High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 54	99.27 1	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 33	94.17 92	86.07 45	98.48 17	97.22 19

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 20	93.73 60	85.72 30	96.79 1	95.51 8	88.86 12	95.63 8	96.99 8	84.81 67	93.16 133	91.10 1	97.53 70	96.58 30
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 4	92.54 5	92.37 5	95.93 15	85.81 29	92.99 12	94.23 23	85.21 35	92.51 55	95.13 43	90.65 9	95.34 52	88.06 8	98.15 34	95.95 41

SR-MVS-dyc-post		92.41 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 13	94.16 28	88.75 14	93.79 28	94.43 67	88.83 24	95.51 44	87.16 29	97.60 64	92.73 156

SR-MVS		92.23 6	92.34 7	91.91 15	94.89 37	87.85 8	92.51 23	93.87 46	88.20 19	93.24 39	94.02 90	90.15 16	95.67 34	86.82 33	97.34 74	92.19 183

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 15	82.88 59	91.77 68	93.94 98	90.55 12	95.73 31	88.50 6	98.23 27	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 77	85.17 35	92.47 25	95.05 14	87.65 22	93.21 40	94.39 72	90.09 17	95.08 61	86.67 35	97.60 64	94.18 95

COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 61	86.15 20	93.37 10	95.10 13	90.28 9	92.11 61	95.03 45	89.75 20	94.93 65	79.95 110	98.27 25	95.04 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 28	82.52 62	92.39 58	94.14 84	89.15 23	95.62 35	87.35 24	98.24 26	94.56 76
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 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 17	92.60 94	83.09 56	91.54 70	94.25 78	87.67 41	95.51 44	87.21 28	98.11 35	93.12 144

CP-MVS		91.67 12	91.58 19	91.96 12	95.29 30	87.62 9	93.38 9	93.36 60	83.16 55	91.06 80	94.00 91	88.26 30	95.71 32	87.28 27	98.39 20	92.55 165

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 62	85.07 36	89.99 99	94.03 89	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 30	91.81 119	84.07 44	92.00 64	94.40 71	86.63 51	95.28 55	88.59 5	98.31 23	92.30 176

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 89	88.22 18	88.53 129	97.64 2	83.45 81	94.55 78	86.02 48	98.60 12	96.67 27

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 68	81.99 65	91.40 72	94.17 83	87.51 42	95.87 19	87.74 13	97.76 55	93.99 103

LPG-MVS_test		91.47 17	91.68 16	90.82 33	94.75 40	81.69 59	90.00 57	94.27 20	82.35 63	93.67 33	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 66	81.91 67	90.88 86	94.21 79	87.75 39	95.87 19	87.60 18	97.71 58	93.83 111

HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 21	93.29 67	81.99 65	91.47 71	93.96 95	88.35 29	95.56 39	87.74 13	97.74 57	92.85 153

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 28	94.22 25	80.14 88	91.29 76	93.97 92	87.93 38	95.87 19	88.65 4	97.96 45	94.12 99

APDe-MVS		91.22 21	91.92 11	89.14 64	92.97 79	78.04 89	92.84 15	94.14 32	83.33 53	93.90 24	95.73 27	88.77 25	96.41 2	87.60 18	97.98 42	92.98 150
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 22	90.95 39	91.93 13	95.67 22	85.85 27	90.00 57	93.90 43	80.32 85	91.74 69	94.41 70	88.17 32	95.98 11	86.37 38	97.99 40	93.96 105

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 37	93.48 58	82.82 60	92.60 54	93.97 92	88.19 31	96.29 5	87.61 17	98.20 31	94.39 87
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 24	90.91 40	91.83 18	96.18 10	86.88 13	92.20 27	93.03 81	82.59 61	88.52 130	94.37 73	86.74 50	95.41 50	86.32 39	98.21 29	93.19 140
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 49	80.98 79	91.38 73	93.80 102	87.20 46	95.80 25	87.10 31	97.69 59	93.93 106

MP-MVS-pluss		90.81 26	91.08 33	89.99 46	95.97 13	79.88 71	88.13 98	94.51 18	75.79 140	92.94 44	94.96 46	88.36 28	95.01 63	90.70 2	98.40 19	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 27	91.50 21	88.44 76	93.00 78	76.26 116	89.65 70	95.55 7	87.72 21	93.89 26	94.94 47	91.62 3	93.44 124	78.35 126	98.76 3	95.61 48

ACMMP_NAP		90.65 28	91.07 35	89.42 59	95.93 15	79.54 76	89.95 61	93.68 53	77.65 119	91.97 65	94.89 48	88.38 27	95.45 48	89.27 3	97.87 50	93.27 136

ACMM	79.39 9	90.65 28	90.99 37	89.63 55	95.03 33	83.53 47	89.62 71	93.35 61	79.20 100	93.83 27	93.60 110	90.81 7	92.96 139	85.02 56	98.45 18	92.41 170
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 30	90.34 47	91.38 24	89.03 183	84.23 45	93.58 6	94.68 17	90.65 7	90.33 93	93.95 97	84.50 69	95.37 51	80.87 100	95.50 143	94.53 79

ACMP	79.16 10	90.54 31	90.60 45	90.35 41	94.36 43	80.98 65	89.16 81	94.05 37	79.03 103	92.87 46	93.74 106	90.60 11	95.21 58	82.87 78	98.76 3	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 32	91.08 33	88.88 67	93.38 68	78.65 83	89.15 82	94.05 37	84.68 40	93.90 24	94.11 87	88.13 34	96.30 4	84.51 62	97.81 52	91.70 199
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 33	91.64 17	86.93 97	94.18 46	72.65 142	90.47 51	93.69 51	83.77 47	94.11 22	94.27 74	90.28 14	95.84 23	86.03 46	97.92 46	92.29 177

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 69	75.37 147	92.84 48	95.28 38	85.58 62	96.09 7	87.92 10	97.76 55	93.88 109
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 35	90.80 42	88.68 74	92.86 83	77.09 104	91.19 40	95.74 5	81.38 73	92.28 59	93.80 102	86.89 49	94.64 73	85.52 51	97.51 71	94.30 91

v7n		90.13 36	90.96 38	87.65 89	91.95 109	71.06 170	89.99 59	93.05 78	86.53 26	94.29 18	96.27 17	82.69 88	94.08 95	86.25 42	97.63 61	97.82 8

PMVS	80.48 6	90.08 37	90.66 44	88.34 79	96.71 3	92.97 1	90.31 54	89.57 182	88.51 17	90.11 95	95.12 44	90.98 6	88.92 247	77.55 140	97.07 81	83.13 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 38	91.09 32	87.00 95	91.55 126	72.64 144	96.19 2	94.10 35	85.33 33	93.49 36	94.64 59	81.12 117	95.88 17	87.41 22	95.94 126	92.48 167

DVP-MVS		90.06 39	91.32 28	86.29 109	94.16 49	72.56 148	90.54 48	91.01 140	83.61 50	93.75 30	94.65 56	89.76 18	95.78 28	86.42 36	97.97 43	90.55 229
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 39	91.92 11	84.47 146	96.56 6	58.83 303	89.04 83	92.74 91	91.40 5	96.12 4	96.06 22	87.23 45	95.57 38	79.42 118	98.74 5	99.00 2

PEN-MVS		90.03 41	91.88 14	84.48 145	96.57 5	58.88 300	88.95 84	93.19 70	91.62 4	96.01 6	96.16 20	87.02 47	95.60 36	78.69 123	98.72 8	98.97 3

OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 75	80.37 68	91.91 33	93.11 74	81.10 77	95.32 10	97.24 5	72.94 207	94.85 67	85.07 54	97.78 53	97.26 16

DTE-MVSNet		89.98 43	91.91 13	84.21 155	96.51 7	57.84 310	88.93 85	92.84 88	91.92 3	96.16 3	96.23 18	86.95 48	95.99 10	79.05 120	98.57 14	98.80 6

XVG-ACMP-BASELINE		89.98 43	89.84 50	90.41 39	94.91 36	84.50 44	89.49 76	93.98 39	79.68 92	92.09 62	93.89 100	83.80 76	93.10 136	82.67 82	98.04 36	93.64 123

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 135	81.66 62	91.25 38	94.13 33	88.89 11	88.83 124	94.26 77	77.55 149	95.86 22	84.88 58	95.87 130	95.24 58

WR-MVS_H		89.91 46	91.31 29	85.71 124	96.32 9	62.39 255	89.54 74	93.31 65	90.21 10	95.57 9	95.66 29	81.42 114	95.90 15	80.94 99	98.80 2	98.84 5

OPM-MVS		89.80 47	89.97 48	89.27 61	94.76 39	79.86 72	86.76 120	92.78 90	78.78 106	92.51 55	93.64 109	88.13 34	93.84 105	84.83 59	97.55 67	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 48	89.27 59	91.30 25	93.51 64	84.79 40	89.89 63	90.63 150	70.00 217	94.55 15	96.67 11	87.94 37	93.59 116	84.27 64	95.97 123	95.52 49

anonymousdsp		89.73 49	88.88 66	92.27 7	89.82 168	86.67 14	90.51 50	90.20 167	69.87 218	95.06 11	96.14 21	84.28 72	93.07 137	87.68 15	96.34 105	97.09 21

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 94	82.98 53	91.98 31	90.08 170	71.54 199	94.28 20	96.54 13	81.57 112	94.27 84	86.26 40	96.49 99	97.09 21

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 116	93.91 42	80.07 89	86.75 164	93.26 114	93.64 2	90.93 194	84.60 61	90.75 263	93.97 104

APD-MVS		89.54 52	89.63 54	89.26 62	92.57 88	81.34 64	90.19 56	93.08 77	80.87 81	91.13 78	93.19 115	86.22 57	95.97 12	82.23 88	97.18 79	90.45 231
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 53	88.81 68	91.19 28	93.38 68	84.72 41	89.70 66	90.29 164	69.27 221	94.39 16	96.38 15	86.02 60	93.52 120	83.96 66	95.92 128	95.34 53

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 29	92.30 101	79.74 91	87.50 149	92.38 143	81.42 114	93.28 129	83.07 74	97.24 77	91.67 200

ACMH	76.49 14	89.34 55	91.14 31	83.96 160	92.50 91	70.36 176	89.55 72	93.84 47	81.89 68	94.70 13	95.44 34	90.69 8	88.31 257	83.33 70	98.30 24	93.20 139
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 71	86.02 29	93.12 41	95.30 36	84.94 64	89.44 239	74.12 176	96.10 118	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 71	86.02 29	93.12 41	95.30 36	84.94 64	89.44 239	74.12 176	96.10 118	94.45 82

CP-MVSNet		89.27 58	90.91 40	84.37 147	96.34 8	58.61 306	88.66 92	92.06 107	90.78 6	95.67 7	95.17 42	81.80 110	95.54 41	79.00 121	98.69 9	98.95 4

XVG-OURS		89.18 59	88.83 67	90.23 43	94.28 44	86.11 22	85.91 131	93.60 56	80.16 87	89.13 121	93.44 112	83.82 75	90.98 192	83.86 68	95.30 151	93.60 125

DeepC-MVS	82.31 4	89.15 60	89.08 62	89.37 60	93.64 62	79.07 79	88.54 93	94.20 26	73.53 166	89.71 106	94.82 51	85.09 63	95.77 30	84.17 65	98.03 38	93.26 137
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 61	90.72 43	84.31 153	97.00 2	64.33 231	89.67 69	88.38 197	88.84 13	94.29 18	97.57 3	90.48 13	91.26 184	72.57 200	97.65 60	97.34 15

