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.

SD-MVS		99.41 46	99.52 8	99.05 170	99.74 76	99.68 54	99.46 159	99.52 92	99.11 11	99.88 6	99.91 8	99.43 1	97.70 359	98.72 111	99.93 12	99.77 72
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
TSAR-MVS + MP.		99.58 5	99.50 10	99.81 41	99.91 1	99.66 59	99.63 65	99.39 218	98.91 46	99.78 35	99.85 34	99.36 2	99.94 58	98.84 93	99.88 38	99.82 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PC_three_1452									98.18 112	99.84 15	99.70 140	99.31 3	98.52 344	98.30 167	99.80 89	99.81 46

SteuartSystems-ACMMP		99.54 10	99.42 17	99.87 12	99.82 39	99.81 27	99.59 85	99.51 106	98.62 67	99.79 30	99.83 47	99.28 4	99.97 12	98.48 147	99.90 25	99.84 22
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		99.59 3	99.50 10	99.88 6	99.51 161	99.88 8	99.87 6	99.51 106	98.99 31	99.88 6	99.81 67	99.27 5	99.96 20	98.85 90	99.80 89	99.81 46

OPU-MVS				99.64 80	99.56 153	99.72 47	99.60 78				99.70 140	99.27 5	99.42 256	98.24 169	99.80 89	99.79 62

SED-MVS		99.61 2	99.52 8	99.88 6	99.84 33	99.90 2	99.60 78	99.48 147	99.08 16	99.91 2	99.81 67	99.20 7	99.96 20	98.91 76	99.85 60	99.79 62

test_241102_ONE					99.84 33	99.90 2		99.48 147	99.07 18	99.91 2	99.74 124	99.20 7	99.76 184

MSLP-MVS++		99.46 26	99.47 13	99.44 126	99.60 143	99.16 128	99.41 179	99.71 13	98.98 34	99.45 122	99.78 102	99.19 9	99.54 240	99.28 39	99.84 67	99.63 133

SMA-MVS		99.44 32	99.30 46	99.85 28	99.73 84	99.83 17	99.56 106	99.47 165	97.45 194	99.78 35	99.82 54	99.18 10	99.91 96	98.79 102	99.89 35	99.81 46
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
APDe-MVS		99.66 1	99.57 3	99.92 1	99.77 53	99.89 4	99.75 33	99.56 58	99.02 21	99.88 6	99.85 34	99.18 10	99.96 20	99.22 44	99.92 13	99.90 1

HPM-MVS_fast		99.51 15	99.40 20	99.85 28	99.91 1	99.79 33	99.76 31	99.56 58	97.72 165	99.76 44	99.75 118	99.13 12	99.92 85	99.07 60	99.92 13	99.85 18

PGM-MVS		99.45 28	99.31 43	99.86 21	99.87 16	99.78 40	99.58 93	99.65 32	97.84 150	99.71 55	99.80 83	99.12 13	99.97 12	98.33 163	99.87 42	99.83 33

test_one_0601					99.81 42	99.88 8		99.49 134	98.97 37	99.65 79	99.81 67	99.09 14

test_0728_THIRD									98.99 31	99.81 25	99.80 83	99.09 14	99.96 20	98.85 90	99.90 25	99.88 8

HFP-MVS		99.49 17	99.37 25	99.86 21	99.87 16	99.80 29	99.66 53	99.67 22	98.15 114	99.68 62	99.69 149	99.06 16	99.96 20	98.69 116	99.87 42	99.84 22

#test#		99.43 36	99.29 50	99.86 21	99.87 16	99.80 29	99.55 115	99.67 22	97.83 151	99.68 62	99.69 149	99.06 16	99.96 20	98.39 155	99.87 42	99.84 22

TSAR-MVS + GP.		99.36 53	99.36 27	99.36 132	99.67 110	98.61 194	99.07 273	99.33 250	99.00 28	99.82 23	99.81 67	99.06 16	99.84 142	99.09 58	99.42 142	99.65 122

pcd_1.5k_mvsjas		8.27 347	11.03 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	99.01 19	0.00 380	0.00 378	0.00 378	0.00 376

PS-MVSNAJss		98.92 119	98.92 102	98.90 195	98.78 312	98.53 199	99.78 26	99.54 75	98.07 128	99.00 226	99.76 113	99.01 19	99.37 263	99.13 54	97.23 262	98.81 233

PS-MVSNAJ		99.32 57	99.32 36	99.30 143	99.57 149	98.94 162	98.97 301	99.46 175	98.92 45	99.71 55	99.24 299	99.01 19	99.98 7	99.35 29	99.66 126	98.97 223

EI-MVSNet-Vis-set		99.58 5	99.56 5	99.64 80	99.78 48	99.15 132	99.61 77	99.45 187	99.01 24	99.89 5	99.82 54	99.01 19	99.92 85	99.56 9	99.95 8	99.85 18

Regformer-1		99.53 12	99.47 13	99.72 64	99.71 95	99.44 98	99.49 145	99.46 175	98.95 40	99.83 20	99.76 113	99.01 19	99.93 73	99.17 50	99.87 42	99.80 56

Regformer-2		99.54 10	99.47 13	99.75 54	99.71 95	99.52 88	99.49 145	99.49 134	98.94 41	99.83 20	99.76 113	99.01 19	99.94 58	99.15 53	99.87 42	99.80 56

patch_mono-2		99.26 66	99.62 1	98.16 278	99.81 42	94.59 341	99.52 124	99.64 33	99.33 2	99.73 50	99.90 10	99.00 25	99.99 1	99.69 1	99.98 2	99.89 2

Regformer-4		99.59 3	99.54 6	99.73 61	99.76 57	99.41 101	99.58 93	99.49 134	99.02 21	99.88 6	99.80 83	99.00 25	99.94 58	99.45 22	99.92 13	99.84 22

EI-MVSNet-UG-set		99.58 5	99.57 3	99.64 80	99.78 48	99.14 133	99.60 78	99.45 187	99.01 24	99.90 4	99.83 47	98.98 27	99.93 73	99.59 6	99.95 8	99.86 15

Regformer-3		99.57 8	99.53 7	99.68 68	99.76 57	99.29 114	99.58 93	99.44 196	99.01 24	99.87 12	99.80 83	98.97 28	99.91 96	99.44 24	99.92 13	99.83 33

region2R		99.48 21	99.35 30	99.87 12	99.88 12	99.80 29	99.65 60	99.66 27	98.13 116	99.66 73	99.68 156	98.96 29	99.96 20	98.62 125	99.87 42	99.84 22

segment_acmp												98.96 29

CNVR-MVS		99.42 41	99.30 46	99.78 48	99.62 135	99.71 49	99.26 239	99.52 92	98.82 52	99.39 141	99.71 136	98.96 29	99.85 136	98.59 133	99.80 89	99.77 72

xxxxxxxxxxxxxcwj		99.43 36	99.32 36	99.75 54	99.76 57	99.59 73	99.14 261	99.53 86	99.00 28	99.71 55	99.80 83	98.95 32	99.93 73	98.19 172	99.84 67	99.74 83

SF-MVS		99.38 51	99.24 61	99.79 46	99.79 46	99.68 54	99.57 99	99.54 75	97.82 156	99.71 55	99.80 83	98.95 32	99.93 73	98.19 172	99.84 67	99.74 83

ACMMPR		99.49 17	99.36 27	99.86 21	99.87 16	99.79 33	99.66 53	99.67 22	98.15 114	99.67 68	99.69 149	98.95 32	99.96 20	98.69 116	99.87 42	99.84 22

test_241102_TWO								99.48 147	99.08 16	99.88 6	99.81 67	98.94 35	99.96 20	98.91 76	99.84 67	99.88 8

DVP-MVS		99.57 8	99.47 13	99.88 6	99.85 26	99.89 4	99.57 99	99.37 233	99.10 12	99.81 25	99.80 83	98.94 35	99.96 20	98.93 73	99.86 53	99.81 46
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
test0726					99.85 26	99.89 4	99.62 71	99.50 126	99.10 12	99.86 13	99.82 54	98.94 35

xiu_mvs_v2_base		99.26 66	99.25 60	99.29 146	99.53 157	98.91 166	99.02 287	99.45 187	98.80 56	99.71 55	99.26 297	98.94 35	99.98 7	99.34 33	99.23 157	98.98 222

CP-MVS		99.45 28	99.32 36	99.85 28	99.83 37	99.75 43	99.69 42	99.52 92	98.07 128	99.53 109	99.63 182	98.93 39	99.97 12	98.74 107	99.91 18	99.83 33

ZNCC-MVS		99.47 24	99.33 34	99.87 12	99.87 16	99.81 27	99.64 63	99.67 22	98.08 127	99.55 106	99.64 176	98.91 40	99.96 20	98.72 111	99.90 25	99.82 40

MCST-MVS		99.43 36	99.30 46	99.82 38	99.79 46	99.74 46	99.29 223	99.40 214	98.79 57	99.52 111	99.62 188	98.91 40	99.90 111	98.64 123	99.75 105	99.82 40

HPM-MVS		99.42 41	99.28 54	99.83 36	99.90 4	99.72 47	99.81 16	99.54 75	97.59 177	99.68 62	99.63 182	98.91 40	99.94 58	98.58 134	99.91 18	99.84 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				99.54 96	99.75 68	98.95 159		99.51 106	97.07 232	99.43 127	99.70 140	98.87 43	99.94 58	97.76 209	99.64 129	99.72 96

APD-MVS_3200maxsize		99.48 21	99.35 30	99.85 28	99.76 57	99.83 17	99.63 65	99.54 75	98.36 91	99.79 30	99.82 54	98.86 44	99.95 47	98.62 125	99.81 85	99.78 70

DeepC-MVS_fast	98.69 1	99.49 17	99.39 21	99.77 50	99.63 129	99.59 73	99.36 203	99.46 175	99.07 18	99.79 30	99.82 54	98.85 45	99.92 85	98.68 118	99.87 42	99.82 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			99.10 74		99.72 89		99.40 187	99.51 106	97.53 187	99.64 83	99.78 102	98.84 46	99.91 96	97.63 222	99.82 82

CDPH-MVS		99.13 84	98.91 104	99.80 43	99.75 68	99.71 49	99.15 259	99.41 208	96.60 268	99.60 94	99.55 211	98.83 47	99.90 111	97.48 238	99.83 76	99.78 70

ACMMP_NAP		99.47 24	99.34 32	99.88 6	99.87 16	99.86 13	99.47 156	99.48 147	98.05 133	99.76 44	99.86 28	98.82 48	99.93 73	98.82 100	99.91 18	99.84 22

XVS		99.53 12	99.42 17	99.87 12	99.85 26	99.83 17	99.69 42	99.68 19	98.98 34	99.37 146	99.74 124	98.81 49	99.94 58	98.79 102	99.86 53	99.84 22

X-MVStestdata		96.55 298	95.45 313	99.87 12	99.85 26	99.83 17	99.69 42	99.68 19	98.98 34	99.37 146	64.01 377	98.81 49	99.94 58	98.79 102	99.86 53	99.84 22

MP-MVS-pluss		99.37 52	99.20 65	99.88 6	99.90 4	99.87 12	99.30 219	99.52 92	97.18 220	99.60 94	99.79 95	98.79 51	99.95 47	98.83 96	99.91 18	99.83 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mPP-MVS		99.44 32	99.30 46	99.86 21	99.88 12	99.79 33	99.69 42	99.48 147	98.12 118	99.50 114	99.75 118	98.78 52	99.97 12	98.57 136	99.89 35	99.83 33

