Xiaochen Zhou

I am a third-year PhD student at Purdue University, where I major in computer graphics, computer vision, image processing and machine learning. I am a research assistant in HPCG Lab, advised by Bedrich Benes.

I graduated from Washington University in st. Louis with a Master degree and I defensed my bachelor degree at Beihang University. I achieved amazing internship experience in Microsoft, Meta and Megvii Face++.

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I'm interested in computer vision, machine learning, optimization, and image processing. More specifially, I'm interested in 3D modeling and reconstruction.

Urban Brush: Intuitive and Controllable Urban Layout Editing
Xiaochen Zhou, Pascal Chang, Marie-Paule Cani, Bedrich Benes
UIST, 2021  

We designed a new modeling architecture for urban modeling. We provided the real-time local editing tools called atomic brushes to edit the elements of the local region in the urban city.

Image Extension with Patch Matching and GAN
Master defense project, 2020

This project is the master defense project. We firstly extract the layout of the source image with additional information. Second, the layout image is extended via optimized patch matching algorithms. Then we implemented coarse-to-fine progressive Unet GAN to reconstruct the completed image through layouts.

Contour-to-Image Reconstruction through Neural Network
Implementation practice of paper Smart, Sparse Contours to Represent and Edit Images, 2018

In this project, a generative adversial network is trained to generate and edit image through contour domain, where canny edge detection is used to extract contour information through original input.

Learning Discriminative 3D Shape Representations by View Discerning Networks
Biao Leng, Cheng Zhang, Xiaochen Zhou, Cheng Xu, Kai Xu
TVCG, 2018  

Two score units are devised to evaluate the quality of each projected image with score vectors.

Improved Panoramic Representation via Bidirectional Recurrent View Aggregation for 3D Model Retrieval
Cheng Xu, Cheng Zhang, Xiaochen Zhou, Biao Leng
IEEE Computer Graphic and Application, 2018  

A novel deep neural network, recurrent panorama network (RePanoNet), is designed to extract features in a panoramic view, encouraging the network to recognize the original 3D shape.

Emphasizing 3D Properties in Recurrent Multi-View Aggregation for 3D Shape Retrieval
Cheng Xu, Biao Leng, Cheng Zhang, Xiaochen Zhou
AAAI, 2018  

We designed an encoder-decoder recurrent feature aggregation network (ERFA-Net) to emphasize the 3D properties of 3D shapes in multi-view features aggregation.

Foundations Of Computer Science - Spring 2022 Programming With Multimedia Objects - Fall 2021 Computer Vision - Fall 2019

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