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 46	96.29 16	88.16 33	94.17 91	86.07 45	98.48 17	97.22 18

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 69	93.16 133	91.10 1	97.53 69	96.58 29
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 54	95.13 42	90.65 9	95.34 51	88.06 8	98.15 33	95.95 40

SR-MVS-dyc-post		92.41 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 13	94.16 29	88.75 14	93.79 28	94.43 66	88.83 24	95.51 43	87.16 29	97.60 63	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 48	88.20 19	93.24 38	94.02 89	90.15 16	95.67 33	86.82 33	97.34 73	92.19 187

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 14	82.88 59	91.77 67	93.94 97	90.55 12	95.73 30	88.50 6	98.23 27	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 8	92.24 8	91.48 21	93.02 76	85.17 35	92.47 25	95.05 13	87.65 22	93.21 39	94.39 71	90.09 17	95.08 60	86.67 35	97.60 63	94.18 94

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 12	90.28 9	92.11 60	95.03 44	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 29	82.52 62	92.39 57	94.14 83	89.15 23	95.62 34	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 98	83.09 56	91.54 69	94.25 77	87.67 41	95.51 43	87.21 28	98.11 34	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 62	83.16 55	91.06 79	94.00 90	88.26 30	95.71 31	87.28 27	98.39 20	92.55 166

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 64	85.07 36	89.99 97	94.03 88	86.57 52	95.80 24	87.35 24	97.62 61	94.20 91

MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 29	91.81 125	84.07 44	92.00 63	94.40 70	86.63 51	95.28 54	88.59 5	98.31 23	92.30 179

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 92	88.22 18	88.53 127	97.64 2	83.45 83	94.55 78	86.02 48	98.60 12	96.67 26

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 71	81.99 65	91.40 71	94.17 82	87.51 42	95.87 18	87.74 13	97.76 54	93.99 102

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 50	91.18 4	95.52 41	85.36 52	98.73 6	95.23 58

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 68	81.91 67	90.88 85	94.21 78	87.75 39	95.87 18	87.60 18	97.71 57	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 69	81.99 65	91.47 70	93.96 94	88.35 29	95.56 38	87.74 13	97.74 56	92.85 153

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 27	94.22 25	80.14 88	91.29 75	93.97 91	87.93 38	95.87 18	88.65 4	97.96 44	94.12 98

APDe-MVS		91.22 21	91.92 11	89.14 63	92.97 78	78.04 89	92.84 15	94.14 33	83.33 53	93.90 24	95.73 26	88.77 25	96.41 2	87.60 18	97.98 41	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 45	80.32 85	91.74 68	94.41 69	88.17 32	95.98 11	86.37 38	97.99 39	93.96 105

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 36	93.48 60	82.82 60	92.60 53	93.97 91	88.19 31	96.29 5	87.61 17	98.20 30	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 26	93.03 84	82.59 61	88.52 128	94.37 72	86.74 50	95.41 49	86.32 39	98.21 28	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 34	93.74 51	80.98 79	91.38 72	93.80 101	87.20 46	95.80 24	87.10 31	97.69 58	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 17	75.79 139	92.94 43	94.96 45	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 75	93.00 77	76.26 116	89.65 70	95.55 7	87.72 21	93.89 26	94.94 46	91.62 3	93.44 123	78.35 126	98.76 3	95.61 47

ACMMP_NAP		90.65 28	91.07 35	89.42 58	95.93 15	79.54 76	89.95 61	93.68 55	77.65 119	91.97 64	94.89 47	88.38 27	95.45 47	89.27 3	97.87 49	93.27 136

ACMM	79.39 9	90.65 28	90.99 37	89.63 54	95.03 33	83.53 47	89.62 71	93.35 63	79.20 100	93.83 27	93.60 109	90.81 7	92.96 139	85.02 56	98.45 18	92.41 173
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 16	90.65 7	90.33 91	93.95 96	84.50 71	95.37 50	80.87 100	95.50 141	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 38	79.03 103	92.87 45	93.74 105	90.60 11	95.21 57	82.87 77	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 66	93.38 67	78.65 83	89.15 82	94.05 38	84.68 40	93.90 24	94.11 86	88.13 34	96.30 4	84.51 61	97.81 51	91.70 203
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 96	94.18 46	72.65 141	90.47 51	93.69 53	83.77 47	94.11 22	94.27 73	90.28 14	95.84 22	86.03 46	97.92 45	92.29 180

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 72	75.37 146	92.84 47	95.28 37	85.58 64	96.09 7	87.92 10	97.76 54	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 73	92.86 82	77.09 104	91.19 40	95.74 5	81.38 73	92.28 58	93.80 101	86.89 49	94.64 73	85.52 51	97.51 70	94.30 90

v7n		90.13 36	90.96 38	87.65 88	91.95 108	71.06 169	89.99 59	93.05 81	86.53 26	94.29 18	96.27 17	82.69 90	94.08 95	86.25 42	97.63 60	97.82 8

PMVS	80.48 6	90.08 37	90.66 44	88.34 78	96.71 3	92.97 1	90.31 54	89.57 188	88.51 17	90.11 93	95.12 43	90.98 6	88.92 250	77.55 140	97.07 80	83.13 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 38	91.09 32	87.00 94	91.55 125	72.64 143	96.19 2	94.10 36	85.33 33	93.49 35	94.64 58	81.12 119	95.88 16	87.41 22	95.94 125	92.48 169

DVP-MVS		90.06 39	91.32 28	86.29 108	94.16 49	72.56 147	90.54 48	91.01 146	83.61 50	93.75 30	94.65 55	89.76 18	95.78 27	86.42 36	97.97 42	90.55 234
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	58.83 306	89.04 83	92.74 94	91.40 5	96.12 4	96.06 22	87.23 45	95.57 37	79.42 118	98.74 5	99.00 2

PEN-MVS		90.03 41	91.88 14	84.48 145	96.57 5	58.88 303	88.95 84	93.19 73	91.62 4	96.01 6	96.16 20	87.02 47	95.60 35	78.69 123	98.72 8	98.97 3

OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 74	80.37 68	91.91 32	93.11 77	81.10 77	95.32 10	97.24 5	72.94 209	94.85 67	85.07 54	97.78 52	97.26 15

DTE-MVSNet		89.98 43	91.91 13	84.21 155	96.51 7	57.84 313	88.93 85	92.84 91	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 40	79.68 92	92.09 61	93.89 99	83.80 78	93.10 136	82.67 81	98.04 35	93.64 123

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 134	81.66 62	91.25 38	94.13 34	88.89 11	88.83 122	94.26 76	77.55 151	95.86 21	84.88 57	95.87 129	95.24 57

WR-MVS_H		89.91 46	91.31 29	85.71 123	96.32 9	62.39 257	89.54 74	93.31 67	90.21 10	95.57 9	95.66 28	81.42 116	95.90 15	80.94 99	98.80 2	98.84 5

OPM-MVS		89.80 47	89.97 48	89.27 60	94.76 39	79.86 72	86.76 121	92.78 93	78.78 106	92.51 54	93.64 108	88.13 34	93.84 104	84.83 58	97.55 66	94.10 100
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 63	84.79 40	89.89 63	90.63 156	70.00 219	94.55 15	96.67 11	87.94 37	93.59 115	84.27 63	95.97 122	95.52 48

anonymousdsp		89.73 49	88.88 66	92.27 7	89.82 167	86.67 14	90.51 50	90.20 173	69.87 220	95.06 11	96.14 21	84.28 74	93.07 137	87.68 15	96.34 105	97.09 20

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 93	82.98 53	91.98 30	90.08 176	71.54 201	94.28 20	96.54 13	81.57 114	94.27 83	86.26 40	96.49 99	97.09 20

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 117	93.91 44	80.07 89	86.75 165	93.26 113	93.64 2	90.93 196	84.60 60	90.75 266	93.97 104

APD-MVS		89.54 52	89.63 54	89.26 61	92.57 87	81.34 64	90.19 56	93.08 80	80.87 81	91.13 77	93.19 114	86.22 59	95.97 12	82.23 87	97.18 78	90.45 236
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 67	84.72 41	89.70 66	90.29 170	69.27 223	94.39 16	96.38 15	86.02 62	93.52 119	83.96 65	95.92 127	95.34 52

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 28	92.30 106	79.74 91	87.50 150	92.38 142	81.42 116	93.28 128	83.07 73	97.24 76	91.67 204

ACMH	76.49 14	89.34 55	91.14 31	83.96 160	92.50 90	70.36 175	89.55 72	93.84 49	81.89 68	94.70 13	95.44 33	90.69 8	88.31 260	83.33 69	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	88.67 193	85.64 31	90.61 46	93.17 74	86.02 29	93.12 40	95.30 35	84.94 66	89.44 242	74.12 180	96.10 117	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 193	85.64 31	90.61 46	93.17 74	86.02 29	93.12 40	95.30 35	84.94 66	89.44 242	74.12 180	96.10 117	94.45 82

CP-MVSNet		89.27 58	90.91 40	84.37 147	96.34 8	58.61 309	88.66 92	92.06 112	90.78 6	95.67 7	95.17 41	81.80 112	95.54 40	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	85.91 132	93.60 58	80.16 87	89.13 119	93.44 111	83.82 77	90.98 194	83.86 67	95.30 150	93.60 125

DeepC-MVS	82.31 4	89.15 60	89.08 62	89.37 59	93.64 62	79.07 79	88.54 93	94.20 26	73.53 165	89.71 104	94.82 50	85.09 65	95.77 29	84.17 64	98.03 37	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	64.33 232	89.67 69	88.38 202	88.84 13	94.29 18	97.57 3	90.48 13	91.26 185	72.57 205	97.65 59	97.34 14

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 45	78.43 111	89.16 117	92.25 149	72.03 222	96.36 3	88.21 7	90.93 260	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	86.22 111	91.63 119	77.07 105	89.82 64	93.77 50	78.90 104	92.88 44	92.29 147	86.11 60	90.22 218	86.24 43	97.24 76	91.36 211
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	91.27 139	78.20 113	86.69 168	92.28 148	80.36 128	95.06 61	86.17 44	96.49 99	90.22 240

test_0402		88.65 65	89.58 56	85.88 119	92.55 88	72.22 155	84.01 167	89.44 190	88.63 16	94.38 17	95.77 25	86.38 58	93.59 115	79.84 111	95.21 151	91.82 199

DP-MVS		88.60 66	89.01 63	87.36 90	91.30 132	77.50 97	87.55 105	92.97 87	87.95 20	89.62 108	92.87 128	84.56 70	93.89 101	77.65 138	96.62 93	90.70 227

APD_test1		88.40 67	87.91 75	89.88 47	89.50 171	86.65 16	89.98 60	91.91 118	84.26 42	90.87 86	93.92 98	82.18 103	89.29 246	73.75 187	94.81 170	93.70 119

Anonymous20231211		88.40 67	89.62 55	84.73 140	90.46 153	65.27 222	88.86 86	93.02 85	87.15 23	93.05 42	97.10 6	82.28 102	92.02 166	76.70 150	97.99 39	96.88 24

PS-MVSNAJss		88.31 69	87.90 76	89.56 56	93.31 69	77.96 92	87.94 101	91.97 115	70.73 210	94.19 21	96.67 11	76.94 161	94.57 76	83.07 73	96.28 107	96.15 32

OMC-MVS		88.19 70	87.52 80	90.19 44	91.94 110	81.68 61	87.49 108	93.17 74	76.02 133	88.64 125	91.22 176	84.24 75	93.37 126	77.97 136	97.03 81	95.52 48

CS-MVS		88.14 71	87.67 79	89.54 57	89.56 169	79.18 78	90.47 51	94.77 15	79.37 98	84.32 217	89.33 229	83.87 76	94.53 79	82.45 83	94.89 166	94.90 65

