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		86.90 1	88.67 1	81.57 24	91.50 1	63.30 125	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 70	68.08 96	97.05 1	96.93 1

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 67	74.51 51	96.15 2	92.88 7

abl_6		84.92 3	85.70 3	82.57 17	86.72 49	79.27 8	87.56 7	86.08 27	77.48 13	88.12 14	91.53 29	81.18 8	84.31 76	78.12 24	94.47 38	84.15 128

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 42	81.06 3	87.20 28	90.28 72	79.20 14	85.58 49	78.04 27	94.08 57	83.55 141

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 55	79.18 9	87.23 9	86.27 22	77.51 11	87.65 19	90.73 48	79.20 14	85.58 49	78.11 25	94.46 39	84.89 96

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 37	91.70 25	78.23 20	85.20 59	79.45 13	94.91 25	88.15 47

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 41	77.42 15	86.15 38	90.24 74	81.69 5	85.94 34	77.77 29	93.58 68	83.09 157

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 30	71.03 45	85.85 42	90.58 52	78.77 17	85.78 42	79.37 16	95.17 17	84.62 108
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
LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 79	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 124	81.53 3	92.15 91	88.91 39
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		84.12 10	84.63 11	82.60 15	88.21 39	74.40 36	85.24 29	87.21 16	70.69 48	85.14 55	90.42 60	78.99 16	86.62 13	80.83 6	94.93 24	86.79 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 64	71.96 42	84.70 63	90.56 53	77.12 26	86.18 26	79.24 18	95.36 13	82.49 176

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 91	72.41 36	85.11 56	90.85 44	76.65 30	84.89 64	79.30 17	94.63 35	82.35 179

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 112	74.39 37	87.18 11	88.18 7	78.98 7	86.11 40	91.47 31	79.70 12	85.76 43	66.91 111	95.46 11	87.89 49
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 73	70.23 49	84.49 65	90.67 51	75.15 43	86.37 18	79.58 11	94.26 52	84.18 127

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 32	82.83 115	73.53 45	85.50 28	87.45 14	74.11 21	86.45 35	90.52 56	80.02 11	84.48 71	77.73 30	94.34 50	85.93 76

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 72	70.19 51	83.86 75	90.72 50	75.20 42	86.27 21	79.41 15	94.25 53	83.95 132

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 57	72.71 29	82.87 85	90.39 64	73.86 56	86.31 19	78.84 20	94.03 58	84.64 106

LPG-MVS_test		83.47 18	84.33 14	80.90 37	87.00 43	70.41 66	82.04 61	86.35 19	69.77 53	87.75 16	91.13 36	81.83 3	86.20 24	77.13 37	95.96 5	86.08 72

HFP-MVS		83.39 19	84.03 18	81.48 26	89.25 21	75.69 28	87.01 15	84.27 68	70.23 49	84.47 66	90.43 58	76.79 27	85.94 34	79.58 11	94.23 54	82.82 166

MTAPA		83.19 20	83.87 20	81.13 34	91.16 2	78.16 14	84.87 32	80.63 129	72.08 39	84.93 57	90.79 45	74.65 49	84.42 73	80.98 4	94.75 29	80.82 205

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 82	71.31 43	81.26 106	90.96 41	74.57 51	84.69 68	78.41 22	94.78 28	82.74 170
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		83.12 22	83.68 23	81.45 28	89.14 26	73.28 47	86.32 24	85.97 29	67.39 64	84.02 72	90.39 64	74.73 48	86.46 15	80.73 7	94.43 43	84.60 111

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 61	85.40 38	67.96 62	84.91 61	90.88 42	75.59 38	86.57 14	78.16 23	94.71 32	83.82 133

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 30	85.14 74	71.00 60	85.53 27	84.78 51	70.91 46	85.64 45	90.41 61	75.55 40	87.69 3	79.75 8	95.08 20	85.36 86
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		83.01 25	83.63 25	81.13 34	91.16 2	78.16 14	82.72 57	80.63 129	72.08 39	84.93 57	90.79 45	74.65 49	84.42 73	80.98 4	94.75 29	80.82 205

APDe-MVS		82.88 26	84.14 16	79.08 58	84.80 81	66.72 95	86.54 21	85.11 44	72.00 41	86.65 33	91.75 24	78.20 21	87.04 8	77.93 28	94.32 51	83.47 146

GST-MVS		82.79 27	83.27 32	81.34 31	88.99 28	73.29 46	85.94 26	85.13 43	68.58 60	84.14 71	90.21 76	73.37 61	86.41 16	79.09 19	93.98 61	84.30 126

ACMP	69.50 8	82.64 28	83.38 29	80.40 42	86.50 51	69.44 73	82.30 58	86.08 27	66.80 69	86.70 32	89.99 79	81.64 6	85.95 33	74.35 52	96.11 3	85.81 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MP-MVS-pluss		82.54 29	83.46 28	79.76 46	88.88 32	68.44 82	81.57 64	86.33 21	63.17 110	85.38 52	91.26 35	76.33 32	84.67 69	83.30 1	94.96 23	86.17 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
#test#		82.40 30	82.71 41	81.48 26	89.25 21	75.69 28	84.47 36	84.27 68	64.45 94	84.47 66	90.43 58	76.79 27	85.94 34	76.01 39	94.23 54	82.82 166

ACMMP_NAP		82.33 31	83.28 31	79.46 54	89.28 19	69.09 80	83.62 45	84.98 47	64.77 91	83.97 73	91.02 40	75.53 41	85.93 37	82.00 2	94.36 48	83.35 151

SMA-MVS		82.12 32	82.68 42	80.43 41	88.90 31	69.52 71	85.12 30	84.76 52	63.53 104	84.23 70	91.47 31	72.02 67	87.16 6	79.74 10	94.36 48	84.61 109
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
ACMM	69.25 9	82.11 33	83.31 30	78.49 69	88.17 40	73.96 39	83.11 53	84.52 63	66.40 74	87.45 23	89.16 97	81.02 9	80.52 148	74.27 53	95.73 7	80.98 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPE-MVS		82.00 34	83.02 36	78.95 63	85.36 71	67.25 89	82.91 54	84.98 47	73.52 26	85.43 51	90.03 78	76.37 31	86.97 10	74.56 50	94.02 60	82.62 172
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		81.78 35	83.48 27	76.67 92	86.12 59	61.06 142	83.62 45	84.72 54	72.61 32	87.38 25	89.70 84	77.48 24	85.89 40	75.29 45	94.39 44	83.08 158

PMVS	70.70 6	81.70 36	83.15 34	77.36 85	90.35 6	82.82 2	82.15 59	79.22 154	74.08 22	87.16 30	91.97 19	84.80 2	76.97 208	64.98 123	93.61 66	72.28 291
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UA-Net		81.56 37	82.28 44	79.40 55	88.91 30	69.16 78	84.67 35	80.01 144	75.34 17	79.80 123	94.91 2	69.79 87	80.25 153	72.63 64	94.46 39	88.78 43

CPTT-MVS		81.51 38	81.76 47	80.76 39	89.20 24	78.75 12	86.48 22	82.03 102	68.80 56	80.92 111	88.52 109	72.00 68	82.39 109	74.80 47	93.04 75	81.14 195

DVP-MVS++		81.24 39	82.74 40	76.76 91	83.14 104	60.90 146	91.64 1	85.49 34	74.03 23	84.93 57	90.38 66	66.82 114	85.90 38	77.43 33	90.78 122	83.49 143

ACMH+	66.64 10	81.20 40	82.48 43	77.35 86	81.16 138	62.39 130	80.51 71	87.80 8	73.02 28	87.57 21	91.08 38	80.28 10	82.44 108	64.82 124	96.10 4	87.21 59

testtj		81.19 41	81.70 49	79.67 51	83.95 94	69.77 70	83.58 48	84.63 59	72.13 38	82.85 87	88.36 114	75.00 46	86.79 11	71.99 73	92.84 78	82.44 177

DVP-MVS		81.15 42	83.12 35	75.24 114	86.16 57	60.78 148	83.77 43	80.58 132	72.48 34	85.83 43	90.41 61	78.57 18	85.69 45	75.86 42	94.39 44	79.24 230
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
APD-MVS		81.13 43	81.73 48	79.36 56	84.47 86	70.53 65	83.85 41	83.70 80	69.43 55	83.67 77	88.96 103	75.89 36	86.41 16	72.62 65	92.95 76	81.14 195
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 135	72.03 50	84.38 37	86.23 25	77.28 16	80.65 114	90.18 77	59.80 180	87.58 4	73.06 61	91.34 104	89.01 35

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 25	86.70 50	70.03 69	82.06 60	87.00 17	59.89 133	80.91 112	90.53 54	72.19 65	88.56 1	73.67 57	94.52 37	85.92 77
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		80.99 46	81.63 51	79.07 59	86.86 48	69.39 74	79.41 88	84.00 78	65.64 78	85.54 49	89.28 90	76.32 33	83.47 90	74.03 54	93.57 69	84.35 123
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		80.99 46	80.85 55	81.41 29	78.37 172	71.37 56	87.45 8	85.87 31	77.48 13	81.98 95	89.95 81	69.14 91	85.26 55	66.15 112	91.24 106	87.61 53

SF-MVS		80.72 48	81.80 46	77.48 82	82.03 126	64.40 117	83.41 50	88.46 6	65.28 85	84.29 68	89.18 94	73.73 59	83.22 95	76.01 39	93.77 63	84.81 102

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 62	87.01 42	72.91 48	80.23 80	85.56 33	66.56 73	85.64 45	89.57 86	69.12 92	80.55 147	72.51 66	93.37 70	83.48 145

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 45	64.94 90	81.05 108	88.38 113	57.10 207	87.10 7	79.75 8	83.87 230	84.31 124
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
PEN-MVS		80.46 51	82.91 37	73.11 143	89.83 9	39.02 309	77.06 121	82.61 95	80.04 5	90.60 6	92.85 9	74.93 47	85.21 58	63.15 142	95.15 18	95.09 2

PS-CasMVS		80.41 52	82.86 39	73.07 145	89.93 7	39.21 306	77.15 119	81.28 115	79.74 6	90.87 4	92.73 11	75.03 45	84.93 63	63.83 134	95.19 16	95.07 3

DTE-MVSNet		80.35 53	82.89 38	72.74 156	89.84 8	37.34 323	77.16 118	81.81 105	80.45 4	90.92 3	92.95 7	74.57 51	86.12 29	63.65 135	94.68 33	94.76 6

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 71	87.00 43	67.23 90	79.24 89	88.61 5	56.65 169	84.29 68	89.18 94	73.73 59	83.22 95	76.01 39	93.77 63	84.81 102

SD-MVS		80.28 55	81.55 52	76.47 97	83.57 98	67.83 86	83.39 51	85.35 40	64.42 95	86.14 39	87.07 130	74.02 55	80.97 138	77.70 31	92.32 89	80.62 213
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
WR-MVS_H		80.22 56	82.17 45	74.39 121	89.46 15	42.69 284	78.24 105	82.24 98	78.21 10	89.57 9	92.10 18	68.05 103	85.59 48	66.04 114	95.62 9	94.88 5

