  Autonomous perception requires high-quality environment sensing in the form
of 3D bounding boxes of dynamic objects. The primary sensors used in automotive
systems are light-based cameras and LiDARs. However, they are known to fail in
adverse weather conditions. Radars can potentially solve this problem as they
are barely affected by adverse weather conditions. However, specular
reflections of wireless signals cause poor performance of radar point clouds.
We introduce Pointillism, a system that combines data from multiple spatially
separated radars with an optimal separation to mitigate these problems. We
introduce a novel concept of Cross Potential Point Clouds, which uses the
spatial diversity induced by multiple radars and solves the problem of noise
and sparsity in radar point clouds. Furthermore, we present the design of
RP-net, a novel deep learning architecture, designed explicitly for radar's
sparse data distribution, to enable accurate 3D bounding box estimation. The
spatial techniques designed and proposed in this paper are fundamental to
radars point cloud distribution and would benefit other radar sensing
applications.
