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 65	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 46	97.23 2	95.32 2	99.01 2	97.26 6	80.16 135	98.99 1	95.15 1	99.14 2	96.47 30

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 22	93.73 64	85.72 34	96.79 1	95.51 9	88.86 16	95.63 10	96.99 10	84.81 72	93.16 137	91.10 2	97.53 72	96.58 28
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
TDRefinement		93.52 3	93.39 4	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 50	96.29 19	88.16 35	94.17 97	86.07 50	98.48 18	97.22 17

reproduce-ours		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 213

our_new_method		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 213

reproduce_model		92.89 5	93.18 7	92.01 13	94.20 49	88.23 9	92.87 13	94.32 21	90.25 11	95.65 9	95.74 30	87.75 41	95.72 36	89.60 4	98.27 26	92.08 202

RE-MVS-def			92.61 8		94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	90.64 10		87.16 34	97.60 66	92.73 165

HPM-MVS_fast		92.50 8	92.54 9	92.37 6	95.93 16	85.81 33	92.99 12	94.23 27	85.21 40	92.51 58	95.13 48	90.65 9	95.34 55	88.06 13	98.15 37	95.95 41

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 5	94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	88.83 26	95.51 47	87.16 34	97.60 66	92.73 165

SR-MVS		92.23 10	92.34 11	91.91 17	94.89 38	87.85 10	92.51 24	93.87 51	88.20 23	93.24 42	94.02 94	90.15 16	95.67 38	86.82 38	97.34 76	92.19 198

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 25	93.02 80	85.17 39	92.47 26	95.05 14	87.65 27	93.21 43	94.39 77	90.09 17	95.08 66	86.67 40	97.60 66	94.18 100

HPM-MVS		92.13 11	92.20 13	91.91 17	95.58 26	84.67 46	93.51 8	94.85 15	82.88 64	91.77 70	93.94 102	90.55 12	95.73 35	88.50 11	98.23 31	95.33 56
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	83.01 3	91.97 13	91.95 14	92.04 11	93.68 65	86.15 24	93.37 10	95.10 13	90.28 10	92.11 63	95.03 50	89.75 20	94.93 70	79.95 122	98.27 26	95.04 67
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APDe-MVS		91.22 25	91.92 15	89.14 66	92.97 82	78.04 93	92.84 16	94.14 36	83.33 58	93.90 28	95.73 31	88.77 27	96.41 3	87.60 23	97.98 45	92.98 158
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PS-CasMVS		90.06 43	91.92 15	84.47 156	96.56 6	58.83 320	89.04 88	92.74 97	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 132	98.74 6	99.00 2

DTE-MVSNet		89.98 47	91.91 17	84.21 165	96.51 7	57.84 328	88.93 90	92.84 94	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 135	98.57 15	98.80 6

PEN-MVS		90.03 45	91.88 18	84.48 155	96.57 5	58.88 317	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 138	98.72 9	98.97 3

ACMMP		91.91 14	91.87 19	92.03 12	95.53 27	85.91 28	93.35 11	94.16 32	82.52 67	92.39 61	94.14 89	89.15 25	95.62 39	87.35 29	98.24 30	94.56 81
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
LPG-MVS_test		91.47 21	91.68 20	90.82 37	94.75 41	81.69 63	90.00 62	94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 61	98.73 7	95.23 61

SED-MVS		90.46 37	91.64 21	86.93 99	94.18 50	72.65 146	90.47 55	93.69 56	83.77 52	94.11 26	94.27 79	90.28 14	95.84 24	86.03 51	97.92 49	92.29 192

MTAPA		91.52 18	91.60 22	91.29 30	96.59 4	86.29 21	92.02 33	91.81 128	84.07 49	92.00 66	94.40 76	86.63 54	95.28 58	88.59 10	98.31 24	92.30 190

CP-MVS		91.67 16	91.58 23	91.96 14	95.29 31	87.62 13	93.38 9	93.36 65	83.16 60	91.06 82	94.00 95	88.26 32	95.71 37	87.28 32	98.39 21	92.55 176

UA-Net		91.49 19	91.53 24	91.39 27	94.98 35	82.95 58	93.52 7	92.79 95	88.22 22	88.53 134	97.64 3	83.45 86	94.55 83	86.02 54	98.60 13	96.67 25

ACMH+	77.89 11	90.73 31	91.50 25	88.44 78	93.00 81	76.26 119	89.65 75	95.55 8	87.72 26	93.89 30	94.94 52	91.62 3	93.44 128	78.35 142	98.76 4	95.61 50

mPP-MVS		91.69 15	91.47 26	92.37 6	96.04 13	88.48 8	92.72 18	92.60 103	83.09 61	91.54 72	94.25 83	87.67 44	95.51 47	87.21 33	98.11 38	93.12 152

HFP-MVS		91.30 23	91.39 27	91.02 33	95.43 29	84.66 47	92.58 22	93.29 72	81.99 70	91.47 73	93.96 99	88.35 31	95.56 42	87.74 18	97.74 59	92.85 162

XVS		91.54 17	91.36 28	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	94.03 93	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 41	93.91 60	80.97 70	91.49 40	93.48 63	82.82 65	92.60 57	93.97 96	88.19 33	96.29 6	87.61 22	98.20 34	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		91.49 19	91.35 30	91.92 16	95.74 20	85.88 30	92.58 22	93.25 73	81.99 70	91.40 74	94.17 88	87.51 45	95.87 20	87.74 18	97.76 57	93.99 107

ZNCC-MVS		91.26 24	91.34 31	91.01 34	95.73 21	83.05 56	92.18 31	94.22 29	80.14 92	91.29 78	93.97 96	87.93 40	95.87 20	88.65 9	97.96 48	94.12 104

DVP-MVS		90.06 43	91.32 32	86.29 111	94.16 53	72.56 152	90.54 52	91.01 150	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 41	97.97 46	90.55 251
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
WR-MVS_H		89.91 50	91.31 33	85.71 128	96.32 9	62.39 273	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 121	95.90 17	80.94 111	98.80 3	98.84 5

region2R		91.44 22	91.30 34	91.87 19	95.75 19	85.90 29	92.63 21	93.30 71	81.91 72	90.88 88	94.21 84	87.75 41	95.87 20	87.60 23	97.71 60	93.83 116

ACMH	76.49 14	89.34 59	91.14 35	83.96 171	92.50 94	70.36 185	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 269	83.33 82	98.30 25	93.20 147
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		90.07 42	91.09 36	87.00 97	91.55 129	72.64 148	96.19 2	94.10 39	85.33 38	93.49 39	94.64 64	81.12 124	95.88 18	87.41 27	95.94 128	92.48 179

DPE-MVS		90.53 36	91.08 37	88.88 69	93.38 71	78.65 87	89.15 87	94.05 41	84.68 45	93.90 28	94.11 91	88.13 36	96.30 5	84.51 73	97.81 55	91.70 217
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		90.81 30	91.08 37	89.99 50	95.97 14	79.88 75	88.13 102	94.51 18	75.79 148	92.94 47	94.96 51	88.36 30	95.01 68	90.70 3	98.40 20	95.09 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 32	91.07 39	89.42 61	95.93 16	79.54 80	89.95 66	93.68 58	77.65 127	91.97 67	94.89 53	88.38 29	95.45 51	89.27 5	97.87 53	93.27 144

GST-MVS		90.96 29	91.01 40	90.82 37	95.45 28	82.73 59	91.75 38	93.74 54	80.98 83	91.38 75	93.80 106	87.20 49	95.80 28	87.10 36	97.69 61	93.93 110

ACMM	79.39 9	90.65 32	90.99 41	89.63 57	95.03 34	83.53 51	89.62 76	93.35 66	79.20 105	93.83 31	93.60 116	90.81 7	92.96 144	85.02 67	98.45 19	92.41 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 177	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 100	86.25 47	97.63 63	97.82 8

PGM-MVS		91.20 26	90.95 43	91.93 15	95.67 23	85.85 31	90.00 62	93.90 48	80.32 89	91.74 71	94.41 75	88.17 34	95.98 13	86.37 43	97.99 43	93.96 109

MP-MVS		91.14 28	90.91 44	91.83 20	96.18 11	86.88 17	92.20 30	93.03 86	82.59 66	88.52 135	94.37 78	86.74 53	95.41 53	86.32 44	98.21 32	93.19 148
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVSNet		89.27 62	90.91 44	84.37 157	96.34 8	58.61 323	88.66 97	92.06 117	90.78 7	95.67 8	95.17 47	81.80 117	95.54 44	79.00 136	98.69 10	98.95 4

SF-MVS		90.27 39	90.80 46	88.68 76	92.86 86	77.09 108	91.19 44	95.74 6	81.38 78	92.28 62	93.80 106	86.89 52	94.64 78	85.52 60	97.51 73	94.30 96

UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 163	97.00 2	64.33 248	89.67 74	88.38 213	88.84 17	94.29 22	97.57 4	90.48 13	91.26 189	72.57 220	97.65 62	97.34 14

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 196	88.51 21	90.11 96	95.12 49	90.98 6	88.92 255	77.55 156	97.07 83	83.13 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	79.16 10	90.54 35	90.60 49	90.35 45	94.36 46	80.98 69	89.16 86	94.05 41	79.03 108	92.87 49	93.74 111	90.60 11	95.21 61	82.87 90	98.76 4	94.87 71
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 38	90.48 50	89.83 54	95.31 30	79.52 81	90.98 47	93.24 74	75.37 157	92.84 51	95.28 44	85.58 67	96.09 8	87.92 15	97.76 57	93.88 113
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
LS3D		90.60 34	90.34 51	91.38 28	89.03 185	84.23 49	93.58 6	94.68 17	90.65 8	90.33 94	93.95 101	84.50 74	95.37 54	80.87 112	95.50 145	94.53 84

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 76	86.76 127	92.78 96	78.78 111	92.51 58	93.64 115	88.13 36	93.84 109	84.83 70	97.55 69	94.10 105
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD-MVS		88.96 67	89.88 53	86.22 115	91.63 123	77.07 109	89.82 69	93.77 53	78.90 109	92.88 48	92.29 160	86.11 63	90.22 223	86.24 48	97.24 79	91.36 226
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
XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 43	94.91 37	84.50 48	89.49 81	93.98 43	79.68 97	92.09 64	93.89 104	83.80 81	93.10 140	82.67 94	98.04 39	93.64 129

tt0805		88.09 77	89.79 55	82.98 202	93.26 75	63.94 252	91.10 45	89.64 193	85.07 41	90.91 86	91.09 198	89.16 24	91.87 175	82.03 101	95.87 132	93.13 150

OurMVSNet-221017-0		90.01 46	89.74 56	90.83 36	93.16 78	80.37 72	91.91 36	93.11 79	81.10 81	95.32 14	97.24 7	72.94 221	94.85 72	85.07 65	97.78 56	97.26 15

3Dnovator+	83.92 2	89.97 49	89.66 57	90.92 35	91.27 138	81.66 66	91.25 42	94.13 37	88.89 15	88.83 126	94.26 82	77.55 159	95.86 23	84.88 68	95.87 132	95.24 60

APD-MVS		89.54 56	89.63 58	89.26 64	92.57 91	81.34 68	90.19 61	93.08 82	80.87 85	91.13 80	93.19 122	86.22 62	95.97 14	82.23 100	97.18 81	90.45 253
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 71	89.62 59	84.73 147	90.46 157	65.27 238	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 106	92.02 170	76.70 166	97.99 43	96.88 23

test_0402		88.65 69	89.58 60	85.88 124	92.55 92	72.22 160	84.01 182	89.44 199	88.63 20	94.38 21	95.77 29	86.38 61	93.59 120	79.84 123	95.21 154	91.82 211

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 46	94.47 43	85.95 27	86.84 123	93.91 47	80.07 93	86.75 177	93.26 121	93.64 2	90.93 200	84.60 72	90.75 281	93.97 108

9.14			89.29 62		91.84 119		88.80 93	95.32 12	75.14 159	91.07 81	92.89 136	87.27 47	93.78 110	83.69 81	97.55 69

mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 160	70.00 232	94.55 19	96.67 14	87.94 39	93.59 120	84.27 75	95.97 124	95.52 51

testf1		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 247	74.12 195	96.10 119	94.45 87

APD_test2		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 247	74.12 195	96.10 119	94.45 87

DeepC-MVS	82.31 4	89.15 64	89.08 66	89.37 62	93.64 66	79.07 83	88.54 98	94.20 30	73.53 177	89.71 107	94.82 56	85.09 68	95.77 34	84.17 76	98.03 41	93.26 145
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_djsdf		89.62 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 183	71.54 212	94.28 24	96.54 16	81.57 119	94.27 89	86.26 45	96.49 100	97.09 19

