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

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

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	88.92 248	77.55 139	97.07 86	83.13 313
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
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	92.30 166

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	92.30 166

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

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

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

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

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

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	93.19 129
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FOURS1					96.08 12	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

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	93.12 131

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
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

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	93.27 125

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

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	92.25 140	72.03 214	96.36 2	88.21 8	90.93 251	92.98 136
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
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	93.83 102

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 78	87.51 44	95.87 18	87.74 11	97.76 56	93.99 95

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 87	87.93 40	95.87 18	88.65 4	97.96 47	94.12 92

TSAR-MVS + MP.		88.14 75	87.82 80	89.09 65	95.72 22	76.74 113	92.49 25	91.19 136	67.85 238	86.63 166	94.84 47	79.58 134	95.96 13	87.62 14	94.50 175	94.56 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
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 97

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 84	86.57 56	95.80 24	87.35 23	97.62 64	94.20 87

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	16.05 369	86.57 56	95.80 24	87.35 23	97.62 64	94.20 87

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 94	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
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 80	89.15 23	95.62 34	87.35 23	98.24 28	94.56 73
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
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 97	87.20 48	95.80 24	87.10 31	97.69 61	93.93 98

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 90	88.35 30	95.56 37	87.74 11	97.74 58	92.85 140

#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 90	88.35 30	95.56 37	84.88 55	97.74 58	92.85 140

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	93.88 100
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
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 86	88.26 32	95.71 31	87.28 26	98.39 21	92.55 154

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	93.81 107

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	92.38 133	81.42 114	93.28 134	83.07 73	97.24 82	91.67 190

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	93.60 104	90.81 8	92.96 146	85.02 54	98.45 19	92.41 160
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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

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	92.28 139	80.36 128	95.06 66	86.17 42	96.49 106	90.22 221

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 95	83.80 79	93.10 143	82.67 78	98.04 37	93.64 115

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 85	90.15 17	95.67 33	86.82 32	97.34 79	92.19 174

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	93.64 103	88.13 36	93.84 110	84.83 57	97.55 69	94.10 93
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
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

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	93.71 110

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 79	88.83 24	95.51 44	87.11 30	97.54 72	92.54 155

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	93.26 107	93.64 2	90.93 203	84.60 59	90.75 256	93.97 96

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	93.74 101	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
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	93.44 105	83.82 78	90.98 201	83.86 66	95.30 151	93.60 117

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	92.02 178

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	92.29 168

IU-MVS					94.18 49	72.64 140		90.82 145	56.98 313	89.67 108				85.78 47	97.92 48	93.28 124

test_241102_ONE					94.18 49	72.65 138		93.69 54	83.62 46	94.11 23	93.78 100	90.28 15	95.50 47

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	90.55 216
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					94.16 52	72.56 144	90.63 44	93.90 46	83.61 47	93.75 31	94.49 60	89.76 19

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	92.73 145

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	92.73 145

MIMVSNet1		83.63 155	84.59 136	80.74 222	94.06 56	62.77 234	82.72 194	84.53 256	77.57 120	90.34 90	95.92 23	76.88 165	85.83 289	61.88 271	97.42 77	93.62 116

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 96	86.45 58	93.73 113	73.22 183	98.66 11	97.69 9

新几何1				82.95 183	93.96 58	78.56 88		80.24 281	55.45 318	83.93 217	91.08 168	71.19 219	88.33 257	65.84 246	93.07 207	81.95 326

1121		80.86 194	79.81 211	84.02 159	93.93 59	78.70 86	81.64 220	80.18 282	55.43 319	83.67 219	91.15 166	71.29 218	91.41 190	67.95 233	93.06 208	81.96 325

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 87	88.19 33	96.29 4	87.61 15	98.20 32	94.39 82
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 61	77.77 96				92.84 48

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

xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 70	93.75 63	77.44 101	86.31 123	95.27 12	70.80 205	92.28 58	93.80 97	86.89 51	94.64 78	85.52 48	97.51 74	94.30 85

save fliter					93.75 63	77.44 101	86.31 123	89.72 175	70.80 205

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
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
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	93.26 126
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 52	89.27 63	91.30 28	93.51 68	84.79 40	89.89 59	90.63 150	70.00 216	94.55 15	96.67 11	87.94 39	93.59 121	84.27 62	95.97 125	95.52 48

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	92.95 116	74.84 177	95.22 59	80.78 99	95.83 132	94.46 78

plane_prior7					93.45 69	77.31 105

WR-MVS		83.56 156	84.40 142	81.06 217	93.43 71	54.88 310	78.67 264	85.02 250	81.24 75	90.74 85	91.56 157	72.85 203	91.08 199	68.00 231	98.04 37	97.23 16

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 82	88.13 36	96.30 3	84.51 60	97.81 54	91.70 189
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
jajsoiax		89.41 58	88.81 72	91.19 32	93.38 72	84.72 41	89.70 61	90.29 164	69.27 220	94.39 17	96.38 15	86.02 64	93.52 125	83.96 64	95.92 129	95.34 52

PS-MVSNAJss		88.31 73	87.90 79	89.56 59	93.31 74	77.96 94	87.94 95	91.97 113	70.73 207	94.19 22	96.67 11	76.94 159	94.57 82	83.07 73	96.28 114	96.15 32

test222					93.31 74	76.54 114	79.38 252	77.79 294	52.59 332	82.36 237	90.84 179	66.83 238			91.69 236	81.25 334

DU-MVS		86.80 91	86.99 92	86.21 116	93.24 76	67.02 196	83.16 185	92.21 106	81.73 70	90.92 81	91.97 144	77.20 153	93.99 102	74.16 171	98.35 22	97.61 10

NR-MVSNet		86.00 104	86.22 103	85.34 133	93.24 76	64.56 215	82.21 213	90.46 153	80.99 78	88.42 133	91.97 144	77.56 149	93.85 108	72.46 193	98.65 12	97.61 10

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

UniMVSNet (Re)		86.87 88	86.98 93	86.55 105	93.11 79	68.48 186	83.80 165	92.87 90	80.37 84	89.61 112	91.81 151	77.72 147	94.18 94	75.00 167	98.53 16	96.99 22

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 89

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

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	92.98 136

114514_t		83.10 166	82.54 171	84.77 142	92.90 83	69.10 184	86.65 117	90.62 151	54.66 322	81.46 252	90.81 180	76.98 158	94.38 86	72.62 191	96.18 119	90.82 207

testdata				79.54 243	92.87 84	72.34 149		80.14 283	59.91 297	85.47 190	91.75 153	67.96 232	85.24 293	68.57 229	92.18 228	81.06 339

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	91.47 159	82.94 88	94.71 75	84.67 58	96.27 116	92.62 152

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 97	86.89 51	94.64 78	85.52 48	97.51 74	94.30 85

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 118	92.86 86	67.02 196	82.55 200	91.56 123	83.08 55	90.92 81	91.82 150	78.25 143	93.99 102	74.16 171	98.35 22	97.49 13

plane_prior1					92.83 88

原ACMM1				84.60 146	92.81 89	74.01 129		91.50 125	62.59 275	82.73 233	90.67 185	76.53 166	94.25 89	69.24 217	95.69 139	85.55 281

plane_prior6					92.61 90	76.54 114						74.84 177

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	93.19 108	86.22 61	95.97 12	82.23 83	97.18 84	90.45 218
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 70	89.58 59	85.88 123	92.55 92	72.22 152	84.01 156	89.44 182	88.63 18	94.38 18	95.77 24	86.38 60	93.59 121	79.84 108	95.21 152	91.82 186

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

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	88.31 258	83.33 71	98.30 26	93.20 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 208	81.68 181	75.94 288	92.46 95	47.98 349	76.70 289	81.67 273	73.45 167	84.87 198	92.82 120	74.66 182	86.51 279	61.66 274	96.85 92	93.33 122

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	80.18 271	89.15 213	77.04 157	93.28 134	65.82 247	92.28 224	92.21 173

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 97	82.98 54	91.98 31	90.08 170	71.54 196	94.28 21	96.54 13	81.57 112	94.27 87	86.26 38	96.49 106	97.09 19

TEST9					92.34 98	79.70 76	83.94 158	90.32 158	65.41 264	84.49 205	90.97 173	82.03 104	93.63 117

train_agg		85.98 106	85.28 121	88.07 86	92.34 98	79.70 76	83.94 158	90.32 158	65.79 254	84.49 205	90.97 173	81.93 106	93.63 117	81.21 92	96.54 104	90.88 205

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	90.89 177	80.85 121	95.29 55	81.14 93	95.32 148	92.34 164

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	92.55 130	84.99 68	96.15 5	81.26 91	96.61 101	91.83 185

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

CDPH-MVS		86.17 103	85.54 117	88.05 87	92.25 102	75.45 122	83.85 162	92.01 111	65.91 253	86.19 174	91.75 153	83.77 80	94.98 68	77.43 142	96.71 98	93.73 109

ECVR-MVS		78.53 226	78.85 217	77.56 271	92.22 104	47.49 351	82.61 196	69.24 345	72.43 184	85.28 191	94.20 77	51.91 310	90.07 234	65.36 250	96.45 108	95.11 61

