nuScenes Revisited: Progress and Challenges in Autonomous Driving

A new framework named BEVDilation has been introduced, focusing on the integration of LiDAR and camera data for enhanced 3D object detection. This approach emphasizes LiDAR information to mitigate performance degradation caused by the geometric discrepancies between the two sensors, utilizing image features as implicit guidance to improve spatial alignment and address point cloud limitations.

The Driving Gaussian Grounded Transformer (DGGT) has been introduced as a novel framework for fast and scalable 4D reconstruction of dynamic driving scenes using unposed images, addressing the limitations of existing methods that require known camera calibration and per-scene optimization. This approach allows for reconstruction directly from sparse images and supports long sequences with multiple views.

LiDARCrafter has been introduced as a unified framework for dynamic 4D world modeling from LiDAR sequences, addressing challenges in controllability, temporal coherence, and evaluation standardization. The framework utilizes natural language inputs to generate structured scene graphs, which guide a tri-branch diffusion network in creating object structures and motion trajectories.

A novel pre-training approach named Alligat0R has been introduced, focusing on co-visibility segmentation for relative camera pose regression, replacing the previous cross-view completion method. This technique enhances performance in both covisible and non-covisible regions by predicting pixel visibility across images, supported by the large-scale Cub3 dataset containing 5 million image pairs with dense annotations.