Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
This addresses domain transfer issues in LiDAR perception for autonomous robots, though it is incremental as it builds on prior generative and simulation techniques.
The paper tackles the domain gap in 3D LiDAR data for autonomous robots by proposing a generative model for LiDAR range images, which improves fidelity and diversity over existing methods and enhances LiDAR semantic segmentation in Sim2Real applications.
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.