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.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 58	91.63 1	86.34 1	97.97 1	94.77 3	66.57 129	95.38 1	87.74 1	97.72 1	93.00 7

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 128	84.80 35	87.77 11	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 106	97.05 2	96.93 1

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 16	79.37 15	84.79 69	74.51 56	96.15 3	92.88 8

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 18	82.72 194

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 18	82.72 194

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 39	79.26 7	89.12 12	92.10 21	77.52 26	85.92 39	80.47 9	95.20 20	82.10 211

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	79.20 16	85.58 51	78.11 28	94.46 41	84.89 112

HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 17	73.69 27	86.17 41	91.70 33	78.23 22	85.20 61	79.45 17	94.91 30	88.15 50

SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 41	77.42 17	86.15 42	90.24 77	81.69 5	85.94 36	77.77 31	93.58 66	83.09 179

ACMMP		84.22 10	84.84 13	82.35 18	89.23 22	76.66 26	87.65 7	85.89 27	71.03 48	85.85 46	90.58 58	78.77 18	85.78 44	79.37 20	95.17 22	84.62 125
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 10	84.98 12	81.94 24	84.82 77	75.40 29	91.60 3	87.80 9	73.52 29	88.90 15	93.06 9	71.39 74	81.53 127	81.53 5	92.15 86	88.91 40
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 12	84.63 14	82.60 14	88.21 36	74.40 35	85.24 31	87.21 15	70.69 51	85.14 58	90.42 65	78.99 17	86.62 15	80.83 7	94.93 29	86.79 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 12	84.55 15	82.80 11	89.42 18	79.74 6	88.19 5	84.43 62	71.96 44	84.70 65	90.56 59	77.12 29	86.18 28	79.24 22	95.36 15	82.49 201

mPP-MVS		84.01 14	84.39 16	82.88 7	90.65 4	81.38 4	87.08 13	82.79 91	72.41 40	85.11 59	90.85 51	76.65 32	84.89 66	79.30 21	94.63 38	82.35 204

COLMAP_ROB	72.78 3	83.75 15	84.11 20	82.68 13	82.97 109	74.39 36	87.18 11	88.18 8	78.98 8	86.11 44	91.47 38	79.70 14	85.76 45	66.91 125	95.46 14	87.89 52
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		83.62 16	83.93 22	82.69 12	89.78 11	77.51 22	87.01 17	84.19 72	70.23 52	84.49 67	90.67 57	75.15 45	86.37 20	79.58 15	94.26 54	84.18 143

APD-MVS_3200maxsize		83.57 17	84.33 17	81.31 32	82.83 112	73.53 44	85.50 30	87.45 14	74.11 23	86.45 39	90.52 62	80.02 10	84.48 73	77.73 32	94.34 52	85.93 86

region2R		83.54 18	83.86 24	82.58 15	89.82 10	77.53 18	87.06 16	84.23 71	70.19 54	83.86 74	90.72 56	75.20 44	86.27 23	79.41 19	94.25 55	83.95 149

XVS		83.51 19	83.73 25	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	90.39 69	73.86 56	86.31 21	78.84 24	94.03 58	84.64 123

LPG-MVS_test		83.47 20	84.33 17	80.90 36	87.00 40	70.41 64	82.04 62	86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 82

lecture		83.41 21	85.02 11	78.58 66	83.87 94	67.26 91	84.47 37	88.27 7	73.64 28	87.35 31	91.96 24	78.55 21	82.92 100	81.59 4	95.50 11	85.56 97

HFP-MVS		83.39 22	84.03 21	81.48 27	89.25 21	75.69 28	87.01 17	84.27 68	70.23 52	84.47 68	90.43 64	76.79 30	85.94 36	79.58 15	94.23 56	82.82 190

MTAPA		83.19 23	83.87 23	81.13 34	91.16 3	78.16 12	84.87 33	80.63 140	72.08 42	84.93 60	90.79 52	74.65 50	84.42 75	80.98 6	94.75 34	80.82 242

MP-MVS		83.19 23	83.54 28	82.14 20	90.54 5	79.00 9	86.42 25	83.59 81	71.31 45	81.26 104	90.96 46	74.57 51	84.69 70	78.41 26	94.78 33	82.74 193
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		83.12 25	83.68 26	81.45 28	89.14 25	73.28 46	86.32 26	85.97 26	67.39 68	84.02 72	90.39 69	74.73 49	86.46 17	80.73 8	94.43 45	84.60 128

PGM-MVS		83.07 26	83.25 35	82.54 16	89.57 14	77.21 24	82.04 62	85.40 37	67.96 65	84.91 63	90.88 49	75.59 40	86.57 16	78.16 27	94.71 36	83.82 151

SteuartSystems-ACMMP		83.07 26	83.64 27	81.35 30	85.14 73	71.00 58	85.53 29	84.78 50	70.91 49	85.64 49	90.41 66	75.55 42	87.69 5	79.75 12	95.08 25	85.36 102
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		82.88 28	84.14 19	79.08 56	84.80 79	66.72 97	86.54 23	85.11 43	72.00 43	86.65 36	91.75 32	78.20 23	87.04 11	77.93 30	94.32 53	83.47 164
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
GST-MVS		82.79 29	83.27 34	81.34 31	88.99 27	73.29 45	85.94 28	85.13 42	68.58 63	84.14 71	90.21 79	73.37 60	86.41 18	79.09 23	93.98 61	84.30 142

ACMP	69.50 8	82.64 30	83.38 31	80.40 41	86.50 46	69.44 73	82.30 59	86.08 25	66.80 73	86.70 35	89.99 82	81.64 6	85.95 35	74.35 58	96.11 4	85.81 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MP-MVS-pluss		82.54 31	83.46 30	79.76 45	88.88 31	68.44 82	81.57 65	86.33 20	63.17 118	85.38 56	91.26 41	76.33 34	84.67 71	83.30 2	94.96 28	86.17 81
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		82.33 32	83.28 33	79.46 51	89.28 19	69.09 80	83.62 47	84.98 46	64.77 100	83.97 73	91.02 45	75.53 43	85.93 38	82.00 3	94.36 50	83.35 170

SMA-MVS		82.12 33	82.68 43	80.43 40	88.90 30	69.52 71	85.12 32	84.76 51	63.53 112	84.23 70	91.47 38	72.02 68	87.16 8	79.74 14	94.36 50	84.61 126
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 34	83.31 32	78.49 68	88.17 37	73.96 38	83.11 54	84.52 61	66.40 78	87.45 26	89.16 100	81.02 8	80.52 150	74.27 59	95.73 8	80.98 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPE-MVS		82.00 35	83.02 38	78.95 61	85.36 69	67.25 92	82.91 55	84.98 46	73.52 29	85.43 55	90.03 81	76.37 33	86.97 13	74.56 54	94.02 60	82.62 198
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		81.78 36	83.48 29	76.67 90	86.12 54	61.06 148	83.62 47	84.72 53	72.61 36	87.38 28	89.70 87	77.48 27	85.89 42	75.29 47	94.39 46	83.08 180

PMVS	70.70 6	81.70 37	83.15 36	77.36 84	90.35 6	82.82 3	82.15 60	79.22 171	74.08 24	87.16 33	91.97 23	84.80 2	76.97 211	64.98 138	93.61 65	72.28 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UA-Net		81.56 38	82.28 45	79.40 52	88.91 29	69.16 78	84.67 36	80.01 154	75.34 19	79.80 120	94.91 2	69.79 92	80.25 154	72.63 72	94.46 41	88.78 44

CPTT-MVS		81.51 39	81.76 48	80.76 38	89.20 23	78.75 10	86.48 24	82.03 106	68.80 59	80.92 109	88.52 117	72.00 69	82.39 110	74.80 49	93.04 72	81.14 232

DVP-MVS++		81.24 40	82.74 42	76.76 89	83.14 102	60.90 152	91.64 1	85.49 33	74.03 25	84.93 60	90.38 71	66.82 122	85.90 40	77.43 35	90.78 120	83.49 161

ACMH+	66.64 10	81.20 41	82.48 44	77.35 85	81.16 134	62.39 133	80.51 73	87.80 9	73.02 31	87.57 24	91.08 44	80.28 9	82.44 109	64.82 140	96.10 5	87.21 61

DVP-MVS		81.15 42	83.12 37	75.24 113	86.16 52	60.78 154	83.77 45	80.58 142	72.48 38	85.83 47	90.41 66	78.57 19	85.69 47	75.86 43	94.39 46	79.24 273
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 49	79.36 53	84.47 84	70.53 63	83.85 43	83.70 79	69.43 58	83.67 76	88.96 107	75.89 38	86.41 18	72.62 73	92.95 73	81.14 232
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 56	82.87 8	81.41 130	72.03 49	84.38 39	86.23 24	77.28 18	80.65 113	90.18 80	59.80 212	87.58 6	73.06 68	91.34 99	89.01 36

DeepC-MVS	72.44 4	81.00 45	80.83 55	81.50 26	86.70 45	70.03 68	82.06 61	87.00 16	59.89 144	80.91 110	90.53 60	72.19 65	88.56 2	73.67 64	94.52 40	85.92 87
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 57	86.86 44	69.39 74	79.41 91	84.00 77	65.64 83	85.54 53	89.28 93	76.32 35	83.47 90	74.03 61	93.57 67	84.35 139
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		80.99 46	80.85 54	81.41 29	78.37 172	71.37 54	87.45 8	85.87 28	77.48 16	81.98 93	89.95 84	69.14 95	85.26 57	66.15 127	91.24 101	87.61 56

SF-MVS		80.72 48	81.80 47	77.48 81	82.03 122	64.40 119	83.41 51	88.46 6	65.28 91	84.29 69	89.18 98	73.73 59	83.22 94	76.01 42	93.77 63	84.81 119

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 60	87.01 39	72.91 47	80.23 81	85.56 32	66.56 77	85.64 49	89.57 89	69.12 96	80.55 149	72.51 74	93.37 68	83.48 163

MSP-MVS		80.49 50	79.67 63	82.96 6	89.70 12	77.46 23	87.16 12	85.10 44	64.94 99	81.05 107	88.38 121	57.10 248	87.10 9	79.75 12	83.87 254	84.31 140
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 39	73.11 145	89.83 9	39.02 373	77.06 120	82.61 97	80.04 5	90.60 7	92.85 12	74.93 48	85.21 60	63.15 160	95.15 23	95.09 2

PS-CasMVS		80.41 52	82.86 41	73.07 146	89.93 7	39.21 370	77.15 118	81.28 122	79.74 6	90.87 5	92.73 14	75.03 47	84.93 65	63.83 152	95.19 21	95.07 3

DTE-MVSNet		80.35 53	82.89 40	72.74 164	89.84 8	37.34 390	77.16 117	81.81 110	80.45 4	90.92 4	92.95 10	74.57 51	86.12 31	63.65 153	94.68 37	94.76 6

SD-MVS		80.28 54	81.55 52	76.47 95	83.57 96	67.83 86	83.39 52	85.35 40	64.42 102	86.14 43	87.07 142	74.02 55	80.97 141	77.70 33	92.32 84	80.62 250
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 55	82.17 46	74.39 121	89.46 15	42.69 341	78.24 104	82.24 102	78.21 13	89.57 10	92.10 21	68.05 107	85.59 50	66.04 130	95.62 10	94.88 5

HPM-MVS++		79.89 56	79.80 62	80.18 43	89.02 26	78.44 11	83.49 50	80.18 150	64.71 101	78.11 144	88.39 120	65.46 142	83.14 95	77.64 34	91.20 102	78.94 277

XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 68	86.46 47	74.79 33	77.15 118	85.39 38	66.73 74	80.39 116	88.85 109	74.43 54	78.33 191	74.73 51	85.79 214	82.35 204

XVG-OURS		79.51 58	79.82 61	78.58 66	86.11 57	74.96 32	76.33 132	84.95 48	66.89 71	82.75 87	88.99 106	66.82 122	78.37 189	74.80 49	90.76 123	82.40 203

CP-MVSNet		79.48 59	81.65 50	72.98 150	89.66 13	39.06 372	76.76 121	80.46 144	78.91 9	90.32 8	91.70 33	68.49 100	84.89 66	63.40 157	95.12 24	95.01 4

OMC-MVS		79.41 60	78.79 68	81.28 33	80.62 138	70.71 62	80.91 70	84.76 51	62.54 123	81.77 96	86.65 159	71.46 72	83.53 88	67.95 110	92.44 80	89.60 24

v7n		79.37 61	80.41 57	76.28 97	78.67 171	55.81 203	79.22 93	82.51 99	70.72 50	87.54 25	92.44 17	68.00 109	81.34 129	72.84 70	91.72 89	91.69 11

TSAR-MVS + MP.		79.05 62	78.81 67	79.74 46	88.94 28	67.52 89	86.61 22	81.38 120	51.71 252	77.15 160	91.42 40	65.49 141	87.20 7	79.44 18	87.17 197	84.51 134
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 63	78.67 70	79.72 47	84.81 78	73.93 39	80.65 72	76.50 214	51.98 250	87.40 27	91.86 29	76.09 37	78.53 181	68.58 101	90.20 129	86.69 71

test_djsdf		78.88 64	78.27 74	80.70 39	81.42 129	71.24 56	83.98 41	75.72 225	52.27 243	87.37 30	92.25 19	68.04 108	80.56 147	72.28 77	91.15 104	90.32 21

HQP_MVS		78.77 65	78.78 69	78.72 63	85.18 70	65.18 111	82.74 56	85.49 33	65.45 86	78.23 141	89.11 101	60.83 196	86.15 29	71.09 82	90.94 112	84.82 117

anonymousdsp		78.60 66	77.80 78	81.00 35	78.01 179	74.34 37	80.09 82	76.12 220	50.51 274	89.19 11	90.88 49	71.45 73	77.78 203	73.38 65	90.60 125	90.90 17

