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.

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

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

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

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

PC_three_1452									68.21 234	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 5

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 51
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
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 21

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 38

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 82
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					95.27 5	71.25 63	93.60 6	94.11 8	77.33 49	92.81 3	95.79 3	80.98 9

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

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 24

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 21
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
ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 61	94.05 15	70.80 181	87.59 33	93.51 56	77.57 14	96.63 31	83.31 55	95.77 26	94.72 20

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 28

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 34

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 81	93.82 19	73.07 148	84.86 61	92.89 71	76.22 17	96.33 39	84.89 35	95.13 41	94.40 31

CSCG		86.41 47	86.19 49	87.07 50	92.91 65	72.48 38	90.81 57	93.56 26	73.95 130	83.16 88	91.07 110	75.94 18	95.19 88	79.94 92	94.38 60	93.55 75

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 93

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 47	92.35 80	74.62 116	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
xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 66	89.69 168	74.31 122	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 16

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 16

9.14			88.26 15		92.84 68		91.52 45	94.75 1	73.93 132	88.57 24	94.67 19	75.57 23	95.79 59	86.77 23	95.76 28

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	74.47 119	88.86 22	94.61 21	75.23 24	95.84 58	86.62 26	95.92 21	94.78 18

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	71.76 164	87.28 34	94.27 36	75.18 25	96.08 49	85.16 30	95.77 26	93.80 62

agg_prior1		86.22 49	86.09 52	86.62 59	92.85 66	71.94 54	88.59 120	91.78 109	68.96 223	84.41 69	93.18 64	74.94 26	94.93 98	84.75 38	95.33 38	93.01 96

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 70
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
DELS-MVS		85.41 62	85.30 62	85.77 75	88.49 171	67.93 139	85.52 217	93.44 32	78.70 29	83.63 85	89.03 161	74.57 28	95.71 62	80.26 90	94.04 65	93.66 65
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-2		86.63 43	86.53 42	86.95 51	89.33 134	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 129	74.45 29	95.61 63	84.38 43	90.63 101	94.01 48

train_agg		86.43 45	86.20 48	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 228	85.00 54	93.10 65	74.43 30	95.41 77	84.97 32	95.71 30	93.02 95

test_8					93.13 58	72.57 36	88.68 118	91.84 106	68.69 228	84.87 60	93.10 65	74.43 30	95.16 89

Regformer-1		86.41 47	86.33 44	86.64 58	89.33 134	70.93 75	88.43 123	91.39 122	82.14 3	86.65 38	90.09 129	74.39 32	95.01 97	83.97 51	90.63 101	93.97 50

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 232	85.00 54	93.10 65	74.36 33	95.41 77

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 22	93.63 23	74.77 112	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
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 52

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

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	90.22 127	74.15 37	95.37 83	81.82 75	91.88 85	92.65 108

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 179	87.75 31	94.07 45	74.01 38	96.70 26	84.66 39	94.84 48

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.
APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 39	92.83 61	73.01 150	88.58 23	94.52 23	73.36 40	96.49 37	84.26 46	95.01 42	92.70 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
canonicalmvs		85.91 53	85.87 54	86.04 72	89.84 120	69.44 108	90.45 69	93.00 47	76.70 71	88.01 29	91.23 104	73.28 41	93.91 140	81.50 77	88.80 122	94.77 19

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 31	93.49 30	74.75 113	88.33 25	94.43 32	73.27 42	97.02 16	84.18 49	94.84 48	93.82 59

segment_acmp												73.08 43

DPM-MVS		84.93 70	84.29 75	86.84 53	90.20 109	73.04 24	87.12 167	93.04 43	69.80 201	82.85 93	91.22 105	73.06 44	96.02 51	76.72 121	94.63 53	91.46 145

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 42

nrg030		83.88 75	83.53 77	84.96 91	86.77 220	69.28 109	90.46 68	92.67 67	74.79 111	82.95 90	91.33 103	72.70 46	93.09 180	80.79 85	79.28 236	92.50 111

Regformer-4		85.68 58	85.45 58	86.35 62	88.95 153	69.67 100	88.29 133	91.29 124	81.73 5	85.36 49	90.01 132	72.62 47	95.35 84	83.28 58	87.57 134	94.03 46

Regformer-3		85.23 64	85.07 66	85.70 76	88.95 153	69.01 112	88.29 133	89.91 162	80.95 8	85.01 53	90.01 132	72.45 48	94.19 126	82.50 71	87.57 134	93.90 54

CDPH-MVS		85.76 56	85.29 63	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 233	84.61 66	93.48 57	72.32 49	96.15 48	79.00 94	95.43 34	94.28 37

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 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.11 67	85.14 65	85.01 89	87.20 212	65.77 178	87.75 151	92.83 61	77.84 37	84.36 72	92.38 80	72.15 51	93.93 139	81.27 79	90.48 103	95.33 1

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 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		84.93 70	84.98 67	84.80 100	87.30 210	65.39 186	87.30 163	92.88 58	77.62 40	84.04 78	92.26 81	71.81 53	93.96 133	81.31 78	90.30 105	95.03 4

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 35

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 76

UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 169	88.46 173	63.46 223	87.13 166	92.37 79	80.19 14	78.38 150	89.14 156	71.66 56	93.05 182	70.05 175	76.46 264	92.25 121

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 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		85.14 66	84.75 72	86.32 65	91.65 86	72.70 31	85.98 200	90.33 150	76.11 83	82.08 100	91.61 95	71.36 58	94.17 128	81.02 80	92.58 78	92.08 127

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 43

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 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 65

#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 65

EI-MVSNet-Vis-set		84.19 74	83.81 76	85.31 80	88.18 180	67.85 140	87.66 153	89.73 167	80.05 16	82.95 90	89.59 145	70.74 63	94.82 106	80.66 86	84.72 170	93.28 84

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 54

CS-MVS-test		85.02 69	85.21 64	84.46 108	89.28 141	65.70 179	91.16 52	93.56 26	77.83 38	81.80 105	89.89 134	70.67 64	95.61 63	80.39 87	92.34 83	92.06 128

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	91.43 101	70.34 66	97.23 12	84.26 46	93.36 70	94.37 32

alignmvs		85.48 59	85.32 61	85.96 74	89.51 127	69.47 105	89.74 85	92.47 74	76.17 82	87.73 32	91.46 100	70.32 67	93.78 145	81.51 76	88.95 119	94.63 23

CS-MVS		84.53 73	84.97 68	83.23 154	87.54 204	63.27 228	88.82 110	93.50 28	75.98 87	83.07 89	89.73 139	70.29 68	95.23 86	82.07 74	93.70 69	91.18 151

EI-MVSNet-UG-set		83.81 76	83.38 79	85.09 87	87.87 189	67.53 146	87.44 159	89.66 169	79.74 18	82.23 99	89.41 154	70.24 69	94.74 109	79.95 91	83.92 178	92.99 98

MVS_Test		83.15 87	83.06 83	83.41 146	86.86 216	63.21 230	86.11 198	92.00 97	74.31 122	82.87 92	89.44 153	70.03 70	93.21 170	77.39 113	88.50 128	93.81 60

FC-MVSNet-test		81.52 115	82.02 100	80.03 236	88.42 175	55.97 319	87.95 144	93.42 34	77.10 58	77.38 170	90.98 116	69.96 71	91.79 224	68.46 194	84.50 172	92.33 116

FIs		82.07 103	82.42 90	81.04 217	88.80 160	58.34 283	88.26 135	93.49 30	76.93 62	78.47 149	91.04 111	69.92 72	92.34 205	69.87 179	84.97 167	92.44 115

UniMVSNet (Re)		81.60 114	81.11 111	83.09 160	88.38 176	64.41 204	87.60 154	93.02 46	78.42 32	78.56 146	88.16 184	69.78 73	93.26 169	69.58 182	76.49 263	91.60 138

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 35
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Effi-MVS+		83.62 80	83.08 82	85.24 83	88.38 176	67.45 147	88.89 106	89.15 185	75.50 95	82.27 98	88.28 180	69.61 75	94.45 116	77.81 108	87.84 132	93.84 58

PHI-MVS		86.43 45	86.17 50	87.24 46	90.88 97	70.96 72	92.27 30	94.07 11	72.45 153	85.22 52	91.90 87	69.47 76	96.42 38	83.28 58	95.94 20	94.35 33

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 99	93.07 92

ETV-MVS		84.90 72	84.67 73	85.59 77	89.39 132	68.66 126	88.74 115	92.64 71	79.97 17	84.10 76	85.71 248	69.32 78	95.38 80	80.82 83	91.37 93	92.72 103

旧先验1					91.96 82	65.79 177		86.37 246			93.08 69	69.31 79			92.74 75	88.74 243

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 46

EIA-MVS		83.31 86	82.80 88	84.82 98	89.59 123	65.59 181	88.21 136	92.68 66	74.66 115	78.96 137	86.42 236	69.06 81	95.26 85	75.54 131	90.09 109	93.62 72

EPP-MVSNet		83.40 84	83.02 84	84.57 104	90.13 110	64.47 202	92.32 28	90.73 138	74.45 121	79.35 134	91.10 108	69.05 82	95.12 90	72.78 155	87.22 142	94.13 41

DROMVSNet		86.01 51	86.38 43	84.91 95	89.31 139	66.27 167	92.32 28	93.63 23	79.37 21	84.17 75	91.88 88	69.04 83	95.43 75	83.93 52	93.77 67	93.01 96

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 49

mvs_anonymous		79.42 163	79.11 151	80.34 230	84.45 252	57.97 289	82.59 268	87.62 225	67.40 240	76.17 201	88.56 173	68.47 85	89.59 268	70.65 170	86.05 159	93.47 78

