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	80.16 143	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	92.08 224

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	91.76 235

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	91.76 235

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	93.63 140

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	92.73 181

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	92.19 219

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	94.18 110

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	92.60 108	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 164

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	92.55 194

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	94.20 107

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 134	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 211

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 100	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	93.99 117

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	93.83 126

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	92.85 178

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	94.12 114

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	92.98 174
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	93.96 119

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 90	82.59 72	88.52 143	94.37 82	86.74 54	95.41 53	86.32 48	98.21 34	93.19 160
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	93.93 120

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	93.27 155

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	92.41 201
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	91.70 239
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	92.29 213

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	93.88 123
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	94.30 106

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 184	89.99 65	93.05 87	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	89.57 209	88.51 21	90.11 102	95.12 53	90.98 7	88.92 270	77.55 164	97.07 88	83.13 396
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	81.12 131	95.88 18	87.41 30	95.94 133	92.48 197

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 160	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 276
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	58.83 349	89.04 89	92.74 102	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	58.88 346	88.95 90	93.19 79	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 83	81.10 87	95.32 14	97.24 10	72.94 243	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	57.84 357	88.93 91	92.84 98	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	93.64 139

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	77.55 171	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	62.39 293	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 128	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	92.78 101	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 75	97.55 74	94.10 115
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	90.63 170	70.00 248	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	90.20 191	69.87 249	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	90.08 194	71.54 228	94.28 25	96.54 19	81.57 126	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	90.93 210	84.60 78	90.75 306	93.97 118

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 86	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 106	97.18 86	90.45 278
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	90.29 188	69.27 256	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	92.30 117	79.74 103	87.50 175	92.38 162	81.42 128	93.28 141	83.07 92	97.24 84	91.67 240

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	88.31 286	83.33 88	98.30 27	93.20 159
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 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 262	74.12 212	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 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 262	74.12 212	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 352	88.66 98	92.06 123	90.78 7	95.67 8	95.17 51	81.80 123	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	90.98 207	83.86 85	95.30 161	93.60 143

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	93.26 157
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	64.33 264	89.67 75	88.38 229	88.84 17	94.29 23	97.57 7	90.48 14	91.26 198	72.57 242	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	72.03 257	96.36 4	88.21 13	90.93 298	92.98 174
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	90.22 236	86.24 52	97.24 84	91.36 248
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	91.27 152	78.20 126	86.69 195	92.28 170	80.36 141	95.06 67	86.17 53	96.49 105	90.22 282

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	74.07 221	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	74.07 221	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	89.44 212	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 233

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 94	87.95 26	89.62 118	92.87 146	84.56 77	93.89 113	77.65 162	96.62 100	90.70 268

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 129	84.26 53	90.87 92	93.92 109	82.18 112	89.29 266	73.75 220	94.81 181	93.70 134

Anonymous20231211		88.40 74	89.62 60	84.73 158	90.46 165	65.27 254	88.86 92	93.02 91	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	91.97 126	70.73 239	94.19 26	96.67 17	76.94 183	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 80	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	84.32 258	89.33 271	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	91.19 155	67.85 282	86.63 196	94.84 59	79.58 149	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	63.94 268	91.10 46	89.64 206	85.07 45	90.91 88	91.09 213	89.16 25	91.87 183	82.03 107	95.87 139	93.13 162

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	85.12 234	89.67 265	84.47 79	95.46 50	82.56 101	96.26 116	93.77 132

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 156	79.26 111	89.68 115	94.81 63	82.44 101	87.74 297	76.54 179	88.74 340	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	70.81 255	96.14 121	94.16 111

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 265	84.38 185	91.29 149	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 233	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	91.23 153	77.31 139	87.07 185	91.47 198	82.94 95	94.71 77	84.67 77	96.27 115	92.62 189

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	74.84 208	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	88.21 236	86.81 32	91.87 70	97.65 5	85.51 71	87.91 292	74.22 207	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	86.76 267	84.54 50	83.58 276	93.78 114	73.36 238	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	62.26 297	91.49 41	92.62 106	88.07 25	88.07 155	96.17 26	72.24 252	95.79 31	84.85 74	94.16 203	92.58 192

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 158	78.77 119	84.85 245	90.89 223	80.85 134	95.29 56	81.14 115	95.32 158	92.34 209

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 113	70.25 245	89.35 126	90.68 233	82.85 96	94.57 84	79.55 135	95.95 132	92.00 228

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 237	81.66 81	94.64 18	96.53 20	65.94 294	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	89.74 201	74.40 178	89.92 110	93.41 125	80.45 139	90.63 224	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	81.64 317	87.25 316	82.43 102	94.53 87	77.65 162	96.46 107	94.14 113

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 221	83.80 203	92.87 96	80.37 94	89.61 120	91.81 184	77.72 168	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	92.09 122	78.87 117	84.27 263	94.05 98	78.35 160	93.65 121	80.54 124	91.58 285	92.08 224
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	82.55 242	91.56 139	83.08 68	90.92 86	91.82 183	78.25 161	93.99 107	74.16 210	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 225	92.21 118	81.73 80	90.92 86	91.97 176	77.20 177	93.99 107	74.16 210	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	85.94 295	78.30 92	86.93 125	92.20 119	65.94 301	89.16 129	93.16 132	83.10 93	89.89 251	87.81 20	94.43 194	93.35 150

tt0320-xc		86.67 102	88.41 81	81.44 254	93.45 72	60.44 324	83.96 195	88.50 225	87.26 29	90.90 90	97.90 3	85.61 68	86.40 323	70.14 266	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	88.64 222	86.30 37	89.60 121	92.59 155	69.22 275	94.91 71	73.89 217	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 255	93.57 69	60.73 321	84.37 186	88.61 224	87.00 31	90.75 93	97.98 2	85.54 70	86.45 321	69.75 271	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 275	86.64 135	92.20 119	75.42 163	92.81 54	94.50 72	74.05 224	94.06 106	83.88 84	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 240	92.22 109	62.28 296	84.66 177	89.15 216	83.54 63	89.85 111	97.32 8	88.08 39	86.80 314	70.43 263	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	85.12 234	90.34 246	66.19 291	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 87	73.15 202	84.76 247	87.70 305	78.87 154	94.18 100	80.67 122	96.29 112	92.73 181

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 130	80.35 95	89.54 124	88.01 292	79.09 152	92.13 174	75.51 194	95.06 169	90.41 279

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 125	70.56 240	84.96 240	90.69 232	80.01 145	95.14 64	78.37 149	95.78 145	91.82 233
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	62.35 295	86.42 139	91.33 148	76.78 143	92.73 56	94.48 74	73.41 235	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	90.71 167	85.85 40	89.94 109	95.24 50	82.13 113	90.40 231	69.19 278	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 216	86.91 266	70.38 192	85.31 162	92.61 107	75.59 159	88.32 150	92.87 146	82.22 111	88.63 278	88.80 9	92.82 248	89.83 292

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 309	78.25 93	85.82 151	91.82 132	65.33 315	88.55 141	92.35 168	82.62 100	89.80 253	86.87 40	94.32 198	93.18 161

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 124	65.91 303	86.19 208	91.75 188	83.77 86	94.98 69	77.43 167	96.71 98	93.73 133

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 256	90.46 176	80.99 88	88.42 146	91.97 176	77.56 170	93.85 114	72.46 243	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	90.32 183	65.79 305	84.49 252	90.97 217	81.93 119	93.63 123	81.21 114	96.54 103	90.88 262

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 200	80.42 93	87.76 169	93.24 129	73.76 229	91.54 189	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 226	92.25 107	56.44 368	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 210	93.41 138	75.11 200	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 318	78.21 94	85.40 161	91.39 146	65.32 316	87.72 171	91.81 184	82.33 105	89.78 254	86.68 42	94.20 201	92.99 172

Effi-MVS+-dtu		85.82 121	83.38 186	93.14 4	87.13 254	91.15 3	87.70 113	88.42 228	74.57 172	83.56 277	85.65 340	78.49 159	94.21 96	72.04 245	92.88 244	94.05 116

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	69.17 257	85.80 218	89.56 266	80.76 135	92.13 174	73.21 238	95.51 152	93.25 158
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	87.54 308	86.45 59	91.06 205	75.76 192	93.76 214	92.54 195

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	87.54 308	86.45 59	91.06 205	75.76 192	93.76 214	92.54 195

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 161	87.10 257	69.98 197	84.28 187	92.68 103	74.77 169	87.90 162	92.36 167	73.94 225	90.41 230	85.95 60	92.74 250	93.66 135

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 240	81.51 83	87.05 186	91.83 182	66.18 293	95.29 56	70.75 258	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	91.39 146	72.33 219	87.59 173	90.25 251	84.85 75	92.37 168	78.00 158	91.94 275	93.66 135

MVS_0304		85.37 128	84.58 158	87.75 93	85.28 308	73.36 141	86.54 138	85.71 284	77.56 137	81.78 315	92.47 160	70.29 269	96.02 11	85.59 63	95.96 130	93.87 124

FIs		85.35 129	86.27 114	82.60 225	91.86 122	57.31 361	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 231	92.91 156	73.97 216	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 297	76.13 125	85.15 166	92.32 116	61.40 351	91.33 78	90.85 226	83.76 87	86.16 329	84.31 80	93.28 232	92.15 222

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 288	62.77 282	83.03 227	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	86.30 283	62.37 294	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	91.58 137	74.41 176	86.55 197	91.49 195	78.54 155	93.97 109	73.71 221	93.21 236	92.59 191

K. test v3		85.14 134	84.73 151	86.37 115	91.13 151	69.63 203	85.45 159	76.68 369	84.06 56	92.44 61	96.99 13	62.03 321	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 320	74.71 132	85.87 149	90.35 182	77.94 129	83.82 270	96.96 15	77.75 166	80.03 388	78.44 147	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 136	84.53 161	86.88 106	84.01 333	72.76 151	83.91 199	85.18 294	80.44 92	88.75 136	85.49 344	80.08 144	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	90.61 172	67.64 286	84.88 243	92.05 174	82.30 107	88.36 284	83.84 86	91.10 291	92.62 189

MGCFI-Net		85.04 138	85.95 121	82.31 234	87.52 244	63.59 271	86.23 143	93.96 45	73.46 190	88.07 155	87.83 303	86.46 58	90.87 215	76.17 186	93.89 211	92.47 199

EI-MVSNet-UG-set		85.04 138	84.44 164	86.85 107	83.87 337	72.52 160	83.82 201	85.15 295	80.27 97	88.75 136	85.45 346	79.95 146	91.90 181	81.92 111	90.80 305	96.13 39