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 43	78.43 111	89.16 119	92.25 150	72.03 220	96.36 3	88.21 7	90.93 257	92.98 150
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 63	89.88 49	86.22 112	91.63 120	77.07 105	89.82 64	93.77 48	78.90 104	92.88 45	92.29 148	86.11 58	90.22 215	86.24 43	97.24 77	91.36 207
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 64	88.45 71	90.38 40	94.92 35	85.85 27	89.70 66	91.27 133	78.20 113	86.69 167	92.28 149	80.36 126	95.06 62	86.17 44	96.49 99	90.22 235

test_0402		88.65 65	89.58 56	85.88 120	92.55 89	72.22 156	84.01 167	89.44 184	88.63 16	94.38 17	95.77 26	86.38 56	93.59 116	79.84 111	95.21 152	91.82 195

DP-MVS		88.60 66	89.01 63	87.36 91	91.30 133	77.50 97	87.55 105	92.97 84	87.95 20	89.62 110	92.87 129	84.56 68	93.89 102	77.65 138	96.62 93	90.70 223

APD_test1		88.40 67	87.91 75	89.88 47	89.50 172	86.65 16	89.98 60	91.91 113	84.26 42	90.87 87	93.92 99	82.18 101	89.29 243	73.75 183	94.81 171	93.70 119

Anonymous20231211		88.40 67	89.62 55	84.73 140	90.46 154	65.27 221	88.86 86	93.02 82	87.15 23	93.05 43	97.10 6	82.28 100	92.02 165	76.70 150	97.99 40	96.88 25

PS-MVSNAJss		88.31 69	87.90 76	89.56 57	93.31 70	77.96 92	87.94 101	91.97 110	70.73 208	94.19 21	96.67 11	76.94 159	94.57 76	83.07 74	96.28 108	96.15 33

RRT_MVS		88.30 70	87.83 77	89.70 52	93.62 63	75.70 121	92.36 26	89.06 189	77.34 122	93.63 35	95.83 25	65.40 255	95.90 15	85.01 57	98.23 27	97.49 13

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 111	81.68 61	87.49 107	93.17 71	76.02 134	88.64 127	91.22 176	84.24 73	93.37 127	77.97 136	97.03 82	95.52 49

CS-MVS		88.14 72	87.67 80	89.54 58	89.56 170	79.18 78	90.47 51	94.77 16	79.37 98	84.32 215	89.33 227	83.87 74	94.53 79	82.45 84	94.89 167	94.90 65

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 65	95.72 21	76.74 108	92.49 24	91.19 136	67.85 241	86.63 168	94.84 50	79.58 132	95.96 13	87.62 16	94.50 179	94.56 76
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 51	82.98 187	93.26 72	63.94 235	91.10 41	89.64 179	85.07 36	90.91 84	91.09 181	89.16 22	91.87 170	82.03 89	95.87 130	93.13 142

EC-MVSNet		88.01 75	88.32 72	87.09 93	89.28 177	72.03 158	90.31 54	96.31 3	80.88 80	85.12 196	89.67 222	84.47 70	95.46 47	82.56 83	96.26 111	93.77 117

RPSCF		88.00 76	86.93 94	91.22 27	90.08 161	89.30 4	89.68 68	91.11 137	79.26 99	89.68 107	94.81 54	82.44 92	87.74 261	76.54 153	88.74 288	96.61 29

AllTest		87.97 77	87.40 85	89.68 53	91.59 121	83.40 48	89.50 75	95.44 10	79.47 94	88.00 141	93.03 121	82.66 89	91.47 177	70.81 209	96.14 115	94.16 96

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 98	76.51 112	91.09 42	91.87 114	72.61 186	92.16 60	95.23 41	66.01 249	95.59 37	86.02 48	97.78 53	97.24 17

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 132	94.02 54	64.13 232	84.38 160	91.29 132	84.88 39	92.06 63	93.84 101	86.45 54	93.73 107	73.22 191	98.66 10	97.69 9

nrg030		87.85 80	88.49 70	85.91 118	90.07 163	69.73 180	87.86 102	94.20 26	74.04 158	92.70 53	94.66 55	85.88 61	91.50 176	79.72 113	97.32 75	96.50 31

CNVR-MVS		87.81 81	87.68 79	88.21 81	92.87 81	77.30 103	85.25 143	91.23 134	77.31 124	87.07 158	91.47 170	82.94 86	94.71 70	84.67 60	96.27 110	92.62 163

HQP_MVS		87.75 82	87.43 84	88.70 73	93.45 65	76.42 113	89.45 77	93.61 54	79.44 96	86.55 169	92.95 126	74.84 180	95.22 56	80.78 102	95.83 132	94.46 80

MM		87.64 83	87.15 87	89.09 65	89.51 171	76.39 115	88.68 91	86.76 227	84.54 41	83.58 232	93.78 104	73.36 203	96.48 1	87.98 9	96.21 112	94.41 86

NCCC		87.36 84	86.87 95	88.83 68	92.32 97	78.84 82	86.58 124	91.09 138	78.77 107	84.85 204	90.89 189	80.85 120	95.29 53	81.14 97	95.32 148	92.34 174

DeepPCF-MVS	81.24 5	87.28 85	86.21 105	90.49 38	91.48 130	84.90 38	83.41 185	92.38 99	70.25 214	89.35 118	90.68 198	82.85 87	94.57 76	79.55 115	95.95 125	92.00 190

SixPastTwentyTwo		87.20 86	87.45 83	86.45 106	92.52 90	69.19 189	87.84 103	88.05 205	81.66 70	94.64 14	96.53 14	65.94 250	94.75 69	83.02 76	96.83 87	95.41 51

CS-MVS-test		87.00 87	86.43 101	88.71 72	89.46 173	77.46 98	89.42 79	95.73 6	77.87 117	81.64 266	87.25 261	82.43 93	94.53 79	77.65 138	96.46 101	94.14 98

UniMVSNet (Re)		86.87 88	86.98 93	86.55 104	93.11 76	68.48 193	83.80 176	92.87 86	80.37 83	89.61 112	91.81 161	77.72 146	94.18 90	75.00 169	98.53 15	96.99 24

Vis-MVSNet		86.86 89	86.58 98	87.72 86	92.09 105	77.43 100	87.35 108	92.09 106	78.87 105	84.27 220	94.05 88	78.35 140	93.65 109	80.54 106	91.58 245	92.08 187
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	86.17 115	92.86 83	67.02 205	82.55 211	91.56 122	83.08 57	90.92 82	91.82 160	78.25 141	93.99 97	74.16 174	98.35 21	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 113	93.24 73	67.02 205	83.16 194	92.21 102	81.73 69	90.92 82	91.97 154	77.20 153	93.99 97	74.16 174	98.35 21	97.61 10

casdiffmvs_mvg		86.72 92	87.51 82	84.36 149	87.09 230	65.22 222	84.16 162	94.23 23	77.89 116	91.28 77	93.66 108	84.35 71	92.71 145	80.07 107	94.87 170	95.16 61
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 92	85.94 257	78.30 85	86.93 114	92.20 103	65.94 253	89.16 119	93.16 117	83.10 84	89.89 228	87.81 11	94.43 182	93.35 132

IS-MVSNet		86.66 94	86.82 97	86.17 115	92.05 107	66.87 208	91.21 39	88.64 194	86.30 28	89.60 113	92.59 137	69.22 233	94.91 66	73.89 180	97.89 49	96.72 26

v10		86.54 95	87.10 89	84.84 136	88.16 206	63.28 241	86.64 123	92.20 103	75.42 146	92.81 50	94.50 63	74.05 191	94.06 96	83.88 67	96.28 108	97.17 20

pmmvs6		86.52 96	88.06 74	81.90 207	92.22 101	62.28 258	84.66 153	89.15 187	83.54 52	89.85 103	97.32 4	88.08 36	86.80 276	70.43 217	97.30 76	96.62 28

PHI-MVS		86.38 97	85.81 113	88.08 82	88.44 200	77.34 101	89.35 80	93.05 78	73.15 177	84.76 205	87.70 252	78.87 136	94.18 90	80.67 104	96.29 107	92.73 156

MVS_0304		86.35 98	85.92 109	87.66 88	89.21 180	73.16 139	88.40 95	83.63 269	81.27 74	80.87 276	94.12 86	71.49 224	95.71 32	87.79 12	96.50 98	94.11 100

CSCG		86.26 99	86.47 100	85.60 126	90.87 146	74.26 128	87.98 100	91.85 115	80.35 84	89.54 116	88.01 245	79.09 134	92.13 161	75.51 162	95.06 159	90.41 232

DeepC-MVS_fast	80.27 8	86.23 100	85.65 117	87.96 85	91.30 133	76.92 106	87.19 109	91.99 109	70.56 209	84.96 200	90.69 197	80.01 129	95.14 59	78.37 125	95.78 137	91.82 195
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 101	86.83 96	84.36 149	87.82 210	62.35 257	86.42 126	91.33 131	76.78 128	92.73 52	94.48 65	73.41 200	93.72 108	83.10 73	95.41 144	97.01 23

Anonymous20240529		86.20 102	87.13 88	83.42 176	90.19 159	64.55 229	84.55 155	90.71 147	85.85 31	89.94 102	95.24 40	82.13 102	90.40 211	69.19 231	96.40 104	95.31 55

test_fmvsmconf0.1_n		86.18 103	85.88 111	87.08 94	85.26 267	78.25 86	85.82 134	91.82 117	65.33 266	88.55 128	92.35 147	82.62 91	89.80 230	86.87 32	94.32 185	93.18 141

CDPH-MVS		86.17 104	85.54 118	88.05 84	92.25 99	75.45 122	83.85 173	92.01 108	65.91 255	86.19 178	91.75 164	83.77 77	94.98 64	77.43 143	96.71 91	93.73 118

NR-MVSNet		86.00 105	86.22 104	85.34 130	93.24 73	64.56 228	82.21 223	90.46 154	80.99 78	88.42 132	91.97 154	77.56 148	93.85 103	72.46 201	98.65 11	97.61 10

train_agg		85.98 106	85.28 123	88.07 83	92.34 95	79.70 74	83.94 169	90.32 159	65.79 256	84.49 209	90.97 185	81.93 106	93.63 111	81.21 96	96.54 96	90.88 217

FC-MVSNet-test		85.93 107	87.05 91	82.58 198	92.25 99	56.44 321	85.75 135	93.09 76	77.33 123	91.94 66	94.65 56	74.78 182	93.41 126	75.11 168	98.58 13	97.88 7

test_fmvsmconf_n		85.88 108	85.51 119	86.99 96	84.77 274	78.21 87	85.40 142	91.39 129	65.32 267	87.72 145	91.81 161	82.33 96	89.78 231	86.68 34	94.20 188	92.99 149

Effi-MVS+-dtu		85.82 109	83.38 154	93.14 3	87.13 226	91.15 2	87.70 104	88.42 196	74.57 154	83.56 233	85.65 284	78.49 139	94.21 88	72.04 203	92.88 218	94.05 102

TAPA-MVS	77.73 12	85.71 110	84.83 129	88.37 78	88.78 191	79.72 73	87.15 111	93.50 57	69.17 222	85.80 187	89.56 223	80.76 121	92.13 161	73.21 196	95.51 142	93.25 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 111	86.14 106	83.58 172	87.97 207	67.13 203	87.55 105	94.32 19	73.44 168	88.47 131	87.54 255	86.45 54	91.06 191	75.76 161	93.76 197	92.54 166

EPP-MVSNet		85.47 112	85.04 126	86.77 101	91.52 129	69.37 184	91.63 36	87.98 207	81.51 72	87.05 159	91.83 159	66.18 248	95.29 53	70.75 212	96.89 84	95.64 46

GeoE		85.45 113	85.81 113	84.37 147	90.08 161	67.07 204	85.86 133	91.39 129	72.33 192	87.59 147	90.25 210	84.85 66	92.37 155	78.00 134	91.94 238	93.66 120