OurMVSNet-221017-0		78.57 67	78.53 72	78.67 64	80.48 139	64.16 120	80.24 80	82.06 105	61.89 127	88.77 16	93.32 6	57.15 246	82.60 106	70.08 92	92.80 75	89.25 30

jajsoiax		78.51 68	78.16 76	79.59 49	84.65 81	73.83 41	80.42 75	76.12 220	51.33 261	87.19 32	91.51 37	73.79 58	78.44 185	68.27 104	90.13 133	86.49 75

CNVR-MVS		78.49 69	78.59 71	78.16 72	85.86 63	67.40 90	78.12 107	81.50 115	63.92 106	77.51 154	86.56 163	68.43 102	84.82 68	73.83 62	91.61 93	82.26 208

DeepPCF-MVS	71.07 5	78.48 70	77.14 87	82.52 17	84.39 87	77.04 25	76.35 130	84.05 75	56.66 180	80.27 117	85.31 191	68.56 99	87.03 12	67.39 117	91.26 100	83.50 160

DP-MVS		78.44 71	79.29 65	75.90 102	81.86 125	65.33 109	79.05 94	84.63 59	74.83 22	80.41 115	86.27 170	71.68 70	83.45 91	62.45 165	92.40 81	78.92 278

NCCC		78.25 72	78.04 77	78.89 62	85.61 65	69.45 72	79.80 88	80.99 132	65.77 82	75.55 197	86.25 172	67.42 114	85.42 52	70.10 91	90.88 118	81.81 221

test_0402		78.17 73	79.48 64	74.24 123	83.50 97	59.15 171	72.52 181	74.60 236	75.34 19	88.69 17	91.81 31	75.06 46	82.37 111	65.10 136	88.68 166	81.20 230

MM		78.15 74	77.68 79	79.55 50	80.10 142	65.47 107	80.94 69	78.74 181	71.22 46	72.40 267	88.70 111	60.51 200	87.70 4	77.40 37	89.13 158	85.48 99

AllTest		77.66 75	77.43 81	78.35 70	79.19 158	70.81 59	78.60 98	88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 186	55.60 240	90.90 116	85.81 88

PS-MVSNAJss		77.54 76	77.35 85	78.13 74	84.88 76	66.37 99	78.55 99	79.59 163	53.48 233	86.29 40	92.43 18	62.39 171	80.25 154	67.90 111	90.61 124	87.77 53

Elysia		77.52 77	77.43 81	77.78 77	79.01 164	60.26 160	76.55 123	84.34 64	67.82 66	78.73 132	87.94 130	58.68 225	83.79 81	74.70 52	89.10 160	89.28 28

StellarMVS		77.52 77	77.43 81	77.78 77	79.01 164	60.26 160	76.55 123	84.34 64	67.82 66	78.73 132	87.94 130	58.68 225	83.79 81	74.70 52	89.10 160	89.28 28

ACMH	63.62 14	77.50 79	80.11 59	69.68 217	79.61 149	56.28 197	78.81 96	83.62 80	63.41 116	87.14 34	90.23 78	76.11 36	73.32 264	67.58 112	94.44 44	79.44 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		77.33 80	77.06 88	78.14 73	84.21 88	63.98 123	76.07 136	83.45 82	54.20 218	77.68 152	87.18 138	69.98 89	85.37 53	68.01 108	92.72 78	85.08 109

DeepC-MVS_fast	69.89 7	77.17 81	76.33 93	79.70 48	83.90 92	67.94 84	80.06 84	83.75 78	56.73 179	74.88 214	85.32 190	65.54 140	87.79 3	65.61 135	91.14 105	83.35 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		77.08 82	77.39 84	76.14 100	76.86 205	56.87 195	80.32 79	87.52 13	63.45 114	74.66 219	84.52 204	69.87 91	84.94 64	69.76 95	89.59 145	86.60 72

MVSMamba_PlusPlus		76.88 83	78.21 75	72.88 158	80.83 135	48.71 264	83.28 53	82.79 91	72.78 32	79.17 127	91.94 25	56.47 255	83.95 78	70.51 90	86.15 209	85.99 85

X-MVStestdata		76.81 84	74.79 107	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	9.95 466	73.86 56	86.31 21	78.84 24	94.03 58	84.64 123

UniMVSNet_ETH3D		76.74 85	79.02 66	69.92 215	89.27 20	43.81 328	74.47 159	71.70 266	72.33 41	85.50 54	93.65 4	77.98 24	76.88 215	54.60 257	91.64 91	89.08 34

CS-MVS		76.51 86	76.00 96	78.06 75	77.02 194	64.77 116	80.78 71	82.66 96	60.39 140	74.15 231	83.30 236	69.65 93	82.07 117	69.27 98	86.75 204	87.36 59

train_agg		76.38 87	76.55 91	75.86 103	85.47 67	69.32 76	76.42 128	78.69 182	54.00 223	76.97 162	86.74 153	66.60 127	81.10 135	72.50 75	91.56 94	77.15 304

NormalMVS		76.15 88	75.08 105	79.36 53	83.87 94	70.01 69	79.92 86	84.34 64	58.60 156	75.21 205	84.02 217	52.85 276	81.82 121	61.45 173	95.50 11	86.24 77

TranMVSNet+NR-MVSNet		76.13 89	77.66 80	71.56 183	84.61 82	42.57 343	70.98 214	78.29 191	68.67 62	83.04 80	89.26 94	72.99 62	80.75 146	55.58 243	95.47 13	91.35 12

tt0805		76.12 90	78.43 73	69.20 227	81.32 131	41.37 349	76.72 122	77.64 200	63.78 109	82.06 92	87.88 132	79.78 11	79.05 171	64.33 144	92.40 81	87.17 65

SixPastTwentyTwo		75.77 91	76.34 92	74.06 126	81.69 127	54.84 214	76.47 125	75.49 227	64.10 105	87.73 21	92.24 20	50.45 294	81.30 131	67.41 115	91.46 96	86.04 84

RPSCF		75.76 92	74.37 113	79.93 44	74.81 234	77.53 18	77.53 112	79.30 168	59.44 147	78.88 130	89.80 86	71.26 75	73.09 266	57.45 221	80.89 303	89.17 33

v10		75.69 93	76.20 94	74.16 124	74.44 243	48.69 265	75.84 140	82.93 90	59.02 152	85.92 45	89.17 99	58.56 227	82.74 104	70.73 86	89.14 157	91.05 14

testf1		75.66 94	76.57 89	72.95 151	67.07 370	67.62 87	76.10 134	80.68 137	64.95 97	86.58 37	90.94 47	71.20 76	71.68 290	60.46 185	91.13 106	79.56 267

APD_test2		75.66 94	76.57 89	72.95 151	67.07 370	67.62 87	76.10 134	80.68 137	64.95 97	86.58 37	90.94 47	71.20 76	71.68 290	60.46 185	91.13 106	79.56 267

Anonymous20231211		75.54 96	77.19 86	70.59 196	77.67 185	45.70 312	74.73 153	80.19 149	68.80 59	82.95 83	92.91 11	66.26 131	76.76 217	58.41 211	92.77 76	89.30 27

MVS_0304		75.45 97	74.66 109	77.83 76	75.58 224	61.53 141	78.29 102	77.18 208	63.15 120	69.97 303	87.20 137	57.54 243	87.05 10	74.05 60	88.96 163	84.89 112

Effi-MVS+-dtu		75.43 98	72.28 164	84.91 3	77.05 192	83.58 2	78.47 100	77.70 199	57.68 165	74.89 213	78.13 335	64.80 150	84.26 77	56.46 231	85.32 224	86.88 68

F-COLMAP		75.29 99	73.99 123	79.18 55	81.73 126	71.90 50	81.86 64	82.98 88	59.86 145	72.27 268	84.00 219	64.56 153	83.07 98	51.48 281	87.19 196	82.56 200

casdiffmvs_mvg		75.26 100	76.18 95	72.52 169	72.87 276	49.47 258	72.94 178	84.71 55	59.49 146	80.90 111	88.81 110	70.07 88	79.71 162	67.40 116	88.39 169	88.40 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HQP-MVS		75.24 101	75.01 106	75.94 101	82.37 116	58.80 178	77.32 114	84.12 73	59.08 148	71.58 279	85.96 184	58.09 234	85.30 55	67.38 119	89.16 154	83.73 156

TAPA-MVS	65.27 12	75.16 102	74.29 116	77.77 79	74.86 233	68.08 83	77.89 108	84.04 76	55.15 196	76.19 190	83.39 230	66.91 120	80.11 158	60.04 194	90.14 132	85.13 106
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS-MVSNet		75.10 103	75.42 103	74.15 125	79.23 156	48.05 278	79.43 89	78.04 195	70.09 55	79.17 127	88.02 129	53.04 275	83.60 85	58.05 215	93.76 64	90.79 18

v8		75.07 104	75.64 100	73.35 138	73.42 260	47.46 290	75.20 143	81.45 117	60.05 142	85.64 49	89.26 94	58.08 236	81.80 124	69.71 97	87.97 177	90.79 18

APD_test1		75.04 105	75.38 104	74.02 127	69.89 329	70.15 66	76.46 126	79.71 159	65.50 85	82.99 82	88.60 116	66.94 119	72.35 278	59.77 197	88.54 167	79.56 267

UniMVSNet (Re)		75.00 106	75.48 102	73.56 136	83.14 102	47.92 280	70.41 223	81.04 130	63.67 110	79.54 122	86.37 168	62.83 164	81.82 121	57.10 225	95.25 17	90.94 16

PHI-MVS		74.92 107	74.36 114	76.61 91	76.40 210	62.32 134	80.38 76	83.15 86	54.16 220	73.23 251	80.75 282	62.19 176	83.86 80	68.02 107	90.92 115	83.65 157

DU-MVS		74.91 108	75.57 101	72.93 154	83.50 97	45.79 309	69.47 236	80.14 151	65.22 92	81.74 98	87.08 140	61.82 181	81.07 137	56.21 233	94.98 26	91.93 9

UniMVSNet_NR-MVSNet		74.90 109	75.65 99	72.64 167	83.04 107	45.79 309	69.26 242	78.81 177	66.66 76	81.74 98	86.88 147	63.26 160	81.07 137	56.21 233	94.98 26	91.05 14

SPE-MVS-test		74.89 110	74.23 117	76.86 88	77.01 195	62.94 131	78.98 95	84.61 60	58.62 155	70.17 300	80.80 281	66.74 126	81.96 119	61.74 170	89.40 152	85.69 95

nrg030		74.87 111	75.99 97	71.52 184	74.90 232	49.88 257	74.10 166	82.58 98	54.55 209	83.50 78	89.21 96	71.51 71	75.74 229	61.24 177	92.34 83	88.94 39

Vis-MVSNet		74.85 112	74.56 110	75.72 104	81.63 128	64.64 117	76.35 130	79.06 173	62.85 121	73.33 249	88.41 119	62.54 169	79.59 165	63.94 151	82.92 268	82.94 184
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_9		74.56 113	74.30 115	75.34 110	77.17 191	64.87 115	72.62 180	76.17 219	54.54 210	78.32 140	86.14 176	65.14 148	75.72 230	73.10 67	85.55 218	85.42 100

MSLP-MVS++		74.48 114	75.78 98	70.59 196	84.66 80	62.40 132	78.65 97	84.24 70	60.55 139	77.71 151	81.98 262	63.12 161	77.64 205	62.95 161	88.14 172	71.73 365

AdaColmap		74.22 115	74.56 110	73.20 142	81.95 123	60.97 150	79.43 89	80.90 133	65.57 84	72.54 265	81.76 267	70.98 79	85.26 57	47.88 320	90.00 134	73.37 343

CSCG		74.12 116	74.39 112	73.33 139	79.35 153	61.66 140	77.45 113	81.98 107	62.47 125	79.06 129	80.19 293	61.83 180	78.79 177	59.83 196	87.35 187	79.54 270

SymmetryMVS		74.00 117	72.85 150	77.43 83	85.17 72	70.01 69	79.92 86	68.48 314	58.60 156	75.21 205	84.02 217	52.85 276	81.82 121	61.45 173	89.99 136	80.47 253

test_fmvsmconf0.01_n		73.91 118	73.64 130	74.71 114	69.79 333	66.25 100	75.90 138	79.90 155	46.03 327	76.48 184	85.02 194	67.96 111	73.97 257	74.47 57	87.22 194	83.90 150

PAPM_NR		73.91 118	74.16 119	73.16 143	81.90 124	53.50 225	81.28 67	81.40 118	66.17 80	73.30 250	83.31 235	59.96 207	83.10 97	58.45 210	81.66 290	82.87 188

EPP-MVSNet		73.86 120	73.38 136	75.31 111	78.19 175	53.35 227	80.45 74	77.32 204	65.11 95	76.47 185	86.80 148	49.47 300	83.77 83	53.89 266	92.72 78	88.81 43

K. test v3		73.67 121	73.61 132	73.87 129	79.78 146	55.62 207	74.69 155	62.04 359	66.16 81	84.76 64	93.23 8	49.47 300	80.97 141	65.66 134	86.67 205	85.02 111

NR-MVSNet		73.62 122	74.05 122	72.33 174	83.50 97	43.71 329	65.65 304	77.32 204	64.32 103	75.59 196	87.08 140	62.45 170	81.34 129	54.90 252	95.63 9	91.93 9

balanced_conf03		73.59 123	74.06 121	72.17 178	77.48 188	47.72 285	81.43 66	82.20 103	54.38 211	79.19 126	87.68 134	54.41 267	83.57 86	63.98 148	85.78 215	85.22 103

DP-MVS Recon		73.57 124	72.69 154	76.23 98	82.85 111	63.39 126	74.32 161	82.96 89	57.75 164	70.35 296	81.98 262	64.34 155	84.41 76	49.69 297	89.95 137	80.89 240

