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_0728_THIRD									97.32 32	99.45 11	99.46 12	97.88 1	99.94 4	98.47 21	99.86 1	99.85 4

PC_three_1452									95.08 143	99.60 5	99.16 67	97.86 2	98.47 264	97.52 80	99.72 53	99.74 36

DVP-MVS++		99.08 2	98.89 2	99.64 3	99.17 101	99.23 7	99.69 1	98.88 51	97.32 32	99.53 9	99.47 9	97.81 3	99.94 4	98.47 21	99.72 53	99.74 36

OPU-MVS				99.37 23	99.24 95	99.05 14	99.02 66				99.16 67	97.81 3	99.37 164	97.24 88	99.73 46	99.70 53

SteuartSystems-ACMMP		98.90 6	98.75 6	99.36 24	99.22 97	98.43 38	99.10 51	98.87 58	97.38 29	99.35 17	99.40 16	97.78 5	99.87 48	97.77 58	99.85 4	99.78 16
Skip Steuart: Steuart Systems R&D Blog.
test_one_0601					99.66 28	99.25 2		98.86 64	97.55 16	99.20 25	99.47 9	97.57 6

SED-MVS		99.09 1	98.91 1	99.63 4	99.71 21	99.24 5	99.02 66	98.87 58	97.65 10	99.73 1	99.48 7	97.53 7	99.94 4	98.43 25	99.81 11	99.70 53

test_241102_ONE					99.71 21	99.24 5		98.87 58	97.62 12	99.73 1	99.39 17	97.53 7	99.74 111

DVP-MVS		99.03 3	98.83 5	99.63 4	99.72 13	99.25 2	98.97 76	98.58 153	97.62 12	99.45 11	99.46 12	97.42 9	99.94 4	98.47 21	99.81 11	99.69 56
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					99.72 13	99.25 2	99.06 56	98.88 51	97.62 12	99.56 6	99.50 5	97.42 9

test_241102_TWO								98.87 58	97.65 10	99.53 9	99.48 7	97.34 11	99.94 4	98.43 25	99.80 18	99.83 7

DPE-MVS		98.92 5	98.67 8	99.65 2	99.58 34	99.20 9	98.42 180	98.91 45	97.58 15	99.54 8	99.46 12	97.10 12	99.94 4	97.64 69	99.84 9	99.83 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS		98.78 7	98.56 11	99.45 17	99.32 71	98.87 19	98.47 172	98.81 80	97.72 7	98.76 60	99.16 67	97.05 13	99.78 100	98.06 40	99.66 62	99.69 56

segment_acmp												96.85 14

patch_mono-2		98.36 47	98.87 3	96.82 206	99.53 38	90.68 311	98.64 146	99.29 8	97.88 5	99.19 28	99.52 3	96.80 15	99.97 1	99.11 1	99.86 1	99.82 10

MCST-MVS		98.65 14	98.37 24	99.48 13	99.60 33	98.87 19	98.41 181	98.68 125	97.04 54	98.52 77	98.80 120	96.78 16	99.83 60	97.93 46	99.61 72	99.74 36

APDe-MVS		99.02 4	98.84 4	99.55 9	99.57 35	98.96 16	99.39 9	98.93 39	97.38 29	99.41 13	99.54 1	96.66 17	99.84 57	98.86 3	99.85 4	99.87 1

NCCC		98.61 18	98.35 27	99.38 20	99.28 85	98.61 29	98.45 173	98.76 103	97.82 6	98.45 81	98.93 105	96.65 18	99.83 60	97.38 85	99.41 105	99.71 49

SD-MVS		98.64 15	98.68 7	98.53 94	99.33 68	98.36 47	98.90 87	98.85 68	97.28 35	99.72 3	99.39 17	96.63 19	97.60 331	98.17 35	99.85 4	99.64 75
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
PHI-MVS		98.34 51	98.06 54	99.18 50	99.15 107	98.12 63	99.04 59	99.09 21	93.32 221	98.83 56	99.10 76	96.54 20	99.83 60	97.70 66	99.76 35	99.59 86

SMA-MVS		98.58 24	98.25 41	99.56 8	99.51 42	99.04 15	98.95 80	98.80 91	93.67 208	99.37 16	99.52 3	96.52 21	99.89 39	98.06 40	99.81 11	99.76 29
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
MSLP-MVS++		98.56 29	98.57 10	98.55 90	99.26 88	96.80 115	98.71 131	99.05 25	97.28 35	98.84 54	99.28 43	96.47 22	99.40 162	98.52 19	99.70 56	99.47 106

TSAR-MVS + MP.		98.78 7	98.62 9	99.24 43	99.69 26	98.28 53	99.14 42	98.66 136	96.84 62	99.56 6	99.31 38	96.34 23	99.70 119	98.32 32	99.73 46	99.73 41
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xxxxxxxxxxxxxcwj		98.70 10	98.50 16	99.30 33	99.46 54	98.38 40	98.21 207	98.52 164	97.95 3	99.32 18	99.39 17	96.22 24	99.84 57	97.72 61	99.73 46	99.67 66

SF-MVS		98.59 21	98.32 36	99.41 19	99.54 37	98.71 22	99.04 59	98.81 80	95.12 138	99.32 18	99.39 17	96.22 24	99.84 57	97.72 61	99.73 46	99.67 66

TSAR-MVS + GP.		98.38 45	98.24 44	98.81 77	99.22 97	97.25 101	98.11 226	98.29 213	97.19 44	98.99 43	99.02 88	96.22 24	99.67 126	98.52 19	98.56 145	99.51 97

TEST9					99.31 73	98.50 34	97.92 241	98.73 112	92.63 245	97.74 124	98.68 132	96.20 27	99.80 84

train_agg		97.97 62	97.52 76	99.33 30	99.31 73	98.50 34	97.92 241	98.73 112	92.98 234	97.74 124	98.68 132	96.20 27	99.80 84	96.59 123	99.57 80	99.68 62

test_8					99.29 81	98.44 36	97.89 247	98.72 114	92.98 234	97.70 127	98.66 135	96.20 27	99.80 84

agg_prior1		97.95 66	97.51 78	99.28 38	99.30 78	98.38 40	97.81 254	98.72 114	93.16 228	97.57 137	98.66 135	96.14 30	99.81 75	96.63 122	99.56 85	99.66 70

Regformer-2		98.69 12	98.52 14	99.19 46	99.35 63	98.01 67	98.37 184	98.81 80	97.48 20	99.21 24	99.21 54	96.13 31	99.80 84	98.40 29	99.73 46	99.75 31

DeepPCF-MVS	96.37 2	97.93 68	98.48 20	96.30 253	99.00 120	89.54 326	97.43 277	98.87 58	98.16 2	99.26 21	99.38 24	96.12 32	99.64 130	98.30 33	99.77 29	99.72 45

Regformer-1		98.66 13	98.51 15	99.12 60	99.35 63	97.81 79	98.37 184	98.76 103	97.49 19	99.20 25	99.21 54	96.08 33	99.79 96	98.42 27	99.73 46	99.75 31

HFP-MVS		98.63 17	98.40 21	99.32 31	99.72 13	98.29 51	99.23 27	98.96 33	96.10 93	98.94 44	99.17 62	96.06 34	99.92 25	97.62 70	99.78 26	99.75 31

#test#		98.54 33	98.27 39	99.32 31	99.72 13	98.29 51	98.98 75	98.96 33	95.65 109	98.94 44	99.17 62	96.06 34	99.92 25	97.21 90	99.78 26	99.75 31

9.14			98.06 54		99.47 51		98.71 131	98.82 74	94.36 172	99.16 31	99.29 42	96.05 36	99.81 75	97.00 95	99.71 55

CP-MVS		98.57 27	98.36 25	99.19 46	99.66 28	97.86 73	99.34 16	98.87 58	95.96 96	98.60 74	99.13 71	96.05 36	99.94 4	97.77 58	99.86 1	99.77 23

MSP-MVS		98.74 9	98.55 12	99.29 34	99.75 4	98.23 54	99.26 24	98.88 51	97.52 17	99.41 13	98.78 122	96.00 38	99.79 96	97.79 57	99.59 76	99.85 4
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
MVS_111021_HR		98.47 39	98.34 31	98.88 75	99.22 97	97.32 93	97.91 243	99.58 3	97.20 43	98.33 89	99.00 93	95.99 39	99.64 130	98.05 42	99.76 35	99.69 56

test_prior3		98.22 58	97.90 63	99.19 46	99.31 73	98.22 55	97.80 255	98.84 69	96.12 91	97.89 118	98.69 130	95.96 40	99.70 119	96.89 105	99.60 73	99.65 72

test_prior2							97.80 255		96.12 91	97.89 118	98.69 130	95.96 40		96.89 105	99.60 73

CDPH-MVS		97.94 67	97.49 79	99.28 38	99.47 51	98.44 36	97.91 243	98.67 133	92.57 249	98.77 59	98.85 113	95.93 42	99.72 113	95.56 162	99.69 57	99.68 62

region2R		98.61 18	98.38 23	99.29 34	99.74 8	98.16 60	99.23 27	98.93 39	96.15 88	98.94 44	99.17 62	95.91 43	99.94 4	97.55 77	99.79 22	99.78 16

XVS		98.70 10	98.49 18	99.34 26	99.70 24	98.35 48	99.29 20	98.88 51	97.40 26	98.46 78	99.20 58	95.90 44	99.89 39	97.85 53	99.74 44	99.78 16

X-MVStestdata		94.06 272	92.30 292	99.34 26	99.70 24	98.35 48	99.29 20	98.88 51	97.40 26	98.46 78	43.50 372	95.90 44	99.89 39	97.85 53	99.74 44	99.78 16

Regformer-4		98.64 15	98.53 13	98.99 66	99.43 60	97.37 92	98.40 182	98.79 96	97.46 23	99.09 35	99.31 38	95.86 46	99.80 84	98.64 7	99.76 35	99.79 13

Regformer-3		98.59 21	98.50 16	98.86 76	99.43 60	97.05 106	98.40 182	98.68 125	97.43 25	99.06 36	99.31 38	95.80 47	99.77 105	98.62 9	99.76 35	99.78 16

CS-MVS		98.41 42	98.49 18	98.18 122	99.08 112	96.33 139	99.67 3	98.49 176	97.17 45	98.93 48	99.10 76	95.79 48	99.12 186	98.67 6	99.48 96	99.10 155

ZD-MVS					99.46 54	98.70 23		98.79 96	93.21 225	98.67 66	98.97 95	95.70 49	99.83 60	96.07 139	99.58 79

HPM-MVS++		98.58 24	98.25 41	99.55 9	99.50 44	99.08 11	98.72 130	98.66 136	97.51 18	98.15 92	98.83 117	95.70 49	99.92 25	97.53 79	99.67 59	99.66 70

ACMMPR		98.59 21	98.36 25	99.29 34	99.74 8	98.15 61	99.23 27	98.95 35	96.10 93	98.93 48	99.19 61	95.70 49	99.94 4	97.62 70	99.79 22	99.78 16

旧先验1					99.29 81	97.48 88		98.70 121			99.09 82	95.56 52			99.47 98	99.61 81

PGM-MVS		98.49 37	98.23 45	99.27 41	99.72 13	98.08 64	98.99 72	99.49 5	95.43 119	99.03 38	99.32 36	95.56 52	99.94 4	96.80 116	99.77 29	99.78 16