FIs		85.35 114	86.27 103	82.60 197	91.86 113	57.31 314	85.10 147	93.05 78	75.83 139	91.02 81	93.97 92	73.57 196	92.91 143	73.97 179	98.02 39	97.58 12

test_fmvsmvis_n_1920		85.22 115	85.36 122	84.81 137	85.80 259	76.13 119	85.15 146	92.32 100	61.40 296	91.33 74	90.85 192	83.76 78	86.16 289	84.31 63	93.28 208	92.15 185

casdiffmvs		85.21 116	85.85 112	83.31 179	86.17 252	62.77 248	83.03 196	93.93 41	74.69 153	88.21 137	92.68 136	82.29 99	91.89 169	77.87 137	93.75 199	95.27 57
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		85.20 117	85.93 108	83.02 186	86.30 247	62.37 256	84.55 155	93.96 40	74.48 155	87.12 153	92.03 153	82.30 98	91.94 166	78.39 124	94.21 187	94.74 73

K. test v3		85.14 118	84.73 130	86.37 107	91.13 140	69.63 182	85.45 140	76.68 318	84.06 45	92.44 57	96.99 8	62.03 273	94.65 72	80.58 105	93.24 209	94.83 72

EI-MVSNet-Vis-set		85.12 119	84.53 137	86.88 98	84.01 287	72.76 141	83.91 172	85.18 249	80.44 82	88.75 125	85.49 286	80.08 128	91.92 167	82.02 90	90.85 261	95.97 39

EI-MVSNet-UG-set		85.04 120	84.44 139	86.85 99	83.87 291	72.52 150	83.82 174	85.15 250	80.27 86	88.75 125	85.45 288	79.95 130	91.90 168	81.92 93	90.80 262	96.13 34

X-MVStestdata		85.04 120	82.70 167	92.08 8	95.64 23	86.25 18	92.64 18	93.33 62	85.07 36	89.99 99	16.05 404	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

MSLP-MVS++		85.00 122	86.03 107	81.90 207	91.84 116	71.56 167	86.75 121	93.02 82	75.95 137	87.12 153	89.39 225	77.98 142	89.40 242	77.46 141	94.78 172	84.75 312

F-COLMAP		84.97 123	83.42 153	89.63 55	92.39 93	83.40 48	88.83 87	91.92 112	73.19 176	80.18 289	89.15 231	77.04 157	93.28 129	65.82 262	92.28 229	92.21 182

3Dnovator	80.37 7	84.80 124	84.71 133	85.06 134	86.36 245	74.71 125	88.77 89	90.00 172	75.65 142	84.96 200	93.17 116	74.06 190	91.19 186	78.28 128	91.09 251	89.29 255

IterMVS-LS		84.73 125	84.98 127	83.96 160	87.35 221	63.66 236	83.25 190	89.88 174	76.06 132	89.62 110	92.37 146	73.40 202	92.52 150	78.16 131	94.77 174	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 126	84.34 143	85.49 129	90.18 160	75.86 120	79.23 264	87.13 217	73.35 169	85.56 191	89.34 226	83.60 80	90.50 209	76.64 151	94.05 192	90.09 240

HQP-MVS		84.61 127	84.06 146	86.27 110	91.19 136	70.66 172	84.77 148	92.68 92	73.30 172	80.55 281	90.17 214	72.10 216	94.61 74	77.30 145	94.47 180	93.56 128

v1192		84.57 128	84.69 134	84.21 155	87.75 212	62.88 245	83.02 197	91.43 126	69.08 224	89.98 101	90.89 189	72.70 211	93.62 114	82.41 85	94.97 164	96.13 34

FMVSNet1		84.55 129	85.45 120	81.85 209	90.27 158	61.05 271	86.83 117	88.27 202	78.57 110	89.66 109	95.64 30	75.43 173	90.68 204	69.09 232	95.33 147	93.82 112

v1144		84.54 130	84.72 132	84.00 158	87.67 215	62.55 252	82.97 199	90.93 143	70.32 213	89.80 104	90.99 184	73.50 197	93.48 122	81.69 95	94.65 177	95.97 39

Gipuma		84.44 131	86.33 102	78.78 255	84.20 285	73.57 132	89.55 72	90.44 155	84.24 43	84.38 212	94.89 48	76.35 170	80.40 336	76.14 157	96.80 89	82.36 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 132	83.93 149	85.63 125	91.59 121	71.58 165	83.52 182	92.13 105	61.82 289	83.96 226	89.75 221	79.93 131	93.46 123	78.33 127	94.34 184	91.87 194

VDDNet		84.35 133	85.39 121	81.25 220	95.13 31	59.32 293	85.42 141	81.11 289	86.41 27	87.41 150	96.21 19	73.61 195	90.61 207	66.33 255	96.85 85	93.81 115

ETV-MVS		84.31 134	83.91 150	85.52 127	88.58 196	70.40 175	84.50 159	93.37 59	78.76 108	84.07 224	78.72 362	80.39 125	95.13 60	73.82 182	92.98 216	91.04 213

v1240		84.30 135	84.51 138	83.65 169	87.65 216	61.26 268	82.85 203	91.54 123	67.94 239	90.68 90	90.65 201	71.71 222	93.64 110	82.84 79	94.78 172	96.07 36

MVS_111021_LR		84.28 136	83.76 151	85.83 122	89.23 179	83.07 51	80.99 239	83.56 270	72.71 184	86.07 181	89.07 232	81.75 111	86.19 288	77.11 147	93.36 204	88.24 268

h-mvs33		84.25 137	82.76 166	88.72 71	91.82 118	82.60 56	84.00 168	84.98 256	71.27 201	86.70 165	90.55 203	63.04 270	93.92 100	78.26 129	94.20 188	89.63 247

v144192		84.24 138	84.41 140	83.71 168	87.59 218	61.57 264	82.95 200	91.03 139	67.82 242	89.80 104	90.49 204	73.28 204	93.51 121	81.88 94	94.89 167	96.04 38

dcpmvs_2		84.23 139	85.14 124	81.50 216	88.61 195	61.98 262	82.90 202	93.11 74	68.66 230	92.77 51	92.39 142	78.50 138	87.63 263	76.99 149	92.30 226	94.90 65

v1921920		84.23 139	84.37 142	83.79 164	87.64 217	61.71 263	82.91 201	91.20 135	67.94 239	90.06 96	90.34 207	72.04 219	93.59 116	82.32 86	94.91 165	96.07 36

VDD-MVS		84.23 139	84.58 136	83.20 183	91.17 139	65.16 224	83.25 190	84.97 257	79.79 90	87.18 152	94.27 74	74.77 183	90.89 197	69.24 228	96.54 96	93.55 130

v2v482		84.09 142	84.24 144	83.62 170	87.13 226	61.40 265	82.71 206	89.71 177	72.19 195	89.55 114	91.41 171	70.70 228	93.20 131	81.02 98	93.76 197	96.25 32

EG-PatchMatch MVS		84.08 143	84.11 145	83.98 159	92.22 101	72.61 147	82.20 225	87.02 222	72.63 185	88.86 122	91.02 183	78.52 137	91.11 189	73.41 188	91.09 251	88.21 269

DP-MVS Recon		84.05 144	83.22 156	86.52 105	91.73 119	75.27 123	83.23 192	92.40 97	72.04 196	82.04 256	88.33 241	77.91 144	93.95 99	66.17 256	95.12 157	90.34 234

TransMVSNet (Re)		84.02 145	85.74 115	78.85 254	91.00 143	55.20 331	82.29 219	87.26 213	79.65 93	88.38 134	95.52 33	83.00 85	86.88 274	67.97 246	96.60 94	94.45 82

Baseline_NR-MVSNet		84.00 146	85.90 110	78.29 266	91.47 131	53.44 340	82.29 219	87.00 225	79.06 102	89.55 114	95.72 28	77.20 153	86.14 290	72.30 202	98.51 16	95.28 56

TSAR-MVS + GP.		83.95 147	82.69 168	87.72 86	89.27 178	81.45 63	83.72 178	81.58 288	74.73 152	85.66 188	86.06 279	72.56 213	92.69 147	75.44 164	95.21 152	89.01 263

alignmvs		83.94 148	83.98 148	83.80 163	87.80 211	67.88 200	84.54 157	91.42 128	73.27 175	88.41 133	87.96 246	72.33 214	90.83 199	76.02 159	94.11 190	92.69 160

Effi-MVS+		83.90 149	84.01 147	83.57 173	87.22 224	65.61 220	86.55 125	92.40 97	78.64 109	81.34 271	84.18 307	83.65 79	92.93 141	74.22 173	87.87 300	92.17 184

CANet		83.79 150	82.85 165	86.63 102	86.17 252	72.21 157	83.76 177	91.43 126	77.24 125	74.39 339	87.45 257	75.36 174	95.42 49	77.03 148	92.83 219	92.25 181

pm-mvs1		83.69 151	84.95 128	79.91 241	90.04 165	59.66 290	82.43 215	87.44 210	75.52 144	87.85 143	95.26 39	81.25 116	85.65 299	68.74 238	96.04 120	94.42 85

AdaColmap		83.66 152	83.69 152	83.57 173	90.05 164	72.26 155	86.29 129	90.00 172	78.19 114	81.65 265	87.16 263	83.40 82	94.24 87	61.69 296	94.76 175	84.21 321

MIMVSNet1		83.63 153	84.59 135	80.74 229	94.06 53	62.77 248	82.72 205	84.53 262	77.57 121	90.34 92	95.92 24	76.88 165	85.83 297	61.88 294	97.42 72	93.62 124

test_fmvsm_n_1920		83.60 154	82.89 164	85.74 123	85.22 268	77.74 95	84.12 164	90.48 153	59.87 316	86.45 177	91.12 180	75.65 171	85.89 295	82.28 87	90.87 259	93.58 126

WR-MVS		83.56 155	84.40 141	81.06 225	93.43 67	54.88 332	78.67 272	85.02 254	81.24 75	90.74 89	91.56 168	72.85 208	91.08 190	68.00 245	98.04 36	97.23 18

CNLPA		83.55 156	83.10 161	84.90 135	89.34 176	83.87 46	84.54 157	88.77 191	79.09 101	83.54 234	88.66 238	74.87 179	81.73 327	66.84 251	92.29 228	89.11 257

LCM-MVSNet-Re		83.48 157	85.06 125	78.75 256	85.94 257	55.75 326	80.05 248	94.27 20	76.47 129	96.09 5	94.54 62	83.31 83	89.75 234	59.95 306	94.89 167	90.75 220

hse-mvs2		83.47 158	81.81 181	88.47 75	91.03 142	82.27 57	82.61 207	83.69 267	71.27 201	86.70 165	86.05 280	63.04 270	92.41 153	78.26 129	93.62 203	90.71 222

V42		83.47 158	83.37 155	83.75 166	83.16 304	63.33 240	81.31 233	90.23 166	69.51 220	90.91 84	90.81 194	74.16 189	92.29 159	80.06 108	90.22 270	95.62 47

VPA-MVSNet		83.47 158	84.73 130	79.69 245	90.29 157	57.52 313	81.30 235	88.69 193	76.29 130	87.58 148	94.44 66	80.60 124	87.20 268	66.60 254	96.82 88	94.34 89

PAPM_NR		83.23 161	83.19 158	83.33 178	90.90 145	65.98 216	88.19 97	90.78 146	78.13 115	80.87 276	87.92 249	73.49 199	92.42 152	70.07 221	88.40 290	91.60 202

CLD-MVS		83.18 162	82.64 169	84.79 138	89.05 182	67.82 201	77.93 280	92.52 95	68.33 232	85.07 197	81.54 338	82.06 103	92.96 139	69.35 227	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		83.17 163	85.68 116	75.65 300	81.24 322	45.26 383	79.94 250	92.91 85	83.83 46	91.33 74	96.88 10	80.25 127	85.92 292	68.89 235	95.89 129	95.76 43