APD-MVS		99.27 64	99.08 77	99.84 35	99.75 68	99.79 33	99.50 135	99.50 126	97.16 222	99.77 37	99.82 54	98.78 52	99.94 58	97.56 231	99.86 53	99.80 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAPA-MVS	97.07 15	97.74 250	97.34 270	98.94 185	99.70 102	97.53 251	99.25 241	99.51 106	91.90 351	99.30 160	99.63 182	98.78 52	99.64 226	88.09 363	99.87 42	99.65 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TEST9					99.67 110	99.65 62	99.05 278	99.41 208	96.22 296	98.95 232	99.49 233	98.77 55	99.91 96

agg_prior1		99.01 112	98.76 125	99.76 53	99.67 110	99.62 66	98.99 294	99.40 214	96.26 292	98.87 245	99.49 233	98.77 55	99.91 96	97.69 219	99.72 112	99.75 78

train_agg		99.02 109	98.77 123	99.77 50	99.67 110	99.65 62	99.05 278	99.41 208	96.28 289	98.95 232	99.49 233	98.76 57	99.91 96	97.63 222	99.72 112	99.75 78

test_8					99.67 110	99.61 68	99.03 284	99.41 208	96.28 289	98.93 236	99.48 239	98.76 57	99.91 96

API-MVS		99.04 106	99.03 84	99.06 168	99.40 197	99.31 112	99.55 115	99.56 58	98.54 73	99.33 156	99.39 265	98.76 57	99.78 176	96.98 270	99.78 96	98.07 341

RE-MVS-def			99.34 32		99.76 57	99.82 23	99.63 65	99.52 92	98.38 87	99.76 44	99.82 54	98.75 60		98.61 128	99.81 85	99.77 72

CS-MVS		99.50 16	99.49 12	99.52 108	99.76 57	99.35 106	99.90 1	99.55 67	98.56 71	99.77 37	99.70 140	98.75 60	99.77 178	99.64 3	99.78 96	99.42 181

DP-MVS Recon		99.12 90	98.95 100	99.65 75	99.74 76	99.70 51	99.27 230	99.57 52	96.40 285	99.42 130	99.68 156	98.75 60	99.80 169	97.98 191	99.72 112	99.44 179

Test By Simon												98.75 60

ACMMP		99.45 28	99.32 36	99.82 38	99.89 9	99.67 57	99.62 71	99.69 18	98.12 118	99.63 84	99.84 43	98.73 64	99.96 20	98.55 142	99.83 76	99.81 46
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
DPE-MVS		99.46 26	99.32 36	99.91 2	99.78 48	99.88 8	99.36 203	99.51 106	98.73 61	99.88 6	99.84 43	98.72 65	99.96 20	98.16 177	99.87 42	99.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		99.34 55	99.19 66	99.79 46	99.61 139	99.65 62	99.30 219	99.48 147	98.86 48	99.21 184	99.63 182	98.72 65	99.90 111	98.25 168	99.63 131	99.80 56

DeepPCF-MVS	98.18 3	98.81 136	99.37 25	97.12 326	99.60 143	91.75 363	98.61 337	99.44 196	99.35 1	99.83 20	99.85 34	98.70 67	99.81 164	99.02 64	99.91 18	99.81 46

SR-MVS		99.43 36	99.29 50	99.86 21	99.75 68	99.83 17	99.59 85	99.62 34	98.21 108	99.73 50	99.79 95	98.68 68	99.96 20	98.44 153	99.77 101	99.79 62

test_prior3		99.21 72	99.05 79	99.68 68	99.67 110	99.48 93	98.96 302	99.56 58	98.34 93	99.01 221	99.52 223	98.68 68	99.83 153	97.96 192	99.74 108	99.74 83

test_prior2							98.96 302		98.34 93	99.01 221	99.52 223	98.68 68		97.96 192	99.74 108

DPM-MVS		98.95 117	98.71 129	99.66 71	99.63 129	99.55 80	98.64 336	99.10 297	97.93 142	99.42 130	99.55 211	98.67 71	99.80 169	95.80 305	99.68 123	99.61 137

原ACMM1				99.65 75	99.73 84	99.33 108		99.47 165	97.46 191	99.12 200	99.66 168	98.67 71	99.91 96	97.70 218	99.69 118	99.71 103

HPM-MVS++		99.39 50	99.23 63	99.87 12	99.75 68	99.84 16	99.43 170	99.51 106	98.68 65	99.27 168	99.53 220	98.64 73	99.96 20	98.44 153	99.80 89	99.79 62

abl_6		99.44 32	99.31 43	99.83 36	99.85 26	99.75 43	99.66 53	99.59 44	98.13 116	99.82 23	99.81 67	98.60 74	99.96 20	98.46 151	99.88 38	99.79 62

ZD-MVS					99.71 95	99.79 33		99.61 36	96.84 250	99.56 102	99.54 216	98.58 75	99.96 20	96.93 275	99.75 105

PHI-MVS		99.30 59	99.17 68	99.70 67	99.56 153	99.52 88	99.58 93	99.80 8	97.12 226	99.62 88	99.73 131	98.58 75	99.90 111	98.61 128	99.91 18	99.68 112

dcpmvs_2		99.23 71	99.58 2	98.16 278	99.83 37	94.68 340	99.76 31	99.52 92	99.07 18	99.98 1	99.88 19	98.56 77	99.93 73	99.67 2	99.98 2	99.87 13

test1172		99.43 36	99.29 50	99.85 28	99.75 68	99.82 23	99.60 78	99.56 58	98.28 99	99.74 48	99.79 95	98.53 78	99.95 47	98.55 142	99.78 96	99.79 62

SR-MVS-dyc-post		99.45 28	99.31 43	99.85 28	99.76 57	99.82 23	99.63 65	99.52 92	98.38 87	99.76 44	99.82 54	98.53 78	99.95 47	98.61 128	99.81 85	99.77 72

GST-MVS		99.40 49	99.24 61	99.85 28	99.86 22	99.79 33	99.60 78	99.67 22	97.97 139	99.63 84	99.68 156	98.52 80	99.95 47	98.38 157	99.86 53	99.81 46

ETH3D-3000-0.1		99.21 72	99.02 87	99.77 50	99.73 84	99.69 52	99.38 196	99.51 106	97.45 194	99.61 90	99.75 118	98.51 81	99.91 96	97.45 243	99.83 76	99.71 103

MVS_111021_LR		99.41 46	99.33 34	99.65 75	99.77 53	99.51 90	98.94 308	99.85 6	98.82 52	99.65 79	99.74 124	98.51 81	99.80 169	98.83 96	99.89 35	99.64 129

MVS_111021_HR		99.41 46	99.32 36	99.66 71	99.72 89	99.47 95	98.95 306	99.85 6	98.82 52	99.54 107	99.73 131	98.51 81	99.74 187	98.91 76	99.88 38	99.77 72

旧先验1					99.74 76	99.59 73		99.54 75			99.69 149	98.47 84			99.68 123	99.73 90

DELS-MVS		99.48 21	99.42 17	99.65 75	99.72 89	99.40 103	99.05 278	99.66 27	99.14 7	99.57 101	99.80 83	98.46 85	99.94 58	99.57 8	99.84 67	99.60 139
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
PAPR		98.63 153	98.34 162	99.51 112	99.40 197	99.03 145	98.80 321	99.36 234	96.33 286	99.00 226	99.12 314	98.46 85	99.84 142	95.23 318	99.37 151	99.66 118

zzz-MVS		99.49 17	99.36 27	99.89 4	99.90 4	99.86 13	99.36 203	99.47 165	98.79 57	99.68 62	99.81 67	98.43 87	99.97 12	98.88 79	99.90 25	99.83 33

MTAPA		99.52 14	99.39 21	99.89 4	99.90 4	99.86 13	99.66 53	99.47 165	98.79 57	99.68 62	99.81 67	98.43 87	99.97 12	98.88 79	99.90 25	99.83 33

新几何1				99.75 54	99.75 68	99.59 73		99.54 75	96.76 254	99.29 163	99.64 176	98.43 87	99.94 58	96.92 277	99.66 126	99.72 96

F-COLMAP		99.19 74	99.04 82	99.64 80	99.78 48	99.27 117	99.42 177	99.54 75	97.29 210	99.41 134	99.59 198	98.42 90	99.93 73	98.19 172	99.69 118	99.73 90

ETV-MVS		99.26 66	99.21 64	99.40 128	99.46 182	99.30 113	99.56 106	99.52 92	98.52 75	99.44 126	99.27 295	98.41 91	99.86 130	99.10 57	99.59 134	99.04 215

1121		99.09 99	98.87 109	99.75 54	99.74 76	99.60 70	99.27 230	99.48 147	96.82 253	99.25 175	99.65 169	98.38 92	99.93 73	97.53 234	99.67 125	99.73 90

test12				99.75 54	99.64 126	99.61 68		99.29 272		99.21 184		98.38 92	99.89 119		99.74 108	99.74 83

CSCG		99.32 57	99.32 36	99.32 138	99.85 26	98.29 217	99.71 39	99.66 27	98.11 120	99.41 134	99.80 83	98.37 94	99.96 20	98.99 66	99.96 7	99.72 96

PAPM_NR		99.04 106	98.84 115	99.66 71	99.74 76	99.44 98	99.39 191	99.38 224	97.70 167	99.28 165	99.28 292	98.34 95	99.85 136	96.96 272	99.45 140	99.69 108

TAMVS		99.12 90	99.08 77	99.24 154	99.46 182	98.55 197	99.51 129	99.46 175	98.09 123	99.45 122	99.82 54	98.34 95	99.51 241	98.70 113	98.93 182	99.67 115

ETH3D cwj APD-0.16		99.06 103	98.84 115	99.72 64	99.51 161	99.60 70	99.23 244	99.44 196	97.04 235	99.39 141	99.67 162	98.30 97	99.92 85	97.27 250	99.69 118	99.64 129

MP-MVS		99.33 56	99.15 69	99.87 12	99.88 12	99.82 23	99.66 53	99.46 175	98.09 123	99.48 118	99.74 124	98.29 98	99.96 20	97.93 195	99.87 42	99.82 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test222					99.75 68	99.49 91	98.91 311	99.49 134	96.42 283	99.34 155	99.65 169	98.28 99			99.69 118	99.72 96

PLC	97.94 4	99.02 109	98.85 114	99.53 102	99.66 119	99.01 148	99.24 243	99.52 92	96.85 249	99.27 168	99.48 239	98.25 100	99.91 96	97.76 209	99.62 132	99.65 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSP-MVS		99.42 41	99.27 56	99.88 6	99.89 9	99.80 29	99.67 49	99.50 126	98.70 63	99.77 37	99.49 233	98.21 101	99.95 47	98.46 151	99.77 101	99.88 8
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
DROMVSNet		99.44 32	99.39 21	99.58 90	99.56 153	99.49 91	99.88 2	99.58 50	98.38 87	99.73 50	99.69 149	98.20 102	99.70 210	99.64 3	99.82 82	99.54 152

xiu_mvs_v1_base_debu		99.29 61	99.27 56	99.34 133	99.63 129	98.97 153	99.12 263	99.51 106	98.86 48	99.84 15	99.47 242	98.18 103	99.99 1	99.50 13	99.31 152	99.08 208

xiu_mvs_v1_base		99.29 61	99.27 56	99.34 133	99.63 129	98.97 153	99.12 263	99.51 106	98.86 48	99.84 15	99.47 242	98.18 103	99.99 1	99.50 13	99.31 152	99.08 208

xiu_mvs_v1_base_debi		99.29 61	99.27 56	99.34 133	99.63 129	98.97 153	99.12 263	99.51 106	98.86 48	99.84 15	99.47 242	98.18 103	99.99 1	99.50 13	99.31 152	99.08 208