DP-MVS		88.60 70	89.01 67	87.36 93	91.30 136	77.50 101	87.55 109	92.97 90	87.95 25	89.62 111	92.87 137	84.56 73	93.89 106	77.65 154	96.62 95	90.70 244

CPTT-MVS		89.39 58	88.98 69	90.63 40	95.09 33	86.95 16	92.09 32	92.30 111	79.74 96	87.50 162	92.38 153	81.42 121	93.28 133	83.07 86	97.24 79	91.67 218

anonymousdsp		89.73 53	88.88 70	92.27 8	89.82 171	86.67 18	90.51 54	90.20 180	69.87 233	95.06 15	96.14 25	84.28 77	93.07 141	87.68 20	96.34 106	97.09 19

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 188	92.03 110	62.26 277	91.49 40	92.62 101	88.07 24	88.07 147	96.17 23	72.24 230	95.79 31	84.85 69	94.16 194	92.58 174

XVG-OURS		89.18 63	88.83 72	90.23 47	94.28 47	86.11 26	85.91 141	93.60 61	80.16 91	89.13 123	93.44 118	83.82 80	90.98 198	83.86 79	95.30 153	93.60 132

jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 177	69.27 236	94.39 20	96.38 18	86.02 65	93.52 124	83.96 77	95.92 130	95.34 55

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 137	94.02 58	64.13 249	84.38 175	91.29 141	84.88 44	92.06 65	93.84 105	86.45 58	93.73 111	73.22 211	98.66 11	97.69 9

nrg030		87.85 82	88.49 75	85.91 122	90.07 166	69.73 192	87.86 106	94.20 30	74.04 169	92.70 56	94.66 60	85.88 66	91.50 181	79.72 125	97.32 77	96.50 29

HPM-MVS++		88.93 68	88.45 76	90.38 44	94.92 36	85.85 31	89.70 71	91.27 142	78.20 119	86.69 180	92.28 161	80.36 133	95.06 67	86.17 49	96.49 100	90.22 257

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 163	90.31 59	96.31 4	80.88 84	85.12 213	89.67 242	84.47 75	95.46 50	82.56 95	96.26 111	93.77 122

MSP-MVS		89.08 66	88.16 78	91.83 20	95.76 18	86.14 25	92.75 17	93.90 48	78.43 116	89.16 121	92.25 162	72.03 235	96.36 4	88.21 12	90.93 274	92.98 158
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
pmmvs6		86.52 99	88.06 79	81.90 225	92.22 103	62.28 276	84.66 168	89.15 202	83.54 57	89.85 104	97.32 5	88.08 38	86.80 291	70.43 237	97.30 78	96.62 26

APD_test1		88.40 71	87.91 80	89.88 51	89.50 175	86.65 20	89.98 65	91.91 123	84.26 47	90.87 89	93.92 103	82.18 108	89.29 251	73.75 203	94.81 173	93.70 124

PS-MVSNAJss		88.31 73	87.90 81	89.56 59	93.31 73	77.96 96	87.94 105	91.97 120	70.73 223	94.19 25	96.67 14	76.94 169	94.57 81	83.07 86	96.28 108	96.15 33

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 145	67.85 260	86.63 181	94.84 55	79.58 140	95.96 15	87.62 21	94.50 182	94.56 81
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		87.81 83	87.68 83	88.21 83	92.87 84	77.30 107	85.25 156	91.23 143	77.31 132	87.07 171	91.47 186	82.94 91	94.71 75	84.67 71	96.27 110	92.62 172

CS-MVS		88.14 75	87.67 84	89.54 60	89.56 173	79.18 82	90.47 55	94.77 16	79.37 103	84.32 234	89.33 247	83.87 79	94.53 84	82.45 96	94.89 169	94.90 69

OMC-MVS		88.19 74	87.52 85	90.19 48	91.94 114	81.68 65	87.49 112	93.17 76	76.02 142	88.64 131	91.22 193	84.24 78	93.37 131	77.97 152	97.03 84	95.52 51

casdiffmvs_mvg		86.72 95	87.51 86	84.36 159	87.09 244	65.22 239	84.16 178	94.23 27	77.89 123	91.28 79	93.66 114	84.35 76	92.71 150	80.07 119	94.87 172	95.16 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
SixPastTwentyTwo		87.20 89	87.45 87	86.45 108	92.52 93	69.19 201	87.84 107	88.05 220	81.66 75	94.64 18	96.53 17	65.94 268	94.75 74	83.02 88	96.83 89	95.41 53

HQP_MVS		87.75 84	87.43 88	88.70 75	93.45 68	76.42 116	89.45 82	93.61 59	79.44 101	86.55 182	92.95 134	74.84 191	95.22 59	80.78 114	95.83 134	94.46 85

AllTest		87.97 80	87.40 89	89.68 55	91.59 124	83.40 52	89.50 80	95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 229	96.14 116	94.16 101

fmvsm_s_conf0.5_n_3		86.19 105	87.27 90	82.95 204	86.91 249	70.38 184	85.31 155	92.61 102	75.59 152	88.32 142	92.87 137	82.22 107	88.63 263	88.80 8	92.82 230	89.83 267

MM		87.64 85	87.15 91	89.09 67	89.51 174	76.39 118	88.68 96	86.76 243	84.54 46	83.58 252	93.78 108	73.36 216	96.48 2	87.98 14	96.21 112	94.41 91

Anonymous20240529		86.20 104	87.13 92	83.42 190	90.19 162	64.55 246	84.55 170	90.71 157	85.85 36	89.94 103	95.24 46	82.13 109	90.40 219	69.19 250	96.40 105	95.31 57

v10		86.54 98	87.10 93	84.84 141	88.16 211	63.28 259	86.64 130	92.20 113	75.42 156	92.81 53	94.50 68	74.05 203	94.06 101	83.88 78	96.28 108	97.17 18

UniMVSNet_NR-MVSNet		86.84 93	87.06 94	86.17 118	92.86 86	67.02 222	82.55 228	91.56 131	83.08 62	90.92 84	91.82 174	78.25 149	93.99 102	74.16 193	98.35 22	97.49 13

FC-MVSNet-test		85.93 110	87.05 95	82.58 214	92.25 101	56.44 339	85.75 146	93.09 81	77.33 131	91.94 68	94.65 61	74.78 193	93.41 130	75.11 187	98.58 14	97.88 7

DU-MVS		86.80 94	86.99 96	86.21 116	93.24 76	67.02 222	83.16 211	92.21 112	81.73 74	90.92 84	91.97 167	77.20 163	93.99 102	74.16 193	98.35 22	97.61 10

UniMVSNet (Re)		86.87 91	86.98 97	86.55 106	93.11 79	68.48 208	83.80 191	92.87 92	80.37 87	89.61 113	91.81 175	77.72 156	94.18 95	75.00 188	98.53 16	96.99 22

RPSCF		88.00 79	86.93 98	91.22 31	90.08 164	89.30 5	89.68 73	91.11 146	79.26 104	89.68 108	94.81 59	82.44 97	87.74 276	76.54 168	88.74 311	96.61 27

NCCC		87.36 87	86.87 99	88.83 70	92.32 100	78.84 86	86.58 131	91.09 148	78.77 112	84.85 222	90.89 207	80.85 127	95.29 56	81.14 109	95.32 150	92.34 188

v8		86.22 103	86.83 100	84.36 159	87.82 219	62.35 275	86.42 134	91.33 140	76.78 136	92.73 55	94.48 70	73.41 213	93.72 112	83.10 85	95.41 146	97.01 21

IS-MVSNet		86.66 97	86.82 101	86.17 118	92.05 109	66.87 225	91.21 43	88.64 208	86.30 33	89.60 114	92.59 146	69.22 252	94.91 71	73.89 200	97.89 52	96.72 24

Vis-MVSNet		86.86 92	86.58 102	87.72 89	92.09 107	77.43 104	87.35 113	92.09 116	78.87 110	84.27 239	94.05 92	78.35 148	93.65 113	80.54 118	91.58 261	92.08 202
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		86.68 96	86.52 103	87.18 94	85.94 274	78.30 89	86.93 120	92.20 113	65.94 276	89.16 121	93.16 124	83.10 89	89.89 236	87.81 17	94.43 186	93.35 139

CSCG		86.26 101	86.47 104	85.60 130	90.87 149	74.26 132	87.98 104	91.85 124	80.35 88	89.54 117	88.01 266	79.09 142	92.13 166	75.51 181	95.06 161	90.41 254

SPE-MVS-test		87.00 90	86.43 105	88.71 74	89.46 176	77.46 102	89.42 84	95.73 7	77.87 125	81.64 288	87.25 285	82.43 98	94.53 84	77.65 154	96.46 102	94.14 103

Gipuma		84.44 142	86.33 106	78.78 273	84.20 304	73.57 136	89.55 77	90.44 166	84.24 48	84.38 231	94.89 53	76.35 180	80.40 357	76.14 175	96.80 91	82.36 375
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FIs		85.35 120	86.27 107	82.60 213	91.86 116	57.31 332	85.10 160	93.05 83	75.83 147	91.02 83	93.97 96	73.57 209	92.91 148	73.97 199	98.02 42	97.58 12

NR-MVSNet		86.00 108	86.22 108	85.34 135	93.24 76	64.56 245	82.21 240	90.46 165	80.99 82	88.42 138	91.97 167	77.56 158	93.85 107	72.46 221	98.65 12	97.61 10

DeepPCF-MVS	81.24 5	87.28 88	86.21 109	90.49 42	91.48 133	84.90 42	83.41 202	92.38 108	70.25 229	89.35 119	90.68 217	82.85 92	94.57 81	79.55 129	95.95 127	92.00 206

sasdasda		85.50 114	86.14 110	83.58 184	87.97 213	67.13 219	87.55 109	94.32 21	73.44 180	88.47 136	87.54 278	86.45 58	91.06 196	75.76 179	93.76 204	92.54 177

canonicalmvs		85.50 114	86.14 110	83.58 184	87.97 213	67.13 219	87.55 109	94.32 21	73.44 180	88.47 136	87.54 278	86.45 58	91.06 196	75.76 179	93.76 204	92.54 177

MSLP-MVS++		85.00 131	86.03 112	81.90 225	91.84 119	71.56 172	86.75 128	93.02 87	75.95 145	87.12 166	89.39 245	77.98 151	89.40 250	77.46 157	94.78 174	84.75 337

MGCFI-Net		85.04 128	85.95 113	82.31 220	87.52 229	63.59 255	86.23 138	93.96 44	73.46 178	88.07 147	87.83 273	86.46 57	90.87 205	76.17 174	93.89 201	92.47 181

baseline		85.20 123	85.93 114	83.02 200	86.30 263	62.37 274	84.55 170	93.96 44	74.48 166	87.12 166	92.03 166	82.30 103	91.94 171	78.39 140	94.21 191	94.74 78

Baseline_NR-MVSNet		84.00 159	85.90 115	78.29 284	91.47 134	53.44 362	82.29 236	87.00 242	79.06 107	89.55 115	95.72 32	77.20 163	86.14 305	72.30 222	98.51 17	95.28 58

test_fmvsmconf0.1_n		86.18 106	85.88 116	87.08 96	85.26 284	78.25 90	85.82 145	91.82 126	65.33 290	88.55 133	92.35 159	82.62 96	89.80 238	86.87 37	94.32 189	93.18 149

casdiffmvs		85.21 122	85.85 117	83.31 193	86.17 268	62.77 266	83.03 213	93.93 46	74.69 164	88.21 144	92.68 145	82.29 105	91.89 174	77.87 153	93.75 207	95.27 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		85.45 118	85.81 118	84.37 157	90.08 164	67.07 221	85.86 144	91.39 138	72.33 204	87.59 160	90.25 229	84.85 71	92.37 160	78.00 150	91.94 251	93.66 125

PHI-MVS		86.38 100	85.81 118	88.08 84	88.44 205	77.34 105	89.35 85	93.05 83	73.15 190	84.76 223	87.70 275	78.87 144	94.18 95	80.67 116	96.29 107	92.73 165

mmtdpeth		85.13 125	85.78 120	83.17 198	84.65 294	74.71 128	85.87 143	90.35 171	77.94 122	83.82 246	96.96 12	77.75 154	80.03 360	78.44 139	96.21 112	94.79 77

fmvsm_s_conf0.5_n_8		85.48 116	85.75 121	84.68 150	87.10 242	69.98 189	84.28 176	92.68 98	74.77 162	87.90 153	92.36 158	73.94 204	90.41 218	85.95 56	92.74 232	93.66 125

TransMVSNet (Re)		84.02 158	85.74 122	78.85 272	91.00 146	55.20 351	82.29 236	87.26 229	79.65 98	88.38 140	95.52 37	83.00 90	86.88 289	67.97 265	96.60 96	94.45 87

