Xiaochen Zhou

I am a fourth-year PhD student at Purdue University, where I major in computer graphics, 3D computer vision, 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 worked as a research scientist intern in Microsoft, Meta and Megvii Face++.

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In this paper, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining.

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.

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

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.

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

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