Core Insights - 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 [5][6]. 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 [5][8]. - The method employs learnable Bezier curves to represent the motion trajectories of dynamic targets, effectively utilizing temporal information and calibrating pose errors [5][9]. - 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 reconstruction [5][15]. Group 2: Advantages and Future Directions - The approach aims to build high-quality street scenes for training autonomous models, reducing data collection costs and reliance on bounding box accuracy [7][8]. - Future explorations will focus on creating a true autonomous driving world model, with current work limited to trajectory interpolation [7]. - The method enhances the realism of closed-loop evaluations by providing high-quality scene reconstructions, enabling safe and cost-effective simulations of critical extreme scenarios [8][9]. Group 3: Experimental Results - BezierGS achieved superior performance metrics compared to existing methods, with significant improvements in PSNR, SSIM, and Dyn-PSNR across both datasets [37][38]. - In the Waymo dataset, BezierGS showed a PSNR increase of 1.87 dB and a Dyn-PSNR improvement of 2.66 dB, indicating its effectiveness in rendering dynamic content [38][40]. - The nuPlan benchmark results also highlighted BezierGS's ability to correct pose errors automatically, leading to enhanced reconstruction quality [42][43].

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 ...