.66 319	69.30 376	89.11 340

TranMVSNet+NR-MVSNet		83.24 296	81.71 301	87.83 293	87.71 371	78.81 247	96.13 229	94.82 236	84.52 198	76.18 326	90.78 294	64.07 298	94.60 369	74.60 320	66.59 403	90.09 320

Anonymous20240529		83.15 297	80.60 318	90.80 216	95.74 128	78.27 268	96.81 176	94.92 229	60.10 441	81.89 259	92.54 261	45.82 410	98.82 132	79.25 269	78.32 322	95.31 247

eth_miper_zixun_eth		83.12 298	82.01 296	86.47 324	91.85 294	74.80 340	94.33 306	93.18 350	79.11 328	75.74 334	87.25 346	72.71 216	95.32 338	76.78 296	67.13 398	89.27 336

MS-PatchMatch		83.05 299	81.82 300	86.72 323	89.64 343	79.10 238	94.88 291	94.59 257	79.70 316	70.67 378	89.65 309	50.43 391	96.82 267	70.82 350	95.99 123	84.25 419

V42		83.04 300	81.53 304	87.57 303	86.27 386	79.09 239	95.87 243	94.11 294	80.35 300	77.22 307	86.79 354	65.32 288	96.02 297	77.74 282	70.14 365	87.61 379

tpmvs		83.04 300	80.77 314	89.84 249	95.43 137	77.96 280	85.59 420	95.32 213	75.31 369	76.27 323	83.70 398	73.89 202	97.41 219	59.53 402	81.93 297	94.14 276

test_djsdf		83.00 302	82.45 291	84.64 357	84.07 414	69.78 391	94.80 295	94.48 261	80.74 286	75.41 337	87.70 337	61.32 324	95.10 351	83.77 216	79.76 303	89.04 344

v1144		82.90 303	81.27 308	87.78 295	86.29 385	79.07 240	96.14 227	93.93 301	80.05 309	77.38 303	86.80 353	65.50 284	95.93 304	75.21 313	70.13 366	88.33 366

test0.0.03 1		82.79 304	82.48 290	83.74 370	86.81 379	72.22 363	96.52 195	95.03 225	83.76 229	73.00 358	93.20 250	72.30 224	88.88 429	64.15 383	77.52 325	90.12 318

FMVSNet2		82.79 304	80.44 320	89.83 250	92.66 248	85.43 59	95.42 264	94.35 275	79.06 330	74.46 344	87.28 343	56.38 364	94.31 375	69.72 355	74.68 339	89.76 325

D2MVS		82.67 306	81.55 303	86.04 332	87.77 370	76.47 315	95.21 275	96.58 98	82.66 257	70.26 381	85.46 378	60.39 326	95.80 310	76.40 301	79.18 311	85.83 407

MVP-Stereo		82.65 307	81.67 302	85.59 342	86.10 390	78.29 266	93.33 336	92.82 359	77.75 344	69.17 388	87.98 333	59.28 335	95.76 314	71.77 338	96.88 98	82.73 427
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 308	80.79 313	87.79 294	86.11 389	80.49 197	93.55 330	93.18 350	77.29 350	73.35 354	89.40 312	65.26 289	95.05 354	75.32 312	73.61 343	87.83 374

v144192		82.43 309	80.73 315	87.54 304	85.81 394	78.22 270	95.98 233	93.78 319	79.09 329	77.11 308	86.49 358	64.66 297	95.91 305	74.20 323	69.42 374	88.49 360

GBi-Net		82.42 310	80.43 321	88.39 280	92.66 248	81.95 143	94.30 308	93.38 340	79.06 330	75.82 331	85.66 371	56.38 364	93.84 384	71.23 343	75.38 333	89.38 330

test1		82.42 310	80.43 321	88.39 280	92.66 248	81.95 143	94.30 308	93.38 340	79.06 330	75.82 331	85.66 371	56.38 364	93.84 384	71.23 343	75.38 333	89.38 330

v148		82.41 312	80.89 312	86.99 317	86.18 388	76.81 311	96.27 216	93.82 314	80.49 293	75.28 338	86.11 369	67.32 270	95.75 315	75.48 311	67.03 400	88.42 364

v1192		82.31 313	80.55 319	87.60 300	85.94 391	78.47 262	95.85 245	93.80 317	79.33 322	76.97 310	86.51 357	63.33 304	95.87 306	73.11 331	70.13 366	88.46 362

LS3D		82.22 314	79.94 329	89.06 264	97.43 84	74.06 349	93.20 342	92.05 373	61.90 431	73.33 355	95.21 183	59.35 333	99.21 101	54.54 424	92.48 173	93.90 282

jajsoiax		82.12 315	81.15 310	85.03 351	84.19 412	70.70 383	94.22 313	93.95 300	83.07 244	73.48 350	89.75 308	49.66 395	95.37 335	82.24 238	79.76 303	89.02 346

v1921920		82.02 316	80.23 323	87.41 308	85.62 395	77.92 283	95.79 249	93.69 326	78.86 333	76.67 313	86.44 360	62.50 307	95.83 308	72.69 333	69.77 372	88.47 361

myMVS_eth3d		81.93 317	82.18 293	81.18 395	92.13 279	67.18 405	93.97 317	94.23 285	82.43 260	73.39 351	93.57 246	76.98 139	87.86 435	50.53 435	82.34 292	88.51 358

v8		81.88 318	80.06 327	87.32 310	86.63 380	79.04 241	94.41 301	93.65 328	78.77 334	73.19 357	85.57 375	66.87 275	95.81 309	73.84 327	67.61 393	87.11 388

mvs_tets		81.74 319	80.71 316	84.84 352	84.22 411	70.29 387	93.91 320	93.78 319	82.77 254	73.37 353	89.46 311	47.36 406	95.31 339	81.99 239	79.55 309	88.92 352

v1240		81.70 320	79.83 331	87.30 312	85.50 396	77.70 295	95.48 261	93.44 336	78.46 338	76.53 317	86.44 360	60.85 325	95.84 307	71.59 340	70.17 364	88.35 365

