T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition
Zhiyuan Yang Yunjiao Zhou Lihua Xie Jianfei Yang
3D point cloud has been widely used in many mobile application scenarios,
including autonomous driving and 3D sensing on mobile devices. However,
existing 3D point cloud models tend to be large and cumbersome, making them
hard to deploy on edged devices due to their high memory requirements and
non-real-time latency. There has been a lack of research on how to compress 3D
point cloud models into lightweight models. In this paper, we propose a method
called T3DNet (Tiny 3D Network with augmEntation and disTillation) to address
this issue. We find that the tiny model after network augmentation is much
easier for a teacher to distill. Instead of gradually reducing the parameters
through techniques such as pruning or quantization, we pre-define a tiny model
and improve its performance through auxiliary supervision from augmented
networks and the original model. We evaluate our method on several public
datasets, including ModelNet40, ShapeNet, and ScanObjectNN. Our method can
achieve high compression rates without significant accuracy sacrifice,
achieving state-of-the-art performances on three datasets against existing
methods. Amazingly, our T3DNet is 58 times smaller and 54 times faster than the
original model yet with only 1.4% accuracy descent on the ModelNet40 dataset.