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 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 69	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 47	97.23 2	95.32 2	99.01 2	97.26 9	80.16 144	98.99 1	95.15 1	99.14 2	96.47 35

TDRefinement		93.52 3	93.39 5	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 51	96.29 22	88.16 36	94.17 102	86.07 54	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 66	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 76	93.16 145	91.10 2	97.53 77	96.58 33
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
reproduce_model		92.89 5	93.18 8	92.01 13	94.20 51	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 42	95.72 36	89.60 5	98.27 28	92.08 226

reproduce-ours		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 26	89.13 7	98.26 30	91.76 237

our_new_method		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 26	89.13 7	98.26 30	91.76 237

HPM-MVS_fast		92.50 8	92.54 10	92.37 6	95.93 16	85.81 33	92.99 12	94.23 28	85.21 44	92.51 59	95.13 52	90.65 10	95.34 55	88.06 16	98.15 39	95.95 46

lecture		92.43 9	93.50 3	89.21 66	94.43 44	79.31 84	92.69 19	95.72 8	88.48 22	94.43 20	95.73 34	91.34 4	94.68 78	90.26 4	98.44 20	93.63 141

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 47	87.16 37	97.60 71	92.73 183

SR-MVS		92.23 11	92.34 12	91.91 17	94.89 38	87.85 10	92.51 25	93.87 52	88.20 24	93.24 43	94.02 100	90.15 17	95.67 38	86.82 41	97.34 81	92.19 221

HPM-MVS		92.13 12	92.20 14	91.91 17	95.58 26	84.67 46	93.51 8	94.85 16	82.88 70	91.77 72	93.94 108	90.55 13	95.73 35	88.50 12	98.23 33	95.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 13	92.24 13	91.48 25	93.02 85	85.17 39	92.47 27	95.05 15	87.65 28	93.21 44	94.39 81	90.09 18	95.08 66	86.67 43	97.60 71	94.18 111

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 67	86.15 24	93.37 10	95.10 14	90.28 10	92.11 64	95.03 54	89.75 21	94.93 70	79.95 128	98.27 28	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 15	91.87 20	92.03 12	95.53 27	85.91 28	93.35 11	94.16 33	82.52 73	92.39 62	94.14 93	89.15 26	95.62 39	87.35 32	98.24 32	94.56 90
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
mPP-MVS		91.69 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 109	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 166

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 69	83.16 66	91.06 84	94.00 101	88.26 33	95.71 37	87.28 35	98.39 23	92.55 196

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	94.03 99	86.57 56	95.80 28	87.35 32	97.62 69	94.20 108

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 136	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 213

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 101	88.22 23	88.53 142	97.64 6	83.45 90	94.55 86	86.02 58	98.60 13	96.67 30

ACMMPR		91.49 20	91.35 31	91.92 16	95.74 20	85.88 30	92.58 23	93.25 77	81.99 76	91.40 76	94.17 92	87.51 46	95.87 20	87.74 21	97.76 60	93.99 118

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 63	94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	98.73 7	95.23 66

region2R		91.44 23	91.30 35	91.87 19	95.75 19	85.90 29	92.63 22	93.30 75	81.91 78	90.88 91	94.21 88	87.75 42	95.87 20	87.60 26	97.71 63	93.83 127

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 76	81.99 76	91.47 75	93.96 105	88.35 32	95.56 42	87.74 21	97.74 62	92.85 180

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 99	91.29 80	93.97 102	87.93 41	95.87 20	88.65 10	97.96 51	94.12 115

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 87	78.04 96	92.84 16	94.14 37	83.33 64	93.90 29	95.73 34	88.77 28	96.41 3	87.60 26	97.98 48	92.98 176
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 27	90.95 44	91.93 15	95.67 23	85.85 31	90.00 63	93.90 49	80.32 96	91.74 73	94.41 79	88.17 35	95.98 13	86.37 47	97.99 46	93.96 120

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 62	80.97 70	91.49 41	93.48 67	82.82 71	92.60 58	93.97 102	88.19 34	96.29 6	87.61 25	98.20 36	94.39 102
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 45	91.83 20	96.18 11	86.88 17	92.20 31	93.03 91	82.59 72	88.52 143	94.37 82	86.74 54	95.41 53	86.32 48	98.21 34	93.19 162
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 30	91.01 41	90.82 37	95.45 28	82.73 59	91.75 39	93.74 55	80.98 89	91.38 77	93.80 112	87.20 50	95.80 28	87.10 39	97.69 65	93.93 121

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 105	94.51 19	75.79 155	92.94 48	94.96 55	88.36 31	95.01 68	90.70 3	98.40 22	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 32	91.50 26	88.44 81	93.00 86	76.26 122	89.65 76	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 136	78.35 150	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 40	89.42 62	95.93 16	79.54 82	89.95 67	93.68 59	77.65 134	91.97 68	94.89 57	88.38 30	95.45 51	89.27 6	97.87 56	93.27 157

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 77	93.35 70	79.20 112	93.83 32	93.60 122	90.81 8	92.96 152	85.02 72	98.45 19	92.41 203
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 52	91.38 28	89.03 199	84.23 49	93.58 6	94.68 18	90.65 8	90.33 100	93.95 107	84.50 78	95.37 54	80.87 118	95.50 153	94.53 93

ACMP	79.16 10	90.54 36	90.60 50	90.35 45	94.36 48	80.98 69	89.16 87	94.05 42	79.03 115	92.87 50	93.74 117	90.60 12	95.21 61	82.87 96	98.76 4	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 37	91.08 38	88.88 71	93.38 76	78.65 90	89.15 88	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 79	97.81 58	91.70 241
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 38	91.64 22	86.93 105	94.18 52	72.65 152	90.47 56	93.69 57	83.77 58	94.11 27	94.27 83	90.28 15	95.84 24	86.03 55	97.92 52	92.29 215

SMA-MVS		90.31 39	90.48 51	89.83 55	95.31 30	79.52 83	90.98 48	93.24 78	75.37 164	92.84 52	95.28 48	85.58 69	96.09 8	87.92 18	97.76 60	93.88 124
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
SF-MVS		90.27 40	90.80 47	88.68 78	92.86 91	77.09 111	91.19 45	95.74 6	81.38 84	92.28 63	93.80 112	86.89 53	94.64 81	85.52 64	97.51 78	94.30 107

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 184	89.99 65	93.05 88	86.53 35	94.29 23	96.27 23	82.69 97	94.08 105	86.25 51	97.63 67	97.82 8

PMVS	80.48 6	90.08 42	90.66 49	88.34 84	96.71 3	92.97 2	90.31 60	89.57 211	88.51 21	90.11 102	95.12 53	90.98 7	88.92 272	77.55 164	97.07 88	83.13 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 43	91.09 37	87.00 103	91.55 135	72.64 154	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 132	95.88 18	87.41 30	95.94 133	92.48 199

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 162	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 278
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
PS-CasMVS		90.06 44	91.92 16	84.47 167	96.56 6	58.83 351	89.04 89	92.74 103	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 138	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 166	96.57 5	58.88 348	88.95 90	93.19 80	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 146	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 47	89.74 57	90.83 36	93.16 83	80.37 74	91.91 37	93.11 84	81.10 87	95.32 14	97.24 10	72.94 245	94.85 72	85.07 69	97.78 59	97.26 16

DTE-MVSNet		89.98 48	91.91 18	84.21 176	96.51 7	57.84 359	88.93 91	92.84 99	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 142	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 48	89.84 55	90.41 43	94.91 37	84.50 48	89.49 82	93.98 44	79.68 104	92.09 65	93.89 110	83.80 85	93.10 148	82.67 100	98.04 41	93.64 140

3Dnovator+	83.92 2	89.97 50	89.66 58	90.92 35	91.27 144	81.66 66	91.25 43	94.13 38	88.89 15	88.83 134	94.26 86	77.55 173	95.86 23	84.88 73	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 294	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 129	95.90 17	80.94 117	98.80 3	98.84 5

OPM-MVS		89.80 52	89.97 53	89.27 64	94.76 40	79.86 78	86.76 132	92.78 102	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 75	97.55 74	94.10 116
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 53	89.27 64	91.30 29	93.51 70	84.79 44	89.89 69	90.63 172	70.00 249	94.55 19	96.67 17	87.94 40	93.59 128	84.27 81	95.97 129	95.52 56

anonymousdsp		89.73 54	88.88 74	92.27 8	89.82 180	86.67 18	90.51 55	90.20 193	69.87 250	95.06 15	96.14 28	84.28 81	93.07 149	87.68 23	96.34 111	97.09 20

test_djsdf		89.62 55	89.01 68	91.45 26	92.36 103	82.98 57	91.98 35	90.08 196	71.54 228	94.28 25	96.54 19	81.57 127	94.27 92	86.26 49	96.49 105	97.09 20

XVG-OURS-SEG-HR		89.59 56	89.37 62	90.28 46	94.47 43	85.95 27	86.84 128	93.91 48	80.07 100	86.75 191	93.26 128	93.64 2	90.93 211	84.60 78	90.75 306	93.97 119

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 87	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 106	97.18 86	90.45 280
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 58	88.81 77	91.19 32	93.38 76	84.72 45	89.70 72	90.29 190	69.27 257	94.39 21	96.38 21	86.02 66	93.52 132	83.96 83	95.92 135	95.34 60

CPTT-MVS		89.39 59	88.98 70	90.63 40	95.09 33	86.95 16	92.09 33	92.30 118	79.74 103	87.50 175	92.38 162	81.42 129	93.28 141	83.07 92	97.24 84	91.67 242

ACMH	76.49 14	89.34 60	91.14 36	83.96 183	92.50 99	70.36 193	89.55 78	93.84 53	81.89 79	94.70 17	95.44 44	90.69 9	88.31 288	83.33 88	98.30 27	93.20 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 81	86.02 38	93.12 45	95.30 46	84.94 73	89.44 264	74.12 213	96.10 124	94.45 96

APD_test2		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 81	86.02 38	93.12 45	95.30 46	84.94 73	89.44 264	74.12 213	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 354	88.66 98	92.06 125	90.78 7	95.67 8	95.17 51	81.80 124	95.54 44	79.00 143	98.69 10	98.95 4

XVG-OURS		89.18 64	88.83 76	90.23 47	94.28 49	86.11 26	85.91 147	93.60 62	80.16 98	89.13 131	93.44 124	83.82 84	90.98 208	83.86 85	95.30 161	93.60 144

DeepC-MVS	82.31 4	89.15 65	89.08 67	89.37 63	93.64 68	79.07 86	88.54 101	94.20 31	73.53 189	89.71 114	94.82 60	85.09 72	95.77 34	84.17 82	98.03 43	93.26 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 66	90.72 48	84.31 174	97.00 2	64.33 265	89.67 75	88.38 231	88.84 17	94.29 23	97.57 7	90.48 14	91.26 199	72.57 243	97.65 66	97.34 15

MSP-MVS		89.08 67	88.16 84	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 123	89.16 129	92.25 171	72.03 259	96.36 4	88.21 13	90.93 298	92.98 176
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
SD-MVS		88.96 68	89.88 54	86.22 121	91.63 129	77.07 112	89.82 70	93.77 54	78.90 116	92.88 49	92.29 169	86.11 64	90.22 238	86.24 52	97.24 84	91.36 250
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
HPM-MVS++		88.93 69	88.45 80	90.38 44	94.92 36	85.85 31	89.70 72	91.27 154	78.20 126	86.69 195	92.28 170	80.36 142	95.06 67	86.17 53	96.49 105	90.22 284

Elysia		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 223	94.82 73	88.19 14	95.92 135	96.80 27

StellarMVS		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 223	94.82 73	88.19 14	95.92 135	96.80 27

test_0402		88.65 72	89.58 61	85.88 130	92.55 97	72.22 166	84.01 193	89.44 214	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 235

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 95	87.95 26	89.62 118	92.87 146	84.56 77	93.89 113	77.65 162	96.62 100	90.70 270

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 131	84.26 53	90.87 92	93.92 109	82.18 112	89.29 268	73.75 221	94.81 181	93.70 135

Anonymous20231211		88.40 74	89.62 60	84.73 158	90.46 165	65.27 254	88.86 92	93.02 92	87.15 30	93.05 47	97.10 11	82.28 110	92.02 178	76.70 174	97.99 46	96.88 26

PS-MVSNAJss		88.31 76	87.90 87	89.56 60	93.31 78	77.96 99	87.94 110	91.97 128	70.73 240	94.19 26	96.67 17	76.94 185	94.57 84	83.07 92	96.28 113	96.15 38