X-MVStestdata		85.04 138	82.70 204	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 465	86.57 56	95.80 28	87.35 32	97.62 69	94.20 107

MSLP-MVS++		85.00 141	86.03 120	81.90 240	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 180	89.39 269	77.98 163	89.40 265	77.46 165	94.78 182	84.75 368

F-COLMAP		84.97 142	83.42 185	89.63 58	92.39 102	83.40 52	88.83 93	91.92 128	73.19 201	80.18 339	89.15 275	77.04 181	93.28 141	65.82 311	92.28 264	92.21 218

SSM_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 137	74.41 176	85.68 219	91.49 195	78.54 155	93.69 120	73.71 221	93.47 225	92.38 206

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 305	90.42 59	92.37 115	71.48 230	88.72 138	93.13 133	70.16 271	95.15 63	79.26 140	94.11 204	92.41 201

3Dnovator	80.37 7	84.80 144	84.71 154	85.06 149	86.36 281	74.71 132	88.77 95	90.00 196	75.65 157	84.96 240	93.17 131	74.06 223	91.19 200	78.28 152	91.09 292	89.29 302

SymmetryMVS		84.79 146	83.54 180	88.55 79	92.44 101	80.42 72	88.63 99	82.37 329	74.56 173	85.12 234	90.34 246	66.19 291	94.20 97	76.57 177	95.68 149	91.03 256

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 269	83.25 221	89.88 199	76.06 147	89.62 118	92.37 165	73.40 237	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	79.23 307	87.13 257	73.35 194	85.56 226	89.34 270	83.60 89	90.50 227	76.64 176	94.05 208	90.09 288

HQP-MVS		84.61 149	84.06 173	86.27 118	91.19 147	70.66 187	84.77 170	92.68 103	73.30 197	80.55 331	90.17 256	72.10 253	94.61 82	77.30 169	94.47 192	93.56 146

v1192		84.57 150	84.69 156	84.21 176	87.75 235	62.88 279	83.02 228	91.43 143	69.08 259	89.98 108	90.89 223	72.70 247	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	88.64 222	64.43 325	88.77 135	91.78 186	78.07 162	87.95 291	85.85 61	92.18 268	92.30 211

FMVSNet1		84.55 152	85.45 135	81.85 242	90.27 169	61.05 312	86.83 129	88.27 233	78.57 122	89.66 117	95.64 38	75.43 200	90.68 221	69.09 279	95.33 157	93.82 127

v1144		84.54 153	84.72 153	84.00 180	87.67 239	62.55 286	82.97 230	90.93 163	70.32 244	89.80 112	90.99 216	73.50 232	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 299	84.20 330	73.57 140	89.55 78	90.44 177	84.24 54	84.38 255	94.89 57	76.35 196	80.40 385	76.14 187	96.80 96	82.36 406
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	88.45 227	68.64 268	86.29 207	91.31 204	74.97 206	88.42 282	87.87 19	90.07 320	94.95 75

MCST-MVS		84.36 156	83.93 176	85.63 135	91.59 130	71.58 177	83.52 212	92.13 121	61.82 344	83.96 268	89.75 264	79.93 147	93.46 135	78.33 151	94.34 197	91.87 232

VDDNet		84.35 157	85.39 137	81.25 257	95.13 32	59.32 338	85.42 160	81.11 340	86.41 36	87.41 176	96.21 25	73.61 230	90.61 225	66.33 304	96.85 92	93.81 130

ETV-MVS		84.31 158	83.91 177	85.52 138	88.58 215	70.40 191	84.50 184	93.37 68	78.76 120	84.07 266	78.72 419	80.39 140	95.13 65	73.82 219	92.98 242	91.04 255

v1240		84.30 159	84.51 162	83.65 193	87.65 240	61.26 309	82.85 234	91.54 140	67.94 279	90.68 95	90.65 237	71.71 261	93.64 122	82.84 97	94.78 182	96.07 41

MVS_111021_LR		84.28 160	83.76 178	85.83 132	89.23 193	83.07 55	80.99 277	83.56 317	72.71 211	86.07 211	89.07 277	81.75 125	86.19 328	77.11 171	93.36 228	88.24 321

h-mvs33		84.25 161	82.76 203	88.72 75	91.82 127	82.60 60	84.00 194	84.98 301	71.27 231	86.70 193	90.55 242	63.04 318	93.92 112	78.26 153	94.20 201	89.63 294

v144192		84.24 162	84.41 165	83.71 192	87.59 242	61.57 304	82.95 231	91.03 159	67.82 283	89.80 112	90.49 243	73.28 239	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 252	88.61 214	61.98 301	82.90 233	93.11 83	68.66 267	92.77 55	92.39 161	78.50 158	87.63 300	76.99 173	92.30 261	94.90 76

v1921920		84.23 163	84.37 167	83.79 188	87.64 241	61.71 303	82.91 232	91.20 154	67.94 279	90.06 103	90.34 246	72.04 256	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	84.97 302	79.79 102	87.18 179	94.27 83	74.77 211	90.89 213	69.24 275	96.54 103	93.55 148

v2v482		84.09 166	84.24 170	83.62 194	87.13 254	61.40 306	82.71 237	89.71 204	72.19 222	89.55 122	91.41 199	70.70 267	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	82.20 258	87.02 263	72.63 212	88.86 132	91.02 215	78.52 157	91.11 203	73.41 228	91.09 292	88.21 322

fmvsm_s_conf0.5_n_6		84.05 168	84.14 171	83.81 186	87.75 235	71.17 182	83.42 215	91.10 157	67.90 281	84.53 250	90.70 231	73.01 242	88.73 276	85.09 68	93.72 219	91.53 245

DP-MVS Recon		84.05 168	83.22 189	86.52 113	91.73 128	75.27 130	83.23 223	92.40 111	72.04 224	82.04 306	88.33 288	77.91 165	93.95 111	66.17 305	95.12 167	90.34 281

viewmacassd2359aftdt		84.04 170	84.78 150	81.81 245	86.43 275	60.32 326	81.95 260	92.82 99	71.56 227	86.06 212	92.98 139	81.79 124	90.28 232	76.18 185	93.24 233	94.82 84

TransMVSNet (Re)		84.02 171	85.74 130	78.85 298	91.00 154	55.20 380	82.29 252	87.26 252	79.65 105	88.38 148	95.52 41	83.00 94	86.88 312	67.97 293	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 172	85.90 123	78.29 310	91.47 140	53.44 392	82.29 252	87.00 266	79.06 114	89.55 122	95.72 36	77.20 177	86.14 330	72.30 244	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 173	84.46 163	82.53 229	86.11 291	70.65 189	82.45 247	89.17 215	67.72 285	86.74 192	91.49 195	79.20 150	85.86 339	84.71 76	92.60 254	91.07 254

TSAR-MVS + GP.		83.95 174	82.69 205	87.72 94	89.27 192	81.45 67	83.72 205	81.58 338	74.73 170	85.66 222	86.06 335	72.56 249	92.69 160	75.44 196	95.21 162	89.01 315

LuminaMVS		83.94 175	83.51 181	85.23 144	89.78 181	71.74 172	84.76 173	87.27 251	72.60 213	89.31 127	90.60 241	64.04 307	90.95 208	79.08 141	94.11 204	92.99 172

alignmvs		83.94 175	83.98 175	83.80 187	87.80 234	67.88 228	84.54 182	91.42 145	73.27 200	88.41 147	87.96 293	72.33 250	90.83 216	76.02 189	94.11 204	92.69 185

Effi-MVS+		83.90 177	84.01 174	83.57 198	87.22 252	65.61 253	86.55 137	92.40 111	78.64 121	81.34 322	84.18 365	83.65 88	92.93 154	74.22 207	87.87 354	92.17 221

fmvsm_s_conf0.1_n_2		83.82 178	83.49 182	84.84 152	85.99 294	70.19 195	80.93 278	87.58 247	67.26 292	87.94 161	92.37 165	71.40 263	88.01 288	86.03 55	91.87 276	96.31 36

mvs5depth		83.82 178	84.54 160	81.68 248	82.23 362	68.65 219	86.89 126	89.90 198	80.02 101	87.74 170	97.86 4	64.19 306	82.02 373	76.37 181	95.63 151	94.35 103

CANet		83.79 180	82.85 202	86.63 110	86.17 288	72.21 167	83.76 204	91.43 143	77.24 140	74.39 394	87.45 312	75.36 201	95.42 52	77.03 172	92.83 247	92.25 217

pm-mvs1		83.69 181	84.95 147	79.91 284	90.04 177	59.66 335	82.43 248	87.44 248	75.52 161	87.85 165	95.26 49	81.25 130	85.65 343	68.74 285	96.04 126	94.42 100

AdaColmap		83.66 182	83.69 179	83.57 198	90.05 176	72.26 165	86.29 141	90.00 196	78.19 127	81.65 316	87.16 318	83.40 91	94.24 95	61.69 347	94.76 185	84.21 378

MIMVSNet1		83.63 183	84.59 157	80.74 268	94.06 59	62.77 282	82.72 236	84.53 309	77.57 136	90.34 99	95.92 31	76.88 189	85.83 340	61.88 345	97.42 79	93.62 141

fmvsm_s_conf0.5_n_2		83.62 184	83.29 188	84.62 162	85.43 306	70.18 196	80.61 283	87.24 253	67.14 293	87.79 167	91.87 178	71.79 260	87.98 290	86.00 59	91.77 279	95.71 50

test_fmvsm_n_1920		83.60 185	82.89 199	85.74 133	85.22 310	77.74 102	84.12 191	90.48 174	59.87 371	86.45 206	91.12 212	75.65 198	85.89 337	82.28 105	90.87 301	93.58 144

WR-MVS		83.56 186	84.40 166	81.06 262	93.43 75	54.88 381	78.67 316	85.02 299	81.24 85	90.74 94	91.56 193	72.85 244	91.08 204	68.00 292	98.04 41	97.23 17

CNLPA		83.55 187	83.10 194	84.90 151	89.34 190	83.87 50	84.54 182	88.77 219	79.09 113	83.54 278	88.66 285	74.87 207	81.73 375	66.84 299	92.29 263	89.11 308

LCM-MVSNet-Re		83.48 188	85.06 143	78.75 300	85.94 295	55.75 374	80.05 289	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 257	59.95 358	94.89 177	90.75 265

hse-mvs2		83.47 189	81.81 219	88.47 80	91.03 153	82.27 61	82.61 238	83.69 315	71.27 231	86.70 193	86.05 336	63.04 318	92.41 166	78.26 153	93.62 223	90.71 267

