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	63.30 127	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 74	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 50	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 39	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 40	90.24 76	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 44	90.58 54	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 57	90.42 62	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 65	90.56 55	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 58	90.85 46	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 42	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 67	90.67 53	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 37	90.52 58	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 77	90.72 52	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 87	90.39 66	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 68	90.43 60	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	80.63 131	72.08 39	84.93 59	90.79 47	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	81.26 108	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 74	90.39 66	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 63	90.88 44	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 47	90.41 63	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	80.63 131	72.08 39	84.93 59	90.79 47	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 97	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 73	90.21 78	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 81	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	63.17 112	85.38 54	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 96	84.47 68	90.43 60	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 93	83.97 75	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	63.53 106	84.23 72	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 99	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 91	82.91 54	84.98 47	73.52 26	85.43 53	90.03 80	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	61.06 144	83.62 45	84.72 54	72.61 32	87.38 25	89.70 86	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	79.22 156	74.08 22	87.16 30	91.97 19	84.80 2	76.97 208	64.98 123	93.61 66	72.28 294
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	80.01 146	75.34 17	79.80 125	94.91 2	69.79 89	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	80.92 113	88.52 111	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	60.90 148	91.64 1	85.49 34	74.03 23	84.93 59	90.38 68	66.82 116	85.90 38	77.43 33	90.78 124	83.49 143

ACMH+	66.64 10	81.20 40	82.48 43	77.35 86	81.16 138	62.39 132	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 89	88.36 116	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	60.78 150	83.77 43	80.58 134	72.48 34	85.83 45	90.41 63	78.57 18	85.69 45	75.86 42	94.39 44	79.24 232
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 79	88.96 105	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	80.65 116	90.18 79	59.80 182	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	59.89 135	80.91 114	90.53 56	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 51	89.28 92	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 97	89.95 83	69.14 93	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	64.40 119	83.41 50	88.46 6	65.28 85	84.29 70	89.18 96	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 47	89.57 88	69.12 94	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 92	81.05 110	88.38 115	57.10 209	87.10 7	79.75 8	83.87 232	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	39.02 313	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	39.21 310	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	72.74 158	89.84 8	37.34 328	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 92	79.24 89	88.61 5	56.65 171	84.29 70	89.18 96	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 97	86.14 41	87.07 132	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	42.69 288	78.24 105	82.24 98	78.21 10	89.57 9	92.10 18	68.05 105	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	80.18 143	64.71 95	78.11 145	88.39 114	65.46 132	83.14 97	77.64 32	91.20 107	78.94 235

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	80.39 120	88.85 107	74.43 54	78.33 189	74.73 49	85.79 204	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 91	88.99 104	66.82 116	78.37 187	74.80 47	90.76 127	82.40 178

CP-MVSNet		79.48 60	81.65 50	72.98 148	89.66 13	39.06 312	76.76 122	80.46 136	78.91 8	90.32 7	91.70 25	68.49 100	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	62.54 116	81.77 100	86.65 149	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	55.81 186	79.22 91	82.51 97	70.72 47	87.54 22	92.44 14	68.00 107	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	64.57 116	80.26 78	87.60 12	60.74 128	82.47 94	88.03 124	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 89	86.61 20	81.38 113	51.71 232	77.15 156	91.42 34	65.49 131	87.20 5	79.44 14	87.17 190	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	76.50 195	51.98 229	87.40 24	91.86 21	76.09 35	78.53 179	68.58 91	90.20 133	86.69 67

test_djsdf		78.88 66	78.27 76	80.70 40	81.42 134	71.24 58	83.98 39	75.72 202	52.27 224	87.37 27	92.25 16	68.04 106	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	65.18 109	82.74 55	85.49 34	65.45 80	78.23 142	89.11 100	60.83 172	86.15 27	71.09 75	90.94 116	84.82 100

anonymousdsp		78.60 68	77.80 80	81.00 36	78.01 178	74.34 38	80.09 81	76.12 197	50.51 248	89.19 10	90.88 44	71.45 74	77.78 201	73.38 58	90.60 129	90.90 18

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 66	80.48 143	64.16 120	80.24 79	82.06 101	61.89 120	88.77 12	93.32 4	57.15 207	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	76.12 197	51.33 238	87.19 29	91.51 30	73.79 58	78.44 183	68.27 94	90.13 138	86.49 69

CNVR-MVS		78.49 71	78.59 74	78.16 74	85.86 66	67.40 90	78.12 108	81.50 109	63.92 101	77.51 153	86.56 153	68.43 102	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	56.66 170	80.27 121	85.31 180	68.56 99	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	65.33 107	79.05 93	84.63 59	74.83 20	80.41 119	86.27 160	71.68 71	83.45 91	62.45 146	92.40 87	78.92 236

ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 84	80.09 147	66.16 102	79.08 92	86.13 26	57.55 161	80.93 112	87.76 127	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	75.55 187	86.25 162	67.42 110	85.42 51	70.10 81	90.88 122	81.81 188

RRT_MVS		78.18 76	77.69 81	79.66 52	83.14 104	61.34 141	83.29 52	80.34 141	57.43 162	86.65 33	91.79 23	50.52 243	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	59.15 166	72.52 169	74.60 213	75.34 17	88.69 13	91.81 22	75.06 44	82.37 110	65.10 121	88.68 166	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	80.09 123	88.17 120	70.33 83	78.43 184	55.60 202	90.90 120	85.81 78

PS-MVSNAJss		77.54 79	77.35 85	78.13 76	84.88 78	66.37 100	78.55 99	79.59 151	53.48 215	86.29 38	92.43 15	62.39 154	80.25 153	67.90 102	90.61 128	87.77 50

ACMH	63.62 14	77.50 80	80.11 60	69.68 199	79.61 151	56.28 182	78.81 95	83.62 81	63.41 110	87.14 31	90.23 77	76.11 34	73.32 247	67.58 103	94.44 42	79.44 230
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	63.98 122	76.07 134	83.45 83	54.20 202	77.68 152	87.18 128	69.98 86	85.37 52	68.01 98	92.72 84	85.08 93

mvsmamba		77.20 82	76.37 94	79.69 50	80.34 145	61.52 139	80.58 70	82.12 100	53.54 214	83.93 76	91.03 39	49.49 249	85.97 32	73.26 59	93.08 74	91.59 12

DeepC-MVS_fast	69.89 7	77.17 83	76.33 96	79.70 49	83.90 96	67.94 84	80.06 83	83.75 79	56.73 169	74.88 195	85.32 179	65.54 130	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	56.87 180	80.32 77	87.52 13	63.45 108	74.66 201	84.52 188	69.87 88	84.94 62	69.76 86	89.59 149	86.60 68

test_part1		76.97 85	78.21 77	73.25 140	77.87 180	45.76 264	78.27 104	87.26 15	66.69 71	85.31 55	91.43 33	55.95 218	84.24 78	65.71 116	95.43 12	89.75 24

X-MVStestdata		76.81 86	74.79 108	82.85 10	89.43 16	77.61 18	86.80 18	84.66 57	72.71 29	82.87 87	9.95 381	73.86 56	86.31 19	78.84 20	94.03 58	84.64 106

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 198	89.27 20	43.81 276	74.47 156	71.70 235	72.33 37	85.50 52	93.65 3	77.98 22	76.88 211	54.60 212	91.64 95	89.08 33

test_prior3		76.71 88	77.19 86	75.27 112	82.15 124	59.85 159	75.57 139	84.33 66	58.92 144	76.53 176	86.78 140	67.83 108	83.39 92	69.81 84	92.76 82	82.58 173

CS-MVS		76.51 89	76.00 98	78.06 77	77.02 191	64.77 114	80.78 68	82.66 94	60.39 131	74.15 207	83.30 208	69.65 90	82.07 116	69.27 88	86.75 195	87.36 57

train_agg		76.38 90	76.55 92	75.86 105	85.47 69	69.32 76	76.42 125	78.69 165	54.00 207	76.97 159	86.74 143	66.60 120	81.10 132	72.50 67	91.56 99	77.15 255

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 175	84.61 84	42.57 290	70.98 196	78.29 174	68.67 59	83.04 84	89.26 93	72.99 63	80.75 144	55.58 205	95.47 10	91.35 13

agg_prior1		75.89 92	76.41 93	74.31 123	84.44 88	66.02 103	76.12 131	78.62 168	54.40 196	76.95 161	86.85 137	66.44 123	80.34 150	72.45 68	91.42 102	76.57 259

SixPastTwentyTwo		75.77 93	76.34 95	74.06 127	81.69 132	54.84 191	76.47 123	75.49 204	64.10 100	87.73 18	92.24 17	50.45 245	81.30 128	67.41 105	91.46 101	86.04 74

RPSCF		75.76 94	74.37 114	79.93 45	74.81 222	77.53 20	77.53 113	79.30 155	59.44 138	78.88 134	89.80 85	71.26 76	73.09 249	57.45 185	80.89 268	89.17 32

ETH3 D test6400		75.73 95	76.00 98	74.92 115	81.75 130	56.93 178	78.31 102	84.60 62	52.83 220	77.15 156	85.14 182	68.59 98	84.03 79	65.44 119	90.20 133	83.82 133

v10		75.69 96	76.20 97	74.16 125	74.44 233	48.69 229	75.84 138	82.93 90	59.02 143	85.92 43	89.17 98	58.56 192	82.74 104	70.73 77	89.14 160	91.05 15

FMVS1		75.66 97	76.57 90	72.95 149	67.07 307	67.62 87	76.10 132	80.68 129	64.95 90	86.58 35	90.94 42	71.20 77	71.68 269	60.46 162	91.13 111	79.56 227

APD_test		75.66 97	76.57 90	72.95 149	67.07 307	67.62 87	76.10 132	80.68 129	64.95 90	86.58 35	90.94 42	71.20 77	71.68 269	60.46 162	91.13 111	79.56 227

