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 68	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 24	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 29	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 47	88.20 19	93.24 39	94.02 90	90.15 16	95.67 34	86.82 33	97.34 74	92.19 184

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 29	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 95	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 61	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 63	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	91.81 120	84.07 44	92.00 64	94.40 71	86.63 51	95.28 55	88.59 5	98.31 23	92.30 177

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 90	88.22 18	88.53 129	97.64 2	83.45 82	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 69	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 21	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 67	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 68	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 26	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 33	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 44	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 59	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 82	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 50	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 54	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 62	79.20 100	93.83 27	93.60 110	90.81 7	92.96 139	85.02 56	98.45 18	92.41 171
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 70	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 38	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 38	84.68 40	93.90 24	94.11 87	88.13 34	96.30 4	84.51 62	97.81 52	91.70 200
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 52	83.77 47	94.11 22	94.27 74	90.28 14	95.84 23	86.03 46	97.92 46	92.29 178

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 70	75.37 147	92.84 48	95.28 38	85.58 63	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 79	86.53 26	94.29 18	96.27 17	82.69 89	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	89.57 183	88.51 17	90.11 95	95.12 44	90.98 6	88.92 248	77.55 140	97.07 81	83.13 340
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 36	85.33 33	93.49 36	94.64 59	81.12 118	95.88 17	87.41 22	95.94 126	92.48 168

DVP-MVS		90.06 39	91.32 28	86.29 109	94.16 49	72.56 148	90.54 48	91.01 141	83.61 50	93.75 30	94.65 56	89.76 18	95.78 28	86.42 36	97.97 43	90.55 230
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	58.83 304	89.04 83	92.74 92	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	58.88 301	88.95 84	93.19 71	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 75	81.10 77	95.32 10	97.24 5	72.94 208	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	57.84 311	88.93 85	92.84 89	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 62	93.89 100	83.80 77	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 34	88.89 11	88.83 124	94.26 77	77.55 150	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	62.39 256	89.54 74	93.31 66	90.21 10	95.57 9	95.66 29	81.42 115	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	86.76 121	92.78 91	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	90.63 151	70.00 218	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	90.20 168	69.87 219	95.06 11	96.14 21	84.28 73	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	90.08 171	71.54 200	94.28 20	96.54 13	81.57 113	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	86.84 117	93.91 43	80.07 89	86.75 165	93.26 114	93.64 2	90.93 195	84.60 61	90.75 264	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 78	80.87 81	91.13 78	93.19 115	86.22 58	95.97 12	82.23 88	97.18 79	90.45 232
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	90.29 165	69.27 222	94.39 16	96.38 15	86.02 61	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	92.30 102	79.74 91	87.50 150	92.38 143	81.42 115	93.28 129	83.07 74	97.24 77	91.67 201

ACMH	76.49 14	89.34 55	91.14 31	83.96 160	92.50 91	70.36 176	89.55 72	93.84 48	81.89 68	94.70 13	95.44 34	90.69 8	88.31 258	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 72	86.02 29	93.12 41	95.30 36	84.94 65	89.44 240	74.12 177	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 72	86.02 29	93.12 41	95.30 36	84.94 65	89.44 240	74.12 177	96.10 118	94.45 82

CP-MVSNet		89.27 58	90.91 40	84.37 147	96.34 8	58.61 307	88.66 92	92.06 108	90.78 6	95.67 7	95.17 42	81.80 111	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	85.91 132	93.60 57	80.16 87	89.13 121	93.44 112	83.82 76	90.98 193	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 27	73.53 166	89.71 106	94.82 51	85.09 64	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	64.33 232	89.67 69	88.38 198	88.84 13	94.29 18	97.57 3	90.48 13	91.26 184	72.57 201	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 44	78.43 111	89.16 119	92.25 150	72.03 221	96.36 3	88.21 7	90.93 258	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 49	78.90 104	92.88 45	92.29 148	86.11 59	90.22 216	86.24 43	97.24 77	91.36 208
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	91.27 134	78.20 113	86.69 168	92.28 149	80.36 127	95.06 62	86.17 44	96.49 99	90.22 236

test_0402		88.65 65	89.58 56	85.88 120	92.55 89	72.22 156	84.01 168	89.44 185	88.63 16	94.38 17	95.77 26	86.38 57	93.59 116	79.84 111	95.21 152	91.82 196

DP-MVS		88.60 66	89.01 63	87.36 91	91.30 133	77.50 97	87.55 105	92.97 85	87.95 20	89.62 110	92.87 129	84.56 69	93.89 102	77.65 138	96.62 93	90.70 224

APD_test1		88.40 67	87.91 75	89.88 47	89.50 172	86.65 16	89.98 60	91.91 114	84.26 42	90.87 87	93.92 99	82.18 102	89.29 244	73.75 184	94.81 171	93.70 119

Anonymous20231211		88.40 67	89.62 55	84.73 140	90.46 154	65.27 222	88.86 86	93.02 83	87.15 23	93.05 43	97.10 6	82.28 101	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	91.97 111	70.73 209	94.19 21	96.67 11	76.94 160	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	89.06 190	77.34 122	93.63 35	95.83 25	65.40 256	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	87.49 108	93.17 72	76.02 134	88.64 127	91.22 176	84.24 74	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	84.32 216	89.33 227	83.87 75	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	91.19 137	67.85 242	86.63 169	94.84 50	79.58 133	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	82.98 188	93.26 72	63.94 236	91.10 41	89.64 180	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	85.12 197	89.67 222	84.47 71	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	91.11 138	79.26 99	89.68 107	94.81 54	82.44 93	87.74 262	76.54 153	88.74 289	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	88.00 142	93.03 121	82.66 90	91.47 177	70.81 210	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	91.87 115	72.61 187	92.16 60	95.23 41	66.01 250	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	64.13 233	84.38 161	91.29 133	84.88 39	92.06 63	93.84 101	86.45 54	93.73 107	73.22 192	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 27	74.04 158	92.70 53	94.66 55	85.88 62	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	85.25 144	91.23 135	77.31 124	87.07 159	91.47 170	82.94 87	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 55	79.44 96	86.55 170	92.95 126	74.84 181	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	86.76 228	84.54 41	83.58 233	93.78 104	73.36 204	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	86.58 125	91.09 139	78.77 107	84.85 205	90.89 189	80.85 121	95.29 53	81.14 97	95.32 148	92.34 175

DeepPCF-MVS	81.24 5	87.28 85	86.21 105	90.49 38	91.48 130	84.90 38	83.41 186	92.38 100	70.25 215	89.35 118	90.68 198	82.85 88	94.57 76	79.55 115	95.95 125	92.00 191

SixPastTwentyTwo		87.20 86	87.45 83	86.45 106	92.52 90	69.19 189	87.84 103	88.05 206	81.66 70	94.64 14	96.53 14	65.94 251	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	81.64 267	87.25 262	82.43 94	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	83.80 177	92.87 87	80.37 83	89.61 112	91.81 161	77.72 147	94.18 90	75.00 170	98.53 15	96.99 24

Vis-MVSNet		86.86 89	86.58 98	87.72 86	92.09 105	77.43 100	87.35 109	92.09 107	78.87 105	84.27 221	94.05 88	78.35 141	93.65 109	80.54 106	91.58 246	92.08 188
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	67.02 206	82.55 212	91.56 123	83.08 57	90.92 82	91.82 160	78.25 142	93.99 97	74.16 175	98.35 21	97.49 13

DU-MVS		86.80 91	86.99 92	86.21 113	93.24 73	67.02 206	83.16 195	92.21 103	81.73 69	90.92 82	91.97 154	77.20 154	93.99 97	74.16 175	98.35 21	97.61 10

casdiffmvs_mvg		86.72 92	87.51 82	84.36 149	87.09 231	65.22 223	84.16 163	94.23 24	77.89 116	91.28 77	93.66 108	84.35 72	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	85.94 258	78.30 85	86.93 115	92.20 104	65.94 254	89.16 119	93.16 117	83.10 85	89.89 229	87.81 11	94.43 182	93.35 132

IS-MVSNet		86.66 94	86.82 97	86.17 115	92.05 107	66.87 209	91.21 39	88.64 195	86.30 28	89.60 113	92.59 137	69.22 234	94.91 66	73.89 181	97.89 49	96.72 26

v10		86.54 95	87.10 89	84.84 136	88.16 206	63.28 242	86.64 124	92.20 104	75.42 146	92.81 50	94.50 63	74.05 192	94.06 96	83.88 67	96.28 108	97.17 20

pmmvs6		86.52 96	88.06 74	81.90 208	92.22 101	62.28 259	84.66 154	89.15 188	83.54 52	89.85 103	97.32 4	88.08 36	86.80 277	70.43 218	97.30 76	96.62 28

PHI-MVS		86.38 97	85.81 114	88.08 82	88.44 200	77.34 101	89.35 80	93.05 79	73.15 178	84.76 206	87.70 252	78.87 137	94.18 90	80.67 104	96.29 107	92.73 156

MVS_0304		86.35 98	85.92 110	87.66 88	89.21 180	73.16 139	88.40 95	83.63 270	81.27 74	80.87 277	94.12 86	71.49 225	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	91.85 116	80.35 84	89.54 116	88.01 245	79.09 135	92.13 161	75.51 163	95.06 159	90.41 233

DeepC-MVS_fast	80.27 8	86.23 100	85.65 118	87.96 85	91.30 133	76.92 106	87.19 110	91.99 110	70.56 210	84.96 201	90.69 197	80.01 130	95.14 59	78.37 125	95.78 137	91.82 196
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	87.82 211	62.35 258	86.42 127	91.33 132	76.78 128	92.73 52	94.48 65	73.41 201	93.72 108	83.10 73	95.41 144	97.01 23

Anonymous20240529		86.20 102	87.13 88	83.42 177	90.19 159	64.55 230	84.55 156	90.71 148	85.85 31	89.94 102	95.24 40	82.13 103	90.40 212	69.19 232	96.40 104	95.31 55

test_fmvsmconf0.1_n		86.18 103	85.88 112	87.08 94	85.26 268	78.25 86	85.82 135	91.82 118	65.33 267	88.55 128	92.35 147	82.62 92	89.80 231	86.87 32	94.32 185	93.18 141

