Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data

TL;DR

This paper introduces a task-optimized adaptive sampling framework for 3D single-photon multispectral LiDAR that significantly reduces data acquisition time by focusing on the most informative scene regions, enabling faster processing.

Contribution

It presents a novel Bayesian-based adaptive sampling method tailored for high-dimensional LiDAR data, optimizing data collection based on task-specific uncertainties.

Findings

Reduces acquisition time compared to fixed sampling.

Effective in object classification and target detection.

Works with both sequential and parallel scanning modes.

Abstract

3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge to LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that enables fast acquisition and processing of high-dimensional single-photon LiDAR data. Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties. The task is performed by considering a Bayesian model that is carefully built to allow fast per-pixel computations while delivering parameter estimates with quantified uncertainties. The framework is demonstrated on multispectral 3D single-photon LiDAR imaging when considering object classification and/or target detection as tasks. It is also analysed for both sequential and parallel scanning modes for different detector array sizes. Results on simulated and real data show the benefit of the proposed optimized sampling strategy when compared to fixed sampling strategies.