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.

test_vis1_n		97.92 243	97.44 283	99.34 151	99.53 172	98.08 246	99.74 46	99.49 153	99.15 25	100.00 1	99.94 6	79.51 415	99.98 14	99.88 17	99.76 121	99.97 4

fmvsm_s_conf0.5_n_3		99.37 59	99.20 74	99.87 16	99.75 79	99.70 52	99.48 190	99.66 28	99.45 8	99.99 2	99.93 10	94.64 239	99.97 22	99.94 12	99.97 7	99.95 9

fmvsm_s_conf0.5_n_2		99.32 67	99.13 81	99.89 8	99.80 53	99.77 41	99.44 208	99.58 65	99.47 4	99.99 2	99.93 10	94.04 263	99.96 34	99.96 8	99.93 27	99.93 18

fmvsm_s_conf0.1_n_a		99.26 78	99.06 91	99.85 34	99.52 178	99.62 72	99.54 149	99.62 43	98.69 88	99.99 2	99.96 1	94.47 248	99.94 76	99.88 17	99.92 30	99.98 2

fmvsm_s_conf0.1_n		99.29 72	99.10 85	99.86 27	99.70 108	99.65 64	99.53 158	99.62 43	98.74 84	99.99 2	99.95 3	94.53 246	99.94 76	99.89 16	99.96 13	99.97 4

test_vis1_n_1920		98.63 172	98.40 179	99.31 158	99.86 20	97.94 258	99.67 69	99.62 43	99.43 11	99.99 2	99.91 23	87.29 383	100.00 1	99.92 15	99.92 30	99.98 2

test_fmvs1_n		98.41 183	98.14 195	99.21 178	99.82 43	97.71 271	99.74 46	99.49 153	99.32 18	99.99 2	99.95 3	85.32 396	99.97 22	99.82 20	99.84 86	99.96 7

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 32	99.82 25	99.54 149	99.66 28	99.46 7	99.98 8	99.89 35	97.27 129	99.99 4	99.97 1	99.95 18	99.95 9

fmvsm_s_conf0.1_n_2		99.37 59	99.22 72	99.81 50	99.77 65	99.75 44	99.46 200	99.60 56	99.47 4	99.98 8	99.94 6	94.98 212	99.95 65	99.97 1	99.79 113	99.73 103

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 34	99.86 20	99.61 74	99.56 130	99.63 41	99.48 3	99.98 8	99.83 76	98.75 58	99.99 4	99.97 1	99.96 13	99.94 13

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 34	99.84 32	99.63 71	99.56 130	99.63 41	99.47 4	99.98 8	99.82 85	98.75 58	99.99 4	99.97 1	99.97 7	99.94 13

fmvsm_s_conf0.5_n_a		99.56 17	99.47 21	99.85 34	99.83 40	99.64 70	99.52 159	99.65 35	99.10 35	99.98 8	99.92 17	97.35 125	99.96 34	99.94 12	99.92 30	99.95 9

fmvsm_s_conf0.5_n		99.51 22	99.40 31	99.85 34	99.84 32	99.65 64	99.51 168	99.67 23	99.13 28	99.98 8	99.92 17	96.60 153	99.96 34	99.95 10	99.96 13	99.95 9

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 59	99.84 32	99.44 103	99.58 117	99.69 18	99.43 11	99.98 8	99.91 23	98.62 73	100.00 1	99.97 1	99.95 18	99.90 19

test_fmvs1		98.88 139	98.79 140	99.16 183	99.69 112	97.61 275	99.55 144	99.49 153	99.32 18	99.98 8	99.91 23	91.41 333	99.96 34	99.82 20	99.92 30	99.90 19

dcpmvs_2		99.23 84	99.58 7	98.16 312	99.83 40	94.68 378	99.76 37	99.52 109	99.07 43	99.98 8	99.88 43	98.56 77	99.93 94	99.67 27	99.98 4	99.87 33

test_cas_vis1_n_1920		99.16 92	99.01 104	99.61 95	99.81 47	98.86 185	99.65 81	99.64 38	99.39 14	99.97 17	99.94 6	93.20 285	99.98 14	99.55 38	99.91 37	99.99 1

mvsany_test1		99.50 24	99.46 24	99.62 94	99.61 149	99.09 148	98.94 359	99.48 165	99.10 35	99.96 18	99.91 23	98.85 42	99.96 34	99.72 23	99.58 149	99.82 60

mamv4		99.33 65	99.42 26	99.07 191	99.67 118	97.73 266	99.42 220	99.60 56	98.15 147	99.94 19	99.91 23	98.42 88	99.94 76	99.72 23	99.96 13	99.54 172

test_fmvsmconf_n		99.70 3	99.64 4	99.87 16	99.80 53	99.66 60	99.48 190	99.64 38	99.45 8	99.92 20	99.92 17	98.62 73	99.99 4	99.96 8	99.99 1	99.96 7

SED-MVS		99.61 8	99.52 12	99.88 10	99.84 32	99.90 2	99.60 102	99.48 165	99.08 41	99.91 21	99.81 99	99.20 7	99.96 34	98.91 114	99.85 78	99.79 80

test_241102_ONE					99.84 32	99.90 2		99.48 165	99.07 43	99.91 21	99.74 151	99.20 7	99.76 215

reproduce_model		99.63 7	99.54 11	99.90 5	99.78 58	99.88 8	99.56 130	99.55 82	99.15 25	99.90 23	99.90 30	99.00 22	99.97 22	99.11 88	99.91 37	99.86 35

EI-MVSNet-UG-set		99.58 13	99.57 8	99.64 87	99.78 58	99.14 143	99.60 102	99.45 206	99.01 48	99.90 23	99.83 76	98.98 24	99.93 94	99.59 33	99.95 18	99.86 35

reproduce-ours		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 159	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 96	99.90 46	99.85 39

our_new_method		99.61 8	99.52 12	99.90 5	99.76 69	99.88 8	99.52 159	99.54 91	99.13 28	99.89 25	99.89 35	98.96 25	99.96 34	99.04 96	99.90 46	99.85 39

EI-MVSNet-Vis-set		99.58 13	99.56 10	99.64 87	99.78 58	99.15 142	99.61 101	99.45 206	99.01 48	99.89 25	99.82 85	99.01 18	99.92 106	99.56 37	99.95 18	99.85 39

DVP-MVS++		99.59 12	99.50 17	99.88 10	99.51 181	99.88 8	99.87 8	99.51 123	98.99 53	99.88 28	99.81 99	99.27 5	99.96 34	98.85 127	99.80 106	99.81 67

test_241102_TWO								99.48 165	99.08 41	99.88 28	99.81 99	98.94 32	99.96 34	98.91 114	99.84 86	99.88 28

DPE-MVS		99.46 35	99.32 47	99.91 3	99.78 58	99.88 8	99.36 247	99.51 123	98.73 85	99.88 28	99.84 71	98.72 64	99.96 34	98.16 212	99.87 63	99.88 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SD-MVS		99.41 52	99.52 12	99.05 195	99.74 87	99.68 55	99.46 200	99.52 109	99.11 34	99.88 28	99.91 23	99.43 1	97.70 407	98.72 145	99.93 27	99.77 88
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
APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 65	99.89 4	99.75 42	99.56 74	99.02 46	99.88 28	99.85 61	99.18 10	99.96 34	99.22 78	99.92 30	99.90 19
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVSMamba_PlusPlus		99.46 35	99.41 30	99.64 87	99.68 116	99.50 95	99.75 42	99.50 143	98.27 130	99.87 33	99.92 17	98.09 104	99.94 76	99.65 29	99.95 18	99.47 199

test_fmvsmconf0.1_n		99.55 18	99.45 25	99.86 27	99.44 211	99.65 64	99.50 175	99.61 50	99.45 8	99.87 33	99.92 17	97.31 126	99.97 22	99.95 10	99.99 1	99.97 4

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 62	99.38 229	99.37 109	99.58 117	99.62 43	99.41 13	99.87 33	99.92 17	98.81 47	100.00 1	99.97 1	99.93 27	99.94 13

balanced_conf03		99.46 35	99.39 33	99.67 76	99.55 168	99.58 82	99.74 46	99.51 123	98.42 113	99.87 33	99.84 71	98.05 107	99.91 118	99.58 35	99.94 25	99.52 179

test0726					99.85 26	99.89 4	99.62 95	99.50 143	99.10 35	99.86 37	99.82 85	98.94 32

Vis-MVSNet		99.12 105	98.97 110	99.56 105	99.78 58	99.10 147	99.68 66	99.66 28	98.49 104	99.86 37	99.87 52	94.77 228	99.84 168	99.19 80	99.41 161	99.74 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BP-MVS1		99.12 105	98.94 118	99.65 81	99.51 181	99.30 121	99.67 69	98.92 346	98.48 105	99.84 39	99.69 176	94.96 213	99.92 106	99.62 32	99.79 113	99.71 119

PC_three_1452									98.18 145	99.84 39	99.70 166	99.31 3	98.52 390	98.30 202	99.80 106	99.81 67

IU-MVS					99.84 32	99.88 8		99.32 280	98.30 127	99.84 39				98.86 125	99.85 78	99.89 22

xiu_mvs_v1_base_debu		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 316	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 250

xiu_mvs_v1_base		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 316	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 250

xiu_mvs_v1_base_debi		99.29 72	99.27 64	99.34 151	99.63 139	98.97 165	99.12 316	99.51 123	98.86 68	99.84 39	99.47 268	98.18 100	99.99 4	99.50 45	99.31 170	99.08 250

DeepPCF-MVS	98.18 3	98.81 154	99.37 37	97.12 365	99.60 154	91.75 405	98.61 390	99.44 214	99.35 16	99.83 45	99.85 61	98.70 66	99.81 194	99.02 100	99.91 37	99.81 67

TSAR-MVS + GP.		99.36 62	99.36 39	99.36 149	99.67 118	98.61 210	99.07 326	99.33 270	99.00 51	99.82 46	99.81 99	99.06 16	99.84 168	99.09 92	99.42 160	99.65 137

FOURS1					99.91 1	99.93 1	99.87 8	99.56 74	99.10 35	99.81 47

DVP-MVS		99.57 16	99.47 21	99.88 10	99.85 26	99.89 4	99.57 124	99.37 250	99.10 35	99.81 47	99.80 112	98.94 32	99.96 34	98.93 111	99.86 71	99.81 67
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
test_0728_THIRD									98.99 53	99.81 47	99.80 112	99.09 14	99.96 34	98.85 127	99.90 46	99.88 28

RRT-MVS		98.91 137	98.75 143	99.39 147	99.46 204	98.61 210	99.76 37	99.50 143	98.06 167	99.81 47	99.88 43	93.91 270	99.94 76	99.11 88	99.27 173	99.61 153

MVSFormer		99.17 90	99.12 83	99.29 166	99.51 181	98.94 175	99.88 4	99.46 195	97.55 226	99.80 51	99.65 196	97.39 121	99.28 318	99.03 98	99.85 78	99.65 137

lupinMVS		99.13 99	99.01 104	99.46 135	99.51 181	98.94 175	99.05 331	99.16 315	97.86 187	99.80 51	99.56 234	97.39 121	99.86 155	98.94 108	99.85 78	99.58 164

tttt0517		98.42 181	98.14 195	99.28 170	99.66 128	98.38 233	99.74 46	96.85 413	97.68 212	99.79 53	99.74 151	91.39 334	99.89 142	98.83 133	99.56 150	99.57 167

APD-MVS_3200maxsize		99.48 30	99.35 41	99.85 34	99.76 69	99.83 19	99.63 90	99.54 91	98.36 120	99.79 53	99.82 85	98.86 41	99.95 65	98.62 159	99.81 102	99.78 86

jason		99.13 99	99.03 96	99.45 136	99.46 204	98.87 182	99.12 316	99.26 298	98.03 172	99.79 53	99.65 196	97.02 139	99.85 161	99.02 100	99.90 46	99.65 137
jason: jason.
SteuartSystems-ACMMP		99.54 19	99.42 26	99.87 16	99.82 43	99.81 29	99.59 109	99.51 123	98.62 93	99.79 53	99.83 76	99.28 4	99.97 22	98.48 181	99.90 46	99.84 45
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.69 1	99.49 26	99.39 33	99.77 62	99.63 139	99.59 77	99.36 247	99.46 195	99.07 43	99.79 53	99.82 85	98.85 42	99.92 106	98.68 152	99.87 63	99.82 60
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n		99.22 85	99.03 96	99.79 56	98.42 386	99.48 98	99.55 144	99.51 123	99.39 14	99.78 58	99.93 10	94.80 223	99.95 65	99.93 14	99.95 18	99.94 13

CS-MVS		99.50 24	99.48 19	99.54 108	99.76 69	99.42 105	99.90 1	99.55 82	98.56 98	99.78 58	99.70 166	98.65 71	99.79 204	99.65 29	99.78 115	99.41 214