PVSNet_0	77.72 15	81.70 320	78.95 339	89.94 246	90.77 318	76.72 313	95.96 234	96.95 47	85.01 185	70.24 382	88.53 323	52.32 381	98.20 165	86.68 195	44.08 454	94.89 259

miper_lstm_enhance		81.66 322	80.66 317	84.67 356	91.19 304	71.97 370	91.94 360	93.19 348	77.86 343	72.27 366	85.26 379	73.46 208	93.42 392	73.71 328	67.05 399	88.61 356

DP-MVS		81.47 323	78.28 342	91.04 206	98.14 55	78.48 259	95.09 287	86.97 424	61.14 437	71.12 375	92.78 260	59.59 330	99.38 88	53.11 428	86.61 251	95.27 250

v10		81.43 324	79.53 333	87.11 315	86.38 382	78.87 243	94.31 307	93.43 338	77.88 342	73.24 356	85.26 379	65.44 285	95.75 315	72.14 337	67.71 392	86.72 392

pmmvs5		81.34 325	79.54 332	86.73 322	85.02 403	76.91 308	96.22 219	91.65 381	77.65 345	73.55 349	88.61 320	55.70 367	94.43 373	74.12 324	73.35 346	88.86 354

SD_0403		81.29 326	81.13 311	81.78 391	90.20 329	60.43 435	89.97 381	91.31 390	83.87 223	71.78 369	93.08 255	63.86 299	89.61 426	60.00 401	86.07 260	95.30 248

ADS-MVSNet		81.26 327	78.36 341	89.96 245	93.78 200	79.78 215	79.48 438	93.60 331	73.09 388	80.14 277	79.99 421	62.15 312	95.24 342	59.49 403	83.52 277	94.85 261

Baseline_NR-MVSNet		81.22 328	80.07 326	84.68 355	85.32 401	75.12 339	96.48 198	88.80 415	76.24 364	77.28 306	86.40 363	67.61 264	94.39 374	75.73 309	66.73 402	84.54 416

tt0805		81.20 329	79.06 338	87.61 299	86.50 381	72.97 360	93.66 325	95.48 199	74.11 378	76.23 324	91.99 273	41.36 426	97.40 221	77.44 290	74.78 338	92.45 296

SSC-MVS3.2		81.06 330	79.49 334	85.75 337	89.78 337	73.00 359	94.40 304	95.23 217	83.76 229	76.61 316	87.82 336	49.48 396	94.88 357	66.80 366	71.56 356	89.38 330

WR-MVS_H		81.02 331	80.09 324	83.79 368	88.08 367	71.26 381	94.46 299	96.54 102	80.08 308	72.81 361	86.82 352	70.36 249	92.65 397	64.18 382	67.50 394	87.46 385

CP-MVSNet		81.01 332	80.08 325	83.79 368	87.91 369	70.51 384	94.29 312	95.65 188	80.83 283	72.54 364	88.84 317	63.71 300	92.32 402	68.58 360	68.36 384	88.55 357

anonymousdsp		80.98 333	79.97 328	84.01 365	81.73 426	70.44 386	92.49 352	93.58 333	77.10 354	72.98 359	86.31 364	57.58 351	94.90 356	79.32 267	78.63 318	86.69 393

UniMVSNet_ETH3D		80.86 334	78.75 340	87.22 314	86.31 384	72.02 368	91.95 359	93.76 324	73.51 383	75.06 341	90.16 304	43.04 419	95.66 320	76.37 302	78.55 319	93.98 280

testing3		80.74 335	81.17 309	79.44 405	91.15 306	63.48 424	97.16 140	95.76 181	80.83 283	71.36 372	93.15 253	78.22 113	87.30 440	43.19 449	79.67 306	87.55 383

IterMVS		80.67 336	79.16 336	85.20 348	89.79 336	76.08 323	92.97 346	91.86 375	80.28 302	71.20 374	85.14 384	57.93 349	91.34 414	72.52 335	70.74 361	88.18 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 337	77.77 347	89.14 263	93.43 215	77.24 302	91.89 361	90.18 403	69.86 409	68.02 390	91.94 278	52.21 383	98.84 131	59.32 405	83.12 281	91.35 301

IterMVS-SCA-FT		80.51 338	79.10 337	84.73 354	89.63 344	74.66 341	92.98 345	91.81 377	80.05 309	71.06 376	85.18 382	58.04 345	91.40 413	72.48 336	70.70 363	88.12 370

PS-CasMVS		80.27 339	79.18 335	83.52 374	87.56 373	69.88 390	94.08 315	95.29 214	80.27 303	72.08 367	88.51 324	59.22 336	92.23 404	67.49 362	68.15 387	88.45 363

pm-mvs1		80.05 340	78.02 345	86.15 330	85.42 397	75.81 332	95.11 284	92.69 362	77.13 352	70.36 380	87.43 341	58.44 341	95.27 341	71.36 342	64.25 410	87.36 386

