  3D object detection with LiDAR point clouds plays an important role in
autonomous driving perception module that requires high speed, stability and
accuracy. However, the existing point-based methods are challenging to reach
the speed requirements because of too many raw points, and the voxel-based
methods are unable to ensure stable speed because of the 3D sparse convolution.
In contrast, the 2D grid-based methods, such as PointPillar, can easily achieve
a stable and efficient speed based on simple 2D convolution, but it is hard to
get the competitive accuracy limited by the coarse-grained point clouds
representation. So we propose an improved pillar with fine-grained feature
based on PointPillar that can significantly improve detection accuracy. It
consists of two modules, including height-aware sub-pillar and sparsity-based
tiny-pillar, which get fine-grained representation respectively in the vertical
and horizontal direction of 3D space. For height-aware sub-pillar, we introduce
a height position encoding to keep height information of each sub-pillar during
projecting to a 2D pseudo image. For sparsity-based tiny-pillar, we introduce
sparsity-based CNN backbone stacked by dense feature and sparse attention
module to extract feature with larger receptive field efficiently. Experimental
results show that our proposed method significantly outperforms previous
state-of-the-art 3D detection methods on the Waymo Open Dataset. The related
code will be released to facilitate the academic and industrial study.