TSAR-MVS + MP.		88.14 71	87.82 77	89.09 64	95.72 21	76.74 108	92.49 24	91.19 142	67.85 243	86.63 169	94.84 49	79.58 134	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 73	89.79 51	82.98 188	93.26 71	63.94 236	91.10 41	89.64 185	85.07 36	90.91 83	91.09 181	89.16 22	91.87 171	82.03 88	95.87 129	93.13 142

EC-MVSNet		88.01 74	88.32 72	87.09 92	89.28 176	72.03 157	90.31 54	96.31 3	80.88 80	85.12 197	89.67 225	84.47 72	95.46 46	82.56 82	96.26 110	93.77 117

RPSCF		88.00 75	86.93 93	91.22 27	90.08 160	89.30 4	89.68 68	91.11 143	79.26 99	89.68 105	94.81 53	82.44 94	87.74 264	76.54 153	88.74 291	96.61 28

AllTest		87.97 76	87.40 84	89.68 52	91.59 120	83.40 48	89.50 75	95.44 9	79.47 94	88.00 142	93.03 120	82.66 91	91.47 178	70.81 214	96.14 114	94.16 95

mvsmamba		87.87 77	87.23 85	89.78 51	92.31 97	76.51 112	91.09 42	91.87 119	72.61 187	92.16 59	95.23 40	66.01 256	95.59 36	86.02 48	97.78 52	97.24 16

TranMVSNet+NR-MVSNet		87.86 78	88.76 69	85.18 132	94.02 54	64.13 233	84.38 161	91.29 138	84.88 39	92.06 62	93.84 100	86.45 55	93.73 106	73.22 196	98.66 10	97.69 9

nrg030		87.85 79	88.49 70	85.91 117	90.07 162	69.73 179	87.86 102	94.20 26	74.04 157	92.70 52	94.66 54	85.88 63	91.50 177	79.72 113	97.32 74	96.50 30

CNVR-MVS		87.81 80	87.68 78	88.21 80	92.87 80	77.30 103	85.25 144	91.23 140	77.31 123	87.07 159	91.47 170	82.94 88	94.71 70	84.67 59	96.27 109	92.62 163

HQP_MVS		87.75 81	87.43 83	88.70 72	93.45 64	76.42 113	89.45 77	93.61 56	79.44 96	86.55 170	92.95 125	74.84 182	95.22 55	80.78 102	95.83 131	94.46 80

MM		87.64 82	87.15 86	89.09 64	89.51 170	76.39 115	88.68 91	86.76 230	84.54 41	83.58 233	93.78 103	73.36 205	96.48 1	87.98 9	96.21 111	94.41 86

NCCC		87.36 83	86.87 94	88.83 67	92.32 96	78.84 82	86.58 125	91.09 144	78.77 107	84.85 206	90.89 189	80.85 122	95.29 52	81.14 97	95.32 147	92.34 177

DeepPCF-MVS	81.24 5	87.28 84	86.21 104	90.49 38	91.48 129	84.90 38	83.41 185	92.38 104	70.25 216	89.35 116	90.68 198	82.85 89	94.57 76	79.55 115	95.95 124	92.00 194

SixPastTwentyTwo		87.20 85	87.45 82	86.45 105	92.52 89	69.19 188	87.84 103	88.05 209	81.66 70	94.64 14	96.53 14	65.94 257	94.75 69	83.02 75	96.83 86	95.41 50

CS-MVS-test		87.00 86	86.43 100	88.71 71	89.46 172	77.46 98	89.42 79	95.73 6	77.87 117	81.64 267	87.25 265	82.43 95	94.53 79	77.65 138	96.46 101	94.14 97

UniMVSNet (Re)		86.87 87	86.98 92	86.55 103	93.11 75	68.48 192	83.80 176	92.87 89	80.37 83	89.61 110	91.81 161	77.72 148	94.18 89	75.00 171	98.53 15	96.99 23

Vis-MVSNet		86.86 88	86.58 97	87.72 85	92.09 104	77.43 100	87.35 109	92.09 111	78.87 105	84.27 222	94.05 87	78.35 142	93.65 108	80.54 106	91.58 248	92.08 191
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 89	87.06 89	86.17 114	92.86 82	67.02 206	82.55 213	91.56 128	83.08 57	90.92 81	91.82 160	78.25 143	93.99 97	74.16 177	98.35 21	97.49 13

DU-MVS		86.80 90	86.99 91	86.21 112	93.24 72	67.02 206	83.16 194	92.21 107	81.73 69	90.92 81	91.97 154	77.20 155	93.99 97	74.16 177	98.35 21	97.61 10

casdiffmvs_mvg		86.72 91	87.51 81	84.36 149	87.09 233	65.22 223	84.16 163	94.23 23	77.89 116	91.28 76	93.66 107	84.35 73	92.71 146	80.07 107	94.87 169	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 92	86.52 98	87.18 91	85.94 261	78.30 85	86.93 115	92.20 108	65.94 255	89.16 117	93.16 116	83.10 86	89.89 231	87.81 11	94.43 183	93.35 132

IS-MVSNet		86.66 93	86.82 96	86.17 114	92.05 106	66.87 209	91.21 39	88.64 199	86.30 28	89.60 111	92.59 136	69.22 238	94.91 66	73.89 184	97.89 48	96.72 25

v10		86.54 94	87.10 88	84.84 136	88.16 207	63.28 243	86.64 124	92.20 108	75.42 145	92.81 49	94.50 62	74.05 193	94.06 96	83.88 66	96.28 107	97.17 19

pmmvs6		86.52 95	88.06 74	81.90 211	92.22 100	62.28 260	84.66 154	89.15 193	83.54 52	89.85 101	97.32 4	88.08 36	86.80 279	70.43 222	97.30 75	96.62 27

PHI-MVS		86.38 96	85.81 114	88.08 81	88.44 201	77.34 101	89.35 80	93.05 81	73.15 178	84.76 207	87.70 255	78.87 138	94.18 89	80.67 104	96.29 106	92.73 156

MVS_0304		86.35 97	85.92 110	87.66 87	89.21 179	73.16 138	88.40 95	83.63 272	81.27 74	80.87 277	94.12 85	71.49 226	95.71 31	87.79 12	96.50 98	94.11 99

CSCG		86.26 98	86.47 99	85.60 125	90.87 145	74.26 127	87.98 100	91.85 121	80.35 84	89.54 114	88.01 247	79.09 136	92.13 162	75.51 164	95.06 158	90.41 237

DeepC-MVS_fast	80.27 8	86.23 99	85.65 119	87.96 84	91.30 132	76.92 106	87.19 110	91.99 114	70.56 211	84.96 202	90.69 197	80.01 131	95.14 58	78.37 125	95.78 135	91.82 199
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 95	84.36 149	87.82 212	62.35 259	86.42 127	91.33 137	76.78 127	92.73 51	94.48 64	73.41 202	93.72 107	83.10 72	95.41 142	97.01 22

Anonymous20240529		86.20 101	87.13 87	83.42 178	90.19 158	64.55 230	84.55 156	90.71 153	85.85 31	89.94 100	95.24 39	82.13 104	90.40 214	69.19 234	96.40 104	95.31 54

test_fmvsmconf0.1_n		86.18 102	85.88 112	87.08 93	85.26 270	78.25 86	85.82 135	91.82 123	65.33 270	88.55 126	92.35 146	82.62 93	89.80 233	86.87 32	94.32 186	93.18 141

CDPH-MVS		86.17 103	85.54 120	88.05 83	92.25 98	75.45 121	83.85 173	92.01 113	65.91 257	86.19 179	91.75 164	83.77 79	94.98 64	77.43 143	96.71 90	93.73 118

NR-MVSNet		86.00 104	86.22 103	85.34 130	93.24 72	64.56 229	82.21 226	90.46 160	80.99 78	88.42 131	91.97 154	77.56 150	93.85 102	72.46 206	98.65 11	97.61 10

train_agg		85.98 105	85.28 125	88.07 82	92.34 94	79.70 74	83.94 169	90.32 165	65.79 258	84.49 211	90.97 185	81.93 108	93.63 110	81.21 95	96.54 96	90.88 221

FC-MVSNet-test		85.93 106	87.05 90	82.58 201	92.25 98	56.44 324	85.75 136	93.09 79	77.33 122	91.94 65	94.65 55	74.78 184	93.41 125	75.11 170	98.58 13	97.88 7

test_fmvsmconf_n		85.88 107	85.51 121	86.99 95	84.77 278	78.21 87	85.40 143	91.39 135	65.32 271	87.72 146	91.81 161	82.33 98	89.78 234	86.68 34	94.20 189	92.99 149

Effi-MVS+-dtu		85.82 108	83.38 156	93.14 3	87.13 229	91.15 2	87.70 104	88.42 201	74.57 153	83.56 234	85.65 288	78.49 141	94.21 87	72.04 208	92.88 221	94.05 101

iter_conf05_11		85.73 109	85.77 116	85.60 125	88.77 192	67.74 201	91.49 36	94.17 28	71.86 200	88.07 139	92.18 152	68.84 242	95.06 61	81.20 96	95.33 145	93.99 102

TAPA-MVS	77.73 12	85.71 110	84.83 131	88.37 77	88.78 191	79.72 73	87.15 112	93.50 59	69.17 224	85.80 188	89.56 226	80.76 123	92.13 162	73.21 201	95.51 140	93.25 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 111	86.14 105	83.58 173	87.97 208	67.13 203	87.55 105	94.32 18	73.44 168	88.47 129	87.54 258	86.45 55	91.06 192	75.76 162	93.76 199	92.54 167

canonicalmvs		85.50 111	86.14 105	83.58 173	87.97 208	67.13 203	87.55 105	94.32 18	73.44 168	88.47 129	87.54 258	86.45 55	91.06 192	75.76 162	93.76 199	92.54 167

EPP-MVSNet		85.47 113	85.04 128	86.77 100	91.52 128	69.37 183	91.63 35	87.98 211	81.51 72	87.05 160	91.83 159	66.18 255	95.29 52	70.75 217	96.89 83	95.64 45

GeoE		85.45 114	85.81 114	84.37 147	90.08 160	67.07 205	85.86 134	91.39 135	72.33 193	87.59 148	90.25 211	84.85 68	92.37 156	78.00 134	91.94 241	93.66 120

FIs		85.35 115	86.27 102	82.60 200	91.86 112	57.31 317	85.10 148	93.05 81	75.83 138	91.02 80	93.97 91	73.57 198	92.91 143	73.97 183	98.02 38	97.58 12

test_fmvsmvis_n_1920		85.22 116	85.36 124	84.81 137	85.80 263	76.13 119	85.15 147	92.32 105	61.40 301	91.33 73	90.85 192	83.76 80	86.16 292	84.31 62	93.28 211	92.15 189

casdiffmvs		85.21 117	85.85 113	83.31 181	86.17 255	62.77 250	83.03 196	93.93 43	74.69 152	88.21 136	92.68 135	82.29 101	91.89 170	77.87 137	93.75 202	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	85.93 109	83.02 187	86.30 250	62.37 258	84.55 156	93.96 41	74.48 154	87.12 154	92.03 153	82.30 100	91.94 167	78.39 124	94.21 188	94.74 73

K. test v3		85.14 119	84.73 132	86.37 106	91.13 139	69.63 181	85.45 141	76.68 321	84.06 45	92.44 56	96.99 8	62.03 277	94.65 72	80.58 105	93.24 212	94.83 72

