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.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 52	99.27 1	99.54 1

FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 47	91.91 15	84.21 155	96.51 8	57.84 287	88.93 80	92.84 93	91.92 3	96.16 3	96.23 18	86.95 50	95.99 10	79.05 117	98.57 15	98.80 6

PS-CasMVS		90.06 43	91.92 13	84.47 148	96.56 7	58.83 282	89.04 77	92.74 96	91.40 5	96.12 4	96.06 22	87.23 47	95.57 36	79.42 115	98.74 6	99.00 2

LCM-MVSNet-Re		83.48 158	85.06 124	78.75 251	85.94 248	55.75 304	80.05 243	94.27 23	76.47 128	96.09 5	94.54 58	83.31 85	89.75 241	59.95 287	94.89 168	90.75 210

PEN-MVS		90.03 45	91.88 16	84.48 147	96.57 6	58.88 279	88.95 78	93.19 77	91.62 4	96.01 6	96.16 20	87.02 49	95.60 35	78.69 120	98.72 9	98.97 3

CP-MVSNet		89.27 61	90.91 43	84.37 149	96.34 9	58.61 284	88.66 87	92.06 110	90.78 6	95.67 7	95.17 39	81.80 110	95.54 41	79.00 118	98.69 10	98.95 4

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 23	93.73 65	85.72 32	96.79 1	95.51 8	88.86 14	95.63 8	96.99 8	84.81 70	93.16 139	91.10 1	97.53 73	96.58 29
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
WR-MVS_H		89.91 50	91.31 31	85.71 127	96.32 10	62.39 240	89.54 69	93.31 70	90.21 10	95.57 9	95.66 28	81.42 114	95.90 15	80.94 96	98.80 3	98.84 5

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 78	80.37 70	91.91 34	93.11 79	81.10 77	95.32 10	97.24 5	72.94 202	94.85 72	85.07 52	97.78 55	97.26 15

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 174	86.67 16	90.51 47	90.20 167	69.87 219	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 112	97.09 19

wuyk23d		75.13 260	79.30 214	62.63 339	75.56 348	75.18 124	80.89 234	73.10 327	75.06 151	94.76 12	95.32 34	87.73 42	52.85 368	34.16 367	97.11 85	59.85 364

ACMH	76.49 14	89.34 60	91.14 34	83.96 162	92.50 94	70.36 170	89.55 67	93.84 50	81.89 69	94.70 13	95.44 33	90.69 9	88.31 260	83.33 71	98.30 26	93.20 130
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 86	87.45 85	86.45 107	92.52 93	69.19 182	87.84 97	88.05 203	81.66 71	94.64 14	96.53 14	65.94 242	94.75 74	83.02 75	96.83 94	95.41 50

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 68	84.79 40	89.89 59	90.63 150	70.00 218	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 127	95.52 48

abl_6		93.02 4	93.16 4	92.60 4	94.73 43	88.99 7	93.26 12	94.19 30	89.11 12	94.43 16	95.27 36	91.86 3	95.09 64	87.54 18	98.02 40	93.71 112

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 72	84.72 41	89.70 61	90.29 164	69.27 222	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 131	95.34 52

test_0402		88.65 70	89.58 59	85.88 123	92.55 92	72.22 152	84.01 157	89.44 182	88.63 18	94.38 18	95.77 24	86.38 60	93.59 121	79.84 108	95.21 154	91.82 188

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 153	97.00 2	64.33 218	89.67 64	88.38 197	88.84 15	94.29 19	97.57 3	90.48 14	91.26 193	72.57 192	97.65 62	97.34 14

v7n		90.13 40	90.96 41	87.65 91	91.95 112	71.06 165	89.99 56	93.05 82	86.53 28	94.29 19	96.27 17	82.69 90	94.08 100	86.25 40	97.63 63	97.82 8

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 97	82.98 54	91.98 31	90.08 170	71.54 198	94.28 21	96.54 13	81.57 112	94.27 87	86.26 38	96.49 106	97.09 19

PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 74	77.96 94	87.94 95	91.97 113	70.73 209	94.19 22	96.67 11	76.94 159	94.57 82	83.07 73	96.28 114	96.15 32

SED-MVS		90.46 37	91.64 19	86.93 98	94.18 49	72.65 138	90.47 48	93.69 54	83.77 44	94.11 23	94.27 71	90.28 15	95.84 22	86.03 44	97.92 48	92.29 170

test_241102_ONE					94.18 49	72.65 138		93.69 54	83.62 46	94.11 23	93.78 102	90.28 15	95.50 47

DPE-MVS		90.53 35	91.08 36	88.88 66	93.38 72	78.65 87	89.15 76	94.05 40	84.68 38	93.90 25	94.11 84	88.13 36	96.30 3	84.51 60	97.81 54	91.70 191
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		91.22 24	91.92 13	89.14 64	92.97 82	78.04 91	92.84 16	94.14 35	83.33 51	93.90 25	95.73 25	88.77 26	96.41 1	87.60 16	97.98 44	92.98 138

ACMH+	77.89 11	90.73 30	91.50 23	88.44 79	93.00 81	76.26 119	89.65 65	95.55 7	87.72 23	93.89 27	94.94 44	91.62 4	93.44 129	78.35 124	98.76 4	95.61 47

ACMM	79.39 9	90.65 31	90.99 40	89.63 56	95.03 35	83.53 48	89.62 66	93.35 66	79.20 100	93.83 28	93.60 106	90.81 8	92.96 146	85.02 54	98.45 19	92.41 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	88.83 24	95.51 44	87.16 28	97.60 66	92.73 147

RE-MVS-def			92.61 5		94.13 54	88.95 8	92.87 14	94.16 31	88.75 16	93.79 29	94.43 63	90.64 11		87.16 28	97.60 66	92.73 147

DVP-MVS		90.06 43	91.32 30	86.29 111	94.16 52	72.56 144	90.54 45	91.01 140	83.61 47	93.75 31	94.65 53	89.76 19	95.78 27	86.42 34	97.97 45	90.55 218
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									85.33 32	93.75 31	94.65 53	87.44 45	95.78 27	87.41 21	98.21 30	92.98 138

test0726					94.16 52	72.56 144	90.63 44	93.90 46	83.61 47	93.75 31	94.49 60	89.76 19

LPG-MVS_test		91.47 19	91.68 18	90.82 38	94.75 41	81.69 60	90.00 54	94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

LGP-MVS_train				90.82 38	94.75 41	81.69 60		94.27 23	82.35 63	93.67 34	94.82 48	91.18 5	95.52 42	85.36 50	98.73 7	95.23 58

test1172		92.40 7	92.41 7	92.37 6	94.68 44	89.04 6	91.98 31	93.62 57	90.14 11	93.63 36	94.16 81	88.83 24	95.51 44	87.11 30	97.54 72	92.54 157

test_part1		87.15 87	87.82 80	85.15 136	88.88 188	63.04 230	87.98 93	94.85 16	82.52 61	93.61 37	95.73 25	67.51 233	95.71 31	80.48 104	98.83 2	96.69 25

DVP-MVS++		90.07 42	91.09 35	87.00 97	91.55 131	72.64 140	96.19 2	94.10 38	85.33 32	93.49 38	94.64 56	81.12 118	95.88 16	87.41 21	95.94 129	92.48 159

test_241102_TWO								93.71 53	83.77 44	93.49 38	94.27 71	89.27 22	95.84 22	86.03 44	97.82 53	92.04 179

test_one_0601					93.85 62	73.27 134		94.11 37	86.57 27	93.47 40	94.64 56	88.42 27

SR-MVS		92.23 8	92.34 9	91.91 17	94.89 39	87.85 11	92.51 24	93.87 49	88.20 21	93.24 41	94.02 87	90.15 17	95.67 33	86.82 32	97.34 79	92.19 176

APD-MVS_3200maxsize		92.05 10	92.24 10	91.48 24	93.02 80	85.17 35	92.47 26	95.05 15	87.65 24	93.21 42	94.39 69	90.09 18	95.08 65	86.67 33	97.60 66	94.18 91

Anonymous20231211		88.40 72	89.62 58	84.73 143	90.46 162	65.27 209	88.86 81	93.02 86	87.15 25	93.05 43	97.10 6	82.28 98	92.02 173	76.70 147	97.99 42	96.88 23

MP-MVS-pluss		90.81 29	91.08 36	89.99 51	95.97 14	79.88 73	88.13 92	94.51 21	75.79 141	92.94 44	94.96 43	88.36 29	95.01 67	90.70 2	98.40 20	95.09 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 67	89.88 53	86.22 114	91.63 124	77.07 109	89.82 60	93.77 51	78.90 104	92.88 45	92.29 140	86.11 62	90.22 225	86.24 41	97.24 82	91.36 199
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
ACMP	79.16 10	90.54 34	90.60 48	90.35 46	94.36 46	80.98 66	89.16 75	94.05 40	79.03 103	92.87 46	93.74 103	90.60 12	95.21 61	82.87 76	98.76 4	94.87 64
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 15	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 35	94.17 96	86.07 43	98.48 18	97.22 17

SMA-MVS		90.31 38	90.48 49	89.83 52	95.31 31	79.52 79	90.98 41	93.24 76	75.37 148	92.84 48	95.28 35	85.58 66	96.09 7	87.92 10	97.76 56	93.88 102
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_part2					93.86 61	77.77 96				92.84 48

v10		86.54 94	87.10 89	84.84 140	88.16 202	63.28 227	86.64 118	92.20 107	75.42 147	92.81 50	94.50 59	74.05 187	94.06 101	83.88 65	96.28 114	97.17 18

v8		86.22 101	86.83 96	84.36 151	87.82 207	62.35 242	86.42 121	91.33 131	76.78 127	92.73 51	94.48 61	73.41 196	93.72 114	83.10 72	95.41 146	97.01 21

nrg030		87.85 80	88.49 74	85.91 121	90.07 170	69.73 173	87.86 96	94.20 28	74.04 160	92.70 52	94.66 52	85.88 65	91.50 184	79.72 109	97.32 80	96.50 30

SteuartSystems-ACMMP		91.16 26	91.36 26	90.55 42	93.91 60	80.97 67	91.49 37	93.48 63	82.82 58	92.60 53	93.97 89	88.19 33	96.29 4	87.61 15	98.20 32	94.39 84
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 51	89.97 52	89.27 62	94.76 40	79.86 74	86.76 115	92.78 95	78.78 106	92.51 54	93.64 105	88.13 36	93.84 110	84.83 57	97.55 69	94.10 95
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 31	92.99 13	94.23 26	85.21 34	92.51 54	95.13 40	90.65 10	95.34 54	88.06 9	98.15 35	95.95 40

K. test v3		85.14 117	84.73 130	86.37 108	91.13 146	69.63 175	85.45 134	76.68 301	84.06 42	92.44 56	96.99 8	62.03 261	94.65 77	80.58 102	93.24 205	94.83 70

