Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification

TL;DR

This paper introduces an efficient learned point cloud simplification method for LiDAR data that improves speed and accuracy in object detection and classification tasks, especially under aggressive downsampling.

Contribution

It presents a novel attention-based sampling approach that outperforms traditional methods like FPS and RS in speed and accuracy, trained end-to-end for LiDAR point cloud simplification.

Findings

Faster than Farthest Point Sampling (FPS) with comparable or better accuracy.

More reliable accuracy preservation at high sampling ratios compared to Random Sampling (RS).

Achieves significant speed gains especially under aggressive downsampling.

Abstract

LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, pedestrians, and traffic signs. While this dense data enables accurate perception, it also increases computational cost and power consumption, which can limit real-time deployment. Existing point cloud sampling methods typically face a trade-off: very fast approaches tend to reduce accuracy, while more accurate methods are computationally expensive. To address this limitation, we propose an efficient learned point cloud simplification method for LiDAR data. The method combines a feature embedding module with an attention-based sampling module to prioritize task-relevant regions and is trained end-to-end. We evaluate the method against farthest point sampling (FPS) and random sampling (RS) on 3D object detection on the KITTI dataset and on object classification across four datasets. The method was consistently faster than FPS and achieved similar, and in some settings better, accuracy, with the largest gains under aggressive downsampling. It was slower than RS, but it typically preserved accuracy more reliably at high sampling ratios.