HPM-MVS++		79.89 57	79.80 63	80.18 44	89.02 27	78.44 13	83.49 49	80.18 141	64.71 93	78.11 143	88.39 112	65.46 130	83.14 97	77.64 32	91.20 107	78.94 233

XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 69	86.46 52	74.79 34	77.15 119	85.39 39	66.73 70	80.39 118	88.85 105	74.43 54	78.33 189	74.73 49	85.79 202	82.35 179

XVG-OURS		79.51 59	79.82 62	78.58 68	86.11 62	74.96 33	76.33 129	84.95 49	66.89 66	82.75 89	88.99 102	66.82 114	78.37 187	74.80 47	90.76 125	82.40 178

CP-MVSNet		79.48 60	81.65 50	72.98 148	89.66 13	39.06 308	76.76 122	80.46 134	78.91 8	90.32 7	91.70 25	68.49 98	84.89 64	63.40 139	95.12 19	95.01 4

OMC-MVS		79.41 61	78.79 70	81.28 33	80.62 142	70.71 64	80.91 67	84.76 52	62.54 114	81.77 98	86.65 147	71.46 73	83.53 89	67.95 101	92.44 86	89.60 25

v7n		79.37 62	80.41 58	76.28 99	78.67 171	55.81 183	79.22 91	82.51 97	70.72 47	87.54 22	92.44 14	68.00 105	81.34 126	72.84 62	91.72 93	91.69 10

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 89	80.67 141	64.57 114	80.26 78	87.60 12	60.74 126	82.47 92	88.03 122	71.73 70	81.81 120	73.12 60	93.61 66	85.09 91

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 47	88.94 29	67.52 87	86.61 20	81.38 113	51.71 230	77.15 154	91.42 34	65.49 129	87.20 5	79.44 14	87.17 188	84.51 118
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mvs_tets		78.93 65	78.67 73	79.72 48	84.81 80	73.93 40	80.65 69	76.50 193	51.98 227	87.40 24	91.86 21	76.09 35	78.53 179	68.58 91	90.20 131	86.69 67

test_djsdf		78.88 66	78.27 76	80.70 40	81.42 134	71.24 58	83.98 39	75.72 200	52.27 222	87.37 27	92.25 16	68.04 104	80.56 145	72.28 69	91.15 109	90.32 22

HQP_MVS		78.77 67	78.78 71	78.72 65	85.18 72	65.18 107	82.74 55	85.49 34	65.45 80	78.23 140	89.11 98	60.83 170	86.15 27	71.09 75	90.94 114	84.82 100

anonymousdsp		78.60 68	77.80 80	81.00 36	78.01 178	74.34 38	80.09 81	76.12 195	50.51 246	89.19 10	90.88 42	71.45 74	77.78 201	73.38 58	90.60 127	90.90 18

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 66	80.48 143	64.16 118	80.24 79	82.06 101	61.89 118	88.77 12	93.32 4	57.15 205	82.60 107	70.08 82	92.80 80	89.25 29

jajsoiax		78.51 70	78.16 78	79.59 53	84.65 83	73.83 42	80.42 73	76.12 195	51.33 236	87.19 29	91.51 30	73.79 58	78.44 183	68.27 94	90.13 136	86.49 69

CNVR-MVS		78.49 71	78.59 74	78.16 74	85.86 66	67.40 88	78.12 108	81.50 109	63.92 99	77.51 151	86.56 151	68.43 100	84.82 66	73.83 55	91.61 97	82.26 182

DeepPCF-MVS	71.07 5	78.48 72	77.14 88	82.52 19	84.39 90	77.04 25	76.35 127	84.05 76	56.66 168	80.27 119	85.31 178	68.56 97	87.03 9	67.39 106	91.26 105	83.50 142

DP-MVS		78.44 73	79.29 67	75.90 104	81.86 129	65.33 105	79.05 93	84.63 59	74.83 20	80.41 117	86.27 158	71.68 71	83.45 91	62.45 146	92.40 87	78.92 234

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 84	80.09 147	66.16 100	79.08 92	86.13 26	57.55 159	80.93 110	87.76 125	71.98 69	82.73 105	72.11 72	92.83 79	83.25 153

NCCC		78.25 75	78.04 79	78.89 64	85.61 68	69.45 72	79.80 85	80.99 124	65.77 77	75.55 184	86.25 160	67.42 108	85.42 51	70.10 81	90.88 120	81.81 188

RRT_MVS		78.18 76	77.69 81	79.66 52	83.14 104	61.34 139	83.29 52	80.34 139	57.43 160	86.65 33	91.79 23	50.52 241	86.01 30	71.36 74	94.65 34	91.62 11

test_0402		78.17 77	79.48 66	74.24 124	83.50 99	59.15 164	72.52 167	74.60 211	75.34 17	88.69 13	91.81 22	75.06 44	82.37 110	65.10 121	88.68 164	81.20 193

AllTest		77.66 78	77.43 83	78.35 72	79.19 160	70.81 61	78.60 98	88.64 3	65.37 83	80.09 121	88.17 118	70.33 81	78.43 184	55.60 200	90.90 118	85.81 78

PS-MVSNAJss		77.54 79	77.35 85	78.13 76	84.88 78	66.37 98	78.55 99	79.59 149	53.48 213	86.29 36	92.43 15	62.39 152	80.25 153	67.90 102	90.61 126	87.77 50

ACMH	63.62 14	77.50 80	80.11 60	69.68 197	79.61 151	56.28 179	78.81 95	83.62 81	63.41 108	87.14 31	90.23 75	76.11 34	73.32 247	67.58 103	94.44 42	79.44 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		77.33 81	77.06 89	78.14 75	84.21 91	63.98 120	76.07 132	83.45 83	54.20 200	77.68 150	87.18 126	69.98 84	85.37 52	68.01 98	92.72 84	85.08 93

mvsmamba		77.20 82	76.37 92	79.69 50	80.34 145	61.52 137	80.58 70	82.12 100	53.54 212	83.93 74	91.03 39	49.49 247	85.97 32	73.26 59	93.08 74	91.59 12

DeepC-MVS_fast	69.89 7	77.17 83	76.33 94	79.70 49	83.90 96	67.94 84	80.06 83	83.75 79	56.73 167	74.88 192	85.32 177	65.54 128	87.79 2	65.61 118	91.14 110	83.35 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DROMVSNet		77.08 84	77.39 84	76.14 102	76.86 198	56.87 177	80.32 77	87.52 13	63.45 106	74.66 198	84.52 186	69.87 86	84.94 62	69.76 86	89.59 147	86.60 68

test_part1		76.97 85	78.21 77	73.25 140	77.87 180	45.76 260	78.27 104	87.26 15	66.69 71	85.31 53	91.43 33	55.95 216	84.24 78	65.71 116	95.43 12	89.75 24

X-MVStestdata		76.81 86	74.79 106	82.85 10	89.43 16	77.61 18	86.80 18	84.66 57	72.71 29	82.87 85	9.95 376	73.86 56	86.31 19	78.84 20	94.03 58	84.64 106

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 196	89.27 20	43.81 272	74.47 154	71.70 233	72.33 37	85.50 50	93.65 3	77.98 22	76.88 211	54.60 210	91.64 95	89.08 33

test_prior3		76.71 88	77.19 86	75.27 112	82.15 124	59.85 157	75.57 137	84.33 66	58.92 142	76.53 173	86.78 138	67.83 106	83.39 92	69.81 84	92.76 82	82.58 173

CS-MVS		76.51 89	76.00 96	78.06 77	77.02 191	64.77 112	80.78 68	82.66 94	60.39 129	74.15 204	83.30 206	69.65 88	82.07 116	69.27 88	86.75 193	87.36 57

train_agg		76.38 90	76.55 90	75.86 105	85.47 69	69.32 76	76.42 125	78.69 163	54.00 205	76.97 157	86.74 141	66.60 118	81.10 132	72.50 67	91.56 99	77.15 252

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 173	84.61 84	42.57 286	70.98 194	78.29 172	68.67 59	83.04 82	89.26 91	72.99 63	80.75 144	55.58 203	95.47 10	91.35 13

agg_prior1		75.89 92	76.41 91	74.31 123	84.44 88	66.02 101	76.12 131	78.62 166	54.40 194	76.95 159	86.85 135	66.44 121	80.34 150	72.45 68	91.42 102	76.57 256

SixPastTwentyTwo		75.77 93	76.34 93	74.06 127	81.69 132	54.84 188	76.47 123	75.49 202	64.10 98	87.73 18	92.24 17	50.45 243	81.30 128	67.41 105	91.46 101	86.04 74

RPSCF		75.76 94	74.37 112	79.93 45	74.81 222	77.53 20	77.53 113	79.30 153	59.44 136	78.88 132	89.80 83	71.26 76	73.09 249	57.45 183	80.89 266	89.17 32

ETH3 D test6400		75.73 95	76.00 96	74.92 115	81.75 130	56.93 175	78.31 102	84.60 62	52.83 218	77.15 154	85.14 180	68.59 96	84.03 79	65.44 119	90.20 131	83.82 133

v10		75.69 96	76.20 95	74.16 125	74.44 233	48.69 226	75.84 136	82.93 90	59.02 141	85.92 41	89.17 96	58.56 190	82.74 104	70.73 77	89.14 158	91.05 15

Anonymous20231211		75.54 97	77.19 86	70.59 182	77.67 185	45.70 262	74.73 150	80.19 140	68.80 56	82.95 84	92.91 8	66.26 122	76.76 214	58.41 179	92.77 81	89.30 28

Effi-MVS+-dtu		75.43 98	72.28 150	84.91 2	77.05 188	83.58 1	78.47 100	77.70 179	57.68 153	74.89 191	78.13 271	64.80 136	84.26 77	56.46 192	85.32 209	86.88 63

Regformer-2		75.32 99	74.47 110	77.88 78	74.22 235	66.65 96	72.77 165	77.54 181	68.47 61	80.44 116	72.08 318	70.60 80	80.97 138	70.08 82	84.02 228	86.01 75

F-COLMAP		75.29 100	73.99 118	79.18 57	81.73 131	71.90 51	81.86 63	82.98 88	59.86 134	72.27 229	84.00 193	64.56 139	83.07 100	51.48 231	87.19 187	82.56 175

HQP-MVS		75.24 101	75.01 105	75.94 103	82.37 119	58.80 167	77.32 115	84.12 74	59.08 137	71.58 237	85.96 171	58.09 195	85.30 54	67.38 107	89.16 155	83.73 139

TAPA-MVS	65.27 12	75.16 102	74.29 114	77.77 79	74.86 221	68.08 83	77.89 109	84.04 77	55.15 182	76.19 180	83.39 200	66.91 112	80.11 158	60.04 167	90.14 135	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS-MVSNet		75.10 103	75.42 104	74.15 126	79.23 158	48.05 235	79.43 86	78.04 175	70.09 52	79.17 130	88.02 123	53.04 226	83.60 87	58.05 181	93.76 65	90.79 19

v8		75.07 104	75.64 101	73.35 137	73.42 248	47.46 244	75.20 141	81.45 111	60.05 131	85.64 45	89.26 91	58.08 197	81.80 121	69.71 87	87.97 172	90.79 19