ACMMP		91.91 12	91.87 17	92.03 13	95.53 27	85.91 26	93.35 11	94.16 31	82.52 61	92.39 57	94.14 82	89.15 23	95.62 34	87.35 23	98.24 28	94.56 75
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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 70	93.75 63	77.44 101	86.31 123	95.27 12	70.80 207	92.28 58	93.80 99	86.89 51	94.64 78	85.52 48	97.51 74	94.30 87

SF-MVS		90.27 39	90.80 45	88.68 74	92.86 86	77.09 108	91.19 40	95.74 5	81.38 74	92.28 58	93.80 99	86.89 51	94.64 78	85.52 48	97.51 74	94.30 87

COLMAP_ROB	83.01 3	91.97 11	91.95 12	92.04 12	93.68 66	86.15 22	93.37 10	95.10 14	90.28 9	92.11 60	95.03 42	89.75 21	94.93 69	79.95 107	98.27 27	95.04 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 38	84.50 45	89.49 71	93.98 42	79.68 93	92.09 61	93.89 97	83.80 79	93.10 143	82.67 78	98.04 37	93.64 117

TranMVSNet+NR-MVSNet		87.86 79	88.76 73	85.18 135	94.02 57	64.13 219	84.38 151	91.29 132	84.88 37	92.06 62	93.84 98	86.45 58	93.73 113	73.22 183	98.66 11	97.69 9

zzz-MVS		91.27 22	91.26 33	91.29 29	96.59 4	86.29 18	88.94 79	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 168

MTAPA		91.52 16	91.60 20	91.29 29	96.59 4	86.29 18	92.02 30	91.81 119	84.07 40	92.00 63	94.40 67	86.63 54	95.28 57	88.59 5	98.31 24	92.30 168

ACMMP_NAP		90.65 31	91.07 38	89.42 60	95.93 16	79.54 78	89.95 57	93.68 56	77.65 117	91.97 65	94.89 45	88.38 28	95.45 49	89.27 3	97.87 52	93.27 127

FC-MVSNet-test		85.93 107	87.05 91	82.58 193	92.25 102	56.44 299	85.75 130	93.09 80	77.33 121	91.94 66	94.65 53	74.78 179	93.41 131	75.11 166	98.58 14	97.88 7

lessismore_v0				85.95 120	91.10 147	70.99 166		70.91 340		91.79 67	94.42 65	61.76 262	92.93 148	79.52 114	93.03 211	93.93 100

HPM-MVS		92.13 9	92.20 11	91.91 17	95.58 26	84.67 42	93.51 8	94.85 16	82.88 57	91.77 68	93.94 96	90.55 13	95.73 30	88.50 7	98.23 29	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 25	90.95 42	91.93 15	95.67 23	85.85 29	90.00 54	93.90 46	80.32 86	91.74 69	94.41 66	88.17 34	95.98 11	86.37 36	97.99 42	93.96 99

mPP-MVS		91.69 13	91.47 24	92.37 6	96.04 13	88.48 10	92.72 18	92.60 99	83.09 54	91.54 70	94.25 75	87.67 43	95.51 44	87.21 27	98.11 36	93.12 133

HFP-MVS		91.30 21	91.39 25	91.02 33	95.43 29	84.66 43	92.58 22	93.29 73	81.99 66	91.47 71	93.96 92	88.35 30	95.56 37	87.74 11	97.74 58	92.85 142

#test#		90.49 36	90.31 51	91.02 33	95.43 29	84.66 43	90.65 43	93.29 73	77.00 125	91.47 71	93.96 92	88.35 30	95.56 37	84.88 55	97.74 58	92.85 142

ACMMPR		91.49 17	91.35 28	91.92 16	95.74 20	85.88 28	92.58 22	93.25 75	81.99 66	91.40 73	94.17 80	87.51 44	95.87 18	87.74 11	97.76 56	93.99 97

GST-MVS		90.96 28	91.01 39	90.82 38	95.45 28	82.73 56	91.75 35	93.74 52	80.98 79	91.38 74	93.80 99	87.20 48	95.80 24	87.10 31	97.69 61	93.93 100

ANet_high		83.17 165	85.68 115	75.65 291	81.24 300	45.26 359	79.94 245	92.91 89	83.83 43	91.33 75	96.88 10	80.25 129	85.92 289	68.89 223	95.89 132	95.76 42

ZNCC-MVS		91.26 23	91.34 29	91.01 35	95.73 21	83.05 53	92.18 28	94.22 27	80.14 89	91.29 76	93.97 89	87.93 40	95.87 18	88.65 4	97.96 47	94.12 94

APD-MVS		89.54 56	89.63 57	89.26 63	92.57 91	81.34 65	90.19 52	93.08 81	80.87 81	91.13 77	93.19 110	86.22 61	95.97 12	82.23 83	97.18 84	90.45 220
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 62		91.84 120		88.80 83	95.32 11	75.14 150	91.07 78	92.89 120	87.27 46	93.78 112	83.69 68	97.55 69

CP-MVS		91.67 14	91.58 21	91.96 14	95.29 32	87.62 12	93.38 9	93.36 65	83.16 53	91.06 79	94.00 88	88.26 32	95.71 31	87.28 26	98.39 21	92.55 156

FIs		85.35 114	86.27 102	82.60 192	91.86 117	57.31 292	85.10 138	93.05 82	75.83 140	91.02 80	93.97 89	73.57 192	92.91 150	73.97 174	98.02 40	97.58 12

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 118	92.86 86	67.02 196	82.55 202	91.56 123	83.08 55	90.92 81	91.82 152	78.25 143	93.99 102	74.16 171	98.35 22	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 116	93.24 76	67.02 196	83.16 187	92.21 106	81.73 70	90.92 81	91.97 146	77.20 153	93.99 102	74.16 171	98.35 22	97.61 10

V42		83.47 159	83.37 156	83.75 167	83.16 285	63.33 226	81.31 227	90.23 166	69.51 221	90.91 83	90.81 182	74.16 185	92.29 165	80.06 105	90.22 264	95.62 46

region2R		91.44 20	91.30 32	91.87 19	95.75 19	85.90 27	92.63 21	93.30 72	81.91 68	90.88 84	94.21 76	87.75 41	95.87 18	87.60 16	97.71 60	93.83 104

WR-MVS		83.56 156	84.40 142	81.06 217	93.43 71	54.88 310	78.67 266	85.02 250	81.24 75	90.74 85	91.56 159	72.85 203	91.08 199	68.00 231	98.04 37	97.23 16

v1240		84.30 138	84.51 139	83.65 169	87.65 212	61.26 251	82.85 194	91.54 124	67.94 238	90.68 86	90.65 188	71.71 216	93.64 116	82.84 77	94.78 170	96.07 35

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 75	91.94 114	77.27 107	88.71 85	95.26 13	76.08 132	90.66 87	92.69 127	84.48 73	93.83 111	83.38 70	97.48 76	94.47 79

testtj		89.51 57	89.48 60	89.59 58	92.26 101	80.80 68	90.14 53	93.54 61	83.37 50	90.57 88	92.55 132	84.99 68	96.15 5	81.26 91	96.61 101	91.83 187

ZD-MVS					92.22 104	80.48 69		91.85 116	71.22 204	90.38 89	92.98 115	86.06 63	96.11 6	81.99 85	96.75 97

MIMVSNet1		83.63 155	84.59 136	80.74 222	94.06 56	62.77 234	82.72 196	84.53 256	77.57 120	90.34 90	95.92 23	76.88 165	85.83 291	61.88 273	97.42 77	93.62 118

LS3D		90.60 33	90.34 50	91.38 27	89.03 184	84.23 46	93.58 6	94.68 19	90.65 7	90.33 91	93.95 95	84.50 72	95.37 53	80.87 97	95.50 145	94.53 78

KD-MVS_self_test		81.93 183	83.14 160	78.30 260	84.75 260	52.75 322	80.37 240	89.42 183	70.24 216	90.26 92	93.39 108	74.55 184	86.77 277	68.61 227	96.64 99	95.38 51

PMVS	80.48 6	90.08 41	90.66 47	88.34 82	96.71 3	92.97 1	90.31 50	89.57 180	88.51 19	90.11 93	95.12 41	90.98 7	88.92 250	77.55 139	97.07 86	83.13 315
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PC_three_1452									58.96 301	90.06 94	91.33 163	80.66 124	93.03 145	75.78 158	95.94 129	92.48 159

v1921920		84.23 142	84.37 143	83.79 165	87.64 213	61.71 246	82.91 193	91.20 135	67.94 238	90.06 94	90.34 193	72.04 213	93.59 121	82.32 82	94.91 166	96.07 35

ITE_SJBPF				90.11 50	90.72 156	84.97 37		90.30 161	81.56 72	90.02 96	91.20 167	82.40 94	90.81 209	73.58 180	94.66 174	94.56 75

XVS		91.54 15	91.36 26	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	94.03 86	86.57 56	95.80 24	87.35 23	97.62 64	94.20 89

X-MVStestdata		85.04 121	82.70 165	92.08 10	95.64 24	86.25 20	92.64 19	93.33 67	85.07 35	89.99 97	16.05 371	86.57 56	95.80 24	87.35 23	97.62 64	94.20 89

v1192		84.57 130	84.69 134	84.21 155	87.75 209	62.88 232	83.02 190	91.43 127	69.08 225	89.98 99	90.89 179	72.70 206	93.62 120	82.41 80	94.97 165	96.13 33

Anonymous20240529		86.20 102	87.13 88	83.42 174	90.19 166	64.55 216	84.55 146	90.71 147	85.85 31	89.94 100	95.24 38	82.13 100	90.40 220	69.19 220	96.40 110	95.31 54

Regformer-2		86.74 92	86.08 106	88.73 71	84.18 273	79.20 81	83.52 173	89.33 184	83.33 51	89.92 101	85.07 282	83.23 86	93.16 139	83.39 69	92.72 220	93.83 104

pmmvs6		86.52 95	88.06 78	81.90 202	92.22 104	62.28 243	84.66 144	89.15 186	83.54 49	89.85 102	97.32 4	88.08 38	86.80 276	70.43 209	97.30 81	96.62 27

Regformer-4		86.41 96	85.71 114	88.52 75	84.27 269	77.57 99	84.07 154	88.00 205	82.82 58	89.84 103	85.48 270	82.06 102	92.77 152	83.83 67	91.04 247	95.22 60

v144192		84.24 141	84.41 141	83.71 168	87.59 214	61.57 247	82.95 192	91.03 139	67.82 241	89.80 104	90.49 191	73.28 199	93.51 126	81.88 88	94.89 168	96.04 37

v1144		84.54 133	84.72 132	84.00 160	87.67 211	62.55 238	82.97 191	90.93 143	70.32 214	89.80 104	90.99 174	73.50 193	93.48 127	81.69 89	94.65 175	95.97 38

