  The field of 3D object detection from point clouds is rapidly advancing in
computer vision, aiming to accurately and efficiently detect and localize
objects in three-dimensional space. Current 3D detectors commonly fall short in
terms of flexibility and scalability, with ample room for advancements in
performance. In this paper, our objective is to address these limitations by
introducing two frameworks for 3D object detection with minimal hand-crafted
design. Firstly, we propose CT3D, which sequentially performs raw-point-based
embedding, a standard Transformer encoder, and a channel-wise decoder for point
features within each proposal. Secondly, we present an enhanced network called
CT3D++, which incorporates geometric and semantic fusion-based embedding to
extract more valuable and comprehensive proposal-aware information.
Additionally, CT3D ++ utilizes a point-to-key bidirectional encoder for more
efficient feature encoding with reduced computational cost. By replacing the
corresponding components of CT3D with these novel modules, CT3D++ achieves
state-of-the-art performance on both the KITTI dataset and the large-scale
Way\-mo Open Dataset. The source code for our frameworks will be made
accessible at https://github.com/hlsheng1/CT3D-plusplus.