EI-MVSNet-Vis-set		85.12 120	84.53 139	86.88 97	84.01 291	72.76 140	83.91 172	85.18 252	80.44 82	88.75 123	85.49 290	80.08 130	91.92 168	82.02 89	90.85 264	95.97 38

MGCFI-Net		85.04 121	85.95 108	82.31 207	87.52 221	63.59 239	86.23 130	93.96 41	73.46 166	88.07 139	87.83 253	86.46 54	90.87 201	76.17 157	93.89 197	92.47 171

EI-MVSNet-UG-set		85.04 121	84.44 141	86.85 98	83.87 295	72.52 149	83.82 174	85.15 253	80.27 86	88.75 123	85.45 292	79.95 132	91.90 169	81.92 92	90.80 265	96.13 33

X-MVStestdata		85.04 121	82.70 170	92.08 8	95.64 23	86.25 18	92.64 18	93.33 64	85.07 36	89.99 97	16.05 409	86.57 52	95.80 24	87.35 24	97.62 61	94.20 91

MSLP-MVS++		85.00 124	86.03 107	81.90 211	91.84 115	71.56 166	86.75 122	93.02 85	75.95 136	87.12 154	89.39 227	77.98 144	89.40 245	77.46 141	94.78 171	84.75 315

F-COLMAP		84.97 125	83.42 155	89.63 54	92.39 92	83.40 48	88.83 87	91.92 117	73.19 177	80.18 290	89.15 233	77.04 159	93.28 128	65.82 265	92.28 232	92.21 185

3Dnovator	80.37 7	84.80 126	84.71 135	85.06 134	86.36 248	74.71 124	88.77 89	90.00 178	75.65 141	84.96 202	93.17 115	74.06 192	91.19 187	78.28 128	91.09 254	89.29 258

IterMVS-LS		84.73 127	84.98 129	83.96 160	87.35 224	63.66 237	83.25 190	89.88 180	76.06 131	89.62 108	92.37 145	73.40 204	92.52 151	78.16 131	94.77 173	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 128	84.34 145	85.49 129	90.18 159	75.86 120	79.23 267	87.13 221	73.35 170	85.56 192	89.34 228	83.60 82	90.50 212	76.64 152	94.05 194	90.09 245

HQP-MVS		84.61 129	84.06 148	86.27 109	91.19 135	70.66 171	84.77 149	92.68 95	73.30 173	80.55 282	90.17 216	72.10 218	94.61 74	77.30 145	94.47 180	93.56 128

v1192		84.57 130	84.69 136	84.21 155	87.75 214	62.88 247	83.02 197	91.43 132	69.08 226	89.98 99	90.89 189	72.70 213	93.62 113	82.41 84	94.97 163	96.13 33

FMVSNet1		84.55 131	85.45 122	81.85 213	90.27 157	61.05 276	86.83 118	88.27 206	78.57 110	89.66 107	95.64 29	75.43 175	90.68 207	69.09 235	95.33 145	93.82 112

v1144		84.54 132	84.72 134	84.00 158	87.67 217	62.55 254	82.97 199	90.93 149	70.32 215	89.80 102	90.99 184	73.50 199	93.48 121	81.69 94	94.65 176	95.97 38

Gipuma		84.44 133	86.33 101	78.78 258	84.20 289	73.57 131	89.55 72	90.44 161	84.24 43	84.38 214	94.89 47	76.35 172	80.40 339	76.14 158	96.80 88	82.36 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 134	83.93 151	85.63 124	91.59 120	71.58 164	83.52 182	92.13 110	61.82 294	83.96 227	89.75 224	79.93 133	93.46 122	78.33 127	94.34 185	91.87 198

VDDNet		84.35 135	85.39 123	81.25 222	95.13 31	59.32 296	85.42 142	81.11 292	86.41 27	87.41 151	96.21 19	73.61 197	90.61 210	66.33 258	96.85 84	93.81 115

ETV-MVS		84.31 136	83.91 152	85.52 127	88.58 197	70.40 174	84.50 160	93.37 61	78.76 108	84.07 225	78.72 365	80.39 127	95.13 59	73.82 186	92.98 219	91.04 217

v1240		84.30 137	84.51 140	83.65 170	87.65 218	61.26 273	82.85 204	91.54 129	67.94 241	90.68 88	90.65 201	71.71 224	93.64 109	82.84 78	94.78 171	96.07 35

MVS_111021_LR		84.28 138	83.76 153	85.83 121	89.23 178	83.07 51	80.99 242	83.56 273	72.71 185	86.07 182	89.07 234	81.75 113	86.19 291	77.11 147	93.36 207	88.24 271

h-mvs33		84.25 139	82.76 169	88.72 70	91.82 117	82.60 56	84.00 168	84.98 259	71.27 203	86.70 166	90.55 204	63.04 274	93.92 100	78.26 129	94.20 189	89.63 250

v144192		84.24 140	84.41 142	83.71 169	87.59 220	61.57 269	82.95 200	91.03 145	67.82 244	89.80 102	90.49 205	73.28 206	93.51 120	81.88 93	94.89 166	96.04 37

dcpmvs_2		84.23 141	85.14 126	81.50 219	88.61 196	61.98 266	82.90 202	93.11 77	68.66 232	92.77 50	92.39 141	78.50 140	87.63 266	76.99 149	92.30 229	94.90 65

v1921920		84.23 141	84.37 144	83.79 165	87.64 219	61.71 268	82.91 201	91.20 141	67.94 241	90.06 94	90.34 208	72.04 221	93.59 115	82.32 85	94.91 164	96.07 35

VDD-MVS		84.23 141	84.58 138	83.20 184	91.17 138	65.16 225	83.25 190	84.97 260	79.79 90	87.18 153	94.27 73	74.77 185	90.89 199	69.24 231	96.54 96	93.55 130

v2v482		84.09 144	84.24 146	83.62 171	87.13 229	61.40 270	82.71 207	89.71 183	72.19 196	89.55 112	91.41 171	70.70 230	93.20 131	81.02 98	93.76 199	96.25 31

EG-PatchMatch MVS		84.08 145	84.11 147	83.98 159	92.22 100	72.61 146	82.20 228	87.02 226	72.63 186	88.86 120	91.02 183	78.52 139	91.11 190	73.41 193	91.09 254	88.21 272

DP-MVS Recon		84.05 146	83.22 158	86.52 104	91.73 118	75.27 122	83.23 192	92.40 102	72.04 197	82.04 257	88.33 243	77.91 146	93.95 99	66.17 259	95.12 156	90.34 239

TransMVSNet (Re)		84.02 147	85.74 117	78.85 257	91.00 142	55.20 334	82.29 222	87.26 217	79.65 93	88.38 133	95.52 32	83.00 87	86.88 277	67.97 249	96.60 94	94.45 82

Baseline_NR-MVSNet		84.00 148	85.90 111	78.29 269	91.47 130	53.44 343	82.29 222	87.00 229	79.06 102	89.55 112	95.72 27	77.20 155	86.14 293	72.30 207	98.51 16	95.28 55

TSAR-MVS + GP.		83.95 149	82.69 171	87.72 85	89.27 177	81.45 63	83.72 178	81.58 291	74.73 151	85.66 189	86.06 283	72.56 215	92.69 148	75.44 166	95.21 151	89.01 266

alignmvs		83.94 150	83.98 150	83.80 164	87.80 213	67.88 199	84.54 158	91.42 134	73.27 176	88.41 132	87.96 248	72.33 216	90.83 202	76.02 160	94.11 192	92.69 160

Effi-MVS+		83.90 151	84.01 149	83.57 175	87.22 227	65.61 221	86.55 126	92.40 102	78.64 109	81.34 272	84.18 311	83.65 81	92.93 141	74.22 176	87.87 303	92.17 188

CANet		83.79 152	82.85 168	86.63 101	86.17 255	72.21 156	83.76 177	91.43 132	77.24 124	74.39 342	87.45 261	75.36 176	95.42 48	77.03 148	92.83 222	92.25 184

pm-mvs1		83.69 153	84.95 130	79.91 244	90.04 164	59.66 293	82.43 217	87.44 214	75.52 143	87.85 144	95.26 38	81.25 118	85.65 302	68.74 241	96.04 119	94.42 85

AdaColmap		83.66 154	83.69 154	83.57 175	90.05 163	72.26 154	86.29 129	90.00 178	78.19 114	81.65 266	87.16 267	83.40 84	94.24 86	61.69 299	94.76 174	84.21 324

MIMVSNet1		83.63 155	84.59 137	80.74 232	94.06 53	62.77 250	82.72 206	84.53 265	77.57 121	90.34 90	95.92 24	76.88 167	85.83 300	61.88 297	97.42 71	93.62 124

test_fmvsm_n_1920		83.60 156	82.89 167	85.74 122	85.22 272	77.74 95	84.12 165	90.48 159	59.87 320	86.45 178	91.12 180	75.65 173	85.89 298	82.28 86	90.87 262	93.58 126

WR-MVS		83.56 157	84.40 143	81.06 228	93.43 66	54.88 335	78.67 275	85.02 257	81.24 75	90.74 87	91.56 168	72.85 210	91.08 191	68.00 248	98.04 35	97.23 17

CNLPA		83.55 158	83.10 163	84.90 135	89.34 175	83.87 46	84.54 158	88.77 196	79.09 101	83.54 235	88.66 240	74.87 181	81.73 330	66.84 254	92.29 231	89.11 260

LCM-MVSNet-Re		83.48 159	85.06 127	78.75 259	85.94 261	55.75 329	80.05 251	94.27 20	76.47 128	96.09 5	94.54 61	83.31 85	89.75 237	59.95 309	94.89 166	90.75 224

hse-mvs2		83.47 160	81.81 184	88.47 74	91.03 141	82.27 57	82.61 209	83.69 270	71.27 203	86.70 166	86.05 284	63.04 274	92.41 154	78.26 129	93.62 206	90.71 226

V42		83.47 160	83.37 157	83.75 167	83.16 308	63.33 242	81.31 236	90.23 172	69.51 222	90.91 83	90.81 194	74.16 191	92.29 160	80.06 108	90.22 273	95.62 46

VPA-MVSNet		83.47 160	84.73 132	79.69 248	90.29 156	57.52 316	81.30 238	88.69 198	76.29 129	87.58 149	94.44 65	80.60 126	87.20 271	66.60 257	96.82 87	94.34 88

PAPM_NR		83.23 163	83.19 160	83.33 180	90.90 144	65.98 217	88.19 97	90.78 152	78.13 115	80.87 277	87.92 251	73.49 201	92.42 153	70.07 224	88.40 293	91.60 206

iter_conf05		83.19 164	82.97 165	83.85 163	89.06 181	61.92 267	82.41 218	93.28 70	65.43 264	84.98 201	89.78 222	68.44 244	94.48 81	76.66 151	96.64 91	95.15 61

CLD-MVS		83.18 165	82.64 172	84.79 138	89.05 182	67.82 200	77.93 284	92.52 100	68.33 234	85.07 198	81.54 341	82.06 105	92.96 139	69.35 230	97.91 47	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		83.17 166	85.68 118	75.65 303	81.24 325	45.26 386	79.94 253	92.91 88	83.83 46	91.33 73	96.88 10	80.25 129	85.92 295	68.89 238	95.89 128	95.76 42

FA-MVS(test-final)		83.13 167	83.02 164	83.43 177	86.16 257	66.08 216	88.00 99	88.36 203	75.55 142	85.02 199	92.75 133	65.12 261	92.50 152	74.94 172	91.30 252	91.72 201

114514_t		83.10 168	82.54 175	84.77 139	92.90 79	69.10 190	86.65 123	90.62 157	54.66 349	81.46 269	90.81 194	76.98 160	94.38 82	72.62 204	96.18 112	90.82 223