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 61	93.64 67	79.07 82	88.54 88	94.20 28	73.53 166	89.71 106	94.82 48	85.09 67	95.77 29	84.17 63	98.03 39	93.26 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 77	86.93 94	91.22 31	90.08 168	89.30 5	89.68 63	91.11 137	79.26 99	89.68 107	94.81 51	82.44 93	87.74 264	76.54 149	88.74 280	96.61 28

IU-MVS					94.18 49	72.64 140		90.82 145	56.98 315	89.67 108				85.78 47	97.92 48	93.28 126

FMVSNet1		84.55 131	85.45 119	81.85 204	90.27 165	61.05 254	86.83 112	88.27 200	78.57 110	89.66 109	95.64 29	75.43 171	90.68 213	69.09 221	95.33 149	93.82 106

IterMVS-LS		84.73 127	84.98 126	83.96 162	87.35 217	63.66 222	83.25 183	89.88 174	76.06 133	89.62 110	92.37 138	73.40 198	92.52 157	78.16 130	94.77 172	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 71	89.01 66	87.36 94	91.30 138	77.50 100	87.55 99	92.97 88	87.95 22	89.62 110	92.87 121	84.56 71	93.89 107	77.65 137	96.62 100	90.70 212

UniMVSNet (Re)		86.87 88	86.98 93	86.55 105	93.11 79	68.48 186	83.80 166	92.87 90	80.37 84	89.61 112	91.81 153	77.72 147	94.18 94	75.00 167	98.53 16	96.99 22

IS-MVSNet		86.66 93	86.82 97	86.17 118	92.05 110	66.87 198	91.21 39	88.64 193	86.30 30	89.60 113	92.59 129	69.22 225	94.91 70	73.89 175	97.89 51	96.72 24

v2v482		84.09 144	84.24 145	83.62 170	87.13 222	61.40 248	82.71 197	89.71 176	72.19 193	89.55 114	91.41 162	70.70 221	93.20 136	81.02 94	93.76 192	96.25 31

Baseline_NR-MVSNet		84.00 148	85.90 109	78.29 261	91.47 136	53.44 318	82.29 211	87.00 225	79.06 102	89.55 114	95.72 27	77.20 153	86.14 287	72.30 194	98.51 17	95.28 55

CSCG		86.26 99	86.47 99	85.60 129	90.87 152	74.26 128	87.98 93	91.85 116	80.35 85	89.54 116	88.01 230	79.09 136	92.13 167	75.51 160	95.06 161	90.41 221

ambc				82.98 182	90.55 161	64.86 212	88.20 90	89.15 186		89.40 117	93.96 92	71.67 217	91.38 192	78.83 119	96.55 103	92.71 150

DeepPCF-MVS	81.24 5	87.28 85	86.21 104	90.49 43	91.48 135	84.90 38	83.41 178	92.38 104	70.25 215	89.35 118	90.68 186	82.85 89	94.57 82	79.55 111	95.95 128	92.00 181

Regformer-1		86.00 104	85.50 118	87.49 92	84.18 273	76.90 111	83.52 173	87.94 207	82.18 65	89.19 119	85.07 282	82.28 98	91.89 177	82.40 81	92.72 220	93.69 113

MSP-MVS		89.08 65	88.16 77	91.83 21	95.76 18	86.14 23	92.75 17	93.90 46	78.43 111	89.16 120	92.25 142	72.03 214	96.36 2	88.21 8	90.93 253	92.98 138
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
XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 24	85.91 126	93.60 60	80.16 88	89.13 121	93.44 107	83.82 78	90.98 201	83.86 66	95.30 153	93.60 119

ETH3D cwj APD-0.16		87.83 81	87.62 83	88.47 77	91.21 141	78.20 89	87.26 104	94.54 20	72.05 194	88.89 122	92.31 139	83.86 77	94.24 90	81.59 90	96.87 91	92.97 141

MDA-MVSNet-bldmvs		77.47 237	76.90 241	79.16 247	79.03 327	64.59 213	66.58 345	75.67 307	73.15 177	88.86 123	88.99 218	66.94 236	81.23 320	64.71 254	88.22 287	91.64 193

EG-PatchMatch MVS		84.08 145	84.11 146	83.98 161	92.22 104	72.61 143	82.20 217	87.02 222	72.63 183	88.86 123	91.02 173	78.52 139	91.11 198	73.41 182	91.09 245	88.21 253

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 140	81.66 63	91.25 38	94.13 36	88.89 13	88.83 125	94.26 74	77.55 150	95.86 21	84.88 55	95.87 133	95.24 57

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 100	83.87 279	72.52 146	83.82 164	85.15 246	80.27 87	88.75 126	85.45 273	79.95 132	91.90 176	81.92 87	90.80 257	96.13 33

EI-MVSNet-Vis-set		85.12 118	84.53 138	86.88 99	84.01 276	72.76 137	83.91 162	85.18 245	80.44 83	88.75 126	85.49 269	80.08 130	91.92 175	82.02 84	90.85 256	95.97 38

OMC-MVS		88.19 74	87.52 84	90.19 49	91.94 114	81.68 62	87.49 101	93.17 78	76.02 135	88.64 128	91.22 165	84.24 76	93.37 132	77.97 135	97.03 87	95.52 48

Regformer-3		85.06 120	84.67 135	86.22 114	84.27 269	73.43 132	84.07 154	85.26 243	80.77 82	88.62 129	85.48 270	80.56 126	90.39 221	81.99 85	91.04 247	94.85 68

UA-Net		91.49 17	91.53 22	91.39 26	94.98 36	82.95 55	93.52 7	92.79 94	88.22 20	88.53 130	97.64 2	83.45 83	94.55 85	86.02 46	98.60 13	96.67 26

MP-MVS		91.14 27	90.91 43	91.83 21	96.18 11	86.88 15	92.20 27	93.03 85	82.59 60	88.52 131	94.37 70	86.74 53	95.41 51	86.32 37	98.21 30	93.19 131
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		85.50 111	86.14 105	83.58 171	87.97 203	67.13 194	87.55 99	94.32 22	73.44 168	88.47 132	87.54 239	86.45 58	91.06 200	75.76 159	93.76 192	92.54 157

NR-MVSNet		86.00 104	86.22 103	85.34 133	93.24 76	64.56 215	82.21 215	90.46 153	80.99 78	88.42 133	91.97 146	77.56 149	93.85 108	72.46 193	98.65 12	97.61 10

alignmvs		83.94 150	83.98 149	83.80 164	87.80 208	67.88 191	84.54 148	91.42 129	73.27 175	88.41 134	87.96 231	72.33 209	90.83 208	76.02 156	94.11 186	92.69 151

TransMVSNet (Re)		84.02 147	85.74 113	78.85 249	91.00 149	55.20 309	82.29 211	87.26 213	79.65 94	88.38 135	95.52 32	83.00 87	86.88 274	67.97 232	96.60 102	94.45 82

PM-MVS		80.20 210	79.00 216	83.78 166	88.17 201	86.66 17	81.31 227	66.81 354	69.64 220	88.33 136	90.19 198	64.58 245	83.63 310	71.99 197	90.03 265	81.06 341

tttt0517		81.07 191	79.58 212	85.52 130	88.99 186	66.45 202	87.03 109	75.51 309	73.76 164	88.32 137	90.20 197	37.96 360	94.16 99	79.36 116	95.13 157	95.93 41

casdiffmvs		85.21 115	85.85 110	83.31 176	86.17 245	62.77 234	83.03 189	93.93 44	74.69 154	88.21 138	92.68 128	82.29 97	91.89 177	77.87 136	93.75 194	95.27 56

v148		82.31 173	82.48 172	81.81 207	85.59 251	59.66 269	81.47 225	86.02 233	72.85 180	88.05 139	90.65 188	70.73 220	90.91 205	75.15 165	91.79 236	94.87 64

AllTest		87.97 78	87.40 87	89.68 54	91.59 125	83.40 49	89.50 70	95.44 9	79.47 95	88.00 140	93.03 113	82.66 91	91.47 185	70.81 201	96.14 121	94.16 92

TestCases				89.68 54	91.59 125	83.40 49		95.44 9	79.47 95	88.00 140	93.03 113	82.66 91	91.47 185	70.81 201	96.14 121	94.16 92

pm-mvs1		83.69 153	84.95 127	79.91 235	90.04 172	59.66 269	82.43 206	87.44 210	75.52 144	87.85 142	95.26 37	81.25 117	85.65 293	68.74 225	96.04 124	94.42 83

PCF-MVS	74.62 15	82.15 177	80.92 194	85.84 124	89.43 176	72.30 150	80.53 238	91.82 118	57.36 313	87.81 143	89.92 204	77.67 148	93.63 117	58.69 292	95.08 160	91.58 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet2		81.31 188	81.61 183	80.41 228	86.38 235	58.75 283	83.93 161	86.58 228	72.43 184	87.65 144	92.98 115	63.78 251	90.22 225	66.86 236	93.92 190	92.27 172

GeoE		85.45 113	85.81 111	84.37 149	90.08 168	67.07 195	85.86 129	91.39 130	72.33 189	87.59 145	90.25 196	84.85 69	92.37 161	78.00 133	91.94 235	93.66 114

VPA-MVSNet		83.47 159	84.73 130	79.69 240	90.29 164	57.52 291	81.30 229	88.69 192	76.29 129	87.58 146	94.44 62	80.60 125	87.20 269	66.60 240	96.82 95	94.34 86

CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 34	86.95 14	92.09 29	92.30 105	79.74 92	87.50 147	92.38 135	81.42 114	93.28 134	83.07 73	97.24 82	91.67 192

VDDNet		84.35 136	85.39 120	81.25 212	95.13 33	59.32 272	85.42 135	81.11 275	86.41 29	87.41 148	96.21 19	73.61 191	90.61 216	66.33 241	96.85 92	93.81 109

c3_l		81.64 185	81.59 184	81.79 208	80.86 306	59.15 276	78.61 267	90.18 168	68.36 231	87.20 149	87.11 248	69.39 223	91.62 182	78.16 130	94.43 180	94.60 74

VDD-MVS		84.23 142	84.58 137	83.20 178	91.17 145	65.16 211	83.25 183	84.97 253	79.79 91	87.18 150	94.27 71	74.77 180	90.89 206	69.24 217	96.54 104	93.55 123

MSLP-MVS++		85.00 123	86.03 107	81.90 202	91.84 120	71.56 163	86.75 116	93.02 86	75.95 138	87.12 151	89.39 209	77.98 144	89.40 246	77.46 140	94.78 170	84.75 292

baseline		85.20 116	85.93 108	83.02 181	86.30 240	62.37 241	84.55 146	93.96 43	74.48 157	87.12 151	92.03 145	82.30 96	91.94 174	78.39 122	94.21 183	94.74 71

YYNet1		70.06 300	70.44 296	68.90 321	73.76 358	53.42 319	58.99 359	67.20 350	58.42 304	87.10 153	85.39 275	59.82 274	67.32 354	59.79 288	83.50 326	85.96 278

