ICCV 2025！复旦BezierGS：利用贝塞尔曲线实现极简标注驾驶场景SOTA重建~

Core Viewpoint - The article discusses the latest work from Fudan University on a method called BezierGS, which utilizes Bezier curves for dynamic urban scene reconstruction, crucial for developing closed-loop simulations in autonomous driving [4][5]. Group 1: Methodology and Contributions - BezierGS addresses the limitations of existing methods that rely on precise pose annotations for dynamic targets, which restricts large-scale scene reconstruction [4][7]. - The method employs learnable Bezier curves to represent the motion trajectories of dynamic targets, effectively utilizing temporal information and calibrating pose errors [4][8]. - Extensive 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 and novel view synthesis [4][14]. Group 2: Advantages and Future Directions - The approach aims to build a high-quality street scene for training autonomous models, reducing data collection costs and reliance on bounding box accuracy, which is often imprecise in current datasets [6]. - Future exploration will focus on creating a true autonomous driving world model, although the current work is limited to trajectory interpolation and cannot extrapolate beyond the trajectory [6]. - The introduction of additional supervision for dynamic target rendering enhances the separation and reconstruction of scene elements, leading to more accurate simulations [8][49]. Group 3: Experimental Results - The experiments conducted on the Waymo and nuPlan datasets show significant improvements in reconstruction quality, with BezierGS achieving higher PSNR and SSIM scores compared to existing methods [36][41]. - Specifically, in the Waymo dataset, BezierGS achieved a PSNR of 33.98 and an SSIM of 0.934, outperforming other methods by notable margins [36][37]. - In the nuPlan benchmark, BezierGS demonstrated a PSNR improvement of 3.04 dB and a reduction in LPIPS by 16.35%, showcasing its effectiveness in handling complex dynamic scenes [41].