UGNet		82.78 169	81.64 186	86.21 112	86.20 254	76.24 117	86.86 116	85.68 244	77.07 125	73.76 346	92.82 129	69.64 233	91.82 173	69.04 237	93.69 203	90.56 233
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		82.75 170	81.93 182	85.19 131	82.08 314	80.15 70	85.53 139	88.76 197	68.01 238	85.58 191	87.75 254	71.80 223	86.85 278	74.02 182	93.87 198	88.58 269

EI-MVSNet		82.61 171	82.42 177	83.20 184	83.25 305	63.66 237	83.50 183	85.07 254	76.06 131	86.55 170	85.10 298	73.41 202	90.25 215	78.15 133	90.67 268	95.68 44

QAPM		82.59 172	82.59 174	82.58 201	86.44 243	66.69 210	89.94 62	90.36 164	67.97 240	84.94 204	92.58 138	72.71 212	92.18 161	70.63 220	87.73 305	88.85 267

fmvsm_s_conf0.1_n_a		82.58 173	81.93 182	84.50 144	87.68 216	73.35 132	86.14 131	77.70 310	61.64 299	85.02 199	91.62 166	77.75 147	86.24 288	82.79 79	87.07 312	93.91 108

Fast-Effi-MVS+-dtu		82.54 174	81.41 195	85.90 118	85.60 264	76.53 111	83.07 195	89.62 187	73.02 180	79.11 302	83.51 317	80.74 124	90.24 217	68.76 240	89.29 282	90.94 219

MVS_Test		82.47 175	83.22 158	80.22 241	82.62 313	57.75 315	82.54 214	91.96 116	71.16 207	82.89 245	92.52 140	77.41 152	90.50 212	80.04 109	87.84 304	92.40 174

v148		82.31 176	82.48 176	81.81 216	85.59 265	59.66 293	81.47 235	86.02 240	72.85 181	88.05 141	90.65 201	70.73 229	90.91 198	75.15 169	91.79 242	94.87 67

API-MVS		82.28 177	82.61 173	81.30 221	86.29 251	69.79 177	88.71 90	87.67 213	78.42 112	82.15 256	84.15 312	77.98 144	91.59 176	65.39 268	92.75 223	82.51 350

MVSFormer		82.23 178	81.57 191	84.19 157	85.54 266	69.26 185	91.98 30	90.08 176	71.54 201	76.23 323	85.07 301	58.69 299	94.27 83	86.26 40	88.77 289	89.03 264

fmvsm_s_conf0.5_n_a		82.21 179	81.51 194	84.32 152	86.56 241	73.35 132	85.46 140	77.30 314	61.81 295	84.51 210	90.88 191	77.36 153	86.21 290	82.72 80	86.97 317	93.38 131

EIA-MVS		82.19 180	81.23 201	85.10 133	87.95 210	69.17 189	83.22 193	93.33 64	70.42 212	78.58 305	79.77 357	77.29 154	94.20 88	71.51 210	88.96 287	91.93 197

fmvsm_s_conf0.1_n		82.17 181	81.59 189	83.94 162	86.87 239	71.57 165	85.19 146	77.42 313	62.27 293	84.47 213	91.33 173	76.43 169	85.91 296	83.14 70	87.14 310	94.33 89

PCF-MVS	74.62 15	82.15 182	80.92 205	85.84 120	89.43 173	72.30 153	80.53 246	91.82 123	57.36 336	87.81 145	89.92 220	77.67 149	93.63 110	58.69 314	95.08 157	91.58 207
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 183	80.31 212	87.45 89	90.86 146	80.29 69	85.88 133	90.65 155	68.17 237	76.32 322	86.33 278	73.12 208	92.61 150	61.40 302	90.02 276	89.44 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 184	81.54 192	83.60 172	83.94 292	73.90 129	83.35 187	86.10 237	58.97 322	83.80 229	90.36 207	74.23 190	86.94 276	82.90 76	90.22 273	89.94 247

GBi-Net		82.02 185	82.07 179	81.85 213	86.38 245	61.05 276	86.83 118	88.27 206	72.43 188	86.00 183	95.64 29	63.78 268	90.68 207	65.95 261	93.34 208	93.82 112

test1		82.02 185	82.07 179	81.85 213	86.38 245	61.05 276	86.83 118	88.27 206	72.43 188	86.00 183	95.64 29	63.78 268	90.68 207	65.95 261	93.34 208	93.82 112

OpenMVS	76.72 13	81.98 187	82.00 181	81.93 210	84.42 284	68.22 194	88.50 94	89.48 189	66.92 250	81.80 264	91.86 156	72.59 214	90.16 220	71.19 213	91.25 253	87.40 287

KD-MVS_self_test		81.93 188	83.14 162	78.30 268	84.75 279	52.75 347	80.37 248	89.42 191	70.24 217	90.26 92	93.39 112	74.55 189	86.77 280	68.61 243	96.64 91	95.38 51

fmvsm_s_conf0.5_n		81.91 189	81.30 198	83.75 167	86.02 260	71.56 166	84.73 152	77.11 317	62.44 290	84.00 226	90.68 198	76.42 170	85.89 298	83.14 70	87.11 311	93.81 115

SDMVSNet		81.90 190	83.17 161	78.10 272	88.81 189	62.45 256	76.08 314	86.05 239	73.67 162	83.41 236	93.04 118	82.35 97	80.65 337	70.06 225	95.03 159	91.21 213

mamv4		81.86 191	81.52 193	82.87 195	85.42 268	62.26 261	82.66 208	92.62 97	65.43 264	79.34 299	90.22 212	69.65 232	94.15 94	74.14 179	94.16 191	92.21 185

tfpnnormal		81.79 192	82.95 166	78.31 267	88.93 186	55.40 330	80.83 245	82.85 279	76.81 126	85.90 187	94.14 83	74.58 188	86.51 284	66.82 255	95.68 139	93.01 148

MVSMamba_pp		81.67 193	81.33 197	82.70 199	85.24 271	62.25 263	82.88 203	92.53 99	62.64 284	79.42 295	90.65 201	69.37 235	93.26 130	74.78 173	94.44 182	92.58 164

c3_l		81.64 194	81.59 189	81.79 217	80.86 331	59.15 300	78.61 276	90.18 174	68.36 233	87.20 152	87.11 269	69.39 234	91.62 175	78.16 131	94.43 183	94.60 75

PVSNet_Blended_VisFu		81.55 195	80.49 210	84.70 142	91.58 123	73.24 136	84.21 162	91.67 127	62.86 283	80.94 275	87.16 267	67.27 249	92.87 145	69.82 227	88.94 288	87.99 278

fmvsm_l_conf0.5_n_a		81.46 196	80.87 206	83.25 182	83.73 297	73.21 137	83.00 198	85.59 246	58.22 328	82.96 244	90.09 218	72.30 217	86.65 282	81.97 91	89.95 277	89.88 248

DELS-MVS		81.44 197	81.25 199	82.03 209	84.27 288	62.87 248	76.47 308	92.49 101	70.97 208	81.64 267	83.83 314	75.03 179	92.70 147	74.29 175	92.22 235	90.51 235
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	81.61 188	80.41 238	86.38 245	58.75 307	83.93 171	86.58 232	72.43 188	87.65 147	92.98 122	63.78 268	90.22 218	66.86 252	93.92 196	92.27 182

TinyColmap		81.25 199	82.34 178	77.99 275	85.33 269	60.68 283	82.32 221	88.33 204	71.26 205	86.97 161	92.22 151	77.10 158	86.98 275	62.37 291	95.17 153	86.31 298

AUN-MVS		81.18 200	78.78 233	88.39 76	90.93 143	82.14 58	82.51 215	83.67 271	64.69 275	80.29 286	85.91 287	51.07 337	92.38 155	76.29 156	93.63 205	90.65 231

tttt0517		81.07 201	79.58 225	85.52 127	88.99 185	66.45 213	87.03 114	75.51 329	73.76 161	88.32 135	90.20 213	37.96 390	94.16 93	79.36 119	95.13 154	95.93 41

Fast-Effi-MVS+		81.04 202	80.57 207	82.46 205	87.50 222	63.22 244	78.37 279	89.63 186	68.01 238	81.87 260	82.08 335	82.31 99	92.65 149	67.10 251	88.30 299	91.51 209

BH-untuned		80.96 203	80.99 203	80.84 231	88.55 198	68.23 193	80.33 249	88.46 200	72.79 184	86.55 170	86.76 273	74.72 186	91.77 174	61.79 298	88.99 286	82.52 349

eth_miper_zixun_eth		80.84 204	80.22 216	82.71 197	81.41 323	60.98 279	77.81 286	90.14 175	67.31 248	86.95 162	87.24 266	64.26 264	92.31 158	75.23 168	91.61 246	94.85 71

xiu_mvs_v1_base_debu		80.84 204	80.14 218	82.93 191	88.31 202	71.73 160	79.53 258	87.17 218	65.43 264	79.59 292	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

xiu_mvs_v1_base		80.84 204	80.14 218	82.93 191	88.31 202	71.73 160	79.53 258	87.17 218	65.43 264	79.59 292	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

xiu_mvs_v1_base_debi		80.84 204	80.14 218	82.93 191	88.31 202	71.73 160	79.53 258	87.17 218	65.43 264	79.59 292	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

IterMVS-SCA-FT		80.64 208	79.41 226	84.34 151	83.93 293	69.66 180	76.28 310	81.09 293	72.43 188	86.47 176	90.19 214	60.46 284	93.15 134	77.45 142	86.39 323	90.22 240

BH-RMVSNet		80.53 209	80.22 216	81.49 220	87.19 228	66.21 215	77.79 287	86.23 235	74.21 156	83.69 230	88.50 241	73.25 207	90.75 204	63.18 288	87.90 302	87.52 285

Anonymous202405211		80.51 210	81.19 202	78.49 264	88.48 199	57.26 318	76.63 304	82.49 282	81.21 76	84.30 220	92.24 150	67.99 246	86.24 288	62.22 292	95.13 154	91.98 196

DIV-MVS_self_test		80.43 211	80.23 214	81.02 229	79.99 339	59.25 297	77.07 297	87.02 226	67.38 245	86.19 179	89.22 230	63.09 272	90.16 220	76.32 154	95.80 133	93.66 120

cl____		80.42 212	80.23 214	81.02 229	79.99 339	59.25 297	77.07 297	87.02 226	67.37 246	86.18 181	89.21 231	63.08 273	90.16 220	76.31 155	95.80 133	93.65 122

diffmvs		80.40 213	80.48 211	80.17 242	79.02 351	60.04 287	77.54 291	90.28 171	66.65 253	82.40 251	87.33 264	73.50 199	87.35 269	77.98 135	89.62 280	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		80.37 214	78.41 240	86.23 110	76.75 365	73.28 134	87.18 111	77.45 312	76.24 130	68.14 374	88.93 236	65.41 260	93.85 102	69.47 229	96.12 116	91.55 208
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	81.24 224	79.82 341	58.95 302	77.66 288	89.66 184	65.75 261	85.99 186	85.11 297	68.29 245	91.42 182	76.03 159	92.03 237	93.33 133

MG-MVS		80.32 216	80.94 204	78.47 265	88.18 205	52.62 350	82.29 222	85.01 258	72.01 198	79.24 301	92.54 139	69.36 236	93.36 127	70.65 219	89.19 285	89.45 252

bld_raw_dy_0_64		80.29 217	80.03 222	81.09 226	86.14 258	59.69 292	78.24 280	91.87 119	63.91 279	78.46 307	84.08 313	69.23 237	92.89 144	73.70 189	94.61 177	90.69 228