V42		83.47 189	83.37 187	83.75 190	83.16 356	63.33 274	81.31 271	90.23 190	69.51 253	90.91 88	90.81 228	74.16 220	92.29 172	80.06 126	90.22 318	95.62 54

VPA-MVSNet		83.47 189	84.73 151	79.69 289	90.29 168	57.52 360	81.30 273	88.69 221	76.29 145	87.58 174	94.44 75	80.60 138	87.20 306	66.60 302	96.82 95	94.34 104

mamba_0408		83.44 192	82.88 200	85.11 147	89.13 195	68.97 214	72.73 391	91.28 150	72.90 205	85.68 219	90.61 239	76.78 190	93.97 109	73.37 230	93.47 225	92.38 206

PAPM_NR		83.23 193	83.19 191	83.33 204	90.90 156	65.98 249	88.19 104	90.78 166	78.13 128	80.87 327	87.92 297	73.49 234	92.42 165	70.07 267	88.40 343	91.60 242

CLD-MVS		83.18 194	82.64 206	84.79 155	89.05 198	67.82 229	77.93 326	92.52 109	68.33 271	85.07 237	81.54 394	82.06 116	92.96 152	69.35 274	97.91 54	93.57 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 195	85.68 131	75.65 348	81.24 374	45.26 436	79.94 291	92.91 95	83.83 57	91.33 78	96.88 16	80.25 142	85.92 333	68.89 282	95.89 138	95.76 48

FA-MVS(test-final)		83.13 196	83.02 195	83.43 201	86.16 290	66.08 248	88.00 108	88.36 230	75.55 160	85.02 238	92.75 152	65.12 300	92.50 164	74.94 202	91.30 289	91.72 237

114514_t		83.10 197	82.54 209	84.77 156	92.90 88	69.10 213	86.65 134	90.62 171	54.66 403	81.46 319	90.81 228	76.98 182	94.38 90	72.62 241	96.18 119	90.82 264

RRT-MVS		82.97 198	83.44 183	81.57 250	85.06 313	58.04 355	87.20 119	90.37 180	77.88 131	88.59 140	93.70 119	63.17 315	93.05 150	76.49 180	88.47 342	93.62 141

viewmanbaseed2359cas		82.95 199	83.43 184	81.52 251	85.18 311	60.03 331	81.36 270	92.38 113	69.55 252	84.84 246	91.38 200	79.85 148	90.09 245	74.22 207	92.09 270	94.43 99

BP-MVS1		82.81 200	81.67 221	86.23 119	87.88 232	68.53 220	86.06 146	84.36 310	75.65 157	85.14 233	90.19 253	45.84 407	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 201	81.64 222	86.21 122	86.20 287	76.24 123	86.86 127	85.68 285	77.07 141	73.76 398	92.82 148	69.64 272	91.82 185	69.04 281	93.69 220	90.56 275
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		82.75 202	81.93 217	85.19 145	82.08 363	80.15 76	85.53 157	88.76 220	68.01 276	85.58 225	87.75 304	71.80 259	86.85 313	74.02 215	93.87 212	88.58 318

EI-MVSNet		82.61 203	82.42 211	83.20 208	83.25 353	63.66 269	83.50 213	85.07 296	76.06 147	86.55 197	85.10 352	73.41 235	90.25 233	78.15 157	90.67 312	95.68 52

QAPM		82.59 204	82.59 208	82.58 226	86.44 274	66.69 240	89.94 68	90.36 181	67.97 278	84.94 242	92.58 157	72.71 246	92.18 173	70.63 261	87.73 357	88.85 316

fmvsm_s_conf0.1_n_a		82.58 205	81.93 217	84.50 165	87.68 238	73.35 142	86.14 145	77.70 358	61.64 349	85.02 238	91.62 190	77.75 166	86.24 325	82.79 98	87.07 365	93.91 122

Fast-Effi-MVS+-dtu		82.54 206	81.41 231	85.90 129	85.60 302	76.53 118	83.07 226	89.62 208	73.02 204	79.11 349	83.51 370	80.74 136	90.24 235	68.76 284	89.29 330	90.94 259

MVS_Test		82.47 207	83.22 189	80.22 280	82.62 361	57.75 359	82.54 243	91.96 127	71.16 235	82.89 289	92.52 159	77.41 172	90.50 227	80.04 127	87.84 356	92.40 203

viewdifsd2359ckpt11		82.46 208	82.98 197	80.88 265	83.53 340	61.00 315	79.46 300	85.97 280	69.48 254	87.89 163	91.31 204	82.10 114	88.61 279	74.28 205	92.86 245	93.02 168

viewmsd2359difaftdt		82.46 208	82.99 196	80.88 265	83.52 341	61.00 315	79.46 300	85.97 280	69.48 254	87.89 163	91.31 204	82.10 114	88.61 279	74.28 205	92.86 245	93.02 168

v148		82.31 210	82.48 210	81.81 245	85.59 303	59.66 335	81.47 268	86.02 278	72.85 207	88.05 157	90.65 237	70.73 266	90.91 212	75.15 199	91.79 277	94.87 78

API-MVS		82.28 211	82.61 207	81.30 256	86.29 284	69.79 198	88.71 96	87.67 246	78.42 124	82.15 302	84.15 366	77.98 163	91.59 188	65.39 314	92.75 249	82.51 405

MVSFormer		82.23 212	81.57 227	84.19 178	85.54 304	69.26 208	91.98 35	90.08 194	71.54 228	76.23 374	85.07 355	58.69 343	94.27 92	86.26 49	88.77 338	89.03 313

fmvsm_s_conf0.5_n_a		82.21 213	81.51 230	84.32 173	86.56 271	73.35 142	85.46 158	77.30 362	61.81 345	84.51 251	90.88 225	77.36 173	86.21 327	82.72 99	86.97 370	93.38 149

EIA-MVS		82.19 214	81.23 238	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 241	78.58 354	79.77 410	77.29 174	94.20 97	71.51 251	88.96 336	91.93 231

GDP-MVS		82.17 215	80.85 246	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 269	83.73 272	89.54 267	45.07 418	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 215	81.59 225	83.94 185	86.87 269	71.57 178	85.19 165	77.42 361	62.27 343	84.47 254	91.33 202	76.43 193	85.91 335	83.14 89	87.14 363	94.33 105

PCF-MVS	74.62 15	82.15 217	80.92 244	85.84 131	89.43 188	72.30 164	80.53 284	91.82 132	57.36 387	87.81 166	89.92 261	77.67 169	93.63 123	58.69 363	95.08 168	91.58 243
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 218	80.31 253	87.45 97	90.86 158	80.29 75	85.88 148	90.65 169	68.17 274	76.32 373	86.33 330	73.12 241	92.61 162	61.40 350	90.02 322	89.44 297
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 219	81.54 229	83.60 195	83.94 334	73.90 138	83.35 218	86.10 274	58.97 373	83.80 271	90.36 245	74.23 218	86.94 311	82.90 95	90.22 318	89.94 290

fmvsm_s_conf0.5_n_7		82.04 220	82.05 215	82.01 238	86.98 265	71.07 183	78.70 314	89.45 211	68.07 275	78.14 356	91.61 191	74.19 219	85.92 333	79.61 134	91.73 280	89.05 312

GBi-Net		82.02 221	82.07 213	81.85 242	86.38 278	61.05 312	86.83 129	88.27 233	72.43 214	86.00 213	95.64 38	63.78 311	90.68 221	65.95 307	93.34 229	93.82 127

test1		82.02 221	82.07 213	81.85 242	86.38 278	61.05 312	86.83 129	88.27 233	72.43 214	86.00 213	95.64 38	63.78 311	90.68 221	65.95 307	93.34 229	93.82 127

OpenMVS	76.72 13	81.98 223	82.00 216	81.93 239	84.42 325	68.22 223	88.50 102	89.48 210	66.92 296	81.80 313	91.86 179	72.59 248	90.16 239	71.19 254	91.25 290	87.40 338

KD-MVS_self_test		81.93 224	83.14 193	78.30 309	84.75 319	52.75 396	80.37 286	89.42 213	70.24 246	90.26 101	93.39 126	74.55 217	86.77 315	68.61 287	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 225	81.30 235	83.75 190	86.02 293	71.56 179	84.73 174	77.11 365	62.44 340	84.00 267	90.68 233	76.42 194	85.89 337	83.14 89	87.11 364	93.81 130

SDMVSNet		81.90 226	83.17 192	78.10 313	88.81 207	62.45 292	76.08 360	86.05 277	73.67 186	83.41 279	93.04 135	82.35 104	80.65 382	70.06 268	95.03 170	91.21 250

tfpnnormal		81.79 227	82.95 198	78.31 308	88.93 203	55.40 376	80.83 281	82.85 324	76.81 142	85.90 217	94.14 93	74.58 215	86.51 319	66.82 300	95.68 149	93.01 171

AstraMVS		81.67 228	81.40 232	82.48 231	87.06 262	66.47 243	81.41 269	81.68 335	68.78 264	88.00 158	90.95 221	65.70 296	87.86 296	76.66 175	92.38 258	93.12 164

c3_l		81.64 229	81.59 225	81.79 247	80.86 380	59.15 343	78.61 317	90.18 192	68.36 270	87.20 178	87.11 320	69.39 273	91.62 187	78.16 155	94.43 194	94.60 89

guyue		81.57 230	81.37 234	82.15 235	86.39 276	66.13 247	81.54 267	83.21 319	69.79 250	87.77 168	89.95 259	65.36 299	87.64 299	75.88 190	92.49 256	92.67 186

PVSNet_Blended_VisFu		81.55 231	80.49 251	84.70 160	91.58 133	73.24 146	84.21 188	91.67 136	62.86 334	80.94 325	87.16 318	67.27 285	92.87 157	69.82 270	88.94 337	87.99 328

fmvsm_l_conf0.5_n_a		81.46 232	80.87 245	83.25 206	83.73 339	73.21 147	83.00 229	85.59 287	58.22 379	82.96 288	90.09 258	72.30 251	86.65 317	81.97 110	89.95 323	89.88 291

SSM_04072		81.44 233	82.88 200	77.10 328	89.13 195	68.97 214	72.73 391	91.28 150	72.90 205	85.68 219	90.61 239	76.78 190	69.94 425	73.37 230	93.47 225	92.38 206

DELS-MVS		81.44 233	81.25 236	82.03 237	84.27 329	62.87 280	76.47 354	92.49 110	70.97 237	81.64 317	83.83 367	75.03 204	92.70 159	74.29 204	92.22 267	90.51 277
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		81.31 235	81.61 224	80.41 276	86.38 278	58.75 350	83.93 198	86.58 269	72.43 214	87.65 172	92.98 139	63.78 311	90.22 236	66.86 297	93.92 210	92.27 215

