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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous

Woomin Jun1,2, Jisang Yoo2,3, Sungjin Lee1,2

  • 1Electronic Engineering, Dong Seoul University, Seongnam 13117, Republic of Korea.

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Summary
This summary is machine-generated.

This study enhances monocular depth estimation (MDE) for autonomous driving using novel data augmentation and the RMS algorithm. These methods improve 3D perception accuracy and efficiency for real-time applications.

Keywords:
absolute relative errorautonomous drivingdata augmentationmonocular depth estimationpruningquantization

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous driving safety relies on accurate 3D image recognition.
  • Camera-based depth estimation is replacing LIDAR due to cost and limitations in detecting small, distant objects.
  • Monocular Depth Estimation (MDE) offers a cost-effective solution for 3D environmental data acquisition.

Purpose of the Study:

  • To enhance Monocular Depth Estimation (MDE) accuracy using novel synthetic-based data augmentation techniques.
  • To introduce the Real-time Monocular Depth Estimation configuration considering Resolution, Efficiency, and Latency (RMS) algorithm for optimizing neural networks.
  • To validate the performance of the proposed methods on an on-device autonomous driving platform.

Main Methods:

  • Proposed synthetic-based data augmentation strategies: Mask, Mask-Scale, and CutFlip.
  • Developed the RMS algorithm: a three-step process involving model selection, accuracy refinement with data augmentation and loss functions, and network compression.
  • Utilized techniques such as quantization, pruning, and FP16 for model compression.

Main Results:

  • Synthetic data augmentation improved MDE model accuracy by 4.0%.
  • The IEBins model achieved the best REL performance (0.0480) under RMS constraints.
  • A combination of data augmentation (Flip, Mask, CutFlip) and SigLoss yielded the best REL performance (0.0461).
  • FP16 compression reduced model size by 83.4% with minimal impact on performance and latency.

Conclusions:

  • Novel data augmentation and the RMS algorithm significantly enhance MDE accuracy and efficiency for autonomous driving.
  • The proposed methods enable cost-effective, real-time 3D perception on edge devices.
  • Optimized deployment strategies were derived for various autonomous driving scenarios on the NVIDIA Jetson AGX Orin platform.