Anonymous20231211		75.54 99	77.19 86	70.59 184	77.67 185	45.70 266	74.73 152	80.19 142	68.80 56	82.95 86	92.91 8	66.26 124	76.76 214	58.41 181	92.77 81	89.30 28

Effi-MVS+-dtu		75.43 100	72.28 152	84.91 2	77.05 188	83.58 1	78.47 100	77.70 181	57.68 155	74.89 194	78.13 273	64.80 138	84.26 77	56.46 194	85.32 211	86.88 63

Regformer-2		75.32 101	74.47 112	77.88 78	74.22 235	66.65 98	72.77 167	77.54 183	68.47 61	80.44 118	72.08 320	70.60 82	80.97 138	70.08 82	84.02 230	86.01 75

F-COLMAP		75.29 102	73.99 120	79.18 57	81.73 131	71.90 51	81.86 63	82.98 88	59.86 136	72.27 232	84.00 195	64.56 141	83.07 100	51.48 234	87.19 189	82.56 175

HQP-MVS		75.24 103	75.01 107	75.94 103	82.37 119	58.80 169	77.32 115	84.12 74	59.08 139	71.58 240	85.96 173	58.09 197	85.30 54	67.38 107	89.16 157	83.73 139

TAPA-MVS	65.27 12	75.16 104	74.29 116	77.77 79	74.86 221	68.08 83	77.89 109	84.04 77	55.15 184	76.19 183	83.39 202	66.91 114	80.11 158	60.04 169	90.14 137	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS-MVSNet		75.10 105	75.42 106	74.15 126	79.23 158	48.05 239	79.43 86	78.04 177	70.09 52	79.17 132	88.02 125	53.04 228	83.60 87	58.05 183	93.76 65	90.79 19

v8		75.07 106	75.64 103	73.35 137	73.42 248	47.46 248	75.20 143	81.45 111	60.05 133	85.64 47	89.26 93	58.08 199	81.80 121	69.71 87	87.97 174	90.79 19

UniMVSNet (Re)		75.00 107	75.48 105	73.56 135	83.14 104	47.92 241	70.41 204	81.04 123	63.67 104	79.54 127	86.37 158	62.83 148	81.82 119	57.10 188	95.25 15	90.94 17

PHI-MVS		74.92 108	74.36 115	76.61 93	76.40 201	62.32 133	80.38 74	83.15 86	54.16 204	73.23 221	80.75 235	62.19 157	83.86 81	68.02 97	90.92 119	83.65 140

DU-MVS		74.91 109	75.57 104	72.93 152	83.50 99	45.79 262	69.47 212	80.14 144	65.22 86	81.74 102	87.08 130	61.82 160	81.07 134	56.21 197	94.98 21	91.93 8

UniMVSNet_NR-MVSNet		74.90 110	75.65 102	72.64 161	83.04 110	45.79 262	69.26 215	78.81 162	66.66 72	81.74 102	86.88 136	63.26 146	81.07 134	56.21 197	94.98 21	91.05 15

CS-MVS-test		74.89 111	74.23 117	76.86 90	77.01 192	62.94 130	78.98 94	84.61 61	58.62 147	70.17 260	80.80 234	66.74 119	81.96 117	61.74 149	89.40 155	85.69 84

nrg030		74.87 112	75.99 100	71.52 176	74.90 220	49.88 225	74.10 160	82.58 96	54.55 195	83.50 81	89.21 95	71.51 72	75.74 223	61.24 153	92.34 88	88.94 38

Vis-MVSNet		74.85 113	74.56 110	75.72 106	81.63 133	64.64 115	76.35 127	79.06 158	62.85 114	73.33 219	88.41 113	62.54 152	79.59 164	63.94 133	82.92 242	82.94 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-4		74.64 114	73.67 126	77.55 80	74.74 224	64.49 118	72.91 165	75.42 206	67.45 63	80.24 122	72.07 322	68.98 95	80.19 157	70.29 80	80.91 266	87.98 48

MSLP-MVS++		74.48 115	75.78 101	70.59 184	84.66 82	62.40 131	78.65 97	84.24 71	60.55 130	77.71 151	81.98 222	63.12 147	77.64 203	62.95 143	88.14 169	71.73 299

Regformer-1		74.28 116	73.63 128	76.21 101	74.22 235	64.12 121	72.77 167	75.46 205	66.86 68	79.27 130	72.08 320	69.29 92	78.74 176	68.73 90	84.02 230	85.77 83

AdaColmap		74.22 117	74.56 110	73.20 141	81.95 127	60.97 146	79.43 86	80.90 125	65.57 79	72.54 230	81.76 226	70.98 80	85.26 55	47.88 265	90.00 139	73.37 282

CSCG		74.12 118	74.39 113	73.33 138	79.35 155	61.66 138	77.45 114	81.98 103	62.47 118	79.06 133	80.19 244	61.83 159	78.79 175	59.83 171	87.35 183	79.54 229

PAPM_NR		73.91 119	74.16 118	73.16 142	81.90 128	53.50 201	81.28 65	81.40 112	66.17 75	73.30 220	83.31 207	59.96 178	83.10 99	58.45 180	81.66 260	82.87 164

EPP-MVSNet		73.86 120	73.38 132	75.31 111	78.19 174	53.35 203	80.45 72	77.32 187	65.11 88	76.47 178	86.80 138	49.47 250	83.77 82	53.89 221	92.72 84	88.81 42

mvs-test1		73.81 121	70.69 174	83.18 5	77.05 188	81.39 3	75.39 141	77.70 181	57.68 155	71.19 250	74.72 298	64.80 138	83.66 86	56.46 194	81.19 264	84.50 119

K. test v3		73.67 122	73.61 129	73.87 129	79.78 149	55.62 189	74.69 154	62.04 305	66.16 76	84.76 64	93.23 5	49.47 250	80.97 138	65.66 117	86.67 196	85.02 95

NR-MVSNet		73.62 123	74.05 119	72.33 168	83.50 99	43.71 277	65.65 269	77.32 187	64.32 98	75.59 186	87.08 130	62.45 153	81.34 126	54.90 208	95.63 8	91.93 8

DP-MVS Recon		73.57 124	72.69 146	76.23 100	82.85 114	63.39 125	74.32 157	82.96 89	57.75 154	70.35 257	81.98 222	64.34 143	84.41 75	49.69 248	89.95 141	80.89 203

CNLPA		73.44 125	73.03 141	74.66 116	78.27 173	75.29 31	75.99 135	78.49 170	65.39 82	75.67 185	83.22 212	61.23 168	66.77 306	53.70 223	85.33 210	81.92 187

MCST-MVS		73.42 126	73.34 134	73.63 134	81.28 136	59.17 165	74.80 150	83.13 87	45.50 284	72.84 225	83.78 198	65.15 135	80.99 136	64.54 125	89.09 162	80.73 210

v1192		73.40 127	73.42 130	73.32 139	74.65 230	48.67 230	72.21 172	81.73 106	52.76 221	81.85 98	84.56 187	57.12 208	82.24 114	68.58 91	87.33 184	89.06 34

114514_t		73.40 127	73.33 135	73.64 133	84.15 93	57.11 177	78.20 106	80.02 145	43.76 299	72.55 229	86.07 171	64.00 144	83.35 94	60.14 167	91.03 115	80.45 216

FC-MVSNet-test		73.32 129	74.78 109	68.93 212	79.21 159	36.57 330	71.82 184	79.54 153	57.63 160	82.57 93	90.38 68	59.38 185	78.99 171	57.91 184	94.56 36	91.23 14

v1144		73.29 130	73.39 131	73.01 146	74.12 240	48.11 237	72.01 176	81.08 122	53.83 211	81.77 100	84.68 185	58.07 200	81.91 118	68.10 95	86.86 192	88.99 37

GeoE		73.14 131	73.77 125	71.26 179	78.09 176	52.64 206	74.32 157	79.56 152	56.32 174	76.35 181	83.36 206	70.76 81	77.96 197	63.32 140	81.84 254	83.18 156

baseline		73.10 132	73.96 121	70.51 186	71.46 268	46.39 260	72.08 174	84.40 65	55.95 177	76.62 172	86.46 156	67.20 111	78.03 196	64.22 128	87.27 187	87.11 62

h-mvs33		73.08 133	71.61 162	77.48 82	83.89 97	72.89 49	70.47 202	71.12 250	54.28 198	77.89 146	83.41 201	49.04 253	80.98 137	63.62 136	90.77 126	78.58 239

TSAR-MVS + GP.		73.08 133	71.60 163	77.54 81	78.99 167	70.73 63	74.96 145	69.38 261	60.73 129	74.39 205	78.44 267	57.72 204	82.78 103	60.16 166	89.60 148	79.11 234

v1240		73.06 135	73.14 137	72.84 155	74.74 224	47.27 251	71.88 183	81.11 119	51.80 231	82.28 96	84.21 192	56.22 216	82.34 111	68.82 89	87.17 190	88.91 39

casdiffmvs		73.06 135	73.84 122	70.72 182	71.32 269	46.71 256	70.93 197	84.26 70	55.62 180	77.46 154	87.10 129	67.09 112	77.81 199	63.95 131	86.83 193	87.64 52

IterMVS-LS		73.01 137	73.12 139	72.66 160	73.79 244	49.90 222	71.63 185	78.44 171	58.22 149	80.51 117	86.63 150	58.15 196	79.62 162	62.51 144	88.20 168	88.48 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet		73.00 138	71.84 157	76.48 96	75.82 210	61.28 142	74.81 148	80.37 139	63.17 112	62.43 315	80.50 239	61.10 170	85.16 61	64.00 130	84.34 226	83.01 161

v144192		72.99 139	73.06 140	72.77 156	74.58 231	47.48 247	71.90 182	80.44 137	51.57 234	81.46 106	84.11 194	58.04 201	82.12 115	67.98 99	87.47 180	88.70 44

