Abstract
To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges in producing high-quality, diverse, and controllable foreground objects. To address the needs of object-aware tasks in 3D perception, we introduce OLiDM, a novel framework capable of generating high-fidelity LiDAR data at both the object and the scene levels. OLiDM consists of two pivotal components: the Object-Scene Progressive Generation (OPG) module and the Object Semantic Alignment (OSA) module. OPG adapts to user-specific prompts to generate desired foreground objects, which are subsequently employed as conditions in scene generation, ensuring controllable outputs at both the object and scene levels. This also facilitates the association of user-defined object-level annotations with the generated LiDAR scenes. Moreover, OSA aims to rectify the misalignment between foreground objects and background scenes, enhancing the overall quality of the generated objects. The broad effectiveness of OLiDM is demonstrated across various LiDAR generation tasks, as well as in 3D perception tasks. Specifically, on the KITTI-360 dataset, OLiDM surpasses prior state-of-the-art methods such as UltraLiDAR by 17.5 in FPD. Additionally, in sparse-to-dense LiDAR completion, OLiDM achieves a significant improvement over LiDARGen, with a 57.47\% increase in semantic IoU. Moreover, OLiDM enhances the performance of mainstream 3D detectors by 2.4\% in mAP and 1.9\% in NDS, underscoring its potential in advancing object-aware 3D tasks. Code is available at: this https URL.
Abstract (translated)
为了提高复杂场景下自动驾驶的安全性，已经提出了多种方法来模拟激光雷达点云数据。然而，这些方法在生成高质量、多样化且可控制的前景对象方面常常面临挑战。为了解决三维感知中物体感知任务的需求，我们引入了OLiDM（Object-Level Aware LiDAR Data Model），这是一种能够在对象和场景层面生成高保真度激光雷达数据的新框架。OLiDM由两个关键组件构成：对象-场景渐进生成(OPG)模块和对象语义对齐(OSA)模块。OPG能够适应用户特定的提示，生成所需前景对象，并将其用作场景生成中的条件，确保在对象和场景层面均能产生可控制的输出。这也有助于将用户定义的对象级注释与生成的激光雷达场景关联起来。此外，OSA旨在纠正前景对象和背景场景之间的错位，提高生成物体的整体质量。OLiDM的有效性已经通过各种激光雷达生成任务以及三维感知任务得到了广泛的验证。具体来说，在KITTI-360数据集上，OLiDM在FPD指标上超越了UltraLiDAR等先前的领先方法17.5分。此外，在稀疏到稠密的激光雷达完成任务中，OLiDM相比于LiDARGen实现了语义IoU 57.47%的显著提升。而且，OLiDM提升了主流三维检测器的表现，mAP提高了2.4%，NDS提高了1.9%，突显了其在推进物体感知三维任务中的潜力。代码可在以下链接获取：此 https URL。
URL
https://arxiv.org/abs/2412.17226
