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.

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 28

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 28

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 53	96.48 8	94.88 9

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 25	92.85 60	80.26 13	87.78 30	94.27 36	75.89 19	96.81 22	87.45 19	96.44 9	93.05 92

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 22	93.63 23	74.77 111	92.29 7	95.97 2	74.28 34	97.24 11	88.58 13	96.91 1	94.87 11
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
3Dnovator+	77.84 4	85.48 59	84.47 74	88.51 6	91.08 92	73.49 17	93.18 11	93.78 21	80.79 10	76.66 186	93.37 60	60.40 188	96.75 25	77.20 114	93.73 68	95.29 2

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 63	93.00 47	80.90 9	88.06 28	94.06 46	76.43 16	96.84 20	88.48 14	95.99 19	94.34 33

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 22	80.19 14	88.10 27	94.80 16	73.76 39	97.11 13	87.51 18	95.82 24	94.90 8
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 64	94.67 20
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
NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 51	92.83 61	81.50 6	85.79 45	93.47 59	73.02 45	97.00 17	84.90 33	94.94 44	94.10 41

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 65	85.24 51	94.32 35	71.76 54	96.93 18	85.53 29	95.79 25	94.32 34

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 70	84.45 68	94.52 23	69.09 80	96.70 26	84.37 44	94.83 50	94.03 45

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 67	84.66 65	94.52 23	68.81 84	96.65 29	84.53 41	94.90 45	94.00 48

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 42	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 50
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVS		87.18 34	86.91 37	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	94.17 40	67.45 93	96.60 34	83.06 60	94.50 56	94.07 43

X-MVStestdata		80.37 143	77.83 179	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	12.47 369	67.45 93	96.60 34	83.06 60	94.50 56	94.07 43

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 56	93.59 25	76.27 81	88.14 26	95.09 15	71.06 59	96.67 28	87.67 16	96.37 14	94.09 42

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 67	84.91 56	94.44 30	70.78 61	96.61 32	84.53 41	94.89 46	93.66 64

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 27	93.24 37	75.23 100	84.91 56	94.44 30	70.78 61	96.61 32	83.75 54	94.89 46	93.66 64

MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 21	92.65 70	77.57 42	83.84 80	94.40 34	72.24 50	96.28 41	85.65 28	95.30 40	93.62 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 73	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 84

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 184	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 84

PGM-MVS		86.68 41	86.27 46	87.90 21	94.22 35	73.38 19	90.22 74	93.04 43	75.53 94	83.86 79	94.42 33	67.87 90	96.64 30	82.70 69	94.57 55	93.66 64

DVP-MVS++.		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 33	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 5

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 24	93.43 33	76.89 63	84.68 62	93.99 50	70.67 64	96.82 21	84.18 49	95.01 42	93.90 53

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 51	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 9

xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 66	89.69 168	74.31 121	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 15

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 31	93.49 30	74.75 112	88.33 25	94.43 32	73.27 42	97.02 16	84.18 49	94.84 48	93.82 58

DeepC-MVS_fast	79.65 3	86.91 38	86.62 41	87.76 29	93.52 52	72.37 44	91.26 48	93.04 43	76.62 72	84.22 73	93.36 61	71.44 57	96.76 24	80.82 83	95.33 38	94.16 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 27

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 81	93.82 19	73.07 147	84.86 61	92.89 71	76.22 17	96.33 39	84.89 35	95.13 41	94.40 30

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 47	92.35 80	74.62 115	88.90 21	93.85 52	75.75 20	96.00 53	87.80 15	94.63 53	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mPP-MVS		86.67 42	86.32 45	87.72 32	94.41 24	73.55 13	92.74 19	92.22 87	76.87 64	82.81 95	94.25 38	66.44 102	96.24 42	82.88 64	94.28 62	93.38 78

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 38

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 61	94.05 15	70.80 180	87.59 33	93.51 56	77.57 14	96.63 31	83.31 55	95.77 26	94.72 19

DeepC-MVS	79.81 2	87.08 37	86.88 38	87.69 36	91.16 91	72.32 47	90.31 71	93.94 17	77.12 57	82.82 94	94.23 39	72.13 52	97.09 14	84.83 36	95.37 35	93.65 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		87.11 35	86.92 36	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 72	83.68 82	94.46 27	67.93 88	95.95 56	84.20 48	94.39 59	93.23 84

SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 22	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 15

MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 83	93.50 28	75.17 103	86.34 40	95.29 12	70.86 60	96.00 53	88.78 12	96.04 16	94.58 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		86.45 44	86.10 51	87.51 40	90.09 111	70.94 74	89.70 87	92.59 72	81.78 4	81.32 111	91.43 100	70.34 66	97.23 12	84.26 46	93.36 70	94.37 31

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 46	94.70 3	74.47 118	88.86 22	94.61 21	75.23 24	95.84 58	86.62 26	95.92 21	94.78 17

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 32	93.33 36	76.07 84	83.81 81	93.95 51	69.77 74	96.01 52	85.15 31	94.66 52	94.32 34
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 87	72.45 41	90.02 77	94.37 4	71.76 163	87.28 34	94.27 36	75.18 25	96.08 49	85.16 30	95.77 26	93.80 61

ACMMP		85.89 54	85.39 59	87.38 44	93.59 51	72.63 34	92.74 19	93.18 41	76.78 67	80.73 120	93.82 53	64.33 123	96.29 40	82.67 70	90.69 99	93.23 84
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
DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 49	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 81
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
PHI-MVS		86.43 45	86.17 50	87.24 46	90.88 97	70.96 72	92.27 30	94.07 11	72.45 152	85.22 52	91.90 86	69.47 76	96.42 38	83.28 58	95.94 20	94.35 32

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 39	92.83 61	73.01 149	88.58 23	94.52 23	73.36 40	96.49 37	84.26 46	95.01 42	92.70 103
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		85.76 56	85.29 63	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 232	84.61 66	93.48 57	72.32 49	96.15 48	79.00 94	95.43 34	94.28 36

train_agg		86.43 45	86.20 48	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 227	85.00 54	93.10 65	74.43 30	95.41 77	84.97 32	95.71 30	93.02 94

CSCG		86.41 47	86.19 49	87.07 50	92.91 65	72.48 38	90.81 57	93.56 26	73.95 129	83.16 88	91.07 109	75.94 18	95.19 88	79.94 92	94.38 60	93.55 74

Regformer-2		86.63 43	86.53 42	86.95 51	89.33 133	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 128	74.45 29	95.61 63	84.38 43	90.63 100	94.01 47

SR-MVS		86.73 39	86.67 40	86.91 52	94.11 40	72.11 51	92.37 26	92.56 73	74.50 116	86.84 37	94.65 20	67.31 95	95.77 60	84.80 37	92.85 74	92.84 101

DPM-MVS		84.93 70	84.29 75	86.84 53	90.20 109	73.04 24	87.12 166	93.04 43	69.80 200	82.85 93	91.22 104	73.06 44	96.02 51	76.72 121	94.63 53	91.46 144

TSAR-MVS + GP.		85.71 57	85.33 60	86.84 53	91.34 89	72.50 37	89.07 101	87.28 232	76.41 74	85.80 44	90.22 126	74.15 37	95.37 83	81.82 75	91.88 84	92.65 107

test12				86.80 55	92.63 73	70.70 81		91.79 108		82.71 96		71.67 55	96.16 47		94.50 56	93.54 75

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 110	89.57 89	93.39 35	77.53 46	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 23

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 40	93.19 40	77.87 36	90.32 17	94.00 48	74.83 27	93.78 145	87.63 17	94.27 63	93.65 69
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
Regformer-1		86.41 47	86.33 44	86.64 58	89.33 133	70.93 75	88.43 123	91.39 122	82.14 3	86.65 38	90.09 128	74.39 32	95.01 97	83.97 51	90.63 100	93.97 49

agg_prior1		86.22 49	86.09 52	86.62 59	92.85 66	71.94 54	88.59 120	91.78 109	68.96 222	84.41 69	93.18 64	74.94 26	94.93 98	84.75 38	95.33 38	93.01 95

3Dnovator	76.31 5	83.38 85	82.31 94	86.59 60	87.94 187	72.94 29	90.64 60	92.14 91	77.21 53	75.47 211	92.83 73	58.56 195	94.72 110	73.24 151	92.71 76	92.13 125

HPM-MVS_fast		85.35 63	84.95 70	86.57 61	93.69 48	70.58 85	92.15 34	91.62 113	73.89 132	82.67 97	94.09 44	62.60 145	95.54 69	80.93 81	92.93 72	93.57 73

Regformer-4		85.68 58	85.45 58	86.35 62	88.95 152	69.67 100	88.29 133	91.29 124	81.73 5	85.36 49	90.01 131	72.62 47	95.35 84	83.28 58	87.57 133	94.03 45

test_prior3		86.73 39	86.86 39	86.33 63	92.61 74	69.59 101	88.85 108	92.97 54	75.41 96	84.91 56	93.54 54	74.28 34	95.48 71	83.31 55	95.86 22	93.91 51

test_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 71			93.91 51

MVS_111021_HR		85.14 66	84.75 72	86.32 65	91.65 86	72.70 31	85.98 199	90.33 150	76.11 83	82.08 100	91.61 94	71.36 58	94.17 128	81.02 80	92.58 78	92.08 126

SR-MVS-dyc-post		85.77 55	85.61 56	86.23 66	93.06 62	70.63 82	91.88 37	92.27 83	73.53 140	85.69 46	94.45 28	65.00 120	95.56 66	82.75 65	91.87 85	92.50 110

APD-MVS_3200maxsize		85.97 52	85.88 53	86.22 67	92.69 72	69.53 103	91.93 36	92.99 49	73.54 139	85.94 41	94.51 26	65.80 112	95.61 63	83.04 62	92.51 79	93.53 76

DP-MVS Recon		83.11 90	82.09 97	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 187	79.17 134	91.03 112	64.12 125	96.03 50	68.39 194	90.14 107	91.50 141