VPNet		80.25 218	81.68 185	75.94 301	92.46 91	47.98 373	76.70 302	81.67 289	73.45 167	84.87 205	92.82 129	74.66 187	86.51 284	61.66 300	96.85 84	93.33 133

MAR-MVS		80.24 219	78.74 235	84.73 140	86.87 239	78.18 88	85.75 136	87.81 212	65.67 263	77.84 311	78.50 366	73.79 196	90.53 211	61.59 301	90.87 262	85.49 308
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	79.00 230	83.78 166	88.17 206	86.66 15	81.31 236	66.81 381	69.64 221	88.33 134	90.19 214	64.58 262	83.63 321	71.99 209	90.03 275	81.06 368

Anonymous20240521		80.18 221	81.25 199	76.95 288	83.15 309	60.84 281	82.46 216	85.99 241	68.76 230	86.78 163	93.73 106	59.13 296	77.44 351	73.71 188	97.55 66	92.56 165

LFMVS		80.15 222	80.56 208	78.89 256	89.19 180	55.93 326	85.22 145	73.78 341	82.96 58	84.28 221	92.72 134	57.38 308	90.07 227	63.80 282	95.75 136	90.68 229

DPM-MVS		80.10 223	79.18 229	82.88 194	90.71 149	69.74 178	78.87 272	90.84 150	60.29 316	75.64 332	85.92 286	67.28 248	93.11 135	71.24 212	91.79 242	85.77 304

MSDG		80.06 224	79.99 224	80.25 240	83.91 294	68.04 198	77.51 292	89.19 192	77.65 119	81.94 258	83.45 319	76.37 171	86.31 287	63.31 287	86.59 320	86.41 296

FE-MVS		79.98 225	78.86 231	83.36 179	86.47 242	66.45 213	89.73 65	84.74 264	72.80 183	84.22 224	91.38 172	44.95 371	93.60 114	63.93 281	91.50 249	90.04 246

sd_testset		79.95 226	81.39 196	75.64 304	88.81 189	58.07 311	76.16 313	82.81 280	73.67 162	83.41 236	93.04 118	80.96 121	77.65 350	58.62 315	95.03 159	91.21 213

ab-mvs		79.67 227	80.56 208	76.99 287	88.48 199	56.93 320	84.70 153	86.06 238	68.95 228	80.78 279	93.08 117	75.30 177	84.62 310	56.78 324	90.90 261	89.43 254

VNet		79.31 228	80.27 213	76.44 295	87.92 211	53.95 339	75.58 320	84.35 266	74.39 155	82.23 254	90.72 196	72.84 211	84.39 313	60.38 308	93.98 195	90.97 218

thisisatest0530		79.07 229	77.33 249	84.26 154	87.13 229	64.58 228	83.66 180	75.95 324	68.86 229	85.22 196	87.36 263	38.10 388	93.57 118	75.47 165	94.28 187	94.62 74

cl22		78.97 230	78.21 242	81.24 224	77.74 355	59.01 301	77.46 294	87.13 221	65.79 258	84.32 217	85.10 298	58.96 298	90.88 200	75.36 167	92.03 237	93.84 110

patch_mono-2		78.89 231	79.39 227	77.41 284	84.78 277	68.11 196	75.60 318	83.11 276	60.96 309	79.36 297	89.89 221	75.18 178	72.97 362	73.32 195	92.30 229	91.15 215

RPMNet		78.88 232	78.28 241	80.68 235	79.58 342	62.64 252	82.58 211	94.16 29	74.80 150	75.72 330	92.59 136	48.69 345	95.56 38	73.48 192	82.91 358	83.85 329

PAPR		78.84 233	78.10 243	81.07 227	85.17 273	60.22 286	82.21 226	90.57 158	62.51 286	75.32 336	84.61 306	74.99 180	92.30 159	59.48 312	88.04 301	90.68 229

PVSNet_BlendedMVS		78.80 234	77.84 244	81.65 218	84.43 282	63.41 240	79.49 261	90.44 161	61.70 298	75.43 333	87.07 270	69.11 239	91.44 180	60.68 306	92.24 233	90.11 244

FMVSNet3		78.80 234	78.55 237	79.57 250	82.89 312	56.89 322	81.76 230	85.77 243	69.04 227	86.00 183	90.44 206	51.75 335	90.09 226	65.95 261	93.34 208	91.72 201

test_yl		78.71 236	78.51 238	79.32 253	84.32 286	58.84 304	78.38 277	85.33 249	75.99 134	82.49 249	86.57 274	58.01 302	90.02 229	62.74 289	92.73 224	89.10 261

DCV-MVSNet		78.71 236	78.51 238	79.32 253	84.32 286	58.84 304	78.38 277	85.33 249	75.99 134	82.49 249	86.57 274	58.01 302	90.02 229	62.74 289	92.73 224	89.10 261

test1111		78.53 238	78.85 232	77.56 281	92.22 100	47.49 375	82.61 209	69.24 370	72.43 188	85.28 195	94.20 79	51.91 333	90.07 227	65.36 269	96.45 102	95.11 62

ECVR-MVS		78.44 239	78.63 236	77.88 277	91.85 113	48.95 369	83.68 179	69.91 367	72.30 194	84.26 223	94.20 79	51.89 334	89.82 232	63.58 283	96.02 120	94.87 67

pmmvs-eth3d		78.42 240	77.04 251	82.57 203	87.44 223	74.41 126	80.86 244	79.67 301	55.68 343	84.69 208	90.31 210	60.91 282	85.42 303	62.20 293	91.59 247	87.88 281

mvs_anonymous		78.13 241	78.76 234	76.23 300	79.24 348	50.31 366	78.69 274	84.82 262	61.60 300	83.09 243	92.82 129	73.89 195	87.01 272	68.33 247	86.41 322	91.37 210

TAMVS		78.08 242	76.36 257	83.23 183	90.62 150	72.87 139	79.08 268	80.01 300	61.72 297	81.35 271	86.92 272	63.96 267	88.78 254	50.61 362	93.01 218	88.04 277

miper_enhance_ethall		77.83 243	76.93 252	80.51 236	76.15 371	58.01 312	75.47 322	88.82 195	58.05 330	83.59 232	80.69 345	64.41 263	91.20 186	73.16 202	92.03 237	92.33 178

Vis-MVSNet (Re-imp)		77.82 244	77.79 245	77.92 276	88.82 188	51.29 360	83.28 188	71.97 355	74.04 157	82.23 254	89.78 222	57.38 308	89.41 244	57.22 323	95.41 142	93.05 146

CANet_DTU		77.81 245	77.05 250	80.09 243	81.37 324	59.90 290	83.26 189	88.29 205	69.16 225	67.83 377	83.72 315	60.93 281	89.47 239	69.22 233	89.70 279	90.88 221

OpenMVS_ROB	70.19 17	77.77 246	77.46 246	78.71 260	84.39 285	61.15 274	81.18 240	82.52 281	62.45 289	83.34 238	87.37 262	66.20 254	88.66 256	64.69 276	85.02 339	86.32 297

SSC-MVS		77.55 247	81.64 186	65.29 367	90.46 153	20.33 413	73.56 338	68.28 372	85.44 32	88.18 138	94.64 58	70.93 228	81.33 332	71.25 211	92.03 237	94.20 91

MDA-MVSNet-bldmvs		77.47 248	76.90 253	79.16 255	79.03 350	64.59 227	66.58 377	75.67 327	73.15 178	88.86 120	88.99 235	66.94 250	81.23 333	64.71 275	88.22 300	91.64 205

jason		77.42 249	75.75 263	82.43 206	87.10 232	69.27 184	77.99 283	81.94 287	51.47 367	77.84 311	85.07 301	60.32 286	89.00 248	70.74 218	89.27 284	89.03 264
jason: jason.
CDS-MVSNet		77.32 250	75.40 266	83.06 186	89.00 184	72.48 150	77.90 285	82.17 285	60.81 310	78.94 303	83.49 318	59.30 294	88.76 255	54.64 342	92.37 228	87.93 280
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	76.75 254	78.52 263	87.01 235	61.30 272	75.55 321	87.12 224	61.24 306	74.45 341	78.79 364	77.20 155	90.93 196	64.62 278	84.80 346	83.32 338

MVSTER		77.09 252	75.70 264	81.25 222	75.27 379	61.08 275	77.49 293	85.07 254	60.78 311	86.55 170	88.68 239	43.14 380	90.25 215	73.69 190	90.67 268	92.42 172

PS-MVSNAJ		77.04 253	76.53 256	78.56 262	87.09 233	61.40 270	75.26 323	87.13 221	61.25 305	74.38 343	77.22 377	76.94 161	90.94 195	64.63 277	84.83 345	83.35 337

IterMVS		76.91 254	76.34 258	78.64 261	80.91 329	64.03 234	76.30 309	79.03 304	64.88 274	83.11 241	89.16 232	59.90 290	84.46 311	68.61 243	85.15 337	87.42 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 255	75.67 265	80.34 239	80.48 337	62.16 265	73.50 339	84.80 263	57.61 334	82.24 253	87.54 258	51.31 336	87.65 265	70.40 223	93.19 214	91.23 212

CL-MVSNet_self_test		76.81 256	77.38 248	75.12 307	86.90 237	51.34 358	73.20 342	80.63 297	68.30 235	81.80 264	88.40 242	66.92 251	80.90 334	55.35 336	94.90 165	93.12 144

TR-MVS		76.77 257	75.79 262	79.72 247	86.10 259	65.79 219	77.14 295	83.02 277	65.20 272	81.40 270	82.10 333	66.30 253	90.73 206	55.57 333	85.27 333	82.65 344

USDC		76.63 258	76.73 255	76.34 297	83.46 299	57.20 319	80.02 252	88.04 210	52.14 363	83.65 231	91.25 175	63.24 271	86.65 282	54.66 341	94.11 192	85.17 310

BH-w/o		76.57 259	76.07 261	78.10 272	86.88 238	65.92 218	77.63 289	86.33 233	65.69 262	80.89 276	79.95 354	68.97 241	90.74 205	53.01 352	85.25 334	77.62 379

Patchmtry		76.56 260	77.46 246	73.83 313	79.37 347	46.60 379	82.41 218	76.90 318	73.81 160	85.56 192	92.38 142	48.07 348	83.98 318	63.36 286	95.31 149	90.92 220

PVSNet_Blended		76.49 261	75.40 266	79.76 246	84.43 282	63.41 240	75.14 324	90.44 161	57.36 336	75.43 333	78.30 367	69.11 239	91.44 180	60.68 306	87.70 306	84.42 320

miper_lstm_enhance		76.45 262	76.10 260	77.51 282	76.72 366	60.97 280	64.69 381	85.04 256	63.98 278	83.20 240	88.22 244	56.67 312	78.79 348	73.22 196	93.12 215	92.78 155

lupinMVS		76.37 263	74.46 275	82.09 208	85.54 266	69.26 185	76.79 300	80.77 296	50.68 374	76.23 323	82.82 327	58.69 299	88.94 249	69.85 226	88.77 289	88.07 274

cascas		76.29 264	74.81 271	80.72 234	84.47 281	62.94 246	73.89 336	87.34 215	55.94 342	75.16 338	76.53 382	63.97 266	91.16 188	65.00 272	90.97 259	88.06 276

WB-MVS		76.06 265	80.01 223	64.19 370	89.96 166	20.58 412	72.18 347	68.19 373	83.21 54	86.46 177	93.49 110	70.19 231	78.97 346	65.96 260	90.46 272	93.02 147

thres600view7		75.97 266	75.35 268	77.85 279	87.01 235	51.84 356	80.45 247	73.26 346	75.20 147	83.10 242	86.31 280	45.54 362	89.05 247	55.03 339	92.24 233	92.66 161