OMC-MVS		88.19 77	87.52 91	90.19 48	91.94 120	81.68 65	87.49 117	93.17 81	76.02 149	88.64 139	91.22 208	84.24 82	93.37 139	77.97 160	97.03 89	95.52 56

CS-MVS		88.14 78	87.67 90	89.54 61	89.56 184	79.18 85	90.47 56	94.77 17	79.37 110	84.32 259	89.33 273	83.87 83	94.53 87	82.45 102	94.89 177	94.90 76

TSAR-MVS + MP.		88.14 78	87.82 88	89.09 69	95.72 22	76.74 115	92.49 26	91.19 157	67.85 283	86.63 196	94.84 59	79.58 150	95.96 15	87.62 24	94.50 190	94.56 90
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 80	89.79 56	82.98 214	93.26 80	63.94 269	91.10 46	89.64 208	85.07 45	90.91 88	91.09 213	89.16 25	91.87 183	82.03 107	95.87 139	93.13 164

EC-MVSNet		88.01 81	88.32 83	87.09 100	89.28 191	72.03 169	90.31 60	96.31 4	80.88 90	85.12 235	89.67 267	84.47 79	95.46 50	82.56 101	96.26 116	93.77 133

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 158	79.26 111	89.68 115	94.81 63	82.44 101	87.74 299	76.54 179	88.74 342	96.61 32

AllTest		87.97 83	87.40 95	89.68 56	91.59 130	83.40 52	89.50 81	95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 256	96.14 121	94.16 112

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 266	84.38 185	91.29 151	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 234	98.66 11	97.69 9

nrg030		87.85 85	88.49 79	85.91 128	90.07 175	69.73 201	87.86 111	94.20 31	74.04 181	92.70 57	94.66 64	85.88 67	91.50 190	79.72 131	97.32 82	96.50 34

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 163	91.23 155	77.31 139	87.07 185	91.47 198	82.94 95	94.71 77	84.67 77	96.27 115	92.62 191

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 60	79.44 108	86.55 197	92.95 143	74.84 210	95.22 59	80.78 120	95.83 141	94.46 94

sc_t1		87.70 88	88.94 71	83.99 181	93.47 71	67.15 232	85.05 168	88.21 238	86.81 32	91.87 70	97.65 5	85.51 71	87.91 294	74.22 208	97.63 67	96.92 25

MM		87.64 89	87.15 97	89.09 69	89.51 185	76.39 121	88.68 97	86.76 269	84.54 50	83.58 278	93.78 114	73.36 240	96.48 2	87.98 17	96.21 117	94.41 101

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 200	92.03 116	62.26 298	91.49 41	92.62 107	88.07 25	88.07 155	96.17 26	72.24 254	95.79 31	84.85 74	94.16 203	92.58 194

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 160	78.77 119	84.85 246	90.89 224	80.85 135	95.29 56	81.14 115	95.32 158	92.34 211

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 114	70.25 246	89.35 126	90.68 234	82.85 96	94.57 84	79.55 135	95.95 132	92.00 230

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 239	81.66 81	94.64 18	96.53 20	65.94 296	94.75 76	83.02 94	96.83 94	95.41 58

fmvsm_s_conf0.5_n_9		87.04 94	87.02 102	87.08 101	89.67 182	75.87 126	84.60 178	89.74 203	74.40 178	89.92 110	93.41 125	80.45 140	90.63 226	86.66 44	94.37 196	94.73 87

SPE-MVS-test		87.00 95	86.43 112	88.71 76	89.46 187	77.46 105	89.42 85	95.73 7	77.87 132	81.64 319	87.25 318	82.43 102	94.53 87	77.65 162	96.46 107	94.14 114

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 221	83.80 203	92.87 97	80.37 94	89.61 120	91.81 184	77.72 169	94.18 100	75.00 201	98.53 16	96.99 24

Vis-MVSNet		86.86 97	86.58 109	87.72 94	92.09 113	77.43 107	87.35 118	92.09 124	78.87 117	84.27 264	94.05 98	78.35 161	93.65 121	80.54 124	91.58 285	92.08 226
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 98	87.06 100	86.17 124	92.86 91	67.02 236	82.55 243	91.56 141	83.08 68	90.92 86	91.82 183	78.25 162	93.99 107	74.16 211	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 226	92.21 119	81.73 80	90.92 86	91.97 176	77.20 179	93.99 107	74.16 211	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 170	87.09 259	65.22 255	84.16 189	94.23 28	77.89 130	91.28 81	93.66 120	84.35 80	92.71 158	80.07 125	94.87 180	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 101	86.52 110	87.18 99	85.94 296	78.30 92	86.93 125	92.20 120	65.94 303	89.16 129	93.16 132	83.10 93	89.89 253	87.81 20	94.43 194	93.35 152

tt0320-xc		86.67 102	88.41 81	81.44 256	93.45 72	60.44 326	83.96 195	88.50 227	87.26 29	90.90 90	97.90 3	85.61 68	86.40 325	70.14 268	98.01 45	97.47 14

IS-MVSNet		86.66 103	86.82 108	86.17 124	92.05 115	66.87 239	91.21 44	88.64 224	86.30 37	89.60 121	92.59 155	69.22 277	94.91 71	73.89 218	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 257	93.57 69	60.73 323	84.37 186	88.61 226	87.00 31	90.75 93	97.98 2	85.54 70	86.45 323	69.75 273	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 276	86.64 135	92.20 120	75.42 163	92.81 54	94.50 72	74.05 226	94.06 106	83.88 84	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 242	92.22 109	62.28 297	84.66 177	89.15 218	83.54 63	89.85 111	97.32 8	88.08 39	86.80 316	70.43 265	97.30 83	96.62 31

NormalMVS		86.47 107	85.32 139	89.94 51	94.43 44	80.42 72	88.63 99	93.59 63	74.56 173	85.12 235	90.34 247	66.19 293	94.20 97	76.57 177	98.44 20	95.19 68

PHI-MVS		86.38 108	85.81 126	88.08 89	88.44 219	77.34 108	89.35 86	93.05 88	73.15 202	84.76 248	87.70 307	78.87 155	94.18 100	80.67 122	96.29 112	92.73 183

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 132	80.35 95	89.54 124	88.01 294	79.09 153	92.13 174	75.51 194	95.06 169	90.41 281

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 127	70.56 241	84.96 241	90.69 233	80.01 146	95.14 64	78.37 149	95.78 145	91.82 235
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 111	86.83 107	84.36 170	87.82 233	62.35 296	86.42 139	91.33 150	76.78 143	92.73 56	94.48 74	73.41 237	93.72 119	83.10 91	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 202	90.19 170	64.55 262	84.55 180	90.71 169	85.85 40	89.94 109	95.24 50	82.13 113	90.40 233	69.19 280	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 216	86.91 267	70.38 192	85.31 162	92.61 108	75.59 159	88.32 150	92.87 146	82.22 111	88.63 280	88.80 9	92.82 248	89.83 294

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 311	78.25 93	85.82 151	91.82 134	65.33 317	88.55 141	92.35 168	82.62 100	89.80 255	86.87 40	94.32 198	93.18 163

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 126	65.91 305	86.19 208	91.75 188	83.77 86	94.98 69	77.43 167	96.71 98	93.73 134

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 257	90.46 178	80.99 88	88.42 146	91.97 176	77.56 172	93.85 114	72.46 244	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 196	90.32 185	65.79 307	84.49 253	90.97 218	81.93 119	93.63 123	81.21 114	96.54 103	90.88 264

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 202	80.42 93	87.76 169	93.24 129	73.76 231	91.54 189	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 227	92.25 107	56.44 370	85.75 152	93.09 86	77.33 138	91.94 69	94.65 65	74.78 212	93.41 138	75.11 200	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 320	78.21 94	85.40 161	91.39 148	65.32 318	87.72 171	91.81 184	82.33 105	89.78 256	86.68 42	94.20 201	92.99 174

Effi-MVS+-dtu		85.82 121	83.38 187	93.14 4	87.13 254	91.15 3	87.70 113	88.42 230	74.57 172	83.56 279	85.65 342	78.49 160	94.21 96	72.04 246	92.88 244	94.05 117

TAPA-MVS	77.73 12	85.71 122	84.83 149	88.37 83	88.78 209	79.72 79	87.15 122	93.50 66	69.17 258	85.80 218	89.56 268	80.76 136	92.13 174	73.21 239	95.51 152	93.25 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 123	86.14 117	83.58 196	87.97 227	67.13 233	87.55 114	94.32 22	73.44 192	88.47 144	87.54 310	86.45 59	91.06 206	75.76 192	93.76 214	92.54 197

canonicalmvs		85.50 123	86.14 117	83.58 196	87.97 227	67.13 233	87.55 114	94.32 22	73.44 192	88.47 144	87.54 310	86.45 59	91.06 206	75.76 192	93.76 214	92.54 197

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 161	87.10 257	69.98 197	84.28 187	92.68 104	74.77 169	87.90 162	92.36 167	73.94 227	90.41 232	85.95 60	92.74 250	93.66 136

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 242	81.51 83	87.05 186	91.83 182	66.18 295	95.29 56	70.75 259	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 168	90.08 173	67.07 235	85.86 150	91.39 148	72.33 219	87.59 173	90.25 252	84.85 75	92.37 168	78.00 158	91.94 275	93.66 136

MVS_0304		85.37 128	84.58 158	87.75 93	85.28 310	73.36 141	86.54 138	85.71 286	77.56 137	81.78 317	92.47 160	70.29 271	96.02 11	85.59 63	95.96 130	93.87 125

FIs		85.35 129	86.27 114	82.60 226	91.86 122	57.31 363	85.10 167	93.05 88	75.83 154	91.02 85	93.97 102	73.57 233	92.91 156	73.97 217	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 298	76.13 125	85.15 166	92.32 117	61.40 353	91.33 78	90.85 227	83.76 87	86.16 331	84.31 80	93.28 232	92.15 224

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 289	62.77 283	83.03 228	93.93 47	74.69 171	88.21 152	92.68 154	82.29 109	91.89 182	77.87 161	93.75 217	95.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		85.20 132	85.93 122	83.02 212	86.30 284	62.37 295	84.55 180	93.96 45	74.48 175	87.12 180	92.03 175	82.30 107	91.94 179	78.39 148	94.21 200	94.74 86

SSM_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 204	86.17 144	91.58 139	74.41 176	86.55 197	91.49 195	78.54 156	93.97 109	73.71 222	93.21 236	92.59 193

K. test v3		85.14 134	84.73 151	86.37 115	91.13 151	69.63 203	85.45 159	76.68 371	84.06 56	92.44 61	96.99 13	62.03 323	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 322	74.71 132	85.87 149	90.35 184	77.94 129	83.82 271	96.96 15	77.75 167	80.03 390	78.44 147	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 136	84.53 161	86.88 106	84.01 335	72.76 151	83.91 199	85.18 296	80.44 92	88.75 136	85.49 346	80.08 145	91.92 180	82.02 108	90.85 303	95.97 44

fmvsm_l_conf0.5_n_3		85.11 137	84.96 146	85.56 137	87.49 246	75.69 128	84.71 175	90.61 174	67.64 287	84.88 244	92.05 174	82.30 107	88.36 286	83.84 86	91.10 291	92.62 191

MGCFI-Net		85.04 138	85.95 121	82.31 235	87.52 244	63.59 272	86.23 143	93.96 45	73.46 190	88.07 155	87.83 305	86.46 58	90.87 216	76.17 186	93.89 211	92.47 201

EI-MVSNet-UG-set		85.04 138	84.44 164	86.85 107	83.87 339	72.52 160	83.82 201	85.15 297	80.27 97	88.75 136	85.45 348	79.95 147	91.90 181	81.92 111	90.80 305	96.13 39

X-MVStestdata		85.04 138	82.70 205	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 467	86.57 56	95.80 28	87.35 32	97.62 69	94.20 108

MSLP-MVS++		85.00 141	86.03 120	81.90 242	91.84 125	71.56 179	86.75 133	93.02 92	75.95 152	87.12 180	89.39 271	77.98 164	89.40 267	77.46 165	94.78 182	84.75 370

F-COLMAP		84.97 142	83.42 186	89.63 58	92.39 102	83.40 52	88.83 93	91.92 130	73.19 201	80.18 341	89.15 277	77.04 183	93.28 141	65.82 313	92.28 264	92.21 220

SSM_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 139	74.41 176	85.68 219	91.49 195	78.54 156	93.69 120	73.71 222	93.47 225	92.38 208

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 306	90.42 59	92.37 116	71.48 230	88.72 138	93.13 133	70.16 273	95.15 63	79.26 140	94.11 204	92.41 203

