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].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 今天自动驾驶之心为大家分享 复旦大学ICCV2025中稿的最新工作！ BezierGS：基于贝塞尔曲线高斯泼溅的动态城市场景重建！ 如 果您有相关工作需要分享，请在文末联系我们！ 自动驾驶课程学习与技术交流群事宜，也欢迎添加小助理微信AIDriver004做进一步咨询 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Zipei Ma等 编辑 | 自动驾驶之心 1. 构建一个高质量街景世界，供自驾模型在其中训练、探索，减少数据采集的成本； 2. 减少对bounding box精确性的依赖，目前业界以及开源自驾数据集采集的准确性不是很高，bounding box的标注不精确； 3. 这篇是对自驾世界的学习与探索，未来会探索一个真正的自驾世界模型，该工作只能实现轨迹内插，无法轨迹外插。 论文链接：https://arxiv.org/abs/2506.22099 代码代码：https://github.com/fudan-zvg/BezierGS 随着需要实时传感器反馈的端到端自动驾驶系统的兴起，现 ...