UniMVSNet (Re)		75.00 105	75.48 103	73.56 135	83.14 104	47.92 237	70.41 202	81.04 123	63.67 102	79.54 125	86.37 156	62.83 146	81.82 119	57.10 186	95.25 15	90.94 17

PHI-MVS		74.92 106	74.36 113	76.61 93	76.40 201	62.32 131	80.38 74	83.15 86	54.16 202	73.23 218	80.75 233	62.19 155	83.86 81	68.02 97	90.92 117	83.65 140

DU-MVS		74.91 107	75.57 102	72.93 150	83.50 99	45.79 258	69.47 210	80.14 142	65.22 86	81.74 100	87.08 128	61.82 158	81.07 134	56.21 195	94.98 21	91.93 8

UniMVSNet_NR-MVSNet		74.90 108	75.65 100	72.64 159	83.04 110	45.79 258	69.26 213	78.81 160	66.66 72	81.74 100	86.88 134	63.26 144	81.07 134	56.21 195	94.98 21	91.05 15

CS-MVS-test		74.89 109	74.23 115	76.86 90	77.01 192	62.94 128	78.98 94	84.61 61	58.62 145	70.17 257	80.80 232	66.74 117	81.96 117	61.74 149	89.40 153	85.69 84

nrg030		74.87 110	75.99 98	71.52 174	74.90 220	49.88 222	74.10 158	82.58 96	54.55 193	83.50 79	89.21 93	71.51 72	75.74 223	61.24 153	92.34 88	88.94 38

Vis-MVSNet		74.85 111	74.56 108	75.72 106	81.63 133	64.64 113	76.35 127	79.06 156	62.85 112	73.33 216	88.41 111	62.54 150	79.59 164	63.94 133	82.92 240	82.94 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-4		74.64 112	73.67 124	77.55 80	74.74 224	64.49 116	72.91 163	75.42 204	67.45 63	80.24 120	72.07 320	68.98 93	80.19 157	70.29 80	80.91 264	87.98 48

MSLP-MVS++		74.48 113	75.78 99	70.59 182	84.66 82	62.40 129	78.65 97	84.24 71	60.55 128	77.71 149	81.98 220	63.12 145	77.64 203	62.95 143	88.14 167	71.73 296

Regformer-1		74.28 114	73.63 126	76.21 101	74.22 235	64.12 119	72.77 165	75.46 203	66.86 68	79.27 128	72.08 318	69.29 90	78.74 176	68.73 90	84.02 228	85.77 83

AdaColmap		74.22 115	74.56 108	73.20 141	81.95 127	60.97 144	79.43 86	80.90 125	65.57 79	72.54 227	81.76 224	70.98 78	85.26 55	47.88 262	90.00 137	73.37 279

CSCG		74.12 116	74.39 111	73.33 138	79.35 155	61.66 136	77.45 114	81.98 103	62.47 116	79.06 131	80.19 242	61.83 157	78.79 175	59.83 169	87.35 181	79.54 227

PAPM_NR		73.91 117	74.16 116	73.16 142	81.90 128	53.50 198	81.28 65	81.40 112	66.17 75	73.30 217	83.31 205	59.96 176	83.10 99	58.45 178	81.66 258	82.87 164

EPP-MVSNet		73.86 118	73.38 130	75.31 111	78.19 174	53.35 200	80.45 72	77.32 185	65.11 88	76.47 175	86.80 136	49.47 248	83.77 82	53.89 219	92.72 84	88.81 42

mvs-test1		73.81 119	70.69 172	83.18 5	77.05 188	81.39 3	75.39 139	77.70 179	57.68 153	71.19 247	74.72 296	64.80 136	83.66 86	56.46 192	81.19 262	84.50 119

K. test v3		73.67 120	73.61 127	73.87 129	79.78 149	55.62 186	74.69 152	62.04 302	66.16 76	84.76 62	93.23 5	49.47 248	80.97 138	65.66 117	86.67 194	85.02 95

NR-MVSNet		73.62 121	74.05 117	72.33 166	83.50 99	43.71 273	65.65 267	77.32 185	64.32 96	75.59 183	87.08 128	62.45 151	81.34 126	54.90 206	95.63 8	91.93 8

DP-MVS Recon		73.57 122	72.69 144	76.23 100	82.85 114	63.39 123	74.32 155	82.96 89	57.75 152	70.35 254	81.98 220	64.34 141	84.41 75	49.69 245	89.95 139	80.89 203

CNLPA		73.44 123	73.03 139	74.66 116	78.27 173	75.29 31	75.99 133	78.49 168	65.39 82	75.67 182	83.22 210	61.23 166	66.77 304	53.70 221	85.33 208	81.92 187

MCST-MVS		73.42 124	73.34 132	73.63 134	81.28 136	59.17 163	74.80 148	83.13 87	45.50 282	72.84 222	83.78 196	65.15 133	80.99 136	64.54 125	89.09 160	80.73 210

v1192		73.40 125	73.42 128	73.32 139	74.65 230	48.67 227	72.21 170	81.73 106	52.76 219	81.85 96	84.56 185	57.12 206	82.24 114	68.58 91	87.33 182	89.06 34

114514_t		73.40 125	73.33 133	73.64 133	84.15 93	57.11 174	78.20 106	80.02 143	43.76 297	72.55 226	86.07 169	64.00 142	83.35 94	60.14 165	91.03 113	80.45 216

FC-MVSNet-test		73.32 127	74.78 107	68.93 210	79.21 159	36.57 325	71.82 182	79.54 151	57.63 158	82.57 91	90.38 66	59.38 183	78.99 171	57.91 182	94.56 36	91.23 14

v1144		73.29 128	73.39 129	73.01 146	74.12 240	48.11 234	72.01 174	81.08 122	53.83 209	81.77 98	84.68 183	58.07 198	81.91 118	68.10 95	86.86 190	88.99 37

GeoE		73.14 129	73.77 123	71.26 177	78.09 176	52.64 203	74.32 155	79.56 150	56.32 172	76.35 178	83.36 204	70.76 79	77.96 197	63.32 140	81.84 252	83.18 156

baseline		73.10 130	73.96 119	70.51 184	71.46 268	46.39 256	72.08 172	84.40 65	55.95 175	76.62 169	86.46 154	67.20 109	78.03 196	64.22 128	87.27 185	87.11 62

h-mvs33		73.08 131	71.61 160	77.48 82	83.89 97	72.89 49	70.47 200	71.12 248	54.28 196	77.89 144	83.41 199	49.04 251	80.98 137	63.62 136	90.77 124	78.58 237

TSAR-MVS + GP.		73.08 131	71.60 161	77.54 81	78.99 167	70.73 63	74.96 143	69.38 259	60.73 127	74.39 202	78.44 265	57.72 202	82.78 103	60.16 164	89.60 146	79.11 232

v1240		73.06 133	73.14 135	72.84 153	74.74 224	47.27 247	71.88 181	81.11 119	51.80 229	82.28 94	84.21 190	56.22 214	82.34 111	68.82 89	87.17 188	88.91 39

casdiffmvs		73.06 133	73.84 120	70.72 180	71.32 269	46.71 252	70.93 195	84.26 70	55.62 178	77.46 152	87.10 127	67.09 110	77.81 199	63.95 131	86.83 191	87.64 52

IterMVS-LS		73.01 135	73.12 137	72.66 158	73.79 244	49.90 219	71.63 183	78.44 169	58.22 147	80.51 115	86.63 148	58.15 194	79.62 162	62.51 144	88.20 166	88.48 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet		73.00 136	71.84 155	76.48 96	75.82 210	61.28 140	74.81 146	80.37 137	63.17 110	62.43 310	80.50 237	61.10 168	85.16 61	64.00 130	84.34 224	83.01 161

v144192		72.99 137	73.06 138	72.77 154	74.58 231	47.48 243	71.90 180	80.44 135	51.57 232	81.46 104	84.11 192	58.04 199	82.12 115	67.98 99	87.47 178	88.70 44

MVS_111021_HR		72.98 138	72.97 141	72.99 147	80.82 139	65.47 104	68.81 220	72.77 224	57.67 155	75.76 181	82.38 217	71.01 77	77.17 206	61.38 152	86.15 198	76.32 257

v1921920		72.96 139	72.98 140	72.89 152	74.67 227	47.58 242	71.92 179	80.69 128	51.70 231	81.69 102	83.89 194	56.58 212	82.25 113	68.34 93	87.36 180	88.82 41

CLD-MVS		72.88 140	72.36 149	74.43 120	77.03 190	54.30 192	68.77 223	83.43 84	52.12 224	76.79 167	74.44 300	69.54 89	83.91 80	55.88 198	93.25 73	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Regformer-3		72.86 141	72.28 150	74.62 117	74.74 224	60.18 154	72.91 163	71.76 232	64.74 92	78.42 138	72.07 320	67.00 111	76.28 218	67.97 100	80.91 264	87.39 55

bld_raw_dy_0_64		72.85 142	72.76 143	73.09 144	85.08 76	64.80 111	78.72 96	64.22 287	51.92 228	83.13 81	90.26 73	39.21 306	69.91 280	70.73 77	91.60 98	84.56 113

EI-MVSNet-Vis-set		72.78 143	71.87 154	75.54 109	74.77 223	59.02 165	72.24 169	71.56 236	63.92 99	78.59 134	71.59 326	66.22 123	78.60 178	67.58 103	80.32 272	89.00 36

ETV-MVS		72.72 144	72.16 153	74.38 122	76.90 196	55.95 180	73.34 161	84.67 56	62.04 117	72.19 232	70.81 330	65.90 126	85.24 57	58.64 176	84.96 216	81.95 186

PCF-MVS	63.80 13	72.70 145	71.69 157	75.72 106	78.10 175	60.01 156	73.04 162	81.50 109	45.34 286	79.66 124	84.35 189	65.15 133	82.65 106	48.70 253	89.38 154	84.50 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 146	71.68 158	75.47 110	74.67 227	58.64 170	72.02 173	71.50 237	63.53 104	78.58 136	71.39 329	65.98 124	78.53 179	67.30 109	80.18 274	89.23 30

Anonymous20240529		72.56 147	73.79 122	68.86 212	76.89 197	45.21 264	68.80 222	77.25 187	67.16 65	76.89 162	90.44 57	65.95 125	74.19 243	50.75 237	90.00 137	87.18 61

FIs		72.56 147	73.80 121	68.84 213	78.74 170	37.74 319	71.02 193	79.83 145	56.12 173	80.88 113	89.45 88	58.18 192	78.28 190	56.63 188	93.36 71	90.51 21

v2v482		72.55 149	72.58 146	72.43 163	72.92 259	46.72 251	71.41 186	79.13 155	55.27 180	81.17 107	85.25 179	55.41 217	81.13 131	67.25 110	85.46 204	89.43 27

hse-mvs2		72.32 150	70.66 173	77.31 87	83.10 109	71.77 52	69.19 215	71.45 239	54.28 196	77.89 144	78.26 267	49.04 251	79.23 167	63.62 136	89.13 159	80.92 202

canonicalmvs		72.29 151	73.38 130	69.04 205	74.23 234	47.37 245	73.93 159	83.18 85	54.36 195	76.61 170	81.64 226	72.03 66	75.34 227	57.12 185	87.28 184	84.40 121

