Browsing by Subject "Point cloud processing"

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    Graph signal processing based object classification for automotive RADAR point clouds
    (Elsevier, 2023-04-11) Sevimli, R.A.; Üçüncü, M.; Koç, Aykut
    As the automotive industry evolves, visual perception systems to provide awareness of surroundings to autonomous vehicles have become vital. Conventional deep neural networks have been effective on 2D Euclidean problems during the previous decade. However, analyzing point clouds, particularly RADAR data, is not well-studied due to their irregular structures and geometry, which are unsuitable for 2D signal processing. To this end, we propose graph signal processing (GSP) based classification methods for RADAR point clouds. GSP is designed to process spatially irregular signals and can directly create feature vectors from graphs. To validate our proposed methods experimentally, publicly available nuScenes and RadarScenes point cloud datasets are used in our study. Extensive experiments on these challenging benchmarks show that our proposed approaches outperform state-of-the-art baselines
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    RadGT: graph and transformer-based automotive radar point cloud segmentation
    (Institute of Electrical and Electronics Engineers, 2023-10-25) Sevimli, R. A.; Ucuncu, M.; Koç, Aykut
    The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.