CDPH-MVS		86.17 104	85.54 119	88.05 84	92.25 99	75.45 122	83.85 174	92.01 109	65.91 256	86.19 179	91.75 164	83.77 78	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	64.56 229	82.21 224	90.46 155	80.99 78	88.42 133	91.97 154	77.56 149	93.85 103	72.46 202	98.65 11	97.61 10

train_agg		85.98 106	85.28 124	88.07 83	92.34 95	79.70 74	83.94 170	90.32 160	65.79 257	84.49 210	90.97 185	81.93 107	93.63 111	81.21 96	96.54 96	90.88 218

FC-MVSNet-test		85.93 107	87.05 91	82.58 199	92.25 99	56.44 322	85.75 136	93.09 77	77.33 123	91.94 66	94.65 56	74.78 183	93.41 126	75.11 169	98.58 13	97.88 7

test_fmvsmconf_n		85.88 108	85.51 120	86.99 96	84.77 275	78.21 87	85.40 143	91.39 130	65.32 268	87.72 146	91.81 161	82.33 97	89.78 232	86.68 34	94.20 188	92.99 149

Effi-MVS+-dtu		85.82 109	83.38 155	93.14 3	87.13 227	91.15 2	87.70 104	88.42 197	74.57 154	83.56 234	85.65 285	78.49 140	94.21 88	72.04 204	92.88 219	94.05 102

TAPA-MVS	77.73 12	85.71 110	84.83 130	88.37 78	88.78 191	79.72 73	87.15 112	93.50 58	69.17 223	85.80 188	89.56 223	80.76 122	92.13 161	73.21 197	95.51 142	93.25 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		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

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		85.47 113	85.04 127	86.77 101	91.52 129	69.37 184	91.63 36	87.98 208	81.51 72	87.05 160	91.83 159	66.18 249	95.29 53	70.75 213	96.89 84	95.64 46

GeoE		85.45 114	85.81 114	84.37 147	90.08 161	67.07 205	85.86 134	91.39 130	72.33 193	87.59 148	90.25 210	84.85 67	92.37 155	78.00 134	91.94 239	93.66 120

FIs		85.35 115	86.27 103	82.60 198	91.86 113	57.31 315	85.10 148	93.05 79	75.83 139	91.02 81	93.97 92	73.57 197	92.91 143	73.97 180	98.02 39	97.58 12

test_fmvsmvis_n_1920		85.22 116	85.36 123	84.81 137	85.80 260	76.13 119	85.15 147	92.32 101	61.40 297	91.33 74	90.85 192	83.76 79	86.16 290	84.31 63	93.28 209	92.15 186

casdiffmvs		85.21 117	85.85 113	83.31 180	86.17 253	62.77 249	83.03 197	93.93 42	74.69 153	88.21 138	92.68 136	82.29 100	91.89 169	77.87 137	93.75 200	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		85.20 118	85.93 109	83.02 187	86.30 248	62.37 257	84.55 156	93.96 41	74.48 155	87.12 154	92.03 153	82.30 99	91.94 166	78.39 124	94.21 187	94.74 73

K. test v3		85.14 119	84.73 131	86.37 107	91.13 140	69.63 182	85.45 141	76.68 319	84.06 45	92.44 57	96.99 8	62.03 274	94.65 72	80.58 105	93.24 210	94.83 72

EI-MVSNet-Vis-set		85.12 120	84.53 138	86.88 98	84.01 288	72.76 141	83.91 173	85.18 250	80.44 82	88.75 125	85.49 287	80.08 129	91.92 167	82.02 90	90.85 262	95.97 39

EI-MVSNet-UG-set		85.04 121	84.44 140	86.85 99	83.87 292	72.52 150	83.82 175	85.15 251	80.27 86	88.75 125	85.45 289	79.95 131	91.90 168	81.92 93	90.80 263	96.13 34

X-MVStestdata		85.04 121	82.70 168	92.08 8	95.64 23	86.25 18	92.64 18	93.33 63	85.07 36	89.99 99	16.05 405	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

MSLP-MVS++		85.00 123	86.03 108	81.90 208	91.84 116	71.56 167	86.75 122	93.02 83	75.95 137	87.12 154	89.39 225	77.98 143	89.40 243	77.46 141	94.78 172	84.75 313

F-COLMAP		84.97 124	83.42 154	89.63 55	92.39 93	83.40 48	88.83 87	91.92 113	73.19 177	80.18 290	89.15 231	77.04 158	93.28 129	65.82 263	92.28 230	92.21 183

3Dnovator	80.37 7	84.80 125	84.71 134	85.06 134	86.36 246	74.71 125	88.77 89	90.00 173	75.65 142	84.96 201	93.17 116	74.06 191	91.19 186	78.28 128	91.09 252	89.29 256

IterMVS-LS		84.73 126	84.98 128	83.96 160	87.35 222	63.66 237	83.25 191	89.88 175	76.06 132	89.62 110	92.37 146	73.40 203	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		84.63 127	84.34 144	85.49 129	90.18 160	75.86 120	79.23 265	87.13 218	73.35 170	85.56 192	89.34 226	83.60 81	90.50 210	76.64 151	94.05 192	90.09 241

HQP-MVS		84.61 128	84.06 147	86.27 110	91.19 136	70.66 172	84.77 149	92.68 93	73.30 173	80.55 282	90.17 214	72.10 217	94.61 74	77.30 145	94.47 180	93.56 128

v1192		84.57 129	84.69 135	84.21 155	87.75 213	62.88 246	83.02 198	91.43 127	69.08 225	89.98 101	90.89 189	72.70 212	93.62 114	82.41 85	94.97 164	96.13 34

FMVSNet1		84.55 130	85.45 121	81.85 210	90.27 158	61.05 272	86.83 118	88.27 203	78.57 110	89.66 109	95.64 30	75.43 174	90.68 205	69.09 233	95.33 147	93.82 112

v1144		84.54 131	84.72 133	84.00 158	87.67 216	62.55 253	82.97 200	90.93 144	70.32 214	89.80 104	90.99 184	73.50 198	93.48 122	81.69 95	94.65 177	95.97 39

Gipuma		84.44 132	86.33 102	78.78 256	84.20 286	73.57 132	89.55 72	90.44 156	84.24 43	84.38 213	94.89 48	76.35 171	80.40 337	76.14 157	96.80 89	82.36 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MCST-MVS		84.36 133	83.93 150	85.63 125	91.59 121	71.58 165	83.52 183	92.13 106	61.82 290	83.96 227	89.75 221	79.93 132	93.46 123	78.33 127	94.34 184	91.87 195

VDDNet		84.35 134	85.39 122	81.25 221	95.13 31	59.32 294	85.42 142	81.11 290	86.41 27	87.41 151	96.21 19	73.61 196	90.61 208	66.33 256	96.85 85	93.81 115

ETV-MVS		84.31 135	83.91 151	85.52 127	88.58 196	70.40 175	84.50 160	93.37 60	78.76 108	84.07 225	78.72 363	80.39 126	95.13 60	73.82 183	92.98 217	91.04 214

v1240		84.30 136	84.51 139	83.65 169	87.65 217	61.26 269	82.85 204	91.54 124	67.94 240	90.68 90	90.65 201	71.71 223	93.64 110	82.84 79	94.78 172	96.07 36

MVS_111021_LR		84.28 137	83.76 152	85.83 122	89.23 179	83.07 51	80.99 240	83.56 271	72.71 185	86.07 182	89.07 232	81.75 112	86.19 289	77.11 147	93.36 205	88.24 269

h-mvs33		84.25 138	82.76 167	88.72 71	91.82 118	82.60 56	84.00 169	84.98 257	71.27 202	86.70 166	90.55 203	63.04 271	93.92 100	78.26 129	94.20 188	89.63 248

v144192		84.24 139	84.41 141	83.71 168	87.59 219	61.57 265	82.95 201	91.03 140	67.82 243	89.80 104	90.49 204	73.28 205	93.51 121	81.88 94	94.89 167	96.04 38

dcpmvs_2		84.23 140	85.14 125	81.50 217	88.61 195	61.98 263	82.90 203	93.11 75	68.66 231	92.77 51	92.39 142	78.50 139	87.63 264	76.99 149	92.30 227	94.90 65

v1921920		84.23 140	84.37 143	83.79 164	87.64 218	61.71 264	82.91 202	91.20 136	67.94 240	90.06 96	90.34 207	72.04 220	93.59 116	82.32 86	94.91 165	96.07 36

VDD-MVS		84.23 140	84.58 137	83.20 184	91.17 139	65.16 225	83.25 191	84.97 258	79.79 90	87.18 153	94.27 74	74.77 184	90.89 198	69.24 229	96.54 96	93.55 130

v2v482		84.09 143	84.24 145	83.62 170	87.13 227	61.40 266	82.71 207	89.71 178	72.19 196	89.55 114	91.41 171	70.70 229	93.20 131	81.02 98	93.76 197	96.25 32

EG-PatchMatch MVS		84.08 144	84.11 146	83.98 159	92.22 101	72.61 147	82.20 226	87.02 223	72.63 186	88.86 122	91.02 183	78.52 138	91.11 189	73.41 189	91.09 252	88.21 270

DP-MVS Recon		84.05 145	83.22 157	86.52 105	91.73 119	75.27 123	83.23 193	92.40 98	72.04 197	82.04 257	88.33 241	77.91 145	93.95 99	66.17 257	95.12 157	90.34 235

TransMVSNet (Re)		84.02 146	85.74 116	78.85 255	91.00 143	55.20 332	82.29 220	87.26 214	79.65 93	88.38 135	95.52 33	83.00 86	86.88 275	67.97 247	96.60 94	94.45 82

Baseline_NR-MVSNet		84.00 147	85.90 111	78.29 267	91.47 131	53.44 341	82.29 220	87.00 226	79.06 102	89.55 114	95.72 28	77.20 154	86.14 291	72.30 203	98.51 16	95.28 56

TSAR-MVS + GP.		83.95 148	82.69 169	87.72 86	89.27 178	81.45 63	83.72 179	81.58 289	74.73 152	85.66 189	86.06 280	72.56 214	92.69 147	75.44 165	95.21 152	89.01 264

alignmvs		83.94 149	83.98 149	83.80 163	87.80 212	67.88 200	84.54 158	91.42 129	73.27 176	88.41 134	87.96 246	72.33 215	90.83 200	76.02 159	94.11 190	92.69 160

