偏振波前激光雷达：从偏振波前学习大场景重建
Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts
AI总结：
本文提出偏振波前激光雷达 PolLidar，利用时间分辨偏振波前并结合学习式重建方法，在户外场景中同时估计法线、距离和材料属性，相比现有 SfP 和 ToF 方法显著降低重建误差。
摘要：
激光雷达已成为三维视觉的基础感知模态，尤其是在大型户外场景和自动驾驶中。传统激光雷达传感器通过向场景发射激光脉冲并测量反射光的飞行时间（ToF），能够提供厘米级精度的距离信息。然而，接收光的偏振通常未被考虑，而偏振取决于表面朝向和材料属性。因此，偏振模态有潜力在距离测量之外改进场景重建。在这项工作中，我们提出了一种新型长距离偏振波前激光雷达传感器（PolLidar），该传感器对发射和接收光的偏振进行调制。与传统激光雷达传感器不同，PolLidar 允许获取原始的时间分辨偏振波前。我们利用偏振波前，通过一种新颖的学习式重建方法，在户外场景中估计法线、距离和材料属性。为了训练和评估该方法，我们引入了一个仿真和真实世界的长距离数据集，其中包含配对的原始激光雷达数据、真值距离和法线图。我们发现，与现有的偏振形状恢复（SfP）和 ToF 方法相比，所提出的方法将法线重建的平均角度误差降低了 53%，将距离重建的平均绝对误差降低了 41%。代码和数据已在 https://light.princeton.edu/pollidar 开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
