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 34	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 54	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 43	97.23 2	95.32 2	99.01 2	97.26 6	80.16 130	98.99 1	95.15 1	99.14 2	96.47 30

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 47	96.29 18	88.16 33	94.17 92	86.07 45	98.48 18	97.22 17

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 21	93.73 61	85.72 31	96.79 1	95.51 9	88.86 13	95.63 9	96.99 10	84.81 69	93.16 132	91.10 2	97.53 69	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
HPM-MVS_fast		92.50 5	92.54 6	92.37 6	95.93 16	85.81 30	92.99 12	94.23 24	85.21 37	92.51 55	95.13 44	90.65 9	95.34 52	88.06 9	98.15 34	95.95 40

SR-MVS-dyc-post		92.41 6	92.41 7	92.39 5	94.13 52	88.95 6	92.87 13	94.16 29	88.75 15	93.79 29	94.43 68	88.83 24	95.51 44	87.16 29	97.60 63	92.73 157

SR-MVS		92.23 7	92.34 8	91.91 16	94.89 38	87.85 9	92.51 23	93.87 48	88.20 20	93.24 39	94.02 90	90.15 16	95.67 35	86.82 33	97.34 73	92.19 188

HPM-MVS		92.13 8	92.20 10	91.91 16	95.58 26	84.67 43	93.51 8	94.85 15	82.88 61	91.77 67	93.94 98	90.55 12	95.73 33	88.50 7	98.23 28	95.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 9	92.24 9	91.48 22	93.02 77	85.17 36	92.47 25	95.05 14	87.65 24	93.21 40	94.39 73	90.09 17	95.08 63	86.67 35	97.60 63	94.18 94

COLMAP_ROB	83.01 3	91.97 10	91.95 11	92.04 11	93.68 62	86.15 21	93.37 10	95.10 13	90.28 10	92.11 60	95.03 46	89.75 20	94.93 67	79.95 110	98.27 26	95.04 63
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 11	91.87 16	92.03 12	95.53 27	85.91 25	93.35 11	94.16 29	82.52 64	92.39 58	94.14 85	89.15 23	95.62 36	87.35 24	98.24 27	94.56 75
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 12	91.47 23	92.37 6	96.04 13	88.48 8	92.72 17	92.60 97	83.09 58	91.54 69	94.25 79	87.67 41	95.51 44	87.21 28	98.11 35	93.12 145

CP-MVS		91.67 13	91.58 20	91.96 13	95.29 31	87.62 10	93.38 9	93.36 62	83.16 57	91.06 79	94.00 91	88.26 30	95.71 34	87.28 27	98.39 21	92.55 167

XVS		91.54 14	91.36 25	92.08 9	95.64 24	86.25 19	92.64 18	93.33 64	85.07 38	89.99 97	94.03 89	86.57 52	95.80 26	87.35 24	97.62 61	94.20 91

MTAPA		91.52 15	91.60 19	91.29 27	96.59 4	86.29 18	92.02 30	91.81 122	84.07 46	92.00 63	94.40 72	86.63 51	95.28 55	88.59 6	98.31 24	92.30 181

UA-Net		91.49 16	91.53 21	91.39 24	94.98 35	82.95 55	93.52 7	92.79 91	88.22 19	88.53 130	97.64 3	83.45 83	94.55 80	86.02 48	98.60 13	96.67 25

ACMMPR		91.49 16	91.35 27	91.92 15	95.74 20	85.88 27	92.58 21	93.25 70	81.99 67	91.40 71	94.17 84	87.51 42	95.87 20	87.74 13	97.76 54	93.99 101

LPG-MVS_test		91.47 18	91.68 17	90.82 34	94.75 41	81.69 60	90.00 59	94.27 21	82.35 65	93.67 34	94.82 52	91.18 4	95.52 42	85.36 52	98.73 7	95.23 58

region2R		91.44 19	91.30 31	91.87 18	95.75 19	85.90 26	92.63 20	93.30 68	81.91 69	90.88 85	94.21 80	87.75 39	95.87 20	87.60 18	97.71 57	93.83 110

HFP-MVS		91.30 20	91.39 24	91.02 30	95.43 29	84.66 44	92.58 21	93.29 69	81.99 67	91.47 70	93.96 95	88.35 29	95.56 39	87.74 13	97.74 56	92.85 154

ZNCC-MVS		91.26 21	91.34 28	91.01 31	95.73 21	83.05 53	92.18 28	94.22 26	80.14 89	91.29 75	93.97 92	87.93 38	95.87 20	88.65 5	97.96 45	94.12 98

APDe-MVS		91.22 22	91.92 12	89.14 63	92.97 79	78.04 90	92.84 15	94.14 33	83.33 55	93.90 25	95.73 29	88.77 25	96.41 3	87.60 18	97.98 42	92.98 151
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 23	90.95 40	91.93 14	95.67 23	85.85 28	90.00 59	93.90 45	80.32 86	91.74 68	94.41 71	88.17 32	95.98 13	86.37 38	97.99 40	93.96 103

SteuartSystems-ACMMP		91.16 24	91.36 25	90.55 38	93.91 57	80.97 67	91.49 37	93.48 60	82.82 62	92.60 54	93.97 92	88.19 31	96.29 6	87.61 17	98.20 31	94.39 86
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 25	90.91 41	91.83 19	96.18 11	86.88 14	92.20 27	93.03 83	82.59 63	88.52 131	94.37 74	86.74 50	95.41 50	86.32 39	98.21 29	93.19 141
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 26	91.01 37	90.82 34	95.45 28	82.73 56	91.75 35	93.74 51	80.98 80	91.38 72	93.80 102	87.20 46	95.80 26	87.10 31	97.69 58	93.93 104

MP-MVS-pluss		90.81 27	91.08 34	89.99 47	95.97 14	79.88 72	88.13 99	94.51 18	75.79 143	92.94 44	94.96 47	88.36 28	95.01 65	90.70 3	98.40 20	95.09 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 28	91.50 22	88.44 75	93.00 78	76.26 116	89.65 72	95.55 8	87.72 23	93.89 27	94.94 48	91.62 3	93.44 123	78.35 127	98.76 4	95.61 47

ACMMP_NAP		90.65 29	91.07 36	89.42 58	95.93 16	79.54 77	89.95 63	93.68 55	77.65 122	91.97 64	94.89 49	88.38 27	95.45 48	89.27 4	97.87 50	93.27 137

ACMM	79.39 9	90.65 29	90.99 38	89.63 54	95.03 34	83.53 48	89.62 73	93.35 63	79.20 102	93.83 28	93.60 112	90.81 7	92.96 139	85.02 56	98.45 19	92.41 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 31	90.34 48	91.38 25	89.03 182	84.23 46	93.58 6	94.68 17	90.65 8	90.33 91	93.95 97	84.50 71	95.37 51	80.87 100	95.50 141	94.53 78

ACMP	79.16 10	90.54 32	90.60 46	90.35 42	94.36 44	80.98 66	89.16 83	94.05 38	79.03 105	92.87 46	93.74 107	90.60 11	95.21 58	82.87 78	98.76 4	94.87 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 33	91.08 34	88.88 66	93.38 68	78.65 84	89.15 84	94.05 38	84.68 42	93.90 25	94.11 87	88.13 34	96.30 5	84.51 62	97.81 52	91.70 204
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 34	91.64 18	86.93 96	94.18 47	72.65 141	90.47 52	93.69 53	83.77 49	94.11 23	94.27 75	90.28 14	95.84 24	86.03 46	97.92 46	92.29 182

SMA-MVS		90.31 35	90.48 47	89.83 51	95.31 30	79.52 78	90.98 44	93.24 71	75.37 150	92.84 48	95.28 40	85.58 64	96.09 8	87.92 11	97.76 54	93.88 107
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 36	90.80 43	88.68 73	92.86 83	77.09 105	91.19 41	95.74 6	81.38 75	92.28 59	93.80 102	86.89 49	94.64 75	85.52 51	97.51 70	94.30 90

v7n		90.13 37	90.96 39	87.65 88	91.95 109	71.06 169	89.99 61	93.05 80	86.53 28	94.29 19	96.27 19	82.69 90	94.08 95	86.25 42	97.63 60	97.82 8

PMVS	80.48 6	90.08 38	90.66 45	88.34 78	96.71 3	92.97 2	90.31 56	89.57 187	88.51 18	90.11 93	95.12 45	90.98 6	88.92 249	77.55 141	97.07 80	83.13 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 39	91.09 33	87.00 94	91.55 126	72.64 143	96.19 2	94.10 36	85.33 35	93.49 36	94.64 60	81.12 119	95.88 18	87.41 22	95.94 124	92.48 170

DVP-MVS		90.06 40	91.32 29	86.29 108	94.16 50	72.56 147	90.54 49	91.01 143	83.61 52	93.75 31	94.65 57	89.76 18	95.78 30	86.42 36	97.97 43	90.55 235
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 40	91.92 12	84.47 146	96.56 6	58.83 305	89.04 85	92.74 93	91.40 6	96.12 5	96.06 25	87.23 45	95.57 38	79.42 118	98.74 6	99.00 2

PEN-MVS		90.03 42	91.88 15	84.48 145	96.57 5	58.88 302	88.95 86	93.19 72	91.62 5	96.01 7	96.16 23	87.02 47	95.60 37	78.69 124	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 43	89.74 53	90.83 33	93.16 75	80.37 69	91.91 33	93.11 76	81.10 78	95.32 11	97.24 7	72.94 210	94.85 69	85.07 54	97.78 53	97.26 15

DTE-MVSNet		89.98 44	91.91 14	84.21 155	96.51 7	57.84 313	88.93 87	92.84 90	91.92 4	96.16 4	96.23 20	86.95 48	95.99 12	79.05 121	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 44	89.84 51	90.41 40	94.91 37	84.50 45	89.49 78	93.98 40	79.68 94	92.09 61	93.89 100	83.80 78	93.10 135	82.67 82	98.04 36	93.64 122

3Dnovator+	83.92 2	89.97 46	89.66 54	90.92 32	91.27 135	81.66 63	91.25 39	94.13 34	88.89 12	88.83 123	94.26 78	77.55 152	95.86 23	84.88 57	95.87 128	95.24 57

WR-MVS_H		89.91 47	91.31 30	85.71 123	96.32 9	62.39 258	89.54 76	93.31 67	90.21 11	95.57 10	95.66 31	81.42 116	95.90 17	80.94 99	98.80 3	98.84 5

OPM-MVS		89.80 48	89.97 49	89.27 60	94.76 40	79.86 73	86.76 124	92.78 92	78.78 108	92.51 55	93.64 111	88.13 34	93.84 104	84.83 59	97.55 66	94.10 99
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 49	89.27 60	91.30 26	93.51 64	84.79 41	89.89 65	90.63 153	70.00 223	94.55 16	96.67 13	87.94 37	93.59 115	84.27 64	95.97 120	95.52 48

anonymousdsp		89.73 50	88.88 67	92.27 8	89.82 168	86.67 15	90.51 51	90.20 171	69.87 224	95.06 12	96.14 24	84.28 74	93.07 136	87.68 15	96.34 103	97.09 19

test_djsdf		89.62 51	89.01 64	91.45 23	92.36 94	82.98 54	91.98 31	90.08 174	71.54 203	94.28 21	96.54 15	81.57 114	94.27 84	86.26 40	96.49 97	97.09 19

XVG-OURS-SEG-HR		89.59 52	89.37 58	90.28 43	94.47 43	85.95 24	86.84 120	93.91 44	80.07 90	86.75 169	93.26 117	93.64 2	90.93 195	84.60 61	90.75 265	93.97 102

APD-MVS		89.54 53	89.63 55	89.26 61	92.57 88	81.34 65	90.19 58	93.08 79	80.87 82	91.13 77	93.19 118	86.22 59	95.97 14	82.23 88	97.18 78	90.45 237
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 54	88.81 70	91.19 29	93.38 68	84.72 42	89.70 68	90.29 168	69.27 227	94.39 17	96.38 17	86.02 62	93.52 119	83.96 66	95.92 126	95.34 52

CPTT-MVS		89.39 55	88.98 66	90.63 37	95.09 33	86.95 13	92.09 29	92.30 105	79.74 93	87.50 154	92.38 148	81.42 116	93.28 128	83.07 74	97.24 76	91.67 205