Effi-MVS+		83.90 150	84.01 148	83.57 174	87.22 225	65.61 221	86.55 126	92.40 98	78.64 109	81.34 272	84.18 308	83.65 80	92.93 141	74.22 174	87.87 301	92.17 185

CANet		83.79 151	82.85 166	86.63 102	86.17 253	72.21 157	83.76 178	91.43 127	77.24 125	74.39 340	87.45 258	75.36 175	95.42 49	77.03 148	92.83 220	92.25 182

pm-mvs1		83.69 152	84.95 129	79.91 242	90.04 165	59.66 291	82.43 216	87.44 211	75.52 144	87.85 144	95.26 39	81.25 117	85.65 300	68.74 239	96.04 120	94.42 85

AdaColmap		83.66 153	83.69 153	83.57 174	90.05 164	72.26 155	86.29 130	90.00 173	78.19 114	81.65 266	87.16 264	83.40 83	94.24 87	61.69 297	94.76 175	84.21 322

MIMVSNet1		83.63 154	84.59 136	80.74 230	94.06 53	62.77 249	82.72 206	84.53 263	77.57 121	90.34 92	95.92 24	76.88 166	85.83 298	61.88 295	97.42 72	93.62 124

test_fmvsm_n_1920		83.60 155	82.89 165	85.74 123	85.22 269	77.74 95	84.12 165	90.48 154	59.87 317	86.45 178	91.12 180	75.65 172	85.89 296	82.28 87	90.87 260	93.58 126

WR-MVS		83.56 156	84.40 142	81.06 226	93.43 67	54.88 333	78.67 273	85.02 255	81.24 75	90.74 89	91.56 168	72.85 209	91.08 190	68.00 246	98.04 36	97.23 18

CNLPA		83.55 157	83.10 162	84.90 135	89.34 176	83.87 46	84.54 158	88.77 192	79.09 101	83.54 235	88.66 238	74.87 180	81.73 328	66.84 252	92.29 229	89.11 258

LCM-MVSNet-Re		83.48 158	85.06 126	78.75 257	85.94 258	55.75 327	80.05 249	94.27 21	76.47 129	96.09 5	94.54 62	83.31 84	89.75 235	59.95 307	94.89 167	90.75 221

hse-mvs2		83.47 159	81.81 182	88.47 75	91.03 142	82.27 57	82.61 208	83.69 268	71.27 202	86.70 166	86.05 281	63.04 271	92.41 153	78.26 129	93.62 204	90.71 223

V42		83.47 159	83.37 156	83.75 166	83.16 305	63.33 241	81.31 234	90.23 167	69.51 221	90.91 84	90.81 194	74.16 190	92.29 159	80.06 108	90.22 271	95.62 47

VPA-MVSNet		83.47 159	84.73 131	79.69 246	90.29 157	57.52 314	81.30 236	88.69 194	76.29 130	87.58 149	94.44 66	80.60 125	87.20 269	66.60 255	96.82 88	94.34 89

PAPM_NR		83.23 162	83.19 159	83.33 179	90.90 145	65.98 217	88.19 97	90.78 147	78.13 115	80.87 277	87.92 249	73.49 200	92.42 152	70.07 222	88.40 291	91.60 203

CLD-MVS		83.18 163	82.64 170	84.79 138	89.05 182	67.82 201	77.93 281	92.52 96	68.33 233	85.07 198	81.54 339	82.06 104	92.96 139	69.35 228	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		83.17 164	85.68 117	75.65 301	81.24 323	45.26 384	79.94 251	92.91 86	83.83 46	91.33 74	96.88 10	80.25 128	85.92 293	68.89 236	95.89 129	95.76 43

FA-MVS(test-final)		83.13 165	83.02 163	83.43 176	86.16 255	66.08 216	88.00 99	88.36 199	75.55 143	85.02 199	92.75 134	65.12 257	92.50 151	74.94 171	91.30 250	91.72 198

114514_t		83.10 166	82.54 173	84.77 139	92.90 80	69.10 191	86.65 123	90.62 152	54.66 346	81.46 269	90.81 194	76.98 159	94.38 83	72.62 200	96.18 113	90.82 220

UGNet		82.78 167	81.64 184	86.21 113	86.20 252	76.24 117	86.86 116	85.68 242	77.07 126	73.76 344	92.82 130	69.64 231	91.82 172	69.04 235	93.69 201	90.56 229
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		82.75 168	81.93 180	85.19 131	82.08 312	80.15 70	85.53 139	88.76 193	68.01 237	85.58 191	87.75 251	71.80 222	86.85 276	74.02 179	93.87 196	88.58 267

EI-MVSNet		82.61 169	82.42 175	83.20 184	83.25 302	63.66 237	83.50 184	85.07 252	76.06 132	86.55 170	85.10 295	73.41 201	90.25 213	78.15 133	90.67 266	95.68 45

QAPM		82.59 170	82.59 172	82.58 199	86.44 241	66.69 210	89.94 62	90.36 159	67.97 239	84.94 203	92.58 139	72.71 211	92.18 160	70.63 216	87.73 303	88.85 265

fmvsm_s_conf0.1_n_a		82.58 171	81.93 180	84.50 144	87.68 215	73.35 133	86.14 131	77.70 308	61.64 295	85.02 199	91.62 166	77.75 146	86.24 286	82.79 80	87.07 310	93.91 108

Fast-Effi-MVS+-dtu		82.54 172	81.41 192	85.90 119	85.60 261	76.53 111	83.07 196	89.62 182	73.02 180	79.11 300	83.51 313	80.74 123	90.24 215	68.76 238	89.29 280	90.94 216

MVS_Test		82.47 173	83.22 157	80.22 239	82.62 311	57.75 313	82.54 213	91.96 112	71.16 206	82.89 245	92.52 141	77.41 151	90.50 210	80.04 109	87.84 302	92.40 172

v148		82.31 174	82.48 174	81.81 213	85.59 262	59.66 291	81.47 233	86.02 238	72.85 181	88.05 141	90.65 201	70.73 228	90.91 197	75.15 168	91.79 240	94.87 67

API-MVS		82.28 175	82.61 171	81.30 220	86.29 249	69.79 178	88.71 90	87.67 210	78.42 112	82.15 256	84.15 309	77.98 143	91.59 175	65.39 266	92.75 221	82.51 348

MVSFormer		82.23 176	81.57 189	84.19 157	85.54 265	69.26 186	91.98 31	90.08 171	71.54 200	76.23 320	85.07 298	58.69 296	94.27 84	86.26 40	88.77 287	89.03 262

fmvsm_s_conf0.5_n_a		82.21 177	81.51 191	84.32 152	86.56 239	73.35 133	85.46 140	77.30 312	61.81 291	84.51 209	90.88 191	77.36 152	86.21 288	82.72 81	86.97 315	93.38 131

EIA-MVS		82.19 178	81.23 197	85.10 133	87.95 209	69.17 190	83.22 194	93.33 63	70.42 211	78.58 303	79.77 355	77.29 153	94.20 89	71.51 206	88.96 285	91.93 194

fmvsm_s_conf0.1_n		82.17 179	81.59 187	83.94 162	86.87 237	71.57 166	85.19 146	77.42 311	62.27 289	84.47 212	91.33 173	76.43 168	85.91 294	83.14 71	87.14 308	94.33 90

PCF-MVS	74.62 15	82.15 180	80.92 202	85.84 121	89.43 174	72.30 154	80.53 244	91.82 118	57.36 333	87.81 145	89.92 218	77.67 148	93.63 111	58.69 312	95.08 158	91.58 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 181	80.31 209	87.45 90	90.86 147	80.29 69	85.88 133	90.65 150	68.17 236	76.32 319	86.33 275	73.12 207	92.61 149	61.40 300	90.02 274	89.44 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 182	81.54 190	83.60 171	83.94 289	73.90 130	83.35 188	86.10 235	58.97 319	83.80 229	90.36 206	74.23 189	86.94 274	82.90 77	90.22 271	89.94 243

GBi-Net		82.02 183	82.07 177	81.85 210	86.38 243	61.05 272	86.83 118	88.27 203	72.43 188	86.00 183	95.64 30	63.78 265	90.68 205	65.95 259	93.34 206	93.82 112

test1		82.02 183	82.07 177	81.85 210	86.38 243	61.05 272	86.83 118	88.27 203	72.43 188	86.00 183	95.64 30	63.78 265	90.68 205	65.95 259	93.34 206	93.82 112

OpenMVS	76.72 13	81.98 185	82.00 179	81.93 207	84.42 281	68.22 195	88.50 94	89.48 184	66.92 249	81.80 264	91.86 156	72.59 213	90.16 218	71.19 209	91.25 251	87.40 285

KD-MVS_self_test		81.93 186	83.14 161	78.30 266	84.75 276	52.75 345	80.37 246	89.42 186	70.24 216	90.26 94	93.39 113	74.55 188	86.77 278	68.61 241	96.64 92	95.38 52

fmvsm_s_conf0.5_n		81.91 187	81.30 194	83.75 166	86.02 257	71.56 167	84.73 152	77.11 315	62.44 286	84.00 226	90.68 198	76.42 169	85.89 296	83.14 71	87.11 309	93.81 115

SDMVSNet		81.90 188	83.17 160	78.10 270	88.81 189	62.45 255	76.08 312	86.05 237	73.67 163	83.41 236	93.04 119	82.35 96	80.65 335	70.06 223	95.03 160	91.21 210

tfpnnormal		81.79 189	82.95 164	78.31 265	88.93 186	55.40 328	80.83 243	82.85 277	76.81 127	85.90 187	94.14 84	74.58 187	86.51 282	66.82 253	95.68 141	93.01 148

c3_l		81.64 190	81.59 187	81.79 214	80.86 329	59.15 298	78.61 274	90.18 169	68.36 232	87.20 152	87.11 266	69.39 232	91.62 174	78.16 131	94.43 182	94.60 75