GA-MVS		75.83 267	74.61 272	79.48 252	81.87 316	59.25 297	73.42 340	82.88 278	68.68 231	79.75 291	81.80 338	50.62 339	89.46 240	66.85 253	85.64 330	89.72 249

MVP-Stereo		75.81 268	73.51 284	82.71 197	89.35 174	73.62 130	80.06 250	85.20 251	60.30 315	73.96 344	87.94 249	57.89 306	89.45 241	52.02 356	74.87 390	85.06 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 269	75.20 269	77.27 285	75.01 382	69.47 182	78.93 269	84.88 261	46.67 381	87.08 158	87.84 252	50.44 341	71.62 367	77.42 144	88.53 292	90.72 225

thres100view900		75.45 270	75.05 270	76.66 294	87.27 225	51.88 355	81.07 241	73.26 346	75.68 140	83.25 239	86.37 277	45.54 362	88.80 251	51.98 357	90.99 256	89.31 256

ET-MVSNet_ETH3D		75.28 271	72.77 292	82.81 196	83.03 311	68.11 196	77.09 296	76.51 322	60.67 313	77.60 316	80.52 349	38.04 389	91.15 189	70.78 216	90.68 267	89.17 259

thres400		75.14 272	74.23 277	77.86 278	86.24 252	52.12 352	79.24 265	73.87 339	73.34 171	81.82 262	84.60 307	46.02 356	88.80 251	51.98 357	90.99 256	92.66 161

wuyk23d		75.13 273	79.30 228	62.63 373	75.56 375	75.18 123	80.89 243	73.10 348	75.06 149	94.76 12	95.32 34	87.73 40	52.85 404	34.16 403	97.11 79	59.85 400

EU-MVSNet		75.12 274	74.43 276	77.18 286	83.11 310	59.48 295	85.71 138	82.43 283	39.76 401	85.64 190	88.76 237	44.71 373	87.88 263	73.86 185	85.88 329	84.16 325

HyFIR lowres test		75.12 274	72.66 294	82.50 204	91.44 131	65.19 224	72.47 345	87.31 216	46.79 380	80.29 286	84.30 309	52.70 330	92.10 165	51.88 361	86.73 318	90.22 240

CMPMVS	59.41 20	75.12 274	73.57 282	79.77 245	75.84 374	67.22 202	81.21 239	82.18 284	50.78 372	76.50 319	87.66 256	55.20 322	82.99 324	62.17 295	90.64 271	89.09 263
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 277	72.98 290	80.73 233	84.95 274	71.71 163	76.23 311	77.59 311	52.83 357	77.73 315	86.38 276	56.35 315	84.97 307	57.72 322	87.05 313	85.51 307

tfpn200view9		74.86 278	74.23 277	76.74 293	86.24 252	52.12 352	79.24 265	73.87 339	73.34 171	81.82 262	84.60 307	46.02 356	88.80 251	51.98 357	90.99 256	89.31 256

1112_ss		74.82 279	73.74 280	78.04 274	89.57 168	60.04 287	76.49 307	87.09 225	54.31 350	73.66 347	79.80 355	60.25 287	86.76 281	58.37 316	84.15 350	87.32 288

EGC-MVSNET		74.79 280	69.99 320	89.19 62	94.89 37	87.00 11	91.89 33	86.28 234	1.09 410	2.23 412	95.98 23	81.87 111	89.48 238	79.76 112	95.96 123	91.10 216

ppachtmachnet_test		74.73 281	74.00 279	76.90 290	80.71 334	56.89 322	71.53 353	78.42 306	58.24 327	79.32 300	82.92 326	57.91 305	84.26 315	65.60 267	91.36 251	89.56 251

Patchmatch-RL test		74.48 282	73.68 281	76.89 291	84.83 276	66.54 211	72.29 346	69.16 371	57.70 332	86.76 164	86.33 278	45.79 361	82.59 325	69.63 228	90.65 270	81.54 359

PatchMatch-RL		74.48 282	73.22 287	78.27 270	87.70 215	85.26 34	75.92 316	70.09 365	64.34 276	76.09 326	81.25 343	65.87 258	78.07 349	53.86 344	83.82 352	71.48 388

XXY-MVS		74.44 284	76.19 259	69.21 345	84.61 280	52.43 351	71.70 350	77.18 316	60.73 312	80.60 280	90.96 187	75.44 174	69.35 373	56.13 329	88.33 295	85.86 303

test2506		74.12 285	73.39 285	76.28 298	91.85 113	44.20 389	84.06 166	48.20 408	72.30 194	81.90 259	94.20 79	27.22 409	89.77 235	64.81 274	96.02 120	94.87 67

CR-MVSNet		74.00 286	73.04 289	76.85 292	79.58 342	62.64 252	82.58 211	76.90 318	50.50 375	75.72 330	92.38 142	48.07 348	84.07 317	68.72 242	82.91 358	83.85 329

Test_1112_low_res		73.90 287	73.08 288	76.35 296	90.35 155	55.95 325	73.40 341	86.17 236	50.70 373	73.14 348	85.94 285	58.31 301	85.90 297	56.51 326	83.22 355	87.20 289

test20.03		73.75 288	74.59 274	71.22 333	81.11 327	51.12 362	70.15 363	72.10 354	70.42 212	80.28 288	91.50 169	64.21 265	74.72 361	46.96 380	94.58 178	87.82 283

test_fmvs2		73.57 289	72.80 291	75.90 302	72.74 394	68.84 191	77.07 297	84.32 267	45.14 387	82.89 245	84.22 310	48.37 346	70.36 370	73.40 194	87.03 314	88.52 270

SCA		73.32 290	72.57 296	75.58 305	81.62 320	55.86 327	78.89 271	71.37 360	61.73 296	74.93 339	83.42 320	60.46 284	87.01 272	58.11 320	82.63 363	83.88 326

baseline1		73.26 291	73.54 283	72.43 327	84.92 275	47.79 374	79.89 254	74.00 337	65.93 256	78.81 304	86.28 281	56.36 314	81.63 331	56.63 325	79.04 379	87.87 282

1314		73.22 292	72.56 297	75.20 306	80.41 338	57.84 313	81.64 233	85.36 248	51.68 366	73.10 349	76.65 381	61.45 279	85.19 305	63.54 284	79.21 377	82.59 345

MVS		73.21 293	72.59 295	75.06 308	80.97 328	60.81 282	81.64 233	85.92 242	46.03 385	71.68 356	77.54 372	68.47 243	89.77 235	55.70 332	85.39 331	74.60 385

HY-MVS	64.64 18	73.03 294	72.47 298	74.71 309	83.36 303	54.19 337	82.14 229	81.96 286	56.76 341	69.57 369	86.21 282	60.03 288	84.83 309	49.58 368	82.65 361	85.11 311

thisisatest0515		73.00 295	70.52 312	80.46 237	81.45 322	59.90 290	73.16 343	74.31 336	57.86 331	76.08 327	77.78 370	37.60 391	92.12 164	65.00 272	91.45 250	89.35 255

EPNet_dtu		72.87 296	71.33 308	77.49 283	77.72 356	60.55 284	82.35 220	75.79 325	66.49 254	58.39 403	81.06 344	53.68 326	85.98 294	53.55 347	92.97 220	85.95 301
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 297	71.41 307	76.28 298	83.25 305	60.34 285	83.50 183	79.02 305	37.77 405	76.33 321	85.10 298	49.60 344	87.41 268	70.54 221	77.54 385	81.08 366

CHOSEN 1792x2688		72.45 298	70.56 311	78.13 271	90.02 165	63.08 245	68.72 368	83.16 275	42.99 395	75.92 328	85.46 291	57.22 310	85.18 306	49.87 366	81.67 365	86.14 299

testgi		72.36 299	74.61 272	65.59 364	80.56 336	42.82 394	68.29 369	73.35 345	66.87 251	81.84 261	89.93 219	72.08 220	66.92 386	46.05 383	92.54 226	87.01 291

thres200		72.34 300	71.55 306	74.70 310	83.48 298	51.60 357	75.02 325	73.71 342	70.14 218	78.56 306	80.57 348	46.20 354	88.20 261	46.99 379	89.29 282	84.32 321

FPMVS		72.29 301	72.00 300	73.14 318	88.63 195	85.00 36	74.65 329	67.39 375	71.94 199	77.80 313	87.66 256	50.48 340	75.83 357	49.95 364	79.51 373	58.58 402

FMVSNet5		72.10 302	71.69 302	73.32 316	81.57 321	53.02 346	76.77 301	78.37 307	63.31 280	76.37 320	91.85 157	36.68 392	78.98 345	47.87 376	92.45 227	87.95 279

our_test_3		71.85 303	71.59 303	72.62 324	80.71 334	53.78 340	69.72 365	71.71 359	58.80 324	78.03 308	80.51 350	56.61 313	78.84 347	62.20 293	86.04 328	85.23 309

PAPM		71.77 304	70.06 318	76.92 289	86.39 244	53.97 338	76.62 305	86.62 231	53.44 354	63.97 393	84.73 305	57.79 307	92.34 157	39.65 394	81.33 369	84.45 319

IB-MVS	62.13 19	71.64 305	68.97 329	79.66 249	80.80 333	62.26 261	73.94 335	76.90 318	63.27 281	68.63 373	76.79 379	33.83 396	91.84 172	59.28 313	87.26 308	84.88 313
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		71.63 306	72.30 299	69.62 342	76.47 368	52.70 349	70.03 364	80.97 294	59.18 321	79.36 297	88.21 245	60.50 283	69.12 374	58.33 318	77.62 384	87.04 290

testing3		71.53 307	70.79 309	73.77 314	88.89 187	41.86 396	76.60 306	59.12 398	72.83 182	80.97 273	82.08 335	19.80 414	87.33 270	65.12 271	91.68 245	92.13 190

test_vis3_rt		71.42 308	70.67 310	73.64 315	69.66 401	70.46 173	66.97 376	89.73 181	42.68 397	88.20 137	83.04 322	43.77 375	60.07 398	65.35 270	86.66 319	90.39 238

Anonymous20231206		71.38 309	71.88 301	69.88 340	86.31 249	54.37 336	70.39 361	74.62 332	52.57 359	76.73 318	88.76 237	59.94 289	72.06 364	44.35 387	93.23 213	83.23 340

test_vis1_n_1920		71.30 310	71.58 305	70.47 336	77.58 358	59.99 289	74.25 330	84.22 268	51.06 369	74.85 340	79.10 361	55.10 323	68.83 376	68.86 239	79.20 378	82.58 346

MIMVSNet		71.09 311	71.59 303	69.57 343	87.23 226	50.07 367	78.91 270	71.83 356	60.20 318	71.26 357	91.76 163	55.08 324	76.09 355	41.06 392	87.02 315	82.54 348

test_fmvs1_n		70.94 312	70.41 315	72.53 326	73.92 384	66.93 208	75.99 315	84.21 269	43.31 394	79.40 296	79.39 359	43.47 376	68.55 378	69.05 236	84.91 342	82.10 353

MS-PatchMatch		70.93 313	70.22 316	73.06 319	81.85 317	62.50 255	73.82 337	77.90 308	52.44 360	75.92 328	81.27 342	55.67 319	81.75 329	55.37 335	77.70 383	74.94 384

pmmvs5		70.73 314	70.07 317	72.72 322	77.03 363	52.73 348	74.14 331	75.65 328	50.36 376	72.17 354	85.37 295	55.42 321	80.67 336	52.86 353	87.59 307	84.77 314