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 85

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 185	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 85

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 85

PAPM_NR		83.02 91	82.41 91	84.82 98	92.47 77	66.37 165	87.93 146	91.80 107	73.82 134	77.32 172	90.66 119	67.90 89	94.90 102	70.37 172	89.48 116	93.19 89

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 65

PAPR		81.66 113	80.89 116	83.99 132	90.27 107	64.00 210	86.76 181	91.77 111	68.84 226	77.13 180	89.50 146	67.63 91	94.88 104	67.55 200	88.52 127	93.09 91

Fast-Effi-MVS+		80.81 129	79.92 130	83.47 142	88.85 155	64.51 199	85.53 215	89.39 174	70.79 182	78.49 148	85.06 264	67.54 92	93.58 154	67.03 209	86.58 151	92.32 117

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 44

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	12.47 371	67.45 93	96.60 34	83.06 60	94.50 56	94.07 44

SR-MVS		86.73 39	86.67 40	86.91 52	94.11 40	72.11 51	92.37 26	92.56 73	74.50 117	86.84 37	94.65 20	67.31 95	95.77 60	84.80 37	92.85 74	92.84 102

NR-MVSNet		80.23 145	79.38 143	82.78 178	87.80 192	63.34 226	86.31 192	91.09 132	79.01 27	72.17 258	89.07 159	67.20 96	92.81 191	66.08 215	75.65 275	92.20 123

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	90.16 187

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 79	66.75 98	95.11 91	72.85 154	91.58 90	92.45 114

EI-MVSNet		80.52 140	79.98 129	82.12 188	84.28 253	63.19 232	86.41 189	88.95 195	74.18 127	78.69 142	87.54 199	66.62 99	92.43 199	72.57 157	80.57 220	90.74 167

IterMVS-LS		80.06 148	79.38 143	82.11 189	85.89 229	63.20 231	86.79 178	89.34 175	74.19 126	75.45 215	86.72 220	66.62 99	92.39 201	72.58 156	76.86 258	90.75 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		78.59 184	77.76 183	81.08 216	82.66 291	61.56 253	83.65 253	89.15 185	68.87 225	75.55 211	83.79 280	66.49 101	92.03 216	73.25 150	76.39 266	89.64 214

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 79

c3_l		78.75 179	77.91 176	81.26 210	82.89 286	61.56 253	84.09 248	89.13 187	69.97 197	75.56 210	84.29 272	66.36 103	92.09 215	73.47 147	75.48 279	90.12 190

GeoE		81.71 110	81.01 114	83.80 137	89.51 127	64.45 203	88.97 103	88.73 204	71.27 174	78.63 145	89.76 138	66.32 104	93.20 172	69.89 178	86.02 160	93.74 63

WR-MVS_H		78.51 185	78.49 161	78.56 261	88.02 186	56.38 314	88.43 123	92.67 67	77.14 56	73.89 241	87.55 198	66.25 105	89.24 274	58.92 272	73.55 304	90.06 197

PCF-MVS	73.52 7	80.38 142	78.84 156	85.01 89	87.71 196	68.99 113	83.65 253	91.46 121	63.00 289	77.77 164	90.28 124	66.10 106	95.09 95	61.40 252	88.22 131	90.94 161
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		83.72 78	82.92 86	86.14 69	84.22 255	69.48 104	91.05 54	85.27 257	81.30 7	76.83 182	91.65 92	66.09 107	95.56 66	76.00 126	93.85 66	93.38 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				84.35 114	93.01 64	68.79 116		92.44 75	63.96 283	81.09 116	91.57 96	66.06 108	95.45 73	67.19 206	94.82 51	88.81 240

PVSNet_BlendedMVS		80.60 137	80.02 128	82.36 187	88.85 155	65.40 184	86.16 197	92.00 97	69.34 210	78.11 157	86.09 243	66.02 109	94.27 120	71.52 161	82.06 203	87.39 269

PVSNet_Blended		80.98 123	80.34 124	82.90 170	88.85 155	65.40 184	84.43 240	92.00 97	67.62 237	78.11 157	85.05 265	66.02 109	94.27 120	71.52 161	89.50 115	89.01 230

diffmvs		82.10 101	81.88 103	82.76 180	83.00 282	63.78 215	83.68 252	89.76 165	72.94 151	82.02 101	89.85 136	65.96 111	90.79 251	82.38 73	87.30 141	93.71 64

APD-MVS_3200maxsize		85.97 52	85.88 53	86.22 67	92.69 72	69.53 103	91.93 36	92.99 49	73.54 140	85.94 41	94.51 26	65.80 112	95.61 63	83.04 62	92.51 79	93.53 77

miper_enhance_ethall		77.87 204	76.86 202	80.92 219	81.65 305	61.38 255	82.68 267	88.98 192	65.52 262	75.47 212	82.30 298	65.76 113	92.00 218	72.95 153	76.39 266	89.39 219

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 91	90.80 99	69.76 98	88.74 115	91.70 112	69.39 208	78.96 137	88.46 175	65.47 114	94.87 105	74.42 136	88.57 125	90.24 185

API-MVS		81.99 105	81.23 109	84.26 118	90.94 95	70.18 93	91.10 53	89.32 176	71.51 171	78.66 144	88.28 180	65.26 115	95.10 94	64.74 226	91.23 96	87.51 267

TranMVSNet+NR-MVSNet		80.84 126	80.31 125	82.42 185	87.85 190	62.33 242	87.74 152	91.33 123	80.55 11	77.99 160	89.86 135	65.23 116	92.62 192	67.05 208	75.24 289	92.30 119

IS-MVSNet		83.15 87	82.81 87	84.18 120	89.94 118	63.30 227	91.59 42	88.46 209	79.04 26	79.49 132	92.16 82	65.10 117	94.28 119	67.71 198	91.86 88	94.95 5

DU-MVS		81.12 122	80.52 121	82.90 170	87.80 192	63.46 223	87.02 170	91.87 105	79.01 27	78.38 150	89.07 159	65.02 118	93.05 182	70.05 175	76.46 264	92.20 123

Baseline_NR-MVSNet		78.15 195	78.33 168	77.61 276	85.79 230	56.21 317	86.78 179	85.76 253	73.60 138	77.93 161	87.57 197	65.02 118	88.99 278	67.14 207	75.33 285	87.63 263

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	92.90 100

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	73.53 141	85.69 46	94.45 28	65.00 120	95.56 66	82.75 65	91.87 86	92.50 111

VNet		82.21 100	82.41 91	81.62 199	90.82 98	60.93 258	84.47 236	89.78 164	76.36 79	84.07 77	91.88 88	64.71 122	90.26 257	70.68 169	88.89 120	93.66 65

Test By Simon												64.33 123

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	90.69 100	93.23 85
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
DP-MVS Recon		83.11 90	82.09 97	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 188	79.17 135	91.03 113	64.12 125	96.03 50	68.39 195	90.14 108	91.50 142

CLD-MVS		82.31 99	81.65 105	84.29 117	88.47 172	67.73 143	85.81 208	92.35 80	75.78 89	78.33 152	86.58 231	64.01 126	94.35 117	76.05 124	87.48 139	90.79 164
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RE-MVS-def			85.48 57		93.06 62	70.63 82	91.88 37	92.27 83	73.53 141	85.69 46	94.45 28	63.87 127		82.75 65	91.87 86	92.50 111

MVS		78.19 194	76.99 200	81.78 196	85.66 232	66.99 155	84.66 230	90.47 144	55.08 345	72.02 260	85.27 258	63.83 128	94.11 131	66.10 214	89.80 113	84.24 319

WR-MVS		79.49 159	79.22 149	80.27 232	88.79 161	58.35 282	85.06 222	88.61 207	78.56 30	77.65 165	88.34 178	63.81 129	90.66 254	64.98 224	77.22 252	91.80 136

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	90.04 131	63.44 130	95.39 79	76.04 125	76.90 256	92.31 118

1121		80.84 126	79.77 133	84.05 126	93.11 60	70.78 79	84.66 230	85.42 256	57.37 335	81.76 109	92.02 84	63.41 131	94.12 129	67.28 203	92.93 72	87.26 274

VPA-MVSNet		80.60 137	80.55 120	80.76 222	88.07 184	60.80 261	86.86 175	91.58 115	75.67 93	80.24 125	89.45 152	63.34 132	90.25 258	70.51 171	79.22 237	91.23 150

新几何1				83.42 144	93.13 58	70.71 80		85.48 255	57.43 334	81.80 105	91.98 85	63.28 133	92.27 207	64.60 227	92.99 71	87.27 273

HY-MVS	69.67 12	77.95 201	77.15 196	80.36 229	87.57 203	60.21 270	83.37 260	87.78 223	66.11 253	75.37 218	87.06 215	63.27 134	90.48 256	61.38 253	82.43 200	90.40 180

XXY-MVS		75.41 243	75.56 221	74.96 301	83.59 266	57.82 293	80.59 288	83.87 276	66.54 250	74.93 232	88.31 179	63.24 135	80.09 338	62.16 244	76.85 259	86.97 282

ab-mvs		79.51 158	78.97 154	81.14 214	88.46 173	60.91 259	83.84 250	89.24 181	70.36 190	79.03 136	88.87 164	63.23 136	90.21 259	65.12 222	82.57 199	92.28 120

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 139	89.15 147	68.03 138	84.46 238	90.02 158	70.67 185	81.30 114	86.53 234	63.17 137	94.19 126	75.60 130	88.54 126	88.57 247

pcd_1.5k_mvsjas		5.26 345	7.02 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	63.15 138	0.00 378	0.00 376	0.00 376	0.00 374

