Ghosts in the Point Clouds: De-glaring LiDAR in the Transient Domain
For developers and users of compact LiDAR systems, this work addresses a safety-critical artifact that can create phantom objects and obscure real ones, offering a practical solution compatible with existing pipelines.
The paper identifies internal-multipath glare as a failure mode in compact solid-state LiDARs, introduces a model (TGSF) to represent it, and proposes a training-free algorithm that suppresses glare artifacts by operating on raw detections before point-cloud formation, demonstrating substantial artifact reduction on real hardware.
Modern LiDARs are rapidly transitioning from bulky, mechanically scanned systems to ultra-compact, low-cost, solid-state arrays. This miniaturization-while enabling scalability, affordability, and camera-like data structures-introduces a new and severe failure mode: internal-multipath glare. When light from a bright or retroreflective surface reflects and scatters within the LiDAR, light that should reach a single pixel spreads across the pixel array. The resulting artifacts create phantom objects, obscure real ones, and produce safety-critical "ghosts in the point clouds." This paper introduces a physically grounded sensing model and algorithmic techniques for addressing this effect. We show that internal glare can be represented as a linear, scene-independent operator-the Transient Glare Spread Function (TGSF)-acting on the transient measurements. Building on this model, we develop a training-free approach that operates on low-level LiDAR detections (or echoes) prior to point-cloud formation, leveraging knowledge of the glare spread function to reason about the likelihood of each detection arising from glare. The resulting approach is compatible with existing LiDAR signal-processing pipelines, and deployable on unmodified commercial sensors. Using experiments with real single-photon LiDAR hardware, we demonstrate substantial suppression of severe glare artifacts while preserving true scene structure.