ZD-MVS					92.22 104	80.48 69		91.85 116	71.22 202	90.38 89	92.98 113	86.06 63	96.11 6	81.99 85	96.75 97

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	86.80 274	70.43 209	97.30 81	96.62 27

EG-PatchMatch MVS		84.08 145	84.11 146	83.98 161	92.22 104	72.61 143	82.20 215	87.02 222	72.63 183	88.86 123	91.02 171	78.52 139	91.11 198	73.41 182	91.09 243	88.21 251

test_8					92.09 108	78.87 84	83.82 163	90.31 160	65.79 254	84.36 208	90.96 175	81.93 106	93.44 129

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 83	78.35 142	93.65 115	80.54 103	91.58 239	92.08 176
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
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	92.59 127	69.22 225	94.91 70	73.89 175	97.89 51	96.72 24

旧先验1					91.97 111	71.77 157		81.78 272			91.84 148	73.92 188			93.65 195	83.61 303

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

NP-MVS					91.95 112	74.55 126					90.17 198

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	92.69 125	84.48 73	93.83 111	83.38 70	97.48 76	94.47 77

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	91.22 163	84.24 76	93.37 132	77.97 135	97.03 87	95.52 48

OPU-MVS				88.27 83	91.89 116	77.83 95	90.47 48				91.22 163	81.12 118	94.68 76	74.48 168	95.35 146	92.29 168

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 87	73.57 192	92.91 150	73.97 174	98.02 40	97.58 12

9.14			89.29 62		91.84 118		88.80 83	95.32 11	75.14 150	91.07 78	92.89 118	87.27 46	93.78 112	83.69 68	97.55 69

MSLP-MVS++		85.00 123	86.03 107	81.90 202	91.84 118	71.56 163	86.75 116	93.02 86	75.95 138	87.12 151	89.39 207	77.98 144	89.40 244	77.46 140	94.78 168	84.75 290

h-mvs33		84.25 140	82.76 164	88.72 72	91.82 120	82.60 57	84.00 157	84.98 252	71.27 198	86.70 163	90.55 188	63.04 257	93.92 106	78.26 127	94.20 182	89.63 228

DP-MVS Recon		84.05 146	83.22 157	86.52 106	91.73 121	75.27 123	83.23 183	92.40 102	72.04 193	82.04 242	88.33 224	77.91 146	93.95 105	66.17 242	95.12 157	90.34 220

SD-MVS		88.96 67	89.88 53	86.22 114	91.63 122	77.07 109	89.82 60	93.77 51	78.90 104	92.88 45	92.29 138	86.11 62	90.22 225	86.24 41	97.24 82	91.36 197
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
AllTest		87.97 78	87.40 87	89.68 54	91.59 123	83.40 49	89.50 70	95.44 9	79.47 95	88.00 140	93.03 111	82.66 91	91.47 185	70.81 201	96.14 121	94.16 90

TestCases				89.68 54	91.59 123	83.40 49		95.44 9	79.47 95	88.00 140	93.03 111	82.66 91	91.47 185	70.81 201	96.14 121	94.16 90

MCST-MVS		84.36 135	83.93 150	85.63 128	91.59 123	71.58 162	83.52 171	92.13 108	61.82 281	83.96 216	89.75 204	79.93 133	93.46 128	78.33 125	94.34 179	91.87 184

agg_prior1		85.72 109	85.20 123	87.28 95	91.58 126	77.69 97	83.69 168	90.30 161	66.29 250	84.32 209	91.07 170	82.13 100	93.18 137	81.02 94	96.36 111	90.98 201

agg_prior					91.58 126	77.69 97		90.30 161		84.32 209			93.18 137

PVSNet_Blended_VisFu		81.55 186	80.49 198	84.70 145	91.58 126	73.24 135	84.21 152	91.67 122	62.86 274	80.94 257	87.16 244	67.27 235	92.87 151	69.82 213	88.94 275	87.99 255

DVP-MVS++.		90.07 42	91.09 35	87.00 97	91.55 129	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	95.94 127	92.48 157

MSC_two_6792asdad				88.81 68	91.55 129	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 161

No_MVS				88.81 68	91.55 129	77.99 92		91.01 140					96.05 8	87.45 19	98.17 33	92.40 161

EPP-MVSNet		85.47 112	85.04 125	86.77 102	91.52 132	69.37 176	91.63 36	87.98 206	81.51 73	87.05 156	91.83 149	66.18 241	95.29 55	70.75 204	96.89 90	95.64 45

DeepPCF-MVS	81.24 5	87.28 85	86.21 104	90.49 43	91.48 133	84.90 38	83.41 176	92.38 104	70.25 213	89.35 118	90.68 184	82.85 89	94.57 82	79.55 111	95.95 126	92.00 179

Baseline_NR-MVSNet		84.00 148	85.90 109	78.29 261	91.47 134	53.44 318	82.29 209	87.00 225	79.06 102	89.55 114	95.72 27	77.20 153	86.14 285	72.30 194	98.51 17	95.28 55

HyFIR lowres test		75.12 260	72.66 278	82.50 196	91.44 135	65.19 210	72.47 322	87.31 212	46.79 353	80.29 267	84.30 291	52.70 309	92.10 170	51.88 331	86.73 297	90.22 221

DP-MVS		88.60 71	89.01 66	87.36 94	91.30 136	77.50 100	87.55 99	92.97 88	87.95 22	89.62 110	92.87 119	84.56 71	93.89 107	77.65 137	96.62 100	90.70 210

DeepC-MVS_fast	80.27 8	86.23 100	85.65 116	87.96 88	91.30 136	76.92 110	87.19 105	91.99 112	70.56 208	84.96 195	90.69 183	80.01 131	95.14 62	78.37 123	95.78 136	91.82 186
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 138	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	95.87 131	95.24 57

ETH3D cwj APD-0.16		87.83 81	87.62 83	88.47 77	91.21 139	78.20 89	87.26 104	94.54 20	72.05 192	88.89 122	92.31 137	83.86 77	94.24 90	81.59 90	96.87 91	92.97 139

HQP-NCC					91.19 140		84.77 140		73.30 172	80.55 264

ACMP_Plane					91.19 140		84.77 140		73.30 172	80.55 264

HQP-MVS		84.61 129	84.06 147	86.27 112	91.19 140	70.66 167	84.77 140	92.68 97	73.30 172	80.55 264	90.17 198	72.10 210	94.61 80	77.30 143	94.47 176	93.56 119

VDD-MVS		84.23 142	84.58 137	83.20 178	91.17 143	65.16 211	83.25 181	84.97 253	79.79 91	87.18 150	94.27 71	74.77 180	90.89 206	69.24 217	96.54 104	93.55 121

K. test v3		85.14 117	84.73 130	86.37 108	91.13 144	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	93.24 203	94.83 68

lessismore_v0				85.95 120	91.10 145	70.99 166		70.91 340		91.79 67	94.42 65	61.76 262	92.93 148	79.52 114	93.03 209	93.93 98

hse-mvs2		83.47 159	81.81 180	88.47 77	91.03 146	82.27 58	82.61 196	83.69 258	71.27 198	86.70 163	86.05 260	63.04 257	92.41 159	78.26 127	93.62 197	90.71 209

TransMVSNet (Re)		84.02 147	85.74 113	78.85 249	91.00 147	55.20 309	82.29 209	87.26 213	79.65 94	88.38 135	95.52 32	83.00 87	86.88 272	67.97 232	96.60 102	94.45 80

AUN-MVS		81.18 190	78.78 218	88.39 80	90.93 148	82.14 59	82.51 202	83.67 259	64.69 268	80.29 267	85.91 263	51.07 313	92.38 160	76.29 152	93.63 196	90.65 213

PAPM_NR		83.23 163	83.19 159	83.33 175	90.90 149	65.98 205	88.19 91	90.78 146	78.13 115	80.87 259	87.92 232	73.49 195	92.42 158	70.07 211	88.40 279	91.60 192

CSCG		86.26 99	86.47 99	85.60 129	90.87 150	74.26 128	87.98 93	91.85 116	80.35 85	89.54 116	88.01 228	79.09 136	92.13 167	75.51 160	95.06 159	90.41 219

PLC	73.85 16	82.09 178	80.31 200	87.45 93	90.86 151	80.29 71	85.88 128	90.65 149	68.17 232	76.32 298	86.33 254	73.12 201	92.61 156	61.40 277	90.02 264	89.44 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETH3 D test6400		85.09 119	84.87 128	85.75 126	90.80 152	69.34 177	85.90 127	93.31 70	65.43 260	86.11 177	89.95 200	80.92 120	94.86 71	75.90 157	95.57 141	93.05 133

test12				86.57 104	90.74 153	72.63 142		90.69 148		82.76 232		79.20 135	94.80 73		95.32 148	92.27 170

ITE_SJBPF				90.11 50	90.72 154	84.97 37		90.30 161	81.56 72	90.02 96	91.20 165	82.40 94	90.81 209	73.58 180	94.66 172	94.56 73

DPM-MVS		80.10 213	79.18 215	82.88 187	90.71 155	69.74 172	78.87 261	90.84 144	60.29 295	75.64 307	85.92 262	67.28 234	93.11 142	71.24 199	91.79 234	85.77 280