EPNet		83.72 78	82.92 86	86.14 69	84.22 254	69.48 104	91.05 54	85.27 257	81.30 7	76.83 181	91.65 91	66.09 107	95.56 66	76.00 126	93.85 66	93.38 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		86.20 50	86.22 47	86.12 70	93.95 42	69.89 96	91.79 41	92.28 82	75.07 104	86.40 39	94.58 22	65.00 120	95.56 66	84.34 45	92.60 77	92.90 99

abl_6		85.23 64	84.95 70	86.07 71	92.23 79	70.48 86	90.80 58	92.08 92	73.51 142	85.26 50	94.16 41	62.75 144	95.92 57	82.46 72	91.30 94	91.81 134

canonicalmvs		85.91 53	85.87 54	86.04 72	89.84 119	69.44 108	90.45 69	93.00 47	76.70 71	88.01 29	91.23 103	73.28 41	93.91 140	81.50 77	88.80 121	94.77 18

h-mvs33		83.15 87	82.19 95	86.02 73	90.56 102	70.85 78	88.15 140	89.16 184	76.02 85	84.67 63	91.39 101	61.54 163	95.50 70	82.71 67	75.48 278	91.72 136

alignmvs		85.48 59	85.32 61	85.96 74	89.51 126	69.47 105	89.74 85	92.47 74	76.17 82	87.73 32	91.46 99	70.32 67	93.78 145	81.51 76	88.95 118	94.63 22

DELS-MVS		85.41 62	85.30 62	85.77 75	88.49 170	67.93 139	85.52 216	93.44 32	78.70 29	83.63 85	89.03 160	74.57 28	95.71 62	80.26 90	94.04 65	93.66 64
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
Regformer-3		85.23 64	85.07 66	85.70 76	88.95 152	69.01 112	88.29 133	89.91 162	80.95 8	85.01 53	90.01 131	72.45 48	94.19 126	82.50 71	87.57 133	93.90 53

ETV-MVS		84.90 72	84.67 73	85.59 77	89.39 131	68.66 126	88.74 115	92.64 71	79.97 17	84.10 76	85.71 247	69.32 78	95.38 80	80.82 83	91.37 92	92.72 102

UA-Net		85.08 68	84.96 69	85.45 78	92.07 81	68.07 137	89.78 84	90.86 137	82.48 2	84.60 67	93.20 63	69.35 77	95.22 87	71.39 164	90.88 98	93.07 91

Vis-MVSNet		83.46 82	82.80 88	85.43 79	90.25 108	68.74 120	90.30 72	90.13 156	76.33 80	80.87 119	92.89 71	61.00 177	94.20 125	72.45 158	90.97 96	93.35 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet-Vis-set		84.19 74	83.81 76	85.31 80	88.18 179	67.85 140	87.66 152	89.73 167	80.05 16	82.95 90	89.59 144	70.74 63	94.82 106	80.66 86	84.72 169	93.28 83

mvs-test1		80.88 124	79.40 142	85.29 81	85.13 242	69.75 99	89.28 92	88.10 213	74.99 106	76.44 192	86.72 219	57.27 207	94.26 124	73.53 144	83.18 189	91.87 131

MAR-MVS		81.84 107	80.70 117	85.27 82	91.32 90	71.53 60	89.82 81	90.92 134	69.77 201	78.50 146	86.21 239	62.36 151	94.52 114	65.36 219	92.05 83	89.77 210
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
Effi-MVS+		83.62 80	83.08 82	85.24 83	88.38 175	67.45 147	88.89 106	89.15 185	75.50 95	82.27 98	88.28 179	69.61 75	94.45 116	77.81 108	87.84 131	93.84 57

MVSFormer		82.85 93	82.05 99	85.24 83	87.35 204	70.21 88	90.50 64	90.38 146	68.55 229	81.32 111	89.47 147	61.68 160	93.46 163	78.98 95	90.26 105	92.05 128

OPM-MVS		83.50 81	82.95 85	85.14 85	88.79 160	70.95 73	89.13 100	91.52 116	77.55 45	80.96 118	91.75 89	60.71 180	94.50 115	79.67 93	86.51 152	89.97 202
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		83.64 79	83.14 81	85.14 85	90.08 112	68.71 122	91.25 49	92.44 75	79.12 24	78.92 138	91.00 113	60.42 186	95.38 80	78.71 97	86.32 154	91.33 146

EI-MVSNet-UG-set		83.81 76	83.38 79	85.09 87	87.87 188	67.53 146	87.44 158	89.66 169	79.74 18	82.23 99	89.41 153	70.24 69	94.74 109	79.95 91	83.92 177	92.99 97

QAPM		80.88 124	79.50 140	85.03 88	88.01 186	68.97 114	91.59 42	92.00 97	66.63 248	75.15 225	92.16 81	57.70 201	95.45 73	63.52 229	88.76 122	90.66 168

casdiffmvs		85.11 67	85.14 65	85.01 89	87.20 211	65.77 178	87.75 150	92.83 61	77.84 37	84.36 72	92.38 79	72.15 51	93.93 139	81.27 79	90.48 102	95.33 1

PCF-MVS	73.52 7	80.38 142	78.84 156	85.01 89	87.71 195	68.99 113	83.65 252	91.46 121	63.00 288	77.77 163	90.28 123	66.10 106	95.09 95	61.40 251	88.22 130	90.94 160
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
nrg030		83.88 75	83.53 77	84.96 91	86.77 219	69.28 109	90.46 68	92.67 67	74.79 110	82.95 90	91.33 102	72.70 46	93.09 180	80.79 85	79.28 235	92.50 110

VDD-MVS		83.01 92	82.36 93	84.96 91	91.02 94	66.40 164	88.91 105	88.11 212	77.57 42	84.39 71	93.29 62	52.19 243	93.91 140	77.05 116	88.70 123	94.57 25

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 91	90.80 99	69.76 98	88.74 115	91.70 112	69.39 207	78.96 136	88.46 174	65.47 114	94.87 105	74.42 136	88.57 124	90.24 184

CPTT-MVS		83.73 77	83.33 80	84.92 94	93.28 55	70.86 77	92.09 35	90.38 146	68.75 226	79.57 130	92.83 73	60.60 184	93.04 184	80.92 82	91.56 90	90.86 162

DROMVSNet		86.01 51	86.38 43	84.91 95	89.31 138	66.27 167	92.32 28	93.63 23	79.37 21	84.17 75	91.88 87	69.04 83	95.43 75	83.93 52	93.77 67	93.01 95

test_part1		82.78 94	82.08 98	84.89 96	90.66 100	66.97 158	90.96 55	92.93 57	77.19 54	80.53 122	90.04 130	63.44 130	95.39 79	76.04 125	76.90 255	92.31 117

OMC-MVS		82.69 95	81.97 102	84.85 97	88.75 162	67.42 148	87.98 142	90.87 136	74.92 108	79.72 129	91.65 91	62.19 155	93.96 133	75.26 133	86.42 153	93.16 89

EIA-MVS		83.31 86	82.80 88	84.82 98	89.59 122	65.59 181	88.21 136	92.68 66	74.66 114	78.96 136	86.42 235	69.06 81	95.26 85	75.54 131	90.09 108	93.62 71

PAPM_NR		83.02 91	82.41 91	84.82 98	92.47 77	66.37 165	87.93 146	91.80 107	73.82 133	77.32 171	90.66 118	67.90 89	94.90 102	70.37 172	89.48 115	93.19 88

baseline		84.93 70	84.98 67	84.80 100	87.30 209	65.39 186	87.30 162	92.88 58	77.62 40	84.04 78	92.26 80	71.81 53	93.96 133	81.31 78	90.30 104	95.03 4

lupinMVS		81.39 118	80.27 127	84.76 101	87.35 204	70.21 88	85.55 212	86.41 244	62.85 291	81.32 111	88.61 169	61.68 160	92.24 209	78.41 103	90.26 105	91.83 132

jason		81.39 118	80.29 126	84.70 102	86.63 221	69.90 95	85.95 200	86.77 240	63.24 284	81.07 117	89.47 147	61.08 176	92.15 212	78.33 104	90.07 110	92.05 128
jason: jason.
ET-MVSNet_ETH3D		78.63 182	76.63 211	84.64 103	86.73 220	69.47 105	85.01 222	84.61 264	69.54 205	66.51 314	86.59 228	50.16 269	91.75 224	76.26 122	84.24 175	92.69 105

EPP-MVSNet		83.40 84	83.02 84	84.57 104	90.13 110	64.47 202	92.32 28	90.73 138	74.45 120	79.35 133	91.10 107	69.05 82	95.12 90	72.78 155	87.22 141	94.13 40

UGNet		80.83 128	79.59 138	84.54 105	88.04 184	68.09 136	89.42 90	88.16 211	76.95 61	76.22 196	89.46 149	49.30 280	93.94 136	68.48 192	90.31 103	91.60 137
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
LPG-MVS_test		82.08 102	81.27 108	84.50 106	89.23 143	68.76 118	90.22 74	91.94 101	75.37 98	76.64 187	91.51 96	54.29 226	94.91 100	78.44 101	83.78 178	89.83 207

LGP-MVS_train				84.50 106	89.23 143	68.76 118		91.94 101	75.37 98	76.64 187	91.51 96	54.29 226	94.91 100	78.44 101	83.78 178	89.83 207

CS-MVS-test		85.02 69	85.21 64	84.46 108	89.28 140	65.70 179	91.16 52	93.56 26	77.83 38	81.80 105	89.89 133	70.67 64	95.61 63	80.39 87	92.34 82	92.06 127

MSLP-MVS++		85.43 61	85.76 55	84.45 109	91.93 83	70.24 87	90.71 59	92.86 59	77.46 48	84.22 73	92.81 75	67.16 97	92.94 186	80.36 88	94.35 61	90.16 186

Effi-MVS+-dtu		80.03 149	78.57 160	84.42 110	85.13 242	68.74 120	88.77 112	88.10 213	74.99 106	74.97 230	83.49 283	57.27 207	93.36 166	73.53 144	80.88 213	91.18 150

RRT_MVS		79.88 152	78.38 165	84.38 111	85.42 236	70.60 84	88.71 117	88.75 203	72.30 157	78.83 140	89.14 155	44.44 312	92.18 211	78.50 100	79.33 234	90.35 180

HQP-MVS		82.61 97	82.02 100	84.37 112	89.33 133	66.98 156	89.17 95	92.19 89	76.41 74	77.23 174	90.23 125	60.17 189	95.11 91	77.47 111	85.99 160	91.03 156

ACMP	74.13 6	81.51 117	80.57 119	84.36 113	89.42 129	68.69 125	89.97 79	91.50 120	74.46 119	75.04 229	90.41 122	53.82 231	94.54 112	77.56 110	82.91 192	89.86 206
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
原ACMM1				84.35 114	93.01 64	68.79 116		92.44 75	63.96 282	81.09 116	91.57 95	66.06 108	95.45 73	67.19 205	94.82 51	88.81 239