ACMH	76.49 14	89.34 56	91.14 32	83.96 160	92.50 91	70.36 175	89.55 74	93.84 49	81.89 70	94.70 14	95.44 36	90.69 8	88.31 259	83.33 70	98.30 25	93.20 140
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 57	89.12 61	89.84 49	88.67 192	85.64 32	90.61 47	93.17 73	86.02 31	93.12 41	95.30 38	84.94 66	89.44 241	74.12 180	96.10 115	94.45 81

APD_test2		89.30 57	89.12 61	89.84 49	88.67 192	85.64 32	90.61 47	93.17 73	86.02 31	93.12 41	95.30 38	84.94 66	89.44 241	74.12 180	96.10 115	94.45 81

CP-MVSNet		89.27 59	90.91 41	84.37 147	96.34 8	58.61 308	88.66 94	92.06 111	90.78 7	95.67 8	95.17 43	81.80 112	95.54 41	79.00 122	98.69 10	98.95 4

XVG-OURS		89.18 60	88.83 69	90.23 44	94.28 45	86.11 23	85.91 137	93.60 58	80.16 88	89.13 120	93.44 114	83.82 77	90.98 193	83.86 68	95.30 149	93.60 125

DeepC-MVS	82.31 4	89.15 61	89.08 63	89.37 59	93.64 63	79.07 80	88.54 95	94.20 27	73.53 169	89.71 104	94.82 52	85.09 65	95.77 32	84.17 65	98.03 38	93.26 138
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 62	90.72 44	84.31 153	97.00 2	64.33 233	89.67 71	88.38 201	88.84 14	94.29 19	97.57 4	90.48 13	91.26 184	72.57 205	97.65 59	97.34 14

MSP-MVS		89.08 63	88.16 75	91.83 19	95.76 18	86.14 22	92.75 16	93.90 45	78.43 113	89.16 118	92.25 155	72.03 224	96.36 4	88.21 8	90.93 258	92.98 151
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 64	89.88 50	86.22 111	91.63 120	77.07 106	89.82 66	93.77 50	78.90 106	92.88 45	92.29 153	86.11 60	90.22 217	86.24 43	97.24 76	91.36 212
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 65	88.45 73	90.38 41	94.92 36	85.85 28	89.70 68	91.27 136	78.20 115	86.69 172	92.28 154	80.36 128	95.06 64	86.17 44	96.49 97	90.22 241

test_0402		88.65 66	89.58 57	85.88 119	92.55 89	72.22 155	84.01 172	89.44 189	88.63 17	94.38 18	95.77 28	86.38 58	93.59 115	79.84 111	95.21 150	91.82 200

DP-MVS		88.60 67	89.01 64	87.36 90	91.30 133	77.50 98	87.55 106	92.97 86	87.95 22	89.62 108	92.87 133	84.56 70	93.89 101	77.65 139	96.62 92	90.70 229

APD_test1		88.40 68	87.91 77	89.88 48	89.50 172	86.65 17	89.98 62	91.91 117	84.26 44	90.87 86	93.92 99	82.18 103	89.29 245	73.75 188	94.81 169	93.70 118

Anonymous20231211		88.40 68	89.62 56	84.73 139	90.46 154	65.27 223	88.86 88	93.02 84	87.15 25	93.05 43	97.10 8	82.28 102	92.02 165	76.70 151	97.99 40	96.88 23

PS-MVSNAJss		88.31 70	87.90 78	89.56 56	93.31 70	77.96 93	87.94 102	91.97 114	70.73 214	94.19 22	96.67 13	76.94 162	94.57 78	83.07 74	96.28 105	96.15 32

OMC-MVS		88.19 71	87.52 82	90.19 45	91.94 111	81.68 62	87.49 109	93.17 73	76.02 137	88.64 127	91.22 181	84.24 75	93.37 126	77.97 137	97.03 81	95.52 48

CS-MVS		88.14 72	87.67 81	89.54 57	89.56 170	79.18 79	90.47 52	94.77 16	79.37 100	84.32 222	89.33 232	83.87 76	94.53 81	82.45 84	94.89 165	94.90 64

TSAR-MVS + MP.		88.14 72	87.82 79	89.09 64	95.72 22	76.74 109	92.49 24	91.19 139	67.85 247	86.63 173	94.84 51	79.58 135	95.96 15	87.62 16	94.50 177	94.56 75
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 74	89.79 52	82.98 189	93.26 72	63.94 237	91.10 42	89.64 184	85.07 38	90.91 83	91.09 186	89.16 22	91.87 170	82.03 89	95.87 128	93.13 143

EC-MVSNet		88.01 75	88.32 74	87.09 92	89.28 177	72.03 157	90.31 56	96.31 4	80.88 81	85.12 203	89.67 228	84.47 72	95.46 47	82.56 83	96.26 108	93.77 116

RPSCF		88.00 76	86.93 94	91.22 28	90.08 161	89.30 5	89.68 70	91.11 140	79.26 101	89.68 105	94.81 55	82.44 94	87.74 263	76.54 153	88.74 292	96.61 27

AllTest		87.97 77	87.40 86	89.68 52	91.59 121	83.40 49	89.50 77	95.44 10	79.47 96	88.00 145	93.03 125	82.66 91	91.47 177	70.81 214	96.14 112	94.16 95

TranMVSNet+NR-MVSNet		87.86 78	88.76 71	85.18 131	94.02 55	64.13 234	84.38 166	91.29 135	84.88 41	92.06 62	93.84 101	86.45 55	93.73 106	73.22 196	98.66 11	97.69 9

nrg030		87.85 79	88.49 72	85.91 117	90.07 163	69.73 179	87.86 103	94.20 27	74.04 161	92.70 53	94.66 56	85.88 63	91.50 176	79.72 113	97.32 74	96.50 29

CNVR-MVS		87.81 80	87.68 80	88.21 80	92.87 81	77.30 104	85.25 149	91.23 137	77.31 127	87.07 163	91.47 175	82.94 88	94.71 72	84.67 60	96.27 107	92.62 164

HQP_MVS		87.75 81	87.43 85	88.70 72	93.45 65	76.42 113	89.45 79	93.61 56	79.44 98	86.55 174	92.95 130	74.84 183	95.22 56	80.78 102	95.83 130	94.46 79

MM		87.64 82	87.15 87	89.09 64	89.51 171	76.39 115	88.68 93	86.76 229	84.54 43	83.58 238	93.78 104	73.36 206	96.48 2	87.98 10	96.21 109	94.41 85

MVSMamba_PlusPlus		87.53 83	88.86 68	83.54 176	92.03 107	62.26 262	91.49 37	92.62 96	88.07 21	88.07 142	96.17 22	72.24 219	95.79 29	84.85 58	94.16 188	92.58 165

NCCC		87.36 84	86.87 95	88.83 67	92.32 97	78.84 83	86.58 128	91.09 141	78.77 109	84.85 211	90.89 195	80.85 122	95.29 53	81.14 97	95.32 146	92.34 179

DeepPCF-MVS	81.24 5	87.28 85	86.21 105	90.49 39	91.48 130	84.90 39	83.41 190	92.38 102	70.25 220	89.35 116	90.68 204	82.85 89	94.57 78	79.55 115	95.95 123	92.00 195

SixPastTwentyTwo		87.20 86	87.45 84	86.45 105	92.52 90	69.19 188	87.84 104	88.05 208	81.66 72	94.64 15	96.53 16	65.94 254	94.75 71	83.02 76	96.83 86	95.41 50

CS-MVS-test		87.00 87	86.43 101	88.71 71	89.46 173	77.46 99	89.42 81	95.73 7	77.87 120	81.64 273	87.25 269	82.43 95	94.53 81	77.65 139	96.46 99	94.14 97

UniMVSNet (Re)		86.87 88	86.98 93	86.55 103	93.11 76	68.48 193	83.80 181	92.87 88	80.37 84	89.61 110	91.81 166	77.72 149	94.18 90	75.00 173	98.53 16	96.99 22

Vis-MVSNet		86.86 89	86.58 98	87.72 86	92.09 104	77.43 101	87.35 110	92.09 110	78.87 107	84.27 227	94.05 88	78.35 143	93.65 108	80.54 106	91.58 246	92.08 192
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 114	92.86 83	67.02 207	82.55 216	91.56 125	83.08 59	90.92 81	91.82 165	78.25 144	93.99 97	74.16 178	98.35 22	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 112	93.24 73	67.02 207	83.16 199	92.21 106	81.73 71	90.92 81	91.97 159	77.20 156	93.99 97	74.16 178	98.35 22	97.61 10

casdiffmvs_mvg		86.72 92	87.51 83	84.36 149	87.09 233	65.22 224	84.16 168	94.23 24	77.89 118	91.28 76	93.66 110	84.35 73	92.71 145	80.07 107	94.87 168	95.16 60
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 93	86.52 99	87.18 91	85.94 260	78.30 86	86.93 117	92.20 107	65.94 259	89.16 118	93.16 120	83.10 86	89.89 230	87.81 12	94.43 180	93.35 132

IS-MVSNet		86.66 94	86.82 97	86.17 114	92.05 106	66.87 210	91.21 40	88.64 198	86.30 30	89.60 111	92.59 141	69.22 238	94.91 68	73.89 185	97.89 49	96.72 24

v10		86.54 95	87.10 89	84.84 135	88.16 206	63.28 244	86.64 127	92.20 107	75.42 149	92.81 50	94.50 64	74.05 194	94.06 96	83.88 67	96.28 105	97.17 18

pmmvs6		86.52 96	88.06 76	81.90 210	92.22 100	62.28 261	84.66 159	89.15 192	83.54 54	89.85 101	97.32 5	88.08 36	86.80 278	70.43 222	97.30 75	96.62 26

PHI-MVS		86.38 97	85.81 114	88.08 81	88.44 200	77.34 102	89.35 82	93.05 80	73.15 182	84.76 212	87.70 259	78.87 139	94.18 90	80.67 104	96.29 104	92.73 157

CSCG		86.26 98	86.47 100	85.60 125	90.87 146	74.26 127	87.98 101	91.85 118	80.35 85	89.54 114	88.01 251	79.09 137	92.13 161	75.51 166	95.06 157	90.41 238

DeepC-MVS_fast	80.27 8	86.23 99	85.65 118	87.96 84	91.30 133	76.92 107	87.19 112	91.99 113	70.56 215	84.96 207	90.69 203	80.01 132	95.14 61	78.37 126	95.78 134	91.82 200
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 100	86.83 96	84.36 149	87.82 212	62.35 260	86.42 131	91.33 134	76.78 131	92.73 52	94.48 66	73.41 203	93.72 107	83.10 73	95.41 142	97.01 21

Anonymous20240529		86.20 101	87.13 88	83.42 178	90.19 159	64.55 231	84.55 161	90.71 150	85.85 33	89.94 100	95.24 42	82.13 104	90.40 213	69.19 234	96.40 102	95.31 54

test_fmvsmconf0.1_n		86.18 102	85.88 112	87.08 93	85.26 269	78.25 87	85.82 140	91.82 120	65.33 272	88.55 129	92.35 152	82.62 93	89.80 232	86.87 32	94.32 183	93.18 142

CDPH-MVS		86.17 103	85.54 119	88.05 83	92.25 98	75.45 121	83.85 178	92.01 112	65.91 261	86.19 183	91.75 169	83.77 79	94.98 66	77.43 144	96.71 90	93.73 117

NR-MVSNet		86.00 104	86.22 104	85.34 129	93.24 73	64.56 230	82.21 228	90.46 157	80.99 79	88.42 134	91.97 159	77.56 151	93.85 102	72.46 206	98.65 12	97.61 10

train_agg		85.98 105	85.28 125	88.07 82	92.34 95	79.70 75	83.94 174	90.32 163	65.79 262	84.49 216	90.97 190	81.93 108	93.63 110	81.21 96	96.54 95	90.88 223

FC-MVSNet-test		85.93 106	87.05 91	82.58 200	92.25 98	56.44 324	85.75 141	93.09 78	77.33 126	91.94 65	94.65 57	74.78 185	93.41 125	75.11 172	98.58 14	97.88 7

test_fmvsmconf_n		85.88 107	85.51 120	86.99 95	84.77 277	78.21 88	85.40 148	91.39 132	65.32 273	87.72 150	91.81 166	82.33 98	89.78 233	86.68 34	94.20 186	92.99 150

Effi-MVS+-dtu		85.82 108	83.38 159	93.14 4	87.13 229	91.15 3	87.70 105	88.42 200	74.57 157	83.56 239	85.65 292	78.49 142	94.21 88	72.04 208	92.88 219	94.05 100