TAMVS		78.08 230	76.36 244	83.23 177	90.62 156	72.87 136	79.08 258	80.01 284	61.72 283	81.35 254	86.92 248	63.96 250	88.78 252	50.61 332	93.01 210	88.04 254

test_prior3		86.31 98	86.31 101	86.32 109	90.59 157	71.99 155	83.37 177	92.85 91	75.43 145	84.58 203	91.57 155	81.92 108	94.17 96	79.54 112	96.97 88	92.80 142

test_prior				86.32 109	90.59 157	71.99 155		92.85 91					94.17 96			92.80 142

ambc				82.98 182	90.55 159	64.86 212	88.20 90	89.15 186		89.40 117	93.96 90	71.67 217	91.38 192	78.83 119	96.55 103	92.71 148

Anonymous20231211		88.40 72	89.62 58	84.73 143	90.46 160	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

Test_1112_low_res		73.90 271	73.08 273	76.35 284	90.35 161	55.95 300	73.40 319	86.17 231	50.70 346	73.14 320	85.94 261	58.31 284	85.90 288	56.51 301	83.22 325	87.20 265

VPA-MVSNet		83.47 159	84.73 130	79.69 240	90.29 162	57.52 291	81.30 227	88.69 192	76.29 129	87.58 146	94.44 62	80.60 125	87.20 267	66.60 240	96.82 95	94.34 84

FMVSNet1		84.55 131	85.45 119	81.85 204	90.27 163	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	95.33 147	93.82 104

Anonymous20240529		86.20 102	87.13 88	83.42 174	90.19 164	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

MVS_111021_HR		84.63 128	84.34 144	85.49 132	90.18 165	75.86 121	79.23 257	87.13 217	73.35 169	85.56 188	89.34 208	83.60 82	90.50 218	76.64 148	94.05 186	90.09 226

GeoE		85.45 113	85.81 111	84.37 149	90.08 166	67.07 195	85.86 129	91.39 130	72.33 189	87.59 145	90.25 194	84.85 69	92.37 161	78.00 133	91.94 233	93.66 112

RPSCF		88.00 77	86.93 94	91.22 31	90.08 166	89.30 5	89.68 63	91.11 137	79.26 99	89.68 107	94.81 51	82.44 93	87.74 262	76.54 149	88.74 278	96.61 28

nrg030		87.85 80	88.49 74	85.91 121	90.07 168	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

AdaColmap		83.66 154	83.69 153	83.57 172	90.05 169	72.26 151	86.29 125	90.00 172	78.19 114	81.65 250	87.16 244	83.40 84	94.24 90	61.69 273	94.76 171	84.21 295

pm-mvs1		83.69 153	84.95 127	79.91 235	90.04 170	59.66 269	82.43 204	87.44 210	75.52 144	87.85 142	95.26 37	81.25 117	85.65 291	68.74 225	96.04 124	94.42 81

CHOSEN 1792x2688		72.45 281	70.56 292	78.13 263	90.02 171	63.08 229	68.72 335	83.16 261	42.99 362	75.92 303	85.46 270	57.22 293	85.18 295	49.87 336	81.67 333	86.14 275

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 172	86.67 16	90.51 47	90.20 167	69.87 217	95.06 11	96.14 21	84.28 75	93.07 144	87.68 13	96.34 112	97.09 19

1112_ss		74.82 265	73.74 266	78.04 265	89.57 173	60.04 265	76.49 293	87.09 221	54.31 323	73.66 319	79.80 334	60.25 270	86.76 277	58.37 291	84.15 321	87.32 264

PCF-MVS	74.62 15	82.15 177	80.92 194	85.84 124	89.43 174	72.30 150	80.53 236	91.82 118	57.36 311	87.81 143	89.92 202	77.67 148	93.63 117	58.69 290	95.08 158	91.58 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 255	73.51 270	82.71 190	89.35 175	73.62 130	80.06 240	85.20 244	60.30 294	73.96 317	87.94 230	57.89 289	89.45 242	52.02 327	74.87 354	85.06 287
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 157	83.10 161	84.90 139	89.34 176	83.87 47	84.54 148	88.77 190	79.09 101	83.54 223	88.66 221	74.87 176	81.73 316	66.84 238	92.29 223	89.11 239

DROMVSNet		88.01 76	88.32 76	87.09 96	89.28 177	72.03 154	90.31 50	96.31 3	80.88 80	85.12 193	89.67 205	84.47 74	95.46 48	82.56 79	96.26 117	93.77 108

TSAR-MVS + GP.		83.95 149	82.69 166	87.72 89	89.27 178	81.45 64	83.72 167	81.58 274	74.73 153	85.66 185	86.06 259	72.56 208	92.69 154	75.44 162	95.21 152	89.01 245

MVS_111021_LR		84.28 139	83.76 152	85.83 125	89.23 179	83.07 52	80.99 231	83.56 260	72.71 182	86.07 178	89.07 215	81.75 111	86.19 284	77.11 145	93.36 199	88.24 250

LFMVS		80.15 212	80.56 196	78.89 248	89.19 180	55.93 301	85.22 137	73.78 321	82.96 56	84.28 213	92.72 124	57.38 291	90.07 234	63.80 258	95.75 137	90.68 211

CLD-MVS		83.18 164	82.64 167	84.79 141	89.05 181	67.82 192	77.93 272	92.52 100	68.33 230	85.07 194	81.54 320	82.06 102	92.96 146	69.35 216	97.91 50	93.57 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 33	90.34 50	91.38 27	89.03 182	84.23 46	93.58 6	94.68 19	90.65 7	90.33 91	93.95 93	84.50 72	95.37 53	80.87 97	95.50 143	94.53 76

CDS-MVSNet		77.32 238	75.40 253	83.06 180	89.00 183	72.48 147	77.90 273	82.17 269	60.81 290	78.94 281	83.49 298	59.30 277	88.76 253	54.64 316	92.37 222	87.93 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 191	79.58 212	85.52 130	88.99 184	66.45 202	87.03 109	75.51 309	73.76 164	88.32 137	90.20 195	37.96 359	94.16 99	79.36 116	95.13 155	95.93 41

tfpnnormal		81.79 184	82.95 162	78.31 259	88.93 185	55.40 305	80.83 234	82.85 264	76.81 126	85.90 183	94.14 80	74.58 183	86.51 279	66.82 239	95.68 140	93.01 135

test_part1		87.15 87	87.82 80	85.15 136	88.88 186	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

Vis-MVSNet (Re-imp)		77.82 233	77.79 230	77.92 267	88.82 187	51.29 336	83.28 179	71.97 334	74.04 160	82.23 239	89.78 203	57.38 291	89.41 243	57.22 298	95.41 144	93.05 133

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 81	88.78 188	79.72 75	87.15 107	93.50 62	69.17 221	85.80 184	89.56 206	80.76 122	92.13 167	73.21 188	95.51 142	93.25 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FPMVS		72.29 284	72.00 284	73.14 301	88.63 189	85.00 36	74.65 311	67.39 347	71.94 195	77.80 290	87.66 235	50.48 316	75.83 335	49.95 334	79.51 340	58.58 364

ETV-MVS		84.31 137	83.91 151	85.52 130	88.58 190	70.40 169	84.50 150	93.37 64	78.76 108	84.07 215	78.72 339	80.39 127	95.13 63	73.82 177	92.98 211	91.04 199

BH-untuned		80.96 193	80.99 192	80.84 221	88.55 191	68.23 187	80.33 239	88.46 194	72.79 181	86.55 167	86.76 249	74.72 181	91.77 181	61.79 272	88.99 273	82.52 319

Anonymous202405211		80.51 201	81.19 190	78.49 256	88.48 192	57.26 293	76.63 290	82.49 266	81.21 76	84.30 212	92.24 141	67.99 231	86.24 283	62.22 267	95.13 155	91.98 182

ab-mvs		79.67 215	80.56 196	76.99 275	88.48 192	56.93 295	84.70 143	86.06 232	68.95 225	80.78 260	93.08 110	75.30 173	84.62 300	56.78 299	90.90 252	89.43 233

PHI-MVS		86.38 97	85.81 111	88.08 85	88.44 194	77.34 104	89.35 74	93.05 82	73.15 177	84.76 200	87.70 234	78.87 138	94.18 94	80.67 101	96.29 113	92.73 145

xiu_mvs_v1_base_debu		80.84 195	80.14 206	82.93 184	88.31 195	71.73 158	79.53 248	87.17 214	65.43 260	79.59 274	82.73 310	76.94 159	90.14 230	73.22 183	88.33 280	86.90 269

xiu_mvs_v1_base		80.84 195	80.14 206	82.93 184	88.31 195	71.73 158	79.53 248	87.17 214	65.43 260	79.59 274	82.73 310	76.94 159	90.14 230	73.22 183	88.33 280	86.90 269

xiu_mvs_v1_base_debi		80.84 195	80.14 206	82.93 184	88.31 195	71.73 158	79.53 248	87.17 214	65.43 260	79.59 274	82.73 310	76.94 159	90.14 230	73.22 183	88.33 280	86.90 269

