  We introduce SLCF-Net, a novel approach for the Semantic Scene Completion
(SSC) task that sequentially fuses LiDAR and camera data. It jointly estimates
missing geometry and semantics in a scene from sequences of RGB images and
sparse LiDAR measurements. The images are semantically segmented by a
pre-trained 2D U-Net and a dense depth prior is estimated from a
depth-conditioned pipeline fueled by Depth Anything. To associate the 2D image
features with the 3D scene volume, we introduce Gaussian-decay Depth-prior
Projection (GDP). This module projects the 2D features into the 3D volume along
the line of sight with a Gaussian-decay function, centered around the depth
prior. Volumetric semantics is computed by a 3D U-Net. We propagate the hidden
3D U-Net state using the sensor motion and design a novel loss to ensure
temporal consistency. We evaluate our approach on the SemanticKITTI dataset and
compare it with leading SSC approaches. The SLCF-Net excels in all SSC metrics
and shows great temporal consistency.