CS-MVS-test		99.42 41	99.39 21	99.52 108	99.77 53	99.35 106	99.80 20	99.57 52	98.56 71	99.77 37	99.44 248	98.16 106	99.77 178	99.64 3	99.78 96	99.42 181

testtj		99.12 90	98.87 109	99.86 21	99.72 89	99.79 33	99.44 164	99.51 106	97.29 210	99.59 97	99.74 124	98.15 107	99.96 20	96.74 283	99.69 118	99.81 46

EIA-MVS		99.18 76	99.09 76	99.45 122	99.49 173	99.18 125	99.67 49	99.53 86	97.66 173	99.40 139	99.44 248	98.10 108	99.81 164	98.94 71	99.62 132	99.35 189

CNLPA		99.14 82	98.99 92	99.59 87	99.58 147	99.41 101	99.16 255	99.44 196	98.45 81	99.19 190	99.49 233	98.08 109	99.89 119	97.73 213	99.75 105	99.48 169

114514_t		98.93 118	98.67 133	99.72 64	99.85 26	99.53 85	99.62 71	99.59 44	92.65 349	99.71 55	99.78 102	98.06 110	99.90 111	98.84 93	99.91 18	99.74 83

CDS-MVSNet		99.09 99	99.03 84	99.25 152	99.42 189	98.73 183	99.45 160	99.46 175	98.11 120	99.46 121	99.77 109	98.01 111	99.37 263	98.70 113	98.92 184	99.66 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		99.13 84	99.02 87	99.45 122	99.57 149	98.63 191	99.07 273	99.34 243	98.99 31	99.61 90	99.82 54	97.98 112	99.87 127	97.00 268	99.80 89	99.85 18

EI-MVSNet		98.67 149	98.67 133	98.68 228	99.35 206	97.97 232	99.50 135	99.38 224	96.93 246	99.20 187	99.83 47	97.87 113	99.36 267	98.38 157	97.56 242	98.71 252

IterMVS-LS		98.46 158	98.42 157	98.58 235	99.59 145	98.00 230	99.37 199	99.43 204	96.94 245	99.07 212	99.59 198	97.87 113	99.03 319	98.32 165	95.62 300	98.71 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		98.98 114	98.80 120	99.53 102	99.76 57	99.19 123	98.75 326	99.55 67	97.25 214	99.47 119	99.77 109	97.82 115	99.87 127	96.93 275	99.90 25	99.54 152

OMC-MVS		99.08 101	99.04 82	99.20 157	99.67 110	98.22 221	99.28 225	99.52 92	98.07 128	99.66 73	99.81 67	97.79 116	99.78 176	97.79 206	99.81 85	99.60 139

LS3D		99.27 64	99.12 72	99.74 59	99.18 250	99.75 43	99.56 106	99.57 52	98.45 81	99.49 117	99.85 34	97.77 117	99.94 58	98.33 163	99.84 67	99.52 158

PVSNet_Blended_VisFu		99.36 53	99.28 54	99.61 85	99.86 22	99.07 142	99.47 156	99.93 2	97.66 173	99.71 55	99.86 28	97.73 118	99.96 20	99.47 20	99.82 82	99.79 62

1314		98.68 148	98.54 152	99.11 165	98.89 296	98.65 189	99.27 230	99.49 134	96.89 247	97.99 318	99.56 208	97.72 119	99.83 153	97.74 212	99.27 155	98.84 232

MVS_Test		99.10 98	98.97 96	99.48 116	99.49 173	99.14 133	99.67 49	99.34 243	97.31 208	99.58 99	99.76 113	97.65 120	99.82 160	98.87 83	99.07 172	99.46 176

PVSNet_BlendedMVS		98.86 124	98.80 120	99.03 173	99.76 57	98.79 180	99.28 225	99.91 3	97.42 200	99.67 68	99.37 269	97.53 121	99.88 124	98.98 67	97.29 261	98.42 324

PVSNet_Blended		99.08 101	98.97 96	99.42 127	99.76 57	98.79 180	98.78 323	99.91 3	96.74 255	99.67 68	99.49 233	97.53 121	99.88 124	98.98 67	99.85 60	99.60 139

UA-Net		99.42 41	99.29 50	99.80 43	99.62 135	99.55 80	99.50 135	99.70 15	98.79 57	99.77 37	99.96 1	97.45 123	99.96 20	98.92 75	99.90 25	99.89 2

ETH3 D test6400		98.70 145	98.35 161	99.73 61	99.69 105	99.60 70	99.16 255	99.45 187	95.42 318	99.27 168	99.60 195	97.39 124	99.91 96	95.36 316	99.83 76	99.70 105

MVSFormer		99.17 78	99.12 72	99.29 146	99.51 161	98.94 162	99.88 2	99.46 175	97.55 182	99.80 28	99.65 169	97.39 124	99.28 282	99.03 62	99.85 60	99.65 122

lupinMVS		99.13 84	99.01 91	99.46 121	99.51 161	98.94 162	99.05 278	99.16 291	97.86 146	99.80 28	99.56 208	97.39 124	99.86 130	98.94 71	99.85 60	99.58 147

DP-MVS		99.16 80	98.95 100	99.78 48	99.77 53	99.53 85	99.41 179	99.50 126	97.03 237	99.04 218	99.88 19	97.39 124	99.92 85	98.66 121	99.90 25	99.87 13

sss		99.17 78	99.05 79	99.53 102	99.62 135	98.97 153	99.36 203	99.62 34	97.83 151	99.67 68	99.65 169	97.37 128	99.95 47	99.19 47	99.19 160	99.68 112

mvs_anonymous		99.03 108	98.99 92	99.16 161	99.38 201	98.52 203	99.51 129	99.38 224	97.79 157	99.38 144	99.81 67	97.30 129	99.45 246	99.35 29	98.99 179	99.51 164

miper_ehance_all_eth		98.18 182	98.10 177	98.41 257	99.23 237	97.72 247	98.72 329	99.31 263	96.60 268	98.88 243	99.29 290	97.29 130	99.13 306	97.60 224	95.99 289	98.38 329

CPTT-MVS		99.11 95	98.90 105	99.74 59	99.80 45	99.46 96	99.59 85	99.49 134	97.03 237	99.63 84	99.69 149	97.27 131	99.96 20	97.82 204	99.84 67	99.81 46

PMMVS		98.80 139	98.62 143	99.34 133	99.27 229	98.70 185	98.76 325	99.31 263	97.34 205	99.21 184	99.07 316	97.20 132	99.82 160	98.56 139	98.87 187	99.52 158

EPP-MVSNet		99.13 84	98.99 92	99.53 102	99.65 124	99.06 143	99.81 16	99.33 250	97.43 198	99.60 94	99.88 19	97.14 133	99.84 142	99.13 54	98.94 181	99.69 108

c3_l		98.12 190	98.04 186	98.38 261	99.30 220	97.69 250	98.81 320	99.33 250	96.67 260	98.83 251	99.34 278	97.11 134	98.99 325	97.58 226	95.34 306	98.48 315

canonicalmvs		99.02 109	98.86 113	99.51 112	99.42 189	99.32 109	99.80 20	99.48 147	98.63 66	99.31 158	98.81 334	97.09 135	99.75 186	99.27 41	97.90 230	99.47 174

MAR-MVS		98.86 124	98.63 138	99.54 96	99.37 203	99.66 59	99.45 160	99.54 75	96.61 266	99.01 221	99.40 261	97.09 135	99.86 130	97.68 221	99.53 138	99.10 203
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
miper_enhance_ethall		98.16 184	98.08 181	98.41 257	98.96 291	97.72 247	98.45 346	99.32 260	96.95 243	98.97 230	99.17 306	97.06 137	99.22 292	97.86 200	95.99 289	98.29 332

jason		99.13 84	99.03 84	99.45 122	99.46 182	98.87 169	99.12 263	99.26 276	98.03 136	99.79 30	99.65 169	97.02 138	99.85 136	99.02 64	99.90 25	99.65 122
jason: jason.
our_test_3		97.65 266	97.68 226	97.55 315	98.62 330	94.97 334	98.84 317	99.30 267	96.83 252	98.19 309	99.34 278	97.01 139	99.02 321	95.00 322	96.01 287	98.64 284

MVS		97.28 285	96.55 294	99.48 116	98.78 312	98.95 159	99.27 230	99.39 218	83.53 364	98.08 313	99.54 216	96.97 140	99.87 127	94.23 330	99.16 161	99.63 133

Fast-Effi-MVS+-dtu		98.77 142	98.83 119	98.60 231	99.41 192	96.99 278	99.52 124	99.49 134	98.11 120	99.24 176	99.34 278	96.96 141	99.79 172	97.95 194	99.45 140	99.02 218

1112_ss		98.98 114	98.77 123	99.59 87	99.68 109	99.02 146	99.25 241	99.48 147	97.23 217	99.13 198	99.58 201	96.93 142	99.90 111	98.87 83	98.78 193	99.84 22

WTY-MVS		99.06 103	98.88 108	99.61 85	99.62 135	99.16 128	99.37 199	99.56 58	98.04 134	99.53 109	99.62 188	96.84 143	99.94 58	98.85 90	98.49 206	99.72 96

FC-MVSNet-test		98.75 143	98.62 143	99.15 163	99.08 272	99.45 97	99.86 9	99.60 41	98.23 105	98.70 270	99.82 54	96.80 144	99.22 292	99.07 60	96.38 280	98.79 235

Effi-MVS+-dtu		98.78 140	98.89 107	98.47 250	99.33 211	96.91 284	99.57 99	99.30 267	98.47 78	99.41 134	98.99 325	96.78 145	99.74 187	98.73 109	99.38 144	98.74 248

mvs-test1		98.86 124	98.84 115	98.89 198	99.33 211	97.77 244	99.44 164	99.30 267	98.47 78	99.10 205	99.43 251	96.78 145	99.95 47	98.73 109	99.02 177	98.96 225

Test_1112_low_res		98.89 120	98.66 136	99.57 92	99.69 105	98.95 159	99.03 284	99.47 165	96.98 239	99.15 196	99.23 300	96.77 147	99.89 119	98.83 96	98.78 193	99.86 15

FIs		98.78 140	98.63 138	99.23 156	99.18 250	99.54 82	99.83 13	99.59 44	98.28 99	98.79 257	99.81 67	96.75 148	99.37 263	99.08 59	96.38 280	98.78 236

PVSNet	96.02 17	98.85 132	98.84 115	98.89 198	99.73 84	97.28 257	98.32 353	99.60 41	97.86 146	99.50 114	99.57 205	96.75 148	99.86 130	98.56 139	99.70 117	99.54 152

nrg030		98.64 152	98.42 157	99.28 149	99.05 278	99.69 52	99.81 16	99.46 175	98.04 134	99.01 221	99.82 54	96.69 150	99.38 260	99.34 33	94.59 320	98.78 236