PS-MVSNAJss		82.07 103	81.31 107	84.34 115	86.51 222	67.27 152	89.27 93	91.51 117	71.75 164	79.37 132	90.22 126	63.15 138	94.27 120	77.69 109	82.36 200	91.49 142

thisisatest0530		79.40 164	77.76 183	84.31 116	87.69 197	65.10 192	87.36 159	84.26 271	70.04 194	77.42 168	88.26 181	49.94 272	94.79 108	70.20 173	84.70 170	93.03 93

CLD-MVS		82.31 99	81.65 105	84.29 117	88.47 171	67.73 143	85.81 207	92.35 80	75.78 89	78.33 151	86.58 230	64.01 126	94.35 117	76.05 124	87.48 138	90.79 163
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 105	81.23 109	84.26 118	90.94 95	70.18 93	91.10 53	89.32 176	71.51 170	78.66 143	88.28 179	65.26 115	95.10 94	64.74 225	91.23 95	87.51 266

114514_t		80.68 135	79.51 139	84.20 119	94.09 41	67.27 152	89.64 88	91.11 131	58.75 325	74.08 239	90.72 117	58.10 197	95.04 96	69.70 180	89.42 116	90.30 182

IS-MVSNet		83.15 87	82.81 87	84.18 120	89.94 117	63.30 227	91.59 42	88.46 209	79.04 26	79.49 131	92.16 81	65.10 117	94.28 119	67.71 197	91.86 87	94.95 5

MVS_111021_LR		82.61 97	82.11 96	84.11 121	88.82 157	71.58 59	85.15 219	86.16 249	74.69 113	80.47 123	91.04 110	62.29 152	90.55 254	80.33 89	90.08 109	90.20 185

Anonymous20240529		80.19 147	78.89 155	84.10 122	90.60 101	64.75 196	88.95 104	90.90 135	65.97 256	80.59 121	91.17 106	49.97 271	93.73 151	69.16 186	82.70 197	93.81 59

OpenMVS	72.83 10	79.77 153	78.33 168	84.09 123	85.17 239	69.91 94	90.57 62	90.97 133	66.70 244	72.17 257	91.91 85	54.70 223	93.96 133	61.81 248	90.95 97	88.41 250

hse-mvs2		81.72 109	80.94 115	84.07 124	88.72 163	67.68 144	85.87 203	87.26 233	76.02 85	84.67 63	88.22 182	61.54 163	93.48 161	82.71 67	73.44 305	91.06 154

AdaColmap		80.58 139	79.42 141	84.06 125	93.09 61	68.91 115	89.36 91	88.97 194	69.27 210	75.70 208	89.69 139	57.20 209	95.77 60	63.06 235	88.41 128	87.50 267

AUN-MVS		79.21 169	77.60 188	84.05 126	88.71 164	67.61 145	85.84 205	87.26 233	69.08 217	77.23 174	88.14 187	53.20 236	93.47 162	75.50 132	73.45 304	91.06 154

1121		80.84 126	79.77 133	84.05 126	93.11 60	70.78 79	84.66 229	85.42 256	57.37 334	81.76 109	92.02 83	63.41 131	94.12 129	67.28 202	92.93 72	87.26 273

VDDNet		81.52 115	80.67 118	84.05 126	90.44 105	64.13 209	89.73 86	85.91 252	71.11 175	83.18 87	93.48 57	50.54 266	93.49 160	73.40 148	88.25 129	94.54 26

xiu_mvs_v1_base_debu		80.80 131	79.72 135	84.03 129	87.35 204	70.19 90	85.56 209	88.77 199	69.06 218	81.83 102	88.16 183	50.91 260	92.85 188	78.29 105	87.56 135	89.06 224

xiu_mvs_v1_base		80.80 131	79.72 135	84.03 129	87.35 204	70.19 90	85.56 209	88.77 199	69.06 218	81.83 102	88.16 183	50.91 260	92.85 188	78.29 105	87.56 135	89.06 224

xiu_mvs_v1_base_debi		80.80 131	79.72 135	84.03 129	87.35 204	70.19 90	85.56 209	88.77 199	69.06 218	81.83 102	88.16 183	50.91 260	92.85 188	78.29 105	87.56 135	89.06 224

PAPR		81.66 113	80.89 116	83.99 132	90.27 107	64.00 210	86.76 180	91.77 111	68.84 225	77.13 179	89.50 145	67.63 91	94.88 104	67.55 199	88.52 126	93.09 90

XVG-OURS		80.41 141	79.23 148	83.97 133	85.64 232	69.02 111	83.03 265	90.39 145	71.09 176	77.63 165	91.49 98	54.62 225	91.35 235	75.71 127	83.47 185	91.54 139

XVG-OURS-SEG-HR		80.81 129	79.76 134	83.96 134	85.60 233	68.78 117	83.54 257	90.50 143	70.66 185	76.71 185	91.66 90	60.69 181	91.26 237	76.94 118	81.58 207	91.83 132

HyFIR lowres test		77.53 210	75.40 225	83.94 135	89.59 122	66.62 161	80.36 288	88.64 206	56.29 340	76.45 189	85.17 260	57.64 202	93.28 168	61.34 253	83.10 191	91.91 130

tttt0517		79.40 164	77.91 176	83.90 136	88.10 182	63.84 213	88.37 130	84.05 273	71.45 171	76.78 183	89.12 157	49.93 274	94.89 103	70.18 174	83.18 189	92.96 98

GeoE		81.71 110	81.01 114	83.80 137	89.51 126	64.45 203	88.97 103	88.73 204	71.27 173	78.63 144	89.76 137	66.32 104	93.20 172	69.89 178	86.02 159	93.74 62

PS-MVSNAJ		81.69 111	81.02 113	83.70 138	89.51 126	68.21 135	84.28 243	90.09 157	70.79 181	81.26 115	85.62 251	63.15 138	94.29 118	75.62 129	88.87 120	88.59 245

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 139	89.15 146	68.03 138	84.46 237	90.02 158	70.67 184	81.30 114	86.53 233	63.17 137	94.19 126	75.60 130	88.54 125	88.57 246

ACMM	73.20 8	80.78 134	79.84 132	83.58 140	89.31 138	68.37 130	89.99 78	91.60 114	70.28 191	77.25 172	89.66 140	53.37 234	93.53 159	74.24 139	82.85 193	88.85 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		81.82 108	81.23 109	83.57 141	91.89 84	63.43 225	89.84 80	81.85 303	77.04 60	83.21 86	93.10 65	52.26 242	93.43 165	71.98 159	89.95 111	93.85 55

Fast-Effi-MVS+		80.81 129	79.92 130	83.47 142	88.85 154	64.51 199	85.53 214	89.39 174	70.79 181	78.49 147	85.06 263	67.54 92	93.58 154	67.03 208	86.58 150	92.32 116

CHOSEN 1792x2688		77.63 209	75.69 218	83.44 143	89.98 116	68.58 128	78.70 306	87.50 228	56.38 339	75.80 207	86.84 215	58.67 194	91.40 234	61.58 250	85.75 163	90.34 181

新几何1				83.42 144	93.13 58	70.71 80		85.48 255	57.43 333	81.80 105	91.98 84	63.28 133	92.27 206	64.60 226	92.99 71	87.27 272

DP-MVS		76.78 222	74.57 234	83.42 144	93.29 54	69.46 107	88.55 122	83.70 277	63.98 281	70.20 274	88.89 162	54.01 230	94.80 107	46.66 337	81.88 205	86.01 299

MVS_Test		83.15 87	83.06 83	83.41 146	86.86 215	63.21 230	86.11 197	92.00 97	74.31 121	82.87 92	89.44 152	70.03 70	93.21 170	77.39 113	88.50 127	93.81 59

LS3D		76.95 220	74.82 232	83.37 147	90.45 104	67.36 151	89.15 99	86.94 238	61.87 301	69.52 286	90.61 119	51.71 254	94.53 113	46.38 340	86.71 149	88.21 252

IB-MVS	68.01 15	75.85 236	73.36 248	83.31 148	84.76 246	66.03 169	83.38 258	85.06 259	70.21 193	69.40 287	81.05 307	45.76 305	94.66 111	65.10 222	75.49 277	89.25 221
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
MG-MVS		83.41 83	83.45 78	83.28 149	92.74 71	62.28 244	88.17 138	89.50 172	75.22 101	81.49 110	92.74 78	66.75 98	95.11 91	72.85 154	91.58 89	92.45 113

jajsoiax		79.29 167	77.96 174	83.27 150	84.68 248	66.57 163	89.25 94	90.16 155	69.20 214	75.46 213	89.49 146	45.75 306	93.13 178	76.84 119	80.80 215	90.11 190

test_djsdf		80.30 144	79.32 145	83.27 150	83.98 259	65.37 187	90.50 64	90.38 146	68.55 229	76.19 197	88.70 165	56.44 213	93.46 163	78.98 95	80.14 225	90.97 159

test_yl		81.17 120	80.47 122	83.24 152	89.13 147	63.62 216	86.21 194	89.95 160	72.43 155	81.78 107	89.61 142	57.50 204	93.58 154	70.75 167	86.90 145	92.52 108

DCV-MVSNet		81.17 120	80.47 122	83.24 152	89.13 147	63.62 216	86.21 194	89.95 160	72.43 155	81.78 107	89.61 142	57.50 204	93.58 154	70.75 167	86.90 145	92.52 108

CS-MVS		84.53 73	84.97 68	83.23 154	87.54 203	63.27 228	88.82 110	93.50 28	75.98 87	83.07 89	89.73 138	70.29 68	95.23 86	82.07 74	93.70 69	91.18 150

mvs_tets		79.13 171	77.77 182	83.22 155	84.70 247	66.37 165	89.17 95	90.19 154	69.38 208	75.40 216	89.46 149	44.17 314	93.15 176	76.78 120	80.70 217	90.14 187

thisisatest0515		77.33 214	75.38 226	83.18 156	85.27 238	63.80 214	82.11 272	83.27 286	65.06 265	75.91 203	83.84 277	49.54 276	94.27 120	67.24 204	86.19 156	91.48 143

CDS-MVSNet		79.07 173	77.70 185	83.17 157	87.60 198	68.23 134	84.40 241	86.20 248	67.49 238	76.36 193	86.54 232	61.54 163	90.79 250	61.86 247	87.33 139	90.49 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7n		78.97 176	77.58 189	83.14 158	83.45 267	65.51 182	88.32 131	91.21 127	73.69 135	72.41 254	86.32 238	57.93 198	93.81 144	69.18 185	75.65 274	90.11 190