MDA-MVSNet_test_wron		70.05 301	70.44 296	68.88 322	73.84 357	53.47 317	58.93 360	67.28 349	58.43 303	87.09 154	85.40 274	59.80 275	67.25 355	59.66 289	83.54 325	85.92 280

CNVR-MVS		87.81 82	87.68 82	88.21 84	92.87 84	77.30 106	85.25 136	91.23 134	77.31 122	87.07 155	91.47 161	82.94 88	94.71 75	84.67 58	96.27 116	92.62 154

EPP-MVSNet		85.47 112	85.04 125	86.77 102	91.52 134	69.37 176	91.63 36	87.98 206	81.51 73	87.05 156	91.83 151	66.18 241	95.29 55	70.75 204	96.89 90	95.64 45

RRT_MVS		83.25 162	81.08 191	89.74 53	80.55 313	79.32 80	86.41 122	86.69 226	72.33 189	87.00 157	91.08 170	44.98 344	95.55 40	84.47 61	96.24 118	94.36 85

TinyColmap		81.25 189	82.34 174	77.99 266	85.33 254	60.68 261	82.32 210	88.33 198	71.26 202	86.97 158	92.22 144	77.10 156	86.98 273	62.37 268	95.17 156	86.31 276

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 190	81.41 298	60.98 257	77.81 276	90.14 169	67.31 245	86.95 159	87.24 245	64.26 247	92.31 163	75.23 164	91.61 239	94.85 68

Anonymous20240521		80.18 211	81.25 187	76.95 277	83.15 286	60.84 259	82.46 205	85.99 234	68.76 229	86.78 160	93.73 104	59.13 279	77.44 331	73.71 178	97.55 69	92.56 155

Patchmatch-RL test		74.48 268	73.68 268	76.89 280	84.83 258	66.54 201	72.29 325	69.16 347	57.70 309	86.76 161	86.33 256	45.79 334	82.59 313	69.63 214	90.65 262	81.54 332

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 25	86.84 111	93.91 45	80.07 90	86.75 162	93.26 109	93.64 2	90.93 203	84.60 59	90.75 258	93.97 98

h-mvs33		84.25 140	82.76 164	88.72 72	91.82 122	82.60 57	84.00 158	84.98 252	71.27 200	86.70 163	90.55 190	63.04 257	93.92 106	78.26 127	94.20 184	89.63 230

hse-mvs2		83.47 159	81.81 180	88.47 77	91.03 148	82.27 58	82.61 198	83.69 258	71.27 200	86.70 163	86.05 262	63.04 257	92.41 159	78.26 127	93.62 199	90.71 211

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 37	85.85 29	89.70 61	91.27 133	78.20 113	86.69 165	92.28 141	80.36 128	95.06 66	86.17 42	96.49 106	90.22 223

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 65	95.72 22	76.74 113	92.49 25	91.19 136	67.85 240	86.63 166	94.84 47	79.58 134	95.96 13	87.62 14	94.50 177	94.56 75
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet		82.61 169	82.42 173	83.20 178	83.25 283	63.66 222	83.50 176	85.07 247	76.06 133	86.55 167	85.10 279	73.41 196	90.25 222	78.15 132	90.67 260	95.68 44

HQP_MVS		87.75 83	87.43 86	88.70 73	93.45 69	76.42 117	89.45 72	93.61 58	79.44 97	86.55 167	92.95 118	74.84 177	95.22 59	80.78 99	95.83 134	94.46 80

plane_prior3						76.85 112			77.79 116	86.55 167

BH-untuned		80.96 193	80.99 192	80.84 221	88.55 193	68.23 187	80.33 241	88.46 194	72.79 181	86.55 167	86.76 251	74.72 181	91.77 181	61.79 274	88.99 275	82.52 321

MVSTER		77.09 241	75.70 252	81.25 212	75.27 352	61.08 253	77.49 283	85.07 247	60.78 293	86.55 167	88.68 222	43.14 350	90.25 222	73.69 179	90.67 260	92.42 161

旧先验2							81.73 220		56.88 316	86.54 172			84.90 299	72.81 190

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 152	83.93 277	69.66 174	76.28 298	81.09 276	72.43 184	86.47 173	90.19 198	60.46 267	93.15 141	77.45 141	86.39 303	90.22 223

DIV-MVS_self_test		80.43 202	80.23 202	81.02 218	79.99 316	59.25 273	77.07 287	87.02 222	67.38 243	86.19 174	89.22 212	63.09 255	90.16 227	76.32 150	95.80 136	93.66 114

CDPH-MVS		86.17 103	85.54 117	88.05 87	92.25 102	75.45 122	83.85 163	92.01 111	65.91 255	86.19 174	91.75 155	83.77 80	94.98 68	77.43 142	96.71 98	93.73 111

cl____		80.42 203	80.23 202	81.02 218	79.99 316	59.25 273	77.07 287	87.02 222	67.37 244	86.18 176	89.21 213	63.08 256	90.16 227	76.31 151	95.80 136	93.65 116

ETH3 D test6400		85.09 119	84.87 128	85.75 126	90.80 154	69.34 177	85.90 127	93.31 70	65.43 262	86.11 177	89.95 202	80.92 120	94.86 71	75.90 157	95.57 143	93.05 135

MVS_111021_LR		84.28 139	83.76 152	85.83 125	89.23 181	83.07 52	80.99 233	83.56 260	72.71 182	86.07 178	89.07 217	81.75 111	86.19 286	77.11 145	93.36 201	88.24 252

GBi-Net		82.02 179	82.07 176	81.85 204	86.38 235	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 202	93.82 106

test1		82.02 179	82.07 176	81.85 204	86.38 235	61.05 254	86.83 112	88.27 200	72.43 184	86.00 179	95.64 29	63.78 251	90.68 213	65.95 243	93.34 202	93.82 106

FMVSNet3		78.80 222	78.55 222	79.57 242	82.89 289	56.89 297	81.76 219	85.77 236	69.04 226	86.00 179	90.44 192	51.75 312	90.09 233	65.95 243	93.34 202	91.72 190

miper_ehance_all_eth		80.34 206	80.04 209	81.24 214	79.82 318	58.95 278	77.66 278	89.66 177	65.75 259	85.99 182	85.11 278	68.29 230	91.42 189	76.03 155	92.03 231	93.33 124

tfpnnormal		81.79 184	82.95 162	78.31 259	88.93 187	55.40 305	80.83 236	82.85 264	76.81 126	85.90 183	94.14 82	74.58 183	86.51 281	66.82 239	95.68 142	93.01 137

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 81	88.78 190	79.72 75	87.15 107	93.50 62	69.17 223	85.80 184	89.56 208	80.76 122	92.13 167	73.21 188	95.51 144	93.25 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		83.95 149	82.69 166	87.72 89	89.27 180	81.45 64	83.72 168	81.58 274	74.73 153	85.66 185	86.06 261	72.56 208	92.69 154	75.44 162	95.21 154	89.01 247

EU-MVSNet		75.12 261	74.43 263	77.18 275	83.11 287	59.48 271	85.71 132	82.43 267	39.76 367	85.64 186	88.76 220	44.71 346	87.88 263	73.86 176	85.88 307	84.16 298

LF4IMVS		82.75 168	81.93 179	85.19 134	82.08 291	80.15 72	85.53 133	88.76 191	68.01 235	85.58 187	87.75 235	71.80 215	86.85 275	74.02 173	93.87 191	88.58 250

Patchmtry		76.56 249	77.46 233	73.83 300	79.37 324	46.60 356	82.41 207	76.90 298	73.81 163	85.56 188	92.38 135	48.07 322	83.98 307	63.36 263	95.31 152	90.92 206

MVS_111021_HR		84.63 128	84.34 144	85.49 132	90.18 167	75.86 121	79.23 259	87.13 217	73.35 169	85.56 188	89.34 210	83.60 82	90.50 218	76.64 148	94.05 188	90.09 228

testdata				79.54 243	92.87 84	72.34 149		80.14 283	59.91 299	85.47 190	91.75 155	67.96 232	85.24 295	68.57 229	92.18 230	81.06 341

test1111		78.53 226	78.85 217	77.56 272	92.22 104	47.49 352	82.61 198	69.24 346	72.43 184	85.28 191	94.20 77	51.91 310	90.07 234	65.36 250	96.45 108	95.11 61

thisisatest0530		79.07 218	77.33 236	84.26 154	87.13 222	64.58 214	83.66 171	75.95 304	68.86 228	85.22 192	87.36 243	38.10 358	93.57 124	75.47 161	94.28 182	94.62 72

DROMVSNet		88.01 76	88.32 76	87.09 96	89.28 179	72.03 154	90.31 50	96.31 3	80.88 80	85.12 193	89.67 207	84.47 74	95.46 48	82.56 79	96.26 117	93.77 110

CLD-MVS		83.18 164	82.64 167	84.79 141	89.05 183	67.82 192	77.93 274	92.52 100	68.33 232	85.07 194	81.54 322	82.06 102	92.96 146	69.35 216	97.91 50	93.57 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DeepC-MVS_fast	80.27 8	86.23 100	85.65 116	87.96 88	91.30 138	76.92 110	87.19 105	91.99 112	70.56 210	84.96 195	90.69 185	80.01 131	95.14 62	78.37 123	95.78 138	91.82 188
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 126	84.71 133	85.06 138	86.36 238	74.71 125	88.77 84	90.00 172	75.65 143	84.96 195	93.17 111	74.06 186	91.19 195	78.28 126	91.09 245	89.29 239

QAPM		82.59 170	82.59 170	82.58 193	86.44 233	66.69 200	89.94 58	90.36 157	67.97 237	84.94 197	92.58 131	72.71 205	92.18 166	70.63 207	87.73 292	88.85 248

VPNet		80.25 208	81.68 181	75.94 290	92.46 95	47.98 350	76.70 291	81.67 273	73.45 167	84.87 198	92.82 122	74.66 182	86.51 281	61.66 276	96.85 92	93.33 124

NCCC		87.36 84	86.87 95	88.83 67	92.32 100	78.84 85	86.58 119	91.09 138	78.77 107	84.85 199	90.89 179	80.85 121	95.29 55	81.14 93	95.32 150	92.34 166

PHI-MVS		86.38 97	85.81 111	88.08 85	88.44 196	77.34 104	89.35 74	93.05 82	73.15 177	84.76 200	87.70 236	78.87 138	94.18 94	80.67 101	96.29 113	92.73 147

pmmvs-eth3d		78.42 228	77.04 239	82.57 195	87.44 216	74.41 127	80.86 235	79.67 285	55.68 319	84.69 201	90.31 195	60.91 265	85.42 294	62.20 270	91.59 240	87.88 260