TAPA-MVS	77.73 12	85.71 109	84.83 131	88.37 77	88.78 191	79.72 74	87.15 114	93.50 59	69.17 228	85.80 192	89.56 229	80.76 123	92.13 161	73.21 201	95.51 140	93.25 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 110	86.14 106	83.58 172	87.97 208	67.13 204	87.55 106	94.32 19	73.44 172	88.47 132	87.54 262	86.45 55	91.06 191	75.76 164	93.76 197	92.54 168

canonicalmvs		85.50 110	86.14 106	83.58 172	87.97 208	67.13 204	87.55 106	94.32 19	73.44 172	88.47 132	87.54 262	86.45 55	91.06 191	75.76 164	93.76 197	92.54 168

EPP-MVSNet		85.47 112	85.04 128	86.77 100	91.52 129	69.37 183	91.63 36	87.98 210	81.51 74	87.05 164	91.83 164	66.18 253	95.29 53	70.75 217	96.89 83	95.64 45

GeoE		85.45 113	85.81 114	84.37 147	90.08 161	67.07 206	85.86 139	91.39 132	72.33 196	87.59 152	90.25 216	84.85 68	92.37 155	78.00 135	91.94 239	93.66 119

MVS_0304		85.37 114	84.58 138	87.75 85	85.28 268	73.36 132	86.54 130	85.71 244	77.56 125	81.78 271	92.47 146	70.29 232	96.02 11	85.59 50	95.96 121	93.87 108

FIs		85.35 115	86.27 103	82.60 199	91.86 113	57.31 317	85.10 153	93.05 80	75.83 142	91.02 80	93.97 92	73.57 199	92.91 143	73.97 184	98.02 39	97.58 12

test_fmvsmvis_n_1920		85.22 116	85.36 124	84.81 136	85.80 262	76.13 119	85.15 152	92.32 104	61.40 302	91.33 73	90.85 198	83.76 80	86.16 291	84.31 63	93.28 209	92.15 190

casdiffmvs		85.21 117	85.85 113	83.31 181	86.17 255	62.77 251	83.03 201	93.93 43	74.69 156	88.21 139	92.68 140	82.29 101	91.89 169	77.87 138	93.75 200	95.27 56
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		85.20 118	85.93 110	83.02 187	86.30 250	62.37 259	84.55 161	93.96 41	74.48 158	87.12 158	92.03 158	82.30 100	91.94 166	78.39 125	94.21 185	94.74 72

K. test v3		85.14 119	84.73 132	86.37 106	91.13 140	69.63 181	85.45 146	76.68 322	84.06 47	92.44 57	96.99 10	62.03 276	94.65 74	80.58 105	93.24 210	94.83 71

EI-MVSNet-Vis-set		85.12 120	84.53 141	86.88 97	84.01 290	72.76 140	83.91 177	85.18 253	80.44 83	88.75 124	85.49 295	80.08 131	91.92 167	82.02 90	90.85 263	95.97 38

MGCFI-Net		85.04 121	85.95 109	82.31 206	87.52 221	63.59 240	86.23 135	93.96 41	73.46 170	88.07 142	87.83 257	86.46 54	90.87 200	76.17 159	93.89 195	92.47 172

EI-MVSNet-UG-set		85.04 121	84.44 143	86.85 98	83.87 294	72.52 149	83.82 179	85.15 254	80.27 87	88.75 124	85.45 297	79.95 133	91.90 168	81.92 93	90.80 264	96.13 33

X-MVStestdata		85.04 121	82.70 172	92.08 9	95.64 24	86.25 19	92.64 18	93.33 64	85.07 38	89.99 97	16.05 414	86.57 52	95.80 26	87.35 24	97.62 61	94.20 91

MSLP-MVS++		85.00 124	86.03 108	81.90 210	91.84 116	71.56 166	86.75 125	93.02 84	75.95 140	87.12 158	89.39 230	77.98 145	89.40 244	77.46 142	94.78 170	84.75 318

F-COLMAP		84.97 125	83.42 158	89.63 54	92.39 93	83.40 49	88.83 89	91.92 116	73.19 181	80.18 295	89.15 236	77.04 160	93.28 128	65.82 266	92.28 230	92.21 187

balanced_conf03		84.80 126	85.40 122	83.00 188	88.95 185	61.44 269	90.42 55	92.37 103	71.48 205	88.72 126	93.13 121	70.16 234	95.15 60	79.26 120	94.11 189	92.41 174

3Dnovator	80.37 7	84.80 126	84.71 135	85.06 133	86.36 248	74.71 124	88.77 91	90.00 176	75.65 145	84.96 207	93.17 119	74.06 193	91.19 186	78.28 129	91.09 252	89.29 260

IterMVS-LS		84.73 128	84.98 129	83.96 160	87.35 224	63.66 238	83.25 195	89.88 179	76.06 135	89.62 108	92.37 151	73.40 205	92.52 150	78.16 132	94.77 172	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 129	84.34 147	85.49 128	90.18 160	75.86 120	79.23 269	87.13 220	73.35 174	85.56 197	89.34 231	83.60 82	90.50 211	76.64 152	94.05 192	90.09 247

HQP-MVS		84.61 130	84.06 150	86.27 109	91.19 136	70.66 171	84.77 154	92.68 94	73.30 177	80.55 287	90.17 220	72.10 220	94.61 76	77.30 146	94.47 178	93.56 128

v1192		84.57 131	84.69 136	84.21 155	87.75 214	62.88 248	83.02 202	91.43 129	69.08 230	89.98 99	90.89 195	72.70 214	93.62 113	82.41 85	94.97 162	96.13 33

FMVSNet1		84.55 132	85.45 121	81.85 212	90.27 158	61.05 276	86.83 121	88.27 205	78.57 112	89.66 107	95.64 32	75.43 176	90.68 206	69.09 235	95.33 145	93.82 111

v1144		84.54 133	84.72 134	84.00 158	87.67 217	62.55 255	82.97 204	90.93 146	70.32 219	89.80 102	90.99 189	73.50 200	93.48 121	81.69 95	94.65 175	95.97 38

Gipuma		84.44 134	86.33 102	78.78 258	84.20 288	73.57 131	89.55 74	90.44 158	84.24 45	84.38 219	94.89 49	76.35 173	80.40 341	76.14 160	96.80 88	82.36 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 135	83.93 153	85.63 124	91.59 121	71.58 164	83.52 187	92.13 109	61.82 295	83.96 232	89.75 227	79.93 134	93.46 122	78.33 128	94.34 182	91.87 199

VDDNet		84.35 136	85.39 123	81.25 223	95.13 32	59.32 295	85.42 147	81.11 293	86.41 29	87.41 155	96.21 21	73.61 198	90.61 209	66.33 259	96.85 84	93.81 114

ETV-MVS		84.31 137	83.91 154	85.52 126	88.58 196	70.40 174	84.50 165	93.37 61	78.76 110	84.07 230	78.72 369	80.39 127	95.13 62	73.82 187	92.98 217	91.04 218

v1240		84.30 138	84.51 142	83.65 169	87.65 218	61.26 273	82.85 208	91.54 126	67.94 245	90.68 88	90.65 207	71.71 226	93.64 109	82.84 79	94.78 170	96.07 35

MVS_111021_LR		84.28 139	83.76 155	85.83 121	89.23 179	83.07 52	80.99 244	83.56 274	72.71 189	86.07 186	89.07 237	81.75 113	86.19 290	77.11 148	93.36 205	88.24 274

h-mvs33		84.25 140	82.76 171	88.72 70	91.82 118	82.60 57	84.00 173	84.98 260	71.27 206	86.70 170	90.55 209	63.04 273	93.92 100	78.26 130	94.20 186	89.63 252

v144192		84.24 141	84.41 144	83.71 168	87.59 220	61.57 268	82.95 205	91.03 142	67.82 248	89.80 102	90.49 210	73.28 207	93.51 120	81.88 94	94.89 165	96.04 37

dcpmvs_2		84.23 142	85.14 126	81.50 220	88.61 195	61.98 266	82.90 207	93.11 76	68.66 236	92.77 51	92.39 147	78.50 141	87.63 265	76.99 150	92.30 227	94.90 64

v1921920		84.23 142	84.37 146	83.79 164	87.64 219	61.71 267	82.91 206	91.20 138	67.94 245	90.06 94	90.34 213	72.04 223	93.59 115	82.32 86	94.91 163	96.07 35

VDD-MVS		84.23 142	84.58 138	83.20 184	91.17 139	65.16 226	83.25 195	84.97 261	79.79 92	87.18 157	94.27 75	74.77 186	90.89 198	69.24 231	96.54 95	93.55 130

v2v482		84.09 145	84.24 148	83.62 170	87.13 229	61.40 270	82.71 211	89.71 182	72.19 199	89.55 112	91.41 176	70.70 231	93.20 130	81.02 98	93.76 197	96.25 31

EG-PatchMatch MVS		84.08 146	84.11 149	83.98 159	92.22 100	72.61 146	82.20 230	87.02 225	72.63 190	88.86 121	91.02 188	78.52 140	91.11 189	73.41 193	91.09 252	88.21 275

DP-MVS Recon		84.05 147	83.22 161	86.52 104	91.73 119	75.27 122	83.23 197	92.40 100	72.04 200	82.04 262	88.33 247	77.91 147	93.95 99	66.17 260	95.12 155	90.34 240

TransMVSNet (Re)		84.02 148	85.74 116	78.85 257	91.00 143	55.20 336	82.29 224	87.26 216	79.65 95	88.38 136	95.52 35	83.00 87	86.88 276	67.97 249	96.60 93	94.45 81

Baseline_NR-MVSNet		84.00 149	85.90 111	78.29 269	91.47 131	53.44 346	82.29 224	87.00 228	79.06 104	89.55 112	95.72 30	77.20 156	86.14 292	72.30 207	98.51 17	95.28 55

TSAR-MVS + GP.		83.95 150	82.69 173	87.72 86	89.27 178	81.45 64	83.72 183	81.58 292	74.73 155	85.66 193	86.06 287	72.56 216	92.69 147	75.44 168	95.21 150	89.01 268

alignmvs		83.94 151	83.98 152	83.80 163	87.80 213	67.88 200	84.54 163	91.42 131	73.27 180	88.41 135	87.96 252	72.33 217	90.83 201	76.02 162	94.11 189	92.69 161

Effi-MVS+		83.90 152	84.01 151	83.57 174	87.22 227	65.61 222	86.55 129	92.40 100	78.64 111	81.34 278	84.18 316	83.65 81	92.93 141	74.22 177	87.87 306	92.17 189

mvs5depth		83.82 153	84.54 140	81.68 217	82.23 315	68.65 192	86.89 118	89.90 178	80.02 91	87.74 149	97.86 2	64.19 263	82.02 329	76.37 155	95.63 139	94.35 87

CANet		83.79 154	82.85 170	86.63 101	86.17 255	72.21 156	83.76 182	91.43 129	77.24 128	74.39 346	87.45 265	75.36 177	95.42 49	77.03 149	92.83 220	92.25 186

pm-mvs1		83.69 155	84.95 130	79.91 244	90.04 165	59.66 292	82.43 220	87.44 213	75.52 147	87.85 147	95.26 41	81.25 118	85.65 302	68.74 241	96.04 117	94.42 84

AdaColmap		83.66 156	83.69 156	83.57 174	90.05 164	72.26 154	86.29 133	90.00 176	78.19 116	81.65 272	87.16 271	83.40 84	94.24 87	61.69 301	94.76 173	84.21 328

MIMVSNet1		83.63 157	84.59 137	80.74 232	94.06 54	62.77 251	82.72 210	84.53 267	77.57 124	90.34 90	95.92 27	76.88 168	85.83 300	61.88 299	97.42 71	93.62 123

test_fmvsm_n_1920		83.60 158	82.89 169	85.74 122	85.22 270	77.74 96	84.12 170	90.48 156	59.87 321	86.45 182	91.12 185	75.65 174	85.89 298	82.28 87	90.87 261	93.58 126

WR-MVS		83.56 159	84.40 145	81.06 228	93.43 67	54.88 337	78.67 277	85.02 258	81.24 76	90.74 87	91.56 173	72.85 211	91.08 190	68.00 248	98.04 36	97.23 16

CNLPA		83.55 160	83.10 166	84.90 134	89.34 176	83.87 47	84.54 163	88.77 195	79.09 103	83.54 240	88.66 244	74.87 182	81.73 331	66.84 254	92.29 229	89.11 262

