# Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data Source: [Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:55.903Z Authors: Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume Published: 2022-10-21T06:08:39.000Z ## Abstract 3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferability is constrained by the training configuration and the training is susceptible to peculiar lossy noises called ray-drop. To address the issue, this paper proposes a generative model of LiDAR range images applicable to the data-level domain transfer. Motivated by the fact that LiDAR measurement is based on point-by-point range imaging, we train an implicit image representation-based generative adversarial networks along with a differentiable ray-drop effect. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models. We also showcase upsampling and restoration applications. Furthermore, we introduce a Sim2Real application for LiDAR semantic segmentation. We demonstrate that our method is effective as a realistic ray-drop simulator and outperforms state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on GTA-LiDAR -\> KITTI-FRONTAL](): Semantic segmentation; GTA-LiDAR -\> KITTI-FRONTAL; Car IoU: 67.3. ## Original sources and implementation [Original paper]() [PDF]()