Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection
This addresses the challenge of limited object detail in autonomous systems without needing cameras, though it is incremental as it builds on existing pseudo-LiDAR methods.
The paper tackles the problem of sparse LiDAR data in 3D object detection by proposing a LiDAR-only framework that generates dense pseudo point clouds using scene semantics, resulting in up to a 2.9% performance improvement.
Although LiDAR sensors are crucial for autonomous systems due to providing precise depth information, they struggle with capturing fine object details, especially at a distance, due to sparse and non-uniform data. Recent advances introduced pseudo-LiDAR, i.e., synthetic dense point clouds, using additional modalities such as cameras to enhance 3D object detection. We present a novel LiDAR-only framework that augments raw scans with denser pseudo point clouds by solely relying on LiDAR sensors and scene semantics, omitting the need for cameras. Our framework first utilizes a segmentation model to extract scene semantics from raw point clouds, and then employs a multi-modal domain translator to generate synthetic image segments and depth cues without real cameras. This yields a dense pseudo point cloud enriched with semantic information. We also introduce a new semantically guided projection method, which enhances detection performance by retaining only relevant pseudo points. We applied our framework to different advanced 3D object detection methods and reported up to 2.9% performance upgrade. We also obtained comparable results on the KITTI 3D object detection dataset, in contrast to other state-of-the-art LiDAR-only detectors.