LCM-MVSNet-Re		83.48 161	85.06 127	78.75 259	85.94 260	55.75 330	80.05 253	94.27 21	76.47 132	96.09 6	94.54 63	83.31 85	89.75 236	59.95 312	94.89 165	90.75 226

hse-mvs2		83.47 162	81.81 186	88.47 74	91.03 142	82.27 58	82.61 212	83.69 272	71.27 206	86.70 170	86.05 288	63.04 273	92.41 153	78.26 130	93.62 204	90.71 228

V42		83.47 162	83.37 160	83.75 166	83.16 309	63.33 243	81.31 238	90.23 170	69.51 226	90.91 83	90.81 200	74.16 192	92.29 159	80.06 108	90.22 272	95.62 46

VPA-MVSNet		83.47 162	84.73 132	79.69 248	90.29 157	57.52 316	81.30 240	88.69 197	76.29 133	87.58 153	94.44 67	80.60 126	87.20 270	66.60 257	96.82 87	94.34 88

PAPM_NR		83.23 165	83.19 163	83.33 180	90.90 145	65.98 218	88.19 98	90.78 149	78.13 117	80.87 283	87.92 255	73.49 202	92.42 152	70.07 224	88.40 295	91.60 207

CLD-MVS		83.18 166	82.64 174	84.79 137	89.05 181	67.82 201	77.93 285	92.52 98	68.33 238	85.07 204	81.54 345	82.06 105	92.96 139	69.35 230	97.91 48	93.57 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 167	85.68 117	75.65 303	81.24 327	45.26 389	79.94 255	92.91 87	83.83 48	91.33 73	96.88 12	80.25 129	85.92 295	68.89 238	95.89 127	95.76 42

FA-MVS(test-final)		83.13 168	83.02 167	83.43 177	86.16 257	66.08 217	88.00 100	88.36 202	75.55 146	85.02 205	92.75 138	65.12 258	92.50 151	74.94 174	91.30 250	91.72 202

114514_t		83.10 169	82.54 177	84.77 138	92.90 80	69.10 190	86.65 126	90.62 154	54.66 353	81.46 275	90.81 200	76.98 161	94.38 83	72.62 204	96.18 110	90.82 225

RRT-MVS		82.97 170	83.44 157	81.57 219	85.06 272	58.04 311	87.20 111	90.37 161	77.88 119	88.59 128	93.70 109	63.17 270	93.05 137	76.49 154	88.47 294	93.62 123

UGNet		82.78 171	81.64 188	86.21 112	86.20 254	76.24 117	86.86 119	85.68 245	77.07 129	73.76 350	92.82 134	69.64 235	91.82 172	69.04 237	93.69 201	90.56 234
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		82.75 172	81.93 184	85.19 130	82.08 316	80.15 71	85.53 144	88.76 196	68.01 242	85.58 196	87.75 258	71.80 225	86.85 277	74.02 183	93.87 196	88.58 271

EI-MVSNet		82.61 173	82.42 179	83.20 184	83.25 306	63.66 238	83.50 188	85.07 255	76.06 135	86.55 174	85.10 303	73.41 203	90.25 214	78.15 134	90.67 267	95.68 44

QAPM		82.59 174	82.59 176	82.58 200	86.44 243	66.69 211	89.94 64	90.36 162	67.97 244	84.94 209	92.58 143	72.71 213	92.18 160	70.63 220	87.73 308	88.85 269

fmvsm_s_conf0.1_n_a		82.58 175	81.93 184	84.50 144	87.68 216	73.35 133	86.14 136	77.70 311	61.64 300	85.02 205	91.62 171	77.75 148	86.24 287	82.79 80	87.07 315	93.91 106

Fast-Effi-MVS+-dtu		82.54 176	81.41 196	85.90 118	85.60 263	76.53 112	83.07 200	89.62 186	73.02 184	79.11 305	83.51 321	80.74 124	90.24 216	68.76 240	89.29 283	90.94 221

MVS_Test		82.47 177	83.22 161	80.22 241	82.62 314	57.75 315	82.54 217	91.96 115	71.16 210	82.89 250	92.52 145	77.41 153	90.50 211	80.04 109	87.84 307	92.40 176

v148		82.31 178	82.48 178	81.81 215	85.59 264	59.66 292	81.47 237	86.02 240	72.85 185	88.05 144	90.65 207	70.73 230	90.91 197	75.15 171	91.79 240	94.87 66

API-MVS		82.28 179	82.61 175	81.30 222	86.29 251	69.79 177	88.71 92	87.67 212	78.42 114	82.15 261	84.15 317	77.98 145	91.59 175	65.39 269	92.75 221	82.51 355

MVSFormer		82.23 180	81.57 193	84.19 157	85.54 265	69.26 185	91.98 31	90.08 174	71.54 203	76.23 327	85.07 306	58.69 298	94.27 84	86.26 40	88.77 290	89.03 266

fmvsm_s_conf0.5_n_a		82.21 181	81.51 195	84.32 152	86.56 241	73.35 133	85.46 145	77.30 315	61.81 296	84.51 215	90.88 197	77.36 154	86.21 289	82.72 81	86.97 320	93.38 131

EIA-MVS		82.19 182	81.23 201	85.10 132	87.95 210	69.17 189	83.22 198	93.33 64	70.42 216	78.58 309	79.77 361	77.29 155	94.20 89	71.51 210	88.96 288	91.93 198

fmvsm_s_conf0.1_n		82.17 183	81.59 191	83.94 162	86.87 239	71.57 165	85.19 151	77.42 314	62.27 294	84.47 218	91.33 178	76.43 170	85.91 296	83.14 71	87.14 313	94.33 89

PCF-MVS	74.62 15	82.15 184	80.92 205	85.84 120	89.43 174	72.30 153	80.53 248	91.82 120	57.36 337	87.81 148	89.92 224	77.67 150	93.63 110	58.69 317	95.08 156	91.58 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 185	80.31 212	87.45 89	90.86 147	80.29 70	85.88 138	90.65 152	68.17 241	76.32 326	86.33 282	73.12 209	92.61 149	61.40 304	90.02 275	89.44 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 186	81.54 194	83.60 171	83.94 291	73.90 129	83.35 192	86.10 236	58.97 323	83.80 234	90.36 212	74.23 191	86.94 275	82.90 77	90.22 272	89.94 249

GBi-Net		82.02 187	82.07 181	81.85 212	86.38 245	61.05 276	86.83 121	88.27 205	72.43 191	86.00 187	95.64 32	63.78 266	90.68 206	65.95 262	93.34 206	93.82 111

test1		82.02 187	82.07 181	81.85 212	86.38 245	61.05 276	86.83 121	88.27 205	72.43 191	86.00 187	95.64 32	63.78 266	90.68 206	65.95 262	93.34 206	93.82 111

OpenMVS	76.72 13	81.98 189	82.00 183	81.93 209	84.42 283	68.22 195	88.50 96	89.48 188	66.92 254	81.80 269	91.86 161	72.59 215	90.16 219	71.19 213	91.25 251	87.40 290

KD-MVS_self_test		81.93 190	83.14 165	78.30 268	84.75 278	52.75 350	80.37 250	89.42 190	70.24 221	90.26 92	93.39 115	74.55 190	86.77 279	68.61 243	96.64 91	95.38 51

fmvsm_s_conf0.5_n		81.91 191	81.30 198	83.75 166	86.02 259	71.56 166	84.73 157	77.11 318	62.44 291	84.00 231	90.68 204	76.42 171	85.89 298	83.14 71	87.11 314	93.81 114

SDMVSNet		81.90 192	83.17 164	78.10 272	88.81 189	62.45 257	76.08 317	86.05 239	73.67 166	83.41 241	93.04 123	82.35 97	80.65 338	70.06 225	95.03 158	91.21 214

tfpnnormal		81.79 193	82.95 168	78.31 267	88.93 186	55.40 332	80.83 247	82.85 280	76.81 130	85.90 191	94.14 85	74.58 189	86.51 283	66.82 255	95.68 138	93.01 149

c3_l		81.64 194	81.59 191	81.79 216	80.86 333	59.15 299	78.61 278	90.18 172	68.36 237	87.20 156	87.11 273	69.39 236	91.62 174	78.16 132	94.43 180	94.60 74

PVSNet_Blended_VisFu		81.55 195	80.49 210	84.70 141	91.58 124	73.24 137	84.21 167	91.67 124	62.86 285	80.94 281	87.16 271	67.27 247	92.87 144	69.82 227	88.94 289	87.99 281

fmvsm_l_conf0.5_n_a		81.46 196	80.87 206	83.25 182	83.73 296	73.21 138	83.00 203	85.59 247	58.22 329	82.96 249	90.09 222	72.30 218	86.65 281	81.97 92	89.95 276	89.88 250

DELS-MVS		81.44 197	81.25 199	82.03 208	84.27 287	62.87 249	76.47 311	92.49 99	70.97 212	81.64 273	83.83 318	75.03 180	92.70 146	74.29 176	92.22 233	90.51 236
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		81.31 198	81.61 190	80.41 238	86.38 245	58.75 306	83.93 176	86.58 231	72.43 191	87.65 151	92.98 127	63.78 266	90.22 217	66.86 252	93.92 194	92.27 184

TinyColmap		81.25 199	82.34 180	77.99 275	85.33 267	60.68 283	82.32 223	88.33 203	71.26 208	86.97 165	92.22 157	77.10 159	86.98 274	62.37 293	95.17 152	86.31 301

AUN-MVS		81.18 200	78.78 233	88.39 76	90.93 144	82.14 59	82.51 218	83.67 273	64.69 277	80.29 291	85.91 291	51.07 337	92.38 154	76.29 158	93.63 203	90.65 232

tttt0517		81.07 201	79.58 224	85.52 126	88.99 184	66.45 214	87.03 116	75.51 330	73.76 165	88.32 138	90.20 217	37.96 392	94.16 94	79.36 119	95.13 153	95.93 41

Fast-Effi-MVS+		81.04 202	80.57 207	82.46 204	87.50 222	63.22 245	78.37 281	89.63 185	68.01 242	81.87 265	82.08 339	82.31 99	92.65 148	67.10 251	88.30 301	91.51 210

BH-untuned		80.96 203	80.99 203	80.84 231	88.55 197	68.23 194	80.33 251	88.46 199	72.79 188	86.55 174	86.76 277	74.72 187	91.77 173	61.79 300	88.99 287	82.52 354

eth_miper_zixun_eth		80.84 204	80.22 216	82.71 197	81.41 325	60.98 279	77.81 287	90.14 173	67.31 252	86.95 166	87.24 270	64.26 261	92.31 157	75.23 170	91.61 244	94.85 70

xiu_mvs_v1_base_debu		80.84 204	80.14 218	82.93 192	88.31 201	71.73 160	79.53 260	87.17 217	65.43 268	79.59 297	82.73 333	76.94 162	90.14 222	73.22 196	88.33 297	86.90 295

xiu_mvs_v1_base		80.84 204	80.14 218	82.93 192	88.31 201	71.73 160	79.53 260	87.17 217	65.43 268	79.59 297	82.73 333	76.94 162	90.14 222	73.22 196	88.33 297	86.90 295

xiu_mvs_v1_base_debi		80.84 204	80.14 218	82.93 192	88.31 201	71.73 160	79.53 260	87.17 217	65.43 268	79.59 297	82.73 333	76.94 162	90.14 222	73.22 196	88.33 297	86.90 295

IterMVS-SCA-FT		80.64 208	79.41 225	84.34 151	83.93 292	69.66 180	76.28 313	81.09 294	72.43 191	86.47 180	90.19 218	60.46 283	93.15 133	77.45 143	86.39 326	90.22 241

BH-RMVSNet		80.53 209	80.22 216	81.49 221	87.19 228	66.21 216	77.79 288	86.23 234	74.21 160	83.69 235	88.50 245	73.25 208	90.75 203	63.18 290	87.90 305	87.52 288

Anonymous202405211		80.51 210	81.19 202	78.49 264	88.48 198	57.26 318	76.63 306	82.49 283	81.21 77	84.30 225	92.24 156	67.99 244	86.24 287	62.22 294	95.13 153	91.98 197

