  Though 3D object detection from point clouds has achieved rapid progress in
recent years, the lack of flexible and high-performance proposal refinement
remains a great hurdle for existing state-of-the-art two-stage detectors.
Previous works on refining 3D proposals have relied on human-designed
components such as keypoints sampling, set abstraction and multi-scale feature
fusion to produce powerful 3D object representations. Such methods, however,
have limited ability to capture rich contextual dependencies among points. In
this paper, we leverage the high-quality region proposal network and a
Channel-wise Transformer architecture to constitute our two-stage 3D object
detection framework (CT3D) with minimal hand-crafted design. The proposed CT3D
simultaneously performs proposal-aware embedding and channel-wise context
aggregation for the point features within each proposal. Specifically, CT3D
uses proposal's keypoints for spatial contextual modelling and learns attention
propagation in the encoding module, mapping the proposal to point embeddings.
Next, a new channel-wise decoding module enriches the query-key interaction via
channel-wise re-weighting to effectively merge multi-level contexts, which
contributes to more accurate object predictions. Extensive experiments
demonstrate that our CT3D method has superior performance and excellent
scalability. Remarkably, CT3D achieves the AP of 81.77% in the moderate car
category on the KITTI test 3D detection benchmark, outperforms state-of-the-art
3D detectors.