Effi-MVS+		72.10 152	72.28 150	71.58 172	74.21 238	50.33 215	74.72 151	82.73 92	62.62 113	70.77 250	76.83 281	69.96 85	80.97 138	60.20 162	78.43 291	83.45 148

MVS_111021_LR		72.10 152	71.82 156	72.95 149	79.53 153	73.90 41	70.45 201	66.64 271	56.87 164	76.81 166	81.76 224	68.78 94	71.76 267	61.81 147	83.74 232	73.18 281

pmmvs6		71.82 154	73.66 125	66.31 241	75.94 209	42.01 288	66.99 249	72.53 227	63.45 106	76.43 176	92.78 10	72.95 64	69.69 282	51.41 232	90.46 128	87.22 58

PLC	62.01 16	71.79 155	70.28 175	76.33 98	80.31 146	68.63 81	78.18 107	81.24 116	54.57 192	67.09 284	80.63 235	59.44 181	81.74 123	46.91 269	84.17 225	78.63 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VDDNet		71.60 156	73.13 136	67.02 234	86.29 53	41.11 293	69.97 204	66.50 272	68.72 58	74.74 194	91.70 25	59.90 177	75.81 221	48.58 255	91.72 93	84.15 128

3Dnovator	65.95 11	71.50 157	71.22 166	72.34 165	73.16 252	63.09 126	78.37 101	78.32 170	57.67 155	72.22 231	84.61 184	54.77 218	78.47 181	60.82 160	81.07 263	75.45 263

FA-MVS(test-final)		71.27 158	71.06 167	71.92 170	73.96 241	52.32 206	76.45 124	76.12 195	59.07 140	74.04 208	86.18 161	52.18 231	79.43 166	59.75 170	81.76 253	84.03 130

WR-MVS		71.20 159	72.48 147	67.36 229	84.98 77	35.70 333	64.43 282	68.66 263	65.05 89	81.49 103	86.43 155	57.57 203	76.48 216	50.36 241	93.32 72	89.90 23

V42		71.06 160	70.83 170	71.72 171	67.25 304	47.14 248	65.94 261	80.35 138	51.35 235	83.40 80	83.23 208	59.25 184	78.80 174	65.91 115	80.81 268	89.23 30

FMVSNet1		71.06 160	72.48 147	66.81 235	77.65 186	40.68 297	71.96 176	73.03 219	61.14 122	79.45 127	90.36 69	60.44 172	75.20 229	50.20 242	88.05 169	84.54 114

dcpmvs_2		71.02 162	72.65 145	66.16 242	76.06 208	50.49 214	71.97 175	79.36 152	50.34 247	82.81 88	83.63 197	64.38 140	67.27 298	61.54 151	83.71 234	80.71 212

API-MVS		70.97 163	71.51 163	69.37 199	75.20 215	55.94 181	80.99 66	76.84 190	62.48 115	71.24 245	77.51 277	61.51 162	80.96 142	52.04 227	85.76 203	71.22 301

VDD-MVS		70.81 164	71.44 164	68.91 211	79.07 165	46.51 253	67.82 236	70.83 252	61.23 121	74.07 207	88.69 106	59.86 178	75.62 224	51.11 234	90.28 130	84.61 109

EG-PatchMatch MVS		70.70 165	70.88 169	70.16 190	82.64 118	58.80 167	71.48 184	73.64 215	54.98 183	76.55 171	81.77 223	61.10 168	78.94 172	54.87 207	80.84 267	72.74 286

Baseline_NR-MVSNet		70.62 166	73.19 134	62.92 273	76.97 193	34.44 341	68.84 218	70.88 251	60.25 130	79.50 126	90.53 54	61.82 158	69.11 286	54.67 209	95.27 14	85.22 87

alignmvs		70.54 167	71.00 168	69.15 204	73.50 246	48.04 236	69.85 207	79.62 146	53.94 208	76.54 172	82.00 219	59.00 186	74.68 236	57.32 184	87.21 186	84.72 104

MG-MVS		70.47 168	71.34 165	67.85 224	79.26 157	40.42 301	74.67 153	75.15 208	58.41 146	68.74 273	88.14 121	56.08 215	83.69 85	59.90 168	81.71 257	79.43 229

AUN-MVS		70.22 169	67.88 205	77.22 88	82.96 113	71.61 53	69.08 216	71.39 240	49.17 259	71.70 235	78.07 272	37.62 316	79.21 168	61.81 147	89.15 157	80.82 205

UGNet		70.20 170	69.05 185	73.65 132	76.24 203	63.64 121	75.87 135	72.53 227	61.48 120	60.93 320	86.14 165	52.37 230	77.12 207	50.67 238	85.21 210	80.17 221
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
PVSNet_Blended_VisFu		70.04 171	68.88 188	73.53 136	82.71 116	63.62 122	74.81 146	81.95 104	48.53 264	67.16 283	79.18 257	51.42 237	78.38 186	54.39 214	79.72 281	78.60 236

Fast-Effi-MVS+-dtu		70.00 172	68.74 192	73.77 130	73.47 247	64.53 115	71.36 187	78.14 174	55.81 177	68.84 272	74.71 297	65.36 131	75.75 222	52.00 228	79.00 285	81.03 198

DPM-MVS		69.98 173	69.22 184	72.26 167	82.69 117	58.82 166	70.53 199	81.23 117	47.79 271	64.16 298	80.21 240	51.32 238	83.12 98	60.14 165	84.95 217	74.83 269

MVSFormer		69.93 174	69.03 186	72.63 160	74.93 218	59.19 161	83.98 39	75.72 200	52.27 222	63.53 306	76.74 282	43.19 282	80.56 145	72.28 69	78.67 289	78.14 244

MVS_Test		69.84 175	70.71 171	67.24 230	67.49 303	43.25 280	69.87 206	81.22 118	52.69 220	71.57 240	86.68 144	62.09 156	74.51 238	66.05 113	78.74 287	83.96 131

c3_l		69.82 176	69.89 177	69.61 198	66.24 311	43.48 276	68.12 233	79.61 148	51.43 234	77.72 148	80.18 243	54.61 221	78.15 195	63.62 136	87.50 177	87.20 60

TransMVSNet (Re)		69.62 177	71.63 159	63.57 262	76.51 200	35.93 331	65.75 266	71.29 243	61.05 123	75.02 189	89.90 82	65.88 127	70.41 279	49.79 244	89.48 149	84.38 122

EI-MVSNet		69.61 178	69.01 187	71.41 176	73.94 242	49.90 219	71.31 189	71.32 241	58.22 147	75.40 187	70.44 332	58.16 193	75.85 219	62.51 144	79.81 278	88.48 45

Gipuma		69.55 179	72.83 142	59.70 297	63.63 331	53.97 195	80.08 82	75.93 198	64.24 97	73.49 213	88.93 104	57.89 201	62.46 321	59.75 170	91.55 100	62.67 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tttt0517		69.46 180	67.79 206	74.46 118	75.34 213	52.72 202	75.05 142	63.27 293	54.69 189	78.87 133	84.37 188	26.63 360	81.15 130	63.95 131	87.93 173	89.51 26

eth_miper_zixun_eth		69.42 181	68.73 193	71.50 175	67.99 297	46.42 254	67.58 238	78.81 160	50.72 243	78.13 142	80.34 239	50.15 245	80.34 150	60.18 163	84.65 218	87.74 51

BH-untuned		69.39 182	69.46 179	69.18 203	77.96 179	56.88 176	68.47 230	77.53 182	56.77 166	77.79 147	79.63 249	60.30 174	80.20 156	46.04 275	80.65 269	70.47 306

v148		69.38 183	69.39 180	69.36 200	69.14 290	44.56 267	68.83 219	72.70 225	54.79 187	78.59 134	84.12 191	54.69 219	76.74 215	59.40 173	82.20 246	86.79 64

1121		69.23 184	68.26 197	72.12 169	88.36 37	71.40 55	68.59 225	62.06 300	43.80 296	74.75 193	86.18 161	52.92 227	76.85 212	54.47 211	83.27 238	68.12 322

PAPR		69.20 185	68.66 194	70.82 179	75.15 217	47.77 239	75.31 140	81.11 119	49.62 256	66.33 286	79.27 254	61.53 161	82.96 101	48.12 260	81.50 260	81.74 189

QAPM		69.18 186	69.26 182	68.94 209	71.61 267	52.58 204	80.37 75	78.79 162	49.63 255	73.51 212	85.14 180	53.66 224	79.12 169	55.11 205	75.54 307	75.11 267

LCM-MVSNet-Re		69.10 187	71.57 162	61.70 281	70.37 278	34.30 343	61.45 303	79.62 146	56.81 165	89.59 8	88.16 120	68.44 99	72.94 250	42.30 291	87.33 182	77.85 249

EPNet		69.10 187	67.32 211	74.46 118	68.33 294	61.27 141	77.56 111	63.57 291	60.95 124	56.62 336	82.75 213	51.53 236	81.24 129	54.36 215	90.20 131	80.88 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		68.83 189	68.31 195	70.38 185	70.55 277	48.31 230	63.78 289	82.13 99	54.00 205	68.96 269	75.17 292	58.95 187	80.06 159	58.55 177	82.74 242	82.76 168
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
Fast-Effi-MVS+		68.81 190	68.30 196	70.35 186	74.66 229	48.61 228	66.06 260	78.32 170	50.62 244	71.48 243	75.54 288	68.75 95	79.59 164	50.55 240	78.73 288	82.86 165

OpenMVS	62.51 15	68.76 191	68.75 191	68.78 214	70.56 276	53.91 196	78.29 103	77.35 184	48.85 262	70.22 256	83.52 198	52.65 229	76.93 209	55.31 204	81.99 248	75.49 262

VPA-MVSNet		68.71 192	70.37 174	63.72 260	76.13 205	38.06 317	64.10 284	71.48 238	56.60 171	74.10 206	88.31 115	64.78 138	69.72 281	47.69 264	90.15 134	83.37 150

iter_conf_final		68.69 193	67.00 217	73.76 131	73.68 245	52.33 205	75.96 134	73.54 216	50.56 245	69.90 260	82.85 211	24.76 368	83.73 83	65.40 120	86.33 197	85.22 87

BH-RMVSNet		68.69 193	68.20 201	70.14 191	76.40 201	53.90 197	64.62 279	73.48 217	58.01 149	73.91 210	81.78 222	59.09 185	78.22 191	48.59 254	77.96 296	78.31 240