PVSNet_Blended_VisFu		81.55 191	80.49 207	84.70 142	91.58 124	73.24 137	84.21 162	91.67 122	62.86 280	80.94 275	87.16 264	67.27 243	92.87 144	69.82 225	88.94 286	87.99 276

fmvsm_l_conf0.5_n_a		81.46 192	80.87 203	83.25 181	83.73 294	73.21 138	83.00 199	85.59 244	58.22 325	82.96 244	90.09 216	72.30 216	86.65 280	81.97 92	89.95 275	89.88 244

DELS-MVS		81.44 193	81.25 195	82.03 206	84.27 285	62.87 247	76.47 306	92.49 97	70.97 207	81.64 267	83.83 310	75.03 178	92.70 146	74.29 173	92.22 233	90.51 231
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		81.31 194	81.61 186	80.41 236	86.38 243	58.75 305	83.93 172	86.58 230	72.43 188	87.65 147	92.98 123	63.78 265	90.22 216	66.86 250	93.92 194	92.27 180

bld_raw_dy_0_64		81.25 195	81.17 199	81.49 218	85.55 263	60.85 278	86.36 128	95.45 9	57.08 335	90.81 88	82.69 328	65.85 253	93.91 101	70.37 220	96.34 105	89.72 245

TinyColmap		81.25 195	82.34 176	77.99 273	85.33 267	60.68 282	82.32 219	88.33 201	71.26 204	86.97 161	92.22 152	77.10 157	86.98 273	62.37 289	95.17 154	86.31 296

AUN-MVS		81.18 197	78.78 229	88.39 77	90.93 144	82.14 58	82.51 214	83.67 269	64.69 272	80.29 286	85.91 284	51.07 334	92.38 154	76.29 156	93.63 203	90.65 227

tttt0517		81.07 198	79.58 221	85.52 127	88.99 185	66.45 213	87.03 114	75.51 327	73.76 162	88.32 137	90.20 211	37.96 387	94.16 94	79.36 119	95.13 155	95.93 42

Fast-Effi-MVS+		81.04 199	80.57 204	82.46 203	87.50 220	63.22 243	78.37 277	89.63 181	68.01 237	81.87 260	82.08 332	82.31 98	92.65 148	67.10 249	88.30 297	91.51 206

BH-untuned		80.96 200	80.99 200	80.84 229	88.55 197	68.23 194	80.33 247	88.46 196	72.79 184	86.55 170	86.76 270	74.72 185	91.77 173	61.79 296	88.99 284	82.52 347

eth_miper_zixun_eth		80.84 201	80.22 213	82.71 196	81.41 321	60.98 275	77.81 283	90.14 170	67.31 247	86.95 162	87.24 263	64.26 260	92.31 157	75.23 167	91.61 244	94.85 71

xiu_mvs_v1_base_debu		80.84 201	80.14 215	82.93 191	88.31 201	71.73 161	79.53 256	87.17 215	65.43 263	79.59 292	82.73 325	76.94 160	90.14 221	73.22 192	88.33 293	86.90 290

xiu_mvs_v1_base		80.84 201	80.14 215	82.93 191	88.31 201	71.73 161	79.53 256	87.17 215	65.43 263	79.59 292	82.73 325	76.94 160	90.14 221	73.22 192	88.33 293	86.90 290

xiu_mvs_v1_base_debi		80.84 201	80.14 215	82.93 191	88.31 201	71.73 161	79.53 256	87.17 215	65.43 263	79.59 292	82.73 325	76.94 160	90.14 221	73.22 192	88.33 293	86.90 290

IterMVS-SCA-FT		80.64 205	79.41 222	84.34 151	83.93 290	69.66 181	76.28 308	81.09 291	72.43 188	86.47 176	90.19 212	60.46 281	93.15 134	77.45 142	86.39 321	90.22 236

BH-RMVSNet		80.53 206	80.22 213	81.49 218	87.19 226	66.21 215	77.79 284	86.23 233	74.21 157	83.69 230	88.50 239	73.25 206	90.75 202	63.18 286	87.90 300	87.52 283

Anonymous202405211		80.51 207	81.19 198	78.49 262	88.48 198	57.26 316	76.63 302	82.49 280	81.21 76	84.30 219	92.24 151	67.99 240	86.24 286	62.22 290	95.13 155	91.98 193

DIV-MVS_self_test		80.43 208	80.23 211	81.02 227	79.99 337	59.25 295	77.07 295	87.02 223	67.38 244	86.19 179	89.22 228	63.09 269	90.16 218	76.32 154	95.80 135	93.66 120

cl____		80.42 209	80.23 211	81.02 227	79.99 337	59.25 295	77.07 295	87.02 223	67.37 245	86.18 181	89.21 229	63.08 270	90.16 218	76.31 155	95.80 135	93.65 122

diffmvs		80.40 210	80.48 208	80.17 240	79.02 349	60.04 286	77.54 288	90.28 166	66.65 252	82.40 251	87.33 261	73.50 198	87.35 267	77.98 135	89.62 278	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		80.37 211	78.41 236	86.23 111	76.75 363	73.28 135	87.18 111	77.45 310	76.24 131	68.14 372	88.93 234	65.41 255	93.85 103	69.47 227	96.12 117	91.55 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 212	80.04 218	81.24 223	79.82 339	58.95 300	77.66 285	89.66 179	65.75 260	85.99 186	85.11 294	68.29 239	91.42 181	76.03 158	92.03 235	93.33 133

MG-MVS		80.32 213	80.94 201	78.47 263	88.18 204	52.62 348	82.29 220	85.01 256	72.01 198	79.24 299	92.54 140	69.36 233	93.36 128	70.65 215	89.19 283	89.45 250

VPNet		80.25 214	81.68 183	75.94 299	92.46 92	47.98 371	76.70 300	81.67 287	73.45 167	84.87 204	92.82 130	74.66 186	86.51 282	61.66 298	96.85 85	93.33 133

MAR-MVS		80.24 215	78.74 231	84.73 140	86.87 237	78.18 88	85.75 136	87.81 209	65.67 262	77.84 308	78.50 364	73.79 195	90.53 209	61.59 299	90.87 260	85.49 306
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		80.20 216	79.00 226	83.78 165	88.17 205	86.66 15	81.31 234	66.81 379	69.64 220	88.33 136	90.19 212	64.58 258	83.63 319	71.99 205	90.03 273	81.06 366

Anonymous20240521		80.18 217	81.25 195	76.95 286	83.15 306	60.84 279	82.46 215	85.99 239	68.76 229	86.78 163	93.73 107	59.13 293	77.44 349	73.71 185	97.55 67	92.56 164

LFMVS		80.15 218	80.56 205	78.89 254	89.19 181	55.93 324	85.22 145	73.78 339	82.96 58	84.28 220	92.72 135	57.38 305	90.07 225	63.80 280	95.75 138	90.68 225

DPM-MVS		80.10 219	79.18 225	82.88 194	90.71 150	69.74 179	78.87 270	90.84 145	60.29 312	75.64 330	85.92 283	67.28 242	93.11 135	71.24 208	91.79 240	85.77 302

MSDG		80.06 220	79.99 220	80.25 238	83.91 291	68.04 199	77.51 289	89.19 187	77.65 119	81.94 258	83.45 315	76.37 170	86.31 285	63.31 285	86.59 318	86.41 294

FE-MVS		79.98 221	78.86 227	83.36 178	86.47 240	66.45 213	89.73 65	84.74 262	72.80 183	84.22 224	91.38 172	44.95 368	93.60 115	63.93 279	91.50 247	90.04 242

sd_testset		79.95 222	81.39 193	75.64 302	88.81 189	58.07 309	76.16 311	82.81 278	73.67 163	83.41 236	93.04 119	80.96 120	77.65 348	58.62 313	95.03 160	91.21 210

ab-mvs		79.67 223	80.56 205	76.99 285	88.48 198	56.93 318	84.70 153	86.06 236	68.95 227	80.78 279	93.08 118	75.30 176	84.62 308	56.78 322	90.90 259	89.43 252

VNet		79.31 224	80.27 210	76.44 293	87.92 210	53.95 337	75.58 318	84.35 264	74.39 156	82.23 254	90.72 196	72.84 210	84.39 311	60.38 306	93.98 193	90.97 215

thisisatest0530		79.07 225	77.33 246	84.26 154	87.13 227	64.58 228	83.66 181	75.95 322	68.86 228	85.22 196	87.36 260	38.10 385	93.57 119	75.47 164	94.28 186	94.62 74

cl22		78.97 226	78.21 238	81.24 223	77.74 353	59.01 299	77.46 291	87.13 218	65.79 257	84.32 216	85.10 295	58.96 295	90.88 199	75.36 166	92.03 235	93.84 110

patch_mono-2		78.89 227	79.39 223	77.41 282	84.78 274	68.11 197	75.60 316	83.11 274	60.96 305	79.36 296	89.89 219	75.18 177	72.97 360	73.32 191	92.30 227	91.15 212

RPMNet		78.88 228	78.28 237	80.68 233	79.58 340	62.64 251	82.58 210	94.16 29	74.80 151	75.72 328	92.59 137	48.69 342	95.56 39	73.48 188	82.91 356	83.85 327

PAPR		78.84 229	78.10 239	81.07 225	85.17 270	60.22 285	82.21 224	90.57 153	62.51 282	75.32 334	84.61 303	74.99 179	92.30 158	59.48 310	88.04 299	90.68 225

iter_conf05		78.81 230	77.35 245	83.21 183	82.98 309	60.75 281	84.09 166	88.34 200	63.12 278	84.25 223	89.48 224	31.41 396	94.51 81	76.64 151	95.83 132	94.38 88

PVSNet_BlendedMVS		78.80 231	77.84 240	81.65 216	84.43 279	63.41 239	79.49 259	90.44 156	61.70 294	75.43 331	87.07 267	69.11 235	91.44 179	60.68 304	92.24 231	90.11 240

FMVSNet3		78.80 231	78.55 233	79.57 248	82.89 310	56.89 320	81.76 228	85.77 241	69.04 226	86.00 183	90.44 205	51.75 332	90.09 224	65.95 259	93.34 206	91.72 198

test_yl		78.71 233	78.51 234	79.32 251	84.32 283	58.84 302	78.38 275	85.33 247	75.99 135	82.49 249	86.57 271	58.01 299	90.02 227	62.74 287	92.73 222	89.10 259