3Dnovator	80.37 7	84.80 144	84.71 154	85.06 149	86.36 282	74.71 132	88.77 95	90.00 198	75.65 157	84.96 241	93.17 131	74.06 225	91.19 201	78.28 152	91.09 292	89.29 304

SymmetryMVS		84.79 146	83.54 181	88.55 79	92.44 101	80.42 72	88.63 99	82.37 331	74.56 173	85.12 235	90.34 247	66.19 293	94.20 97	76.57 177	95.68 149	91.03 258

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 270	83.25 221	89.88 201	76.06 147	89.62 118	92.37 165	73.40 239	92.52 163	78.16 155	94.77 184	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 148	84.34 168	85.49 141	90.18 171	75.86 127	79.23 309	87.13 259	73.35 194	85.56 226	89.34 272	83.60 89	90.50 229	76.64 176	94.05 208	90.09 290

HQP-MVS		84.61 149	84.06 173	86.27 118	91.19 147	70.66 187	84.77 170	92.68 104	73.30 197	80.55 333	90.17 257	72.10 255	94.61 82	77.30 169	94.47 192	93.56 148

v1192		84.57 150	84.69 156	84.21 176	87.75 235	62.88 280	83.02 229	91.43 145	69.08 260	89.98 108	90.89 224	72.70 249	93.62 126	82.41 103	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 151	84.71 154	84.11 179	87.92 230	72.09 168	84.80 169	88.64 224	64.43 327	88.77 135	91.78 186	78.07 163	87.95 293	85.85 61	92.18 268	92.30 213

FMVSNet1		84.55 152	85.45 135	81.85 244	90.27 169	61.05 314	86.83 129	88.27 235	78.57 122	89.66 117	95.64 38	75.43 202	90.68 223	69.09 281	95.33 157	93.82 128

v1144		84.54 153	84.72 153	84.00 180	87.67 239	62.55 287	82.97 231	90.93 165	70.32 245	89.80 112	90.99 217	73.50 234	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 301	84.20 332	73.57 140	89.55 78	90.44 179	84.24 54	84.38 256	94.89 57	76.35 198	80.40 387	76.14 187	96.80 96	82.36 408
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 155	84.27 169	84.74 157	87.25 250	70.84 186	83.55 211	88.45 229	68.64 269	86.29 207	91.31 204	74.97 208	88.42 284	87.87 19	90.07 321	94.95 75

MCST-MVS		84.36 156	83.93 177	85.63 135	91.59 130	71.58 177	83.52 212	92.13 122	61.82 346	83.96 269	89.75 265	79.93 148	93.46 135	78.33 151	94.34 197	91.87 234

VDDNet		84.35 157	85.39 137	81.25 259	95.13 32	59.32 340	85.42 160	81.11 342	86.41 36	87.41 176	96.21 25	73.61 232	90.61 227	66.33 306	96.85 92	93.81 131

ETV-MVS		84.31 158	83.91 178	85.52 138	88.58 215	70.40 191	84.50 184	93.37 68	78.76 120	84.07 267	78.72 421	80.39 141	95.13 65	73.82 220	92.98 242	91.04 257

v1240		84.30 159	84.51 162	83.65 193	87.65 240	61.26 310	82.85 235	91.54 142	67.94 280	90.68 95	90.65 238	71.71 263	93.64 122	82.84 97	94.78 182	96.07 41

MVS_111021_LR		84.28 160	83.76 179	85.83 132	89.23 193	83.07 55	80.99 278	83.56 319	72.71 211	86.07 211	89.07 279	81.75 126	86.19 330	77.11 171	93.36 228	88.24 323

h-mvs33		84.25 161	82.76 204	88.72 75	91.82 127	82.60 60	84.00 194	84.98 303	71.27 231	86.70 193	90.55 243	63.04 320	93.92 112	78.26 153	94.20 201	89.63 296

v144192		84.24 162	84.41 165	83.71 192	87.59 242	61.57 305	82.95 232	91.03 161	67.82 284	89.80 112	90.49 244	73.28 241	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 254	88.61 214	61.98 302	82.90 234	93.11 84	68.66 268	92.77 55	92.39 161	78.50 159	87.63 302	76.99 173	92.30 261	94.90 76

v1921920		84.23 163	84.37 167	83.79 188	87.64 241	61.71 304	82.91 233	91.20 156	67.94 280	90.06 103	90.34 247	72.04 258	93.59 128	82.32 104	94.91 175	96.07 41

VDD-MVS		84.23 163	84.58 158	83.20 208	91.17 150	65.16 257	83.25 221	84.97 304	79.79 102	87.18 179	94.27 83	74.77 213	90.89 214	69.24 277	96.54 103	93.55 150

v2v482		84.09 166	84.24 170	83.62 194	87.13 254	61.40 307	82.71 238	89.71 206	72.19 222	89.55 122	91.41 199	70.70 269	93.20 143	81.02 116	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 167	84.11 172	83.98 182	92.22 109	72.61 157	82.20 259	87.02 265	72.63 212	88.86 132	91.02 216	78.52 158	91.11 204	73.41 229	91.09 292	88.21 324

fmvsm_s_conf0.5_n_6		84.05 168	84.14 171	83.81 186	87.75 235	71.17 182	83.42 215	91.10 159	67.90 282	84.53 251	90.70 232	73.01 244	88.73 278	85.09 68	93.72 219	91.53 247

DP-MVS Recon		84.05 168	83.22 190	86.52 113	91.73 128	75.27 130	83.23 223	92.40 112	72.04 224	82.04 308	88.33 290	77.91 166	93.95 111	66.17 307	95.12 167	90.34 283

viewmacassd2359aftdt		84.04 170	84.78 150	81.81 247	86.43 276	60.32 328	81.95 261	92.82 100	71.56 227	86.06 212	92.98 139	81.79 125	90.28 234	76.18 185	93.24 233	94.82 84

TransMVSNet (Re)		84.02 171	85.74 130	78.85 300	91.00 154	55.20 382	82.29 253	87.26 254	79.65 105	88.38 148	95.52 41	83.00 94	86.88 314	67.97 295	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 172	85.90 123	78.29 312	91.47 140	53.44 394	82.29 253	87.00 268	79.06 114	89.55 122	95.72 36	77.20 179	86.14 332	72.30 245	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 173	84.46 163	82.53 230	86.11 292	70.65 189	82.45 248	89.17 217	67.72 286	86.74 192	91.49 195	79.20 151	85.86 341	84.71 76	92.60 254	91.07 256

TSAR-MVS + GP.		83.95 174	82.69 206	87.72 94	89.27 192	81.45 67	83.72 205	81.58 340	74.73 170	85.66 222	86.06 337	72.56 251	92.69 160	75.44 196	95.21 162	89.01 317

LuminaMVS		83.94 175	83.51 182	85.23 144	89.78 181	71.74 172	84.76 173	87.27 253	72.60 213	89.31 127	90.60 242	64.04 309	90.95 209	79.08 141	94.11 204	92.99 174

alignmvs		83.94 175	83.98 175	83.80 187	87.80 234	67.88 228	84.54 182	91.42 147	73.27 200	88.41 147	87.96 295	72.33 252	90.83 217	76.02 189	94.11 204	92.69 187

Effi-MVS+		83.90 177	84.01 174	83.57 198	87.22 252	65.61 253	86.55 137	92.40 112	78.64 121	81.34 324	84.18 367	83.65 88	92.93 154	74.22 208	87.87 356	92.17 223

fmvsm_s_conf0.1_n_2		83.82 178	83.49 183	84.84 152	85.99 295	70.19 195	80.93 279	87.58 249	67.26 293	87.94 161	92.37 165	71.40 265	88.01 290	86.03 55	91.87 276	96.31 36

mvs5depth		83.82 178	84.54 160	81.68 250	82.23 364	68.65 219	86.89 126	89.90 200	80.02 101	87.74 170	97.86 4	64.19 308	82.02 375	76.37 181	95.63 151	94.35 103

CANet		83.79 180	82.85 203	86.63 110	86.17 289	72.21 167	83.76 204	91.43 145	77.24 140	74.39 396	87.45 314	75.36 203	95.42 52	77.03 172	92.83 247	92.25 219

pm-mvs1		83.69 181	84.95 147	79.91 286	90.04 177	59.66 337	82.43 249	87.44 250	75.52 161	87.85 165	95.26 49	81.25 131	85.65 345	68.74 287	96.04 126	94.42 100

AdaColmap		83.66 182	83.69 180	83.57 198	90.05 176	72.26 165	86.29 141	90.00 198	78.19 127	81.65 318	87.16 320	83.40 91	94.24 95	61.69 349	94.76 185	84.21 380

MIMVSNet1		83.63 183	84.59 157	80.74 270	94.06 59	62.77 283	82.72 237	84.53 311	77.57 136	90.34 99	95.92 31	76.88 191	85.83 342	61.88 347	97.42 79	93.62 142

fmvsm_s_conf0.5_n_2		83.62 184	83.29 189	84.62 162	85.43 308	70.18 196	80.61 285	87.24 255	67.14 294	87.79 167	91.87 178	71.79 262	87.98 292	86.00 59	91.77 279	95.71 50

test_fmvsm_n_1920		83.60 185	82.89 200	85.74 133	85.22 312	77.74 102	84.12 191	90.48 176	59.87 373	86.45 206	91.12 212	75.65 200	85.89 339	82.28 105	90.87 301	93.58 146

WR-MVS		83.56 186	84.40 166	81.06 264	93.43 75	54.88 383	78.67 318	85.02 301	81.24 85	90.74 94	91.56 193	72.85 246	91.08 205	68.00 294	98.04 41	97.23 17

CNLPA		83.55 187	83.10 195	84.90 151	89.34 190	83.87 50	84.54 182	88.77 221	79.09 113	83.54 280	88.66 287	74.87 209	81.73 377	66.84 301	92.29 263	89.11 310

viewcassd2359sk11		83.53 188	83.96 176	82.25 236	86.97 266	61.13 312	80.80 283	93.22 79	70.97 237	85.36 230	91.08 214	81.84 123	91.29 198	74.79 203	90.58 317	94.33 105

LCM-MVSNet-Re		83.48 189	85.06 143	78.75 302	85.94 296	55.75 376	80.05 291	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 259	59.95 360	94.89 177	90.75 267

hse-mvs2		83.47 190	81.81 221	88.47 80	91.03 153	82.27 61	82.61 239	83.69 317	71.27 231	86.70 193	86.05 338	63.04 320	92.41 166	78.26 153	93.62 223	90.71 269

V42		83.47 190	83.37 188	83.75 190	83.16 358	63.33 275	81.31 272	90.23 192	69.51 254	90.91 88	90.81 229	74.16 222	92.29 172	80.06 126	90.22 319	95.62 54

VPA-MVSNet		83.47 190	84.73 151	79.69 291	90.29 168	57.52 362	81.30 274	88.69 223	76.29 145	87.58 174	94.44 75	80.60 139	87.20 308	66.60 304	96.82 95	94.34 104

mamba_0408		83.44 193	82.88 201	85.11 147	89.13 195	68.97 214	72.73 393	91.28 152	72.90 205	85.68 219	90.61 240	76.78 192	93.97 109	73.37 231	93.47 225	92.38 208

PAPM_NR		83.23 194	83.19 192	83.33 204	90.90 156	65.98 249	88.19 104	90.78 168	78.13 128	80.87 329	87.92 299	73.49 236	92.42 165	70.07 269	88.40 345	91.60 244

CLD-MVS		83.18 195	82.64 207	84.79 155	89.05 198	67.82 229	77.93 328	92.52 110	68.33 272	85.07 238	81.54 396	82.06 116	92.96 152	69.35 276	97.91 54	93.57 147
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 196	85.68 131	75.65 350	81.24 376	45.26 438	79.94 293	92.91 96	83.83 57	91.33 78	96.88 16	80.25 143	85.92 335	68.89 284	95.89 138	95.76 48

FA-MVS(test-final)		83.13 197	83.02 196	83.43 201	86.16 291	66.08 248	88.00 108	88.36 232	75.55 160	85.02 239	92.75 152	65.12 302	92.50 164	74.94 202	91.30 289	91.72 239

114514_t		83.10 198	82.54 210	84.77 156	92.90 88	69.10 213	86.65 134	90.62 173	54.66 405	81.46 321	90.81 229	76.98 184	94.38 90	72.62 242	96.18 119	90.82 266

RRT-MVS		82.97 199	83.44 184	81.57 252	85.06 315	58.04 357	87.20 119	90.37 182	77.88 131	88.59 140	93.70 119	63.17 317	93.05 150	76.49 180	88.47 344	93.62 142

viewmanbaseed2359cas		82.95 200	83.43 185	81.52 253	85.18 313	60.03 333	81.36 271	92.38 114	69.55 253	84.84 247	91.38 200	79.85 149	90.09 247	74.22 208	92.09 270	94.43 99