CNLPA		73.44 125	73.03 147	74.66 115	78.27 173	75.29 30	75.99 137	78.49 186	65.39 88	75.67 195	83.22 241	61.23 189	66.77 351	53.70 269	85.33 223	81.92 219

MCST-MVS		73.42 126	73.34 139	73.63 133	81.28 132	59.17 170	74.80 151	83.13 87	45.50 331	72.84 258	83.78 226	65.15 146	80.99 139	64.54 141	89.09 162	80.73 246

v1192		73.40 127	73.42 134	73.32 140	74.65 240	48.67 266	72.21 187	81.73 111	52.76 238	81.85 94	84.56 202	57.12 247	82.24 115	68.58 101	87.33 189	89.06 35

114514_t		73.40 127	73.33 140	73.64 132	84.15 90	57.11 193	78.20 105	80.02 153	43.76 354	72.55 264	86.07 182	64.00 156	83.35 93	60.14 192	91.03 111	80.45 254

FC-MVSNet-test		73.32 129	74.78 108	68.93 237	79.21 157	36.57 392	71.82 201	79.54 165	57.63 169	82.57 89	90.38 71	59.38 216	78.99 173	57.91 216	94.56 39	91.23 13

v1144		73.29 130	73.39 135	73.01 148	74.12 249	48.11 276	72.01 193	81.08 129	53.83 227	81.77 96	84.68 197	58.07 237	81.91 120	68.10 105	86.86 200	88.99 38

test_fmvsmconf0.1_n		73.26 131	72.82 153	74.56 116	69.10 340	66.18 102	74.65 157	79.34 167	45.58 330	75.54 198	83.91 222	67.19 117	73.88 260	73.26 66	86.86 200	83.63 158

GeoE		73.14 132	73.77 128	71.26 188	78.09 177	52.64 230	74.32 161	79.56 164	56.32 183	76.35 188	83.36 234	70.76 81	77.96 199	63.32 158	81.84 284	83.18 175

baseline		73.10 133	73.96 124	70.51 198	71.46 297	46.39 306	72.08 190	84.40 63	55.95 188	76.62 176	86.46 166	67.20 116	78.03 198	64.22 145	87.27 193	87.11 66

h-mvs33		73.08 134	71.61 178	77.48 81	83.89 93	72.89 48	70.47 221	71.12 284	54.28 214	77.89 145	83.41 229	49.04 306	80.98 140	63.62 154	90.77 122	78.58 281

TSAR-MVS + GP.		73.08 134	71.60 179	77.54 80	78.99 167	70.73 61	74.96 146	69.38 300	60.73 138	74.39 227	78.44 329	57.72 241	82.78 103	60.16 190	89.60 144	79.11 275

v1240		73.06 136	73.14 142	72.84 160	74.74 236	47.27 294	71.88 200	81.11 126	51.80 251	82.28 91	84.21 209	56.22 257	82.34 112	68.82 100	87.17 197	88.91 40

casdiffmvs		73.06 136	73.84 125	70.72 194	71.32 299	46.71 299	70.93 215	84.26 69	55.62 191	77.46 157	87.10 139	67.09 118	77.81 201	63.95 149	86.83 202	87.64 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		73.01 138	73.12 144	72.66 166	73.79 255	49.90 253	71.63 203	78.44 187	58.22 159	80.51 114	86.63 160	58.15 232	79.62 163	62.51 163	88.20 171	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet		73.00 139	71.84 171	76.48 94	75.82 221	61.28 144	74.81 149	80.37 147	63.17 118	62.43 380	80.50 287	61.10 193	85.16 63	64.00 147	84.34 250	83.01 183

v144192		72.99 140	73.06 146	72.77 162	74.58 241	47.48 289	71.90 199	80.44 145	51.57 254	81.46 102	84.11 215	58.04 238	82.12 116	67.98 109	87.47 184	88.70 45

MVS_111021_HR		72.98 141	72.97 149	72.99 149	80.82 136	65.47 107	68.81 252	72.77 255	57.67 166	75.76 193	82.38 254	71.01 78	77.17 209	61.38 175	86.15 209	76.32 316

fmvsm_s_conf0.5_n_3		72.97 142	74.13 120	69.47 221	71.40 298	58.36 184	73.07 175	80.64 139	56.86 175	75.49 200	84.67 198	67.86 112	72.33 279	75.68 45	81.54 293	77.73 297

v1921920		72.96 143	72.98 148	72.89 157	74.67 237	47.58 287	71.92 198	80.69 136	51.70 253	81.69 100	83.89 223	56.58 253	82.25 114	68.34 103	87.36 186	88.82 42

test_fmvsmconf_n		72.91 144	72.40 161	74.46 117	68.62 344	66.12 103	74.21 165	78.80 179	45.64 329	74.62 221	83.25 238	66.80 125	73.86 261	72.97 69	86.66 206	83.39 167

CLD-MVS		72.88 145	72.36 162	74.43 120	77.03 193	54.30 218	68.77 255	83.43 83	52.12 247	76.79 172	74.44 366	69.54 94	83.91 79	55.88 236	93.25 71	85.09 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
fmvsm_s_conf0.5_n_8		72.87 146	72.85 150	72.93 154	72.25 286	59.01 175	72.35 184	80.13 152	56.32 183	75.74 194	84.12 213	60.14 205	75.05 241	71.71 80	82.90 269	84.75 120

EI-MVSNet-Vis-set		72.78 147	71.87 169	75.54 108	74.77 235	59.02 174	72.24 186	71.56 270	63.92 106	78.59 135	71.59 388	66.22 132	78.60 180	67.58 112	80.32 316	89.00 37

ETV-MVS		72.72 148	72.16 166	74.38 122	76.90 203	55.95 199	73.34 173	84.67 56	62.04 126	72.19 271	70.81 393	65.90 136	85.24 59	58.64 206	84.96 231	81.95 218

PCF-MVS	63.80 13	72.70 149	71.69 173	75.72 104	78.10 176	60.01 163	73.04 176	81.50 115	45.34 336	79.66 121	84.35 208	65.15 146	82.65 105	48.70 309	89.38 153	84.50 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 150	71.68 174	75.47 109	74.67 237	58.64 182	72.02 192	71.50 271	63.53 112	78.58 137	71.39 392	65.98 134	78.53 181	67.30 122	80.18 319	89.23 31

KinetiMVS		72.61 151	72.54 157	72.82 161	71.47 296	55.27 208	68.54 260	76.50 214	61.70 129	74.95 211	86.08 180	59.17 218	76.95 212	69.96 93	84.45 247	86.24 77

Anonymous20240529		72.56 152	73.79 127	68.86 239	76.89 204	45.21 316	68.80 254	77.25 206	67.16 69	76.89 166	90.44 63	65.95 135	74.19 255	50.75 288	90.00 134	87.18 64

FIs		72.56 152	73.80 126	68.84 240	78.74 170	37.74 386	71.02 213	79.83 156	56.12 185	80.88 112	89.45 91	58.18 230	78.28 192	56.63 227	93.36 69	90.51 20

v2v482		72.55 154	72.58 156	72.43 171	72.92 275	46.72 298	71.41 206	79.13 172	55.27 194	81.17 106	85.25 192	55.41 261	81.13 134	67.25 123	85.46 219	89.43 26

SSM_0404		72.51 155	72.15 167	73.60 134	78.20 174	55.86 202	74.41 160	79.83 156	53.69 229	73.98 237	84.18 210	62.26 174	82.50 107	58.21 212	84.60 243	82.43 202

sc_t1		72.50 156	74.23 117	67.33 266	80.05 143	46.99 296	66.58 291	69.48 299	66.28 79	77.62 153	91.83 30	70.98 79	68.62 324	53.86 268	91.40 97	86.37 76

test_fmvsmvis_n_1920		72.36 157	72.49 158	71.96 179	71.29 301	64.06 122	72.79 179	81.82 109	40.23 386	81.25 105	81.04 277	70.62 82	68.69 321	69.74 96	83.60 261	83.14 176

hse-mvs2		72.32 158	70.66 194	77.31 86	83.10 106	71.77 51	69.19 244	71.45 273	54.28 214	77.89 145	78.26 331	49.04 306	79.23 168	63.62 154	89.13 158	80.92 239

sasdasda		72.29 159	73.38 136	69.04 231	74.23 244	47.37 291	73.93 168	83.18 84	54.36 212	76.61 177	81.64 270	72.03 66	75.34 234	57.12 223	87.28 191	84.40 136

canonicalmvs		72.29 159	73.38 136	69.04 231	74.23 244	47.37 291	73.93 168	83.18 84	54.36 212	76.61 177	81.64 270	72.03 66	75.34 234	57.12 223	87.28 191	84.40 136

SSM_0407		72.15 161	71.85 170	73.06 147	76.92 198	55.22 209	73.59 170	79.83 156	53.69 229	73.08 253	84.18 210	62.26 174	81.98 118	58.21 212	84.91 235	81.99 215

Effi-MVS+		72.10 162	72.28 164	71.58 182	74.21 247	50.33 246	74.72 154	82.73 94	62.62 122	70.77 292	76.83 346	69.96 90	80.97 141	60.20 188	78.43 341	83.45 166

MVS_111021_LR		72.10 162	71.82 172	72.95 151	79.53 151	73.90 40	70.45 222	66.64 324	56.87 174	76.81 171	81.76 267	68.78 97	71.76 288	61.81 168	83.74 257	73.18 345

fmvsm_l_conf0.5_n_3		71.98 164	71.68 174	72.88 158	72.84 277	64.15 121	73.48 171	77.11 209	48.97 299	71.31 287	84.18 210	67.98 110	71.60 292	68.86 99	80.43 315	82.89 186

pmmvs6		71.82 165	73.66 129	66.31 282	75.94 219	42.01 345	66.99 283	72.53 259	63.45 114	76.43 186	92.78 13	72.95 63	69.69 313	51.41 283	90.46 126	87.22 60

PLC	62.01 16	71.79 166	70.28 197	76.33 96	80.31 141	68.63 81	78.18 106	81.24 123	54.57 208	67.09 344	80.63 285	59.44 214	81.74 126	46.91 327	84.17 251	78.63 279
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MGCFI-Net		71.70 167	73.10 145	67.49 263	73.23 264	43.08 337	72.06 191	82.43 100	54.58 207	75.97 192	82.00 260	72.42 64	75.22 236	57.84 217	87.34 188	84.18 143

BP-MVS1		71.60 168	70.06 198	76.20 99	74.07 250	55.22 209	74.29 163	73.44 243	57.29 171	73.87 240	84.65 199	32.57 397	83.49 89	72.43 76	87.94 178	89.89 23

VDDNet		71.60 168	73.13 143	67.02 274	86.29 48	41.11 351	69.97 229	66.50 325	68.72 61	74.74 215	91.70 33	59.90 209	75.81 226	48.58 311	91.72 89	84.15 145

tt0320-xc		71.50 170	73.63 131	65.08 292	79.77 147	40.46 363	64.80 319	68.86 308	67.08 70	76.84 170	93.24 7	70.33 84	66.77 351	49.76 296	92.02 87	88.02 51

3Dnovator	65.95 11	71.50 170	71.22 184	72.34 173	73.16 265	63.09 129	78.37 101	78.32 189	57.67 166	72.22 270	84.61 201	54.77 263	78.47 183	60.82 183	81.07 301	75.45 322

fmvsm_s_conf0.5_n_5		71.46 172	71.62 177	70.99 192	73.89 254	59.95 164	73.02 177	73.08 245	45.15 342	77.30 159	84.06 216	64.73 152	70.08 308	71.20 81	82.10 279	82.92 185

viewcassd2359sk11		71.41 173	71.89 168	69.98 213	73.50 257	46.46 303	68.91 248	82.39 101	53.62 231	74.57 223	84.41 206	67.40 115	77.27 208	61.35 176	80.89 303	86.21 80

viewmacassd2359aftdt		71.41 173	72.29 163	68.78 241	71.32 299	44.81 319	70.11 226	81.51 114	52.64 240	74.95 211	86.79 149	66.02 133	74.50 249	62.43 166	84.86 238	87.03 67

tt0320		71.34 175	73.47 133	64.97 294	79.92 145	40.81 356	65.22 311	69.07 304	66.72 75	76.15 191	93.36 5	70.35 83	66.90 344	49.31 304	91.09 109	87.21 61

FA-MVS(test-final)		71.27 176	71.06 186	71.92 180	73.96 251	52.32 232	76.45 127	76.12 220	59.07 151	74.04 236	86.18 173	52.18 281	79.43 167	59.75 198	81.76 285	84.03 147

WR-MVS		71.20 177	72.48 159	67.36 265	84.98 75	35.70 400	64.43 327	68.66 312	65.05 96	81.49 101	86.43 167	57.57 242	76.48 220	50.36 292	93.32 70	89.90 22

LuminaMVS		71.15 178	70.79 191	72.24 177	77.20 190	58.34 185	72.18 188	76.20 218	54.91 198	77.74 149	81.93 264	49.17 305	76.31 222	62.12 167	85.66 217	82.07 212

V42		71.06 179	70.83 189	71.72 181	67.25 366	47.14 295	65.94 298	80.35 148	51.35 260	83.40 79	83.23 239	59.25 217	78.80 176	65.91 131	80.81 307	89.23 31

FMVSNet1		71.06 179	72.48 159	66.81 276	77.65 186	40.68 359	71.96 195	73.03 246	61.14 132	79.45 124	90.36 74	60.44 201	75.20 238	50.20 293	88.05 174	84.54 130

dcpmvs_2		71.02 181	72.65 155	66.16 283	76.06 218	50.49 244	71.97 194	79.36 166	50.34 275	82.81 86	83.63 227	64.38 154	67.27 340	61.54 172	83.71 259	80.71 248

API-MVS		70.97 182	71.51 181	69.37 222	75.20 227	55.94 200	80.99 68	76.84 211	62.48 124	71.24 288	77.51 341	61.51 185	80.96 144	52.04 277	85.76 216	71.22 371