RRT_test8_iter05		78.08 231	77.52 232	79.75 238	80.84 307	52.54 326	80.61 237	88.96 188	67.77 242	84.62 202	89.29 211	33.89 365	92.10 170	77.59 138	94.15 185	94.62 72

test_prior3		86.31 98	86.31 101	86.32 109	90.59 159	71.99 155	83.37 179	92.85 91	75.43 145	84.58 203	91.57 157	81.92 108	94.17 96	79.54 112	96.97 88	92.80 144

test_prior2							83.37 179		75.43 145	84.58 203	91.57 157	81.92 108		79.54 112	96.97 88

TEST9					92.34 98	79.70 76	83.94 159	90.32 158	65.41 266	84.49 205	90.97 175	82.03 104	93.63 117

train_agg		85.98 106	85.28 121	88.07 86	92.34 98	79.70 76	83.94 159	90.32 158	65.79 256	84.49 205	90.97 175	81.93 106	93.63 117	81.21 92	96.54 104	90.88 207

Gipuma		84.44 134	86.33 100	78.78 250	84.20 272	73.57 131	89.55 67	90.44 154	84.24 39	84.38 207	94.89 45	76.35 168	80.40 324	76.14 153	96.80 96	82.36 323
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_8					92.09 108	78.87 84	83.82 164	90.31 160	65.79 256	84.36 208	90.96 177	81.93 106	93.44 129

cl22		78.97 219	78.21 227	81.24 214	77.74 332	59.01 277	77.46 284	87.13 217	65.79 256	84.32 209	85.10 279	58.96 281	90.88 207	75.36 163	92.03 231	93.84 103

agg_prior1		85.72 109	85.20 123	87.28 95	91.58 128	77.69 97	83.69 169	90.30 161	66.29 252	84.32 209	91.07 172	82.13 100	93.18 137	81.02 94	96.36 111	90.98 203

agg_prior					91.58 128	77.69 97		90.30 161		84.32 209			93.18 137

Anonymous202405211		80.51 201	81.19 190	78.49 256	88.48 194	57.26 293	76.63 292	82.49 266	81.21 76	84.30 212	92.24 143	67.99 231	86.24 285	62.22 269	95.13 157	91.98 184

LFMVS		80.15 212	80.56 196	78.89 248	89.19 182	55.93 301	85.22 137	73.78 321	82.96 56	84.28 213	92.72 126	57.38 291	90.07 234	63.80 259	95.75 139	90.68 213

Vis-MVSNet		86.86 89	86.58 98	87.72 89	92.09 108	77.43 103	87.35 102	92.09 109	78.87 105	84.27 214	94.05 85	78.35 142	93.65 115	80.54 103	91.58 241	92.08 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ECVR-MVS		78.44 227	78.63 221	77.88 268	91.85 118	48.95 346	83.68 170	69.91 344	72.30 191	84.26 215	94.20 77	51.89 311	89.82 238	63.58 260	96.02 125	94.87 64

ETV-MVS		84.31 137	83.91 151	85.52 130	88.58 192	70.40 169	84.50 150	93.37 64	78.76 108	84.07 216	78.72 341	80.39 127	95.13 63	73.82 177	92.98 213	91.04 201

MCST-MVS		84.36 135	83.93 150	85.63 128	91.59 125	71.58 162	83.52 173	92.13 108	61.82 283	83.96 217	89.75 206	79.93 133	93.46 128	78.33 125	94.34 181	91.87 186

新几何1				82.95 183	93.96 58	78.56 88		80.24 281	55.45 320	83.93 218	91.08 170	71.19 219	88.33 259	65.84 246	93.07 209	81.95 328

BH-RMVSNet		80.53 200	80.22 204	81.49 210	87.19 221	66.21 203	77.79 277	86.23 230	74.21 159	83.69 219	88.50 224	73.25 200	90.75 210	63.18 265	87.90 289	87.52 263

1121		80.86 194	79.81 211	84.02 159	93.93 59	78.70 86	81.64 222	80.18 282	55.43 321	83.67 220	91.15 168	71.29 218	91.41 190	67.95 233	93.06 210	81.96 327

USDC		76.63 247	76.73 243	76.34 286	83.46 281	57.20 294	80.02 244	88.04 204	52.14 339	83.65 221	91.25 164	63.24 254	86.65 280	54.66 317	94.11 186	85.17 287

miper_enhance_ethall		77.83 233	76.93 240	80.51 226	76.15 345	58.01 286	75.47 306	88.82 189	58.05 307	83.59 222	80.69 326	64.41 246	91.20 194	73.16 189	92.03 231	92.33 167

Effi-MVS+-dtu		85.82 108	83.38 155	93.14 3	87.13 222	91.15 2	87.70 98	88.42 195	74.57 155	83.56 223	85.65 267	78.49 140	94.21 92	72.04 195	92.88 215	94.05 96

CNLPA		83.55 157	83.10 161	84.90 139	89.34 178	83.87 47	84.54 148	88.77 190	79.09 101	83.54 224	88.66 223	74.87 176	81.73 318	66.84 238	92.29 225	89.11 241

bset_n11_16_dypcd		79.19 217	77.97 229	82.86 188	85.81 249	66.85 199	75.02 309	79.31 286	66.07 253	83.50 225	83.37 304	55.04 305	92.10 170	78.63 121	94.99 164	89.63 230

OpenMVS_ROB	70.19 17	77.77 236	77.46 233	78.71 252	84.39 266	61.15 252	81.18 231	82.52 265	62.45 280	83.34 226	87.37 242	66.20 240	88.66 256	64.69 255	85.02 314	86.32 275

thres100view900		75.45 257	75.05 257	76.66 283	87.27 218	51.88 331	81.07 232	73.26 325	75.68 142	83.25 227	86.37 255	45.54 335	88.80 251	51.98 330	90.99 249	89.31 237

miper_lstm_enhance		76.45 251	76.10 248	77.51 273	76.72 340	60.97 258	64.69 348	85.04 249	63.98 272	83.20 228	88.22 227	56.67 294	78.79 329	73.22 183	93.12 208	92.78 146

IterMVS		76.91 243	76.34 246	78.64 253	80.91 304	64.03 220	76.30 297	79.03 289	64.88 269	83.11 229	89.16 214	59.90 273	84.46 303	68.61 227	85.15 313	87.42 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 254	75.35 256	77.85 270	87.01 228	51.84 332	80.45 239	73.26 325	75.20 149	83.10 230	86.31 258	45.54 335	89.05 247	55.03 315	92.24 227	92.66 152

mvs_anonymous		78.13 230	78.76 219	76.23 289	79.24 325	50.31 343	78.69 265	84.82 254	61.60 287	83.09 231	92.82 122	73.89 189	87.01 270	68.33 230	86.41 302	91.37 198

MVS_Test		82.47 172	83.22 157	80.22 231	82.62 290	57.75 289	82.54 203	91.96 114	71.16 205	82.89 232	92.52 134	77.41 151	90.50 218	80.04 106	87.84 291	92.40 163

test12				86.57 104	90.74 155	72.63 142		90.69 148		82.76 233		79.20 135	94.80 73		95.32 150	92.27 172

原ACMM1				84.60 146	92.81 89	74.01 129		91.50 125	62.59 277	82.73 234	90.67 187	76.53 166	94.25 89	69.24 217	95.69 141	85.55 283

test_yl		78.71 224	78.51 223	79.32 245	84.32 267	58.84 280	78.38 268	85.33 241	75.99 136	82.49 235	86.57 252	58.01 285	90.02 236	62.74 266	92.73 218	89.10 242

DCV-MVSNet		78.71 224	78.51 223	79.32 245	84.32 267	58.84 280	78.38 268	85.33 241	75.99 136	82.49 235	86.57 252	58.01 285	90.02 236	62.74 266	92.73 218	89.10 242

diffmvs		80.40 204	80.48 199	80.17 233	79.02 328	60.04 265	77.54 281	90.28 165	66.65 250	82.40 237	87.33 244	73.50 193	87.35 268	77.98 134	89.62 269	93.13 132

test222					93.31 74	76.54 114	79.38 254	77.79 294	52.59 334	82.36 238	90.84 181	66.83 238			91.69 238	81.25 336

D2MVS		76.84 244	75.67 253	80.34 229	80.48 314	62.16 245	73.50 319	84.80 255	57.61 311	82.24 239	87.54 239	51.31 313	87.65 265	70.40 210	93.19 207	91.23 200

VNet		79.31 216	80.27 201	76.44 284	87.92 205	53.95 314	75.58 304	84.35 257	74.39 158	82.23 240	90.72 184	72.84 204	84.39 304	60.38 286	93.98 189	90.97 204

Vis-MVSNet (Re-imp)		77.82 234	77.79 231	77.92 267	88.82 189	51.29 336	83.28 181	71.97 334	74.04 160	82.23 240	89.78 205	57.38 291	89.41 245	57.22 300	95.41 146	93.05 135

API-MVS		82.28 174	82.61 169	81.30 211	86.29 241	69.79 171	88.71 85	87.67 209	78.42 112	82.15 242	84.15 295	77.98 144	91.59 183	65.39 249	92.75 217	82.51 322

DP-MVS Recon		84.05 146	83.22 157	86.52 106	91.73 123	75.27 123	83.23 185	92.40 102	72.04 195	82.04 243	88.33 226	77.91 146	93.95 105	66.17 242	95.12 159	90.34 222

MSDG		80.06 214	79.99 210	80.25 230	83.91 278	68.04 190	77.51 282	89.19 185	77.65 117	81.94 244	83.45 301	76.37 167	86.31 284	63.31 264	86.59 300	86.41 274

test2506		74.12 271	73.39 272	76.28 287	91.85 118	44.20 362	84.06 156	48.20 374	72.30 191	81.90 245	94.20 77	27.22 375	89.77 239	64.81 253	96.02 125	94.87 64

Fast-Effi-MVS+		81.04 192	80.57 195	82.46 197	87.50 215	63.22 228	78.37 270	89.63 178	68.01 235	81.87 246	82.08 317	82.31 95	92.65 155	67.10 235	88.30 286	91.51 197

testgi		72.36 284	74.61 259	65.59 333	80.56 312	42.82 366	68.29 338	73.35 324	66.87 248	81.84 247	89.93 203	72.08 212	66.92 357	46.05 352	92.54 222	87.01 270

tfpn200view9		74.86 265	74.23 264	76.74 282	86.24 242	52.12 328	79.24 257	73.87 319	73.34 170	81.82 248	84.60 291	46.02 329	88.80 251	51.98 330	90.99 249	89.31 237

thres400		75.14 259	74.23 264	77.86 269	86.24 242	52.12 328	79.24 257	73.87 319	73.34 170	81.82 248	84.60 291	46.02 329	88.80 251	51.98 330	90.99 249	92.66 152

CL-MVSNet_self_test		76.81 245	77.38 235	75.12 294	86.90 230	51.34 334	73.20 322	80.63 280	68.30 233	81.80 250	88.40 225	66.92 237	80.90 321	55.35 312	94.90 167	93.12 133