DCV-MVSNet		78.71 233	78.51 234	79.32 251	84.32 283	58.84 302	78.38 275	85.33 247	75.99 135	82.49 249	86.57 271	58.01 299	90.02 227	62.74 287	92.73 222	89.10 259

test1111		78.53 235	78.85 228	77.56 279	92.22 101	47.49 373	82.61 208	69.24 368	72.43 188	85.28 195	94.20 80	51.91 330	90.07 225	65.36 267	96.45 102	95.11 62

ECVR-MVS		78.44 236	78.63 232	77.88 275	91.85 114	48.95 367	83.68 180	69.91 365	72.30 194	84.26 222	94.20 80	51.89 331	89.82 230	63.58 281	96.02 121	94.87 67

pmmvs-eth3d		78.42 237	77.04 249	82.57 201	87.44 221	74.41 127	80.86 242	79.67 299	55.68 340	84.69 207	90.31 209	60.91 279	85.42 301	62.20 291	91.59 245	87.88 279

iter_conf05_11		78.40 238	77.29 247	81.71 215	85.55 263	60.95 277	77.22 292	86.90 227	60.10 315	75.79 327	81.73 336	64.08 262	94.47 82	70.37 220	93.92 194	89.72 245

mvs_anonymous		78.13 239	78.76 230	76.23 298	79.24 346	50.31 364	78.69 272	84.82 260	61.60 296	83.09 243	92.82 130	73.89 194	87.01 270	68.33 245	86.41 320	91.37 207

TAMVS		78.08 240	76.36 255	83.23 182	90.62 151	72.87 140	79.08 266	80.01 298	61.72 293	81.35 271	86.92 269	63.96 264	88.78 252	50.61 360	93.01 216	88.04 275

miper_enhance_ethall		77.83 241	76.93 250	80.51 234	76.15 369	58.01 310	75.47 320	88.82 191	58.05 327	83.59 232	80.69 343	64.41 259	91.20 185	73.16 198	92.03 235	92.33 176

Vis-MVSNet (Re-imp)		77.82 242	77.79 241	77.92 274	88.82 188	51.29 358	83.28 189	71.97 353	74.04 158	82.23 254	89.78 220	57.38 305	89.41 242	57.22 321	95.41 144	93.05 146

CANet_DTU		77.81 243	77.05 248	80.09 241	81.37 322	59.90 289	83.26 190	88.29 202	69.16 224	67.83 375	83.72 311	60.93 278	89.47 237	69.22 231	89.70 277	90.88 218

OpenMVS_ROB	70.19 17	77.77 244	77.46 242	78.71 258	84.39 282	61.15 270	81.18 238	82.52 279	62.45 285	83.34 238	87.37 259	66.20 248	88.66 254	64.69 274	85.02 337	86.32 295

SSC-MVS		77.55 245	81.64 184	65.29 365	90.46 154	20.33 410	73.56 336	68.28 370	85.44 32	88.18 140	94.64 59	70.93 227	81.33 330	71.25 207	92.03 235	94.20 92

MDA-MVSNet-bldmvs		77.47 246	76.90 251	79.16 253	79.03 348	64.59 227	66.58 375	75.67 325	73.15 178	88.86 122	88.99 233	66.94 244	81.23 331	64.71 273	88.22 298	91.64 202

jason		77.42 247	75.75 261	82.43 204	87.10 230	69.27 185	77.99 280	81.94 285	51.47 364	77.84 308	85.07 298	60.32 283	89.00 246	70.74 214	89.27 282	89.03 262
jason: jason.
CDS-MVSNet		77.32 248	75.40 264	83.06 186	89.00 184	72.48 151	77.90 282	82.17 283	60.81 306	78.94 301	83.49 314	59.30 291	88.76 253	54.64 340	92.37 226	87.93 278
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		77.19 249	76.75 252	78.52 261	87.01 233	61.30 268	75.55 319	87.12 221	61.24 302	74.45 339	78.79 362	77.20 154	90.93 195	64.62 276	84.80 344	83.32 336

MVSTER		77.09 250	75.70 262	81.25 221	75.27 377	61.08 271	77.49 290	85.07 252	60.78 307	86.55 170	88.68 237	43.14 377	90.25 213	73.69 186	90.67 266	92.42 170

PS-MVSNAJ		77.04 251	76.53 254	78.56 260	87.09 231	61.40 266	75.26 321	87.13 218	61.25 301	74.38 341	77.22 375	76.94 160	90.94 194	64.63 275	84.83 343	83.35 335

IterMVS		76.91 252	76.34 256	78.64 259	80.91 327	64.03 234	76.30 307	79.03 302	64.88 271	83.11 241	89.16 230	59.90 287	84.46 309	68.61 241	85.15 335	87.42 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 253	75.67 263	80.34 237	80.48 335	62.16 262	73.50 337	84.80 261	57.61 331	82.24 253	87.54 255	51.31 333	87.65 263	70.40 219	93.19 212	91.23 209

CL-MVSNet_self_test		76.81 254	77.38 244	75.12 305	86.90 235	51.34 356	73.20 340	80.63 295	68.30 234	81.80 264	88.40 240	66.92 245	80.90 332	55.35 334	94.90 166	93.12 144

TR-MVS		76.77 255	75.79 260	79.72 245	86.10 256	65.79 219	77.14 293	83.02 275	65.20 269	81.40 270	82.10 330	66.30 247	90.73 204	55.57 331	85.27 331	82.65 342

USDC		76.63 256	76.73 253	76.34 295	83.46 296	57.20 317	80.02 250	88.04 207	52.14 360	83.65 231	91.25 175	63.24 268	86.65 280	54.66 339	94.11 190	85.17 308

BH-w/o		76.57 257	76.07 259	78.10 270	86.88 236	65.92 218	77.63 286	86.33 231	65.69 261	80.89 276	79.95 352	68.97 237	90.74 203	53.01 350	85.25 332	77.62 377

Patchmtry		76.56 258	77.46 242	73.83 311	79.37 345	46.60 377	82.41 217	76.90 316	73.81 161	85.56 192	92.38 143	48.07 345	83.98 316	63.36 284	95.31 150	90.92 217

PVSNet_Blended		76.49 259	75.40 264	79.76 244	84.43 279	63.41 239	75.14 322	90.44 156	57.36 333	75.43 331	78.30 365	69.11 235	91.44 179	60.68 304	87.70 304	84.42 318

miper_lstm_enhance		76.45 260	76.10 258	77.51 280	76.72 364	60.97 276	64.69 379	85.04 254	63.98 275	83.20 240	88.22 242	56.67 309	78.79 346	73.22 192	93.12 213	92.78 155

lupinMVS		76.37 261	74.46 273	82.09 205	85.54 265	69.26 186	76.79 298	80.77 294	50.68 371	76.23 320	82.82 323	58.69 296	88.94 247	69.85 224	88.77 287	88.07 272

cascas		76.29 262	74.81 269	80.72 232	84.47 278	62.94 245	73.89 334	87.34 212	55.94 339	75.16 336	76.53 380	63.97 263	91.16 187	65.00 270	90.97 257	88.06 274

WB-MVS		76.06 263	80.01 219	64.19 368	89.96 167	20.58 409	72.18 345	68.19 371	83.21 54	86.46 177	93.49 111	70.19 230	78.97 344	65.96 258	90.46 270	93.02 147

thres600view7		75.97 264	75.35 266	77.85 277	87.01 233	51.84 354	80.45 245	73.26 344	75.20 148	83.10 242	86.31 277	45.54 359	89.05 245	55.03 337	92.24 231	92.66 161

GA-MVS		75.83 265	74.61 270	79.48 250	81.87 314	59.25 295	73.42 338	82.88 276	68.68 230	79.75 291	81.80 335	50.62 336	89.46 238	66.85 251	85.64 328	89.72 245

MVP-Stereo		75.81 266	73.51 282	82.71 196	89.35 175	73.62 131	80.06 248	85.20 249	60.30 311	73.96 342	87.94 247	57.89 303	89.45 239	52.02 354	74.87 388	85.06 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 267	75.20 267	77.27 283	75.01 380	69.47 183	78.93 267	84.88 259	46.67 378	87.08 158	87.84 250	50.44 338	71.62 365	77.42 144	88.53 290	90.72 222

thres100view900		75.45 268	75.05 268	76.66 292	87.27 223	51.88 353	81.07 239	73.26 344	75.68 141	83.25 239	86.37 274	45.54 359	88.80 249	51.98 355	90.99 254	89.31 254

ET-MVSNet_ETH3D		75.28 269	72.77 290	82.81 195	83.03 308	68.11 197	77.09 294	76.51 320	60.67 309	77.60 313	80.52 347	38.04 386	91.15 188	70.78 212	90.68 265	89.17 257

thres400		75.14 270	74.23 275	77.86 276	86.24 250	52.12 350	79.24 263	73.87 337	73.34 171	81.82 262	84.60 304	46.02 353	88.80 249	51.98 355	90.99 254	92.66 161

wuyk23d		75.13 271	79.30 224	62.63 371	75.56 373	75.18 124	80.89 241	73.10 346	75.06 150	94.76 12	95.32 35	87.73 40	52.85 401	34.16 401	97.11 80	59.85 398

EU-MVSNet		75.12 272	74.43 274	77.18 284	83.11 307	59.48 293	85.71 138	82.43 281	39.76 398	85.64 190	88.76 235	44.71 370	87.88 261	73.86 182	85.88 327	84.16 323

HyFIR lowres test		75.12 272	72.66 292	82.50 202	91.44 132	65.19 224	72.47 343	87.31 213	46.79 377	80.29 286	84.30 306	52.70 327	92.10 164	51.88 359	86.73 316	90.22 236

CMPMVS	59.41 20	75.12 272	73.57 280	79.77 243	75.84 372	67.22 202	81.21 237	82.18 282	50.78 369	76.50 316	87.66 253	55.20 319	82.99 322	62.17 293	90.64 269	89.09 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 275	72.98 288	80.73 231	84.95 271	71.71 164	76.23 309	77.59 309	52.83 354	77.73 312	86.38 273	56.35 312	84.97 305	57.72 320	87.05 311	85.51 305

tfpn200view9		74.86 276	74.23 275	76.74 291	86.24 250	52.12 350	79.24 263	73.87 337	73.34 171	81.82 262	84.60 304	46.02 353	88.80 249	51.98 355	90.99 254	89.31 254