MVS_111021_HR		72.98 140	72.97 143	72.99 147	80.82 139	65.47 106	68.81 222	72.77 226	57.67 157	75.76 184	82.38 219	71.01 79	77.17 206	61.38 152	86.15 200	76.32 260

v1921920		72.96 141	72.98 142	72.89 154	74.67 227	47.58 246	71.92 181	80.69 128	51.70 233	81.69 104	83.89 196	56.58 214	82.25 113	68.34 93	87.36 182	88.82 41

CLD-MVS		72.88 142	72.36 151	74.43 120	77.03 190	54.30 195	68.77 225	83.43 84	52.12 226	76.79 169	74.44 302	69.54 91	83.91 80	55.88 200	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		72.86 143	72.28 152	74.62 117	74.74 224	60.18 156	72.91 165	71.76 234	64.74 94	78.42 140	72.07 322	67.00 113	76.28 218	67.97 100	80.91 266	87.39 55

bld_raw_dy_0_64		72.85 144	72.76 145	73.09 144	85.08 76	64.80 113	78.72 96	64.22 290	51.92 230	83.13 83	90.26 75	39.21 308	69.91 282	70.73 77	91.60 98	84.56 113

EI-MVSNet-Vis-set		72.78 145	71.87 156	75.54 109	74.77 223	59.02 167	72.24 171	71.56 238	63.92 101	78.59 136	71.59 328	66.22 125	78.60 178	67.58 103	80.32 274	89.00 36

ETV-MVS		72.72 146	72.16 155	74.38 122	76.90 196	55.95 183	73.34 163	84.67 56	62.04 119	72.19 235	70.81 332	65.90 128	85.24 57	58.64 178	84.96 218	81.95 186

PCF-MVS	63.80 13	72.70 147	71.69 159	75.72 106	78.10 175	60.01 158	73.04 164	81.50 109	45.34 288	79.66 126	84.35 191	65.15 135	82.65 106	48.70 256	89.38 156	84.50 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 148	71.68 160	75.47 110	74.67 227	58.64 172	72.02 175	71.50 239	63.53 106	78.58 138	71.39 331	65.98 126	78.53 179	67.30 109	80.18 276	89.23 30

Anonymous20240529		72.56 149	73.79 124	68.86 214	76.89 197	45.21 268	68.80 224	77.25 189	67.16 65	76.89 164	90.44 59	65.95 127	74.19 243	50.75 240	90.00 139	87.18 61

FIs		72.56 149	73.80 123	68.84 215	78.74 170	37.74 324	71.02 195	79.83 147	56.12 175	80.88 115	89.45 90	58.18 194	78.28 190	56.63 190	93.36 71	90.51 21

v2v482		72.55 151	72.58 148	72.43 165	72.92 259	46.72 255	71.41 188	79.13 157	55.27 182	81.17 109	85.25 181	55.41 219	81.13 131	67.25 110	85.46 206	89.43 27

hse-mvs2		72.32 152	70.66 175	77.31 87	83.10 109	71.77 52	69.19 217	71.45 241	54.28 198	77.89 146	78.26 269	49.04 253	79.23 167	63.62 136	89.13 161	80.92 202

canonicalmvs		72.29 153	73.38 132	69.04 207	74.23 234	47.37 249	73.93 161	83.18 85	54.36 197	76.61 173	81.64 228	72.03 66	75.34 227	57.12 187	87.28 186	84.40 121

Effi-MVS+		72.10 154	72.28 152	71.58 174	74.21 238	50.33 218	74.72 153	82.73 92	62.62 115	70.77 253	76.83 283	69.96 87	80.97 138	60.20 164	78.43 293	83.45 148

MVS_111021_LR		72.10 154	71.82 158	72.95 149	79.53 153	73.90 41	70.45 203	66.64 273	56.87 166	76.81 168	81.76 226	68.78 96	71.76 267	61.81 147	83.74 234	73.18 284

pmmvs6		71.82 156	73.66 127	66.31 243	75.94 209	42.01 292	66.99 251	72.53 229	63.45 108	76.43 179	92.78 10	72.95 64	69.69 284	51.41 235	90.46 130	87.22 58

PLC	62.01 16	71.79 157	70.28 177	76.33 98	80.31 146	68.63 81	78.18 107	81.24 116	54.57 194	67.09 288	80.63 237	59.44 183	81.74 123	46.91 272	84.17 227	78.63 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VDDNet		71.60 158	73.13 138	67.02 236	86.29 53	41.11 297	69.97 206	66.50 274	68.72 58	74.74 197	91.70 25	59.90 179	75.81 221	48.58 258	91.72 93	84.15 128

3Dnovator	65.95 11	71.50 159	71.22 168	72.34 167	73.16 252	63.09 128	78.37 101	78.32 172	57.67 157	72.22 234	84.61 186	54.77 220	78.47 181	60.82 160	81.07 265	75.45 266

FA-MVS(test-final)		71.27 160	71.06 169	71.92 172	73.96 241	52.32 209	76.45 124	76.12 197	59.07 142	74.04 211	86.18 163	52.18 233	79.43 166	59.75 172	81.76 255	84.03 130

WR-MVS		71.20 161	72.48 149	67.36 231	84.98 77	35.70 338	64.43 284	68.66 265	65.05 89	81.49 105	86.43 157	57.57 205	76.48 216	50.36 244	93.32 72	89.90 23

V42		71.06 162	70.83 172	71.72 173	67.25 304	47.14 252	65.94 263	80.35 140	51.35 237	83.40 82	83.23 210	59.25 186	78.80 174	65.91 115	80.81 270	89.23 30

FMVSNet1		71.06 162	72.48 149	66.81 237	77.65 186	40.68 301	71.96 178	73.03 221	61.14 124	79.45 129	90.36 71	60.44 174	75.20 229	50.20 245	88.05 171	84.54 114

dcpmvs_2		71.02 164	72.65 147	66.16 244	76.06 208	50.49 217	71.97 177	79.36 154	50.34 249	82.81 90	83.63 199	64.38 142	67.27 300	61.54 151	83.71 236	80.71 212

API-MVS		70.97 165	71.51 165	69.37 201	75.20 215	55.94 184	80.99 66	76.84 192	62.48 117	71.24 248	77.51 279	61.51 164	80.96 142	52.04 230	85.76 205	71.22 304

VDD-MVS		70.81 166	71.44 166	68.91 213	79.07 165	46.51 257	67.82 238	70.83 254	61.23 123	74.07 210	88.69 108	59.86 180	75.62 224	51.11 237	90.28 132	84.61 109

EG-PatchMatch MVS		70.70 167	70.88 171	70.16 192	82.64 118	58.80 169	71.48 186	73.64 217	54.98 185	76.55 174	81.77 225	61.10 170	78.94 172	54.87 209	80.84 269	72.74 289

Baseline_NR-MVSNet		70.62 168	73.19 136	62.92 275	76.97 193	34.44 346	68.84 220	70.88 253	60.25 132	79.50 128	90.53 56	61.82 160	69.11 288	54.67 211	95.27 14	85.22 87

alignmvs		70.54 169	71.00 170	69.15 206	73.50 246	48.04 240	69.85 209	79.62 148	53.94 210	76.54 175	82.00 221	59.00 188	74.68 236	57.32 186	87.21 188	84.72 104

MG-MVS		70.47 170	71.34 167	67.85 226	79.26 157	40.42 305	74.67 155	75.15 210	58.41 148	68.74 276	88.14 123	56.08 217	83.69 85	59.90 170	81.71 259	79.43 231

AUN-MVS		70.22 171	67.88 207	77.22 88	82.96 113	71.61 53	69.08 218	71.39 242	49.17 261	71.70 238	78.07 274	37.62 318	79.21 168	61.81 147	89.15 159	80.82 205

UGNet		70.20 172	69.05 187	73.65 132	76.24 203	63.64 123	75.87 137	72.53 229	61.48 122	60.93 325	86.14 167	52.37 232	77.12 207	50.67 241	85.21 212	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		70.04 173	68.88 190	73.53 136	82.71 116	63.62 124	74.81 148	81.95 104	48.53 266	67.16 287	79.18 259	51.42 239	78.38 186	54.39 216	79.72 283	78.60 238

Fast-Effi-MVS+-dtu		70.00 174	68.74 194	73.77 130	73.47 247	64.53 117	71.36 189	78.14 176	55.81 179	68.84 275	74.71 299	65.36 133	75.75 222	52.00 231	79.00 287	81.03 198

DPM-MVS		69.98 175	69.22 186	72.26 169	82.69 117	58.82 168	70.53 201	81.23 117	47.79 273	64.16 303	80.21 242	51.32 240	83.12 98	60.14 167	84.95 219	74.83 272

MVSFormer		69.93 176	69.03 188	72.63 162	74.93 218	59.19 163	83.98 39	75.72 202	52.27 224	63.53 311	76.74 284	43.19 284	80.56 145	72.28 69	78.67 291	78.14 246

MVS_Test		69.84 177	70.71 173	67.24 232	67.49 303	43.25 284	69.87 208	81.22 118	52.69 222	71.57 243	86.68 146	62.09 158	74.51 238	66.05 113	78.74 289	83.96 131

c3_l		69.82 178	69.89 179	69.61 200	66.24 313	43.48 280	68.12 235	79.61 150	51.43 236	77.72 150	80.18 245	54.61 223	78.15 195	63.62 136	87.50 179	87.20 60

TransMVSNet (Re)		69.62 179	71.63 161	63.57 264	76.51 200	35.93 336	65.75 268	71.29 245	61.05 125	75.02 192	89.90 84	65.88 129	70.41 281	49.79 247	89.48 151	84.38 122