OpenMVS	76.72 13	81.98 182	82.00 178	81.93 201	84.42 265	68.22 188	88.50 89	89.48 181	66.92 247	81.80 250	91.86 148	72.59 207	90.16 227	71.19 200	91.25 244	87.40 265

AdaColmap		83.66 154	83.69 153	83.57 172	90.05 171	72.26 151	86.29 125	90.00 172	78.19 114	81.65 252	87.16 246	83.40 84	94.24 90	61.69 275	94.76 173	84.21 297

DELS-MVS		81.44 187	81.25 187	82.03 200	84.27 269	62.87 233	76.47 296	92.49 101	70.97 206	81.64 253	83.83 296	75.03 174	92.70 153	74.29 169	92.22 229	90.51 219
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
114514_t		83.10 166	82.54 171	84.77 142	92.90 83	69.10 184	86.65 117	90.62 151	54.66 324	81.46 254	90.81 182	76.98 158	94.38 86	72.62 191	96.18 119	90.82 209

TR-MVS		76.77 246	75.79 250	79.72 239	86.10 247	65.79 207	77.14 285	83.02 262	65.20 267	81.40 255	82.10 316	66.30 239	90.73 212	55.57 309	85.27 311	82.65 317

TAMVS		78.08 231	76.36 245	83.23 177	90.62 158	72.87 136	79.08 260	80.01 284	61.72 285	81.35 256	86.92 250	63.96 250	88.78 254	50.61 334	93.01 212	88.04 256

Effi-MVS+		83.90 151	84.01 148	83.57 172	87.22 220	65.61 208	86.55 120	92.40 102	78.64 109	81.34 257	84.18 294	83.65 81	92.93 148	74.22 170	87.87 290	92.17 177

new-patchmatchnet		70.10 299	73.37 273	60.29 346	81.23 301	16.95 376	59.54 356	74.62 312	62.93 275	80.97 258	87.93 233	62.83 260	71.90 344	55.24 313	95.01 163	92.00 181

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 145	91.58 128	73.24 135	84.21 152	91.67 122	62.86 276	80.94 259	87.16 246	67.27 235	92.87 151	69.82 213	88.94 277	87.99 257

BH-w/o		76.57 248	76.07 249	78.10 264	86.88 231	65.92 206	77.63 279	86.33 229	65.69 260	80.89 260	79.95 335	68.97 228	90.74 211	53.01 326	85.25 312	77.62 347

PAPM_NR		83.23 163	83.19 159	83.33 175	90.90 151	65.98 205	88.19 91	90.78 146	78.13 115	80.87 261	87.92 234	73.49 195	92.42 158	70.07 211	88.40 281	91.60 194

ab-mvs		79.67 215	80.56 196	76.99 276	88.48 194	56.93 295	84.70 143	86.06 232	68.95 227	80.78 262	93.08 112	75.30 173	84.62 302	56.78 301	90.90 254	89.43 235

mvs-test1		84.55 131	82.12 175	91.84 20	87.13 222	89.54 4	85.05 139	88.42 195	74.57 155	80.60 263	82.98 305	78.49 140	93.98 104	72.04 195	89.77 267	92.00 181

XXY-MVS		74.44 270	76.19 247	69.21 320	84.61 261	52.43 327	71.70 327	77.18 297	60.73 294	80.60 263	90.96 177	75.44 170	69.35 349	56.13 305	88.33 282	85.86 281

HQP4-MVS										80.56 265			94.61 80			93.56 121

HQP-NCC					91.19 142		84.77 140		73.30 172	80.55 266

ACMP_Plane					91.19 142		84.77 140		73.30 172	80.55 266

HQP-MVS		84.61 129	84.06 147	86.27 112	91.19 142	70.66 167	84.77 140	92.68 97	73.30 172	80.55 266	90.17 200	72.10 210	94.61 80	77.30 143	94.47 178	93.56 121

AUN-MVS		81.18 190	78.78 218	88.39 80	90.93 150	82.14 59	82.51 204	83.67 259	64.69 270	80.29 269	85.91 265	51.07 314	92.38 160	76.29 152	93.63 198	90.65 215

HyFIR lowres test		75.12 261	72.66 280	82.50 196	91.44 137	65.19 210	72.47 324	87.31 212	46.79 355	80.29 269	84.30 293	52.70 309	92.10 170	51.88 333	86.73 299	90.22 223

test20.03		73.75 274	74.59 261	71.22 313	81.11 302	51.12 338	70.15 333	72.10 333	70.42 211	80.28 271	91.50 160	64.21 248	74.72 341	46.96 350	94.58 176	87.82 262

CS-MVS		82.02 179	82.63 168	80.19 232	84.80 259	57.56 290	82.39 208	94.72 18	71.24 203	80.22 272	84.89 286	75.85 169	94.56 84	76.08 154	93.49 200	88.46 251

F-COLMAP		84.97 125	83.42 154	89.63 56	92.39 96	83.40 49	88.83 82	91.92 115	73.19 176	80.18 273	89.15 215	77.04 157	93.28 134	65.82 247	92.28 226	92.21 175

CS-MVS-test		85.00 123	85.28 121	84.17 158	87.84 206	66.12 204	87.30 103	95.67 6	77.63 119	80.02 274	85.85 266	81.34 116	95.41 51	78.18 129	93.71 195	90.99 202

GA-MVS		75.83 255	74.61 259	79.48 244	81.87 293	59.25 273	73.42 320	82.88 263	68.68 230	79.75 275	81.80 319	50.62 316	89.46 243	66.85 237	85.64 308	89.72 229

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 184	88.31 197	71.73 158	79.53 250	87.17 214	65.43 262	79.59 276	82.73 312	76.94 159	90.14 230	73.22 183	88.33 282	86.90 271

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 184	88.31 197	71.73 158	79.53 250	87.17 214	65.43 262	79.59 276	82.73 312	76.94 159	90.14 230	73.22 183	88.33 282	86.90 271

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 184	88.31 197	71.73 158	79.53 250	87.17 214	65.43 262	79.59 276	82.73 312	76.94 159	90.14 230	73.22 183	88.33 282	86.90 271

UnsupCasMVSNet_eth		71.63 291	72.30 285	69.62 318	76.47 342	52.70 324	70.03 334	80.97 277	59.18 300	79.36 279	88.21 228	60.50 266	69.12 350	58.33 295	77.62 351	87.04 269

ppachtmachnet_test		74.73 267	74.00 266	76.90 279	80.71 310	56.89 297	71.53 328	78.42 291	58.24 305	79.32 280	82.92 309	57.91 288	84.26 305	65.60 248	91.36 243	89.56 232

MG-MVS		80.32 207	80.94 193	78.47 257	88.18 200	52.62 325	82.29 211	85.01 251	72.01 196	79.24 281	92.54 133	69.36 224	93.36 133	70.65 206	89.19 274	89.45 233

Fast-Effi-MVS+-dtu		82.54 171	81.41 186	85.90 122	85.60 250	76.53 116	83.07 188	89.62 179	73.02 179	79.11 282	83.51 299	80.74 123	90.24 224	68.76 224	89.29 271	90.94 205

CDS-MVSNet		77.32 239	75.40 254	83.06 180	89.00 185	72.48 147	77.90 275	82.17 269	60.81 292	78.94 283	83.49 300	59.30 277	88.76 255	54.64 318	92.37 224	87.93 259
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 276	73.54 270	72.43 309	84.92 257	47.79 351	79.89 246	74.00 317	65.93 254	78.81 284	86.28 259	56.36 296	81.63 319	56.63 302	79.04 347	87.87 261

EIA-MVS		82.19 176	81.23 189	85.10 137	87.95 204	69.17 183	83.22 186	93.33 67	70.42 211	78.58 285	79.77 338	77.29 152	94.20 93	71.51 198	88.96 276	91.93 185

thres200		72.34 285	71.55 291	74.70 297	83.48 280	51.60 333	75.02 309	73.71 322	70.14 217	78.56 286	80.57 329	46.20 327	88.20 261	46.99 349	89.29 271	84.32 296

our_test_3		71.85 288	71.59 289	72.62 307	80.71 310	53.78 315	69.72 335	71.71 338	58.80 302	78.03 287	80.51 331	56.61 295	78.84 328	62.20 270	86.04 306	85.23 286

KD-MVS_2432*1600		66.87 313	65.81 318	70.04 314	67.50 369	47.49 352	62.56 352	79.16 287	61.21 290	77.98 288	80.61 327	25.29 377	82.48 314	53.02 324	84.92 315	80.16 344

miper_refine_blended		66.87 313	65.81 318	70.04 314	67.50 369	47.49 352	62.56 352	79.16 287	61.21 290	77.98 288	80.61 327	25.29 377	82.48 314	53.02 324	84.92 315	80.16 344

jason		77.42 238	75.75 251	82.43 198	87.10 226	69.27 178	77.99 273	81.94 271	51.47 343	77.84 290	85.07 282	60.32 269	89.00 248	70.74 205	89.27 273	89.03 245
jason: jason.
MAR-MVS		80.24 209	78.74 220	84.73 143	86.87 232	78.18 90	85.75 130	87.81 208	65.67 261	77.84 290	78.50 342	73.79 190	90.53 217	61.59 278	90.87 255	85.49 285
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
FPMVS		72.29 286	72.00 286	73.14 303	88.63 191	85.00 36	74.65 313	67.39 348	71.94 197	77.80 292	87.66 237	50.48 317	75.83 337	49.95 336	79.51 342	58.58 366

pmmvs4		74.92 264	72.98 277	80.73 223	84.95 256	71.71 161	76.23 299	77.59 295	52.83 333	77.73 293	86.38 254	56.35 297	84.97 298	57.72 299	87.05 297	85.51 284

ET-MVSNet_ETH3D		75.28 258	72.77 278	82.81 189	83.03 288	68.11 189	77.09 286	76.51 302	60.67 295	77.60 294	80.52 330	38.04 359	91.15 197	70.78 203	90.68 259	89.17 240

UnsupCasMVSNet_bld		69.21 306	69.68 301	67.82 327	79.42 322	51.15 337	67.82 342	75.79 305	54.15 326	77.47 295	85.36 277	59.26 278	70.64 346	48.46 343	79.35 344	81.66 330

Anonymous20231206		71.38 292	71.88 287	69.88 316	86.31 239	54.37 311	70.39 332	74.62 312	52.57 335	76.73 296	88.76 220	59.94 272	72.06 343	44.35 355	93.23 206	83.23 313

CMPMVS	59.41 20	75.12 261	73.57 269	79.77 236	75.84 347	67.22 193	81.21 230	82.18 268	50.78 347	76.50 297	87.66 237	55.20 303	82.99 312	62.17 272	90.64 263	89.09 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 287	71.69 288	73.32 301	81.57 296	53.02 321	76.77 290	78.37 292	63.31 273	76.37 298	91.85 149	36.68 362	78.98 327	47.87 346	92.45 223	87.95 258