1112_ss		74.82 277	73.74 278	78.04 272	89.57 169	60.04 286	76.49 305	87.09 222	54.31 347	73.66 345	79.80 353	60.25 284	86.76 279	58.37 314	84.15 348	87.32 286

EGC-MVSNET		74.79 278	69.99 318	89.19 63	94.89 37	87.00 11	91.89 34	86.28 232	1.09 406	2.23 408	95.98 23	81.87 110	89.48 236	79.76 112	95.96 124	91.10 213

ppachtmachnet_test		74.73 279	74.00 277	76.90 288	80.71 332	56.89 320	71.53 351	78.42 304	58.24 324	79.32 298	82.92 322	57.91 302	84.26 313	65.60 265	91.36 249	89.56 249

Patchmatch-RL test		74.48 280	73.68 279	76.89 289	84.83 273	66.54 211	72.29 344	69.16 369	57.70 329	86.76 164	86.33 275	45.79 358	82.59 323	69.63 226	90.65 268	81.54 357

PatchMatch-RL		74.48 280	73.22 285	78.27 268	87.70 214	85.26 34	75.92 314	70.09 363	64.34 273	76.09 323	81.25 341	65.87 252	78.07 347	53.86 342	83.82 350	71.48 386

XXY-MVS		74.44 282	76.19 257	69.21 343	84.61 277	52.43 349	71.70 348	77.18 314	60.73 308	80.60 280	90.96 187	75.44 173	69.35 371	56.13 327	88.33 293	85.86 301

test2506		74.12 283	73.39 283	76.28 296	91.85 114	44.20 387	84.06 167	48.20 406	72.30 194	81.90 259	94.20 80	27.22 407	89.77 233	64.81 272	96.02 121	94.87 67

CR-MVSNet		74.00 284	73.04 287	76.85 290	79.58 340	62.64 251	82.58 210	76.90 316	50.50 372	75.72 328	92.38 143	48.07 345	84.07 315	68.72 240	82.91 356	83.85 327

Test_1112_low_res		73.90 285	73.08 286	76.35 294	90.35 156	55.95 323	73.40 339	86.17 234	50.70 370	73.14 346	85.94 282	58.31 298	85.90 295	56.51 324	83.22 353	87.20 287

test20.03		73.75 286	74.59 272	71.22 331	81.11 325	51.12 360	70.15 361	72.10 352	70.42 211	80.28 288	91.50 169	64.21 261	74.72 359	46.96 378	94.58 178	87.82 281

test_fmvs2		73.57 287	72.80 289	75.90 300	72.74 392	68.84 192	77.07 295	84.32 265	45.14 384	82.89 245	84.22 307	48.37 343	70.36 368	73.40 190	87.03 312	88.52 268

SCA		73.32 288	72.57 294	75.58 303	81.62 318	55.86 325	78.89 269	71.37 358	61.73 292	74.93 337	83.42 316	60.46 281	87.01 270	58.11 318	82.63 361	83.88 324

baseline1		73.26 289	73.54 281	72.43 325	84.92 272	47.79 372	79.89 252	74.00 335	65.93 255	78.81 302	86.28 278	56.36 311	81.63 329	56.63 323	79.04 377	87.87 280

1314		73.22 290	72.56 295	75.20 304	80.41 336	57.84 311	81.64 231	85.36 246	51.68 363	73.10 347	76.65 379	61.45 276	85.19 303	63.54 282	79.21 375	82.59 343

MVS		73.21 291	72.59 293	75.06 306	80.97 326	60.81 280	81.64 231	85.92 240	46.03 382	71.68 354	77.54 370	68.47 238	89.77 233	55.70 330	85.39 329	74.60 383

HY-MVS	64.64 18	73.03 292	72.47 296	74.71 307	83.36 300	54.19 335	82.14 227	81.96 284	56.76 338	69.57 367	86.21 279	60.03 285	84.83 307	49.58 366	82.65 359	85.11 309

thisisatest0515		73.00 293	70.52 310	80.46 235	81.45 320	59.90 289	73.16 341	74.31 334	57.86 328	76.08 324	77.78 368	37.60 388	92.12 163	65.00 270	91.45 248	89.35 253

EPNet_dtu		72.87 294	71.33 306	77.49 281	77.72 354	60.55 283	82.35 218	75.79 323	66.49 253	58.39 401	81.06 342	53.68 323	85.98 292	53.55 345	92.97 218	85.95 299
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 295	71.41 305	76.28 296	83.25 302	60.34 284	83.50 184	79.02 303	37.77 401	76.33 318	85.10 295	49.60 341	87.41 266	70.54 217	77.54 383	81.08 364

CHOSEN 1792x2688		72.45 296	70.56 309	78.13 269	90.02 166	63.08 244	68.72 366	83.16 273	42.99 392	75.92 325	85.46 288	57.22 307	85.18 304	49.87 364	81.67 363	86.14 297

testgi		72.36 297	74.61 270	65.59 362	80.56 334	42.82 392	68.29 367	73.35 343	66.87 250	81.84 261	89.93 217	72.08 219	66.92 384	46.05 381	92.54 224	87.01 289

thres200		72.34 298	71.55 304	74.70 308	83.48 295	51.60 355	75.02 323	73.71 340	70.14 217	78.56 304	80.57 346	46.20 351	88.20 259	46.99 377	89.29 280	84.32 319

FPMVS		72.29 299	72.00 298	73.14 316	88.63 194	85.00 36	74.65 327	67.39 373	71.94 199	77.80 310	87.66 253	50.48 337	75.83 355	49.95 362	79.51 371	58.58 400

FMVSNet5		72.10 300	71.69 300	73.32 314	81.57 319	53.02 344	76.77 299	78.37 305	63.31 276	76.37 317	91.85 157	36.68 389	78.98 343	47.87 374	92.45 225	87.95 277

our_test_3		71.85 301	71.59 301	72.62 322	80.71 332	53.78 338	69.72 363	71.71 357	58.80 321	78.03 305	80.51 348	56.61 310	78.84 345	62.20 291	86.04 326	85.23 307

PAPM		71.77 302	70.06 316	76.92 287	86.39 242	53.97 336	76.62 303	86.62 229	53.44 351	63.97 391	84.73 302	57.79 304	92.34 156	39.65 392	81.33 367	84.45 317

IB-MVS	62.13 19	71.64 303	68.97 327	79.66 247	80.80 331	62.26 260	73.94 333	76.90 316	63.27 277	68.63 371	76.79 377	33.83 393	91.84 171	59.28 311	87.26 306	84.88 311
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		71.63 304	72.30 297	69.62 340	76.47 366	52.70 347	70.03 362	80.97 292	59.18 318	79.36 296	88.21 243	60.50 280	69.12 372	58.33 316	77.62 382	87.04 288

testing3		71.53 305	70.79 307	73.77 312	88.89 187	41.86 394	76.60 304	59.12 396	72.83 182	80.97 273	82.08 332	19.80 412	87.33 268	65.12 269	91.68 243	92.13 187

test_vis3_rt		71.42 306	70.67 308	73.64 313	69.66 398	70.46 174	66.97 374	89.73 176	42.68 394	88.20 139	83.04 318	43.77 372	60.07 396	65.35 268	86.66 317	90.39 234

Anonymous20231206		71.38 307	71.88 299	69.88 338	86.31 247	54.37 334	70.39 359	74.62 330	52.57 356	76.73 315	88.76 235	59.94 286	72.06 362	44.35 385	93.23 211	83.23 338

test_vis1_n_1920		71.30 308	71.58 303	70.47 334	77.58 356	59.99 288	74.25 328	84.22 266	51.06 366	74.85 338	79.10 359	55.10 320	68.83 374	68.86 237	79.20 376	82.58 344

MIMVSNet		71.09 309	71.59 301	69.57 341	87.23 224	50.07 365	78.91 268	71.83 354	60.20 314	71.26 355	91.76 163	55.08 321	76.09 353	41.06 390	87.02 313	82.54 346

test_fmvs1_n		70.94 310	70.41 313	72.53 324	73.92 382	66.93 208	75.99 313	84.21 267	43.31 391	79.40 295	79.39 357	43.47 373	68.55 376	69.05 234	84.91 340	82.10 351

MS-PatchMatch		70.93 311	70.22 314	73.06 317	81.85 315	62.50 254	73.82 335	77.90 306	52.44 357	75.92 325	81.27 340	55.67 316	81.75 327	55.37 333	77.70 381	74.94 382

pmmvs5		70.73 312	70.07 315	72.72 320	77.03 361	52.73 346	74.14 329	75.65 326	50.36 373	72.17 352	85.37 292	55.42 318	80.67 334	52.86 351	87.59 305	84.77 312

PatchT		70.52 313	72.76 291	63.79 370	79.38 344	33.53 404	77.63 286	65.37 382	73.61 165	71.77 353	92.79 133	44.38 371	75.65 356	64.53 277	85.37 330	82.18 350

test_vis1_n		70.29 314	69.99 318	71.20 332	75.97 371	66.50 212	76.69 301	80.81 293	44.22 387	75.43 331	77.23 374	50.00 339	68.59 375	66.71 254	82.85 358	78.52 376

N_pmnet		70.20 315	68.80 329	74.38 309	80.91 327	84.81 39	59.12 390	76.45 321	55.06 343	75.31 335	82.36 329	55.74 315	54.82 400	47.02 376	87.24 307	83.52 331

tpmvs		70.16 316	69.56 321	71.96 327	74.71 381	48.13 369	79.63 254	75.45 328	65.02 270	70.26 363	81.88 334	45.34 364	85.68 299	58.34 315	75.39 387	82.08 352

new-patchmatchnet		70.10 317	73.37 284	60.29 378	81.23 324	16.95 411	59.54 388	74.62 330	62.93 279	80.97 273	87.93 248	62.83 273	71.90 363	55.24 335	95.01 163	92.00 191

YYNet1		70.06 318	70.44 311	68.90 345	73.76 384	53.42 342	58.99 391	67.20 375	58.42 323	87.10 156	85.39 291	59.82 288	67.32 381	59.79 308	83.50 352	85.96 298