APD-MVS		98.35 49	98.00 58	99.42 18	99.51 42	98.72 21	98.80 113	98.82 74	94.52 167	99.23 23	99.25 49	95.54 54	99.80 84	96.52 127	99.77 29	99.74 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		98.33 53	97.95 60	99.47 14	99.49 48	98.70 23	98.83 103	98.86 64	95.48 116	98.91 52	99.17 62	95.48 55	99.93 19	95.80 152	99.53 90	99.76 29

ZNCC-MVS		98.49 37	98.20 47	99.35 25	99.73 12	98.39 39	99.19 37	98.86 64	95.77 102	98.31 91	99.10 76	95.46 56	99.93 19	97.57 76	99.81 11	99.74 36

ETH3D-3000-0.1		98.35 49	98.00 58	99.38 20	99.47 51	98.68 25	98.67 141	98.84 69	94.66 162	99.11 33	99.25 49	95.46 56	99.81 75	96.80 116	99.73 46	99.63 78

mPP-MVS		98.51 36	98.26 40	99.25 42	99.75 4	98.04 65	99.28 22	98.81 80	96.24 85	98.35 88	99.23 51	95.46 56	99.94 4	97.42 83	99.81 11	99.77 23

EI-MVSNet-Vis-set		98.47 39	98.39 22	98.69 81	99.46 54	96.49 130	98.30 198	98.69 122	97.21 42	98.84 54	99.36 29	95.41 59	99.78 100	98.62 9	99.65 63	99.80 12

ETV-MVS		97.96 63	97.81 64	98.40 106	98.42 165	97.27 96	98.73 126	98.55 158	96.84 62	98.38 85	97.44 249	95.39 60	99.35 165	97.62 70	98.89 128	98.58 196

SR-MVS		98.57 27	98.35 27	99.24 43	99.53 38	98.18 58	99.09 52	98.82 74	96.58 73	99.10 34	99.32 36	95.39 60	99.82 68	97.70 66	99.63 69	99.72 45

ACMMP_NAP		98.61 18	98.30 37	99.55 9	99.62 32	98.95 17	98.82 106	98.81 80	95.80 101	99.16 31	99.47 9	95.37 62	99.92 25	97.89 50	99.75 41	99.79 13

CSCG		97.85 71	97.74 67	98.20 118	99.67 27	95.16 189	99.22 31	99.32 7	93.04 232	97.02 153	98.92 107	95.36 63	99.91 34	97.43 82	99.64 67	99.52 93

test1172		98.56 29	98.35 27	99.16 53	99.53 38	97.94 71	99.09 52	98.83 72	96.52 76	99.05 37	99.34 34	95.34 64	99.82 68	97.86 52	99.64 67	99.73 41

SR-MVS-dyc-post		98.54 33	98.35 27	99.13 57	99.49 48	97.86 73	99.11 48	98.80 91	96.49 77	99.17 29	99.35 31	95.34 64	99.82 68	97.72 61	99.65 63	99.71 49

DP-MVS Recon		97.86 70	97.46 81	99.06 64	99.53 38	98.35 48	98.33 190	98.89 48	92.62 246	98.05 98	98.94 104	95.34 64	99.65 128	96.04 143	99.42 104	99.19 141

APD-MVS_3200maxsize		98.53 35	98.33 35	99.15 56	99.50 44	97.92 72	99.15 41	98.81 80	96.24 85	99.20 25	99.37 25	95.30 67	99.80 84	97.73 60	99.67 59	99.72 45

RE-MVS-def			98.34 31		99.49 48	97.86 73	99.11 48	98.80 91	96.49 77	99.17 29	99.35 31	95.29 68		97.72 61	99.65 63	99.71 49

GST-MVS		98.43 41	98.12 51	99.34 26	99.72 13	98.38 40	99.09 52	98.82 74	95.71 105	98.73 63	99.06 86	95.27 69	99.93 19	97.07 94	99.63 69	99.72 45

DeepC-MVS_fast	96.70 1	98.55 31	98.34 31	99.18 50	99.25 89	98.04 65	98.50 169	98.78 99	97.72 7	98.92 51	99.28 43	95.27 69	99.82 68	97.55 77	99.77 29	99.69 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS-pluss		98.31 55	97.92 62	99.49 12	99.72 13	98.88 18	98.43 178	98.78 99	94.10 178	97.69 128	99.42 15	95.25 71	99.92 25	98.09 39	99.80 18	99.67 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EI-MVSNet-UG-set		98.41 42	98.34 31	98.61 86	99.45 58	96.32 140	98.28 201	98.68 125	97.17 45	98.74 61	99.37 25	95.25 71	99.79 96	98.57 11	99.54 89	99.73 41

原ACMM1				98.65 84	99.32 71	96.62 121		98.67 133	93.27 224	97.81 120	98.97 95	95.18 73	99.83 60	93.84 214	99.46 101	99.50 99

HPM-MVS_fast		98.38 45	98.13 50	99.12 60	99.75 4	97.86 73	99.44 8	98.82 74	94.46 170	98.94 44	99.20 58	95.16 74	99.74 111	97.58 73	99.85 4	99.77 23

test12				99.18 50	99.16 105	98.19 57		98.53 162		98.07 97		95.13 75	99.72 113		99.56 85	99.63 78

HPM-MVS		98.36 47	98.10 53	99.13 57	99.74 8	97.82 77	99.53 5	98.80 91	94.63 163	98.61 73	98.97 95	95.13 75	99.77 105	97.65 68	99.83 10	99.79 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPM-MVS		97.55 89	96.99 102	99.23 45	99.04 115	98.55 31	97.17 299	98.35 199	94.85 153	97.93 115	98.58 143	95.07 77	99.71 118	92.60 249	99.34 110	99.43 114

MVS_111021_LR		98.34 51	98.23 45	98.67 83	99.27 86	96.90 112	97.95 239	99.58 3	97.14 49	98.44 82	99.01 92	95.03 78	99.62 135	97.91 47	99.75 41	99.50 99

EIA-MVS		97.75 74	97.58 71	98.27 112	98.38 167	96.44 132	99.01 68	98.60 146	95.88 98	97.26 142	97.53 242	94.97 79	99.33 167	97.38 85	99.20 115	99.05 162

DELS-MVS		98.40 44	98.20 47	98.99 66	99.00 120	97.66 81	97.75 259	98.89 48	97.71 9	98.33 89	98.97 95	94.97 79	99.88 47	98.42 27	99.76 35	99.42 116
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
PLC	95.07 4	97.20 109	96.78 111	98.44 102	99.29 81	96.31 142	98.14 221	98.76 103	92.41 255	96.39 184	98.31 173	94.92 81	99.78 100	94.06 209	98.77 136	99.23 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		98.55 31	98.25 41	99.46 15	99.76 2	98.64 27	98.55 162	98.74 108	97.27 39	98.02 103	99.39 17	94.81 82	99.96 2	97.91 47	99.79 22	99.77 23

MTAPA		98.58 24	98.29 38	99.46 15	99.76 2	98.64 27	98.90 87	98.74 108	97.27 39	98.02 103	99.39 17	94.81 82	99.96 2	97.91 47	99.79 22	99.77 23

1121		97.37 101	96.77 115	99.16 53	99.34 65	97.99 70	98.19 214	98.68 125	90.14 316	98.01 107	98.97 95	94.80 84	99.87 48	93.36 228	99.46 101	99.61 81

Test By Simon												94.64 85

ETH3D cwj APD-0.16		97.96 63	97.52 76	99.29 34	99.05 113	98.52 32	98.33 190	98.68 125	93.18 226	98.68 65	99.13 71	94.62 86	99.83 60	96.45 129	99.55 88	99.52 93

新几何1				99.16 53	99.34 65	98.01 67		98.69 122	90.06 317	98.13 93	98.95 103	94.60 87	99.89 39	91.97 269	99.47 98	99.59 86

MP-MVS		98.33 53	98.01 57	99.28 38	99.75 4	98.18 58	99.22 31	98.79 96	96.13 90	97.92 116	99.23 51	94.54 88	99.94 4	96.74 121	99.78 26	99.73 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pcd_1.5k_mvsjas		7.88 346	10.50 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	94.51 89	0.00 379	0.00 377	0.00 377	0.00 375

PS-MVSNAJss		96.43 137	96.26 133	96.92 201	95.84 324	95.08 195	99.16 40	98.50 172	95.87 99	93.84 255	98.34 170	94.51 89	98.61 249	96.88 108	93.45 254	97.06 239

PS-MVSNAJ		97.73 75	97.77 65	97.62 162	98.68 149	95.58 173	97.34 286	98.51 167	97.29 34	98.66 70	97.88 210	94.51 89	99.90 37	97.87 51	99.17 117	97.39 230

API-MVS		97.41 98	97.25 90	97.91 138	98.70 146	96.80 115	98.82 106	98.69 122	94.53 165	98.11 94	98.28 176	94.50 92	99.57 139	94.12 206	99.49 94	97.37 232

ACMMP		98.23 57	97.95 60	99.09 62	99.74 8	97.62 84	99.03 62	99.41 6	95.98 95	97.60 136	99.36 29	94.45 93	99.93 19	97.14 91	98.85 132	99.70 53
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
testdata				98.26 114	99.20 100	95.36 182		98.68 125	91.89 272	98.60 74	99.10 76	94.44 94	99.82 68	94.27 201	99.44 103	99.58 90

CS-MVS-test		98.20 60	98.20 47	98.19 120	99.09 111	96.34 138	99.35 14	98.40 190	97.17 45	98.93 48	98.31 173	94.42 95	99.12 186	98.68 5	99.48 96	99.10 155

xiu_mvs_v2_base		97.66 79	97.70 68	97.56 166	98.61 155	95.46 179	97.44 275	98.46 178	97.15 48	98.65 71	98.15 187	94.33 96	99.80 84	97.84 55	98.66 141	97.41 228

PAPR		96.84 123	96.24 134	98.65 84	98.72 145	96.92 111	97.36 284	98.57 154	93.33 220	96.67 168	97.57 239	94.30 97	99.56 141	91.05 284	98.59 143	99.47 106

PAPM_NR		97.46 91	97.11 95	98.50 96	99.50 44	96.41 134	98.63 148	98.60 146	95.18 134	97.06 151	98.06 193	94.26 98	99.57 139	93.80 216	98.87 131	99.52 93

test222					99.23 96	97.17 104	97.40 278	98.66 136	88.68 333	98.05 98	98.96 101	94.14 99			99.53 90	99.61 81

EPP-MVSNet		97.46 91	97.28 89	97.99 134	98.64 152	95.38 181	99.33 19	98.31 205	93.61 211	97.19 144	99.07 85	94.05 100	99.23 174	96.89 105	98.43 153	99.37 119

F-COLMAP		97.09 115	96.80 108	97.97 135	99.45 58	94.95 203	98.55 162	98.62 145	93.02 233	96.17 189	98.58 143	94.01 101	99.81 75	93.95 211	98.90 127	99.14 150

TAPA-MVS	93.98 7	95.35 189	94.56 204	97.74 151	99.13 108	94.83 208	98.33 190	98.64 141	86.62 341	96.29 186	98.61 138	94.00 102	99.29 169	80.00 356	99.41 105	99.09 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETH3 D test6400		97.59 85	97.01 100	99.34 26	99.40 62	98.56 30	98.20 210	98.81 80	91.63 280	98.44 82	98.85 113	93.98 103	99.82 68	94.11 207	99.69 57	99.64 75