CVMVSNet		72.62 282	71.41 292	76.28 287	83.25 283	60.34 263	83.50 176	79.02 290	37.77 368	76.33 299	85.10 279	49.60 319	87.41 267	70.54 208	77.54 352	81.08 339

PLC	73.85 16	82.09 178	80.31 200	87.45 93	90.86 153	80.29 71	85.88 128	90.65 149	68.17 234	76.32 300	86.33 256	73.12 201	92.61 156	61.40 279	90.02 266	89.44 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 175	81.57 185	84.19 157	85.54 252	69.26 179	91.98 31	90.08 170	71.54 198	76.23 301	85.07 282	58.69 282	94.27 87	86.26 38	88.77 278	89.03 245

lupinMVS		76.37 252	74.46 262	82.09 199	85.54 252	69.26 179	76.79 289	80.77 279	50.68 349	76.23 301	82.82 310	58.69 282	88.94 249	69.85 212	88.77 278	88.07 254

PatchMatch-RL		74.48 268	73.22 274	78.27 262	87.70 210	85.26 34	75.92 301	70.09 342	64.34 271	76.09 303	81.25 324	65.87 243	78.07 330	53.86 320	83.82 324	71.48 355

thisisatest0515		73.00 280	70.52 295	80.46 227	81.45 297	59.90 267	73.16 323	74.31 316	57.86 308	76.08 304	77.78 344	37.60 361	92.12 169	65.00 251	91.45 242	89.35 236

MS-PatchMatch		70.93 294	70.22 298	73.06 304	81.85 294	62.50 239	73.82 318	77.90 293	52.44 336	75.92 305	81.27 323	55.67 300	81.75 317	55.37 311	77.70 350	74.94 351

CHOSEN 1792x2688		72.45 283	70.56 294	78.13 263	90.02 173	63.08 229	68.72 337	83.16 261	42.99 364	75.92 305	85.46 272	57.22 293	85.18 297	49.87 338	81.67 335	86.14 277

CR-MVSNet		74.00 272	73.04 276	76.85 281	79.58 319	62.64 236	82.58 200	76.90 298	50.50 350	75.72 307	92.38 135	48.07 322	84.07 306	68.72 226	82.91 330	83.85 302

RPMNet		78.88 220	78.28 226	80.68 225	79.58 319	62.64 236	82.58 200	94.16 31	74.80 152	75.72 307	92.59 129	48.69 320	95.56 37	73.48 181	82.91 330	83.85 302

DPM-MVS		80.10 213	79.18 215	82.88 187	90.71 157	69.74 172	78.87 263	90.84 144	60.29 297	75.64 309	85.92 264	67.28 234	93.11 142	71.24 199	91.79 236	85.77 282

PVSNet_BlendedMVS		78.80 222	77.84 230	81.65 209	84.43 263	63.41 224	79.49 253	90.44 154	61.70 286	75.43 310	87.07 249	69.11 226	91.44 187	60.68 284	92.24 227	90.11 227

PVSNet_Blended		76.49 250	75.40 254	79.76 237	84.43 263	63.41 224	75.14 308	90.44 154	57.36 313	75.43 310	78.30 343	69.11 226	91.44 187	60.68 284	87.70 293	84.42 295

PAPR		78.84 221	78.10 228	81.07 216	85.17 255	60.22 264	82.21 215	90.57 152	62.51 278	75.32 312	84.61 290	74.99 175	92.30 164	59.48 290	88.04 288	90.68 213

N_pmnet		70.20 297	68.80 306	74.38 298	80.91 304	84.81 39	59.12 358	76.45 303	55.06 322	75.31 313	82.36 315	55.74 299	54.82 367	47.02 348	87.24 296	83.52 306

cascas		76.29 253	74.81 258	80.72 224	84.47 262	62.94 231	73.89 317	87.34 211	55.94 318	75.16 314	76.53 352	63.97 249	91.16 196	65.00 251	90.97 252	88.06 255

SCA		73.32 275	72.57 282	75.58 292	81.62 295	55.86 302	78.89 262	71.37 339	61.73 284	74.93 315	83.42 302	60.46 267	87.01 270	58.11 297	82.63 334	83.88 299

xiu_mvs_v2_base		77.19 240	76.75 242	78.52 255	87.01 228	61.30 250	75.55 305	87.12 220	61.24 289	74.45 316	78.79 340	77.20 153	90.93 203	64.62 257	84.80 320	83.32 311

CANet		83.79 152	82.85 163	86.63 103	86.17 245	72.21 153	83.76 167	91.43 127	77.24 123	74.39 317	87.45 241	75.36 172	95.42 50	77.03 146	92.83 216	92.25 174

PS-MVSNAJ		77.04 242	76.53 244	78.56 254	87.09 227	61.40 248	75.26 307	87.13 217	61.25 288	74.38 318	77.22 349	76.94 159	90.94 202	64.63 256	84.83 319	83.35 310

MVP-Stereo		75.81 256	73.51 271	82.71 190	89.35 177	73.62 130	80.06 242	85.20 244	60.30 296	73.96 319	87.94 232	57.89 289	89.45 244	52.02 329	74.87 356	85.06 289
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UGNet		82.78 167	81.64 182	86.21 116	86.20 244	76.24 120	86.86 110	85.68 237	77.07 124	73.76 320	92.82 122	69.64 222	91.82 180	69.04 222	93.69 196	90.56 217
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
1112_ss		74.82 266	73.74 267	78.04 265	89.57 175	60.04 265	76.49 295	87.09 221	54.31 325	73.66 321	79.80 336	60.25 270	86.76 279	58.37 293	84.15 323	87.32 266

Test_1112_low_res		73.90 273	73.08 275	76.35 285	90.35 163	55.95 300	73.40 321	86.17 231	50.70 348	73.14 322	85.94 263	58.31 284	85.90 290	56.51 303	83.22 327	87.20 267

1314		73.22 277	72.56 283	75.20 293	80.41 315	57.84 287	81.64 222	85.36 240	51.68 342	73.10 323	76.65 351	61.45 263	85.19 296	63.54 261	79.21 346	82.59 318

Patchmatch-test		65.91 319	67.38 311	61.48 344	75.51 349	43.21 365	68.84 336	63.79 358	62.48 279	72.80 324	83.42 302	44.89 345	59.52 366	48.27 345	86.45 301	81.70 329

PatchmatchNet		69.71 304	68.83 305	72.33 310	77.66 334	53.60 316	79.29 255	69.99 343	57.66 310	72.53 325	82.93 308	46.45 326	80.08 326	60.91 282	72.09 359	83.31 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 309	68.08 310	67.55 328	78.74 330	43.53 364	75.60 303	67.10 353	54.92 323	72.23 326	88.10 229	42.87 351	75.97 336	52.21 328	80.95 341	83.15 314

pmmvs5		70.73 295	70.07 299	72.72 305	77.03 338	52.73 323	74.14 314	75.65 308	50.36 351	72.17 327	85.37 276	55.42 302	80.67 323	52.86 327	87.59 294	84.77 291

PatchT		70.52 296	72.76 279	63.79 338	79.38 323	33.53 372	77.63 279	65.37 356	73.61 165	71.77 328	92.79 125	44.38 347	75.65 338	64.53 258	85.37 310	82.18 325

MVS		73.21 278	72.59 281	75.06 295	80.97 303	60.81 260	81.64 222	85.92 235	46.03 358	71.68 329	77.54 345	68.47 229	89.77 239	55.70 308	85.39 309	74.60 352

MIMVSNet		71.09 293	71.59 289	69.57 319	87.23 219	50.07 344	78.91 261	71.83 335	60.20 298	71.26 330	91.76 154	55.08 304	76.09 335	41.06 360	87.02 298	82.54 320

WTY-MVS		67.91 310	68.35 308	66.58 331	80.82 308	48.12 349	65.96 346	72.60 329	53.67 329	71.20 331	81.68 321	58.97 280	69.06 351	48.57 342	81.67 335	82.55 319

test0.0.03 1		64.66 322	64.36 323	65.57 334	75.03 354	46.89 355	64.69 348	61.58 363	62.43 281	71.18 332	77.54 345	43.41 348	68.47 352	40.75 361	82.65 332	81.35 333

CostFormer		69.98 302	68.68 307	73.87 299	77.14 336	50.72 341	79.26 256	74.51 314	51.94 341	70.97 333	84.75 288	45.16 343	87.49 266	55.16 314	79.23 345	83.40 309

tpmvs		70.16 298	69.56 302	71.96 311	74.71 356	48.13 348	79.63 248	75.45 310	65.02 268	70.26 334	81.88 318	45.34 340	85.68 292	58.34 294	75.39 355	82.08 326

sss		66.92 312	67.26 312	65.90 332	77.23 335	51.10 339	64.79 347	71.72 337	52.12 340	70.13 335	80.18 333	57.96 287	65.36 362	50.21 335	81.01 340	81.25 336

tpm2		68.45 308	66.83 314	73.30 302	78.93 329	48.50 347	79.76 247	71.76 336	47.50 354	69.92 336	83.60 298	42.07 352	88.40 258	48.44 344	79.51 342	83.01 316

HY-MVS	64.64 18	73.03 279	72.47 284	74.71 296	83.36 282	54.19 312	82.14 218	81.96 270	56.76 317	69.57 337	86.21 260	60.03 271	84.83 301	49.58 339	82.65 332	85.11 288

tpm cat1		66.76 315	65.21 321	71.42 312	77.09 337	50.62 342	78.01 272	73.68 323	44.89 360	68.64 338	79.00 339	45.51 337	82.42 316	49.91 337	70.15 362	81.23 338

IB-MVS	62.13 19	71.64 290	68.97 304	79.66 241	80.80 309	62.26 244	73.94 316	76.90 298	63.27 274	68.63 339	76.79 350	33.83 366	91.84 179	59.28 291	87.26 295	84.88 290
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
EPNet		80.37 205	78.41 225	86.23 113	76.75 339	73.28 133	87.18 106	77.45 296	76.24 131	68.14 340	88.93 219	65.41 244	93.85 108	69.47 215	96.12 123	91.55 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	58.17 21	66.41 317	65.63 320	68.75 323	81.96 292	49.88 345	62.19 354	72.51 331	51.03 345	68.04 341	75.34 355	50.84 315	74.77 339	45.82 353	82.96 328	81.60 331

tpmrst		66.28 318	66.69 316	65.05 336	72.82 364	39.33 367	78.20 271	70.69 341	53.16 332	67.88 342	80.36 332	48.18 321	74.75 340	58.13 296	70.79 361	81.08 339

CANet_DTU		77.81 235	77.05 238	80.09 234	81.37 299	59.90 267	83.26 182	88.29 199	69.16 224	67.83 343	83.72 297	60.93 264	89.47 242	69.22 219	89.70 268	90.88 207