EI-MVSNet		69.61 180	69.01 189	71.41 178	73.94 242	49.90 222	71.31 191	71.32 243	58.22 149	75.40 190	70.44 334	58.16 195	75.85 219	62.51 144	79.81 280	88.48 45

Gipuma		69.55 181	72.83 144	59.70 299	63.63 333	53.97 198	80.08 82	75.93 200	64.24 99	73.49 216	88.93 106	57.89 203	62.46 323	59.75 172	91.55 100	62.67 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tttt0517		69.46 182	67.79 208	74.46 118	75.34 213	52.72 205	75.05 144	63.27 296	54.69 191	78.87 135	84.37 190	26.63 364	81.15 130	63.95 131	87.93 175	89.51 26

eth_miper_zixun_eth		69.42 183	68.73 195	71.50 177	67.99 297	46.42 258	67.58 240	78.81 162	50.72 245	78.13 144	80.34 241	50.15 247	80.34 150	60.18 165	84.65 220	87.74 51

BH-untuned		69.39 184	69.46 181	69.18 205	77.96 179	56.88 179	68.47 232	77.53 184	56.77 168	77.79 149	79.63 251	60.30 176	80.20 156	46.04 278	80.65 271	70.47 309

v148		69.38 185	69.39 182	69.36 202	69.14 290	44.56 271	68.83 221	72.70 227	54.79 189	78.59 136	84.12 193	54.69 221	76.74 215	59.40 175	82.20 248	86.79 64

1121		69.23 186	68.26 199	72.12 171	88.36 37	71.40 55	68.59 227	62.06 303	43.80 298	74.75 196	86.18 163	52.92 229	76.85 212	54.47 213	83.27 240	68.12 325

PAPR		69.20 187	68.66 196	70.82 181	75.15 217	47.77 243	75.31 142	81.11 119	49.62 258	66.33 290	79.27 256	61.53 163	82.96 101	48.12 263	81.50 262	81.74 189

QAPM		69.18 188	69.26 184	68.94 211	71.61 267	52.58 207	80.37 75	78.79 164	49.63 257	73.51 215	85.14 182	53.66 226	79.12 169	55.11 207	75.54 309	75.11 270

LCM-MVSNet-Re		69.10 189	71.57 164	61.70 283	70.37 278	34.30 348	61.45 305	79.62 148	56.81 167	89.59 8	88.16 122	68.44 101	72.94 250	42.30 294	87.33 184	77.85 252

EPNet		69.10 189	67.32 213	74.46 118	68.33 294	61.27 143	77.56 111	63.57 294	60.95 126	56.62 341	82.75 215	51.53 238	81.24 129	54.36 217	90.20 133	80.88 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		68.83 191	68.31 197	70.38 187	70.55 277	48.31 233	63.78 291	82.13 99	54.00 207	68.96 272	75.17 294	58.95 189	80.06 159	58.55 179	82.74 244	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+		68.81 192	68.30 198	70.35 188	74.66 229	48.61 231	66.06 262	78.32 172	50.62 246	71.48 246	75.54 290	68.75 97	79.59 164	50.55 243	78.73 290	82.86 165

OpenMVS	62.51 15	68.76 193	68.75 193	68.78 216	70.56 276	53.91 199	78.29 103	77.35 186	48.85 264	70.22 259	83.52 200	52.65 231	76.93 209	55.31 206	81.99 250	75.49 265

VPA-MVSNet		68.71 194	70.37 176	63.72 262	76.13 205	38.06 322	64.10 286	71.48 240	56.60 173	74.10 209	88.31 117	64.78 140	69.72 283	47.69 267	90.15 136	83.37 150

iter_conf_final		68.69 195	67.00 219	73.76 131	73.68 245	52.33 208	75.96 136	73.54 218	50.56 247	69.90 263	82.85 213	24.76 373	83.73 83	65.40 120	86.33 199	85.22 87

BH-RMVSNet		68.69 195	68.20 203	70.14 193	76.40 201	53.90 200	64.62 281	73.48 219	58.01 151	73.91 213	81.78 224	59.09 187	78.22 191	48.59 257	77.96 298	78.31 242

EIA-MVS		68.59 197	67.16 215	72.90 153	75.18 216	55.64 188	69.39 213	81.29 114	52.44 223	64.53 299	70.69 333	60.33 175	82.30 112	54.27 218	76.31 304	80.75 209

pm-mvs1		68.40 198	69.85 180	64.04 260	73.10 256	39.94 307	64.61 282	70.50 255	55.52 181	73.97 212	89.33 91	63.91 145	68.38 292	49.68 249	88.02 172	83.81 135

miper_ehance_all_eth		68.36 199	68.16 204	68.98 209	65.14 324	43.34 282	67.07 250	78.92 161	49.11 262	76.21 182	77.72 276	53.48 227	77.92 198	61.16 155	84.59 222	85.68 85

GBi-Net		68.30 200	68.79 191	66.81 237	73.14 253	40.68 301	71.96 178	73.03 221	54.81 186	74.72 198	90.36 71	48.63 259	75.20 229	47.12 269	85.37 207	84.54 114

test1		68.30 200	68.79 191	66.81 237	73.14 253	40.68 301	71.96 178	73.03 221	54.81 186	74.72 198	90.36 71	48.63 259	75.20 229	47.12 269	85.37 207	84.54 114

FE-MVS		68.29 202	66.96 220	72.26 169	74.16 239	54.24 196	77.55 112	73.42 220	57.65 159	72.66 227	84.91 184	32.02 341	81.49 125	48.43 260	81.85 253	81.04 197

DIV-MVS_self_test		68.27 203	68.26 199	68.29 221	64.98 325	43.67 278	65.89 264	74.67 211	50.04 254	76.86 166	82.43 217	48.74 257	75.38 225	60.94 158	89.81 144	85.81 78

cl____		68.26 204	68.26 199	68.29 221	64.98 325	43.67 278	65.89 264	74.67 211	50.04 254	76.86 166	82.42 218	48.74 257	75.38 225	60.92 159	89.81 144	85.80 82

TinyColmap		67.98 205	69.28 183	64.08 258	67.98 298	46.82 254	70.04 205	75.26 208	53.05 217	77.36 155	86.79 139	59.39 184	72.59 257	45.64 280	88.01 173	72.83 287

xiu_mvs_v1_base_debu		67.87 206	67.07 216	70.26 189	79.13 162	61.90 135	67.34 244	71.25 246	47.98 269	67.70 280	74.19 307	61.31 165	72.62 254	56.51 191	78.26 295	76.27 261

xiu_mvs_v1_base		67.87 206	67.07 216	70.26 189	79.13 162	61.90 135	67.34 244	71.25 246	47.98 269	67.70 280	74.19 307	61.31 165	72.62 254	56.51 191	78.26 295	76.27 261

xiu_mvs_v1_base_debi		67.87 206	67.07 216	70.26 189	79.13 162	61.90 135	67.34 244	71.25 246	47.98 269	67.70 280	74.19 307	61.31 165	72.62 254	56.51 191	78.26 295	76.27 261

MAR-MVS		67.72 209	66.16 224	72.40 166	74.45 232	64.99 112	74.87 146	77.50 185	48.67 265	65.78 294	68.58 353	57.01 211	77.79 200	46.68 275	81.92 251	74.42 275
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		67.68 210	66.07 225	72.49 164	73.34 250	58.20 174	63.80 290	65.55 280	48.10 268	76.91 163	82.64 216	45.20 271	78.84 173	61.20 154	77.89 299	80.44 217

LF4IMVS		67.50 211	67.31 214	68.08 224	58.86 356	61.93 134	71.43 187	75.90 201	44.67 294	72.42 231	80.20 243	57.16 206	70.44 279	58.99 177	86.12 201	71.88 297

FMVSNet2		67.48 212	68.21 202	65.29 249	73.14 253	38.94 314	68.81 222	71.21 249	54.81 186	76.73 170	86.48 155	48.63 259	74.60 237	47.98 264	86.11 202	82.35 179

MSDG		67.47 213	67.48 212	67.46 230	70.70 273	54.69 193	66.90 254	78.17 175	60.88 127	70.41 256	74.76 296	61.22 169	73.18 248	47.38 268	76.87 301	74.49 274

diffmvs		67.42 214	67.50 211	67.20 233	62.26 338	45.21 268	64.87 278	77.04 190	48.21 267	71.74 237	79.70 250	58.40 193	71.17 273	64.99 122	80.27 275	85.22 87

iter_conf05		67.34 215	65.62 227	72.50 163	69.82 282	47.06 253	72.19 173	76.86 191	45.32 289	72.86 224	82.85 213	20.53 379	83.73 83	61.13 156	89.02 163	86.70 66

cl22		67.14 216	66.51 222	69.03 208	63.20 334	43.46 281	66.88 255	76.25 196	49.22 260	74.48 203	77.88 275	45.49 270	77.40 205	60.64 161	84.59 222	86.24 70

ANet_high		67.08 217	69.94 178	58.51 308	57.55 361	27.09 373	58.43 325	76.80 193	63.56 105	82.40 95	91.93 20	59.82 181	64.98 314	50.10 246	88.86 165	83.46 147

LFMVS		67.06 218	67.89 206	64.56 254	78.02 177	38.25 319	70.81 200	59.60 311	65.18 87	71.06 251	86.56 153	43.85 280	75.22 228	46.35 276	89.63 147	80.21 220

thisisatest0530		67.05 219	65.16 231	72.73 159	73.10 256	50.55 216	71.26 193	63.91 292	50.22 251	74.46 204	80.75 235	26.81 363	80.25 153	59.43 174	86.50 197	87.37 56

MIMVSNet1		66.57 220	69.23 185	58.59 307	81.26 137	37.73 325	64.06 287	57.62 316	57.02 165	78.40 141	90.75 49	62.65 149	58.10 336	41.77 299	89.58 150	79.95 222

tfpnnormal		66.48 221	67.93 205	62.16 281	73.40 249	36.65 329	63.45 293	64.99 283	55.97 176	72.82 226	87.80 126	57.06 210	69.10 289	48.31 262	87.54 177	80.72 211

