Deep Generative Modeling of LiDAR Data Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against stateoftheart point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative data.

CNNbased synthesis of realistic highresolution LiDAR data This paper presents a novel CNNbased approach for synthesizing highresolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with guidance from specialized lossfunctions. First, we utilize a modified perpoint loss that addresses missing LiDAR point measurements. Second, we align the quality of our generated output with realworld sensor data by applying a perceptual loss. In largescale experiments on realworld datasets, we evaluate both the geometric accuracy and semantic segmentation performance using our generated data vs. ground truth. In a mean opinion score testing we further assess the perceptual quality of our generated point clouds. Our results demonstrate a significant quantitative and qualitative improvement in both geometry and semantics over traditional non CNNbased upsampling methods.