Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds

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 possi...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 10; p. 2487
Main Authors Rozsa, Zoltan, Sziranyi, Tamas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Algorithms
Cameras
Comparative analysis
Ground plane
Image processing
LIDAR
Methods
Optical flow (image analysis)
Optical radar
Remote sensing
Sampling
scene flow
sensor fusion
Sensors
Technology application
Three dimensional flow
upsampling
Vehicles
Hungary
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15102487