DIV-MVS_self_test		80.43 211	80.23 214	81.02 229	79.99 341	59.25 296	77.07 299	87.02 225	67.38 249	86.19 183	89.22 233	63.09 271	90.16 219	76.32 156	95.80 132	93.66 119

cl____		80.42 212	80.23 214	81.02 229	79.99 341	59.25 296	77.07 299	87.02 225	67.37 250	86.18 185	89.21 234	63.08 272	90.16 219	76.31 157	95.80 132	93.65 121

diffmvs		80.40 213	80.48 211	80.17 242	79.02 354	60.04 287	77.54 292	90.28 169	66.65 257	82.40 256	87.33 268	73.50 200	87.35 268	77.98 136	89.62 280	93.13 143
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		80.37 214	78.41 240	86.23 110	76.75 368	73.28 135	87.18 113	77.45 313	76.24 134	68.14 378	88.93 239	65.41 257	93.85 102	69.47 229	96.12 114	91.55 209
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 215	80.04 221	81.24 225	79.82 344	58.95 301	77.66 289	89.66 183	65.75 265	85.99 190	85.11 302	68.29 243	91.42 181	76.03 161	92.03 235	93.33 133

MG-MVS		80.32 216	80.94 204	78.47 265	88.18 204	52.62 353	82.29 224	85.01 259	72.01 201	79.24 304	92.54 144	69.36 237	93.36 127	70.65 219	89.19 286	89.45 254

mvsmamba		80.30 217	78.87 230	84.58 143	88.12 207	67.55 202	92.35 26	84.88 262	63.15 283	85.33 200	90.91 194	50.71 339	95.20 59	66.36 258	87.98 304	90.99 219

VPNet		80.25 218	81.68 187	75.94 301	92.46 92	47.98 376	76.70 304	81.67 290	73.45 171	84.87 210	92.82 134	74.66 188	86.51 283	61.66 302	96.85 84	93.33 133

MAR-MVS		80.24 219	78.74 235	84.73 139	86.87 239	78.18 89	85.75 141	87.81 211	65.67 267	77.84 314	78.50 370	73.79 197	90.53 210	61.59 303	90.87 261	85.49 311
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		80.20 220	79.00 229	83.78 165	88.17 205	86.66 16	81.31 238	66.81 384	69.64 225	88.33 137	90.19 218	64.58 259	83.63 321	71.99 209	90.03 274	81.06 373

Anonymous20240521		80.18 221	81.25 199	76.95 288	83.15 310	60.84 281	82.46 219	85.99 241	68.76 234	86.78 167	93.73 108	59.13 295	77.44 354	73.71 189	97.55 66	92.56 166

LFMVS		80.15 222	80.56 208	78.89 256	89.19 180	55.93 326	85.22 150	73.78 342	82.96 60	84.28 226	92.72 139	57.38 307	90.07 226	63.80 284	95.75 135	90.68 230

DPM-MVS		80.10 223	79.18 228	82.88 195	90.71 150	69.74 178	78.87 274	90.84 147	60.29 317	75.64 336	85.92 290	67.28 246	93.11 134	71.24 212	91.79 240	85.77 307

MSDG		80.06 224	79.99 223	80.25 240	83.91 293	68.04 199	77.51 293	89.19 191	77.65 122	81.94 263	83.45 323	76.37 172	86.31 286	63.31 289	86.59 323	86.41 299

FE-MVS		79.98 225	78.86 231	83.36 179	86.47 242	66.45 214	89.73 67	84.74 266	72.80 187	84.22 229	91.38 177	44.95 372	93.60 114	63.93 282	91.50 247	90.04 248

sd_testset		79.95 226	81.39 197	75.64 304	88.81 189	58.07 310	76.16 316	82.81 281	73.67 166	83.41 241	93.04 123	80.96 121	77.65 353	58.62 318	95.03 158	91.21 214

ab-mvs		79.67 227	80.56 208	76.99 287	88.48 198	56.93 320	84.70 158	86.06 238	68.95 232	80.78 284	93.08 122	75.30 178	84.62 310	56.78 327	90.90 259	89.43 256

VNet		79.31 228	80.27 213	76.44 295	87.92 211	53.95 342	75.58 323	84.35 268	74.39 159	82.23 259	90.72 202	72.84 212	84.39 313	60.38 310	93.98 193	90.97 220

thisisatest0530		79.07 229	77.33 249	84.26 154	87.13 229	64.58 229	83.66 185	75.95 325	68.86 233	85.22 202	87.36 267	38.10 389	93.57 118	75.47 167	94.28 184	94.62 73

cl22		78.97 230	78.21 242	81.24 225	77.74 358	59.01 300	77.46 295	87.13 220	65.79 262	84.32 222	85.10 303	58.96 297	90.88 199	75.36 169	92.03 235	93.84 109

patch_mono-2		78.89 231	79.39 226	77.41 284	84.78 276	68.11 197	75.60 321	83.11 277	60.96 310	79.36 301	89.89 225	75.18 179	72.97 366	73.32 195	92.30 227	91.15 216

RPMNet		78.88 232	78.28 241	80.68 235	79.58 345	62.64 253	82.58 214	94.16 29	74.80 154	75.72 334	92.59 141	48.69 346	95.56 39	73.48 192	82.91 362	83.85 333

PAPR		78.84 233	78.10 243	81.07 227	85.17 271	60.22 286	82.21 228	90.57 155	62.51 287	75.32 340	84.61 311	74.99 181	92.30 158	59.48 315	88.04 303	90.68 230

PVSNet_BlendedMVS		78.80 234	77.84 244	81.65 218	84.43 281	63.41 241	79.49 263	90.44 158	61.70 299	75.43 337	87.07 274	69.11 239	91.44 179	60.68 308	92.24 231	90.11 246

FMVSNet3		78.80 234	78.55 237	79.57 250	82.89 313	56.89 322	81.76 232	85.77 243	69.04 231	86.00 187	90.44 211	51.75 335	90.09 225	65.95 262	93.34 206	91.72 202

test_yl		78.71 236	78.51 238	79.32 253	84.32 285	58.84 303	78.38 279	85.33 250	75.99 138	82.49 254	86.57 278	58.01 301	90.02 228	62.74 291	92.73 222	89.10 263

DCV-MVSNet		78.71 236	78.51 238	79.32 253	84.32 285	58.84 303	78.38 279	85.33 250	75.99 138	82.49 254	86.57 278	58.01 301	90.02 228	62.74 291	92.73 222	89.10 263

test1111		78.53 238	78.85 232	77.56 281	92.22 100	47.49 378	82.61 212	69.24 373	72.43 191	85.28 201	94.20 81	51.91 333	90.07 226	65.36 270	96.45 100	95.11 61

ECVR-MVS		78.44 239	78.63 236	77.88 277	91.85 114	48.95 372	83.68 184	69.91 369	72.30 197	84.26 228	94.20 81	51.89 334	89.82 231	63.58 285	96.02 118	94.87 66

pmmvs-eth3d		78.42 240	77.04 252	82.57 202	87.44 223	74.41 126	80.86 246	79.67 302	55.68 346	84.69 213	90.31 215	60.91 281	85.42 303	62.20 295	91.59 245	87.88 284

mvs_anonymous		78.13 241	78.76 234	76.23 300	79.24 351	50.31 369	78.69 276	84.82 264	61.60 301	83.09 248	92.82 134	73.89 196	87.01 271	68.33 247	86.41 325	91.37 211

TAMVS		78.08 242	76.36 258	83.23 183	90.62 151	72.87 139	79.08 270	80.01 301	61.72 298	81.35 277	86.92 276	63.96 265	88.78 253	50.61 365	93.01 216	88.04 280

miper_enhance_ethall		77.83 243	76.93 253	80.51 236	76.15 374	58.01 312	75.47 325	88.82 194	58.05 331	83.59 237	80.69 349	64.41 260	91.20 185	73.16 202	92.03 235	92.33 180

Vis-MVSNet (Re-imp)		77.82 244	77.79 245	77.92 276	88.82 188	51.29 363	83.28 193	71.97 357	74.04 161	82.23 259	89.78 226	57.38 307	89.41 243	57.22 326	95.41 142	93.05 147

CANet_DTU		77.81 245	77.05 251	80.09 243	81.37 326	59.90 290	83.26 194	88.29 204	69.16 229	67.83 381	83.72 319	60.93 280	89.47 238	69.22 233	89.70 279	90.88 223

OpenMVS_ROB	70.19 17	77.77 246	77.46 246	78.71 260	84.39 284	61.15 274	81.18 242	82.52 282	62.45 290	83.34 243	87.37 266	66.20 252	88.66 255	64.69 277	85.02 342	86.32 300

SSC-MVS		77.55 247	81.64 188	65.29 372	90.46 154	20.33 418	73.56 341	68.28 375	85.44 34	88.18 141	94.64 60	70.93 229	81.33 333	71.25 211	92.03 235	94.20 91

MDA-MVSNet-bldmvs		77.47 248	76.90 254	79.16 255	79.03 353	64.59 228	66.58 382	75.67 328	73.15 182	88.86 121	88.99 238	66.94 248	81.23 334	64.71 276	88.22 302	91.64 206

jason		77.42 249	75.75 264	82.43 205	87.10 232	69.27 184	77.99 284	81.94 288	51.47 372	77.84 314	85.07 306	60.32 285	89.00 247	70.74 218	89.27 285	89.03 266
jason: jason.
CDS-MVSNet		77.32 250	75.40 267	83.06 186	89.00 183	72.48 150	77.90 286	82.17 286	60.81 311	78.94 306	83.49 322	59.30 293	88.76 254	54.64 345	92.37 226	87.93 283
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 251	76.75 255	78.52 263	87.01 235	61.30 272	75.55 324	87.12 223	61.24 307	74.45 345	78.79 368	77.20 156	90.93 195	64.62 279	84.80 349	83.32 342

MVSTER		77.09 252	75.70 265	81.25 223	75.27 382	61.08 275	77.49 294	85.07 255	60.78 312	86.55 174	88.68 242	43.14 381	90.25 214	73.69 190	90.67 267	92.42 173

PS-MVSNAJ		77.04 253	76.53 257	78.56 262	87.09 233	61.40 270	75.26 326	87.13 220	61.25 306	74.38 347	77.22 381	76.94 162	90.94 194	64.63 278	84.83 348	83.35 341

IterMVS		76.91 254	76.34 259	78.64 261	80.91 331	64.03 235	76.30 312	79.03 305	64.88 276	83.11 246	89.16 235	59.90 289	84.46 311	68.61 243	85.15 340	87.42 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 255	75.67 266	80.34 239	80.48 339	62.16 265	73.50 342	84.80 265	57.61 335	82.24 258	87.54 262	51.31 336	87.65 264	70.40 223	93.19 212	91.23 213

CL-MVSNet_self_test		76.81 256	77.38 248	75.12 307	86.90 237	51.34 361	73.20 345	80.63 298	68.30 239	81.80 269	88.40 246	66.92 249	80.90 335	55.35 339	94.90 164	93.12 145

TR-MVS		76.77 257	75.79 263	79.72 247	86.10 258	65.79 220	77.14 297	83.02 278	65.20 274	81.40 276	82.10 337	66.30 251	90.73 205	55.57 336	85.27 336	82.65 349

MonoMVSNet		76.66 258	77.26 250	74.86 309	79.86 343	54.34 339	86.26 134	86.08 237	71.08 211	85.59 195	88.68 242	53.95 325	85.93 294	63.86 283	80.02 377	84.32 324

USDC		76.63 259	76.73 256	76.34 297	83.46 299	57.20 319	80.02 254	88.04 209	52.14 368	83.65 236	91.25 180	63.24 269	86.65 281	54.66 344	94.11 189	85.17 313

BH-w/o		76.57 260	76.07 262	78.10 272	86.88 238	65.92 219	77.63 290	86.33 232	65.69 266	80.89 282	79.95 358	68.97 241	90.74 204	53.01 355	85.25 337	77.62 384

Patchmtry		76.56 261	77.46 246	73.83 315	79.37 350	46.60 382	82.41 221	76.90 319	73.81 164	85.56 197	92.38 148	48.07 349	83.98 318	63.36 288	95.31 148	90.92 222

PVSNet_Blended		76.49 262	75.40 267	79.76 246	84.43 281	63.41 241	75.14 327	90.44 158	57.36 337	75.43 337	78.30 371	69.11 239	91.44 179	60.68 308	87.70 309	84.42 323

miper_lstm_enhance		76.45 263	76.10 261	77.51 282	76.72 369	60.97 280	64.69 386	85.04 257	63.98 280	83.20 245	88.22 248	56.67 311	78.79 350	73.22 196	93.12 213	92.78 156

lupinMVS		76.37 264	74.46 276	82.09 207	85.54 265	69.26 185	76.79 302	80.77 297	50.68 379	76.23 327	82.82 331	58.69 298	88.94 248	69.85 226	88.77 290	88.07 277

cascas		76.29 265	74.81 272	80.72 234	84.47 280	62.94 247	73.89 339	87.34 214	55.94 344	75.16 342	76.53 386	63.97 264	91.16 187	65.00 273	90.97 257	88.06 279