SMA-MVS		99.44 43	99.30 55	99.85 34	99.73 94	99.83 19	99.56 130	99.47 186	97.45 239	99.78 58	99.82 85	99.18 10	99.91 118	98.79 138	99.89 57	99.81 67
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
TSAR-MVS + MP.		99.58 13	99.50 17	99.81 50	99.91 1	99.66 60	99.63 90	99.39 234	98.91 66	99.78 58	99.85 61	99.36 2	99.94 76	98.84 130	99.88 60	99.82 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
GDP-MVS		99.08 116	98.89 125	99.64 87	99.53 172	99.34 113	99.64 84	99.48 165	98.32 125	99.77 62	99.66 194	95.14 209	99.93 94	98.97 106	99.50 155	99.64 144

test2506		96.81 334	96.65 330	97.29 361	99.74 87	92.21 404	99.60 102	85.06 435	99.13 28	99.77 62	99.93 10	87.82 381	99.85 161	99.38 57	99.38 162	99.80 76

test_part2					99.81 47	99.83 19				99.77 62

MSP-MVS		99.42 48	99.27 64	99.88 10	99.89 8	99.80 31	99.67 69	99.50 143	98.70 87	99.77 62	99.49 259	98.21 98	99.95 65	98.46 185	99.77 118	99.88 28
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
UA-Net		99.42 48	99.29 59	99.80 53	99.62 145	99.55 85	99.50 175	99.70 15	98.79 78	99.77 62	99.96 1	97.45 120	99.96 34	98.92 113	99.90 46	99.89 22

APD-MVS		99.27 76	99.08 89	99.84 45	99.75 79	99.79 34	99.50 175	99.50 143	97.16 267	99.77 62	99.82 85	98.78 51	99.94 76	97.56 269	99.86 71	99.80 76
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post		99.45 39	99.31 53	99.85 34	99.76 69	99.82 25	99.63 90	99.52 109	98.38 116	99.76 68	99.82 85	98.53 79	99.95 65	98.61 162	99.81 102	99.77 88

RE-MVS-def			99.34 43		99.76 69	99.82 25	99.63 90	99.52 109	98.38 116	99.76 68	99.82 85	98.75 58		98.61 162	99.81 102	99.77 88

ACMMP_NAP		99.47 33	99.34 43	99.88 10	99.87 15	99.86 16	99.47 197	99.48 165	98.05 169	99.76 68	99.86 56	98.82 46	99.93 94	98.82 137	99.91 37	99.84 45

HPM-MVS_fast		99.51 22	99.40 31	99.85 34	99.91 1	99.79 34	99.76 37	99.56 74	97.72 206	99.76 68	99.75 146	99.13 12	99.92 106	99.07 94	99.92 30	99.85 39

MM		99.40 55	99.28 61	99.74 68	99.67 118	99.31 119	99.52 159	98.87 358	99.55 1	99.74 72	99.80 112	96.47 159	99.98 14	99.97 1	99.97 7	99.94 13

VNet		99.11 110	98.90 122	99.73 71	99.52 178	99.56 83	99.41 223	99.39 234	99.01 48	99.74 72	99.78 131	95.56 193	99.92 106	99.52 43	98.18 251	99.72 110

patch_mono-2		99.26 78	99.62 5	98.16 312	99.81 47	94.59 380	99.52 159	99.64 38	99.33 17	99.73 74	99.90 30	99.00 22	99.99 4	99.69 25	99.98 4	99.89 22

SR-MVS		99.43 46	99.29 59	99.86 27	99.75 79	99.83 19	99.59 109	99.62 43	98.21 140	99.73 74	99.79 124	98.68 67	99.96 34	98.44 187	99.77 118	99.79 80

thisisatest0530		98.35 190	98.03 210	99.31 158	99.63 139	98.56 213	99.54 149	96.75 415	97.53 230	99.73 74	99.65 196	91.25 338	99.89 142	98.62 159	99.56 150	99.48 193

SPE-MVS-test		99.49 26	99.48 19	99.54 108	99.78 58	99.30 121	99.89 2	99.58 65	98.56 98	99.73 74	99.69 176	98.55 78	99.82 189	99.69 25	99.85 78	99.48 193

EC-MVSNet		99.44 43	99.39 33	99.58 101	99.56 164	99.49 96	99.88 4	99.58 65	98.38 116	99.73 74	99.69 176	98.20 99	99.70 242	99.64 31	99.82 99	99.54 172

mmtdpeth		96.95 330	96.71 329	97.67 349	99.33 241	94.90 375	99.89 2	99.28 294	98.15 147	99.72 79	98.57 382	86.56 388	99.90 130	99.82 20	89.02 407	98.20 377

diffmvs		99.14 97	99.02 100	99.51 124	99.61 149	98.96 169	99.28 274	99.49 153	98.46 107	99.72 79	99.71 162	96.50 158	99.88 147	99.31 68	99.11 185	99.67 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SF-MVS		99.38 58	99.24 69	99.79 56	99.79 56	99.68 55	99.57 124	99.54 91	97.82 197	99.71 81	99.80 112	98.95 30	99.93 94	98.19 208	99.84 86	99.74 98

xiu_mvs_v2_base		99.26 78	99.25 68	99.29 166	99.53 172	98.91 179	99.02 339	99.45 206	98.80 77	99.71 81	99.26 326	98.94 32	99.98 14	99.34 64	99.23 175	98.98 264

PS-MVSNAJ		99.32 67	99.32 47	99.30 163	99.57 160	98.94 175	98.97 353	99.46 195	98.92 65	99.71 81	99.24 328	99.01 18	99.98 14	99.35 59	99.66 139	98.97 265

PGM-MVS		99.45 39	99.31 53	99.86 27	99.87 15	99.78 40	99.58 117	99.65 35	97.84 192	99.71 81	99.80 112	99.12 13	99.97 22	98.33 198	99.87 63	99.83 55

114514_t		98.93 135	98.67 151	99.72 73	99.85 26	99.53 90	99.62 95	99.59 61	92.65 400	99.71 81	99.78 131	98.06 106	99.90 130	98.84 130	99.91 37	99.74 98

PVSNet_Blended_VisFu		99.36 62	99.28 61	99.61 95	99.86 20	99.07 153	99.47 197	99.93 2	97.66 215	99.71 81	99.86 56	97.73 115	99.96 34	99.47 52	99.82 99	99.79 80

MTAPA		99.52 21	99.39 33	99.89 8	99.90 4	99.86 16	99.66 75	99.47 186	98.79 78	99.68 87	99.81 99	98.43 86	99.97 22	98.88 117	99.90 46	99.83 55

HFP-MVS		99.49 26	99.37 37	99.86 27	99.87 15	99.80 31	99.66 75	99.67 23	98.15 147	99.68 87	99.69 176	99.06 16	99.96 34	98.69 150	99.87 63	99.84 45

VDDNet		97.55 300	97.02 320	99.16 183	99.49 194	98.12 245	99.38 240	99.30 288	95.35 364	99.68 87	99.90 30	82.62 408	99.93 94	99.31 68	98.13 255	99.42 211

HPM-MVS		99.42 48	99.28 61	99.83 46	99.90 4	99.72 48	99.81 20	99.54 91	97.59 220	99.68 87	99.63 208	98.91 37	99.94 76	98.58 168	99.91 37	99.84 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS		97.73 279	97.35 295	98.88 225	99.47 202	97.12 294	99.34 255	98.85 360	98.19 142	99.67 91	99.85 61	82.98 406	99.92 106	99.49 49	98.32 241	99.60 156

ACMMPR		99.49 26	99.36 39	99.86 27	99.87 15	99.79 34	99.66 75	99.67 23	98.15 147	99.67 91	99.69 176	98.95 30	99.96 34	98.69 150	99.87 63	99.84 45

PVSNet_BlendedMVS		98.86 143	98.80 137	99.03 197	99.76 69	98.79 194	99.28 274	99.91 3	97.42 245	99.67 91	99.37 296	97.53 118	99.88 147	98.98 103	97.29 302	98.42 362

PVSNet_Blended		99.08 116	98.97 110	99.42 141	99.76 69	98.79 194	98.78 375	99.91 3	96.74 300	99.67 91	99.49 259	97.53 118	99.88 147	98.98 103	99.85 78	99.60 156

sss		99.17 90	99.05 92	99.53 116	99.62 145	98.97 165	99.36 247	99.62 43	97.83 193	99.67 91	99.65 196	97.37 124	99.95 65	99.19 80	99.19 178	99.68 127

ECVR-MVS		98.04 223	98.05 208	98.00 325	99.74 87	94.37 384	99.59 109	94.98 423	99.13 28	99.66 96	99.93 10	90.67 344	99.84 168	99.40 56	99.38 162	99.80 76

h-mvs33		97.70 285	97.28 307	98.97 205	99.70 108	97.27 286	99.36 247	99.45 206	98.94 62	99.66 96	99.64 202	94.93 215	99.99 4	99.48 50	84.36 414	99.65 137

hse-mvs2		97.50 305	97.14 314	98.59 260	99.49 194	97.05 301	99.28 274	99.22 306	98.94 62	99.66 96	99.42 279	94.93 215	99.65 258	99.48 50	83.80 416	99.08 250

MVS_0304		99.15 94	98.96 114	99.73 71	98.92 334	99.37 109	99.37 242	96.92 412	99.51 2	99.66 96	99.78 131	96.69 150	99.97 22	99.84 19	99.97 7	99.84 45

region2R		99.48 30	99.35 41	99.87 16	99.88 11	99.80 31	99.65 81	99.66 28	98.13 152	99.66 96	99.68 183	98.96 25	99.96 34	98.62 159	99.87 63	99.84 45

RPSCF		98.22 198	98.62 161	96.99 367	99.82 43	91.58 406	99.72 52	99.44 214	96.61 312	99.66 96	99.89 35	95.92 180	99.82 189	97.46 279	99.10 188	99.57 167

OMC-MVS		99.08 116	99.04 94	99.20 179	99.67 118	98.22 239	99.28 274	99.52 109	98.07 163	99.66 96	99.81 99	97.79 113	99.78 209	97.79 243	99.81 102	99.60 156

test1111		98.04 223	98.11 199	97.83 339	99.74 87	93.82 389	99.58 117	95.40 422	99.12 33	99.65 103	99.93 10	90.73 343	99.84 168	99.43 55	99.38 162	99.82 60

test_one_0601					99.81 47	99.88 8		99.49 153	98.97 59	99.65 103	99.81 99	99.09 14

LFMVS		97.90 246	97.35 295	99.54 108	99.52 178	99.01 160	99.39 235	98.24 396	97.10 275	99.65 103	99.79 124	84.79 399	99.91 118	99.28 72	98.38 234	99.69 123

mvsmamba		99.06 119	98.96 114	99.36 149	99.47 202	98.64 206	99.70 56	99.05 330	97.61 219	99.65 103	99.83 76	96.54 156	99.92 106	99.19 80	99.62 145	99.51 187

MVS_111021_LR		99.41 52	99.33 45	99.65 81	99.77 65	99.51 94	98.94 359	99.85 6	98.82 73	99.65 103	99.74 151	98.51 81	99.80 201	98.83 133	99.89 57	99.64 144

SDMVSNet		99.11 110	98.90 122	99.75 65	99.81 47	99.59 77	99.81 20	99.65 35	98.78 81	99.64 108	99.88 43	94.56 242	99.93 94	99.67 27	98.26 243	99.72 110

sd_testset		98.75 161	98.57 168	99.29 166	99.81 47	98.26 237	99.56 130	99.62 43	98.78 81	99.64 108	99.88 43	92.02 317	99.88 147	99.54 39	98.26 243	99.72 110

9.14			99.10 85		99.72 98		99.40 231	99.51 123	97.53 230	99.64 108	99.78 131	98.84 44	99.91 118	97.63 260	99.82 99

GST-MVS		99.40 55	99.24 69	99.85 34	99.86 20	99.79 34	99.60 102	99.67 23	97.97 177	99.63 111	99.68 183	98.52 80	99.95 65	98.38 191	99.86 71	99.81 67

CPTT-MVS		99.11 110	98.90 122	99.74 68	99.80 53	99.46 101	99.59 109	99.49 153	97.03 283	99.63 111	99.69 176	97.27 129	99.96 34	97.82 241	99.84 86	99.81 67

ACMMP		99.45 39	99.32 47	99.82 47	99.89 8	99.67 58	99.62 95	99.69 18	98.12 153	99.63 111	99.84 71	98.73 63	99.96 34	98.55 177	99.83 95	99.81 67
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
DeepC-MVS	98.35 2	99.30 70	99.19 76	99.64 87	99.82 43	99.23 131	99.62 95	99.55 82	98.94 62	99.63 111	99.95 3	95.82 185	99.94 76	99.37 58	99.97 7	99.73 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FE-MVS		98.48 176	98.17 191	99.40 143	99.54 171	98.96 169	99.68 66	98.81 365	95.54 362	99.62 115	99.70 166	93.82 273	99.93 94	97.35 287	99.46 157	99.32 228