FA-MVS(test-final)		83.13 164	83.02 162	83.43 175	86.16 254	66.08 215	88.00 99	88.36 198	75.55 143	85.02 198	92.75 134	65.12 256	92.50 151	74.94 170	91.30 249	91.72 197

114514_t		83.10 165	82.54 172	84.77 139	92.90 80	69.10 191	86.65 122	90.62 151	54.66 345	81.46 268	90.81 194	76.98 158	94.38 83	72.62 199	96.18 113	90.82 219

UGNet		82.78 166	81.64 183	86.21 113	86.20 251	76.24 117	86.86 115	85.68 241	77.07 126	73.76 343	92.82 130	69.64 230	91.82 172	69.04 234	93.69 200	90.56 228
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		82.75 167	81.93 179	85.19 131	82.08 311	80.15 70	85.53 138	88.76 192	68.01 236	85.58 190	87.75 251	71.80 221	86.85 275	74.02 178	93.87 196	88.58 266

EI-MVSNet		82.61 168	82.42 174	83.20 183	83.25 301	63.66 236	83.50 183	85.07 251	76.06 132	86.55 169	85.10 294	73.41 200	90.25 212	78.15 133	90.67 265	95.68 45

QAPM		82.59 169	82.59 171	82.58 198	86.44 240	66.69 209	89.94 62	90.36 158	67.97 238	84.94 202	92.58 139	72.71 210	92.18 160	70.63 215	87.73 302	88.85 264

fmvsm_s_conf0.1_n_a		82.58 170	81.93 179	84.50 144	87.68 214	73.35 133	86.14 130	77.70 307	61.64 294	85.02 198	91.62 166	77.75 145	86.24 285	82.79 80	87.07 309	93.91 108

Fast-Effi-MVS+-dtu		82.54 171	81.41 191	85.90 119	85.60 260	76.53 111	83.07 195	89.62 181	73.02 179	79.11 299	83.51 312	80.74 122	90.24 214	68.76 237	89.29 279	90.94 215

MVS_Test		82.47 172	83.22 156	80.22 238	82.62 310	57.75 312	82.54 212	91.96 111	71.16 205	82.89 244	92.52 141	77.41 150	90.50 209	80.04 109	87.84 301	92.40 171

v148		82.31 173	82.48 173	81.81 212	85.59 261	59.66 290	81.47 232	86.02 237	72.85 180	88.05 140	90.65 201	70.73 227	90.91 196	75.15 167	91.79 239	94.87 67

API-MVS		82.28 174	82.61 170	81.30 219	86.29 248	69.79 178	88.71 90	87.67 209	78.42 112	82.15 255	84.15 308	77.98 142	91.59 175	65.39 265	92.75 220	82.51 347

MVSFormer		82.23 175	81.57 188	84.19 157	85.54 264	69.26 186	91.98 31	90.08 170	71.54 199	76.23 319	85.07 297	58.69 295	94.27 84	86.26 40	88.77 286	89.03 261

fmvsm_s_conf0.5_n_a		82.21 176	81.51 190	84.32 152	86.56 238	73.35 133	85.46 139	77.30 311	61.81 290	84.51 208	90.88 191	77.36 151	86.21 287	82.72 81	86.97 314	93.38 131

EIA-MVS		82.19 177	81.23 196	85.10 133	87.95 208	69.17 190	83.22 193	93.33 62	70.42 210	78.58 302	79.77 354	77.29 152	94.20 89	71.51 205	88.96 284	91.93 193

fmvsm_s_conf0.1_n		82.17 178	81.59 186	83.94 162	86.87 236	71.57 166	85.19 145	77.42 310	62.27 288	84.47 211	91.33 173	76.43 167	85.91 293	83.14 71	87.14 307	94.33 90

PCF-MVS	74.62 15	82.15 179	80.92 201	85.84 121	89.43 174	72.30 154	80.53 243	91.82 117	57.36 332	87.81 144	89.92 218	77.67 147	93.63 111	58.69 311	95.08 158	91.58 203
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 180	80.31 208	87.45 90	90.86 147	80.29 69	85.88 132	90.65 149	68.17 235	76.32 318	86.33 274	73.12 206	92.61 149	61.40 299	90.02 273	89.44 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 181	81.54 189	83.60 171	83.94 288	73.90 130	83.35 187	86.10 234	58.97 318	83.80 228	90.36 206	74.23 188	86.94 273	82.90 77	90.22 270	89.94 242

GBi-Net		82.02 182	82.07 176	81.85 209	86.38 242	61.05 271	86.83 117	88.27 202	72.43 187	86.00 182	95.64 30	63.78 264	90.68 204	65.95 258	93.34 205	93.82 112

test1		82.02 182	82.07 176	81.85 209	86.38 242	61.05 271	86.83 117	88.27 202	72.43 187	86.00 182	95.64 30	63.78 264	90.68 204	65.95 258	93.34 205	93.82 112

OpenMVS	76.72 13	81.98 184	82.00 178	81.93 206	84.42 280	68.22 195	88.50 94	89.48 183	66.92 248	81.80 263	91.86 156	72.59 212	90.16 217	71.19 208	91.25 250	87.40 284

KD-MVS_self_test		81.93 185	83.14 160	78.30 265	84.75 275	52.75 344	80.37 245	89.42 185	70.24 215	90.26 94	93.39 113	74.55 187	86.77 277	68.61 240	96.64 92	95.38 52

fmvsm_s_conf0.5_n		81.91 186	81.30 193	83.75 166	86.02 256	71.56 167	84.73 151	77.11 314	62.44 285	84.00 225	90.68 198	76.42 168	85.89 295	83.14 71	87.11 308	93.81 115

SDMVSNet		81.90 187	83.17 159	78.10 269	88.81 189	62.45 254	76.08 311	86.05 236	73.67 163	83.41 235	93.04 119	82.35 95	80.65 334	70.06 222	95.03 160	91.21 209

tfpnnormal		81.79 188	82.95 163	78.31 264	88.93 186	55.40 327	80.83 242	82.85 276	76.81 127	85.90 186	94.14 84	74.58 186	86.51 281	66.82 252	95.68 141	93.01 148

c3_l		81.64 189	81.59 186	81.79 213	80.86 328	59.15 297	78.61 273	90.18 168	68.36 231	87.20 151	87.11 265	69.39 231	91.62 174	78.16 131	94.43 182	94.60 75

PVSNet_Blended_VisFu		81.55 190	80.49 206	84.70 142	91.58 124	73.24 137	84.21 161	91.67 121	62.86 279	80.94 274	87.16 263	67.27 242	92.87 144	69.82 224	88.94 285	87.99 275

fmvsm_l_conf0.5_n_a		81.46 191	80.87 202	83.25 180	83.73 293	73.21 138	83.00 198	85.59 243	58.22 324	82.96 243	90.09 216	72.30 215	86.65 279	81.97 92	89.95 274	89.88 243

DELS-MVS		81.44 192	81.25 194	82.03 205	84.27 284	62.87 246	76.47 305	92.49 96	70.97 206	81.64 266	83.83 309	75.03 177	92.70 146	74.29 172	92.22 232	90.51 230
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		81.31 193	81.61 185	80.41 235	86.38 242	58.75 304	83.93 171	86.58 229	72.43 187	87.65 146	92.98 123	63.78 264	90.22 215	66.86 249	93.92 194	92.27 179

bld_raw_dy_0_64		81.25 194	81.17 198	81.49 217	85.55 262	60.85 277	86.36 127	95.45 9	57.08 334	90.81 88	82.69 327	65.85 252	93.91 101	70.37 219	96.34 105	89.72 244

TinyColmap		81.25 194	82.34 175	77.99 272	85.33 266	60.68 281	82.32 218	88.33 200	71.26 203	86.97 160	92.22 152	77.10 156	86.98 272	62.37 288	95.17 154	86.31 295

AUN-MVS		81.18 196	78.78 228	88.39 77	90.93 144	82.14 58	82.51 213	83.67 268	64.69 271	80.29 285	85.91 283	51.07 333	92.38 154	76.29 156	93.63 202	90.65 226

tttt0517		81.07 197	79.58 220	85.52 127	88.99 185	66.45 212	87.03 113	75.51 326	73.76 162	88.32 136	90.20 211	37.96 386	94.16 94	79.36 119	95.13 155	95.93 42

Fast-Effi-MVS+		81.04 198	80.57 203	82.46 202	87.50 219	63.22 242	78.37 276	89.63 180	68.01 236	81.87 259	82.08 331	82.31 97	92.65 148	67.10 248	88.30 296	91.51 205

BH-untuned		80.96 199	80.99 199	80.84 228	88.55 197	68.23 194	80.33 246	88.46 195	72.79 183	86.55 169	86.76 269	74.72 184	91.77 173	61.79 295	88.99 283	82.52 346

eth_miper_zixun_eth		80.84 200	80.22 212	82.71 195	81.41 320	60.98 274	77.81 282	90.14 169	67.31 246	86.95 161	87.24 262	64.26 259	92.31 157	75.23 166	91.61 243	94.85 71

xiu_mvs_v1_base_debu		80.84 200	80.14 214	82.93 190	88.31 201	71.73 161	79.53 255	87.17 214	65.43 262	79.59 291	82.73 324	76.94 159	90.14 220	73.22 191	88.33 292	86.90 289

xiu_mvs_v1_base		80.84 200	80.14 214	82.93 190	88.31 201	71.73 161	79.53 255	87.17 214	65.43 262	79.59 291	82.73 324	76.94 159	90.14 220	73.22 191	88.33 292	86.90 289

xiu_mvs_v1_base_debi		80.84 200	80.14 214	82.93 190	88.31 201	71.73 161	79.53 255	87.17 214	65.43 262	79.59 291	82.73 324	76.94 159	90.14 220	73.22 191	88.33 292	86.90 289

IterMVS-SCA-FT		80.64 204	79.41 221	84.34 151	83.93 289	69.66 181	76.28 307	81.09 290	72.43 187	86.47 175	90.19 212	60.46 280	93.15 134	77.45 142	86.39 320	90.22 235

BH-RMVSNet		80.53 205	80.22 212	81.49 217	87.19 225	66.21 214	77.79 283	86.23 232	74.21 157	83.69 229	88.50 239	73.25 205	90.75 201	63.18 285	87.90 299	87.52 282

Anonymous202405211		80.51 206	81.19 197	78.49 261	88.48 198	57.26 315	76.63 301	82.49 279	81.21 76	84.30 218	92.24 151	67.99 239	86.24 285	62.22 289	95.13 155	91.98 192

DIV-MVS_self_test		80.43 207	80.23 210	81.02 226	79.99 336	59.25 294	77.07 294	87.02 222	67.38 243	86.19 178	89.22 228	63.09 268	90.16 217	76.32 154	95.80 135	93.66 120

cl____		80.42 208	80.23 210	81.02 226	79.99 336	59.25 294	77.07 294	87.02 222	67.37 244	86.18 180	89.21 229	63.08 269	90.16 217	76.31 155	95.80 135	93.65 122

diffmvs		80.40 209	80.48 207	80.17 239	79.02 348	60.04 285	77.54 287	90.28 165	66.65 251	82.40 250	87.33 260	73.50 197	87.35 266	77.98 135	89.62 277	93.13 142
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		80.37 210	78.41 235	86.23 111	76.75 362	73.28 135	87.18 110	77.45 309	76.24 131	68.14 371	88.93 234	65.41 254	93.85 103	69.47 226	96.12 117	91.55 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 211	80.04 217	81.24 222	79.82 338	58.95 299	77.66 284	89.66 178	65.75 259	85.99 185	85.11 293	68.29 238	91.42 181	76.03 158	92.03 234	93.33 133