WB-MVS		76.06 266	80.01 222	64.19 375	89.96 167	20.58 417	72.18 350	68.19 376	83.21 56	86.46 181	93.49 113	70.19 233	78.97 348	65.96 261	90.46 271	93.02 148

thres600view7		75.97 267	75.35 269	77.85 279	87.01 235	51.84 359	80.45 249	73.26 347	75.20 151	83.10 247	86.31 284	45.54 363	89.05 246	55.03 342	92.24 231	92.66 162

GA-MVS		75.83 268	74.61 273	79.48 252	81.87 318	59.25 296	73.42 343	82.88 279	68.68 235	79.75 296	81.80 342	50.62 340	89.46 239	66.85 253	85.64 333	89.72 251

MVP-Stereo		75.81 269	73.51 285	82.71 197	89.35 175	73.62 130	80.06 252	85.20 252	60.30 316	73.96 348	87.94 253	57.89 305	89.45 240	52.02 359	74.87 395	85.06 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 270	75.20 270	77.27 285	75.01 385	69.47 182	78.93 271	84.88 262	46.67 386	87.08 162	87.84 256	50.44 342	71.62 371	77.42 145	88.53 293	90.72 227

thres100view900		75.45 271	75.05 271	76.66 294	87.27 225	51.88 358	81.07 243	73.26 347	75.68 144	83.25 244	86.37 281	45.54 363	88.80 250	51.98 360	90.99 254	89.31 258

ET-MVSNet_ETH3D		75.28 272	72.77 293	82.81 196	83.03 312	68.11 197	77.09 298	76.51 323	60.67 314	77.60 319	80.52 353	38.04 390	91.15 188	70.78 216	90.68 266	89.17 261

thres400		75.14 273	74.23 278	77.86 278	86.24 252	52.12 355	79.24 267	73.87 340	73.34 175	81.82 267	84.60 312	46.02 357	88.80 250	51.98 360	90.99 254	92.66 162

wuyk23d		75.13 274	79.30 227	62.63 378	75.56 378	75.18 123	80.89 245	73.10 349	75.06 153	94.76 13	95.32 37	87.73 40	52.85 409	34.16 408	97.11 79	59.85 405

EU-MVSNet		75.12 275	74.43 277	77.18 286	83.11 311	59.48 294	85.71 143	82.43 284	39.76 406	85.64 194	88.76 240	44.71 374	87.88 262	73.86 186	85.88 332	84.16 329

HyFIR lowres test		75.12 275	72.66 295	82.50 203	91.44 132	65.19 225	72.47 348	87.31 215	46.79 385	80.29 291	84.30 314	52.70 330	92.10 164	51.88 364	86.73 321	90.22 241

CMPMVS	59.41 20	75.12 275	73.57 283	79.77 245	75.84 377	67.22 203	81.21 241	82.18 285	50.78 377	76.50 323	87.66 260	55.20 321	82.99 324	62.17 297	90.64 270	89.09 265
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 278	72.98 291	80.73 233	84.95 273	71.71 163	76.23 314	77.59 312	52.83 362	77.73 318	86.38 280	56.35 314	84.97 307	57.72 325	87.05 316	85.51 310

tfpn200view9		74.86 279	74.23 278	76.74 293	86.24 252	52.12 355	79.24 267	73.87 340	73.34 175	81.82 267	84.60 312	46.02 357	88.80 250	51.98 360	90.99 254	89.31 258

1112_ss		74.82 280	73.74 281	78.04 274	89.57 169	60.04 287	76.49 310	87.09 224	54.31 354	73.66 351	79.80 359	60.25 286	86.76 280	58.37 319	84.15 353	87.32 291

EGC-MVSNET		74.79 281	69.99 322	89.19 62	94.89 38	87.00 12	91.89 34	86.28 233	1.09 415	2.23 417	95.98 26	81.87 111	89.48 237	79.76 112	95.96 121	91.10 217

ppachtmachnet_test		74.73 282	74.00 280	76.90 290	80.71 336	56.89 322	71.53 356	78.42 307	58.24 328	79.32 303	82.92 330	57.91 304	84.26 315	65.60 268	91.36 249	89.56 253

Patchmatch-RL test		74.48 283	73.68 282	76.89 291	84.83 275	66.54 212	72.29 349	69.16 374	57.70 333	86.76 168	86.33 282	45.79 362	82.59 325	69.63 228	90.65 269	81.54 364

PatchMatch-RL		74.48 283	73.22 288	78.27 270	87.70 215	85.26 35	75.92 319	70.09 367	64.34 278	76.09 330	81.25 347	65.87 255	78.07 352	53.86 347	83.82 355	71.48 393

XXY-MVS		74.44 285	76.19 260	69.21 349	84.61 279	52.43 354	71.70 353	77.18 317	60.73 313	80.60 285	90.96 192	75.44 175	69.35 378	56.13 332	88.33 297	85.86 306

test2506		74.12 286	73.39 286	76.28 298	91.85 114	44.20 392	84.06 171	48.20 413	72.30 197	81.90 264	94.20 81	27.22 413	89.77 234	64.81 275	96.02 118	94.87 66

CR-MVSNet		74.00 287	73.04 290	76.85 292	79.58 345	62.64 253	82.58 214	76.90 319	50.50 380	75.72 334	92.38 148	48.07 349	84.07 317	68.72 242	82.91 362	83.85 333

Test_1112_low_res		73.90 288	73.08 289	76.35 296	90.35 156	55.95 325	73.40 344	86.17 235	50.70 378	73.14 352	85.94 289	58.31 300	85.90 297	56.51 329	83.22 359	87.20 292

test20.03		73.75 289	74.59 275	71.22 336	81.11 329	51.12 365	70.15 366	72.10 356	70.42 216	80.28 293	91.50 174	64.21 262	74.72 365	46.96 384	94.58 176	87.82 286

test_fmvs2		73.57 290	72.80 292	75.90 302	72.74 398	68.84 191	77.07 299	84.32 269	45.14 392	82.89 250	84.22 315	48.37 347	70.36 375	73.40 194	87.03 317	88.52 272

SCA		73.32 291	72.57 297	75.58 305	81.62 322	55.86 328	78.89 273	71.37 362	61.73 297	74.93 343	83.42 324	60.46 283	87.01 271	58.11 323	82.63 367	83.88 330

baseline1		73.26 292	73.54 284	72.43 329	84.92 274	47.79 377	79.89 256	74.00 338	65.93 260	78.81 307	86.28 285	56.36 313	81.63 332	56.63 328	79.04 384	87.87 285

1314		73.22 293	72.56 298	75.20 306	80.41 340	57.84 313	81.64 235	85.36 249	51.68 371	73.10 353	76.65 385	61.45 278	85.19 305	63.54 286	79.21 382	82.59 350

MVS		73.21 294	72.59 296	75.06 308	80.97 330	60.81 282	81.64 235	85.92 242	46.03 390	71.68 360	77.54 376	68.47 242	89.77 234	55.70 335	85.39 334	74.60 390

HY-MVS	64.64 18	73.03 295	72.47 299	74.71 311	83.36 303	54.19 340	82.14 231	81.96 287	56.76 343	69.57 373	86.21 286	60.03 287	84.83 309	49.58 371	82.65 365	85.11 314

thisisatest0515		73.00 296	70.52 314	80.46 237	81.45 324	59.90 290	73.16 346	74.31 337	57.86 332	76.08 331	77.78 374	37.60 393	92.12 163	65.00 273	91.45 248	89.35 257

EPNet_dtu		72.87 297	71.33 309	77.49 283	77.72 359	60.55 284	82.35 222	75.79 326	66.49 258	58.39 408	81.06 348	53.68 326	85.98 293	53.55 350	92.97 218	85.95 304
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 298	71.41 308	76.28 298	83.25 306	60.34 285	83.50 188	79.02 306	37.77 410	76.33 325	85.10 303	49.60 345	87.41 267	70.54 221	77.54 390	81.08 371

CHOSEN 1792x2688		72.45 299	70.56 313	78.13 271	90.02 166	63.08 246	68.72 371	83.16 276	42.99 400	75.92 332	85.46 296	57.22 309	85.18 306	49.87 369	81.67 369	86.14 302

testgi		72.36 300	74.61 273	65.59 369	80.56 338	42.82 397	68.29 372	73.35 346	66.87 255	81.84 266	89.93 223	72.08 222	66.92 391	46.05 387	92.54 224	87.01 294

thres200		72.34 301	71.55 307	74.70 312	83.48 298	51.60 360	75.02 328	73.71 343	70.14 222	78.56 310	80.57 352	46.20 355	88.20 260	46.99 383	89.29 283	84.32 324

FPMVS		72.29 302	72.00 301	73.14 320	88.63 194	85.00 37	74.65 332	67.39 378	71.94 202	77.80 316	87.66 260	50.48 341	75.83 360	49.95 367	79.51 378	58.58 407

FMVSNet5		72.10 303	71.69 303	73.32 318	81.57 323	53.02 349	76.77 303	78.37 308	63.31 281	76.37 324	91.85 162	36.68 394	78.98 347	47.87 380	92.45 225	87.95 282

our_test_3		71.85 304	71.59 304	72.62 326	80.71 336	53.78 343	69.72 368	71.71 361	58.80 325	78.03 311	80.51 354	56.61 312	78.84 349	62.20 295	86.04 331	85.23 312

PAPM		71.77 305	70.06 320	76.92 289	86.39 244	53.97 341	76.62 307	86.62 230	53.44 358	63.97 398	84.73 310	57.79 306	92.34 156	39.65 399	81.33 373	84.45 322

m2depth		71.72 306	70.67 311	74.86 309	73.08 395	55.88 327	77.41 296	69.27 372	55.86 345	78.66 308	93.77 106	38.01 391	75.39 362	60.12 311	89.87 277	93.31 135

IB-MVS	62.13 19	71.64 307	68.97 332	79.66 249	80.80 335	62.26 262	73.94 338	76.90 319	63.27 282	68.63 377	76.79 383	33.83 398	91.84 171	59.28 316	87.26 311	84.88 316
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		71.63 308	72.30 300	69.62 346	76.47 371	52.70 352	70.03 367	80.97 295	59.18 322	79.36 301	88.21 249	60.50 282	69.12 379	58.33 321	77.62 389	87.04 293

testing3		71.53 309	70.79 310	73.77 316	88.89 187	41.86 399	76.60 309	59.12 403	72.83 186	80.97 279	82.08 339	19.80 419	87.33 269	65.12 272	91.68 243	92.13 191

test_vis3_rt		71.42 310	70.67 311	73.64 317	69.66 405	70.46 173	66.97 381	89.73 180	42.68 402	88.20 140	83.04 326	43.77 376	60.07 403	65.35 271	86.66 322	90.39 239

Anonymous20231206		71.38 311	71.88 302	69.88 343	86.31 249	54.37 338	70.39 364	74.62 333	52.57 364	76.73 322	88.76 240	59.94 288	72.06 368	44.35 391	93.23 211	83.23 344

test_vis1_n_1920		71.30 312	71.58 306	70.47 339	77.58 361	59.99 289	74.25 333	84.22 270	51.06 374	74.85 344	79.10 365	55.10 322	68.83 381	68.86 239	79.20 383	82.58 351

MIMVSNet		71.09 313	71.59 304	69.57 347	87.23 226	50.07 370	78.91 272	71.83 358	60.20 319	71.26 361	91.76 168	55.08 323	76.09 358	41.06 396	87.02 318	82.54 353

test_fmvs1_n		70.94 314	70.41 317	72.53 328	73.92 387	66.93 209	75.99 318	84.21 271	43.31 399	79.40 300	79.39 363	43.47 377	68.55 383	69.05 236	84.91 345	82.10 358

MS-PatchMatch		70.93 315	70.22 318	73.06 321	81.85 319	62.50 256	73.82 340	77.90 309	52.44 365	75.92 332	81.27 346	55.67 318	81.75 330	55.37 338	77.70 388	74.94 389

pmmvs5		70.73 316	70.07 319	72.72 324	77.03 366	52.73 351	74.14 334	75.65 329	50.36 381	72.17 358	85.37 300	55.42 320	80.67 337	52.86 356	87.59 310	84.77 317