MG-MVS		80.32 207	80.94 193	78.47 257	88.18 198	52.62 325	82.29 209	85.01 251	72.01 194	79.24 279	92.54 131	69.36 224	93.36 133	70.65 206	89.19 272	89.45 231

PM-MVS		80.20 210	79.00 216	83.78 166	88.17 199	86.66 17	81.31 225	66.81 353	69.64 218	88.33 136	90.19 196	64.58 245	83.63 308	71.99 197	90.03 263	81.06 339

v10		86.54 94	87.10 89	84.84 140	88.16 200	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

canonicalmvs		85.50 111	86.14 105	83.58 171	87.97 201	67.13 194	87.55 99	94.32 22	73.44 168	88.47 132	87.54 237	86.45 58	91.06 200	75.76 159	93.76 190	92.54 155

EIA-MVS		82.19 176	81.23 189	85.10 137	87.95 202	69.17 183	83.22 184	93.33 67	70.42 209	78.58 283	79.77 336	77.29 152	94.20 93	71.51 198	88.96 274	91.93 183

VNet		79.31 216	80.27 201	76.44 283	87.92 203	53.95 314	75.58 302	84.35 257	74.39 158	82.23 239	90.72 182	72.84 204	84.39 302	60.38 284	93.98 187	90.97 202

CS-MVS-test		85.00 123	85.28 121	84.17 158	87.84 204	66.12 204	87.30 103	95.67 6	77.63 119	80.02 272	85.85 264	81.34 116	95.41 51	78.18 129	93.71 193	90.99 200

v8		86.22 101	86.83 96	84.36 151	87.82 205	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	95.41 144	97.01 21

alignmvs		83.94 150	83.98 149	83.80 164	87.80 206	67.88 191	84.54 148	91.42 129	73.27 175	88.41 134	87.96 229	72.33 209	90.83 208	76.02 156	94.11 184	92.69 149

v1192		84.57 130	84.69 134	84.21 155	87.75 207	62.88 232	83.02 188	91.43 127	69.08 223	89.98 99	90.89 177	72.70 206	93.62 120	82.41 80	94.97 163	96.13 33

PatchMatch-RL		74.48 267	73.22 272	78.27 262	87.70 208	85.26 34	75.92 299	70.09 342	64.34 269	76.09 301	81.25 322	65.87 243	78.07 328	53.86 318	83.82 322	71.48 353

v1144		84.54 133	84.72 132	84.00 160	87.67 209	62.55 238	82.97 189	90.93 143	70.32 212	89.80 104	90.99 172	73.50 193	93.48 127	81.69 89	94.65 173	95.97 38

v1240		84.30 138	84.51 139	83.65 169	87.65 210	61.26 251	82.85 192	91.54 124	67.94 236	90.68 86	90.65 186	71.71 216	93.64 116	82.84 77	94.78 168	96.07 35

v1921920		84.23 142	84.37 143	83.79 165	87.64 211	61.71 246	82.91 191	91.20 135	67.94 236	90.06 94	90.34 191	72.04 213	93.59 121	82.32 82	94.91 164	96.07 35

v144192		84.24 141	84.41 141	83.71 168	87.59 212	61.57 247	82.95 190	91.03 139	67.82 239	89.80 104	90.49 189	73.28 199	93.51 126	81.88 88	94.89 166	96.04 37

Fast-Effi-MVS+		81.04 192	80.57 195	82.46 197	87.50 213	63.22 228	78.37 268	89.63 178	68.01 233	81.87 244	82.08 315	82.31 95	92.65 155	67.10 235	88.30 284	91.51 195

pmmvs-eth3d		78.42 227	77.04 238	82.57 195	87.44 214	74.41 127	80.86 233	79.67 285	55.68 317	84.69 201	90.31 193	60.91 265	85.42 292	62.20 268	91.59 238	87.88 258

IterMVS-LS		84.73 127	84.98 126	83.96 162	87.35 215	63.66 222	83.25 181	89.88 174	76.06 133	89.62 110	92.37 136	73.40 198	92.52 157	78.16 130	94.77 170	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres100view900		75.45 256	75.05 256	76.66 282	87.27 216	51.88 331	81.07 230	73.26 325	75.68 142	83.25 226	86.37 253	45.54 334	88.80 249	51.98 328	90.99 247	89.31 235

MIMVSNet		71.09 291	71.59 287	69.57 317	87.23 217	50.07 344	78.91 259	71.83 335	60.20 296	71.26 328	91.76 152	55.08 304	76.09 333	41.06 358	87.02 296	82.54 318

Effi-MVS+		83.90 151	84.01 148	83.57 172	87.22 218	65.61 208	86.55 120	92.40 102	78.64 109	81.34 255	84.18 292	83.65 81	92.93 148	74.22 170	87.87 288	92.17 175

BH-RMVSNet		80.53 200	80.22 204	81.49 210	87.19 219	66.21 203	77.79 275	86.23 230	74.21 159	83.69 218	88.50 222	73.25 200	90.75 210	63.18 263	87.90 287	87.52 261

thisisatest0530		79.07 218	77.33 235	84.26 154	87.13 220	64.58 214	83.66 169	75.95 304	68.86 226	85.22 192	87.36 241	38.10 357	93.57 124	75.47 161	94.28 180	94.62 70

Effi-MVS+-dtu		85.82 108	83.38 155	93.14 3	87.13 220	91.15 2	87.70 98	88.42 195	74.57 155	83.56 222	85.65 265	78.49 140	94.21 92	72.04 195	92.88 213	94.05 94

mvs-test1		84.55 131	82.12 175	91.84 20	87.13 220	89.54 4	85.05 139	88.42 195	74.57 155	80.60 261	82.98 303	78.49 140	93.98 104	72.04 195	89.77 265	92.00 179

v2v482		84.09 144	84.24 145	83.62 170	87.13 220	61.40 248	82.71 195	89.71 176	72.19 191	89.55 114	91.41 160	70.70 221	93.20 136	81.02 94	93.76 190	96.25 31

jason		77.42 237	75.75 250	82.43 198	87.10 224	69.27 178	77.99 271	81.94 271	51.47 341	77.84 288	85.07 280	60.32 269	89.00 246	70.74 205	89.27 271	89.03 243
jason: jason.
PS-MVSNAJ		77.04 241	76.53 243	78.56 254	87.09 225	61.40 248	75.26 305	87.13 217	61.25 286	74.38 316	77.22 347	76.94 159	90.94 202	64.63 255	84.83 317	83.35 308

xiu_mvs_v2_base		77.19 239	76.75 241	78.52 255	87.01 226	61.30 250	75.55 303	87.12 220	61.24 287	74.45 314	78.79 338	77.20 153	90.93 203	64.62 256	84.80 318	83.32 309

thres600view7		75.97 253	75.35 255	77.85 269	87.01 226	51.84 332	80.45 237	73.26 325	75.20 149	83.10 229	86.31 256	45.54 334	89.05 245	55.03 313	92.24 225	92.66 150

CL-MVSNet_self_test		76.81 244	77.38 234	75.12 292	86.90 228	51.34 334	73.20 320	80.63 280	68.30 231	81.80 248	88.40 223	66.92 237	80.90 319	55.35 310	94.90 165	93.12 131

BH-w/o		76.57 247	76.07 248	78.10 264	86.88 229	65.92 206	77.63 277	86.33 229	65.69 258	80.89 258	79.95 333	68.97 228	90.74 211	53.01 324	85.25 310	77.62 345

MAR-MVS		80.24 209	78.74 220	84.73 143	86.87 230	78.18 90	85.75 130	87.81 208	65.67 259	77.84 288	78.50 340	73.79 190	90.53 217	61.59 276	90.87 253	85.49 283
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
QAPM		82.59 170	82.59 170	82.58 193	86.44 231	66.69 200	89.94 58	90.36 157	67.97 235	84.94 197	92.58 129	72.71 205	92.18 166	70.63 207	87.73 290	88.85 246

PAPM		71.77 287	70.06 298	76.92 277	86.39 232	53.97 313	76.62 291	86.62 227	53.44 328	63.97 356	84.73 287	57.79 290	92.34 162	39.65 360	81.33 336	84.45 292

GBi-Net		82.02 179	82.07 176	81.85 204	86.38 233	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	93.34 200	93.82 104

test1		82.02 179	82.07 176	81.85 204	86.38 233	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	93.34 200	93.82 104

FMVSNet2		81.31 188	81.61 183	80.41 228	86.38 233	58.75 283	83.93 160	86.58 228	72.43 184	87.65 144	92.98 113	63.78 251	90.22 225	66.86 236	93.92 188	92.27 170

3Dnovator	80.37 7	84.80 126	84.71 133	85.06 138	86.36 236	74.71 125	88.77 84	90.00 172	75.65 143	84.96 195	93.17 109	74.06 186	91.19 195	78.28 126	91.09 243	89.29 237

Anonymous20231206		71.38 290	71.88 285	69.88 314	86.31 237	54.37 311	70.39 330	74.62 312	52.57 333	76.73 294	88.76 218	59.94 272	72.06 341	44.35 353	93.23 204	83.23 311

baseline		85.20 116	85.93 108	83.02 181	86.30 238	62.37 241	84.55 146	93.96 43	74.48 157	87.12 151	92.03 143	82.30 96	91.94 174	78.39 122	94.21 181	94.74 69