RPMNet		79.85 341	75.92 361	91.64 179	90.16 331	79.75 217	79.02 442	95.44 203	58.43 446	82.27 254	72.55 447	73.03 213	98.41 156	46.10 444	86.25 255	96.75 200

PatchT		79.75 342	76.85 354	88.42 277	89.55 346	75.49 336	77.37 446	94.61 255	63.07 426	82.46 248	73.32 444	75.52 171	93.41 393	51.36 431	84.43 273	96.36 210

Anonymous20231211		79.72 343	77.19 351	87.33 309	95.59 134	77.16 306	95.18 279	94.18 290	59.31 444	72.57 363	86.20 367	47.89 403	95.66 320	74.53 321	69.24 377	89.18 338

test_fmvs2		79.59 344	79.90 330	78.67 410	82.86 423	55.82 447	95.20 276	89.55 407	81.09 279	80.12 279	89.80 307	34.31 442	93.51 391	87.82 180	78.36 321	86.69 393

ADS-MVSNet2		79.57 345	77.53 348	85.71 338	93.78 200	72.13 366	79.48 438	86.11 431	73.09 388	80.14 277	79.99 421	62.15 312	90.14 425	59.49 403	83.52 277	94.85 261

FMVSNet1		79.50 346	76.54 357	88.39 280	88.47 361	81.95 143	94.30 308	93.38 340	73.14 387	72.04 368	85.66 371	43.86 413	93.84 384	65.48 376	72.53 350	89.38 330

PEN-MVS		79.47 347	78.26 343	83.08 377	86.36 383	68.58 398	93.85 323	94.77 240	79.76 314	71.37 371	88.55 321	59.79 328	92.46 398	64.50 380	65.40 405	88.19 368

XVG-ACMP-BASELINE		79.38 348	77.90 346	83.81 367	84.98 404	67.14 409	89.03 391	93.18 350	80.26 304	72.87 360	88.15 331	38.55 432	96.26 288	76.05 305	78.05 323	88.02 371

v7n		79.32 349	77.34 349	85.28 347	84.05 415	72.89 362	93.38 333	93.87 307	75.02 372	70.68 377	84.37 391	59.58 331	95.62 325	67.60 361	67.50 394	87.32 387

MIMVSNet		79.18 350	75.99 360	88.72 273	87.37 375	80.66 187	79.96 436	91.82 376	77.38 349	74.33 345	81.87 409	41.78 422	90.74 420	66.36 374	83.10 282	94.76 263

JIA-IIPM		79.00 351	77.20 350	84.40 363	89.74 341	64.06 421	75.30 450	95.44 203	62.15 430	81.90 258	59.08 456	78.92 99	95.59 327	66.51 372	85.78 264	93.54 287

USDC		78.65 352	76.25 358	85.85 333	87.58 372	74.60 343	89.58 386	90.58 402	84.05 215	63.13 415	88.23 329	40.69 431	96.86 266	66.57 371	75.81 331	86.09 402

DTE-MVSNet		78.37 353	77.06 352	82.32 387	85.22 402	67.17 408	93.40 332	93.66 327	78.71 335	70.53 379	88.29 328	59.06 337	92.23 404	61.38 395	63.28 414	87.56 381

Patchmatch-test		78.25 354	74.72 369	88.83 270	91.20 303	74.10 348	73.91 453	88.70 418	59.89 442	66.82 397	85.12 385	78.38 109	94.54 370	48.84 440	79.58 308	97.86 108

tfpnnormal		78.14 355	75.42 363	86.31 328	88.33 365	79.24 231	94.41 301	96.22 141	73.51 383	69.81 384	85.52 377	55.43 368	95.75 315	47.65 442	67.86 390	83.95 422

mmtdpeth		78.04 356	76.76 355	81.86 390	89.60 345	66.12 413	92.34 356	87.18 423	76.83 359	85.55 200	76.49 435	46.77 407	97.02 250	90.85 131	45.24 451	82.43 431

ACMH	75.40 17	77.99 357	74.96 365	87.10 316	90.67 319	76.41 318	93.19 343	91.64 382	72.47 394	63.44 413	87.61 340	43.34 416	97.16 241	58.34 408	73.94 341	87.72 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 357	75.74 362	84.74 353	90.45 323	72.02 368	86.41 415	91.12 391	72.57 393	66.63 399	87.27 344	54.95 373	96.98 253	56.29 418	75.98 328	85.21 411
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		77.97 359	78.05 344	77.74 414	92.13 279	56.85 443	93.97 317	94.23 285	82.43 260	73.39 351	93.57 246	57.95 348	87.86 435	32.40 457	82.34 292	88.51 358

our_test_3		77.90 360	75.37 364	85.48 344	85.39 398	76.74 312	93.63 326	91.67 380	73.39 386	65.72 404	84.65 390	58.20 344	93.13 395	57.82 410	67.87 389	86.57 395

RPSCF		77.73 361	76.63 356	81.06 396	88.66 359	55.76 448	87.77 404	87.88 421	64.82 424	74.14 346	92.79 259	49.22 397	96.81 268	67.47 363	76.88 326	90.62 308

KD-MVS_2432*1600		77.63 362	74.92 367	85.77 335	90.86 314	79.44 225	88.08 400	93.92 303	76.26 362	67.05 395	82.78 404	72.15 226	91.92 407	61.53 392	41.62 457	85.94 405

miper_refine_blended		77.63 362	74.92 367	85.77 335	90.86 314	79.44 225	88.08 400	93.92 303	76.26 362	67.05 395	82.78 404	72.15 226	91.92 407	61.53 392	41.62 457	85.94 405