MG-MVS		80.32 212	80.94 200	78.47 262	88.18 204	52.62 347	82.29 219	85.01 255	72.01 197	79.24 298	92.54 140	69.36 232	93.36 128	70.65 214	89.19 282	89.45 249

VPNet		80.25 213	81.68 182	75.94 298	92.46 92	47.98 370	76.70 299	81.67 286	73.45 167	84.87 203	92.82 130	74.66 185	86.51 281	61.66 297	96.85 85	93.33 133

MAR-MVS		80.24 214	78.74 230	84.73 140	86.87 236	78.18 88	85.75 135	87.81 208	65.67 261	77.84 307	78.50 363	73.79 194	90.53 208	61.59 298	90.87 259	85.49 305
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		80.20 215	79.00 225	83.78 165	88.17 205	86.66 15	81.31 233	66.81 378	69.64 219	88.33 135	90.19 212	64.58 257	83.63 318	71.99 204	90.03 272	81.06 365

Anonymous20240521		80.18 216	81.25 194	76.95 285	83.15 305	60.84 278	82.46 214	85.99 238	68.76 228	86.78 162	93.73 107	59.13 292	77.44 348	73.71 184	97.55 67	92.56 164

LFMVS		80.15 217	80.56 204	78.89 253	89.19 181	55.93 323	85.22 144	73.78 338	82.96 58	84.28 219	92.72 135	57.38 304	90.07 224	63.80 279	95.75 138	90.68 224

DPM-MVS		80.10 218	79.18 224	82.88 193	90.71 150	69.74 179	78.87 269	90.84 144	60.29 311	75.64 329	85.92 282	67.28 241	93.11 135	71.24 207	91.79 239	85.77 301

MSDG		80.06 219	79.99 219	80.25 237	83.91 290	68.04 199	77.51 288	89.19 186	77.65 119	81.94 257	83.45 314	76.37 169	86.31 284	63.31 284	86.59 317	86.41 293

FE-MVS		79.98 220	78.86 226	83.36 177	86.47 239	66.45 212	89.73 65	84.74 261	72.80 182	84.22 223	91.38 172	44.95 367	93.60 115	63.93 278	91.50 246	90.04 241

sd_testset		79.95 221	81.39 192	75.64 301	88.81 189	58.07 308	76.16 310	82.81 277	73.67 163	83.41 235	93.04 119	80.96 119	77.65 347	58.62 312	95.03 160	91.21 209

ab-mvs		79.67 222	80.56 204	76.99 284	88.48 198	56.93 317	84.70 152	86.06 235	68.95 226	80.78 278	93.08 118	75.30 175	84.62 307	56.78 321	90.90 258	89.43 251

VNet		79.31 223	80.27 209	76.44 292	87.92 209	53.95 336	75.58 317	84.35 263	74.39 156	82.23 253	90.72 196	72.84 209	84.39 310	60.38 305	93.98 193	90.97 214

thisisatest0530		79.07 224	77.33 245	84.26 154	87.13 226	64.58 227	83.66 180	75.95 321	68.86 227	85.22 195	87.36 259	38.10 384	93.57 119	75.47 163	94.28 186	94.62 74

cl22		78.97 225	78.21 237	81.24 222	77.74 352	59.01 298	77.46 290	87.13 217	65.79 256	84.32 215	85.10 294	58.96 294	90.88 198	75.36 165	92.03 234	93.84 110

patch_mono-2		78.89 226	79.39 222	77.41 281	84.78 273	68.11 197	75.60 315	83.11 273	60.96 304	79.36 295	89.89 219	75.18 176	72.97 359	73.32 190	92.30 226	91.15 211

RPMNet		78.88 227	78.28 236	80.68 232	79.58 339	62.64 250	82.58 209	94.16 28	74.80 151	75.72 327	92.59 137	48.69 341	95.56 39	73.48 187	82.91 355	83.85 326

PAPR		78.84 228	78.10 238	81.07 224	85.17 269	60.22 284	82.21 223	90.57 152	62.51 281	75.32 333	84.61 302	74.99 178	92.30 158	59.48 309	88.04 298	90.68 224

iter_conf05		78.81 229	77.35 244	83.21 182	82.98 308	60.75 280	84.09 165	88.34 199	63.12 277	84.25 222	89.48 224	31.41 395	94.51 81	76.64 151	95.83 132	94.38 88

PVSNet_BlendedMVS		78.80 230	77.84 239	81.65 215	84.43 278	63.41 238	79.49 258	90.44 155	61.70 293	75.43 330	87.07 266	69.11 234	91.44 179	60.68 303	92.24 230	90.11 239

FMVSNet3		78.80 230	78.55 232	79.57 247	82.89 309	56.89 319	81.76 227	85.77 240	69.04 225	86.00 182	90.44 205	51.75 331	90.09 223	65.95 258	93.34 205	91.72 197

test_yl		78.71 232	78.51 233	79.32 250	84.32 282	58.84 301	78.38 274	85.33 246	75.99 135	82.49 248	86.57 270	58.01 298	90.02 226	62.74 286	92.73 221	89.10 258

DCV-MVSNet		78.71 232	78.51 233	79.32 250	84.32 282	58.84 301	78.38 274	85.33 246	75.99 135	82.49 248	86.57 270	58.01 298	90.02 226	62.74 286	92.73 221	89.10 258

test1111		78.53 234	78.85 227	77.56 278	92.22 101	47.49 372	82.61 207	69.24 367	72.43 187	85.28 194	94.20 80	51.91 329	90.07 224	65.36 266	96.45 102	95.11 62

ECVR-MVS		78.44 235	78.63 231	77.88 274	91.85 114	48.95 366	83.68 179	69.91 364	72.30 193	84.26 221	94.20 80	51.89 330	89.82 229	63.58 280	96.02 121	94.87 67

pmmvs-eth3d		78.42 236	77.04 248	82.57 200	87.44 220	74.41 127	80.86 241	79.67 298	55.68 339	84.69 206	90.31 209	60.91 278	85.42 300	62.20 290	91.59 244	87.88 278

iter_conf05_11		78.40 237	77.29 246	81.71 214	85.55 262	60.95 276	77.22 291	86.90 226	60.10 314	75.79 326	81.73 335	64.08 261	94.47 82	70.37 219	93.92 194	89.72 244

mvs_anonymous		78.13 238	78.76 229	76.23 297	79.24 345	50.31 363	78.69 271	84.82 259	61.60 295	83.09 242	92.82 130	73.89 193	87.01 269	68.33 244	86.41 319	91.37 206

TAMVS		78.08 239	76.36 254	83.23 181	90.62 151	72.87 140	79.08 265	80.01 297	61.72 292	81.35 270	86.92 268	63.96 263	88.78 251	50.61 359	93.01 215	88.04 274

miper_enhance_ethall		77.83 240	76.93 249	80.51 233	76.15 368	58.01 309	75.47 319	88.82 190	58.05 326	83.59 231	80.69 342	64.41 258	91.20 185	73.16 197	92.03 234	92.33 175

Vis-MVSNet (Re-imp)		77.82 241	77.79 240	77.92 273	88.82 188	51.29 357	83.28 188	71.97 352	74.04 158	82.23 253	89.78 220	57.38 304	89.41 241	57.22 320	95.41 144	93.05 146

CANet_DTU		77.81 242	77.05 247	80.09 240	81.37 321	59.90 288	83.26 189	88.29 201	69.16 223	67.83 374	83.72 310	60.93 277	89.47 236	69.22 230	89.70 276	90.88 217

OpenMVS_ROB	70.19 17	77.77 243	77.46 241	78.71 257	84.39 281	61.15 269	81.18 237	82.52 278	62.45 284	83.34 237	87.37 258	66.20 247	88.66 253	64.69 273	85.02 336	86.32 294

SSC-MVS		77.55 244	81.64 183	65.29 364	90.46 154	20.33 409	73.56 335	68.28 369	85.44 32	88.18 139	94.64 59	70.93 226	81.33 329	71.25 206	92.03 234	94.20 92

MDA-MVSNet-bldmvs		77.47 245	76.90 250	79.16 252	79.03 347	64.59 226	66.58 374	75.67 324	73.15 177	88.86 122	88.99 233	66.94 243	81.23 330	64.71 272	88.22 297	91.64 201

jason		77.42 246	75.75 260	82.43 203	87.10 229	69.27 185	77.99 279	81.94 284	51.47 363	77.84 307	85.07 297	60.32 282	89.00 245	70.74 213	89.27 281	89.03 261
jason: jason.
CDS-MVSNet		77.32 247	75.40 263	83.06 185	89.00 184	72.48 151	77.90 281	82.17 282	60.81 305	78.94 300	83.49 313	59.30 290	88.76 252	54.64 339	92.37 225	87.93 277
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 248	76.75 251	78.52 260	87.01 232	61.30 267	75.55 318	87.12 220	61.24 301	74.45 338	78.79 361	77.20 153	90.93 194	64.62 275	84.80 343	83.32 335

MVSTER		77.09 249	75.70 261	81.25 220	75.27 376	61.08 270	77.49 289	85.07 251	60.78 306	86.55 169	88.68 237	43.14 376	90.25 212	73.69 185	90.67 265	92.42 169

PS-MVSNAJ		77.04 250	76.53 253	78.56 259	87.09 230	61.40 265	75.26 320	87.13 217	61.25 300	74.38 340	77.22 374	76.94 159	90.94 193	64.63 274	84.83 342	83.35 334

IterMVS		76.91 251	76.34 255	78.64 258	80.91 326	64.03 233	76.30 306	79.03 301	64.88 270	83.11 240	89.16 230	59.90 286	84.46 308	68.61 240	85.15 334	87.42 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 252	75.67 262	80.34 236	80.48 334	62.16 261	73.50 336	84.80 260	57.61 330	82.24 252	87.54 255	51.31 332	87.65 262	70.40 218	93.19 211	91.23 208

CL-MVSNet_self_test		76.81 253	77.38 243	75.12 304	86.90 234	51.34 355	73.20 339	80.63 294	68.30 233	81.80 263	88.40 240	66.92 244	80.90 331	55.35 333	94.90 166	93.12 144

TR-MVS		76.77 254	75.79 259	79.72 244	86.10 255	65.79 218	77.14 292	83.02 274	65.20 268	81.40 269	82.10 329	66.30 246	90.73 203	55.57 330	85.27 330	82.65 341

USDC		76.63 255	76.73 252	76.34 294	83.46 295	57.20 316	80.02 249	88.04 206	52.14 359	83.65 230	91.25 175	63.24 267	86.65 279	54.66 338	94.11 190	85.17 307

BH-w/o		76.57 256	76.07 258	78.10 269	86.88 235	65.92 217	77.63 285	86.33 230	65.69 260	80.89 275	79.95 351	68.97 236	90.74 202	53.01 349	85.25 331	77.62 376

Patchmtry		76.56 257	77.46 241	73.83 310	79.37 344	46.60 376	82.41 216	76.90 315	73.81 161	85.56 191	92.38 143	48.07 344	83.98 315	63.36 283	95.31 150	90.92 216

PVSNet_Blended		76.49 258	75.40 263	79.76 243	84.43 278	63.41 238	75.14 321	90.44 155	57.36 332	75.43 330	78.30 364	69.11 234	91.44 179	60.68 303	87.70 303	84.42 317

miper_lstm_enhance		76.45 259	76.10 257	77.51 279	76.72 363	60.97 275	64.69 378	85.04 253	63.98 274	83.20 239	88.22 242	56.67 308	78.79 345	73.22 191	93.12 212	92.78 155

lupinMVS		76.37 260	74.46 272	82.09 204	85.54 264	69.26 186	76.79 297	80.77 293	50.68 370	76.23 319	82.82 322	58.69 295	88.94 246	69.85 223	88.77 286	88.07 271

cascas		76.29 261	74.81 268	80.72 231	84.47 277	62.94 244	73.89 333	87.34 211	55.94 338	75.16 335	76.53 379	63.97 262	91.16 187	65.00 269	90.97 256	88.06 273