ANet_high		83.17 179	85.68 123	75.65 319	81.24 345	45.26 405	79.94 269	92.91 91	83.83 51	91.33 76	96.88 13	80.25 134	85.92 308	68.89 254	95.89 131	95.76 43

DeepC-MVS_fast	80.27 8	86.23 102	85.65 124	87.96 87	91.30 136	76.92 110	87.19 115	91.99 119	70.56 224	84.96 217	90.69 216	80.01 137	95.14 64	78.37 141	95.78 138	91.82 211
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 107	85.54 125	88.05 86	92.25 101	75.45 125	83.85 188	92.01 118	65.91 278	86.19 192	91.75 179	83.77 82	94.98 69	77.43 159	96.71 93	93.73 123

test_fmvsmconf_n		85.88 111	85.51 126	86.99 98	84.77 292	78.21 91	85.40 154	91.39 138	65.32 291	87.72 158	91.81 175	82.33 101	89.78 239	86.68 39	94.20 192	92.99 157

FMVSNet1		84.55 140	85.45 127	81.85 227	90.27 161	61.05 291	86.83 124	88.27 217	78.57 115	89.66 110	95.64 34	75.43 183	90.68 211	69.09 251	95.33 149	93.82 117

balanced_conf03		84.80 133	85.40 128	83.00 201	88.95 188	61.44 284	90.42 58	92.37 109	71.48 214	88.72 130	93.13 125	70.16 248	95.15 63	79.26 134	94.11 195	92.41 183

VDDNet		84.35 145	85.39 129	81.25 238	95.13 32	59.32 310	85.42 153	81.11 309	86.41 32	87.41 163	96.21 22	73.61 208	90.61 214	66.33 275	96.85 87	93.81 120

test_fmvsmvis_n_1920		85.22 121	85.36 130	84.81 143	85.80 276	76.13 122	85.15 159	92.32 110	61.40 321	91.33 76	90.85 210	83.76 83	86.16 304	84.31 74	93.28 218	92.15 200

train_agg		85.98 109	85.28 131	88.07 85	92.34 98	79.70 78	83.94 184	90.32 172	65.79 280	84.49 228	90.97 202	81.93 113	93.63 115	81.21 108	96.54 98	90.88 238

dcpmvs_2		84.23 151	85.14 132	81.50 235	88.61 200	61.98 281	82.90 219	93.11 79	68.66 245	92.77 54	92.39 152	78.50 146	87.63 278	76.99 165	92.30 238	94.90 69

LCM-MVSNet-Re		83.48 173	85.06 133	78.75 274	85.94 274	55.75 345	80.05 267	94.27 24	76.47 137	96.09 6	94.54 67	83.31 88	89.75 242	59.95 328	94.89 169	90.75 241

EPP-MVSNet		85.47 117	85.04 134	86.77 103	91.52 132	69.37 196	91.63 39	87.98 222	81.51 77	87.05 172	91.83 173	66.18 267	95.29 56	70.75 232	96.89 86	95.64 48

IterMVS-LS		84.73 135	84.98 135	83.96 171	87.35 233	63.66 253	83.25 207	89.88 188	76.06 140	89.62 111	92.37 156	73.40 215	92.52 155	78.16 147	94.77 176	95.69 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_3		85.11 127	84.96 136	85.56 131	87.49 231	75.69 124	84.71 166	90.61 162	67.64 262	84.88 220	92.05 165	82.30 103	88.36 267	83.84 80	91.10 267	92.62 172

pm-mvs1		83.69 166	84.95 137	79.91 259	90.04 168	59.66 307	82.43 232	87.44 226	75.52 154	87.85 154	95.26 45	81.25 123	85.65 316	68.74 257	96.04 121	94.42 90

TAPA-MVS	77.73 12	85.71 113	84.83 138	88.37 80	88.78 195	79.72 77	87.15 117	93.50 62	69.17 237	85.80 201	89.56 243	80.76 128	92.13 166	73.21 216	95.51 144	93.25 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 174	84.73 139	79.69 263	90.29 160	57.52 331	81.30 252	88.69 207	76.29 138	87.58 161	94.44 71	80.60 131	87.20 283	66.60 273	96.82 90	94.34 94

K. test v3		85.14 124	84.73 139	86.37 109	91.13 143	69.63 194	85.45 152	76.68 338	84.06 50	92.44 60	96.99 10	62.03 290	94.65 77	80.58 117	93.24 219	94.83 76

v1144		84.54 141	84.72 141	84.00 169	87.67 225	62.55 270	82.97 216	90.93 153	70.32 228	89.80 105	90.99 201	73.50 210	93.48 126	81.69 107	94.65 180	95.97 39

fmvsm_s_conf0.5_n_5		84.56 139	84.71 142	84.11 168	87.92 216	72.09 162	84.80 161	88.64 208	64.43 296	88.77 127	91.78 177	78.07 150	87.95 273	85.85 57	92.18 245	92.30 190

3Dnovator	80.37 7	84.80 133	84.71 142	85.06 139	86.36 261	74.71 128	88.77 94	90.00 185	75.65 150	84.96 217	93.17 123	74.06 202	91.19 191	78.28 144	91.09 268	89.29 277

v1192		84.57 138	84.69 144	84.21 165	87.75 221	62.88 263	83.02 214	91.43 135	69.08 239	89.98 102	90.89 207	72.70 225	93.62 118	82.41 97	94.97 166	96.13 34

MIMVSNet1		83.63 168	84.59 145	80.74 247	94.06 57	62.77 266	82.72 222	84.53 281	77.57 129	90.34 93	95.92 28	76.88 175	85.83 314	61.88 315	97.42 74	93.62 130

MVS_0304		85.37 119	84.58 146	87.75 88	85.28 283	73.36 137	86.54 133	85.71 258	77.56 130	81.78 286	92.47 151	70.29 246	96.02 11	85.59 59	95.96 125	93.87 114

VDD-MVS		84.23 151	84.58 146	83.20 196	91.17 142	65.16 241	83.25 207	84.97 275	79.79 95	87.18 165	94.27 79	74.77 194	90.89 203	69.24 247	96.54 98	93.55 137

mvs5depth		83.82 163	84.54 148	81.68 232	82.23 333	68.65 206	86.89 121	89.90 187	80.02 94	87.74 157	97.86 2	64.19 277	82.02 345	76.37 170	95.63 143	94.35 93

EI-MVSNet-Vis-set		85.12 126	84.53 149	86.88 100	84.01 307	72.76 145	83.91 187	85.18 267	80.44 86	88.75 128	85.49 313	80.08 136	91.92 172	82.02 102	90.85 279	95.97 39

v1240		84.30 147	84.51 150	83.65 181	87.65 226	61.26 288	82.85 220	91.54 132	67.94 257	90.68 91	90.65 220	71.71 238	93.64 114	82.84 91	94.78 174	96.07 36

EI-MVSNet-UG-set		85.04 128	84.44 151	86.85 101	83.87 311	72.52 154	83.82 189	85.15 268	80.27 90	88.75 128	85.45 315	79.95 138	91.90 173	81.92 105	90.80 280	96.13 34

v144192		84.24 150	84.41 152	83.71 180	87.59 228	61.57 283	82.95 217	91.03 149	67.82 261	89.80 105	90.49 223	73.28 217	93.51 125	81.88 106	94.89 169	96.04 38

WR-MVS		83.56 171	84.40 153	81.06 243	93.43 70	54.88 352	78.67 292	85.02 272	81.24 79	90.74 90	91.56 184	72.85 222	91.08 195	68.00 264	98.04 39	97.23 16

v1921920		84.23 151	84.37 154	83.79 176	87.64 227	61.71 282	82.91 218	91.20 144	67.94 257	90.06 97	90.34 226	72.04 234	93.59 120	82.32 98	94.91 167	96.07 36

MVS_111021_HR		84.63 136	84.34 155	85.49 134	90.18 163	75.86 123	79.23 283	87.13 234	73.35 182	85.56 206	89.34 246	83.60 85	90.50 216	76.64 167	94.05 198	90.09 263

fmvsm_s_conf0.5_n_4		84.38 143	84.27 156	84.74 146	87.25 235	70.84 179	83.55 197	88.45 211	68.64 246	86.29 191	91.31 191	74.97 189	88.42 265	87.87 16	90.07 291	94.95 68

v2v482		84.09 154	84.24 157	83.62 182	87.13 239	61.40 285	82.71 223	89.71 191	72.19 207	89.55 115	91.41 187	70.70 244	93.20 135	81.02 110	93.76 204	96.25 32

fmvsm_s_conf0.5_n_6		84.05 156	84.14 158	83.81 174	87.75 221	71.17 175	83.42 201	91.10 147	67.90 259	84.53 226	90.70 215	73.01 220	88.73 261	85.09 64	93.72 209	91.53 223

EG-PatchMatch MVS		84.08 155	84.11 159	83.98 170	92.22 103	72.61 151	82.20 242	87.02 239	72.63 198	88.86 124	91.02 200	78.52 145	91.11 194	73.41 208	91.09 268	88.21 293

HQP-MVS		84.61 137	84.06 160	86.27 112	91.19 139	70.66 180	84.77 162	92.68 98	73.30 185	80.55 302	90.17 234	72.10 231	94.61 79	77.30 161	94.47 184	93.56 135

Effi-MVS+		83.90 162	84.01 161	83.57 186	87.22 237	65.61 237	86.55 132	92.40 106	78.64 114	81.34 293	84.18 334	83.65 84	92.93 146	74.22 192	87.87 325	92.17 199

alignmvs		83.94 161	83.98 162	83.80 175	87.80 220	67.88 215	84.54 172	91.42 137	73.27 188	88.41 139	87.96 267	72.33 228	90.83 206	76.02 177	94.11 195	92.69 169

MCST-MVS		84.36 144	83.93 163	85.63 129	91.59 124	71.58 170	83.52 198	92.13 115	61.82 314	83.96 244	89.75 241	79.93 139	93.46 127	78.33 143	94.34 188	91.87 210

ETV-MVS		84.31 146	83.91 164	85.52 132	88.58 201	70.40 183	84.50 174	93.37 64	78.76 113	84.07 242	78.72 388	80.39 132	95.13 65	73.82 202	92.98 226	91.04 232

MVS_111021_LR		84.28 148	83.76 165	85.83 126	89.23 182	83.07 55	80.99 256	83.56 289	72.71 197	86.07 195	89.07 252	81.75 118	86.19 303	77.11 163	93.36 214	88.24 292

AdaColmap		83.66 167	83.69 166	83.57 186	90.05 167	72.26 159	86.29 136	90.00 185	78.19 120	81.65 287	87.16 287	83.40 87	94.24 92	61.69 317	94.76 177	84.21 347

fmvsm_s_conf0.1_n_2		83.82 163	83.49 167	84.84 141	85.99 273	70.19 187	80.93 257	87.58 225	67.26 268	87.94 152	92.37 156	71.40 240	88.01 271	86.03 51	91.87 252	96.31 31

RRT-MVS		82.97 182	83.44 168	81.57 234	85.06 287	58.04 326	87.20 114	90.37 169	77.88 124	88.59 132	93.70 113	63.17 284	93.05 142	76.49 169	88.47 313	93.62 130

F-COLMAP		84.97 132	83.42 169	89.63 57	92.39 96	83.40 52	88.83 92	91.92 122	73.19 189	80.18 310	89.15 251	77.04 167	93.28 133	65.82 282	92.28 241	92.21 197

Effi-MVS+-dtu		85.82 112	83.38 170	93.14 4	87.13 239	91.15 3	87.70 108	88.42 212	74.57 165	83.56 253	85.65 309	78.49 147	94.21 93	72.04 223	92.88 228	94.05 106

V42		83.47 174	83.37 171	83.75 178	83.16 327	63.33 258	81.31 250	90.23 179	69.51 235	90.91 86	90.81 212	74.16 201	92.29 164	80.06 120	90.22 289	95.62 49

fmvsm_s_conf0.5_n_2		83.62 169	83.29 172	84.62 151	85.43 281	70.18 188	80.61 261	87.24 230	67.14 269	87.79 156	91.87 169	71.79 237	87.98 272	86.00 55	91.77 255	95.71 45

MVS_Test		82.47 190	83.22 173	80.22 256	82.62 332	57.75 330	82.54 229	91.96 121	71.16 219	82.89 264	92.52 150	77.41 160	90.50 216	80.04 121	87.84 326	92.40 185

DP-MVS Recon		84.05 156	83.22 173	86.52 107	91.73 122	75.27 126	83.23 209	92.40 106	72.04 209	82.04 277	88.33 262	77.91 153	93.95 104	66.17 276	95.12 159	90.34 256