ACMH+	76.62 16	77.47 364	74.94 366	85.05 350	91.07 309	71.58 377	93.26 340	90.01 404	71.80 397	64.76 408	88.55 321	41.62 423	96.48 280	62.35 391	71.00 359	87.09 389

Patchmtry		77.36 365	74.59 370	85.67 339	89.75 339	75.75 333	77.85 445	91.12 391	60.28 439	71.23 373	80.35 418	75.45 172	93.56 390	57.94 409	67.34 396	87.68 377

ppachtmachnet_test		77.19 366	74.22 374	86.13 331	85.39 398	78.22 270	93.98 316	91.36 387	71.74 398	67.11 394	84.87 388	56.67 360	93.37 394	52.21 429	64.59 407	86.80 391

OurMVSNet-221017-0		77.18 367	76.06 359	80.55 399	83.78 418	60.00 438	90.35 378	91.05 394	77.01 356	66.62 400	87.92 334	47.73 404	94.03 380	71.63 339	68.44 383	87.62 378

TransMVSNet (Re)		76.94 368	74.38 372	84.62 358	85.92 392	75.25 338	95.28 268	89.18 412	73.88 381	67.22 392	86.46 359	59.64 329	94.10 379	59.24 406	52.57 438	84.50 417

EU-MVSNet		76.92 369	76.95 353	76.83 419	84.10 413	54.73 451	91.77 363	92.71 361	72.74 391	69.57 385	88.69 319	58.03 347	87.43 439	64.91 379	70.00 370	88.33 366

Patchmatch-RL test		76.65 370	74.01 377	84.55 359	77.37 441	64.23 419	78.49 444	82.84 447	78.48 337	64.63 409	73.40 443	76.05 159	91.70 412	76.99 293	57.84 423	97.72 121

FMVSNet5		76.46 371	74.16 375	83.35 376	90.05 334	76.17 321	89.58 386	89.85 405	71.39 400	65.29 407	80.42 417	50.61 390	87.70 438	61.05 397	69.24 377	86.18 400

SixPastTwentyTwo		76.04 372	74.32 373	81.22 394	84.54 407	61.43 433	91.16 371	89.30 411	77.89 341	64.04 410	86.31 364	48.23 398	94.29 376	63.54 387	63.84 412	87.93 373

AllTest		75.92 373	73.06 381	84.47 360	92.18 275	67.29 403	91.07 372	84.43 437	67.63 415	63.48 411	90.18 302	38.20 433	97.16 241	57.04 414	73.37 344	88.97 350

CL-MVSNet_self_test		75.81 374	74.14 376	80.83 398	78.33 437	67.79 402	94.22 313	93.52 334	77.28 351	69.82 383	81.54 412	61.47 323	89.22 428	57.59 412	53.51 434	85.48 409

COLMAP_ROB	73.24 19	75.74 375	73.00 382	83.94 366	92.38 256	69.08 396	91.85 362	86.93 425	61.48 434	65.32 406	90.27 301	42.27 421	96.93 258	50.91 433	75.63 332	85.80 408
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	75.71 376	74.56 371	79.17 407	79.69 432	55.98 445	89.59 385	93.30 345	60.28 439	53.85 446	89.07 314	47.68 405	96.33 286	76.55 298	81.02 298	85.22 410
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 377	73.64 378	80.22 401	80.75 427	63.38 425	93.36 334	90.71 401	73.09 388	67.12 393	83.70 398	50.33 392	90.85 419	53.63 427	70.10 368	86.44 396

EG-PatchMatch MVS		74.92 378	72.02 386	83.62 372	83.76 420	73.28 354	93.62 327	92.04 374	68.57 413	58.88 434	83.80 397	31.87 447	95.57 329	56.97 416	78.67 315	82.00 435

testgi		74.88 379	73.40 379	79.32 406	80.13 431	61.75 430	93.21 341	86.64 429	79.49 320	66.56 401	91.06 288	35.51 440	88.67 430	56.79 417	71.25 357	87.56 381

pmmvs6		74.65 380	71.67 387	83.60 373	79.13 434	69.94 389	93.31 339	90.88 398	61.05 438	65.83 403	84.15 394	43.43 415	94.83 360	66.62 369	60.63 419	86.02 403

test_vis1_rt		73.96 381	72.40 384	78.64 411	83.91 416	61.16 434	95.63 255	68.18 464	76.32 361	60.09 431	74.77 438	29.01 453	97.54 208	87.74 183	75.94 329	77.22 447

K. test v3		73.62 382	71.59 388	79.69 403	82.98 422	59.85 439	90.85 375	88.83 414	77.13 352	58.90 433	82.11 406	43.62 414	91.72 411	65.83 375	54.10 433	87.50 384

pmmvs-eth3d		73.59 383	70.66 391	82.38 385	76.40 445	73.38 351	89.39 390	89.43 409	72.69 392	60.34 430	77.79 427	46.43 409	91.26 416	66.42 373	57.06 424	82.51 428

kuosan		73.55 384	72.39 385	77.01 417	89.68 342	66.72 411	85.24 424	93.44 336	67.76 414	60.04 432	83.40 401	71.90 231	84.25 448	45.34 446	54.75 428	80.06 443

