  We introduce Patch Refinement a two-stage model for accurate 3D object
detection and localization from point cloud data. Patch Refinement is composed
of two independently trained Voxelnet-based networks, a Region Proposal Network
(RPN) and a Local Refinement Network (LRN). We decompose the detection task
into a preliminary Bird's Eye View (BEV) detection step and a local 3D
detection step. Based on the proposed BEV locations by the RPN, we extract
small point cloud subsets ("patches"), which are then processed by the LRN,
which is less limited by memory constraints due to the small area of each
patch. Therefore, we can apply encoding with a higher voxel resolution locally.
The independence of the LRN enables the use of additional augmentation
techniques and allows for an efficient, regression focused training as it uses
only a small fraction of each scene. Evaluated on the KITTI 3D object detection
benchmark, our submission from January 28, 2019, outperformed all previous
entries on all three difficulties of the class car, using only 50 % of the
available training data and only LiDAR information.