BH-RMVSNet		79.61 155	78.44 163	83.14 158	89.38 132	65.93 173	84.95 224	87.15 235	73.56 138	78.19 154	89.79 136	56.67 212	93.36 166	59.53 265	86.74 148	90.13 188

UniMVSNet (Re)		81.60 114	81.11 111	83.09 160	88.38 175	64.41 204	87.60 153	93.02 46	78.42 32	78.56 145	88.16 183	69.78 73	93.26 169	69.58 182	76.49 262	91.60 137

PLC	70.83 11	78.05 198	76.37 215	83.08 161	91.88 85	67.80 141	88.19 137	89.46 173	64.33 275	69.87 283	88.38 176	53.66 232	93.58 154	58.86 272	82.73 195	87.86 258
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1192		79.59 157	78.43 164	83.07 162	83.55 266	64.52 198	86.93 172	90.58 141	70.83 179	77.78 162	85.90 243	59.15 192	93.94 136	73.96 141	77.19 252	90.76 164

v2v482		80.23 145	79.29 146	83.05 163	83.62 264	64.14 208	87.04 168	89.97 159	73.61 136	78.18 155	87.22 207	61.10 175	93.82 143	76.11 123	76.78 260	91.18 150

TAMVS		78.89 178	77.51 190	83.03 164	87.80 191	67.79 142	84.72 228	85.05 260	67.63 235	76.75 184	87.70 192	62.25 153	90.82 249	58.53 276	87.13 142	90.49 175

v1144		80.03 149	79.03 152	83.01 165	83.78 262	64.51 199	87.11 167	90.57 142	71.96 162	78.08 158	86.20 240	61.41 167	93.94 136	74.93 134	77.23 250	90.60 171

cascas		76.72 223	74.64 233	82.99 166	85.78 230	65.88 175	82.33 270	89.21 182	60.85 307	72.74 249	81.02 308	47.28 293	93.75 149	67.48 200	85.02 165	89.34 219

anonymousdsp		78.60 183	77.15 196	82.98 167	80.51 321	67.08 154	87.24 164	89.53 171	65.66 259	75.16 224	87.19 209	52.52 237	92.25 208	77.17 115	79.34 233	89.61 214

v10		79.74 154	78.67 157	82.97 168	84.06 257	64.95 193	87.88 149	90.62 140	73.11 146	75.11 226	86.56 231	61.46 166	94.05 132	73.68 142	75.55 276	89.90 204

UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 169	88.46 172	63.46 223	87.13 165	92.37 79	80.19 14	78.38 149	89.14 155	71.66 56	93.05 182	70.05 175	76.46 263	92.25 120

DU-MVS		81.12 122	80.52 121	82.90 170	87.80 191	63.46 223	87.02 169	91.87 105	79.01 27	78.38 149	89.07 158	65.02 118	93.05 182	70.05 175	76.46 263	92.20 122

PVSNet_Blended		80.98 123	80.34 124	82.90 170	88.85 154	65.40 184	84.43 239	92.00 97	67.62 236	78.11 156	85.05 264	66.02 109	94.27 120	71.52 161	89.50 114	89.01 229

CANet_DTU		80.61 136	79.87 131	82.83 172	85.60 233	63.17 233	87.36 159	88.65 205	76.37 78	75.88 205	88.44 175	53.51 233	93.07 181	73.30 149	89.74 113	92.25 120

V42		79.38 166	78.24 170	82.83 172	81.10 315	65.50 183	85.55 212	89.82 163	71.57 169	78.21 153	86.12 241	60.66 182	93.18 175	75.64 128	75.46 280	89.81 209

Anonymous20231211		78.97 176	77.69 186	82.81 174	90.54 103	64.29 206	90.11 76	91.51 117	65.01 267	76.16 201	88.13 188	50.56 265	93.03 185	69.68 181	77.56 248	91.11 153

v1921920		79.22 168	78.03 173	82.80 175	83.30 270	63.94 212	86.80 176	90.33 150	69.91 198	77.48 167	85.53 252	58.44 196	93.75 149	73.60 143	76.85 258	90.71 167

v8		79.97 151	79.02 153	82.80 175	84.09 256	64.50 201	87.96 143	90.29 153	74.13 128	75.24 223	86.81 216	62.88 143	93.89 142	74.39 137	75.40 282	90.00 198

TAPA-MVS	73.13 9	79.15 170	77.94 175	82.79 177	89.59 122	62.99 237	88.16 139	91.51 117	65.77 257	77.14 178	91.09 108	60.91 178	93.21 170	50.26 320	87.05 143	92.17 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v144192		79.47 160	78.37 166	82.78 178	83.35 268	63.96 211	86.96 170	90.36 149	69.99 195	77.50 166	85.67 249	60.66 182	93.77 147	74.27 138	76.58 261	90.62 169

NR-MVSNet		80.23 145	79.38 143	82.78 178	87.80 191	63.34 226	86.31 191	91.09 132	79.01 27	72.17 257	89.07 158	67.20 96	92.81 191	66.08 214	75.65 274	92.20 122

diffmvs		82.10 101	81.88 103	82.76 180	83.00 280	63.78 215	83.68 251	89.76 165	72.94 150	82.02 101	89.85 135	65.96 111	90.79 250	82.38 73	87.30 140	93.71 63

v1240		78.99 175	77.78 181	82.64 181	83.21 272	63.54 220	86.62 183	90.30 152	69.74 204	77.33 170	85.68 248	57.04 210	93.76 148	73.13 152	76.92 254	90.62 169

Fast-Effi-MVS+-dtu		78.02 199	76.49 212	82.62 182	83.16 276	66.96 159	86.94 171	87.45 230	72.45 152	71.49 264	84.17 272	54.79 222	91.58 228	67.61 198	80.31 222	89.30 220

RPMNet		73.51 257	70.49 273	82.58 183	81.32 312	65.19 189	75.92 322	92.27 83	57.60 332	72.73 250	76.45 342	52.30 241	95.43 75	48.14 332	77.71 245	87.11 279

F-COLMAP		76.38 230	74.33 239	82.50 184	89.28 140	66.95 160	88.41 126	89.03 189	64.05 279	66.83 308	88.61 169	46.78 296	92.89 187	57.48 284	78.55 237	87.67 261

TranMVSNet+NR-MVSNet		80.84 126	80.31 125	82.42 185	87.85 189	62.33 242	87.74 151	91.33 123	80.55 11	77.99 159	89.86 134	65.23 116	92.62 192	67.05 207	75.24 288	92.30 118

MVSTER		79.01 174	77.88 178	82.38 186	83.07 277	64.80 195	84.08 248	88.95 195	69.01 221	78.69 141	87.17 210	54.70 223	92.43 199	74.69 135	80.57 219	89.89 205

PVSNet_BlendedMVS		80.60 137	80.02 128	82.36 187	88.85 154	65.40 184	86.16 196	92.00 97	69.34 209	78.11 156	86.09 242	66.02 109	94.27 120	71.52 161	82.06 202	87.39 268

EI-MVSNet		80.52 140	79.98 129	82.12 188	84.28 252	63.19 232	86.41 188	88.95 195	74.18 126	78.69 141	87.54 198	66.62 99	92.43 199	72.57 157	80.57 219	90.74 166

IterMVS-LS		80.06 148	79.38 143	82.11 189	85.89 228	63.20 231	86.79 177	89.34 175	74.19 125	75.45 214	86.72 219	66.62 99	92.39 201	72.58 156	76.86 257	90.75 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
bset_n11_16_dypcd		77.12 216	75.47 222	82.06 190	81.12 314	65.99 171	81.37 281	83.20 289	69.94 197	76.09 202	83.38 285	47.75 290	92.26 207	78.51 99	77.91 244	87.95 254

BH-untuned		79.47 160	78.60 159	82.05 191	89.19 145	65.91 174	86.07 198	88.52 208	72.18 158	75.42 215	87.69 193	61.15 174	93.54 158	60.38 258	86.83 147	86.70 287

ACMH+	68.96 14	76.01 234	74.01 241	82.03 192	88.60 167	65.31 188	88.86 107	87.55 226	70.25 192	67.75 297	87.47 200	41.27 329	93.19 174	58.37 277	75.94 271	87.60 263

Anonymous202405211		78.25 190	77.01 198	81.99 193	91.03 93	60.67 262	84.77 227	83.90 275	70.65 186	80.00 127	91.20 105	41.08 331	91.43 233	65.21 220	85.26 164	93.85 55

GA-MVS		76.87 221	75.17 230	81.97 194	82.75 286	62.58 239	81.44 280	86.35 247	72.16 160	74.74 233	82.89 289	46.20 301	92.02 216	68.85 190	81.09 211	91.30 148

CNLPA		78.08 196	76.79 205	81.97 194	90.40 106	71.07 69	87.59 154	84.55 265	66.03 255	72.38 255	89.64 141	57.56 203	86.04 305	59.61 264	83.35 186	88.79 240

MVS		78.19 194	76.99 200	81.78 196	85.66 231	66.99 155	84.66 229	90.47 144	55.08 344	72.02 259	85.27 257	63.83 128	94.11 131	66.10 213	89.80 112	84.24 318

ACMH	67.68 16	75.89 235	73.93 242	81.77 197	88.71 164	66.61 162	88.62 119	89.01 191	69.81 199	66.78 309	86.70 224	41.95 328	91.51 232	55.64 296	78.14 243	87.17 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		79.10 172	78.24 170	81.70 198	86.85 216	60.24 268	87.28 163	88.79 198	74.25 124	76.84 180	90.53 121	49.48 277	91.56 229	67.98 195	82.15 201	93.29 82

VNet		82.21 100	82.41 91	81.62 199	90.82 98	60.93 258	84.47 235	89.78 164	76.36 79	84.07 77	91.88 87	64.71 122	90.26 256	70.68 169	88.89 119	93.66 64

XVG-ACMP-BASELINE		76.11 233	74.27 240	81.62 199	83.20 273	64.67 197	83.60 255	89.75 166	69.75 202	71.85 260	87.09 212	32.78 353	92.11 213	69.99 177	80.43 221	88.09 253

eth_miper_zixun_eth		77.92 202	76.69 209	81.61 201	83.00 280	61.98 247	83.15 261	89.20 183	69.52 206	74.86 232	84.35 270	61.76 159	92.56 195	71.50 163	72.89 309	90.28 183