KD-MVS_self_test		66.38 222	67.51 210	62.97 273	61.76 340	34.39 347	58.11 327	75.30 207	50.84 244	77.12 158	85.42 178	56.84 212	69.44 285	51.07 238	91.16 108	85.08 93

Anonymous202405211		66.02 223	66.89 221	63.43 267	74.22 235	38.14 320	59.00 321	66.13 276	63.33 111	69.76 266	85.95 174	51.88 234	70.50 278	44.23 286	87.52 178	81.64 190

miper_enhance_ethall		65.86 224	65.05 238	68.28 223	61.62 342	42.62 289	64.74 279	77.97 178	42.52 308	73.42 218	72.79 317	49.66 248	77.68 202	58.12 182	84.59 222	84.54 114

RPMNet		65.77 225	65.08 237	67.84 227	66.37 310	48.24 235	70.93 197	86.27 22	54.66 192	61.35 319	86.77 142	33.29 330	85.67 47	55.93 199	70.17 340	69.62 317

VPNet		65.58 226	67.56 209	59.65 300	79.72 150	30.17 365	60.27 315	62.14 300	54.19 203	71.24 248	86.63 150	58.80 190	67.62 297	44.17 287	90.87 123	81.18 194

PVSNet_BlendedMVS		65.38 227	64.30 239	68.61 217	69.81 283	49.36 226	65.60 271	78.96 159	45.50 284	59.98 328	78.61 265	51.82 235	78.20 192	44.30 284	84.11 228	78.27 243

TAMVS		65.31 228	63.75 244	69.97 197	82.23 123	59.76 161	66.78 256	63.37 295	45.20 290	69.79 265	79.37 255	47.42 265	72.17 260	34.48 343	85.15 214	77.99 250

test_yl		65.11 229	65.09 235	65.18 250	70.59 274	40.86 299	63.22 298	72.79 224	57.91 152	68.88 273	79.07 262	42.85 287	74.89 233	45.50 281	84.97 215	79.81 223

DCV-MVSNet		65.11 229	65.09 235	65.18 250	70.59 274	40.86 299	63.22 298	72.79 224	57.91 152	68.88 273	79.07 262	42.85 287	74.89 233	45.50 281	84.97 215	79.81 223

mvs_anonymous		65.08 231	65.49 228	63.83 261	63.79 331	37.60 326	66.52 259	69.82 259	43.44 303	73.46 217	86.08 170	58.79 191	71.75 268	51.90 232	75.63 308	82.15 183

FMVSNet3		65.00 232	65.16 231	64.52 255	69.47 286	37.56 327	66.63 257	70.38 256	51.55 235	74.72 198	83.27 209	37.89 317	74.44 239	47.12 269	85.37 207	81.57 191

ECVR-MVS		64.82 233	65.22 229	63.60 263	78.80 168	31.14 362	66.97 252	56.47 326	54.23 200	69.94 262	88.68 109	37.23 319	74.81 235	45.28 283	89.41 153	84.86 98

BH-w/o		64.81 234	64.29 240	66.36 242	76.08 207	54.71 192	65.61 270	75.23 209	50.10 253	71.05 252	71.86 327	54.33 224	79.02 170	38.20 321	76.14 305	65.36 340

EGC-MVSNET		64.77 235	61.17 266	75.60 108	86.90 47	74.47 35	84.04 38	68.62 266	0.60 383	1.13 385	91.61 28	65.32 134	74.15 244	64.01 129	88.28 167	78.17 245

test1111		64.62 236	65.19 230	62.93 274	79.01 166	29.91 366	65.45 272	54.41 334	54.09 205	71.47 247	88.48 112	37.02 320	74.29 242	46.83 274	89.94 142	84.58 112

cascas		64.59 237	62.77 255	70.05 195	75.27 214	50.02 221	61.79 304	71.61 236	42.46 309	63.68 309	68.89 349	49.33 252	80.35 149	47.82 266	84.05 229	79.78 225

TR-MVS		64.59 237	63.54 247	67.73 229	75.75 212	50.83 215	63.39 294	70.29 257	49.33 259	71.55 244	74.55 300	50.94 241	78.46 182	40.43 306	75.69 307	73.89 279

PM-MVS		64.49 239	63.61 246	67.14 235	76.68 199	75.15 32	68.49 231	42.85 366	51.17 241	77.85 148	80.51 238	45.76 266	66.31 309	52.83 229	76.35 303	59.96 358

jason		64.47 240	62.84 254	69.34 204	76.91 195	59.20 162	67.15 249	65.67 277	35.29 343	65.16 296	76.74 284	44.67 275	70.68 275	54.74 210	79.28 286	78.14 246
jason: jason.
xiu_mvs_v2_base		64.43 241	63.96 242	65.85 248	77.72 184	51.32 213	63.63 292	72.31 232	45.06 293	61.70 316	69.66 342	62.56 150	73.93 246	49.06 253	73.91 321	72.31 293

pmmvs-eth3d		64.41 242	63.27 250	67.82 228	75.81 211	60.18 156	69.49 211	62.05 304	38.81 329	74.13 208	82.23 220	43.76 281	68.65 290	42.53 293	80.63 273	74.63 273

CDS-MVSNet		64.33 243	62.66 256	69.35 203	80.44 144	58.28 173	65.26 274	65.66 278	44.36 295	67.30 286	75.54 290	43.27 283	71.77 266	37.68 324	84.44 225	78.01 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		64.27 244	63.73 245	65.90 247	77.82 182	51.42 212	63.33 295	72.33 231	45.09 292	61.60 317	68.04 354	62.39 154	73.95 245	49.07 252	73.87 322	72.34 292

ab-mvs		64.11 245	65.13 234	61.05 290	71.99 265	38.03 323	67.59 239	68.79 264	49.08 263	65.32 295	86.26 161	58.02 202	66.85 304	39.33 310	79.79 282	78.27 243

CANet_DTU		64.04 246	63.83 243	64.66 253	68.39 291	42.97 286	73.45 162	74.50 214	52.05 228	54.78 348	75.44 293	43.99 279	70.42 280	53.49 225	78.41 294	80.59 214

VNet		64.01 247	65.15 233	60.57 294	73.28 251	35.61 339	57.60 329	67.08 271	54.61 193	66.76 289	83.37 204	56.28 215	66.87 302	42.19 295	85.20 213	79.23 233

Anonymous20240521		63.55 248	66.07 225	55.99 315	66.18 315	44.04 275	68.77 225	68.80 263	46.99 278	72.57 228	85.84 175	39.87 304	50.22 346	53.40 228	92.23 90	73.71 281

lupinMVS		63.36 249	61.49 264	68.97 210	74.93 218	59.19 163	65.80 267	64.52 288	34.68 348	63.53 311	74.25 305	43.19 284	70.62 276	53.88 222	78.67 291	77.10 256

ET-MVSNet_ETH3D		63.32 250	60.69 272	71.20 180	70.15 281	55.66 187	65.02 277	64.32 289	43.28 307	68.99 271	72.05 326	25.46 370	78.19 194	54.16 220	82.80 243	79.74 226

MVSTER		63.29 251	61.60 263	68.36 219	59.77 353	46.21 261	60.62 312	71.32 243	41.83 311	75.40 190	79.12 260	30.25 355	75.85 219	56.30 196	79.81 280	83.03 160

OpenMVS_ROB	54.93 17	63.23 252	63.28 249	63.07 271	69.81 283	45.34 267	68.52 230	67.14 270	43.74 300	70.61 255	79.22 257	47.90 263	72.66 253	48.75 255	73.84 323	71.21 305

IterMVS		63.12 253	62.48 257	65.02 252	66.34 312	52.86 204	63.81 289	62.25 299	46.57 281	71.51 245	80.40 240	44.60 276	66.82 305	51.38 236	75.47 310	75.38 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 254	60.47 273	70.61 183	83.04 110	54.10 197	59.93 317	72.24 233	33.67 353	69.00 270	75.63 289	38.69 311	76.93 209	36.60 332	75.45 311	80.81 208

GA-MVS		62.91 255	61.66 260	66.66 241	67.09 306	44.49 272	61.18 309	69.36 262	51.33 238	69.33 268	74.47 301	36.83 321	74.94 232	50.60 242	74.72 316	80.57 215

PVSNet_Blended		62.90 256	61.64 261	66.69 240	69.81 283	49.36 226	61.23 308	78.96 159	42.04 310	59.98 328	68.86 350	51.82 235	78.20 192	44.30 284	77.77 300	72.52 290

USDC		62.80 257	63.10 252	61.89 282	65.19 321	43.30 283	67.42 243	74.20 215	35.80 342	72.25 233	84.48 189	45.67 268	71.95 265	37.95 323	84.97 215	70.42 311

Vis-MVSNet (Re-imp)		62.74 258	63.21 251	61.34 288	72.19 263	31.56 359	67.31 248	53.87 335	53.60 213	69.88 264	83.37 204	40.52 301	70.98 274	41.40 300	86.78 194	81.48 192

patch_mono-2		62.73 259	64.08 241	58.68 306	70.36 279	55.87 185	60.84 311	64.11 291	41.23 315	64.04 304	78.22 270	60.00 177	48.80 348	54.17 219	83.71 236	71.37 301

D2MVS		62.58 260	61.05 268	67.20 233	63.85 330	47.92 241	56.29 332	69.58 260	39.32 324	70.07 261	78.19 271	34.93 326	72.68 252	53.44 226	83.74 234	81.00 200

MVS_0304		62.51 261	62.27 258	63.25 268	69.39 287	48.47 232	64.05 288	62.48 298	59.69 137	54.10 353	81.04 232	45.71 267	66.31 309	41.38 301	82.58 246	74.96 271

