Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object
  Detection
Tiago Cortinhal Idriss Gouigah Eren Erdal Aksoy
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.