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This work shines light on a new perspective on accurate depth estimation in autonomous vehicles, combining Adaptive Bins (AdaBins) and YOLO Monocular Depth Estimation (YOLO-MDE v8). AdaBins is a Transformer-based method that generates adaptive bins for depth prediction, while YOLO-MDE v8 integrates YOLO object detection with depth estimation. The synergy between these techniques offers a comprehensive solution for monocular depth estimation, ensuring precise scene reconstruction and real-time detection and depth estimation capabilities. The paper presents experimental results demonstrating the effectiveness of this approach on the KITTI dataset. Our objective is to provide a robust and efficient solution for monocular depth estimation, paving the way for more accessible and reliable autonomous driving technologies.

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Depth Estimation for Autonomous Vehicles with Enhanced Perception

  • 会议论文

  • 发表日期：

    2025年

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  • K. Venkatraman,
  • Abhay Nanduri,
  • D. Sai Sruthik Reddy

摘要

This work shines light on a new perspective on accurate depth estimation in autonomous vehicles, combining Adaptive Bins (AdaBins) and YOLO Monocular Depth Estimation (YOLO-MDE v8). AdaBins is a Transformer-based method that generates adaptive bins for depth prediction, while YOLO-MDE v8 integrates YOLO object detection with depth estimation. The synergy between these techniques offers a comprehensive solution for monocular depth estimation, ensuring precise scene reconstruction and real-time detection and depth estimation capabilities. The paper presents experimental results demonstrating the effectiveness of this approach on the KITTI dataset. Our objective is to provide a robust and efficient solution for monocular depth estimation, paving the way for more accessible and reliable autonomous driving technologies.