PAPM_NR		83.23 177	83.19 175	83.33 192	90.90 148	65.98 233	88.19 101	90.78 156	78.13 121	80.87 298	87.92 271	73.49 212	92.42 157	70.07 240	88.40 314	91.60 220

SDMVSNet		81.90 207	83.17 176	78.10 287	88.81 193	62.45 272	76.08 333	86.05 253	73.67 174	83.41 255	93.04 127	82.35 100	80.65 354	70.06 241	95.03 162	91.21 228

KD-MVS_self_test		81.93 205	83.14 177	78.30 283	84.75 293	52.75 366	80.37 264	89.42 200	70.24 230	90.26 95	93.39 119	74.55 198	86.77 292	68.61 259	96.64 94	95.38 54

CNLPA		83.55 172	83.10 178	84.90 140	89.34 179	83.87 50	84.54 172	88.77 205	79.09 106	83.54 254	88.66 259	74.87 190	81.73 347	66.84 270	92.29 240	89.11 279

FA-MVS(test-final)		83.13 180	83.02 179	83.43 189	86.16 270	66.08 232	88.00 103	88.36 214	75.55 153	85.02 215	92.75 143	65.12 272	92.50 156	74.94 189	91.30 265	91.72 215

tfpnnormal		81.79 208	82.95 180	78.31 282	88.93 189	55.40 347	80.83 260	82.85 295	76.81 135	85.90 200	94.14 89	74.58 197	86.51 296	66.82 271	95.68 142	93.01 156

test_fmvsm_n_1920		83.60 170	82.89 181	85.74 127	85.22 285	77.74 99	84.12 180	90.48 164	59.87 340	86.45 190	91.12 197	75.65 181	85.89 312	82.28 99	90.87 277	93.58 133

CANet		83.79 165	82.85 182	86.63 104	86.17 268	72.21 161	83.76 192	91.43 135	77.24 133	74.39 364	87.45 281	75.36 184	95.42 52	77.03 164	92.83 229	92.25 196

h-mvs33		84.25 149	82.76 183	88.72 73	91.82 121	82.60 60	84.00 183	84.98 274	71.27 215	86.70 178	90.55 222	63.04 287	93.92 105	78.26 145	94.20 192	89.63 269

X-MVStestdata		85.04 128	82.70 184	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 434	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

TSAR-MVS + GP.		83.95 160	82.69 185	87.72 89	89.27 181	81.45 67	83.72 193	81.58 307	74.73 163	85.66 202	86.06 304	72.56 227	92.69 152	75.44 183	95.21 154	89.01 286

CLD-MVS		83.18 178	82.64 186	84.79 144	89.05 184	67.82 216	77.93 300	92.52 104	68.33 249	85.07 214	81.54 363	82.06 110	92.96 144	69.35 246	97.91 51	93.57 134
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 192	82.61 187	81.30 237	86.29 264	69.79 190	88.71 95	87.67 224	78.42 117	82.15 276	84.15 335	77.98 151	91.59 180	65.39 285	92.75 231	82.51 374

QAPM		82.59 187	82.59 188	82.58 214	86.44 256	66.69 226	89.94 67	90.36 170	67.97 256	84.94 219	92.58 148	72.71 224	92.18 165	70.63 235	87.73 327	88.85 287

114514_t		83.10 181	82.54 189	84.77 145	92.90 83	69.10 203	86.65 129	90.62 161	54.66 372	81.46 290	90.81 212	76.98 168	94.38 87	72.62 219	96.18 114	90.82 240

v148		82.31 191	82.48 190	81.81 230	85.59 278	59.66 307	81.47 249	86.02 254	72.85 193	88.05 149	90.65 220	70.73 243	90.91 202	75.15 186	91.79 253	94.87 71

EI-MVSNet		82.61 186	82.42 191	83.20 196	83.25 324	63.66 253	83.50 199	85.07 269	76.06 140	86.55 182	85.10 321	73.41 213	90.25 220	78.15 149	90.67 283	95.68 47

TinyColmap		81.25 214	82.34 192	77.99 290	85.33 282	60.68 298	82.32 235	88.33 215	71.26 217	86.97 173	92.22 164	77.10 166	86.98 287	62.37 309	95.17 156	86.31 320

GBi-Net		82.02 202	82.07 193	81.85 227	86.38 258	61.05 291	86.83 124	88.27 217	72.43 199	86.00 196	95.64 34	63.78 280	90.68 211	65.95 278	93.34 215	93.82 117

test1		82.02 202	82.07 193	81.85 227	86.38 258	61.05 291	86.83 124	88.27 217	72.43 199	86.00 196	95.64 34	63.78 280	90.68 211	65.95 278	93.34 215	93.82 117

fmvsm_s_conf0.5_n_7		82.04 201	82.05 195	82.01 223	86.98 248	71.07 176	78.70 290	89.45 198	68.07 253	78.14 327	91.61 182	74.19 200	85.92 308	79.61 128	91.73 256	89.05 283

OpenMVS	76.72 13	81.98 204	82.00 196	81.93 224	84.42 299	68.22 210	88.50 99	89.48 197	66.92 271	81.80 284	91.86 170	72.59 226	90.16 225	71.19 228	91.25 266	87.40 308

fmvsm_s_conf0.1_n_a		82.58 188	81.93 197	84.50 154	87.68 224	73.35 138	86.14 139	77.70 327	61.64 319	85.02 215	91.62 181	77.75 154	86.24 300	82.79 92	87.07 334	93.91 112

LF4IMVS		82.75 185	81.93 197	85.19 136	82.08 334	80.15 74	85.53 150	88.76 206	68.01 254	85.58 205	87.75 274	71.80 236	86.85 290	74.02 198	93.87 202	88.58 289

hse-mvs2		83.47 174	81.81 199	88.47 77	91.03 145	82.27 61	82.61 224	83.69 287	71.27 215	86.70 178	86.05 305	63.04 287	92.41 158	78.26 145	93.62 213	90.71 243

VPNet		80.25 233	81.68 200	75.94 317	92.46 95	47.98 392	76.70 320	81.67 305	73.45 179	84.87 221	92.82 139	74.66 196	86.51 296	61.66 318	96.85 87	93.33 140

BP-MVS1		82.81 183	81.67 201	86.23 113	87.88 218	68.53 207	86.06 140	84.36 282	75.65 150	85.14 212	90.19 231	45.84 376	94.42 86	85.18 63	94.72 178	95.75 44

SSC-MVS		77.55 262	81.64 202	65.29 391	90.46 157	20.33 438	73.56 357	68.28 392	85.44 37	88.18 146	94.64 64	70.93 242	81.33 349	71.25 226	92.03 247	94.20 97

UGNet		82.78 184	81.64 202	86.21 116	86.20 267	76.24 120	86.86 122	85.68 259	77.07 134	73.76 368	92.82 139	69.64 249	91.82 177	69.04 253	93.69 210	90.56 250
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
FMVSNet2		81.31 213	81.61 204	80.41 253	86.38 258	58.75 321	83.93 186	86.58 245	72.43 199	87.65 159	92.98 131	63.78 280	90.22 223	66.86 268	93.92 200	92.27 194

fmvsm_s_conf0.1_n		82.17 196	81.59 205	83.94 173	86.87 252	71.57 171	85.19 158	77.42 330	62.27 313	84.47 230	91.33 189	76.43 177	85.91 310	83.14 83	87.14 332	94.33 95

c3_l		81.64 209	81.59 205	81.79 231	80.86 351	59.15 314	78.61 293	90.18 181	68.36 248	87.20 164	87.11 289	69.39 250	91.62 179	78.16 147	94.43 186	94.60 80

MVSFormer		82.23 193	81.57 207	84.19 167	85.54 279	69.26 198	91.98 34	90.08 183	71.54 212	76.23 345	85.07 324	58.69 312	94.27 89	86.26 45	88.77 309	89.03 284

fmvsm_l_conf0.5_n		82.06 200	81.54 208	83.60 183	83.94 308	73.90 134	83.35 204	86.10 250	58.97 342	83.80 247	90.36 225	74.23 199	86.94 288	82.90 89	90.22 289	89.94 265

fmvsm_s_conf0.5_n_a		82.21 194	81.51 209	84.32 162	86.56 254	73.35 138	85.46 151	77.30 331	61.81 315	84.51 227	90.88 209	77.36 161	86.21 302	82.72 93	86.97 339	93.38 138

Fast-Effi-MVS+-dtu		82.54 189	81.41 210	85.90 123	85.60 277	76.53 115	83.07 212	89.62 195	73.02 192	79.11 320	83.51 339	80.74 129	90.24 222	68.76 256	89.29 301	90.94 235

sd_testset		79.95 241	81.39 211	75.64 320	88.81 193	58.07 325	76.16 332	82.81 296	73.67 174	83.41 255	93.04 127	80.96 126	77.65 370	58.62 334	95.03 162	91.21 228

fmvsm_s_conf0.5_n		81.91 206	81.30 212	83.75 178	86.02 272	71.56 172	84.73 165	77.11 334	62.44 310	84.00 243	90.68 217	76.42 178	85.89 312	83.14 83	87.11 333	93.81 120

Anonymous20240521		80.18 236	81.25 213	76.95 303	83.15 328	60.84 296	82.46 231	85.99 255	68.76 243	86.78 175	93.73 112	59.13 309	77.44 371	73.71 204	97.55 69	92.56 175

DELS-MVS		81.44 212	81.25 213	82.03 222	84.27 303	62.87 264	76.47 327	92.49 105	70.97 221	81.64 288	83.83 336	75.03 187	92.70 151	74.29 191	92.22 244	90.51 252
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
EIA-MVS		82.19 195	81.23 215	85.10 138	87.95 215	69.17 202	83.22 210	93.33 67	70.42 225	78.58 325	79.77 379	77.29 162	94.20 94	71.51 225	88.96 307	91.93 209

Anonymous202405211		80.51 225	81.19 216	78.49 279	88.48 203	57.26 333	76.63 322	82.49 298	81.21 80	84.30 237	92.24 163	67.99 258	86.24 300	62.22 310	95.13 157	91.98 208

BH-untuned		80.96 218	80.99 217	80.84 246	88.55 202	68.23 209	80.33 265	88.46 210	72.79 196	86.55 182	86.76 293	74.72 195	91.77 178	61.79 316	88.99 306	82.52 373

MG-MVS		80.32 231	80.94 218	78.47 280	88.18 209	52.62 369	82.29 236	85.01 273	72.01 210	79.24 319	92.54 149	69.36 251	93.36 132	70.65 234	89.19 304	89.45 271

PCF-MVS	74.62 15	82.15 198	80.92 219	85.84 125	89.43 177	72.30 158	80.53 262	91.82 126	57.36 356	87.81 155	89.92 238	77.67 157	93.63 115	58.69 333	95.08 160	91.58 221
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_l_conf0.5_n_a		81.46 211	80.87 220	83.25 194	83.73 313	73.21 143	83.00 215	85.59 261	58.22 348	82.96 263	90.09 236	72.30 229	86.65 294	81.97 104	89.95 294	89.88 266

GDP-MVS		82.17 196	80.85 221	86.15 120	88.65 198	68.95 204	85.65 149	93.02 87	68.42 247	83.73 248	89.54 244	45.07 387	94.31 88	79.66 127	93.87 202	95.19 63

Fast-Effi-MVS+		81.04 217	80.57 222	82.46 218	87.50 230	63.22 260	78.37 296	89.63 194	68.01 254	81.87 280	82.08 357	82.31 102	92.65 153	67.10 267	88.30 320	91.51 224

LFMVS		80.15 237	80.56 223	78.89 271	89.19 183	55.93 341	85.22 157	73.78 358	82.96 63	84.28 238	92.72 144	57.38 321	90.07 232	63.80 300	95.75 139	90.68 245

ab-mvs		79.67 242	80.56 223	76.99 302	88.48 203	56.93 335	84.70 167	86.06 252	68.95 241	80.78 299	93.08 126	75.30 185	84.62 324	56.78 343	90.90 275	89.43 273

PVSNet_Blended_VisFu		81.55 210	80.49 225	84.70 149	91.58 127	73.24 142	84.21 177	91.67 130	62.86 304	80.94 296	87.16 287	67.27 261	92.87 149	69.82 243	88.94 308	87.99 299

diffmvs		80.40 228	80.48 226	80.17 257	79.02 372	60.04 302	77.54 307	90.28 178	66.65 274	82.40 271	87.33 284	73.50 210	87.35 281	77.98 151	89.62 298	93.13 150
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PLC	73.85 16	82.09 199	80.31 227	87.45 92	90.86 150	80.29 73	85.88 142	90.65 159	68.17 252	76.32 344	86.33 299	73.12 219	92.61 154	61.40 320	90.02 293	89.44 272
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 243	80.27 228	76.44 311	87.92 216	53.95 358	75.58 339	84.35 283	74.39 167	82.23 274	90.72 214	72.84 223	84.39 328	60.38 326	93.98 199	90.97 234

cl____		80.42 227	80.23 229	81.02 244	79.99 359	59.25 311	77.07 315	87.02 239	67.37 265	86.18 194	89.21 249	63.08 286	90.16 225	76.31 172	95.80 136	93.65 128