MDA-MVSNet_test_wron		73.54 385	70.43 393	82.86 379	84.55 406	71.85 372	91.74 364	91.32 389	67.63 415	46.73 451	81.09 415	55.11 371	90.42 423	55.91 420	59.76 420	86.31 398

YYNet1		73.53 386	70.43 393	82.85 380	84.52 408	71.73 375	91.69 365	91.37 386	67.63 415	46.79 450	81.21 414	55.04 372	90.43 422	55.93 419	59.70 421	86.38 397

UnsupCasMVSNet_eth		73.25 387	70.57 392	81.30 393	77.53 439	66.33 412	87.24 408	93.89 306	80.38 297	57.90 438	81.59 410	42.91 420	90.56 421	65.18 378	48.51 445	87.01 390

DSMNet-mixed		73.13 388	72.45 383	75.19 425	77.51 440	46.82 456	85.09 425	82.01 449	67.61 419	69.27 387	81.33 413	50.89 386	86.28 443	54.54 424	83.80 276	92.46 295

OpenMVS_ROB	68.52 20	73.02 389	69.57 396	83.37 375	80.54 430	71.82 373	93.60 329	88.22 419	62.37 429	61.98 422	83.15 403	35.31 441	95.47 331	45.08 447	75.88 330	82.82 425

test_0402		72.68 390	69.54 397	82.09 388	88.67 358	71.81 374	92.72 350	86.77 428	61.52 433	62.21 421	83.91 396	43.22 417	93.76 387	34.60 455	72.23 354	80.72 442

TinyColmap		72.41 391	68.99 400	82.68 381	88.11 366	69.59 393	88.41 396	85.20 433	65.55 421	57.91 437	84.82 389	30.80 449	95.94 303	51.38 430	68.70 380	82.49 430

sc_t1		72.37 392	68.03 403	85.39 345	83.78 418	70.51 384	91.27 370	83.70 444	52.46 451	68.29 389	82.02 407	30.58 450	94.81 361	64.50 380	55.69 426	90.85 307

test20.03		72.36 393	71.15 389	75.98 423	77.79 438	59.16 440	92.40 354	89.35 410	74.09 379	61.50 425	84.32 392	48.09 399	85.54 446	50.63 434	62.15 417	83.24 423

LF4IMVS		72.36 393	70.82 390	76.95 418	79.18 433	56.33 444	86.12 417	86.11 431	69.30 411	63.06 416	86.66 355	33.03 445	92.25 403	65.33 377	68.64 381	82.28 432

Anonymous20240521		72.06 395	69.91 395	78.50 412	77.11 442	61.67 432	91.62 367	90.97 396	65.52 422	62.37 420	79.05 424	36.32 436	90.96 418	57.75 411	68.52 382	82.87 424

dmvs_testset		72.00 396	73.36 380	67.91 431	83.83 417	31.90 471	85.30 423	77.12 456	82.80 253	63.05 417	92.46 262	61.54 321	82.55 453	42.22 452	71.89 355	89.29 335

MDA-MVSNet-bldmvs		71.45 397	67.94 404	81.98 389	85.33 400	68.50 399	92.35 355	88.76 416	70.40 403	42.99 454	81.96 408	46.57 408	91.31 415	48.75 441	54.39 432	86.11 401

mvs5depth		71.40 398	68.36 402	80.54 400	75.31 449	65.56 415	79.94 437	85.14 434	69.11 412	71.75 370	81.59 410	41.02 428	93.94 382	60.90 398	50.46 441	82.10 433

MVS-HIRNet		71.36 399	67.00 405	84.46 362	90.58 320	69.74 392	79.15 441	87.74 422	46.09 455	61.96 423	50.50 459	45.14 411	95.64 323	53.74 426	88.11 237	88.00 372

KD-MVS_self_test		70.97 400	69.31 398	75.95 424	76.24 447	55.39 449	87.45 405	90.94 397	70.20 406	62.96 418	77.48 429	44.01 412	88.09 433	61.25 396	53.26 435	84.37 418

tt0320		70.21 401	66.07 409	82.64 382	83.42 421	70.82 382	89.63 384	84.10 440	49.75 454	62.71 419	77.28 430	33.35 443	92.45 400	58.78 407	55.62 427	84.64 415

tt0320-xc		69.70 402	65.27 414	82.99 378	84.33 409	71.92 371	89.56 388	82.08 448	50.11 452	61.87 424	77.50 428	30.48 451	92.34 401	60.30 399	51.20 440	84.71 414

ttmdpeth		69.58 403	66.92 407	77.54 416	75.95 448	62.40 428	88.09 399	84.32 439	62.87 428	65.70 405	86.25 366	36.53 435	88.53 432	55.65 422	46.96 450	81.70 438

test_fmvs3		69.56 404	69.19 399	70.67 429	69.01 455	47.05 455	90.87 374	86.81 426	71.31 401	66.79 398	77.15 431	16.40 460	83.17 451	81.84 240	62.51 416	81.79 437

dongtai		69.47 405	68.98 401	70.93 428	86.87 378	58.45 441	88.19 398	93.18 350	63.98 425	56.04 442	80.17 420	70.97 244	79.24 455	33.46 456	47.94 447	75.09 449

MIMVSNet1		69.44 406	66.65 408	77.84 413	76.48 444	62.84 427	87.42 406	88.97 413	66.96 420	57.75 440	79.72 423	32.77 446	85.83 445	46.32 443	63.42 413	84.85 413