API-MVS		82.28 174	82.61 169	81.30 211	86.29 239	69.79 171	88.71 85	87.67 209	78.42 112	82.15 241	84.15 293	77.98 144	91.59 183	65.39 249	92.75 215	82.51 320

tfpn200view9		74.86 264	74.23 263	76.74 281	86.24 240	52.12 328	79.24 255	73.87 319	73.34 170	81.82 246	84.60 289	46.02 328	88.80 249	51.98 328	90.99 247	89.31 235

thres400		75.14 258	74.23 263	77.86 268	86.24 240	52.12 328	79.24 255	73.87 319	73.34 170	81.82 246	84.60 289	46.02 328	88.80 249	51.98 328	90.99 247	92.66 150

UGNet		82.78 167	81.64 182	86.21 116	86.20 242	76.24 120	86.86 110	85.68 237	77.07 124	73.76 318	92.82 120	69.64 222	91.82 180	69.04 222	93.69 194	90.56 215
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
CANet		83.79 152	82.85 163	86.63 103	86.17 243	72.21 153	83.76 166	91.43 127	77.24 123	74.39 315	87.45 239	75.36 172	95.42 50	77.03 146	92.83 214	92.25 172

casdiffmvs		85.21 115	85.85 110	83.31 176	86.17 243	62.77 234	83.03 187	93.93 44	74.69 154	88.21 138	92.68 126	82.29 97	91.89 177	77.87 136	93.75 192	95.27 56

TR-MVS		76.77 245	75.79 249	79.72 239	86.10 245	65.79 207	77.14 283	83.02 262	65.20 265	81.40 253	82.10 314	66.30 239	90.73 212	55.57 307	85.27 309	82.65 315

LCM-MVSNet-Re		83.48 158	85.06 124	78.75 251	85.94 246	55.75 304	80.05 241	94.27 23	76.47 128	96.09 5	94.54 58	83.31 85	89.75 239	59.95 285	94.89 166	90.75 208

bset_n11_16_dypcd		79.19 217	77.97 228	82.86 188	85.81 247	66.85 199	75.02 307	79.31 286	66.07 251	83.50 224	83.37 302	55.04 305	92.10 170	78.63 121	94.99 162	89.63 228

Fast-Effi-MVS+-dtu		82.54 171	81.41 186	85.90 122	85.60 248	76.53 116	83.07 186	89.62 179	73.02 179	79.11 280	83.51 297	80.74 123	90.24 224	68.76 224	89.29 269	90.94 203

v148		82.31 173	82.48 172	81.81 207	85.59 249	59.66 269	81.47 223	86.02 233	72.85 180	88.05 139	90.65 186	70.73 220	90.91 205	75.15 165	91.79 234	94.87 64

MVSFormer		82.23 175	81.57 185	84.19 157	85.54 250	69.26 179	91.98 31	90.08 170	71.54 196	76.23 299	85.07 280	58.69 282	94.27 87	86.26 38	88.77 276	89.03 243

lupinMVS		76.37 251	74.46 261	82.09 199	85.54 250	69.26 179	76.79 287	80.77 279	50.68 347	76.23 299	82.82 308	58.69 282	88.94 247	69.85 212	88.77 276	88.07 252

TinyColmap		81.25 189	82.34 174	77.99 266	85.33 252	60.68 261	82.32 208	88.33 198	71.26 200	86.97 158	92.22 142	77.10 156	86.98 271	62.37 266	95.17 154	86.31 274

PAPR		78.84 221	78.10 227	81.07 216	85.17 253	60.22 264	82.21 213	90.57 152	62.51 276	75.32 310	84.61 288	74.99 175	92.30 164	59.48 288	88.04 286	90.68 211

pmmvs4		74.92 263	72.98 275	80.73 223	84.95 254	71.71 161	76.23 297	77.59 295	52.83 331	77.73 291	86.38 252	56.35 297	84.97 296	57.72 297	87.05 295	85.51 282

baseline1		73.26 274	73.54 269	72.43 307	84.92 255	47.79 350	79.89 244	74.00 317	65.93 252	78.81 282	86.28 257	56.36 296	81.63 317	56.63 300	79.04 345	87.87 259

Patchmatch-RL test		74.48 267	73.68 267	76.89 279	84.83 256	66.54 201	72.29 323	69.16 346	57.70 307	86.76 161	86.33 254	45.79 333	82.59 311	69.63 214	90.65 260	81.54 330

CS-MVS		82.02 179	82.63 168	80.19 232	84.80 257	57.56 290	82.39 206	94.72 18	71.24 201	80.22 270	84.89 284	75.85 169	94.56 84	76.08 154	93.49 198	88.46 249

KD-MVS_self_test		81.93 183	83.14 160	78.30 260	84.75 258	52.75 322	80.37 238	89.42 183	70.24 214	90.26 92	93.39 106	74.55 184	86.77 275	68.61 227	96.64 99	95.38 51

XXY-MVS		74.44 269	76.19 246	69.21 318	84.61 259	52.43 327	71.70 325	77.18 297	60.73 292	80.60 261	90.96 175	75.44 170	69.35 347	56.13 303	88.33 280	85.86 279

cascas		76.29 252	74.81 257	80.72 224	84.47 260	62.94 231	73.89 315	87.34 211	55.94 316	75.16 312	76.53 350	63.97 249	91.16 196	65.00 251	90.97 250	88.06 253

PVSNet_BlendedMVS		78.80 222	77.84 229	81.65 209	84.43 261	63.41 224	79.49 251	90.44 154	61.70 284	75.43 308	87.07 247	69.11 226	91.44 187	60.68 282	92.24 225	90.11 225

PVSNet_Blended		76.49 249	75.40 253	79.76 237	84.43 261	63.41 224	75.14 306	90.44 154	57.36 311	75.43 308	78.30 341	69.11 226	91.44 187	60.68 282	87.70 291	84.42 293

OpenMVS	76.72 13	81.98 182	82.00 178	81.93 201	84.42 263	68.22 188	88.50 89	89.48 181	66.92 245	81.80 248	91.86 146	72.59 207	90.16 227	71.19 200	91.25 242	87.40 263

OpenMVS_ROB	70.19 17	77.77 235	77.46 232	78.71 252	84.39 264	61.15 252	81.18 229	82.52 265	62.45 278	83.34 225	87.37 240	66.20 240	88.66 254	64.69 254	85.02 312	86.32 273

test_yl		78.71 224	78.51 222	79.32 245	84.32 265	58.84 280	78.38 266	85.33 241	75.99 136	82.49 234	86.57 250	58.01 285	90.02 236	62.74 264	92.73 216	89.10 240

DCV-MVSNet		78.71 224	78.51 222	79.32 245	84.32 265	58.84 280	78.38 266	85.33 241	75.99 136	82.49 234	86.57 250	58.01 285	90.02 236	62.74 264	92.73 216	89.10 240

Regformer-3		85.06 120	84.67 135	86.22 114	84.27 267	73.43 132	84.07 154	85.26 243	80.77 82	88.62 129	85.48 268	80.56 126	90.39 221	81.99 85	91.04 245	94.85 66

Regformer-4		86.41 96	85.71 114	88.52 75	84.27 267	77.57 99	84.07 154	88.00 205	82.82 58	89.84 103	85.48 268	82.06 102	92.77 152	83.83 67	91.04 245	95.22 60

DELS-MVS		81.44 187	81.25 187	82.03 200	84.27 267	62.87 233	76.47 294	92.49 101	70.97 204	81.64 251	83.83 294	75.03 174	92.70 153	74.29 169	92.22 227	90.51 217
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
Gipuma		84.44 134	86.33 100	78.78 250	84.20 270	73.57 131	89.55 67	90.44 154	84.24 39	84.38 207	94.89 45	76.35 168	80.40 322	76.14 153	96.80 96	82.36 321
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-1		86.00 104	85.50 118	87.49 92	84.18 271	76.90 111	83.52 171	87.94 207	82.18 65	89.19 119	85.07 280	82.28 98	91.89 177	82.40 81	92.72 218	93.69 111

Regformer-2		86.74 92	86.08 106	88.73 71	84.18 271	79.20 81	83.52 171	89.33 184	83.33 51	89.92 101	85.07 280	83.23 86	93.16 139	83.39 69	92.72 218	93.83 102

MVS_0304		78.17 228	77.23 236	80.99 220	84.13 273	69.07 185	81.39 224	80.81 278	76.28 130	67.53 343	89.11 214	62.87 259	86.77 275	60.90 281	92.01 232	87.13 266

EI-MVSNet-Vis-set		85.12 118	84.53 138	86.88 99	84.01 274	72.76 137	83.91 161	85.18 245	80.44 83	88.75 126	85.49 267	80.08 130	91.92 175	82.02 84	90.85 254	95.97 38

IterMVS-SCA-FT		80.64 199	79.41 213	84.34 152	83.93 275	69.66 174	76.28 296	81.09 276	72.43 184	86.47 173	90.19 196	60.46 267	93.15 141	77.45 141	86.39 301	90.22 221

MSDG		80.06 214	79.99 210	80.25 230	83.91 276	68.04 190	77.51 280	89.19 185	77.65 117	81.94 243	83.45 299	76.37 167	86.31 282	63.31 262	86.59 298	86.41 272

