DRUM：基于扩散的反射率感知无配对映射用于Sim2Real激光雷达分割
DRUM: Diffusion-based Raydrop-aware Unpaired Mapping for Sim2Real LiDAR Segmentation
AI总结：
DRUM通过利用未标记真实数据预训练的扩散模型，结合反射强度和射线丢失噪声特性，提升Sim2Real激光雷达分割性能。
摘要：
基于激光雷达的语义分割是自主移动机器人的重要组件，但大规模标注激光雷达点云成本高昂且耗时。尽管模拟器可提供带标签的合成数据，但训练在合成数据上的模型在真实数据上表现不佳，因存在数据级领域差距。为解决此问题，我们提出DRUM，一种新的Sim2Real翻译框架。我们利用在未标记真实数据上预训练的扩散模型作为生成先验，并通过再现两个关键测量特征：反射强度和射线丢失噪声来翻译合成数据。为提高样本保真度，我们引入了射线丢失感知的掩码引导机制，该机制有选择地强制与输入合成数据一致，同时保留由扩散先验引起的现实射线丢失噪声。实验结果表明，DRUM在多种激光雷达数据表示上一致提升了Sim2Real性能。项目页面可在https://miya-tomoya.github.io/drum上获取。
英文摘要：
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 https://miya-tomoya.github.io/drum.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
