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An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
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Efficient and Scalable Object Localization in 3D on Mobile Device.

Neetika Gupta1, Naimul Mefraz Khan1

  • 1Department of Electrical, Computer & Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

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|July 25, 2022
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Summary
This summary is machine-generated.

This study introduces a novel method for 3D object localization on mobile devices, combining Convolutional Neural Network (CNN) models with Augmented Reality (AR). It efficiently determines real-world coordinates for objects using limited device resources.

Keywords:
ARCoreobject detectionobject localization

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Area of Science:

  • Computer Vision
  • Mobile Augmented Reality
  • 3D Object Detection

Background:

  • 2D object detection lacks real-world context like size and position.
  • Existing methods struggle with efficient object classification and localization on mobile devices.
  • Mobile offloading techniques are resource-intensive.

Purpose of the Study:

  • To propose a novel 3D object localization solution for mobile devices.
  • To enable efficient object recognition and real-world coordinate determination.
  • To overcome the limitations of 2D object detection on resource-constrained mobile platforms.

Main Methods:

  • Combining a 2D object detection Convolutional Neural Network (CNN) with Augmented Reality (AR) technologies.
  • Leveraging Simultaneous Localization and Mapping (SLAM) from ARCore for plane detection and camera information.
  • Generating 3D cuboid proposals from 2D bounding boxes using AR capabilities.

Main Results:

  • The proposed method achieves fast and efficient identification of everyday objects.
  • Real-world coordinates of detected objects are accurately determined.
  • The approach is optimized for mobile devices with limited resources.

Conclusions:

  • The novel approach successfully integrates 2D CNNs with AR for 3D object localization on mobile devices.
  • This method provides a robust solution for understanding object size and position in real-world environments.
  • The technique is suitable for real-time applications on mobile platforms without requiring significant computational offloading.