PDC: Piecewise Depth Completion utilizing Superpixels

• URL: http://arxiv.org/abs/2107.06711v1
• Date: Wed, 14 Jul 2021 13:58:39 GMT
• Title: PDC: Piecewise Depth Completion utilizing Superpixels
• Authors: Dennis Teutscher, Patrick Mangat, Oliver Wasenm\"uller
• Abstract summary: Current approaches often rely on CNN-based methods with several known drawbacks. We propose our novel Piecewise Depth Completion (PDC), which works completely without deep learning. In our evaluation, we can show both the influence of the individual proposed processing steps and the overall performance of our method on the challenging KITTI dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth completion from sparse LiDAR and high-resolution RGB data is one of the foundations for autonomous driving techniques. Current approaches often rely on CNN-based methods with several known drawbacks: flying pixel at depth discontinuities, overfitting to both a given data set as well as error metric, and many more. Thus, we propose our novel Piecewise Depth Completion (PDC), which works completely without deep learning. PDC segments the RGB image into superpixels corresponding the regions with similar depth value. Superpixels corresponding to same objects are gathered using a cost map. At the end, we receive detailed depth images with state of the art accuracy. In our evaluation, we can show both the influence of the individual proposed processing steps and the overall performance of our method on the challenging KITTI dataset.

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