PatchT		70.52 315	72.76 293	63.79 372	79.38 346	33.53 406	77.63 289	65.37 384	73.61 164	71.77 355	92.79 132	44.38 374	75.65 358	64.53 279	85.37 332	82.18 352

test_vis1_n		70.29 316	69.99 320	71.20 334	75.97 373	66.50 212	76.69 303	80.81 295	44.22 390	75.43 333	77.23 376	50.00 342	68.59 377	66.71 256	82.85 360	78.52 378

N_pmnet		70.20 317	68.80 331	74.38 311	80.91 329	84.81 39	59.12 393	76.45 323	55.06 346	75.31 337	82.36 332	55.74 318	54.82 403	47.02 378	87.24 309	83.52 333

tpmvs		70.16 318	69.56 323	71.96 329	74.71 383	48.13 371	79.63 256	75.45 330	65.02 273	70.26 365	81.88 337	45.34 367	85.68 301	58.34 317	75.39 389	82.08 354

new-patchmatchnet		70.10 319	73.37 286	60.29 380	81.23 326	16.95 415	59.54 391	74.62 332	62.93 282	80.97 273	87.93 250	62.83 276	71.90 365	55.24 337	95.01 162	92.00 194

YYNet1		70.06 320	70.44 313	68.90 347	73.76 386	53.42 344	58.99 394	67.20 377	58.42 326	87.10 156	85.39 294	59.82 291	67.32 383	59.79 310	83.50 354	85.96 300

MDA-MVSNet_test_wron		70.05 321	70.44 313	68.88 348	73.84 385	53.47 342	58.93 395	67.28 376	58.43 325	87.09 157	85.40 293	59.80 292	67.25 384	59.66 311	83.54 353	85.92 302

CostFormer		69.98 322	68.68 332	73.87 312	77.14 361	50.72 364	79.26 264	74.51 334	51.94 365	70.97 360	84.75 304	45.16 370	87.49 267	55.16 338	79.23 376	83.40 336

testing91		69.94 323	68.99 328	72.80 321	83.81 296	45.89 382	71.57 352	73.64 344	68.24 236	70.77 363	77.82 369	34.37 395	84.44 312	53.64 346	87.00 316	88.07 274

baseline2		69.77 324	66.89 340	78.41 266	79.51 344	58.09 310	76.23 311	69.57 368	57.50 335	64.82 391	77.45 374	46.02 356	88.44 257	53.08 349	77.83 381	88.70 268

PatchmatchNet		69.71 325	68.83 330	72.33 328	77.66 357	53.60 341	79.29 263	69.99 366	57.66 333	72.53 352	82.93 325	46.45 353	80.08 341	60.91 305	72.09 393	83.31 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 326	69.05 326	71.14 335	69.15 402	65.77 220	73.98 334	83.32 274	42.83 396	77.77 314	78.27 368	43.39 379	68.50 379	68.39 246	84.38 349	79.15 376

JIA-IIPM		69.41 327	66.64 344	77.70 280	73.19 389	71.24 168	75.67 317	65.56 383	70.42 212	65.18 387	92.97 124	33.64 397	83.06 322	53.52 348	69.61 399	78.79 377

Syy-MVS		69.40 328	70.03 319	67.49 357	81.72 318	38.94 399	71.00 355	61.99 389	61.38 302	70.81 361	72.36 392	61.37 280	79.30 343	64.50 280	85.18 335	84.22 322

testing99		69.27 329	68.15 335	72.63 323	83.29 304	45.45 384	71.15 354	71.08 361	67.34 247	70.43 364	77.77 371	32.24 398	84.35 314	53.72 345	86.33 324	88.10 273

UnsupCasMVSNet_bld		69.21 330	69.68 322	67.82 355	79.42 345	51.15 361	67.82 373	75.79 325	54.15 351	77.47 317	85.36 296	59.26 295	70.64 369	48.46 373	79.35 375	81.66 357

test_cas_vis1_n_1920		69.20 331	69.12 324	69.43 344	73.68 387	62.82 249	70.38 362	77.21 315	46.18 384	80.46 285	78.95 363	52.03 332	65.53 391	65.77 266	77.45 386	79.95 374

gg-mvs-nofinetune		68.96 332	69.11 325	68.52 353	76.12 372	45.32 385	83.59 181	55.88 403	86.68 24	64.62 392	97.01 7	30.36 401	83.97 319	44.78 386	82.94 357	76.26 381

WB-MVSnew		68.72 333	69.01 327	67.85 354	83.22 307	43.98 390	74.93 326	65.98 382	55.09 345	73.83 345	79.11 360	65.63 259	71.89 366	38.21 399	85.04 338	87.69 284

tpm2		68.45 334	66.83 341	73.30 317	78.93 352	48.50 370	79.76 255	71.76 357	47.50 379	69.92 367	83.60 316	42.07 382	88.40 258	48.44 374	79.51 373	83.01 343

tpm		67.95 335	68.08 336	67.55 356	78.74 353	43.53 392	75.60 318	67.10 380	54.92 347	72.23 353	88.10 246	42.87 381	75.97 356	52.21 355	80.95 372	83.15 341

WTY-MVS		67.91 336	68.35 333	66.58 361	80.82 332	48.12 372	65.96 378	72.60 349	53.67 353	71.20 358	81.68 340	58.97 297	69.06 375	48.57 372	81.67 365	82.55 347

testing11		67.38 337	65.93 345	71.73 331	83.37 302	46.60 379	70.95 357	69.40 369	62.47 288	66.14 380	76.66 380	31.22 399	84.10 316	49.10 370	84.10 351	84.49 317

test-LLR		67.21 338	66.74 342	68.63 351	76.45 369	55.21 332	67.89 370	67.14 378	62.43 291	65.08 388	72.39 390	43.41 377	69.37 371	61.00 303	84.89 343	81.31 361

testing222		66.93 339	65.30 351	71.81 330	83.38 301	45.83 383	72.06 348	67.50 374	64.12 277	69.68 368	76.37 383	27.34 408	83.00 323	38.88 395	88.38 294	86.62 295

sss		66.92 340	67.26 338	65.90 363	77.23 360	51.10 363	64.79 380	71.72 358	52.12 364	70.13 366	80.18 352	57.96 304	65.36 392	50.21 363	81.01 371	81.25 363

KD-MVS_2432*1600		66.87 341	65.81 347	70.04 338	67.50 403	47.49 375	62.56 385	79.16 302	61.21 307	77.98 309	80.61 346	25.29 411	82.48 326	53.02 350	84.92 340	80.16 372

miper_refine_blended		66.87 341	65.81 347	70.04 338	67.50 403	47.49 375	62.56 385	79.16 302	61.21 307	77.98 309	80.61 346	25.29 411	82.48 326	53.02 350	84.92 340	80.16 372

dmvs_re		66.81 343	66.98 339	66.28 362	76.87 364	58.68 308	71.66 351	72.24 352	60.29 316	69.52 370	73.53 389	52.38 331	64.40 394	44.90 385	81.44 368	75.76 382

tpm cat1		66.76 344	65.21 352	71.42 332	77.09 362	50.62 365	78.01 282	73.68 343	44.89 388	68.64 372	79.00 362	45.51 364	82.42 328	49.91 365	70.15 396	81.23 365

UWE-MVS		66.43 345	65.56 350	69.05 346	84.15 290	40.98 397	73.06 344	64.71 385	54.84 348	76.18 325	79.62 358	29.21 403	80.50 338	38.54 398	89.75 278	85.66 305

PVSNet	58.17 21	66.41 346	65.63 349	68.75 349	81.96 315	49.88 368	62.19 387	72.51 351	51.03 370	68.04 375	75.34 387	50.84 338	74.77 359	45.82 384	82.96 356	81.60 358

tpmrst		66.28 347	66.69 343	65.05 368	72.82 393	39.33 398	78.20 281	70.69 364	53.16 356	67.88 376	80.36 351	48.18 347	74.75 360	58.13 319	70.79 395	81.08 366

Patchmatch-test		65.91 348	67.38 337	61.48 378	75.51 376	43.21 393	68.84 367	63.79 387	62.48 287	72.80 351	83.42 320	44.89 372	59.52 400	48.27 375	86.45 321	81.70 356

ADS-MVSNet2		65.87 349	63.64 357	72.55 325	73.16 390	56.92 321	67.10 374	74.81 331	49.74 377	66.04 382	82.97 323	46.71 351	77.26 352	42.29 389	69.96 397	83.46 334

test_vis1_rt		65.64 350	64.09 354	70.31 337	66.09 407	70.20 176	61.16 388	81.60 290	38.65 402	72.87 350	69.66 395	52.84 328	60.04 399	56.16 328	77.77 382	80.68 370

mvsany_test3		65.48 351	62.97 359	73.03 320	69.99 400	76.17 118	64.83 379	43.71 410	43.68 392	80.25 289	87.05 271	52.83 329	63.09 397	51.92 360	72.44 392	79.84 375

test-mter		65.00 352	63.79 356	68.63 351	76.45 369	55.21 332	67.89 370	67.14 378	50.98 371	65.08 388	72.39 390	28.27 406	69.37 371	61.00 303	84.89 343	81.31 361

ETVMVS		64.67 353	63.34 358	68.64 350	83.44 300	41.89 395	69.56 366	61.70 394	61.33 304	68.74 371	75.76 385	28.76 404	79.35 342	34.65 402	86.16 327	84.67 316

myMVS_eth3d		64.66 354	63.89 355	66.97 359	81.72 318	37.39 402	71.00 355	61.99 389	61.38 302	70.81 361	72.36 392	20.96 413	79.30 343	49.59 367	85.18 335	84.22 322

test0.0.03 1		64.66 354	64.36 353	65.57 365	75.03 381	46.89 378	64.69 381	61.58 395	62.43 291	71.18 359	77.54 372	43.41 377	68.47 380	40.75 393	82.65 361	81.35 360

test_f		64.31 356	65.85 346	59.67 381	66.54 406	62.24 264	57.76 397	70.96 362	40.13 399	84.36 215	82.09 334	46.93 350	51.67 405	61.99 296	81.89 364	65.12 396

pmmvs3		62.47 357	60.02 370	69.80 341	71.58 397	64.00 235	70.52 360	58.44 401	39.77 400	66.05 381	75.84 384	27.10 410	72.28 363	46.15 382	84.77 347	73.11 386

EPMVS		62.47 357	62.63 361	62.01 374	70.63 399	38.74 400	74.76 327	52.86 405	53.91 352	67.71 378	80.01 353	39.40 386	66.60 387	55.54 334	68.81 401	80.68 370

ADS-MVSNet		61.90 359	62.19 363	61.03 379	73.16 390	36.42 404	67.10 374	61.75 392	49.74 377	66.04 382	82.97 323	46.71 351	63.21 395	42.29 389	69.96 397	83.46 334

PMMVS		61.65 360	60.38 367	65.47 366	65.40 410	69.26 185	63.97 383	61.73 393	36.80 406	60.11 398	68.43 397	59.42 293	66.35 388	48.97 371	78.57 380	60.81 399

E-PMN		61.59 361	61.62 364	61.49 377	66.81 405	55.40 330	53.77 400	60.34 397	66.80 252	58.90 401	65.50 400	40.48 385	66.12 389	55.72 331	86.25 325	62.95 398

TESTMET0.1,1		61.29 362	60.32 368	64.19 370	72.06 395	51.30 359	67.89 370	62.09 388	45.27 386	60.65 397	69.01 396	27.93 407	64.74 393	56.31 327	81.65 367	76.53 380

MVS-HIRNet		61.16 363	62.92 360	55.87 384	79.09 349	35.34 405	71.83 349	57.98 402	46.56 382	59.05 400	91.14 179	49.95 343	76.43 354	38.74 396	71.92 394	55.84 403

