Semantic Scene Completion

Semantic scene completion (SSC) aims to reconstruct complete 3D scenes, including both geometry and semantic labels, from partial or incomplete sensor data like sparse LiDAR point clouds or single images. Current research heavily utilizes deep learning, focusing on transformer-based architectures, diffusion models, and hybrid approaches combining neural radiance fields (NeRFs) with transformers to improve accuracy and handle occlusions. This field is crucial for advancing autonomous driving and robotics, providing richer scene understanding for safer and more efficient navigation by enabling robust perception in challenging conditions.