DIV-MVS_self_test		80.43 226	80.23 229	81.02 244	79.99 359	59.25 311	77.07 315	87.02 239	67.38 264	86.19 192	89.22 248	63.09 285	90.16 225	76.32 171	95.80 136	93.66 125

eth_miper_zixun_eth		80.84 219	80.22 231	82.71 211	81.41 343	60.98 294	77.81 302	90.14 182	67.31 267	86.95 174	87.24 286	64.26 275	92.31 162	75.23 185	91.61 259	94.85 75

BH-RMVSNet		80.53 224	80.22 231	81.49 236	87.19 238	66.21 231	77.79 303	86.23 248	74.21 168	83.69 249	88.50 260	73.25 218	90.75 208	63.18 306	87.90 324	87.52 306

xiu_mvs_v1_base_debu		80.84 219	80.14 233	82.93 206	88.31 206	71.73 166	79.53 274	87.17 231	65.43 286	79.59 312	82.73 351	76.94 169	90.14 228	73.22 211	88.33 316	86.90 314

xiu_mvs_v1_base		80.84 219	80.14 233	82.93 206	88.31 206	71.73 166	79.53 274	87.17 231	65.43 286	79.59 312	82.73 351	76.94 169	90.14 228	73.22 211	88.33 316	86.90 314

xiu_mvs_v1_base_debi		80.84 219	80.14 233	82.93 206	88.31 206	71.73 166	79.53 274	87.17 231	65.43 286	79.59 312	82.73 351	76.94 169	90.14 228	73.22 211	88.33 316	86.90 314

miper_ehance_all_eth		80.34 230	80.04 236	81.24 240	79.82 362	58.95 316	77.66 304	89.66 192	65.75 283	85.99 199	85.11 320	68.29 257	91.42 186	76.03 176	92.03 247	93.33 140

WB-MVS		76.06 281	80.01 237	64.19 394	89.96 170	20.58 437	72.18 366	68.19 393	83.21 59	86.46 189	93.49 117	70.19 247	78.97 365	65.96 277	90.46 288	93.02 155

MSDG		80.06 239	79.99 238	80.25 255	83.91 310	68.04 214	77.51 308	89.19 201	77.65 127	81.94 278	83.45 341	76.37 179	86.31 299	63.31 305	86.59 342	86.41 318

tttt0517		81.07 216	79.58 239	85.52 132	88.99 187	66.45 229	87.03 119	75.51 346	73.76 173	88.32 142	90.20 230	37.96 408	94.16 99	79.36 133	95.13 157	95.93 42

IterMVS-SCA-FT		80.64 223	79.41 240	84.34 161	83.93 309	69.66 193	76.28 329	81.09 310	72.43 199	86.47 188	90.19 231	60.46 297	93.15 138	77.45 158	86.39 345	90.22 257

patch_mono-2		78.89 246	79.39 241	77.41 299	84.78 291	68.11 212	75.60 337	83.11 292	60.96 329	79.36 316	89.89 239	75.18 186	72.97 385	73.32 210	92.30 238	91.15 230

wuyk23d		75.13 289	79.30 242	62.63 397	75.56 397	75.18 127	80.89 258	73.10 365	75.06 160	94.76 16	95.32 41	87.73 43	52.85 429	34.16 427	97.11 82	59.85 425

DPM-MVS		80.10 238	79.18 243	82.88 209	90.71 153	69.74 191	78.87 288	90.84 154	60.29 336	75.64 354	85.92 307	67.28 260	93.11 139	71.24 227	91.79 253	85.77 326

PM-MVS		80.20 235	79.00 244	83.78 177	88.17 210	86.66 19	81.31 250	66.81 402	69.64 234	88.33 141	90.19 231	64.58 273	83.63 336	71.99 224	90.03 292	81.06 393

mvsmamba		80.30 232	78.87 245	84.58 153	88.12 212	67.55 217	92.35 29	84.88 276	63.15 302	85.33 209	90.91 206	50.71 353	95.20 62	66.36 274	87.98 323	90.99 233

FE-MVS		79.98 240	78.86 246	83.36 191	86.47 255	66.45 229	89.73 70	84.74 280	72.80 195	84.22 241	91.38 188	44.95 388	93.60 119	63.93 298	91.50 262	90.04 264

test1111		78.53 253	78.85 247	77.56 296	92.22 103	47.49 394	82.61 224	69.24 390	72.43 199	85.28 210	94.20 85	51.91 347	90.07 232	65.36 286	96.45 103	95.11 65

AUN-MVS		81.18 215	78.78 248	88.39 79	90.93 147	82.14 62	82.51 230	83.67 288	64.69 295	80.29 306	85.91 308	51.07 351	92.38 159	76.29 173	93.63 212	90.65 248

mvs_anonymous		78.13 256	78.76 249	76.23 316	79.24 369	50.31 385	78.69 291	84.82 278	61.60 320	83.09 262	92.82 139	73.89 206	87.01 284	68.33 263	86.41 344	91.37 225

MAR-MVS		80.24 234	78.74 250	84.73 147	86.87 252	78.18 92	85.75 146	87.81 223	65.67 285	77.84 331	78.50 389	73.79 207	90.53 215	61.59 319	90.87 277	85.49 330
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
ECVR-MVS		78.44 254	78.63 251	77.88 292	91.85 117	48.95 388	83.68 194	69.91 386	72.30 205	84.26 240	94.20 85	51.89 348	89.82 237	63.58 301	96.02 122	94.87 71

FMVSNet3		78.80 249	78.55 252	79.57 265	82.89 331	56.89 337	81.76 244	85.77 257	69.04 240	86.00 196	90.44 224	51.75 349	90.09 231	65.95 278	93.34 215	91.72 215

test_yl		78.71 251	78.51 253	79.32 268	84.32 301	58.84 318	78.38 294	85.33 264	75.99 143	82.49 269	86.57 295	58.01 315	90.02 234	62.74 307	92.73 233	89.10 280

DCV-MVSNet		78.71 251	78.51 253	79.32 268	84.32 301	58.84 318	78.38 294	85.33 264	75.99 143	82.49 269	86.57 295	58.01 315	90.02 234	62.74 307	92.73 233	89.10 280

EPNet		80.37 229	78.41 255	86.23 113	76.75 386	73.28 140	87.18 116	77.45 329	76.24 139	68.14 397	88.93 254	65.41 271	93.85 107	69.47 245	96.12 118	91.55 222
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		78.88 247	78.28 256	80.68 250	79.58 363	62.64 268	82.58 226	94.16 32	74.80 161	75.72 352	92.59 146	48.69 360	95.56 42	73.48 207	82.91 381	83.85 352

cl22		78.97 245	78.21 257	81.24 240	77.74 376	59.01 315	77.46 311	87.13 234	65.79 280	84.32 234	85.10 321	58.96 311	90.88 204	75.36 184	92.03 247	93.84 115

PAPR		78.84 248	78.10 258	81.07 242	85.17 286	60.22 301	82.21 240	90.57 163	62.51 306	75.32 358	84.61 329	74.99 188	92.30 163	59.48 331	88.04 322	90.68 245

PVSNet_BlendedMVS		78.80 249	77.84 259	81.65 233	84.43 297	63.41 256	79.49 277	90.44 166	61.70 318	75.43 355	87.07 290	69.11 253	91.44 184	60.68 324	92.24 242	90.11 262

Vis-MVSNet (Re-imp)		77.82 259	77.79 260	77.92 291	88.82 192	51.29 379	83.28 205	71.97 374	74.04 169	82.23 274	89.78 240	57.38 321	89.41 249	57.22 342	95.41 146	93.05 154

Patchmtry		76.56 276	77.46 261	73.83 331	79.37 368	46.60 398	82.41 233	76.90 335	73.81 172	85.56 206	92.38 153	48.07 363	83.98 333	63.36 304	95.31 152	90.92 236

OpenMVS_ROB	70.19 17	77.77 261	77.46 261	78.71 275	84.39 300	61.15 289	81.18 254	82.52 297	62.45 309	83.34 257	87.37 282	66.20 266	88.66 262	64.69 293	85.02 361	86.32 319

CL-MVSNet_self_test		76.81 271	77.38 263	75.12 323	86.90 250	51.34 377	73.20 361	80.63 314	68.30 250	81.80 284	88.40 261	66.92 263	80.90 351	55.35 356	94.90 168	93.12 152

thisisatest0530		79.07 244	77.33 264	84.26 164	87.13 239	64.58 244	83.66 195	75.95 341	68.86 242	85.22 211	87.36 283	38.10 405	93.57 123	75.47 182	94.28 190	94.62 79

MonoMVSNet		76.66 273	77.26 265	74.86 325	79.86 361	54.34 355	86.26 137	86.08 251	71.08 220	85.59 204	88.68 257	53.95 339	85.93 307	63.86 299	80.02 397	84.32 343

CANet_DTU		77.81 260	77.05 266	80.09 258	81.37 344	59.90 305	83.26 206	88.29 216	69.16 238	67.83 400	83.72 337	60.93 294	89.47 244	69.22 249	89.70 297	90.88 238

pmmvs-eth3d		78.42 255	77.04 267	82.57 216	87.44 232	74.41 131	80.86 259	79.67 318	55.68 365	84.69 224	90.31 228	60.91 295	85.42 317	62.20 311	91.59 260	87.88 302

miper_enhance_ethall		77.83 258	76.93 268	80.51 251	76.15 393	58.01 327	75.47 341	88.82 204	58.05 350	83.59 251	80.69 367	64.41 274	91.20 190	73.16 217	92.03 247	92.33 189

MDA-MVSNet-bldmvs		77.47 263	76.90 269	79.16 270	79.03 371	64.59 243	66.58 401	75.67 344	73.15 190	88.86 124	88.99 253	66.94 262	81.23 350	64.71 292	88.22 321	91.64 219

xiu_mvs_v2_base		77.19 266	76.75 270	78.52 278	87.01 246	61.30 287	75.55 340	87.12 237	61.24 326	74.45 363	78.79 387	77.20 163	90.93 200	64.62 295	84.80 368	83.32 361

USDC		76.63 274	76.73 271	76.34 313	83.46 316	57.20 334	80.02 268	88.04 221	52.14 388	83.65 250	91.25 192	63.24 283	86.65 294	54.66 361	94.11 195	85.17 332

PS-MVSNAJ		77.04 268	76.53 272	78.56 277	87.09 244	61.40 285	75.26 342	87.13 234	61.25 325	74.38 365	77.22 401	76.94 169	90.94 199	64.63 294	84.83 367	83.35 360

TAMVS		78.08 257	76.36 273	83.23 195	90.62 154	72.87 144	79.08 284	80.01 317	61.72 317	81.35 292	86.92 292	63.96 279	88.78 259	50.61 382	93.01 225	88.04 298

IterMVS		76.91 269	76.34 274	78.64 276	80.91 349	64.03 250	76.30 328	79.03 321	64.88 294	83.11 260	89.16 250	59.90 303	84.46 326	68.61 259	85.15 359	87.42 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 300	76.19 275	69.21 366	84.61 295	52.43 370	71.70 369	77.18 333	60.73 332	80.60 300	90.96 204	75.44 182	69.35 397	56.13 348	88.33 316	85.86 325

miper_lstm_enhance		76.45 278	76.10 276	77.51 297	76.72 387	60.97 295	64.69 405	85.04 271	63.98 299	83.20 259	88.22 263	56.67 325	78.79 367	73.22 211	93.12 222	92.78 164

BH-w/o		76.57 275	76.07 277	78.10 287	86.88 251	65.92 234	77.63 305	86.33 246	65.69 284	80.89 297	79.95 376	68.97 255	90.74 209	53.01 372	85.25 356	77.62 404

TR-MVS		76.77 272	75.79 278	79.72 262	86.10 271	65.79 235	77.14 313	83.02 293	65.20 292	81.40 291	82.10 355	66.30 265	90.73 210	55.57 353	85.27 355	82.65 368

jason		77.42 264	75.75 279	82.43 219	87.10 242	69.27 197	77.99 299	81.94 303	51.47 392	77.84 331	85.07 324	60.32 299	89.00 253	70.74 233	89.27 303	89.03 284
jason: jason.
MVSTER		77.09 267	75.70 280	81.25 238	75.27 401	61.08 290	77.49 310	85.07 269	60.78 331	86.55 182	88.68 257	43.14 397	90.25 220	73.69 205	90.67 283	92.42 182