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 100	83.87 277	72.52 146	83.82 163	85.15 246	80.27 87	88.75 126	85.45 271	79.95 132	91.90 176	81.92 87	90.80 255	96.13 33

thres200		72.34 283	71.55 289	74.70 295	83.48 278	51.60 333	75.02 307	73.71 322	70.14 215	78.56 284	80.57 327	46.20 326	88.20 259	46.99 347	89.29 269	84.32 294

USDC		76.63 246	76.73 242	76.34 285	83.46 279	57.20 294	80.02 242	88.04 204	52.14 337	83.65 220	91.25 162	63.24 254	86.65 278	54.66 315	94.11 184	85.17 285

HY-MVS	64.64 18	73.03 277	72.47 282	74.71 294	83.36 280	54.19 312	82.14 216	81.96 270	56.76 315	69.57 335	86.21 258	60.03 271	84.83 299	49.58 337	82.65 330	85.11 286

EI-MVSNet		82.61 169	82.42 173	83.20 178	83.25 281	63.66 222	83.50 174	85.07 247	76.06 133	86.55 167	85.10 277	73.41 196	90.25 222	78.15 132	90.67 258	95.68 44

CVMVSNet		72.62 280	71.41 290	76.28 286	83.25 281	60.34 263	83.50 174	79.02 290	37.77 366	76.33 297	85.10 277	49.60 318	87.41 265	70.54 208	77.54 350	81.08 337

V42		83.47 159	83.37 156	83.75 167	83.16 283	63.33 226	81.31 225	90.23 166	69.51 219	90.91 83	90.81 180	74.16 185	92.29 165	80.06 105	90.22 262	95.62 46

Anonymous20240521		80.18 211	81.25 187	76.95 276	83.15 284	60.84 259	82.46 203	85.99 234	68.76 227	86.78 160	93.73 102	59.13 279	77.44 329	73.71 178	97.55 69	92.56 153

EU-MVSNet		75.12 260	74.43 262	77.18 274	83.11 285	59.48 271	85.71 132	82.43 267	39.76 365	85.64 186	88.76 218	44.71 345	87.88 261	73.86 176	85.88 305	84.16 296

ET-MVSNet_ETH3D		75.28 257	72.77 276	82.81 189	83.03 286	68.11 189	77.09 284	76.51 302	60.67 293	77.60 292	80.52 328	38.04 358	91.15 197	70.78 203	90.68 257	89.17 238

FMVSNet3		78.80 222	78.55 221	79.57 242	82.89 287	56.89 297	81.76 217	85.77 236	69.04 224	86.00 179	90.44 190	51.75 311	90.09 233	65.95 243	93.34 200	91.72 188

MVS_Test		82.47 172	83.22 157	80.22 231	82.62 288	57.75 289	82.54 201	91.96 114	71.16 203	82.89 231	92.52 132	77.41 151	90.50 218	80.04 106	87.84 289	92.40 161

LF4IMVS		82.75 168	81.93 179	85.19 134	82.08 289	80.15 72	85.53 133	88.76 191	68.01 233	85.58 187	87.75 233	71.80 215	86.85 273	74.02 173	93.87 189	88.58 248

PVSNet	58.17 21	66.41 315	65.63 318	68.75 321	81.96 290	49.88 345	62.19 352	72.51 331	51.03 343	68.04 339	75.34 353	50.84 314	74.77 337	45.82 351	82.96 326	81.60 329

GA-MVS		75.83 254	74.61 258	79.48 244	81.87 291	59.25 273	73.42 318	82.88 263	68.68 228	79.75 273	81.80 317	50.62 315	89.46 241	66.85 237	85.64 306	89.72 227

MS-PatchMatch		70.93 292	70.22 296	73.06 302	81.85 292	62.50 239	73.82 316	77.90 293	52.44 334	75.92 303	81.27 321	55.67 300	81.75 315	55.37 309	77.70 348	74.94 349

SCA		73.32 273	72.57 280	75.58 290	81.62 293	55.86 302	78.89 260	71.37 339	61.73 282	74.93 313	83.42 300	60.46 267	87.01 268	58.11 295	82.63 332	83.88 297

FMVSNet5		72.10 285	71.69 286	73.32 299	81.57 294	53.02 321	76.77 288	78.37 292	63.31 271	76.37 296	91.85 147	36.68 361	78.98 325	47.87 344	92.45 221	87.95 256

thisisatest0515		73.00 278	70.52 293	80.46 227	81.45 295	59.90 267	73.16 321	74.31 316	57.86 306	76.08 302	77.78 342	37.60 360	92.12 169	65.00 251	91.45 240	89.35 234

eth_miper_zixun_eth		80.84 195	80.22 204	82.71 190	81.41 296	60.98 257	77.81 274	90.14 169	67.31 243	86.95 159	87.24 243	64.26 247	92.31 163	75.23 164	91.61 237	94.85 66

CANet_DTU		77.81 234	77.05 237	80.09 234	81.37 297	59.90 267	83.26 180	88.29 199	69.16 222	67.83 341	83.72 295	60.93 264	89.47 240	69.22 219	89.70 266	90.88 205

ANet_high		83.17 165	85.68 115	75.65 289	81.24 298	45.26 358	79.94 243	92.91 89	83.83 43	91.33 75	96.88 10	80.25 129	85.92 287	68.89 223	95.89 130	95.76 42

new-patchmatchnet		70.10 297	73.37 271	60.29 344	81.23 299	16.95 374	59.54 354	74.62 312	62.93 273	80.97 256	87.93 231	62.83 260	71.90 342	55.24 311	95.01 161	92.00 179

test20.03		73.75 272	74.59 260	71.22 311	81.11 300	51.12 338	70.15 331	72.10 333	70.42 209	80.28 269	91.50 158	64.21 248	74.72 339	46.96 348	94.58 174	87.82 260

MVS		73.21 276	72.59 279	75.06 293	80.97 301	60.81 260	81.64 220	85.92 235	46.03 356	71.68 327	77.54 343	68.47 229	89.77 238	55.70 306	85.39 307	74.60 350

N_pmnet		70.20 295	68.80 304	74.38 296	80.91 302	84.81 39	59.12 356	76.45 303	55.06 320	75.31 311	82.36 313	55.74 299	54.82 365	47.02 346	87.24 294	83.52 304

IterMVS		76.91 242	76.34 245	78.64 253	80.91 302	64.03 220	76.30 295	79.03 289	64.88 267	83.11 228	89.16 212	59.90 273	84.46 301	68.61 227	85.15 311	87.42 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		81.64 185	81.59 184	81.79 208	80.86 304	59.15 276	78.61 265	90.18 168	68.36 229	87.20 149	87.11 246	69.39 223	91.62 182	78.16 130	94.43 178	94.60 72

RRT_test8_iter05		78.08 230	77.52 231	79.75 238	80.84 305	52.54 326	80.61 235	88.96 188	67.77 240	84.62 202	89.29 209	33.89 364	92.10 170	77.59 138	94.15 183	94.62 70

WTY-MVS		67.91 308	68.35 306	66.58 329	80.82 306	48.12 348	65.96 344	72.60 329	53.67 327	71.20 329	81.68 319	58.97 280	69.06 349	48.57 340	81.67 333	82.55 317

IB-MVS	62.13 19	71.64 288	68.97 302	79.66 241	80.80 307	62.26 244	73.94 314	76.90 298	63.27 272	68.63 337	76.79 348	33.83 365	91.84 179	59.28 289	87.26 293	84.88 288
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
our_test_3		71.85 286	71.59 287	72.62 305	80.71 308	53.78 315	69.72 333	71.71 338	58.80 300	78.03 285	80.51 329	56.61 295	78.84 326	62.20 268	86.04 304	85.23 284

ppachtmachnet_test		74.73 266	74.00 265	76.90 278	80.71 308	56.89 297	71.53 326	78.42 291	58.24 303	79.32 278	82.92 307	57.91 288	84.26 303	65.60 248	91.36 241	89.56 230

testgi		72.36 282	74.61 258	65.59 331	80.56 310	42.82 364	68.29 336	73.35 324	66.87 246	81.84 245	89.93 201	72.08 212	66.92 355	46.05 350	92.54 220	87.01 268

RRT_MVS		83.25 162	81.08 191	89.74 53	80.55 311	79.32 80	86.41 122	86.69 226	72.33 189	87.00 157	91.08 168	44.98 343	95.55 40	84.47 61	96.24 118	94.36 83

D2MVS		76.84 243	75.67 252	80.34 229	80.48 312	62.16 245	73.50 317	84.80 255	57.61 309	82.24 238	87.54 237	51.31 312	87.65 263	70.40 210	93.19 205	91.23 198

1314		73.22 275	72.56 281	75.20 291	80.41 313	57.84 287	81.64 220	85.36 240	51.68 340	73.10 321	76.65 349	61.45 263	85.19 294	63.54 259	79.21 344	82.59 316

cl____		80.42 203	80.23 202	81.02 218	79.99 314	59.25 273	77.07 285	87.02 222	67.37 242	86.18 176	89.21 211	63.08 256	90.16 227	76.31 151	95.80 134	93.65 114

