TULIP: Transformer for Upsampling of LiDAR Point Clouds

• URL: http://arxiv.org/abs/2312.06733v4
• Date: Fri, 3 May 2024 16:46:57 GMT
• Title: TULIP: Transformer for Upsampling of LiDAR Point Clouds
• Authors: Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter, Peyman Moghadam, Vaishakh Patil,
• Abstract summary: LiDAR Up is a challenging task for the perception systems of robots and autonomous vehicles. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. We propose T geometries, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input.
• Score: 32.77657816997911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles, due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. Although their methods can generate high-resolution range images with fine-grained details, the resulting 3D point clouds often blur out details and predict invalid points. In this paper, we propose TULIP, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input. We also follow a range image-based approach but specifically modify the patch and window geometries of a Swin-Transformer-based network to better fit the characteristics of range images. We conducted several experiments on three public real-world and simulated datasets. TULIP outperforms state-of-the-art methods in all relevant metrics and generates robust and more realistic point clouds than prior works.

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