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Monocular Depth Estimation from a Fisheye Camera Based on Knowledge Distillation.

Eunjin Son1, Jiho Choi1, Jimin Song1

  • 1Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea.

Sensors (Basel, Switzerland)
|December 23, 2023
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Summary
This summary is machine-generated.

Researchers developed a new dataset for fisheye camera depth estimation and used knowledge distillation to improve model performance. This technique enhances collision avoidance in autonomous systems by refining depth predictions from wide-angle views.

Keywords:
fisheye cameraknowledge distillationmonocular depth estimationparking lot datasetsupervised depth estimation

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Monocular depth estimation predicts distances from single images, crucial for autonomous driving and robotics.
  • Fisheye cameras offer wide fields of view, essential for collision avoidance in parking, but depth estimation from their distorted images is challenging.
  • Existing research predominantly uses pinhole camera models, lacking focus on fisheye lens specificities and public datasets.

Purpose of the Study:

  • Introduce JBNU-Depth360, a novel dataset for fisheye camera depth estimation in underground parking lots.
  • Enhance the performance of state-of-the-art depth estimation models using a knowledge distillation technique.
  • Evaluate the effectiveness of the proposed method on both the new JBNU-Depth360 and existing KITTI-360 datasets.

Main Methods:

  • Collected 4221 fisheye image and LiDAR projection pairs from six driving sequences for the JBNU-Depth360 dataset.
  • Implemented a teacher-student knowledge distillation framework to transfer information from dense depth predictions and sparse LiDAR data.
  • Trained and evaluated existing depth estimation models on fisheye images using the JBNU-Depth360 and KITTI-360 datasets.

Main Results:

  • The JBNU-Depth360 dataset comprises 4221 fisheye images and corresponding LiDAR point clouds.
  • Self-distillation significantly improved depth estimation accuracy, reducing AbsRel error by 1.81% and SILog error by 1.55% on the JBNU-Depth360 dataset.
  • Experimental results confirm the benefits of self-distillation for enhancing depth estimation performance with fisheye camera data.

Conclusions:

  • The JBNU-Depth360 dataset addresses the need for fisheye camera data in depth estimation research.
  • Knowledge distillation is an effective technique for improving monocular depth estimation from fisheye images.
  • The proposed approach contributes to safer and more robust perception systems for autonomous applications.