Fenggen Yu(余锋根)
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. My research interests are on 3D Shape Recognition and Reconstruction, Geometric Modeling and Geometric Deep Learning.
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.
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.
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.