GDP-MVS		70.84 183	69.24 213	75.62 106	76.44 209	55.65 205	74.62 158	82.78 93	49.63 285	72.10 272	83.79 225	31.86 405	82.84 102	64.93 139	87.01 199	88.39 49

VDD-MVS		70.81 184	71.44 182	68.91 238	79.07 163	46.51 302	67.82 270	70.83 288	61.23 131	74.07 234	88.69 112	59.86 210	75.62 231	51.11 285	90.28 128	84.61 126

fmvsm_l_conf0.5_n_9		70.73 185	71.08 185	69.67 218	70.44 317	58.80 178	70.21 225	75.11 232	48.15 308	73.50 245	82.69 249	65.69 138	68.05 332	70.87 85	83.02 267	82.16 209

EG-PatchMatch MVS		70.70 186	70.88 188	70.16 208	82.64 115	58.80 178	71.48 204	73.64 241	54.98 197	76.55 180	81.77 266	61.10 193	78.94 174	54.87 253	80.84 306	72.74 353

Baseline_NR-MVSNet		70.62 187	73.19 141	62.92 317	76.97 196	34.44 408	68.84 249	70.88 287	60.25 141	79.50 123	90.53 60	61.82 181	69.11 318	54.67 256	95.27 16	85.22 103

alignmvs		70.54 188	71.00 187	69.15 229	73.50 257	48.04 279	69.85 232	79.62 160	53.94 226	76.54 181	82.00 260	59.00 220	74.68 246	57.32 222	87.21 195	84.72 121

MG-MVS		70.47 189	71.34 183	67.85 256	79.26 155	40.42 364	74.67 156	75.15 231	58.41 158	68.74 327	88.14 128	56.08 258	83.69 84	59.90 195	81.71 289	79.43 272

RRT-MVS		70.33 190	70.73 192	69.14 230	71.93 291	45.24 315	75.10 144	75.08 233	60.85 137	78.62 134	87.36 136	49.54 299	78.64 179	60.16 190	77.90 349	83.55 159

mamba_0408		70.32 191	69.35 209	73.24 141	76.92 198	55.22 209	56.61 387	79.27 169	52.14 245	73.08 253	83.14 242	60.53 198	82.50 107	57.51 219	84.91 235	81.99 215

viewmanbaseed2359cas		70.24 192	70.83 189	68.48 246	69.99 328	44.55 323	69.48 235	81.01 131	50.87 266	73.61 242	84.84 196	64.00 156	74.31 253	60.24 187	83.43 263	86.56 73

AUN-MVS		70.22 193	67.88 240	77.22 87	82.96 110	71.61 52	69.08 245	71.39 274	49.17 293	71.70 275	78.07 336	37.62 376	79.21 169	61.81 168	89.15 156	80.82 242

UGNet		70.20 194	69.05 216	73.65 131	76.24 212	63.64 124	75.87 139	72.53 259	61.48 130	60.93 391	86.14 176	52.37 280	77.12 210	50.67 289	85.21 225	80.17 261
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
fmvsm_s_conf0.5_n_4		70.18 195	69.83 203	71.24 189	71.65 293	58.59 183	69.29 241	71.66 267	48.69 301	71.62 276	82.11 258	59.94 208	70.03 309	74.52 55	78.96 334	85.10 107

fmvsm_s_conf0.5_n_6		70.08 196	69.97 199	70.39 199	72.99 274	58.93 176	68.84 249	76.40 216	49.08 295	68.75 326	81.65 269	57.34 244	71.97 285	70.91 84	83.81 256	80.26 258

PVSNet_Blended_VisFu		70.04 197	68.88 219	73.53 137	82.71 113	63.62 125	74.81 149	81.95 108	48.53 303	67.16 343	79.18 320	51.42 287	78.38 188	54.39 261	79.72 328	78.60 280

Fast-Effi-MVS+-dtu		70.00 198	68.74 223	73.77 130	73.47 259	64.53 118	71.36 207	78.14 194	55.81 190	68.84 324	74.71 363	65.36 143	75.75 228	52.00 278	79.00 333	81.03 235

DPM-MVS		69.98 199	69.22 215	72.26 175	82.69 114	58.82 177	70.53 220	81.23 124	47.79 314	64.16 361	80.21 291	51.32 288	83.12 96	60.14 192	84.95 232	74.83 328

MVSFormer		69.93 200	69.03 217	72.63 168	74.93 230	59.19 168	83.98 41	75.72 225	52.27 243	63.53 374	76.74 347	43.19 339	80.56 147	72.28 77	78.67 338	78.14 290

viewdifsd2359ckpt13		69.89 201	69.74 204	70.32 203	70.82 304	48.73 263	72.39 183	81.39 119	48.20 306	72.73 260	82.73 246	62.61 166	76.50 219	55.87 237	80.93 302	85.73 94

MVS_Test		69.84 202	70.71 193	67.24 268	67.49 364	43.25 336	69.87 231	81.22 125	52.69 239	71.57 282	86.68 156	62.09 177	74.51 248	66.05 129	78.74 336	83.96 148

c3_l		69.82 203	69.89 201	69.61 219	66.24 377	43.48 332	68.12 267	79.61 162	51.43 256	77.72 150	80.18 294	54.61 266	78.15 197	63.62 154	87.50 183	87.20 63

test_fmvsm_n_1920		69.63 204	68.45 227	73.16 143	70.56 312	65.86 105	70.26 224	78.35 188	37.69 403	74.29 229	78.89 325	61.10 193	68.10 330	65.87 132	79.07 332	85.53 98

TransMVSNet (Re)		69.62 205	71.63 176	63.57 306	76.51 208	35.93 398	65.75 303	71.29 278	61.05 133	75.02 209	89.90 85	65.88 137	70.41 306	49.79 295	89.48 148	84.38 138

EI-MVSNet		69.61 206	69.01 218	71.41 186	73.94 252	49.90 253	71.31 209	71.32 276	58.22 159	75.40 202	70.44 395	58.16 231	75.85 224	62.51 163	79.81 325	88.48 46

Gipuma		69.55 207	72.83 152	59.70 346	63.63 400	53.97 221	80.08 83	75.93 223	64.24 104	73.49 246	88.93 108	57.89 240	62.46 373	59.75 198	91.55 95	62.67 428
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tttt0517		69.46 208	67.79 242	74.46 117	75.34 225	52.72 229	75.05 145	63.27 352	54.69 204	78.87 131	84.37 207	26.63 432	81.15 133	63.95 149	87.93 179	89.51 25

eth_miper_zixun_eth		69.42 209	68.73 224	71.50 185	67.99 355	46.42 304	67.58 272	78.81 177	50.72 269	78.13 143	80.34 290	50.15 296	80.34 152	60.18 189	84.65 241	87.74 54

BH-untuned		69.39 210	69.46 207	69.18 228	77.96 180	56.88 194	68.47 263	77.53 201	56.77 177	77.79 148	79.63 304	60.30 204	80.20 157	46.04 335	80.65 311	70.47 378

v148		69.38 211	69.39 208	69.36 223	69.14 339	44.56 322	68.83 251	72.70 257	54.79 202	78.59 135	84.12 213	54.69 264	76.74 218	59.40 201	82.20 277	86.79 69

viewdifsd2359ckpt11		69.22 212	69.68 205	67.83 258	68.17 352	46.57 300	66.42 293	68.93 306	50.60 272	77.47 156	83.95 220	68.16 104	73.84 262	58.49 208	84.92 233	83.10 177

viewmsd2359difaftdt		69.22 212	69.68 205	67.83 258	68.17 352	46.57 300	66.42 293	68.93 306	50.60 272	77.48 155	83.94 221	68.16 104	73.84 262	58.49 208	84.92 233	83.10 177

PAPR		69.20 214	68.66 225	70.82 193	75.15 229	47.77 283	75.31 142	81.11 126	49.62 287	66.33 346	79.27 317	61.53 184	82.96 99	48.12 317	81.50 295	81.74 225

QAPM		69.18 215	69.26 212	68.94 236	71.61 294	52.58 231	80.37 77	78.79 180	49.63 285	73.51 244	85.14 193	53.66 271	79.12 170	55.11 246	75.54 367	75.11 327

fmvsm_s_conf0.1_n_2		69.14 216	68.42 228	71.28 187	68.30 349	57.60 191	65.06 314	69.91 294	48.24 304	74.56 224	82.84 244	55.55 260	69.73 311	70.66 88	80.69 310	86.52 74

LCM-MVSNet-Re		69.10 217	71.57 180	61.70 326	70.37 319	34.30 410	61.45 349	79.62 160	56.81 176	89.59 9	88.16 127	68.44 101	72.94 267	42.30 356	87.33 189	77.85 296

EPNet		69.10 217	67.32 248	74.46 117	68.33 348	61.27 145	77.56 110	63.57 349	60.95 135	56.62 415	82.75 245	51.53 286	81.24 132	54.36 262	90.20 129	80.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_2		68.93 219	68.23 233	71.02 191	67.78 359	57.58 192	64.74 321	69.56 298	48.16 307	74.38 228	82.32 255	56.00 259	69.68 314	70.65 89	80.52 314	85.80 92

mvsmamba		68.87 220	67.30 250	73.57 135	76.58 207	53.70 224	84.43 38	74.25 238	45.38 335	76.63 175	84.55 203	35.85 383	85.27 56	49.54 300	78.49 340	81.75 224

DELS-MVS		68.83 221	68.31 229	70.38 200	70.55 314	48.31 272	63.78 334	82.13 104	54.00 223	68.96 315	75.17 359	58.95 221	80.06 159	58.55 207	82.74 272	82.76 191
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+		68.81 222	68.30 230	70.35 202	74.66 239	48.61 271	66.06 297	78.32 189	50.62 271	71.48 285	75.54 354	68.75 98	79.59 165	50.55 291	78.73 337	82.86 189

mmtdpeth		68.76 223	70.55 195	63.40 310	67.06 372	56.26 198	68.73 257	71.22 282	55.47 193	70.09 301	88.64 115	65.29 145	56.89 397	58.94 204	89.50 147	77.04 309

OpenMVS	62.51 15	68.76 223	68.75 222	68.78 241	70.56 312	53.91 222	78.29 102	77.35 203	48.85 300	70.22 298	83.52 228	52.65 279	76.93 213	55.31 244	81.99 280	75.49 321

VPA-MVSNet		68.71 225	70.37 196	63.72 304	76.13 214	38.06 384	64.10 330	71.48 272	56.60 182	74.10 233	88.31 122	64.78 151	69.72 312	47.69 322	90.15 131	83.37 169

BH-RMVSNet		68.69 226	68.20 235	70.14 209	76.40 210	53.90 223	64.62 324	73.48 242	58.01 161	73.91 239	81.78 265	59.09 219	78.22 193	48.59 310	77.96 348	78.31 285

EIA-MVS		68.59 227	67.16 251	72.90 156	75.18 228	55.64 206	69.39 237	81.29 121	52.44 242	64.53 357	70.69 394	60.33 203	82.30 113	54.27 263	76.31 361	80.75 245

pm-mvs1		68.40 228	69.85 202	64.04 302	73.10 269	39.94 367	64.61 325	70.50 290	55.52 192	73.97 238	89.33 92	63.91 158	68.38 326	49.68 298	88.02 175	83.81 152

miper_ehance_all_eth		68.36 229	68.16 236	68.98 234	65.14 389	43.34 334	67.07 282	78.92 176	49.11 294	76.21 189	77.72 338	53.48 272	77.92 200	61.16 179	84.59 244	85.68 96

GBi-Net		68.30 230	68.79 220	66.81 276	73.14 266	40.68 359	71.96 195	73.03 246	54.81 199	74.72 216	90.36 74	48.63 312	75.20 238	47.12 324	85.37 220	84.54 130

test1		68.30 230	68.79 220	66.81 276	73.14 266	40.68 359	71.96 195	73.03 246	54.81 199	74.72 216	90.36 74	48.63 312	75.20 238	47.12 324	85.37 220	84.54 130

FE-MVS		68.29 232	66.96 256	72.26 175	74.16 248	54.24 219	77.55 111	73.42 244	57.65 168	72.66 262	84.91 195	32.02 404	81.49 128	48.43 313	81.85 283	81.04 234

diffmvs_AUTHOR		68.27 233	68.59 226	67.32 267	63.76 398	45.37 313	65.31 309	77.19 207	49.25 291	72.68 261	82.19 257	59.62 213	71.17 295	65.75 133	81.53 294	85.42 100

DIV-MVS_self_test		68.27 233	68.26 231	68.29 250	64.98 390	43.67 330	65.89 299	74.67 234	50.04 281	76.86 168	82.43 252	48.74 310	75.38 232	60.94 181	89.81 140	85.81 88

cl____		68.26 235	68.26 231	68.29 250	64.98 390	43.67 330	65.89 299	74.67 234	50.04 281	76.86 168	82.42 253	48.74 310	75.38 232	60.92 182	89.81 140	85.80 92

TinyColmap		67.98 236	69.28 211	64.08 300	67.98 356	46.82 297	70.04 227	75.26 229	53.05 235	77.36 158	86.79 149	59.39 215	72.59 274	45.64 338	88.01 176	72.83 351

xiu_mvs_v1_base_debu		67.87 237	67.07 253	70.26 204	79.13 160	61.90 137	67.34 276	71.25 279	47.98 310	67.70 336	74.19 371	61.31 186	72.62 271	56.51 228	78.26 344	76.27 317

xiu_mvs_v1_base		67.87 237	67.07 253	70.26 204	79.13 160	61.90 137	67.34 276	71.25 279	47.98 310	67.70 336	74.19 371	61.31 186	72.62 271	56.51 228	78.26 344	76.27 317

xiu_mvs_v1_base_debi		67.87 237	67.07 253	70.26 204	79.13 160	61.90 137	67.34 276	71.25 279	47.98 310	67.70 336	74.19 371	61.31 186	72.62 271	56.51 228	78.26 344	76.27 317