BP-MVS1		82.81 201	81.67 223	86.23 119	87.88 232	68.53 220	86.06 146	84.36 312	75.65 157	85.14 234	90.19 254	45.84 409	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 202	81.64 224	86.21 122	86.20 288	76.24 123	86.86 127	85.68 287	77.07 141	73.76 400	92.82 148	69.64 274	91.82 185	69.04 283	93.69 220	90.56 277
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
LF4IMVS		82.75 203	81.93 219	85.19 145	82.08 365	80.15 76	85.53 157	88.76 222	68.01 277	85.58 225	87.75 306	71.80 261	86.85 315	74.02 216	93.87 212	88.58 320

EI-MVSNet		82.61 204	82.42 212	83.20 208	83.25 355	63.66 270	83.50 213	85.07 298	76.06 147	86.55 197	85.10 354	73.41 237	90.25 235	78.15 157	90.67 312	95.68 52

QAPM		82.59 205	82.59 209	82.58 227	86.44 275	66.69 240	89.94 68	90.36 183	67.97 279	84.94 243	92.58 157	72.71 248	92.18 173	70.63 262	87.73 359	88.85 318

fmvsm_s_conf0.1_n_a		82.58 206	81.93 219	84.50 165	87.68 238	73.35 142	86.14 145	77.70 360	61.64 351	85.02 239	91.62 190	77.75 167	86.24 327	82.79 98	87.07 367	93.91 123

Fast-Effi-MVS+-dtu		82.54 207	81.41 233	85.90 129	85.60 303	76.53 118	83.07 227	89.62 210	73.02 204	79.11 351	83.51 372	80.74 137	90.24 237	68.76 286	89.29 332	90.94 261

MVS_Test		82.47 208	83.22 190	80.22 282	82.62 363	57.75 361	82.54 244	91.96 129	71.16 235	82.89 291	92.52 159	77.41 174	90.50 229	80.04 127	87.84 358	92.40 205

viewdifsd2359ckpt11		82.46 209	82.98 198	80.88 267	83.53 342	61.00 317	79.46 302	85.97 282	69.48 255	87.89 163	91.31 204	82.10 114	88.61 281	74.28 206	92.86 245	93.02 170

viewmsd2359difaftdt		82.46 209	82.99 197	80.88 267	83.52 343	61.00 317	79.46 302	85.97 282	69.48 255	87.89 163	91.31 204	82.10 114	88.61 281	74.28 206	92.86 245	93.02 170

v148		82.31 211	82.48 211	81.81 247	85.59 304	59.66 337	81.47 269	86.02 280	72.85 207	88.05 157	90.65 238	70.73 268	90.91 213	75.15 199	91.79 277	94.87 78

API-MVS		82.28 212	82.61 208	81.30 258	86.29 285	69.79 198	88.71 96	87.67 248	78.42 124	82.15 304	84.15 368	77.98 164	91.59 188	65.39 316	92.75 249	82.51 407

MVSFormer		82.23 213	81.57 229	84.19 178	85.54 305	69.26 208	91.98 35	90.08 196	71.54 228	76.23 376	85.07 357	58.69 345	94.27 92	86.26 49	88.77 340	89.03 315

viewdifsd2359ckpt13		82.22 214	81.98 218	82.95 216	85.48 307	64.44 263	83.17 225	92.11 123	65.97 302	83.72 274	89.73 266	77.60 171	90.80 219	70.61 263	89.42 330	93.59 145

fmvsm_s_conf0.5_n_a		82.21 215	81.51 232	84.32 173	86.56 272	73.35 142	85.46 158	77.30 364	61.81 347	84.51 252	90.88 226	77.36 175	86.21 329	82.72 99	86.97 372	93.38 151

EIA-MVS		82.19 216	81.23 240	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 242	78.58 356	79.77 412	77.29 176	94.20 97	71.51 252	88.96 338	91.93 233

GDP-MVS		82.17 217	80.85 248	86.15 126	88.65 212	68.95 217	85.65 155	93.02 92	68.42 270	83.73 273	89.54 269	45.07 420	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 217	81.59 227	83.94 185	86.87 270	71.57 178	85.19 165	77.42 363	62.27 345	84.47 255	91.33 202	76.43 195	85.91 337	83.14 89	87.14 365	94.33 105

PCF-MVS	74.62 15	82.15 219	80.92 246	85.84 131	89.43 188	72.30 164	80.53 286	91.82 134	57.36 389	87.81 166	89.92 262	77.67 170	93.63 123	58.69 365	95.08 168	91.58 245
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 220	80.31 255	87.45 97	90.86 158	80.29 75	85.88 148	90.65 171	68.17 275	76.32 375	86.33 332	73.12 243	92.61 162	61.40 352	90.02 323	89.44 299
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 221	81.54 231	83.60 195	83.94 336	73.90 138	83.35 218	86.10 276	58.97 375	83.80 272	90.36 246	74.23 220	86.94 313	82.90 95	90.22 319	89.94 292

fmvsm_s_conf0.5_n_7		82.04 222	82.05 216	82.01 240	86.98 265	71.07 183	78.70 316	89.45 213	68.07 276	78.14 358	91.61 191	74.19 221	85.92 335	79.61 134	91.73 280	89.05 314

GBi-Net		82.02 223	82.07 214	81.85 244	86.38 279	61.05 314	86.83 129	88.27 235	72.43 214	86.00 213	95.64 38	63.78 313	90.68 223	65.95 309	93.34 229	93.82 128

test1		82.02 223	82.07 214	81.85 244	86.38 279	61.05 314	86.83 129	88.27 235	72.43 214	86.00 213	95.64 38	63.78 313	90.68 223	65.95 309	93.34 229	93.82 128

OpenMVS	76.72 13	81.98 225	82.00 217	81.93 241	84.42 327	68.22 223	88.50 102	89.48 212	66.92 297	81.80 315	91.86 179	72.59 250	90.16 241	71.19 255	91.25 290	87.40 340

KD-MVS_self_test		81.93 226	83.14 194	78.30 311	84.75 321	52.75 398	80.37 288	89.42 215	70.24 247	90.26 101	93.39 126	74.55 219	86.77 317	68.61 289	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 227	81.30 237	83.75 190	86.02 294	71.56 179	84.73 174	77.11 367	62.44 342	84.00 268	90.68 234	76.42 196	85.89 339	83.14 89	87.11 366	93.81 131

SDMVSNet		81.90 228	83.17 193	78.10 315	88.81 207	62.45 293	76.08 362	86.05 279	73.67 186	83.41 281	93.04 135	82.35 104	80.65 384	70.06 270	95.03 170	91.21 252

tfpnnormal		81.79 229	82.95 199	78.31 310	88.93 203	55.40 378	80.83 282	82.85 326	76.81 142	85.90 217	94.14 93	74.58 217	86.51 321	66.82 302	95.68 149	93.01 173

AstraMVS		81.67 230	81.40 234	82.48 232	87.06 262	66.47 243	81.41 270	81.68 337	68.78 265	88.00 158	90.95 222	65.70 298	87.86 298	76.66 175	92.38 258	93.12 166

c3_l		81.64 231	81.59 227	81.79 249	80.86 382	59.15 345	78.61 319	90.18 194	68.36 271	87.20 178	87.11 322	69.39 275	91.62 187	78.16 155	94.43 194	94.60 89

guyue		81.57 232	81.37 236	82.15 237	86.39 277	66.13 247	81.54 268	83.21 321	69.79 251	87.77 168	89.95 260	65.36 301	87.64 301	75.88 190	92.49 256	92.67 188

PVSNet_Blended_VisFu		81.55 233	80.49 253	84.70 160	91.58 133	73.24 146	84.21 188	91.67 138	62.86 336	80.94 327	87.16 320	67.27 287	92.87 157	69.82 272	88.94 339	87.99 330

fmvsm_l_conf0.5_n_a		81.46 234	80.87 247	83.25 206	83.73 341	73.21 147	83.00 230	85.59 289	58.22 381	82.96 290	90.09 259	72.30 253	86.65 319	81.97 110	89.95 324	89.88 293

SSM_04072		81.44 235	82.88 201	77.10 330	89.13 195	68.97 214	72.73 393	91.28 152	72.90 205	85.68 219	90.61 240	76.78 192	69.94 427	73.37 231	93.47 225	92.38 208

DELS-MVS		81.44 235	81.25 238	82.03 239	84.27 331	62.87 281	76.47 356	92.49 111	70.97 237	81.64 319	83.83 369	75.03 206	92.70 159	74.29 205	92.22 267	90.51 279
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
FMVSNet2		81.31 237	81.61 226	80.41 278	86.38 279	58.75 352	83.93 198	86.58 271	72.43 214	87.65 172	92.98 139	63.78 313	90.22 238	66.86 299	93.92 210	92.27 217

TinyColmap		81.25 238	82.34 213	77.99 318	85.33 309	60.68 324	82.32 252	88.33 233	71.26 233	86.97 187	92.22 173	77.10 182	86.98 312	62.37 341	95.17 164	86.31 353

diffmvs_AUTHOR		81.24 239	81.55 230	80.30 280	80.61 387	60.22 329	77.98 327	90.48 176	67.77 285	83.34 283	89.50 270	74.69 215	87.42 304	78.78 145	90.81 304	93.27 157

AUN-MVS		81.18 240	78.78 278	88.39 82	90.93 155	82.14 62	82.51 245	83.67 318	64.69 326	80.29 337	85.91 341	51.07 384	92.38 167	76.29 184	93.63 222	90.65 274

IMVS_0407		81.08 241	81.23 240	80.62 275	85.76 299	62.46 289	82.46 246	87.91 243	65.23 319	82.12 305	87.92 299	77.27 177	90.18 240	71.67 248	90.74 307	89.20 305

tttt0517		81.07 242	79.58 268	85.52 138	88.99 201	66.45 244	87.03 124	75.51 379	73.76 185	88.32 150	90.20 253	37.96 441	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 243	80.57 250	82.46 233	87.50 245	63.22 277	78.37 322	89.63 209	68.01 277	81.87 311	82.08 390	82.31 106	92.65 161	67.10 298	88.30 351	91.51 248

BH-untuned		80.96 244	80.99 244	80.84 269	88.55 216	68.23 222	80.33 289	88.46 228	72.79 210	86.55 197	86.76 326	74.72 214	91.77 186	61.79 348	88.99 337	82.52 406

IMVS_0403		80.93 245	81.00 243	80.72 272	85.76 299	62.46 289	81.82 262	87.91 243	65.23 319	82.07 307	87.92 299	75.91 199	90.50 229	71.67 248	90.74 307	89.20 305

eth_miper_zixun_eth		80.84 246	80.22 259	82.71 224	81.41 374	60.98 319	77.81 330	90.14 195	67.31 292	86.95 188	87.24 319	64.26 306	92.31 170	75.23 198	91.61 283	94.85 82

xiu_mvs_v1_base_debu		80.84 246	80.14 261	82.93 219	88.31 220	71.73 173	79.53 298	87.17 256	65.43 313	79.59 343	82.73 384	76.94 185	90.14 244	73.22 234	88.33 347	86.90 347

xiu_mvs_v1_base		80.84 246	80.14 261	82.93 219	88.31 220	71.73 173	79.53 298	87.17 256	65.43 313	79.59 343	82.73 384	76.94 185	90.14 244	73.22 234	88.33 347	86.90 347

xiu_mvs_v1_base_debi		80.84 246	80.14 261	82.93 219	88.31 220	71.73 173	79.53 298	87.17 256	65.43 313	79.59 343	82.73 384	76.94 185	90.14 244	73.22 234	88.33 347	86.90 347

IterMVS-SCA-FT		80.64 250	79.41 269	84.34 172	83.93 337	69.66 202	76.28 358	81.09 343	72.43 214	86.47 204	90.19 254	60.46 330	93.15 146	77.45 166	86.39 378	90.22 284

BH-RMVSNet		80.53 251	80.22 259	81.49 255	87.19 253	66.21 246	77.79 331	86.23 274	74.21 180	83.69 275	88.50 288	73.25 242	90.75 220	63.18 337	87.90 355	87.52 338

VortexMVS		80.51 252	80.63 249	80.15 284	83.36 351	61.82 303	80.63 284	88.00 241	67.11 295	87.23 177	89.10 278	63.98 310	88.00 291	73.63 226	92.63 253	90.64 275