MG-MVS		97.81 72	97.60 70	98.44 102	99.12 109	95.97 155	97.75 259	98.78 99	96.89 61	98.46 78	99.22 53	93.90 104	99.68 125	94.81 182	99.52 92	99.67 66

DROMVSNet		98.21 59	98.11 52	98.49 98	98.34 174	97.26 100	99.61 4	98.43 185	96.78 64	98.87 53	98.84 115	93.72 105	99.01 207	98.91 2	99.50 93	99.19 141

CDS-MVSNet		96.99 117	96.69 117	97.90 139	98.05 199	95.98 150	98.20 210	98.33 202	93.67 208	96.95 154	98.49 151	93.54 106	98.42 270	95.24 173	97.74 176	99.31 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		97.02 116	96.79 110	97.70 155	98.06 198	95.31 186	98.52 164	98.31 205	93.95 187	97.05 152	98.61 138	93.49 107	98.52 259	95.33 167	97.81 172	99.29 131

abl_6		98.30 56	98.03 56	99.13 57	99.56 36	97.76 80	99.13 45	98.82 74	96.14 89	99.26 21	99.37 25	93.33 108	99.93 19	96.96 99	99.67 59	99.69 56

CNLPA		97.45 94	97.03 99	98.73 79	99.05 113	97.44 91	98.07 228	98.53 162	95.32 127	96.80 165	98.53 147	93.32 109	99.72 113	94.31 200	99.31 112	99.02 164

OMC-MVS		97.55 89	97.34 87	98.20 118	99.33 68	95.92 162	98.28 201	98.59 148	95.52 115	97.97 110	99.10 76	93.28 110	99.49 152	95.09 175	98.88 129	99.19 141

UA-Net		97.96 63	97.62 69	98.98 68	98.86 132	97.47 89	98.89 91	99.08 22	96.67 70	98.72 64	99.54 1	93.15 111	99.81 75	94.87 178	98.83 133	99.65 72

CPTT-MVS		97.72 76	97.32 88	98.92 72	99.64 30	97.10 105	99.12 47	98.81 80	92.34 257	98.09 96	99.08 84	93.01 112	99.92 25	96.06 142	99.77 29	99.75 31

114514_t		96.93 119	96.27 132	98.92 72	99.50 44	97.63 83	98.85 99	98.90 46	84.80 352	97.77 121	99.11 74	92.84 113	99.66 127	94.85 179	99.77 29	99.47 106

PVSNet_Blended_VisFu		97.70 77	97.46 81	98.44 102	99.27 86	95.91 163	98.63 148	99.16 18	94.48 169	97.67 129	98.88 110	92.80 114	99.91 34	97.11 92	99.12 118	99.50 99

PVSNet_BlendedMVS		96.73 126	96.60 121	97.12 186	99.25 89	95.35 184	98.26 204	99.26 9	94.28 173	97.94 113	97.46 246	92.74 115	99.81 75	96.88 108	93.32 257	96.20 323

PVSNet_Blended		97.38 100	97.12 94	98.14 123	99.25 89	95.35 184	97.28 291	99.26 9	93.13 229	97.94 113	98.21 183	92.74 115	99.81 75	96.88 108	99.40 107	99.27 133

MVS_Test		97.28 104	97.00 101	98.13 125	98.33 176	95.97 155	98.74 122	98.07 253	94.27 174	98.44 82	98.07 192	92.48 117	99.26 170	96.43 131	98.19 160	99.16 147

miper_enhance_ethall		95.10 203	94.75 196	96.12 261	97.53 234	93.73 249	96.61 330	98.08 251	92.20 266	93.89 251	96.65 307	92.44 118	98.30 290	94.21 203	91.16 283	96.34 317

MVSFormer		97.57 87	97.49 79	97.84 141	98.07 196	95.76 169	99.47 6	98.40 190	94.98 146	98.79 57	98.83 117	92.34 119	98.41 277	96.91 101	99.59 76	99.34 120

lupinMVS		97.44 95	97.22 92	98.12 127	98.07 196	95.76 169	97.68 263	97.76 274	94.50 168	98.79 57	98.61 138	92.34 119	99.30 168	97.58 73	99.59 76	99.31 126

CHOSEN 280x420		97.18 110	97.18 93	97.20 180	98.81 137	93.27 266	95.78 343	99.15 19	95.25 131	96.79 166	98.11 190	92.29 121	99.07 197	98.56 12	99.85 4	99.25 135

canonicalmvs		97.67 78	97.23 91	98.98 68	98.70 146	98.38 40	99.34 16	98.39 193	96.76 66	97.67 129	97.40 252	92.26 122	99.49 152	98.28 34	96.28 213	99.08 160

IterMVS-LS		95.46 178	95.21 176	96.22 256	98.12 193	93.72 250	98.32 195	98.13 238	93.71 201	94.26 234	97.31 256	92.24 123	98.10 305	94.63 185	90.12 294	96.84 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		95.96 154	95.83 146	96.36 249	97.93 205	93.70 251	98.12 224	98.27 214	93.70 203	95.07 203	99.02 88	92.23 124	98.54 257	94.68 184	93.46 252	96.84 262

WTY-MVS		97.37 101	96.92 105	98.72 80	98.86 132	96.89 114	98.31 196	98.71 118	95.26 130	97.67 129	98.56 146	92.21 125	99.78 100	95.89 147	96.85 192	99.48 104

Effi-MVS+		97.12 113	96.69 117	98.39 107	98.19 187	96.72 119	97.37 282	98.43 185	93.71 201	97.65 132	98.02 195	92.20 126	99.25 171	96.87 111	97.79 173	99.19 141

1112_ss		96.63 128	96.00 142	98.50 96	98.56 157	96.37 135	98.18 218	98.10 244	92.92 237	94.84 209	98.43 156	92.14 127	99.58 138	94.35 197	96.51 203	99.56 92

LS3D		97.16 111	96.66 120	98.68 82	98.53 160	97.19 103	98.93 84	98.90 46	92.83 242	95.99 194	99.37 25	92.12 128	99.87 48	93.67 220	99.57 80	98.97 169

nrg030		96.28 144	95.72 149	97.96 137	96.90 278	98.15 61	99.39 9	98.31 205	95.47 117	94.42 227	98.35 166	92.09 129	98.69 241	97.50 81	89.05 311	97.04 240

mvs_anonymous		96.70 127	96.53 125	97.18 182	98.19 187	93.78 244	98.31 196	98.19 224	94.01 183	94.47 221	98.27 179	92.08 130	98.46 265	97.39 84	97.91 168	99.31 126

FC-MVSNet-test		96.42 138	96.05 139	97.53 168	96.95 273	97.27 96	99.36 12	99.23 13	95.83 100	93.93 249	98.37 164	92.00 131	98.32 286	96.02 144	92.72 265	97.00 242

FIs		96.51 135	96.12 137	97.67 158	97.13 264	97.54 87	99.36 12	99.22 15	95.89 97	94.03 247	98.35 166	91.98 132	98.44 268	96.40 132	92.76 264	97.01 241

sss		97.39 99	96.98 103	98.61 86	98.60 156	96.61 123	98.22 206	98.93 39	93.97 186	98.01 107	98.48 152	91.98 132	99.85 54	96.45 129	98.15 161	99.39 117

miper_ehance_all_eth		95.01 207	94.69 199	95.97 266	97.70 219	93.31 265	97.02 306	98.07 253	92.23 263	93.51 267	96.96 289	91.85 134	98.15 301	93.68 218	91.16 283	96.44 314

DP-MVS		96.59 131	95.93 143	98.57 88	99.34 65	96.19 146	98.70 135	98.39 193	89.45 327	94.52 219	99.35 31	91.85 134	99.85 54	92.89 245	98.88 129	99.68 62

Test_1112_low_res		96.34 141	95.66 157	98.36 108	98.56 157	95.94 158	97.71 261	98.07 253	92.10 267	94.79 213	97.29 257	91.75 136	99.56 141	94.17 204	96.50 204	99.58 90

UniMVSNet_NR-MVSNet		95.71 168	95.15 178	97.40 174	96.84 281	96.97 108	98.74 122	99.24 11	95.16 135	93.88 252	97.72 226	91.68 137	98.31 288	95.81 150	87.25 332	96.92 248

UniMVSNet (Re)		95.78 165	95.19 177	97.58 164	96.99 272	97.47 89	98.79 117	99.18 17	95.60 110	93.92 250	97.04 280	91.68 137	98.48 261	95.80 152	87.66 327	96.79 266

HY-MVS	93.96 8	96.82 124	96.23 135	98.57 88	98.46 164	97.00 107	98.14 221	98.21 221	93.95 187	96.72 167	97.99 199	91.58 139	99.76 107	94.51 193	96.54 202	98.95 172

xiu_mvs_v1_base_debu		97.60 82	97.56 73	97.72 152	98.35 169	95.98 150	97.86 250	98.51 167	97.13 50	99.01 40	98.40 160	91.56 140	99.80 84	98.53 13	98.68 137	97.37 232

xiu_mvs_v1_base		97.60 82	97.56 73	97.72 152	98.35 169	95.98 150	97.86 250	98.51 167	97.13 50	99.01 40	98.40 160	91.56 140	99.80 84	98.53 13	98.68 137	97.37 232

xiu_mvs_v1_base_debi		97.60 82	97.56 73	97.72 152	98.35 169	95.98 150	97.86 250	98.51 167	97.13 50	99.01 40	98.40 160	91.56 140	99.80 84	98.53 13	98.68 137	97.37 232

MAR-MVS		96.91 120	96.40 128	98.45 101	98.69 148	96.90 112	98.66 144	98.68 125	92.40 256	97.07 150	97.96 202	91.54 143	99.75 109	93.68 218	98.92 126	98.69 186
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
CANet		98.05 61	97.76 66	98.90 74	98.73 141	97.27 96	98.35 187	98.78 99	97.37 31	97.72 126	98.96 101	91.53 144	99.92 25	98.79 4	99.65 63	99.51 97

c3_l		94.79 222	94.43 214	95.89 271	97.75 214	93.12 272	97.16 300	98.03 260	92.23 263	93.46 270	97.05 279	91.39 145	98.01 313	93.58 223	89.21 309	96.53 301

EPNet		97.28 104	96.87 107	98.51 95	94.98 340	96.14 147	98.90 87	97.02 320	98.28 1	95.99 194	99.11 74	91.36 146	99.89 39	96.98 96	99.19 116	99.50 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline		97.64 80	97.44 83	98.25 115	98.35 169	96.20 144	99.00 70	98.32 203	96.33 84	98.03 101	99.17 62	91.35 147	99.16 180	98.10 38	98.29 159	99.39 117

1314		96.25 146	95.73 148	97.79 146	97.13 264	95.55 176	98.19 214	98.59 148	93.47 215	92.03 311	97.82 219	91.33 148	99.49 152	94.62 187	98.44 151	98.32 206

diffmvs		97.58 86	97.40 85	98.13 125	98.32 178	95.81 168	98.06 229	98.37 196	96.20 87	98.74 61	98.89 109	91.31 149	99.25 171	98.16 36	98.52 146	99.34 120