PAPM		77.68 208	76.40 214	81.51 202	87.29 210	61.85 249	83.78 250	89.59 170	64.74 269	71.23 265	88.70 165	62.59 146	93.66 153	52.66 307	87.03 144	89.01 229

v148		78.72 180	77.80 180	81.47 203	82.73 287	61.96 248	86.30 192	88.08 215	73.26 145	76.18 198	85.47 254	62.46 149	92.36 203	71.92 160	73.82 301	90.09 192

LTVRE_ROB	69.57 13	76.25 231	74.54 236	81.41 204	88.60 167	64.38 205	79.24 299	89.12 188	70.76 183	69.79 285	87.86 190	49.09 282	93.20 172	56.21 295	80.16 223	86.65 288
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
GBi-Net		78.40 186	77.40 191	81.40 205	87.60 198	63.01 234	88.39 127	89.28 177	71.63 166	75.34 218	87.28 203	54.80 219	91.11 240	62.72 236	79.57 228	90.09 192

test1		78.40 186	77.40 191	81.40 205	87.60 198	63.01 234	88.39 127	89.28 177	71.63 166	75.34 218	87.28 203	54.80 219	91.11 240	62.72 236	79.57 228	90.09 192

FMVSNet1		77.44 211	76.12 217	81.40 205	86.81 218	63.01 234	88.39 127	89.28 177	70.49 188	74.39 236	87.28 203	49.06 283	91.11 240	60.91 255	78.52 238	90.09 192

RRT_test8_iter05		78.38 188	77.40 191	81.34 208	86.00 227	58.86 277	86.55 186	91.26 125	72.13 161	75.91 203	87.42 201	44.97 309	93.73 151	77.02 117	75.30 285	91.45 145

baseline2		75.70 237	73.83 245	81.30 209	83.26 271	61.79 251	82.57 268	80.65 312	66.81 241	66.88 306	83.42 284	57.86 200	92.19 210	63.47 230	79.57 228	89.91 203

c3_l		78.75 179	77.91 176	81.26 210	82.89 284	61.56 253	84.09 247	89.13 187	69.97 196	75.56 209	84.29 271	66.36 103	92.09 214	73.47 147	75.48 278	90.12 189

cl22		78.07 197	77.01 198	81.23 211	82.37 296	61.83 250	83.55 256	87.98 217	68.96 222	75.06 228	83.87 275	61.40 168	91.88 222	73.53 144	76.39 265	89.98 201

FMVSNet2		78.20 193	77.21 195	81.20 212	87.60 198	62.89 238	87.47 157	89.02 190	71.63 166	75.29 222	87.28 203	54.80 219	91.10 243	62.38 240	79.38 232	89.61 214

TR-MVS		77.44 211	76.18 216	81.20 212	88.24 178	63.24 229	84.61 233	86.40 245	67.55 237	77.81 161	86.48 234	54.10 228	93.15 176	57.75 283	82.72 196	87.20 274

ab-mvs		79.51 158	78.97 154	81.14 214	88.46 172	60.91 259	83.84 249	89.24 181	70.36 189	79.03 135	88.87 163	63.23 136	90.21 258	65.12 221	82.57 198	92.28 119

MVP-Stereo		76.12 232	74.46 238	81.13 215	85.37 237	69.79 97	84.42 240	87.95 218	65.03 266	67.46 300	85.33 256	53.28 235	91.73 226	58.01 281	83.27 187	81.85 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
miper_ehance_all_eth		78.59 184	77.76 183	81.08 216	82.66 289	61.56 253	83.65 252	89.15 185	68.87 224	75.55 210	83.79 279	66.49 101	92.03 215	73.25 150	76.39 265	89.64 213

FIs		82.07 103	82.42 90	81.04 217	88.80 159	58.34 282	88.26 135	93.49 30	76.93 62	78.47 148	91.04 110	69.92 72	92.34 204	69.87 179	84.97 166	92.44 114

FMVSNet3		77.88 203	76.85 203	80.97 218	86.84 217	62.36 241	86.52 187	88.77 199	71.13 174	75.34 218	86.66 226	54.07 229	91.10 243	62.72 236	79.57 228	89.45 217

miper_enhance_ethall		77.87 204	76.86 202	80.92 219	81.65 303	61.38 255	82.68 266	88.98 192	65.52 261	75.47 211	82.30 297	65.76 113	92.00 217	72.95 153	76.39 265	89.39 218

BH-w/o		78.21 192	77.33 194	80.84 220	88.81 158	65.13 191	84.87 225	87.85 222	69.75 202	74.52 235	84.74 267	61.34 169	93.11 179	58.24 279	85.84 162	84.27 317

COLMAP_ROB	66.92 17	73.01 264	70.41 275	80.81 221	87.13 213	65.63 180	88.30 132	84.19 272	62.96 289	63.80 332	87.69 193	38.04 341	92.56 195	46.66 337	74.91 290	84.24 318
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		80.60 137	80.55 120	80.76 222	88.07 183	60.80 261	86.86 174	91.58 115	75.67 93	80.24 125	89.45 151	63.34 132	90.25 257	70.51 171	79.22 236	91.23 149

EG-PatchMatch MVS		74.04 252	71.82 261	80.71 223	84.92 245	67.42 148	85.86 204	88.08 215	66.04 254	64.22 328	83.85 276	35.10 349	92.56 195	57.44 285	80.83 214	82.16 336

ECVR-MVS		79.61 155	79.26 147	80.67 224	90.08 112	54.69 324	87.89 148	77.44 335	74.88 109	80.27 124	92.79 76	48.96 285	92.45 198	68.55 191	92.50 80	94.86 12

cl____		77.72 206	76.76 206	80.58 225	82.49 293	60.48 265	83.09 262	87.87 220	69.22 212	74.38 237	85.22 259	62.10 156	91.53 230	71.09 165	75.41 281	89.73 212

DIV-MVS_self_test		77.72 206	76.76 206	80.58 225	82.48 294	60.48 265	83.09 262	87.86 221	69.22 212	74.38 237	85.24 258	62.10 156	91.53 230	71.09 165	75.40 282	89.74 211

MSDG		73.36 260	70.99 269	80.49 227	84.51 250	65.80 176	80.71 285	86.13 250	65.70 258	65.46 319	83.74 280	44.60 310	90.91 248	51.13 313	76.89 256	84.74 313

pmmvs4		74.03 253	71.91 259	80.39 228	81.96 300	68.32 131	81.45 279	82.14 299	59.32 319	69.87 283	85.13 261	52.40 240	88.13 290	60.21 260	74.74 292	84.73 314

HY-MVS	69.67 12	77.95 201	77.15 196	80.36 229	87.57 202	60.21 269	83.37 259	87.78 223	66.11 252	75.37 217	87.06 214	63.27 134	90.48 255	61.38 252	82.43 199	90.40 179

mvs_anonymous		79.42 163	79.11 151	80.34 230	84.45 251	57.97 288	82.59 267	87.62 225	67.40 239	76.17 200	88.56 172	68.47 85	89.59 267	70.65 170	86.05 158	93.47 77

1112_ss		77.40 213	76.43 213	80.32 231	89.11 151	60.41 267	83.65 252	87.72 224	62.13 299	73.05 247	86.72 219	62.58 147	89.97 261	62.11 245	80.80 215	90.59 172

WR-MVS		79.49 159	79.22 149	80.27 232	88.79 160	58.35 281	85.06 221	88.61 207	78.56 30	77.65 164	88.34 177	63.81 129	90.66 253	64.98 223	77.22 251	91.80 135

1314		76.53 224	75.30 229	80.21 233	83.93 260	62.32 243	84.66 229	88.81 197	60.23 311	70.16 277	84.07 274	55.30 217	90.73 252	67.37 201	83.21 188	87.59 265

ECVR-MVS11		79.43 162	79.18 150	80.15 234	89.99 115	53.31 337	87.33 161	77.05 338	75.04 105	80.23 126	92.77 77	48.97 284	92.33 205	68.87 189	92.40 81	94.81 14

IterMVS-SCA-FT		75.43 241	73.87 244	80.11 235	82.69 288	64.85 194	81.57 278	83.47 283	69.16 215	70.49 271	84.15 273	51.95 249	88.15 289	69.23 184	72.14 314	87.34 270

FC-MVSNet-test		81.52 115	82.02 100	80.03 236	88.42 174	55.97 318	87.95 144	93.42 34	77.10 58	77.38 169	90.98 115	69.96 71	91.79 223	68.46 193	84.50 171	92.33 115

testdata				79.97 237	90.90 96	64.21 207		84.71 262	59.27 320	85.40 48	92.91 70	62.02 158	89.08 276	68.95 188	91.37 92	86.63 289

SCA		74.22 250	72.33 257	79.91 238	84.05 258	62.17 245	79.96 293	79.29 326	66.30 251	72.38 255	80.13 317	51.95 249	88.60 284	59.25 267	77.67 247	88.96 233

thres400		76.50 225	75.37 227	79.86 239	89.13 147	57.65 294	85.17 217	83.60 278	73.41 143	76.45 189	86.39 236	52.12 244	91.95 218	48.33 328	83.75 180	90.00 198

test_0402		72.79 267	70.44 274	79.84 240	88.13 180	65.99 171	85.93 201	84.29 269	65.57 260	67.40 302	85.49 253	46.92 295	92.61 193	35.88 358	74.38 295	80.94 342

OurMVSNet-221017-0		74.26 249	72.42 256	79.80 241	83.76 263	59.59 273	85.92 202	86.64 241	66.39 250	66.96 305	87.58 195	39.46 335	91.60 227	65.76 217	69.27 326	88.22 251

SixPastTwentyTwo		73.37 258	71.26 268	79.70 242	85.08 244	57.89 290	85.57 208	83.56 280	71.03 177	65.66 318	85.88 244	42.10 326	92.57 194	59.11 269	63.34 343	88.65 244

thres600view7		76.50 225	75.44 223	79.68 243	89.40 130	57.16 299	85.53 214	83.23 287	73.79 134	76.26 195	87.09 212	51.89 251	91.89 221	48.05 333	83.72 183	90.00 198

CR-MVSNet		73.37 258	71.27 267	79.67 244	81.32 312	65.19 189	75.92 322	80.30 318	59.92 314	72.73 250	81.19 305	52.50 238	86.69 300	59.84 262	77.71 245	87.11 279

D2MVS		74.82 245	73.21 249	79.64 245	79.81 328	62.56 240	80.34 289	87.35 231	64.37 274	68.86 290	82.66 293	46.37 298	90.10 260	67.91 196	81.24 210	86.25 292