Anonymous202405211		80.51 252	81.19 242	78.49 307	88.48 217	57.26 364	76.63 351	82.49 329	81.21 86	84.30 262	92.24 172	67.99 283	86.24 327	62.22 342	95.13 165	91.98 232

DIV-MVS_self_test		80.43 254	80.23 257	81.02 265	79.99 392	59.25 342	77.07 344	87.02 265	67.38 289	86.19 208	89.22 274	63.09 318	90.16 241	76.32 182	95.80 143	93.66 136

cl____		80.42 255	80.23 257	81.02 265	79.99 392	59.25 342	77.07 344	87.02 265	67.37 290	86.18 210	89.21 275	63.08 319	90.16 241	76.31 183	95.80 143	93.65 139

diffmvs		80.40 256	80.48 254	80.17 283	79.02 405	60.04 331	77.54 335	90.28 191	66.65 300	82.40 298	87.33 317	73.50 234	87.35 306	77.98 159	89.62 328	93.13 164
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet		80.37 257	78.41 286	86.23 119	76.75 419	73.28 144	87.18 121	77.45 362	76.24 146	68.14 430	88.93 281	65.41 300	93.85 114	69.47 275	96.12 123	91.55 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 258	80.04 264	81.24 261	79.82 395	58.95 347	77.66 332	89.66 207	65.75 310	85.99 216	85.11 353	68.29 282	91.42 195	76.03 188	92.03 271	93.33 153

MG-MVS		80.32 259	80.94 245	78.47 308	88.18 223	52.62 401	82.29 253	85.01 302	72.01 225	79.24 350	92.54 158	69.36 276	93.36 140	70.65 261	89.19 335	89.45 298

mvsmamba		80.30 260	78.87 275	84.58 164	88.12 226	67.55 230	92.35 30	84.88 305	63.15 334	85.33 231	90.91 223	50.71 386	95.20 62	66.36 305	87.98 354	90.99 259

VPNet		80.25 261	81.68 222	75.94 346	92.46 100	47.98 425	76.70 349	81.67 338	73.45 191	84.87 245	92.82 148	74.66 216	86.51 321	61.66 350	96.85 92	93.33 153

MAR-MVS		80.24 262	78.74 280	84.73 158	86.87 270	78.18 95	85.75 152	87.81 247	65.67 312	77.84 362	78.50 422	73.79 230	90.53 228	61.59 351	90.87 301	85.49 363
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
PM-MVS		80.20 263	79.00 274	83.78 189	88.17 224	86.66 19	81.31 272	66.81 435	69.64 252	88.33 149	90.19 254	64.58 303	83.63 366	71.99 247	90.03 322	81.06 426

Anonymous20240521		80.18 264	81.25 238	76.95 332	83.15 359	60.84 321	82.46 246	85.99 281	68.76 266	86.78 189	93.73 118	59.13 342	77.44 401	73.71 222	97.55 74	92.56 195

LFMVS		80.15 265	80.56 251	78.89 299	89.19 194	55.93 372	85.22 164	73.78 391	82.96 69	84.28 263	92.72 153	57.38 354	90.07 249	63.80 331	95.75 146	90.68 271

DPM-MVS		80.10 266	79.18 273	82.88 222	90.71 161	69.74 200	78.87 314	90.84 166	60.29 369	75.64 385	85.92 340	67.28 286	93.11 147	71.24 254	91.79 277	85.77 359

MSDG		80.06 267	79.99 266	80.25 281	83.91 338	68.04 227	77.51 336	89.19 216	77.65 134	81.94 309	83.45 374	76.37 197	86.31 326	63.31 336	86.59 375	86.41 351

FE-MVS		79.98 268	78.86 276	83.36 203	86.47 274	66.45 244	89.73 71	84.74 309	72.80 209	84.22 266	91.38 200	44.95 421	93.60 127	63.93 329	91.50 286	90.04 291

sd_testset		79.95 269	81.39 235	75.64 351	88.81 207	58.07 356	76.16 361	82.81 327	73.67 186	83.41 281	93.04 135	80.96 134	77.65 400	58.62 366	95.03 170	91.21 252

ab-mvs		79.67 270	80.56 251	76.99 331	88.48 217	56.93 366	84.70 176	86.06 278	68.95 263	80.78 330	93.08 134	75.30 204	84.62 353	56.78 375	90.90 299	89.43 300

VNet		79.31 271	80.27 256	76.44 340	87.92 230	53.95 390	75.58 368	84.35 313	74.39 179	82.23 302	90.72 231	72.84 247	84.39 358	60.38 358	93.98 209	90.97 260

thisisatest0530		79.07 272	77.33 296	84.26 175	87.13 254	64.58 260	83.66 208	75.95 374	68.86 264	85.22 233	87.36 316	38.10 438	93.57 131	75.47 195	94.28 199	94.62 88

cl22		78.97 273	78.21 288	81.24 261	77.74 409	59.01 346	77.46 339	87.13 259	65.79 307	84.32 259	85.10 354	58.96 344	90.88 215	75.36 197	92.03 271	93.84 126

patch_mono-2		78.89 274	79.39 270	77.41 327	84.78 319	68.11 225	75.60 366	83.11 323	60.96 361	79.36 347	89.89 263	75.18 205	72.97 416	73.32 233	92.30 261	91.15 254

RPMNet		78.88 275	78.28 287	80.68 274	79.58 396	62.64 285	82.58 241	94.16 33	74.80 168	75.72 383	92.59 155	48.69 393	95.56 42	73.48 228	82.91 414	83.85 385

PAPR		78.84 276	78.10 289	81.07 263	85.17 314	60.22 329	82.21 257	90.57 175	62.51 338	75.32 389	84.61 362	74.99 207	92.30 171	59.48 363	88.04 353	90.68 271

viewmambaseed2359dif		78.80 277	78.47 285	79.78 287	80.26 391	59.28 341	77.31 341	87.13 259	60.42 367	82.37 299	88.67 286	74.58 217	87.87 297	67.78 297	87.73 359	92.19 221

PVSNet_BlendedMVS		78.80 277	77.84 290	81.65 251	84.43 325	63.41 273	79.49 301	90.44 179	61.70 350	75.43 386	87.07 323	69.11 278	91.44 193	60.68 356	92.24 265	90.11 289

FMVSNet3		78.80 277	78.55 282	79.57 293	82.89 362	56.89 368	81.76 263	85.77 285	69.04 261	86.00 213	90.44 245	51.75 382	90.09 247	65.95 309	93.34 229	91.72 239

test_yl		78.71 280	78.51 283	79.32 296	84.32 329	58.84 349	78.38 320	85.33 293	75.99 150	82.49 296	86.57 328	58.01 348	90.02 251	62.74 338	92.73 251	89.10 311

DCV-MVSNet		78.71 280	78.51 283	79.32 296	84.32 329	58.84 349	78.38 320	85.33 293	75.99 150	82.49 296	86.57 328	58.01 348	90.02 251	62.74 338	92.73 251	89.10 311

test1111		78.53 282	78.85 277	77.56 324	92.22 109	47.49 427	82.61 239	69.24 423	72.43 214	85.28 232	94.20 89	51.91 380	90.07 249	65.36 317	96.45 108	95.11 72

FE-MVSNET		78.46 283	79.36 271	75.75 348	86.53 273	54.53 385	78.03 324	85.35 292	69.01 262	85.41 229	90.68 234	64.27 305	85.73 343	62.59 340	92.35 260	87.00 346

icg_test_0407_2		78.46 283	79.68 267	74.78 358	85.76 299	62.46 289	68.51 422	87.91 243	65.23 319	82.12 305	87.92 299	77.27 177	72.67 417	71.67 248	90.74 307	89.20 305

ECVR-MVS		78.44 285	78.63 281	77.88 320	91.85 123	48.95 421	83.68 207	69.91 419	72.30 220	84.26 265	94.20 89	51.89 381	89.82 254	63.58 332	96.02 127	94.87 78

pmmvs-eth3d		78.42 286	77.04 299	82.57 229	87.44 247	74.41 135	80.86 281	79.67 351	55.68 398	84.69 249	90.31 251	60.91 328	85.42 346	62.20 343	91.59 284	87.88 334

mvs_anonymous		78.13 287	78.76 279	76.23 345	79.24 402	50.31 417	78.69 317	84.82 307	61.60 352	83.09 289	92.82 148	73.89 229	87.01 309	68.33 293	86.41 377	91.37 249

TAMVS		78.08 288	76.36 306	83.23 207	90.62 162	72.87 150	79.08 310	80.01 350	61.72 349	81.35 323	86.92 325	63.96 312	88.78 276	50.61 414	93.01 241	88.04 329

miper_enhance_ethall		77.83 289	76.93 300	80.51 276	76.15 426	58.01 358	75.47 370	88.82 220	58.05 383	83.59 277	80.69 400	64.41 304	91.20 200	73.16 240	92.03 271	92.33 212

Vis-MVSNet (Re-imp)		77.82 290	77.79 291	77.92 319	88.82 206	51.29 411	83.28 219	71.97 407	74.04 181	82.23 302	89.78 264	57.38 354	89.41 266	57.22 374	95.41 154	93.05 169

CANet_DTU		77.81 291	77.05 298	80.09 285	81.37 375	59.90 335	83.26 220	88.29 234	69.16 259	67.83 433	83.72 370	60.93 327	89.47 261	69.22 279	89.70 327	90.88 264

OpenMVS_ROB	70.19 17	77.77 292	77.46 293	78.71 303	84.39 328	61.15 311	81.18 276	82.52 328	62.45 341	83.34 283	87.37 315	66.20 292	88.66 279	64.69 324	85.02 394	86.32 352

SSC-MVS		77.55 293	81.64 224	65.29 424	90.46 165	20.33 471	73.56 386	68.28 425	85.44 41	88.18 154	94.64 68	70.93 267	81.33 379	71.25 253	92.03 271	94.20 108

MDA-MVSNet-bldmvs		77.47 294	76.90 301	79.16 298	79.03 404	64.59 259	66.58 434	75.67 377	73.15 202	88.86 132	88.99 280	66.94 288	81.23 380	64.71 323	88.22 352	91.64 243

jason		77.42 295	75.75 312	82.43 234	87.10 257	69.27 207	77.99 326	81.94 335	51.47 425	77.84 362	85.07 357	60.32 332	89.00 270	70.74 260	89.27 334	89.03 315
jason: jason.
CDS-MVSNet		77.32 296	75.40 316	83.06 211	89.00 200	72.48 161	77.90 329	82.17 333	60.81 362	78.94 353	83.49 373	59.30 340	88.76 277	54.64 394	92.37 259	87.93 333
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 297	77.75 292	75.73 349	85.76 299	62.46 289	70.84 408	87.91 243	65.23 319	72.21 408	87.92 299	67.48 285	75.53 409	71.67 248	90.74 307	89.20 305

xiu_mvs_v2_base		77.19 298	76.75 303	78.52 306	87.01 263	61.30 309	75.55 369	87.12 263	61.24 358	74.45 395	78.79 420	77.20 179	90.93 211	64.62 326	84.80 401	83.32 394

MVSTER		77.09 299	75.70 313	81.25 259	75.27 434	61.08 313	77.49 338	85.07 298	60.78 363	86.55 197	88.68 284	43.14 430	90.25 235	73.69 225	90.67 312	92.42 202

PS-MVSNAJ		77.04 300	76.53 305	78.56 305	87.09 259	61.40 307	75.26 371	87.13 259	61.25 357	74.38 397	77.22 434	76.94 185	90.94 210	64.63 325	84.83 400	83.35 393

IterMVS		76.91 301	76.34 307	78.64 304	80.91 380	64.03 267	76.30 357	79.03 354	64.88 325	83.11 287	89.16 276	59.90 336	84.46 356	68.61 289	85.15 392	87.42 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 302	75.67 314	80.34 279	80.48 389	62.16 301	73.50 387	84.80 308	57.61 387	82.24 301	87.54 310	51.31 383	87.65 300	70.40 266	93.19 237	91.23 251

CL-MVSNet_self_test		76.81 303	77.38 295	75.12 354	86.90 268	51.34 409	73.20 390	80.63 347	68.30 273	81.80 315	88.40 289	66.92 289	80.90 381	55.35 388	94.90 176	93.12 166

TR-MVS		76.77 304	75.79 311	79.72 290	86.10 293	65.79 251	77.14 342	83.02 324	65.20 323	81.40 322	82.10 388	66.30 291	90.73 222	55.57 385	85.27 388	82.65 401

MonoMVSNet		76.66 305	77.26 297	74.86 356	79.86 394	54.34 387	86.26 142	86.08 277	71.08 236	85.59 224	88.68 284	53.95 372	85.93 334	63.86 330	80.02 430	84.32 376