CHOSEN 280x420		99.12 105	99.13 81	99.08 190	99.66 128	97.89 259	98.43 400	99.71 13	98.88 67	99.62 115	99.76 143	96.63 152	99.70 242	99.46 53	99.99 1	99.66 133

PHI-MVS		99.30 70	99.17 78	99.70 74	99.56 164	99.52 93	99.58 117	99.80 8	97.12 271	99.62 115	99.73 157	98.58 75	99.90 130	98.61 162	99.91 37	99.68 127

test_yl		98.86 143	98.63 156	99.54 108	99.49 194	99.18 135	99.50 175	99.07 327	98.22 138	99.61 118	99.51 253	95.37 199	99.84 168	98.60 165	98.33 237	99.59 160

DCV-MVSNet		98.86 143	98.63 156	99.54 108	99.49 194	99.18 135	99.50 175	99.07 327	98.22 138	99.61 118	99.51 253	95.37 199	99.84 168	98.60 165	98.33 237	99.59 160

MG-MVS		99.13 99	99.02 100	99.45 136	99.57 160	98.63 207	99.07 326	99.34 263	98.99 53	99.61 118	99.82 85	97.98 109	99.87 152	97.00 306	99.80 106	99.85 39

MP-MVS-pluss		99.37 59	99.20 74	99.88 10	99.90 4	99.87 15	99.30 264	99.52 109	97.18 265	99.60 121	99.79 124	98.79 50	99.95 65	98.83 133	99.91 37	99.83 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS		99.13 99	98.91 121	99.80 53	99.75 79	99.71 50	99.15 310	99.41 225	96.60 315	99.60 121	99.55 237	98.83 45	99.90 130	97.48 276	99.83 95	99.78 86

EPP-MVSNet		99.13 99	98.99 106	99.53 116	99.65 134	99.06 154	99.81 20	99.33 270	97.43 243	99.60 121	99.88 43	97.14 132	99.84 168	99.13 86	98.94 199	99.69 123

HyFIR lowres test		99.11 110	98.92 119	99.65 81	99.90 4	99.37 109	99.02 339	99.91 3	97.67 214	99.59 124	99.75 146	95.90 182	99.73 226	99.53 41	99.02 196	99.86 35

FA-MVS(test-final)		98.75 161	98.53 172	99.41 142	99.55 168	99.05 156	99.80 25	99.01 335	96.59 317	99.58 125	99.59 222	95.39 198	99.90 130	97.78 244	99.49 156	99.28 231

MVS_Test		99.10 114	98.97 110	99.48 130	99.49 194	99.14 143	99.67 69	99.34 263	97.31 254	99.58 125	99.76 143	97.65 117	99.82 189	98.87 120	99.07 191	99.46 204

MDTV_nov1_ep13_2view						95.18 370	99.35 252		96.84 296	99.58 125		95.19 208		97.82 241		99.46 204

DELS-MVS		99.48 30	99.42 26	99.65 81	99.72 98	99.40 108	99.05 331	99.66 28	99.14 27	99.57 128	99.80 112	98.46 84	99.94 76	99.57 36	99.84 86	99.60 156
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
ZD-MVS					99.71 103	99.79 34		99.61 50	96.84 296	99.56 129	99.54 242	98.58 75	99.96 34	96.93 313	99.75 123

CR-MVSNet		98.17 205	97.93 222	98.87 229	99.18 283	98.49 224	99.22 299	99.33 270	96.96 287	99.56 129	99.38 293	94.33 252	99.00 363	94.83 367	98.58 222	99.14 242

RPMNet		96.72 335	95.90 348	99.19 180	99.18 283	98.49 224	99.22 299	99.52 109	88.72 414	99.56 129	97.38 408	94.08 262	99.95 65	86.87 416	98.58 222	99.14 242

IS-MVSNet		99.05 121	98.87 128	99.57 103	99.73 94	99.32 115	99.75 42	99.20 310	98.02 174	99.56 129	99.86 56	96.54 156	99.67 250	98.09 215	99.13 184	99.73 103

ZNCC-MVS		99.47 33	99.33 45	99.87 16	99.87 15	99.81 29	99.64 84	99.67 23	98.08 162	99.55 133	99.64 202	98.91 37	99.96 34	98.72 145	99.90 46	99.82 60

thisisatest0515		98.14 208	97.79 234	99.19 180	99.50 192	98.50 223	98.61 390	96.82 414	96.95 289	99.54 134	99.43 277	91.66 329	99.86 155	98.08 219	99.51 154	99.22 239

MVS_111021_HR		99.41 52	99.32 47	99.66 77	99.72 98	99.47 100	98.95 357	99.85 6	98.82 73	99.54 134	99.73 157	98.51 81	99.74 220	98.91 114	99.88 60	99.77 88

CP-MVS		99.45 39	99.32 47	99.85 34	99.83 40	99.75 44	99.69 60	99.52 109	98.07 163	99.53 136	99.63 208	98.93 36	99.97 22	98.74 142	99.91 37	99.83 55

WTY-MVS		99.06 119	98.88 127	99.61 95	99.62 145	99.16 138	99.37 242	99.56 74	98.04 170	99.53 136	99.62 213	96.84 144	99.94 76	98.85 127	98.49 230	99.72 110

MCST-MVS		99.43 46	99.30 55	99.82 47	99.79 56	99.74 47	99.29 269	99.40 231	98.79 78	99.52 138	99.62 213	98.91 37	99.90 130	98.64 156	99.75 123	99.82 60

PatchT		97.03 329	96.44 335	98.79 244	98.99 324	98.34 234	99.16 307	99.07 327	92.13 401	99.52 138	97.31 411	94.54 245	98.98 365	88.54 409	98.73 215	99.03 258

CANet		99.25 82	99.14 80	99.59 98	99.41 219	99.16 138	99.35 252	99.57 69	98.82 73	99.51 140	99.61 217	96.46 160	99.95 65	99.59 33	99.98 4	99.65 137

mPP-MVS		99.44 43	99.30 55	99.86 27	99.88 11	99.79 34	99.69 60	99.48 165	98.12 153	99.50 141	99.75 146	98.78 51	99.97 22	98.57 171	99.89 57	99.83 55

PatchMatch-RL		98.84 153	98.62 161	99.52 122	99.71 103	99.28 124	99.06 329	99.77 9	97.74 205	99.50 141	99.53 246	95.41 197	99.84 168	97.17 300	99.64 142	99.44 209

PVSNet	96.02 17	98.85 150	98.84 134	98.89 223	99.73 94	97.28 285	98.32 406	99.60 56	97.86 187	99.50 141	99.57 231	96.75 148	99.86 155	98.56 174	99.70 133	99.54 172

LS3D		99.27 76	99.12 83	99.74 68	99.18 283	99.75 44	99.56 130	99.57 69	98.45 109	99.49 144	99.85 61	97.77 114	99.94 76	98.33 198	99.84 86	99.52 179

MP-MVS		99.33 65	99.15 79	99.87 16	99.88 11	99.82 25	99.66 75	99.46 195	98.09 158	99.48 145	99.74 151	98.29 95	99.96 34	97.93 230	99.87 63	99.82 60
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
旧先验2							98.96 354		96.70 303	99.47 146			99.94 76	98.19 208

MSDG		98.98 131	98.80 137	99.53 116	99.76 69	99.19 133	98.75 378	99.55 82	97.25 259	99.47 146	99.77 139	97.82 112	99.87 152	96.93 313	99.90 46	99.54 172

CDS-MVSNet		99.09 115	99.03 96	99.25 173	99.42 214	98.73 198	99.45 202	99.46 195	98.11 155	99.46 148	99.77 139	98.01 108	99.37 301	98.70 147	98.92 202	99.66 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSLP-MVS++		99.46 35	99.47 21	99.44 140	99.60 154	99.16 138	99.41 223	99.71 13	98.98 56	99.45 149	99.78 131	99.19 9	99.54 276	99.28 72	99.84 86	99.63 149

XVG-OURS		98.73 164	98.68 150	98.88 225	99.70 108	97.73 266	98.92 361	99.55 82	98.52 102	99.45 149	99.84 71	95.27 203	99.91 118	98.08 219	98.84 208	99.00 261

casdiffmvs_mvg		99.15 94	99.02 100	99.55 107	99.66 128	99.09 148	99.64 84	99.56 74	98.26 132	99.45 149	99.87 52	96.03 174	99.81 194	99.54 39	99.15 182	99.73 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		98.33 191	98.48 174	97.90 333	99.16 293	94.78 376	99.31 262	99.11 320	97.27 257	99.45 149	99.59 222	95.33 201	99.84 168	98.48 181	98.61 219	99.09 249

TAMVS		99.12 105	99.08 89	99.24 175	99.46 204	98.55 214	99.51 168	99.46 195	98.09 158	99.45 149	99.82 85	98.34 93	99.51 278	98.70 147	98.93 200	99.67 130

MonoMVSNet		98.38 187	98.47 175	98.12 317	98.59 379	96.19 345	99.72 52	98.79 368	97.89 184	99.44 154	99.52 249	96.13 170	98.90 379	98.64 156	97.54 283	99.28 231

ETV-MVS		99.26 78	99.21 73	99.40 143	99.46 204	99.30 121	99.56 130	99.52 109	98.52 102	99.44 154	99.27 324	98.41 90	99.86 155	99.10 91	99.59 148	99.04 257

CANet_DTU		98.97 133	98.87 128	99.25 173	99.33 241	98.42 232	99.08 325	99.30 288	99.16 24	99.43 156	99.75 146	95.27 203	99.97 22	98.56 174	99.95 18	99.36 222

SCA		98.19 202	98.16 192	98.27 306	99.30 250	95.55 356	99.07 326	98.97 339	97.57 223	99.43 156	99.57 231	92.72 296	99.74 220	97.58 264	99.20 177	99.52 179

testdata				99.54 108	99.75 79	98.95 172		99.51 123	97.07 277	99.43 156	99.70 166	98.87 40	99.94 76	97.76 248	99.64 142	99.72 110

DPM-MVS		98.95 134	98.71 147	99.66 77	99.63 139	99.55 85	98.64 389	99.10 321	97.93 180	99.42 159	99.55 237	98.67 69	99.80 201	95.80 345	99.68 137	99.61 153

XVG-OURS-SEG-HR		98.69 166	98.62 161	98.89 223	99.71 103	97.74 265	99.12 316	99.54 91	98.44 112	99.42 159	99.71 162	94.20 256	99.92 106	98.54 178	98.90 204	99.00 261

baseline		99.15 94	99.02 100	99.53 116	99.66 128	99.14 143	99.72 52	99.48 165	98.35 121	99.42 159	99.84 71	96.07 172	99.79 204	99.51 44	99.14 183	99.67 130

DP-MVS Recon		99.12 105	98.95 116	99.65 81	99.74 87	99.70 52	99.27 279	99.57 69	96.40 331	99.42 159	99.68 183	98.75 58	99.80 201	97.98 227	99.72 129	99.44 209

Effi-MVS+-dtu		98.78 158	98.89 125	98.47 280	99.33 241	96.91 314	99.57 124	99.30 288	98.47 106	99.41 163	98.99 356	96.78 146	99.74 220	98.73 144	99.38 162	98.74 287

casdiffmvs		99.13 99	98.98 109	99.56 105	99.65 134	99.16 138	99.56 130	99.50 143	98.33 124	99.41 163	99.86 56	95.92 180	99.83 181	99.45 54	99.16 179	99.70 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MIMVSNet		97.73 279	97.45 278	98.57 264	99.45 210	97.50 278	99.02 339	98.98 338	96.11 351	99.41 163	99.14 339	90.28 346	98.74 385	95.74 346	98.93 200	99.47 199

CSCG		99.32 67	99.32 47	99.32 157	99.85 26	98.29 235	99.71 55	99.66 28	98.11 155	99.41 163	99.80 112	98.37 92	99.96 34	98.99 102	99.96 13	99.72 110

F-COLMAP		99.19 86	99.04 94	99.64 87	99.78 58	99.27 126	99.42 220	99.54 91	97.29 256	99.41 163	99.59 222	98.42 88	99.93 94	98.19 208	99.69 134	99.73 103

EIA-MVS		99.18 88	99.09 88	99.45 136	99.49 194	99.18 135	99.67 69	99.53 104	97.66 215	99.40 168	99.44 275	98.10 103	99.81 194	98.94 108	99.62 145	99.35 223

MDTV_nov1_ep13			98.32 184		99.11 301	94.44 382	99.27 279	98.74 374	97.51 233	99.40 168	99.62 213	94.78 225	99.76 215	97.59 263	98.81 212

CVMVSNet		98.57 174	98.67 151	98.30 300	99.35 236	95.59 355	99.50 175	99.55 82	98.60 95	99.39 170	99.83 76	94.48 247	99.45 284	98.75 141	98.56 225	99.85 39

CNVR-MVS		99.42 48	99.30 55	99.78 59	99.62 145	99.71 50	99.26 288	99.52 109	98.82 73	99.39 170	99.71 162	98.96 25	99.85 161	98.59 167	99.80 106	99.77 88

