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.

通过使用LiDAR传感器和场景语义，我们提供了一种新的仅依靠LiDAR传感器的增强型原始扫描的稠密伪点云框架，无需摄像机，该框架首先利用分割模型从原始点云中提取场景语义，然后使用多模态的域转换器生成伪图像分割和深度提示，从而生成一种富含语义信息的稠密伪点云，并通过引入新的语义引导投影方法增强检测性能。

针对三维物体检测的基于语义感知的仅激光雷达伪点云生成