Evaluation of lab-based, hybrid, and in-situ radiometric calibration for TLS

This is the authors' abstract. We don't add key points for this paper.

Radiometric compensation is essential for obtaining accurate reflectance values from terrestrial laser scanning (TLS) intensities, yet it remains unclear which strategy best compensates for the impact of measurement configuration (angle of incidence (AOI), and distance) in terms of precision and accuracy. Herein, precision refers to the consistency of compensated intensities within a dataset, while accuracy describes their agreement with true reflectance values. Whether precision or accuracy should be prioritized depends on the downstream application: segmentation typically benefits from high precision, whereas material probing requires high accuracy. Compared to the distance effect, the AOI effect is harder to model because it is inherently material-dependent. Consequently, this paper places a particular focus on optimizing AOI compensation. We compare three calibration strategies: lab-based, in-situ, and hybrid. We extend our analysis by i) comparing global and material-dependent AOI compensation, i.e., using single representative vs. multiple locally adapted compensation functions, and ii) assessing the impact of the choice of laboratory calibration targets. We find that all calibration strategies yield similar precision and accuracy. The choice of strategy is thus primarily driven by operational constraints and convenience (e.g., availability of lab facilities vs. in-situ reference targets). Moreover, we show that the material-dependent AOI compensation increases precision by approximately 10%, while yielding similar accuracy as using a common (global) AOI compensation function. All three strategies lead to absolute reflectances that agree with spectrometer measurements (reference) within about 5% (median of differences). Our results confirm the potential of using TLS intensities for material probing and can serve as a guideline for choosing the TLS radiometric compensation strategy providing the best trade-off in terms of adaptability, generalizability, precision and accuracy across different datasets acquired by a common instrument.