Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts
This work addresses the challenge of enhancing scene reconstruction for applications like autonomous driving by leveraging polarization data, representing a novel method for a known bottleneck in lidar sensing.
The paper tackles the problem of improving 3D scene reconstruction in large outdoor scenarios by introducing a novel polarization wavefront lidar sensor (PolLidar) and a learned method that estimates normals, distance, and material properties from polarimetric wavefronts, resulting in improvements of 53% in normal reconstruction and 41% in distance reconstruction compared to existing methods.
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 https://light.princeton.edu/pollidar.