CHOSEN 280x420		99.12 90	99.13 71	99.08 166	99.66 119	97.89 238	98.43 347	99.71 13	98.88 47	99.62 88	99.76 113	96.63 151	99.70 210	99.46 21	99.99 1	99.66 118

eth_miper_zixun_eth		98.05 200	97.96 195	98.33 264	99.26 231	97.38 255	98.56 342	99.31 263	96.65 262	98.88 243	99.52 223	96.58 152	99.12 310	97.39 247	95.53 303	98.47 317

cdsmvs_eth3d_5k		24.64 345	32.85 348	0.00 361	0.00 384	0.00 385	0.00 372	99.51 106	0.00 379	0.00 380	99.56 208	96.58 152	0.00 380	0.00 378	0.00 378	0.00 376

IS-MVSNet		99.05 105	98.87 109	99.57 92	99.73 84	99.32 109	99.75 33	99.20 286	98.02 137	99.56 102	99.86 28	96.54 154	99.67 216	98.09 181	99.13 165	99.73 90

diffmvs		99.14 82	99.02 87	99.51 112	99.61 139	98.96 157	99.28 225	99.49 134	98.46 80	99.72 54	99.71 136	96.50 155	99.88 124	99.31 36	99.11 166	99.67 115

CANet		99.25 69	99.14 70	99.59 87	99.41 192	99.16 128	99.35 209	99.57 52	98.82 52	99.51 113	99.61 192	96.46 156	99.95 47	99.59 6	99.98 2	99.65 122

ppachtmachnet_test		97.49 279	97.45 250	97.61 312	98.62 330	95.24 328	98.80 321	99.46 175	96.11 307	98.22 308	99.62 188	96.45 157	98.97 333	93.77 334	95.97 292	98.61 303

HY-MVS	97.30 7	98.85 132	98.64 137	99.47 119	99.42 189	99.08 140	99.62 71	99.36 234	97.39 203	99.28 165	99.68 156	96.44 158	99.92 85	98.37 159	98.22 215	99.40 186

UniMVSNet_NR-MVSNet		98.22 176	97.97 193	98.96 182	98.92 294	98.98 150	99.48 151	99.53 86	97.76 160	98.71 264	99.46 246	96.43 159	99.22 292	98.57 136	92.87 343	98.69 260

Effi-MVS+		98.81 136	98.59 149	99.48 116	99.46 182	99.12 137	98.08 359	99.50 126	97.50 190	99.38 144	99.41 258	96.37 160	99.81 164	99.11 56	98.54 203	99.51 164

AdaColmap		99.01 112	98.80 120	99.66 71	99.56 153	99.54 82	99.18 253	99.70 15	98.18 112	99.35 152	99.63 182	96.32 161	99.90 111	97.48 238	99.77 101	99.55 150

UniMVSNet (Re)		98.29 173	98.00 190	99.13 164	99.00 284	99.36 105	99.49 145	99.51 106	97.95 140	98.97 230	99.13 311	96.30 162	99.38 260	98.36 161	93.34 336	98.66 280

LCM-MVSNet-Re		97.83 233	98.15 172	96.87 332	99.30 220	92.25 361	99.59 85	98.26 348	97.43 198	96.20 347	99.13 311	96.27 163	98.73 342	98.17 176	98.99 179	99.64 129

PAPM		97.59 269	97.09 285	99.07 167	99.06 275	98.26 220	98.30 354	99.10 297	94.88 327	98.08 313	99.34 278	96.27 163	99.64 226	89.87 356	98.92 184	99.31 193

Fast-Effi-MVS+		98.70 145	98.43 156	99.51 112	99.51 161	99.28 115	99.52 124	99.47 165	96.11 307	99.01 221	99.34 278	96.20 165	99.84 142	97.88 198	98.82 190	99.39 187

EPNet_dtu		98.03 203	97.96 195	98.23 274	98.27 342	95.54 321	99.23 244	98.75 330	99.02 21	97.82 323	99.71 136	96.11 166	99.48 242	93.04 343	99.65 128	99.69 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline		99.15 81	99.02 87	99.53 102	99.66 119	99.14 133	99.72 37	99.48 147	98.35 92	99.42 130	99.84 43	96.07 167	99.79 172	99.51 12	99.14 164	99.67 115

D2MVS		98.41 163	98.50 153	98.15 281	99.26 231	96.62 294	99.40 187	99.61 36	97.71 166	98.98 228	99.36 272	96.04 168	99.67 216	98.70 113	97.41 257	98.15 339

miper_lstm_enhance		98.00 210	97.91 201	98.28 272	99.34 210	97.43 254	98.88 313	99.36 234	96.48 278	98.80 255	99.55 211	95.98 169	98.91 337	97.27 250	95.50 304	98.51 313

EPNet		98.86 124	98.71 129	99.30 143	97.20 359	98.18 222	99.62 71	98.91 319	99.28 3	98.63 281	99.81 67	95.96 170	99.99 1	99.24 43	99.72 112	99.73 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest		98.87 121	98.72 127	99.31 139	99.86 22	98.48 209	99.56 106	99.61 36	97.85 148	99.36 149	99.85 34	95.95 171	99.85 136	96.66 289	99.83 76	99.59 143

TestCases				99.31 139	99.86 22	98.48 209		99.61 36	97.85 148	99.36 149	99.85 34	95.95 171	99.85 136	96.66 289	99.83 76	99.59 143

3Dnovator	97.25 9	99.24 70	99.05 79	99.81 41	99.12 263	99.66 59	99.84 10	99.74 10	99.09 15	98.92 237	99.90 10	95.94 173	99.98 7	98.95 70	99.92 13	99.79 62

casdiffmvs		99.13 84	98.98 95	99.56 94	99.65 124	99.16 128	99.56 106	99.50 126	98.33 96	99.41 134	99.86 28	95.92 174	99.83 153	99.45 22	99.16 161	99.70 105

RPSCF		98.22 176	98.62 143	96.99 327	99.82 39	91.58 364	99.72 37	99.44 196	96.61 266	99.66 73	99.89 14	95.92 174	99.82 160	97.46 241	99.10 169	99.57 148

pmmvs4		98.13 188	97.90 202	98.81 216	98.61 332	98.87 169	98.99 294	99.21 285	96.44 281	99.06 216	99.58 201	95.90 176	99.11 311	97.18 260	96.11 286	98.46 321

HyFIR lowres test		99.11 95	98.92 102	99.65 75	99.90 4	99.37 104	99.02 287	99.91 3	97.67 172	99.59 97	99.75 118	95.90 176	99.73 194	99.53 10	99.02 177	99.86 15

COLMAP_ROB	97.56 6	98.86 124	98.75 126	99.17 160	99.88 12	98.53 199	99.34 212	99.59 44	97.55 182	98.70 270	99.89 14	95.83 178	99.90 111	98.10 180	99.90 25	99.08 208
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	98.35 2	99.30 59	99.19 66	99.64 80	99.82 39	99.23 121	99.62 71	99.55 67	98.94 41	99.63 84	99.95 2	95.82 179	99.94 58	99.37 28	99.97 5	99.73 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
QAPM		98.67 149	98.30 166	99.80 43	99.20 245	99.67 57	99.77 28	99.72 11	94.74 330	98.73 262	99.90 10	95.78 180	99.98 7	96.96 272	99.88 38	99.76 77

BH-untuned		98.42 161	98.36 159	98.59 232	99.49 173	96.70 290	99.27 230	99.13 295	97.24 216	98.80 255	99.38 266	95.75 181	99.74 187	97.07 266	99.16 161	99.33 192

test_djsdf		98.67 149	98.57 150	98.98 179	98.70 323	98.91 166	99.88 2	99.46 175	97.55 182	99.22 181	99.88 19	95.73 182	99.28 282	99.03 62	97.62 237	98.75 244

DIV-MVS_self_test		98.01 208	97.85 208	98.48 246	99.24 236	97.95 236	98.71 330	99.35 239	96.50 273	98.60 286	99.54 216	95.72 183	99.03 319	97.21 254	95.77 295	98.46 321

bset_n11_16_dypcd		98.16 184	97.97 193	98.73 223	98.26 343	98.28 219	97.99 361	98.01 354	97.68 169	99.10 205	99.63 182	95.68 184	99.15 302	98.78 105	96.55 275	98.75 244

3Dnovator+	97.12 13	99.18 76	98.97 96	99.82 38	99.17 256	99.68 54	99.81 16	99.51 106	99.20 5	98.72 263	99.89 14	95.68 184	99.97 12	98.86 88	99.86 53	99.81 46

cl____		98.01 208	97.84 209	98.55 240	99.25 235	97.97 232	98.71 330	99.34 243	96.47 280	98.59 287	99.54 216	95.65 186	99.21 297	97.21 254	95.77 295	98.46 321

VNet		99.11 95	98.90 105	99.73 61	99.52 159	99.56 78	99.41 179	99.39 218	99.01 24	99.74 48	99.78 102	95.56 187	99.92 85	99.52 11	98.18 220	99.72 96

WR-MVS_H		98.13 188	97.87 207	98.90 195	99.02 282	98.84 173	99.70 40	99.59 44	97.27 212	98.40 298	99.19 305	95.53 188	99.23 289	98.34 162	93.78 332	98.61 303

CHOSEN 1792x2688		99.19 74	99.10 74	99.45 122	99.89 9	98.52 203	99.39 191	99.94 1	98.73 61	99.11 202	99.89 14	95.50 189	99.94 58	99.50 13	99.97 5	99.89 2

Vis-MVSNet (Re-imp)		98.87 121	98.72 127	99.31 139	99.71 95	98.88 168	99.80 20	99.44 196	97.91 144	99.36 149	99.78 102	95.49 190	99.43 255	97.91 196	99.11 166	99.62 135

PatchMatch-RL		98.84 135	98.62 143	99.52 108	99.71 95	99.28 115	99.06 276	99.77 9	97.74 164	99.50 114	99.53 220	95.41 191	99.84 142	97.17 261	99.64 129	99.44 179

RRT_MVS		98.60 154	98.44 155	99.05 170	98.88 297	99.14 133	99.49 145	99.38 224	97.76 160	99.29 163	99.86 28	95.38 192	99.36 267	98.81 101	97.16 266	98.64 284

test_yl		98.86 124	98.63 138	99.54 96	99.49 173	99.18 125	99.50 135	99.07 302	98.22 106	99.61 90	99.51 227	95.37 193	99.84 142	98.60 131	98.33 209	99.59 143

DCV-MVSNet		98.86 124	98.63 138	99.54 96	99.49 173	99.18 125	99.50 135	99.07 302	98.22 106	99.61 90	99.51 227	95.37 193	99.84 142	98.60 131	98.33 209	99.59 143

tpmrst		98.33 169	98.48 154	97.90 298	99.16 258	94.78 338	99.31 217	99.11 296	97.27 212	99.45 122	99.59 198	95.33 195	99.84 142	98.48 147	98.61 196	99.09 207

MVP-Stereo		97.81 238	97.75 220	97.99 292	97.53 352	96.60 295	98.96 302	98.85 325	97.22 218	97.23 334	99.36 272	95.28 196	99.46 245	95.51 311	99.78 96	97.92 353
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CANet_DTU		98.97 116	98.87 109	99.25 152	99.33 211	98.42 214	99.08 272	99.30 267	99.16 6	99.43 127	99.75 118	95.27 197	99.97 12	98.56 139	99.95 8	99.36 188

XVG-OURS		98.73 144	98.68 132	98.88 201	99.70 102	97.73 246	98.92 309	99.55 67	98.52 75	99.45 122	99.84 43	95.27 197	99.91 96	98.08 185	98.84 189	99.00 219

