Perception task is a key technology for autonomous vehicles. Among the perception tasks, image perception has achieved powerful performance in contrast to perception tasks with other sensors. This is because of a lack of data. When comparing large amounts of image data, other sensors’ data is not sufficient, particularly in LiDAR data. To deal with the lack of data problem, many translation methods have been proposed. Although most of them focus only on image translation and LiDAR translation between simulated and real data, [1] proposed by us is the first LiDAR translation method that can convert sunny data to data of adverse weather conditions while maintaining 3D point cloud information. It is possible to generate fake LiDAR data for weather changes from sunny data. However, there is a big limitation in that noisy points are generated for a huge domain gap, such as the translation between sunny and heavily foggy conditions. Thus, we propose an advanced LiDAR translation for a huge domain gap to handle adverse weather change.


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    Title :

    Advanced LiDAR Translation for Huge Domain Gap to Handle Adverse Weather Change


    Contributors:


    Publication date :

    2023-06-01


    Size :

    2276057 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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