*.pcd

Pavement planar coefficients are critical for a wide range of civil engineering applications, including 3D city modeling, extraction of pavement design parameters, and assessment of pavement conditions. However, existing plane fitting methods often struggle to maintain accuracy and stability in complex road environments, particularly when the point cloud is affected by non-pavement objects such as trees, curbstones, pedestrians, and vehicles. This paper presents REoPC, a robust two-stage estimation method based on road point clouds acquired using hybrid solid-state LiDAR.

This dataset accompanies the study  “Universal Metrics to Characterize the Performance of Imaging 3D Measurement Systems with a Focus on Static Indoor Scenes” and provides all measurement data, processing scripts, and evaluation code necessary to reproduce the results. It includes raw and processed point cloud data from six state-of-the-art 3D measurement systems, captured under standardized conditions. Additionally, the dataset contains high-speed sensor measurements of the cameras’ active illumination, offering insights into their optical emission characteristics.