Fenggen Yu

Bio & cv (click here)

I am 2rd year Ph.d. student of Gruvi Lab, school of Computing Science at Simon Fraser University, under supervision of Prof. Hao(Richard) Zhang. I received my bachelor degree and master degree from Nanjing University, under supervision of Associate Prof. Yan Zhang.

Research Interests

3D Shape Analysis, 3D Shape Generation and Reconstruction

News

Sep 3, 2020. VDAC: Volume Decompose-and-Carve for Subtractive Manufacturing is accepted to SIGGRAPH ASIA 2020.

Oct 30, 2019. Our extended PartNet datasets PartNet_symh has been released.

Sep 3, 2019. I start my Ph.d. career at SFU!

June 20, 2019. Our PartNet is shown as poster at CVPR 19, long Beach, US!

Apr 11, 2019. I gave presentation about our paper PartNet at VALSE 19, Hefei, CHN!

Selected Publications

(More in here)

Ali Mahdavi-Amiri, Fenggen Yu, Haisen Zhao, Adriana Schulz and Hao(Richard) Zhang.

VDAC: Volume Decompose-and-Carve for Subtractive Manufacturing.

Accepted to SIGGRAPH ASIA 2020|[Paper]|[Project page]

We introduce carvable volume decomposition for efficient 3-axis CNC machining of 3D freeform objects, where our goal is to develop a fully automatic method to jointly optimize setup and path planning.

Fenggen Yu, Kun Liu, Yan Zhang, Chenyang Zhu, Kai Xu.

PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation.

Accepted to CVPR 2019|[Paper]|[Code & data]

Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for topdown recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks.

Fenggen Yu, Yan Zhang, Kai Xu, Ali Mahdavi-Amiri and Hao(Richard) Zhang.

Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines.

Accepted to ACM Transactions on Graphics (to be presented at SIGGRAPH 2018), 37(2)|[Paper]|[Code & data].

We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision. Given a collection of 3D shapes spanning multiple object categories and styles, we perform style co-analysis over projected feature lines of each 3D shape and then backproject the learned style features onto the 3D shapes.

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