PM-MVS		69.32 407	66.93 406	76.49 420	73.60 452	55.84 446	85.91 418	79.32 454	74.72 374	61.09 427	78.18 426	21.76 456	91.10 417	70.86 348	56.90 425	82.51 428

FE-MVSNET		69.26 408	66.03 410	78.93 408	73.82 451	68.33 400	89.65 383	84.06 441	70.21 405	57.79 439	76.94 434	41.48 425	86.98 442	45.85 445	54.51 431	81.48 440

TDRefinement		69.20 409	65.78 412	79.48 404	66.04 460	62.21 429	88.21 397	86.12 430	62.92 427	61.03 428	85.61 374	33.23 444	94.16 378	55.82 421	53.02 436	82.08 434

new-patchmatchnet		68.85 410	65.93 411	77.61 415	73.57 453	63.94 422	90.11 380	88.73 417	71.62 399	55.08 444	73.60 442	40.84 429	87.22 441	51.35 432	48.49 446	81.67 439

UnsupCasMVSNet_bld		68.60 411	64.50 415	80.92 397	74.63 450	67.80 401	83.97 429	92.94 357	65.12 423	54.63 445	68.23 452	35.97 438	92.17 406	60.13 400	44.83 452	82.78 426

mvsany_test3		67.19 412	65.34 413	72.72 427	63.08 461	48.57 454	83.12 432	78.09 455	72.07 395	61.21 426	77.11 432	22.94 455	87.78 437	78.59 274	51.88 439	81.80 436

MVStest1		66.93 413	63.01 417	78.69 409	78.56 435	71.43 379	85.51 422	86.81 426	49.79 453	48.57 449	84.15 394	53.46 379	83.31 449	43.14 450	37.15 460	81.34 441

new_pmnet		66.18 414	63.18 416	75.18 426	76.27 446	61.74 431	83.79 430	84.66 436	56.64 448	51.57 447	71.85 450	31.29 448	87.93 434	49.98 436	62.55 415	75.86 448

pmmvs3		65.75 415	62.18 418	76.45 421	67.12 459	64.54 417	88.68 394	85.05 435	54.77 450	57.54 441	73.79 441	29.40 452	86.21 444	55.49 423	47.77 448	78.62 445

test_f		64.01 416	62.13 419	69.65 430	63.00 462	45.30 461	83.66 431	80.68 451	61.30 435	55.70 443	72.62 446	14.23 462	84.64 447	69.84 353	58.11 422	79.00 444

N_pmnet		61.30 417	60.20 420	64.60 436	84.32 410	17.00 477	91.67 366	10.98 475	61.77 432	58.45 436	78.55 425	49.89 394	91.83 410	42.27 451	63.94 411	84.97 412

WB-MVS		57.26 418	56.22 421	60.39 442	69.29 454	35.91 469	86.39 416	70.06 462	59.84 443	46.46 452	72.71 445	51.18 385	78.11 456	15.19 466	34.89 461	67.14 455

test_method		56.77 419	54.53 423	63.49 438	76.49 443	40.70 464	75.68 449	74.24 458	19.47 466	48.73 448	71.89 449	19.31 457	65.80 466	57.46 413	47.51 449	83.97 421

APD_test1		56.56 420	53.58 424	65.50 433	67.93 458	46.51 458	77.24 448	72.95 459	38.09 457	42.75 455	75.17 437	13.38 463	82.78 452	40.19 453	54.53 430	67.23 454

SSC-MVS		56.01 421	54.96 422	59.17 443	68.42 456	34.13 470	84.98 426	69.23 463	58.08 447	45.36 453	71.67 451	50.30 393	77.46 457	14.28 467	32.33 462	65.91 456

FPMVS		55.09 422	52.93 425	61.57 440	55.98 464	40.51 465	83.11 433	83.41 446	37.61 458	34.95 459	71.95 448	14.40 461	76.95 458	29.81 458	65.16 406	67.25 453

test_vis3_rt		54.10 423	51.04 426	63.27 439	58.16 463	46.08 460	84.17 428	49.32 474	56.48 449	36.56 458	49.48 461	8.03 470	91.91 409	67.29 364	49.87 442	51.82 460

LCM-MVSNet		52.52 424	48.24 427	65.35 434	47.63 471	41.45 463	72.55 454	83.62 445	31.75 459	37.66 457	57.92 457	9.19 469	76.76 459	49.26 438	44.60 453	77.84 446

EGC-MVSNET		52.46 425	47.56 428	67.15 432	81.98 425	60.11 437	82.54 434	72.44 460	0.11 472	0.70 473	74.59 439	25.11 454	83.26 450	29.04 459	61.51 418	58.09 457

PMMVS2		50.90 426	46.31 429	64.67 435	55.53 465	46.67 457	77.30 447	71.02 461	40.89 456	34.16 460	59.32 455	9.83 468	76.14 461	40.09 454	28.63 463	71.21 450

ANet_high		46.22 427	41.28 434	61.04 441	39.91 473	46.25 459	70.59 457	76.18 457	58.87 445	23.09 465	48.00 462	12.58 465	66.54 465	28.65 460	13.62 466	70.35 451

testf1		45.70 428	42.41 430	55.58 444	53.29 468	40.02 466	68.96 458	62.67 468	27.45 461	29.85 461	61.58 453	5.98 471	73.83 463	28.49 461	43.46 455	52.90 458

APD_test2		45.70 428	42.41 430	55.58 444	53.29 468	40.02 466	68.96 458	62.67 468	27.45 461	29.85 461	61.58 453	5.98 471	73.83 463	28.49 461	43.46 455	52.90 458