PatchT		70.52 317	72.76 294	63.79 377	79.38 349	33.53 411	77.63 290	65.37 388	73.61 168	71.77 359	92.79 137	44.38 375	75.65 361	64.53 280	85.37 335	82.18 357

test_vis1_n		70.29 318	69.99 322	71.20 337	75.97 376	66.50 213	76.69 305	80.81 296	44.22 395	75.43 337	77.23 380	50.00 343	68.59 382	66.71 256	82.85 364	78.52 383

N_pmnet		70.20 319	68.80 334	74.38 313	80.91 331	84.81 40	59.12 398	76.45 324	55.06 349	75.31 341	82.36 336	55.74 317	54.82 408	47.02 382	87.24 312	83.52 337

tpmvs		70.16 320	69.56 325	71.96 332	74.71 386	48.13 374	79.63 258	75.45 331	65.02 275	70.26 369	81.88 341	45.34 368	85.68 301	58.34 320	75.39 394	82.08 359

new-patchmatchnet		70.10 321	73.37 287	60.29 385	81.23 328	16.95 420	59.54 396	74.62 333	62.93 284	80.97 279	87.93 254	62.83 275	71.90 369	55.24 340	95.01 161	92.00 195

YYNet1		70.06 322	70.44 315	68.90 351	73.76 389	53.42 347	58.99 399	67.20 380	58.42 327	87.10 160	85.39 299	59.82 290	67.32 388	59.79 313	83.50 358	85.96 303

MVStest1		70.05 323	69.26 326	72.41 330	58.62 417	55.59 331	76.61 308	65.58 386	53.44 358	89.28 117	93.32 116	22.91 417	71.44 373	74.08 182	89.52 281	90.21 245

MDA-MVSNet_test_wron		70.05 323	70.44 315	68.88 352	73.84 388	53.47 345	58.93 400	67.28 379	58.43 326	87.09 161	85.40 298	59.80 291	67.25 389	59.66 314	83.54 357	85.92 305

CostFormer		69.98 325	68.68 335	73.87 314	77.14 364	50.72 367	79.26 266	74.51 335	51.94 370	70.97 364	84.75 309	45.16 371	87.49 266	55.16 341	79.23 381	83.40 340

testing91		69.94 326	68.99 331	72.80 323	83.81 295	45.89 385	71.57 355	73.64 345	68.24 240	70.77 367	77.82 373	34.37 397	84.44 312	53.64 349	87.00 319	88.07 277

baseline2		69.77 327	66.89 344	78.41 266	79.51 347	58.09 309	76.23 314	69.57 370	57.50 336	64.82 396	77.45 378	46.02 357	88.44 256	53.08 352	77.83 386	88.70 270

PatchmatchNet		69.71 328	68.83 333	72.33 331	77.66 360	53.60 344	79.29 265	69.99 368	57.66 334	72.53 356	82.93 329	46.45 354	80.08 343	60.91 307	72.09 398	83.31 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 329	69.05 329	71.14 338	69.15 406	65.77 221	73.98 337	83.32 275	42.83 401	77.77 317	78.27 372	43.39 380	68.50 384	68.39 246	84.38 352	79.15 381

JIA-IIPM		69.41 330	66.64 348	77.70 280	73.19 392	71.24 168	75.67 320	65.56 387	70.42 216	65.18 392	92.97 129	33.64 400	83.06 322	53.52 351	69.61 404	78.79 382

Syy-MVS		69.40 331	70.03 321	67.49 361	81.72 320	38.94 404	71.00 358	61.99 394	61.38 303	70.81 365	72.36 397	61.37 279	79.30 345	64.50 281	85.18 338	84.22 326

testing99		69.27 332	68.15 339	72.63 325	83.29 304	45.45 387	71.15 357	71.08 363	67.34 251	70.43 368	77.77 375	32.24 402	84.35 314	53.72 348	86.33 327	88.10 276

UnsupCasMVSNet_bld		69.21 333	69.68 324	67.82 359	79.42 348	51.15 364	67.82 376	75.79 326	54.15 355	77.47 320	85.36 301	59.26 294	70.64 374	48.46 377	79.35 380	81.66 362

test_cas_vis1_n_1920		69.20 334	69.12 327	69.43 348	73.68 390	62.82 250	70.38 365	77.21 316	46.18 389	80.46 290	78.95 367	52.03 332	65.53 396	65.77 267	77.45 391	79.95 379

gg-mvs-nofinetune		68.96 335	69.11 328	68.52 357	76.12 375	45.32 388	83.59 186	55.88 408	86.68 26	64.62 397	97.01 9	30.36 405	83.97 319	44.78 390	82.94 361	76.26 386

WBMVS		68.76 336	68.43 336	69.75 345	83.29 304	40.30 402	67.36 378	72.21 355	57.09 340	77.05 321	85.53 294	33.68 399	80.51 339	48.79 375	90.90 259	88.45 273

WB-MVSnew		68.72 337	69.01 330	67.85 358	83.22 308	43.98 393	74.93 329	65.98 385	55.09 348	73.83 349	79.11 364	65.63 256	71.89 370	38.21 404	85.04 341	87.69 287

tpm2		68.45 338	66.83 345	73.30 319	78.93 355	48.50 373	79.76 257	71.76 359	47.50 384	69.92 371	83.60 320	42.07 383	88.40 257	48.44 378	79.51 378	83.01 347

tpm		67.95 339	68.08 340	67.55 360	78.74 356	43.53 395	75.60 321	67.10 383	54.92 350	72.23 357	88.10 250	42.87 382	75.97 359	52.21 358	80.95 376	83.15 345

WTY-MVS		67.91 340	68.35 337	66.58 366	80.82 334	48.12 375	65.96 383	72.60 350	53.67 357	71.20 362	81.68 344	58.97 296	69.06 380	48.57 376	81.67 369	82.55 352

testing11		67.38 341	65.93 349	71.73 334	83.37 302	46.60 382	70.95 360	69.40 371	62.47 289	66.14 385	76.66 384	31.22 403	84.10 316	49.10 373	84.10 354	84.49 320

test-LLR		67.21 342	66.74 346	68.63 355	76.45 372	55.21 334	67.89 373	67.14 381	62.43 292	65.08 393	72.39 395	43.41 378	69.37 376	61.00 305	84.89 346	81.31 366

testing222		66.93 343	65.30 355	71.81 333	83.38 301	45.83 386	72.06 351	67.50 377	64.12 279	69.68 372	76.37 387	27.34 412	83.00 323	38.88 400	88.38 296	86.62 298

sss		66.92 344	67.26 342	65.90 368	77.23 363	51.10 366	64.79 385	71.72 360	52.12 369	70.13 370	80.18 356	57.96 303	65.36 397	50.21 366	81.01 375	81.25 368

KD-MVS_2432*1600		66.87 345	65.81 351	70.04 341	67.50 407	47.49 378	62.56 390	79.16 303	61.21 308	77.98 312	80.61 350	25.29 415	82.48 326	53.02 353	84.92 343	80.16 377

miper_refine_blended		66.87 345	65.81 351	70.04 341	67.50 407	47.49 378	62.56 390	79.16 303	61.21 308	77.98 312	80.61 350	25.29 415	82.48 326	53.02 353	84.92 343	80.16 377

dmvs_re		66.81 347	66.98 343	66.28 367	76.87 367	58.68 307	71.66 354	72.24 353	60.29 317	69.52 374	73.53 394	52.38 331	64.40 399	44.90 389	81.44 372	75.76 387

tpm cat1		66.76 348	65.21 356	71.42 335	77.09 365	50.62 368	78.01 283	73.68 344	44.89 393	68.64 376	79.00 366	45.51 365	82.42 328	49.91 368	70.15 401	81.23 370

UWE-MVS		66.43 349	65.56 354	69.05 350	84.15 289	40.98 400	73.06 347	64.71 390	54.84 351	76.18 329	79.62 362	29.21 407	80.50 340	38.54 403	89.75 278	85.66 308

PVSNet	58.17 21	66.41 350	65.63 353	68.75 353	81.96 317	49.88 371	62.19 392	72.51 352	51.03 375	68.04 379	75.34 391	50.84 338	74.77 363	45.82 388	82.96 360	81.60 363

tpmrst		66.28 351	66.69 347	65.05 373	72.82 397	39.33 403	78.20 282	70.69 366	53.16 361	67.88 380	80.36 355	48.18 348	74.75 364	58.13 322	70.79 400	81.08 371

Patchmatch-test		65.91 352	67.38 341	61.48 383	75.51 379	43.21 396	68.84 370	63.79 392	62.48 288	72.80 355	83.42 324	44.89 373	59.52 405	48.27 379	86.45 324	81.70 361

ADS-MVSNet2		65.87 353	63.64 361	72.55 327	73.16 393	56.92 321	67.10 379	74.81 332	49.74 382	66.04 387	82.97 327	46.71 352	77.26 355	42.29 393	69.96 402	83.46 338

test_vis1_rt		65.64 354	64.09 358	70.31 340	66.09 411	70.20 176	61.16 393	81.60 291	38.65 407	72.87 354	69.66 400	52.84 328	60.04 404	56.16 331	77.77 387	80.68 375

mvsany_test3		65.48 355	62.97 364	73.03 322	69.99 404	76.17 118	64.83 384	43.71 415	43.68 397	80.25 294	87.05 275	52.83 329	63.09 402	51.92 363	72.44 397	79.84 380

test-mter		65.00 356	63.79 360	68.63 355	76.45 372	55.21 334	67.89 373	67.14 381	50.98 376	65.08 393	72.39 395	28.27 410	69.37 376	61.00 305	84.89 346	81.31 366

ETVMVS		64.67 357	63.34 363	68.64 354	83.44 300	41.89 398	69.56 369	61.70 399	61.33 305	68.74 375	75.76 389	28.76 408	79.35 344	34.65 407	86.16 330	84.67 319

myMVS_eth3d		64.66 358	63.89 359	66.97 364	81.72 320	37.39 407	71.00 358	61.99 394	61.38 303	70.81 365	72.36 397	20.96 418	79.30 345	49.59 370	85.18 338	84.22 326

test0.0.03 1		64.66 358	64.36 357	65.57 370	75.03 384	46.89 381	64.69 386	61.58 400	62.43 292	71.18 363	77.54 376	43.41 378	68.47 385	40.75 398	82.65 365	81.35 365

UBG		64.34 360	63.35 362	67.30 362	83.50 297	40.53 401	67.46 377	65.02 389	54.77 352	67.54 383	74.47 393	32.99 401	78.50 351	40.82 397	83.58 356	82.88 348

test_f		64.31 361	65.85 350	59.67 386	66.54 410	62.24 264	57.76 402	70.96 364	40.13 404	84.36 220	82.09 338	46.93 351	51.67 410	61.99 298	81.89 368	65.12 401

pmmvs3		62.47 362	60.02 375	69.80 344	71.58 401	64.00 236	70.52 363	58.44 406	39.77 405	66.05 386	75.84 388	27.10 414	72.28 367	46.15 386	84.77 350	73.11 391

EPMVS		62.47 362	62.63 366	62.01 379	70.63 403	38.74 405	74.76 330	52.86 410	53.91 356	67.71 382	80.01 357	39.40 387	66.60 392	55.54 337	68.81 406	80.68 375

ADS-MVSNet		61.90 364	62.19 368	61.03 384	73.16 393	36.42 409	67.10 379	61.75 397	49.74 382	66.04 387	82.97 327	46.71 352	63.21 400	42.29 393	69.96 402	83.46 338

PMMVS		61.65 365	60.38 372	65.47 371	65.40 414	69.26 185	63.97 388	61.73 398	36.80 411	60.11 403	68.43 402	59.42 292	66.35 393	48.97 374	78.57 385	60.81 404

E-PMN		61.59 366	61.62 369	61.49 382	66.81 409	55.40 332	53.77 405	60.34 402	66.80 256	58.90 406	65.50 405	40.48 386	66.12 394	55.72 334	86.25 328	62.95 403

TESTMET0.1,1		61.29 367	60.32 373	64.19 375	72.06 399	51.30 362	67.89 373	62.09 393	45.27 391	60.65 402	69.01 401	27.93 411	64.74 398	56.31 330	81.65 371	76.53 385

