Abstract
LiDAR-based semantic segmentation is a key component for autonomous mobile robots, yet large-scale annotation of LiDAR point clouds is prohibitively expensive and time-consuming. Although simulators can provide labeled synthetic data, models trained on synthetic data often underperform on real-world data due to a data-level domain gap. To address this issue, we propose DRUM, a novel Sim2Real translation framework. We leverage a diffusion model pre-trained on unlabeled real-world data as a generative prior and translate synthetic data by reproducing two key measurement characteristics: reflectance intensity and raydrop noise. To improve sample fidelity, we introduce a raydrop-aware masked guidance mechanism that selectively enforces consistency with the input synthetic data while preserving realistic raydrop noise induced by the diffusion prior. Experimental results demonstrate that DRUM consistently improves Sim2Real performance across multiple representations of LiDAR data. The project page is available at this https URL.
Abstract (translated)
基于激光雷达的语义分割是自主移动机器人的关键组成部分，然而对激光雷达点云进行大规模标注的成本极高且耗时。尽管模拟器可提供带标注的合成数据，但由于数据层面的域差异，在合成数据上训练的模型在真实数据上往往表现不佳。为应对此问题，我们提出了DRUM——一种新颖的Sim2Real（仿真到现实）翻译框架。我们利用在无标注真实数据上预训练的扩散模型作为生成先验，并通过复现两种关键测量特性（反射强度与雨滴噪声）来转换合成数据。为提升样本保真度，我们引入了雨滴感知掩码引导机制，该机制在保留扩散先验所引入的真实雨滴噪声的同时，选择性地强化与输入合成数据的一致性。实验结果表明，DRUM在多种激光雷达数据表示形式上均持续提升了Sim2Real性能。项目页面请访问此链接。
URL
https://arxiv.org/abs/2603.26263