MDA-MVSNet_test_wron		70.05 319	70.44 311	68.88 346	73.84 383	53.47 340	58.93 392	67.28 374	58.43 322	87.09 157	85.40 290	59.80 289	67.25 382	59.66 309	83.54 351	85.92 300

CostFormer		69.98 320	68.68 330	73.87 310	77.14 359	50.72 362	79.26 262	74.51 332	51.94 362	70.97 358	84.75 301	45.16 367	87.49 265	55.16 336	79.23 374	83.40 334

testing91		69.94 321	68.99 326	72.80 319	83.81 293	45.89 380	71.57 350	73.64 342	68.24 235	70.77 361	77.82 367	34.37 392	84.44 310	53.64 344	87.00 314	88.07 272

baseline2		69.77 322	66.89 338	78.41 264	79.51 342	58.09 308	76.23 309	69.57 366	57.50 332	64.82 389	77.45 372	46.02 353	88.44 255	53.08 347	77.83 379	88.70 266

PatchmatchNet		69.71 323	68.83 328	72.33 326	77.66 355	53.60 339	79.29 261	69.99 364	57.66 330	72.53 350	82.93 321	46.45 350	80.08 339	60.91 303	72.09 391	83.31 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 324	69.05 324	71.14 333	69.15 399	65.77 220	73.98 332	83.32 272	42.83 393	77.77 311	78.27 366	43.39 376	68.50 377	68.39 244	84.38 347	79.15 374

JIA-IIPM		69.41 325	66.64 342	77.70 278	73.19 387	71.24 169	75.67 315	65.56 381	70.42 211	65.18 385	92.97 125	33.64 394	83.06 320	53.52 346	69.61 397	78.79 375

Syy-MVS		69.40 326	70.03 317	67.49 355	81.72 316	38.94 397	71.00 353	61.99 387	61.38 298	70.81 359	72.36 390	61.37 277	79.30 341	64.50 278	85.18 333	84.22 320

testing99		69.27 327	68.15 333	72.63 321	83.29 301	45.45 382	71.15 352	71.08 359	67.34 246	70.43 362	77.77 369	32.24 395	84.35 312	53.72 343	86.33 322	88.10 271

UnsupCasMVSNet_bld		69.21 328	69.68 320	67.82 353	79.42 343	51.15 359	67.82 371	75.79 323	54.15 348	77.47 314	85.36 293	59.26 292	70.64 367	48.46 371	79.35 373	81.66 355

test_cas_vis1_n_1920		69.20 329	69.12 322	69.43 342	73.68 385	62.82 248	70.38 360	77.21 313	46.18 381	80.46 285	78.95 361	52.03 329	65.53 389	65.77 264	77.45 384	79.95 372

gg-mvs-nofinetune		68.96 330	69.11 323	68.52 351	76.12 370	45.32 383	83.59 182	55.88 401	86.68 24	64.62 390	97.01 7	30.36 399	83.97 317	44.78 384	82.94 355	76.26 379

WB-MVSnew		68.72 331	69.01 325	67.85 352	83.22 304	43.98 388	74.93 324	65.98 380	55.09 342	73.83 343	79.11 358	65.63 254	71.89 364	38.21 397	85.04 336	87.69 282

tpm2		68.45 332	66.83 339	73.30 315	78.93 350	48.50 368	79.76 253	71.76 355	47.50 376	69.92 365	83.60 312	42.07 379	88.40 256	48.44 372	79.51 371	83.01 341

tpm		67.95 333	68.08 334	67.55 354	78.74 351	43.53 390	75.60 316	67.10 378	54.92 344	72.23 351	88.10 244	42.87 378	75.97 354	52.21 353	80.95 370	83.15 339

WTY-MVS		67.91 334	68.35 331	66.58 359	80.82 330	48.12 370	65.96 376	72.60 347	53.67 350	71.20 356	81.68 338	58.97 294	69.06 373	48.57 370	81.67 363	82.55 345

testing11		67.38 335	65.93 343	71.73 329	83.37 299	46.60 377	70.95 355	69.40 367	62.47 284	66.14 378	76.66 378	31.22 397	84.10 314	49.10 368	84.10 349	84.49 315

test-LLR		67.21 336	66.74 340	68.63 349	76.45 367	55.21 330	67.89 368	67.14 376	62.43 287	65.08 386	72.39 388	43.41 374	69.37 369	61.00 301	84.89 341	81.31 359

testing222		66.93 337	65.30 349	71.81 328	83.38 298	45.83 381	72.06 346	67.50 372	64.12 274	69.68 366	76.37 381	27.34 406	83.00 321	38.88 393	88.38 292	86.62 293

sss		66.92 338	67.26 336	65.90 361	77.23 358	51.10 361	64.79 378	71.72 356	52.12 361	70.13 364	80.18 350	57.96 301	65.36 390	50.21 361	81.01 369	81.25 361

KD-MVS_2432*1600		66.87 339	65.81 345	70.04 336	67.50 400	47.49 373	62.56 383	79.16 300	61.21 303	77.98 306	80.61 344	25.29 409	82.48 324	53.02 348	84.92 338	80.16 370

miper_refine_blended		66.87 339	65.81 345	70.04 336	67.50 400	47.49 373	62.56 383	79.16 300	61.21 303	77.98 306	80.61 344	25.29 409	82.48 324	53.02 348	84.92 338	80.16 370

dmvs_re		66.81 341	66.98 337	66.28 360	76.87 362	58.68 306	71.66 349	72.24 350	60.29 312	69.52 368	73.53 387	52.38 328	64.40 392	44.90 383	81.44 366	75.76 380

tpm cat1		66.76 342	65.21 350	71.42 330	77.09 360	50.62 363	78.01 279	73.68 341	44.89 385	68.64 370	79.00 360	45.51 361	82.42 326	49.91 363	70.15 394	81.23 363

UWE-MVS		66.43 343	65.56 348	69.05 344	84.15 287	40.98 395	73.06 342	64.71 383	54.84 345	76.18 322	79.62 356	29.21 401	80.50 336	38.54 396	89.75 276	85.66 303

PVSNet	58.17 21	66.41 344	65.63 347	68.75 347	81.96 313	49.88 366	62.19 385	72.51 349	51.03 367	68.04 373	75.34 385	50.84 335	74.77 357	45.82 382	82.96 354	81.60 356

tpmrst		66.28 345	66.69 341	65.05 366	72.82 391	39.33 396	78.20 278	70.69 362	53.16 353	67.88 374	80.36 349	48.18 344	74.75 358	58.13 317	70.79 393	81.08 364

Patchmatch-test		65.91 346	67.38 335	61.48 376	75.51 374	43.21 391	68.84 365	63.79 385	62.48 283	72.80 349	83.42 316	44.89 369	59.52 398	48.27 373	86.45 319	81.70 354

ADS-MVSNet2		65.87 347	63.64 355	72.55 323	73.16 388	56.92 319	67.10 372	74.81 329	49.74 374	66.04 380	82.97 319	46.71 348	77.26 350	42.29 387	69.96 395	83.46 332

test_vis1_rt		65.64 348	64.09 352	70.31 335	66.09 404	70.20 177	61.16 386	81.60 288	38.65 399	72.87 348	69.66 393	52.84 325	60.04 397	56.16 326	77.77 380	80.68 368

mvsany_test3		65.48 349	62.97 357	73.03 318	69.99 397	76.17 118	64.83 377	43.71 408	43.68 389	80.25 289	87.05 268	52.83 326	63.09 395	51.92 358	72.44 390	79.84 373

test-mter		65.00 350	63.79 354	68.63 349	76.45 367	55.21 330	67.89 368	67.14 376	50.98 368	65.08 386	72.39 388	28.27 404	69.37 369	61.00 301	84.89 341	81.31 359

ETVMVS		64.67 351	63.34 356	68.64 348	83.44 297	41.89 393	69.56 364	61.70 392	61.33 300	68.74 369	75.76 383	28.76 402	79.35 340	34.65 400	86.16 325	84.67 314

myMVS_eth3d		64.66 352	63.89 353	66.97 357	81.72 316	37.39 400	71.00 353	61.99 387	61.38 298	70.81 359	72.36 390	20.96 411	79.30 341	49.59 365	85.18 333	84.22 320

test0.0.03 1		64.66 352	64.36 351	65.57 363	75.03 379	46.89 376	64.69 379	61.58 393	62.43 287	71.18 357	77.54 370	43.41 374	68.47 378	40.75 391	82.65 359	81.35 358

test_f		64.31 354	65.85 344	59.67 379	66.54 403	62.24 261	57.76 393	70.96 360	40.13 396	84.36 214	82.09 331	46.93 347	51.67 402	61.99 294	81.89 362	65.12 394

pmmvs3		62.47 355	60.02 368	69.80 339	71.58 395	64.00 235	70.52 358	58.44 399	39.77 397	66.05 379	75.84 382	27.10 408	72.28 361	46.15 380	84.77 345	73.11 384

EPMVS		62.47 355	62.63 359	62.01 372	70.63 396	38.74 398	74.76 325	52.86 403	53.91 349	67.71 376	80.01 351	39.40 383	66.60 385	55.54 332	68.81 399	80.68 368

ADS-MVSNet		61.90 357	62.19 361	61.03 377	73.16 388	36.42 402	67.10 372	61.75 390	49.74 374	66.04 380	82.97 319	46.71 348	63.21 393	42.29 387	69.96 395	83.46 332

PMMVS		61.65 358	60.38 365	65.47 364	65.40 407	69.26 186	63.97 381	61.73 391	36.80 402	60.11 396	68.43 395	59.42 290	66.35 386	48.97 369	78.57 378	60.81 397

E-PMN		61.59 359	61.62 362	61.49 375	66.81 402	55.40 328	53.77 396	60.34 395	66.80 251	58.90 399	65.50 398	40.48 382	66.12 387	55.72 329	86.25 323	62.95 396

TESTMET0.1,1		61.29 360	60.32 366	64.19 368	72.06 393	51.30 357	67.89 368	62.09 386	45.27 383	60.65 395	69.01 394	27.93 405	64.74 391	56.31 325	81.65 365	76.53 378

MVS-HIRNet		61.16 361	62.92 358	55.87 382	79.09 347	35.34 403	71.83 347	57.98 400	46.56 379	59.05 398	91.14 179	49.95 340	76.43 352	38.74 394	71.92 392	55.84 401