TinyColmap		81.25 236	82.34 212	77.99 316	85.33 307	60.68 322	82.32 251	88.33 231	71.26 233	86.97 187	92.22 173	77.10 180	86.98 310	62.37 339	95.17 164	86.31 351

diffmvs_AUTHOR		81.24 237	81.55 228	80.30 278	80.61 385	60.22 327	77.98 325	90.48 174	67.77 284	83.34 281	89.50 268	74.69 213	87.42 302	78.78 145	90.81 304	93.27 155

AUN-MVS		81.18 238	78.78 276	88.39 82	90.93 155	82.14 62	82.51 244	83.67 316	64.69 324	80.29 335	85.91 339	51.07 382	92.38 167	76.29 184	93.63 222	90.65 272

IMVS_0407		81.08 239	81.23 238	80.62 273	85.76 298	62.46 288	82.46 245	87.91 241	65.23 317	82.12 303	87.92 297	77.27 175	90.18 238	71.67 247	90.74 307	89.20 303

tttt0517		81.07 240	79.58 266	85.52 138	88.99 201	66.45 244	87.03 124	75.51 377	73.76 185	88.32 150	90.20 252	37.96 439	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 241	80.57 248	82.46 232	87.50 245	63.22 276	78.37 320	89.63 207	68.01 276	81.87 309	82.08 388	82.31 106	92.65 161	67.10 296	88.30 349	91.51 246

BH-untuned		80.96 242	80.99 242	80.84 267	88.55 216	68.23 222	80.33 287	88.46 226	72.79 210	86.55 197	86.76 324	74.72 212	91.77 186	61.79 346	88.99 335	82.52 404

IMVS_0403		80.93 243	81.00 241	80.72 270	85.76 298	62.46 288	81.82 261	87.91 241	65.23 317	82.07 305	87.92 297	75.91 197	90.50 227	71.67 247	90.74 307	89.20 303

eth_miper_zixun_eth		80.84 244	80.22 257	82.71 223	81.41 372	60.98 317	77.81 328	90.14 193	67.31 291	86.95 188	87.24 317	64.26 304	92.31 170	75.23 198	91.61 283	94.85 82

xiu_mvs_v1_base_debu		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 311	79.59 341	82.73 382	76.94 183	90.14 242	73.22 233	88.33 345	86.90 345

xiu_mvs_v1_base		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 311	79.59 341	82.73 382	76.94 183	90.14 242	73.22 233	88.33 345	86.90 345

xiu_mvs_v1_base_debi		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 311	79.59 341	82.73 382	76.94 183	90.14 242	73.22 233	88.33 345	86.90 345

IterMVS-SCA-FT		80.64 248	79.41 267	84.34 172	83.93 335	69.66 202	76.28 356	81.09 341	72.43 214	86.47 204	90.19 253	60.46 328	93.15 146	77.45 166	86.39 376	90.22 282

BH-RMVSNet		80.53 249	80.22 257	81.49 253	87.19 253	66.21 246	77.79 329	86.23 272	74.21 180	83.69 273	88.50 286	73.25 240	90.75 218	63.18 335	87.90 353	87.52 336

VortexMVS		80.51 250	80.63 247	80.15 282	83.36 349	61.82 302	80.63 282	88.00 239	67.11 294	87.23 177	89.10 276	63.98 308	88.00 289	73.63 225	92.63 253	90.64 273

Anonymous202405211		80.51 250	81.19 240	78.49 305	88.48 217	57.26 362	76.63 349	82.49 327	81.21 86	84.30 261	92.24 172	67.99 281	86.24 325	62.22 340	95.13 165	91.98 230

DIV-MVS_self_test		80.43 252	80.23 255	81.02 263	79.99 390	59.25 340	77.07 342	87.02 263	67.38 288	86.19 208	89.22 272	63.09 316	90.16 239	76.32 182	95.80 143	93.66 135

cl____		80.42 253	80.23 255	81.02 263	79.99 390	59.25 340	77.07 342	87.02 263	67.37 289	86.18 210	89.21 273	63.08 317	90.16 239	76.31 183	95.80 143	93.65 138

diffmvs		80.40 254	80.48 252	80.17 281	79.02 403	60.04 329	77.54 333	90.28 189	66.65 299	82.40 296	87.33 315	73.50 232	87.35 304	77.98 159	89.62 327	93.13 162
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		80.37 255	78.41 284	86.23 119	76.75 417	73.28 144	87.18 121	77.45 360	76.24 146	68.14 428	88.93 279	65.41 298	93.85 114	69.47 273	96.12 123	91.55 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 256	80.04 262	81.24 259	79.82 393	58.95 345	77.66 330	89.66 205	65.75 308	85.99 216	85.11 351	68.29 280	91.42 195	76.03 188	92.03 271	93.33 151

MG-MVS		80.32 257	80.94 243	78.47 306	88.18 223	52.62 399	82.29 252	85.01 300	72.01 225	79.24 348	92.54 158	69.36 274	93.36 140	70.65 260	89.19 333	89.45 296

mvsmamba		80.30 258	78.87 273	84.58 164	88.12 226	67.55 230	92.35 30	84.88 303	63.15 332	85.33 230	90.91 222	50.71 384	95.20 62	66.36 303	87.98 352	90.99 257

VPNet		80.25 259	81.68 220	75.94 344	92.46 100	47.98 423	76.70 347	81.67 336	73.45 191	84.87 244	92.82 148	74.66 214	86.51 319	61.66 348	96.85 92	93.33 151

MAR-MVS		80.24 260	78.74 278	84.73 158	86.87 269	78.18 95	85.75 152	87.81 245	65.67 310	77.84 360	78.50 420	73.79 228	90.53 226	61.59 349	90.87 301	85.49 361
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		80.20 261	79.00 272	83.78 189	88.17 224	86.66 19	81.31 271	66.81 433	69.64 251	88.33 149	90.19 253	64.58 301	83.63 364	71.99 246	90.03 321	81.06 424

Anonymous20240521		80.18 262	81.25 236	76.95 330	83.15 357	60.84 319	82.46 245	85.99 279	68.76 265	86.78 189	93.73 118	59.13 340	77.44 399	73.71 221	97.55 74	92.56 193

LFMVS		80.15 263	80.56 249	78.89 297	89.19 194	55.93 370	85.22 164	73.78 389	82.96 69	84.28 262	92.72 153	57.38 352	90.07 247	63.80 329	95.75 146	90.68 269

DPM-MVS		80.10 264	79.18 271	82.88 221	90.71 161	69.74 200	78.87 312	90.84 164	60.29 367	75.64 383	85.92 338	67.28 284	93.11 147	71.24 253	91.79 277	85.77 357

MSDG		80.06 265	79.99 264	80.25 279	83.91 336	68.04 227	77.51 334	89.19 214	77.65 134	81.94 307	83.45 372	76.37 195	86.31 324	63.31 334	86.59 373	86.41 349

FE-MVS		79.98 266	78.86 274	83.36 203	86.47 273	66.45 244	89.73 71	84.74 307	72.80 209	84.22 265	91.38 200	44.95 419	93.60 127	63.93 327	91.50 286	90.04 289

sd_testset		79.95 267	81.39 233	75.64 349	88.81 207	58.07 354	76.16 359	82.81 325	73.67 186	83.41 279	93.04 135	80.96 133	77.65 398	58.62 364	95.03 170	91.21 250

ab-mvs		79.67 268	80.56 249	76.99 329	88.48 217	56.93 364	84.70 176	86.06 276	68.95 262	80.78 328	93.08 134	75.30 202	84.62 351	56.78 373	90.90 299	89.43 298

VNet		79.31 269	80.27 254	76.44 338	87.92 230	53.95 388	75.58 366	84.35 311	74.39 179	82.23 300	90.72 230	72.84 245	84.39 356	60.38 356	93.98 209	90.97 258

thisisatest0530		79.07 270	77.33 294	84.26 175	87.13 254	64.58 260	83.66 208	75.95 372	68.86 263	85.22 232	87.36 314	38.10 436	93.57 131	75.47 195	94.28 199	94.62 88

cl22		78.97 271	78.21 286	81.24 259	77.74 407	59.01 344	77.46 337	87.13 257	65.79 305	84.32 258	85.10 352	58.96 342	90.88 214	75.36 197	92.03 271	93.84 125

patch_mono-2		78.89 272	79.39 268	77.41 325	84.78 317	68.11 225	75.60 364	83.11 321	60.96 359	79.36 345	89.89 262	75.18 203	72.97 414	73.32 232	92.30 261	91.15 252

RPMNet		78.88 273	78.28 285	80.68 272	79.58 394	62.64 284	82.58 240	94.16 33	74.80 168	75.72 381	92.59 155	48.69 391	95.56 42	73.48 227	82.91 412	83.85 383

PAPR		78.84 274	78.10 287	81.07 261	85.17 312	60.22 327	82.21 256	90.57 173	62.51 336	75.32 387	84.61 360	74.99 205	92.30 171	59.48 361	88.04 351	90.68 269

viewmambaseed2359dif		78.80 275	78.47 283	79.78 285	80.26 389	59.28 339	77.31 339	87.13 257	60.42 365	82.37 297	88.67 284	74.58 215	87.87 295	67.78 295	87.73 357	92.19 219

PVSNet_BlendedMVS		78.80 275	77.84 288	81.65 249	84.43 323	63.41 272	79.49 299	90.44 177	61.70 348	75.43 384	87.07 321	69.11 276	91.44 193	60.68 354	92.24 265	90.11 287

FMVSNet3		78.80 275	78.55 280	79.57 291	82.89 360	56.89 366	81.76 262	85.77 283	69.04 260	86.00 213	90.44 244	51.75 380	90.09 245	65.95 307	93.34 229	91.72 237

test_yl		78.71 278	78.51 281	79.32 294	84.32 327	58.84 347	78.38 318	85.33 291	75.99 150	82.49 294	86.57 326	58.01 346	90.02 249	62.74 336	92.73 251	89.10 309

DCV-MVSNet		78.71 278	78.51 281	79.32 294	84.32 327	58.84 347	78.38 318	85.33 291	75.99 150	82.49 294	86.57 326	58.01 346	90.02 249	62.74 336	92.73 251	89.10 309

test1111		78.53 280	78.85 275	77.56 322	92.22 109	47.49 425	82.61 238	69.24 421	72.43 214	85.28 231	94.20 89	51.91 378	90.07 247	65.36 315	96.45 108	95.11 72