CL-MVSNet_self_test		62.44 262	63.40 248	59.55 301	72.34 262	32.38 355	56.39 331	64.84 284	51.21 240	67.46 284	81.01 233	50.75 242	63.51 321	38.47 319	88.12 170	82.75 169

MDA-MVSNet-bldmvs		62.34 263	61.73 259	64.16 256	61.64 341	49.90 222	48.11 353	57.24 322	53.31 216	80.95 111	79.39 254	49.00 255	61.55 327	45.92 279	80.05 277	81.03 198

miper_lstm_enhance		61.97 264	61.63 262	62.98 272	60.04 349	45.74 265	47.53 355	70.95 251	44.04 296	73.06 222	78.84 264	39.72 305	60.33 329	55.82 201	84.64 221	82.88 163

wuyk23d		61.97 264	66.25 223	49.12 336	58.19 360	60.77 152	66.32 260	52.97 342	55.93 178	90.62 5	86.91 135	73.07 62	35.98 376	20.63 379	91.63 96	50.62 367

thres600view7		61.82 266	61.38 265	63.12 270	71.81 266	34.93 343	64.64 280	56.99 323	54.78 190	70.33 258	79.74 249	32.07 339	72.42 259	38.61 317	83.46 238	82.02 184

PAPM		61.79 267	60.37 274	66.05 245	76.09 206	41.87 293	69.30 214	76.79 194	40.64 321	53.80 354	79.62 252	44.38 277	82.92 102	29.64 361	73.11 326	73.36 283

MVP-Stereo		61.56 268	59.22 280	68.58 218	79.28 156	60.44 154	69.20 216	71.57 237	43.58 302	56.42 342	78.37 268	39.57 307	76.46 217	34.86 342	60.16 364	68.86 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	48.73 20	61.54 269	60.89 269	63.52 265	61.08 344	51.55 211	68.07 236	68.00 269	33.88 350	65.87 292	81.25 230	37.91 316	67.71 295	49.32 251	82.60 245	71.31 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		61.23 270	60.85 270	62.38 279	78.80 168	27.88 372	67.33 247	37.42 379	54.23 200	67.55 283	88.68 109	17.87 384	74.39 240	46.33 277	89.41 153	84.86 98

thres100view900		61.17 271	61.09 267	61.39 287	72.14 264	35.01 342	65.42 273	56.99 323	55.23 183	70.71 254	79.90 247	32.07 339	72.09 261	35.61 339	81.73 256	77.08 257

Patchmtry		60.91 272	63.01 253	54.62 318	66.10 316	26.27 376	67.47 242	56.40 327	54.05 206	72.04 236	86.66 147	33.19 331	60.17 330	43.69 288	87.45 181	77.42 253

EU-MVSNet		60.82 273	60.80 271	60.86 293	68.37 292	41.16 296	72.27 170	68.27 268	26.96 372	69.08 269	75.71 288	32.09 338	67.44 298	55.59 204	78.90 288	73.97 277

pmmvs4		60.78 274	59.04 282	66.00 246	73.06 258	57.67 176	64.53 283	60.22 309	36.91 337	65.96 291	77.27 280	39.66 306	68.54 291	38.87 314	74.89 315	71.80 298

thres400		60.77 275	59.97 276	63.15 269	70.78 271	35.35 340	63.27 296	57.47 317	53.00 218	68.31 277	77.09 281	32.45 336	72.09 261	35.61 339	81.73 256	82.02 184

MVS		60.62 276	59.97 276	62.58 277	68.13 296	47.28 250	68.59 227	73.96 216	32.19 357	59.94 330	68.86 350	50.48 244	77.64 203	41.85 298	75.74 306	62.83 350

thisisatest0515		60.48 277	57.86 291	68.34 220	67.25 304	46.42 258	60.58 313	62.14 300	40.82 319	63.58 310	69.12 345	26.28 366	78.34 188	48.83 254	82.13 249	80.26 219

tfpn200view9		60.35 278	59.97 276	61.51 285	70.78 271	35.35 340	63.27 296	57.47 317	53.00 218	68.31 277	77.09 281	32.45 336	72.09 261	35.61 339	81.73 256	77.08 257

ppachtmachnet_test		60.26 279	59.61 279	62.20 280	67.70 301	44.33 273	58.18 326	60.96 308	40.75 320	65.80 293	72.57 318	41.23 294	63.92 318	46.87 273	82.42 247	78.33 241

Patchmatch-RL test		59.95 280	59.12 281	62.44 278	72.46 261	54.61 194	59.63 318	47.51 359	41.05 318	74.58 202	74.30 304	31.06 349	65.31 311	51.61 233	79.85 279	67.39 328

1314		59.83 281	58.86 284	62.74 276	65.71 318	44.78 270	68.59 227	72.63 228	33.54 355	61.05 323	67.29 359	43.62 282	71.26 272	49.49 250	67.84 351	72.19 295

IB-MVS	49.67 18	59.69 282	56.96 297	67.90 225	68.19 295	50.30 219	61.42 306	65.18 282	47.57 275	55.83 345	67.15 360	23.77 376	79.60 163	43.56 290	79.97 278	73.79 280
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		59.48 283	58.99 283	60.96 292	77.84 181	42.39 291	61.42 306	68.45 267	37.96 332	59.93 331	67.46 356	45.11 273	65.07 313	40.89 304	71.81 331	75.41 267

FPMVS		59.43 284	60.07 275	57.51 311	77.62 187	71.52 54	62.33 302	50.92 347	57.40 163	69.40 267	80.00 246	39.14 309	61.92 326	37.47 327	66.36 353	39.09 377

CVMVSNet		59.21 285	58.44 288	61.51 285	73.94 242	47.76 244	71.31 191	64.56 287	26.91 373	60.34 327	70.44 334	36.24 323	67.65 296	53.57 224	68.66 348	69.12 322

CR-MVSNet		58.96 286	58.49 287	60.36 296	66.37 310	48.24 235	70.93 197	56.40 327	32.87 356	61.35 319	86.66 147	33.19 331	63.22 322	48.50 259	70.17 340	69.62 317

EPNet_dtu		58.93 287	58.52 286	60.16 298	67.91 299	47.70 245	69.97 206	58.02 315	49.73 256	47.28 368	73.02 316	38.14 313	62.34 324	36.57 333	85.99 203	70.43 310
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		58.78 288	58.69 285	59.04 305	79.41 154	38.13 321	57.62 328	66.98 272	34.74 346	59.62 332	77.56 278	42.92 286	63.65 320	38.66 316	70.73 337	75.35 269

PatchMatch-RL		58.68 289	57.72 292	61.57 284	76.21 204	73.59 44	61.83 303	49.00 354	47.30 277	61.08 321	68.97 347	50.16 246	59.01 333	36.06 338	68.84 347	52.10 366

SCA		58.57 290	58.04 290	60.17 297	70.17 280	41.07 298	65.19 275	53.38 340	43.34 306	61.00 324	73.48 311	45.20 271	69.38 286	40.34 307	70.31 339	70.05 312

CHOSEN 1792x2688		58.09 291	56.30 302	63.45 266	79.95 148	50.93 214	54.07 340	65.59 279	28.56 368	61.53 318	74.33 303	41.09 297	66.52 308	33.91 346	67.69 352	72.92 286

HY-MVS	49.31 19	57.96 292	57.59 293	59.10 304	66.85 309	36.17 333	65.13 276	65.39 281	39.24 326	54.69 350	78.14 272	44.28 278	67.18 301	33.75 347	70.79 336	73.95 278

baseline1		57.82 293	58.36 289	56.19 314	69.17 289	30.76 364	62.94 300	55.21 330	46.04 283	63.83 307	78.47 266	41.20 295	63.68 319	39.44 309	68.99 346	74.13 276

thres200		57.55 294	57.02 296	59.17 302	67.89 300	34.93 343	58.91 323	57.25 321	50.24 250	64.01 305	71.46 330	32.49 335	71.39 271	31.31 353	79.57 284	71.19 306

CostFormer		57.35 295	56.14 303	60.97 291	63.76 332	38.43 316	67.50 241	60.22 309	37.14 336	59.12 333	76.34 286	32.78 333	71.99 264	39.12 313	69.27 345	72.47 291

FMVS2		56.78 296	55.99 305	59.12 303	53.96 377	48.09 238	58.76 324	66.22 275	27.54 370	76.66 171	68.69 352	25.32 372	51.31 343	53.42 227	73.38 324	77.97 251

our_test_3		56.46 297	56.51 300	56.30 313	67.70 301	39.66 309	55.36 337	52.34 345	40.57 322	63.85 306	69.91 341	40.04 303	58.22 335	43.49 291	75.29 314	71.03 308

tpm2		56.12 298	54.64 311	60.55 295	66.24 313	36.01 334	68.14 234	56.77 325	33.60 354	58.25 336	75.52 292	30.25 355	74.33 241	33.27 348	69.76 344	71.32 302

tpmvs		55.84 299	55.45 309	57.01 312	60.33 348	33.20 353	65.89 264	59.29 313	47.52 276	56.04 343	73.60 310	31.05 350	68.06 294	40.64 305	64.64 356	69.77 315

gg-mvs-nofinetune		55.75 300	56.75 299	52.72 324	62.87 335	28.04 371	68.92 219	41.36 374	71.09 44	50.80 360	92.63 12	20.74 378	66.86 303	29.97 359	72.41 328	63.25 349

test20.03		55.74 301	57.51 294	50.42 329	59.89 352	32.09 357	50.63 347	49.01 353	50.11 252	65.07 297	83.23 210	45.61 269	48.11 351	30.22 357	83.82 233	71.07 307