DIV-MVS_self_test		80.43 202	80.23 202	81.02 218	79.99 314	59.25 273	77.07 285	87.02 222	67.38 241	86.19 174	89.22 210	63.09 255	90.16 227	76.32 150	95.80 134	93.66 112

miper_ehance_all_eth		80.34 206	80.04 209	81.24 214	79.82 316	58.95 278	77.66 276	89.66 177	65.75 257	85.99 182	85.11 276	68.29 230	91.42 189	76.03 155	92.03 229	93.33 122

CR-MVSNet		74.00 270	73.04 274	76.85 280	79.58 317	62.64 236	82.58 198	76.90 298	50.50 348	75.72 305	92.38 133	48.07 321	84.07 304	68.72 226	82.91 328	83.85 300

RPMNet		78.88 220	78.28 225	80.68 225	79.58 317	62.64 236	82.58 198	94.16 31	74.80 152	75.72 305	92.59 127	48.69 319	95.56 37	73.48 181	82.91 328	83.85 300

baseline2		69.77 301	66.89 311	78.41 258	79.51 319	58.09 285	76.23 297	69.57 344	57.50 310	64.82 354	77.45 345	46.02 328	88.44 255	53.08 321	77.83 347	88.70 247

UnsupCasMVSNet_bld		69.21 304	69.68 299	67.82 325	79.42 320	51.15 337	67.82 340	75.79 305	54.15 324	77.47 293	85.36 275	59.26 278	70.64 344	48.46 341	79.35 342	81.66 328

PatchT		70.52 294	72.76 277	63.79 336	79.38 321	33.53 370	77.63 277	65.37 355	73.61 165	71.77 326	92.79 123	44.38 346	75.65 336	64.53 257	85.37 308	82.18 323

Patchmtry		76.56 248	77.46 232	73.83 298	79.37 322	46.60 355	82.41 205	76.90 298	73.81 163	85.56 188	92.38 133	48.07 321	83.98 305	63.36 261	95.31 150	90.92 204

mvs_anonymous		78.13 229	78.76 219	76.23 287	79.24 323	50.31 343	78.69 263	84.82 254	61.60 285	83.09 230	92.82 120	73.89 189	87.01 268	68.33 230	86.41 300	91.37 196

MVS-HIRNet		61.16 327	62.92 324	55.87 347	79.09 324	35.34 369	71.83 324	57.98 367	46.56 354	59.05 363	91.14 167	49.95 317	76.43 332	38.74 361	71.92 358	55.84 365

MDA-MVSNet-bldmvs		77.47 236	76.90 240	79.16 247	79.03 325	64.59 213	66.58 343	75.67 307	73.15 177	88.86 123	88.99 216	66.94 236	81.23 318	64.71 253	88.22 285	91.64 191

diffmvs		80.40 204	80.48 199	80.17 233	79.02 326	60.04 265	77.54 279	90.28 165	66.65 248	82.40 236	87.33 242	73.50 193	87.35 266	77.98 134	89.62 267	93.13 130

tpm2		68.45 306	66.83 312	73.30 300	78.93 327	48.50 346	79.76 245	71.76 336	47.50 352	69.92 334	83.60 296	42.07 351	88.40 256	48.44 342	79.51 340	83.01 314

tpm		67.95 307	68.08 308	67.55 326	78.74 328	43.53 362	75.60 301	67.10 352	54.92 321	72.23 324	88.10 227	42.87 350	75.97 334	52.21 326	80.95 339	83.15 312

MDTV_nov1_ep13			68.29 307		78.03 329	43.87 361	74.12 313	72.22 332	52.17 335	67.02 344	85.54 266	45.36 338	80.85 320	55.73 304	84.42 320

cl22		78.97 219	78.21 226	81.24 214	77.74 330	59.01 277	77.46 282	87.13 217	65.79 254	84.32 209	85.10 277	58.96 281	90.88 207	75.36 163	92.03 229	93.84 101

EPNet_dtu		72.87 279	71.33 291	77.49 273	77.72 331	60.55 262	82.35 207	75.79 305	66.49 249	58.39 366	81.06 323	53.68 307	85.98 286	53.55 319	92.97 212	85.95 277
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 302	68.83 303	72.33 308	77.66 332	53.60 316	79.29 253	69.99 343	57.66 308	72.53 323	82.93 306	46.45 325	80.08 324	60.91 280	72.09 357	83.31 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 310	67.26 310	65.90 330	77.23 333	51.10 339	64.79 345	71.72 337	52.12 338	70.13 333	80.18 331	57.96 287	65.36 360	50.21 333	81.01 338	81.25 334

CostFormer		69.98 300	68.68 305	73.87 297	77.14 334	50.72 341	79.26 254	74.51 314	51.94 339	70.97 331	84.75 286	45.16 342	87.49 264	55.16 312	79.23 343	83.40 307

tpm cat1		66.76 313	65.21 319	71.42 310	77.09 335	50.62 342	78.01 270	73.68 323	44.89 358	68.64 336	79.00 337	45.51 336	82.42 314	49.91 335	70.15 360	81.23 336

pmmvs5		70.73 293	70.07 297	72.72 303	77.03 336	52.73 323	74.14 312	75.65 308	50.36 349	72.17 325	85.37 274	55.42 302	80.67 321	52.86 325	87.59 292	84.77 289

EPNet		80.37 205	78.41 224	86.23 113	76.75 337	73.28 133	87.18 106	77.45 296	76.24 131	68.14 338	88.93 217	65.41 244	93.85 108	69.47 215	96.12 123	91.55 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		76.45 250	76.10 247	77.51 272	76.72 338	60.97 258	64.69 346	85.04 249	63.98 270	83.20 227	88.22 225	56.67 294	78.79 327	73.22 183	93.12 206	92.78 144

CHOSEN 280x420		59.08 331	56.52 336	66.76 328	76.51 339	64.39 217	49.62 362	59.00 364	43.86 360	55.66 368	68.41 360	35.55 363	68.21 351	43.25 354	76.78 352	67.69 358

UnsupCasMVSNet_eth		71.63 289	72.30 283	69.62 316	76.47 340	52.70 324	70.03 332	80.97 277	59.18 298	79.36 277	88.21 226	60.50 266	69.12 348	58.33 293	77.62 349	87.04 267

test-LLR		67.21 309	66.74 313	68.63 322	76.45 341	55.21 307	67.89 337	67.14 350	62.43 279	65.08 351	72.39 355	43.41 347	69.37 345	61.00 278	84.89 315	81.31 332

test-mter		65.00 319	63.79 322	68.63 322	76.45 341	55.21 307	67.89 337	67.14 350	50.98 344	65.08 351	72.39 355	28.27 372	69.37 345	61.00 278	84.89 315	81.31 332

miper_enhance_ethall		77.83 232	76.93 239	80.51 226	76.15 343	58.01 286	75.47 304	88.82 189	58.05 305	83.59 221	80.69 324	64.41 246	91.20 194	73.16 189	92.03 229	92.33 165

gg-mvs-nofinetune		68.96 305	69.11 301	68.52 324	76.12 344	45.32 357	83.59 170	55.88 368	86.68 26	64.62 355	97.01 7	30.36 369	83.97 306	44.78 352	82.94 327	76.26 347

CMPMVS	59.41 20	75.12 260	73.57 268	79.77 236	75.84 345	67.22 193	81.21 228	82.18 268	50.78 345	76.50 295	87.66 235	55.20 303	82.99 310	62.17 270	90.64 261	89.09 242
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
wuyk23d		75.13 259	79.30 214	62.63 337	75.56 346	75.18 124	80.89 232	73.10 327	75.06 151	94.76 12	95.32 34	87.73 42	52.85 366	34.16 365	97.11 85	59.85 362

Patchmatch-test		65.91 317	67.38 309	61.48 342	75.51 347	43.21 363	68.84 334	63.79 357	62.48 277	72.80 322	83.42 300	44.89 344	59.52 364	48.27 343	86.45 299	81.70 327

new_pmnet		55.69 333	57.66 335	49.76 349	75.47 348	30.59 371	59.56 353	51.45 371	43.62 361	62.49 357	75.48 352	40.96 353	49.15 368	37.39 363	72.52 356	69.55 356

gm-plane-assit					75.42 349	44.97 360			52.17 335		72.36 357		87.90 260	54.10 317

MVSTER		77.09 240	75.70 251	81.25 212	75.27 350	61.08 253	77.49 281	85.07 247	60.78 291	86.55 167	88.68 220	43.14 349	90.25 222	73.69 179	90.67 258	92.42 159

PVSNet_0	51.08 22	56.10 332	54.97 337	59.48 345	75.12 351	53.28 320	55.16 359	61.89 359	44.30 359	59.16 362	62.48 364	54.22 306	65.91 359	35.40 364	47.01 367	59.25 363

test0.0.03 1		64.66 320	64.36 321	65.57 332	75.03 352	46.89 354	64.69 346	61.58 362	62.43 279	71.18 330	77.54 343	43.41 347	68.47 350	40.75 359	82.65 330	81.35 331

