生成可控制的连续激光雷达场景，提升自动驾驶仿真真实度。
DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving
- 用多模态条件与序列噪声预测模型生成连贯点云
- nuScenes上FRD达743.13，比SOTA提升37.2%
- 支持前景物体精准定位与背景真实还原，适合自动驾驶测试
真实激光雷达点云的生成在自动驾驶系统开发与评估中至关重要。尽管近期3D激光雷达点云生成方法已有显著进展，但仍存在缺乏序列生成能力、难以精确生成前景物体与真实背景等局限，制约其实际应用。本文提出DriveLiDAR4D，一种包含多模态条件与新型序列噪声预测模型LiDAR4DNet的激光雷达生成流水线，可生成时序一致、前景可控且背景逼真的激光雷达场景。据我们所知，这是首个以端到端方式实现完整场景操作能力的序列化激光雷达场景生成工作。我们在nuScenes和KITTI数据集上进行评估，在nuScenes上取得743.13的FRD得分与16.96的FVD得分，分别较当前最优方法UniScene提升37.2%和24.1%。
原文摘要 · Abstract (English)
The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant improvements, they still face notable limitations, including the lack of sequential generation capabilities and the inability to produce accurately positioned foreground objects and realistic backgrounds. These shortcomings hinder their practical applicability. In this paper, we introduce DriveLiDAR4D, a novel LiDAR generation pipeline consisting of multimodal conditions and a novel sequential noise prediction model LiDAR4DNet, capable of producing temporally consistent LiDAR scenes with highly controllable foreground objects and realistic backgrounds. To the best of our knowledge, this is the first work to address the sequential generation of LiDAR scenes with full scene manipulation capability in an end-to-end manner. We evaluated DriveLiDAR4D on the nuScenes and KITTI datasets, where we achieved an FRD score of 743.13 and an FVD score of 16.96 on the nuScenes dataset, surpassing the current state-of-the-art (SOTA) method, UniScene, with an performance boost of 37.2% in FRD and 24.1% in FVD, respectively.
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