标题： 自适应LiDAR扫描：通过多模态融合利用时间线索进行高效的3D目标检测 显示英文标题

标题： Adaptive LiDAR Scanning: Harnessing Temporal Cues for Efficient 3D Object Detection via Multi-Modal Fusion

摘要： 多传感器融合使用LiDAR和RGB相机显著增强了3D目标检测任务。 然而，传统的LiDAR传感器进行密集、无状态的扫描，忽略了现实场景中的强时间连续性。 这导致了大量的传感冗余和过高的功耗，限制了它们在资源受限平台上的实用性。 为了解决这种低效问题，我们提出了一种预测性、历史感知的自适应扫描框架，该框架基于过去观察结果预测感兴趣的区域（ROI）。 我们的方法引入了一个轻量级预测网络，将历史空间和时间上下文提炼为精炼的查询嵌入。 这些嵌入引导一个可微分的Mask Generator网络，该网络利用Gumbel-Softmax采样生成二进制掩码，识别下一帧的关键ROI。 我们的方法通过仅在这些ROI内进行密集LiDAR扫描并在其他地方稀疏采样，显著减少了不必要的数据采集。 在nuScenes和Lyft基准测试上的实验表明，我们的自适应扫描策略将LiDAR能耗降低了超过65%，同时在3D目标检测性能方面与传统LiDAR-相机融合方法相比保持竞争力甚至更优。 显示英文摘要

摘要： Multi-sensor fusion using LiDAR and RGB cameras significantly enhances 3D object detection task. However, conventional LiDAR sensors perform dense, stateless scans, ignoring the strong temporal continuity in real-world scenes. This leads to substantial sensing redundancy and excessive power consumption, limiting their practicality on resource-constrained platforms. To address this inefficiency, we propose a predictive, history-aware adaptive scanning framework that anticipates informative regions of interest (ROI) based on past observations. Our approach introduces a lightweight predictor network that distills historical spatial and temporal contexts into refined query embeddings. These embeddings guide a differentiable Mask Generator network, which leverages Gumbel-Softmax sampling to produce binary masks identifying critical ROIs for the upcoming frame. Our method significantly reduces unnecessary data acquisition by concentrating dense LiDAR scanning only within these ROIs and sparsely sampling elsewhere. Experiments on nuScenes and Lyft benchmarks demonstrate that our adaptive scanning strategy reduces LiDAR energy consumption by over 65% while maintaining competitive or even superior 3D object detection performance compared to traditional LiDAR-camera fusion methods with dense LiDAR scanning.