BH-w/o		98.00 210	97.89 206	98.32 266	99.35 206	96.20 307	99.01 292	98.90 321	96.42 283	98.38 299	99.00 324	95.26 199	99.72 198	96.06 299	98.61 196	99.03 216

EU-MVSNet		97.98 212	98.03 187	97.81 305	98.72 320	96.65 293	99.66 53	99.66 27	98.09 123	98.35 302	99.82 54	95.25 200	98.01 352	97.41 246	95.30 307	98.78 236

GeoE		98.85 132	98.62 143	99.53 102	99.61 139	99.08 140	99.80 20	99.51 106	97.10 230	99.31 158	99.78 102	95.23 201	99.77 178	98.21 170	99.03 175	99.75 78

MDTV_nov1_ep13_2view						95.18 331	99.35 209		96.84 250	99.58 99		95.19 202		97.82 204		99.46 176

JIA-IIPM		97.50 276	97.02 287	98.93 187	98.73 318	97.80 243	99.30 219	98.97 310	91.73 352	98.91 238	94.86 365	95.10 203	99.71 204	97.58 226	97.98 228	99.28 195

NR-MVSNet		97.97 215	97.61 233	99.02 174	98.87 301	99.26 118	99.47 156	99.42 206	97.63 175	97.08 339	99.50 230	95.07 204	99.13 306	97.86 200	93.59 334	98.68 265

tpmvs		97.98 212	98.02 189	97.84 301	99.04 279	94.73 339	99.31 217	99.20 286	96.10 311	98.76 260	99.42 254	94.94 205	99.81 164	96.97 271	98.45 207	98.97 223

h-mvs33		97.70 258	97.28 276	98.97 181	99.70 102	97.27 258	99.36 203	99.45 187	98.94 41	99.66 73	99.64 176	94.93 206	99.99 1	99.48 18	84.36 360	99.65 122

hse-mvs2		97.50 276	97.14 283	98.59 232	99.49 173	97.05 271	99.28 225	99.22 282	98.94 41	99.66 73	99.42 254	94.93 206	99.65 223	99.48 18	83.80 362	99.08 208

v8		97.95 216	97.63 232	98.93 187	98.95 292	98.81 179	99.80 20	99.41 208	96.03 312	99.10 205	99.42 254	94.92 208	99.30 280	96.94 274	94.08 329	98.66 280

PatchmatchNet		98.31 170	98.36 159	98.19 276	99.16 258	95.32 327	99.27 230	98.92 316	97.37 204	99.37 146	99.58 201	94.90 209	99.70 210	97.43 245	99.21 158	99.54 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v7n		97.87 225	97.52 241	98.92 189	98.76 316	98.58 195	99.84 10	99.46 175	96.20 297	98.91 238	99.70 140	94.89 210	99.44 251	96.03 300	93.89 331	98.75 244

sam_mvs1												94.86 211				99.52 158

DU-MVS		98.08 194	97.79 211	98.96 182	98.87 301	98.98 150	99.41 179	99.45 187	97.87 145	98.71 264	99.50 230	94.82 212	99.22 292	98.57 136	92.87 343	98.68 265

Baseline_NR-MVSNet		97.76 243	97.45 250	98.68 228	99.09 270	98.29 217	99.41 179	98.85 325	95.65 316	98.63 281	99.67 162	94.82 212	99.10 313	98.07 188	92.89 342	98.64 284

patchmatchnet-post											98.70 339	94.79 214	99.74 187

Patchmatch-RL test		95.84 311	95.81 309	95.95 340	95.61 365	90.57 365	98.24 355	98.39 347	95.10 324	95.20 353	98.67 340	94.78 215	97.77 357	96.28 297	90.02 352	99.51 164

alignmvs		98.81 136	98.56 151	99.58 90	99.43 188	99.42 100	99.51 129	98.96 312	98.61 68	99.35 152	98.92 331	94.78 215	99.77 178	99.35 29	98.11 226	99.54 152

MDTV_nov1_ep13			98.32 164		99.11 265	94.44 343	99.27 230	98.74 333	97.51 189	99.40 139	99.62 188	94.78 215	99.76 184	97.59 225	98.81 192

Vis-MVSNet		99.12 90	98.97 96	99.56 94	99.78 48	99.10 138	99.68 47	99.66 27	98.49 77	99.86 13	99.87 25	94.77 218	99.84 142	99.19 47	99.41 143	99.74 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
anonymousdsp		98.44 159	98.28 167	98.94 185	98.50 338	98.96 157	99.77 28	99.50 126	97.07 232	98.87 245	99.77 109	94.76 219	99.28 282	98.66 121	97.60 238	98.57 309

v10		97.85 228	97.52 241	98.86 208	98.99 285	98.67 187	99.75 33	99.41 208	95.70 315	98.98 228	99.41 258	94.75 220	99.23 289	96.01 301	94.63 319	98.67 272

OpenMVS	96.50 16	98.47 157	98.12 175	99.52 108	99.04 279	99.53 85	99.82 14	99.72 11	94.56 333	98.08 313	99.88 19	94.73 221	99.98 7	97.47 240	99.76 104	99.06 214

sam_mvs												94.72 222

v148		97.79 241	97.55 237	98.50 243	98.74 317	97.72 247	99.54 118	99.33 250	96.26 292	98.90 240	99.51 227	94.68 223	99.14 303	97.83 203	93.15 340	98.63 292

v1144		97.98 212	97.69 225	98.85 211	98.87 301	98.66 188	99.54 118	99.35 239	96.27 291	99.23 180	99.35 275	94.67 224	99.23 289	96.73 284	95.16 310	98.68 265

V42		98.06 195	97.79 211	98.86 208	98.98 288	98.84 173	99.69 42	99.34 243	96.53 272	99.30 160	99.37 269	94.67 224	99.32 277	97.57 230	94.66 318	98.42 324

test_post											65.99 375	94.65 226	99.73 194

baseline1		98.31 170	97.95 197	99.38 131	99.50 171	98.74 182	99.59 85	98.93 314	98.41 85	99.14 197	99.60 195	94.59 227	99.79 172	98.48 147	93.29 337	99.61 137

DSMNet-mixed		97.25 286	97.35 267	96.95 330	97.84 349	93.61 355	99.57 99	96.63 367	96.13 306	98.87 245	98.61 343	94.59 227	97.70 359	95.08 320	98.86 188	99.55 150

Patchmatch-test		97.93 217	97.65 229	98.77 221	99.18 250	97.07 269	99.03 284	99.14 294	96.16 302	98.74 261	99.57 205	94.56 229	99.72 198	93.36 339	99.11 166	99.52 158

PCF-MVS	97.08 14	97.66 265	97.06 286	99.47 119	99.61 139	99.09 139	98.04 360	99.25 278	91.24 354	98.51 290	99.70 140	94.55 230	99.91 96	92.76 347	99.85 60	99.42 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchT		97.03 291	96.44 296	98.79 219	98.99 285	98.34 216	99.16 255	99.07 302	92.13 350	99.52 111	97.31 360	94.54 231	98.98 326	88.54 361	98.73 195	99.03 216

CVMVSNet		98.57 155	98.67 133	98.30 268	99.35 206	95.59 318	99.50 135	99.55 67	98.60 69	99.39 141	99.83 47	94.48 232	99.45 246	98.75 106	98.56 202	99.85 18

test-LLR		98.06 195	97.90 202	98.55 240	98.79 309	97.10 265	98.67 332	97.75 357	97.34 205	98.61 284	98.85 332	94.45 233	99.45 246	97.25 252	99.38 144	99.10 203

test0.0.03 1		97.71 257	97.42 259	98.56 238	98.41 341	97.82 242	98.78 323	98.63 342	97.34 205	98.05 317	98.98 328	94.45 233	98.98 326	95.04 321	97.15 267	98.89 229

v144192		97.92 220	97.60 234	98.87 205	98.83 307	98.65 189	99.55 115	99.34 243	96.20 297	99.32 157	99.40 261	94.36 235	99.26 286	96.37 296	95.03 313	98.70 256

CR-MVSNet		98.17 183	97.93 200	98.87 205	99.18 250	98.49 207	99.22 249	99.33 250	96.96 241	99.56 102	99.38 266	94.33 236	99.00 324	94.83 324	98.58 199	99.14 200

Patchmtry		97.75 247	97.40 261	98.81 216	99.10 268	98.87 169	99.11 269	99.33 250	94.83 328	98.81 253	99.38 266	94.33 236	99.02 321	96.10 298	95.57 301	98.53 311

tpm cat1		97.39 282	97.36 265	97.50 317	99.17 256	93.73 351	99.43 170	99.31 263	91.27 353	98.71 264	99.08 315	94.31 238	99.77 178	96.41 295	98.50 205	99.00 219

TranMVSNet+NR-MVSNet		97.93 217	97.66 228	98.76 222	98.78 312	98.62 192	99.65 60	99.49 134	97.76 160	98.49 292	99.60 195	94.23 239	98.97 333	98.00 190	92.90 341	98.70 256

v2v482		98.06 195	97.77 216	98.92 189	98.90 295	98.82 177	99.57 99	99.36 234	96.65 262	99.19 190	99.35 275	94.20 240	99.25 287	97.72 215	94.97 314	98.69 260

XVG-OURS-SEG-HR		98.69 147	98.62 143	98.89 198	99.71 95	97.74 245	99.12 263	99.54 75	98.44 84	99.42 130	99.71 136	94.20 240	99.92 85	98.54 144	98.90 186	99.00 219

ab-mvs		98.86 124	98.63 138	99.54 96	99.64 126	99.19 123	99.44 164	99.54 75	97.77 159	99.30 160	99.81 67	94.20 240	99.93 73	99.17 50	98.82 190	99.49 168

test_post1							99.23 244				65.14 376	94.18 243	99.71 204	97.58 226

ADS-MVSNet2		98.02 205	98.07 184	97.87 299	99.33 211	95.19 330	99.23 244	99.08 300	96.24 294	99.10 205	99.67 162	94.11 244	98.93 336	96.81 280	99.05 173	99.48 169

ADS-MVSNet		98.20 179	98.08 181	98.56 238	99.33 211	96.48 298	99.23 244	99.15 292	96.24 294	99.10 205	99.67 162	94.11 244	99.71 204	96.81 280	99.05 173	99.48 169

RPMNet		96.72 296	95.90 306	99.19 158	99.18 250	98.49 207	99.22 249	99.52 92	88.72 360	99.56 102	97.38 357	94.08 246	99.95 47	86.87 367	98.58 199	99.14 200

v1192		97.81 238	97.44 255	98.91 193	98.88 297	98.68 186	99.51 129	99.34 243	96.18 299	99.20 187	99.34 278	94.03 247	99.36 267	95.32 317	95.18 309	98.69 260

v1921920		97.80 240	97.45 250	98.84 212	98.80 308	98.53 199	99.52 124	99.34 243	96.15 304	99.24 176	99.47 242	93.98 248	99.29 281	95.40 314	95.13 311	98.69 260

Anonymous20231206		96.22 304	96.03 303	96.79 334	97.31 357	94.14 347	99.63 65	99.08 300	96.17 300	97.04 340	99.06 318	93.94 249	97.76 358	86.96 366	95.06 312	98.47 317

WR-MVS		98.06 195	97.73 222	99.06 168	98.86 304	99.25 119	99.19 252	99.35 239	97.30 209	98.66 273	99.43 251	93.94 249	99.21 297	98.58 134	94.28 325	98.71 252