PS-MVSNAJss		82.07 103	81.31 107	84.34 115	86.51 223	67.27 152	89.27 93	91.51 117	71.75 165	79.37 133	90.22 127	63.15 138	94.27 120	77.69 109	82.36 201	91.49 143

PS-MVSNAJ		81.69 111	81.02 113	83.70 138	89.51 127	68.21 135	84.28 244	90.09 157	70.79 182	81.26 115	85.62 252	63.15 138	94.29 118	75.62 129	88.87 121	88.59 246

WTY-MVS		75.65 239	75.68 220	75.57 295	86.40 224	56.82 305	77.92 316	82.40 297	65.10 265	76.18 199	87.72 192	63.13 141	80.90 335	60.31 260	81.96 204	89.00 232

TransMVSNet (Re)		75.39 244	74.56 236	77.86 270	85.50 236	57.10 302	86.78 179	86.09 251	72.17 160	71.53 264	87.34 203	63.01 142	89.31 273	56.84 292	61.83 346	87.17 276

v8		79.97 151	79.02 153	82.80 175	84.09 257	64.50 201	87.96 143	90.29 153	74.13 129	75.24 224	86.81 217	62.88 143	93.89 142	74.39 137	75.40 283	90.00 199

abl_6		85.23 64	84.95 70	86.07 71	92.23 79	70.48 86	90.80 58	92.08 92	73.51 143	85.26 50	94.16 41	62.75 144	95.92 57	82.46 72	91.30 95	91.81 135

HPM-MVS_fast		85.35 63	84.95 70	86.57 61	93.69 48	70.58 85	92.15 34	91.62 113	73.89 133	82.67 97	94.09 44	62.60 145	95.54 69	80.93 81	92.93 72	93.57 74

PAPM		77.68 208	76.40 215	81.51 202	87.29 211	61.85 249	83.78 251	89.59 170	64.74 270	71.23 266	88.70 166	62.59 146	93.66 153	52.66 308	87.03 145	89.01 230

1112_ss		77.40 213	76.43 214	80.32 231	89.11 152	60.41 268	83.65 253	87.72 224	62.13 300	73.05 248	86.72 220	62.58 147	89.97 262	62.11 246	80.80 216	90.59 173

LCM-MVSNet-Re		77.05 218	76.94 201	77.36 279	87.20 212	51.60 345	80.06 293	80.46 316	75.20 102	67.69 299	86.72 220	62.48 148	88.98 279	63.44 232	89.25 118	91.51 141

v148		78.72 180	77.80 180	81.47 203	82.73 289	61.96 248	86.30 193	88.08 215	73.26 146	76.18 199	85.47 255	62.46 149	92.36 203	71.92 160	73.82 302	90.09 193

baseline1		76.98 220	76.75 208	77.66 274	88.13 181	55.66 322	85.12 221	81.89 301	73.04 149	76.79 183	88.90 162	62.43 150	87.78 295	63.30 234	71.18 321	89.55 217

MAR-MVS		81.84 107	80.70 117	85.27 82	91.32 90	71.53 60	89.82 81	90.92 134	69.77 202	78.50 147	86.21 240	62.36 151	94.52 114	65.36 220	92.05 84	89.77 211
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS_111021_LR		82.61 97	82.11 96	84.11 121	88.82 158	71.58 59	85.15 220	86.16 249	74.69 114	80.47 123	91.04 111	62.29 152	90.55 255	80.33 89	90.08 110	90.20 186

TAMVS		78.89 178	77.51 190	83.03 164	87.80 192	67.79 142	84.72 229	85.05 260	67.63 236	76.75 185	87.70 193	62.25 153	90.82 250	58.53 277	87.13 143	90.49 176

CP-MVSNet		78.22 191	78.34 167	77.84 271	87.83 191	54.54 327	87.94 145	91.17 129	77.65 39	73.48 243	88.49 174	62.24 154	88.43 287	62.19 243	74.07 297	90.55 174

OMC-MVS		82.69 95	81.97 102	84.85 97	88.75 163	67.42 148	87.98 142	90.87 136	74.92 108	79.72 130	91.65 92	62.19 155	93.96 133	75.26 133	86.42 154	93.16 90

cl____		77.72 206	76.76 206	80.58 225	82.49 295	60.48 266	83.09 263	87.87 220	69.22 213	74.38 238	85.22 260	62.10 156	91.53 231	71.09 165	75.41 282	89.73 213

DIV-MVS_self_test		77.72 206	76.76 206	80.58 225	82.48 296	60.48 266	83.09 263	87.86 221	69.22 213	74.38 238	85.24 259	62.10 156	91.53 231	71.09 165	75.40 283	89.74 212

testdata				79.97 237	90.90 96	64.21 207		84.71 262	59.27 321	85.40 48	92.91 70	62.02 158	89.08 277	68.95 188	91.37 93	86.63 290

eth_miper_zixun_eth		77.92 202	76.69 209	81.61 201	83.00 282	61.98 247	83.15 262	89.20 183	69.52 207	74.86 233	84.35 271	61.76 159	92.56 195	71.50 163	72.89 310	90.28 184

MVSFormer		82.85 93	82.05 99	85.24 83	87.35 205	70.21 88	90.50 64	90.38 146	68.55 230	81.32 111	89.47 148	61.68 160	93.46 163	78.98 95	90.26 106	92.05 129

lupinMVS		81.39 118	80.27 127	84.76 101	87.35 205	70.21 88	85.55 213	86.41 244	62.85 292	81.32 111	88.61 170	61.68 160	92.24 210	78.41 103	90.26 106	91.83 133

cdsmvs_eth3d_5k		19.96 339	26.61 341	0.00 359	0.00 382	0.00 383	0.00 370	89.26 180	0.00 377	0.00 378	88.61 170	61.62 162	0.00 378	0.00 376	0.00 376	0.00 374

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	91.39 102	61.54 163	95.50 70	82.71 67	75.48 279	91.72 137

hse-mvs2		81.72 109	80.94 115	84.07 124	88.72 164	67.68 144	85.87 204	87.26 233	76.02 85	84.67 63	88.22 183	61.54 163	93.48 161	82.71 67	73.44 306	91.06 155

CDS-MVSNet		79.07 173	77.70 185	83.17 157	87.60 199	68.23 134	84.40 242	86.20 248	67.49 239	76.36 194	86.54 233	61.54 163	90.79 251	61.86 248	87.33 140	90.49 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v10		79.74 154	78.67 157	82.97 168	84.06 258	64.95 193	87.88 149	90.62 140	73.11 147	75.11 227	86.56 232	61.46 166	94.05 132	73.68 142	75.55 277	89.90 205

v1144		80.03 149	79.03 152	83.01 165	83.78 263	64.51 199	87.11 168	90.57 142	71.96 163	78.08 159	86.20 241	61.41 167	93.94 136	74.93 134	77.23 251	90.60 172

cl22		78.07 197	77.01 198	81.23 211	82.37 298	61.83 250	83.55 257	87.98 217	68.96 223	75.06 229	83.87 276	61.40 168	91.88 223	73.53 144	76.39 266	89.98 202

BH-w/o		78.21 192	77.33 194	80.84 220	88.81 159	65.13 191	84.87 226	87.85 222	69.75 203	74.52 236	84.74 268	61.34 169	93.11 179	58.24 280	85.84 163	84.27 318

Test_1112_low_res		76.40 230	75.44 224	79.27 251	89.28 141	58.09 285	81.69 277	87.07 236	59.53 319	72.48 254	86.67 226	61.30 170	89.33 272	60.81 258	80.15 225	90.41 179

Vis-MVSNet (Re-imp)		78.36 189	78.45 162	78.07 269	88.64 167	51.78 344	86.70 182	79.63 324	74.14 128	75.11 227	90.83 117	61.29 171	89.75 265	58.10 281	91.60 89	92.69 106

PEN-MVS		77.73 205	77.69 186	77.84 271	87.07 215	53.91 332	87.91 147	91.18 128	77.56 44	73.14 247	88.82 165	61.23 172	89.17 275	59.95 262	72.37 312	90.43 178

pm-mvs1		77.25 216	76.68 210	78.93 256	84.22 255	58.62 281	86.41 189	88.36 210	71.37 173	73.31 244	88.01 190	61.22 173	89.15 276	64.24 228	73.01 309	89.03 229

BH-untuned		79.47 160	78.60 159	82.05 191	89.19 146	65.91 174	86.07 199	88.52 208	72.18 159	75.42 216	87.69 194	61.15 174	93.54 158	60.38 259	86.83 148	86.70 288

v2v482		80.23 145	79.29 146	83.05 163	83.62 265	64.14 208	87.04 169	89.97 159	73.61 137	78.18 156	87.22 208	61.10 175	93.82 143	76.11 123	76.78 261	91.18 151

jason		81.39 118	80.29 126	84.70 102	86.63 222	69.90 95	85.95 201	86.77 240	63.24 285	81.07 117	89.47 148	61.08 176	92.15 213	78.33 104	90.07 111	92.05 129
jason: jason.
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 97	93.35 81
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	73.13 9	79.15 170	77.94 175	82.79 177	89.59 123	62.99 237	88.16 139	91.51 117	65.77 258	77.14 179	91.09 109	60.91 178	93.21 170	50.26 321	87.05 144	92.17 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PS-CasMVS		78.01 200	78.09 172	77.77 273	87.71 196	54.39 329	88.02 141	91.22 126	77.50 47	73.26 245	88.64 169	60.73 179	88.41 288	61.88 247	73.88 301	90.53 175