PAPM		94.95 213	94.00 237	97.78 147	97.04 269	95.65 171	96.03 339	98.25 219	91.23 296	94.19 239	97.80 221	91.27 150	98.86 229	82.61 350	97.61 180	98.84 178

casdiffmvs		97.63 81	97.41 84	98.28 111	98.33 176	96.14 147	98.82 106	98.32 203	96.38 82	97.95 111	99.21 54	91.23 151	99.23 174	98.12 37	98.37 154	99.48 104

jason		97.32 103	97.08 97	98.06 131	97.45 242	95.59 172	97.87 249	97.91 269	94.79 154	98.55 76	98.83 117	91.12 152	99.23 174	97.58 73	99.60 73	99.34 120
jason: jason.
IS-MVSNet		97.22 106	96.88 106	98.25 115	98.85 134	96.36 136	99.19 37	97.97 263	95.39 121	97.23 143	98.99 94	91.11 153	98.93 218	94.60 188	98.59 143	99.47 106

PMMVS		96.60 129	96.33 130	97.41 172	97.90 207	93.93 240	97.35 285	98.41 188	92.84 241	97.76 122	97.45 248	91.10 154	99.20 177	96.26 135	97.91 168	99.11 153

MVS		94.67 230	93.54 268	98.08 129	96.88 279	96.56 127	98.19 214	98.50 172	78.05 361	92.69 293	98.02 195	91.07 155	99.63 133	90.09 295	98.36 156	98.04 213

Fast-Effi-MVS+		96.28 144	95.70 154	98.03 132	98.29 180	95.97 155	98.58 154	98.25 219	91.74 275	95.29 202	97.23 261	91.03 156	99.15 183	92.90 243	97.96 167	98.97 169

Effi-MVS+-dtu		96.29 142	96.56 122	95.51 282	97.89 208	90.22 317	98.80 113	98.10 244	96.57 74	96.45 183	96.66 305	90.81 157	98.91 220	95.72 155	97.99 166	97.40 229

mvs-test1		96.60 129	96.68 119	96.37 248	97.89 208	91.81 286	98.56 160	98.10 244	96.57 74	96.52 179	97.94 204	90.81 157	99.45 160	95.72 155	98.01 165	97.86 218

test_yl		97.22 106	96.78 111	98.54 92	98.73 141	96.60 124	98.45 173	98.31 205	94.70 156	98.02 103	98.42 158	90.80 159	99.70 119	96.81 114	96.79 194	99.34 120

DCV-MVSNet		97.22 106	96.78 111	98.54 92	98.73 141	96.60 124	98.45 173	98.31 205	94.70 156	98.02 103	98.42 158	90.80 159	99.70 119	96.81 114	96.79 194	99.34 120

alignmvs		97.56 88	97.07 98	99.01 65	98.66 150	98.37 46	98.83 103	98.06 258	96.74 67	98.00 109	97.65 231	90.80 159	99.48 156	98.37 30	96.56 201	99.19 141

AdaColmap		97.15 112	96.70 116	98.48 99	99.16 105	96.69 120	98.01 234	98.89 48	94.44 171	96.83 161	98.68 132	90.69 162	99.76 107	94.36 196	99.29 113	98.98 168

cdsmvs_eth3d_5k		23.98 342	31.98 344	0.00 360	0.00 383	0.00 384	0.00 371	98.59 148	0.00 378	0.00 379	98.61 138	90.60 163	0.00 379	0.00 377	0.00 377	0.00 375

eth_miper_zixun_eth		94.68 227	94.41 215	95.47 284	97.64 222	91.71 291	96.73 327	98.07 253	92.71 244	93.64 260	97.21 263	90.54 164	98.17 300	93.38 226	89.76 298	96.54 299

DeepC-MVS	95.98 3	97.88 69	97.58 71	98.77 78	99.25 89	96.93 110	98.83 103	98.75 106	96.96 58	96.89 160	99.50 5	90.46 165	99.87 48	97.84 55	99.76 35	99.52 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		95.05 206	94.46 210	96.81 207	96.86 280	95.82 167	99.24 26	99.24 11	93.87 191	92.53 298	96.84 299	90.37 166	98.24 297	93.24 231	87.93 324	96.38 316

EPNet_dtu		95.21 197	94.95 189	95.99 264	96.17 311	90.45 315	98.16 220	97.27 309	96.77 65	93.14 281	98.33 171	90.34 167	98.42 270	85.57 336	98.81 135	99.09 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		97.79 73	97.40 85	98.96 70	98.88 130	97.55 86	98.63 148	98.93 39	96.74 67	99.02 39	98.84 115	90.33 168	99.83 60	98.53 13	96.66 197	99.50 99

MSDG		95.93 158	95.30 173	97.83 142	98.90 128	95.36 182	96.83 323	98.37 196	91.32 291	94.43 226	98.73 128	90.27 169	99.60 136	90.05 298	98.82 134	98.52 197

LCM-MVSNet-Re		95.22 196	95.32 171	94.91 300	98.18 189	87.85 351	98.75 119	95.66 347	95.11 139	88.96 337	96.85 298	90.26 170	97.65 329	95.65 160	98.44 151	99.22 137

Vis-MVSNet (Re-imp)		96.87 122	96.55 123	97.83 142	98.73 141	95.46 179	99.20 35	98.30 211	94.96 148	96.60 172	98.87 111	90.05 171	98.59 253	93.67 220	98.60 142	99.46 110

miper_lstm_enhance		94.33 252	94.07 232	95.11 295	97.75 214	90.97 304	97.22 294	98.03 260	91.67 279	92.76 290	96.97 287	90.03 172	97.78 327	92.51 256	89.64 300	96.56 296

baseline1		95.84 162	95.12 180	98.01 133	98.49 163	95.98 150	98.73 126	97.03 318	95.37 124	96.22 187	98.19 185	89.96 173	99.16 180	94.60 188	87.48 328	98.90 175

MDTV_nov1_ep13_2view						84.26 359	96.89 318		90.97 302	97.90 117		89.89 174		93.91 212		99.18 146

h-mvs33		96.17 147	95.62 158	97.81 145	99.03 116	94.45 224	98.64 146	98.75 106	97.48 20	98.67 66	98.72 129	89.76 175	99.86 53	97.95 44	81.59 351	99.11 153

hse-mvs2		95.71 168	95.30 173	96.93 198	98.50 161	93.53 256	98.36 186	98.10 244	97.48 20	98.67 66	97.99 199	89.76 175	99.02 205	97.95 44	80.91 355	98.22 208

GeoE		96.58 133	96.07 138	98.10 128	98.35 169	95.89 165	99.34 16	98.12 239	93.12 230	96.09 190	98.87 111	89.71 177	98.97 209	92.95 241	98.08 164	99.43 114

our_test_3		93.65 279	93.30 275	94.69 308	95.45 335	89.68 324	96.91 313	97.65 279	91.97 270	91.66 315	96.88 295	89.67 178	97.93 320	88.02 322	91.49 277	96.48 311

tpmrst		95.63 173	95.69 155	95.44 286	97.54 232	88.54 342	96.97 308	97.56 285	93.50 214	97.52 139	96.93 293	89.49 179	99.16 180	95.25 172	96.42 206	98.64 192

D2MVS		95.18 199	95.08 182	95.48 283	97.10 266	92.07 282	98.30 198	99.13 20	94.02 182	92.90 286	96.73 302	89.48 180	98.73 240	94.48 194	93.60 251	95.65 336

sam_mvs1												89.45 181				99.20 138

patchmatchnet-post											95.10 342	89.42 182	98.89 224

3Dnovator+	94.38 6	97.43 96	96.78 111	99.38 20	97.83 211	98.52 32	99.37 11	98.71 118	97.09 53	92.99 285	99.13 71	89.36 183	99.89 39	96.97 97	99.57 80	99.71 49

NR-MVSNet		94.98 211	94.16 227	97.44 170	96.53 296	97.22 102	98.74 122	98.95 35	94.96 148	89.25 336	97.69 227	89.32 184	98.18 299	94.59 190	87.40 330	96.92 248

HyFIR lowres test		96.90 121	96.49 126	98.14 123	99.33 68	95.56 174	97.38 280	99.65 2	92.34 257	97.61 135	98.20 184	89.29 185	99.10 194	96.97 97	97.60 181	99.77 23

3Dnovator	94.51 5	97.46 91	96.93 104	99.07 63	97.78 213	97.64 82	99.35 14	99.06 23	97.02 55	93.75 259	99.16 67	89.25 186	99.92 25	97.22 89	99.75 41	99.64 75

PatchmatchNet		95.71 168	95.52 160	96.29 254	97.58 227	90.72 310	96.84 322	97.52 292	94.06 179	97.08 148	96.96 289	89.24 187	98.90 223	92.03 267	98.37 154	99.26 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			95.40 162		97.48 236	88.34 345	96.85 321	97.29 307	93.74 198	97.48 140	97.26 258	89.18 188	99.05 198	91.92 270	97.43 184

test_djsdf		96.00 153	95.69 155	96.93 198	95.72 326	95.49 178	99.47 6	98.40 190	94.98 146	94.58 217	97.86 212	89.16 189	98.41 277	96.91 101	94.12 238	96.88 257

DIV-MVS_self_test		94.52 241	94.03 233	95.99 264	97.57 231	93.38 263	97.05 304	97.94 266	91.74 275	92.81 288	97.10 267	89.12 190	98.07 309	92.60 249	90.30 292	96.53 301

QAPM		96.29 142	95.40 162	98.96 70	97.85 210	97.60 85	99.23 27	98.93 39	89.76 322	93.11 282	99.02 88	89.11 191	99.93 19	91.99 268	99.62 71	99.34 120

pmmvs4		94.69 225	93.99 239	96.81 207	95.74 325	95.94 158	97.40 278	97.67 278	90.42 310	93.37 272	97.59 237	89.08 192	98.20 298	92.97 240	91.67 275	96.30 321

cl____		94.51 242	94.01 236	96.02 263	97.58 227	93.40 262	97.05 304	97.96 265	91.73 277	92.76 290	97.08 273	89.06 193	98.13 303	92.61 248	90.29 293	96.52 304

sam_mvs												88.99 194

Patchmatch-test		94.42 248	93.68 263	96.63 221	97.60 225	91.76 288	94.83 354	97.49 296	89.45 327	94.14 241	97.10 267	88.99 194	98.83 232	85.37 339	98.13 162	99.29 131

Patchmatch-RL test		91.49 303	90.85 304	93.41 327	91.37 363	84.40 358	92.81 360	95.93 345	91.87 273	87.25 345	94.87 344	88.99 194	96.53 352	92.54 255	82.00 348	99.30 129

Fast-Effi-MVS+-dtu		95.87 160	95.85 145	95.91 269	97.74 217	91.74 290	98.69 137	98.15 235	95.56 112	94.92 207	97.68 230	88.98 197	98.79 236	93.19 233	97.78 174	97.20 236

BH-untuned		95.95 155	95.72 149	96.65 217	98.55 159	92.26 279	98.23 205	97.79 273	93.73 199	94.62 216	98.01 197	88.97 198	99.00 208	93.04 238	98.51 147	98.68 187

XVG-OURS		96.55 134	96.41 127	96.99 192	98.75 140	93.76 245	97.50 274	98.52 164	95.67 107	96.83 161	99.30 41	88.95 199	99.53 147	95.88 148	96.26 214	97.69 224

PVSNet	91.96 18	96.35 140	96.15 136	96.96 196	99.17 101	92.05 283	96.08 336	98.68 125	93.69 204	97.75 123	97.80 221	88.86 200	99.69 124	94.26 202	99.01 123	99.15 148

test_post											31.83 375	88.83 201	98.91 220

v8		94.47 245	93.77 255	96.57 229	96.36 304	94.83 208	99.05 58	98.19 224	91.92 271	93.16 278	96.97 287	88.82 202	98.48 261	91.69 275	87.79 325	96.39 315