N_pmnet		94.95 320	95.83 308	92.31 346	98.47 339	79.33 373	99.12 263	92.81 379	93.87 338	97.68 326	99.13 311	93.87 251	99.01 323	91.38 351	96.19 284	98.59 307

MVSTER		98.49 156	98.32 164	99.00 177	99.35 206	99.02 146	99.54 118	99.38 224	97.41 201	99.20 187	99.73 131	93.86 252	99.36 267	98.87 83	97.56 242	98.62 294

CP-MVSNet		98.09 192	97.78 214	99.01 175	98.97 290	99.24 120	99.67 49	99.46 175	97.25 214	98.48 293	99.64 176	93.79 253	99.06 315	98.63 124	94.10 328	98.74 248

cascas		97.69 259	97.43 258	98.48 246	98.60 333	97.30 256	98.18 358	99.39 218	92.96 348	98.41 297	98.78 337	93.77 254	99.27 285	98.16 177	98.61 196	98.86 230

v1240		97.69 259	97.32 273	98.79 219	98.85 305	98.43 212	99.48 151	99.36 234	96.11 307	99.27 168	99.36 272	93.76 255	99.24 288	94.46 327	95.23 308	98.70 256

test20.03		96.12 308	95.96 305	96.63 335	97.44 353	95.45 324	99.51 129	99.38 224	96.55 271	96.16 348	99.25 298	93.76 255	96.17 368	87.35 365	94.22 326	98.27 333

baseline2		97.87 225	97.55 237	98.82 214	99.18 250	98.02 229	99.41 179	96.58 368	96.97 240	96.51 344	99.17 306	93.43 257	99.57 236	97.71 216	99.03 175	98.86 230

TransMVSNet (Re)		97.15 288	96.58 293	98.86 208	99.12 263	98.85 172	99.49 145	98.91 319	95.48 317	97.16 337	99.80 83	93.38 258	99.11 311	94.16 332	91.73 348	98.62 294

tfpnnormal		97.84 231	97.47 247	98.98 179	99.20 245	99.22 122	99.64 63	99.61 36	96.32 287	98.27 307	99.70 140	93.35 259	99.44 251	95.69 307	95.40 305	98.27 333

Anonymous20231211		97.88 223	97.54 240	98.90 195	99.71 95	98.53 199	99.48 151	99.57 52	94.16 336	98.81 253	99.68 156	93.23 260	99.42 256	98.84 93	94.42 323	98.76 242

XXY-MVS		98.38 166	98.09 180	99.24 154	99.26 231	99.32 109	99.56 106	99.55 67	97.45 194	98.71 264	99.83 47	93.23 260	99.63 231	98.88 79	96.32 282	98.76 242

jajsoiax		98.43 160	98.28 167	98.88 201	98.60 333	98.43 212	99.82 14	99.53 86	98.19 109	98.63 281	99.80 83	93.22 262	99.44 251	99.22 44	97.50 248	98.77 240

MDA-MVSNet_test_wron		95.45 314	94.60 320	98.01 289	98.16 345	97.21 263	99.11 269	99.24 280	93.49 343	80.73 370	98.98 328	93.02 263	98.18 347	94.22 331	94.45 322	98.64 284

ACMM	97.58 5	98.37 167	98.34 162	98.48 246	99.41 192	97.10 265	99.56 106	99.45 187	98.53 74	99.04 218	99.85 34	93.00 264	99.71 204	98.74 107	97.45 253	98.64 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		98.03 203	97.76 219	98.84 212	99.39 200	98.98 150	99.40 187	99.38 224	96.67 260	99.07 212	99.28 292	92.93 265	98.98 326	97.10 263	96.65 271	98.56 310

DTE-MVSNet		97.51 275	97.19 282	98.46 251	98.63 329	98.13 226	99.84 10	99.48 147	96.68 259	97.97 319	99.67 162	92.92 266	98.56 343	96.88 279	92.60 346	98.70 256

CLD-MVS		98.16 184	98.10 177	98.33 264	99.29 224	96.82 287	98.75 326	99.44 196	97.83 151	99.13 198	99.55 211	92.92 266	99.67 216	98.32 165	97.69 234	98.48 315
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-RMVSNet		98.41 163	98.08 181	99.40 128	99.41 192	98.83 176	99.30 219	98.77 329	97.70 167	98.94 234	99.65 169	92.91 268	99.74 187	96.52 291	99.55 137	99.64 129

YYNet1		95.36 316	94.51 322	97.92 296	97.89 348	97.10 265	99.10 271	99.23 281	93.26 346	80.77 369	99.04 320	92.81 269	98.02 351	94.30 328	94.18 327	98.64 284

mvs_tets		98.40 165	98.23 169	98.91 193	98.67 326	98.51 205	99.66 53	99.53 86	98.19 109	98.65 279	99.81 67	92.75 270	99.44 251	99.31 36	97.48 252	98.77 240

IterMVS		97.83 233	97.77 216	98.02 288	99.58 147	96.27 305	99.02 287	99.48 147	97.22 218	98.71 264	99.70 140	92.75 270	99.13 306	97.46 241	96.00 288	98.67 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UGNet		98.87 121	98.69 131	99.40 128	99.22 241	98.72 184	99.44 164	99.68 19	99.24 4	99.18 193	99.42 254	92.74 272	99.96 20	99.34 33	99.94 11	99.53 157
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
IterMVS-SCA-FT		97.82 236	97.75 220	98.06 285	99.57 149	96.36 302	99.02 287	99.49 134	97.18 220	98.71 264	99.72 135	92.72 273	99.14 303	97.44 244	95.86 294	98.67 272

SCA		98.19 180	98.16 171	98.27 273	99.30 220	95.55 319	99.07 273	98.97 310	97.57 180	99.43 127	99.57 205	92.72 273	99.74 187	97.58 226	99.20 159	99.52 158

HQP_MVS		98.27 175	98.22 170	98.44 255	99.29 224	96.97 280	99.39 191	99.47 165	98.97 37	99.11 202	99.61 192	92.71 275	99.69 214	97.78 207	97.63 235	98.67 272

plane_prior6					99.27 229	96.98 279						92.71 275

CL-MVSNet_self_test		94.49 323	93.97 326	96.08 339	96.16 363	93.67 354	98.33 352	99.38 224	95.13 320	97.33 332	98.15 352	92.69 277	96.57 366	88.67 360	79.87 366	97.99 348

dp		97.75 247	97.80 210	97.59 313	99.10 268	93.71 352	99.32 215	98.88 323	96.48 278	99.08 211	99.55 211	92.67 278	99.82 160	96.52 291	98.58 199	99.24 196

PEN-MVS		97.76 243	97.44 255	98.72 225	98.77 315	98.54 198	99.78 26	99.51 106	97.06 234	98.29 306	99.64 176	92.63 279	98.89 339	98.09 181	93.16 339	98.72 250

LPG-MVS_test		98.22 176	98.13 174	98.49 244	99.33 211	97.05 271	99.58 93	99.55 67	97.46 191	99.24 176	99.83 47	92.58 280	99.72 198	98.09 181	97.51 246	98.68 265

LGP-MVS_train				98.49 244	99.33 211	97.05 271		99.55 67	97.46 191	99.24 176	99.83 47	92.58 280	99.72 198	98.09 181	97.51 246	98.68 265

VPA-MVSNet		98.29 173	97.95 197	99.30 143	99.16 258	99.54 82	99.50 135	99.58 50	98.27 101	99.35 152	99.37 269	92.53 282	99.65 223	99.35 29	94.46 321	98.72 250

TR-MVS		97.76 243	97.41 260	98.82 214	99.06 275	97.87 239	98.87 315	98.56 344	96.63 265	98.68 272	99.22 301	92.49 283	99.65 223	95.40 314	97.79 232	98.95 228

pm-mvs1		97.68 261	97.28 276	98.88 201	99.06 275	98.62 192	99.50 135	99.45 187	96.32 287	97.87 321	99.79 95	92.47 284	99.35 271	97.54 233	93.54 335	98.67 272

HQP2-MVS												92.47 284

HQP-MVS		98.02 205	97.90 202	98.37 262	99.19 247	96.83 285	98.98 298	99.39 218	98.24 102	98.66 273	99.40 261	92.47 284	99.64 226	97.19 258	97.58 240	98.64 284

EPMVS		97.82 236	97.65 229	98.35 263	98.88 297	95.98 311	99.49 145	94.71 374	97.57 180	99.26 173	99.48 239	92.46 287	99.71 204	97.87 199	99.08 171	99.35 189

PS-CasMVS		97.93 217	97.59 236	98.95 184	98.99 285	99.06 143	99.68 47	99.52 92	97.13 224	98.31 304	99.68 156	92.44 288	99.05 316	98.51 145	94.08 329	98.75 244

cl22		97.85 228	97.64 231	98.48 246	99.09 270	97.87 239	98.60 339	99.33 250	97.11 229	98.87 245	99.22 301	92.38 289	99.17 301	98.21 170	95.99 289	98.42 324

CostFormer		97.72 253	97.73 222	97.71 309	99.15 261	94.02 348	99.54 118	99.02 306	94.67 331	99.04 218	99.35 275	92.35 290	99.77 178	98.50 146	97.94 229	99.34 191

OPM-MVS		98.19 180	98.10 177	98.45 252	98.88 297	97.07 269	99.28 225	99.38 224	98.57 70	99.22 181	99.81 67	92.12 291	99.66 219	98.08 185	97.54 244	98.61 303
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ET-MVSNet_ETH3D		96.49 300	95.64 311	99.05 170	99.53 157	98.82 177	98.84 317	97.51 361	97.63 175	84.77 365	99.21 304	92.09 292	98.91 337	98.98 67	92.21 347	99.41 185

AUN-MVS		96.88 292	96.31 298	98.59 232	99.48 180	97.04 274	99.27 230	99.22 282	97.44 197	98.51 290	99.41 258	91.97 293	99.66 219	97.71 216	83.83 361	99.07 213

ACMP	97.20 11	98.06 195	97.94 199	98.45 252	99.37 203	97.01 276	99.44 164	99.49 134	97.54 185	98.45 294	99.79 95	91.95 294	99.72 198	97.91 196	97.49 251	98.62 294
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous202405211		98.30 172	97.98 192	99.26 151	99.57 149	98.16 223	99.41 179	98.55 345	96.03 312	99.19 190	99.74 124	91.87 295	99.92 85	99.16 52	98.29 214	99.70 105

KD-MVS_self_test		95.00 318	94.34 323	96.96 329	97.07 362	95.39 326	99.56 106	99.44 196	95.11 322	97.13 338	97.32 359	91.86 296	97.27 362	90.35 355	81.23 365	98.23 337

tpm		97.67 264	97.55 237	98.03 286	99.02 282	95.01 333	99.43 170	98.54 346	96.44 281	99.12 200	99.34 278	91.83 297	99.60 234	97.75 211	96.46 278	99.48 169

thres100view900		97.76 243	97.45 250	98.69 227	99.72 89	97.86 241	99.59 85	98.74 333	97.93 142	99.26 173	98.62 341	91.75 298	99.83 153	93.22 340	98.18 220	98.37 330

thres600view7		97.86 227	97.51 243	98.92 189	99.72 89	97.95 236	99.59 85	98.74 333	97.94 141	99.27 168	98.62 341	91.75 298	99.86 130	93.73 335	98.19 219	98.96 225