SSC-MVS3.2		73.90 304	75.67 281	68.61 374	84.11 306	41.28 416	64.17 407	72.83 366	72.09 208	79.08 321	87.94 268	70.31 245	73.89 384	55.99 349	94.49 183	90.67 247

D2MVS		76.84 270	75.67 281	80.34 254	80.48 357	62.16 280	73.50 358	84.80 279	57.61 354	82.24 273	87.54 278	51.31 350	87.65 277	70.40 238	93.19 221	91.23 227

PVSNet_Blended		76.49 277	75.40 283	79.76 261	84.43 297	63.41 256	75.14 343	90.44 166	57.36 356	75.43 355	78.30 390	69.11 253	91.44 184	60.68 324	87.70 328	84.42 342

CDS-MVSNet		77.32 265	75.40 283	83.06 199	89.00 186	72.48 155	77.90 301	82.17 301	60.81 330	78.94 322	83.49 340	59.30 307	88.76 260	54.64 362	92.37 237	87.93 301
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 282	75.35 285	77.85 294	87.01 246	51.84 375	80.45 263	73.26 363	75.20 158	83.10 261	86.31 301	45.54 378	89.05 252	55.03 359	92.24 242	92.66 170

test_fmvs3		75.72 285	75.20 286	77.27 300	75.01 404	69.47 195	78.93 285	84.88 276	46.67 406	87.08 170	87.84 272	50.44 356	71.62 390	77.42 160	88.53 312	90.72 242

thres100view900		75.45 286	75.05 287	76.66 309	87.27 234	51.88 374	81.07 255	73.26 363	75.68 149	83.25 258	86.37 298	45.54 378	88.80 256	51.98 377	90.99 270	89.31 275

cascas		76.29 280	74.81 288	80.72 249	84.47 296	62.94 262	73.89 355	87.34 227	55.94 363	75.16 360	76.53 406	63.97 278	91.16 192	65.00 289	90.97 273	88.06 297

GA-MVS		75.83 283	74.61 289	79.48 267	81.87 336	59.25 311	73.42 359	82.88 294	68.68 244	79.75 311	81.80 360	50.62 354	89.46 245	66.85 269	85.64 352	89.72 268

testgi		72.36 317	74.61 289	65.59 388	80.56 356	42.82 413	68.29 390	73.35 362	66.87 272	81.84 281	89.93 237	72.08 233	66.92 411	46.05 405	92.54 235	87.01 313

test20.03		73.75 306	74.59 291	71.22 352	81.11 347	51.12 381	70.15 382	72.10 373	70.42 225	80.28 308	91.50 185	64.21 276	74.72 382	46.96 401	94.58 181	87.82 304

lupinMVS		76.37 279	74.46 292	82.09 221	85.54 279	69.26 198	76.79 318	80.77 313	50.68 399	76.23 345	82.82 349	58.69 312	88.94 254	69.85 242	88.77 309	88.07 295

EU-MVSNet		75.12 290	74.43 293	77.18 301	83.11 329	59.48 309	85.71 148	82.43 299	39.76 426	85.64 203	88.76 255	44.71 390	87.88 275	73.86 201	85.88 351	84.16 348

tfpn200view9		74.86 294	74.23 294	76.74 308	86.24 265	52.12 371	79.24 281	73.87 356	73.34 183	81.82 282	84.60 330	46.02 371	88.80 256	51.98 377	90.99 270	89.31 275

thres400		75.14 288	74.23 294	77.86 293	86.24 265	52.12 371	79.24 281	73.87 356	73.34 183	81.82 282	84.60 330	46.02 371	88.80 256	51.98 377	90.99 270	92.66 170

ppachtmachnet_test		74.73 297	74.00 296	76.90 305	80.71 354	56.89 337	71.53 372	78.42 323	58.24 347	79.32 318	82.92 348	57.91 318	84.26 330	65.60 284	91.36 264	89.56 270

1112_ss		74.82 295	73.74 297	78.04 289	89.57 172	60.04 302	76.49 326	87.09 238	54.31 373	73.66 369	79.80 377	60.25 300	86.76 293	58.37 335	84.15 372	87.32 309

Patchmatch-RL test		74.48 298	73.68 298	76.89 306	84.83 290	66.54 227	72.29 365	69.16 391	57.70 352	86.76 176	86.33 299	45.79 377	82.59 340	69.63 244	90.65 286	81.54 384

CMPMVS	59.41 20	75.12 290	73.57 299	79.77 260	75.84 396	67.22 218	81.21 253	82.18 300	50.78 397	76.50 341	87.66 276	55.20 335	82.99 339	62.17 313	90.64 287	89.09 282
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 309	73.54 300	72.43 345	84.92 289	47.79 393	79.89 270	74.00 354	65.93 277	78.81 323	86.28 302	56.36 327	81.63 348	56.63 344	79.04 404	87.87 303

MVP-Stereo		75.81 284	73.51 301	82.71 211	89.35 178	73.62 135	80.06 266	85.20 266	60.30 335	73.96 366	87.94 268	57.89 319	89.45 246	52.02 376	74.87 415	85.06 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test2506		74.12 301	73.39 302	76.28 314	91.85 117	44.20 408	84.06 181	48.20 433	72.30 205	81.90 279	94.20 85	27.22 433	89.77 240	64.81 291	96.02 122	94.87 71

new-patchmatchnet		70.10 339	73.37 303	60.29 405	81.23 346	16.95 440	59.54 416	74.62 349	62.93 303	80.97 294	87.93 270	62.83 289	71.90 388	55.24 357	95.01 165	92.00 206

reproduce_monomvs		74.09 302	73.23 304	76.65 310	76.52 388	54.54 353	77.50 309	81.40 308	65.85 279	82.86 266	86.67 294	27.38 431	84.53 325	70.24 239	90.66 285	90.89 237

PatchMatch-RL		74.48 298	73.22 305	78.27 285	87.70 223	85.26 38	75.92 335	70.09 384	64.34 297	76.09 348	81.25 365	65.87 269	78.07 369	53.86 364	83.82 374	71.48 413

Test_1112_low_res		73.90 304	73.08 306	76.35 312	90.35 159	55.95 340	73.40 360	86.17 249	50.70 398	73.14 370	85.94 306	58.31 314	85.90 311	56.51 345	83.22 378	87.20 311

CR-MVSNet		74.00 303	73.04 307	76.85 307	79.58 363	62.64 268	82.58 226	76.90 335	50.50 400	75.72 352	92.38 153	48.07 363	84.07 332	68.72 258	82.91 381	83.85 352

pmmvs4		74.92 293	72.98 308	80.73 248	84.95 288	71.71 169	76.23 330	77.59 328	52.83 382	77.73 335	86.38 297	56.35 328	84.97 321	57.72 341	87.05 335	85.51 329

test_fmvs2		73.57 307	72.80 309	75.90 318	72.74 418	68.84 205	77.07 315	84.32 284	45.14 412	82.89 264	84.22 333	48.37 361	70.36 394	73.40 209	87.03 336	88.52 290

ET-MVSNet_ETH3D		75.28 287	72.77 310	82.81 210	83.03 330	68.11 212	77.09 314	76.51 339	60.67 333	77.60 336	80.52 371	38.04 406	91.15 193	70.78 231	90.68 282	89.17 278

PatchT		70.52 335	72.76 311	63.79 396	79.38 367	33.53 430	77.63 305	65.37 407	73.61 176	71.77 377	92.79 142	44.38 391	75.65 378	64.53 296	85.37 354	82.18 377

HyFIR lowres test		75.12 290	72.66 312	82.50 217	91.44 135	65.19 240	72.47 364	87.31 228	46.79 405	80.29 306	84.30 332	52.70 344	92.10 169	51.88 381	86.73 340	90.22 257

MVS		73.21 311	72.59 313	75.06 324	80.97 348	60.81 297	81.64 247	85.92 256	46.03 410	71.68 378	77.54 396	68.47 256	89.77 240	55.70 352	85.39 353	74.60 410

SCA		73.32 308	72.57 314	75.58 321	81.62 340	55.86 343	78.89 287	71.37 379	61.73 316	74.93 361	83.42 342	60.46 297	87.01 284	58.11 339	82.63 386	83.88 349

1314		73.22 310	72.56 315	75.20 322	80.41 358	57.84 328	81.64 247	85.36 263	51.68 391	73.10 371	76.65 405	61.45 292	85.19 319	63.54 302	79.21 402	82.59 369

HY-MVS	64.64 18	73.03 312	72.47 316	74.71 327	83.36 321	54.19 356	82.14 243	81.96 302	56.76 362	69.57 392	86.21 303	60.03 301	84.83 323	49.58 388	82.65 384	85.11 333

UnsupCasMVSNet_eth		71.63 325	72.30 317	69.62 363	76.47 390	52.70 368	70.03 383	80.97 311	59.18 341	79.36 316	88.21 264	60.50 296	69.12 398	58.33 337	77.62 409	87.04 312

FPMVS		72.29 319	72.00 318	73.14 336	88.63 199	85.00 40	74.65 348	67.39 396	71.94 211	77.80 333	87.66 276	50.48 355	75.83 377	49.95 384	79.51 398	58.58 427

Anonymous20231206		71.38 328	71.88 319	69.88 360	86.31 262	54.37 354	70.39 380	74.62 349	52.57 384	76.73 340	88.76 255	59.94 302	72.06 387	44.35 409	93.23 220	83.23 363

FMVSNet5		72.10 320	71.69 320	73.32 334	81.57 341	53.02 365	76.77 319	78.37 324	63.31 300	76.37 342	91.85 171	36.68 410	78.98 364	47.87 397	92.45 236	87.95 300

our_test_3		71.85 321	71.59 321	72.62 342	80.71 354	53.78 359	69.72 385	71.71 378	58.80 344	78.03 328	80.51 372	56.61 326	78.84 366	62.20 311	86.04 350	85.23 331

MIMVSNet		71.09 330	71.59 321	69.57 364	87.23 236	50.07 386	78.91 286	71.83 375	60.20 338	71.26 379	91.76 178	55.08 337	76.09 375	41.06 414	87.02 337	82.54 372

test_vis1_n_1920		71.30 329	71.58 323	70.47 355	77.58 379	59.99 304	74.25 349	84.22 285	51.06 394	74.85 362	79.10 383	55.10 336	68.83 400	68.86 255	79.20 403	82.58 370

thres200		72.34 318	71.55 324	74.70 328	83.48 315	51.60 376	75.02 344	73.71 359	70.14 231	78.56 326	80.57 370	46.20 369	88.20 270	46.99 400	89.29 301	84.32 343

CVMVSNet		72.62 315	71.41 325	76.28 314	83.25 324	60.34 300	83.50 199	79.02 322	37.77 430	76.33 343	85.10 321	49.60 359	87.41 280	70.54 236	77.54 410	81.08 391

EPNet_dtu		72.87 314	71.33 326	77.49 298	77.72 377	60.55 299	82.35 234	75.79 342	66.49 275	58.39 428	81.06 366	53.68 340	85.98 306	53.55 367	92.97 227	85.95 323
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing3-2		70.72 334	70.97 327	69.95 359	88.93 189	34.80 429	69.85 384	66.59 403	78.42 117	77.58 337	85.55 310	31.83 420	82.08 344	46.28 402	93.73 208	92.98 158

testing3		71.53 326	70.79 328	73.77 332	88.89 191	41.86 415	76.60 325	59.12 422	72.83 194	80.97 294	82.08 357	19.80 439	87.33 282	65.12 288	91.68 258	92.13 201

ttmdpeth		71.72 323	70.67 329	74.86 325	73.08 415	55.88 342	77.41 312	69.27 389	55.86 364	78.66 324	93.77 110	38.01 407	75.39 379	60.12 327	89.87 295	93.31 142

test_vis3_rt		71.42 327	70.67 329	73.64 333	69.66 425	70.46 182	66.97 400	89.73 189	42.68 422	88.20 145	83.04 344	43.77 392	60.07 423	65.35 287	86.66 341	90.39 255

CHOSEN 1792x2688		72.45 316	70.56 331	78.13 286	90.02 169	63.08 261	68.72 389	83.16 291	42.99 420	75.92 350	85.46 314	57.22 323	85.18 320	49.87 386	81.67 388	86.14 321

thisisatest0515		73.00 313	70.52 332	80.46 252	81.45 342	59.90 305	73.16 362	74.31 353	57.86 351	76.08 349	77.78 393	37.60 409	92.12 168	65.00 289	91.45 263	89.35 274

