Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data

• URL: http://arxiv.org/abs/2210.11750v1
• Date: Fri, 21 Oct 2022 06:08:39 GMT
• Title: Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
• Authors: Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume
• Abstract summary: 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. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models.
• Score: 3.9447103367861542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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