LTVRE_ROB	97.16 12	98.02 205	97.90 202	98.40 259	99.23 237	96.80 288	99.70 40	99.60 41	97.12 226	98.18 310	99.70 140	91.73 300	99.72 198	98.39 155	97.45 253	98.68 265
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
OurMVSNet-221017-0		97.88 223	97.77 216	98.19 276	98.71 322	96.53 296	99.88 2	99.00 307	97.79 157	98.78 258	99.94 3	91.68 301	99.35 271	97.21 254	96.99 269	98.69 260

tfpn200view9		97.72 253	97.38 263	98.72 225	99.69 105	97.96 234	99.50 135	98.73 338	97.83 151	99.17 194	98.45 346	91.67 302	99.83 153	93.22 340	98.18 220	98.37 330

thres400		97.77 242	97.38 263	98.92 189	99.69 105	97.96 234	99.50 135	98.73 338	97.83 151	99.17 194	98.45 346	91.67 302	99.83 153	93.22 340	98.18 220	98.96 225

thisisatest0515		98.14 187	97.79 211	99.19 158	99.50 171	98.50 206	98.61 337	96.82 365	96.95 243	99.54 107	99.43 251	91.66 304	99.86 130	98.08 185	99.51 139	99.22 197

thres200		97.61 268	97.28 276	98.62 230	99.64 126	98.03 228	99.26 239	98.74 333	97.68 169	99.09 210	98.32 350	91.66 304	99.81 164	92.88 344	98.22 215	98.03 344

new_pmnet		96.38 303	96.03 303	97.41 318	98.13 346	95.16 332	99.05 278	99.20 286	93.94 337	97.39 331	98.79 335	91.61 306	99.04 317	90.43 354	95.77 295	98.05 343

pmmvs5		97.52 273	97.30 275	98.16 278	98.57 335	96.73 289	99.27 230	98.90 321	96.14 305	98.37 300	99.53 220	91.54 307	99.14 303	97.51 236	95.87 293	98.63 292

tttt0517		98.42 161	98.14 173	99.28 149	99.66 119	98.38 215	99.74 36	96.85 364	97.68 169	99.79 30	99.74 124	91.39 308	99.89 119	98.83 96	99.56 135	99.57 148

tpm2		97.44 281	97.34 270	97.74 308	99.15 261	94.36 345	99.45 160	98.94 313	93.45 345	98.90 240	99.44 248	91.35 309	99.59 235	97.31 248	98.07 227	99.29 194

MVS-HIRNet		95.75 312	95.16 316	97.51 316	99.30 220	93.69 353	98.88 313	95.78 369	85.09 363	98.78 258	92.65 367	91.29 310	99.37 263	94.85 323	99.85 60	99.46 176

thisisatest0530		98.35 168	98.03 187	99.31 139	99.63 129	98.56 196	99.54 118	96.75 366	97.53 187	99.73 50	99.65 169	91.25 311	99.89 119	98.62 125	99.56 135	99.48 169

testgi		97.65 266	97.50 244	98.13 282	99.36 205	96.45 299	99.42 177	99.48 147	97.76 160	97.87 321	99.45 247	91.09 312	98.81 340	94.53 326	98.52 204	99.13 202

ITE_SJBPF				98.08 283	99.29 224	96.37 301		98.92 316	98.34 93	98.83 251	99.75 118	91.09 312	99.62 232	95.82 303	97.40 258	98.25 335

DeepMVS_CX				93.34 344	99.29 224	82.27 370		99.22 282	85.15 362	96.33 346	99.05 319	90.97 314	99.73 194	93.57 337	97.77 233	98.01 345

ACMH	97.28 8	98.10 191	97.99 191	98.44 255	99.41 192	96.96 282	99.60 78	99.56 58	98.09 123	98.15 311	99.91 8	90.87 315	99.70 210	98.88 79	97.45 253	98.67 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		98.04 201	98.11 176	97.83 302	99.74 76	93.82 349	99.58 93	95.40 371	99.12 10	99.65 79	99.93 4	90.73 316	99.84 142	99.43 25	99.38 144	99.82 40

ECVR-MVS		98.04 201	98.05 185	98.00 291	99.74 76	94.37 344	99.59 85	94.98 372	99.13 8	99.66 73	99.93 4	90.67 317	99.84 142	99.40 26	99.38 144	99.80 56

SixPastTwentyTwo		97.50 276	97.33 272	98.03 286	98.65 327	96.23 306	99.77 28	98.68 341	97.14 223	97.90 320	99.93 4	90.45 318	99.18 300	97.00 268	96.43 279	98.67 272

MIMVSNet		97.73 251	97.45 250	98.57 236	99.45 187	97.50 252	99.02 287	98.98 309	96.11 307	99.41 134	99.14 310	90.28 319	98.74 341	95.74 306	98.93 182	99.47 174

GBi-Net		97.68 261	97.48 245	98.29 269	99.51 161	97.26 260	99.43 170	99.48 147	96.49 274	99.07 212	99.32 285	90.26 320	98.98 326	97.10 263	96.65 271	98.62 294

test1		97.68 261	97.48 245	98.29 269	99.51 161	97.26 260	99.43 170	99.48 147	96.49 274	99.07 212	99.32 285	90.26 320	98.98 326	97.10 263	96.65 271	98.62 294

FMVSNet2		97.72 253	97.36 265	98.80 218	99.51 161	98.84 173	99.45 160	99.42 206	96.49 274	98.86 250	99.29 290	90.26 320	98.98 326	96.44 293	96.56 274	98.58 308

Anonymous20240529		98.09 192	97.68 226	99.34 133	99.66 119	98.44 211	99.40 187	99.43 204	93.67 340	99.22 181	99.89 14	90.23 323	99.93 73	99.26 42	98.33 209	99.66 118

ACMH+	97.24 10	97.92 220	97.78 214	98.32 266	99.46 182	96.68 292	99.56 106	99.54 75	98.41 85	97.79 325	99.87 25	90.18 324	99.66 219	98.05 189	97.18 265	98.62 294

LF4IMVS		97.52 273	97.46 249	97.70 310	98.98 288	95.55 319	99.29 223	98.82 328	98.07 128	98.66 273	99.64 176	89.97 325	99.61 233	97.01 267	96.68 270	97.94 351

GA-MVS		97.85 228	97.47 247	99.00 177	99.38 201	97.99 231	98.57 340	99.15 292	97.04 235	98.90 240	99.30 288	89.83 326	99.38 260	96.70 286	98.33 209	99.62 135

test_part1		97.75 247	97.24 280	99.29 146	99.59 145	99.63 65	99.65 60	99.49 134	96.17 300	98.44 295	99.69 149	89.80 327	99.47 243	98.68 118	93.66 333	98.78 236

PVSNet_0	94.43 19	96.09 309	95.47 312	97.94 294	99.31 219	94.34 346	97.81 362	99.70 15	97.12 226	97.46 329	98.75 338	89.71 328	99.79 172	97.69 219	81.69 364	99.68 112

Anonymous20240521		96.20 306	95.89 307	97.13 325	97.72 351	94.96 335	99.79 25	99.29 272	93.01 347	97.20 336	99.03 321	89.69 329	98.36 346	91.16 352	96.13 285	98.07 341

XVG-ACMP-BASELINE		97.83 233	97.71 224	98.20 275	99.11 265	96.33 303	99.41 179	99.52 92	98.06 132	99.05 217	99.50 230	89.64 330	99.73 194	97.73 213	97.38 259	98.53 311

gg-mvs-nofinetune		96.17 307	95.32 315	98.73 223	98.79 309	98.14 225	99.38 196	94.09 375	91.07 356	98.07 316	91.04 370	89.62 331	99.35 271	96.75 282	99.09 170	98.68 265

DWT-MVSNet_test		97.53 272	97.40 261	97.93 295	99.03 281	94.86 337	99.57 99	98.63 342	96.59 270	98.36 301	98.79 335	89.32 332	99.74 187	98.14 179	98.16 224	99.20 199

GG-mvs-BLEND				98.45 252	98.55 336	98.16 223	99.43 170	93.68 376		97.23 334	98.46 345	89.30 333	99.22 292	95.43 313	98.22 215	97.98 349

USDC		97.34 283	97.20 281	97.75 307	99.07 273	95.20 329	98.51 344	99.04 305	97.99 138	98.31 304	99.86 28	89.02 334	99.55 239	95.67 309	97.36 260	98.49 314

MS-PatchMatch		97.24 287	97.32 273	96.99 327	98.45 340	93.51 356	98.82 319	99.32 260	97.41 201	98.13 312	99.30 288	88.99 335	99.56 237	95.68 308	99.80 89	97.90 354

VPNet		97.84 231	97.44 255	99.01 175	99.21 243	98.94 162	99.48 151	99.57 52	98.38 87	99.28 165	99.73 131	88.89 336	99.39 258	99.19 47	93.27 338	98.71 252

K. test v3		97.10 290	96.79 291	98.01 289	98.72 320	96.33 303	99.87 6	97.05 363	97.59 177	96.16 348	99.80 83	88.71 337	99.04 317	96.69 287	96.55 275	98.65 282

lessismore_v0				97.79 306	98.69 324	95.44 325		94.75 373		95.71 352	99.87 25	88.69 338	99.32 277	95.89 302	94.93 316	98.62 294

TDRefinement		95.42 315	94.57 321	97.97 293	89.83 374	96.11 309	99.48 151	98.75 330	96.74 255	96.68 343	99.88 19	88.65 339	99.71 204	98.37 159	82.74 363	98.09 340

TESTMET0.1,1		97.55 270	97.27 279	98.40 259	98.93 293	96.53 296	98.67 332	97.61 360	96.96 241	98.64 280	99.28 292	88.63 340	99.45 246	97.30 249	99.38 144	99.21 198

test_0402		96.64 297	96.24 299	97.85 300	98.85 305	96.43 300	99.44 164	99.26 276	93.52 342	96.98 341	99.52 223	88.52 341	99.20 299	92.58 349	97.50 248	97.93 352

UnsupCasMVSNet_eth		96.44 301	96.12 301	97.40 319	98.65 327	95.65 316	99.36 203	99.51 106	97.13 224	96.04 350	98.99 325	88.40 342	98.17 348	96.71 285	90.27 351	98.40 327

MDA-MVSNet-bldmvs		94.96 319	93.98 325	97.92 296	98.24 344	97.27 258	99.15 259	99.33 250	93.80 339	80.09 371	99.03 321	88.31 343	97.86 356	93.49 338	94.36 324	98.62 294

test-mter		97.49 279	97.13 284	98.55 240	98.79 309	97.10 265	98.67 332	97.75 357	96.65 262	98.61 284	98.85 332	88.23 344	99.45 246	97.25 252	99.38 144	99.10 203

TinyColmap		97.12 289	96.89 289	97.83 302	99.07 273	95.52 322	98.57 340	98.74 333	97.58 179	97.81 324	99.79 95	88.16 345	99.56 237	95.10 319	97.21 263	98.39 328

pmmvs-eth3d		95.34 317	94.73 319	97.15 323	95.53 367	95.94 312	99.35 209	99.10 297	95.13 320	93.55 358	97.54 355	88.15 346	97.91 354	94.58 325	89.69 354	97.61 356

RRT_test8_iter05		97.72 253	97.60 234	98.08 283	99.23 237	96.08 310	99.63 65	99.49 134	97.54 185	98.94 234	99.81 67	87.99 347	99.35 271	99.21 46	96.51 277	98.81 233