AllTest		70.96 278	68.09 290	79.58 246	85.15 240	63.62 216	84.58 234	79.83 322	62.31 297	60.32 342	86.73 217	32.02 354	88.96 280	50.28 318	71.57 318	86.15 295

TestCases				79.58 246	85.15 240	63.62 216		79.83 322	62.31 297	60.32 342	86.73 217	32.02 354	88.96 280	50.28 318	71.57 318	86.15 295

tfpn200view9		76.42 228	75.37 227	79.55 248	89.13 147	57.65 294	85.17 217	83.60 278	73.41 143	76.45 189	86.39 236	52.12 244	91.95 218	48.33 328	83.75 180	89.07 222

thres100view900		76.50 225	75.55 221	79.33 249	89.52 125	56.99 302	85.83 206	83.23 287	73.94 130	76.32 194	87.12 211	51.89 251	91.95 218	48.33 328	83.75 180	89.07 222

CostFormer		75.24 244	73.90 243	79.27 250	82.65 290	58.27 283	80.80 282	82.73 295	61.57 302	75.33 221	83.13 287	55.52 215	91.07 246	64.98 223	78.34 242	88.45 248

Test_1112_low_res		76.40 229	75.44 223	79.27 250	89.28 140	58.09 284	81.69 276	87.07 236	59.53 318	72.48 253	86.67 225	61.30 170	89.33 271	60.81 257	80.15 224	90.41 178

DWT-MVSNet_test		73.70 255	71.86 260	79.21 252	82.91 283	58.94 276	82.34 269	82.17 298	65.21 262	71.05 268	78.31 331	44.21 313	90.17 259	63.29 234	77.28 249	88.53 247

K. test v3		71.19 276	68.51 284	79.21 252	83.04 279	57.78 293	84.35 242	76.91 339	72.90 151	62.99 335	82.86 290	39.27 336	91.09 245	61.65 249	52.66 356	88.75 241

lessismore_v0				78.97 254	81.01 316	57.15 300		65.99 362		61.16 340	82.82 291	39.12 337	91.34 236	59.67 263	46.92 361	88.43 249

pm-mvs1		77.25 215	76.68 210	78.93 255	84.22 254	58.62 280	86.41 188	88.36 210	71.37 172	73.31 243	88.01 189	61.22 173	89.15 275	64.24 227	73.01 308	89.03 228

thres200		75.55 239	74.47 237	78.82 256	87.78 194	57.85 291	83.07 264	83.51 281	72.44 154	75.84 206	84.42 269	52.08 246	91.75 224	47.41 335	83.64 184	86.86 283

VPNet		78.69 181	78.66 158	78.76 257	88.31 177	55.72 320	84.45 238	86.63 242	76.79 66	78.26 152	90.55 120	59.30 191	89.70 266	66.63 209	77.05 253	90.88 161

tpm2		73.26 261	71.46 263	78.63 258	83.34 269	56.71 307	80.65 286	80.40 317	56.63 338	73.55 241	82.02 302	51.80 253	91.24 238	56.35 294	78.42 241	87.95 254

pmmvs6		74.69 246	73.39 247	78.61 259	81.38 309	57.48 297	86.64 182	87.95 218	64.99 268	70.18 275	86.61 227	50.43 267	89.52 268	62.12 244	70.18 324	88.83 238

WR-MVS_H		78.51 185	78.49 161	78.56 260	88.02 185	56.38 313	88.43 123	92.67 67	77.14 56	73.89 240	87.55 197	66.25 105	89.24 273	58.92 271	73.55 303	90.06 196

RPSCF		73.23 262	71.46 263	78.54 261	82.50 292	59.85 270	82.18 271	82.84 294	58.96 322	71.15 267	89.41 153	45.48 308	84.77 315	58.82 273	71.83 316	91.02 158

pmmvs-eth3d		70.50 283	67.83 294	78.52 262	77.37 343	66.18 168	81.82 273	81.51 305	58.90 323	63.90 331	80.42 315	42.69 321	86.28 304	58.56 275	65.30 339	83.11 329

PatchmatchNet		73.12 263	71.33 266	78.49 263	83.18 274	60.85 260	79.63 295	78.57 328	64.13 276	71.73 261	79.81 322	51.20 258	85.97 306	57.40 286	76.36 268	88.66 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS		74.29 248	72.94 252	78.35 264	81.53 306	63.49 222	81.58 277	82.49 296	68.06 234	69.99 280	83.69 281	51.66 255	85.54 308	65.85 216	71.64 317	86.01 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ITE_SJBPF				78.22 265	81.77 302	60.57 263		83.30 285	69.25 211	67.54 299	87.20 208	36.33 346	87.28 298	54.34 300	74.62 293	86.80 284

ppachtmachnet_test		70.04 287	67.34 300	78.14 266	79.80 329	61.13 256	79.19 301	80.59 313	59.16 321	65.27 321	79.29 323	46.75 297	87.29 297	49.33 324	66.72 333	86.00 301

tfpnnormal		74.39 247	73.16 250	78.08 267	86.10 226	58.05 285	84.65 232	87.53 227	70.32 190	71.22 266	85.63 250	54.97 218	89.86 262	43.03 349	75.02 289	86.32 291

Vis-MVSNet (Re-imp)		78.36 189	78.45 162	78.07 268	88.64 166	51.78 342	86.70 181	79.63 324	74.14 127	75.11 226	90.83 116	61.29 171	89.75 264	58.10 280	91.60 88	92.69 105

TransMVSNet (Re)		75.39 243	74.56 235	77.86 269	85.50 235	57.10 301	86.78 178	86.09 251	72.17 159	71.53 263	87.34 202	63.01 142	89.31 272	56.84 291	61.83 344	87.17 275

PEN-MVS		77.73 205	77.69 186	77.84 270	87.07 214	53.91 331	87.91 147	91.18 128	77.56 44	73.14 246	88.82 164	61.23 172	89.17 274	59.95 261	72.37 311	90.43 177

CP-MVSNet		78.22 191	78.34 167	77.84 270	87.83 190	54.54 326	87.94 145	91.17 129	77.65 39	73.48 242	88.49 173	62.24 154	88.43 286	62.19 242	74.07 296	90.55 173

PS-CasMVS		78.01 200	78.09 172	77.77 272	87.71 195	54.39 328	88.02 141	91.22 126	77.50 47	73.26 244	88.64 168	60.73 179	88.41 287	61.88 246	73.88 300	90.53 174

baseline1		76.98 219	76.75 208	77.66 273	88.13 180	55.66 321	85.12 220	81.89 301	73.04 148	76.79 182	88.90 161	62.43 150	87.78 294	63.30 233	71.18 320	89.55 216

OpenMVS_ROB	64.09 19	70.56 282	68.19 287	77.65 274	80.26 322	59.41 275	85.01 222	82.96 293	58.76 324	65.43 320	82.33 296	37.63 343	91.23 239	45.34 345	76.03 270	82.32 334

Patchmatch-RL test		70.24 285	67.78 296	77.61 275	77.43 342	59.57 274	71.16 338	70.33 353	62.94 290	68.65 292	72.77 349	50.62 264	85.49 309	69.58 182	66.58 335	87.77 260

Baseline_NR-MVSNet		78.15 195	78.33 168	77.61 275	85.79 229	56.21 316	86.78 178	85.76 253	73.60 137	77.93 160	87.57 196	65.02 118	88.99 277	67.14 206	75.33 284	87.63 262

DTE-MVSNet		76.99 218	76.80 204	77.54 277	86.24 224	53.06 339	87.52 155	90.66 139	77.08 59	72.50 252	88.67 167	60.48 185	89.52 268	57.33 287	70.74 322	90.05 197

LCM-MVSNet-Re		77.05 217	76.94 201	77.36 278	87.20 211	51.60 343	80.06 291	80.46 316	75.20 102	67.69 298	86.72 219	62.48 148	88.98 278	63.44 231	89.25 117	91.51 140

tpm cat1		70.57 281	68.31 286	77.35 279	82.41 295	57.95 289	78.08 311	80.22 320	52.04 350	68.54 294	77.66 337	52.00 248	87.84 293	51.77 309	72.07 315	86.25 292

MS-PatchMatch		73.83 254	72.67 253	77.30 280	83.87 261	66.02 170	81.82 273	84.66 263	61.37 305	68.61 293	82.82 291	47.29 292	88.21 288	59.27 266	84.32 174	77.68 351

MVS_0304		72.48 268	70.89 271	77.24 281	82.20 297	59.68 271	84.11 246	83.49 282	67.10 240	66.87 307	80.59 313	35.00 350	87.40 296	59.07 270	79.58 227	84.63 315

EPNet_dtu		75.46 240	74.86 231	77.23 282	82.57 291	54.60 325	86.89 173	83.09 291	71.64 165	66.25 316	85.86 245	55.99 214	88.04 291	54.92 298	86.55 151	89.05 227
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		74.11 251	73.11 251	77.13 283	80.11 324	59.62 272	72.23 336	86.92 239	66.76 243	70.40 272	82.92 288	56.93 211	82.92 326	69.06 187	72.63 310	88.87 236

TDRefinement		67.49 301	64.34 310	76.92 284	73.47 358	61.07 257	84.86 226	82.98 292	59.77 315	58.30 348	85.13 261	26.06 359	87.89 292	47.92 334	60.59 348	81.81 338

JIA-IIPM		66.32 310	62.82 320	76.82 285	77.09 344	61.72 252	65.34 354	75.38 342	58.04 329	64.51 326	62.32 356	42.05 327	86.51 302	51.45 312	69.22 327	82.21 335

PatchMatch-RL		72.38 270	70.90 270	76.80 286	88.60 167	67.38 150	79.53 296	76.17 341	62.75 293	69.36 288	82.00 303	45.51 307	84.89 314	53.62 303	80.58 218	78.12 350

tpmvs		71.09 277	69.29 280	76.49 287	82.04 299	56.04 317	78.92 304	81.37 307	64.05 279	67.18 304	78.28 332	49.74 275	89.77 263	49.67 323	72.37 311	83.67 323

CMPMVS	51.72 21	70.19 286	68.16 288	76.28 288	73.15 360	57.55 296	79.47 297	83.92 274	48.02 354	56.48 353	84.81 265	43.13 318	86.42 303	62.67 239	81.81 206	84.89 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 284	68.37 285	76.21 289	80.60 319	56.23 315	79.19 301	86.49 243	60.89 306	61.29 339	85.47 254	31.78 356	89.47 270	53.37 304	76.21 269	82.94 333

gg-mvs-nofinetune		69.95 288	67.96 291	75.94 290	83.07 277	54.51 327	77.23 317	70.29 354	63.11 286	70.32 273	62.33 355	43.62 316	88.69 283	53.88 302	87.76 132	84.62 316

