面向激光雷达系统自动化设计的任务驱动隐式表示
Task-Driven Implicit Representations for Automated Design of LiDAR Systems
把激光雷达传感器配置编码为6D连续空间中的隐式密度，用流生成模型学习任务相关的采样分布，再用EM拟合出具体传感器参数——这套「任务驱动隐式表示+参数拟合」的思路可直接迁移到相机阵列、事件相机、ToF等成像系统的联合设计，也可作为生成式传感器仿真的新范式。
原文依据 we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论成像系统设计是一个复杂、耗时且在很大程度上依赖人工的过程；激光雷达设计广泛存在于移动设备、自动驾驶车辆和航空成像平台中，其独特的空间与时序采样需求进一步增加了设计复杂度。本文提出一个面向任意约束条件下自动化、任务驱动激光雷达系统设计的框架。为此，我们将激光雷达配置表示在连续的六维设计空间中，并通过基于流的生成建模在该空间内学习任务特定的隐式密度。随后，我们将传感器建模为六维空间中的参数化分布，并利用期望最大化将这些分布拟合到所学的隐式密度上，从而合成新的激光雷达系统，实现高效且约束感知的激光雷达系统设计。我们在三维视觉的多种任务上验证了该方法，实现了面向人脸扫描、机器人跟踪和目标检测等受真实世界启发的应用的自动化激光雷达系统设计。
英文原文摘要 1001 字符
Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.