BH-w/o		95.38 185	95.08 182	96.26 255	98.34 174	91.79 287	97.70 262	97.43 301	92.87 240	94.24 236	97.22 262	88.66 203	98.84 230	91.55 277	97.70 178	98.16 211

tpmvs		94.60 233	94.36 217	95.33 289	97.46 238	88.60 341	96.88 319	97.68 277	91.29 293	93.80 257	96.42 316	88.58 204	99.24 173	91.06 282	96.04 221	98.17 210

DU-MVS		95.42 182	94.76 195	97.40 174	96.53 296	96.97 108	98.66 144	98.99 30	95.43 119	93.88 252	97.69 227	88.57 205	98.31 288	95.81 150	87.25 332	96.92 248

Baseline_NR-MVSNet		94.35 251	93.81 251	95.96 267	96.20 309	94.05 238	98.61 151	96.67 337	91.44 285	93.85 254	97.60 236	88.57 205	98.14 302	94.39 195	86.93 335	95.68 335

PCF-MVS	93.45 11	94.68 227	93.43 272	98.42 105	98.62 154	96.77 117	95.48 348	98.20 223	84.63 353	93.34 273	98.32 172	88.55 207	99.81 75	84.80 343	98.96 125	98.68 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v148		94.29 255	93.76 257	95.91 269	96.10 314	92.93 274	98.58 154	97.97 263	92.59 248	93.47 269	96.95 291	88.53 208	98.32 286	92.56 253	87.06 334	96.49 310

PatchMatch-RL		96.59 131	96.03 141	98.27 112	99.31 73	96.51 129	97.91 243	99.06 23	93.72 200	96.92 158	98.06 193	88.50 209	99.65 128	91.77 273	99.00 124	98.66 190

V42		94.78 223	94.14 229	96.70 214	96.33 306	95.22 188	98.97 76	98.09 249	92.32 259	94.31 232	97.06 277	88.39 210	98.55 256	92.90 243	88.87 315	96.34 317

v7n		94.19 261	93.43 272	96.47 239	95.90 321	94.38 229	99.26 24	98.34 201	91.99 269	92.76 290	97.13 266	88.31 211	98.52 259	89.48 310	87.70 326	96.52 304

TranMVSNet+NR-MVSNet		95.14 201	94.48 208	97.11 187	96.45 301	96.36 136	99.03 62	99.03 26	95.04 144	93.58 262	97.93 205	88.27 212	98.03 312	94.13 205	86.90 337	96.95 247

MVSTER		96.06 150	95.72 149	97.08 189	98.23 182	95.93 161	98.73 126	98.27 214	94.86 152	95.07 203	98.09 191	88.21 213	98.54 257	96.59 123	93.46 252	96.79 266

RRT_MVS		96.04 151	95.53 159	97.56 166	97.07 268	97.32 93	98.57 159	98.09 249	95.15 136	95.02 205	98.44 155	88.20 214	98.58 255	96.17 138	93.09 261	96.79 266

CHOSEN 1792x2688		97.12 113	96.80 108	98.08 129	99.30 78	94.56 222	98.05 230	99.71 1	93.57 212	97.09 147	98.91 108	88.17 215	99.89 39	96.87 111	99.56 85	99.81 11

CR-MVSNet		94.76 224	94.15 228	96.59 226	97.00 270	93.43 259	94.96 350	97.56 285	92.46 250	96.93 156	96.24 319	88.15 216	97.88 325	87.38 325	96.65 198	98.46 199

Patchmtry		93.22 287	92.35 291	95.84 273	96.77 283	93.09 273	94.66 355	97.56 285	87.37 339	92.90 286	96.24 319	88.15 216	97.90 321	87.37 326	90.10 295	96.53 301

v10		94.29 255	93.55 267	96.51 236	96.39 303	94.80 210	98.99 72	98.19 224	91.35 289	93.02 284	96.99 285	88.09 218	98.41 277	90.50 291	88.41 319	96.33 319

ppachtmachnet_test		93.22 287	92.63 287	94.97 299	95.45 335	90.84 306	96.88 319	97.88 270	90.60 305	92.08 310	97.26 258	88.08 219	97.86 326	85.12 340	90.33 291	96.22 322

Vis-MVSNet		97.42 97	97.11 95	98.34 109	98.66 150	96.23 143	99.22 31	99.00 28	96.63 72	98.04 100	99.21 54	88.05 220	99.35 165	96.01 145	99.21 114	99.45 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1144		94.59 235	93.92 242	96.60 225	96.21 308	94.78 212	98.59 152	98.14 237	91.86 274	94.21 238	97.02 282	87.97 221	98.41 277	91.72 274	89.57 301	96.61 289

PatchT		93.06 291	91.97 296	96.35 250	96.69 289	92.67 276	94.48 356	97.08 314	86.62 341	97.08 148	92.23 358	87.94 222	97.90 321	78.89 360	96.69 196	98.49 198

ADS-MVSNet2		94.58 236	94.40 216	95.11 295	98.00 200	88.74 339	96.04 337	97.30 306	90.15 314	96.47 181	96.64 308	87.89 223	97.56 333	90.08 296	97.06 188	99.02 164

ADS-MVSNet		95.00 208	94.45 212	96.63 221	98.00 200	91.91 285	96.04 337	97.74 276	90.15 314	96.47 181	96.64 308	87.89 223	98.96 213	90.08 296	97.06 188	99.02 164

XVG-OURS-SEG-HR		96.51 135	96.34 129	97.02 191	98.77 139	93.76 245	97.79 257	98.50 172	95.45 118	96.94 155	99.09 82	87.87 225	99.55 146	96.76 120	95.83 223	97.74 221

test_post1							96.68 328				30.43 376	87.85 226	98.69 241	92.59 251

test-LLR		95.10 203	94.87 192	95.80 274	96.77 283	89.70 322	96.91 313	95.21 350	95.11 139	94.83 211	95.72 334	87.71 227	98.97 209	93.06 236	98.50 148	98.72 183

test0.0.03 1		94.08 270	93.51 269	95.80 274	95.53 332	92.89 275	97.38 280	95.97 343	95.11 139	92.51 300	96.66 305	87.71 227	96.94 343	87.03 327	93.67 247	97.57 226

JIA-IIPM		93.35 282	92.49 289	95.92 268	96.48 300	90.65 312	95.01 349	96.96 322	85.93 347	96.08 191	87.33 363	87.70 229	98.78 237	91.35 279	95.58 225	98.34 204

v2v482		94.69 225	94.03 233	96.65 217	96.17 311	94.79 211	98.67 141	98.08 251	92.72 243	94.00 248	97.16 265	87.69 230	98.45 266	92.91 242	88.87 315	96.72 275

CVMVSNet		95.43 181	96.04 140	93.57 325	97.93 205	83.62 360	98.12 224	98.59 148	95.68 106	96.56 173	99.02 88	87.51 231	97.51 335	93.56 224	97.44 183	99.60 84

WR-MVS		95.15 200	94.46 210	97.22 179	96.67 291	96.45 131	98.21 207	98.81 80	94.15 176	93.16 278	97.69 227	87.51 231	98.30 290	95.29 170	88.62 317	96.90 255

anonymousdsp		95.42 182	94.91 190	96.94 197	95.10 339	95.90 164	99.14 42	98.41 188	93.75 196	93.16 278	97.46 246	87.50 233	98.41 277	95.63 161	94.03 240	96.50 309

v144192		94.39 250	93.70 261	96.48 238	96.06 316	94.35 230	98.58 154	98.16 234	91.45 284	94.33 231	97.02 282	87.50 233	98.45 266	91.08 281	89.11 310	96.63 287

baseline2		95.11 202	94.52 206	96.87 203	96.65 292	93.56 253	98.27 203	94.10 364	93.45 216	92.02 312	97.43 250	87.45 235	99.19 178	93.88 213	97.41 185	97.87 217

EU-MVSNet		93.66 277	94.14 229	92.25 338	95.96 320	83.38 361	98.52 164	98.12 239	94.69 158	92.61 295	98.13 189	87.36 236	96.39 354	91.82 271	90.00 296	96.98 243

CP-MVSNet		94.94 215	94.30 219	96.83 205	96.72 288	95.56 174	99.11 48	98.95 35	93.89 189	92.42 304	97.90 207	87.19 237	98.12 304	94.32 199	88.21 321	96.82 265

bset_n11_16_dypcd		94.89 217	94.27 220	96.76 209	94.41 348	95.15 191	95.67 344	95.64 348	95.53 113	94.65 215	97.52 243	87.10 238	98.29 293	96.58 125	91.35 278	96.83 264

HQP_MVS		96.14 148	95.90 144	96.85 204	97.42 243	94.60 220	98.80 113	98.56 156	97.28 35	95.34 199	98.28 176	87.09 239	99.03 202	96.07 139	94.27 230	96.92 248

plane_prior6					97.35 248	94.61 218						87.09 239

RPSCF		94.87 218	95.40 162	93.26 331	98.89 129	82.06 365	98.33 190	98.06 258	90.30 313	96.56 173	99.26 46	87.09 239	99.49 152	93.82 215	96.32 209	98.24 207

RPMNet		92.81 293	91.34 301	97.24 178	97.00 270	93.43 259	94.96 350	98.80 91	82.27 356	96.93 156	92.12 359	86.98 242	99.82 68	76.32 364	96.65 198	98.46 199

v1192		94.32 253	93.58 266	96.53 234	96.10 314	94.45 224	98.50 169	98.17 232	91.54 282	94.19 239	97.06 277	86.95 243	98.43 269	90.14 294	89.57 301	96.70 279

CANet_DTU		96.96 118	96.55 123	98.21 117	98.17 191	96.07 149	97.98 237	98.21 221	97.24 41	97.13 146	98.93 105	86.88 244	99.91 34	95.00 177	99.37 109	98.66 190

HQP2-MVS												86.75 245

HQP-MVS		95.72 167	95.40 162	96.69 215	97.20 257	94.25 234	98.05 230	98.46 178	96.43 79	94.45 222	97.73 224	86.75 245	98.96 213	95.30 168	94.18 234	96.86 261

OpenMVS	93.04 13	95.83 163	95.00 185	98.32 110	97.18 261	97.32 93	99.21 34	98.97 31	89.96 318	91.14 319	99.05 87	86.64 247	99.92 25	93.38 226	99.47 98	97.73 222

cl22		94.68 227	94.19 224	96.13 260	98.11 194	93.60 252	96.94 310	98.31 205	92.43 254	93.32 274	96.87 297	86.51 248	98.28 295	94.10 208	91.16 283	96.51 307

ET-MVSNet_ETH3D		94.13 265	92.98 280	97.58 164	98.22 183	96.20 144	97.31 289	95.37 349	94.53 165	79.56 361	97.63 235	86.51 248	97.53 334	96.91 101	90.74 288	99.02 164

YYNet1		90.70 312	89.39 315	94.62 311	94.79 345	90.65 312	97.20 295	97.46 297	87.54 338	72.54 365	95.74 330	86.51 248	96.66 350	86.00 333	86.76 339	96.54 299