MDA-MVSNet-bldmvs		66.68 306	63.66 314	75.75 291	79.28 336	60.56 264	73.92 333	78.35 329	64.43 272	50.13 359	79.87 321	44.02 315	83.67 321	46.10 341	56.86 351	83.03 331

PVSNet	64.34 18	72.08 273	70.87 272	75.69 292	86.21 225	56.44 311	74.37 332	80.73 311	62.06 300	70.17 276	82.23 299	42.86 320	83.31 324	54.77 299	84.45 173	87.32 271

pmmvs5		71.55 274	70.20 277	75.61 293	77.83 340	56.39 312	81.74 275	80.89 308	57.76 330	67.46 300	84.49 268	49.26 281	85.32 311	57.08 289	75.29 286	85.11 310

our_test_3		69.14 292	67.00 302	75.57 294	79.80 329	58.80 278	77.96 312	77.81 331	59.55 317	62.90 336	78.25 333	47.43 291	83.97 319	51.71 310	67.58 332	83.93 322

WTY-MVS		75.65 238	75.68 219	75.57 294	86.40 223	56.82 304	77.92 314	82.40 297	65.10 264	76.18 198	87.72 191	63.13 141	80.90 333	60.31 259	81.96 203	89.00 231

Patchmtry		70.74 279	69.16 281	75.49 296	80.72 317	54.07 330	74.94 331	80.30 318	58.34 326	70.01 278	81.19 305	52.50 238	86.54 301	53.37 304	71.09 321	85.87 302

GG-mvs-BLEND				75.38 297	81.59 305	55.80 319	79.32 298	69.63 356		67.19 303	73.67 348	43.24 317	88.90 282	50.41 315	84.50 171	81.45 339

ambc				75.24 298	73.16 359	50.51 350	63.05 358	87.47 229		64.28 327	77.81 336	17.80 365	89.73 265	57.88 282	60.64 347	85.49 303

CL-MVSNet_self_test		72.37 271	71.46 263	75.09 299	79.49 334	53.53 333	80.76 284	85.01 261	69.12 216	70.51 270	82.05 301	57.92 199	84.13 318	52.27 308	66.00 337	87.60 263

XXY-MVS		75.41 242	75.56 220	74.96 300	83.59 265	57.82 292	80.59 287	83.87 276	66.54 249	74.93 231	88.31 178	63.24 135	80.09 336	62.16 243	76.85 258	86.97 281

MIMVSNet		70.69 280	69.30 279	74.88 301	84.52 249	56.35 314	75.87 324	79.42 325	64.59 270	67.76 296	82.41 295	41.10 330	81.54 331	46.64 339	81.34 208	86.75 286

ADS-MVSNet2		66.20 313	63.33 315	74.82 302	79.92 326	58.75 279	67.55 351	75.19 343	53.37 347	65.25 322	75.86 343	42.32 323	80.53 335	41.57 352	68.91 328	85.18 307

TinyColmap		67.30 304	64.81 308	74.76 303	81.92 301	56.68 308	80.29 290	81.49 306	60.33 309	56.27 354	83.22 286	24.77 360	87.66 295	45.52 343	69.47 325	79.95 346

test-LLR		72.94 266	72.43 255	74.48 304	81.35 310	58.04 286	78.38 307	77.46 333	66.66 245	69.95 281	79.00 326	48.06 288	79.24 337	66.13 211	84.83 167	86.15 295

test-mter		71.41 275	70.39 276	74.48 304	81.35 310	58.04 286	78.38 307	77.46 333	60.32 310	69.95 281	79.00 326	36.08 347	79.24 337	66.13 211	84.83 167	86.15 295

tpm		72.37 271	71.71 262	74.35 306	82.19 298	52.00 340	79.22 300	77.29 336	64.56 271	72.95 248	83.68 282	51.35 256	83.26 325	58.33 278	75.80 272	87.81 259

CVMVSNet		72.99 265	72.58 254	74.25 307	84.28 252	50.85 348	86.41 188	83.45 284	44.56 355	73.23 245	87.54 198	49.38 278	85.70 307	65.90 215	78.44 240	86.19 294

FMVSNet5		69.50 290	67.96 291	74.15 308	82.97 282	55.35 322	80.01 292	82.12 300	62.56 295	63.02 333	81.53 304	36.92 344	81.92 329	48.42 327	74.06 297	85.17 309

MIMVSNet1		68.58 297	66.78 304	73.98 309	80.07 325	51.82 341	80.77 283	84.37 266	64.40 273	59.75 345	82.16 300	36.47 345	83.63 322	42.73 350	70.33 323	86.48 290

Anonymous20240521		68.80 295	67.22 301	73.55 310	74.33 353	54.11 329	83.18 260	85.61 254	58.15 327	61.68 338	80.94 310	30.71 357	81.27 332	57.00 290	73.34 307	85.28 306

sss		73.60 256	73.64 246	73.51 311	82.80 285	55.01 323	76.12 320	81.69 304	62.47 296	74.68 234	85.85 246	57.32 206	78.11 343	60.86 256	80.93 212	87.39 268

KD-MVS_2432*1600		66.22 311	63.89 312	73.21 312	75.47 351	53.42 335	70.76 341	84.35 267	64.10 277	66.52 312	78.52 329	34.55 351	84.98 312	50.40 316	50.33 359	81.23 340

miper_refine_blended		66.22 311	63.89 312	73.21 312	75.47 351	53.42 335	70.76 341	84.35 267	64.10 277	66.52 312	78.52 329	34.55 351	84.98 312	50.40 316	50.33 359	81.23 340

PM-MVS		66.41 309	64.14 311	73.20 314	73.92 355	56.45 310	78.97 303	64.96 365	63.88 283	64.72 325	80.24 316	19.84 364	83.44 323	66.24 210	64.52 341	79.71 347

tpmrst		72.39 269	72.13 258	73.18 315	80.54 320	49.91 351	79.91 294	79.08 327	63.11 286	71.69 262	79.95 319	55.32 216	82.77 327	65.66 218	73.89 299	86.87 282

TESTMET0.1,1		69.89 289	69.00 282	72.55 316	79.27 337	56.85 303	78.38 307	74.71 347	57.64 331	68.09 295	77.19 339	37.75 342	76.70 348	63.92 228	84.09 176	84.10 321

KD-MVS_self_test		68.81 294	67.59 299	72.46 317	74.29 354	45.45 358	77.93 313	87.00 237	63.12 285	63.99 330	78.99 328	42.32 323	84.77 315	56.55 293	64.09 342	87.16 277

CHOSEN 280x420		66.51 308	64.71 309	71.90 318	81.45 307	63.52 221	57.98 359	68.95 360	53.57 346	62.59 337	76.70 340	46.22 300	75.29 355	55.25 297	79.68 226	76.88 353

EPMVS		69.02 293	68.16 288	71.59 319	79.61 332	49.80 353	77.40 316	66.93 361	62.82 292	70.01 278	79.05 324	45.79 304	77.86 345	56.58 292	75.26 287	87.13 278

YYNet1		65.03 314	62.91 318	71.38 320	75.85 347	56.60 309	69.12 348	74.66 348	57.28 335	54.12 355	77.87 335	45.85 303	74.48 357	49.95 321	61.52 346	83.05 330

MDA-MVSNet_test_wron		65.03 314	62.92 317	71.37 321	75.93 346	56.73 305	69.09 349	74.73 346	57.28 335	54.03 356	77.89 334	45.88 302	74.39 358	49.89 322	61.55 345	82.99 332

UnsupCasMVSNet_eth		67.33 303	65.99 306	71.37 321	73.48 357	51.47 345	75.16 327	85.19 258	65.20 263	60.78 341	80.93 312	42.35 322	77.20 347	57.12 288	53.69 355	85.44 304

PMMVS		69.34 291	68.67 283	71.35 323	75.67 348	62.03 246	75.17 326	73.46 349	50.00 353	68.68 291	79.05 324	52.07 247	78.13 342	61.16 254	82.77 194	73.90 354

EU-MVSNet		68.53 298	67.61 298	71.31 324	78.51 339	47.01 357	84.47 235	84.27 270	42.27 356	66.44 315	84.79 266	40.44 333	83.76 320	58.76 274	68.54 331	83.17 327

Anonymous20231206		68.60 296	67.80 295	71.02 325	80.23 323	50.75 349	78.30 310	80.47 315	56.79 337	66.11 317	82.63 294	46.35 299	78.95 339	43.62 348	75.70 273	83.36 326

dp		66.80 305	65.43 307	70.90 326	79.74 331	48.82 354	75.12 329	74.77 345	59.61 316	64.08 329	77.23 338	42.89 319	80.72 334	48.86 326	66.58 335	83.16 328

PatchT		68.46 299	67.85 293	70.29 327	80.70 318	43.93 361	72.47 335	74.88 344	60.15 312	70.55 269	76.57 341	49.94 272	81.59 330	50.58 314	74.83 291	85.34 305

UnsupCasMVSNet_bld		63.70 319	61.53 323	70.21 328	73.69 356	51.39 346	72.82 334	81.89 301	55.63 342	57.81 349	71.80 351	38.67 338	78.61 340	49.26 325	52.21 357	80.63 343

Patchmatch-test		64.82 316	63.24 316	69.57 329	79.42 335	49.82 352	63.49 357	69.05 359	51.98 351	59.95 344	80.13 317	50.91 260	70.98 361	40.66 354	73.57 302	87.90 257

LF4IMVS		64.02 318	62.19 321	69.50 330	70.90 362	53.29 338	76.13 319	77.18 337	52.65 349	58.59 346	80.98 309	23.55 361	76.52 349	53.06 306	66.66 334	78.68 349

test20.03		67.45 302	66.95 303	68.94 331	75.48 350	44.84 360	77.50 315	77.67 332	66.66 245	63.01 334	83.80 278	47.02 294	78.40 341	42.53 351	68.86 330	83.58 324

test0.0.03 1		68.00 300	67.69 297	68.90 332	77.55 341	47.43 355	75.70 325	72.95 351	66.66 245	66.56 310	82.29 298	48.06 288	75.87 352	44.97 346	74.51 294	83.41 325