Effi-MVS+		98.81 154	98.59 167	99.48 130	99.46 204	99.12 146	98.08 413	99.50 143	97.50 234	99.38 172	99.41 283	96.37 164	99.81 194	99.11 88	98.54 227	99.51 187

mvs_anonymous		99.03 124	98.99 106	99.16 183	99.38 229	98.52 220	99.51 168	99.38 242	97.79 198	99.38 172	99.81 99	97.30 127	99.45 284	99.35 59	98.99 197	99.51 187

XVS		99.53 20	99.42 26	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	99.74 151	98.81 47	99.94 76	98.79 138	99.86 71	99.84 45

X-MVStestdata		96.55 338	95.45 357	99.87 16	99.85 26	99.83 19	99.69 60	99.68 20	98.98 56	99.37 174	64.01 431	98.81 47	99.94 76	98.79 138	99.86 71	99.84 45

PatchmatchNet		98.31 192	98.36 180	98.19 310	99.16 293	95.32 366	99.27 279	98.92 346	97.37 249	99.37 174	99.58 226	94.90 218	99.70 242	97.43 282	99.21 176	99.54 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AllTest		98.87 140	98.72 145	99.31 158	99.86 20	98.48 226	99.56 130	99.61 50	97.85 190	99.36 177	99.85 61	95.95 177	99.85 161	96.66 326	99.83 95	99.59 160

TestCases				99.31 158	99.86 20	98.48 226		99.61 50	97.85 190	99.36 177	99.85 61	95.95 177	99.85 161	96.66 326	99.83 95	99.59 160

Vis-MVSNet (Re-imp)		98.87 140	98.72 145	99.31 158	99.71 103	98.88 181	99.80 25	99.44 214	97.91 182	99.36 177	99.78 131	95.49 196	99.43 293	97.91 231	99.11 185	99.62 151

alignmvs		98.81 154	98.56 170	99.58 101	99.43 212	99.42 105	99.51 168	98.96 341	98.61 94	99.35 180	98.92 366	94.78 225	99.77 211	99.35 59	98.11 256	99.54 172

VPA-MVSNet		98.29 195	97.95 219	99.30 163	99.16 293	99.54 87	99.50 175	99.58 65	98.27 130	99.35 180	99.37 296	92.53 305	99.65 258	99.35 59	94.46 365	98.72 289

AdaColmap		99.01 129	98.80 137	99.66 77	99.56 164	99.54 87	99.18 305	99.70 15	98.18 145	99.35 180	99.63 208	96.32 165	99.90 130	97.48 276	99.77 118	99.55 170

test222					99.75 79	99.49 96	98.91 363	99.49 153	96.42 329	99.34 183	99.65 196	98.28 96			99.69 134	99.72 110

API-MVS		99.04 122	99.03 96	99.06 193	99.40 224	99.31 119	99.55 144	99.56 74	98.54 100	99.33 184	99.39 291	98.76 55	99.78 209	96.98 308	99.78 115	98.07 384

v144192		97.92 243	97.60 261	98.87 229	98.83 348	98.65 204	99.55 144	99.34 263	96.20 342	99.32 185	99.40 287	94.36 251	99.26 322	96.37 335	95.03 356	98.70 296

sasdasda		99.02 125	98.86 130	99.51 124	99.42 214	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 371	97.09 134	99.75 218	99.27 74	97.90 262	99.47 199

GeoE		98.85 150	98.62 161	99.53 116	99.61 149	99.08 151	99.80 25	99.51 123	97.10 275	99.31 186	99.78 131	95.23 207	99.77 211	98.21 206	99.03 194	99.75 94

canonicalmvs		99.02 125	98.86 130	99.51 124	99.42 214	99.32 115	99.80 25	99.48 165	98.63 91	99.31 186	98.81 371	97.09 134	99.75 218	99.27 74	97.90 262	99.47 199

V42		98.06 217	97.79 234	98.86 232	98.98 327	98.84 187	99.69 60	99.34 263	96.53 319	99.30 189	99.37 296	94.67 236	99.32 313	97.57 268	94.66 362	98.42 362

ab-mvs		98.86 143	98.63 156	99.54 108	99.64 136	99.19 133	99.44 208	99.54 91	97.77 201	99.30 189	99.81 99	94.20 256	99.93 94	99.17 84	98.82 210	99.49 192

TAPA-MVS	97.07 15	97.74 277	97.34 298	98.94 210	99.70 108	97.53 276	99.25 290	99.51 123	91.90 402	99.30 189	99.63 208	98.78 51	99.64 261	88.09 411	99.87 63	99.65 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
新几何1				99.75 65	99.75 79	99.59 77		99.54 91	96.76 299	99.29 192	99.64 202	98.43 86	99.94 76	96.92 315	99.66 139	99.72 110

MGCFI-Net		99.01 129	98.85 132	99.50 129	99.42 214	99.26 127	99.82 16	99.48 165	98.60 95	99.28 193	98.81 371	97.04 138	99.76 215	99.29 71	97.87 265	99.47 199

test_fmvs2		97.25 321	97.30 304	97.09 366	99.43 212	93.31 397	99.73 50	98.87 358	98.83 72	99.28 193	99.80 112	84.45 401	99.66 253	97.88 233	97.45 293	98.30 370

VPNet		97.84 257	97.44 283	99.01 199	99.21 275	98.94 175	99.48 190	99.57 69	98.38 116	99.28 193	99.73 157	88.89 363	99.39 296	99.19 80	93.27 384	98.71 291

HY-MVS	97.30 7	98.85 150	98.64 155	99.47 133	99.42 214	99.08 151	99.62 95	99.36 251	97.39 248	99.28 193	99.68 183	96.44 162	99.92 106	98.37 193	98.22 246	99.40 216

PAPM_NR		99.04 122	98.84 134	99.66 77	99.74 87	99.44 103	99.39 235	99.38 242	97.70 210	99.28 193	99.28 321	98.34 93	99.85 161	96.96 310	99.45 158	99.69 123

testing3-2		97.84 257	97.70 249	98.24 307	99.53 172	95.37 365	99.55 144	98.67 384	98.46 107	99.27 198	99.34 306	86.58 387	99.83 181	99.32 67	98.63 218	99.52 179

HPM-MVS++		99.39 57	99.23 71	99.87 16	99.75 79	99.84 18	99.43 213	99.51 123	98.68 90	99.27 198	99.53 246	98.64 72	99.96 34	98.44 187	99.80 106	99.79 80

v1240		97.69 286	97.32 302	98.79 244	98.85 345	98.43 230	99.48 190	99.36 251	96.11 351	99.27 198	99.36 299	93.76 276	99.24 325	94.46 370	95.23 351	98.70 296

thres600view7		97.86 252	97.51 269	98.92 214	99.72 98	97.95 256	99.59 109	98.74 374	97.94 179	99.27 198	98.62 379	91.75 323	99.86 155	93.73 380	98.19 250	98.96 267

PLC	97.94 4	99.02 125	98.85 132	99.53 116	99.66 128	99.01 160	99.24 292	99.52 109	96.85 295	99.27 198	99.48 265	98.25 97	99.91 118	97.76 248	99.62 145	99.65 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thres100view900		97.76 271	97.45 278	98.69 254	99.72 98	97.86 262	99.59 109	98.74 374	97.93 180	99.26 203	98.62 379	91.75 323	99.83 181	93.22 385	98.18 251	98.37 368

EPMVS		97.82 263	97.65 254	98.35 295	98.88 338	95.98 348	99.49 186	94.71 425	97.57 223	99.26 203	99.48 265	92.46 310	99.71 236	97.87 235	99.08 190	99.35 223

Fast-Effi-MVS+-dtu		98.77 160	98.83 136	98.60 259	99.41 219	96.99 308	99.52 159	99.49 153	98.11 155	99.24 205	99.34 306	96.96 142	99.79 204	97.95 229	99.45 158	99.02 260

v1921920		97.80 267	97.45 278	98.84 236	98.80 349	98.53 216	99.52 159	99.34 263	96.15 348	99.24 205	99.47 268	93.98 266	99.29 317	95.40 356	95.13 354	98.69 300

LPG-MVS_test		98.22 198	98.13 197	98.49 273	99.33 241	97.05 301	99.58 117	99.55 82	97.46 236	99.24 205	99.83 76	92.58 303	99.72 230	98.09 215	97.51 286	98.68 305

LGP-MVS_train				98.49 273	99.33 241	97.05 301		99.55 82	97.46 236	99.24 205	99.83 76	92.58 303	99.72 230	98.09 215	97.51 286	98.68 305

v1144		97.98 234	97.69 250	98.85 235	98.87 341	98.66 203	99.54 149	99.35 258	96.27 337	99.23 209	99.35 302	94.67 236	99.23 326	96.73 321	95.16 353	98.68 305

Anonymous20240529		98.09 213	97.68 251	99.34 151	99.66 128	98.44 229	99.40 231	99.43 220	93.67 388	99.22 210	99.89 35	90.23 350	99.93 94	99.26 76	98.33 237	99.66 133

OPM-MVS		98.19 202	98.10 200	98.45 283	98.88 338	97.07 299	99.28 274	99.38 242	98.57 97	99.22 210	99.81 99	92.12 315	99.66 253	98.08 219	97.54 283	98.61 342
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_djsdf		98.67 168	98.57 168	98.98 203	98.70 367	98.91 179	99.88 4	99.46 195	97.55 226	99.22 210	99.88 43	95.73 188	99.28 318	99.03 98	97.62 276	98.75 284

test12				99.75 65	99.64 136	99.61 74		99.29 292		99.21 213		98.38 91	99.89 142		99.74 126	99.74 98

NCCC		99.34 64	99.19 76	99.79 56	99.61 149	99.65 64	99.30 264	99.48 165	98.86 68	99.21 213	99.63 208	98.72 64	99.90 130	98.25 204	99.63 144	99.80 76

PMMVS		98.80 157	98.62 161	99.34 151	99.27 259	98.70 200	98.76 377	99.31 284	97.34 251	99.21 213	99.07 345	97.20 131	99.82 189	98.56 174	98.87 205	99.52 179

v1192		97.81 265	97.44 283	98.91 218	98.88 338	98.68 201	99.51 168	99.34 263	96.18 344	99.20 216	99.34 306	94.03 264	99.36 305	95.32 358	95.18 352	98.69 300

EI-MVSNet		98.67 168	98.67 151	98.68 255	99.35 236	97.97 252	99.50 175	99.38 242	96.93 292	99.20 216	99.83 76	97.87 110	99.36 305	98.38 191	97.56 281	98.71 291

MVSTER		98.49 175	98.32 184	99.00 201	99.35 236	99.02 158	99.54 149	99.38 242	97.41 246	99.20 216	99.73 157	93.86 272	99.36 305	98.87 120	97.56 281	98.62 333

UWE-MVS		97.58 299	97.29 306	98.48 275	99.09 307	96.25 342	99.01 344	96.61 418	97.86 187	99.19 219	99.01 353	88.72 365	99.90 130	97.38 285	98.69 216	99.28 231

Anonymous202405211		98.30 194	97.98 215	99.26 172	99.57 160	98.16 241	99.41 223	98.55 389	96.03 356	99.19 219	99.74 151	91.87 320	99.92 106	99.16 85	98.29 242	99.70 121

v2v482		98.06 217	97.77 239	98.92 214	98.90 336	98.82 191	99.57 124	99.36 251	96.65 307	99.19 219	99.35 302	94.20 256	99.25 323	97.72 254	94.97 357	98.69 300

CNLPA		99.14 97	98.99 106	99.59 98	99.58 158	99.41 107	99.16 307	99.44 214	98.45 109	99.19 219	99.49 259	98.08 105	99.89 142	97.73 252	99.75 123	99.48 193

UGNet		98.87 140	98.69 149	99.40 143	99.22 274	98.72 199	99.44 208	99.68 20	99.24 21	99.18 223	99.42 279	92.74 295	99.96 34	99.34 64	99.94 25	99.53 178
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
tfpn200view9		97.72 281	97.38 291	98.72 250	99.69 112	97.96 254	99.50 175	98.73 380	97.83 193	99.17 224	98.45 386	91.67 327	99.83 181	93.22 385	98.18 251	98.37 368

thres400		97.77 270	97.38 291	98.92 214	99.69 112	97.96 254	99.50 175	98.73 380	97.83 193	99.17 224	98.45 386	91.67 327	99.83 181	93.22 385	98.18 251	98.96 267

Test_1112_low_res		98.89 138	98.66 154	99.57 103	99.69 112	98.95 172	99.03 336	99.47 186	96.98 285	99.15 226	99.23 329	96.77 147	99.89 142	98.83 133	98.78 213	99.86 35

baseline1		98.31 192	97.95 219	99.38 148	99.50 192	98.74 197	99.59 109	98.93 343	98.41 114	99.14 227	99.60 220	94.59 240	99.79 204	98.48 181	93.29 383	99.61 153