WB-MVS		76.06 262	80.01 218	64.19 367	89.96 167	20.58 408	72.18 344	68.19 370	83.21 54	86.46 176	93.49 111	70.19 229	78.97 343	65.96 257	90.46 269	93.02 147

thres600view7		75.97 263	75.35 265	77.85 276	87.01 232	51.84 353	80.45 244	73.26 343	75.20 148	83.10 241	86.31 276	45.54 358	89.05 244	55.03 336	92.24 230	92.66 161

GA-MVS		75.83 264	74.61 269	79.48 249	81.87 313	59.25 294	73.42 337	82.88 275	68.68 229	79.75 290	81.80 334	50.62 335	89.46 237	66.85 250	85.64 327	89.72 244

MVP-Stereo		75.81 265	73.51 281	82.71 195	89.35 175	73.62 131	80.06 247	85.20 248	60.30 310	73.96 341	87.94 247	57.89 302	89.45 238	52.02 353	74.87 387	85.06 309
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 266	75.20 266	77.27 282	75.01 379	69.47 183	78.93 266	84.88 258	46.67 377	87.08 157	87.84 250	50.44 337	71.62 364	77.42 144	88.53 289	90.72 221

thres100view900		75.45 267	75.05 267	76.66 291	87.27 222	51.88 352	81.07 238	73.26 343	75.68 141	83.25 238	86.37 273	45.54 358	88.80 248	51.98 354	90.99 253	89.31 253

ET-MVSNet_ETH3D		75.28 268	72.77 289	82.81 194	83.03 307	68.11 197	77.09 293	76.51 319	60.67 308	77.60 312	80.52 346	38.04 385	91.15 188	70.78 211	90.68 264	89.17 256

thres400		75.14 269	74.23 274	77.86 275	86.24 249	52.12 349	79.24 262	73.87 336	73.34 170	81.82 261	84.60 303	46.02 352	88.80 248	51.98 354	90.99 253	92.66 161

wuyk23d		75.13 270	79.30 223	62.63 370	75.56 372	75.18 124	80.89 240	73.10 345	75.06 150	94.76 12	95.32 35	87.73 40	52.85 400	34.16 400	97.11 80	59.85 397

EU-MVSNet		75.12 271	74.43 273	77.18 283	83.11 306	59.48 292	85.71 137	82.43 280	39.76 397	85.64 189	88.76 235	44.71 369	87.88 260	73.86 181	85.88 326	84.16 322

HyFIR lowres test		75.12 271	72.66 291	82.50 201	91.44 132	65.19 223	72.47 342	87.31 212	46.79 376	80.29 285	84.30 305	52.70 326	92.10 164	51.88 358	86.73 315	90.22 235

CMPMVS	59.41 20	75.12 271	73.57 279	79.77 242	75.84 371	67.22 202	81.21 236	82.18 281	50.78 368	76.50 315	87.66 253	55.20 318	82.99 321	62.17 292	90.64 268	89.09 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 274	72.98 287	80.73 230	84.95 270	71.71 164	76.23 308	77.59 308	52.83 353	77.73 311	86.38 272	56.35 311	84.97 304	57.72 319	87.05 310	85.51 304

tfpn200view9		74.86 275	74.23 274	76.74 290	86.24 249	52.12 349	79.24 262	73.87 336	73.34 170	81.82 261	84.60 303	46.02 352	88.80 248	51.98 354	90.99 253	89.31 253

1112_ss		74.82 276	73.74 277	78.04 271	89.57 169	60.04 285	76.49 304	87.09 221	54.31 346	73.66 344	79.80 352	60.25 283	86.76 278	58.37 313	84.15 347	87.32 285

EGC-MVSNET		74.79 277	69.99 317	89.19 63	94.89 37	87.00 11	91.89 34	86.28 231	1.09 405	2.23 407	95.98 23	81.87 109	89.48 235	79.76 112	95.96 124	91.10 212

ppachtmachnet_test		74.73 278	74.00 276	76.90 287	80.71 331	56.89 319	71.53 350	78.42 303	58.24 323	79.32 297	82.92 321	57.91 301	84.26 312	65.60 264	91.36 248	89.56 248

Patchmatch-RL test		74.48 279	73.68 278	76.89 288	84.83 272	66.54 210	72.29 343	69.16 368	57.70 328	86.76 163	86.33 274	45.79 357	82.59 322	69.63 225	90.65 267	81.54 356

PatchMatch-RL		74.48 279	73.22 284	78.27 267	87.70 213	85.26 34	75.92 313	70.09 362	64.34 272	76.09 322	81.25 340	65.87 251	78.07 346	53.86 341	83.82 349	71.48 385

XXY-MVS		74.44 281	76.19 256	69.21 342	84.61 276	52.43 348	71.70 347	77.18 313	60.73 307	80.60 279	90.96 187	75.44 172	69.35 370	56.13 326	88.33 292	85.86 300

test2506		74.12 282	73.39 282	76.28 295	91.85 114	44.20 386	84.06 166	48.20 405	72.30 193	81.90 258	94.20 80	27.22 406	89.77 232	64.81 271	96.02 121	94.87 67

CR-MVSNet		74.00 283	73.04 286	76.85 289	79.58 339	62.64 250	82.58 209	76.90 315	50.50 371	75.72 327	92.38 143	48.07 344	84.07 314	68.72 239	82.91 355	83.85 326

Test_1112_low_res		73.90 284	73.08 285	76.35 293	90.35 156	55.95 322	73.40 338	86.17 233	50.70 369	73.14 345	85.94 281	58.31 297	85.90 294	56.51 323	83.22 352	87.20 286

test20.03		73.75 285	74.59 271	71.22 330	81.11 324	51.12 359	70.15 360	72.10 351	70.42 210	80.28 287	91.50 169	64.21 260	74.72 358	46.96 377	94.58 178	87.82 280

test_fmvs2		73.57 286	72.80 288	75.90 299	72.74 391	68.84 192	77.07 294	84.32 264	45.14 383	82.89 244	84.22 306	48.37 342	70.36 367	73.40 189	87.03 311	88.52 267

SCA		73.32 287	72.57 293	75.58 302	81.62 317	55.86 324	78.89 268	71.37 357	61.73 291	74.93 336	83.42 315	60.46 280	87.01 269	58.11 317	82.63 360	83.88 323

baseline1		73.26 288	73.54 280	72.43 324	84.92 271	47.79 371	79.89 251	74.00 334	65.93 254	78.81 301	86.28 277	56.36 310	81.63 328	56.63 322	79.04 376	87.87 279

1314		73.22 289	72.56 294	75.20 303	80.41 335	57.84 310	81.64 230	85.36 245	51.68 362	73.10 346	76.65 378	61.45 275	85.19 302	63.54 281	79.21 374	82.59 342

MVS		73.21 290	72.59 292	75.06 305	80.97 325	60.81 279	81.64 230	85.92 239	46.03 381	71.68 353	77.54 369	68.47 237	89.77 232	55.70 329	85.39 328	74.60 382

HY-MVS	64.64 18	73.03 291	72.47 295	74.71 306	83.36 299	54.19 334	82.14 226	81.96 283	56.76 337	69.57 366	86.21 278	60.03 284	84.83 306	49.58 365	82.65 358	85.11 308

thisisatest0515		73.00 292	70.52 309	80.46 234	81.45 319	59.90 288	73.16 340	74.31 333	57.86 327	76.08 323	77.78 367	37.60 387	92.12 163	65.00 269	91.45 247	89.35 252

EPNet_dtu		72.87 293	71.33 305	77.49 280	77.72 353	60.55 282	82.35 217	75.79 322	66.49 252	58.39 400	81.06 341	53.68 322	85.98 291	53.55 344	92.97 217	85.95 298
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 294	71.41 304	76.28 295	83.25 301	60.34 283	83.50 183	79.02 302	37.77 400	76.33 317	85.10 294	49.60 340	87.41 265	70.54 216	77.54 382	81.08 363

CHOSEN 1792x2688		72.45 295	70.56 308	78.13 268	90.02 166	63.08 243	68.72 365	83.16 272	42.99 391	75.92 324	85.46 287	57.22 306	85.18 303	49.87 363	81.67 362	86.14 296

testgi		72.36 296	74.61 269	65.59 361	80.56 333	42.82 391	68.29 366	73.35 342	66.87 249	81.84 260	89.93 217	72.08 218	66.92 383	46.05 380	92.54 223	87.01 288

thres200		72.34 297	71.55 303	74.70 307	83.48 294	51.60 354	75.02 322	73.71 339	70.14 216	78.56 303	80.57 345	46.20 350	88.20 258	46.99 376	89.29 279	84.32 318

FPMVS		72.29 298	72.00 297	73.14 315	88.63 194	85.00 36	74.65 326	67.39 372	71.94 198	77.80 309	87.66 253	50.48 336	75.83 354	49.95 361	79.51 370	58.58 399

FMVSNet5		72.10 299	71.69 299	73.32 313	81.57 318	53.02 343	76.77 298	78.37 304	63.31 275	76.37 316	91.85 157	36.68 388	78.98 342	47.87 373	92.45 224	87.95 276

our_test_3		71.85 300	71.59 300	72.62 321	80.71 331	53.78 337	69.72 362	71.71 356	58.80 320	78.03 304	80.51 347	56.61 309	78.84 344	62.20 290	86.04 325	85.23 306

PAPM		71.77 301	70.06 315	76.92 286	86.39 241	53.97 335	76.62 302	86.62 228	53.44 350	63.97 390	84.73 301	57.79 303	92.34 156	39.65 391	81.33 366	84.45 316

IB-MVS	62.13 19	71.64 302	68.97 326	79.66 246	80.80 330	62.26 259	73.94 332	76.90 315	63.27 276	68.63 370	76.79 376	33.83 392	91.84 171	59.28 310	87.26 305	84.88 310
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		71.63 303	72.30 296	69.62 339	76.47 365	52.70 346	70.03 361	80.97 291	59.18 317	79.36 295	88.21 243	60.50 279	69.12 371	58.33 315	77.62 381	87.04 287

testing3		71.53 304	70.79 306	73.77 311	88.89 187	41.86 393	76.60 303	59.12 395	72.83 181	80.97 272	82.08 331	19.80 411	87.33 267	65.12 268	91.68 242	92.13 186

test_vis3_rt		71.42 305	70.67 307	73.64 312	69.66 397	70.46 174	66.97 373	89.73 175	42.68 393	88.20 138	83.04 317	43.77 371	60.07 395	65.35 267	86.66 316	90.39 233

Anonymous20231206		71.38 306	71.88 298	69.88 337	86.31 246	54.37 333	70.39 358	74.62 329	52.57 355	76.73 314	88.76 235	59.94 285	72.06 361	44.35 384	93.23 210	83.23 337

test_vis1_n_1920		71.30 307	71.58 302	70.47 333	77.58 355	59.99 287	74.25 327	84.22 265	51.06 365	74.85 337	79.10 358	55.10 319	68.83 373	68.86 236	79.20 375	82.58 343

MIMVSNet		71.09 308	71.59 300	69.57 340	87.23 223	50.07 364	78.91 267	71.83 353	60.20 313	71.26 354	91.76 163	55.08 320	76.09 352	41.06 389	87.02 312	82.54 345

test_fmvs1_n		70.94 309	70.41 312	72.53 323	73.92 381	66.93 207	75.99 312	84.21 266	43.31 390	79.40 294	79.39 356	43.47 372	68.55 375	69.05 233	84.91 339	82.10 350