YYNet1		70.06 340	70.44 333	68.90 368	73.76 408	53.42 363	58.99 419	67.20 398	58.42 346	87.10 168	85.39 317	59.82 304	67.32 408	59.79 329	83.50 377	85.96 322

MDA-MVSNet_test_wron		70.05 341	70.44 333	68.88 369	73.84 407	53.47 361	58.93 420	67.28 397	58.43 345	87.09 169	85.40 316	59.80 305	67.25 409	59.66 330	83.54 376	85.92 324

test_fmvs1_n		70.94 331	70.41 335	72.53 344	73.92 406	66.93 224	75.99 334	84.21 286	43.31 419	79.40 315	79.39 381	43.47 393	68.55 402	69.05 252	84.91 364	82.10 378

MS-PatchMatch		70.93 332	70.22 336	73.06 337	81.85 337	62.50 271	73.82 356	77.90 325	52.44 385	75.92 350	81.27 364	55.67 332	81.75 346	55.37 355	77.70 408	74.94 409

pmmvs5		70.73 333	70.07 337	72.72 340	77.03 384	52.73 367	74.14 350	75.65 345	50.36 401	72.17 376	85.37 318	55.42 334	80.67 353	52.86 373	87.59 329	84.77 336

PAPM		71.77 322	70.06 338	76.92 304	86.39 257	53.97 357	76.62 323	86.62 244	53.44 377	63.97 417	84.73 328	57.79 320	92.34 161	39.65 417	81.33 392	84.45 341

Syy-MVS		69.40 349	70.03 339	67.49 379	81.72 338	38.94 421	71.00 374	61.99 413	61.38 322	70.81 383	72.36 417	61.37 293	79.30 362	64.50 297	85.18 357	84.22 345

test_vis1_n		70.29 336	69.99 340	71.20 353	75.97 395	66.50 228	76.69 321	80.81 312	44.22 415	75.43 355	77.23 400	50.00 357	68.59 401	66.71 272	82.85 383	78.52 403

EGC-MVSNET		74.79 296	69.99 340	89.19 65	94.89 38	87.00 15	91.89 37	86.28 247	1.09 435	2.23 437	95.98 27	81.87 116	89.48 243	79.76 124	95.96 125	91.10 231

UnsupCasMVSNet_bld		69.21 351	69.68 342	67.82 377	79.42 366	51.15 380	67.82 394	75.79 342	54.15 374	77.47 338	85.36 319	59.26 308	70.64 393	48.46 394	79.35 400	81.66 382

tpmvs		70.16 338	69.56 343	71.96 348	74.71 405	48.13 390	79.63 272	75.45 347	65.02 293	70.26 387	81.88 359	45.34 383	85.68 315	58.34 336	75.39 414	82.08 379

MVStest1		70.05 341	69.26 344	72.41 346	58.62 437	55.59 346	76.61 324	65.58 405	53.44 377	89.28 120	93.32 120	22.91 437	71.44 392	74.08 197	89.52 299	90.21 261

test_cas_vis1_n_1920		69.20 352	69.12 345	69.43 365	73.68 409	62.82 265	70.38 381	77.21 332	46.18 409	80.46 305	78.95 385	52.03 346	65.53 416	65.77 283	77.45 411	79.95 399

gg-mvs-nofinetune		68.96 353	69.11 346	68.52 375	76.12 394	45.32 404	83.59 196	55.88 427	86.68 29	64.62 416	97.01 9	30.36 424	83.97 334	44.78 408	82.94 380	76.26 406

test_fmvs1		69.57 347	69.05 347	71.14 354	69.15 426	65.77 236	73.98 353	83.32 290	42.83 421	77.77 334	78.27 391	43.39 396	68.50 403	68.39 262	84.38 371	79.15 401

WB-MVSnew		68.72 355	69.01 348	67.85 376	83.22 326	43.98 409	74.93 345	65.98 404	55.09 367	73.83 367	79.11 382	65.63 270	71.89 389	38.21 422	85.04 360	87.69 305

testing91		69.94 344	68.99 349	72.80 339	83.81 312	45.89 401	71.57 371	73.64 361	68.24 251	70.77 385	77.82 392	34.37 413	84.44 327	53.64 366	87.00 338	88.07 295

IB-MVS	62.13 19	71.64 324	68.97 350	79.66 264	80.80 353	62.26 277	73.94 354	76.90 335	63.27 301	68.63 396	76.79 403	33.83 414	91.84 176	59.28 332	87.26 330	84.88 335
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
PatchmatchNet		69.71 346	68.83 351	72.33 347	77.66 378	53.60 360	79.29 279	69.99 385	57.66 353	72.53 374	82.93 347	46.45 368	80.08 359	60.91 323	72.09 418	83.31 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 337	68.80 352	74.38 329	80.91 349	84.81 43	59.12 418	76.45 340	55.06 368	75.31 359	82.36 354	55.74 331	54.82 428	47.02 399	87.24 331	83.52 356

CostFormer		69.98 343	68.68 353	73.87 330	77.14 382	50.72 383	79.26 280	74.51 351	51.94 390	70.97 382	84.75 327	45.16 386	87.49 279	55.16 358	79.23 401	83.40 359

WBMVS		68.76 354	68.43 354	69.75 362	83.29 322	40.30 419	67.36 396	72.21 372	57.09 359	77.05 339	85.53 312	33.68 415	80.51 355	48.79 392	90.90 275	88.45 291

WTY-MVS		67.91 358	68.35 355	66.58 384	80.82 352	48.12 391	65.96 402	72.60 367	53.67 376	71.20 380	81.68 362	58.97 310	69.06 399	48.57 393	81.67 388	82.55 371

MDTV_nov1_ep13			68.29 356		78.03 375	43.87 410	74.12 351	72.22 371	52.17 386	67.02 403	85.54 311	45.36 382	80.85 352	55.73 350	84.42 370

testing99		69.27 350	68.15 357	72.63 341	83.29 322	45.45 403	71.15 373	71.08 380	67.34 266	70.43 386	77.77 394	32.24 419	84.35 329	53.72 365	86.33 346	88.10 294

tpm		67.95 357	68.08 358	67.55 378	78.74 374	43.53 411	75.60 337	67.10 401	54.92 369	72.23 375	88.10 265	42.87 398	75.97 376	52.21 375	80.95 396	83.15 364

Patchmatch-test		65.91 370	67.38 359	61.48 402	75.51 398	43.21 412	68.84 388	63.79 411	62.48 307	72.80 373	83.42 342	44.89 389	59.52 425	48.27 396	86.45 343	81.70 381

sss		66.92 362	67.26 360	65.90 386	77.23 381	51.10 382	64.79 404	71.72 377	52.12 389	70.13 388	80.18 374	57.96 317	65.36 417	50.21 383	81.01 394	81.25 388

dmvs_re		66.81 365	66.98 361	66.28 385	76.87 385	58.68 322	71.66 370	72.24 370	60.29 336	69.52 393	73.53 414	52.38 345	64.40 419	44.90 407	81.44 391	75.76 407

baseline2		69.77 345	66.89 362	78.41 281	79.51 365	58.09 324	76.23 330	69.57 387	57.50 355	64.82 415	77.45 398	46.02 371	88.44 264	53.08 369	77.83 406	88.70 288

tpm2		68.45 356	66.83 363	73.30 335	78.93 373	48.50 389	79.76 271	71.76 376	47.50 404	69.92 389	83.60 338	42.07 399	88.40 266	48.44 395	79.51 398	83.01 366

test-LLR		67.21 360	66.74 364	68.63 372	76.45 391	55.21 349	67.89 391	67.14 399	62.43 311	65.08 412	72.39 415	43.41 394	69.37 395	61.00 321	84.89 365	81.31 386

tpmrst		66.28 369	66.69 365	65.05 392	72.82 417	39.33 420	78.20 297	70.69 383	53.16 380	67.88 399	80.36 373	48.18 362	74.75 381	58.13 338	70.79 420	81.08 391

JIA-IIPM		69.41 348	66.64 366	77.70 295	73.19 412	71.24 174	75.67 336	65.56 406	70.42 225	65.18 411	92.97 133	33.64 416	83.06 337	53.52 368	69.61 424	78.79 402

testing11		67.38 359	65.93 367	71.73 350	83.37 320	46.60 398	70.95 376	69.40 388	62.47 308	66.14 404	76.66 404	31.22 421	84.10 331	49.10 390	84.10 373	84.49 339

myMVS_eth3d28		65.83 372	65.85 368	65.78 387	83.42 318	35.71 427	67.29 397	68.01 394	67.58 263	69.80 390	77.72 395	32.29 418	74.30 383	37.49 423	89.06 305	87.32 309

test_f		64.31 380	65.85 368	59.67 406	66.54 430	62.24 279	57.76 422	70.96 381	40.13 424	84.36 232	82.09 356	46.93 365	51.67 430	61.99 314	81.89 387	65.12 421

KD-MVS_2432*1600		66.87 363	65.81 370	70.04 357	67.50 427	47.49 394	62.56 410	79.16 319	61.21 327	77.98 329	80.61 368	25.29 435	82.48 341	53.02 370	84.92 362	80.16 397

miper_refine_blended		66.87 363	65.81 370	70.04 357	67.50 427	47.49 394	62.56 410	79.16 319	61.21 327	77.98 329	80.61 368	25.29 435	82.48 341	53.02 370	84.92 362	80.16 397

PVSNet	58.17 21	66.41 368	65.63 372	68.75 370	81.96 335	49.88 387	62.19 412	72.51 369	51.03 395	68.04 398	75.34 411	50.84 352	74.77 380	45.82 406	82.96 379	81.60 383

UWE-MVS		66.43 367	65.56 373	69.05 367	84.15 305	40.98 417	73.06 363	64.71 409	54.84 370	76.18 347	79.62 380	29.21 426	80.50 356	38.54 421	89.75 296	85.66 327

testing222		66.93 361	65.30 374	71.81 349	83.38 319	45.83 402	72.06 367	67.50 395	64.12 298	69.68 391	76.37 407	27.34 432	83.00 338	38.88 418	88.38 315	86.62 317

tpm cat1		66.76 366	65.21 375	71.42 351	77.09 383	50.62 384	78.01 298	73.68 360	44.89 413	68.64 395	79.00 384	45.51 380	82.42 343	49.91 385	70.15 421	81.23 390

test0.0.03 1		64.66 377	64.36 376	65.57 389	75.03 403	46.89 397	64.69 405	61.58 419	62.43 311	71.18 381	77.54 396	43.41 394	68.47 404	40.75 416	82.65 384	81.35 385

test_vis1_rt		65.64 373	64.09 377	70.31 356	66.09 431	70.20 186	61.16 413	81.60 306	38.65 427	72.87 372	69.66 420	52.84 342	60.04 424	56.16 347	77.77 407	80.68 395

myMVS_eth3d		64.66 377	63.89 378	66.97 382	81.72 338	37.39 424	71.00 374	61.99 413	61.38 322	70.81 383	72.36 417	20.96 438	79.30 362	49.59 387	85.18 357	84.22 345

test-mter		65.00 375	63.79 379	68.63 372	76.45 391	55.21 349	67.89 391	67.14 399	50.98 396	65.08 412	72.39 415	28.27 429	69.37 395	61.00 321	84.89 365	81.31 386

ADS-MVSNet2		65.87 371	63.64 380	72.55 343	73.16 413	56.92 336	67.10 398	74.81 348	49.74 402	66.04 406	82.97 345	46.71 366	77.26 372	42.29 411	69.96 422	83.46 357

UBG		64.34 379	63.35 381	67.30 380	83.50 314	40.53 418	67.46 395	65.02 408	54.77 371	67.54 402	74.47 413	32.99 417	78.50 368	40.82 415	83.58 375	82.88 367

ETVMVS		64.67 376	63.34 382	68.64 371	83.44 317	41.89 414	69.56 387	61.70 418	61.33 324	68.74 394	75.76 409	28.76 427	79.35 361	34.65 426	86.16 349	84.67 338

mvsany_test3		65.48 374	62.97 383	73.03 338	69.99 424	76.17 121	64.83 403	43.71 435	43.68 417	80.25 309	87.05 291	52.83 343	63.09 422	51.92 380	72.44 417	79.84 400

MVS-HIRNet		61.16 387	62.92 384	55.87 409	79.09 370	35.34 428	71.83 368	57.98 426	46.56 407	59.05 425	91.14 196	49.95 358	76.43 374	38.74 419	71.92 419	55.84 428