MAR-MVS		67.72 240	66.16 265	72.40 172	74.45 242	64.99 114	74.87 147	77.50 202	48.67 302	65.78 350	68.58 420	57.01 250	77.79 202	46.68 330	81.92 281	74.42 336
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		67.68 241	66.07 267	72.49 170	73.34 262	58.20 188	63.80 333	65.55 333	48.10 309	76.91 165	82.64 250	45.20 326	78.84 175	61.20 178	77.89 350	80.44 255

LF4IMVS		67.50 242	67.31 249	68.08 253	58.86 429	61.93 136	71.43 205	75.90 224	44.67 347	72.42 266	80.20 292	57.16 245	70.44 304	58.99 203	86.12 211	71.88 362

fmvsm_l_conf0.5_n		67.48 243	66.88 259	69.28 226	67.41 365	62.04 135	70.69 219	69.85 295	39.46 389	69.59 308	81.09 276	58.15 232	68.73 320	67.51 114	78.16 347	77.07 308

FMVSNet2		67.48 243	68.21 234	65.29 289	73.14 266	38.94 374	68.81 252	71.21 283	54.81 199	76.73 173	86.48 165	48.63 312	74.60 247	47.98 319	86.11 212	82.35 204

MSDG		67.47 245	67.48 246	67.46 264	70.70 308	54.69 216	66.90 286	78.17 192	60.88 136	70.41 295	74.76 361	61.22 191	73.18 265	47.38 323	76.87 357	74.49 334

diffmvs		67.42 246	67.50 245	67.20 269	62.26 406	45.21 316	64.87 317	77.04 210	48.21 305	71.74 274	79.70 302	58.40 229	71.17 295	64.99 137	80.27 317	85.22 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n_a		67.37 247	66.36 263	70.37 201	70.86 303	61.17 146	74.00 167	57.18 378	40.77 381	68.83 325	80.88 279	63.11 162	67.61 336	66.94 124	74.72 374	82.33 207

fmvsm_s_conf0.5_n_7		67.30 248	66.92 257	68.43 247	72.78 278	58.22 187	60.90 355	72.51 261	49.62 287	63.66 371	80.65 284	58.56 227	68.63 323	62.83 162	80.76 308	78.45 283

IMVS_0407		67.26 249	67.35 247	66.97 275	72.47 280	48.64 267	69.03 246	72.98 249	45.33 337	68.91 320	79.37 312	61.91 178	75.77 227	55.06 247	81.11 297	76.49 310

SSM_04072		67.23 250	69.35 209	60.89 338	76.92 198	55.22 209	56.61 387	79.27 169	52.14 245	73.08 253	83.14 242	60.53 198	45.46 434	57.51 219	84.91 235	81.99 215

cl22		67.14 251	66.51 262	69.03 233	63.20 401	43.46 333	66.88 287	76.25 217	49.22 292	74.48 225	77.88 337	45.49 325	77.40 207	60.64 184	84.59 244	86.24 77

AstraMVS		67.11 252	66.84 260	67.92 254	70.75 307	51.36 236	64.77 320	67.06 322	49.03 297	75.40 202	82.05 259	51.26 289	70.65 300	58.89 205	82.32 276	81.77 223

ANet_high		67.08 253	69.94 200	58.51 358	57.55 435	27.09 443	58.43 376	76.80 212	63.56 111	82.40 90	91.93 26	59.82 211	64.98 364	50.10 294	88.86 165	83.46 165

IMVS_0403		67.07 254	67.08 252	67.03 273	72.47 280	48.64 267	68.44 264	72.98 249	45.33 337	68.63 328	79.37 312	60.38 202	75.97 223	55.06 247	81.11 297	76.49 310

LFMVS		67.06 255	67.89 239	64.56 296	78.02 178	38.25 381	70.81 218	59.60 366	65.18 93	71.06 290	86.56 163	43.85 335	75.22 236	46.35 332	89.63 143	80.21 260

thisisatest0530		67.05 256	65.16 279	72.73 165	73.10 269	50.55 243	71.26 211	63.91 347	50.22 278	74.46 226	80.75 282	26.81 431	80.25 154	59.43 200	86.50 207	87.37 58

fmvsm_s_conf0.5_n_a		67.00 257	65.95 271	70.17 207	69.72 334	61.16 147	73.34 173	56.83 381	40.96 378	68.36 330	80.08 296	62.84 163	67.57 337	66.90 126	74.50 378	81.78 222

guyue		66.95 258	66.74 261	67.56 262	70.12 327	51.14 238	65.05 315	68.68 311	49.98 283	74.64 220	80.83 280	50.77 291	70.34 307	57.72 218	82.89 270	81.21 229

fmvsm_l_conf0.5_n_a		66.66 259	65.97 270	68.72 243	67.09 368	61.38 143	70.03 228	69.15 303	38.59 397	68.41 329	80.36 289	56.56 254	68.32 327	66.10 128	77.45 353	76.46 314

fmvsm_s_conf0.1_n		66.60 260	65.54 273	69.77 216	68.99 341	59.15 171	72.12 189	56.74 383	40.72 383	68.25 333	80.14 295	61.18 192	66.92 343	67.34 121	74.40 379	83.23 174

MIMVSNet1		66.57 261	69.23 214	58.59 357	81.26 133	37.73 387	64.06 331	57.62 371	57.02 173	78.40 139	90.75 53	62.65 165	58.10 394	41.77 362	89.58 146	79.95 262

tfpnnormal		66.48 262	67.93 238	62.16 323	73.40 261	36.65 391	63.45 336	64.99 337	55.97 187	72.82 259	87.80 133	57.06 249	69.10 319	48.31 315	87.54 181	80.72 247

KD-MVS_self_test		66.38 263	67.51 244	62.97 315	61.76 408	34.39 409	58.11 379	75.30 228	50.84 268	77.12 161	85.42 189	56.84 251	69.44 315	51.07 286	91.16 103	85.08 109

SDMVSNet		66.36 264	67.85 241	61.88 325	73.04 272	46.14 308	58.54 374	71.36 275	51.42 257	68.93 318	82.72 247	65.62 139	62.22 376	54.41 260	84.67 239	77.28 300

mvs5depth		66.35 265	67.98 237	61.47 330	62.43 404	51.05 239	69.38 238	69.24 302	56.74 178	73.62 241	89.06 104	46.96 320	58.63 390	55.87 237	88.49 168	74.73 330

fmvsm_s_conf0.5_n		66.34 266	65.27 276	69.57 220	68.20 350	59.14 173	71.66 202	56.48 384	40.92 379	67.78 335	79.46 307	61.23 189	66.90 344	67.39 117	74.32 382	82.66 197

Anonymous202405211		66.02 267	66.89 258	63.43 309	74.22 246	38.14 382	59.00 369	66.13 327	63.33 117	69.76 307	85.95 185	51.88 282	70.50 303	44.23 346	87.52 182	81.64 226

VortexMVS		65.93 268	66.04 269	65.58 288	67.63 363	47.55 288	64.81 318	72.75 256	47.37 318	75.17 207	79.62 305	49.28 303	71.00 297	55.20 245	82.51 274	78.21 288

miper_enhance_ethall		65.86 269	65.05 287	68.28 252	61.62 410	42.62 342	64.74 321	77.97 196	42.52 365	73.42 248	72.79 381	49.66 298	77.68 204	58.12 214	84.59 244	84.54 130

RPMNet		65.77 270	65.08 286	67.84 257	66.37 374	48.24 274	70.93 215	86.27 21	54.66 205	61.35 385	86.77 152	33.29 391	85.67 49	55.93 235	70.17 412	69.62 387

viewmambaseed2359dif		65.63 271	65.13 282	67.11 272	64.57 393	44.73 321	64.12 329	72.48 262	43.08 364	71.59 277	81.17 274	58.90 222	72.46 275	52.94 275	77.33 354	84.13 146

VPNet		65.58 272	67.56 243	59.65 347	79.72 148	30.17 432	60.27 361	62.14 355	54.19 219	71.24 288	86.63 160	58.80 223	67.62 335	44.17 347	90.87 119	81.18 231

PVSNet_BlendedMVS		65.38 273	64.30 289	68.61 244	69.81 330	49.36 259	65.60 306	78.96 174	45.50 331	59.98 394	78.61 327	51.82 283	78.20 194	44.30 344	84.11 252	78.27 286

TAMVS		65.31 274	63.75 295	69.97 214	82.23 120	59.76 166	66.78 288	63.37 351	45.20 341	69.79 306	79.37 312	47.42 319	72.17 280	34.48 413	85.15 227	77.99 294

test_yl		65.11 275	65.09 284	65.18 290	70.59 310	40.86 354	63.22 341	72.79 253	57.91 162	68.88 322	79.07 323	42.85 342	74.89 243	45.50 340	84.97 228	79.81 263

DCV-MVSNet		65.11 275	65.09 284	65.18 290	70.59 310	40.86 354	63.22 341	72.79 253	57.91 162	68.88 322	79.07 323	42.85 342	74.89 243	45.50 340	84.97 228	79.81 263

mvs_anonymous		65.08 277	65.49 274	63.83 303	63.79 397	37.60 388	66.52 292	69.82 296	43.44 359	73.46 247	86.08 180	58.79 224	71.75 289	51.90 279	75.63 366	82.15 210

FMVSNet3		65.00 278	65.16 279	64.52 297	69.47 335	37.56 389	66.63 289	70.38 291	51.55 255	74.72 216	83.27 237	37.89 374	74.44 250	47.12 324	85.37 220	81.57 227

ECVR-MVS		64.82 279	65.22 277	63.60 305	78.80 168	31.14 427	66.97 284	56.47 385	54.23 216	69.94 304	88.68 113	37.23 377	74.81 245	45.28 343	89.41 150	84.86 115

BH-w/o		64.81 280	64.29 290	66.36 281	76.08 217	54.71 215	65.61 305	75.23 230	50.10 280	71.05 291	71.86 387	54.33 268	79.02 172	38.20 385	76.14 362	65.36 414

EGC-MVSNET		64.77 281	61.17 319	75.60 107	86.90 43	74.47 34	84.04 40	68.62 313	0.60 468	1.13 470	91.61 36	65.32 144	74.15 256	64.01 146	88.28 170	78.17 289

test1111		64.62 282	65.19 278	62.93 316	79.01 164	29.91 433	65.45 307	54.41 395	54.09 221	71.47 286	88.48 118	37.02 378	74.29 254	46.83 329	89.94 138	84.58 129

cascas		64.59 283	62.77 309	70.05 211	75.27 226	50.02 250	61.79 347	71.61 268	42.46 366	63.68 370	68.89 416	49.33 302	80.35 151	47.82 321	84.05 253	79.78 265

TR-MVS		64.59 283	63.54 298	67.73 261	75.75 223	50.83 242	63.39 337	70.29 292	49.33 290	71.55 283	74.55 364	50.94 290	78.46 184	40.43 370	75.69 365	73.89 340

PM-MVS		64.49 285	63.61 297	67.14 271	76.68 206	75.15 31	68.49 262	42.85 447	51.17 264	77.85 147	80.51 286	45.76 322	66.31 355	52.83 276	76.35 360	59.96 437

jason		64.47 286	62.84 307	69.34 225	76.91 201	59.20 167	67.15 281	65.67 330	35.29 417	65.16 354	76.74 347	44.67 330	70.68 299	54.74 255	79.28 331	78.14 290
jason: jason.
xiu_mvs_v2_base		64.43 287	63.96 293	65.85 287	77.72 184	51.32 237	63.63 335	72.31 264	45.06 345	61.70 382	69.66 407	62.56 167	73.93 259	49.06 306	73.91 384	72.31 358

pmmvs-eth3d		64.41 288	63.27 302	67.82 260	75.81 222	60.18 162	69.49 234	62.05 358	38.81 396	74.13 232	82.23 256	43.76 336	68.65 322	42.53 355	80.63 313	74.63 331

CDS-MVSNet		64.33 289	62.66 310	69.35 224	80.44 140	58.28 186	65.26 310	65.66 331	44.36 349	67.30 342	75.54 354	43.27 338	71.77 287	37.68 389	84.44 248	78.01 293
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		64.27 290	63.73 296	65.90 286	77.82 182	51.42 235	63.33 338	72.33 263	45.09 344	61.60 383	68.04 422	62.39 171	73.95 258	49.07 305	73.87 385	72.34 357

ab-mvs		64.11 291	65.13 282	61.05 335	71.99 290	38.03 385	67.59 271	68.79 310	49.08 295	65.32 353	86.26 171	58.02 239	66.85 349	39.33 374	79.79 327	78.27 286

CANet_DTU		64.04 292	63.83 294	64.66 295	68.39 345	42.97 339	73.45 172	74.50 237	52.05 249	54.78 426	75.44 357	43.99 334	70.42 305	53.49 271	78.41 342	80.59 251

VNet		64.01 293	65.15 281	60.57 341	73.28 263	35.61 401	57.60 381	67.08 321	54.61 206	66.76 345	83.37 232	56.28 256	66.87 347	42.19 358	85.20 226	79.23 274

icg_test_0407_2		63.88 294	65.59 272	58.75 355	72.47 280	48.64 267	53.19 411	72.98 249	45.33 337	68.91 320	79.37 312	61.91 178	51.11 412	55.06 247	81.11 297	76.49 310

sd_testset		63.55 295	65.38 275	58.07 360	73.04 272	38.83 376	57.41 382	65.44 334	51.42 257	68.93 318	82.72 247	63.76 159	58.11 393	41.05 366	84.67 239	77.28 300

Anonymous20240521		63.55 295	66.07 267	55.99 373	66.18 379	44.04 327	68.77 255	68.80 309	46.99 320	72.57 263	85.84 186	39.87 360	50.22 416	53.40 274	92.23 85	73.71 342

lupinMVS		63.36 297	61.49 317	68.97 235	74.93 230	59.19 168	65.80 302	64.52 343	34.68 423	63.53 374	74.25 369	43.19 339	70.62 301	53.88 267	78.67 338	77.10 305

