Lidar has become a cornerstone sensing modality for 3D vision, especially for
large outdoor scenarios and autonomous driving. Conventional lidar sensors are
capable of providing centimeter-accurate distance information by emitting laser
pulses into a scene and measuring the time-of-flight (ToF) of the reflection.
However, the polarization of the received light that depends on the surface
orientation and material properties is usually not considered. As such, the
polarization modality has the potential to improve scene reconstruction beyond
distance measurements. In this work, we introduce a novel long-range
polarization wavefront lidar sensor (PolLidar) that modulates the polarization
of the emitted and received light. Departing from conventional lidar sensors,
PolLidar allows access to the raw time-resolved polarimetric wavefronts. We
leverage polarimetric wavefronts to estimate normals, distance, and material
properties in outdoor scenarios with a novel learned reconstruction method. To
train and evaluate the method, we introduce a simulated and real-world
long-range dataset with paired raw lidar data, ground truth distance, and
normal maps. We find that the proposed method improves normal and distance
reconstruction by 53\% mean angular error and 41\% mean absolute error compared
to existing shape-from-polarization (SfP) and ToF methods. Code and data are
open-sourced at this https URL

该研究介绍了一种新颖的长距离极化波前激光雷达传感器（PolLidar），利用极化波前估计室外场景中的法线、距离和材料特性，并提高了场景重建的准确性。