FE-MVSNET		78.46 281	79.36 269	75.75 346	86.53 272	54.53 383	78.03 322	85.35 290	69.01 261	85.41 229	90.68 233	64.27 303	85.73 341	62.59 338	92.35 260	87.00 344

icg_test_0407_2		78.46 281	79.68 265	74.78 356	85.76 298	62.46 288	68.51 420	87.91 241	65.23 317	82.12 303	87.92 297	77.27 175	72.67 415	71.67 247	90.74 307	89.20 303

ECVR-MVS		78.44 283	78.63 279	77.88 318	91.85 123	48.95 419	83.68 207	69.91 417	72.30 220	84.26 264	94.20 89	51.89 379	89.82 252	63.58 330	96.02 127	94.87 78

pmmvs-eth3d		78.42 284	77.04 297	82.57 228	87.44 247	74.41 135	80.86 280	79.67 349	55.68 396	84.69 248	90.31 250	60.91 326	85.42 344	62.20 341	91.59 284	87.88 332

mvs_anonymous		78.13 285	78.76 277	76.23 343	79.24 400	50.31 415	78.69 315	84.82 305	61.60 350	83.09 287	92.82 148	73.89 227	87.01 307	68.33 291	86.41 375	91.37 247

TAMVS		78.08 286	76.36 304	83.23 207	90.62 162	72.87 150	79.08 308	80.01 348	61.72 347	81.35 321	86.92 323	63.96 310	88.78 274	50.61 412	93.01 241	88.04 327

miper_enhance_ethall		77.83 287	76.93 298	80.51 274	76.15 424	58.01 356	75.47 368	88.82 218	58.05 381	83.59 275	80.69 398	64.41 302	91.20 199	73.16 239	92.03 271	92.33 210

Vis-MVSNet (Re-imp)		77.82 288	77.79 289	77.92 317	88.82 206	51.29 409	83.28 219	71.97 405	74.04 181	82.23 300	89.78 263	57.38 352	89.41 264	57.22 372	95.41 154	93.05 167

CANet_DTU		77.81 289	77.05 296	80.09 283	81.37 373	59.90 333	83.26 220	88.29 232	69.16 258	67.83 431	83.72 368	60.93 325	89.47 259	69.22 277	89.70 326	90.88 262

OpenMVS_ROB	70.19 17	77.77 290	77.46 291	78.71 301	84.39 326	61.15 310	81.18 275	82.52 326	62.45 339	83.34 281	87.37 313	66.20 290	88.66 277	64.69 322	85.02 392	86.32 350

SSC-MVS		77.55 291	81.64 222	65.29 422	90.46 165	20.33 469	73.56 384	68.28 423	85.44 41	88.18 154	94.64 68	70.93 265	81.33 377	71.25 252	92.03 271	94.20 107

MDA-MVSNet-bldmvs		77.47 292	76.90 299	79.16 296	79.03 402	64.59 259	66.58 432	75.67 375	73.15 202	88.86 132	88.99 278	66.94 286	81.23 378	64.71 321	88.22 350	91.64 241

jason		77.42 293	75.75 310	82.43 233	87.10 257	69.27 207	77.99 324	81.94 333	51.47 423	77.84 360	85.07 355	60.32 330	89.00 268	70.74 259	89.27 332	89.03 313
jason: jason.
CDS-MVSNet		77.32 294	75.40 314	83.06 211	89.00 200	72.48 161	77.90 327	82.17 331	60.81 360	78.94 351	83.49 371	59.30 338	88.76 275	54.64 392	92.37 259	87.93 331
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 295	77.75 290	75.73 347	85.76 298	62.46 288	70.84 406	87.91 241	65.23 317	72.21 406	87.92 297	67.48 283	75.53 407	71.67 247	90.74 307	89.20 303

xiu_mvs_v2_base		77.19 296	76.75 301	78.52 304	87.01 263	61.30 308	75.55 367	87.12 261	61.24 356	74.45 393	78.79 418	77.20 177	90.93 210	64.62 324	84.80 399	83.32 392

MVSTER		77.09 297	75.70 311	81.25 257	75.27 432	61.08 311	77.49 336	85.07 296	60.78 361	86.55 197	88.68 282	43.14 428	90.25 233	73.69 224	90.67 312	92.42 200

PS-MVSNAJ		77.04 298	76.53 303	78.56 303	87.09 259	61.40 306	75.26 369	87.13 257	61.25 355	74.38 395	77.22 432	76.94 183	90.94 209	64.63 323	84.83 398	83.35 391

IterMVS		76.91 299	76.34 305	78.64 302	80.91 378	64.03 266	76.30 355	79.03 352	64.88 323	83.11 285	89.16 274	59.90 334	84.46 354	68.61 287	85.15 390	87.42 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 300	75.67 312	80.34 277	80.48 387	62.16 300	73.50 385	84.80 306	57.61 385	82.24 299	87.54 308	51.31 381	87.65 298	70.40 264	93.19 237	91.23 249

CL-MVSNet_self_test		76.81 301	77.38 293	75.12 352	86.90 267	51.34 407	73.20 388	80.63 345	68.30 272	81.80 313	88.40 287	66.92 287	80.90 379	55.35 386	94.90 176	93.12 164

TR-MVS		76.77 302	75.79 309	79.72 288	86.10 292	65.79 251	77.14 340	83.02 322	65.20 321	81.40 320	82.10 386	66.30 289	90.73 220	55.57 383	85.27 386	82.65 399

MonoMVSNet		76.66 303	77.26 295	74.86 354	79.86 392	54.34 385	86.26 142	86.08 275	71.08 236	85.59 224	88.68 282	53.95 370	85.93 332	63.86 328	80.02 428	84.32 374

USDC		76.63 304	76.73 302	76.34 340	83.46 344	57.20 363	80.02 290	88.04 238	52.14 419	83.65 274	91.25 207	63.24 314	86.65 317	54.66 391	94.11 204	85.17 363

BH-w/o		76.57 305	76.07 308	78.10 313	86.88 268	65.92 250	77.63 331	86.33 270	65.69 309	80.89 326	79.95 407	68.97 278	90.74 219	53.01 402	85.25 387	77.62 435

Patchmtry		76.56 306	77.46 291	73.83 362	79.37 399	46.60 429	82.41 249	76.90 366	73.81 184	85.56 226	92.38 162	48.07 394	83.98 361	63.36 333	95.31 160	90.92 260

PVSNet_Blended		76.49 307	75.40 314	79.76 287	84.43 323	63.41 272	75.14 370	90.44 177	57.36 387	75.43 384	78.30 421	69.11 276	91.44 193	60.68 354	87.70 359	84.42 373

miper_lstm_enhance		76.45 308	76.10 307	77.51 323	76.72 418	60.97 318	64.69 436	85.04 298	63.98 328	83.20 284	88.22 289	56.67 356	78.79 395	73.22 233	93.12 238	92.78 180

lupinMVS		76.37 309	74.46 323	82.09 236	85.54 304	69.26 208	76.79 345	80.77 344	50.68 430	76.23 374	82.82 380	58.69 343	88.94 269	69.85 269	88.77 338	88.07 324

cascas		76.29 310	74.81 319	80.72 270	84.47 322	62.94 278	73.89 382	87.34 249	55.94 394	75.16 389	76.53 437	63.97 309	91.16 201	65.00 318	90.97 297	88.06 326

SD_0403		76.08 311	76.77 300	73.98 360	87.08 261	49.45 418	83.62 209	84.68 308	63.31 329	75.13 390	87.47 311	71.85 258	84.56 352	49.97 414	87.86 355	87.94 330

WB-MVS		76.06 312	80.01 263	64.19 425	89.96 179	20.58 468	72.18 395	68.19 424	83.21 65	86.46 205	93.49 123	70.19 270	78.97 393	65.96 306	90.46 317	93.02 168

thres600view7		75.97 313	75.35 316	77.85 320	87.01 263	51.84 405	80.45 285	73.26 394	75.20 165	83.10 286	86.31 332	45.54 409	89.05 267	55.03 389	92.24 265	92.66 187

GA-MVS		75.83 314	74.61 320	79.48 293	81.87 365	59.25 340	73.42 386	82.88 323	68.68 266	79.75 340	81.80 391	50.62 385	89.46 260	66.85 298	85.64 383	89.72 293

MVP-Stereo		75.81 315	73.51 332	82.71 223	89.35 189	73.62 139	80.06 288	85.20 293	60.30 366	73.96 396	87.94 294	57.89 350	89.45 261	52.02 406	74.87 446	85.06 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 316	75.20 317	77.27 326	75.01 435	69.47 205	78.93 309	84.88 303	46.67 437	87.08 184	87.84 302	50.44 387	71.62 420	77.42 168	88.53 341	90.72 266

thres100view900		75.45 317	75.05 318	76.66 336	87.27 249	51.88 404	81.07 276	73.26 394	75.68 156	83.25 283	86.37 329	45.54 409	88.80 271	51.98 407	90.99 294	89.31 300

ET-MVSNet_ETH3D		75.28 318	72.77 341	82.81 222	83.03 359	68.11 225	77.09 341	76.51 370	60.67 363	77.60 365	80.52 402	38.04 437	91.15 202	70.78 257	90.68 311	89.17 307

thres400		75.14 319	74.23 325	77.86 319	86.24 285	52.12 401	79.24 305	73.87 387	73.34 195	81.82 311	84.60 361	46.02 402	88.80 271	51.98 407	90.99 294	92.66 187

wuyk23d		75.13 320	79.30 270	62.63 428	75.56 428	75.18 131	80.89 279	73.10 396	75.06 167	94.76 16	95.32 45	87.73 44	52.85 460	34.16 458	97.11 87	59.85 456

EU-MVSNet		75.12 321	74.43 324	77.18 327	83.11 358	59.48 337	85.71 154	82.43 328	39.76 457	85.64 223	88.76 280	44.71 421	87.88 294	73.86 218	85.88 382	84.16 379

HyFIR lowres test		75.12 321	72.66 343	82.50 230	91.44 141	65.19 256	72.47 393	87.31 250	46.79 436	80.29 335	84.30 363	52.70 375	92.10 177	51.88 411	86.73 371	90.22 282

CMPMVS	59.41 20	75.12 321	73.57 330	79.77 286	75.84 427	67.22 231	81.21 274	82.18 330	50.78 428	76.50 370	87.66 306	55.20 366	82.99 367	62.17 343	90.64 316	89.09 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 324	72.98 339	80.73 269	84.95 314	71.71 176	76.23 357	77.59 359	52.83 413	77.73 364	86.38 328	56.35 359	84.97 348	57.72 371	87.05 366	85.51 360

tfpn200view9		74.86 325	74.23 325	76.74 335	86.24 285	52.12 401	79.24 305	73.87 387	73.34 195	81.82 311	84.60 361	46.02 402	88.80 271	51.98 407	90.99 294	89.31 300

