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Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
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- Nakashima, Kazuto
- Kyushu University
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- Iwashita, Yumi
- Jet Propulsion Laboratory, California Institute of Technology
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- Kurazume, Ryo
- Kyushu University
Description
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.
Journal
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- IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 1256-1266, 2023
Institute of Electrical and Electronics Engineers (IEEE)
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Keywords
Details 詳細情報について
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- CRID
- 1050021370926484864
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- HANDLE
- 2324/7232999
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- Text Lang
- en
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- Article Type
- conference paper
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- Data Source
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- IRDB