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。