1112_ss		98.98 131	98.77 141	99.59 98	99.68 116	99.02 158	99.25 290	99.48 165	97.23 262	99.13 228	99.58 226	96.93 143	99.90 130	98.87 120	98.78 213	99.84 45

CLD-MVS		98.16 206	98.10 200	98.33 296	99.29 254	96.82 319	98.75 378	99.44 214	97.83 193	99.13 228	99.55 237	92.92 289	99.67 250	98.32 200	97.69 272	98.48 354
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
原ACMM1				99.65 81	99.73 94	99.33 114		99.47 186	97.46 236	99.12 230	99.66 194	98.67 69	99.91 118	97.70 257	99.69 134	99.71 119

tpm		97.67 292	97.55 263	98.03 320	99.02 319	95.01 372	99.43 213	98.54 390	96.44 327	99.12 230	99.34 306	91.83 322	99.60 270	97.75 250	96.46 318	99.48 193

HQP_MVS		98.27 197	98.22 190	98.44 286	99.29 254	96.97 310	99.39 235	99.47 186	98.97 59	99.11 232	99.61 217	92.71 298	99.69 247	97.78 244	97.63 274	98.67 312

plane_prior3						97.00 307			98.69 88	99.11 232

CHOSEN 1792x2688		99.19 86	99.10 85	99.45 136	99.89 8	98.52 220	99.39 235	99.94 1	98.73 85	99.11 232	99.89 35	95.50 195	99.94 76	99.50 45	99.97 7	99.89 22

v8		97.95 239	97.63 258	98.93 212	98.95 331	98.81 193	99.80 25	99.41 225	96.03 356	99.10 235	99.42 279	94.92 217	99.30 316	96.94 312	94.08 374	98.66 320

ADS-MVSNet2		98.02 227	98.07 207	97.87 335	99.33 241	95.19 369	99.23 295	99.08 324	96.24 339	99.10 235	99.67 189	94.11 260	98.93 376	96.81 318	99.05 192	99.48 193

ADS-MVSNet		98.20 201	98.08 204	98.56 267	99.33 241	96.48 333	99.23 295	99.15 316	96.24 339	99.10 235	99.67 189	94.11 260	99.71 236	96.81 318	99.05 192	99.48 193

thres200		97.61 297	97.28 307	98.62 258	99.64 136	98.03 248	99.26 288	98.74 374	97.68 212	99.09 238	98.32 392	91.66 329	99.81 194	92.88 390	98.22 246	98.03 387

dp		97.75 275	97.80 233	97.59 353	99.10 304	93.71 392	99.32 259	98.88 356	96.48 324	99.08 239	99.55 237	92.67 301	99.82 189	96.52 330	98.58 222	99.24 237

WB-MVSnew		97.65 294	97.65 254	97.63 350	98.78 353	97.62 274	99.13 313	98.33 393	97.36 250	99.07 240	98.94 362	95.64 192	99.15 340	92.95 389	98.68 217	96.12 416

GBi-Net		97.68 289	97.48 272	98.29 301	99.51 181	97.26 288	99.43 213	99.48 165	96.49 321	99.07 240	99.32 314	90.26 347	98.98 365	97.10 301	96.65 313	98.62 333

test1		97.68 289	97.48 272	98.29 301	99.51 181	97.26 288	99.43 213	99.48 165	96.49 321	99.07 240	99.32 314	90.26 347	98.98 365	97.10 301	96.65 313	98.62 333

FMVSNet3		98.03 225	97.76 243	98.84 236	99.39 227	98.98 162	99.40 231	99.38 242	96.67 305	99.07 240	99.28 321	92.93 288	98.98 365	97.10 301	96.65 313	98.56 349

IterMVS-LS		98.46 178	98.42 177	98.58 263	99.59 156	98.00 250	99.37 242	99.43 220	96.94 291	99.07 240	99.59 222	97.87 110	99.03 358	98.32 200	95.62 342	98.71 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		98.08 215	98.16 192	97.85 336	99.55 168	94.67 379	99.70 56	98.92 346	98.15 147	99.06 245	99.35 302	93.67 278	99.25 323	97.77 247	97.25 303	99.64 144

pmmvs4		98.13 209	97.90 224	98.81 241	98.61 376	98.87 182	98.99 347	99.21 309	96.44 327	99.06 245	99.58 226	95.90 182	99.11 349	97.18 299	96.11 327	98.46 359

XVG-ACMP-BASELINE		97.83 260	97.71 248	98.20 309	99.11 301	96.33 338	99.41 223	99.52 109	98.06 167	99.05 247	99.50 256	89.64 357	99.73 226	97.73 252	97.38 300	98.53 350

CostFormer		97.72 281	97.73 246	97.71 347	99.15 297	94.02 388	99.54 149	99.02 334	94.67 379	99.04 248	99.35 302	92.35 313	99.77 211	98.50 180	97.94 261	99.34 226

DP-MVS		99.16 92	98.95 116	99.78 59	99.77 65	99.53 90	99.41 223	99.50 143	97.03 283	99.04 248	99.88 43	97.39 121	99.92 106	98.66 154	99.90 46	99.87 33

ACMM	97.58 5	98.37 189	98.34 182	98.48 275	99.41 219	97.10 295	99.56 130	99.45 206	98.53 101	99.04 248	99.85 61	93.00 287	99.71 236	98.74 142	97.45 293	98.64 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		98.70 165	98.43 176	99.51 124	99.51 181	99.28 124	99.52 159	99.47 186	96.11 351	99.01 251	99.34 306	96.20 169	99.84 168	97.88 233	98.82 210	99.39 217

nrg030		98.64 171	98.42 177	99.28 170	99.05 316	99.69 54	99.81 20	99.46 195	98.04 170	99.01 251	99.82 85	96.69 150	99.38 298	99.34 64	94.59 364	98.78 276

test_prior2							98.96 354		98.34 122	99.01 251	99.52 249	98.68 67		97.96 228	99.74 126

MAR-MVS		98.86 143	98.63 156	99.54 108	99.37 232	99.66 60	99.45 202	99.54 91	96.61 312	99.01 251	99.40 287	97.09 134	99.86 155	97.68 259	99.53 153	99.10 245
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
UWE-MVS-28		97.36 315	97.24 311	97.75 344	98.84 347	94.44 382	99.24 292	97.58 408	97.98 176	99.00 255	99.00 354	91.35 335	99.53 277	93.75 379	98.39 233	99.27 235

PS-MVSNAJss		98.92 136	98.92 119	98.90 220	98.78 353	98.53 216	99.78 32	99.54 91	98.07 163	99.00 255	99.76 143	99.01 18	99.37 301	99.13 86	97.23 304	98.81 274

PAPR		98.63 172	98.34 182	99.51 124	99.40 224	99.03 157	98.80 373	99.36 251	96.33 332	99.00 255	99.12 343	98.46 84	99.84 168	95.23 360	99.37 169	99.66 133

D2MVS		98.41 183	98.50 173	98.15 315	99.26 262	96.62 328	99.40 231	99.61 50	97.71 207	98.98 258	99.36 299	96.04 173	99.67 250	98.70 147	97.41 298	98.15 380

v10		97.85 253	97.52 267	98.86 232	98.99 324	98.67 202	99.75 42	99.41 225	95.70 360	98.98 258	99.41 283	94.75 230	99.23 326	96.01 341	94.63 363	98.67 312

miper_enhance_ethall		98.16 206	98.08 204	98.41 289	98.96 330	97.72 268	98.45 399	99.32 280	96.95 289	98.97 260	99.17 335	97.06 137	99.22 330	97.86 236	95.99 331	98.29 371

UniMVSNet (Re)		98.29 195	98.00 213	99.13 188	99.00 321	99.36 112	99.49 186	99.51 123	97.95 178	98.97 260	99.13 340	96.30 166	99.38 298	98.36 195	93.34 382	98.66 320

WBMVS		97.74 277	97.50 270	98.46 281	99.24 268	97.43 280	99.21 301	99.42 222	97.45 239	98.96 262	99.41 283	88.83 364	99.23 326	98.94 108	96.02 328	98.71 291

TEST9					99.67 118	99.65 64	99.05 331	99.41 225	96.22 341	98.95 263	99.49 259	98.77 54	99.91 118

train_agg		99.02 125	98.77 141	99.77 62	99.67 118	99.65 64	99.05 331	99.41 225	96.28 335	98.95 263	99.49 259	98.76 55	99.91 118	97.63 260	99.72 129	99.75 94

BH-RMVSNet		98.41 183	98.08 204	99.40 143	99.41 219	98.83 190	99.30 264	98.77 370	97.70 210	98.94 265	99.65 196	92.91 291	99.74 220	96.52 330	99.55 152	99.64 144

test_8					99.67 118	99.61 74	99.03 336	99.41 225	96.28 335	98.93 266	99.48 265	98.76 55	99.91 118

3Dnovator	97.25 9	99.24 83	99.05 92	99.81 50	99.12 299	99.66 60	99.84 12	99.74 10	99.09 40	98.92 267	99.90 30	95.94 179	99.98 14	98.95 107	99.92 30	99.79 80

v7n		97.87 250	97.52 267	98.92 214	98.76 360	98.58 212	99.84 12	99.46 195	96.20 342	98.91 268	99.70 166	94.89 219	99.44 289	96.03 339	93.89 377	98.75 284

JIA-IIPM		97.50 305	97.02 320	98.93 212	98.73 362	97.80 264	99.30 264	98.97 339	91.73 403	98.91 268	94.86 418	95.10 210	99.71 236	97.58 264	97.98 259	99.28 231

v148		97.79 269	97.55 263	98.50 272	98.74 361	97.72 268	99.54 149	99.33 270	96.26 338	98.90 270	99.51 253	94.68 235	99.14 341	97.83 240	93.15 386	98.63 331

GA-MVS		97.85 253	97.47 275	99.00 201	99.38 229	97.99 251	98.57 393	99.15 316	97.04 282	98.90 270	99.30 317	89.83 354	99.38 298	96.70 323	98.33 237	99.62 151

tpm2		97.44 312	97.34 298	97.74 346	99.15 297	94.36 385	99.45 202	98.94 342	93.45 393	98.90 270	99.44 275	91.35 335	99.59 271	97.31 288	98.07 257	99.29 230

tt0805		97.97 237	97.77 239	98.57 264	99.59 156	96.61 329	99.45 202	99.08 324	98.21 140	98.88 273	99.80 112	88.66 368	99.70 242	98.58 168	97.72 271	99.39 217

miper_ehance_all_eth		98.18 204	98.10 200	98.41 289	99.23 270	97.72 268	98.72 381	99.31 284	96.60 315	98.88 273	99.29 319	97.29 128	99.13 344	97.60 262	95.99 331	98.38 367

eth_miper_zixun_eth		98.05 222	97.96 217	98.33 296	99.26 262	97.38 282	98.56 395	99.31 284	96.65 307	98.88 273	99.52 249	96.58 154	99.12 348	97.39 284	95.53 346	98.47 356

cl22		97.85 253	97.64 257	98.48 275	99.09 307	97.87 260	98.60 392	99.33 270	97.11 274	98.87 276	99.22 330	92.38 312	99.17 339	98.21 206	95.99 331	98.42 362

agg_prior					99.67 118	99.62 72		99.40 231		98.87 276			99.91 118

anonymousdsp		98.44 179	98.28 187	98.94 210	98.50 383	98.96 169	99.77 34	99.50 143	97.07 277	98.87 276	99.77 139	94.76 229	99.28 318	98.66 154	97.60 277	98.57 348

DSMNet-mixed		97.25 321	97.35 295	96.95 370	97.84 394	93.61 395	99.57 124	96.63 417	96.13 350	98.87 276	98.61 381	94.59 240	97.70 407	95.08 362	98.86 206	99.55 170

FMVSNet2		97.72 281	97.36 293	98.80 243	99.51 181	98.84 187	99.45 202	99.42 222	96.49 321	98.86 280	99.29 319	90.26 347	98.98 365	96.44 332	96.56 316	98.58 347

reproduce_monomvs		97.89 247	97.87 229	97.96 329	99.51 181	95.45 361	99.60 102	99.25 300	99.17 23	98.85 281	99.49 259	89.29 360	99.64 261	99.35 59	96.31 323	98.78 276

c3_l		98.12 211	98.04 209	98.38 293	99.30 250	97.69 272	98.81 372	99.33 270	96.67 305	98.83 282	99.34 306	97.11 133	98.99 364	97.58 264	95.34 349	98.48 354

ITE_SJBPF				98.08 318	99.29 254	96.37 336		98.92 346	98.34 122	98.83 282	99.75 146	91.09 339	99.62 268	95.82 343	97.40 299	98.25 374

myMVS_eth3d28		97.69 286	97.34 298	98.73 248	99.27 259	97.52 277	99.33 257	98.78 369	98.03 172	98.82 284	98.49 384	86.64 386	99.46 282	98.44 187	98.24 245	99.23 238