EIA-MVS		68.59 195	67.16 213	72.90 151	75.18 216	55.64 185	69.39 211	81.29 114	52.44 221	64.53 294	70.69 331	60.33 173	82.30 112	54.27 216	76.31 302	80.75 209

pm-mvs1		68.40 196	69.85 178	64.04 258	73.10 256	39.94 303	64.61 280	70.50 253	55.52 179	73.97 209	89.33 89	63.91 143	68.38 290	49.68 246	88.02 170	83.81 135

miper_ehance_all_eth		68.36 197	68.16 202	68.98 207	65.14 322	43.34 278	67.07 248	78.92 159	49.11 260	76.21 179	77.72 274	53.48 225	77.92 198	61.16 155	84.59 220	85.68 85

GBi-Net		68.30 198	68.79 189	66.81 235	73.14 253	40.68 297	71.96 176	73.03 219	54.81 184	74.72 195	90.36 69	48.63 257	75.20 229	47.12 266	85.37 205	84.54 114

test1		68.30 198	68.79 189	66.81 235	73.14 253	40.68 297	71.96 176	73.03 219	54.81 184	74.72 195	90.36 69	48.63 257	75.20 229	47.12 266	85.37 205	84.54 114

FE-MVS		68.29 200	66.96 218	72.26 167	74.16 239	54.24 193	77.55 112	73.42 218	57.65 157	72.66 224	84.91 182	32.02 339	81.49 125	48.43 257	81.85 251	81.04 197

DIV-MVS_self_test		68.27 201	68.26 197	68.29 219	64.98 323	43.67 274	65.89 262	74.67 209	50.04 252	76.86 164	82.43 215	48.74 255	75.38 225	60.94 158	89.81 142	85.81 78

cl____		68.26 202	68.26 197	68.29 219	64.98 323	43.67 274	65.89 262	74.67 209	50.04 252	76.86 164	82.42 216	48.74 255	75.38 225	60.92 159	89.81 142	85.80 82

TinyColmap		67.98 203	69.28 181	64.08 256	67.98 298	46.82 250	70.04 203	75.26 206	53.05 215	77.36 153	86.79 137	59.39 182	72.59 257	45.64 277	88.01 171	72.83 284

xiu_mvs_v1_base_debu		67.87 204	67.07 214	70.26 187	79.13 162	61.90 133	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 189	78.26 293	76.27 258

xiu_mvs_v1_base		67.87 204	67.07 214	70.26 187	79.13 162	61.90 133	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 189	78.26 293	76.27 258

xiu_mvs_v1_base_debi		67.87 204	67.07 214	70.26 187	79.13 162	61.90 133	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 189	78.26 293	76.27 258

MAR-MVS		67.72 207	66.16 222	72.40 164	74.45 232	64.99 110	74.87 144	77.50 183	48.67 263	65.78 290	68.58 350	57.01 209	77.79 200	46.68 272	81.92 249	74.42 272
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
IterMVS-SCA-FT		67.68 208	66.07 223	72.49 162	73.34 250	58.20 172	63.80 288	65.55 277	48.10 266	76.91 161	82.64 214	45.20 269	78.84 173	61.20 154	77.89 297	80.44 217

LF4IMVS		67.50 209	67.31 212	68.08 222	58.86 354	61.93 132	71.43 185	75.90 199	44.67 292	72.42 228	80.20 241	57.16 204	70.44 277	58.99 175	86.12 199	71.88 294

FMVSNet2		67.48 210	68.21 200	65.29 247	73.14 253	38.94 310	68.81 220	71.21 247	54.81 184	76.73 168	86.48 153	48.63 257	74.60 237	47.98 261	86.11 200	82.35 179

MSDG		67.47 211	67.48 210	67.46 228	70.70 273	54.69 190	66.90 252	78.17 173	60.88 125	70.41 253	74.76 294	61.22 167	73.18 248	47.38 265	76.87 299	74.49 271

diffmvs		67.42 212	67.50 209	67.20 231	62.26 336	45.21 264	64.87 276	77.04 188	48.21 265	71.74 234	79.70 248	58.40 191	71.17 271	64.99 122	80.27 273	85.22 87

iter_conf05		67.34 213	65.62 225	72.50 161	69.82 282	47.06 249	72.19 171	76.86 189	45.32 287	72.86 221	82.85 211	20.53 374	83.73 83	61.13 156	89.02 161	86.70 66

cl22		67.14 214	66.51 220	69.03 206	63.20 332	43.46 277	66.88 253	76.25 194	49.22 258	74.48 200	77.88 273	45.49 268	77.40 205	60.64 161	84.59 220	86.24 70

ANet_high		67.08 215	69.94 176	58.51 305	57.55 359	27.09 368	58.43 322	76.80 191	63.56 103	82.40 93	91.93 20	59.82 179	64.98 312	50.10 243	88.86 163	83.46 147

LFMVS		67.06 216	67.89 204	64.56 252	78.02 177	38.25 314	70.81 198	59.60 308	65.18 87	71.06 248	86.56 151	43.85 278	75.22 228	46.35 273	89.63 145	80.21 220

thisisatest0530		67.05 217	65.16 229	72.73 157	73.10 256	50.55 213	71.26 191	63.91 289	50.22 249	74.46 201	80.75 233	26.81 359	80.25 153	59.43 172	86.50 195	87.37 56

MIMVSNet1		66.57 218	69.23 183	58.59 304	81.26 137	37.73 320	64.06 285	57.62 313	57.02 163	78.40 139	90.75 47	62.65 147	58.10 334	41.77 296	89.58 148	79.95 222

tfpnnormal		66.48 219	67.93 203	62.16 279	73.40 249	36.65 324	63.45 291	64.99 280	55.97 174	72.82 223	87.80 124	57.06 208	69.10 287	48.31 259	87.54 175	80.72 211

KD-MVS_self_test		66.38 220	67.51 208	62.97 271	61.76 338	34.39 342	58.11 324	75.30 205	50.84 242	77.12 156	85.42 176	56.84 210	69.44 283	51.07 235	91.16 108	85.08 93

Anonymous202405211		66.02 221	66.89 219	63.43 265	74.22 235	38.14 315	59.00 319	66.13 273	63.33 109	69.76 263	85.95 172	51.88 232	70.50 276	44.23 283	87.52 176	81.64 190

miper_enhance_ethall		65.86 222	65.05 236	68.28 221	61.62 340	42.62 285	64.74 277	77.97 176	42.52 306	73.42 215	72.79 315	49.66 246	77.68 202	58.12 180	84.59 220	84.54 114

RPMNet		65.77 223	65.08 235	67.84 225	66.37 308	48.24 232	70.93 195	86.27 22	54.66 190	61.35 314	86.77 140	33.29 328	85.67 47	55.93 197	70.17 337	69.62 314

VPNet		65.58 224	67.56 207	59.65 298	79.72 150	30.17 360	60.27 313	62.14 297	54.19 201	71.24 245	86.63 148	58.80 188	67.62 295	44.17 284	90.87 121	81.18 194

PVSNet_BlendedMVS		65.38 225	64.30 237	68.61 215	69.81 283	49.36 223	65.60 269	78.96 157	45.50 282	59.98 323	78.61 263	51.82 233	78.20 192	44.30 281	84.11 226	78.27 241

TAMVS		65.31 226	63.75 242	69.97 195	82.23 123	59.76 159	66.78 254	63.37 292	45.20 288	69.79 262	79.37 253	47.42 263	72.17 260	34.48 339	85.15 212	77.99 248

test_yl		65.11 227	65.09 233	65.18 248	70.59 274	40.86 295	63.22 296	72.79 222	57.91 150	68.88 270	79.07 260	42.85 285	74.89 233	45.50 278	84.97 213	79.81 223

DCV-MVSNet		65.11 227	65.09 233	65.18 248	70.59 274	40.86 295	63.22 296	72.79 222	57.91 150	68.88 270	79.07 260	42.85 285	74.89 233	45.50 278	84.97 213	79.81 223

mvs_anonymous		65.08 229	65.49 226	63.83 259	63.79 329	37.60 321	66.52 257	69.82 257	43.44 301	73.46 214	86.08 168	58.79 189	71.75 268	51.90 229	75.63 306	82.15 183

FMVSNet3		65.00 230	65.16 229	64.52 253	69.47 286	37.56 322	66.63 255	70.38 254	51.55 233	74.72 195	83.27 207	37.89 315	74.44 239	47.12 266	85.37 205	81.57 191

ECVR-MVS		64.82 231	65.22 227	63.60 261	78.80 168	31.14 357	66.97 250	56.47 323	54.23 198	69.94 259	88.68 107	37.23 317	74.81 235	45.28 280	89.41 151	84.86 98

BH-w/o		64.81 232	64.29 238	66.36 240	76.08 207	54.71 189	65.61 268	75.23 207	50.10 251	71.05 249	71.86 325	54.33 222	79.02 170	38.20 317	76.14 303	65.36 337

EGC-MVSNET		64.77 233	61.17 264	75.60 108	86.90 47	74.47 35	84.04 38	68.62 264	0.60 378	1.13 380	91.61 28	65.32 132	74.15 244	64.01 129	88.28 165	78.17 243

test1111		64.62 234	65.19 228	62.93 272	79.01 166	29.91 361	65.45 270	54.41 331	54.09 203	71.47 244	88.48 110	37.02 318	74.29 242	46.83 271	89.94 140	84.58 112

cascas		64.59 235	62.77 253	70.05 193	75.27 214	50.02 218	61.79 302	71.61 234	42.46 307	63.68 304	68.89 347	49.33 250	80.35 149	47.82 263	84.05 227	79.78 225

TR-MVS		64.59 235	63.54 245	67.73 227	75.75 212	50.83 212	63.39 292	70.29 255	49.33 257	71.55 241	74.55 298	50.94 239	78.46 182	40.43 303	75.69 305	73.89 276

PM-MVS		64.49 237	63.61 244	67.14 233	76.68 199	75.15 32	68.49 229	42.85 362	51.17 239	77.85 146	80.51 236	45.76 264	66.31 307	52.83 226	76.35 301	59.96 355

jason		64.47 238	62.84 252	69.34 202	76.91 195	59.20 160	67.15 247	65.67 274	35.29 341	65.16 292	76.74 282	44.67 273	70.68 273	54.74 208	79.28 284	78.14 244
jason: jason.
xiu_mvs_v2_base		64.43 239	63.96 240	65.85 246	77.72 184	51.32 210	63.63 290	72.31 230	45.06 291	61.70 311	69.66 340	62.56 148	73.93 246	49.06 250	73.91 319	72.31 290

pmmvs-eth3d		64.41 240	63.27 248	67.82 226	75.81 211	60.18 154	69.49 209	62.05 301	38.81 327	74.13 205	82.23 218	43.76 279	68.65 288	42.53 290	80.63 271	74.63 270

CDS-MVSNet		64.33 241	62.66 254	69.35 201	80.44 144	58.28 171	65.26 272	65.66 275	44.36 293	67.30 282	75.54 288	43.27 281	71.77 266	37.68 320	84.44 223	78.01 247
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		64.27 242	63.73 243	65.90 245	77.82 182	51.42 209	63.33 293	72.33 229	45.09 290	61.60 312	68.04 351	62.39 152	73.95 245	49.07 249	73.87 320	72.34 289

ab-mvs		64.11 243	65.13 232	61.05 288	71.99 265	38.03 318	67.59 237	68.79 262	49.08 261	65.32 291	86.26 159	58.02 200	66.85 302	39.33 307	79.79 280	78.27 241