1112_ss		74.82 326	73.74 328	78.04 315	89.57 183	60.04 329	76.49 353	87.09 262	54.31 404	73.66 399	79.80 408	60.25 331	86.76 316	58.37 365	84.15 403	87.32 339

EGC-MVSNET		74.79 327	69.99 371	89.19 67	94.89 38	87.00 15	91.89 38	86.28 271	1.09 466	2.23 468	95.98 30	81.87 122	89.48 258	79.76 130	95.96 130	91.10 253

ppachtmachnet_test		74.73 328	74.00 327	76.90 332	80.71 383	56.89 366	71.53 401	78.42 354	58.24 378	79.32 347	82.92 379	57.91 349	84.26 358	65.60 313	91.36 288	89.56 295

Patchmatch-RL test		74.48 329	73.68 329	76.89 333	84.83 316	66.54 241	72.29 394	69.16 422	57.70 383	86.76 190	86.33 330	45.79 408	82.59 368	69.63 272	90.65 315	81.54 415

PatchMatch-RL		74.48 329	73.22 336	78.27 311	87.70 237	85.26 38	75.92 362	70.09 415	64.34 326	76.09 377	81.25 396	65.87 295	78.07 397	53.86 394	83.82 405	71.48 444

XXY-MVS		74.44 331	76.19 306	69.21 397	84.61 321	52.43 400	71.70 398	77.18 364	60.73 362	80.60 329	90.96 219	75.44 199	69.35 428	56.13 378	88.33 345	85.86 356

test2506		74.12 332	73.39 333	76.28 341	91.85 123	44.20 439	84.06 192	48.20 464	72.30 220	81.90 308	94.20 89	27.22 464	89.77 255	64.81 320	96.02 127	94.87 78

reproduce_monomvs		74.09 333	73.23 335	76.65 337	76.52 419	54.54 382	77.50 335	81.40 339	65.85 304	82.86 291	86.67 325	27.38 462	84.53 353	70.24 265	90.66 314	90.89 261

CR-MVSNet		74.00 334	73.04 338	76.85 334	79.58 394	62.64 284	82.58 240	76.90 366	50.50 431	75.72 381	92.38 162	48.07 394	84.07 360	68.72 286	82.91 412	83.85 383

SSC-MVS3.2		73.90 335	75.67 312	68.61 405	84.11 332	41.28 447	64.17 438	72.83 397	72.09 223	79.08 350	87.94 294	70.31 268	73.89 413	55.99 379	94.49 191	90.67 271

Test_1112_low_res		73.90 335	73.08 337	76.35 339	90.35 167	55.95 369	73.40 387	86.17 273	50.70 429	73.14 400	85.94 337	58.31 345	85.90 336	56.51 375	83.22 409	87.20 341

test20.03		73.75 337	74.59 322	71.22 383	81.11 376	51.12 411	70.15 412	72.10 404	70.42 241	80.28 337	91.50 194	64.21 305	74.72 411	46.96 432	94.58 189	87.82 334

test_fmvs2		73.57 338	72.80 340	75.90 345	72.74 449	68.84 218	77.07 342	84.32 312	45.14 443	82.89 289	84.22 364	48.37 392	70.36 424	73.40 229	87.03 367	88.52 319

SCA		73.32 339	72.57 345	75.58 350	81.62 369	55.86 372	78.89 311	71.37 410	61.73 346	74.93 391	83.42 373	60.46 328	87.01 307	58.11 369	82.63 417	83.88 380

baseline1		73.26 340	73.54 331	72.43 376	84.92 315	47.79 424	79.89 292	74.00 385	65.93 302	78.81 352	86.28 333	56.36 358	81.63 376	56.63 374	79.04 435	87.87 333

1314		73.22 341	72.56 346	75.20 351	80.41 388	57.84 357	81.64 265	85.36 289	51.68 422	73.10 401	76.65 436	61.45 323	85.19 346	63.54 331	79.21 433	82.59 400

MVS		73.21 342	72.59 344	75.06 353	80.97 377	60.81 320	81.64 265	85.92 282	46.03 441	71.68 409	77.54 427	68.47 279	89.77 255	55.70 382	85.39 384	74.60 441

HY-MVS	64.64 18	73.03 343	72.47 347	74.71 357	83.36 349	54.19 386	82.14 259	81.96 332	56.76 393	69.57 423	86.21 334	60.03 332	84.83 350	49.58 419	82.65 415	85.11 364

thisisatest0515		73.00 344	70.52 363	80.46 275	81.45 371	59.90 333	73.16 389	74.31 384	57.86 382	76.08 378	77.78 424	37.60 440	92.12 176	65.00 318	91.45 287	89.35 299

EPNet_dtu		72.87 345	71.33 357	77.49 324	77.72 408	60.55 323	82.35 250	75.79 373	66.49 300	58.39 459	81.06 397	53.68 371	85.98 331	53.55 397	92.97 243	85.95 354
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 346	71.41 356	76.28 341	83.25 353	60.34 325	83.50 213	79.02 353	37.77 461	76.33 372	85.10 352	49.60 390	87.41 303	70.54 262	77.54 441	81.08 422

CHOSEN 1792x2688		72.45 347	70.56 362	78.13 312	90.02 178	63.08 277	68.72 419	83.16 320	42.99 451	75.92 379	85.46 345	57.22 354	85.18 347	49.87 417	81.67 419	86.14 352

testgi		72.36 348	74.61 320	65.59 419	80.56 386	42.82 444	68.29 421	73.35 393	66.87 297	81.84 310	89.93 260	72.08 255	66.92 442	46.05 436	92.54 255	87.01 343

thres200		72.34 349	71.55 355	74.70 358	83.48 343	51.60 406	75.02 371	73.71 390	70.14 247	78.56 355	80.57 401	46.20 400	88.20 287	46.99 431	89.29 330	84.32 374

FPMVS		72.29 350	72.00 349	73.14 367	88.63 213	85.00 40	74.65 375	67.39 427	71.94 226	77.80 362	87.66 306	50.48 386	75.83 405	49.95 415	79.51 429	58.58 458

FMVSNet5		72.10 351	71.69 351	73.32 365	81.57 370	53.02 395	76.77 346	78.37 355	63.31 329	76.37 371	91.85 180	36.68 441	78.98 392	47.87 428	92.45 257	87.95 329

our_test_3		71.85 352	71.59 352	72.62 373	80.71 383	53.78 389	69.72 415	71.71 409	58.80 375	78.03 357	80.51 403	56.61 357	78.84 394	62.20 341	86.04 381	85.23 362

PAPM		71.77 353	70.06 369	76.92 331	86.39 276	53.97 387	76.62 350	86.62 268	53.44 408	63.97 448	84.73 359	57.79 351	92.34 169	39.65 448	81.33 423	84.45 372

ttmdpeth		71.72 354	70.67 360	74.86 354	73.08 446	55.88 371	77.41 338	69.27 420	55.86 395	78.66 353	93.77 116	38.01 438	75.39 408	60.12 357	89.87 324	93.31 153

IB-MVS	62.13 19	71.64 355	68.97 381	79.66 290	80.80 382	62.26 297	73.94 381	76.90 366	63.27 331	68.63 427	76.79 434	33.83 445	91.84 184	59.28 362	87.26 361	84.88 366
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		71.63 356	72.30 348	69.62 394	76.47 421	52.70 398	70.03 413	80.97 342	59.18 372	79.36 345	88.21 290	60.50 327	69.12 429	58.33 367	77.62 440	87.04 342

testing3		71.53 357	70.79 359	73.77 363	88.89 205	41.86 446	76.60 352	59.12 453	72.83 208	80.97 323	82.08 388	19.80 470	87.33 305	65.12 317	91.68 282	92.13 223

test_vis3_rt		71.42 358	70.67 360	73.64 364	69.66 456	70.46 190	66.97 431	89.73 202	42.68 453	88.20 153	83.04 375	43.77 423	60.07 454	65.35 316	86.66 372	90.39 280

Anonymous20231206		71.38 359	71.88 350	69.88 391	86.31 282	54.37 384	70.39 410	74.62 380	52.57 415	76.73 369	88.76 280	59.94 333	72.06 417	44.35 440	93.23 235	83.23 394

test_vis1_n_1920		71.30 360	71.58 354	70.47 386	77.58 410	59.99 332	74.25 376	84.22 313	51.06 425	74.85 392	79.10 414	55.10 367	68.83 431	68.86 283	79.20 434	82.58 401

MIMVSNet		71.09 361	71.59 352	69.57 395	87.23 251	50.07 416	78.91 310	71.83 406	60.20 369	71.26 410	91.76 187	55.08 368	76.09 403	41.06 445	87.02 368	82.54 403

test_fmvs1_n		70.94 362	70.41 366	72.53 375	73.92 437	66.93 238	75.99 361	84.21 314	43.31 450	79.40 344	79.39 412	43.47 424	68.55 433	69.05 280	84.91 395	82.10 409

MS-PatchMatch		70.93 363	70.22 367	73.06 368	81.85 366	62.50 287	73.82 383	77.90 356	52.44 416	75.92 379	81.27 395	55.67 363	81.75 374	55.37 385	77.70 439	74.94 440

pmmvs5		70.73 364	70.07 368	72.72 371	77.03 415	52.73 397	74.14 377	75.65 376	50.36 432	72.17 407	85.37 349	55.42 365	80.67 381	52.86 403	87.59 360	84.77 367

testing3-2		70.72 365	70.97 358	69.95 390	88.93 203	34.80 460	69.85 414	66.59 434	78.42 124	77.58 366	85.55 341	31.83 451	82.08 372	46.28 433	93.73 218	92.98 174

PatchT		70.52 366	72.76 342	63.79 427	79.38 398	33.53 461	77.63 331	65.37 438	73.61 188	71.77 408	92.79 151	44.38 422	75.65 406	64.53 325	85.37 385	82.18 408

test_vis1_n		70.29 367	69.99 371	71.20 384	75.97 426	66.50 242	76.69 348	80.81 343	44.22 446	75.43 384	77.23 431	50.00 388	68.59 432	66.71 301	82.85 414	78.52 434

N_pmnet		70.20 368	68.80 383	74.38 359	80.91 378	84.81 43	59.12 449	76.45 371	55.06 399	75.31 388	82.36 385	55.74 362	54.82 459	47.02 430	87.24 362	83.52 387

tpmvs		70.16 369	69.56 374	71.96 379	74.71 436	48.13 421	79.63 294	75.45 378	65.02 322	70.26 418	81.88 390	45.34 414	85.68 342	58.34 366	75.39 445	82.08 410

new-patchmatchnet		70.10 370	73.37 334	60.29 436	81.23 375	16.95 471	59.54 447	74.62 380	62.93 333	80.97 323	87.93 296	62.83 320	71.90 418	55.24 387	95.01 173	92.00 228

