We study the problem of efficient object detection of 3D LiDAR point clouds.
To reduce the memory and computational cost, existing point-based pipelines
usually adopt task-agnostic random sampling or farthest point sampling to
progressively downsample input point clouds, despite the fact that not all
points are equally important to the task of object detection. In particular,
the foreground points are inherently more important than background points for
object detectors. Motivated by this, we propose a highly-efficient single-stage
point-based 3D detector in this paper, termed IA-SSD. The key of our approach
is to exploit two learnable, task-oriented, instance-aware downsampling
strategies to hierarchically select the foreground points belonging to objects
of interest. Additionally, we also introduce a contextual centroid perception
module to further estimate precise instance centers. Finally, we build our
IA-SSD following the encoder-only architecture for efficiency. Extensive
experiments conducted on several large-scale detection benchmarks demonstrate
the competitive performance of our IA-SSD. Thanks to the low memory footprint
and a high degree of parallelism, it achieves a superior speed of 80+
frames-per-second on the KITTI dataset with a single RTX2080Ti GPU. The code is
available at https://github.com/yifanzhang713/IA-SSD.

本文提出了一种高效的基于点云的 3D 检测器 IA-SSD，利用可学习的面向任务的实例感知下采样策略分层选择感兴趣物体的前景点，进一步估计精确的实例中心，并采用仅编码器架构实现。大规模检测基准测试表明，该模型具有卓越的速度性能，能够以 80 个以上的速度在 KITTI 数据集上进行实时检测。

不是所有点都相同：学习针对 3D LiDAR 点云的高效点检测器

Not All Points Are Equal: Learning Highly Efficient Point-based  Detectors for 3D LiDAR Point Clouds

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。