MS-PatchMatch		70.93 310	70.22 313	73.06 316	81.85 314	62.50 253	73.82 334	77.90 305	52.44 356	75.92 324	81.27 339	55.67 315	81.75 326	55.37 332	77.70 380	74.94 381

pmmvs5		70.73 311	70.07 314	72.72 319	77.03 360	52.73 345	74.14 328	75.65 325	50.36 372	72.17 351	85.37 291	55.42 317	80.67 333	52.86 350	87.59 304	84.77 311

PatchT		70.52 312	72.76 290	63.79 369	79.38 343	33.53 403	77.63 285	65.37 381	73.61 165	71.77 352	92.79 133	44.38 370	75.65 355	64.53 276	85.37 329	82.18 349

test_vis1_n		70.29 313	69.99 317	71.20 331	75.97 370	66.50 211	76.69 300	80.81 292	44.22 386	75.43 330	77.23 373	50.00 338	68.59 374	66.71 253	82.85 357	78.52 375

N_pmnet		70.20 314	68.80 328	74.38 308	80.91 326	84.81 39	59.12 389	76.45 320	55.06 342	75.31 334	82.36 328	55.74 314	54.82 399	47.02 375	87.24 306	83.52 330

tpmvs		70.16 315	69.56 320	71.96 326	74.71 380	48.13 368	79.63 253	75.45 327	65.02 269	70.26 362	81.88 333	45.34 363	85.68 298	58.34 314	75.39 386	82.08 351

new-patchmatchnet		70.10 316	73.37 283	60.29 377	81.23 323	16.95 410	59.54 387	74.62 329	62.93 278	80.97 272	87.93 248	62.83 272	71.90 362	55.24 334	95.01 163	92.00 190

YYNet1		70.06 317	70.44 310	68.90 344	73.76 383	53.42 341	58.99 390	67.20 374	58.42 322	87.10 155	85.39 290	59.82 287	67.32 380	59.79 307	83.50 351	85.96 297

MDA-MVSNet_test_wron		70.05 318	70.44 310	68.88 345	73.84 382	53.47 339	58.93 391	67.28 373	58.43 321	87.09 156	85.40 289	59.80 288	67.25 381	59.66 308	83.54 350	85.92 299

CostFormer		69.98 319	68.68 329	73.87 309	77.14 358	50.72 361	79.26 261	74.51 331	51.94 361	70.97 357	84.75 300	45.16 366	87.49 264	55.16 335	79.23 373	83.40 333

testing91		69.94 320	68.99 325	72.80 318	83.81 292	45.89 379	71.57 349	73.64 341	68.24 234	70.77 360	77.82 366	34.37 391	84.44 309	53.64 343	87.00 313	88.07 271

baseline2		69.77 321	66.89 337	78.41 263	79.51 341	58.09 307	76.23 308	69.57 365	57.50 331	64.82 388	77.45 371	46.02 352	88.44 254	53.08 346	77.83 378	88.70 265

PatchmatchNet		69.71 322	68.83 327	72.33 325	77.66 354	53.60 338	79.29 260	69.99 363	57.66 329	72.53 349	82.93 320	46.45 349	80.08 338	60.91 302	72.09 390	83.31 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 323	69.05 323	71.14 332	69.15 398	65.77 219	73.98 331	83.32 271	42.83 392	77.77 310	78.27 365	43.39 375	68.50 376	68.39 243	84.38 346	79.15 373

JIA-IIPM		69.41 324	66.64 341	77.70 277	73.19 386	71.24 169	75.67 314	65.56 380	70.42 210	65.18 384	92.97 125	33.64 393	83.06 319	53.52 345	69.61 396	78.79 374

Syy-MVS		69.40 325	70.03 316	67.49 354	81.72 315	38.94 396	71.00 352	61.99 386	61.38 297	70.81 358	72.36 389	61.37 276	79.30 340	64.50 277	85.18 332	84.22 319

testing99		69.27 326	68.15 332	72.63 320	83.29 300	45.45 381	71.15 351	71.08 358	67.34 245	70.43 361	77.77 368	32.24 394	84.35 311	53.72 342	86.33 321	88.10 270

UnsupCasMVSNet_bld		69.21 327	69.68 319	67.82 352	79.42 342	51.15 358	67.82 370	75.79 322	54.15 347	77.47 313	85.36 292	59.26 291	70.64 366	48.46 370	79.35 372	81.66 354

test_cas_vis1_n_1920		69.20 328	69.12 321	69.43 341	73.68 384	62.82 247	70.38 359	77.21 312	46.18 380	80.46 284	78.95 360	52.03 328	65.53 388	65.77 263	77.45 383	79.95 371

gg-mvs-nofinetune		68.96 329	69.11 322	68.52 350	76.12 369	45.32 382	83.59 181	55.88 400	86.68 24	64.62 389	97.01 7	30.36 398	83.97 316	44.78 383	82.94 354	76.26 378

WB-MVSnew		68.72 330	69.01 324	67.85 351	83.22 303	43.98 387	74.93 323	65.98 379	55.09 341	73.83 342	79.11 357	65.63 253	71.89 363	38.21 396	85.04 335	87.69 281

tpm2		68.45 331	66.83 338	73.30 314	78.93 349	48.50 367	79.76 252	71.76 354	47.50 375	69.92 364	83.60 311	42.07 378	88.40 255	48.44 371	79.51 370	83.01 340

tpm		67.95 332	68.08 333	67.55 353	78.74 350	43.53 389	75.60 315	67.10 377	54.92 343	72.23 350	88.10 244	42.87 377	75.97 353	52.21 352	80.95 369	83.15 338

WTY-MVS		67.91 333	68.35 330	66.58 358	80.82 329	48.12 369	65.96 375	72.60 346	53.67 349	71.20 355	81.68 337	58.97 293	69.06 372	48.57 369	81.67 362	82.55 344

testing11		67.38 334	65.93 342	71.73 328	83.37 298	46.60 376	70.95 354	69.40 366	62.47 283	66.14 377	76.66 377	31.22 396	84.10 313	49.10 367	84.10 348	84.49 314

test-LLR		67.21 335	66.74 339	68.63 348	76.45 366	55.21 329	67.89 367	67.14 375	62.43 286	65.08 385	72.39 387	43.41 373	69.37 368	61.00 300	84.89 340	81.31 358

testing222		66.93 336	65.30 348	71.81 327	83.38 297	45.83 380	72.06 345	67.50 371	64.12 273	69.68 365	76.37 380	27.34 405	83.00 320	38.88 392	88.38 291	86.62 292

sss		66.92 337	67.26 335	65.90 360	77.23 357	51.10 360	64.79 377	71.72 355	52.12 360	70.13 363	80.18 349	57.96 300	65.36 389	50.21 360	81.01 368	81.25 360

KD-MVS_2432*1600		66.87 338	65.81 344	70.04 335	67.50 399	47.49 372	62.56 382	79.16 299	61.21 302	77.98 305	80.61 343	25.29 408	82.48 323	53.02 347	84.92 337	80.16 369

miper_refine_blended		66.87 338	65.81 344	70.04 335	67.50 399	47.49 372	62.56 382	79.16 299	61.21 302	77.98 305	80.61 343	25.29 408	82.48 323	53.02 347	84.92 337	80.16 369

dmvs_re		66.81 340	66.98 336	66.28 359	76.87 361	58.68 305	71.66 348	72.24 349	60.29 311	69.52 367	73.53 386	52.38 327	64.40 391	44.90 382	81.44 365	75.76 379

tpm cat1		66.76 341	65.21 349	71.42 329	77.09 359	50.62 362	78.01 278	73.68 340	44.89 384	68.64 369	79.00 359	45.51 360	82.42 325	49.91 362	70.15 393	81.23 362

UWE-MVS		66.43 342	65.56 347	69.05 343	84.15 286	40.98 394	73.06 341	64.71 382	54.84 344	76.18 321	79.62 355	29.21 400	80.50 335	38.54 395	89.75 275	85.66 302

PVSNet	58.17 21	66.41 343	65.63 346	68.75 346	81.96 312	49.88 365	62.19 384	72.51 348	51.03 366	68.04 372	75.34 384	50.84 334	74.77 356	45.82 381	82.96 353	81.60 355

tpmrst		66.28 344	66.69 340	65.05 365	72.82 390	39.33 395	78.20 277	70.69 361	53.16 352	67.88 373	80.36 348	48.18 343	74.75 357	58.13 316	70.79 392	81.08 363

Patchmatch-test		65.91 345	67.38 334	61.48 375	75.51 373	43.21 390	68.84 364	63.79 384	62.48 282	72.80 348	83.42 315	44.89 368	59.52 397	48.27 372	86.45 318	81.70 353

ADS-MVSNet2		65.87 346	63.64 354	72.55 322	73.16 387	56.92 318	67.10 371	74.81 328	49.74 373	66.04 379	82.97 318	46.71 347	77.26 349	42.29 386	69.96 394	83.46 331

test_vis1_rt		65.64 347	64.09 351	70.31 334	66.09 403	70.20 177	61.16 385	81.60 287	38.65 398	72.87 347	69.66 392	52.84 324	60.04 396	56.16 325	77.77 379	80.68 367

mvsany_test3		65.48 348	62.97 356	73.03 317	69.99 396	76.17 118	64.83 376	43.71 407	43.68 388	80.25 288	87.05 267	52.83 325	63.09 394	51.92 357	72.44 389	79.84 372

test-mter		65.00 349	63.79 353	68.63 348	76.45 366	55.21 329	67.89 367	67.14 375	50.98 367	65.08 385	72.39 387	28.27 403	69.37 368	61.00 300	84.89 340	81.31 358

ETVMVS		64.67 350	63.34 355	68.64 347	83.44 296	41.89 392	69.56 363	61.70 391	61.33 299	68.74 368	75.76 382	28.76 401	79.35 339	34.65 399	86.16 324	84.67 313

myMVS_eth3d		64.66 351	63.89 352	66.97 356	81.72 315	37.39 399	71.00 352	61.99 386	61.38 297	70.81 358	72.36 389	20.96 410	79.30 340	49.59 364	85.18 332	84.22 319

test0.0.03 1		64.66 351	64.36 350	65.57 362	75.03 378	46.89 375	64.69 378	61.58 392	62.43 286	71.18 356	77.54 369	43.41 373	68.47 377	40.75 390	82.65 358	81.35 357

test_f		64.31 353	65.85 343	59.67 378	66.54 402	62.24 260	57.76 392	70.96 359	40.13 395	84.36 213	82.09 330	46.93 346	51.67 401	61.99 293	81.89 361	65.12 393

pmmvs3		62.47 354	60.02 367	69.80 338	71.58 394	64.00 234	70.52 357	58.44 398	39.77 396	66.05 378	75.84 381	27.10 407	72.28 360	46.15 379	84.77 344	73.11 383

EPMVS		62.47 354	62.63 358	62.01 371	70.63 395	38.74 397	74.76 324	52.86 402	53.91 348	67.71 375	80.01 350	39.40 382	66.60 384	55.54 331	68.81 398	80.68 367

ADS-MVSNet		61.90 356	62.19 360	61.03 376	73.16 387	36.42 401	67.10 371	61.75 389	49.74 373	66.04 379	82.97 318	46.71 347	63.21 392	42.29 386	69.96 394	83.46 331

PMMVS		61.65 357	60.38 364	65.47 363	65.40 406	69.26 186	63.97 380	61.73 390	36.80 401	60.11 395	68.43 394	59.42 289	66.35 385	48.97 368	78.57 377	60.81 396

E-PMN		61.59 358	61.62 361	61.49 374	66.81 401	55.40 327	53.77 395	60.34 394	66.80 250	58.90 398	65.50 397	40.48 381	66.12 386	55.72 328	86.25 322	62.95 395

