Feature Matching of Multi-view 3D Models Based on Hash Binary Encoding

Title:

Feature Matching of Multi-view 3D Models Based on Hash Binary Encoding

Creator:

Li, H., Zhao, T., Li, N., Cai, Q., and Du, J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:03e22a9e-e6b4-4d19-8d16-75cf51185581
uuid:03e22a9e-e6b4-4d19-8d16-75cf51185581
doi:10.14311/NNW.2017.27.005

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Image data and 3D model data have emerged as resourceful foundation for information with proliferation of image capturing devices and social media. In this paper, a feature matching method based on hash binary encoding for multi-view 3D models in social media is proposed. SIFT algorithm is first used to extract features of the depth image, and then RANSAC is utilized as a filter. Finally, a cascade hash binary encoding algorithm is adapted to match the feature of multi-view models. Experimental results on SHREC2014 dataset have shown the effectiveness of the proposed method.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:1

Harvested from:

CDK

Metadata only:

false