Sub-Footprint Effect Correction in FW-LiDAR Point Clouds via Intra-Footprint Target Unmixing

2026-05-10

AI Summary

This study addresses the nonlinear distortion of LiDAR intensity caused by heterogeneous target mixing within a single laser footprint, which undermines the reliability of intensity information in complex scenes. The work proposes the first physics-driven sub-footprint intensity correction framework that explicitly models the spatiotemporal laser beam distribution to characterize the forward mixing process of multiple targets within a footprint. By integrating full-waveform features, geometric constraints, and a parametric model, the method decomposes the implicit mixed signal into individual sub-target contributions, thereby solving the sub-footprint inversion problem in an explicit and physically consistent manner. Evaluated on both controlled and real-world datasets, the approach significantly improves intensity consistency for homogeneous targets and semantic separability for heterogeneous ones, offering a principled solution for intensity-based applications.

Abstract

Sub-footprint target mixing within a laser footprint significantly increases LiDAR intensity uncertainty, especially in complex environments where heterogeneous materials inside one footprint cause nonlinear distortions that impair intensity-based applications. However, the forward mixing inherent to the single-pixel detection mode of LiDAR systems blurs sub-footprint contributions, making sub-footprint effects difficult to address effectively in existing studies. To address this issue, we introduce a novel, physics-based framework that explicitly resolves sub-footprint intensity correction in full-waveform LiDAR (FW-LiDAR) point clouds. The key innovation is to make the otherwise implicit intra-footprint mixing process explicit: we first develop a spatiotemporal laser-beam distribution model to physically characterize within-footprint forward mixing of multi-target returns. Building on this formulation, we incorporate ancillary information including waveform parameters and surface geometry as constraints to pose a well-defined inverse unmixing problem and decompose each footprint into fractional contributions from multiple sub-targets. We then recover sub-footprint-corrected intensities by inverting the observed mixtures through a unified combination of parametric and model-driven approaches. To the best of our knowledge, few prior studies explicitly establish sub-footprint inversion and correction within a single laser footprint, and our framework offers a principled, physics-grounded solution. Experiments on both controlled and real-world LiDAR datasets demonstrate that the proposed method significantly enhances semantic separability across heterogeneous targets and intensity consistency across homogeneous targets.

Problem

• sub-footprint effect
• LiDAR intensity uncertainty
• intra-footprint mixing
• full-waveform LiDAR
• target unmixing

Innovation

• sub-footprint unmixing
• FW-LiDAR
• intensity correction
• physics-based modeling
• inverse problem