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Related Experiment Video

Updated: Feb 14, 2026

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A Monocular Depth Estimation Method for Autonomous Driving Vehicles Based on Gaussian Neural Radiance Fields.

Ziqin Nie1,2,3, Zhouxing Zhao4, Jieying Pan1,2

  • 1School of Transportation Science and Engineering, Beihang University, Beijing 102206, China.

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Summary

This study introduces a novel Neural Radiance Field (NeRF)-based method for monocular depth estimation in autonomous driving. The approach enhances accuracy and efficiency in complex scenes, overcoming current limitations.

Keywords:
Gaussian-Probability samplingNeural Radiance Fieldsadaptive channel attentionautonomous drivingmonocular depth estimation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Monocular depth estimation is crucial for autonomous driving, providing scene depth from single images.
  • Current methods struggle with visual artifacts, scale ambiguity, and occlusion, limiting performance in complex environments.

Purpose of the Study:

  • To develop an improved monocular depth estimation method for autonomous driving using Neural Radiance Fields (NeRF).
  • To address limitations of existing approaches, including visual artifacts, scale ambiguity, and occlusion handling.

Main Methods:

  • Introduced a Gaussian probability-based ray sampling strategy to manage large, complex scenes and reduce computational load.
  • Designed a lightweight spherical network with fine-grained adaptive channel attention for detailed feature extraction.
  • Mapped pixel-level features to 3D spatial locations for enhanced NeRF model generalizability.

Main Results:

  • The proposed NeRF-based method demonstrated superior performance on the KITTI benchmark for depth estimation tasks.
  • Achieved significant improvements over traditional monocular depth estimation techniques.
  • Showcased enhanced model efficiency and generalization capabilities.

Conclusions:

  • The developed method offers significant advancements for practical monocular depth estimation in autonomous driving.
  • The approach effectively tackles challenges in complex environments, improving reliability and applicability.
  • This work paves the way for more robust perception systems in autonomous vehicles.