Task-Driven Implicit Representations for Automated Design of LiDAR Systems

TL;DR

This paper introduces a novel, automated framework for designing LiDAR systems tailored to specific tasks by learning a continuous configuration space and synthesizing sensor designs through flow-based generative modeling, streamlining the complex design process.

Contribution

It presents a task-driven, generative modeling approach for LiDAR system design that automates and optimizes configurations under various constraints, a significant advancement over manual methods.

Findings

Successfully designed LiDAR systems for face scanning, robotic tracking, and object detection.

Demonstrated efficient, constraint-aware system synthesis in diverse real-world scenarios.

Validated the approach's ability to generate task-specific LiDAR configurations.

Abstract

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.