Gipuma		45.11 430	42.05 432	54.30 446	80.69 428	51.30 453	35.80 464	83.81 443	28.13 460	27.94 464	34.53 464	11.41 467	76.70 460	21.45 463	54.65 429	34.90 464
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 431	41.93 433	40.38 449	20.10 475	26.84 473	61.93 461	59.09 470	14.81 468	28.51 463	80.58 416	35.53 439	48.33 470	63.70 386	13.11 467	45.96 463

PMVS	34.80 23	39.19 432	35.53 435	50.18 447	29.72 474	30.30 472	59.60 462	66.20 467	26.06 463	17.91 467	49.53 460	3.12 473	74.09 462	18.19 465	49.40 443	46.14 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 433	29.49 438	46.92 448	41.86 472	36.28 468	50.45 463	56.52 471	18.75 467	18.28 466	37.84 463	2.41 474	58.41 467	18.71 464	20.62 464	46.06 462
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 434	32.39 436	33.65 450	53.35 467	25.70 474	74.07 452	53.33 472	21.08 464	17.17 468	33.63 466	11.85 466	54.84 468	12.98 468	14.04 465	20.42 465

EMVS		31.70 435	31.45 437	32.48 451	50.72 470	23.95 475	74.78 451	52.30 473	20.36 465	16.08 469	31.48 467	12.80 464	53.60 469	11.39 469	13.10 468	19.88 466

cdsmvs_eth3d_5k		21.43 436	28.57 439	0.00 455	0.00 478	0.00 480	0.00 466	95.93 170	0.00 473	0.00 474	97.66 87	63.57 301	0.00 474	0.00 473	0.00 472	0.00 470

wuyk23d		14.10 437	13.89 440	14.72 452	55.23 466	22.91 476	33.83 465	3.56 476	4.94 469	4.11 470	2.28 472	2.06 475	19.66 471	10.23 470	8.74 469	1.59 469

testmvs		9.92 438	12.94 441	0.84 454	0.65 476	0.29 479	93.78 324	0.39 477	0.42 470	2.85 471	15.84 470	0.17 477	0.30 473	2.18 471	0.21 470	1.91 468

test123		9.07 439	11.73 442	1.11 453	0.50 477	0.77 478	89.44 389	0.20 478	0.34 471	2.15 472	10.72 471	0.34 476	0.32 472	1.79 472	0.08 471	2.23 467

ab-mvs-re		8.11 440	10.81 443	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	97.30 110	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

pcd_1.5k_mvsjas		5.92 441	7.89 444	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	71.04 241	0.00 474	0.00 473	0.00 472	0.00 470

mmdepth		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

monomultidepth		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

test_blank		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

uanet_test		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

DCPMVS		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

sosnet-low-res		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

sosnet		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

uncertanet		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

Regformer		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

uanet		0.00 442	0.00 445	0.00 455	0.00 478	0.00 480	0.00 466	0.00 479	0.00 473	0.00 474	0.00 473	0.00 478	0.00 474	0.00 473	0.00 472	0.00 470

WAC-MVS						67.18 405								49.00 439

FOURS1					98.51 39	78.01 278	98.13 62	96.21 142	83.04 245	94.39 64

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 58					99.81 22	98.08 24	98.81 24	99.43 11

PC_three_1452									91.12 48	98.33 4	98.42 37	92.51 2	99.81 22	98.96 6	99.37 1	99.70 3

No_MVS				97.14 3	99.05 9	92.19 4		96.83 58					99.81 22	98.08 24	98.81 24	99.43 11

test_one_0601					98.91 18	84.56 84		96.70 78	88.06 100	96.57 31	98.77 10	88.04 21

eth-test2					0.00 478

eth-test					0.00 478

ZD-MVS					99.09 8	83.22 109		96.60 95	82.88 251	93.61 75	98.06 65	82.93 60	99.14 111	95.51 60	98.49 39

RE-MVS-def			91.18 110		97.76 69	76.03 325	96.20 221	95.44 203	80.56 291	90.72 123	97.84 79	73.36 210		91.99 114	96.79 103	97.75 118

IU-MVS					99.03 15	85.34 61		96.86 56	92.05 39	98.74 1				98.15 20	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 14				98.54 24	92.06 3	99.84 13	99.11 5	99.37 1	99.74 1

test_241102_TWO								96.78 61	88.72 82	97.70 12	98.91 2	87.86 22	99.82 19	98.15 20	99.00 15	99.47 9

test_241102_ONE					99.03 15	85.03 74		96.78 61	88.72 82	97.79 9	98.90 5	88.48 17	99.82 19

9.14			94.26 38		98.10 57		98.14 59	96.52 105	84.74 191	94.83 58	98.80 7	82.80 62	99.37 90	95.95 52	98.42 42

save fliter					98.24 51	83.34 106	98.61 44	96.57 99	91.32 45

test_0728_THIRD									88.38 90	96.69 26	98.76 12	89.64 12	99.76 38	97.47 35	98.84 23	99.38 14

test_0728_SECOND				95.14 20	99.04 14	86.14 39	99.06 21	96.77 67					99.84 13	97.90 28	98.85 21	99.45 10

test0726					99.05 9	85.18 66	99.11 17	96.78 61	88.75 80	97.65 15	98.91 2	87.69 23