KD-MVS_2432*1600		94.62 321	93.72 327	97.31 320	97.19 360	95.82 314	98.34 350	99.20 286	95.00 325	97.57 327	98.35 348	87.95 348	98.10 349	92.87 345	77.00 368	98.01 345

miper_refine_blended		94.62 321	93.72 327	97.31 320	97.19 360	95.82 314	98.34 350	99.20 286	95.00 325	97.57 327	98.35 348	87.95 348	98.10 349	92.87 345	77.00 368	98.01 345

new-patchmatchnet		94.48 324	94.08 324	95.67 341	95.08 368	92.41 360	99.18 253	99.28 274	94.55 334	93.49 359	97.37 358	87.86 350	97.01 364	91.57 350	88.36 355	97.61 356

test2506		96.81 294	96.65 292	97.29 322	99.74 76	92.21 362	99.60 78	85.06 381	99.13 8	99.77 37	99.93 4	87.82 351	99.85 136	99.38 27	99.38 144	99.80 56

FMVSNet5		96.43 302	96.19 300	97.15 323	99.11 265	95.89 313	99.32 215	99.52 92	94.47 335	98.34 303	99.07 316	87.54 352	97.07 363	92.61 348	95.72 298	98.47 317

pmmvs6		96.53 299	96.09 302	97.82 304	98.69 324	95.47 323	99.37 199	99.47 165	93.46 344	97.41 330	99.78 102	87.06 353	99.33 275	96.92 277	92.70 345	98.65 282

pmmvs3		94.09 327	93.25 330	96.60 336	94.76 369	94.49 342	98.92 309	98.18 352	89.66 357	96.48 345	98.06 353	86.28 354	97.33 361	89.68 357	87.20 357	97.97 350

IB-MVS	95.67 18	96.22 304	95.44 314	98.57 236	99.21 243	96.70 290	98.65 335	97.74 359	96.71 257	97.27 333	98.54 344	86.03 355	99.92 85	98.47 150	86.30 358	99.10 203
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
tmp_tt		82.80 335	81.52 338	86.66 351	66.61 381	68.44 379	92.79 370	97.92 355	68.96 369	80.04 372	99.85 34	85.77 356	96.15 369	97.86 200	43.89 374	95.39 365

CMPMVS	69.68 23	94.13 326	94.90 318	91.84 347	97.24 358	80.01 372	98.52 343	99.48 147	89.01 358	91.99 362	99.67 162	85.67 357	99.13 306	95.44 312	97.03 268	96.39 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		95.51 313	95.04 317	96.92 331	97.38 354	95.60 317	99.52 124	99.50 126	93.65 341	96.97 342	99.17 306	85.28 358	96.56 367	88.36 362	95.55 302	98.60 306

LFMVS		97.90 222	97.35 267	99.54 96	99.52 159	99.01 148	99.39 191	98.24 349	97.10 230	99.65 79	99.79 95	84.79 359	99.91 96	99.28 39	98.38 208	99.69 108

EGC-MVSNET		82.80 335	77.86 341	97.62 311	97.91 347	96.12 308	99.33 214	99.28 274	8.40 378	25.05 379	99.27 295	84.11 360	99.33 275	89.20 358	98.22 215	97.42 360

FMVSNet1		96.84 293	96.36 297	98.29 269	99.32 218	97.26 260	99.43 170	99.48 147	95.11 322	98.55 288	99.32 285	83.95 361	98.98 326	95.81 304	96.26 283	98.62 294

VDD-MVS		97.73 251	97.35 267	98.88 201	99.47 181	97.12 264	99.34 212	98.85 325	98.19 109	99.67 68	99.85 34	82.98 362	99.92 85	99.49 17	98.32 213	99.60 139

EG-PatchMatch MVS		95.97 310	95.69 310	96.81 333	97.78 350	92.79 359	99.16 255	98.93 314	96.16 302	94.08 357	99.22 301	82.72 363	99.47 243	95.67 309	97.50 248	98.17 338

VDDNet		97.55 270	97.02 287	99.16 161	99.49 173	98.12 227	99.38 196	99.30 267	95.35 319	99.68 62	99.90 10	82.62 364	99.93 73	99.31 36	98.13 225	99.42 181

UniMVSNet_ETH3D		97.32 284	96.81 290	98.87 205	99.40 197	97.46 253	99.51 129	99.53 86	95.86 314	98.54 289	99.77 109	82.44 365	99.66 219	98.68 118	97.52 245	99.50 167

OpenMVS_ROB	92.34 20	94.38 325	93.70 329	96.41 338	97.38 354	93.17 357	99.06 276	98.75 330	86.58 361	94.84 356	98.26 351	81.53 366	99.32 277	89.01 359	97.87 231	96.76 361

test_method		91.10 330	91.36 333	90.31 350	95.85 364	73.72 378	94.89 367	99.25 278	68.39 370	95.82 351	99.02 323	80.50 367	98.95 335	93.64 336	94.89 317	98.25 335

MVS_0304		96.79 295	96.52 295	97.59 313	99.22 241	94.92 336	99.04 283	99.59 44	96.49 274	98.43 296	98.99 325	80.48 368	99.39 258	97.15 262	99.27 155	98.47 317

UnsupCasMVSNet_bld		93.53 328	92.51 331	96.58 337	97.38 354	93.82 349	98.24 355	99.48 147	91.10 355	93.10 360	96.66 361	74.89 369	98.37 345	94.03 333	87.71 356	97.56 358

Gipuma		90.99 331	90.15 334	93.51 343	98.73 318	90.12 366	93.98 368	99.45 187	79.32 366	92.28 361	94.91 364	69.61 370	97.98 353	87.42 364	95.67 299	92.45 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 329	92.23 332	95.14 342	95.61 365	89.98 367	99.37 199	98.21 350	94.80 329	95.04 355	97.69 354	65.06 371	97.90 355	94.30 328	89.98 353	97.54 359

EMVS		80.02 338	79.22 340	82.43 356	91.19 371	76.40 375	97.55 365	92.49 380	66.36 373	83.01 368	91.27 369	64.63 372	85.79 375	65.82 374	60.65 372	85.08 371

E-PMN		80.61 337	79.88 339	82.81 354	90.75 372	76.38 376	97.69 363	95.76 370	66.44 372	83.52 366	92.25 368	62.54 373	87.16 374	68.53 373	61.40 371	84.89 372

ambc				93.06 345	92.68 370	82.36 369	98.47 345	98.73 338		95.09 354	97.41 356	55.55 374	99.10 313	96.42 294	91.32 349	97.71 355

FPMVS		84.93 334	85.65 335	82.75 355	86.77 376	63.39 380	98.35 349	98.92 316	74.11 367	83.39 367	98.98 328	50.85 375	92.40 372	84.54 369	94.97 314	92.46 366

PMMVS2		86.87 332	85.37 336	91.35 349	90.21 373	83.80 368	98.89 312	97.45 362	83.13 365	91.67 363	95.03 363	48.49 376	94.70 370	85.86 368	77.62 367	95.54 364

LCM-MVSNet		86.80 333	85.22 337	91.53 348	87.81 375	80.96 371	98.23 357	98.99 308	71.05 368	90.13 364	96.51 362	48.45 377	96.88 365	90.51 353	85.30 359	96.76 361

ANet_high		77.30 339	74.86 343	84.62 353	75.88 379	77.61 374	97.63 364	93.15 378	88.81 359	64.27 374	89.29 371	36.51 378	83.93 376	75.89 371	52.31 373	92.33 368

test123		39.01 344	42.50 346	28.53 359	39.17 382	20.91 383	98.75 326	19.17 384	19.83 377	38.57 376	66.67 374	33.16 379	15.42 378	37.50 377	29.66 376	49.26 373

testmvs		39.17 343	43.78 345	25.37 360	36.04 383	16.84 384	98.36 348	26.56 382	20.06 376	38.51 377	67.32 373	29.64 380	15.30 379	37.59 376	39.90 375	43.98 374

wuyk23d		40.18 342	41.29 347	36.84 358	86.18 377	49.12 382	79.73 371	22.81 383	27.64 375	25.46 378	28.45 378	21.98 381	48.89 377	55.80 375	23.56 377	12.51 375

PMVS	70.75 22	75.98 341	74.97 342	79.01 357	70.98 380	55.18 381	93.37 369	98.21 350	65.08 374	61.78 375	93.83 366	21.74 382	92.53 371	78.59 370	91.12 350	89.34 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	76.82 21	76.91 340	74.31 344	84.70 352	85.38 378	76.05 377	96.88 366	93.17 377	67.39 371	71.28 373	89.01 372	21.66 383	87.69 373	71.74 372	72.29 370	90.35 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_blank		0.13 348	0.17 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	1.57 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		8.30 346	11.06 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	99.58 201	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.02 349	0.03 352	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.27 380	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.91 1	99.93 1	99.87 6	99.56 58	99.10 12	99.81 25

MSC_two_6792asdad				99.87 12	99.51 161	99.76 41		99.33 250					99.96 20	98.87 83	99.84 67	99.89 2

No_MVS				99.87 12	99.51 161	99.76 41		99.33 250					99.96 20	98.87 83	99.84 67	99.89 2

eth-test2					0.00 384

eth-test					0.00 384

IU-MVS					99.84 33	99.88 8		99.32 260	98.30 98	99.84 15				98.86 88	99.85 60	99.89 2

save fliter					99.76 57	99.59 73	99.14 261	99.40 214	99.00 28

test_0728_SECOND				99.91 2	99.84 33	99.89 4	99.57 99	99.51 106					99.96 20	98.93 73	99.86 53	99.88 8

GSMVS																99.52 158

test_part2					99.81 42	99.83 17				99.77 37

MTGPA								99.47 165

MTMP							99.54 118	98.88 323

gm-plane-assit					98.54 337	92.96 358			94.65 332		99.15 309		99.64 226	97.56 231

test9_res														97.49 237	99.72 112	99.75 78

agg_prior2														97.21 254	99.73 111	99.75 78

agg_prior					99.67 110	99.62 66		99.40 214		98.87 245			99.91 96

test_prior4						99.56 78	98.99 294

test_prior				99.68 68	99.67 110	99.48 93		99.56 58					99.83 153			99.74 83

旧先验2							98.96 302		96.70 258	99.47 119			99.94 58	98.19 172

新几何2							99.01 292

无先验							98.99 294	99.51 106	96.89 247				99.93 73	97.53 234		99.72 96

原ACMM2							98.95 306

testdata2													99.95 47	96.67 288

testdata1							98.85 316		98.32 97

plane_prior7					99.29 224	97.03 275

plane_prior5								99.47 165					99.69 214	97.78 207	97.63 235	98.67 272

plane_prior4											99.61 192

plane_prior3						97.00 277			98.69 64	99.11 202

plane_prior2							99.39 191		98.97 37

plane_prior1					99.26 231

plane_prior						96.97 280	99.21 251		98.45 81						97.60 238

n2								0.00 385

nn								0.00 385

door-mid								98.05 353

test11								99.35 239

door								97.92 355

HQP5-MVS						96.83 285

HQP-NCC					99.19 247		98.98 298		98.24 102	98.66 273

ACMP_Plane					99.19 247		98.98 298		98.24 102	98.66 273

BP-MVS														97.19 258

HQP4-MVS										98.66 273			99.64 226			98.64 284

HQP3-MVS								99.39 218							97.58 240

NP-MVS					99.23 237	96.92 283					99.40 261

ACMMP++_ref															97.19 264

ACMMP++															97.43 256

High-resolution multi-view benchmark