OPM-MVS		83.50 81	82.95 85	85.14 85	88.79 161	70.95 73	89.13 100	91.52 116	77.55 45	80.96 118	91.75 90	60.71 180	94.50 115	79.67 93	86.51 153	89.97 203
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		80.81 129	79.76 134	83.96 134	85.60 234	68.78 117	83.54 258	90.50 143	70.66 186	76.71 186	91.66 91	60.69 181	91.26 238	76.94 118	81.58 208	91.83 133

v144192		79.47 160	78.37 166	82.78 178	83.35 270	63.96 211	86.96 171	90.36 149	69.99 196	77.50 167	85.67 250	60.66 182	93.77 147	74.27 138	76.58 262	90.62 170

V42		79.38 166	78.24 170	82.83 172	81.10 317	65.50 183	85.55 213	89.82 163	71.57 170	78.21 154	86.12 242	60.66 182	93.18 175	75.64 128	75.46 281	89.81 210

CPTT-MVS		83.73 77	83.33 80	84.92 94	93.28 55	70.86 77	92.09 35	90.38 146	68.75 227	79.57 131	92.83 73	60.60 184	93.04 184	80.92 82	91.56 91	90.86 163

DTE-MVSNet		76.99 219	76.80 204	77.54 278	86.24 225	53.06 340	87.52 156	90.66 139	77.08 59	72.50 253	88.67 168	60.48 185	89.52 269	57.33 288	70.74 323	90.05 198

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	78.92 139	91.00 114	60.42 186	95.38 80	78.71 97	86.32 155	91.33 147

plane_prior6					89.84 120	68.70 124						60.42 186

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	76.66 187	93.37 60	60.40 188	96.75 25	77.20 114	93.73 68	95.29 2

HQP2-MVS												60.17 189

HQP-MVS		82.61 97	82.02 100	84.37 112	89.33 134	66.98 156	89.17 95	92.19 89	76.41 74	77.23 175	90.23 126	60.17 189	95.11 91	77.47 111	85.99 161	91.03 157

VPNet		78.69 181	78.66 158	78.76 258	88.31 178	55.72 321	84.45 239	86.63 242	76.79 66	78.26 153	90.55 121	59.30 191	89.70 267	66.63 210	77.05 254	90.88 162

v1192		79.59 157	78.43 164	83.07 162	83.55 267	64.52 198	86.93 173	90.58 141	70.83 180	77.78 163	85.90 244	59.15 192	93.94 136	73.96 141	77.19 253	90.76 165

test222					91.50 88	68.26 133	84.16 245	83.20 289	54.63 346	79.74 129	91.63 94	58.97 193			91.42 92	86.77 286

CHOSEN 1792x2688		77.63 209	75.69 219	83.44 143	89.98 117	68.58 128	78.70 308	87.50 228	56.38 340	75.80 208	86.84 216	58.67 194	91.40 235	61.58 251	85.75 164	90.34 182

3Dnovator	76.31 5	83.38 85	82.31 94	86.59 60	87.94 188	72.94 29	90.64 60	92.14 91	77.21 53	75.47 212	92.83 73	58.56 195	94.72 110	73.24 151	92.71 76	92.13 126

v1921920		79.22 168	78.03 173	82.80 175	83.30 272	63.94 212	86.80 177	90.33 150	69.91 199	77.48 168	85.53 253	58.44 196	93.75 149	73.60 143	76.85 259	90.71 168

114514_t		80.68 135	79.51 139	84.20 119	94.09 41	67.27 152	89.64 88	91.11 131	58.75 326	74.08 240	90.72 118	58.10 197	95.04 96	69.70 180	89.42 117	90.30 183

v7n		78.97 176	77.58 189	83.14 158	83.45 269	65.51 182	88.32 131	91.21 127	73.69 136	72.41 255	86.32 239	57.93 198	93.81 144	69.18 185	75.65 275	90.11 191

CL-MVSNet_self_test		72.37 272	71.46 264	75.09 300	79.49 336	53.53 334	80.76 285	85.01 261	69.12 217	70.51 271	82.05 302	57.92 199	84.13 319	52.27 309	66.00 338	87.60 264

baseline2		75.70 238	73.83 246	81.30 209	83.26 273	61.79 251	82.57 269	80.65 312	66.81 242	66.88 307	83.42 285	57.86 200	92.19 211	63.47 231	79.57 229	89.91 204

QAPM		80.88 124	79.50 140	85.03 88	88.01 187	68.97 114	91.59 42	92.00 97	66.63 249	75.15 226	92.16 82	57.70 201	95.45 73	63.52 230	88.76 123	90.66 169

HyFIR lowres test		77.53 210	75.40 226	83.94 135	89.59 123	66.62 161	80.36 290	88.64 206	56.29 341	76.45 190	85.17 261	57.64 202	93.28 168	61.34 254	83.10 192	91.91 131

CNLPA		78.08 196	76.79 205	81.97 194	90.40 106	71.07 69	87.59 155	84.55 265	66.03 256	72.38 256	89.64 142	57.56 203	86.04 306	59.61 265	83.35 187	88.79 241

test_yl		81.17 120	80.47 122	83.24 152	89.13 148	63.62 216	86.21 195	89.95 160	72.43 156	81.78 107	89.61 143	57.50 204	93.58 154	70.75 167	86.90 146	92.52 109

DCV-MVSNet		81.17 120	80.47 122	83.24 152	89.13 148	63.62 216	86.21 195	89.95 160	72.43 156	81.78 107	89.61 143	57.50 204	93.58 154	70.75 167	86.90 146	92.52 109

sss		73.60 257	73.64 247	73.51 312	82.80 287	55.01 324	76.12 322	81.69 304	62.47 297	74.68 235	85.85 247	57.32 206	78.11 345	60.86 257	80.93 213	87.39 269

Effi-MVS+-dtu		80.03 149	78.57 160	84.42 110	85.13 243	68.74 120	88.77 112	88.10 213	74.99 106	74.97 231	83.49 284	57.27 207	93.36 166	73.53 144	80.88 214	91.18 151

mvs-test1		80.88 124	79.40 142	85.29 81	85.13 243	69.75 99	89.28 92	88.10 213	74.99 106	76.44 193	86.72 220	57.27 207	94.26 124	73.53 144	83.18 190	91.87 132

AdaColmap		80.58 139	79.42 141	84.06 125	93.09 61	68.91 115	89.36 91	88.97 194	69.27 211	75.70 209	89.69 140	57.20 209	95.77 60	63.06 236	88.41 129	87.50 268

v1240		78.99 175	77.78 181	82.64 181	83.21 274	63.54 220	86.62 184	90.30 152	69.74 205	77.33 171	85.68 249	57.04 210	93.76 148	73.13 152	76.92 255	90.62 170

miper_lstm_enhance		74.11 252	73.11 252	77.13 284	80.11 326	59.62 273	72.23 338	86.92 239	66.76 244	70.40 273	82.92 289	56.93 211	82.92 327	69.06 187	72.63 311	88.87 237

BH-RMVSNet		79.61 155	78.44 163	83.14 158	89.38 133	65.93 173	84.95 225	87.15 235	73.56 139	78.19 155	89.79 137	56.67 212	93.36 166	59.53 266	86.74 149	90.13 189

test_djsdf		80.30 144	79.32 145	83.27 150	83.98 260	65.37 187	90.50 64	90.38 146	68.55 230	76.19 198	88.70 166	56.44 213	93.46 163	78.98 95	80.14 226	90.97 160

EPNet_dtu		75.46 241	74.86 232	77.23 283	82.57 293	54.60 326	86.89 174	83.09 291	71.64 166	66.25 317	85.86 246	55.99 214	88.04 292	54.92 299	86.55 152	89.05 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CostFormer		75.24 245	73.90 244	79.27 251	82.65 292	58.27 284	80.80 283	82.73 295	61.57 303	75.33 222	83.13 288	55.52 215	91.07 247	64.98 224	78.34 243	88.45 249

tpmrst		72.39 270	72.13 259	73.18 316	80.54 322	49.91 353	79.91 296	79.08 327	63.11 287	71.69 263	79.95 320	55.32 216	82.77 328	65.66 219	73.89 300	86.87 283

1314		76.53 225	75.30 230	80.21 233	83.93 261	62.32 243	84.66 230	88.81 197	60.23 312	70.16 278	84.07 275	55.30 217	90.73 253	67.37 202	83.21 189	87.59 266

tfpnnormal		74.39 248	73.16 251	78.08 268	86.10 227	58.05 286	84.65 233	87.53 227	70.32 191	71.22 267	85.63 251	54.97 218	89.86 263	43.03 350	75.02 290	86.32 292

GBi-Net		78.40 186	77.40 191	81.40 205	87.60 199	63.01 234	88.39 127	89.28 177	71.63 167	75.34 219	87.28 204	54.80 219	91.11 241	62.72 237	79.57 229	90.09 193

test1		78.40 186	77.40 191	81.40 205	87.60 199	63.01 234	88.39 127	89.28 177	71.63 167	75.34 219	87.28 204	54.80 219	91.11 241	62.72 237	79.57 229	90.09 193

FMVSNet2		78.20 193	77.21 195	81.20 212	87.60 199	62.89 238	87.47 158	89.02 190	71.63 167	75.29 223	87.28 204	54.80 219	91.10 244	62.38 241	79.38 233	89.61 215

Fast-Effi-MVS+-dtu		78.02 199	76.49 212	82.62 182	83.16 278	66.96 159	86.94 172	87.45 230	72.45 153	71.49 265	84.17 273	54.79 222	91.58 229	67.61 199	80.31 223	89.30 221

MVSTER		79.01 174	77.88 178	82.38 186	83.07 279	64.80 195	84.08 249	88.95 195	69.01 222	78.69 142	87.17 211	54.70 223	92.43 199	74.69 135	80.57 220	89.89 206

