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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 multiview 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.

FEATURE MATCHING OF MULTI-VIEW 3D MODELS BASED ON HASH BINARY ENCODING