ET-MVSNet_ETH3D		63.32 298	60.69 325	71.20 190	70.15 325	55.66 204	65.02 316	64.32 344	43.28 363	68.99 314	72.05 386	25.46 438	78.19 196	54.16 265	82.80 271	79.74 266

MVSTER		63.29 299	61.60 316	68.36 248	59.77 424	46.21 307	60.62 358	71.32 276	41.83 369	75.40 202	79.12 321	30.25 420	75.85 224	56.30 232	79.81 325	83.03 182

OpenMVS_ROB	54.93 17	63.23 300	63.28 301	63.07 313	69.81 330	45.34 314	68.52 261	67.14 320	43.74 355	70.61 294	79.22 318	47.90 317	72.66 270	48.75 308	73.84 386	71.21 372

IterMVS		63.12 301	62.48 311	65.02 293	66.34 376	52.86 228	63.81 332	62.25 354	46.57 323	71.51 284	80.40 288	44.60 331	66.82 350	51.38 284	75.47 368	75.38 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 302	60.47 326	70.61 195	83.04 107	54.10 220	59.93 364	72.24 265	33.67 428	69.00 313	75.63 353	38.69 368	76.93 213	36.60 399	75.45 369	80.81 244

GA-MVS		62.91 303	61.66 313	66.66 280	67.09 368	44.49 324	61.18 353	69.36 301	51.33 261	69.33 311	74.47 365	36.83 379	74.94 242	50.60 290	74.72 374	80.57 252

PVSNet_Blended		62.90 304	61.64 314	66.69 279	69.81 330	49.36 259	61.23 352	78.96 174	42.04 367	59.98 394	68.86 417	51.82 283	78.20 194	44.30 344	77.77 351	72.52 354

USDC		62.80 305	63.10 304	61.89 324	65.19 386	43.30 335	67.42 275	74.20 239	35.80 416	72.25 269	84.48 205	45.67 323	71.95 286	37.95 387	84.97 228	70.42 380

FE-MVSNET		62.77 306	64.36 288	57.97 363	70.52 315	33.96 411	61.66 348	67.88 318	50.67 270	73.18 252	82.58 251	48.03 315	68.22 328	43.21 352	81.55 292	71.74 364

MonoMVSNet		62.75 307	63.42 299	60.73 340	65.60 383	40.77 357	72.49 182	70.56 289	52.49 241	75.07 208	79.42 309	39.52 364	69.97 310	46.59 331	69.06 418	71.44 367

Vis-MVSNet (Re-imp)		62.74 308	63.21 303	61.34 333	72.19 288	31.56 424	67.31 280	53.87 397	53.60 232	69.88 305	83.37 232	40.52 356	70.98 298	41.40 364	86.78 203	81.48 228

patch_mono-2		62.73 309	64.08 292	58.68 356	70.36 320	55.87 201	60.84 356	64.11 346	41.23 374	64.04 362	78.22 332	60.00 206	48.80 420	54.17 264	83.71 259	71.37 368

D2MVS		62.58 310	61.05 321	67.20 269	63.85 396	47.92 280	56.29 390	69.58 297	39.32 390	70.07 302	78.19 333	34.93 386	72.68 269	53.44 272	83.74 257	81.00 237

CL-MVSNet_self_test		62.44 311	63.40 300	59.55 349	72.34 285	32.38 419	56.39 389	64.84 339	51.21 263	67.46 340	81.01 278	50.75 292	63.51 371	38.47 383	88.12 173	82.75 192

MDA-MVSNet-bldmvs		62.34 312	61.73 312	64.16 298	61.64 409	49.90 253	48.11 432	57.24 377	53.31 234	80.95 108	79.39 311	49.00 308	61.55 378	45.92 336	80.05 320	81.03 235

IMVS_0404		62.18 313	63.05 305	59.58 348	72.47 280	48.64 267	55.47 397	72.98 249	45.33 337	55.80 421	79.37 312	49.84 297	53.60 407	55.06 247	81.11 297	76.49 310

miper_lstm_enhance		61.97 314	61.63 315	62.98 314	60.04 418	45.74 311	47.53 434	70.95 285	44.04 350	73.06 256	78.84 326	39.72 361	60.33 381	55.82 239	84.64 242	82.88 187

wuyk23d		61.97 314	66.25 264	49.12 412	58.19 434	60.77 156	66.32 295	52.97 405	55.93 189	90.62 6	86.91 146	73.07 61	35.98 460	20.63 462	91.63 92	50.62 449

thres600view7		61.82 316	61.38 318	63.12 312	71.81 292	34.93 405	64.64 323	56.99 379	54.78 203	70.33 297	79.74 300	32.07 402	72.42 277	38.61 381	83.46 262	82.02 213

SSC-MVS		61.79 317	66.08 266	48.89 414	76.91 201	10.00 472	53.56 410	47.37 432	68.20 64	76.56 179	89.21 96	54.13 269	57.59 395	54.75 254	74.07 383	79.08 276

PAPM		61.79 317	60.37 327	66.05 284	76.09 215	41.87 346	69.30 240	76.79 213	40.64 384	53.80 431	79.62 305	44.38 332	82.92 100	29.64 435	73.11 390	73.36 344

SD_0403		61.63 319	62.83 308	58.03 361	72.21 287	32.43 418	69.33 239	69.00 305	44.54 348	62.01 381	79.42 309	55.27 262	66.88 346	36.07 406	77.63 352	74.78 329

MVP-Stereo		61.56 320	59.22 334	68.58 245	79.28 154	60.44 158	69.20 243	71.57 269	43.58 357	56.42 416	78.37 330	39.57 363	76.46 221	34.86 412	60.16 445	68.86 394
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	48.73 20	61.54 321	60.89 322	63.52 307	61.08 412	51.55 234	68.07 268	68.00 317	33.88 425	65.87 348	81.25 273	37.91 373	67.71 333	49.32 303	82.60 273	71.31 370
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2506		61.23 322	60.85 323	62.38 321	78.80 168	27.88 441	67.33 279	37.42 460	54.23 216	67.55 339	88.68 113	17.87 464	74.39 251	46.33 333	89.41 150	84.86 115

thres100view900		61.17 323	61.09 320	61.39 331	72.14 289	35.01 404	65.42 308	56.99 379	55.23 195	70.71 293	79.90 298	32.07 402	72.09 281	35.61 408	81.73 286	77.08 306

Patchmtry		60.91 324	63.01 306	54.62 380	66.10 380	26.27 449	67.47 274	56.40 386	54.05 222	72.04 273	86.66 157	33.19 392	60.17 382	43.69 348	87.45 185	77.42 298

EU-MVSNet		60.82 325	60.80 324	60.86 339	68.37 346	41.16 350	72.27 185	68.27 316	26.96 448	69.08 312	75.71 352	32.09 401	67.44 338	55.59 242	78.90 335	73.97 338

pmmvs4		60.78 326	59.04 336	66.00 285	73.06 271	57.67 190	64.53 326	60.22 364	36.91 409	65.96 347	77.27 342	39.66 362	68.54 325	38.87 378	74.89 373	71.80 363

thres400		60.77 327	59.97 329	63.15 311	70.78 305	35.35 402	63.27 339	57.47 372	53.00 236	68.31 331	77.09 344	32.45 399	72.09 281	35.61 408	81.73 286	82.02 213

MVS		60.62 328	59.97 329	62.58 319	68.13 354	47.28 293	68.59 258	73.96 240	32.19 432	59.94 396	68.86 417	50.48 293	77.64 205	41.85 361	75.74 364	62.83 426

thisisatest0515		60.48 329	57.86 347	68.34 249	67.25 366	46.42 304	60.58 359	62.14 355	40.82 380	63.58 373	69.12 411	26.28 434	78.34 190	48.83 307	82.13 278	80.26 258

tfpn200view9		60.35 330	59.97 329	61.51 328	70.78 305	35.35 402	63.27 339	57.47 372	53.00 236	68.31 331	77.09 344	32.45 399	72.09 281	35.61 408	81.73 286	77.08 306

ppachtmachnet_test		60.26 331	59.61 332	62.20 322	67.70 361	44.33 325	58.18 378	60.96 362	40.75 382	65.80 349	72.57 382	41.23 349	63.92 368	46.87 328	82.42 275	78.33 284

WB-MVS		60.04 332	64.19 291	47.59 417	76.09 215	10.22 471	52.44 417	46.74 434	65.17 94	74.07 234	87.48 135	53.48 272	55.28 401	49.36 302	72.84 391	77.28 300

Patchmatch-RL test		59.95 333	59.12 335	62.44 320	72.46 284	54.61 217	59.63 365	47.51 431	41.05 377	74.58 222	74.30 368	31.06 414	65.31 361	51.61 280	79.85 324	67.39 401

1314		59.83 334	58.86 338	62.74 318	65.71 382	44.78 320	68.59 258	72.63 258	33.54 430	61.05 389	67.29 428	43.62 337	71.26 294	49.49 301	67.84 426	72.19 360

IB-MVS	49.67 18	59.69 335	56.96 354	67.90 255	68.19 351	50.30 247	61.42 350	65.18 336	47.57 316	55.83 419	67.15 429	23.77 444	79.60 164	43.56 350	79.97 321	73.79 341
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		59.48 336	58.99 337	60.96 337	77.84 181	42.39 344	61.42 350	68.45 315	37.96 401	59.93 397	67.46 425	45.11 328	65.07 363	40.89 368	71.81 400	75.41 323

FPMVS		59.43 337	60.07 328	57.51 365	77.62 187	71.52 53	62.33 345	50.92 414	57.40 170	69.40 310	80.00 297	39.14 366	61.92 377	37.47 392	66.36 429	39.09 460

CVMVSNet		59.21 338	58.44 342	61.51 328	73.94 252	47.76 284	71.31 209	64.56 342	26.91 450	60.34 393	70.44 395	36.24 382	67.65 334	53.57 270	68.66 421	69.12 392

CR-MVSNet		58.96 339	58.49 341	60.36 343	66.37 374	48.24 274	70.93 215	56.40 386	32.87 431	61.35 385	86.66 157	33.19 392	63.22 372	48.50 312	70.17 412	69.62 387

reproduce_monomvs		58.94 340	58.14 345	61.35 332	59.70 425	40.98 353	60.24 362	63.51 350	45.85 328	68.95 316	75.31 358	18.27 462	65.82 357	51.47 282	79.97 321	77.26 303

EPNet_dtu		58.93 341	58.52 340	60.16 345	67.91 357	47.70 286	69.97 229	58.02 370	49.73 284	47.28 451	73.02 380	38.14 370	62.34 374	36.57 400	85.99 213	70.43 379
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		58.78 342	58.69 339	59.04 354	79.41 152	38.13 383	57.62 380	66.98 323	34.74 421	59.62 400	77.56 340	42.92 341	63.65 370	38.66 380	70.73 408	75.35 325

PatchMatch-RL		58.68 343	57.72 348	61.57 327	76.21 213	73.59 43	61.83 346	49.00 426	47.30 319	61.08 387	68.97 413	50.16 295	59.01 387	36.06 407	68.84 420	52.10 447

SCA		58.57 344	58.04 346	60.17 344	70.17 323	41.07 352	65.19 312	53.38 403	43.34 362	61.00 390	73.48 375	45.20 326	69.38 316	40.34 371	70.31 411	70.05 381

testing3		58.28 345	58.38 343	58.00 362	77.45 189	26.12 450	60.78 357	43.00 446	56.02 186	70.18 299	75.76 351	13.27 472	67.24 341	48.02 318	80.89 303	80.65 249

CHOSEN 1792x2688		58.09 346	56.30 359	63.45 308	79.95 144	50.93 241	54.07 408	65.59 332	28.56 444	61.53 384	74.33 367	41.09 352	66.52 354	33.91 416	67.69 427	72.92 348

HY-MVS	49.31 19	57.96 347	57.59 350	59.10 353	66.85 373	36.17 395	65.13 313	65.39 335	39.24 393	54.69 428	78.14 334	44.28 333	67.18 342	33.75 418	70.79 407	73.95 339

baseline1		57.82 348	58.36 344	56.19 372	69.17 338	30.76 430	62.94 343	55.21 390	46.04 326	63.83 367	78.47 328	41.20 350	63.68 369	39.44 373	68.99 419	74.13 337

thres200		57.55 349	57.02 353	59.17 351	67.89 358	34.93 405	58.91 372	57.25 376	50.24 277	64.01 363	71.46 390	32.49 398	71.39 293	31.31 426	79.57 329	71.19 373

CostFormer		57.35 350	56.14 360	60.97 336	63.76 398	38.43 378	67.50 273	60.22 364	37.14 408	59.12 402	76.34 349	32.78 395	71.99 284	39.12 377	69.27 417	72.47 355