MDA-MVSNet_test_wron		90.71 311	89.38 316	94.68 309	94.83 343	90.78 309	97.19 296	97.46 297	87.60 337	72.41 366	95.72 334	86.51 248	96.71 349	85.92 334	86.80 338	96.56 296

v1921920		94.20 260	93.47 271	96.40 247	95.98 319	94.08 237	98.52 164	98.15 235	91.33 290	94.25 235	97.20 264	86.41 252	98.42 270	90.04 299	89.39 307	96.69 284

COLMAP_ROB	93.27 12	95.33 191	94.87 192	96.71 212	99.29 81	93.24 268	98.58 154	98.11 242	89.92 319	93.57 263	99.10 76	86.37 253	99.79 96	90.78 287	98.10 163	97.09 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVP-Stereo		94.28 257	93.92 242	95.35 288	94.95 341	92.60 277	97.97 238	97.65 279	91.61 281	90.68 324	97.09 271	86.32 254	98.42 270	89.70 305	99.34 110	95.02 347
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CLD-MVS		95.62 174	95.34 168	96.46 242	97.52 235	93.75 247	97.27 292	98.46 178	95.53 113	94.42 227	98.00 198	86.21 255	98.97 209	96.25 136	94.37 228	96.66 285
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpm cat1		93.36 281	92.80 283	95.07 297	97.58 227	87.97 349	96.76 325	97.86 271	82.17 357	93.53 264	96.04 327	86.13 256	99.13 185	89.24 313	95.87 222	98.10 212

PEN-MVS		94.42 248	93.73 259	96.49 237	96.28 307	94.84 206	99.17 39	99.00 28	93.51 213	92.23 307	97.83 218	86.10 257	97.90 321	92.55 254	86.92 336	96.74 272

v1240		94.06 272	93.29 276	96.34 251	96.03 318	93.90 241	98.44 176	98.17 232	91.18 299	94.13 242	97.01 284	86.05 258	98.42 270	89.13 315	89.50 305	96.70 279

CostFormer		94.95 213	94.73 197	95.60 281	97.28 251	89.06 333	97.53 273	96.89 328	89.66 324	96.82 163	96.72 303	86.05 258	98.95 217	95.53 163	96.13 219	98.79 180

ACMM	93.85 9	95.69 171	95.38 166	96.61 223	97.61 224	93.84 243	98.91 86	98.44 182	95.25 131	94.28 233	98.47 153	86.04 260	99.12 186	95.50 164	93.95 243	96.87 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DTE-MVSNet		93.98 274	93.26 277	96.14 259	96.06 316	94.39 228	99.20 35	98.86 64	93.06 231	91.78 313	97.81 220	85.87 261	97.58 332	90.53 290	86.17 341	96.46 313

VPA-MVSNet		95.75 166	95.11 181	97.69 156	97.24 253	97.27 96	98.94 82	99.23 13	95.13 137	95.51 198	97.32 255	85.73 262	98.91 220	97.33 87	89.55 303	96.89 256

EPMVS		94.99 209	94.48 208	96.52 235	97.22 255	91.75 289	97.23 293	91.66 370	94.11 177	97.28 141	96.81 300	85.70 263	98.84 230	93.04 238	97.28 186	98.97 169

TransMVSNet (Re)		92.67 295	91.51 300	96.15 258	96.58 294	94.65 213	98.90 87	96.73 333	90.86 303	89.46 335	97.86 212	85.62 264	98.09 307	86.45 330	81.12 352	95.71 334

AUN-MVS		94.53 240	93.73 259	96.92 201	98.50 161	93.52 257	98.34 188	98.10 244	93.83 194	95.94 196	97.98 201	85.59 265	99.03 202	94.35 197	80.94 354	98.22 208

dp		94.15 264	93.90 245	94.90 301	97.31 250	86.82 356	96.97 308	97.19 312	91.22 297	96.02 193	96.61 310	85.51 266	99.02 205	90.00 300	94.30 229	98.85 176

LPG-MVS_test		95.62 174	95.34 168	96.47 239	97.46 238	93.54 254	98.99 72	98.54 160	94.67 160	94.36 229	98.77 124	85.39 267	99.11 191	95.71 157	94.15 236	96.76 270

LGP-MVS_train				96.47 239	97.46 238	93.54 254		98.54 160	94.67 160	94.36 229	98.77 124	85.39 267	99.11 191	95.71 157	94.15 236	96.76 270

PS-CasMVS		94.67 230	93.99 239	96.71 212	96.68 290	95.26 187	99.13 45	99.03 26	93.68 206	92.33 305	97.95 203	85.35 269	98.10 305	93.59 222	88.16 323	96.79 266

ab-mvs		96.42 138	95.71 152	98.55 90	98.63 153	96.75 118	97.88 248	98.74 108	93.84 192	96.54 177	98.18 186	85.34 270	99.75 109	95.93 146	96.35 207	99.15 148

N_pmnet		87.12 327	87.77 326	85.17 347	95.46 334	61.92 374	97.37 282	70.66 380	85.83 348	88.73 341	96.04 327	85.33 271	97.76 328	80.02 355	90.48 290	95.84 331

OPM-MVS		95.69 171	95.33 170	96.76 209	96.16 313	94.63 215	98.43 178	98.39 193	96.64 71	95.02 205	98.78 122	85.15 272	99.05 198	95.21 174	94.20 233	96.60 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BH-RMVSNet		95.92 159	95.32 171	97.69 156	98.32 178	94.64 214	98.19 214	97.45 299	94.56 164	96.03 192	98.61 138	85.02 273	99.12 186	90.68 289	99.06 119	99.30 129

DSMNet-mixed		92.52 297	92.58 288	92.33 336	94.15 350	82.65 363	98.30 198	94.26 361	89.08 331	92.65 294	95.73 332	85.01 274	95.76 357	86.24 331	97.76 175	98.59 194

tfpnnormal		93.66 277	92.70 286	96.55 233	96.94 274	95.94 158	98.97 76	99.19 16	91.04 301	91.38 317	97.34 253	84.94 275	98.61 249	85.45 338	89.02 313	95.11 344

LTVRE_ROB	92.95 15	94.60 233	93.90 245	96.68 216	97.41 246	94.42 226	98.52 164	98.59 148	91.69 278	91.21 318	98.35 166	84.87 276	99.04 201	91.06 282	93.44 255	96.60 290
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
XXY-MVS		95.20 198	94.45 212	97.46 169	96.75 286	96.56 127	98.86 98	98.65 140	93.30 223	93.27 275	98.27 179	84.85 277	98.87 227	94.82 181	91.26 282	96.96 245

thisisatest0515		95.61 176	94.89 191	97.76 149	98.15 192	95.15 191	96.77 324	94.41 358	92.95 236	97.18 145	97.43 250	84.78 278	99.45 160	94.63 185	97.73 177	98.68 187

CL-MVSNet_self_test		90.11 315	89.14 318	93.02 333	91.86 362	88.23 347	96.51 333	98.07 253	90.49 306	90.49 326	94.41 346	84.75 279	95.34 359	80.79 354	74.95 362	95.50 337

AllTest		95.24 195	94.65 200	96.99 192	99.25 89	93.21 269	98.59 152	98.18 227	91.36 287	93.52 265	98.77 124	84.67 280	99.72 113	89.70 305	97.87 170	98.02 214

TestCases				96.99 192	99.25 89	93.21 269		98.18 227	91.36 287	93.52 265	98.77 124	84.67 280	99.72 113	89.70 305	97.87 170	98.02 214

thres200		95.25 194	94.57 203	97.28 177	98.81 137	94.92 204	98.20 210	97.11 313	95.24 133	96.54 177	96.22 323	84.58 282	99.53 147	87.93 323	96.50 204	97.39 230

pm-mvs1		93.94 275	93.06 279	96.59 226	96.49 299	95.16 189	98.95 80	98.03 260	92.32 259	91.08 320	97.84 215	84.54 283	98.41 277	92.16 261	86.13 343	96.19 324

ACMP	93.49 10	95.34 190	94.98 187	96.43 244	97.67 220	93.48 258	98.73 126	98.44 182	94.94 151	92.53 298	98.53 147	84.50 284	99.14 184	95.48 165	94.00 241	96.66 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres100view900		95.38 185	94.70 198	97.41 172	98.98 124	94.92 204	98.87 96	96.90 326	95.38 122	96.61 171	96.88 295	84.29 285	99.56 141	88.11 319	96.29 210	97.76 219

thres600view7		95.49 177	94.77 194	97.67 158	98.98 124	95.02 196	98.85 99	96.90 326	95.38 122	96.63 170	96.90 294	84.29 285	99.59 137	88.65 318	96.33 208	98.40 201

FMVSNet3		94.97 212	94.26 221	97.11 187	98.18 189	96.62 121	98.56 160	98.26 218	93.67 208	94.09 243	97.10 267	84.25 287	98.01 313	92.08 263	92.14 268	96.70 279

tfpn200view9		95.32 192	94.62 201	97.43 171	98.94 126	94.98 200	98.68 138	96.93 324	95.33 125	96.55 175	96.53 311	84.23 288	99.56 141	88.11 319	96.29 210	97.76 219

thres400		95.38 185	94.62 201	97.65 161	98.94 126	94.98 200	98.68 138	96.93 324	95.33 125	96.55 175	96.53 311	84.23 288	99.56 141	88.11 319	96.29 210	98.40 201

cascas		94.63 232	93.86 248	96.93 198	96.91 277	94.27 232	96.00 340	98.51 167	85.55 350	94.54 218	96.23 321	84.20 290	98.87 227	95.80 152	96.98 191	97.66 225

tpm		94.13 265	93.80 252	95.12 294	96.50 298	87.91 350	97.44 275	95.89 346	92.62 246	96.37 185	96.30 318	84.13 291	98.30 290	93.24 231	91.66 276	99.14 150

tttt0517		96.07 149	95.51 161	97.78 147	98.41 166	94.84 206	99.28 22	94.33 360	94.26 175	97.64 133	98.64 137	84.05 292	99.47 158	95.34 166	97.60 181	99.03 163

IterMVS		94.09 269	93.85 249	94.80 306	97.99 202	90.35 316	97.18 297	98.12 239	93.68 206	92.46 303	97.34 253	84.05 292	97.41 336	92.51 256	91.33 279	96.62 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		94.11 267	93.87 247	94.85 303	97.98 204	90.56 314	97.18 297	98.11 242	93.75 196	92.58 296	97.48 245	83.97 294	97.41 336	92.48 258	91.30 280	96.58 292

SCA		95.46 178	95.13 179	96.46 242	97.67 220	91.29 300	97.33 287	97.60 283	94.68 159	96.92 158	97.10 267	83.97 294	98.89 224	92.59 251	98.32 158	99.20 138

TR-MVS		94.94 215	94.20 223	97.17 183	97.75 214	94.14 236	97.59 270	97.02 320	92.28 262	95.75 197	97.64 233	83.88 296	98.96 213	89.77 302	96.15 218	98.40 201

jajsoiax		95.45 180	95.03 184	96.73 211	95.42 337	94.63 215	99.14 42	98.52 164	95.74 103	93.22 276	98.36 165	83.87 297	98.65 247	96.95 100	94.04 239	96.91 253

