Lidar Upsampling With Sliced Wasserstein Distance

Savkin A, Wang Y, Wirkert S, Navab N, Tombari F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Robotics and Automation Letters IEEE

Book Volume: 8

Pages Range: 392-399

Journal Issue: 1

DOI: 10.1109/LRA.2022.3214791

Abstract

Lidar became an important component of the perception systems in autonomous driving. But challenges of training data acquisition and annotation made emphasized the role of the sensor to sensor domain adaptation. In this letter, we address the problem of lidar upsampling. Learning on lidar point clouds is rather a challenging task due to their irregular and sparse structure. Here we propose a method for lidar point cloud upsampling which can reconstruct fine-grained lidar scan patterns. The key idea is to utilize edge-aware dense convolutions for both feature extraction and feature expansion. Additionally applying a more accurate Sliced Wasserstein Distance facilitates learning of the fine lidar sweep structures. This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction. We conduct several experiments to evaluate our method and demonstrate that it provides better upsampling.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) BMW AG - Bayerische Motoren Werke / BMW Group DE Germany (DE)

How to cite

APA:

Savkin, A., Wang, Y., Wirkert, S., Navab, N., & Tombari, F. (2023). Lidar Upsampling With Sliced Wasserstein Distance. IEEE Robotics and Automation Letters, 8(1), 392-399. https://doi.org/10.1109/LRA.2022.3214791

MLA:

Savkin, Artem, et al. "Lidar Upsampling With Sliced Wasserstein Distance." IEEE Robotics and Automation Letters 8.1 (2023): 392-399.

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