TESTMET0.1,1		61.29 359	60.32 365	64.19 367	72.06 392	51.30 356	67.89 367	62.09 385	45.27 382	60.65 394	69.01 393	27.93 404	64.74 390	56.31 324	81.65 364	76.53 377

MVS-HIRNet		61.16 360	62.92 357	55.87 381	79.09 346	35.34 402	71.83 346	57.98 399	46.56 378	59.05 397	91.14 179	49.95 339	76.43 351	38.74 393	71.92 391	55.84 400

EMVS		61.10 361	60.81 363	61.99 372	65.96 404	55.86 324	53.10 396	58.97 397	67.06 247	56.89 401	63.33 398	40.98 379	67.03 382	54.79 337	86.18 323	63.08 394

DSMNet-mixed		60.98 362	61.61 362	59.09 380	72.88 389	45.05 384	74.70 325	46.61 406	26.20 402	65.34 383	90.32 208	55.46 316	63.12 393	41.72 388	81.30 367	69.09 389

dp		60.70 363	60.29 366	61.92 373	72.04 393	38.67 398	70.83 355	64.08 383	51.28 364	60.75 393	77.28 372	36.59 389	71.58 365	47.41 374	62.34 400	75.52 380

dmvs_testset		60.59 364	62.54 359	54.72 383	77.26 356	27.74 406	74.05 330	61.00 393	60.48 309	65.62 382	67.03 396	55.93 313	68.23 378	32.07 403	69.46 397	68.17 390

CHOSEN 280x420		59.08 365	56.52 370	66.76 357	76.51 364	64.39 230	49.62 397	59.00 396	43.86 387	55.66 402	68.41 395	35.55 390	68.21 379	43.25 385	76.78 385	67.69 391

mvsany_test1		58.48 366	56.47 371	64.50 366	65.90 405	68.21 196	56.95 393	42.11 408	38.30 399	65.69 381	77.19 375	56.96 307	59.35 398	46.16 378	58.96 401	65.93 392

PVSNet_0	51.08 22	56.10 367	54.97 372	59.48 379	75.12 377	53.28 342	55.16 394	61.89 388	44.30 385	59.16 396	62.48 399	54.22 321	65.91 387	35.40 398	47.01 402	59.25 398

new_pmnet		55.69 368	57.66 369	49.76 384	75.47 374	30.59 404	59.56 386	51.45 403	43.62 389	62.49 391	75.48 383	40.96 380	49.15 403	37.39 397	72.52 388	69.55 388

PMMVS2		55.64 369	59.27 368	44.74 385	64.30 407	12.32 411	40.60 398	49.79 404	53.19 351	65.06 387	84.81 299	53.60 323	49.76 402	32.68 402	89.41 278	72.15 384

MVE	40.22 23	51.82 370	50.47 373	55.87 381	62.66 408	51.91 351	31.61 400	39.28 409	40.65 394	50.76 403	74.98 385	56.24 312	44.67 404	33.94 401	64.11 399	71.04 387
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 371	29.60 374	33.06 386	17.99 410	3.84 413	13.62 401	73.92 335	2.79 404	18.29 406	53.41 401	28.53 402	43.25 405	22.56 404	35.27 404	52.11 401

cdsmvs_eth3d_5k		20.81 372	27.75 375	0.00 391	0.00 414	0.00 416	0.00 402	85.44 244	0.00 409	0.00 410	82.82 322	81.46 113	0.00 410	0.00 409	0.00 408	0.00 406

tmp_tt		20.25 373	24.50 376	7.49 388	4.47 411	8.70 412	34.17 399	25.16 411	1.00 406	32.43 405	18.49 403	39.37 383	9.21 407	21.64 405	43.75 403	4.57 403

ab-mvs-re		6.65 374	8.87 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	79.80 352	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		6.41 375	8.55 378	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	76.94 159	0.00 410	0.00 409	0.00 408	0.00 406

test123		6.27 376	8.08 379	0.84 389	1.11 413	0.57 414	62.90 381	0.82 413	0.54 407	1.07 409	2.75 408	1.26 412	0.30 408	1.04 407	1.26 407	1.66 404

testmvs		5.91 377	7.65 380	0.72 390	1.20 412	0.37 415	59.14 388	0.67 414	0.49 408	1.11 408	2.76 407	0.94 413	0.24 409	1.02 408	1.47 406	1.55 405

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						37.39 399								52.61 351

FOURS1					96.08 11	87.41 10	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 69	91.55 126	77.99 90		91.01 140					96.05 8	87.45 20	98.17 32	92.40 171

PC_three_1452									58.96 319	90.06 96	91.33 173	80.66 123	93.03 138	75.78 160	95.94 126	92.48 167

No_MVS				88.81 69	91.55 126	77.99 90		91.01 140					96.05 8	87.45 20	98.17 32	92.40 171

test_one_0601					93.85 58	73.27 136		94.11 34	86.57 25	93.47 38	94.64 59	88.42 26

eth-test2					0.00 414

eth-test					0.00 414

ZD-MVS					92.22 101	80.48 67		91.85 115	71.22 204	90.38 91	92.98 123	86.06 59	96.11 6	81.99 91	96.75 90

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 28	88.75 14	93.79 28	94.43 67	90.64 10		87.16 29	97.60 64	92.73 156

IU-MVS					94.18 46	72.64 144		90.82 145	56.98 335	89.67 108				85.78 50	97.92 46	93.28 135

OPU-MVS				88.27 80	91.89 112	77.83 93	90.47 51				91.22 176	81.12 117	94.68 71	74.48 171	95.35 146	92.29 177

test_241102_TWO								93.71 50	83.77 47	93.49 36	94.27 74	89.27 21	95.84 23	86.03 46	97.82 51	92.04 188

test_241102_ONE					94.18 46	72.65 142		93.69 51	83.62 49	94.11 22	93.78 104	90.28 14	95.50 46

9.14			89.29 58		91.84 116		88.80 88	95.32 12	75.14 149	91.07 79	92.89 128	87.27 44	93.78 106	83.69 69	97.55 67

save fliter					93.75 59	77.44 99	86.31 128	89.72 176	70.80 207

test_0728_THIRD									85.33 33	93.75 30	94.65 56	87.44 43	95.78 28	87.41 22	98.21 29	92.98 150

test_0728_SECOND				86.79 100	94.25 45	72.45 152	90.54 48	94.10 35					95.88 17	86.42 36	97.97 43	92.02 189

test0726					94.16 49	72.56 148	90.63 45	93.90 43	83.61 50	93.75 30	94.49 64	89.76 18

GSMVS																83.88 323

test_part2					93.86 57	77.77 94				92.84 48

sam_mvs1												46.11 351				83.88 323

sam_mvs												45.92 356

ambc				82.98 187	90.55 153	64.86 225	88.20 96	89.15 187		89.40 117	93.96 95	71.67 223	91.38 183	78.83 122	96.55 95	92.71 159

MTGPA								91.81 119

test_post1							78.85 270				3.13 405	45.19 365	80.13 337	58.11 317

test_post											3.10 406	45.43 361	77.22 350

patchmatchnet-post											81.71 336	45.93 355	87.01 269

GG-mvs-BLEND				67.16 355	73.36 385	46.54 378	84.15 163	55.04 401		58.64 399	61.95 400	29.93 399	83.87 317	38.71 394	76.92 384	71.07 386

MTMP							90.66 44	33.14 410

gm-plane-assit					75.42 375	44.97 385			52.17 357		72.36 389		87.90 259	54.10 340

test9_res														80.83 101	96.45 102	90.57 227

TEST9					92.34 95	79.70 74	83.94 169	90.32 159	65.41 265	84.49 209	90.97 185	82.03 104	93.63 111

test_8					92.09 105	78.87 81	83.82 174	90.31 161	65.79 256	84.36 213	90.96 187	81.93 106	93.44 124

agg_prior2														79.68 114	96.16 114	90.22 235

agg_prior					91.58 124	77.69 96		90.30 162		84.32 215			93.18 132

TestCases				89.68 53	91.59 121	83.40 48		95.44 10	79.47 94	88.00 141	93.03 121	82.66 89	91.47 177	70.81 209	96.14 115	94.16 96

test_prior4						78.97 80	84.59 154

test_prior2							83.37 186		75.43 145	84.58 207	91.57 167	81.92 108		79.54 116	96.97 83

test_prior				86.32 108	90.59 152	71.99 159		92.85 87					94.17 92			92.80 154

旧先验2							81.73 228		56.88 336	86.54 174			84.90 305	72.81 198

新几何2							81.72 229

新几何1				82.95 189	93.96 55	78.56 84		80.24 295	55.45 340	83.93 227	91.08 182	71.19 225	88.33 256	65.84 261	93.07 213	81.95 352

旧先验1					91.97 108	71.77 160		81.78 285			91.84 158	73.92 192			93.65 201	83.61 329

无先验							82.81 204	85.62 242	58.09 325				91.41 182	67.95 247		84.48 315

原ACMM2							82.26 222

原ACMM1				84.60 143	92.81 86	74.01 129		91.50 124	62.59 280	82.73 247	90.67 200	76.53 166	94.25 86	69.24 228	95.69 140	85.55 303

test222					93.31 70	76.54 109	79.38 259	77.79 306	52.59 354	82.36 251	90.84 193	66.83 245			91.69 241	81.25 360

testdata2													86.43 283	63.52 282

segment_acmp												81.94 105

testdata				79.54 248	92.87 81	72.34 153		80.14 296	59.91 315	85.47 193	91.75 164	67.96 240	85.24 301	68.57 242	92.18 233	81.06 365

testdata1							79.62 254		73.95 160

test12				86.57 103	90.74 148	72.63 146		90.69 148		82.76 246		79.20 133	94.80 68		95.32 148	92.27 179

plane_prior7					93.45 65	77.31 102

plane_prior6					92.61 87	76.54 109						74.84 180

plane_prior5								93.61 54					95.22 56	80.78 102	95.83 132	94.46 80

plane_prior4											92.95 126

plane_prior3						76.85 107			77.79 118	86.55 169

plane_prior2							89.45 77		79.44 96

plane_prior1					92.83 85

plane_prior						76.42 113	87.15 111		75.94 138						95.03 160

n2								0.00 415

nn								0.00 415

door-mid								74.45 332

lessismore_v0				85.95 117	91.10 141	70.99 171		70.91 360		91.79 67	94.42 69	61.76 274	92.93 141	79.52 117	93.03 214	93.93 106

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 20	82.35 63	93.67 33	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

test11								91.46 125

door								72.57 347

HQP5-MVS						70.66 172

HQP-NCC					91.19 136		84.77 148		73.30 172	80.55 281

ACMP_Plane					91.19 136		84.77 148		73.30 172	80.55 281

BP-MVS														77.30 145

HQP4-MVS										80.56 280			94.61 74			93.56 128

HQP3-MVS								92.68 92							94.47 180

HQP2-MVS												72.10 216

NP-MVS					91.95 109	74.55 126					90.17 214

MDTV_nov1_ep13_2view						27.60 407	70.76 356		46.47 379	61.27 392		45.20 364		49.18 366		83.75 328

MDTV_nov1_ep13			68.29 331		78.03 351	43.87 388	74.12 329	72.22 350	52.17 357	67.02 376	85.54 285	45.36 362	80.85 332	55.73 327	84.42 345

ACMMP++_ref															95.74 139

ACMMP++															97.35 73

Test By Simon												79.09 134

ITE_SJBPF				90.11 45	90.72 149	84.97 37		90.30 162	81.56 71	90.02 98	91.20 178	82.40 94	90.81 200	73.58 186	94.66 176	94.56 76

DeepMVS_CX				24.13 387	32.95 409	29.49 405		21.63 412	12.07 403	37.95 404	45.07 402	30.84 397	19.21 406	17.94 406	33.06 405	23.69 402

High-resolution multi-view benchmark