用隐式建模自动设计满足任务需求的激光雷达系统。
Task-Driven Implicit Representations for Automated Design of LiDAR Systems
- 在六维连续空间中表示激光雷达配置，通过生成模型学习任务相关密度。
- 基于期望最大化算法拟合传感器分布，实现约束感知的设计优化。
- 适用于人脸扫描、机器人追踪等真实场景，提升设计效率。
成像系统设计过程复杂且高度依赖人工；激光雷达因在移动设备、自动驾驶及航拍平台中的广泛应用，其空间与时间采样要求更复杂。本文提出一种在任意约束条件下自动化、任务驱动的激光雷达系统设计框架。通过将激光雷达配置表示在连续六维设计空间中，并利用基于流的生成模型学习特定任务的隐式密度，实现新系统的合成。我们把传感器建模为六维空间中的参数化分布，通过期望最大化算法将其拟合到学习到的隐式密度上，从而实现高效、约束感知的系统设计。该方法在三维视觉多个任务中验证有效，支持面向真实应用场景（如人脸扫描、机器人追踪、目标检测）的自动化激光雷达系统设计。
原文摘要 · Abstract (English)
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.
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