YYNet1		70.06 371	70.44 364	68.90 399	73.76 439	53.42 393	58.99 450	67.20 429	58.42 377	87.10 182	85.39 348	59.82 335	67.32 439	59.79 359	83.50 408	85.96 353

MVStest1		70.05 372	69.26 375	72.41 377	58.62 468	55.59 375	76.61 351	65.58 436	53.44 408	89.28 128	93.32 127	22.91 468	71.44 422	74.08 214	89.52 328	90.21 286

MDA-MVSNet_test_wron		70.05 372	70.44 364	68.88 400	73.84 438	53.47 391	58.93 451	67.28 428	58.43 376	87.09 183	85.40 347	59.80 336	67.25 440	59.66 360	83.54 407	85.92 355

CostFormer		69.98 374	68.68 384	73.87 361	77.14 413	50.72 413	79.26 304	74.51 382	51.94 421	70.97 413	84.75 358	45.16 417	87.49 301	55.16 388	79.23 432	83.40 390

testing91		69.94 375	68.99 380	72.80 370	83.81 338	45.89 432	71.57 400	73.64 392	68.24 273	70.77 416	77.82 423	34.37 444	84.44 355	53.64 396	87.00 369	88.07 324

baseline2		69.77 376	66.89 393	78.41 307	79.51 396	58.09 353	76.23 357	69.57 418	57.50 386	64.82 446	77.45 429	46.02 402	88.44 281	53.08 399	77.83 437	88.70 317

PatchmatchNet		69.71 377	68.83 382	72.33 378	77.66 409	53.60 390	79.29 303	69.99 416	57.66 384	72.53 404	82.93 378	46.45 399	80.08 387	60.91 353	72.09 449	83.31 393
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 378	69.05 378	71.14 385	69.15 457	65.77 252	73.98 380	83.32 318	42.83 452	77.77 363	78.27 422	43.39 427	68.50 434	68.39 290	84.38 402	79.15 432

JIA-IIPM		69.41 379	66.64 397	77.70 321	73.19 443	71.24 181	75.67 363	65.56 437	70.42 241	65.18 442	92.97 142	33.64 447	83.06 365	53.52 398	69.61 455	78.79 433

Syy-MVS		69.40 380	70.03 370	67.49 410	81.72 367	38.94 452	71.00 403	61.99 444	61.38 352	70.81 414	72.36 448	61.37 324	79.30 390	64.50 326	85.18 388	84.22 376

testing99		69.27 381	68.15 388	72.63 372	83.29 351	45.45 434	71.15 402	71.08 411	67.34 290	70.43 417	77.77 425	32.24 450	84.35 357	53.72 395	86.33 377	88.10 323

UnsupCasMVSNet_bld		69.21 382	69.68 373	67.82 408	79.42 397	51.15 410	67.82 425	75.79 373	54.15 405	77.47 367	85.36 350	59.26 339	70.64 423	48.46 425	79.35 431	81.66 413

test_cas_vis1_n_1920		69.20 383	69.12 376	69.43 396	73.68 440	62.82 281	70.38 411	77.21 363	46.18 440	80.46 334	78.95 416	52.03 377	65.53 447	65.77 312	77.45 442	79.95 430

gg-mvs-nofinetune		68.96 384	69.11 377	68.52 406	76.12 425	45.32 435	83.59 210	55.88 458	86.68 33	64.62 447	97.01 12	30.36 455	83.97 362	44.78 439	82.94 411	76.26 437

WBMVS		68.76 385	68.43 385	69.75 393	83.29 351	40.30 450	67.36 427	72.21 403	57.09 390	77.05 368	85.53 343	33.68 446	80.51 383	48.79 423	90.90 299	88.45 320

WB-MVSnew		68.72 386	69.01 379	67.85 407	83.22 355	43.98 440	74.93 372	65.98 435	55.09 398	73.83 397	79.11 413	65.63 297	71.89 419	38.21 453	85.04 391	87.69 335

tpm2		68.45 387	66.83 394	73.30 366	78.93 404	48.50 420	79.76 293	71.76 407	47.50 435	69.92 420	83.60 369	42.07 430	88.40 283	48.44 426	79.51 429	83.01 397

tpm		67.95 388	68.08 389	67.55 409	78.74 405	43.53 442	75.60 364	67.10 432	54.92 400	72.23 405	88.10 291	42.87 429	75.97 404	52.21 405	80.95 427	83.15 395

WTY-MVS		67.91 389	68.35 386	66.58 415	80.82 381	48.12 422	65.96 433	72.60 398	53.67 407	71.20 411	81.68 393	58.97 341	69.06 430	48.57 424	81.67 419	82.55 402

testing11		67.38 390	65.93 398	71.73 381	83.37 348	46.60 429	70.95 405	69.40 419	62.47 338	66.14 435	76.66 435	31.22 452	84.10 359	49.10 421	84.10 404	84.49 370

test-LLR		67.21 391	66.74 395	68.63 403	76.45 422	55.21 378	67.89 422	67.14 430	62.43 341	65.08 443	72.39 446	43.41 425	69.37 426	61.00 351	84.89 396	81.31 417

testing222		66.93 392	65.30 405	71.81 380	83.38 347	45.83 433	72.06 396	67.50 426	64.12 327	69.68 422	76.37 438	27.34 463	83.00 366	38.88 449	88.38 344	86.62 348

sss		66.92 393	67.26 391	65.90 417	77.23 412	51.10 412	64.79 435	71.72 408	52.12 420	70.13 419	80.18 405	57.96 348	65.36 448	50.21 413	81.01 425	81.25 419

KD-MVS_2432*1600		66.87 394	65.81 401	70.04 388	67.50 458	47.49 425	62.56 441	79.16 350	61.21 357	77.98 358	80.61 399	25.29 466	82.48 369	53.02 400	84.92 393	80.16 428

miper_refine_blended		66.87 394	65.81 401	70.04 388	67.50 458	47.49 425	62.56 441	79.16 350	61.21 357	77.98 358	80.61 399	25.29 466	82.48 369	53.02 400	84.92 393	80.16 428

dmvs_re		66.81 396	66.98 392	66.28 416	76.87 416	58.68 351	71.66 399	72.24 401	60.29 367	69.52 424	73.53 445	52.38 376	64.40 450	44.90 438	81.44 422	75.76 438

tpm cat1		66.76 397	65.21 406	71.42 382	77.09 414	50.62 414	78.01 323	73.68 391	44.89 444	68.64 426	79.00 415	45.51 411	82.42 371	49.91 416	70.15 452	81.23 421

UWE-MVS		66.43 398	65.56 404	69.05 398	84.15 331	40.98 448	73.06 390	64.71 440	54.84 401	76.18 376	79.62 411	29.21 457	80.50 384	38.54 452	89.75 325	85.66 358

PVSNet	58.17 21	66.41 399	65.63 403	68.75 401	81.96 364	49.88 417	62.19 443	72.51 400	51.03 426	68.04 429	75.34 442	50.84 383	74.77 409	45.82 437	82.96 410	81.60 414

tpmrst		66.28 400	66.69 396	65.05 423	72.82 448	39.33 451	78.20 321	70.69 414	53.16 411	67.88 430	80.36 404	48.18 393	74.75 410	58.13 368	70.79 451	81.08 422

Patchmatch-test		65.91 401	67.38 390	61.48 433	75.51 429	43.21 443	68.84 418	63.79 442	62.48 337	72.80 403	83.42 373	44.89 420	59.52 456	48.27 427	86.45 374	81.70 412

ADS-MVSNet2		65.87 402	63.64 411	72.55 374	73.16 444	56.92 365	67.10 429	74.81 379	49.74 433	66.04 437	82.97 376	46.71 397	77.26 400	42.29 442	69.96 453	83.46 388

myMVS_eth3d28		65.83 403	65.85 399	65.78 418	83.42 346	35.71 458	67.29 428	68.01 425	67.58 287	69.80 421	77.72 426	32.29 449	74.30 412	37.49 454	89.06 334	87.32 339

test_vis1_rt		65.64 404	64.09 408	70.31 387	66.09 462	70.20 194	61.16 444	81.60 337	38.65 458	72.87 402	69.66 451	52.84 373	60.04 455	56.16 377	77.77 438	80.68 426

mvsany_test3		65.48 405	62.97 414	73.03 369	69.99 455	76.17 124	64.83 434	43.71 466	43.68 448	80.25 338	87.05 322	52.83 374	63.09 453	51.92 410	72.44 448	79.84 431

test-mter		65.00 406	63.79 410	68.63 403	76.45 422	55.21 378	67.89 422	67.14 430	50.98 427	65.08 443	72.39 446	28.27 460	69.37 426	61.00 351	84.89 396	81.31 417

ETVMVS		64.67 407	63.34 413	68.64 402	83.44 345	41.89 445	69.56 417	61.70 449	61.33 354	68.74 425	75.76 440	28.76 458	79.35 389	34.65 457	86.16 380	84.67 369

myMVS_eth3d		64.66 408	63.89 409	66.97 413	81.72 367	37.39 455	71.00 403	61.99 444	61.38 352	70.81 414	72.36 448	20.96 469	79.30 390	49.59 418	85.18 388	84.22 376

test0.0.03 1		64.66 408	64.36 407	65.57 420	75.03 434	46.89 428	64.69 436	61.58 450	62.43 341	71.18 412	77.54 427	43.41 425	68.47 435	40.75 447	82.65 415	81.35 416

UBG		64.34 410	63.35 412	67.30 411	83.50 342	40.53 449	67.46 426	65.02 439	54.77 402	67.54 433	74.47 444	32.99 448	78.50 396	40.82 446	83.58 406	82.88 398

test_f		64.31 411	65.85 399	59.67 437	66.54 461	62.24 299	57.76 453	70.96 412	40.13 455	84.36 256	82.09 387	46.93 396	51.67 461	61.99 344	81.89 418	65.12 452

pmmvs3		62.47 412	60.02 425	69.80 392	71.58 452	64.00 267	70.52 409	58.44 456	39.77 456	66.05 436	75.84 439	27.10 465	72.28 416	46.15 435	84.77 400	73.11 442

EPMVS		62.47 412	62.63 416	62.01 429	70.63 454	38.74 453	74.76 373	52.86 460	53.91 406	67.71 432	80.01 406	39.40 434	66.60 443	55.54 384	68.81 457	80.68 426

