Summary of Aydiv: Adaptable Yielding 3d Object Detection Via Integrated Contextual Vision Transformer, by Tanmoy Dam et al.
AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer
by Tanmoy Dam, Sanjay Bhargav Dharavath, Sameer Alam, Nimrod Lilith, Supriyo Chakraborty, Mir Feroskhan
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes AYDIV, a novel framework for integrating LiDAR and camera data in autonomous driving systems. The fusion of these data sources has shown promise in short-distance object detection, but faces challenges in long-distance detection due to differences in data representation. AYDIV addresses this issue through three phases: Global Contextual Fusion Alignment Transformer (GCFAT) extracts camera features, Sparse Fused Feature Attention (SFFA) fine-tunes LiDAR-camera fusion, and Volumetric Grid Attention (VGA) provides comprehensive spatial data fusion. The framework is tested on the Waymo Open Dataset (WOD) and Argoverse2 Dataset, achieving improved performance over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AYDIV is a new way to combine LiDAR and camera data for self-driving cars. This helps with detecting objects at a distance. Right now, combining these two types of data can be tricky because they are very different. AYDIV solves this problem by using three steps: one that gets camera features, one that makes sure the LiDAR and camera data match up well, and one that puts all the data together. This makes it better at detecting objects at a distance than other methods. |
Keywords
» Artificial intelligence » Alignment » Attention » Object detection » Transformer