Anonymous20231206		91.66 302	91.10 302	93.33 329	94.02 354	87.35 353	98.58 154	97.26 310	90.48 307	90.16 328	96.31 317	83.83 298	96.53 352	79.36 358	89.90 297	96.12 325

thisisatest0530		96.01 152	95.36 167	97.97 135	98.38 167	95.52 177	98.88 94	94.19 362	94.04 180	97.64 133	98.31 173	83.82 299	99.46 159	95.29 170	97.70 178	98.93 173

tpm2		94.19 261	93.76 257	95.46 285	97.23 254	89.04 334	97.31 289	96.85 332	87.08 340	96.21 188	96.79 301	83.75 300	98.74 239	92.43 259	96.23 216	98.59 194

mvs_tets		95.41 184	95.00 185	96.65 217	95.58 330	94.42 226	99.00 70	98.55 158	95.73 104	93.21 277	98.38 163	83.45 301	98.63 248	97.09 93	94.00 241	96.91 253

OurMVSNet-221017-0		94.21 259	94.00 237	94.85 303	95.60 329	89.22 331	98.89 91	97.43 301	95.29 128	92.18 308	98.52 150	82.86 302	98.59 253	93.46 225	91.76 273	96.74 272

UGNet		96.78 125	96.30 131	98.19 120	98.24 181	95.89 165	98.88 94	98.93 39	97.39 28	96.81 164	97.84 215	82.60 303	99.90 37	96.53 126	99.49 94	98.79 180
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
pmmvs5		93.65 279	92.97 281	95.68 278	95.49 333	92.37 278	98.20 210	97.28 308	89.66 324	92.58 296	97.26 258	82.14 304	98.09 307	93.18 234	90.95 287	96.58 292

DWT-MVSNet_test		94.82 219	94.36 217	96.20 257	97.35 248	90.79 308	98.34 188	96.57 339	92.91 238	95.33 201	96.44 315	82.00 305	99.12 186	94.52 192	95.78 224	98.70 185

test_part1		94.82 219	93.82 250	97.82 144	98.84 135	97.82 77	99.03 62	98.81 80	92.31 261	92.51 300	97.89 209	81.96 306	98.67 245	94.80 183	88.24 320	96.98 243

ACMH	92.88 16	94.55 238	93.95 241	96.34 251	97.63 223	93.26 267	98.81 112	98.49 176	93.43 217	89.74 331	98.53 147	81.91 307	99.08 196	93.69 217	93.30 258	96.70 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				95.44 286	97.42 243	91.32 299		97.50 294	95.09 142	93.59 261	98.35 166	81.70 308	98.88 226	89.71 304	93.39 256	96.12 325

Anonymous20231211		94.10 268	93.26 277	96.61 223	99.11 110	94.28 231	99.01 68	98.88 51	86.43 343	92.81 288	97.57 239	81.66 309	98.68 244	94.83 180	89.02 313	96.88 257

test1111		95.94 157	95.78 147	96.41 245	98.99 123	90.12 318	99.04 59	92.45 368	96.99 57	98.03 101	99.27 45	81.40 310	99.48 156	96.87 111	99.04 120	99.63 78

ECVR-MVS		95.95 155	95.71 152	96.65 217	99.02 117	90.86 305	99.03 62	91.80 369	96.96 58	98.10 95	99.26 46	81.31 311	99.51 151	96.90 104	99.04 120	99.59 86

GBi-Net		94.49 243	93.80 252	96.56 230	98.21 184	95.00 197	98.82 106	98.18 227	92.46 250	94.09 243	97.07 274	81.16 312	97.95 317	92.08 263	92.14 268	96.72 275

test1		94.49 243	93.80 252	96.56 230	98.21 184	95.00 197	98.82 106	98.18 227	92.46 250	94.09 243	97.07 274	81.16 312	97.95 317	92.08 263	92.14 268	96.72 275

FMVSNet2		94.47 245	93.61 265	97.04 190	98.21 184	96.43 133	98.79 117	98.27 214	92.46 250	93.50 268	97.09 271	81.16 312	98.00 315	91.09 280	91.93 271	96.70 279

GA-MVS		94.81 221	94.03 233	97.14 184	97.15 263	93.86 242	96.76 325	97.58 284	94.00 184	94.76 214	97.04 280	80.91 315	98.48 261	91.79 272	96.25 215	99.09 157

SixPastTwentyTwo		93.34 283	92.86 282	94.75 307	95.67 327	89.41 329	98.75 119	96.67 337	93.89 189	90.15 329	98.25 181	80.87 316	98.27 296	90.90 285	90.64 289	96.57 294

ACMH+	92.99 14	94.30 254	93.77 255	95.88 272	97.81 212	92.04 284	98.71 131	98.37 196	93.99 185	90.60 325	98.47 153	80.86 317	99.05 198	92.75 247	92.40 267	96.55 298

gg-mvs-nofinetune		92.21 299	90.58 306	97.13 185	96.75 286	95.09 194	95.85 341	89.40 373	85.43 351	94.50 220	81.98 366	80.80 318	98.40 283	92.16 261	98.33 157	97.88 216

test20.03		90.89 310	90.38 308	92.43 335	93.48 356	88.14 348	98.33 190	97.56 285	93.40 218	87.96 343	96.71 304	80.69 319	94.13 365	79.15 359	86.17 341	95.01 348

VPNet		94.99 209	94.19 224	97.40 174	97.16 262	96.57 126	98.71 131	98.97 31	95.67 107	94.84 209	98.24 182	80.36 320	98.67 245	96.46 128	87.32 331	96.96 245

GG-mvs-BLEND				96.59 226	96.34 305	94.98 200	96.51 333	88.58 374		93.10 283	94.34 350	80.34 321	98.05 311	89.53 308	96.99 190	96.74 272

KD-MVS_self_test		90.38 313	89.38 316	93.40 328	92.85 359	88.94 337	97.95 239	97.94 266	90.35 312	90.25 327	93.96 351	79.82 322	95.94 356	84.62 345	76.69 360	95.33 339

PVSNet_0	88.72 19	91.28 305	90.03 311	95.00 298	97.99 202	87.29 354	94.84 353	98.50 172	92.06 268	89.86 330	95.19 340	79.81 323	99.39 163	92.27 260	69.79 365	98.33 205

MS-PatchMatch		93.84 276	93.63 264	94.46 317	96.18 310	89.45 327	97.76 258	98.27 214	92.23 263	92.13 309	97.49 244	79.50 324	98.69 241	89.75 303	99.38 108	95.25 340

MVS-HIRNet		89.46 322	88.40 321	92.64 334	97.58 227	82.15 364	94.16 359	93.05 367	75.73 363	90.90 321	82.52 365	79.42 325	98.33 285	83.53 348	98.68 137	97.43 227

MDA-MVSNet-bldmvs		89.97 317	88.35 322	94.83 305	95.21 338	91.34 296	97.64 266	97.51 293	88.36 335	71.17 367	96.13 325	79.22 326	96.63 351	83.65 347	86.27 340	96.52 304

XVG-ACMP-BASELINE		94.54 239	94.14 229	95.75 277	96.55 295	91.65 292	98.11 226	98.44 182	94.96 148	94.22 237	97.90 207	79.18 327	99.11 191	94.05 210	93.85 245	96.48 311

RRT_test8_iter05		94.56 237	94.19 224	95.67 279	97.60 225	91.34 296	98.93 84	98.42 187	94.75 155	93.39 271	97.87 211	79.00 328	98.61 249	96.78 118	90.99 286	97.07 238

Anonymous20240529		95.10 203	94.22 222	97.75 150	99.01 119	94.26 233	98.87 96	98.83 72	85.79 349	96.64 169	98.97 95	78.73 329	99.85 54	96.27 134	94.89 227	99.12 152

TESTMET0.1,1		94.18 263	93.69 262	95.63 280	96.92 275	89.12 332	96.91 313	94.78 355	93.17 227	94.88 208	96.45 314	78.52 330	98.92 219	93.09 235	98.50 148	98.85 176

pmmvs-eth3d		90.36 314	89.05 319	94.32 319	91.10 364	92.12 280	97.63 269	96.95 323	88.86 332	84.91 355	93.13 354	78.32 331	96.74 346	88.70 317	81.81 350	94.09 355

KD-MVS_2432*1600		89.61 320	87.96 324	94.54 312	94.06 352	91.59 293	95.59 346	97.63 281	89.87 320	88.95 338	94.38 348	78.28 332	96.82 344	84.83 341	68.05 366	95.21 341

miper_refine_blended		89.61 320	87.96 324	94.54 312	94.06 352	91.59 293	95.59 346	97.63 281	89.87 320	88.95 338	94.38 348	78.28 332	96.82 344	84.83 341	68.05 366	95.21 341

Anonymous202405211		95.28 193	94.49 207	97.67 158	99.00 120	93.75 247	98.70 135	97.04 317	90.66 304	96.49 180	98.80 120	78.13 334	99.83 60	96.21 137	95.36 226	99.44 113

IB-MVS	91.98 17	93.27 285	91.97 296	97.19 181	97.47 237	93.41 261	97.09 303	95.99 342	93.32 221	92.47 302	95.73 332	78.06 335	99.53 147	94.59 190	82.98 346	98.62 193
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
LF4IMVS		93.14 290	92.79 284	94.20 320	95.88 322	88.67 340	97.66 265	97.07 315	93.81 195	91.71 314	97.65 231	77.96 336	98.81 234	91.47 278	91.92 272	95.12 343

test-mter		94.08 270	93.51 269	95.80 274	96.77 283	89.70 322	96.91 313	95.21 350	92.89 239	94.83 211	95.72 334	77.69 337	98.97 209	93.06 236	98.50 148	98.72 183

USDC		93.33 284	92.71 285	95.21 291	96.83 282	90.83 307	96.91 313	97.50 294	93.84 192	90.72 323	98.14 188	77.69 337	98.82 233	89.51 309	93.21 260	95.97 329

test_0402		91.32 304	90.27 309	94.48 315	96.60 293	91.12 302	98.50 169	97.22 311	86.10 346	88.30 342	96.98 286	77.65 339	97.99 316	78.13 362	92.94 263	94.34 351

K. test v3		92.55 296	91.91 298	94.48 315	95.64 328	89.24 330	99.07 55	94.88 354	94.04 180	86.78 347	97.59 237	77.64 340	97.64 330	92.08 263	89.43 306	96.57 294

TDRefinement		91.06 308	89.68 313	95.21 291	85.35 370	91.49 295	98.51 168	97.07 315	91.47 283	88.83 340	97.84 215	77.31 341	99.09 195	92.79 246	77.98 358	95.04 346

test2506		94.44 247	93.91 244	96.04 262	99.02 117	88.99 336	99.06 56	79.47 379	96.96 58	98.36 86	99.26 46	77.21 342	99.52 150	96.78 118	99.04 120	99.59 86

new_pmnet		90.06 316	89.00 320	93.22 332	94.18 349	88.32 346	96.42 335	96.89 328	86.19 344	85.67 353	93.62 352	77.18 343	97.10 340	81.61 352	89.29 308	94.23 352

Anonymous20240521		91.18 306	90.44 307	93.42 326	93.70 355	88.47 343	98.94 82	97.56 285	88.46 334	89.56 334	95.08 343	77.15 344	96.97 342	83.92 346	89.55 303	94.82 349

new-patchmatchnet		88.50 324	87.45 327	91.67 340	90.31 366	85.89 357	97.16 300	97.33 305	89.47 326	83.63 357	92.77 355	76.38 345	95.06 362	82.70 349	77.29 359	94.06 356

lessismore_v0				94.45 318	94.93 342	88.44 344		91.03 371		86.77 348	97.64 233	76.23 346	98.42 270	90.31 293	85.64 344	96.51 307