Anonymous20231211		97.88 248	97.54 266	98.90 220	99.71 103	98.53 216	99.48 190	99.57 69	94.16 384	98.81 285	99.68 183	93.23 282	99.42 294	98.84 130	94.42 367	98.76 282

Patchmtry		97.75 275	97.40 290	98.81 241	99.10 304	98.87 182	99.11 322	99.33 270	94.83 376	98.81 285	99.38 293	94.33 252	99.02 360	96.10 337	95.57 344	98.53 350

miper_lstm_enhance		98.00 232	97.91 223	98.28 305	99.34 240	97.43 280	98.88 365	99.36 251	96.48 324	98.80 287	99.55 237	95.98 175	98.91 377	97.27 290	95.50 347	98.51 352

BH-untuned		98.42 181	98.36 180	98.59 260	99.49 194	96.70 322	99.27 279	99.13 319	97.24 261	98.80 287	99.38 293	95.75 187	99.74 220	97.07 304	99.16 179	99.33 227

FIs		98.78 158	98.63 156	99.23 177	99.18 283	99.54 87	99.83 15	99.59 61	98.28 128	98.79 289	99.81 99	96.75 148	99.37 301	99.08 93	96.38 320	98.78 276

OurMVSNet-221017-0		97.88 248	97.77 239	98.19 310	98.71 366	96.53 331	99.88 4	99.00 336	97.79 198	98.78 290	99.94 6	91.68 326	99.35 308	97.21 293	96.99 311	98.69 300

MVS-HIRNet		95.75 355	95.16 360	97.51 355	99.30 250	93.69 393	98.88 365	95.78 420	85.09 417	98.78 290	92.65 420	91.29 337	99.37 301	94.85 366	99.85 78	99.46 204

tpmvs		97.98 234	98.02 212	97.84 338	99.04 317	94.73 377	99.31 262	99.20 310	96.10 355	98.76 292	99.42 279	94.94 214	99.81 194	96.97 309	98.45 231	98.97 265

Patchmatch-test		97.93 240	97.65 254	98.77 246	99.18 283	97.07 299	99.03 336	99.14 318	96.16 346	98.74 293	99.57 231	94.56 242	99.72 230	93.36 384	99.11 185	99.52 179

QAPM		98.67 168	98.30 186	99.80 53	99.20 277	99.67 58	99.77 34	99.72 11	94.74 378	98.73 294	99.90 30	95.78 186	99.98 14	96.96 310	99.88 60	99.76 93

3Dnovator+	97.12 13	99.18 88	98.97 110	99.82 47	99.17 291	99.68 55	99.81 20	99.51 123	99.20 22	98.72 295	99.89 35	95.68 190	99.97 22	98.86 125	99.86 71	99.81 67

IterMVS-SCA-FT		97.82 263	97.75 244	98.06 319	99.57 160	96.36 337	99.02 339	99.49 153	97.18 265	98.71 296	99.72 161	92.72 296	99.14 341	97.44 281	95.86 336	98.67 312

UniMVSNet_NR-MVSNet		98.22 198	97.97 216	98.96 206	98.92 334	98.98 162	99.48 190	99.53 104	97.76 202	98.71 296	99.46 272	96.43 163	99.22 330	98.57 171	92.87 389	98.69 300

DU-MVS		98.08 215	97.79 234	98.96 206	98.87 341	98.98 162	99.41 223	99.45 206	97.87 186	98.71 296	99.50 256	94.82 221	99.22 330	98.57 171	92.87 389	98.68 305

tpm cat1		97.39 314	97.36 293	97.50 356	99.17 291	93.73 391	99.43 213	99.31 284	91.27 404	98.71 296	99.08 344	94.31 254	99.77 211	96.41 334	98.50 229	99.00 261

XXY-MVS		98.38 187	98.09 203	99.24 175	99.26 262	99.32 115	99.56 130	99.55 82	97.45 239	98.71 296	99.83 76	93.23 282	99.63 267	98.88 117	96.32 322	98.76 282

IterMVS		97.83 260	97.77 239	98.02 322	99.58 158	96.27 341	99.02 339	99.48 165	97.22 263	98.71 296	99.70 166	92.75 293	99.13 344	97.46 279	96.00 330	98.67 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		98.75 161	98.62 161	99.15 187	99.08 310	99.45 102	99.86 11	99.60 56	98.23 137	98.70 302	99.82 85	96.80 145	99.22 330	99.07 94	96.38 320	98.79 275

COLMAP_ROB	97.56 6	98.86 143	98.75 143	99.17 182	99.88 11	98.53 216	99.34 255	99.59 61	97.55 226	98.70 302	99.89 35	95.83 184	99.90 130	98.10 214	99.90 46	99.08 250
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		97.76 271	97.41 289	98.82 238	99.06 313	97.87 260	98.87 367	98.56 388	96.63 311	98.68 304	99.22 330	92.49 306	99.65 258	95.40 356	97.79 269	98.95 269

WR-MVS		98.06 217	97.73 246	99.06 193	98.86 344	99.25 129	99.19 303	99.35 258	97.30 255	98.66 305	99.43 277	93.94 267	99.21 335	98.58 168	94.28 369	98.71 291

HQP-NCC					99.19 280		98.98 350		98.24 134	98.66 305

ACMP_Plane					99.19 280		98.98 350		98.24 134	98.66 305

HQP4-MVS										98.66 305			99.64 261			98.64 324

HQP-MVS		98.02 227	97.90 224	98.37 294	99.19 280	96.83 317	98.98 350	99.39 234	98.24 134	98.66 305	99.40 287	92.47 307	99.64 261	97.19 297	97.58 279	98.64 324

LF4IMVS		97.52 302	97.46 277	97.70 348	98.98 327	95.55 356	99.29 269	98.82 363	98.07 163	98.66 305	99.64 202	89.97 352	99.61 269	97.01 305	96.68 312	97.94 395

mvs_tets		98.40 186	98.23 189	98.91 218	98.67 370	98.51 222	99.66 75	99.53 104	98.19 142	98.65 311	99.81 99	92.75 293	99.44 289	99.31 68	97.48 292	98.77 280

UBG		97.85 253	97.48 272	98.95 208	99.25 266	97.64 273	99.24 292	98.74 374	97.90 183	98.64 312	98.20 396	88.65 369	99.81 194	98.27 203	98.40 232	99.42 211

TESTMET0.1,1		97.55 300	97.27 310	98.40 291	98.93 332	96.53 331	98.67 384	97.61 407	96.96 287	98.64 312	99.28 321	88.63 371	99.45 284	97.30 289	99.38 162	99.21 240

jajsoiax		98.43 180	98.28 187	98.88 225	98.60 377	98.43 230	99.82 16	99.53 104	98.19 142	98.63 314	99.80 112	93.22 284	99.44 289	99.22 78	97.50 288	98.77 280

Baseline_NR-MVSNet		97.76 271	97.45 278	98.68 255	99.09 307	98.29 235	99.41 223	98.85 360	95.65 361	98.63 314	99.67 189	94.82 221	99.10 351	98.07 222	92.89 388	98.64 324

EPNet		98.86 143	98.71 147	99.30 163	97.20 406	98.18 240	99.62 95	98.91 351	99.28 20	98.63 314	99.81 99	95.96 176	99.99 4	99.24 77	99.72 129	99.73 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR		98.06 217	97.90 224	98.55 269	98.79 350	97.10 295	98.67 384	97.75 404	97.34 251	98.61 317	98.85 368	94.45 249	99.45 284	97.25 291	99.38 162	99.10 245

test-mter		97.49 310	97.13 316	98.55 269	98.79 350	97.10 295	98.67 384	97.75 404	96.65 307	98.61 317	98.85 368	88.23 375	99.45 284	97.25 291	99.38 162	99.10 245

DIV-MVS_self_test		98.01 230	97.85 231	98.48 275	99.24 268	97.95 256	98.71 382	99.35 258	96.50 320	98.60 319	99.54 242	95.72 189	99.03 358	97.21 293	95.77 337	98.46 359

cl____		98.01 230	97.84 232	98.55 269	99.25 266	97.97 252	98.71 382	99.34 263	96.47 326	98.59 320	99.54 242	95.65 191	99.21 335	97.21 293	95.77 337	98.46 359

ETVMVS		97.50 305	96.90 324	99.29 166	99.23 270	98.78 196	99.32 259	98.90 353	97.52 232	98.56 321	98.09 402	84.72 400	99.69 247	97.86 236	97.88 264	99.39 217

FMVSNet1		96.84 333	96.36 337	98.29 301	99.32 248	97.26 288	99.43 213	99.48 165	95.11 368	98.55 322	99.32 314	83.95 403	98.98 365	95.81 344	96.26 324	98.62 333

UniMVSNet_ETH3D		97.32 318	96.81 326	98.87 229	99.40 224	97.46 279	99.51 168	99.53 104	95.86 359	98.54 323	99.77 139	82.44 409	99.66 253	98.68 152	97.52 285	99.50 191

AUN-MVS		96.88 332	96.31 338	98.59 260	99.48 201	97.04 304	99.27 279	99.22 306	97.44 242	98.51 324	99.41 283	91.97 318	99.66 253	97.71 255	83.83 415	99.07 255

PCF-MVS	97.08 14	97.66 293	97.06 319	99.47 133	99.61 149	99.09 148	98.04 414	99.25 300	91.24 405	98.51 324	99.70 166	94.55 244	99.91 118	92.76 393	99.85 78	99.42 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		97.93 240	97.66 253	98.76 247	98.78 353	98.62 208	99.65 81	99.49 153	97.76 202	98.49 326	99.60 220	94.23 255	98.97 372	98.00 226	92.90 387	98.70 296

CP-MVSNet		98.09 213	97.78 237	99.01 199	98.97 329	99.24 130	99.67 69	99.46 195	97.25 259	98.48 327	99.64 202	93.79 274	99.06 354	98.63 158	94.10 373	98.74 287

ACMP	97.20 11	98.06 217	97.94 221	98.45 283	99.37 232	97.01 306	99.44 208	99.49 153	97.54 229	98.45 328	99.79 124	91.95 319	99.72 230	97.91 231	97.49 291	98.62 333
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		97.69 286	97.43 287	98.48 275	98.60 377	97.30 284	98.18 411	99.39 234	92.96 396	98.41 329	98.78 375	93.77 275	99.27 321	98.16 212	98.61 219	98.86 271

WR-MVS_H		98.13 209	97.87 229	98.90 220	99.02 319	98.84 187	99.70 56	99.59 61	97.27 257	98.40 330	99.19 334	95.53 194	99.23 326	98.34 197	93.78 379	98.61 342

BH-w/o		98.00 232	97.89 228	98.32 298	99.35 236	96.20 344	99.01 344	98.90 353	96.42 329	98.38 331	99.00 354	95.26 205	99.72 230	96.06 338	98.61 219	99.03 258

pmmvs5		97.52 302	97.30 304	98.16 312	98.57 380	96.73 321	99.27 279	98.90 353	96.14 349	98.37 332	99.53 246	91.54 332	99.14 341	97.51 273	95.87 335	98.63 331

EU-MVSNet		97.98 234	98.03 210	97.81 342	98.72 364	96.65 327	99.66 75	99.66 28	98.09 158	98.35 333	99.82 85	95.25 206	98.01 400	97.41 283	95.30 350	98.78 276

FMVSNet5		96.43 342	96.19 341	97.15 362	99.11 301	95.89 350	99.32 259	99.52 109	94.47 383	98.34 334	99.07 345	87.54 382	97.07 412	92.61 394	95.72 340	98.47 356

testing91		97.44 312	97.02 320	98.71 252	99.18 283	96.89 316	99.19 303	99.04 331	97.78 200	98.31 335	98.29 393	85.41 395	99.85 161	98.01 225	97.95 260	99.39 217

PS-CasMVS		97.93 240	97.59 262	98.95 208	98.99 324	99.06 154	99.68 66	99.52 109	97.13 269	98.31 335	99.68 183	92.44 311	99.05 355	98.51 179	94.08 374	98.75 284

USDC		97.34 317	97.20 312	97.75 344	99.07 311	95.20 368	98.51 397	99.04 331	97.99 175	98.31 335	99.86 56	89.02 361	99.55 275	95.67 350	97.36 301	98.49 353