PVSNet_0	57.27 20	61.67 321	59.27 324	68.85 333	79.61 332	57.44 298	68.01 350	73.44 350	55.93 341	58.54 347	70.41 352	44.58 311	77.55 346	47.01 336	35.91 362	71.55 356

ADS-MVSNet		64.36 317	62.88 319	68.78 334	79.92 326	47.17 356	67.55 351	71.18 352	53.37 347	65.25 322	75.86 343	42.32 323	73.99 359	41.57 352	68.91 328	85.18 307

pmmvs3		57.79 323	54.26 327	68.37 335	64.02 366	56.72 306	75.12 329	65.17 363	40.20 358	52.93 357	69.86 353	20.36 363	75.48 354	45.45 344	55.25 354	72.90 355

LCM-MVSNet		54.25 325	49.68 331	67.97 336	53.73 369	45.28 359	66.85 353	80.78 310	35.96 362	39.45 362	62.23 357	8.70 373	78.06 344	48.24 331	51.20 358	80.57 344

testgi		66.67 307	66.53 305	67.08 337	75.62 349	41.69 364	75.93 321	76.50 340	66.11 252	65.20 324	86.59 228	35.72 348	74.71 356	43.71 347	73.38 306	84.84 312

ANet_high		50.57 329	46.10 332	63.99 338	48.67 372	39.13 365	70.99 340	80.85 309	61.39 304	31.18 364	57.70 360	17.02 366	73.65 360	31.22 360	15.89 369	79.18 348

MVS-HIRNet		59.14 322	57.67 325	63.57 339	81.65 303	43.50 362	71.73 337	65.06 364	39.59 360	51.43 358	57.73 359	38.34 340	82.58 328	39.53 355	73.95 298	64.62 359

new-patchmatchnet		61.73 320	61.73 322	61.70 340	72.74 361	24.50 374	69.16 347	78.03 330	61.40 303	56.72 352	75.53 345	38.42 339	76.48 350	45.95 342	57.67 350	84.13 320

DSMNet-mixed		57.77 324	56.90 326	60.38 341	67.70 364	35.61 367	69.18 346	53.97 369	32.30 365	57.49 350	79.88 320	40.39 334	68.57 363	38.78 356	72.37 311	76.97 352

FPMVS		53.68 326	51.64 329	59.81 342	65.08 365	51.03 347	69.48 345	69.58 357	41.46 357	40.67 361	72.32 350	16.46 367	70.00 362	24.24 364	65.42 338	58.40 361

PMVS	37.38 22	44.16 331	40.28 334	55.82 343	40.82 374	42.54 363	65.12 355	63.99 366	34.43 363	24.48 366	57.12 361	3.92 375	76.17 351	17.10 367	55.52 353	48.75 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 330	41.86 333	55.16 344	77.03 345	51.52 344	32.50 365	80.52 314	32.46 364	27.12 365	35.02 365	9.52 372	75.50 353	22.31 365	60.21 349	38.45 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		50.91 328	50.29 330	52.78 345	68.58 363	34.94 369	63.71 356	56.63 368	39.73 359	44.95 360	65.47 354	21.93 362	58.48 365	34.98 359	56.62 352	64.92 358

N_pmnet		52.79 327	53.26 328	51.40 346	78.99 338	7.68 377	69.52 344	3.89 377	51.63 352	57.01 351	74.98 346	40.83 332	65.96 364	37.78 357	64.67 340	80.56 345

PMMVS2		40.82 332	38.86 335	46.69 347	53.84 368	16.45 375	48.61 362	49.92 370	37.49 361	31.67 363	60.97 358	8.14 374	56.42 366	28.42 361	30.72 364	67.19 357

MVE	26.22 23	30.37 336	25.89 340	43.81 348	44.55 373	35.46 368	28.87 366	39.07 373	18.20 368	18.58 370	40.18 364	2.68 376	47.37 370	17.07 368	23.78 366	48.60 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 334	29.28 338	38.23 349	27.03 376	6.50 378	20.94 367	62.21 367	4.05 371	22.35 369	52.50 362	13.33 368	47.58 369	27.04 363	34.04 363	60.62 360

E-PMN		31.77 333	30.64 336	35.15 350	52.87 370	27.67 371	57.09 360	47.86 371	24.64 366	16.40 371	33.05 366	11.23 370	54.90 367	14.46 369	18.15 367	22.87 366

EMVS		30.81 335	29.65 337	34.27 351	50.96 371	25.95 373	56.58 361	46.80 372	24.01 367	15.53 372	30.68 367	12.47 369	54.43 368	12.81 370	17.05 368	22.43 367

DeepMVS_CX				27.40 352	40.17 375	26.90 372		24.59 376	17.44 369	23.95 367	48.61 363	9.77 371	26.48 371	18.06 366	24.47 365	28.83 365

wuyk23d		16.82 339	15.94 342	19.46 353	58.74 367	31.45 370	39.22 363	3.74 378	6.84 370	6.04 373	2.70 372	1.27 377	24.29 372	10.54 371	14.40 371	2.63 369

tmp_tt		18.61 338	21.40 341	10.23 354	4.82 377	10.11 376	34.70 364	30.74 375	1.48 372	23.91 368	26.07 368	28.42 358	13.41 373	27.12 362	15.35 370	7.17 368

test123		6.12 341	8.11 344	0.14 355	0.06 379	0.09 379	71.05 339	0.03 380	0.04 374	0.25 375	1.30 374	0.05 378	0.03 375	0.21 373	0.01 373	0.29 370

testmvs		6.04 342	8.02 345	0.10 356	0.08 378	0.03 380	69.74 343	0.04 379	0.05 373	0.31 374	1.68 373	0.02 379	0.04 374	0.24 372	0.02 372	0.25 371

test_blank		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet_test		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

cdsmvs_eth3d_5k		19.96 337	26.61 339	0.00 357	0.00 380	0.00 381	0.00 368	89.26 180	0.00 375	0.00 376	88.61 169	61.62 162	0.00 376	0.00 374	0.00 374	0.00 372

pcd_1.5k_mvsjas		5.26 343	7.02 346	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	63.15 138	0.00 376	0.00 374	0.00 374	0.00 372

sosnet-low-res		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

sosnet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uncertanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

Regformer		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

ab-mvs-re		7.23 340	9.64 343	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	86.72 219	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

uanet		0.00 344	0.00 347	0.00 357	0.00 380	0.00 381	0.00 368	0.00 381	0.00 375	0.00 376	0.00 375	0.00 380	0.00 376	0.00 374	0.00 374	0.00 372

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 117	91.30 15

PC_three_1452									68.21 233	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 5

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 380

eth-test					0.00 380

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 178	87.75 31	94.07 45	74.01 38	96.70 26	84.66 39	94.84 48

RE-MVS-def			85.48 57		93.06 62	70.63 82	91.88 37	92.27 83	73.53 140	85.69 46	94.45 28	63.87 127		82.75 65	91.87 85	92.50 110

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 241	92.39 6				88.94 11	96.63 4	94.85 13

test_241102_TWO								94.06 12	77.24 51	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 37

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 55	93.10 1	95.39 11	82.99 1	97.27 10

9.14			88.26 15		92.84 68		91.52 45	94.75 1	73.93 131	88.57 24	94.67 19	75.57 23	95.79 59	86.77 23	95.76 28

save fliter					93.80 44	72.35 45	90.47 66	91.17 129	74.31 121

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 20

test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 49	92.81 3	95.79 3	80.98 9

GSMVS																88.96 233

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 257				88.96 233

sam_mvs												50.01 270

MTGPA								92.02 94

test_post1							78.90 305				5.43 371	48.81 287	85.44 310	59.25 267

test_post											5.46 370	50.36 268	84.24 317

patchmatchnet-post											74.00 347	51.12 259	88.60 284

MTMP							92.18 33	32.83 374

gm-plane-assit					81.40 308	53.83 332			62.72 294		80.94 310		92.39 201	63.40 232

test9_res														84.90 33	95.70 31	92.87 100

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 231	85.00 54	93.10 65	74.36 33	95.41 77

test_8					93.13 58	72.57 36	88.68 118	91.84 106	68.69 227	84.87 60	93.10 65	74.43 30	95.16 89

agg_prior2														82.91 63	95.45 33	92.70 103

agg_prior					92.85 66	71.94 54		91.78 109		84.41 69			94.93 98

test_prior4						72.60 35	89.01 102

test_prior2							88.85 108		75.41 96	84.91 56	93.54 54	74.28 34		83.31 55	95.86 22

旧先验2							86.56 185		58.10 328	87.04 35			88.98 278	74.07 140

新几何2							86.29 193

旧先验1					91.96 82	65.79 177		86.37 246			93.08 69	69.31 79			92.74 75	88.74 242

无先验							87.48 156	88.98 192	60.00 313				94.12 129	67.28 202		88.97 232

原ACMM2							86.86 174

test222					91.50 88	68.26 133	84.16 244	83.20 289	54.63 345	79.74 128	91.63 93	58.97 193			91.42 91	86.77 285

testdata2													91.01 247	62.37 241

segment_acmp												73.08 43

testdata1							84.14 245		75.71 90

plane_prior7					90.08 112	68.51 129

plane_prior6					89.84 119	68.70 124						60.42 186

plane_prior5								92.44 75					95.38 80	78.71 97	86.32 154	91.33 146

plane_prior4											91.00 113

plane_prior3						68.60 127			78.44 31	78.92 138

plane_prior2							91.25 49		79.12 24

plane_prior1					89.90 118

plane_prior						68.71 122	90.38 70		77.62 40						86.16 157

n2								0.00 381

nn								0.00 381

door-mid								69.98 355

test11								92.23 86

door								69.44 358

HQP5-MVS						66.98 156

HQP-NCC					89.33 133		89.17 95		76.41 74	77.23 174

ACMP_Plane					89.33 133		89.17 95		76.41 74	77.23 174

BP-MVS														77.47 111

HQP4-MVS										77.24 173			95.11 91			91.03 156

HQP3-MVS								92.19 89							85.99 160

HQP2-MVS												60.17 189

NP-MVS					89.62 121	68.32 131					90.24 124

MDTV_nov1_ep13_2view						37.79 366	75.16 327		55.10 343	66.53 311		49.34 279		53.98 301		87.94 256

MDTV_nov1_ep13			69.97 278		83.18 274	53.48 334	77.10 318	80.18 321	60.45 308	69.33 289	80.44 314	48.89 286	86.90 299	51.60 311	78.51 239

ACMMP++_ref															81.95 204

ACMMP++															81.25 209

Test By Simon												64.33 123

High-resolution multi-view benchmark