Report
AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer
| العنوان: | AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer |
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| المؤلفون: | Dam, Tanmoy, Dharavath, Sanjay Bhargav, Alam, Sameer, Lilith, Nimrod, Chakraborty, Supriyo, Feroskhan, Mir |
| المصدر: | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Robotics |
| الوصف: | Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR's sparse data and the dense resolution of cameras. Besides, discrepancies in the two data representations further complicate fusion methods. We introduce AYDIV, a novel framework integrating a tri-phase alignment process specifically designed to enhance long-distance detection even amidst data discrepancies. AYDIV consists of the Global Contextual Fusion Alignment Transformer (GCFAT), which improves the extraction of camera features and provides a deeper understanding of large-scale patterns; the Sparse Fused Feature Attention (SFFA), which fine-tunes the fusion of LiDAR and camera details; and the Volumetric Grid Attention (VGA) for a comprehensive spatial data fusion. AYDIV's performance on the Waymo Open Dataset (WOD) with an improvement of 1.24% in mAPH value(L2 difficulty) and the Argoverse2 Dataset with a performance improvement of 7.40% in AP value demonstrates its efficacy in comparison to other existing fusion-based methods. Our code is publicly available at https://github.com/sanjay-810/AYDIV2 Comment: This paper has been accepted for ICRA 2024, and copyright will automatically transfer to IEEE upon its availability on the IEEE portal |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/ICRA57147.2024.10610908 |
| URL الوصول: | http://arxiv.org/abs/2402.07680 |
| رقم الانضمام: | edsarx.2402.07680 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/ICRA57147.2024.10610908 |
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