Although point-based networks are demonstrated to be accurate for 3D point
cloud modeling, they are still falling behind their voxel-based competitors in
3D detection. We observe that the prevailing set abstraction design for
down-sampling points may maintain too much unimportant background information
that can affect feature learning for detecting objects. To tackle this issue,
we propose a novel set abstraction method named Semantics-Augmented Set
Abstraction (SASA). Technically, we first add a binary segmentation module as
the side output to help identify foreground points. Based on the estimated
point-wise foreground scores, we then propose a semantics-guided point sampling
algorithm to help retain more important foreground points during down-sampling.
In practice, SASA shows to be effective in identifying valuable points related
to foreground objects and improving feature learning for point-based 3D
detection. Additionally, it is an easy-to-plug-in module and able to boost
various point-based detectors, including single-stage and two-stage ones.
Extensive experiments on the popular KITTI and nuScenes datasets validate the
superiority of SASA, lifting point-based detection models to reach comparable
performance to state-of-the-art voxel-based methods.

本论文提出了一种名为 SASA 的新型集抽象方法，通过添加二进制分割模块和基于预测的前景分数的语义引导点采样算法来识别与前景物体相关的有价值的点，进而提升点云三维检测的性能。实验表明，SASA 可提升各种基于点云的检测器的性能，与体素方法媲美。