MS-PatchMatch		55.59 302	54.89 310	57.68 310	69.18 288	49.05 228	61.00 310	62.93 297	35.98 340	58.36 335	68.93 348	36.71 322	66.59 307	37.62 326	63.30 359	57.39 362

baseline2		55.57 303	52.74 317	64.05 259	65.26 320	44.11 274	62.38 301	54.43 333	39.03 327	51.21 358	67.35 358	33.66 329	72.45 258	37.14 329	64.22 358	75.60 264

XXY-MVS		55.19 304	57.40 295	48.56 338	64.45 328	34.84 345	51.54 346	53.59 337	38.99 328	63.79 308	79.43 253	56.59 213	45.57 355	36.92 331	71.29 333	65.25 341

FMVSNet5		55.08 305	55.54 308	53.71 319	65.80 317	33.50 352	56.22 333	52.50 344	43.72 301	61.06 322	83.38 203	25.46 370	54.87 339	30.11 358	81.64 261	72.75 288

PatchmatchNet		54.60 306	54.27 312	55.59 316	65.17 323	39.08 311	66.92 253	51.80 346	39.89 323	58.39 334	73.12 315	31.69 343	58.33 334	43.01 292	58.38 370	69.38 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		54.39 307	56.12 304	49.20 334	72.57 260	30.91 363	59.98 316	48.43 356	41.66 312	55.94 344	83.86 197	41.19 296	50.42 345	26.05 369	75.38 312	66.27 336

Anonymous20231206		54.13 308	55.82 306	49.04 337	70.89 270	35.96 335	51.73 345	50.87 348	34.86 344	62.49 314	79.22 257	42.52 290	44.29 363	27.95 367	81.88 252	66.88 332

JIA-IIPM		54.03 309	51.62 321	61.25 289	59.14 355	55.21 190	59.10 320	47.72 357	50.85 243	50.31 364	85.81 176	20.10 381	63.97 317	36.16 337	55.41 375	64.55 347

tpm cat1		54.02 310	52.63 318	58.19 309	64.85 327	39.86 308	66.26 261	57.28 320	32.16 358	56.90 339	70.39 336	32.75 334	65.30 312	34.29 344	58.79 367	69.41 319

testgi		54.00 311	56.86 298	45.45 346	58.20 359	25.81 377	49.05 349	49.50 352	45.43 287	67.84 279	81.17 231	51.81 237	43.20 367	29.30 362	79.41 285	67.34 330

PatchT		53.35 312	56.47 301	43.99 352	64.19 329	17.46 384	59.15 319	43.10 365	52.11 227	54.74 349	86.95 134	29.97 358	49.98 347	43.62 289	74.40 319	64.53 348

new-patchmatchnet		52.89 313	55.76 307	44.26 351	59.94 351	6.31 387	37.36 373	50.76 349	41.10 316	64.28 302	79.82 248	44.77 274	48.43 350	36.24 336	87.61 176	78.03 248

YYNet1		52.58 314	53.50 314	49.85 330	54.15 375	36.45 332	40.53 367	46.55 361	38.09 331	75.52 188	73.31 314	41.08 298	43.88 364	41.10 302	71.14 335	69.21 321

MDA-MVSNet_test_wron		52.57 315	53.49 315	49.81 331	54.24 374	36.47 331	40.48 368	46.58 360	38.13 330	75.47 189	73.32 313	41.05 299	43.85 365	40.98 303	71.20 334	69.10 323

pmmvs5		52.49 316	52.58 319	52.21 326	54.99 372	32.38 355	55.45 336	53.84 336	32.15 359	55.49 347	74.81 295	38.08 314	57.37 337	34.02 345	74.40 319	66.88 332

UnsupCasMVSNet_eth		52.26 317	53.29 316	49.16 335	55.08 371	33.67 351	50.03 348	58.79 314	37.67 333	63.43 313	74.75 297	41.82 292	45.83 354	38.59 318	59.42 366	67.98 327

N_pmnet		52.06 318	51.11 326	54.92 317	59.64 354	71.03 59	37.42 372	61.62 307	33.68 352	57.12 337	72.10 319	37.94 315	31.03 378	29.13 366	71.35 332	62.70 351

KD-MVS_2432*1600		52.05 319	51.58 322	53.44 320	52.11 378	31.20 360	44.88 362	64.83 285	41.53 313	64.37 300	70.03 339	15.61 388	64.20 315	36.25 334	74.61 317	64.93 344

miper_refine_blended		52.05 319	51.58 322	53.44 320	52.11 378	31.20 360	44.88 362	64.83 285	41.53 313	64.37 300	70.03 339	15.61 388	64.20 315	36.25 334	74.61 317	64.93 344

PVSNet	43.83 21	51.56 321	51.17 325	52.73 323	68.34 293	38.27 318	48.22 352	53.56 338	36.41 338	54.29 351	64.94 363	34.60 327	54.20 342	30.34 356	69.87 342	65.71 339

tpm		50.60 322	52.42 320	45.14 348	65.18 322	26.29 375	60.30 314	43.50 364	37.41 334	57.01 338	79.09 261	30.20 357	42.32 368	32.77 350	66.36 353	66.81 334

test-LLR		50.43 323	50.69 328	49.64 332	60.76 345	41.87 293	53.18 342	45.48 362	43.41 304	49.41 365	60.47 370	29.22 360	44.73 361	42.09 296	72.14 329	62.33 354

tpmrst		50.15 324	51.38 324	46.45 343	56.05 366	24.77 378	64.40 285	49.98 350	36.14 339	53.32 355	69.59 343	35.16 325	48.69 349	39.24 311	58.51 369	65.89 337

UnsupCasMVSNet_bld		50.01 325	51.03 327	46.95 339	58.61 357	32.64 354	48.31 351	53.27 341	34.27 349	60.47 326	71.53 329	41.40 293	47.07 352	30.68 355	60.78 363	61.13 356

WTY-MVS		49.39 326	50.31 329	46.62 342	61.22 343	32.00 358	46.61 358	49.77 351	33.87 351	54.12 352	69.55 344	41.96 291	45.40 357	31.28 354	64.42 357	62.47 353

ADS-MVSNet2		48.76 327	47.25 335	53.29 322	55.90 368	40.54 304	47.34 356	54.99 332	31.41 364	50.48 361	72.06 324	31.23 346	54.26 341	25.93 370	55.93 372	65.07 342

test-mter		48.56 328	48.20 333	49.64 332	60.76 345	41.87 293	53.18 342	45.48 362	31.91 362	49.41 365	60.47 370	18.34 382	44.73 361	42.09 296	72.14 329	62.33 354

Patchmatch-test		47.93 329	49.96 330	41.84 355	57.42 362	24.26 379	48.75 350	41.49 373	39.30 325	56.79 340	73.48 311	30.48 354	33.87 377	29.29 363	72.61 327	67.39 328

test0.0.03 1		47.72 330	48.31 332	45.93 344	55.53 370	29.39 367	46.40 359	41.21 375	43.41 304	55.81 346	67.65 355	29.22 360	43.77 366	25.73 372	69.87 342	64.62 346

sss		47.59 331	48.32 331	45.40 347	56.73 365	33.96 349	45.17 361	48.51 355	32.11 361	52.37 357	65.79 361	40.39 302	41.91 371	31.85 351	61.97 361	60.35 357

pmmvs3		46.71 332	45.09 340	51.55 328	56.76 364	48.25 234	55.78 335	39.53 378	24.13 376	50.35 363	63.40 364	15.90 387	51.08 344	29.29 363	70.69 338	55.33 365

EPMVS		45.74 333	46.53 336	43.39 353	54.14 376	22.33 381	55.02 338	35.00 382	34.69 347	51.09 359	70.20 338	25.92 368	42.04 370	37.19 328	55.50 374	65.78 338

MVS-HIRNet		45.53 334	47.29 334	40.24 358	62.29 337	26.82 374	56.02 334	37.41 380	29.74 367	43.69 377	81.27 229	33.96 328	55.48 338	24.46 375	56.79 371	38.43 378

TESTMET0.1,1		45.17 335	44.93 341	45.89 345	56.02 367	38.31 317	53.18 342	41.94 372	27.85 369	44.86 373	56.47 372	17.93 383	41.50 372	38.08 322	68.06 349	57.85 360

E-PMN		45.17 335	45.36 339	44.60 350	50.07 380	42.75 287	38.66 370	42.29 370	46.39 282	39.55 378	51.15 376	26.00 367	45.37 358	37.68 324	76.41 302	45.69 373

PMMVS		44.69 337	43.95 345	46.92 340	50.05 381	53.47 202	48.08 354	42.40 368	22.36 378	44.01 376	53.05 374	42.60 289	45.49 356	31.69 352	61.36 362	41.79 375

ADS-MVSNet		44.62 338	45.58 338	41.73 356	55.90 368	20.83 382	47.34 356	39.94 377	31.41 364	50.48 361	72.06 324	31.23 346	39.31 374	25.93 370	55.93 372	65.07 342

EMVS		44.61 339	44.45 344	45.10 349	48.91 382	43.00 285	37.92 371	41.10 376	46.75 280	38.00 380	48.43 378	26.42 365	46.27 353	37.11 330	75.38 312	46.03 372

dp		44.09 340	44.88 342	41.72 357	58.53 358	23.18 380	54.70 339	42.38 369	34.80 345	44.25 375	65.61 362	24.48 375	44.80 360	29.77 360	49.42 377	57.18 363

FMVS		43.79 341	45.63 337	38.24 361	42.29 386	38.58 315	34.76 374	47.68 358	22.22 379	67.34 285	63.15 365	31.82 342	30.60 379	39.19 312	62.28 360	45.53 374

mvsany_test		43.76 342	41.01 346	52.01 327	48.09 383	57.74 175	42.47 365	23.85 387	23.30 377	64.80 298	62.17 368	27.12 362	40.59 373	29.17 365	48.11 378	57.69 361