DWT-MVSNet_test		66.43 314	64.37 320	72.63 304	74.86 353	50.86 340	76.52 292	72.74 328	54.06 325	65.50 348	68.30 361	32.13 367	84.84 298	61.63 275	73.59 355	82.19 322

tpmvs		70.16 296	69.56 300	71.96 309	74.71 354	48.13 347	79.63 246	75.45 310	65.02 266	70.26 332	81.88 316	45.34 339	85.68 290	58.34 292	75.39 353	82.08 324

MDA-MVSNet_test_wron		70.05 299	70.44 294	68.88 320	73.84 355	53.47 317	58.93 358	67.28 348	58.43 301	87.09 154	85.40 272	59.80 275	67.25 353	59.66 287	83.54 323	85.92 278

YYNet1		70.06 298	70.44 294	68.90 319	73.76 356	53.42 319	58.99 357	67.20 349	58.42 302	87.10 153	85.39 273	59.82 274	67.32 352	59.79 286	83.50 324	85.96 276

GG-mvs-BLEND				67.16 327	73.36 357	46.54 356	84.15 153	55.04 369		58.64 365	61.95 365	29.93 370	83.87 307	38.71 362	76.92 351	71.07 354

JIA-IIPM		69.41 303	66.64 315	77.70 270	73.19 358	71.24 164	75.67 300	65.56 354	70.42 209	65.18 350	92.97 115	33.64 366	83.06 309	53.52 320	69.61 363	78.79 344

ADS-MVSNet2		65.87 318	63.64 323	72.55 306	73.16 359	56.92 296	67.10 341	74.81 311	49.74 350	66.04 346	82.97 304	46.71 323	77.26 330	42.29 355	69.96 361	83.46 305

ADS-MVSNet		61.90 323	62.19 326	61.03 343	73.16 359	36.42 368	67.10 341	61.75 360	49.74 350	66.04 346	82.97 304	46.71 323	63.21 362	42.29 355	69.96 361	83.46 305

DSMNet-mixed		60.98 329	61.61 328	59.09 346	72.88 361	45.05 359	74.70 310	46.61 373	26.20 368	65.34 349	90.32 192	55.46 301	63.12 363	41.72 357	81.30 337	69.09 357

tpmrst		66.28 316	66.69 314	65.05 334	72.82 362	39.33 365	78.20 269	70.69 341	53.16 330	67.88 340	80.36 330	48.18 320	74.75 338	58.13 294	70.79 359	81.08 337

TESTMET0.1,1		61.29 326	60.32 331	64.19 335	72.06 363	51.30 335	67.89 337	62.09 358	45.27 357	60.65 360	69.01 358	27.93 373	64.74 361	56.31 302	81.65 335	76.53 346

dp		60.70 330	60.29 332	61.92 340	72.04 364	38.67 367	70.83 327	64.08 356	51.28 342	60.75 359	77.28 346	36.59 362	71.58 343	47.41 345	62.34 366	75.52 348

pmmvs3		62.47 321	60.02 333	69.80 315	71.58 365	64.00 221	70.52 329	58.44 366	39.77 364	66.05 345	75.84 351	27.10 374	72.28 340	46.15 349	84.77 319	73.11 351

EPMVS		62.47 321	62.63 325	62.01 338	70.63 366	38.74 366	74.76 309	52.86 370	53.91 326	67.71 342	80.01 332	39.40 355	66.60 356	55.54 308	68.81 364	80.68 341

KD-MVS_2432*1600		66.87 311	65.81 316	70.04 312	67.50 367	47.49 351	62.56 350	79.16 287	61.21 288	77.98 286	80.61 325	25.29 375	82.48 312	53.02 322	84.92 313	80.16 342

miper_refine_blended		66.87 311	65.81 316	70.04 312	67.50 367	47.49 351	62.56 350	79.16 287	61.21 288	77.98 286	80.61 325	25.29 375	82.48 312	53.02 322	84.92 313	80.16 342

E-PMN		61.59 325	61.62 327	61.49 341	66.81 369	55.40 305	53.77 360	60.34 363	66.80 247	58.90 364	65.50 362	40.48 354	66.12 358	55.72 305	86.25 302	62.95 360

EMVS		61.10 328	60.81 329	61.99 339	65.96 370	55.86 302	53.10 361	58.97 365	67.06 244	56.89 367	63.33 363	40.98 352	67.03 354	54.79 314	86.18 303	63.08 359

PMMVS		61.65 324	60.38 330	65.47 333	65.40 371	69.26 179	63.97 348	61.73 361	36.80 367	60.11 361	68.43 359	59.42 276	66.35 357	48.97 339	78.57 346	60.81 361

PMMVS2		55.64 334	59.27 334	44.74 350	64.30 372	12.32 375	40.60 363	49.79 372	53.19 329	65.06 353	84.81 285	53.60 308	49.76 367	32.68 367	89.41 268	72.15 352

MVE	40.22 23	51.82 335	50.47 338	55.87 347	62.66 373	51.91 330	31.61 365	39.28 374	40.65 363	50.76 369	74.98 354	56.24 298	44.67 369	33.94 366	64.11 365	71.04 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 352	32.95 374	29.49 372		21.63 377	12.07 369	37.95 370	45.07 367	30.84 368	19.21 371	17.94 370	33.06 370	23.69 367

test_method		30.46 336	29.60 339	33.06 351	17.99 375	3.84 377	13.62 366	73.92 318	2.79 370	18.29 372	53.41 366	28.53 371	43.25 370	22.56 368	35.27 369	52.11 366

tmp_tt		20.25 338	24.50 341	7.49 353	4.47 376	8.70 376	34.17 364	25.16 376	1.00 371	32.43 371	18.49 368	39.37 356	9.21 372	21.64 369	43.75 368	4.57 368

testmvs		5.91 342	7.65 345	0.72 355	1.20 377	0.37 379	59.14 355	0.67 379	0.49 373	1.11 373	2.76 372	0.94 378	0.24 374	1.02 372	1.47 371	1.55 370

test123		6.27 341	8.08 344	0.84 354	1.11 378	0.57 378	62.90 349	0.82 378	0.54 372	1.07 374	2.75 373	1.26 377	0.30 373	1.04 371	1.26 372	1.66 369

test_blank		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

eth-test2					0.00 379

eth-test					0.00 379

uanet_test		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

cdsmvs_eth3d_5k		20.81 337	27.75 340	0.00 356	0.00 379	0.00 380	0.00 367	85.44 239	0.00 374	0.00 375	82.82 308	81.46 113	0.00 375	0.00 373	0.00 373	0.00 371

pcd_1.5k_mvsjas		6.41 340	8.55 343	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	76.94 159	0.00 375	0.00 373	0.00 373	0.00 371

sosnet-low-res		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

sosnet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

uncertanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

Regformer		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

ab-mvs-re		6.65 339	8.87 342	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	79.80 334	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

uanet		0.00 343	0.00 346	0.00 356	0.00 379	0.00 380	0.00 367	0.00 380	0.00 374	0.00 375	0.00 374	0.00 379	0.00 375	0.00 373	0.00 373	0.00 371

PC_three_1452									58.96 299	90.06 94	91.33 161	80.66 124	93.03 145	75.78 158	95.94 127	92.48 157

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	92.04 177

test_0728_THIRD									85.33 32	93.75 31	94.65 53	87.44 45	95.78 27	87.41 21	98.21 30	92.98 136

GSMVS																83.88 297

sam_mvs1												46.11 327				83.88 297

sam_mvs												45.92 332

MTGPA								91.81 119

test_post1							78.85 262				3.13 370	45.19 341	80.13 323	58.11 295

test_post											3.10 371	45.43 337	77.22 331

patchmatchnet-post											81.71 318	45.93 331	87.01 268

MTMP							90.66 42	33.14 375

test9_res														80.83 98	96.45 108	90.57 214

agg_prior2														79.68 110	96.16 120	90.22 221

test_prior4						78.97 83	84.59 145

test_prior2							83.37 177		75.43 145	84.58 203	91.57 155	81.92 108		79.54 112	96.97 88

旧先验2							81.73 218		56.88 314	86.54 172			84.90 297	72.81 190

新几何2							81.72 219

无先验							82.81 193	85.62 238	58.09 304				91.41 190	67.95 233		84.48 291

原ACMM2							82.26 212

testdata2													86.43 281	63.52 260

segment_acmp												81.94 105

testdata1							79.62 247		73.95 162

plane_prior5								93.61 58					95.22 59	80.78 99	95.83 132	94.46 78

plane_prior4											92.95 116

plane_prior3						76.85 112			77.79 116	86.55 167

plane_prior2							89.45 72		79.44 97

plane_prior						76.42 117	87.15 107		75.94 139						95.03 160

n2								0.00 380

nn								0.00 380

door-mid								74.45 315

test11								91.46 126

door								72.57 330

HQP5-MVS						70.66 167

BP-MVS														77.30 143

HQP4-MVS										80.56 263			94.61 80			93.56 119

HQP3-MVS								92.68 97							94.47 176

HQP2-MVS												72.10 210

MDTV_nov1_ep13_2view						27.60 373	70.76 328		46.47 355	61.27 358		45.20 340		49.18 338		83.75 302

ACMMP++_ref															95.74 138

ACMMP++															97.35 78

Test By Simon												79.09 136

High-resolution multi-view benchmark