OpenMVS	72.83 10	79.77 153	78.33 168	84.09 123	85.17 240	69.91 94	90.57 62	90.97 133	66.70 245	72.17 258	91.91 86	54.70 223	93.96 133	61.81 249	90.95 98	88.41 251

XVG-OURS		80.41 141	79.23 148	83.97 133	85.64 233	69.02 111	83.03 266	90.39 145	71.09 177	77.63 166	91.49 99	54.62 225	91.35 236	75.71 127	83.47 186	91.54 140

LPG-MVS_test		82.08 102	81.27 108	84.50 106	89.23 144	68.76 118	90.22 74	91.94 101	75.37 98	76.64 188	91.51 97	54.29 226	94.91 100	78.44 101	83.78 179	89.83 208

LGP-MVS_train				84.50 106	89.23 144	68.76 118		91.94 101	75.37 98	76.64 188	91.51 97	54.29 226	94.91 100	78.44 101	83.78 179	89.83 208

TR-MVS		77.44 211	76.18 217	81.20 212	88.24 179	63.24 229	84.61 234	86.40 245	67.55 238	77.81 162	86.48 235	54.10 228	93.15 176	57.75 284	82.72 197	87.20 275

FMVSNet3		77.88 203	76.85 203	80.97 218	86.84 218	62.36 241	86.52 188	88.77 199	71.13 175	75.34 219	86.66 227	54.07 229	91.10 244	62.72 237	79.57 229	89.45 218

DP-MVS		76.78 223	74.57 235	83.42 144	93.29 54	69.46 107	88.55 122	83.70 277	63.98 282	70.20 275	88.89 163	54.01 230	94.80 107	46.66 338	81.88 206	86.01 300

ACMP	74.13 6	81.51 117	80.57 119	84.36 113	89.42 130	68.69 125	89.97 79	91.50 120	74.46 120	75.04 230	90.41 123	53.82 231	94.54 112	77.56 110	82.91 193	89.86 207
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	70.83 11	78.05 198	76.37 216	83.08 161	91.88 85	67.80 141	88.19 137	89.46 173	64.33 276	69.87 284	88.38 177	53.66 232	93.58 154	58.86 273	82.73 196	87.86 259
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet_DTU		80.61 136	79.87 131	82.83 172	85.60 234	63.17 233	87.36 160	88.65 205	76.37 78	75.88 206	88.44 176	53.51 233	93.07 181	73.30 149	89.74 114	92.25 121

ACMM	73.20 8	80.78 134	79.84 132	83.58 140	89.31 139	68.37 130	89.99 78	91.60 114	70.28 192	77.25 173	89.66 141	53.37 234	93.53 159	74.24 139	82.85 194	88.85 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 233	74.46 239	81.13 215	85.37 238	69.79 97	84.42 241	87.95 218	65.03 267	67.46 301	85.33 257	53.28 235	91.73 227	58.01 282	83.27 188	81.85 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		79.21 169	77.60 188	84.05 126	88.71 165	67.61 145	85.84 206	87.26 233	69.08 218	77.23 175	88.14 188	53.20 236	93.47 162	75.50 132	73.45 305	91.06 155

anonymousdsp		78.60 183	77.15 196	82.98 167	80.51 323	67.08 154	87.24 165	89.53 171	65.66 260	75.16 225	87.19 210	52.52 237	92.25 209	77.17 115	79.34 234	89.61 215

CR-MVSNet		73.37 259	71.27 268	79.67 245	81.32 314	65.19 189	75.92 324	80.30 318	59.92 315	72.73 251	81.19 306	52.50 238	86.69 301	59.84 263	77.71 246	87.11 280

Patchmtry		70.74 280	69.16 282	75.49 297	80.72 319	54.07 331	74.94 333	80.30 318	58.34 327	70.01 279	81.19 306	52.50 238	86.54 302	53.37 305	71.09 322	85.87 303

pmmvs4		74.03 254	71.91 260	80.39 228	81.96 302	68.32 131	81.45 280	82.14 299	59.32 320	69.87 284	85.13 262	52.40 240	88.13 291	60.21 261	74.74 293	84.73 315

RPMNet		73.51 258	70.49 274	82.58 183	81.32 314	65.19 189	75.92 324	92.27 83	57.60 333	72.73 251	76.45 343	52.30 241	95.43 75	48.14 333	77.71 246	87.11 280

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	89.95 112	93.85 56

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	88.70 124	94.57 26

tfpn200view9		76.42 229	75.37 228	79.55 249	89.13 148	57.65 295	85.17 218	83.60 278	73.41 144	76.45 190	86.39 237	52.12 244	91.95 219	48.33 329	83.75 181	89.07 223

thres400		76.50 226	75.37 228	79.86 239	89.13 148	57.65 295	85.17 218	83.60 278	73.41 144	76.45 190	86.39 237	52.12 244	91.95 219	48.33 329	83.75 181	90.00 199

thres200		75.55 240	74.47 238	78.82 257	87.78 195	57.85 292	83.07 265	83.51 281	72.44 155	75.84 207	84.42 270	52.08 246	91.75 225	47.41 336	83.64 185	86.86 284

PMMVS		69.34 292	68.67 284	71.35 324	75.67 350	62.03 246	75.17 328	73.46 349	50.00 354	68.68 292	79.05 325	52.07 247	78.13 344	61.16 255	82.77 195	73.90 355

tpm cat1		70.57 282	68.31 287	77.35 280	82.41 297	57.95 290	78.08 313	80.22 320	52.04 351	68.54 295	77.66 338	52.00 248	87.84 294	51.77 310	72.07 316	86.25 293

IterMVS-SCA-FT		75.43 242	73.87 245	80.11 235	82.69 290	64.85 194	81.57 279	83.47 283	69.16 216	70.49 272	84.15 274	51.95 249	88.15 290	69.23 184	72.14 315	87.34 271

SCA		74.22 251	72.33 258	79.91 238	84.05 259	62.17 245	79.96 295	79.29 326	66.30 252	72.38 256	80.13 318	51.95 249	88.60 285	59.25 268	77.67 248	88.96 234

thres100view900		76.50 226	75.55 222	79.33 250	89.52 126	56.99 303	85.83 207	83.23 287	73.94 131	76.32 195	87.12 212	51.89 251	91.95 219	48.33 329	83.75 181	89.07 223

thres600view7		76.50 226	75.44 224	79.68 244	89.40 131	57.16 300	85.53 215	83.23 287	73.79 135	76.26 196	87.09 213	51.89 251	91.89 222	48.05 334	83.72 184	90.00 199

tpm2		73.26 262	71.46 264	78.63 259	83.34 271	56.71 308	80.65 287	80.40 317	56.63 339	73.55 242	82.02 303	51.80 253	91.24 239	56.35 295	78.42 242	87.95 255

LS3D		76.95 221	74.82 233	83.37 147	90.45 104	67.36 151	89.15 99	86.94 238	61.87 302	69.52 287	90.61 120	51.71 254	94.53 113	46.38 341	86.71 150	88.21 253

IterMVS		74.29 249	72.94 253	78.35 265	81.53 308	63.49 222	81.58 278	82.49 296	68.06 235	69.99 281	83.69 282	51.66 255	85.54 309	65.85 217	71.64 318	86.01 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 272	71.71 263	74.35 307	82.19 300	52.00 342	79.22 302	77.29 336	64.56 272	72.95 249	83.68 283	51.35 256	83.26 326	58.33 279	75.80 273	87.81 260

sam_mvs1												51.32 257				88.96 234

PatchmatchNet		73.12 264	71.33 267	78.49 264	83.18 276	60.85 260	79.63 297	78.57 328	64.13 277	71.73 262	79.81 323	51.20 258	85.97 307	57.40 287	76.36 269	88.66 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											74.00 349	51.12 259	88.60 285

xiu_mvs_v1_base_debu		80.80 131	79.72 135	84.03 129	87.35 205	70.19 90	85.56 210	88.77 199	69.06 219	81.83 102	88.16 184	50.91 260	92.85 188	78.29 105	87.56 136	89.06 225

xiu_mvs_v1_base		80.80 131	79.72 135	84.03 129	87.35 205	70.19 90	85.56 210	88.77 199	69.06 219	81.83 102	88.16 184	50.91 260	92.85 188	78.29 105	87.56 136	89.06 225

xiu_mvs_v1_base_debi		80.80 131	79.72 135	84.03 129	87.35 205	70.19 90	85.56 210	88.77 199	69.06 219	81.83 102	88.16 184	50.91 260	92.85 188	78.29 105	87.56 136	89.06 225

Patchmatch-test		64.82 317	63.24 317	69.57 330	79.42 337	49.82 354	63.49 359	69.05 359	51.98 352	59.95 345	80.13 318	50.91 260	70.98 363	40.66 355	73.57 303	87.90 258

Patchmatch-RL test		70.24 286	67.78 297	77.61 276	77.43 344	59.57 275	71.16 340	70.33 353	62.94 291	68.65 293	72.77 351	50.62 264	85.49 310	69.58 182	66.58 336	87.77 261

Anonymous20231211		78.97 176	77.69 186	82.81 174	90.54 103	64.29 206	90.11 76	91.51 117	65.01 268	76.16 202	88.13 189	50.56 265	93.03 185	69.68 181	77.56 249	91.11 154

