Towards 3D object recognition with contractive autoencoders

Bo Liu; Lingcheng Kong; Jianghai Zhao; Jinghua Wu; Zhiying Tan

doi:10.1117/12.2243988

29 August 2016 Towards 3D object recognition with contractive autoencoders

Bo Liu, Lingcheng Kong, Jianghai Zhao, Jinghua Wu, Zhiying Tan

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100330S (2016) https://doi.org/10.1117/12.2243988
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Nowadays, object recognition in 3D scenes has become an emerging challenge with various applications. However, an object can’t be represented well by artificial features derived only from 2D images or depth images separately, and supervised learning method usually requires lots of manually labeled data. To address those limitations, we propose a cross-modality deep learning framework based on contractive autoencoders for 3D scenes object recognition. In particular, we use contractive autoencoding to learn feature representations from 2D and depth images at the same time in an unsupervised way, it is possible to capture their joint information to reinforce detector training. Experiments on 3D image dataset demonstrate the effectiveness of the proposed method for 3D scene object recognition.

Citation Download Citation

Bo Liu, Lingcheng Kong, Jianghai Zhao, Jinghua Wu, and Zhiying Tan "Towards 3D object recognition with contractive autoencoders", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330S (29 August 2016); https://doi.org/10.1117/12.2243988

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available