  Multi-class 3D object detection aims to localize and classify objects of
multiple categories from point clouds. Due to the nature of point clouds, i.e.
unstructured, sparse and noisy, some features benefit-ting multi-class
discrimination are underexploited, such as shape information. In this paper, we
propose a novel 3D shape signature to explore the shape information from point
clouds. By incorporating operations of symmetry, convex hull and chebyshev
fitting, the proposed shape sig-nature is not only compact and effective but
also robust to the noise, which serves as a soft constraint to improve the
feature capability of multi-class discrimination. Based on the proposed shape
signature, we develop the shape signature networks (SSN) for 3D object
detection, which consist of pyramid feature encoding part, shape-aware grouping
heads and explicit shape encoding objective. Experiments show that the proposed
method performs remarkably better than existing methods on two large-scale
datasets. Furthermore, our shape signature can act as a plug-and-play component
and ablation study shows its effectiveness and good scalability