PEN-MVS		97.76 271	97.44 283	98.72 250	98.77 358	98.54 215	99.78 32	99.51 123	97.06 279	98.29 338	99.64 202	92.63 302	98.89 380	98.09 215	93.16 385	98.72 289

tfpnnormal		97.84 257	97.47 275	98.98 203	99.20 277	99.22 132	99.64 84	99.61 50	96.32 333	98.27 339	99.70 166	93.35 281	99.44 289	95.69 348	95.40 348	98.27 372

testing99		97.36 315	96.94 323	98.63 257	99.18 283	96.70 322	99.30 264	98.93 343	97.71 207	98.23 340	98.26 394	84.92 398	99.84 168	98.04 224	97.85 267	99.35 223

testing222		97.16 324	96.50 333	99.16 183	99.16 293	98.47 228	99.27 279	98.66 385	97.71 207	98.23 340	98.15 397	82.28 411	99.84 168	97.36 286	97.66 273	99.18 241

ppachtmachnet_test		97.49 310	97.45 278	97.61 352	98.62 374	95.24 367	98.80 373	99.46 195	96.11 351	98.22 342	99.62 213	96.45 161	98.97 372	93.77 378	95.97 334	98.61 342

testing11		97.50 305	97.10 317	98.71 252	99.20 277	96.91 314	99.29 269	98.82 363	97.89 184	98.21 343	98.40 388	85.63 393	99.83 181	98.45 186	98.04 258	99.37 221

our_test_3		97.65 294	97.68 251	97.55 354	98.62 374	94.97 373	98.84 369	99.30 288	96.83 298	98.19 344	99.34 306	97.01 140	99.02 360	95.00 364	96.01 329	98.64 324

LTVRE_ROB	97.16 12	98.02 227	97.90 224	98.40 291	99.23 270	96.80 320	99.70 56	99.60 56	97.12 271	98.18 345	99.70 166	91.73 325	99.72 230	98.39 190	97.45 293	98.68 305
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
ACMH	97.28 8	98.10 212	97.99 214	98.44 286	99.41 219	96.96 312	99.60 102	99.56 74	98.09 158	98.15 346	99.91 23	90.87 342	99.70 242	98.88 117	97.45 293	98.67 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		97.24 323	97.32 302	96.99 367	98.45 385	93.51 396	98.82 371	99.32 280	97.41 246	98.13 347	99.30 317	88.99 362	99.56 273	95.68 349	99.80 106	97.90 398

MVS		97.28 319	96.55 332	99.48 130	98.78 353	98.95 172	99.27 279	99.39 234	83.53 418	98.08 348	99.54 242	96.97 141	99.87 152	94.23 374	99.16 179	99.63 149

PAPM		97.59 298	97.09 318	99.07 191	99.06 313	98.26 237	98.30 407	99.10 321	94.88 374	98.08 348	99.34 306	96.27 167	99.64 261	89.87 404	98.92 202	99.31 229

OpenMVS	96.50 16	98.47 177	98.12 198	99.52 122	99.04 317	99.53 90	99.82 16	99.72 11	94.56 381	98.08 348	99.88 43	94.73 231	99.98 14	97.47 278	99.76 121	99.06 256

gg-mvs-nofinetune		96.17 347	95.32 359	98.73 248	98.79 350	98.14 243	99.38 240	94.09 426	91.07 407	98.07 351	91.04 424	89.62 358	99.35 308	96.75 320	99.09 189	98.68 305

test0.0.03 1		97.71 284	97.42 288	98.56 267	98.41 387	97.82 263	98.78 375	98.63 386	97.34 251	98.05 352	98.98 358	94.45 249	98.98 365	95.04 363	97.15 308	98.89 270

APD_test1		95.87 352	96.49 334	94.00 387	99.53 172	84.01 416	99.54 149	99.32 280	95.91 358	97.99 353	99.85 61	85.49 394	99.88 147	91.96 396	98.84 208	98.12 381

1314		98.68 167	98.54 171	99.11 189	98.89 337	98.65 204	99.27 279	99.49 153	96.89 293	97.99 353	99.56 234	97.72 116	99.83 181	97.74 251	99.27 173	98.84 273

DTE-MVSNet		97.51 304	97.19 313	98.46 281	98.63 373	98.13 244	99.84 12	99.48 165	96.68 304	97.97 355	99.67 189	92.92 289	98.56 389	96.88 317	92.60 393	98.70 296

SixPastTwentyTwo		97.50 305	97.33 301	98.03 320	98.65 371	96.23 343	99.77 34	98.68 383	97.14 268	97.90 356	99.93 10	90.45 345	99.18 338	97.00 306	96.43 319	98.67 312

testing3		97.28 319	96.76 328	98.82 238	99.37 232	98.07 247	99.45 202	99.36 251	97.56 225	97.89 357	98.95 361	83.70 404	98.82 381	96.03 339	98.56 225	99.58 164

pm-mvs1		97.68 289	97.28 307	98.88 225	99.06 313	98.62 208	99.50 175	99.45 206	96.32 333	97.87 358	99.79 124	92.47 307	99.35 308	97.54 271	93.54 381	98.67 312

testgi		97.65 294	97.50 270	98.13 316	99.36 235	96.45 334	99.42 220	99.48 165	97.76 202	97.87 358	99.45 274	91.09 339	98.81 382	94.53 369	98.52 228	99.13 244

EPNet_dtu		98.03 225	97.96 217	98.23 308	98.27 388	95.54 358	99.23 295	98.75 371	99.02 46	97.82 360	99.71 162	96.11 171	99.48 279	93.04 388	99.65 141	99.69 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 326	96.89 325	97.83 339	99.07 311	95.52 359	98.57 393	98.74 374	97.58 222	97.81 361	99.79 124	88.16 376	99.56 273	95.10 361	97.21 305	98.39 366

ACMH+	97.24 10	97.92 243	97.78 237	98.32 298	99.46 204	96.68 326	99.56 130	99.54 91	98.41 114	97.79 362	99.87 52	90.18 351	99.66 253	98.05 223	97.18 307	98.62 333

N_pmnet		94.95 364	95.83 350	92.31 394	98.47 384	79.33 426	99.12 316	92.81 432	93.87 386	97.68 363	99.13 340	93.87 271	99.01 362	91.38 399	96.19 325	98.59 346

KD-MVS_2432*1600		94.62 365	93.72 373	97.31 359	97.19 407	95.82 351	98.34 403	99.20 310	95.00 372	97.57 364	98.35 390	87.95 378	98.10 397	92.87 391	77.00 422	98.01 388

miper_refine_blended		94.62 365	93.72 373	97.31 359	97.19 407	95.82 351	98.34 403	99.20 310	95.00 372	97.57 364	98.35 390	87.95 378	98.10 397	92.87 391	77.00 422	98.01 388

PVSNet_0	94.43 19	96.09 349	95.47 356	97.94 330	99.31 249	94.34 386	97.81 415	99.70 15	97.12 271	97.46 366	98.75 376	89.71 355	99.79 204	97.69 258	81.69 418	99.68 127

Syy-MVS		97.09 328	97.14 314	96.95 370	99.00 321	92.73 401	99.29 269	99.39 234	97.06 279	97.41 367	98.15 397	93.92 269	98.68 387	91.71 397	98.34 235	99.45 207

myMVS_eth3d		96.89 331	96.37 336	98.43 288	99.00 321	97.16 292	99.29 269	99.39 234	97.06 279	97.41 367	98.15 397	83.46 405	98.68 387	95.27 359	98.34 235	99.45 207

pmmvs6		96.53 339	96.09 344	97.82 341	98.69 368	95.47 360	99.37 242	99.47 186	93.46 392	97.41 367	99.78 131	87.06 385	99.33 311	96.92 315	92.70 391	98.65 322

new_pmnet		96.38 343	96.03 345	97.41 357	98.13 391	95.16 371	99.05 331	99.20 310	93.94 385	97.39 370	98.79 374	91.61 331	99.04 356	90.43 402	95.77 337	98.05 386

CL-MVSNet_self_test		94.49 367	93.97 371	96.08 381	96.16 412	93.67 394	98.33 405	99.38 242	95.13 366	97.33 371	98.15 397	92.69 300	96.57 415	88.67 408	79.87 420	97.99 392

IB-MVS	95.67 18	96.22 344	95.44 358	98.57 264	99.21 275	96.70 322	98.65 388	97.74 406	96.71 302	97.27 372	98.54 383	86.03 390	99.92 106	98.47 184	86.30 412	99.10 245
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
GG-mvs-BLEND				98.45 283	98.55 381	98.16 241	99.43 213	93.68 427		97.23 373	98.46 385	89.30 359	99.22 330	95.43 355	98.22 246	97.98 393

MVP-Stereo		97.81 265	97.75 244	97.99 326	97.53 399	96.60 330	98.96 354	98.85 360	97.22 263	97.23 373	99.36 299	95.28 202	99.46 282	95.51 352	99.78 115	97.92 397
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20240521		96.20 346	95.89 349	97.13 364	97.72 398	94.96 374	99.79 31	99.29 292	93.01 395	97.20 375	99.03 350	89.69 356	98.36 393	91.16 400	96.13 326	98.07 384

TransMVSNet (Re)		97.15 325	96.58 331	98.86 232	99.12 299	98.85 186	99.49 186	98.91 351	95.48 363	97.16 376	99.80 112	93.38 280	99.11 349	94.16 376	91.73 395	98.62 333

KD-MVS_self_test		95.00 362	94.34 367	96.96 369	97.07 409	95.39 364	99.56 130	99.44 214	95.11 368	97.13 377	97.32 410	91.86 321	97.27 411	90.35 403	81.23 419	98.23 376

NR-MVSNet		97.97 237	97.61 260	99.02 198	98.87 341	99.26 127	99.47 197	99.42 222	97.63 217	97.08 378	99.50 256	95.07 211	99.13 344	97.86 236	93.59 380	98.68 305

Anonymous20231206		96.22 344	96.03 345	96.79 375	97.31 404	94.14 387	99.63 90	99.08 324	96.17 345	97.04 379	99.06 347	93.94 267	97.76 406	86.96 415	95.06 355	98.47 356

test_0402		96.64 337	96.24 339	97.85 336	98.85 345	96.43 335	99.44 208	99.26 298	93.52 390	96.98 380	99.52 249	88.52 372	99.20 337	92.58 395	97.50 288	97.93 396

MIMVSNet1		95.51 356	95.04 361	96.92 372	97.38 401	95.60 354	99.52 159	99.50 143	93.65 389	96.97 381	99.17 335	85.28 397	96.56 416	88.36 410	95.55 345	98.60 345

mvs5depth		96.66 336	96.22 340	97.97 327	97.00 410	96.28 340	98.66 387	99.03 333	96.61 312	96.93 382	99.79 124	87.20 384	99.47 280	96.65 328	94.13 372	98.16 379

dongtai		93.26 374	92.93 378	94.25 386	99.39 227	85.68 414	97.68 417	93.27 428	92.87 397	96.85 383	99.39 291	82.33 410	97.48 409	76.78 422	97.80 268	99.58 164

TDRefinement		95.42 358	94.57 365	97.97 327	89.83 428	96.11 347	99.48 190	98.75 371	96.74 300	96.68 384	99.88 43	88.65 369	99.71 236	98.37 193	82.74 417	98.09 383

baseline2		97.87 250	97.55 263	98.82 238	99.18 283	98.02 249	99.41 223	96.58 419	96.97 286	96.51 385	99.17 335	93.43 279	99.57 272	97.71 255	99.03 194	98.86 271

pmmvs3		94.09 371	93.25 377	96.60 377	94.76 422	94.49 381	98.92 361	98.18 399	89.66 408	96.48 386	98.06 403	86.28 389	97.33 410	89.68 405	87.20 411	97.97 394

DeepMVS_CX				93.34 390	99.29 254	82.27 419		99.22 306	85.15 416	96.33 387	99.05 348	90.97 341	99.73 226	93.57 382	97.77 270	98.01 388

ttmdpeth		97.80 267	97.63 258	98.29 301	98.77 358	97.38 282	99.64 84	99.36 251	98.78 81	96.30 388	99.58 226	92.34 314	99.39 296	98.36 195	95.58 343	98.10 382

LCM-MVSNet-Re		97.83 260	98.15 194	96.87 373	99.30 250	92.25 403	99.59 109	98.26 394	97.43 243	96.20 389	99.13 340	96.27 167	98.73 386	98.17 211	98.99 197	99.64 144

test20.03		96.12 348	95.96 347	96.63 376	97.44 400	95.45 361	99.51 168	99.38 242	96.55 318	96.16 390	99.25 327	93.76 276	96.17 417	87.35 414	94.22 370	98.27 372

K. test v3		97.10 327	96.79 327	98.01 323	98.72 364	96.33 338	99.87 8	97.05 411	97.59 220	96.16 390	99.80 112	88.71 366	99.04 356	96.69 324	96.55 317	98.65 322

UnsupCasMVSNet_eth		96.44 341	96.12 342	97.40 358	98.65 371	95.65 353	99.36 247	99.51 123	97.13 269	96.04 392	98.99 356	88.40 373	98.17 396	96.71 322	90.27 403	98.40 365

test_method		91.10 380	91.36 382	90.31 400	95.85 413	73.72 433	94.89 421	99.25 300	68.39 424	95.82 393	99.02 352	80.50 414	98.95 375	93.64 381	94.89 361	98.25 374

lessismore_v0				97.79 343	98.69 368	95.44 363		94.75 424		95.71 394	99.87 52	88.69 367	99.32 313	95.89 342	94.93 359	98.62 333

test_vis1_rt		95.81 354	95.65 353	96.32 380	99.67 118	91.35 407	99.49 186	96.74 416	98.25 133	95.24 395	98.10 401	74.96 416	99.90 130	99.53 41	98.85 207	97.70 401

dmvs_testset		95.02 361	96.12 342	91.72 396	99.10 304	80.43 424	99.58 117	97.87 403	97.47 235	95.22 396	98.82 370	93.99 265	95.18 421	88.09 411	94.91 360	99.56 169

