  Recent progress on 2D object detection has featured Cascade RCNN, which
capitalizes on a sequence of cascade detectors to progressively improve
proposal quality, towards high-quality object detection. However, there has not
been evidence in support of building such cascade structures for 3D object
detection, a challenging detection scenario with highly sparse LiDAR point
clouds. In this work, we present a simple yet effective cascade architecture,
named 3D Cascade RCNN, that allocates multiple detectors based on the voxelized
point clouds in a cascade paradigm, pursuing higher quality 3D object detector
progressively. Furthermore, we quantitatively define the sparsity level of the
points within 3D bounding box of each object as the point completeness score,
which is exploited as the task weight for each proposal to guide the learning
of each stage detector. The spirit behind is to assign higher weights for
high-quality proposals with relatively complete point distribution, while
down-weight the proposals with extremely sparse points that often incur noise
during training. This design of completeness-aware re-weighting elegantly
upgrades the cascade paradigm to be better applicable for the sparse input
data, without increasing any FLOP budgets. Through extensive experiments on
both the KITTI dataset and Waymo Open Dataset, we validate the superiority of
our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object
detection techniques. The source code is publicly available at
\url{https://github.com/caiqi/Cascasde-3D}.
