根据前后帧信息预测扫描重点，省电超65%还更准
Adaptive LiDAR Scanning: Harnessing Temporal Cues for Efficient 3D Object Detection via Multi-Modal Fusion
- 用历史数据预测下帧关键区域，只在重点区域密集扫描
- 在nuScenes和Lyft上能耗降低65%以上，检测精度不降反升
- 适合车载等算力受限设备，尤其看重能效的自动驾驶场景
融合激光雷达（LiDAR）与可见光相机可显著提升3D目标检测性能。但传统激光雷达采用密集、无状态的扫描方式，忽略了真实场景中强烈的时序连续性，导致大量感知冗余和高功耗，限制了其在资源受限平台的应用。为此，我们提出一种基于历史信息的自适应扫描框架，通过预测下一帧的感兴趣区域（ROI）来优化扫描策略。该方法引入轻量级预测网络，将历史空间与时间上下文提炼为精炼的查询嵌入，再由可微分掩码生成网络利用Gumbel-Softmax采样生成二值掩码，指导激光雷达仅在关键区域进行密集扫描，其余区域稀疏采样。在nuScenes和Lyft基准测试中，该方法将激光雷达能耗降低超过65%，同时保持甚至优于传统全密度扫描方法的3D目标检测性能。
原文摘要 · Abstract (English)
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.
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