USDC		76.63 306	76.73 304	76.34 342	83.46 346	57.20 365	80.02 292	88.04 240	52.14 421	83.65 276	91.25 207	63.24 316	86.65 319	54.66 393	94.11 204	85.17 365

BH-w/o		76.57 307	76.07 310	78.10 315	86.88 269	65.92 250	77.63 333	86.33 272	65.69 311	80.89 328	79.95 409	68.97 280	90.74 221	53.01 404	85.25 389	77.62 437

Patchmtry		76.56 308	77.46 293	73.83 364	79.37 401	46.60 431	82.41 250	76.90 368	73.81 184	85.56 226	92.38 162	48.07 396	83.98 363	63.36 335	95.31 160	90.92 262

PVSNet_Blended		76.49 309	75.40 316	79.76 289	84.43 325	63.41 273	75.14 372	90.44 179	57.36 389	75.43 386	78.30 423	69.11 278	91.44 193	60.68 356	87.70 361	84.42 375

miper_lstm_enhance		76.45 310	76.10 309	77.51 325	76.72 420	60.97 320	64.69 438	85.04 300	63.98 330	83.20 286	88.22 291	56.67 358	78.79 397	73.22 234	93.12 238	92.78 182

lupinMVS		76.37 311	74.46 325	82.09 238	85.54 305	69.26 208	76.79 347	80.77 346	50.68 432	76.23 376	82.82 382	58.69 345	88.94 271	69.85 271	88.77 340	88.07 326

cascas		76.29 312	74.81 321	80.72 272	84.47 324	62.94 279	73.89 384	87.34 251	55.94 396	75.16 391	76.53 439	63.97 311	91.16 202	65.00 320	90.97 297	88.06 328

SD_0403		76.08 313	76.77 302	73.98 362	87.08 261	49.45 420	83.62 209	84.68 310	63.31 331	75.13 392	87.47 313	71.85 260	84.56 354	49.97 416	87.86 357	87.94 332

WB-MVS		76.06 314	80.01 265	64.19 427	89.96 179	20.58 470	72.18 397	68.19 426	83.21 65	86.46 205	93.49 123	70.19 272	78.97 395	65.96 308	90.46 318	93.02 170

thres600view7		75.97 315	75.35 318	77.85 322	87.01 263	51.84 407	80.45 287	73.26 396	75.20 165	83.10 288	86.31 334	45.54 411	89.05 269	55.03 391	92.24 265	92.66 189

GA-MVS		75.83 316	74.61 322	79.48 295	81.87 367	59.25 342	73.42 388	82.88 325	68.68 267	79.75 342	81.80 393	50.62 387	89.46 262	66.85 300	85.64 385	89.72 295

MVP-Stereo		75.81 317	73.51 334	82.71 224	89.35 189	73.62 139	80.06 290	85.20 295	60.30 368	73.96 398	87.94 296	57.89 352	89.45 263	52.02 408	74.87 448	85.06 367
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 318	75.20 319	77.27 328	75.01 437	69.47 205	78.93 311	84.88 305	46.67 439	87.08 184	87.84 304	50.44 389	71.62 422	77.42 168	88.53 343	90.72 268

thres100view900		75.45 319	75.05 320	76.66 338	87.27 249	51.88 406	81.07 277	73.26 396	75.68 156	83.25 285	86.37 331	45.54 411	88.80 273	51.98 409	90.99 294	89.31 302

ET-MVSNet_ETH3D		75.28 320	72.77 343	82.81 223	83.03 361	68.11 225	77.09 343	76.51 372	60.67 365	77.60 367	80.52 404	38.04 439	91.15 203	70.78 258	90.68 311	89.17 309

thres400		75.14 321	74.23 327	77.86 321	86.24 286	52.12 403	79.24 307	73.87 389	73.34 195	81.82 313	84.60 363	46.02 404	88.80 273	51.98 409	90.99 294	92.66 189

wuyk23d		75.13 322	79.30 272	62.63 430	75.56 430	75.18 131	80.89 280	73.10 398	75.06 167	94.76 16	95.32 45	87.73 44	52.85 462	34.16 460	97.11 87	59.85 458

EU-MVSNet		75.12 323	74.43 326	77.18 329	83.11 360	59.48 339	85.71 154	82.43 330	39.76 459	85.64 223	88.76 282	44.71 423	87.88 296	73.86 219	85.88 384	84.16 381

HyFIR lowres test		75.12 323	72.66 345	82.50 231	91.44 141	65.19 256	72.47 395	87.31 252	46.79 438	80.29 337	84.30 365	52.70 377	92.10 177	51.88 413	86.73 373	90.22 284

CMPMVS	59.41 20	75.12 323	73.57 332	79.77 288	75.84 429	67.22 231	81.21 275	82.18 332	50.78 430	76.50 372	87.66 308	55.20 368	82.99 369	62.17 345	90.64 316	89.09 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 326	72.98 341	80.73 271	84.95 316	71.71 176	76.23 359	77.59 361	52.83 415	77.73 366	86.38 330	56.35 361	84.97 350	57.72 373	87.05 368	85.51 362

tfpn200view9		74.86 327	74.23 327	76.74 337	86.24 286	52.12 403	79.24 307	73.87 389	73.34 195	81.82 313	84.60 363	46.02 404	88.80 273	51.98 409	90.99 294	89.31 302

1112_ss		74.82 328	73.74 330	78.04 317	89.57 183	60.04 331	76.49 355	87.09 264	54.31 406	73.66 401	79.80 410	60.25 333	86.76 318	58.37 367	84.15 405	87.32 341

EGC-MVSNET		74.79 329	69.99 373	89.19 67	94.89 38	87.00 15	91.89 38	86.28 273	1.09 468	2.23 470	95.98 30	81.87 122	89.48 260	79.76 130	95.96 130	91.10 255

ppachtmachnet_test		74.73 330	74.00 329	76.90 334	80.71 385	56.89 368	71.53 403	78.42 356	58.24 380	79.32 349	82.92 381	57.91 351	84.26 360	65.60 315	91.36 288	89.56 297

Patchmatch-RL test		74.48 331	73.68 331	76.89 335	84.83 318	66.54 241	72.29 396	69.16 424	57.70 385	86.76 190	86.33 332	45.79 410	82.59 370	69.63 274	90.65 315	81.54 417

PatchMatch-RL		74.48 331	73.22 338	78.27 313	87.70 237	85.26 38	75.92 364	70.09 417	64.34 328	76.09 379	81.25 398	65.87 297	78.07 399	53.86 396	83.82 407	71.48 446

XXY-MVS		74.44 333	76.19 308	69.21 399	84.61 323	52.43 402	71.70 400	77.18 366	60.73 364	80.60 331	90.96 220	75.44 201	69.35 430	56.13 380	88.33 347	85.86 358

test2506		74.12 334	73.39 335	76.28 343	91.85 123	44.20 441	84.06 192	48.20 466	72.30 220	81.90 310	94.20 89	27.22 466	89.77 257	64.81 322	96.02 127	94.87 78

reproduce_monomvs		74.09 335	73.23 337	76.65 339	76.52 421	54.54 384	77.50 337	81.40 341	65.85 306	82.86 293	86.67 327	27.38 464	84.53 355	70.24 267	90.66 314	90.89 263

CR-MVSNet		74.00 336	73.04 340	76.85 336	79.58 396	62.64 285	82.58 241	76.90 368	50.50 433	75.72 383	92.38 162	48.07 396	84.07 362	68.72 288	82.91 414	83.85 385

SSC-MVS3.2		73.90 337	75.67 314	68.61 407	84.11 334	41.28 449	64.17 440	72.83 399	72.09 223	79.08 352	87.94 296	70.31 270	73.89 415	55.99 381	94.49 191	90.67 273

Test_1112_low_res		73.90 337	73.08 339	76.35 341	90.35 167	55.95 371	73.40 389	86.17 275	50.70 431	73.14 402	85.94 339	58.31 347	85.90 338	56.51 377	83.22 411	87.20 343

test20.03		73.75 339	74.59 324	71.22 385	81.11 378	51.12 413	70.15 414	72.10 406	70.42 242	80.28 339	91.50 194	64.21 307	74.72 413	46.96 434	94.58 189	87.82 336

test_fmvs2		73.57 340	72.80 342	75.90 347	72.74 451	68.84 218	77.07 344	84.32 314	45.14 445	82.89 291	84.22 366	48.37 394	70.36 426	73.40 230	87.03 369	88.52 321

SCA		73.32 341	72.57 347	75.58 352	81.62 371	55.86 374	78.89 313	71.37 412	61.73 348	74.93 393	83.42 375	60.46 330	87.01 309	58.11 371	82.63 419	83.88 382

baseline1		73.26 342	73.54 333	72.43 378	84.92 317	47.79 426	79.89 294	74.00 387	65.93 304	78.81 354	86.28 335	56.36 360	81.63 378	56.63 376	79.04 437	87.87 335

1314		73.22 343	72.56 348	75.20 353	80.41 390	57.84 359	81.64 266	85.36 291	51.68 424	73.10 403	76.65 438	61.45 325	85.19 348	63.54 333	79.21 435	82.59 402

MVS		73.21 344	72.59 346	75.06 355	80.97 379	60.81 322	81.64 266	85.92 284	46.03 443	71.68 411	77.54 429	68.47 281	89.77 257	55.70 384	85.39 386	74.60 443

HY-MVS	64.64 18	73.03 345	72.47 349	74.71 359	83.36 351	54.19 388	82.14 260	81.96 334	56.76 395	69.57 425	86.21 336	60.03 334	84.83 352	49.58 421	82.65 417	85.11 366

thisisatest0515		73.00 346	70.52 365	80.46 277	81.45 373	59.90 335	73.16 391	74.31 386	57.86 384	76.08 380	77.78 426	37.60 442	92.12 176	65.00 320	91.45 287	89.35 301

EPNet_dtu		72.87 347	71.33 359	77.49 326	77.72 410	60.55 325	82.35 251	75.79 375	66.49 301	58.39 461	81.06 399	53.68 373	85.98 333	53.55 399	92.97 243	85.95 356
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 348	71.41 358	76.28 343	83.25 355	60.34 327	83.50 213	79.02 355	37.77 463	76.33 374	85.10 354	49.60 392	87.41 305	70.54 264	77.54 443	81.08 424

CHOSEN 1792x2688		72.45 349	70.56 364	78.13 314	90.02 178	63.08 278	68.72 421	83.16 322	42.99 453	75.92 381	85.46 347	57.22 356	85.18 349	49.87 419	81.67 421	86.14 354

testgi		72.36 350	74.61 322	65.59 421	80.56 388	42.82 446	68.29 423	73.35 395	66.87 298	81.84 312	89.93 261	72.08 257	66.92 444	46.05 438	92.54 255	87.01 345

thres200		72.34 351	71.55 357	74.70 360	83.48 345	51.60 408	75.02 373	73.71 392	70.14 248	78.56 357	80.57 403	46.20 402	88.20 289	46.99 433	89.29 332	84.32 376

FPMVS		72.29 352	72.00 351	73.14 369	88.63 213	85.00 40	74.65 377	67.39 429	71.94 226	77.80 364	87.66 308	50.48 388	75.83 407	49.95 417	79.51 431	58.58 460

FMVSNet5		72.10 353	71.69 353	73.32 367	81.57 372	53.02 397	76.77 348	78.37 357	63.31 331	76.37 373	91.85 180	36.68 443	78.98 394	47.87 430	92.45 257	87.95 331

our_test_3		71.85 354	71.59 354	72.62 375	80.71 385	53.78 391	69.72 417	71.71 411	58.80 377	78.03 359	80.51 405	56.61 359	78.84 396	62.20 343	86.04 383	85.23 364

PAPM		71.77 355	70.06 371	76.92 333	86.39 277	53.97 389	76.62 352	86.62 270	53.44 410	63.97 450	84.73 361	57.79 353	92.34 169	39.65 450	81.33 425	84.45 374

ttmdpeth		71.72 356	70.67 362	74.86 356	73.08 448	55.88 373	77.41 340	69.27 422	55.86 397	78.66 355	93.77 116	38.01 440	75.39 410	60.12 359	89.87 325	93.31 155

