Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds
Rózsa, Zoltán and Szirányi, Tamás (2023) Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds. REMOTE SENSING, 15 (10). ISSN 2072-4292 10.3390/rs15102487
|
Text
Rozsa_1_33822096_ny.pdf Download (10MB) | Preview |
Abstract
This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements makes the temporal up-sampling of point clouds possible. The only requirement of the system is a camera with a higher frame rate than the LIDAR equipped to the same vehicle, which is usually provided. The pipeline first utilizes optical flow estimations from the available camera frames. Next, optical expansion is used to upgrade it to 3D scene flow. Following that, ground plane fitting is made on the previous LIDAR point cloud. Finally, the estimated scene flow is applied to the previously measured object points to generate the new point cloud. The framework’s efficiency is proved as state-of-the-art performance is achieved on the KITTI dataset.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány |
| Divisions: | Distributed Events Analysis Research Laboratory |
| SWORD Depositor: | MTMT Injector |
| Depositing User: | MTMT Injector |
| Date Deposited: | 16 May 2023 07:35 |
| Last Modified: | 11 Sep 2023 14:59 |
| URI: | https://eprints.sztaki.hu/id/eprint/10513 |
 |
Update Item |