VDDNet		81.52 115	80.67 118	84.05 126	90.44 105	64.13 209	89.73 86	85.91 252	71.11 176	83.18 87	93.48 57	50.54 266	93.49 160	73.40 148	88.25 130	94.54 27

pmmvs6		74.69 247	73.39 248	78.61 260	81.38 311	57.48 298	86.64 183	87.95 218	64.99 269	70.18 276	86.61 228	50.43 267	89.52 269	62.12 245	70.18 325	88.83 239

test_post											5.46 372	50.36 268	84.24 318

ET-MVSNet_ETH3D		78.63 182	76.63 211	84.64 103	86.73 221	69.47 105	85.01 223	84.61 264	69.54 206	66.51 315	86.59 229	50.16 269	91.75 225	76.26 122	84.24 176	92.69 106

sam_mvs												50.01 270

Anonymous20240529		80.19 147	78.89 155	84.10 122	90.60 101	64.75 196	88.95 104	90.90 135	65.97 257	80.59 121	91.17 107	49.97 271	93.73 151	69.16 186	82.70 198	93.81 60

thisisatest0530		79.40 164	77.76 183	84.31 116	87.69 198	65.10 192	87.36 160	84.26 271	70.04 195	77.42 169	88.26 182	49.94 272	94.79 108	70.20 173	84.70 171	93.03 94

PatchT		68.46 300	67.85 294	70.29 328	80.70 320	43.93 363	72.47 337	74.88 344	60.15 313	70.55 270	76.57 342	49.94 272	81.59 331	50.58 315	74.83 292	85.34 306

tttt0517		79.40 164	77.91 176	83.90 136	88.10 183	63.84 213	88.37 130	84.05 273	71.45 172	76.78 184	89.12 158	49.93 274	94.89 103	70.18 174	83.18 190	92.96 99

tpmvs		71.09 278	69.29 281	76.49 288	82.04 301	56.04 318	78.92 306	81.37 307	64.05 280	67.18 305	78.28 333	49.74 275	89.77 264	49.67 324	72.37 312	83.67 324

thisisatest0515		77.33 214	75.38 227	83.18 156	85.27 239	63.80 214	82.11 273	83.27 286	65.06 266	75.91 204	83.84 278	49.54 276	94.27 120	67.24 205	86.19 157	91.48 144

UniMVSNet_ETH3D		79.10 172	78.24 170	81.70 198	86.85 217	60.24 269	87.28 164	88.79 198	74.25 125	76.84 181	90.53 122	49.48 277	91.56 230	67.98 196	82.15 202	93.29 83

CVMVSNet		72.99 266	72.58 255	74.25 308	84.28 253	50.85 350	86.41 189	83.45 284	44.56 356	73.23 246	87.54 199	49.38 278	85.70 308	65.90 216	78.44 241	86.19 295

MDTV_nov1_ep13_2view						37.79 368	75.16 329		55.10 344	66.53 312		49.34 279		53.98 302		87.94 257

UGNet		80.83 128	79.59 138	84.54 105	88.04 185	68.09 136	89.42 90	88.16 211	76.95 61	76.22 197	89.46 150	49.30 280	93.94 136	68.48 193	90.31 104	91.60 138
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		71.55 275	70.20 278	75.61 294	77.83 342	56.39 313	81.74 276	80.89 308	57.76 331	67.46 301	84.49 269	49.26 281	85.32 312	57.08 290	75.29 287	85.11 311

LTVRE_ROB	69.57 13	76.25 232	74.54 237	81.41 204	88.60 168	64.38 205	79.24 301	89.12 188	70.76 184	69.79 286	87.86 191	49.09 282	93.20 172	56.21 296	80.16 224	86.65 289
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
FMVSNet1		77.44 211	76.12 218	81.40 205	86.81 219	63.01 234	88.39 127	89.28 177	70.49 189	74.39 237	87.28 204	49.06 283	91.11 241	60.91 256	78.52 239	90.09 193

test1111		79.43 162	79.18 150	80.15 234	89.99 116	53.31 338	87.33 162	77.05 338	75.04 105	80.23 126	92.77 78	48.97 284	92.33 206	68.87 189	92.40 82	94.81 15

ECVR-MVS		79.61 155	79.26 147	80.67 224	90.08 112	54.69 325	87.89 148	77.44 335	74.88 109	80.27 124	92.79 76	48.96 285	92.45 198	68.55 192	92.50 80	94.86 12

MDTV_nov1_ep13			69.97 279		83.18 276	53.48 335	77.10 320	80.18 321	60.45 309	69.33 290	80.44 315	48.89 286	86.90 300	51.60 312	78.51 240

test_post1							78.90 307				5.43 373	48.81 287	85.44 311	59.25 268

test-LLR		72.94 267	72.43 256	74.48 305	81.35 312	58.04 287	78.38 309	77.46 333	66.66 246	69.95 282	79.00 327	48.06 288	79.24 339	66.13 212	84.83 168	86.15 296

test0.0.03 1		68.00 301	67.69 298	68.90 333	77.55 343	47.43 357	75.70 327	72.95 351	66.66 246	66.56 311	82.29 299	48.06 288	75.87 354	44.97 347	74.51 295	83.41 326

bset_n11_16_dypcd		77.12 217	75.47 223	82.06 190	81.12 316	65.99 171	81.37 282	83.20 289	69.94 198	76.09 203	83.38 286	47.75 290	92.26 208	78.51 99	77.91 245	87.95 255

our_test_3		69.14 293	67.00 303	75.57 295	79.80 331	58.80 279	77.96 314	77.81 331	59.55 318	62.90 337	78.25 334	47.43 291	83.97 320	51.71 311	67.58 333	83.93 323

MS-PatchMatch		73.83 255	72.67 254	77.30 281	83.87 262	66.02 170	81.82 274	84.66 263	61.37 306	68.61 294	82.82 292	47.29 292	88.21 289	59.27 267	84.32 175	77.68 352

cascas		76.72 224	74.64 234	82.99 166	85.78 231	65.88 175	82.33 271	89.21 182	60.85 308	72.74 250	81.02 309	47.28 293	93.75 149	67.48 201	85.02 166	89.34 220

test20.03		67.45 303	66.95 304	68.94 332	75.48 352	44.84 362	77.50 317	77.67 332	66.66 246	63.01 335	83.80 279	47.02 294	78.40 343	42.53 352	68.86 331	83.58 325

test_0402		72.79 268	70.44 275	79.84 240	88.13 181	65.99 171	85.93 202	84.29 269	65.57 261	67.40 303	85.49 254	46.92 295	92.61 193	35.88 359	74.38 296	80.94 343

F-COLMAP		76.38 231	74.33 240	82.50 184	89.28 141	66.95 160	88.41 126	89.03 189	64.05 280	66.83 309	88.61 170	46.78 296	92.89 187	57.48 285	78.55 238	87.67 262

ppachtmachnet_test		70.04 288	67.34 301	78.14 267	79.80 331	61.13 256	79.19 303	80.59 313	59.16 322	65.27 322	79.29 324	46.75 297	87.29 298	49.33 325	66.72 334	86.00 302

D2MVS		74.82 246	73.21 250	79.64 246	79.81 330	62.56 240	80.34 291	87.35 231	64.37 275	68.86 291	82.66 294	46.37 298	90.10 261	67.91 197	81.24 211	86.25 293

Anonymous20231206		68.60 297	67.80 296	71.02 326	80.23 325	50.75 351	78.30 312	80.47 315	56.79 338	66.11 318	82.63 295	46.35 299	78.95 341	43.62 349	75.70 274	83.36 327

CHOSEN 280x420		66.51 309	64.71 310	71.90 319	81.45 309	63.52 221	57.98 361	68.95 360	53.57 347	62.59 338	76.70 341	46.22 300	75.29 357	55.25 298	79.68 227	76.88 354

GA-MVS		76.87 222	75.17 231	81.97 194	82.75 288	62.58 239	81.44 281	86.35 247	72.16 161	74.74 234	82.89 290	46.20 301	92.02 217	68.85 190	81.09 212	91.30 149

MDA-MVSNet_test_wron		65.03 315	62.92 318	71.37 322	75.93 348	56.73 306	69.09 351	74.73 346	57.28 336	54.03 357	77.89 335	45.88 302	74.39 360	49.89 323	61.55 347	82.99 333

YYNet1		65.03 315	62.91 319	71.38 321	75.85 349	56.60 310	69.12 350	74.66 348	57.28 336	54.12 356	77.87 336	45.85 303	74.48 359	49.95 322	61.52 348	83.05 331

EPMVS		69.02 294	68.16 289	71.59 320	79.61 334	49.80 355	77.40 318	66.93 362	62.82 293	70.01 279	79.05 325	45.79 304	77.86 347	56.58 293	75.26 288	87.13 279

IB-MVS	68.01 15	75.85 237	73.36 249	83.31 148	84.76 247	66.03 169	83.38 259	85.06 259	70.21 194	69.40 288	81.05 308	45.76 305	94.66 111	65.10 223	75.49 278	89.25 222
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
jajsoiax		79.29 167	77.96 174	83.27 150	84.68 249	66.57 163	89.25 94	90.16 155	69.20 215	75.46 214	89.49 147	45.75 306	93.13 178	76.84 119	80.80 216	90.11 191

PatchMatch-RL		72.38 271	70.90 271	76.80 287	88.60 168	67.38 150	79.53 298	76.17 341	62.75 294	69.36 289	82.00 304	45.51 307	84.89 315	53.62 304	80.58 219	78.12 351

RPSCF		73.23 263	71.46 264	78.54 262	82.50 294	59.85 271	82.18 272	82.84 294	58.96 323	71.15 268	89.41 154	45.48 308	84.77 316	58.82 274	71.83 317	91.02 159

RRT_test8_iter05		78.38 188	77.40 191	81.34 208	86.00 228	58.86 278	86.55 187	91.26 125	72.13 162	75.91 204	87.42 202	44.97 309	93.73 151	77.02 117	75.30 286	91.45 146