IB-MVS	62.13 19	71.64 357	68.97 383	79.66 292	80.80 384	62.26 298	73.94 383	76.90 368	63.27 333	68.63 429	76.79 436	33.83 447	91.84 184	59.28 364	87.26 363	84.88 368
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
UnsupCasMVSNet_eth		71.63 358	72.30 350	69.62 396	76.47 423	52.70 400	70.03 415	80.97 344	59.18 374	79.36 347	88.21 292	60.50 329	69.12 431	58.33 369	77.62 442	87.04 344

testing3		71.53 359	70.79 361	73.77 365	88.89 205	41.86 448	76.60 354	59.12 455	72.83 208	80.97 325	82.08 390	19.80 472	87.33 307	65.12 319	91.68 282	92.13 225

test_vis3_rt		71.42 360	70.67 362	73.64 366	69.66 458	70.46 190	66.97 433	89.73 204	42.68 455	88.20 153	83.04 377	43.77 425	60.07 456	65.35 318	86.66 374	90.39 282

Anonymous20231206		71.38 361	71.88 352	69.88 393	86.31 283	54.37 386	70.39 412	74.62 382	52.57 417	76.73 371	88.76 282	59.94 335	72.06 419	44.35 442	93.23 235	83.23 396

test_vis1_n_1920		71.30 362	71.58 356	70.47 388	77.58 412	59.99 334	74.25 378	84.22 315	51.06 427	74.85 394	79.10 416	55.10 369	68.83 433	68.86 285	79.20 436	82.58 403

MIMVSNet		71.09 363	71.59 354	69.57 397	87.23 251	50.07 418	78.91 312	71.83 408	60.20 371	71.26 412	91.76 187	55.08 370	76.09 405	41.06 447	87.02 370	82.54 405

test_fmvs1_n		70.94 364	70.41 368	72.53 377	73.92 439	66.93 238	75.99 363	84.21 316	43.31 452	79.40 346	79.39 414	43.47 426	68.55 435	69.05 282	84.91 397	82.10 411

MS-PatchMatch		70.93 365	70.22 369	73.06 370	81.85 368	62.50 288	73.82 385	77.90 358	52.44 418	75.92 381	81.27 397	55.67 365	81.75 376	55.37 387	77.70 441	74.94 442

pmmvs5		70.73 366	70.07 370	72.72 373	77.03 417	52.73 399	74.14 379	75.65 378	50.36 434	72.17 409	85.37 351	55.42 367	80.67 383	52.86 405	87.59 362	84.77 369

testing3-2		70.72 367	70.97 360	69.95 392	88.93 203	34.80 462	69.85 416	66.59 436	78.42 124	77.58 368	85.55 343	31.83 453	82.08 374	46.28 435	93.73 218	92.98 176

PatchT		70.52 368	72.76 344	63.79 429	79.38 400	33.53 463	77.63 333	65.37 440	73.61 188	71.77 410	92.79 151	44.38 424	75.65 408	64.53 327	85.37 387	82.18 410

test_vis1_n		70.29 369	69.99 373	71.20 386	75.97 428	66.50 242	76.69 350	80.81 345	44.22 448	75.43 386	77.23 433	50.00 390	68.59 434	66.71 303	82.85 416	78.52 436

N_pmnet		70.20 370	68.80 385	74.38 361	80.91 380	84.81 43	59.12 451	76.45 373	55.06 401	75.31 390	82.36 387	55.74 364	54.82 461	47.02 432	87.24 364	83.52 389

tpmvs		70.16 371	69.56 376	71.96 381	74.71 438	48.13 423	79.63 296	75.45 380	65.02 324	70.26 420	81.88 392	45.34 416	85.68 344	58.34 368	75.39 447	82.08 412

new-patchmatchnet		70.10 372	73.37 336	60.29 438	81.23 377	16.95 473	59.54 449	74.62 382	62.93 335	80.97 325	87.93 298	62.83 322	71.90 420	55.24 389	95.01 173	92.00 230

YYNet1		70.06 373	70.44 366	68.90 401	73.76 441	53.42 395	58.99 452	67.20 431	58.42 379	87.10 182	85.39 350	59.82 337	67.32 441	59.79 361	83.50 410	85.96 355

MVStest1		70.05 374	69.26 377	72.41 379	58.62 470	55.59 377	76.61 353	65.58 438	53.44 410	89.28 128	93.32 127	22.91 470	71.44 424	74.08 215	89.52 329	90.21 288

MDA-MVSNet_test_wron		70.05 374	70.44 366	68.88 402	73.84 440	53.47 393	58.93 453	67.28 430	58.43 378	87.09 183	85.40 349	59.80 338	67.25 442	59.66 362	83.54 409	85.92 357

CostFormer		69.98 376	68.68 386	73.87 363	77.14 415	50.72 415	79.26 306	74.51 384	51.94 423	70.97 415	84.75 360	45.16 419	87.49 303	55.16 390	79.23 434	83.40 392

testing91		69.94 377	68.99 382	72.80 372	83.81 340	45.89 434	71.57 402	73.64 394	68.24 274	70.77 418	77.82 425	34.37 446	84.44 357	53.64 398	87.00 371	88.07 326

baseline2		69.77 378	66.89 395	78.41 309	79.51 398	58.09 355	76.23 359	69.57 420	57.50 388	64.82 448	77.45 431	46.02 404	88.44 283	53.08 401	77.83 439	88.70 319

PatchmatchNet		69.71 379	68.83 384	72.33 380	77.66 411	53.60 392	79.29 305	69.99 418	57.66 386	72.53 406	82.93 380	46.45 401	80.08 389	60.91 355	72.09 451	83.31 395
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 380	69.05 380	71.14 387	69.15 459	65.77 252	73.98 382	83.32 320	42.83 454	77.77 365	78.27 424	43.39 429	68.50 436	68.39 292	84.38 404	79.15 434

JIA-IIPM		69.41 381	66.64 399	77.70 323	73.19 445	71.24 181	75.67 365	65.56 439	70.42 242	65.18 444	92.97 142	33.64 449	83.06 367	53.52 400	69.61 457	78.79 435

Syy-MVS		69.40 382	70.03 372	67.49 412	81.72 369	38.94 454	71.00 405	61.99 446	61.38 354	70.81 416	72.36 450	61.37 326	79.30 392	64.50 328	85.18 390	84.22 378

testing99		69.27 383	68.15 390	72.63 374	83.29 353	45.45 436	71.15 404	71.08 413	67.34 291	70.43 419	77.77 427	32.24 452	84.35 359	53.72 397	86.33 379	88.10 325

UnsupCasMVSNet_bld		69.21 384	69.68 375	67.82 410	79.42 399	51.15 412	67.82 427	75.79 375	54.15 407	77.47 369	85.36 352	59.26 341	70.64 425	48.46 427	79.35 433	81.66 415

test_cas_vis1_n_1920		69.20 385	69.12 378	69.43 398	73.68 442	62.82 282	70.38 413	77.21 365	46.18 442	80.46 336	78.95 418	52.03 379	65.53 449	65.77 314	77.45 444	79.95 432

gg-mvs-nofinetune		68.96 386	69.11 379	68.52 408	76.12 427	45.32 437	83.59 210	55.88 460	86.68 33	64.62 449	97.01 12	30.36 457	83.97 364	44.78 441	82.94 413	76.26 439

WBMVS		68.76 387	68.43 387	69.75 395	83.29 353	40.30 452	67.36 429	72.21 405	57.09 392	77.05 370	85.53 345	33.68 448	80.51 385	48.79 425	90.90 299	88.45 322

WB-MVSnew		68.72 388	69.01 381	67.85 409	83.22 357	43.98 442	74.93 374	65.98 437	55.09 400	73.83 399	79.11 415	65.63 299	71.89 421	38.21 455	85.04 393	87.69 337

tpm2		68.45 389	66.83 396	73.30 368	78.93 406	48.50 422	79.76 295	71.76 409	47.50 437	69.92 422	83.60 371	42.07 432	88.40 285	48.44 428	79.51 431	83.01 399

tpm		67.95 390	68.08 391	67.55 411	78.74 407	43.53 444	75.60 366	67.10 434	54.92 402	72.23 407	88.10 293	42.87 431	75.97 406	52.21 407	80.95 429	83.15 397

WTY-MVS		67.91 391	68.35 388	66.58 417	80.82 383	48.12 424	65.96 435	72.60 400	53.67 409	71.20 413	81.68 395	58.97 343	69.06 432	48.57 426	81.67 421	82.55 404

testing11		67.38 392	65.93 400	71.73 383	83.37 350	46.60 431	70.95 407	69.40 421	62.47 340	66.14 437	76.66 437	31.22 454	84.10 361	49.10 423	84.10 406	84.49 372

test-LLR		67.21 393	66.74 397	68.63 405	76.45 424	55.21 380	67.89 424	67.14 432	62.43 343	65.08 445	72.39 448	43.41 427	69.37 428	61.00 353	84.89 398	81.31 419

testing222		66.93 394	65.30 407	71.81 382	83.38 349	45.83 435	72.06 398	67.50 428	64.12 329	69.68 424	76.37 440	27.34 465	83.00 368	38.88 451	88.38 346	86.62 350

sss		66.92 395	67.26 393	65.90 419	77.23 414	51.10 414	64.79 437	71.72 410	52.12 422	70.13 421	80.18 407	57.96 350	65.36 450	50.21 415	81.01 427	81.25 421

KD-MVS_2432*1600		66.87 396	65.81 403	70.04 390	67.50 460	47.49 427	62.56 443	79.16 352	61.21 359	77.98 360	80.61 401	25.29 468	82.48 371	53.02 402	84.92 395	80.16 430

miper_refine_blended		66.87 396	65.81 403	70.04 390	67.50 460	47.49 427	62.56 443	79.16 352	61.21 359	77.98 360	80.61 401	25.29 468	82.48 371	53.02 402	84.92 395	80.16 430

dmvs_re		66.81 398	66.98 394	66.28 418	76.87 418	58.68 353	71.66 401	72.24 403	60.29 369	69.52 426	73.53 447	52.38 378	64.40 452	44.90 440	81.44 424	75.76 440

tpm cat1		66.76 399	65.21 408	71.42 384	77.09 416	50.62 416	78.01 325	73.68 393	44.89 446	68.64 428	79.00 417	45.51 413	82.42 373	49.91 418	70.15 454	81.23 423

UWE-MVS		66.43 400	65.56 406	69.05 400	84.15 333	40.98 450	73.06 392	64.71 442	54.84 403	76.18 378	79.62 413	29.21 459	80.50 386	38.54 454	89.75 326	85.66 360

PVSNet	58.17 21	66.41 401	65.63 405	68.75 403	81.96 366	49.88 419	62.19 445	72.51 402	51.03 428	68.04 431	75.34 444	50.84 385	74.77 411	45.82 439	82.96 412	81.60 416

tpmrst		66.28 402	66.69 398	65.05 425	72.82 450	39.33 453	78.20 323	70.69 416	53.16 413	67.88 432	80.36 406	48.18 395	74.75 412	58.13 370	70.79 453	81.08 424

Patchmatch-test		65.91 403	67.38 392	61.48 435	75.51 431	43.21 445	68.84 420	63.79 444	62.48 339	72.80 405	83.42 375	44.89 422	59.52 458	48.27 429	86.45 376	81.70 414

ADS-MVSNet2		65.87 404	63.64 413	72.55 376	73.16 446	56.92 367	67.10 431	74.81 381	49.74 435	66.04 439	82.97 378	46.71 399	77.26 402	42.29 444	69.96 455	83.46 390

myMVS_eth3d28		65.83 405	65.85 401	65.78 420	83.42 348	35.71 460	67.29 430	68.01 427	67.58 288	69.80 423	77.72 428	32.29 451	74.30 414	37.49 456	89.06 336	87.32 341

test_vis1_rt		65.64 406	64.09 410	70.31 389	66.09 464	70.20 194	61.16 446	81.60 339	38.65 460	72.87 404	69.66 453	52.84 375	60.04 457	56.16 379	77.77 440	80.68 428

mvsany_test3		65.48 407	62.97 416	73.03 371	69.99 457	76.17 124	64.83 436	43.71 468	43.68 450	80.25 340	87.05 324	52.83 376	63.09 455	51.92 412	72.44 450	79.84 433

test-mter		65.00 408	63.79 412	68.63 405	76.45 424	55.21 380	67.89 424	67.14 432	50.98 429	65.08 445	72.39 448	28.27 462	69.37 428	61.00 353	84.89 398	81.31 419

ETVMVS		64.67 409	63.34 415	68.64 404	83.44 347	41.89 447	69.56 419	61.70 451	61.33 356	68.74 427	75.76 442	28.76 460	79.35 391	34.65 459	86.16 382	84.67 371

myMVS_eth3d		64.66 410	63.89 411	66.97 415	81.72 369	37.39 457	71.00 405	61.99 446	61.38 354	70.81 416	72.36 450	20.96 471	79.30 392	49.59 420	85.18 390	84.22 378

test0.0.03 1		64.66 410	64.36 409	65.57 422	75.03 436	46.89 430	64.69 438	61.58 452	62.43 343	71.18 414	77.54 429	43.41 427	68.47 437	40.75 449	82.65 417	81.35 418