EMVS		61.10 362	60.81 364	61.99 373	65.96 405	55.86 325	53.10 397	58.97 398	67.06 248	56.89 402	63.33 399	40.98 380	67.03 383	54.79 338	86.18 324	63.08 395

DSMNet-mixed		60.98 363	61.61 363	59.09 381	72.88 390	45.05 385	74.70 326	46.61 407	26.20 403	65.34 384	90.32 208	55.46 317	63.12 394	41.72 389	81.30 368	69.09 390

dp		60.70 364	60.29 367	61.92 374	72.04 394	38.67 399	70.83 356	64.08 384	51.28 365	60.75 394	77.28 373	36.59 390	71.58 366	47.41 375	62.34 401	75.52 381

dmvs_testset		60.59 365	62.54 360	54.72 384	77.26 357	27.74 407	74.05 331	61.00 394	60.48 310	65.62 383	67.03 397	55.93 314	68.23 379	32.07 404	69.46 398	68.17 391

CHOSEN 280x420		59.08 366	56.52 371	66.76 358	76.51 365	64.39 231	49.62 398	59.00 397	43.86 388	55.66 403	68.41 396	35.55 391	68.21 380	43.25 386	76.78 386	67.69 392

mvsany_test1		58.48 367	56.47 372	64.50 367	65.90 406	68.21 196	56.95 394	42.11 409	38.30 400	65.69 382	77.19 376	56.96 308	59.35 399	46.16 379	58.96 402	65.93 393

PVSNet_0	51.08 22	56.10 368	54.97 373	59.48 380	75.12 378	53.28 343	55.16 395	61.89 389	44.30 386	59.16 397	62.48 400	54.22 322	65.91 388	35.40 399	47.01 403	59.25 399

new_pmnet		55.69 369	57.66 370	49.76 385	75.47 375	30.59 405	59.56 387	51.45 404	43.62 390	62.49 392	75.48 384	40.96 381	49.15 404	37.39 398	72.52 389	69.55 389

PMMVS2		55.64 370	59.27 369	44.74 386	64.30 408	12.32 412	40.60 399	49.79 405	53.19 352	65.06 388	84.81 300	53.60 324	49.76 403	32.68 403	89.41 279	72.15 385

MVE	40.22 23	51.82 371	50.47 374	55.87 382	62.66 409	51.91 352	31.61 401	39.28 410	40.65 395	50.76 404	74.98 386	56.24 313	44.67 405	33.94 402	64.11 400	71.04 388
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		30.46 372	29.60 375	33.06 387	17.99 411	3.84 414	13.62 402	73.92 336	2.79 405	18.29 407	53.41 402	28.53 403	43.25 406	22.56 405	35.27 405	52.11 402

cdsmvs_eth3d_5k		20.81 373	27.75 376	0.00 392	0.00 415	0.00 417	0.00 403	85.44 245	0.00 410	0.00 411	82.82 323	81.46 114	0.00 411	0.00 410	0.00 409	0.00 407

tmp_tt		20.25 374	24.50 377	7.49 389	4.47 412	8.70 413	34.17 400	25.16 412	1.00 407	32.43 406	18.49 404	39.37 384	9.21 408	21.64 406	43.75 404	4.57 404

ab-mvs-re		6.65 375	8.87 378	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	79.80 353	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		6.41 376	8.55 379	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	76.94 160	0.00 411	0.00 410	0.00 409	0.00 407

test123		6.27 377	8.08 380	0.84 390	1.11 414	0.57 415	62.90 382	0.82 414	0.54 408	1.07 410	2.75 409	1.26 413	0.30 409	1.04 408	1.26 408	1.66 405

testmvs		5.91 378	7.65 381	0.72 391	1.20 413	0.37 416	59.14 389	0.67 415	0.49 409	1.11 409	2.76 408	0.94 414	0.24 410	1.02 409	1.47 407	1.55 406

test_blank		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet_test		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						37.39 400								52.61 352

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		91.01 141					96.05 8	87.45 20	98.17 32	92.40 172

PC_three_1452									58.96 320	90.06 96	91.33 173	80.66 124	93.03 138	75.78 160	95.94 126	92.48 168

No_MVS				88.81 69	91.55 126	77.99 90		91.01 141					96.05 8	87.45 20	98.17 32	92.40 172

test_one_0601					93.85 58	73.27 136		94.11 35	86.57 25	93.47 38	94.64 59	88.42 26

eth-test2					0.00 415

eth-test					0.00 415

ZD-MVS					92.22 101	80.48 67		91.85 116	71.22 205	90.38 91	92.98 123	86.06 60	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 29	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		90.82 146	56.98 336	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	81.12 118	94.68 71	74.48 172	95.35 146	92.29 178

test_241102_TWO								93.71 51	83.77 47	93.49 36	94.27 74	89.27 21	95.84 23	86.03 46	97.82 51	92.04 189

test_241102_ONE					94.18 46	72.65 142		93.69 52	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	86.31 129	89.72 177	70.80 208

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 36					95.88 17	86.42 36	97.97 43	92.02 190

test0726					94.16 49	72.56 148	90.63 45	93.90 44	83.61 50	93.75 30	94.49 64	89.76 18

GSMVS																83.88 324

test_part2					93.86 57	77.77 94				92.84 48

sam_mvs1												46.11 352				83.88 324

sam_mvs												45.92 357

ambc				82.98 188	90.55 153	64.86 226	88.20 96	89.15 188		89.40 117	93.96 95	71.67 224	91.38 183	78.83 122	96.55 95	92.71 159

MTGPA								91.81 120

test_post1							78.85 271				3.13 406	45.19 366	80.13 338	58.11 318

test_post											3.10 407	45.43 362	77.22 351

patchmatchnet-post											81.71 337	45.93 356	87.01 270

GG-mvs-BLEND				67.16 356	73.36 386	46.54 379	84.15 164	55.04 402		58.64 400	61.95 401	29.93 400	83.87 318	38.71 395	76.92 385	71.07 387

MTMP							90.66 44	33.14 411

gm-plane-assit					75.42 376	44.97 386			52.17 358		72.36 390		87.90 260	54.10 341

test9_res														80.83 101	96.45 102	90.57 228

TEST9					92.34 95	79.70 74	83.94 170	90.32 160	65.41 266	84.49 210	90.97 185	82.03 105	93.63 111

test_8					92.09 105	78.87 81	83.82 175	90.31 162	65.79 257	84.36 214	90.96 187	81.93 107	93.44 124

agg_prior2														79.68 114	96.16 114	90.22 236

agg_prior					91.58 124	77.69 96		90.30 163		84.32 216			93.18 132

TestCases				89.68 53	91.59 121	83.40 48		95.44 10	79.47 94	88.00 142	93.03 121	82.66 90	91.47 177	70.81 210	96.14 115	94.16 96

test_prior4						78.97 80	84.59 155

test_prior2							83.37 187		75.43 145	84.58 208	91.57 167	81.92 109		79.54 116	96.97 83

test_prior				86.32 108	90.59 152	71.99 159		92.85 88					94.17 92			92.80 154

旧先验2							81.73 229		56.88 337	86.54 175			84.90 306	72.81 199

新几何2							81.72 230

新几何1				82.95 190	93.96 55	78.56 84		80.24 296	55.45 341	83.93 228	91.08 182	71.19 226	88.33 257	65.84 262	93.07 214	81.95 353

旧先验1					91.97 108	71.77 160		81.78 286			91.84 158	73.92 193			93.65 202	83.61 330

无先验							82.81 205	85.62 243	58.09 326				91.41 182	67.95 248		84.48 316

原ACMM2							82.26 223

原ACMM1				84.60 143	92.81 86	74.01 129		91.50 125	62.59 281	82.73 248	90.67 200	76.53 167	94.25 86	69.24 229	95.69 140	85.55 304

test222					93.31 70	76.54 109	79.38 260	77.79 307	52.59 355	82.36 252	90.84 193	66.83 246			91.69 242	81.25 361

testdata2													86.43 284	63.52 283

segment_acmp												81.94 106

testdata				79.54 249	92.87 81	72.34 153		80.14 297	59.91 316	85.47 194	91.75 164	67.96 241	85.24 302	68.57 243	92.18 234	81.06 366

testdata1							79.62 255		73.95 160

test12				86.57 103	90.74 148	72.63 146		90.69 149		82.76 247		79.20 134	94.80 68		95.32 148	92.27 180

plane_prior7					93.45 65	77.31 102

plane_prior6					92.61 87	76.54 109						74.84 181

plane_prior5								93.61 55					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	86.55 170

plane_prior2							89.45 77		79.44 96

plane_prior1					92.83 85

plane_prior						76.42 113	87.15 112		75.94 138						95.03 160

n2								0.00 416

nn								0.00 416

door-mid								74.45 333

lessismore_v0				85.95 117	91.10 141	70.99 171		70.91 361		91.79 67	94.42 69	61.76 275	92.93 141	79.52 117	93.03 215	93.93 106

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 21	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								91.46 126

door								72.57 348

HQP5-MVS						70.66 172

HQP-NCC					91.19 136		84.77 149		73.30 173	80.55 282

ACMP_Plane					91.19 136		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 93							94.47 180

HQP2-MVS												72.10 217

NP-MVS					91.95 109	74.55 126					90.17 214

MDTV_nov1_ep13_2view						27.60 408	70.76 357		46.47 380	61.27 393		45.20 365		49.18 367		83.75 329

MDTV_nov1_ep13			68.29 332		78.03 352	43.87 389	74.12 330	72.22 351	52.17 358	67.02 377	85.54 286	45.36 363	80.85 333	55.73 328	84.42 346

ACMMP++_ref															95.74 139

ACMMP++															97.35 73

Test By Simon												79.09 135

ITE_SJBPF				90.11 45	90.72 149	84.97 37		90.30 163	81.56 71	90.02 98	91.20 178	82.40 95	90.81 201	73.58 187	94.66 176	94.56 76

DeepMVS_CX				24.13 388	32.95 410	29.49 406		21.63 413	12.07 404	37.95 405	45.07 403	30.84 398	19.21 407	17.94 407	33.06 406	23.69 403

High-resolution multi-view benchmark