Authors: Helin Cao, Sven Behnke
Abstract: Perception systems play a crucial role in autonomous driving, incorporating
multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors
are widely used to capture sparse point clouds of the vehicle’s surroundings.
However, such systems struggle to perceive occluded areas and gaps in the scene
due to the sparsity of these point clouds and their lack of semantics. To
address these challenges, Semantic Scene Completion (SSC) jointly predicts
unobserved geometry and semantics in the scene given raw LiDAR measurements,
aiming for a more complete scene representation. Building on promising results
of diffusion models in image generation and super-resolution tasks, we propose
their extension to SSC by implementing the noising and denoising diffusion
processes in the point and semantic spaces individually. To control the
generation, we employ semantic LiDAR point clouds as conditional input and
design local and global regularization losses to stabilize the denoising
process. We evaluate our approach on autonomous driving datasets and our
approach outperforms the state-of-the-art for SSC.
Source: http://arxiv.org/abs/2409.18092v1