CANet_DTU		64.04 244	63.83 241	64.66 251	68.39 291	42.97 282	73.45 160	74.50 212	52.05 226	54.78 343	75.44 291	43.99 277	70.42 278	53.49 223	78.41 292	80.59 214

VNet		64.01 245	65.15 231	60.57 292	73.28 251	35.61 334	57.60 326	67.08 269	54.61 191	66.76 285	83.37 202	56.28 213	66.87 300	42.19 292	85.20 211	79.23 231

Anonymous20240521		63.55 246	66.07 223	55.99 312	66.18 313	44.04 271	68.77 223	68.80 261	46.99 276	72.57 225	85.84 173	39.87 302	50.22 343	53.40 225	92.23 90	73.71 278

lupinMVS		63.36 247	61.49 262	68.97 208	74.93 218	59.19 161	65.80 265	64.52 285	34.68 346	63.53 306	74.25 303	43.19 282	70.62 274	53.88 220	78.67 289	77.10 253

ET-MVSNet_ETH3D		63.32 248	60.69 270	71.20 178	70.15 281	55.66 184	65.02 275	64.32 286	43.28 305	68.99 268	72.05 324	25.46 366	78.19 194	54.16 218	82.80 241	79.74 226

MVSTER		63.29 249	61.60 261	68.36 217	59.77 351	46.21 257	60.62 310	71.32 241	41.83 309	75.40 187	79.12 258	30.25 352	75.85 219	56.30 194	79.81 278	83.03 160

OpenMVS_ROB	54.93 17	63.23 250	63.28 247	63.07 269	69.81 283	45.34 263	68.52 228	67.14 268	43.74 298	70.61 252	79.22 255	47.90 261	72.66 253	48.75 252	73.84 321	71.21 302

IterMVS		63.12 251	62.48 255	65.02 250	66.34 310	52.86 201	63.81 287	62.25 296	46.57 279	71.51 242	80.40 238	44.60 274	66.82 303	51.38 233	75.47 308	75.38 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 252	60.47 271	70.61 181	83.04 110	54.10 194	59.93 315	72.24 231	33.67 351	69.00 267	75.63 287	38.69 309	76.93 209	36.60 328	75.45 309	80.81 208

GA-MVS		62.91 253	61.66 258	66.66 239	67.09 306	44.49 268	61.18 307	69.36 260	51.33 236	69.33 265	74.47 299	36.83 319	74.94 232	50.60 239	74.72 314	80.57 215

PVSNet_Blended		62.90 254	61.64 259	66.69 238	69.81 283	49.36 223	61.23 306	78.96 157	42.04 308	59.98 323	68.86 348	51.82 233	78.20 192	44.30 281	77.77 298	72.52 287

USDC		62.80 255	63.10 250	61.89 280	65.19 319	43.30 279	67.42 241	74.20 213	35.80 340	72.25 230	84.48 187	45.67 266	71.95 265	37.95 319	84.97 213	70.42 308

Vis-MVSNet (Re-imp)		62.74 256	63.21 249	61.34 286	72.19 263	31.56 354	67.31 246	53.87 332	53.60 211	69.88 261	83.37 202	40.52 299	70.98 272	41.40 297	86.78 192	81.48 192

patch_mono-2		62.73 257	64.08 239	58.68 303	70.36 279	55.87 182	60.84 309	64.11 288	41.23 313	64.04 299	78.22 268	60.00 175	48.80 345	54.17 217	83.71 234	71.37 298

D2MVS		62.58 258	61.05 266	67.20 231	63.85 328	47.92 237	56.29 329	69.58 258	39.32 322	70.07 258	78.19 269	34.93 324	72.68 252	53.44 224	83.74 232	81.00 200

MVS_0304		62.51 259	62.27 256	63.25 266	69.39 287	48.47 229	64.05 286	62.48 295	59.69 135	54.10 348	81.04 230	45.71 265	66.31 307	41.38 298	82.58 244	74.96 268

CL-MVSNet_self_test		62.44 260	63.40 246	59.55 299	72.34 262	32.38 350	56.39 328	64.84 281	51.21 238	67.46 281	81.01 231	50.75 240	63.51 319	38.47 315	88.12 168	82.75 169

MDA-MVSNet-bldmvs		62.34 261	61.73 257	64.16 254	61.64 339	49.90 219	48.11 350	57.24 319	53.31 214	80.95 109	79.39 252	49.00 253	61.55 325	45.92 276	80.05 275	81.03 198

miper_lstm_enhance		61.97 262	61.63 260	62.98 270	60.04 347	45.74 261	47.53 352	70.95 249	44.04 294	73.06 219	78.84 262	39.72 303	60.33 327	55.82 199	84.64 219	82.88 163

wuyk23d		61.97 262	66.25 221	49.12 332	58.19 358	60.77 150	66.32 258	52.97 339	55.93 176	90.62 5	86.91 133	73.07 62	35.98 372	20.63 374	91.63 96	50.62 363

thres600view7		61.82 264	61.38 263	63.12 268	71.81 266	34.93 338	64.64 278	56.99 320	54.78 188	70.33 255	79.74 247	32.07 337	72.42 259	38.61 313	83.46 236	82.02 184

PAPM		61.79 265	60.37 272	66.05 243	76.09 206	41.87 289	69.30 212	76.79 192	40.64 319	53.80 349	79.62 250	44.38 275	82.92 102	29.64 357	73.11 323	73.36 280

MVP-Stereo		61.56 266	59.22 278	68.58 216	79.28 156	60.44 152	69.20 214	71.57 235	43.58 300	56.42 337	78.37 266	39.57 305	76.46 217	34.86 338	60.16 360	68.86 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	48.73 20	61.54 267	60.89 267	63.52 263	61.08 342	51.55 208	68.07 234	68.00 267	33.88 348	65.87 288	81.25 228	37.91 314	67.71 293	49.32 248	82.60 243	71.31 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		61.23 268	60.85 268	62.38 277	78.80 168	27.88 367	67.33 245	37.42 375	54.23 198	67.55 280	88.68 107	17.87 379	74.39 240	46.33 274	89.41 151	84.86 98

thres100view900		61.17 269	61.09 265	61.39 285	72.14 264	35.01 337	65.42 271	56.99 320	55.23 181	70.71 251	79.90 245	32.07 337	72.09 261	35.61 335	81.73 254	77.08 254

Patchmtry		60.91 270	63.01 251	54.62 315	66.10 314	26.27 371	67.47 240	56.40 324	54.05 204	72.04 233	86.66 145	33.19 329	60.17 328	43.69 285	87.45 179	77.42 250

EU-MVSNet		60.82 271	60.80 269	60.86 291	68.37 292	41.16 292	72.27 168	68.27 266	26.96 369	69.08 266	75.71 286	32.09 336	67.44 296	55.59 202	78.90 286	73.97 274

pmmvs4		60.78 272	59.04 280	66.00 244	73.06 258	57.67 173	64.53 281	60.22 306	36.91 335	65.96 287	77.27 278	39.66 304	68.54 289	38.87 310	74.89 313	71.80 295

thres400		60.77 273	59.97 274	63.15 267	70.78 271	35.35 335	63.27 294	57.47 314	53.00 216	68.31 274	77.09 279	32.45 334	72.09 261	35.61 335	81.73 254	82.02 184

MVS		60.62 274	59.97 274	62.58 275	68.13 296	47.28 246	68.59 225	73.96 214	32.19 355	59.94 325	68.86 348	50.48 242	77.64 203	41.85 295	75.74 304	62.83 347

thisisatest0515		60.48 275	57.86 289	68.34 218	67.25 304	46.42 254	60.58 311	62.14 297	40.82 317	63.58 305	69.12 343	26.28 362	78.34 188	48.83 251	82.13 247	80.26 219

tfpn200view9		60.35 276	59.97 274	61.51 283	70.78 271	35.35 335	63.27 294	57.47 314	53.00 216	68.31 274	77.09 279	32.45 334	72.09 261	35.61 335	81.73 254	77.08 254

ppachtmachnet_test		60.26 277	59.61 277	62.20 278	67.70 301	44.33 269	58.18 323	60.96 305	40.75 318	65.80 289	72.57 316	41.23 292	63.92 316	46.87 270	82.42 245	78.33 239

Patchmatch-RL test		59.95 278	59.12 279	62.44 276	72.46 261	54.61 191	59.63 316	47.51 355	41.05 316	74.58 199	74.30 302	31.06 346	65.31 309	51.61 230	79.85 277	67.39 325

1314		59.83 279	58.86 282	62.74 274	65.71 316	44.78 266	68.59 225	72.63 226	33.54 353	61.05 318	67.29 356	43.62 280	71.26 270	49.49 247	67.84 348	72.19 292

IB-MVS	49.67 18	59.69 280	56.96 295	67.90 223	68.19 295	50.30 216	61.42 304	65.18 279	47.57 273	55.83 340	67.15 357	23.77 371	79.60 163	43.56 287	79.97 276	73.79 277
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
1112_ss		59.48 281	58.99 281	60.96 290	77.84 181	42.39 287	61.42 304	68.45 265	37.96 330	59.93 326	67.46 353	45.11 271	65.07 311	40.89 301	71.81 328	75.41 264

FPMVS		59.43 282	60.07 273	57.51 308	77.62 187	71.52 54	62.33 300	50.92 344	57.40 161	69.40 264	80.00 244	39.14 307	61.92 324	37.47 323	66.36 350	39.09 372

CVMVSNet		59.21 283	58.44 286	61.51 283	73.94 242	47.76 240	71.31 189	64.56 284	26.91 370	60.34 322	70.44 332	36.24 321	67.65 294	53.57 222	68.66 345	69.12 319

CR-MVSNet		58.96 284	58.49 285	60.36 294	66.37 308	48.24 232	70.93 195	56.40 324	32.87 354	61.35 314	86.66 145	33.19 329	63.22 320	48.50 256	70.17 337	69.62 314

EPNet_dtu		58.93 285	58.52 284	60.16 296	67.91 299	47.70 241	69.97 204	58.02 312	49.73 254	47.28 363	73.02 314	38.14 311	62.34 322	36.57 329	85.99 201	70.43 307
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		58.78 286	58.69 283	59.04 302	79.41 154	38.13 316	57.62 325	66.98 270	34.74 344	59.62 327	77.56 276	42.92 284	63.65 318	38.66 312	70.73 334	75.35 266

PatchMatch-RL		58.68 287	57.72 290	61.57 282	76.21 204	73.59 44	61.83 301	49.00 351	47.30 275	61.08 316	68.97 345	50.16 244	59.01 331	36.06 334	68.84 344	52.10 362

SCA		58.57 288	58.04 288	60.17 295	70.17 280	41.07 294	65.19 273	53.38 337	43.34 304	61.00 319	73.48 309	45.20 269	69.38 284	40.34 304	70.31 336	70.05 309

CHOSEN 1792x2688		58.09 289	56.30 300	63.45 264	79.95 148	50.93 211	54.07 337	65.59 276	28.56 366	61.53 313	74.33 301	41.09 295	66.52 306	33.91 342	67.69 349	72.92 283