TinyColmap		92.31 298	91.53 299	94.65 310	96.92 275	89.75 321	96.92 311	96.68 336	90.45 309	89.62 332	97.85 214	76.06 347	98.81 234	86.74 328	92.51 266	95.41 338

pmmvs6		91.77 301	90.63 305	95.17 293	94.69 347	91.24 301	98.67 141	97.92 268	86.14 345	89.62 332	97.56 241	75.79 348	98.34 284	90.75 288	84.56 345	95.94 330

MIMVSNet		93.26 286	92.21 293	96.41 245	97.73 218	93.13 271	95.65 345	97.03 318	91.27 295	94.04 246	96.06 326	75.33 349	97.19 339	86.56 329	96.23 216	98.92 174

UnsupCasMVSNet_eth		90.99 309	89.92 312	94.19 321	94.08 351	89.83 320	97.13 302	98.67 133	93.69 204	85.83 352	96.19 324	75.15 350	96.74 346	89.14 314	79.41 356	96.00 328

LFMVS		95.86 161	94.98 187	98.47 100	98.87 131	96.32 140	98.84 102	96.02 341	93.40 218	98.62 72	99.20 58	74.99 351	99.63 133	97.72 61	97.20 187	99.46 110

CMPMVS	66.06 21	89.70 318	89.67 314	89.78 342	93.19 357	76.56 367	97.00 307	98.35 199	80.97 358	81.57 359	97.75 223	74.75 352	98.61 249	89.85 301	93.63 249	94.17 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		91.81 300	90.92 303	94.49 314	97.21 256	92.09 281	98.00 236	97.55 290	89.31 329	90.86 322	95.61 338	74.48 353	95.32 360	85.57 336	89.70 299	96.07 327

testgi		93.06 291	92.45 290	94.88 302	96.43 302	89.90 319	98.75 119	97.54 291	95.60 110	91.63 316	97.91 206	74.46 354	97.02 341	86.10 332	93.67 247	97.72 223

VDD-MVS		95.82 164	95.23 175	97.61 163	98.84 135	93.98 239	98.68 138	97.40 303	95.02 145	97.95 111	99.34 34	74.37 355	99.78 100	98.64 7	96.80 193	99.08 160

FMVSNet1		93.19 289	92.07 294	96.56 230	97.54 232	95.00 197	98.82 106	98.18 227	90.38 311	92.27 306	97.07 274	73.68 356	97.95 317	89.36 312	91.30 280	96.72 275

VDDNet		95.36 188	94.53 205	97.86 140	98.10 195	95.13 193	98.85 99	97.75 275	90.46 308	98.36 86	99.39 17	73.27 357	99.64 130	97.98 43	96.58 200	98.81 179

UniMVSNet_ETH3D		94.24 258	93.33 274	96.97 195	97.19 260	93.38 263	98.74 122	98.57 154	91.21 298	93.81 256	98.58 143	72.85 358	98.77 238	95.05 176	93.93 244	98.77 182

DeepMVS_CX				86.78 344	97.09 267	72.30 370		95.17 353	75.92 362	84.34 356	95.19 340	70.58 359	95.35 358	79.98 357	89.04 312	92.68 361

OpenMVS_ROB	86.42 20	89.00 323	87.43 328	93.69 324	93.08 358	89.42 328	97.91 243	96.89 328	78.58 360	85.86 351	94.69 345	69.48 360	98.29 293	77.13 363	93.29 259	93.36 360

EGC-MVSNET		75.22 334	69.54 337	92.28 337	94.81 344	89.58 325	97.64 266	96.50 340	1.82 377	5.57 378	95.74 330	68.21 361	96.26 355	73.80 366	91.71 274	90.99 362

EG-PatchMatch MVS		91.13 307	90.12 310	94.17 322	94.73 346	89.00 335	98.13 223	97.81 272	89.22 330	85.32 354	96.46 313	67.71 362	98.42 270	87.89 324	93.82 246	95.08 345

MIMVSNet1		89.67 319	88.28 323	93.82 323	92.81 360	91.08 303	98.01 234	97.45 299	87.95 336	87.90 344	95.87 329	67.63 363	94.56 364	78.73 361	88.18 322	95.83 332

pmmvs3		86.67 328	84.86 331	92.11 339	88.16 367	87.19 355	96.63 329	94.75 356	79.88 359	87.22 346	92.75 356	66.56 364	95.20 361	81.24 353	76.56 361	93.96 357

MVS_0304		92.81 293	92.01 295	95.23 290	97.46 238	91.33 298	98.17 219	98.81 80	91.13 300	93.80 257	95.68 337	66.08 365	98.06 310	90.79 286	96.13 219	96.32 320

tmp_tt		68.90 336	66.97 338	74.68 353	50.78 380	59.95 376	87.13 365	83.47 377	38.80 373	62.21 369	96.23 321	64.70 366	76.91 375	88.91 316	30.49 373	87.19 365

UnsupCasMVSNet_bld		87.17 326	85.12 330	93.31 330	91.94 361	88.77 338	94.92 352	98.30 211	84.30 354	82.30 358	90.04 360	63.96 367	97.25 338	85.85 335	74.47 364	93.93 358

test_method		79.03 329	78.17 332	81.63 349	86.06 369	54.40 379	82.75 368	96.89 328	39.54 372	80.98 360	95.57 339	58.37 368	94.73 363	84.74 344	78.61 357	95.75 333

PM-MVS		87.77 325	86.55 329	91.40 341	91.03 365	83.36 362	96.92 311	95.18 352	91.28 294	86.48 350	93.42 353	53.27 369	96.74 346	89.43 311	81.97 349	94.11 354

ambc				89.49 343	86.66 368	75.78 368	92.66 361	96.72 334		86.55 349	92.50 357	46.01 370	97.90 321	90.32 292	82.09 347	94.80 350

Gipuma		78.40 331	76.75 334	83.38 348	95.54 331	80.43 366	79.42 369	97.40 303	64.67 366	73.46 364	80.82 367	45.65 371	93.14 366	66.32 368	87.43 329	76.56 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		64.07 339	63.26 342	66.53 356	81.73 373	58.81 378	91.85 362	84.75 376	51.93 371	59.09 371	75.13 370	43.32 372	79.09 374	42.03 373	39.47 371	61.69 370

E-PMN		64.94 338	64.25 340	67.02 355	82.28 372	59.36 377	91.83 363	85.63 375	52.69 369	60.22 370	77.28 369	41.06 373	80.12 373	46.15 372	41.14 370	61.57 371

FPMVS		77.62 333	77.14 333	79.05 351	79.25 374	60.97 375	95.79 342	95.94 344	65.96 365	67.93 368	94.40 347	37.73 374	88.88 370	68.83 367	88.46 318	87.29 364

PMMVS2		77.95 332	75.44 336	85.46 346	82.54 371	74.95 369	94.23 358	93.08 366	72.80 364	74.68 363	87.38 362	36.36 375	91.56 368	73.95 365	63.94 368	89.87 363

LCM-MVSNet		78.70 330	76.24 335	86.08 345	77.26 376	71.99 371	94.34 357	96.72 334	61.62 367	76.53 362	89.33 361	33.91 376	92.78 367	81.85 351	74.60 363	93.46 359

ANet_high		69.08 335	65.37 339	80.22 350	65.99 378	71.96 372	90.91 364	90.09 372	82.62 355	49.93 373	78.39 368	29.36 377	81.75 371	62.49 369	38.52 372	86.95 366

PMVS	61.03 23	65.95 337	63.57 341	73.09 354	57.90 379	51.22 380	85.05 367	93.93 365	54.45 368	44.32 374	83.57 364	13.22 378	89.15 369	58.68 370	81.00 353	78.91 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		20.95 344	23.72 347	12.64 358	13.54 382	8.19 382	96.55 332	6.13 383	7.48 376	16.74 376	37.98 374	12.97 379	6.05 377	16.69 375	5.43 376	23.68 372

wuyk23d		30.17 341	30.18 345	30.16 357	78.61 375	43.29 381	66.79 370	14.21 381	17.31 374	14.82 377	11.93 377	11.55 380	41.43 376	37.08 374	19.30 374	5.76 374

MVE	62.14 22	63.28 340	59.38 343	74.99 352	74.33 377	65.47 373	85.55 366	80.50 378	52.02 370	51.10 372	75.00 371	10.91 381	80.50 372	51.60 371	53.40 369	78.99 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.48 343	24.95 346	11.09 359	14.89 381	6.47 383	96.56 331	9.87 382	7.55 375	17.93 375	39.02 373	9.43 382	5.90 378	16.56 376	12.72 375	20.91 373

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		8.20 345	10.94 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	98.43 156	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					99.82 1	98.66 26	99.69 1	98.95 35	97.46 23	99.39 15

MSC_two_6792asdad				99.62 6	99.17 101	99.08 11		98.63 143					99.94 4	98.53 13	99.80 18	99.86 2

No_MVS				99.62 6	99.17 101	99.08 11		98.63 143					99.94 4	98.53 13	99.80 18	99.86 2

eth-test2					0.00 383

eth-test					0.00 383

IU-MVS					99.71 21	99.23 7		98.64 141	95.28 129	99.63 4				98.35 31	99.81 11	99.83 7

save fliter					99.46 54	98.38 40	98.21 207	98.71 118	97.95 3

test_0728_SECOND				99.71 1	99.72 13	99.35 1	98.97 76	98.88 51					99.94 4	98.47 21	99.81 11	99.84 6

GSMVS																99.20 138

test_part2					99.63 31	99.18 10				99.27 20

MTGPA								98.74 108

MTMP							98.89 91	94.14 363

gm-plane-assit					95.88 322	87.47 352			89.74 323		96.94 292		99.19 178	93.32 230

test9_res														96.39 133	99.57 80	99.69 56

agg_prior2														95.87 149	99.57 80	99.68 62

agg_prior					99.30 78	98.38 40		98.72 114		97.57 137			99.81 75

test_prior4						98.01 67	97.86 250

test_prior				99.19 46	99.31 73	98.22 55		98.84 69					99.70 119			99.65 72

旧先验2							97.57 272		91.30 292	98.67 66			99.80 84	95.70 159

新几何2							97.64 266

无先验							97.58 271	98.72 114	91.38 286				99.87 48	93.36 228		99.60 84

原ACMM2							97.67 264

testdata2													99.89 39	91.65 276

testdata1							97.32 288		96.34 83

plane_prior7					97.42 243	94.63 215

plane_prior5								98.56 156					99.03 202	96.07 139	94.27 230	96.92 248

plane_prior4											98.28 176

plane_prior3						94.61 218			97.02 55	95.34 199

plane_prior2							98.80 113		97.28 35

plane_prior1					97.37 247

plane_prior						94.60 220	98.44 176		96.74 67						94.22 232

n2								0.00 384

nn								0.00 384

door-mid								94.37 359

test11								98.66 136

door								94.64 357

HQP5-MVS						94.25 234

HQP-NCC					97.20 257		98.05 230		96.43 79	94.45 222

ACMP_Plane					97.20 257		98.05 230		96.43 79	94.45 222

BP-MVS														95.30 168

HQP4-MVS										94.45 222			98.96 213			96.87 259

HQP3-MVS								98.46 178							94.18 234

NP-MVS					97.28 251	94.51 223					97.73 224

ACMMP++_ref															92.97 262

ACMMP++															93.61 250

High-resolution multi-view benchmark