Core Viewpoint - The article discusses the development of Bezier curve Gaussian splatting (BezierGS) by Fudan University, which addresses the challenges of dynamic target reconstruction in autonomous driving scenarios, improving the accuracy and efficiency of scene element separation and reconstruction [1][2]. Group 1 - BezierGS utilizes learnable Bezier curves to represent the motion trajectories of dynamic targets, leveraging temporal information to calibrate pose errors [1]. - The method introduces additional supervision for dynamic target rendering and consistency constraints between curves, leading to improved reconstruction outcomes [1]. - Experiments on the Waymo Open Dataset and nuPlan benchmark demonstrate that BezierGS outperforms state-of-the-art alternatives in both dynamic and static scene target reconstruction [1]. Group 2 - The article highlights the potential to build a high-quality street scene world for training and exploring self-driving models, which can reduce data collection costs [2]. - It emphasizes the reduction of reliance on the accuracy of bounding box annotations, which are often imprecise in current industry and open-source datasets [2]. - The work represents a step towards exploring a true self-driving world model, although it currently only achieves trajectory interpolation and not extrapolation [2].