HY-MVS	49.31 19	57.96 290	57.59 291	59.10 301	66.85 307	36.17 328	65.13 274	65.39 278	39.24 324	54.69 345	78.14 270	44.28 276	67.18 299	33.75 343	70.79 333	73.95 275

baseline1		57.82 291	58.36 287	56.19 311	69.17 289	30.76 359	62.94 298	55.21 327	46.04 281	63.83 302	78.47 264	41.20 293	63.68 317	39.44 306	68.99 343	74.13 273

thres200		57.55 292	57.02 294	59.17 300	67.89 300	34.93 338	58.91 321	57.25 318	50.24 248	64.01 300	71.46 328	32.49 333	71.39 269	31.31 349	79.57 282	71.19 303

CostFormer		57.35 293	56.14 301	60.97 289	63.76 330	38.43 311	67.50 239	60.22 306	37.14 334	59.12 328	76.34 284	32.78 331	71.99 264	39.12 309	69.27 342	72.47 288

our_test_3		56.46 294	56.51 298	56.30 310	67.70 301	39.66 305	55.36 334	52.34 342	40.57 320	63.85 301	69.91 339	40.04 301	58.22 333	43.49 288	75.29 312	71.03 305

tpm2		56.12 295	54.64 308	60.55 293	66.24 311	36.01 329	68.14 232	56.77 322	33.60 352	58.25 331	75.52 290	30.25 352	74.33 241	33.27 344	69.76 341	71.32 299

tpmvs		55.84 296	55.45 306	57.01 309	60.33 346	33.20 348	65.89 262	59.29 310	47.52 274	56.04 338	73.60 308	31.05 347	68.06 292	40.64 302	64.64 353	69.77 312

gg-mvs-nofinetune		55.75 297	56.75 297	52.72 321	62.87 333	28.04 366	68.92 217	41.36 370	71.09 44	50.80 355	92.63 12	20.74 373	66.86 301	29.97 355	72.41 325	63.25 346

test20.03		55.74 298	57.51 292	50.42 325	59.89 350	32.09 352	50.63 344	49.01 350	50.11 250	65.07 293	83.23 208	45.61 267	48.11 348	30.22 353	83.82 231	71.07 304

MS-PatchMatch		55.59 299	54.89 307	57.68 307	69.18 288	49.05 225	61.00 308	62.93 294	35.98 338	58.36 330	68.93 346	36.71 320	66.59 305	37.62 322	63.30 356	57.39 358

baseline2		55.57 300	52.74 314	64.05 257	65.26 318	44.11 270	62.38 299	54.43 330	39.03 325	51.21 353	67.35 355	33.66 327	72.45 258	37.14 325	64.22 355	75.60 261

XXY-MVS		55.19 301	57.40 293	48.56 334	64.45 326	34.84 340	51.54 343	53.59 334	38.99 326	63.79 303	79.43 251	56.59 211	45.57 352	36.92 327	71.29 330	65.25 338

FMVSNet5		55.08 302	55.54 305	53.71 316	65.80 315	33.50 347	56.22 330	52.50 341	43.72 299	61.06 317	83.38 201	25.46 366	54.87 337	30.11 354	81.64 259	72.75 285

PatchmatchNet		54.60 303	54.27 309	55.59 313	65.17 321	39.08 307	66.92 251	51.80 343	39.89 321	58.39 329	73.12 313	31.69 340	58.33 332	43.01 289	58.38 366	69.38 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		54.39 304	56.12 302	49.20 330	72.57 260	30.91 358	59.98 314	48.43 353	41.66 310	55.94 339	83.86 195	41.19 294	50.42 342	26.05 364	75.38 310	66.27 333

Anonymous20231206		54.13 305	55.82 303	49.04 333	70.89 270	35.96 330	51.73 342	50.87 345	34.86 342	62.49 309	79.22 255	42.52 288	44.29 360	27.95 362	81.88 250	66.88 329

JIA-IIPM		54.03 306	51.62 318	61.25 287	59.14 353	55.21 187	59.10 318	47.72 354	50.85 241	50.31 359	85.81 174	20.10 376	63.97 315	36.16 333	55.41 371	64.55 344

tpm cat1		54.02 307	52.63 315	58.19 306	64.85 325	39.86 304	66.26 259	57.28 317	32.16 356	56.90 334	70.39 334	32.75 332	65.30 310	34.29 340	58.79 363	69.41 316

testgi		54.00 308	56.86 296	45.45 342	58.20 357	25.81 372	49.05 346	49.50 349	45.43 285	67.84 276	81.17 229	51.81 235	43.20 364	29.30 358	79.41 283	67.34 327

PatchT		53.35 309	56.47 299	43.99 348	64.19 327	17.46 379	59.15 317	43.10 361	52.11 225	54.74 344	86.95 132	29.97 355	49.98 344	43.62 286	74.40 317	64.53 345

new-patchmatchnet		52.89 310	55.76 304	44.26 347	59.94 349	6.31 382	37.36 369	50.76 346	41.10 314	64.28 297	79.82 246	44.77 272	48.43 347	36.24 332	87.61 174	78.03 246

YYNet1		52.58 311	53.50 311	49.85 326	54.15 373	36.45 327	40.53 363	46.55 357	38.09 329	75.52 185	73.31 312	41.08 296	43.88 361	41.10 299	71.14 332	69.21 318

MDA-MVSNet_test_wron		52.57 312	53.49 312	49.81 327	54.24 372	36.47 326	40.48 364	46.58 356	38.13 328	75.47 186	73.32 311	41.05 297	43.85 362	40.98 300	71.20 331	69.10 320

pmmvs5		52.49 313	52.58 316	52.21 323	54.99 370	32.38 350	55.45 333	53.84 333	32.15 357	55.49 342	74.81 293	38.08 312	57.37 335	34.02 341	74.40 317	66.88 329

UnsupCasMVSNet_eth		52.26 314	53.29 313	49.16 331	55.08 369	33.67 346	50.03 345	58.79 311	37.67 331	63.43 308	74.75 295	41.82 290	45.83 351	38.59 314	59.42 362	67.98 324

N_pmnet		52.06 315	51.11 323	54.92 314	59.64 352	71.03 59	37.42 368	61.62 304	33.68 350	57.12 332	72.10 317	37.94 313	31.03 374	29.13 361	71.35 329	62.70 348

KD-MVS_2432*1600		52.05 316	51.58 319	53.44 317	52.11 375	31.20 355	44.88 359	64.83 282	41.53 311	64.37 295	70.03 337	15.61 383	64.20 313	36.25 330	74.61 315	64.93 341

miper_refine_blended		52.05 316	51.58 319	53.44 317	52.11 375	31.20 355	44.88 359	64.83 282	41.53 311	64.37 295	70.03 337	15.61 383	64.20 313	36.25 330	74.61 315	64.93 341

PVSNet	43.83 21	51.56 318	51.17 322	52.73 320	68.34 293	38.27 313	48.22 349	53.56 335	36.41 336	54.29 346	64.94 360	34.60 325	54.20 340	30.34 352	69.87 339	65.71 336

tpm		50.60 319	52.42 317	45.14 344	65.18 320	26.29 370	60.30 312	43.50 360	37.41 332	57.01 333	79.09 259	30.20 354	42.32 365	32.77 346	66.36 350	66.81 331

test-LLR		50.43 320	50.69 325	49.64 328	60.76 343	41.87 289	53.18 339	45.48 358	43.41 302	49.41 360	60.47 365	29.22 357	44.73 358	42.09 293	72.14 326	62.33 351

tpmrst		50.15 321	51.38 321	46.45 339	56.05 364	24.77 373	64.40 283	49.98 347	36.14 337	53.32 350	69.59 341	35.16 323	48.69 346	39.24 308	58.51 365	65.89 334

UnsupCasMVSNet_bld		50.01 322	51.03 324	46.95 335	58.61 355	32.64 349	48.31 348	53.27 338	34.27 347	60.47 321	71.53 327	41.40 291	47.07 349	30.68 351	60.78 359	61.13 353

WTY-MVS		49.39 323	50.31 326	46.62 338	61.22 341	32.00 353	46.61 355	49.77 348	33.87 349	54.12 347	69.55 342	41.96 289	45.40 354	31.28 350	64.42 354	62.47 350

ADS-MVSNet2		48.76 324	47.25 332	53.29 319	55.90 366	40.54 300	47.34 353	54.99 329	31.41 362	50.48 356	72.06 322	31.23 343	54.26 339	25.93 365	55.93 368	65.07 339

test-mter		48.56 325	48.20 330	49.64 328	60.76 343	41.87 289	53.18 339	45.48 358	31.91 360	49.41 360	60.47 365	18.34 377	44.73 358	42.09 293	72.14 326	62.33 351

Patchmatch-test		47.93 326	49.96 327	41.84 351	57.42 360	24.26 374	48.75 347	41.49 369	39.30 323	56.79 335	73.48 309	30.48 351	33.87 373	29.29 359	72.61 324	67.39 325

test0.0.03 1		47.72 327	48.31 329	45.93 340	55.53 368	29.39 362	46.40 356	41.21 371	43.41 302	55.81 341	67.65 352	29.22 357	43.77 363	25.73 367	69.87 339	64.62 343

sss		47.59 328	48.32 328	45.40 343	56.73 363	33.96 344	45.17 358	48.51 352	32.11 359	52.37 352	65.79 358	40.39 300	41.91 368	31.85 347	61.97 357	60.35 354

pmmvs3		46.71 329	45.09 336	51.55 324	56.76 362	48.25 231	55.78 332	39.53 374	24.13 373	50.35 358	63.40 361	15.90 382	51.08 341	29.29 359	70.69 335	55.33 361

EPMVS		45.74 330	46.53 333	43.39 349	54.14 374	22.33 376	55.02 335	35.00 378	34.69 345	51.09 354	70.20 336	25.92 364	42.04 367	37.19 324	55.50 370	65.78 335

MVS-HIRNet		45.53 331	47.29 331	40.24 354	62.29 335	26.82 369	56.02 331	37.41 376	29.74 365	43.69 372	81.27 227	33.96 326	55.48 336	24.46 370	56.79 367	38.43 373

TESTMET0.1,1		45.17 332	44.93 337	45.89 341	56.02 365	38.31 312	53.18 339	41.94 368	27.85 367	44.86 368	56.47 367	17.93 378	41.50 369	38.08 318	68.06 346	57.85 357

E-PMN		45.17 332	45.36 335	44.60 346	50.07 377	42.75 283	38.66 366	42.29 366	46.39 280	39.55 373	51.15 371	26.00 363	45.37 355	37.68 320	76.41 300	45.69 369

PMMVS		44.69 334	43.95 341	46.92 336	50.05 378	53.47 199	48.08 351	42.40 364	22.36 374	44.01 371	53.05 369	42.60 287	45.49 353	31.69 348	61.36 358	41.79 370

ADS-MVSNet		44.62 335	45.58 334	41.73 352	55.90 366	20.83 377	47.34 353	39.94 373	31.41 362	50.48 356	72.06 322	31.23 343	39.31 370	25.93 365	55.93 368	65.07 339

