  Varying density of point clouds increases the difficulty of 3D detection. In
this paper, we present a context-aware dynamic network (CADNet) to capture the
variance of density by considering both point context and semantic context.
Point-level contexts are generated from original point clouds to enlarge the
effective receptive filed. They are extracted around the voxelized pillars
based on our extended voxelization method and processed with the context
encoder in parallel with the pillar features. With a large perception range, we
are able to capture the variance of features for potential objects and generate
attentive spatial guidance to help adjust the strengths for different regions.
In the region proposal network, considering the limited representation ability
of traditional convolution where same kernels are shared among different
samples and positions, we propose a decomposable dynamic convolutional layer to
adapt to the variance of input features by learning from local semantic
context. It adaptively generates the position-dependent coefficients for
multiple fixed kernels and combines them to convolve with local feature
windows. Based on our dynamic convolution, we design a dual-path convolution
block to further improve the representation ability. We conduct experiments
with our Network on KITTI dataset and achieve good performance on 3D detection
task for both precision and speed. Our one-stage detector outperforms SECOND
and PointPillars by a large margin and achieves the speed of 30 FPS.