MSDG		73.36 261	70.99 270	80.49 227	84.51 251	65.80 176	80.71 286	86.13 250	65.70 259	65.46 320	83.74 281	44.60 310	90.91 249	51.13 314	76.89 257	84.74 314

PVSNet_0	57.27 20	61.67 322	59.27 325	68.85 334	79.61 334	57.44 299	68.01 352	73.44 350	55.93 342	58.54 348	70.41 354	44.58 311	77.55 348	47.01 337	35.91 364	71.55 357

RRT_MVS		79.88 152	78.38 165	84.38 111	85.42 237	70.60 84	88.71 117	88.75 203	72.30 158	78.83 141	89.14 156	44.44 312	92.18 212	78.50 100	79.33 235	90.35 181

DWT-MVSNet_test		73.70 256	71.86 261	79.21 253	82.91 285	58.94 277	82.34 270	82.17 298	65.21 263	71.05 269	78.31 332	44.21 313	90.17 260	63.29 235	77.28 250	88.53 248

mvs_tets		79.13 171	77.77 182	83.22 155	84.70 248	66.37 165	89.17 95	90.19 154	69.38 209	75.40 217	89.46 150	44.17 314	93.15 176	76.78 120	80.70 218	90.14 188

MDA-MVSNet-bldmvs		66.68 307	63.66 315	75.75 292	79.28 338	60.56 265	73.92 335	78.35 329	64.43 273	50.13 360	79.87 322	44.02 315	83.67 322	46.10 342	56.86 353	83.03 332

gg-mvs-nofinetune		69.95 289	67.96 292	75.94 291	83.07 279	54.51 328	77.23 319	70.29 354	63.11 287	70.32 274	62.33 357	43.62 316	88.69 284	53.88 303	87.76 133	84.62 317

GG-mvs-BLEND				75.38 298	81.59 307	55.80 320	79.32 300	69.63 356		67.19 304	73.67 350	43.24 317	88.90 283	50.41 316	84.50 172	81.45 340

CMPMVS	51.72 21	70.19 287	68.16 289	76.28 289	73.15 362	57.55 297	79.47 299	83.92 274	48.02 355	56.48 354	84.81 266	43.13 318	86.42 304	62.67 240	81.81 207	84.89 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		66.80 306	65.43 308	70.90 327	79.74 333	48.82 356	75.12 331	74.77 345	59.61 317	64.08 330	77.23 339	42.89 319	80.72 336	48.86 327	66.58 336	83.16 329

PVSNet	64.34 18	72.08 274	70.87 273	75.69 293	86.21 226	56.44 312	74.37 334	80.73 311	62.06 301	70.17 277	82.23 300	42.86 320	83.31 325	54.77 300	84.45 174	87.32 272

pmmvs-eth3d		70.50 284	67.83 295	78.52 263	77.37 345	66.18 168	81.82 274	81.51 305	58.90 324	63.90 332	80.42 316	42.69 321	86.28 305	58.56 276	65.30 340	83.11 330

UnsupCasMVSNet_eth		67.33 304	65.99 307	71.37 322	73.48 359	51.47 347	75.16 329	85.19 258	65.20 264	60.78 342	80.93 313	42.35 322	77.20 349	57.12 289	53.69 357	85.44 305

KD-MVS_self_test		68.81 295	67.59 300	72.46 318	74.29 356	45.45 360	77.93 315	87.00 237	63.12 286	63.99 331	78.99 329	42.32 323	84.77 316	56.55 294	64.09 343	87.16 278

ADS-MVSNet2		66.20 314	63.33 316	74.82 303	79.92 328	58.75 280	67.55 353	75.19 343	53.37 348	65.25 323	75.86 344	42.32 323	80.53 337	41.57 353	68.91 329	85.18 308

ADS-MVSNet		64.36 318	62.88 320	68.78 335	79.92 328	47.17 358	67.55 353	71.18 352	53.37 348	65.25 323	75.86 344	42.32 323	73.99 361	41.57 353	68.91 329	85.18 308

SixPastTwentyTwo		73.37 259	71.26 269	79.70 243	85.08 245	57.89 291	85.57 209	83.56 280	71.03 178	65.66 319	85.88 245	42.10 326	92.57 194	59.11 270	63.34 344	88.65 245

JIA-IIPM		66.32 311	62.82 321	76.82 286	77.09 346	61.72 252	65.34 356	75.38 342	58.04 330	64.51 327	62.32 358	42.05 327	86.51 303	51.45 313	69.22 328	82.21 336

ACMH	67.68 16	75.89 236	73.93 243	81.77 197	88.71 165	66.61 162	88.62 119	89.01 191	69.81 200	66.78 310	86.70 225	41.95 328	91.51 233	55.64 297	78.14 244	87.17 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 235	74.01 242	82.03 192	88.60 168	65.31 188	88.86 107	87.55 226	70.25 193	67.75 298	87.47 201	41.27 329	93.19 174	58.37 278	75.94 272	87.60 264

MIMVSNet		70.69 281	69.30 280	74.88 302	84.52 250	56.35 315	75.87 326	79.42 325	64.59 271	67.76 297	82.41 296	41.10 330	81.54 332	46.64 340	81.34 209	86.75 287

Anonymous202405211		78.25 190	77.01 198	81.99 193	91.03 93	60.67 263	84.77 228	83.90 275	70.65 187	80.00 128	91.20 106	41.08 331	91.43 234	65.21 221	85.26 165	93.85 56

N_pmnet		52.79 328	53.26 329	51.40 348	78.99 340	7.68 379	69.52 346	3.89 379	51.63 353	57.01 352	74.98 348	40.83 332	65.96 366	37.78 358	64.67 341	80.56 346

EU-MVSNet		68.53 299	67.61 299	71.31 325	78.51 341	47.01 359	84.47 236	84.27 270	42.27 357	66.44 316	84.79 267	40.44 333	83.76 321	58.76 275	68.54 332	83.17 328

DSMNet-mixed		57.77 325	56.90 327	60.38 343	67.70 366	35.61 369	69.18 348	53.97 371	32.30 366	57.49 351	79.88 321	40.39 334	68.57 365	38.78 357	72.37 312	76.97 353

OurMVSNet-221017-0		74.26 250	72.42 257	79.80 241	83.76 264	59.59 274	85.92 203	86.64 241	66.39 251	66.96 306	87.58 196	39.46 335	91.60 228	65.76 218	69.27 327	88.22 252

K. test v3		71.19 277	68.51 285	79.21 253	83.04 281	57.78 294	84.35 243	76.91 339	72.90 152	62.99 336	82.86 291	39.27 336	91.09 246	61.65 250	52.66 358	88.75 242

lessismore_v0				78.97 255	81.01 318	57.15 301		65.99 363		61.16 341	82.82 292	39.12 337	91.34 237	59.67 264	46.92 363	88.43 250

UnsupCasMVSNet_bld		63.70 320	61.53 324	70.21 329	73.69 358	51.39 348	72.82 336	81.89 301	55.63 343	57.81 350	71.80 353	38.67 338	78.61 342	49.26 326	52.21 359	80.63 344

new-patchmatchnet		61.73 321	61.73 323	61.70 342	72.74 363	24.50 376	69.16 349	78.03 330	61.40 304	56.72 353	75.53 347	38.42 339	76.48 352	45.95 343	57.67 352	84.13 321

MVS-HIRNet		59.14 323	57.67 326	63.57 341	81.65 305	43.50 364	71.73 339	65.06 365	39.59 361	51.43 359	57.73 361	38.34 340	82.58 329	39.53 356	73.95 299	64.62 360

test2506		77.30 215	76.49 212	79.74 242	90.08 112	52.02 341	87.86 150	63.10 368	74.88 109	80.16 127	92.79 76	38.29 341	92.35 204	68.74 191	92.50 80	94.86 12

COLMAP_ROB	66.92 17	73.01 265	70.41 276	80.81 221	87.13 214	65.63 180	88.30 132	84.19 272	62.96 290	63.80 333	87.69 194	38.04 342	92.56 195	46.66 338	74.91 291	84.24 319
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 290	69.00 283	72.55 317	79.27 339	56.85 304	78.38 309	74.71 347	57.64 332	68.09 296	77.19 340	37.75 343	76.70 350	63.92 229	84.09 177	84.10 322

OpenMVS_ROB	64.09 19	70.56 283	68.19 288	77.65 275	80.26 324	59.41 276	85.01 223	82.96 293	58.76 325	65.43 321	82.33 297	37.63 344	91.23 240	45.34 346	76.03 271	82.32 335

FMVSNet5		69.50 291	67.96 292	74.15 309	82.97 284	55.35 323	80.01 294	82.12 300	62.56 296	63.02 334	81.53 305	36.92 345	81.92 330	48.42 328	74.06 298	85.17 310

MIMVSNet1		68.58 298	66.78 305	73.98 310	80.07 327	51.82 343	80.77 284	84.37 266	64.40 274	59.75 346	82.16 301	36.47 346	83.63 323	42.73 351	70.33 324	86.48 291

ITE_SJBPF				78.22 266	81.77 304	60.57 264		83.30 285	69.25 212	67.54 300	87.20 209	36.33 347	87.28 299	54.34 301	74.62 294	86.80 285

test-mter		71.41 276	70.39 277	74.48 305	81.35 312	58.04 287	78.38 309	77.46 333	60.32 311	69.95 282	79.00 327	36.08 348	79.24 339	66.13 212	84.83 168	86.15 296

testgi		66.67 308	66.53 306	67.08 339	75.62 351	41.69 366	75.93 323	76.50 340	66.11 253	65.20 325	86.59 229	35.72 349	74.71 358	43.71 348	73.38 307	84.84 313