ADS-MVSNet		61.90 414	62.19 418	61.03 434	73.16 444	36.42 457	67.10 429	61.75 447	49.74 433	66.04 437	82.97 376	46.71 397	63.21 451	42.29 442	69.96 453	83.46 388

PMMVS		61.65 415	60.38 422	65.47 421	65.40 465	69.26 208	63.97 439	61.73 448	36.80 462	60.11 454	68.43 453	59.42 337	66.35 444	48.97 422	78.57 436	60.81 455

E-PMN		61.59 416	61.62 419	61.49 432	66.81 460	55.40 376	53.77 456	60.34 452	66.80 298	58.90 457	65.50 456	40.48 433	66.12 445	55.72 381	86.25 378	62.95 454

TESTMET0.1,1		61.29 417	60.32 423	64.19 425	72.06 450	51.30 408	67.89 422	62.09 443	45.27 442	60.65 453	69.01 452	27.93 461	64.74 449	56.31 376	81.65 421	76.53 436

MVS-HIRNet		61.16 418	62.92 415	55.87 440	79.09 401	35.34 459	71.83 397	57.98 457	46.56 438	59.05 456	91.14 211	49.95 389	76.43 402	38.74 450	71.92 450	55.84 459

EMVS		61.10 419	60.81 421	61.99 430	65.96 463	55.86 372	53.10 457	58.97 455	67.06 295	56.89 461	63.33 457	40.98 431	67.03 441	54.79 390	86.18 379	63.08 453

DSMNet-mixed		60.98 420	61.61 420	59.09 439	72.88 447	45.05 437	74.70 374	46.61 465	26.20 463	65.34 441	90.32 249	55.46 364	63.12 452	41.72 444	81.30 424	69.09 448

dp		60.70 421	60.29 424	61.92 431	72.04 451	38.67 454	70.83 407	64.08 441	51.28 424	60.75 452	77.28 430	36.59 442	71.58 421	47.41 429	62.34 459	75.52 439

dmvs_testset		60.59 422	62.54 417	54.72 442	77.26 411	27.74 465	74.05 379	61.00 451	60.48 364	65.62 440	67.03 455	55.93 361	68.23 437	32.07 461	69.46 456	68.17 449

CHOSEN 280x420		59.08 423	56.52 429	66.76 414	76.51 420	64.39 263	49.62 458	59.00 454	43.86 447	55.66 462	68.41 454	35.55 443	68.21 438	43.25 441	76.78 444	67.69 450

mvsany_test1		58.48 424	56.47 430	64.50 424	65.90 464	68.21 224	56.95 454	42.11 467	38.30 459	65.69 439	77.19 433	56.96 355	59.35 457	46.16 434	58.96 460	65.93 451

UWE-MVS-28		58.44 425	57.71 427	60.65 435	73.58 441	31.23 462	69.68 416	48.80 463	53.12 412	61.79 450	78.83 417	30.98 453	68.40 436	21.58 464	80.99 426	82.33 407

PVSNet_0	51.08 22	56.10 426	54.97 431	59.48 438	75.12 433	53.28 394	55.16 455	61.89 446	44.30 445	59.16 455	62.48 458	54.22 369	65.91 446	35.40 456	47.01 461	59.25 457

new_pmnet		55.69 427	57.66 428	49.76 443	75.47 430	30.59 463	59.56 446	51.45 461	43.62 449	62.49 449	75.48 441	40.96 432	49.15 463	37.39 455	72.52 447	69.55 447

PMMVS2		55.64 428	59.27 426	44.74 444	64.30 466	12.32 472	40.60 459	49.79 462	53.19 410	65.06 445	84.81 357	53.60 372	49.76 462	32.68 460	89.41 329	72.15 443

MVE	40.22 23	51.82 429	50.47 432	55.87 440	62.66 467	51.91 403	31.61 461	39.28 468	40.65 454	50.76 463	74.98 443	56.24 360	44.67 464	33.94 459	64.11 458	71.04 446
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 430	42.65 433	39.67 445	70.86 453	21.11 467	61.01 445	21.42 472	57.36 387	57.97 460	50.06 461	16.40 471	58.73 458	21.03 465	27.69 465	39.17 461

kuosan		30.83 431	32.17 434	26.83 447	53.36 469	19.02 470	57.90 452	20.44 473	38.29 460	38.01 464	37.82 463	15.18 472	33.45 466	7.74 467	20.76 466	28.03 462

test_method		30.46 432	29.60 435	33.06 446	17.99 471	3.84 474	13.62 462	73.92 386	2.79 465	18.29 467	53.41 460	28.53 459	43.25 465	22.56 462	35.27 463	52.11 460

cdsmvs_eth3d_5k		20.81 433	27.75 436	0.00 452	0.00 475	0.00 477	0.00 463	85.44 288	0.00 470	0.00 471	82.82 380	81.46 127	0.00 471	0.00 470	0.00 469	0.00 467

tmp_tt		20.25 434	24.50 437	7.49 449	4.47 472	8.70 473	34.17 460	25.16 470	1.00 467	32.43 466	18.49 464	39.37 435	9.21 468	21.64 463	43.75 462	4.57 464

ab-mvs-re		6.65 435	8.87 438	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	79.80 408	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

pcd_1.5k_mvsjas		6.41 436	8.55 439	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	76.94 183	0.00 471	0.00 470	0.00 469	0.00 467

test123		6.27 437	8.08 440	0.84 450	1.11 474	0.57 475	62.90 440	0.82 474	0.54 468	1.07 470	2.75 469	1.26 473	0.30 469	1.04 468	1.26 468	1.66 465

testmvs		5.91 438	7.65 441	0.72 451	1.20 473	0.37 476	59.14 448	0.67 475	0.49 469	1.11 469	2.76 468	0.94 474	0.24 470	1.02 469	1.47 467	1.55 466

mmdepth		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

monomultidepth		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

test_blank		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

uanet_test		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

DCPMVS		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

sosnet-low-res		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

sosnet		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

uncertanet		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

Regformer		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

uanet		0.00 439	0.00 442	0.00 452	0.00 475	0.00 477	0.00 463	0.00 476	0.00 470	0.00 471	0.00 470	0.00 475	0.00 471	0.00 470	0.00 469	0.00 467

WAC-MVS						37.39 455								52.61 404

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		91.01 160					96.05 9	87.45 28	98.17 37	92.40 203

PC_three_1452									58.96 374	90.06 103	91.33 202	80.66 137	93.03 151	75.78 191	95.94 133	92.48 197

No_MVS				88.81 73	91.55 135	77.99 97		91.01 160					96.05 9	87.45 28	98.17 37	92.40 203

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					0.00 475

eth-test					0.00 475

ZD-MVS					92.22 109	80.48 71		91.85 130	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	92.73 181

IU-MVS					94.18 52	72.64 154		90.82 165	56.98 391	89.67 116				85.78 62	97.92 52	93.28 154

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 208	81.12 131	94.68 78	74.48 203	95.35 156	92.29 213

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	92.04 226

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	89.72 203	70.80 238

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 174

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	92.02 227

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																83.88 380

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 401				83.88 380

sam_mvs												45.92 406

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 216		89.40 125	93.96 105	71.67 262	91.38 197	78.83 144	96.55 102	92.71 184

MTGPA								91.81 134

test_post1							78.85 313				3.13 466	45.19 416	80.13 386	58.11 369

test_post											3.10 467	45.43 412	77.22 401

patchmatchnet-post											81.71 392	45.93 405	87.01 307

GG-mvs-BLEND				67.16 412	73.36 442	46.54 431	84.15 190	55.04 459		58.64 458	61.95 459	29.93 456	83.87 363	38.71 451	76.92 443	71.07 445

MTMP							90.66 49	33.14 469

gm-plane-assit					75.42 431	44.97 438			52.17 417		72.36 448		87.90 293	54.10 393

test9_res														80.83 119	96.45 108	90.57 274

TEST9					92.34 104	79.70 80	83.94 196	90.32 183	65.41 314	84.49 252	90.97 217	82.03 117	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 185	65.79 305	84.36 256	90.96 219	81.93 119	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 282

agg_prior					91.58 133	77.69 103		90.30 186		84.32 258			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	70.81 255	96.14 121	94.16 111

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 249	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 97					94.17 102			92.80 179

旧先验2							81.73 263		56.88 392	86.54 203			84.90 349	72.81 240

新几何2							81.72 264

新几何1				82.95 216	93.96 61	78.56 91		80.24 346	55.45 397	83.93 269	91.08 214	71.19 264	88.33 285	65.84 310	93.07 239	81.95 411

旧先验1					91.97 117	71.77 171		81.78 334			91.84 181	73.92 226			93.65 221	83.61 386

无先验							82.81 235	85.62 286	58.09 380				91.41 196	67.95 294		84.48 371

原ACMM2							82.26 255

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 141	62.59 335	82.73 293	90.67 236	76.53 192	94.25 94	69.24 275	95.69 148	85.55 359

test222					93.31 78	76.54 116	79.38 302	77.79 357	52.59 414	82.36 298	90.84 227	66.83 288			91.69 281	81.25 419

testdata2													86.43 322	63.52 332

segment_acmp												81.94 118

testdata				79.54 292	92.87 89	72.34 163		80.14 347	59.91 370	85.47 228	91.75 188	67.96 282	85.24 345	68.57 289	92.18 268	81.06 424

testdata1							79.62 295		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 168		82.76 292		79.20 150	94.80 75		95.32 158	92.27 215

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 208

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								0.00 476

nn								0.00 476

door-mid								74.45 383

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 413		91.79 71	94.42 78	61.76 322	92.93 154	79.52 137	93.03 240	93.93 120

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								91.46 142

door								72.57 399

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 331

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 331

BP-MVS														77.30 169

HQP4-MVS										80.56 330			94.61 82			93.56 146

HQP3-MVS								92.68 103							94.47 192

HQP2-MVS												72.10 253

NP-MVS					91.95 118	74.55 134					90.17 256

MDTV_nov1_ep13_2view						27.60 466	70.76 408		46.47 439	61.27 451		45.20 415		49.18 420		83.75 385

MDTV_nov1_ep13			68.29 387		78.03 406	43.87 441	74.12 378	72.22 402	52.17 417	67.02 434	85.54 342	45.36 413	80.85 380	55.73 380	84.42 401

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 152

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 186	81.56 82	90.02 105	91.20 210	82.40 103	90.81 217	73.58 226	94.66 187	94.56 90

DeepMVS_CX				24.13 448	32.95 470	29.49 464		21.63 471	12.07 464	37.95 465	45.07 462	30.84 454	19.21 467	17.94 466	33.06 464	23.69 463

High-resolution multi-view benchmark