EMVS		61.10 364	60.81 366	61.99 375	65.96 408	55.86 327	53.10 401	58.97 400	67.06 249	56.89 405	63.33 401	40.98 383	67.03 385	54.79 340	86.18 326	63.08 397

DSMNet-mixed		60.98 365	61.61 365	59.09 383	72.88 392	45.05 387	74.70 328	46.61 409	26.20 407	65.34 386	90.32 209	55.46 320	63.12 396	41.72 391	81.30 370	69.09 392

dp		60.70 366	60.29 369	61.92 376	72.04 396	38.67 401	70.83 358	64.08 386	51.28 368	60.75 396	77.28 375	36.59 393	71.58 368	47.41 377	62.34 403	75.52 383

dmvs_testset		60.59 367	62.54 362	54.72 386	77.26 359	27.74 409	74.05 333	61.00 396	60.48 314	65.62 385	67.03 399	55.93 317	68.23 381	32.07 406	69.46 400	68.17 393

CHOSEN 280x420		59.08 368	56.52 373	66.76 360	76.51 367	64.39 231	49.62 402	59.00 399	43.86 391	55.66 406	68.41 398	35.55 394	68.21 382	43.25 388	76.78 388	67.69 394

mvsany_test1		58.48 369	56.47 374	64.50 369	65.90 409	68.21 195	56.95 398	42.11 411	38.30 403	65.69 384	77.19 378	56.96 311	59.35 401	46.16 381	58.96 404	65.93 395

PVSNet_0	51.08 22	56.10 370	54.97 375	59.48 382	75.12 380	53.28 345	55.16 399	61.89 391	44.30 389	59.16 399	62.48 402	54.22 325	65.91 390	35.40 401	47.01 405	59.25 401

new_pmnet		55.69 371	57.66 372	49.76 387	75.47 377	30.59 407	59.56 390	51.45 406	43.62 393	62.49 394	75.48 386	40.96 384	49.15 407	37.39 400	72.52 391	69.55 391

PMMVS2		55.64 372	59.27 371	44.74 388	64.30 411	12.32 416	40.60 403	49.79 407	53.19 355	65.06 390	84.81 303	53.60 327	49.76 406	32.68 405	89.41 281	72.15 387

MVE	40.22 23	51.82 373	50.47 376	55.87 384	62.66 412	51.91 354	31.61 405	39.28 412	40.65 398	50.76 407	74.98 388	56.24 316	44.67 408	33.94 404	64.11 402	71.04 390
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 374	42.65 377	39.67 389	70.86 398	21.11 411	61.01 389	21.42 416	57.36 336	57.97 404	50.06 405	16.40 415	58.73 402	21.03 409	27.69 409	39.17 405

kuosan		30.83 375	32.17 378	26.83 391	53.36 413	19.02 414	57.90 396	20.44 417	38.29 404	38.01 408	37.82 407	15.18 416	33.45 410	7.74 411	20.76 410	28.03 406

test_method		30.46 376	29.60 379	33.06 390	17.99 415	3.84 418	13.62 406	73.92 338	2.79 409	18.29 411	53.41 404	28.53 405	43.25 409	22.56 407	35.27 407	52.11 404

cdsmvs_eth3d_5k		20.81 377	27.75 380	0.00 396	0.00 419	0.00 421	0.00 407	85.44 247	0.00 414	0.00 415	82.82 327	81.46 115	0.00 415	0.00 414	0.00 413	0.00 411

tmp_tt		20.25 378	24.50 381	7.49 393	4.47 416	8.70 417	34.17 404	25.16 414	1.00 411	32.43 410	18.49 408	39.37 387	9.21 412	21.64 408	43.75 406	4.57 408

ab-mvs-re		6.65 379	8.87 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	79.80 355	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		6.41 380	8.55 383	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	76.94 161	0.00 415	0.00 414	0.00 413	0.00 411

test123		6.27 381	8.08 384	0.84 394	1.11 418	0.57 419	62.90 384	0.82 418	0.54 412	1.07 414	2.75 413	1.26 417	0.30 413	1.04 412	1.26 412	1.66 409

testmvs		5.91 382	7.65 385	0.72 395	1.20 417	0.37 420	59.14 392	0.67 419	0.49 413	1.11 413	2.76 412	0.94 418	0.24 414	1.02 413	1.47 411	1.55 410

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						37.39 402								52.61 354

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

MSC_two_6792asdad				88.81 68	91.55 125	77.99 90		91.01 146					96.05 8	87.45 20	98.17 31	92.40 174

PC_three_1452									58.96 323	90.06 94	91.33 173	80.66 125	93.03 138	75.78 161	95.94 125	92.48 169

No_MVS				88.81 68	91.55 125	77.99 90		91.01 146					96.05 8	87.45 20	98.17 31	92.40 174

test_one_0601					93.85 58	73.27 135		94.11 35	86.57 25	93.47 37	94.64 58	88.42 26

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					92.22 100	80.48 67		91.85 121	71.22 206	90.38 89	92.98 122	86.06 61	96.11 6	81.99 90	96.75 89

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 29	88.75 14	93.79 28	94.43 66	90.64 10		87.16 29	97.60 63	92.73 156

IU-MVS					94.18 46	72.64 143		90.82 151	56.98 339	89.67 106				85.78 50	97.92 45	93.28 135

OPU-MVS				88.27 79	91.89 111	77.83 93	90.47 51				91.22 176	81.12 119	94.68 71	74.48 174	95.35 144	92.29 180

test_241102_TWO								93.71 52	83.77 47	93.49 35	94.27 73	89.27 21	95.84 22	86.03 46	97.82 50	92.04 192

test_241102_ONE					94.18 46	72.65 141		93.69 53	83.62 49	94.11 22	93.78 103	90.28 14	95.50 45

9.14			89.29 58		91.84 115		88.80 88	95.32 11	75.14 148	91.07 78	92.89 127	87.27 44	93.78 105	83.69 68	97.55 66

save fliter					93.75 59	77.44 99	86.31 128	89.72 182	70.80 209

test_0728_THIRD									85.33 33	93.75 30	94.65 55	87.44 43	95.78 27	87.41 22	98.21 28	92.98 150

test_0728_SECOND				86.79 99	94.25 45	72.45 151	90.54 48	94.10 36					95.88 16	86.42 36	97.97 42	92.02 193

test0726					94.16 49	72.56 147	90.63 45	93.90 45	83.61 50	93.75 30	94.49 63	89.76 18

GSMVS																83.88 326

test_part2					93.86 57	77.77 94				92.84 47

sam_mvs1												46.11 355				83.88 326

sam_mvs												45.92 360

ambc				82.98 188	90.55 152	64.86 226	88.20 96	89.15 193		89.40 115	93.96 94	71.67 225	91.38 184	78.83 122	96.55 95	92.71 159

MTGPA								91.81 125

test_post1							78.85 273				3.13 410	45.19 369	80.13 340	58.11 320

test_post											3.10 411	45.43 365	77.22 353

patchmatchnet-post											81.71 339	45.93 359	87.01 272

GG-mvs-BLEND				67.16 358	73.36 388	46.54 381	84.15 164	55.04 404		58.64 402	61.95 403	29.93 402	83.87 320	38.71 397	76.92 387	71.07 389

MTMP							90.66 44	33.14 413

gm-plane-assit					75.42 378	44.97 388			52.17 361		72.36 392		87.90 262	54.10 343

test9_res														80.83 101	96.45 102	90.57 232

TEST9					92.34 94	79.70 74	83.94 169	90.32 165	65.41 269	84.49 211	90.97 185	82.03 106	93.63 110

test_8					92.09 104	78.87 81	83.82 174	90.31 167	65.79 258	84.36 215	90.96 187	81.93 108	93.44 123

agg_prior2														79.68 114	96.16 113	90.22 240

agg_prior					91.58 123	77.69 96		90.30 168		84.32 217			93.18 132

TestCases				89.68 52	91.59 120	83.40 48		95.44 9	79.47 94	88.00 142	93.03 120	82.66 91	91.47 178	70.81 214	96.14 114	94.16 95

test_prior4						78.97 80	84.59 155

test_prior2							83.37 186		75.43 144	84.58 209	91.57 167	81.92 110		79.54 116	96.97 82

test_prior				86.32 107	90.59 151	71.99 158		92.85 90					94.17 91			92.80 154

旧先验2							81.73 231		56.88 340	86.54 175			84.90 308	72.81 203

新几何2							81.72 232

新几何1				82.95 190	93.96 55	78.56 84		80.24 298	55.45 344	83.93 228	91.08 182	71.19 227	88.33 259	65.84 264	93.07 216	81.95 355

旧先验1					91.97 107	71.77 159		81.78 288			91.84 158	73.92 194			93.65 204	83.61 332

无先验							82.81 205	85.62 245	58.09 329				91.41 183	67.95 250		84.48 318

原ACMM2							82.26 225

原ACMM1				84.60 143	92.81 85	74.01 128		91.50 130	62.59 285	82.73 248	90.67 200	76.53 168	94.25 85	69.24 231	95.69 138	85.55 306

test222					93.31 69	76.54 109	79.38 262	77.79 309	52.59 358	82.36 252	90.84 193	66.83 252			91.69 244	81.25 363

testdata2													86.43 286	63.52 285

segment_acmp												81.94 107

testdata				79.54 251	92.87 80	72.34 152		80.14 299	59.91 319	85.47 194	91.75 164	67.96 247	85.24 304	68.57 245	92.18 236	81.06 368

testdata1							79.62 257		73.95 159

test12				86.57 102	90.74 147	72.63 145		90.69 154		82.76 247		79.20 135	94.80 68		95.32 147	92.27 182

plane_prior7					93.45 64	77.31 102

plane_prior6					92.61 86	76.54 109						74.84 182

plane_prior5								93.61 56					95.22 55	80.78 102	95.83 131	94.46 80

plane_prior4											92.95 125

plane_prior3						76.85 107			77.79 118	86.55 170

plane_prior2							89.45 77		79.44 96

plane_prior1					92.83 84

plane_prior						76.42 113	87.15 112		75.94 137						95.03 159

n2								0.00 420

nn								0.00 420

door-mid								74.45 335

lessismore_v0				85.95 116	91.10 140	70.99 170		70.91 363		91.79 66	94.42 68	61.76 278	92.93 141	79.52 117	93.03 217	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 50	91.18 4	95.52 41	85.36 52	98.73 6	95.23 58

test11								91.46 131

door								72.57 350

HQP5-MVS						70.66 171

HQP-NCC					91.19 135		84.77 149		73.30 173	80.55 282

ACMP_Plane					91.19 135		84.77 149		73.30 173	80.55 282

BP-MVS														77.30 145

HQP4-MVS										80.56 281			94.61 74			93.56 128

HQP3-MVS								92.68 95							94.47 180

HQP2-MVS												72.10 218

NP-MVS					91.95 108	74.55 125					90.17 216

MDTV_nov1_ep13_2view						27.60 410	70.76 359		46.47 383	61.27 395		45.20 368		49.18 369		83.75 331

MDTV_nov1_ep13			68.29 334		78.03 354	43.87 391	74.12 332	72.22 353	52.17 361	67.02 379	85.54 289	45.36 366	80.85 335	55.73 330	84.42 348

ACMMP++_ref															95.74 137

ACMMP++															97.35 72

Test By Simon												79.09 136

ITE_SJBPF				90.11 45	90.72 148	84.97 37		90.30 168	81.56 71	90.02 96	91.20 178	82.40 96	90.81 203	73.58 191	94.66 175	94.56 76

DeepMVS_CX				24.13 392	32.95 414	29.49 408		21.63 415	12.07 408	37.95 409	45.07 406	30.84 400	19.21 411	17.94 410	33.06 408	23.69 407

High-resolution multi-view benchmark