Guocheng Qian (钱国成) is currently a first-year Ph.D. student in Computer Science at KAUST. He is part of the Image and Video Understanding Lab (IVUL) advised by Prof. Bernard Ghanem. Guocheng received his B.Eng degree with first class honors from Xi’an Jiaotong University (XJTU), China in 2018.
“I want to make deep learning less costly and more efficient”, says Guocheng. Guocheng’s research interests lie in efficient deep learning. He currently works on Neural Architecture Search (reduce design cost), self-supervised learning (reduce labeling cost), and efficient neural network (reduce application/deployment cost).
We propose a novel Anisotropic Separable Set Abstraction module that makes PointNet++ faster and more accurate. We denote our modified PointNet++ as ASSANet, whose scaled version sets a new state-of-the-art on S3DIS with much faster speed.