EG-PatchMatch MVS		74.04 253	71.82 262	80.71 223	84.92 246	67.42 148	85.86 205	88.08 215	66.04 255	64.22 329	83.85 277	35.10 350	92.56 195	57.44 286	80.83 215	82.16 337

MVS_0304		72.48 269	70.89 272	77.24 282	82.20 299	59.68 272	84.11 247	83.49 282	67.10 241	66.87 308	80.59 314	35.00 351	87.40 297	59.07 271	79.58 228	84.63 316

KD-MVS_2432*1600		66.22 312	63.89 313	73.21 313	75.47 353	53.42 336	70.76 343	84.35 267	64.10 278	66.52 313	78.52 330	34.55 352	84.98 313	50.40 317	50.33 361	81.23 341

miper_refine_blended		66.22 312	63.89 313	73.21 313	75.47 353	53.42 336	70.76 343	84.35 267	64.10 278	66.52 313	78.52 330	34.55 352	84.98 313	50.40 317	50.33 361	81.23 341

XVG-ACMP-BASELINE		76.11 234	74.27 241	81.62 199	83.20 275	64.67 197	83.60 256	89.75 166	69.75 203	71.85 261	87.09 213	32.78 354	92.11 214	69.99 177	80.43 222	88.09 254

AllTest		70.96 279	68.09 291	79.58 247	85.15 241	63.62 216	84.58 235	79.83 322	62.31 298	60.32 343	86.73 218	32.02 355	88.96 281	50.28 319	71.57 319	86.15 296

TestCases				79.58 247	85.15 241	63.62 216		79.83 322	62.31 298	60.32 343	86.73 218	32.02 355	88.96 281	50.28 319	71.57 319	86.15 296

USDC		70.33 285	68.37 286	76.21 290	80.60 321	56.23 316	79.19 303	86.49 243	60.89 307	61.29 340	85.47 255	31.78 357	89.47 271	53.37 305	76.21 270	82.94 334

Anonymous20240521		68.80 296	67.22 302	73.55 311	74.33 355	54.11 330	83.18 261	85.61 254	58.15 328	61.68 339	80.94 311	30.71 358	81.27 334	57.00 291	73.34 308	85.28 307

tmp_tt		18.61 340	21.40 343	10.23 356	4.82 379	10.11 378	34.70 366	30.74 377	1.48 373	23.91 369	26.07 370	28.42 359	13.41 375	27.12 364	15.35 372	7.17 370

TDRefinement		67.49 302	64.34 311	76.92 285	73.47 360	61.07 257	84.86 227	82.98 292	59.77 316	58.30 349	85.13 262	26.06 360	87.89 293	47.92 335	60.59 350	81.81 339

TinyColmap		67.30 305	64.81 309	74.76 304	81.92 303	56.68 309	80.29 292	81.49 306	60.33 310	56.27 355	83.22 287	24.77 361	87.66 296	45.52 344	69.47 326	79.95 347

EGC-MVSNET		52.07 329	47.05 333	67.14 338	83.51 268	60.71 262	80.50 289	67.75 361	0.07 374	0.43 375	75.85 346	24.26 362	81.54 332	28.82 362	62.25 345	59.16 362

LF4IMVS		64.02 319	62.19 322	69.50 331	70.90 364	53.29 339	76.13 321	77.18 337	52.65 350	58.59 347	80.98 310	23.55 363	76.52 351	53.06 307	66.66 335	78.68 350

new_pmnet		50.91 330	50.29 331	52.78 347	68.58 365	34.94 371	63.71 358	56.63 370	39.73 360	44.95 361	65.47 356	21.93 364	58.48 367	34.98 360	56.62 354	64.92 359

pmmvs3		57.79 324	54.26 328	68.37 336	64.02 368	56.72 307	75.12 331	65.17 364	40.20 359	52.93 358	69.86 355	20.36 365	75.48 356	45.45 345	55.25 356	72.90 356

PM-MVS		66.41 310	64.14 312	73.20 315	73.92 357	56.45 311	78.97 305	64.96 366	63.88 284	64.72 326	80.24 317	19.84 366	83.44 324	66.24 211	64.52 342	79.71 348

ambc				75.24 299	73.16 361	50.51 352	63.05 360	87.47 229		64.28 328	77.81 337	17.80 367	89.73 266	57.88 283	60.64 349	85.49 304

ANet_high		50.57 331	46.10 334	63.99 340	48.67 374	39.13 367	70.99 342	80.85 309	61.39 305	31.18 365	57.70 362	17.02 368	73.65 362	31.22 361	15.89 371	79.18 349

FPMVS		53.68 327	51.64 330	59.81 344	65.08 367	51.03 349	69.48 347	69.58 357	41.46 358	40.67 362	72.32 352	16.46 369	70.00 364	24.24 366	65.42 339	58.40 363

test_method		31.52 336	29.28 340	38.23 351	27.03 378	6.50 380	20.94 369	62.21 369	4.05 372	22.35 370	52.50 364	13.33 370	47.58 371	27.04 365	34.04 365	60.62 361

EMVS		30.81 337	29.65 339	34.27 353	50.96 373	25.95 375	56.58 363	46.80 374	24.01 368	15.53 373	30.68 369	12.47 371	54.43 370	12.81 372	17.05 370	22.43 369

E-PMN		31.77 335	30.64 338	35.15 352	52.87 372	27.67 373	57.09 362	47.86 373	24.64 367	16.40 372	33.05 368	11.23 372	54.90 369	14.46 371	18.15 369	22.87 368

DeepMVS_CX				27.40 354	40.17 377	26.90 374		24.59 378	17.44 370	23.95 368	48.61 365	9.77 373	26.48 373	18.06 368	24.47 367	28.83 367

Gipuma		45.18 332	41.86 335	55.16 346	77.03 347	51.52 346	32.50 367	80.52 314	32.46 365	27.12 366	35.02 367	9.52 374	75.50 355	22.31 367	60.21 351	38.45 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 326	49.68 332	67.97 337	53.73 371	45.28 361	66.85 355	80.78 310	35.96 363	39.45 363	62.23 359	8.70 375	78.06 346	48.24 332	51.20 360	80.57 345

PMMVS2		40.82 334	38.86 337	46.69 349	53.84 370	16.45 377	48.61 364	49.92 372	37.49 362	31.67 364	60.97 360	8.14 376	56.42 368	28.42 363	30.72 366	67.19 358

PMVS	37.38 22	44.16 333	40.28 336	55.82 345	40.82 376	42.54 365	65.12 357	63.99 367	34.43 364	24.48 367	57.12 363	3.92 377	76.17 353	17.10 369	55.52 355	48.75 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 338	25.89 342	43.81 350	44.55 375	35.46 370	28.87 368	39.07 375	18.20 369	18.58 371	40.18 366	2.68 378	47.37 372	17.07 370	23.78 368	48.60 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 341	15.94 344	19.46 355	58.74 369	31.45 372	39.22 365	3.74 380	6.84 371	6.04 374	2.70 374	1.27 379	24.29 374	10.54 373	14.40 373	2.63 371

test123		6.12 343	8.11 346	0.14 357	0.06 381	0.09 381	71.05 341	0.03 382	0.04 376	0.25 377	1.30 376	0.05 380	0.03 377	0.21 375	0.01 375	0.29 372

testmvs		6.04 344	8.02 347	0.10 358	0.08 380	0.03 382	69.74 345	0.04 381	0.05 375	0.31 376	1.68 375	0.02 381	0.04 376	0.24 374	0.02 374	0.25 373

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

ab-mvs-re		7.23 342	9.64 345	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	86.72 220	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 118	91.30 15

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 29

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 29

eth-test2					0.00 382

eth-test					0.00 382

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 242	92.39 6				88.94 11	96.63 4	94.85 14

save fliter					93.80 44	72.35 45	90.47 66	91.17 129	74.31 122

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 39

GSMVS																88.96 234

test_part2					95.06 8	72.65 33				91.80 13

MTGPA								92.02 94

MTMP							92.18 33	32.83 376

gm-plane-assit					81.40 310	53.83 333			62.72 295		80.94 311		92.39 201	63.40 233

test9_res														84.90 33	95.70 31	92.87 101

agg_prior2														82.91 63	95.45 33	92.70 104

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_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 71			93.91 52

旧先验2							86.56 186		58.10 329	87.04 35			88.98 279	74.07 140

新几何2							86.29 194

无先验							87.48 157	88.98 192	60.00 314				94.12 129	67.28 203		88.97 233

原ACMM2							86.86 175

testdata2													91.01 248	62.37 242

testdata1							84.14 246		75.71 90

plane_prior7					90.08 112	68.51 129

plane_prior5								92.44 75					95.38 80	78.71 97	86.32 155	91.33 147

plane_prior4											91.00 114

plane_prior3						68.60 127			78.44 31	78.92 139

plane_prior2							91.25 49		79.12 24

plane_prior1					89.90 119

plane_prior						68.71 122	90.38 70		77.62 40						86.16 158

n2								0.00 383

nn								0.00 383

door-mid								69.98 355

test11								92.23 86

door								69.44 358

HQP5-MVS						66.98 156

HQP-NCC					89.33 134		89.17 95		76.41 74	77.23 175

ACMP_Plane					89.33 134		89.17 95		76.41 74	77.23 175

BP-MVS														77.47 111

HQP4-MVS										77.24 174			95.11 91			91.03 157

HQP3-MVS								92.19 89							85.99 161

NP-MVS					89.62 122	68.32 131					90.24 125

ACMMP++_ref															81.95 205

ACMMP++															81.25 210

High-resolution multi-view benchmark