SSC-MVS3.2		57.01 351	59.50 333	49.57 408	67.73 360	25.95 451	46.68 437	51.75 412	51.41 259	63.84 366	79.66 303	53.28 274	50.34 415	37.85 388	83.28 265	72.41 356

testing3-2		56.85 352	57.62 349	54.53 381	75.84 220	22.23 461	51.26 422	49.10 424	61.04 134	63.74 369	79.73 301	22.29 451	59.44 385	31.16 428	84.43 249	81.92 219

test_fmvs3		56.78 353	55.99 362	59.12 352	53.96 454	48.09 277	58.76 373	66.22 326	27.54 446	76.66 174	68.69 419	25.32 440	51.31 411	53.42 273	73.38 388	77.97 295

our_test_3		56.46 354	56.51 357	56.30 371	67.70 361	39.66 369	55.36 399	52.34 409	40.57 385	63.85 365	69.91 406	40.04 359	58.22 392	43.49 351	75.29 372	71.03 376

ttmdpeth		56.40 355	55.45 366	59.25 350	55.63 445	40.69 358	58.94 371	49.72 420	36.22 412	65.39 351	86.97 144	23.16 447	56.69 398	42.30 356	80.74 309	80.36 256

tpm2		56.12 356	54.64 373	60.55 342	66.24 377	36.01 396	68.14 266	56.77 382	33.60 429	58.25 405	75.52 356	30.25 420	74.33 252	33.27 419	69.76 416	71.32 369

tpmvs		55.84 357	55.45 366	57.01 367	60.33 416	33.20 416	65.89 299	59.29 368	47.52 317	56.04 417	73.60 374	31.05 415	68.06 331	40.64 369	64.64 433	69.77 385

gg-mvs-nofinetune		55.75 358	56.75 356	52.72 390	62.87 402	28.04 440	68.92 247	41.36 455	71.09 47	50.80 441	92.63 15	20.74 454	66.86 348	29.97 433	72.41 394	63.25 425

testing91		55.74 359	55.29 369	57.08 366	70.63 309	30.85 429	54.94 403	56.31 388	50.34 275	57.08 409	70.10 403	24.50 442	65.86 356	36.98 397	76.75 358	74.53 333

test20.03		55.74 359	57.51 351	50.42 401	59.89 423	32.09 421	50.63 423	49.01 425	50.11 279	65.07 355	83.23 239	45.61 324	48.11 425	30.22 431	83.82 255	71.07 375

MS-PatchMatch		55.59 361	54.89 371	57.68 364	69.18 337	49.05 262	61.00 354	62.93 353	35.98 414	58.36 404	68.93 415	36.71 380	66.59 353	37.62 391	63.30 437	57.39 443

baseline2		55.57 362	52.74 383	64.05 301	65.26 385	44.11 326	62.38 344	54.43 394	39.03 394	51.21 439	67.35 427	33.66 390	72.45 276	37.14 394	64.22 435	75.60 320

MVStest1		55.38 363	54.97 370	56.58 370	43.72 467	40.07 366	59.13 367	47.09 433	34.83 419	76.53 182	84.65 199	13.55 471	53.30 408	55.04 251	80.23 318	76.38 315

XXY-MVS		55.19 364	57.40 352	48.56 416	64.45 394	34.84 407	51.54 420	53.59 399	38.99 395	63.79 368	79.43 308	56.59 252	45.57 432	36.92 398	71.29 404	65.25 415

testing99		55.16 365	54.56 374	56.98 368	70.13 326	30.58 431	54.55 406	54.11 396	49.53 289	56.76 413	70.14 402	22.76 449	65.79 358	36.99 396	76.04 363	74.57 332

FMVSNet5		55.08 366	55.54 365	53.71 383	65.80 381	33.50 415	56.22 391	52.50 407	43.72 356	61.06 388	83.38 231	25.46 438	54.87 402	30.11 432	81.64 291	72.75 352

test_fmvs2		54.80 367	54.11 377	56.88 369	51.76 458	49.95 252	56.70 386	65.80 329	26.22 451	69.42 309	65.25 433	31.82 406	49.98 417	49.63 299	70.36 410	70.71 377

PatchmatchNet		54.60 368	54.27 375	55.59 376	65.17 388	39.08 371	66.92 285	51.80 411	39.89 387	58.39 403	73.12 379	31.69 408	58.33 391	43.01 354	58.38 451	69.38 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		54.39 369	56.12 361	49.20 410	72.57 279	30.91 428	59.98 363	48.43 428	41.66 370	55.94 418	83.86 224	41.19 351	50.42 414	26.05 446	75.38 370	66.27 409

Syy-MVS		54.13 370	55.45 366	50.18 402	68.77 342	23.59 455	55.02 400	44.55 440	43.80 352	58.05 406	64.07 435	46.22 321	58.83 388	46.16 334	72.36 395	68.12 397

Anonymous20231206		54.13 370	55.82 363	49.04 413	70.89 302	35.96 397	51.73 419	50.87 415	34.86 418	62.49 379	79.22 318	42.52 345	44.29 443	27.95 442	81.88 282	66.88 405

JIA-IIPM		54.03 372	51.62 392	61.25 334	59.14 428	55.21 213	59.10 368	47.72 429	50.85 267	50.31 445	85.81 187	20.10 456	63.97 367	36.16 404	55.41 456	64.55 422

tpm cat1		54.02 373	52.63 385	58.19 359	64.85 392	39.86 368	66.26 296	57.28 375	32.16 433	56.90 411	70.39 397	32.75 396	65.30 362	34.29 414	58.79 448	69.41 389

testgi		54.00 374	56.86 355	45.45 426	58.20 433	25.81 452	49.05 428	49.50 422	45.43 334	67.84 334	81.17 274	51.81 285	43.20 447	29.30 436	79.41 330	67.34 403

WB-MVSnew		53.94 375	54.76 372	51.49 396	71.53 295	28.05 439	58.22 377	50.36 417	37.94 402	59.16 401	70.17 401	49.21 304	51.94 410	24.49 453	71.80 401	74.47 335

WBMVS		53.38 376	54.14 376	51.11 398	70.16 324	26.66 445	50.52 425	51.64 413	39.32 390	63.08 377	77.16 343	23.53 445	55.56 399	31.99 423	79.88 323	71.11 374

testing222		53.37 377	52.50 387	55.98 374	70.51 316	29.68 434	56.20 392	51.85 410	46.19 325	56.76 413	68.94 414	19.18 460	65.39 360	25.87 449	76.98 356	72.87 350

PatchT		53.35 378	56.47 358	43.99 433	64.19 395	17.46 464	59.15 366	43.10 445	52.11 248	54.74 427	86.95 145	29.97 423	49.98 417	43.62 349	74.40 379	64.53 423

testing11		53.13 379	52.26 389	55.75 375	70.44 317	31.73 423	54.75 404	52.40 408	44.81 346	52.36 436	68.40 421	21.83 452	65.74 359	32.64 422	72.73 392	69.78 384

test_vis1_n_1920		52.96 380	53.50 379	51.32 397	59.15 427	44.90 318	56.13 393	64.29 345	30.56 442	59.87 398	60.68 446	40.16 358	47.47 426	48.25 316	62.46 439	61.58 434

UWE-MVS		52.94 381	52.70 384	53.65 384	73.56 256	27.49 442	57.30 383	49.57 421	38.56 398	62.79 378	71.42 391	19.49 459	60.41 380	24.33 455	77.33 354	73.06 346

new-patchmatchnet		52.89 382	55.76 364	44.26 432	59.94 422	6.31 473	37.36 457	50.76 416	41.10 375	64.28 360	79.82 299	44.77 329	48.43 424	36.24 403	87.61 180	78.03 292

test_fmvs1_n		52.70 383	52.01 390	54.76 378	53.83 455	50.36 245	55.80 395	65.90 328	24.96 455	65.39 351	60.64 447	27.69 429	48.46 422	45.88 337	67.99 424	65.46 413

YYNet1		52.58 384	53.50 379	49.85 404	54.15 451	36.45 394	40.53 450	46.55 436	38.09 400	75.52 199	73.31 378	41.08 353	43.88 444	41.10 365	71.14 406	69.21 391

MDA-MVSNet_test_wron		52.57 385	53.49 381	49.81 405	54.24 450	36.47 393	40.48 451	46.58 435	38.13 399	75.47 201	73.32 377	41.05 354	43.85 445	40.98 367	71.20 405	69.10 393

pmmvs5		52.49 386	52.58 386	52.21 392	54.99 448	32.38 419	55.45 398	53.84 398	32.15 434	55.49 422	74.81 360	38.08 371	57.37 396	34.02 415	74.40 379	66.88 405

UnsupCasMVSNet_eth		52.26 387	53.29 382	49.16 411	55.08 447	33.67 414	50.03 426	58.79 369	37.67 404	63.43 376	74.75 362	41.82 347	45.83 430	38.59 382	59.42 447	67.98 400

N_pmnet		52.06 388	51.11 397	54.92 377	59.64 426	71.03 57	37.42 456	61.62 361	33.68 427	57.12 408	72.10 383	37.94 372	31.03 462	29.13 441	71.35 403	62.70 427

KD-MVS_2432*1600		52.05 389	51.58 393	53.44 386	52.11 456	31.20 425	44.88 443	64.83 340	41.53 371	64.37 358	70.03 404	15.61 468	64.20 365	36.25 401	74.61 376	64.93 419

miper_refine_blended		52.05 389	51.58 393	53.44 386	52.11 456	31.20 425	44.88 443	64.83 340	41.53 371	64.37 358	70.03 404	15.61 468	64.20 365	36.25 401	74.61 376	64.93 419

test_vis3_rt		51.94 391	51.04 398	54.65 379	46.32 465	50.13 249	44.34 445	78.17 192	23.62 459	68.95 316	62.81 439	21.41 453	38.52 458	41.49 363	72.22 397	75.30 326

PVSNet	43.83 21	51.56 392	51.17 396	52.73 389	68.34 347	38.27 380	48.22 431	53.56 401	36.41 411	54.29 429	64.94 434	34.60 387	54.20 405	30.34 430	69.87 414	65.71 412

test_fmvs1		51.51 393	50.86 401	53.48 385	49.72 461	49.35 261	54.11 407	64.96 338	24.64 457	63.66 371	59.61 450	28.33 428	48.45 423	45.38 342	67.30 428	62.66 429

myMVS_eth3d28		51.35 394	51.99 391	49.44 409	69.21 336	22.51 459	49.82 427	49.11 423	49.00 298	55.03 424	70.31 398	22.73 450	52.88 409	24.33 455	78.39 343	72.92 348

test_vis1_n		51.27 395	50.41 405	53.83 382	56.99 437	50.01 251	56.75 385	60.53 363	25.68 453	59.74 399	57.86 451	29.40 425	47.41 427	43.10 353	63.66 436	64.08 424

test_cas_vis1_n_1920		50.90 396	50.92 400	50.83 400	54.12 453	47.80 282	51.44 421	54.61 393	26.95 449	63.95 364	60.85 445	37.86 375	44.97 438	45.53 339	62.97 438	59.72 438

tpm		50.60 397	52.42 388	45.14 428	65.18 387	26.29 448	60.30 360	43.50 443	37.41 406	57.01 410	79.09 322	30.20 422	42.32 448	32.77 421	66.36 429	66.81 407

test-LLR		50.43 398	50.69 403	49.64 406	60.76 413	41.87 346	53.18 412	45.48 438	43.41 360	49.41 446	60.47 448	29.22 426	44.73 440	42.09 359	72.14 398	62.33 432

myMVS_eth3d		50.36 399	50.52 404	49.88 403	68.77 342	22.69 457	55.02 400	44.55 440	43.80 352	58.05 406	64.07 435	14.16 470	58.83 388	33.90 417	72.36 395	68.12 397

ETVMVS		50.32 400	49.87 408	51.68 394	70.30 322	26.66 445	52.33 418	43.93 442	43.54 358	54.91 425	67.95 423	20.01 457	60.17 382	22.47 458	73.40 387	68.22 396

tpmrst		50.15 401	51.38 395	46.45 423	56.05 441	24.77 453	64.40 328	49.98 418	36.14 413	53.32 433	69.59 408	35.16 385	48.69 421	39.24 375	58.51 450	65.89 410

UnsupCasMVSNet_bld		50.01 402	51.03 399	46.95 419	58.61 430	32.64 417	48.31 430	53.27 404	34.27 424	60.47 392	71.53 389	41.40 348	47.07 428	30.68 429	60.78 444	61.13 435

dmvs_re		49.91 403	50.77 402	47.34 418	59.98 419	38.86 375	53.18 412	53.58 400	39.75 388	55.06 423	61.58 444	36.42 381	44.40 442	29.15 440	68.23 422	58.75 440

WTY-MVS		49.39 404	50.31 406	46.62 422	61.22 411	32.00 422	46.61 438	49.77 419	33.87 426	54.12 430	69.55 409	41.96 346	45.40 435	31.28 427	64.42 434	62.47 430

UBG		49.18 405	49.35 409	48.66 415	70.36 320	26.56 447	50.53 424	45.61 437	37.43 405	53.37 432	65.97 430	23.03 448	54.20 405	26.29 444	71.54 402	65.20 416

ADS-MVSNet2		48.76 406	47.25 415	53.29 388	55.90 443	40.54 362	47.34 435	54.99 392	31.41 439	50.48 442	72.06 384	31.23 411	54.26 404	25.93 447	55.93 453	65.07 417

test-mter		48.56 407	48.20 412	49.64 406	60.76 413	41.87 346	53.18 412	45.48 438	31.91 437	49.41 446	60.47 448	18.34 461	44.73 440	42.09 359	72.14 398	62.33 432

Patchmatch-test		47.93 408	49.96 407	41.84 437	57.42 436	24.26 454	48.75 429	41.49 454	39.30 392	56.79 412	73.48 375	30.48 419	33.87 461	29.29 437	72.61 393	67.39 401

test0.0.03 1		47.72 409	48.31 411	45.93 424	55.53 446	29.39 435	46.40 439	41.21 456	43.41 360	55.81 420	67.65 424	29.22 426	43.77 446	25.73 450	69.87 414	64.62 421

sss		47.59 410	48.32 410	45.40 427	56.73 440	33.96 411	45.17 441	48.51 427	32.11 436	52.37 435	65.79 431	40.39 357	41.91 451	31.85 424	61.97 441	60.35 436

pmmvs3		46.71 411	45.09 421	51.55 395	56.76 439	48.25 273	55.78 396	39.53 459	24.13 458	50.35 444	63.40 437	15.90 467	51.08 413	29.29 437	70.69 409	55.33 446

test_vis1_rt		46.70 412	45.24 420	51.06 399	44.58 466	51.04 240	39.91 452	67.56 319	21.84 463	51.94 437	50.79 459	33.83 389	39.77 455	35.25 411	61.50 442	62.38 431