EPMVS		62.47 381	62.63 385	62.01 398	70.63 423	38.74 422	74.76 346	52.86 429	53.91 375	67.71 401	80.01 375	39.40 403	66.60 412	55.54 354	68.81 426	80.68 395

dmvs_testset		60.59 391	62.54 386	54.72 411	77.26 380	27.74 434	74.05 352	61.00 420	60.48 334	65.62 409	67.03 424	55.93 330	68.23 406	32.07 430	69.46 425	68.17 418

ADS-MVSNet		61.90 383	62.19 387	61.03 403	73.16 413	36.42 426	67.10 398	61.75 416	49.74 402	66.04 406	82.97 345	46.71 366	63.21 420	42.29 411	69.96 422	83.46 357

E-PMN		61.59 385	61.62 388	61.49 401	66.81 429	55.40 347	53.77 425	60.34 421	66.80 273	58.90 426	65.50 425	40.48 402	66.12 414	55.72 351	86.25 347	62.95 423

DSMNet-mixed		60.98 389	61.61 389	59.09 408	72.88 416	45.05 406	74.70 347	46.61 434	26.20 432	65.34 410	90.32 227	55.46 333	63.12 421	41.72 413	81.30 393	69.09 417

EMVS		61.10 388	60.81 390	61.99 399	65.96 432	55.86 343	53.10 426	58.97 424	67.06 270	56.89 430	63.33 426	40.98 400	67.03 410	54.79 360	86.18 348	63.08 422

PMMVS		61.65 384	60.38 391	65.47 390	65.40 434	69.26 198	63.97 408	61.73 417	36.80 431	60.11 423	68.43 422	59.42 306	66.35 413	48.97 391	78.57 405	60.81 424

TESTMET0.1,1		61.29 386	60.32 392	64.19 394	72.06 419	51.30 378	67.89 391	62.09 412	45.27 411	60.65 422	69.01 421	27.93 430	64.74 418	56.31 346	81.65 390	76.53 405

dp		60.70 390	60.29 393	61.92 400	72.04 420	38.67 423	70.83 377	64.08 410	51.28 393	60.75 421	77.28 399	36.59 411	71.58 391	47.41 398	62.34 428	75.52 408

pmmvs3		62.47 381	60.02 394	69.80 361	71.58 421	64.00 251	70.52 379	58.44 425	39.77 425	66.05 405	75.84 408	27.10 434	72.28 386	46.15 404	84.77 369	73.11 411

PMMVS2		55.64 397	59.27 395	44.74 413	64.30 435	12.32 441	40.60 428	49.79 431	53.19 379	65.06 414	84.81 326	53.60 341	49.76 431	32.68 429	89.41 300	72.15 412

UWE-MVS-28		58.44 394	57.71 396	60.65 404	73.58 410	31.23 431	69.68 386	48.80 432	53.12 381	61.79 419	78.83 386	30.98 422	68.40 405	21.58 433	80.99 395	82.33 376

new_pmnet		55.69 396	57.66 397	49.76 412	75.47 399	30.59 432	59.56 415	51.45 430	43.62 418	62.49 418	75.48 410	40.96 401	49.15 432	37.39 424	72.52 416	69.55 416

CHOSEN 280x420		59.08 392	56.52 398	66.76 383	76.51 389	64.39 247	49.62 427	59.00 423	43.86 416	55.66 431	68.41 423	35.55 412	68.21 407	43.25 410	76.78 413	67.69 419

mvsany_test1		58.48 393	56.47 399	64.50 393	65.90 433	68.21 211	56.95 423	42.11 436	38.30 428	65.69 408	77.19 402	56.96 324	59.35 426	46.16 403	58.96 429	65.93 420

PVSNet_0	51.08 22	56.10 395	54.97 400	59.48 407	75.12 402	53.28 364	55.16 424	61.89 415	44.30 414	59.16 424	62.48 427	54.22 338	65.91 415	35.40 425	47.01 430	59.25 426

MVE	40.22 23	51.82 398	50.47 401	55.87 409	62.66 436	51.91 373	31.61 430	39.28 437	40.65 423	50.76 432	74.98 412	56.24 329	44.67 433	33.94 428	64.11 427	71.04 415
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 399	42.65 402	39.67 414	70.86 422	21.11 436	61.01 414	21.42 441	57.36 356	57.97 429	50.06 430	16.40 440	58.73 427	21.03 434	27.69 434	39.17 430

kuosan		30.83 400	32.17 403	26.83 416	53.36 438	19.02 439	57.90 421	20.44 442	38.29 429	38.01 433	37.82 432	15.18 441	33.45 435	7.74 436	20.76 435	28.03 431

test_method		30.46 401	29.60 404	33.06 415	17.99 440	3.84 443	13.62 431	73.92 355	2.79 434	18.29 436	53.41 429	28.53 428	43.25 434	22.56 431	35.27 432	52.11 429

cdsmvs_eth3d_5k		20.81 402	27.75 405	0.00 421	0.00 444	0.00 446	0.00 432	85.44 262	0.00 439	0.00 440	82.82 349	81.46 120	0.00 440	0.00 439	0.00 438	0.00 436

tmp_tt		20.25 403	24.50 406	7.49 418	4.47 441	8.70 442	34.17 429	25.16 439	1.00 436	32.43 435	18.49 433	39.37 404	9.21 437	21.64 432	43.75 431	4.57 433

ab-mvs-re		6.65 404	8.87 407	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	79.80 377	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

pcd_1.5k_mvsjas		6.41 405	8.55 408	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	76.94 169	0.00 440	0.00 439	0.00 438	0.00 436

test123		6.27 406	8.08 409	0.84 419	1.11 443	0.57 444	62.90 409	0.82 443	0.54 437	1.07 439	2.75 438	1.26 442	0.30 438	1.04 437	1.26 437	1.66 434

testmvs		5.91 407	7.65 410	0.72 420	1.20 442	0.37 445	59.14 417	0.67 444	0.49 438	1.11 438	2.76 437	0.94 443	0.24 439	1.02 438	1.47 436	1.55 435

mmdepth		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

monomultidepth		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

test_blank		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uanet_test		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

DCPMVS		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

sosnet-low-res		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

sosnet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uncertanet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

Regformer		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

uanet		0.00 408	0.00 411	0.00 421	0.00 444	0.00 446	0.00 432	0.00 445	0.00 439	0.00 440	0.00 439	0.00 444	0.00 440	0.00 439	0.00 438	0.00 436

WAC-MVS						37.39 424								52.61 374

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

MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 150					96.05 9	87.45 25	98.17 35	92.40 185

PC_three_1452									58.96 343	90.06 97	91.33 189	80.66 130	93.03 143	75.78 178	95.94 128	92.48 179

No_MVS				88.81 71	91.55 129	77.99 94		91.01 150					96.05 9	87.45 25	98.17 35	92.40 185

test_one_0601					93.85 62	73.27 141		94.11 38	86.57 30	93.47 41	94.64 64	88.42 28

eth-test2					0.00 444

eth-test					0.00 444

ZD-MVS					92.22 103	80.48 71		91.85 124	71.22 218	90.38 92	92.98 131	86.06 64	96.11 7	81.99 103	96.75 92

IU-MVS					94.18 50	72.64 148		90.82 155	56.98 360	89.67 109				85.78 58	97.92 49	93.28 143

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 193	81.12 124	94.68 76	74.48 190	95.35 148	92.29 192

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 51	97.82 54	92.04 204

test_241102_ONE					94.18 50	72.65 146		93.69 56	83.62 54	94.11 26	93.78 108	90.28 14	95.50 49

save fliter					93.75 63	77.44 103	86.31 135	89.72 190	70.80 222

test_0728_THIRD									85.33 38	93.75 34	94.65 61	87.44 46	95.78 32	87.41 27	98.21 32	92.98 158

test_0728_SECOND				86.79 102	94.25 48	72.45 156	90.54 52	94.10 39					95.88 18	86.42 41	97.97 46	92.02 205

test0726					94.16 53	72.56 152	90.63 49	93.90 48	83.61 55	93.75 34	94.49 69	89.76 18

GSMVS																83.88 349

test_part2					93.86 61	77.77 98				92.84 51

sam_mvs1												46.11 370				83.88 349

sam_mvs												45.92 375

ambc				82.98 202	90.55 156	64.86 242	88.20 100	89.15 202		89.40 118	93.96 99	71.67 239	91.38 188	78.83 137	96.55 97	92.71 168

MTGPA								91.81 128

test_post1							78.85 289				3.13 435	45.19 385	80.13 358	58.11 339

test_post											3.10 436	45.43 381	77.22 373

patchmatchnet-post											81.71 361	45.93 374	87.01 284

GG-mvs-BLEND				67.16 381	73.36 411	46.54 400	84.15 179	55.04 428		58.64 427	61.95 428	29.93 425	83.87 335	38.71 420	76.92 412	71.07 414

MTMP							90.66 48	33.14 438

gm-plane-assit					75.42 400	44.97 407			52.17 386		72.36 417		87.90 274	54.10 363

test9_res														80.83 113	96.45 103	90.57 249

TEST9					92.34 98	79.70 78	83.94 184	90.32 172	65.41 289	84.49 228	90.97 202	82.03 111	93.63 115

test_8					92.09 107	78.87 85	83.82 189	90.31 174	65.79 280	84.36 232	90.96 204	81.93 113	93.44 128

agg_prior2														79.68 126	96.16 115	90.22 257

agg_prior					91.58 127	77.69 100		90.30 175		84.32 234			93.18 136

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 229	96.14 116	94.16 101

test_prior4						78.97 84	84.59 169

test_prior2							83.37 203		75.43 155	84.58 225	91.57 183	81.92 115		79.54 130	96.97 85

test_prior				86.32 110	90.59 155	71.99 164		92.85 93					94.17 97			92.80 163

旧先验2							81.73 245		56.88 361	86.54 187			84.90 322	72.81 218

新几何2							81.72 246

新几何1				82.95 204	93.96 59	78.56 88		80.24 315	55.45 366	83.93 245	91.08 199	71.19 241	88.33 268	65.84 281	93.07 223	81.95 380

旧先验1					91.97 111	71.77 165		81.78 304			91.84 172	73.92 205			93.65 211	83.61 355

无先验							82.81 221	85.62 260	58.09 349				91.41 187	67.95 266		84.48 340

原ACMM2							82.26 239

原ACMM1				84.60 152	92.81 89	74.01 133		91.50 133	62.59 305	82.73 268	90.67 219	76.53 176	94.25 91	69.24 247	95.69 141	85.55 328

test222					93.31 73	76.54 113	79.38 278	77.79 326	52.59 383	82.36 272	90.84 211	66.83 264			91.69 257	81.25 388

testdata2													86.43 298	63.52 303

segment_acmp												81.94 112

testdata				79.54 266	92.87 84	72.34 157		80.14 316	59.91 339	85.47 208	91.75 179	67.96 259	85.24 318	68.57 261	92.18 245	81.06 393

testdata1							79.62 273		73.95 171

test12				86.57 105	90.74 151	72.63 150		90.69 158		82.76 267		79.20 141	94.80 73		95.32 150	92.27 194

plane_prior7					93.45 68	77.31 106

plane_prior6					92.61 90	76.54 113						74.84 191

plane_prior5								93.61 59					95.22 59	80.78 114	95.83 134	94.46 85

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 126	86.55 182

plane_prior2							89.45 82		79.44 101

plane_prior1					92.83 88

plane_prior						76.42 116	87.15 117		75.94 146						95.03 162

n2								0.00 445

nn								0.00 445

door-mid								74.45 352

lessismore_v0				85.95 121	91.10 144	70.99 178		70.91 382		91.79 69	94.42 74	61.76 291	92.93 146	79.52 131	93.03 224	93.93 110

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 61	98.73 7	95.23 61

test11								91.46 134

door								72.57 368

HQP5-MVS						70.66 180

HQP-NCC					91.19 139		84.77 162		73.30 185	80.55 302

ACMP_Plane					91.19 139		84.77 162		73.30 185	80.55 302

BP-MVS														77.30 161

HQP4-MVS										80.56 301			94.61 79			93.56 135

HQP3-MVS								92.68 98							94.47 184

HQP2-MVS												72.10 231

NP-MVS					91.95 112	74.55 130					90.17 234

MDTV_nov1_ep13_2view						27.60 435	70.76 378		46.47 408	61.27 420		45.20 384		49.18 389		83.75 354

ACMMP++_ref															95.74 140

ACMMP++															97.35 75

Test By Simon												79.09 142

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 175	81.56 76	90.02 99	91.20 195	82.40 99	90.81 207	73.58 206	94.66 179	94.56 81

DeepMVS_CX				24.13 417	32.95 439	29.49 433		21.63 440	12.07 433	37.95 434	45.07 431	30.84 423	19.21 436	17.94 435	33.06 433	23.69 432

High-resolution multi-view benchmark