  Currently, existing state-of-the-art 3D object detectors are in two-stage
paradigm. These methods typically comprise two steps: 1) Utilize region
proposal network to propose a fraction of high-quality proposals in a bottom-up
fashion. 2) Resize and pool the semantic features from the proposed regions to
summarize RoI-wise representations for further refinement. Note that these
RoI-wise representations in step 2) are considered individually as an
uncorrelated entry when fed to following detection headers. Nevertheless, we
observe these proposals generated by step 1) offset from ground truth somehow,
emerging in local neighborhood densely with an underlying probability.
Challenges arise in the case where a proposal largely forsakes its boundary
information due to coordinate offset while existing networks lack corresponding
information compensation mechanism. In this paper, we propose BANet for 3D
object detection from point clouds. Specifically, instead of refining each
proposal independently as previous works do, we represent each proposal as a
node for graph construction within a given cut-off threshold, associating
proposals in the form of local neighborhood graph, with boundary correlations
of an object being explicitly exploited. Besiedes, we devise a lightweight
Region Feature Aggregation Network to fully exploit voxel-wise, pixel-wise, and
point-wise feature with expanding receptive fields for more informative
RoI-wise representations. As of Apr. 17th, 2021, our BANet achieves on par
performance on KITTI 3D detection leaderboard and ranks $1^{st}$ on $Moderate$
difficulty of $Car$ category on KITTI BEV detection leaderboard. The source
code will be released once the paper is accepted.