GSMVS																97.54 137

test_part2					98.90 19	85.14 72				96.07 38

sam_mvs1												77.59 124				97.54 137

sam_mvs												75.35 179

ambc				76.02 422	68.11 457	51.43 452	64.97 460	89.59 406		60.49 429	74.49 440	17.17 459	92.46 398	61.50 394	52.85 437	84.17 420

MTGPA								96.33 131

test_post1							85.88 419				30.24 468	73.77 203	95.07 353	73.89 325

test_post											33.80 465	76.17 157	95.97 299

patchmatchnet-post											77.09 433	77.78 122	95.39 333

GG-mvs-BLEND				93.49 78	94.94 158	86.26 37	81.62 435	97.00 40		88.32 163	94.30 221	91.23 5	96.21 292	88.49 173	97.43 76	98.00 96

MTMP							97.53 108	68.16 465

gm-plane-assit					92.27 268	79.64 223			84.47 202		95.15 189		97.93 178	85.81 198

test9_res														96.00 51	99.03 13	98.31 70

TEST9					98.64 31	83.71 97	97.82 83	96.65 86	84.29 209	95.16 48	98.09 60	84.39 42	99.36 91

test_8					98.63 33	83.64 100	97.81 85	96.63 91	84.50 199	95.10 51	98.11 58	84.33 43	99.23 99

agg_prior2														94.30 75	99.00 15	98.57 54

agg_prior					98.59 35	83.13 111		96.56 101		94.19 66			99.16 110

TestCases				84.47 360	92.18 275	67.29 403		84.43 437	67.63 415	63.48 411	90.18 302	38.20 433	97.16 241	57.04 414	73.37 344	88.97 350

test_prior4						82.34 133	97.75 91

test_prior2							98.37 52		86.08 153	94.57 62	98.02 66	83.14 57		95.05 66	98.79 27

test_prior				93.09 92	98.68 26	81.91 147		96.40 121					99.06 118			98.29 72

旧先验2							96.97 161		74.06 380	96.10 37			97.76 189	88.38 175

新几何2							96.42 205

新几何1				93.12 90	97.44 83	81.60 163		96.71 77	74.54 376	91.22 116	97.57 95	79.13 96	99.51 81	77.40 291	98.46 40	98.26 75

旧先验1					97.39 88	79.58 224		96.54 102			98.08 63	84.00 49			97.42 77	97.62 132

无先验							96.87 170	96.78 61	77.39 348				99.52 79	79.95 259		98.43 63

原ACMM2							96.84 171

原ACMM1				91.22 202	97.77 67	78.10 276		96.61 92	81.05 280	91.28 115	97.42 104	77.92 119	98.98 122	79.85 261	98.51 36	96.59 205

test222					96.15 111	78.41 263	95.87 243	96.46 113	71.97 396	89.66 137	97.45 100	76.33 154			98.24 51	98.30 71

testdata2													99.48 83	76.45 300

segment_acmp												82.69 63

testdata				90.13 237	95.92 121	74.17 347		96.49 111	73.49 385	94.82 59	97.99 67	78.80 103	97.93 178	83.53 224	97.52 72	98.29 72

testdata1							95.57 259		87.44 117

test12				94.25 41	98.34 46	85.55 57		96.35 130		92.36 93		80.84 71	99.22 100		98.31 49	97.98 98

plane_prior7					91.86 292	77.55 297

plane_prior6					91.98 288	77.92 283						64.77 293

plane_prior5								94.69 245					97.30 231	87.08 189	82.82 287	90.96 304

plane_prior4											94.15 229

plane_prior3						77.75 293			90.17 65	81.33 263

plane_prior2							97.18 136		89.89 68

plane_prior1					91.95 290

plane_prior						77.96 280	97.52 111		90.36 63						82.96 285

n2								0.00 479

nn								0.00 479

door-mid								79.75 453

lessismore_v0				79.98 402	80.59 429	58.34 442		80.87 450		58.49 435	83.46 400	43.10 418	93.89 383	63.11 389	48.68 444	87.72 375

LGP-MVS_train				86.33 325	90.88 311	73.06 357		94.13 292	82.20 264	76.31 320	93.20 250	54.83 374	96.95 255	83.72 218	80.83 300	88.98 348

test11								96.50 108

door								80.13 452

HQP5-MVS						78.48 259

HQP-NCC					92.08 282		97.63 97		90.52 58	82.30 250

ACMP_Plane					92.08 282		97.63 97		90.52 58	82.30 250

BP-MVS														87.67 185

HQP4-MVS										82.30 250			97.32 229			91.13 302

HQP3-MVS								94.80 237							83.01 283

HQP2-MVS												65.40 286

NP-MVS					92.04 286	78.22 270					94.56 212

MDTV_nov1_ep13_2view						81.74 156	86.80 411		80.65 288	85.65 198		74.26 197		76.52 299		96.98 183

MDTV_nov1_ep13			83.69 263		94.09 193	81.01 175	86.78 412	96.09 151	83.81 227	84.75 211	84.32 392	74.44 196	96.54 278	63.88 384	85.07 270

ACMMP++_ref															78.45 320

ACMMP++															79.05 312

Test By Simon												71.65 234

ITE_SJBPF				82.38 385	87.00 377	65.59 414		89.55 407	79.99 311	69.37 386	91.30 285	41.60 424	95.33 337	62.86 390	74.63 340	86.24 399

DeepMVS_CX				64.06 437	78.53 436	43.26 462		68.11 466	69.94 408	38.55 456	76.14 436	18.53 458	79.34 454	43.72 448	41.62 457	69.57 452

High-resolution multi-view benchmark