Semantic Similarity Metric Learning for Sketch-Based 3D Shape Retrieval

Authors: Xia, Y., Wang, S., You, L. and Zhang, J.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12746 LNCS

Pages: 59-69

eISSN: 1611-3349

ISBN: 9783030779764

ISSN: 0302-9743

DOI: 10.1007/978-3-030-77977-1_5

Abstract:

Since the development of the touch screen technology makes sketches simple to draw and obtain, sketch-based 3D shape retrieval has received increasing attention in the community of computer vision and graphics in recent years. The main challenge is the big domain discrepancy between 2D sketches and 3D shapes. Most existing works tried to simultaneously map sketches and 3D shapes into a joint feature embedding space, which has a low efficiency and high computational cost. In this paper, we propose a novel semantic similarity metric learning method based on a teacher-student strategy for sketch-based 3D shape retrieval. We first extract the pre-learned semantic features of 3D shapes from the teacher network and then use them to guide the feature learning of 2D sketches in the student network. The experiment results show that our method has a better retrieval performance.

http://eprints.bournemouth.ac.uk/35894/

Source: Scopus

Semantic Similarity Metric Learning for Sketch-Based 3D Shape Retrieval

Authors: Xia, Y., Wang, S., You, L. and Zhang, J.J.

Conference: ICCS 2021: International Conference on Computational Science

Pages: 59-69

ISBN: 9783030779764

ISSN: 0302-9743

Abstract:

Since the development of the touch screen technology makes sketches simple to draw and obtain, sketch-based 3D shape retrieval has received increasing attention in the community of computer vision and graphics in recent years. The main challenge is the big domain discrepancy between 2D sketches and 3D shapes. Most existing works tried to simultaneously map sketches and 3D shapes into a joint feature embedding space, which has a low efficiency and high computational cost. In this paper, we propose a novel semantic similarity metric learning method based on a teacher-student strategy for sketch-based 3D shape retrieval. We first extract the pre-learned semantic features of 3D shapes from the teacher network and then use them to guide the feature learning of 2D sketches in the student network. The experiment results show that our method has a better retrieval performance.

http://eprints.bournemouth.ac.uk/35894/

Source: BURO EPrints