EPMVS		45.74 413	46.53 416	43.39 435	54.14 452	22.33 460	55.02 400	35.00 463	34.69 422	51.09 440	70.20 400	25.92 436	42.04 450	37.19 393	55.50 455	65.78 411

MVS-HIRNet		45.53 414	47.29 414	40.24 440	62.29 405	26.82 444	56.02 394	37.41 461	29.74 443	43.69 461	81.27 272	33.96 388	55.48 400	24.46 454	56.79 452	38.43 461

dmvs_testset		45.26 415	47.51 413	38.49 443	59.96 421	14.71 467	58.50 375	43.39 444	41.30 373	51.79 438	56.48 452	39.44 365	49.91 419	21.42 460	55.35 457	50.85 448

TESTMET0.1,1		45.17 416	44.93 422	45.89 425	56.02 442	38.31 379	53.18 412	41.94 453	27.85 445	44.86 457	56.47 453	17.93 463	41.50 453	38.08 386	68.06 423	57.85 441

E-PMN		45.17 416	45.36 419	44.60 430	50.07 459	42.75 340	38.66 454	42.29 451	46.39 324	39.55 462	51.15 458	26.00 435	45.37 436	37.68 389	76.41 359	45.69 455

PMMVS		44.69 418	43.95 427	46.92 420	50.05 460	53.47 226	48.08 433	42.40 449	22.36 461	44.01 460	53.05 456	42.60 344	45.49 433	31.69 425	61.36 443	41.79 458

ADS-MVSNet		44.62 419	45.58 418	41.73 438	55.90 443	20.83 462	47.34 435	39.94 458	31.41 439	50.48 442	72.06 384	31.23 411	39.31 456	25.93 447	55.93 453	65.07 417

EMVS		44.61 420	44.45 425	45.10 429	48.91 462	43.00 338	37.92 455	41.10 457	46.75 322	38.00 464	48.43 461	26.42 433	46.27 429	37.11 395	75.38 370	46.03 454

UWE-MVS-28		44.18 421	44.37 426	43.61 434	60.10 417	16.96 465	52.62 416	33.27 464	36.79 410	48.86 448	69.47 410	19.96 458	45.65 431	13.40 465	64.83 432	68.23 395

dp		44.09 422	44.88 423	41.72 439	58.53 432	23.18 456	54.70 405	42.38 450	34.80 420	44.25 459	65.61 432	24.48 443	44.80 439	29.77 434	49.42 459	57.18 444

test_f		43.79 423	45.63 417	38.24 444	42.29 470	38.58 377	34.76 459	47.68 430	22.22 462	67.34 341	63.15 438	31.82 406	30.60 463	39.19 376	62.28 440	45.53 456

mvsany_test3		43.76 424	41.01 428	52.01 393	48.09 463	57.74 189	42.47 447	23.85 470	23.30 460	64.80 356	62.17 442	27.12 430	40.59 454	29.17 439	48.11 460	57.69 442

DSMNet-mixed		43.18 425	44.66 424	38.75 442	54.75 449	28.88 438	57.06 384	27.42 467	13.47 465	47.27 452	77.67 339	38.83 367	39.29 457	25.32 452	60.12 446	48.08 451

CHOSEN 280x420		41.62 426	39.89 431	46.80 421	61.81 407	51.59 233	33.56 460	35.74 462	27.48 447	37.64 465	53.53 454	23.24 446	42.09 449	27.39 443	58.64 449	46.72 453

PVSNet_0	36.71 22	41.12 427	40.78 430	42.14 436	59.97 420	40.13 365	40.97 449	42.24 452	30.81 441	44.86 457	49.41 460	40.70 355	45.12 437	23.15 457	34.96 463	41.16 459

mvsany_test1		37.88 428	35.74 433	44.28 431	47.28 464	49.90 253	36.54 458	24.37 469	19.56 464	45.76 453	53.46 455	32.99 394	37.97 459	26.17 445	35.52 462	44.99 457

PMMVS2		37.74 429	40.87 429	28.36 446	42.41 469	5.35 474	24.61 461	27.75 466	32.15 434	47.85 450	70.27 399	35.85 383	29.51 464	19.08 463	67.85 425	50.22 450

new_pmnet		37.55 430	39.80 432	30.79 445	56.83 438	16.46 466	39.35 453	30.65 465	25.59 454	45.26 455	61.60 443	24.54 441	28.02 465	21.60 459	52.80 458	47.90 452

MVE	27.91 23	36.69 431	35.64 434	39.84 441	43.37 468	35.85 399	19.49 462	24.61 468	24.68 456	39.05 463	62.63 441	38.67 369	27.10 466	21.04 461	47.25 461	56.56 445
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		31.66 432	32.98 435	27.71 447	58.58 431	12.61 469	45.02 442	14.24 473	41.90 368	47.93 449	43.91 462	10.65 473	41.81 452	14.06 464	20.53 466	28.72 463

kuosan		22.02 433	23.52 437	17.54 449	41.56 471	11.24 470	41.99 448	13.39 474	26.13 452	28.87 466	30.75 464	9.72 474	21.94 468	4.77 469	14.49 467	19.43 464

test_method		19.26 434	19.12 438	19.71 448	9.09 473	1.91 476	7.79 464	53.44 402	1.42 467	10.27 469	35.80 463	17.42 465	25.11 467	12.44 466	24.38 465	32.10 462

cdsmvs_eth3d_5k		17.71 435	23.62 436	0.00 454	0.00 477	0.00 479	0.00 465	70.17 293	0.00 472	0.00 473	74.25 369	68.16 104	0.00 473	0.00 472	0.00 471	0.00 469

tmp_tt		11.98 436	14.73 439	3.72 451	2.28 474	4.62 475	19.44 463	14.50 472	0.47 469	21.55 467	9.58 467	25.78 437	4.57 470	11.61 467	27.37 464	1.96 466

ab-mvs-re		5.62 437	7.50 440	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	67.46 425	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		5.20 438	6.93 441	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	62.39 171	0.00 473	0.00 472	0.00 471	0.00 469

test123		4.43 439	5.78 442	0.39 453	0.97 475	0.28 477	46.33 440	0.45 476	0.31 470	0.62 471	1.50 470	0.61 476	0.11 472	0.56 470	0.63 469	0.77 468

testmvs		4.06 440	5.28 443	0.41 452	0.64 476	0.16 478	42.54 446	0.31 477	0.26 471	0.50 472	1.40 471	0.77 475	0.17 471	0.56 470	0.55 470	0.90 467

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						22.69 457								36.10 405

FOURS1					89.19 24	77.84 14	91.64 1	89.11 3	84.05 3	91.57 3

MSC_two_6792asdad				79.02 58	83.14 102	67.03 94		80.75 134					86.24 24	77.27 38	94.85 31	83.78 153

PC_three_1452									46.98 321	81.83 95	86.28 169	66.55 130	84.47 74	63.31 159	90.78 120	83.49 161

No_MVS				79.02 58	83.14 102	67.03 94		80.75 134					86.24 24	77.27 38	94.85 31	83.78 153

test_one_0601					85.84 64	61.45 142		85.63 31	75.27 21	85.62 52	90.38 71	76.72 31

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					83.91 91	69.36 75		81.09 128	58.91 154	82.73 88	89.11 101	75.77 39	86.63 14	72.73 71	92.93 74

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	81.38 7		78.11 28	94.46 41	84.89 112

IU-MVS					86.12 54	60.90 152		80.38 146	45.49 333	81.31 103				75.64 46	94.39 46	84.65 122

OPU-MVS				78.65 65	83.44 100	66.85 96	83.62 47				86.12 178	66.82 122	86.01 34	61.72 171	89.79 142	83.08 180

test_241102_TWO								84.80 49	72.61 36	84.93 60	89.70 87	77.73 25	85.89 42	75.29 47	94.22 57	83.25 172

test_241102_ONE					86.12 54	61.06 148		84.72 53	72.64 35	87.38 28	89.47 90	77.48 27	85.74 46

9.14			80.22 58		80.68 137		80.35 78	87.69 12	59.90 143	83.00 81	88.20 124	74.57 51	81.75 125	73.75 63	93.78 62

save fliter					87.00 40	67.23 93	79.24 92	77.94 197	56.65 181

test_0728_THIRD									74.03 25	85.83 47	90.41 66	75.58 41	85.69 47	77.43 35	94.74 35	84.31 140

test_0728_SECOND				76.57 92	86.20 49	60.57 157	83.77 45	85.49 33					85.90 40	75.86 43	94.39 46	83.25 172

test0726					86.16 52	60.78 154	83.81 44	85.10 44	72.48 38	85.27 57	89.96 83	78.57 19

GSMVS																70.05 381

test_part2					85.90 60	66.44 98				84.61 66

sam_mvs1												31.41 409				70.05 381

sam_mvs												31.21 413

ambc				70.10 210	77.74 183	50.21 248	74.28 164	77.93 198		79.26 125	88.29 123	54.11 270	79.77 161	64.43 142	91.10 108	80.30 257

MTGPA								80.63 140

test_post1							66.63 289				2.08 468	30.66 418	59.33 386	40.34 371

test_post											1.99 469	30.91 416	54.76 403

patchmatchnet-post											68.99 412	31.32 410	69.38 316

GG-mvs-BLEND				52.24 391	60.64 415	29.21 437	69.73 233	42.41 448		45.47 454	52.33 457	20.43 455	68.16 329	25.52 451	65.42 431	59.36 439

MTMP							84.83 34	19.26 471

gm-plane-assit					62.51 403	33.91 413			37.25 407		62.71 440		72.74 268	38.70 379

test9_res														72.12 79	91.37 98	77.40 299

TEST9					85.47 67	69.32 76	76.42 128	78.69 182	53.73 228	76.97 162	86.74 153	66.84 121	81.10 135

test_8					85.09 74	67.89 85	76.26 133	78.66 184	54.00 223	76.89 166	86.72 155	66.60 127	80.89 145

agg_prior2														70.70 87	90.93 114	78.55 282

agg_prior					84.44 86	66.02 104		78.62 185		76.95 164			80.34 152

TestCases				78.35 70	79.19 158	70.81 59		88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 186	55.60 240	90.90 116	85.81 88

test_prior4						70.14 67	77.57 109

test_prior2							75.57 141		58.92 153	76.53 182	86.78 151	67.83 113		69.81 94	92.76 77

test_prior				75.27 112	82.15 121	59.85 165		84.33 67					83.39 92			82.58 199

旧先验2							71.17 212		45.11 343	78.54 138			61.28 379	59.19 202

新几何2							71.33 208

新几何1				69.99 212	88.37 35	71.34 55		62.08 357	43.85 351	74.99 210	86.11 179	52.85 276	70.57 302	50.99 287	83.23 266	68.05 399

旧先验1					84.55 83	60.36 159		63.69 348			87.05 143	54.65 265			83.34 264	69.66 386

无先验							74.82 148	70.94 286	47.75 315				76.85 216	54.47 258		72.09 361

原ACMM2							74.78 152

原ACMM1				73.90 128	85.90 60	65.15 113		81.67 112	50.97 265	74.25 230	86.16 175	61.60 183	83.54 87	56.75 226	91.08 110	73.00 347

test222					87.30 38	69.15 79	67.85 269	59.59 367	41.06 376	73.05 257	85.72 188	48.03 315			80.65 311	66.92 404

testdata2													67.30 339	48.34 314

segment_acmp												68.30 103

testdata				64.13 299	85.87 62	63.34 127		61.80 360	47.83 313	76.42 187	86.60 162	48.83 309	62.31 375	54.46 259	81.26 296	66.74 408

testdata1							68.34 265		57.24 172

test12				76.51 93	82.28 119	60.94 151		81.64 113		73.60 243		64.88 149	85.19 62		90.42 127	83.38 168

plane_prior7					85.18 70	66.21 101

plane_prior6					84.18 89	65.31 110						60.83 196

plane_prior5								85.49 33					86.15 29	71.09 82	90.94 112	84.82 117

plane_prior4											89.11 101

plane_prior3						65.67 106			63.82 108	78.23 141

plane_prior2							82.74 56		65.45 86

plane_prior1					84.46 85

plane_prior						65.18 111	80.06 84		61.88 128						89.91 139

n2								0.00 478

nn								0.00 478

door-mid								55.02 391

lessismore_v0				72.75 163	79.60 150	56.83 196		57.37 374		83.80 75	89.01 105	47.45 318	78.74 178	64.39 143	86.49 208	82.69 196

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 82

test11								82.71 95

door								52.91 406

HQP5-MVS						58.80 178

HQP-NCC					82.37 116		77.32 114		59.08 148	71.58 279

ACMP_Plane					82.37 116		77.32 114		59.08 148	71.58 279

BP-MVS														67.38 119

HQP4-MVS										71.59 277			85.31 54			83.74 155

HQP3-MVS								84.12 73							89.16 154

HQP2-MVS												58.09 234

NP-MVS					83.34 101	63.07 130					85.97 183

MDTV_nov1_ep13_2view						18.41 463	53.74 409		31.57 438	44.89 456		29.90 424		32.93 420		71.48 366

MDTV_nov1_ep13			54.05 378		65.54 384	29.30 436	59.00 369	55.22 389	35.96 415	52.44 434	75.98 350	30.77 417	59.62 384	38.21 384	73.33 389

ACMMP++_ref															89.47 149

ACMMP++															91.96 88

Test By Simon												62.56 167

ITE_SJBPF				80.35 42	76.94 197	73.60 42		80.48 143	66.87 72	83.64 77	86.18 173	70.25 87	79.90 160	61.12 180	88.95 164	87.56 57

DeepMVS_CX				11.83 450	15.51 472	13.86 468		11.25 475	5.76 466	20.85 468	26.46 465	17.06 466	9.22 469	9.69 468	13.82 468	12.42 465

High-resolution multi-view benchmark