CT3D

CT3D is a two-stage 3D object detection framework that leverages a high-quality region proposal network and a Channel-wise Transformer architecture. The proposed CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation for the point features within each proposal. Specifically, CT3D uses a 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.

In CT3D, the raw points are first fed into the RPN for generating 3D proposals. Then the raw points along with the corresponding proposals are processed by the channel-wise Transformer composed of the proposal-to-point encoding module and the channel-wise decoding module. Specifically, the proposal-to-point encoding module is to modulate each point feature with global proposal-aware context information. After that, the encoded point features are transformed into an effective proposal feature representation by the channel-wise decoding module for confidence prediction and box regression.