MVS-HIRNet		61.16 368	62.92 365	55.87 389	79.09 352	35.34 410	71.83 352	57.98 407	46.56 387	59.05 405	91.14 184	49.95 344	76.43 357	38.74 401	71.92 399	55.84 408

EMVS		61.10 369	60.81 371	61.99 380	65.96 412	55.86 328	53.10 406	58.97 405	67.06 253	56.89 410	63.33 406	40.98 384	67.03 390	54.79 343	86.18 329	63.08 402

DSMNet-mixed		60.98 370	61.61 370	59.09 388	72.88 396	45.05 390	74.70 331	46.61 414	26.20 412	65.34 391	90.32 214	55.46 319	63.12 401	41.72 395	81.30 374	69.09 397

dp		60.70 371	60.29 374	61.92 381	72.04 400	38.67 406	70.83 361	64.08 391	51.28 373	60.75 401	77.28 379	36.59 395	71.58 372	47.41 381	62.34 408	75.52 388

dmvs_testset		60.59 372	62.54 367	54.72 391	77.26 362	27.74 414	74.05 336	61.00 401	60.48 315	65.62 390	67.03 404	55.93 316	68.23 386	32.07 411	69.46 405	68.17 398

CHOSEN 280x420		59.08 373	56.52 378	66.76 365	76.51 370	64.39 232	49.62 407	59.00 404	43.86 396	55.66 411	68.41 403	35.55 396	68.21 387	43.25 392	76.78 393	67.69 399

mvsany_test1		58.48 374	56.47 379	64.50 374	65.90 413	68.21 196	56.95 403	42.11 416	38.30 408	65.69 389	77.19 382	56.96 310	59.35 406	46.16 385	58.96 409	65.93 400

PVSNet_0	51.08 22	56.10 375	54.97 380	59.48 387	75.12 383	53.28 348	55.16 404	61.89 396	44.30 394	59.16 404	62.48 407	54.22 324	65.91 395	35.40 406	47.01 410	59.25 406

new_pmnet		55.69 376	57.66 377	49.76 392	75.47 380	30.59 412	59.56 395	51.45 411	43.62 398	62.49 399	75.48 390	40.96 385	49.15 412	37.39 405	72.52 396	69.55 396

PMMVS2		55.64 377	59.27 376	44.74 393	64.30 415	12.32 421	40.60 408	49.79 412	53.19 360	65.06 395	84.81 308	53.60 327	49.76 411	32.68 410	89.41 282	72.15 392

MVE	40.22 23	51.82 378	50.47 381	55.87 389	62.66 416	51.91 357	31.61 410	39.28 417	40.65 403	50.76 412	74.98 392	56.24 315	44.67 413	33.94 409	64.11 407	71.04 395
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 379	42.65 382	39.67 394	70.86 402	21.11 416	61.01 394	21.42 421	57.36 337	57.97 409	50.06 410	16.40 420	58.73 407	21.03 414	27.69 414	39.17 410

kuosan		30.83 380	32.17 383	26.83 396	53.36 418	19.02 419	57.90 401	20.44 422	38.29 409	38.01 413	37.82 412	15.18 421	33.45 415	7.74 416	20.76 415	28.03 411

test_method		30.46 381	29.60 384	33.06 395	17.99 420	3.84 423	13.62 411	73.92 339	2.79 414	18.29 416	53.41 409	28.53 409	43.25 414	22.56 412	35.27 412	52.11 409

cdsmvs_eth3d_5k		20.81 382	27.75 385	0.00 401	0.00 424	0.00 426	0.00 412	85.44 248	0.00 419	0.00 420	82.82 331	81.46 115	0.00 420	0.00 419	0.00 418	0.00 416

tmp_tt		20.25 383	24.50 386	7.49 398	4.47 421	8.70 422	34.17 409	25.16 419	1.00 416	32.43 415	18.49 413	39.37 388	9.21 417	21.64 413	43.75 411	4.57 413

ab-mvs-re		6.65 384	8.87 387	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	79.80 359	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		6.41 385	8.55 388	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	76.94 162	0.00 420	0.00 419	0.00 418	0.00 416

test123		6.27 386	8.08 389	0.84 399	1.11 423	0.57 424	62.90 389	0.82 423	0.54 417	1.07 419	2.75 418	1.26 422	0.30 418	1.04 417	1.26 417	1.66 414

testmvs		5.91 387	7.65 390	0.72 400	1.20 422	0.37 425	59.14 397	0.67 424	0.49 418	1.11 418	2.76 417	0.94 423	0.24 419	1.02 418	1.47 416	1.55 415

test_blank		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet_test		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

DCPMVS		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

sosnet-low-res		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

sosnet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uncertanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

Regformer		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

WAC-MVS						37.39 407								52.61 357

FOURS1					96.08 12	87.41 11	96.19 2	95.83 5	92.95 3	96.57 3

MSC_two_6792asdad				88.81 68	91.55 126	77.99 91		91.01 143					96.05 9	87.45 20	98.17 32	92.40 176

PC_three_1452									58.96 324	90.06 94	91.33 178	80.66 125	93.03 138	75.78 163	95.94 124	92.48 170

No_MVS				88.81 68	91.55 126	77.99 91		91.01 143					96.05 9	87.45 20	98.17 32	92.40 176

test_one_0601					93.85 59	73.27 136		94.11 35	86.57 27	93.47 38	94.64 60	88.42 26

eth-test2					0.00 424

eth-test					0.00 424

ZD-MVS					92.22 100	80.48 68		91.85 118	71.22 209	90.38 89	92.98 127	86.06 61	96.11 7	81.99 91	96.75 89

RE-MVS-def			92.61 5		94.13 52	88.95 6	92.87 13	94.16 29	88.75 15	93.79 29	94.43 68	90.64 10		87.16 29	97.60 63	92.73 157

IU-MVS					94.18 47	72.64 143		90.82 148	56.98 341	89.67 106				85.78 49	97.92 46	93.28 136

OPU-MVS				88.27 79	91.89 112	77.83 94	90.47 52				91.22 181	81.12 119	94.68 73	74.48 175	95.35 144	92.29 182

test_241102_TWO								93.71 52	83.77 49	93.49 36	94.27 75	89.27 21	95.84 24	86.03 46	97.82 51	92.04 193

test_241102_ONE					94.18 47	72.65 141		93.69 53	83.62 51	94.11 23	93.78 104	90.28 14	95.50 46

9.14			89.29 59		91.84 116		88.80 90	95.32 12	75.14 152	91.07 78	92.89 132	87.27 44	93.78 105	83.69 69	97.55 66

save fliter					93.75 60	77.44 100	86.31 132	89.72 181	70.80 213

test_0728_THIRD									85.33 35	93.75 31	94.65 57	87.44 43	95.78 30	87.41 22	98.21 29	92.98 151

test_0728_SECOND				86.79 99	94.25 46	72.45 151	90.54 49	94.10 36					95.88 18	86.42 36	97.97 43	92.02 194

test0726					94.16 50	72.56 147	90.63 46	93.90 45	83.61 52	93.75 31	94.49 65	89.76 18

GSMVS																83.88 330

test_part2					93.86 58	77.77 95				92.84 48

sam_mvs1												46.11 356				83.88 330

sam_mvs												45.92 361

ambc				82.98 189	90.55 153	64.86 227	88.20 97	89.15 192		89.40 115	93.96 95	71.67 227	91.38 183	78.83 123	96.55 94	92.71 160

MTGPA								91.81 122

test_post1							78.85 275				3.13 415	45.19 370	80.13 342	58.11 323

test_post											3.10 416	45.43 366	77.22 356

patchmatchnet-post											81.71 343	45.93 360	87.01 271

GG-mvs-BLEND				67.16 363	73.36 391	46.54 384	84.15 169	55.04 409		58.64 407	61.95 408	29.93 406	83.87 320	38.71 402	76.92 392	71.07 394

MTMP							90.66 45	33.14 418

gm-plane-assit					75.42 381	44.97 391			52.17 366		72.36 397		87.90 261	54.10 346

test9_res														80.83 101	96.45 100	90.57 233

TEST9					92.34 95	79.70 75	83.94 174	90.32 163	65.41 271	84.49 216	90.97 190	82.03 106	93.63 110

test_8					92.09 104	78.87 82	83.82 179	90.31 165	65.79 262	84.36 220	90.96 192	81.93 108	93.44 123

agg_prior2														79.68 114	96.16 111	90.22 241

agg_prior					91.58 124	77.69 97		90.30 166		84.32 222			93.18 131

TestCases				89.68 52	91.59 121	83.40 49		95.44 10	79.47 96	88.00 145	93.03 125	82.66 91	91.47 177	70.81 214	96.14 112	94.16 95

test_prior4						78.97 81	84.59 160

test_prior2							83.37 191		75.43 148	84.58 214	91.57 172	81.92 110		79.54 116	96.97 82

test_prior				86.32 107	90.59 152	71.99 158		92.85 89					94.17 92			92.80 155

旧先验2							81.73 233		56.88 342	86.54 179			84.90 308	72.81 203

新几何2							81.72 234

新几何1				82.95 191	93.96 56	78.56 85		80.24 299	55.45 347	83.93 233	91.08 187	71.19 228	88.33 258	65.84 265	93.07 214	81.95 360

旧先验1					91.97 108	71.77 159		81.78 289			91.84 163	73.92 195			93.65 202	83.61 336

无先验							82.81 209	85.62 246	58.09 330				91.41 182	67.95 250		84.48 321

原ACMM2							82.26 227

原ACMM1				84.60 142	92.81 86	74.01 128		91.50 127	62.59 286	82.73 253	90.67 206	76.53 169	94.25 86	69.24 231	95.69 137	85.55 309

test222					93.31 70	76.54 110	79.38 264	77.79 310	52.59 363	82.36 257	90.84 199	66.83 250			91.69 242	81.25 368

testdata2													86.43 285	63.52 287

segment_acmp												81.94 107

testdata				79.54 251	92.87 81	72.34 152		80.14 300	59.91 320	85.47 199	91.75 169	67.96 245	85.24 304	68.57 245	92.18 234	81.06 373

testdata1							79.62 259		73.95 163

test12				86.57 102	90.74 148	72.63 145		90.69 151		82.76 252		79.20 136	94.80 70		95.32 146	92.27 184

plane_prior7					93.45 65	77.31 103

plane_prior6					92.61 87	76.54 110						74.84 183

plane_prior5								93.61 56					95.22 56	80.78 102	95.83 130	94.46 79

plane_prior4											92.95 130

plane_prior3						76.85 108			77.79 121	86.55 174

plane_prior2							89.45 79		79.44 98

plane_prior1					92.83 85

plane_prior						76.42 113	87.15 114		75.94 141						95.03 158

n2								0.00 425

nn								0.00 425

door-mid								74.45 336

lessismore_v0				85.95 116	91.10 141	70.99 170		70.91 365		91.79 66	94.42 70	61.76 277	92.93 141	79.52 117	93.03 215	93.93 104

LGP-MVS_train				90.82 34	94.75 41	81.69 60		94.27 21	82.35 65	93.67 34	94.82 52	91.18 4	95.52 42	85.36 52	98.73 7	95.23 58

test11								91.46 128

door								72.57 351

HQP5-MVS						70.66 171

HQP-NCC					91.19 136		84.77 154		73.30 177	80.55 287

ACMP_Plane					91.19 136		84.77 154		73.30 177	80.55 287

BP-MVS														77.30 146

HQP4-MVS										80.56 286			94.61 76			93.56 128

HQP3-MVS								92.68 94							94.47 178

HQP2-MVS												72.10 220

NP-MVS					91.95 109	74.55 125					90.17 220

MDTV_nov1_ep13_2view						27.60 415	70.76 362		46.47 388	61.27 400		45.20 369		49.18 372		83.75 335

MDTV_nov1_ep13			68.29 338		78.03 357	43.87 394	74.12 335	72.22 354	52.17 366	67.02 384	85.54 293	45.36 367	80.85 336	55.73 333	84.42 351

ACMMP++_ref															95.74 136

ACMMP++															97.35 72

Test By Simon												79.09 137

ITE_SJBPF				90.11 46	90.72 149	84.97 38		90.30 166	81.56 73	90.02 96	91.20 183	82.40 96	90.81 202	73.58 191	94.66 174	94.56 75

DeepMVS_CX				24.13 397	32.95 419	29.49 413		21.63 420	12.07 413	37.95 414	45.07 411	30.84 404	19.21 416	17.94 415	33.06 413	23.69 412

High-resolution multi-view benchmark