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J. Paffenholz

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Open access Jul 2026

Experimental Assessment of Measurement Uncertainty for a Green-Wavelength LiDAR System

Abstract. Green-wavelength LiDAR systems enable high-resolution 3D sensing in underwater environments, but the geometric evaluation of measurements acquired across the waterline remains challenging. This is mainly because traceable reference instruments typically operate only in air, while refraction at the waterline systematically affects both the 3D point cloud and the geometry of partially submerged objects. This study presents a controlled experimental framework for assessing waterline-induced effects in an Underwater LiDAR (ULi) system, using a terrestrial laser scanner (TLS), the Z+F IMAGER 5016A (IMAGER), as an above-water reference. A rigid reference frame (RRF) spanning the waterline was deployed in a swimming pool. First, the RRF was scanned by the IMAGER under in-air conditions to establish its reference geometry. Subsequently, in the waterline configuration, the ULi system measured the complete RRF, while the IMAGER captured only its above-water part. The analysis investigated refraction- and interface-related effects on the 3D point cloud and geometry in the above-water, cross-waterline, and underwater parts of the RRF. For a physically meaningful assessment, the evaluation considered overall geometric deviations and rigid-body-invariant internal quantities, including pairwise distances, which are independent of the overall pose of the RRF. Refractive-index sensitivity was analyzed by perturbing the refractive index and quantifying the resulting changes in the derived geometric quantities. The proposed workflow provides a practical and traceable basis for isolating waterline-related refraction effects, evaluating their impact on 3D point cloud geometry, and assessing refractive-index sensitivity.

Yu Lan, Jiale Wang, Ji Yang et al. · 0 citations
Open access Aug 2026

Boundary Cues for Improved 3D Semantic Segmentation

A lightweight boundary-aware learning framework that explicitly models boundary regions during training is proposed, showing that incorporating boundary-aware supervision provides an effective and efficient approach to improving segmentation quality in challenging regions.

Waseem Iqbal, J. Paffenholz · 0 citations