EPMVS		62.47 323	62.63 327	62.01 340	70.63 368	38.74 368	74.76 311	52.86 371	53.91 328	67.71 344	80.01 334	39.40 356	66.60 358	55.54 310	68.81 366	80.68 343

MVS_0304		78.17 229	77.23 237	80.99 220	84.13 275	69.07 185	81.39 226	80.81 278	76.28 130	67.53 345	89.11 216	62.87 259	86.77 277	60.90 283	92.01 234	87.13 268

MDTV_nov1_ep13			68.29 309		78.03 331	43.87 363	74.12 315	72.22 332	52.17 337	67.02 346	85.54 268	45.36 339	80.85 322	55.73 306	84.42 322

pmmvs3		62.47 323	60.02 335	69.80 317	71.58 367	64.00 221	70.52 331	58.44 367	39.77 366	66.05 347	75.84 353	27.10 376	72.28 342	46.15 351	84.77 321	73.11 353

ADS-MVSNet2		65.87 320	63.64 325	72.55 308	73.16 361	56.92 296	67.10 343	74.81 311	49.74 352	66.04 348	82.97 306	46.71 324	77.26 332	42.29 357	69.96 363	83.46 307

ADS-MVSNet		61.90 325	62.19 328	61.03 345	73.16 361	36.42 370	67.10 343	61.75 361	49.74 352	66.04 348	82.97 306	46.71 324	63.21 364	42.29 357	69.96 363	83.46 307

DWT-MVSNet_test		66.43 316	64.37 322	72.63 306	74.86 355	50.86 340	76.52 294	72.74 328	54.06 327	65.50 350	68.30 363	32.13 368	84.84 300	61.63 277	73.59 357	82.19 324

DSMNet-mixed		60.98 331	61.61 330	59.09 348	72.88 363	45.05 360	74.70 312	46.61 375	26.20 370	65.34 351	90.32 194	55.46 301	63.12 365	41.72 359	81.30 339	69.09 359

JIA-IIPM		69.41 305	66.64 317	77.70 271	73.19 360	71.24 164	75.67 302	65.56 355	70.42 211	65.18 352	92.97 117	33.64 367	83.06 311	53.52 322	69.61 365	78.79 346

test-LLR		67.21 311	66.74 315	68.63 324	76.45 343	55.21 307	67.89 339	67.14 351	62.43 281	65.08 353	72.39 357	43.41 348	69.37 347	61.00 280	84.89 317	81.31 334

test-mter		65.00 321	63.79 324	68.63 324	76.45 343	55.21 307	67.89 339	67.14 351	50.98 346	65.08 353	72.39 357	28.27 373	69.37 347	61.00 280	84.89 317	81.31 334

PMMVS2		55.64 336	59.27 336	44.74 352	64.30 374	12.32 377	40.60 365	49.79 373	53.19 331	65.06 355	84.81 287	53.60 308	49.76 369	32.68 369	89.41 270	72.15 354

baseline2		69.77 303	66.89 313	78.41 258	79.51 321	58.09 285	76.23 299	69.57 345	57.50 312	64.82 356	77.45 347	46.02 329	88.44 257	53.08 323	77.83 349	88.70 249

gg-mvs-nofinetune		68.96 307	69.11 303	68.52 326	76.12 346	45.32 358	83.59 172	55.88 369	86.68 26	64.62 357	97.01 7	30.36 370	83.97 308	44.78 354	82.94 329	76.26 349

PAPM		71.77 289	70.06 300	76.92 278	86.39 234	53.97 313	76.62 293	86.62 227	53.44 330	63.97 358	84.73 289	57.79 290	92.34 162	39.65 362	81.33 338	84.45 294

new_pmnet		55.69 335	57.66 337	49.76 351	75.47 350	30.59 373	59.56 355	51.45 372	43.62 363	62.49 359	75.48 354	40.96 354	49.15 370	37.39 365	72.52 358	69.55 358

MDTV_nov1_ep13_2view						27.60 375	70.76 330		46.47 357	61.27 360		45.20 341		49.18 340		83.75 304

dp		60.70 332	60.29 334	61.92 342	72.04 366	38.67 369	70.83 329	64.08 357	51.28 344	60.75 361	77.28 348	36.59 363	71.58 345	47.41 347	62.34 368	75.52 350

TESTMET0.1,1		61.29 328	60.32 333	64.19 337	72.06 365	51.30 335	67.89 339	62.09 359	45.27 359	60.65 362	69.01 360	27.93 374	64.74 363	56.31 304	81.65 337	76.53 348

PMMVS		61.65 326	60.38 332	65.47 335	65.40 373	69.26 179	63.97 350	61.73 362	36.80 369	60.11 363	68.43 361	59.42 276	66.35 359	48.97 341	78.57 348	60.81 363

PVSNet_0	51.08 22	56.10 334	54.97 339	59.48 347	75.12 353	53.28 320	55.16 361	61.89 360	44.30 361	59.16 364	62.48 366	54.22 306	65.91 361	35.40 366	47.01 369	59.25 365

MVS-HIRNet		61.16 329	62.92 326	55.87 349	79.09 326	35.34 371	71.83 326	57.98 368	46.56 356	59.05 365	91.14 169	49.95 318	76.43 334	38.74 363	71.92 360	55.84 367

E-PMN		61.59 327	61.62 329	61.49 343	66.81 371	55.40 305	53.77 362	60.34 364	66.80 249	58.90 366	65.50 364	40.48 355	66.12 360	55.72 307	86.25 304	62.95 362

GG-mvs-BLEND				67.16 329	73.36 359	46.54 357	84.15 153	55.04 370		58.64 367	61.95 367	29.93 371	83.87 309	38.71 364	76.92 353	71.07 356

EPNet_dtu		72.87 281	71.33 293	77.49 274	77.72 333	60.55 262	82.35 209	75.79 305	66.49 251	58.39 368	81.06 325	53.68 307	85.98 288	53.55 321	92.97 214	85.95 279
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EMVS		61.10 330	60.81 331	61.99 341	65.96 372	55.86 302	53.10 363	58.97 366	67.06 246	56.89 369	63.33 365	40.98 353	67.03 356	54.79 316	86.18 305	63.08 361

CHOSEN 280x420		59.08 333	56.52 338	66.76 330	76.51 341	64.39 217	49.62 364	59.00 365	43.86 362	55.66 370	68.41 362	35.55 364	68.21 353	43.25 356	76.78 354	67.69 360

MVE	40.22 23	51.82 337	50.47 340	55.87 349	62.66 375	51.91 330	31.61 367	39.28 376	40.65 365	50.76 371	74.98 356	56.24 298	44.67 371	33.94 368	64.11 367	71.04 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 354	32.95 376	29.49 374		21.63 379	12.07 371	37.95 372	45.07 369	30.84 369	19.21 373	17.94 372	33.06 372	23.69 369

tmp_tt		20.25 340	24.50 343	7.49 355	4.47 378	8.70 378	34.17 366	25.16 378	1.00 373	32.43 373	18.49 370	39.37 357	9.21 374	21.64 371	43.75 370	4.57 370

test_method		30.46 338	29.60 341	33.06 353	17.99 377	3.84 379	13.62 368	73.92 318	2.79 372	18.29 374	53.41 368	28.53 372	43.25 372	22.56 370	35.27 371	52.11 368

testmvs		5.91 344	7.65 347	0.72 357	1.20 379	0.37 381	59.14 357	0.67 381	0.49 375	1.11 375	2.76 374	0.94 380	0.24 376	1.02 374	1.47 373	1.55 372

test123		6.27 343	8.08 346	0.84 356	1.11 380	0.57 380	62.90 351	0.82 380	0.54 374	1.07 376	2.75 375	1.26 379	0.30 375	1.04 373	1.26 374	1.66 371

test_blank		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet_test		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

cdsmvs_eth3d_5k		20.81 339	27.75 342	0.00 358	0.00 381	0.00 382	0.00 369	85.44 239	0.00 376	0.00 377	82.82 310	81.46 113	0.00 377	0.00 375	0.00 375	0.00 373

pcd_1.5k_mvsjas		6.41 342	8.55 345	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	76.94 159	0.00 377	0.00 375	0.00 375	0.00 373

sosnet-low-res		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

sosnet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uncertanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

Regformer		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

ab-mvs-re		6.65 341	8.87 344	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	79.80 336	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

MSC_two_6792asdad				88.81 68	91.55 131	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 163

No_MVS				88.81 68	91.55 131	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 163

eth-test2					0.00 381

eth-test					0.00 381

OPU-MVS				88.27 83	91.89 116	77.83 95	90.47 48				91.22 165	81.12 118	94.68 76	74.48 168	95.35 148	92.29 170

save fliter					93.75 63	77.44 101	86.31 123	89.72 175	70.80 207

test_0728_SECOND				86.79 101	94.25 48	72.45 148	90.54 45	94.10 38					95.88 16	86.42 34	97.97 45	92.02 180

GSMVS																83.88 299

sam_mvs1												46.11 328				83.88 299

sam_mvs												45.92 333

MTGPA								91.81 119

test_post1							78.85 264				3.13 372	45.19 342	80.13 325	58.11 297

test_post											3.10 373	45.43 338	77.22 333

patchmatchnet-post											81.71 320	45.93 332	87.01 270

MTMP							90.66 42	33.14 377

gm-plane-assit					75.42 351	44.97 361			52.17 337		72.36 359		87.90 262	54.10 319

test9_res														80.83 98	96.45 108	90.57 216

agg_prior2														79.68 110	96.16 120	90.22 223

test_prior4						78.97 83	84.59 145

test_prior				86.32 109	90.59 159	71.99 155		92.85 91					94.17 96			92.80 144

新几何2							81.72 221

旧先验1					91.97 111	71.77 157		81.78 272			91.84 150	73.92 188			93.65 197	83.61 305

无先验							82.81 195	85.62 238	58.09 306				91.41 190	67.95 233		84.48 293

原ACMM2							82.26 214

testdata2													86.43 283	63.52 262

segment_acmp												81.94 105

testdata1							79.62 249		73.95 162

plane_prior7					93.45 69	77.31 105

plane_prior6					92.61 90	76.54 114						74.84 177

plane_prior5								93.61 58					95.22 59	80.78 99	95.83 134	94.46 80

plane_prior4											92.95 118

plane_prior2							89.45 72		79.44 97

plane_prior1					92.83 88

plane_prior						76.42 117	87.15 107		75.94 139						95.03 162

n2								0.00 382

nn								0.00 382

door-mid								74.45 315

test11								91.46 126

door								72.57 330

HQP5-MVS						70.66 167

BP-MVS														77.30 143

HQP3-MVS								92.68 97							94.47 178

HQP2-MVS												72.10 210

NP-MVS					91.95 112	74.55 126					90.17 200

ACMMP++_ref															95.74 140

ACMMP++															97.35 78

Test By Simon												79.09 136

High-resolution multi-view benchmark