  Radar-based perception has gained increasing attention in autonomous driving,
yet the inherent sparsity of radars poses challenges. Radar raw data often
contains excessive noise, whereas radar point clouds retain only limited
information. In this work, we holistically treat the sparse nature of radar
data by introducing an adaptive subsampling method together with a tailored
network architecture that exploits the sparsity patterns to discover global and
local dependencies in the radar signal. Our subsampling module selects a subset
of pixels from range-doppler (RD) spectra that contribute most to the
downstream perception tasks. To improve the feature extraction on sparse
subsampled data, we propose a new way of applying graph neural networks on
radar data and design a novel two-branch backbone to capture both global and
local neighbor information. An attentive fusion module is applied to combine
features from both branches. Experiments on the RADIal dataset show that our
SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection
and achieves close to SOTA accuracy in freespace segmentation, meanwhile using
sparse subsampled input data.
