Yinda Zhang

Ph.D. Candidate

Department of Computer Science
Princeton University
Room 418A, 35 Olden Street
Princeton, NJ 08544

Email: yindaz [at] cs [dot] princeton [dot] edu

Google Scholar Profile

Brief Bio.:

Yinda Zhang is a 3rd-year PhD student at Princeton University, advised by Professor Thomas Funkhouser and Professor Jianxiong Xiao. Before that, he received a Bachelor degree from Dept. Automation, Tsinghua University and a Master degree from Dept. ECE, National University of Singapore under the supervision of Prof. Ping Tan and Prof. Shuicheng Yan. He is currently working on 3D context model, 3D deep learning, and scene understanding.


Y. Zhang, M. Bai, P. Kohli, S. Izadi, and J. Xiao.
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
arXiv:1603.04922 [cs.CV] (16 Mar 2016)
Project Webpage

Y. Zhang, S. Song, E. Yumer, M. Savva, J. Lee, H. Jin, T. Funkhouser.
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
arXiv:1612.07429 [cs.CV] (22 Dec 2016)
Project Webpage

Marvin: A Minimalist GPU-only N-dimensional ConvNet Framework
Project Webpage

Y. Zhang, S. Song, P. Tan, and J. Xiao
PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding
Proceedings of the 13th European Conference on Computer Vision (ECCV2014)
Oral Presentation ·  Paper ·  Project Webpage with Code and Data

F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao.
Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
arXiv:1506.03365 [cs.CV] 10 Jun 2015
Paper ·  LSUN dataset

P. Xu, K. A. Ehinger, Y. Zhang, A. Finkelstein, S. R. Kulkarni, and J. Xiao.
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
arXiv:1504.06755 [cs.CV] 25 Apr 2015
Paper ·  Project Webpage, Source Code, and iSUN dataset

Y. Zhang, J. Xiao, J. Hays, P. Tan.
FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013)
Paper ·  Project Webpage, Source Code, Supplimentary Material and Posters


Oral presentation at the main conference: Keynote slides (409MB) and PDF slides (73MB).

Video recording of the talk: http://videolectures.net/eccv2014_zhang_panoramic_scene/.