An Efficient Deep Unrolling Super-Resolution Network for Lidar Automotive Scenes

Considering the high cost of high-resolution Lidar sensors, in this work, a novel Lidar super-resolution method is proposed to improve the performance on numerous autonomous vehicle perception tasks, including that of a Lidar odometer. Specifically, we propose a regularized optimization problem empl...

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Bibliographic Details
Published in:2023 IEEE International Conference on Image Processing (ICIP) pp. 1840 - 1844
Main Authors: Gkillas, Alexandros, Lalos, Aris S., Ampeliotis, Dimitris
Format: Conference Proceeding
Language:English
Published: IEEE 08-10-2023
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Summary:Considering the high cost of high-resolution Lidar sensors, in this work, a novel Lidar super-resolution method is proposed to improve the performance on numerous autonomous vehicle perception tasks, including that of a Lidar odometer. Specifically, we propose a regularized optimization problem employing a learnable regularizer (neural network) to capture the properties of the data. To efficiently solve this problem, a deep unrolling methodology is proposed, thus forming an interpretable and well-justified deep architecture. Extensive experiments on a real-world lidar odometry application highlight that the proposed model exhibits both superior performance as well as a significantly reduced number of trainable parameters i.e., 99.75% less parameters, as compared to other deep learning methods. The source code used for this work can be found at our repository: repository.
DOI:10.1109/ICIP49359.2023.10222856