EMVS		44.61 336	44.45 340	45.10 345	48.91 379	43.00 281	37.92 367	41.10 372	46.75 278	38.00 375	48.43 373	26.42 361	46.27 350	37.11 326	75.38 310	46.03 368

dp		44.09 337	44.88 338	41.72 353	58.53 356	23.18 375	54.70 336	42.38 365	34.80 343	44.25 370	65.61 359	24.48 370	44.80 357	29.77 356	49.42 373	57.18 359

DSMNet-mixed		43.18 338	44.66 339	38.75 356	54.75 371	28.88 365	57.06 327	27.42 381	13.47 375	47.27 364	77.67 275	38.83 308	39.29 371	25.32 369	60.12 361	48.08 365

CHOSEN 280x420		41.62 339	39.89 344	46.80 337	61.81 337	51.59 207	33.56 370	35.74 377	27.48 368	37.64 376	53.53 368	23.24 372	42.09 366	27.39 363	58.64 364	46.72 367

PVSNet_0	36.71 22	41.12 340	40.78 343	42.14 350	59.97 348	40.13 302	40.97 362	42.24 367	30.81 364	44.86 368	49.41 372	40.70 298	45.12 356	23.15 371	34.96 375	41.16 371

PMMVS2		37.74 341	40.87 342	28.36 358	42.41 381	5.35 383	24.61 371	27.75 380	32.15 357	47.85 362	70.27 335	35.85 322	29.51 375	19.08 375	67.85 347	50.22 364

new_pmnet		37.55 342	39.80 345	30.79 357	56.83 361	16.46 380	39.35 365	30.65 379	25.59 371	45.26 366	61.60 364	24.54 369	28.02 376	21.60 372	52.80 372	47.90 366

MVE	27.91 23	36.69 343	35.64 346	39.84 355	43.37 380	35.85 332	19.49 372	24.61 382	24.68 372	39.05 374	62.63 363	38.67 310	27.10 377	21.04 373	47.25 374	56.56 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.26 344	19.12 348	19.71 359	9.09 383	1.91 385	7.79 374	53.44 336	1.42 377	10.27 379	35.80 374	17.42 380	25.11 378	12.44 376	24.38 377	32.10 374

cdsmvs_eth3d_5k		17.71 345	23.62 347	0.00 364	0.00 387	0.00 388	0.00 375	70.17 256	0.00 382	0.00 383	74.25 303	68.16 102	0.00 383	0.00 381	0.00 381	0.00 379

tmp_tt		11.98 346	14.73 349	3.72 361	2.28 384	4.62 384	19.44 373	14.50 384	0.47 379	21.55 377	9.58 377	25.78 365	4.57 380	11.61 377	27.37 376	1.96 376

ab-mvs-re		5.62 347	7.50 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	67.46 353	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		5.20 348	6.93 351	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	62.39 152	0.00 383	0.00 381	0.00 381	0.00 379

test123		4.43 349	5.78 352	0.39 363	0.97 385	0.28 386	46.33 357	0.45 386	0.31 380	0.62 381	1.50 380	0.61 386	0.11 382	0.56 379	0.63 379	0.77 378

testmvs		4.06 350	5.28 353	0.41 362	0.64 386	0.16 387	42.54 361	0.31 387	0.26 381	0.50 382	1.40 381	0.77 385	0.17 381	0.56 379	0.55 380	0.90 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

MSC_two_6792asdad				79.02 60	83.14 104	67.03 92		80.75 126					86.24 22	77.27 35	94.85 26	83.78 136

PC_three_1452									46.98 277	81.83 97	86.28 157	66.55 120	84.47 72	63.31 141	90.78 122	83.49 143

No_MVS				79.02 60	83.14 104	67.03 92		80.75 126					86.24 22	77.27 35	94.85 26	83.78 136

test_one_0601					85.84 67	61.45 138		85.63 32	75.27 19	85.62 48	90.38 66	76.72 29

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					83.91 95	69.36 75		81.09 121	58.91 144	82.73 90	89.11 98	75.77 37	86.63 12	72.73 63	92.93 77

RE-MVS-def			85.50 4		86.19 55	79.18 9	87.23 9	86.27 22	77.51 11	87.65 19	90.73 48	81.38 7		78.11 25	94.46 39	84.89 96

IU-MVS					86.12 59	60.90 146		80.38 136	45.49 284	81.31 105				75.64 44	94.39 44	84.65 105

OPU-MVS				78.65 67	83.44 102	66.85 94	83.62 45				86.12 166	66.82 114	86.01 30	61.72 150	89.79 144	83.08 158

test_241102_TWO								84.80 50	72.61 32	84.93 57	89.70 84	77.73 23	85.89 40	75.29 45	94.22 56	83.25 153

test_241102_ONE					86.12 59	61.06 142		84.72 54	72.64 31	87.38 25	89.47 87	77.48 24	85.74 44

9.14			80.22 59		80.68 140		80.35 76	87.69 11	59.90 132	83.00 83	88.20 117	74.57 51	81.75 122	73.75 56	93.78 62

save fliter					87.00 43	67.23 90	79.24 89	77.94 177	56.65 169

test_0728_THIRD									74.03 23	85.83 43	90.41 61	75.58 39	85.69 45	77.43 33	94.74 31	84.31 124

test_0728_SECOND				76.57 94	86.20 54	60.57 151	83.77 43	85.49 34					85.90 38	75.86 42	94.39 44	83.25 153

test0726					86.16 57	60.78 148	83.81 42	85.10 45	72.48 34	85.27 54	89.96 80	78.57 18

GSMVS																70.05 309

test_part2					85.90 63	66.44 97				84.61 64

sam_mvs1												31.41 341				70.05 309

sam_mvs												31.21 345

ambc				70.10 192	77.74 183	50.21 217	74.28 157	77.93 178		79.26 129	88.29 116	54.11 223	79.77 161	64.43 126	91.10 111	80.30 218

MTGPA								80.63 129

test_post1							66.63 255				2.08 378	30.66 350	59.33 330	40.34 304

test_post											1.99 379	30.91 348	54.76 338

patchmatchnet-post											68.99 344	31.32 342	69.38 284

GG-mvs-BLEND				52.24 322	60.64 345	29.21 364	69.73 208	42.41 363		45.47 365	52.33 370	20.43 375	68.16 291	25.52 368	65.42 352	59.36 356

MTMP							84.83 33	19.26 383

gm-plane-assit					62.51 334	33.91 345			37.25 333		62.71 362		72.74 251	38.70 311

test9_res														72.12 71	91.37 103	77.40 251

TEST9					85.47 69	69.32 76	76.42 125	78.69 163	53.73 210	76.97 157	86.74 141	66.84 113	81.10 132

test_8					85.09 75	67.89 85	76.26 130	78.66 165	54.00 205	76.89 162	86.72 143	66.60 118	80.89 143

agg_prior2														70.70 79	90.93 116	78.55 238

agg_prior					84.44 88	66.02 101		78.62 166		76.95 159			80.34 150

TestCases				78.35 72	79.19 160	70.81 61		88.64 3	65.37 83	80.09 121	88.17 118	70.33 81	78.43 184	55.60 200	90.90 118	85.81 78

test_prior4						70.14 68	77.57 110

test_prior2							75.57 137		58.92 142	76.53 173	86.78 138	67.83 106		69.81 84	92.76 82

test_prior				75.27 112	82.15 124	59.85 157		84.33 66					83.39 92			82.58 173

旧先验2							71.17 192		45.11 289	78.54 137			61.28 326	59.19 174

新几何2							71.33 188

新几何1				69.99 194	88.37 36	71.34 57		62.08 299	43.85 295	74.99 190	86.11 167	52.85 228	70.57 275	50.99 236	83.23 239	68.05 323

旧先验1					84.55 85	60.36 153		63.69 290			87.05 131	54.65 220			83.34 237	69.66 313

无先验							74.82 145	70.94 250	47.75 272				76.85 212	54.47 211		72.09 293

原ACMM2							74.78 149

原ACMM1				73.90 128	85.90 63	65.15 109		81.67 107	50.97 240	74.25 203	86.16 164	61.60 160	83.54 88	56.75 187	91.08 112	73.00 282

test222					87.30 41	69.15 79	67.85 235	59.59 309	41.06 315	73.05 220	85.72 175	48.03 260			80.65 269	66.92 328

testdata2													67.30 297	48.34 258

segment_acmp												68.30 101

testdata				64.13 255	85.87 65	63.34 124		61.80 303	47.83 270	76.42 177	86.60 150	48.83 254	62.31 323	54.46 213	81.26 261	66.74 332

testdata1							68.34 231		57.24 162

test12				76.51 95	82.28 122	60.94 145		81.64 108		73.60 211		64.88 135	85.19 60		90.42 129	83.38 149

plane_prior7					85.18 72	66.21 99

plane_prior6					84.18 92	65.31 106						60.83 170

plane_prior5								85.49 34					86.15 27	71.09 75	90.94 114	84.82 100

plane_prior4											89.11 98

plane_prior3						65.67 103			63.82 101	78.23 140

plane_prior2							82.74 55		65.45 80

plane_prior1					84.46 87

plane_prior						65.18 107	80.06 83		61.88 119						89.91 141

n2								0.00 388

nn								0.00 388

door-mid								55.02 328

lessismore_v0				72.75 155	79.60 152	56.83 178		57.37 316		83.80 76	89.01 101	47.45 262	78.74 176	64.39 127	86.49 196	82.69 171

LGP-MVS_train				80.90 37	87.00 43	70.41 66		86.35 19	69.77 53	87.75 16	91.13 36	81.83 3	86.20 24	77.13 37	95.96 5	86.08 72

test11								82.71 93

door								52.91 340

HQP5-MVS						58.80 167

HQP-NCC					82.37 119		77.32 115		59.08 137	71.58 237

ACMP_Plane					82.37 119		77.32 115		59.08 137	71.58 237

BP-MVS														67.38 107

HQP4-MVS										71.59 236			85.31 53			83.74 138

HQP3-MVS								84.12 74							89.16 155

HQP2-MVS												58.09 195

NP-MVS					83.34 103	63.07 127					85.97 170

MDTV_nov1_ep13_2view						18.41 378	53.74 338		31.57 361	44.89 367		29.90 356		32.93 345		71.48 297

MDTV_nov1_ep13			54.05 310		65.54 317	29.30 363	59.00 319	55.22 326	35.96 339	52.44 351	75.98 285	30.77 349	59.62 329	38.21 316	73.33 322

ACMMP++_ref															89.47 150

ACMMP++															91.96 92

Test By Simon												62.56 148

ITE_SJBPF				80.35 43	76.94 194	73.60 43		80.48 133	66.87 67	83.64 78	86.18 161	70.25 83	79.90 160	61.12 157	88.95 162	87.56 54

DeepMVS_CX				11.83 360	15.51 382	13.86 381		11.25 385	5.76 376	20.85 378	26.46 375	17.06 381	9.22 379	9.69 378	13.82 378	12.42 375

High-resolution multi-view benchmark