Patchmatch-RL test		95.84 353	95.81 351	95.95 382	95.61 415	90.57 408	98.24 408	98.39 392	95.10 370	95.20 397	98.67 378	94.78 225	97.77 405	96.28 336	90.02 404	99.51 187

test_fmvs3		92.10 378	91.77 381	93.08 392	96.19 411	86.25 412	99.82 16	98.62 387	96.65 307	95.19 398	96.90 412	55.05 427	95.93 419	96.63 329	90.92 401	97.06 408

ambc				93.06 393	92.68 424	82.36 418	98.47 398	98.73 380		95.09 399	97.41 407	55.55 425	99.10 351	96.42 333	91.32 396	97.71 399

PM-MVS		92.96 376	92.23 380	95.14 384	95.61 415	89.98 410	99.37 242	98.21 397	94.80 377	95.04 400	97.69 405	65.06 420	97.90 403	94.30 371	89.98 405	97.54 405

OpenMVS_ROB	92.34 20	94.38 369	93.70 375	96.41 379	97.38 401	93.17 398	99.06 329	98.75 371	86.58 415	94.84 401	98.26 394	81.53 412	99.32 313	89.01 407	97.87 265	96.76 409

mvsany_test3		93.77 372	93.45 376	94.74 385	95.78 414	88.01 411	99.64 84	98.25 395	98.28 128	94.31 402	97.97 404	68.89 419	98.51 391	97.50 274	90.37 402	97.71 399

EG-PatchMatch MVS		95.97 351	95.69 352	96.81 374	97.78 395	92.79 400	99.16 307	98.93 343	96.16 346	94.08 403	99.22 330	82.72 407	99.47 280	95.67 350	97.50 288	98.17 378

test_f		91.90 379	91.26 383	93.84 388	95.52 418	85.92 413	99.69 60	98.53 391	95.31 365	93.87 404	96.37 415	55.33 426	98.27 394	95.70 347	90.98 400	97.32 407

pmmvs-eth3d		95.34 360	94.73 363	97.15 362	95.53 417	95.94 349	99.35 252	99.10 321	95.13 366	93.55 405	97.54 406	88.15 377	97.91 402	94.58 368	89.69 406	97.61 402

new-patchmatchnet		94.48 368	94.08 369	95.67 383	95.08 420	92.41 402	99.18 305	99.28 294	94.55 382	93.49 406	97.37 409	87.86 380	97.01 413	91.57 398	88.36 408	97.61 402

UnsupCasMVSNet_bld		93.53 373	92.51 379	96.58 378	97.38 401	93.82 389	98.24 408	99.48 165	91.10 406	93.10 407	96.66 413	74.89 417	98.37 392	94.03 377	87.71 410	97.56 404

WB-MVS		93.10 375	94.10 368	90.12 401	95.51 419	81.88 421	99.73 50	99.27 297	95.05 371	93.09 408	98.91 367	94.70 234	91.89 425	76.62 423	94.02 376	96.58 411

SSC-MVS		92.73 377	93.73 372	89.72 402	95.02 421	81.38 422	99.76 37	99.23 304	94.87 375	92.80 409	98.93 363	94.71 233	91.37 426	74.49 425	93.80 378	96.42 412

Gipuma		90.99 381	90.15 386	93.51 389	98.73 362	90.12 409	93.98 422	99.45 206	79.32 420	92.28 410	94.91 417	69.61 418	97.98 401	87.42 413	95.67 341	92.45 420
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		90.92 382	90.11 387	93.34 390	98.78 353	85.59 415	98.15 412	93.16 430	89.37 411	92.07 411	98.38 389	81.48 413	95.19 420	62.54 429	97.04 309	99.25 236

CMPMVS	69.68 23	94.13 370	94.90 362	91.84 395	97.24 405	80.01 425	98.52 396	99.48 165	89.01 412	91.99 412	99.67 189	85.67 392	99.13 344	95.44 354	97.03 310	96.39 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVStest1		96.08 350	95.48 355	97.89 334	98.93 332	96.70 322	99.56 130	99.35 258	92.69 399	91.81 413	99.46 272	89.90 353	98.96 374	95.00 364	92.61 392	98.00 391

testf1		90.42 383	90.68 384	89.65 403	97.78 395	73.97 431	99.13 313	98.81 365	89.62 409	91.80 414	98.93 363	62.23 423	98.80 383	86.61 417	91.17 397	96.19 414

APD_test2		90.42 383	90.68 384	89.65 403	97.78 395	73.97 431	99.13 313	98.81 365	89.62 409	91.80 414	98.93 363	62.23 423	98.80 383	86.61 417	91.17 397	96.19 414

PMMVS2		86.87 386	85.37 390	91.35 398	90.21 427	83.80 417	98.89 364	97.45 410	83.13 419	91.67 416	95.03 416	48.49 429	94.70 422	85.86 419	77.62 421	95.54 417

LCM-MVSNet		86.80 387	85.22 391	91.53 397	87.81 429	80.96 423	98.23 410	98.99 337	71.05 422	90.13 417	96.51 414	48.45 430	96.88 414	90.51 401	85.30 413	96.76 409

ET-MVSNet_ETH3D		96.49 340	95.64 354	99.05 195	99.53 172	98.82 191	98.84 369	97.51 409	97.63 217	84.77 418	99.21 333	92.09 316	98.91 377	98.98 103	92.21 394	99.41 214

E-PMN		80.61 391	79.88 393	82.81 408	90.75 426	76.38 429	97.69 416	95.76 421	66.44 426	83.52 419	92.25 421	62.54 422	87.16 428	68.53 427	61.40 425	84.89 426

FPMVS		84.93 388	85.65 389	82.75 409	86.77 430	63.39 435	98.35 402	98.92 346	74.11 421	83.39 420	98.98 358	50.85 428	92.40 424	84.54 420	94.97 357	92.46 419

EMVS		80.02 392	79.22 394	82.43 410	91.19 425	76.40 428	97.55 419	92.49 433	66.36 427	83.01 421	91.27 423	64.63 421	85.79 429	65.82 428	60.65 426	85.08 425

test_vis3_rt		87.04 385	85.81 388	90.73 399	93.99 423	81.96 420	99.76 37	90.23 434	92.81 398	81.35 422	91.56 422	40.06 431	99.07 353	94.27 373	88.23 409	91.15 422

YYNet1		95.36 359	94.51 366	97.92 331	97.89 393	97.10 295	99.10 324	99.23 304	93.26 394	80.77 423	99.04 349	92.81 292	98.02 399	94.30 371	94.18 371	98.64 324

MDA-MVSNet_test_wron		95.45 357	94.60 364	98.01 323	98.16 390	97.21 291	99.11 322	99.24 303	93.49 391	80.73 424	98.98 358	93.02 286	98.18 395	94.22 375	94.45 366	98.64 324

MDA-MVSNet-bldmvs		94.96 363	93.98 370	97.92 331	98.24 389	97.27 286	99.15 310	99.33 270	93.80 387	80.09 425	99.03 350	88.31 374	97.86 404	93.49 383	94.36 368	98.62 333

tmp_tt		82.80 389	81.52 392	86.66 405	66.61 435	68.44 434	92.79 424	97.92 401	68.96 423	80.04 426	99.85 61	85.77 391	96.15 418	97.86 236	43.89 428	95.39 418

MVE	76.82 21	76.91 394	74.31 398	84.70 406	85.38 432	76.05 430	96.88 420	93.17 429	67.39 425	71.28 427	89.01 426	21.66 437	87.69 427	71.74 426	72.29 424	90.35 423
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 393	74.86 397	84.62 407	75.88 433	77.61 427	97.63 418	93.15 431	88.81 413	64.27 428	89.29 425	36.51 432	83.93 430	75.89 424	52.31 427	92.33 421

PMVS	70.75 22	75.98 395	74.97 396	79.01 411	70.98 434	55.18 436	93.37 423	98.21 397	65.08 428	61.78 429	93.83 419	21.74 436	92.53 423	78.59 421	91.12 399	89.34 424
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		39.01 398	42.50 400	28.53 413	39.17 436	20.91 438	98.75 378	19.17 438	19.83 431	38.57 430	66.67 428	33.16 433	15.42 432	37.50 432	29.66 430	49.26 427

testmvs		39.17 397	43.78 399	25.37 414	36.04 437	16.84 439	98.36 401	26.56 436	20.06 430	38.51 431	67.32 427	29.64 434	15.30 433	37.59 431	39.90 429	43.98 428

wuyk23d		40.18 396	41.29 401	36.84 412	86.18 431	49.12 437	79.73 425	22.81 437	27.64 429	25.46 432	28.45 432	21.98 435	48.89 431	55.80 430	23.56 431	12.51 429

EGC-MVSNET		82.80 389	77.86 395	97.62 351	97.91 392	96.12 346	99.33 257	99.28 294	8.40 432	25.05 433	99.27 324	84.11 402	99.33 311	89.20 406	98.22 246	97.42 406

mmdepth		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

monomultidepth		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

test_blank		0.13 402	0.17 405	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	1.57 433	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uanet_test		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

DCPMVS		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

cdsmvs_eth3d_5k		24.64 399	32.85 402	0.00 415	0.00 438	0.00 440	0.00 426	99.51 123	0.00 433	0.00 434	99.56 234	96.58 154	0.00 434	0.00 433	0.00 432	0.00 430

pcd_1.5k_mvsjas		8.27 401	11.03 404	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	99.01 18	0.00 434	0.00 433	0.00 432	0.00 430

sosnet-low-res		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

sosnet		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uncertanet		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

Regformer		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

ab-mvs-re		8.30 400	11.06 403	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	99.58 226	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

uanet		0.02 403	0.03 406	0.00 415	0.00 438	0.00 440	0.00 426	0.00 439	0.00 433	0.00 434	0.27 434	0.00 438	0.00 434	0.00 433	0.00 432	0.00 430

WAC-MVS						97.16 292								95.47 353

MSC_two_6792asdad				99.87 16	99.51 181	99.76 42		99.33 270					99.96 34	98.87 120	99.84 86	99.89 22

No_MVS				99.87 16	99.51 181	99.76 42		99.33 270					99.96 34	98.87 120	99.84 86	99.89 22

eth-test2					0.00 438

eth-test					0.00 438

OPU-MVS				99.64 87	99.56 164	99.72 48	99.60 102				99.70 166	99.27 5	99.42 294	98.24 205	99.80 106	99.79 80

save fliter					99.76 69	99.59 77	99.14 312	99.40 231	99.00 51

test_0728_SECOND				99.91 3	99.84 32	99.89 4	99.57 124	99.51 123					99.96 34	98.93 111	99.86 71	99.88 28

GSMVS																99.52 179

sam_mvs1												94.86 220				99.52 179

sam_mvs												94.72 232

MTGPA								99.47 186

test_post1							99.23 295				65.14 430	94.18 259	99.71 236	97.58 264

test_post											65.99 429	94.65 238	99.73 226

patchmatchnet-post											98.70 377	94.79 224	99.74 220

MTMP							99.54 149	98.88 356

gm-plane-assit					98.54 382	92.96 399			94.65 380		99.15 338		99.64 261	97.56 269

test9_res														97.49 275	99.72 129	99.75 94

agg_prior2														97.21 293	99.73 128	99.75 94

test_prior4						99.56 83	98.99 347

test_prior				99.68 75	99.67 118	99.48 98		99.56 74					99.83 181			99.74 98

新几何2							99.01 344

旧先验1					99.74 87	99.59 77		99.54 91			99.69 176	98.47 83			99.68 137	99.73 103

无先验							98.99 347	99.51 123	96.89 293				99.93 94	97.53 272		99.72 110

原ACMM2							98.95 357

testdata2													99.95 65	96.67 325

segment_acmp												98.96 25

testdata1							98.85 368		98.32 125

plane_prior7					99.29 254	97.03 305

plane_prior6					99.27 259	96.98 309						92.71 298

plane_prior5								99.47 186					99.69 247	97.78 244	97.63 274	98.67 312

plane_prior4											99.61 217

plane_prior2							99.39 235		98.97 59

plane_prior1					99.26 262

plane_prior						96.97 310	99.21 301		98.45 109						97.60 277

n2								0.00 439

nn								0.00 439

door-mid								98.05 400

test11								99.35 258

door								97.92 401

HQP5-MVS						96.83 317

BP-MVS														97.19 297

HQP3-MVS								99.39 234							97.58 279

HQP2-MVS												92.47 307

NP-MVS					99.23 270	96.92 313					99.40 287

ACMMP++_ref															97.19 306

ACMMP++															97.43 297

Test By Simon												98.75 58

High-resolution multi-view benchmark