Abstract
LiDAR scene generation has been developing rapidly recently. However, existing methods primarily focus on generating static and single-frame scenes, overlooking the inherently dynamic nature of real-world driving environments. In this work, we introduce DynamicCity, a novel 4D LiDAR generation framework capable of generating large-scale, high-quality LiDAR scenes that capture the temporal evolution of dynamic environments. DynamicCity mainly consists of two key models. 1) A VAE model for learning HexPlane as the compact 4D representation. Instead of using naive averaging operations, DynamicCity employs a novel Projection Module to effectively compress 4D LiDAR features into six 2D feature maps for HexPlane construction, which significantly enhances HexPlane fitting quality (up to 12.56 mIoU gain). Furthermore, we utilize an Expansion & Squeeze Strategy to reconstruct 3D feature volumes in parallel, which improves both network training efficiency and reconstruction accuracy than naively querying each 3D point (up to 7.05 mIoU gain, 2.06x training speedup, and 70.84% memory reduction). 2) A DiT-based diffusion model for HexPlane generation. To make HexPlane feasible for DiT generation, a Padded Rollout Operation is proposed to reorganize all six feature planes of the HexPlane as a squared 2D feature map. In particular, various conditions could be introduced in the diffusion or sampling process, supporting versatile 4D generation applications, such as trajectory- and command-driven generation, inpainting, and layout-conditioned generation. Extensive experiments on the CarlaSC and Waymo datasets demonstrate that DynamicCity significantly outperforms existing state-of-the-art 4D LiDAR generation methods across multiple metrics. The code will be released to facilitate future research.
Abstract (translated)
最近，LiDAR场景生成技术得到了快速发展。然而，现有的方法主要集中在生成静态和单帧场景上，忽视了真实驾驶环境中固有的动态特性。在此工作中，我们引入了DynamicCity，这是一种新颖的4D LiDAR生成框架，能够生成大规模、高质量的LiDAR场景，捕捉动态环境的时间演变。DynamicCity主要包括两个关键模型。 1) 一个VAE（变分自编码器）模型用于学习HexPlane作为紧凑的4D表示形式。与使用简单的平均操作不同，DynamicCity采用了一种新颖的投影模块来有效地将4D LiDAR特征压缩成六个2D特征图以构建HexPlane，这显著提升了HexPlane的拟合质量（最多提高12.56 mIoU）。此外，我们还采用了扩展与压缩策略来并行重建3D特征体积，相较于简单地查询每个3D点，这种方法提高了网络训练效率和重构精度（最高可提升7.05 mIoU，加快2.06倍的训练速度，并减少70.84%的内存使用）。 2) 一个基于DiT（Diffusion Transformer）的扩散模型用于生成HexPlane。为了使HexPlane适用于DiT生成，提出了填充滚动操作来重新组织所有六个特征平面为一个方形的2D特征图。特别地，在扩散或采样过程中可以引入各种条件支持多样的4D生成应用，例如轨迹和指令驱动生成、修复（inpainting）以及布局条件下的生成。 在CarlaSC和Waymo数据集上的大量实验表明，DynamicCity在多个指标上显著优于现有的最先进的4D LiDAR生成方法。代码将被公开发布以促进未来的研究。
URL
https://arxiv.org/abs/2410.18084
