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

Pointillism: Accurate 3D bounding box estimation with multi-radars