학술논문
Feature Matching of Multi-view 3D Models Based on Hash Binary Encoding
Document Type
TEXT
Source
Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:1
Subject
Language
English
Abstract
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.