UBG		64.34 412	63.35 414	67.30 413	83.50 344	40.53 451	67.46 428	65.02 441	54.77 404	67.54 435	74.47 446	32.99 450	78.50 398	40.82 448	83.58 408	82.88 400

test_f		64.31 413	65.85 401	59.67 439	66.54 463	62.24 300	57.76 455	70.96 414	40.13 457	84.36 257	82.09 389	46.93 398	51.67 463	61.99 346	81.89 420	65.12 454

pmmvs3		62.47 414	60.02 427	69.80 394	71.58 454	64.00 268	70.52 411	58.44 458	39.77 458	66.05 438	75.84 441	27.10 467	72.28 418	46.15 437	84.77 402	73.11 444

EPMVS		62.47 414	62.63 418	62.01 431	70.63 456	38.74 455	74.76 375	52.86 462	53.91 408	67.71 434	80.01 408	39.40 436	66.60 445	55.54 386	68.81 459	80.68 428

ADS-MVSNet		61.90 416	62.19 420	61.03 436	73.16 446	36.42 459	67.10 431	61.75 449	49.74 435	66.04 439	82.97 378	46.71 399	63.21 453	42.29 444	69.96 455	83.46 390

PMMVS		61.65 417	60.38 424	65.47 423	65.40 467	69.26 208	63.97 441	61.73 450	36.80 464	60.11 456	68.43 455	59.42 339	66.35 446	48.97 424	78.57 438	60.81 457

E-PMN		61.59 418	61.62 421	61.49 434	66.81 462	55.40 378	53.77 458	60.34 454	66.80 299	58.90 459	65.50 458	40.48 435	66.12 447	55.72 383	86.25 380	62.95 456

TESTMET0.1,1		61.29 419	60.32 425	64.19 427	72.06 452	51.30 410	67.89 424	62.09 445	45.27 444	60.65 455	69.01 454	27.93 463	64.74 451	56.31 378	81.65 423	76.53 438

MVS-HIRNet		61.16 420	62.92 417	55.87 442	79.09 403	35.34 461	71.83 399	57.98 459	46.56 440	59.05 458	91.14 211	49.95 391	76.43 404	38.74 452	71.92 452	55.84 461

EMVS		61.10 421	60.81 423	61.99 432	65.96 465	55.86 374	53.10 459	58.97 457	67.06 296	56.89 463	63.33 459	40.98 433	67.03 443	54.79 392	86.18 381	63.08 455

DSMNet-mixed		60.98 422	61.61 422	59.09 441	72.88 449	45.05 439	74.70 376	46.61 467	26.20 465	65.34 443	90.32 250	55.46 366	63.12 454	41.72 446	81.30 426	69.09 450

dp		60.70 423	60.29 426	61.92 433	72.04 453	38.67 456	70.83 409	64.08 443	51.28 426	60.75 454	77.28 432	36.59 444	71.58 423	47.41 431	62.34 461	75.52 441

dmvs_testset		60.59 424	62.54 419	54.72 444	77.26 413	27.74 467	74.05 381	61.00 453	60.48 366	65.62 442	67.03 457	55.93 363	68.23 439	32.07 463	69.46 458	68.17 451

CHOSEN 280x420		59.08 425	56.52 431	66.76 416	76.51 422	64.39 264	49.62 460	59.00 456	43.86 449	55.66 464	68.41 456	35.55 445	68.21 440	43.25 443	76.78 446	67.69 452

mvsany_test1		58.48 426	56.47 432	64.50 426	65.90 466	68.21 224	56.95 456	42.11 469	38.30 461	65.69 441	77.19 435	56.96 357	59.35 459	46.16 436	58.96 462	65.93 453

UWE-MVS-28		58.44 427	57.71 429	60.65 437	73.58 443	31.23 464	69.68 418	48.80 465	53.12 414	61.79 452	78.83 419	30.98 455	68.40 438	21.58 466	80.99 428	82.33 409

PVSNet_0	51.08 22	56.10 428	54.97 433	59.48 440	75.12 435	53.28 396	55.16 457	61.89 448	44.30 447	59.16 457	62.48 460	54.22 371	65.91 448	35.40 458	47.01 463	59.25 459

new_pmnet		55.69 429	57.66 430	49.76 445	75.47 432	30.59 465	59.56 448	51.45 463	43.62 451	62.49 451	75.48 443	40.96 434	49.15 465	37.39 457	72.52 449	69.55 449

PMMVS2		55.64 430	59.27 428	44.74 446	64.30 468	12.32 474	40.60 461	49.79 464	53.19 412	65.06 447	84.81 359	53.60 374	49.76 464	32.68 462	89.41 331	72.15 445

MVE	40.22 23	51.82 431	50.47 434	55.87 442	62.66 469	51.91 405	31.61 463	39.28 470	40.65 456	50.76 465	74.98 445	56.24 362	44.67 466	33.94 461	64.11 460	71.04 448
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 432	42.65 435	39.67 447	70.86 455	21.11 469	61.01 447	21.42 474	57.36 389	57.97 462	50.06 463	16.40 473	58.73 460	21.03 467	27.69 467	39.17 463

kuosan		30.83 433	32.17 436	26.83 449	53.36 471	19.02 472	57.90 454	20.44 475	38.29 462	38.01 466	37.82 465	15.18 474	33.45 468	7.74 469	20.76 468	28.03 464

test_method		30.46 434	29.60 437	33.06 448	17.99 473	3.84 476	13.62 464	73.92 388	2.79 467	18.29 469	53.41 462	28.53 461	43.25 467	22.56 464	35.27 465	52.11 462

cdsmvs_eth3d_5k		20.81 435	27.75 438	0.00 454	0.00 477	0.00 479	0.00 465	85.44 290	0.00 472	0.00 473	82.82 382	81.46 128	0.00 473	0.00 472	0.00 471	0.00 469

tmp_tt		20.25 436	24.50 439	7.49 451	4.47 474	8.70 475	34.17 462	25.16 472	1.00 469	32.43 468	18.49 466	39.37 437	9.21 470	21.64 465	43.75 464	4.57 466

ab-mvs-re		6.65 437	8.87 440	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	79.80 410	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		6.41 438	8.55 441	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	76.94 185	0.00 473	0.00 472	0.00 471	0.00 469

test123		6.27 439	8.08 442	0.84 452	1.11 476	0.57 477	62.90 442	0.82 476	0.54 470	1.07 472	2.75 471	1.26 475	0.30 471	1.04 470	1.26 470	1.66 467

testmvs		5.91 440	7.65 443	0.72 453	1.20 475	0.37 478	59.14 450	0.67 477	0.49 471	1.11 471	2.76 470	0.94 476	0.24 472	1.02 471	1.47 469	1.55 468

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						37.39 457								52.61 406

FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

MSC_two_6792asdad				88.81 73	91.55 135	77.99 97		91.01 162					96.05 9	87.45 28	98.17 37	92.40 205

PC_three_1452									58.96 376	90.06 103	91.33 202	80.66 138	93.03 151	75.78 191	95.94 133	92.48 199

No_MVS				88.81 73	91.55 135	77.99 97		91.01 162					96.05 9	87.45 28	98.17 37	92.40 205

test_one_0601					93.85 64	73.27 145		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					92.22 109	80.48 71		91.85 132	71.22 234	90.38 98	92.98 139	86.06 65	96.11 7	81.99 109	96.75 97

RE-MVS-def			92.61 9		94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 37	97.60 71	92.73 183

IU-MVS					94.18 52	72.64 154		90.82 167	56.98 393	89.67 116				85.78 62	97.92 52	93.28 156

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 208	81.12 132	94.68 78	74.48 204	95.35 156	92.29 215

test_241102_TWO								93.71 56	83.77 58	93.49 40	94.27 83	89.27 24	95.84 24	86.03 55	97.82 57	92.04 228

test_241102_ONE					94.18 52	72.65 152		93.69 57	83.62 60	94.11 27	93.78 114	90.28 15	95.50 49

9.14			89.29 63		91.84 125		88.80 94	95.32 13	75.14 166	91.07 83	92.89 145	87.27 48	93.78 117	83.69 87	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 205	70.80 239

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 176

test_0728_SECOND				86.79 108	94.25 50	72.45 162	90.54 53	94.10 40					95.88 18	86.42 45	97.97 49	92.02 229

test0726					94.16 55	72.56 158	90.63 50	93.90 49	83.61 61	93.75 35	94.49 73	89.76 19

GSMVS																83.88 382

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 403				83.88 382

sam_mvs												45.92 408

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 218		89.40 125	93.96 105	71.67 264	91.38 197	78.83 144	96.55 102	92.71 186

MTGPA								91.81 136

test_post1							78.85 315				3.13 468	45.19 418	80.13 388	58.11 371

test_post											3.10 469	45.43 414	77.22 403

patchmatchnet-post											81.71 394	45.93 407	87.01 309

GG-mvs-BLEND				67.16 414	73.36 444	46.54 433	84.15 190	55.04 461		58.64 460	61.95 461	29.93 458	83.87 365	38.71 453	76.92 445	71.07 447

MTMP							90.66 49	33.14 471

gm-plane-assit					75.42 433	44.97 440			52.17 419		72.36 450		87.90 295	54.10 395

test9_res														80.83 119	96.45 108	90.57 276

TEST9					92.34 104	79.70 80	83.94 196	90.32 185	65.41 316	84.49 253	90.97 218	82.03 117	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 187	65.79 307	84.36 257	90.96 220	81.93 119	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 284

agg_prior					91.58 133	77.69 103		90.30 188		84.32 259			93.18 144

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 256	96.14 121	94.16 112

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 250	91.57 192	81.92 121		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 98					94.17 102			92.80 181

旧先验2							81.73 264		56.88 394	86.54 203			84.90 351	72.81 241

新几何2							81.72 265

新几何1				82.95 216	93.96 61	78.56 91		80.24 348	55.45 399	83.93 270	91.08 214	71.19 266	88.33 287	65.84 312	93.07 239	81.95 413

旧先验1					91.97 117	71.77 171		81.78 336			91.84 181	73.92 228			93.65 221	83.61 388

无先验							82.81 236	85.62 288	58.09 382				91.41 196	67.95 296		84.48 373

原ACMM2							82.26 256

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 143	62.59 337	82.73 295	90.67 237	76.53 194	94.25 94	69.24 277	95.69 148	85.55 361

test222					93.31 78	76.54 116	79.38 304	77.79 359	52.59 416	82.36 300	90.84 228	66.83 290			91.69 281	81.25 421

testdata2													86.43 324	63.52 334

segment_acmp												81.94 118

testdata				79.54 294	92.87 89	72.34 163		80.14 349	59.91 372	85.47 228	91.75 188	67.96 284	85.24 347	68.57 291	92.18 268	81.06 426

testdata1							79.62 297		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 170		82.76 294		79.20 151	94.80 75		95.32 158	92.27 217

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 210

plane_prior5								93.61 60					95.22 59	80.78 120	95.83 141	94.46 94

plane_prior4											92.95 143

plane_prior3						76.85 114			77.79 133	86.55 197

plane_prior2							89.45 83		79.44 108

plane_prior1					92.83 93

plane_prior						76.42 119	87.15 122		75.94 153						95.03 170

n2								0.00 478

nn								0.00 478

door-mid								74.45 385

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 415		91.79 71	94.42 78	61.76 324	92.93 154	79.52 137	93.03 240	93.93 121

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	98.73 7	95.23 66

test11								91.46 144

door								72.57 401

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 333

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 333

BP-MVS														77.30 169

HQP4-MVS										80.56 332			94.61 82			93.56 148

HQP3-MVS								92.68 104							94.47 192

HQP2-MVS												72.10 255

NP-MVS					91.95 118	74.55 134					90.17 257

MDTV_nov1_ep13_2view						27.60 468	70.76 410		46.47 441	61.27 453		45.20 417		49.18 422		83.75 387

MDTV_nov1_ep13			68.29 389		78.03 408	43.87 443	74.12 380	72.22 404	52.17 419	67.02 436	85.54 344	45.36 415	80.85 382	55.73 382	84.42 403

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 153

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 188	81.56 82	90.02 105	91.20 210	82.40 103	90.81 218	73.58 227	94.66 187	94.56 90

DeepMVS_CX				24.13 450	32.95 472	29.49 466		21.63 473	12.07 466	37.95 467	45.07 464	30.84 456	19.21 469	17.94 468	33.06 466	23.69 465

High-resolution multi-view benchmark