DSMNet-mixed		43.18 343	44.66 343	38.75 360	54.75 373	28.88 370	57.06 330	27.42 385	13.47 380	47.27 369	77.67 277	38.83 310	39.29 375	25.32 374	60.12 365	48.08 369

CHOSEN 280x420		41.62 344	39.89 349	46.80 341	61.81 339	51.59 210	33.56 375	35.74 381	27.48 371	37.64 381	53.53 373	23.24 377	42.09 369	27.39 368	58.64 368	46.72 371

PVSNet_0	36.71 22	41.12 345	40.78 348	42.14 354	59.97 350	40.13 306	40.97 366	42.24 371	30.81 366	44.86 373	49.41 377	40.70 300	45.12 359	23.15 376	34.96 380	41.16 376

PMMVS2		37.74 346	40.87 347	28.36 363	42.41 385	5.35 388	24.61 376	27.75 384	32.15 359	47.85 367	70.27 337	35.85 324	29.51 380	19.08 380	67.85 350	50.22 368

new_pmnet		37.55 347	39.80 350	30.79 362	56.83 363	16.46 385	39.35 369	30.65 383	25.59 374	45.26 371	61.60 369	24.54 374	28.02 381	21.60 377	52.80 376	47.90 370

MVE	27.91 23	36.69 348	35.64 351	39.84 359	43.37 384	35.85 337	19.49 377	24.61 386	24.68 375	39.05 379	62.63 367	38.67 312	27.10 382	21.04 378	47.25 379	56.56 364
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.26 349	19.12 353	19.71 364	9.09 388	1.91 390	7.79 379	53.44 339	1.42 382	10.27 384	35.80 379	17.42 385	25.11 383	12.44 381	24.38 382	32.10 379

cdsmvs_eth3d_5k		17.71 350	23.62 352	0.00 369	0.00 392	0.00 393	0.00 380	70.17 258	0.00 387	0.00 388	74.25 305	68.16 104	0.00 388	0.00 386	0.00 386	0.00 384

tmp_tt		11.98 351	14.73 354	3.72 366	2.28 389	4.62 389	19.44 378	14.50 389	0.47 384	21.55 382	9.58 382	25.78 369	4.57 385	11.61 382	27.37 381	1.96 381

ab-mvs-re		5.62 352	7.50 355	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	67.46 356	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		5.20 353	6.93 356	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	62.39 154	0.00 388	0.00 386	0.00 386	0.00 384

test123		4.43 354	5.78 357	0.39 368	0.97 390	0.28 391	46.33 360	0.45 391	0.31 385	0.62 386	1.50 385	0.61 391	0.11 387	0.56 384	0.63 384	0.77 383

testmvs		4.06 355	5.28 358	0.41 367	0.64 391	0.16 392	42.54 364	0.31 392	0.26 386	0.50 387	1.40 386	0.77 390	0.17 386	0.56 384	0.55 385	0.90 382

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

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 94		80.75 126					86.24 22	77.27 35	94.85 26	83.78 136

PC_three_1452									46.98 279	81.83 99	86.28 159	66.55 122	84.47 72	63.31 141	90.78 124	83.49 143

No_MVS				79.02 60	83.14 104	67.03 94		80.75 126					86.24 22	77.27 35	94.85 26	83.78 136

test_one_0601					85.84 67	61.45 140		85.63 32	75.27 19	85.62 50	90.38 68	76.72 29

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					83.91 95	69.36 75		81.09 121	58.91 146	82.73 92	89.11 100	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 50	81.38 7		78.11 25	94.46 39	84.89 96

IU-MVS					86.12 59	60.90 148		80.38 138	45.49 286	81.31 107				75.64 44	94.39 44	84.65 105

OPU-MVS				78.65 67	83.44 102	66.85 96	83.62 45				86.12 168	66.82 116	86.01 30	61.72 150	89.79 146	83.08 158

test_241102_TWO								84.80 50	72.61 32	84.93 59	89.70 86	77.73 23	85.89 40	75.29 45	94.22 56	83.25 153

test_241102_ONE					86.12 59	61.06 144		84.72 54	72.64 31	87.38 25	89.47 89	77.48 24	85.74 44

9.14			80.22 59		80.68 140		80.35 76	87.69 11	59.90 134	83.00 85	88.20 119	74.57 51	81.75 122	73.75 56	93.78 62

save fliter					87.00 43	67.23 92	79.24 89	77.94 179	56.65 171

test_0728_THIRD									74.03 23	85.83 45	90.41 63	75.58 39	85.69 45	77.43 33	94.74 31	84.31 124

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

test0726					86.16 57	60.78 150	83.81 42	85.10 45	72.48 34	85.27 56	89.96 82	78.57 18

GSMVS																70.05 312

test_part2					85.90 63	66.44 99				84.61 66

sam_mvs1												31.41 344				70.05 312

sam_mvs												31.21 348

ambc				70.10 194	77.74 183	50.21 220	74.28 159	77.93 180		79.26 131	88.29 118	54.11 225	79.77 161	64.43 126	91.10 113	80.30 218

MTGPA								80.63 131

test_post1							66.63 257				2.08 383	30.66 353	59.33 332	40.34 307

test_post											1.99 384	30.91 351	54.76 340

patchmatchnet-post											68.99 346	31.32 345	69.38 286

GG-mvs-BLEND				52.24 325	60.64 347	29.21 369	69.73 210	42.41 367		45.47 370	52.33 375	20.43 380	68.16 293	25.52 373	65.42 355	59.36 359

MTMP							84.83 33	19.26 388

gm-plane-assit					62.51 336	33.91 350			37.25 335		62.71 366		72.74 251	38.70 315

test9_res														72.12 71	91.37 103	77.40 254

TEST9					85.47 69	69.32 76	76.42 125	78.69 165	53.73 212	76.97 159	86.74 143	66.84 115	81.10 132

test_8					85.09 75	67.89 85	76.26 130	78.66 167	54.00 207	76.89 164	86.72 145	66.60 120	80.89 143

agg_prior2														70.70 79	90.93 118	78.55 240

agg_prior					84.44 88	66.02 103		78.62 168		76.95 161			80.34 150

TestCases				78.35 72	79.19 160	70.81 61		88.64 3	65.37 83	80.09 123	88.17 120	70.33 83	78.43 184	55.60 202	90.90 120	85.81 78

test_prior4						70.14 68	77.57 110

test_prior2							75.57 139		58.92 144	76.53 176	86.78 140	67.83 108		69.81 84	92.76 82

test_prior				75.27 112	82.15 124	59.85 159		84.33 66					83.39 92			82.58 173

旧先验2							71.17 194		45.11 291	78.54 139			61.28 328	59.19 176

新几何2							71.33 190

新几何1				69.99 196	88.37 36	71.34 57		62.08 302	43.85 297	74.99 193	86.11 169	52.85 230	70.57 277	50.99 239	83.23 241	68.05 326

旧先验1					84.55 85	60.36 155		63.69 293			87.05 133	54.65 222			83.34 239	69.66 316

无先验							74.82 147	70.94 252	47.75 274				76.85 212	54.47 213		72.09 296

原ACMM2							74.78 151

原ACMM1				73.90 128	85.90 63	65.15 111		81.67 107	50.97 242	74.25 206	86.16 166	61.60 162	83.54 88	56.75 189	91.08 114	73.00 285

test222					87.30 41	69.15 79	67.85 237	59.59 312	41.06 317	73.05 223	85.72 177	48.03 262			80.65 271	66.92 331

testdata2													67.30 299	48.34 261

segment_acmp												68.30 103

testdata				64.13 257	85.87 65	63.34 126		61.80 306	47.83 272	76.42 180	86.60 152	48.83 256	62.31 325	54.46 215	81.26 263	66.74 335

testdata1							68.34 233		57.24 164

test12				76.51 95	82.28 122	60.94 147		81.64 108		73.60 214		64.88 137	85.19 60		90.42 131	83.38 149

plane_prior7					85.18 72	66.21 101

plane_prior6					84.18 92	65.31 108						60.83 172

plane_prior5								85.49 34					86.15 27	71.09 75	90.94 116	84.82 100

plane_prior4											89.11 100

plane_prior3						65.67 105			63.82 103	78.23 142

plane_prior2							82.74 55		65.45 80

plane_prior1					84.46 87

plane_prior						65.18 109	80.06 83		61.88 121						89.91 143

n2								0.00 393

nn								0.00 393

door-mid								55.02 331

lessismore_v0				72.75 157	79.60 152	56.83 181		57.37 319		83.80 78	89.01 103	47.45 264	78.74 176	64.39 127	86.49 198	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								52.91 343

HQP5-MVS						58.80 169

HQP-NCC					82.37 119		77.32 115		59.08 139	71.58 240

ACMP_Plane					82.37 119		77.32 115		59.08 139	71.58 240

BP-MVS														67.38 107

HQP4-MVS										71.59 239			85.31 53			83.74 138

HQP3-MVS								84.12 74							89.16 157

HQP2-MVS												58.09 197

NP-MVS					83.34 103	63.07 129					85.97 172

MDTV_nov1_ep13_2view						18.41 383	53.74 341		31.57 363	44.89 372		29.90 359		32.93 349		71.48 300

MDTV_nov1_ep13			54.05 313		65.54 319	29.30 368	59.00 321	55.22 329	35.96 341	52.44 356	75.98 287	30.77 352	59.62 331	38.21 320	73.33 325

ACMMP++_ref															89.47 152

ACMMP++															91.96 92

Test By Simon												62.56 150

ITE_SJBPF				80.35 43	76.94 194	73.60 43		80.48 135	66.87 67	83.64 80	86.18 163	70.25 85	79.90 160	61.12 157	88.95 164	87.56 54

DeepMVS_CX				11.83 365	15.51 387	13.86 386		11.25 390	5.76 381	20.85 383	26.46 380	17.06 386	9.22 384	9.69 383	13.82 383	12.42 380

High-resolution multi-view benchmark