AI summaryⓘ
The authors address the problem that LiDAR sensors often mix signals from different materials within a single laser footprint, causing errors in measuring intensity. They create a new physics-based method to separate and correct these mixed signals by modeling how the laser beam spreads and interacts with multiple targets at once. Their approach uses extra information like waveform data and surface shape to accurately break down the mixed signals into parts from different sub-targets. Tests show their method improves the ability to distinguish different materials and keeps intensity measurements more consistent. This is one of the first methods to explicitly fix intensity mixing problems inside a single LiDAR footprint.
LiDARFull-waveform LiDARLaser footprintIntensity correctionSub-footprint mixingWaveform parametersInverse problemSemantic separabilitySurface geometryParametric modeling
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.