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Simultaneous Semantic Segmentation and Depth Completion with Constraint of Boundary.

Nan Zou1, Zhiyu Xiang2, Yiman Chen1

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

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|January 26, 2020
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Summary

This study introduces a novel multi-task learning framework for joint semantic segmentation and depth completion, enhancing scene understanding for applications like autonomous driving. The integrated model effectively improves individual task performance by sharing information and utilizing boundary features.

Keywords:
CNNdepth completionmulti-task learningsemantic segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Semantic segmentation and depth completion are crucial for scene understanding in robotics and autonomous driving.
  • Deep learning has advanced these tasks individually, but joint approaches exploring their interdependencies are limited.

Purpose of the Study:

  • To develop a unified multi-task learning framework for simultaneously performing semantic segmentation and depth completion.
  • To investigate the synergistic relationship between semantic segmentation and depth completion through a joint model.

Main Methods:

  • A multi-task Convolutional Neural Network (CNN) architecture with a shared encoder and a decoder incorporating boundary features.
  • An auxiliary boundary detection sub-task to provide boundary features and construct cross-task joint loss functions.
  • End-to-end training utilizing both RGB and sparse depth inputs.

Main Results:

  • The proposed joint model effectively leverages shared information between semantic segmentation and depth completion.
  • Boundary features act as crucial constraints, improving information exchange within the network.
  • Experimental results on synthesized and real-world datasets demonstrate significant performance gains for both individual tasks.

Conclusions:

  • Jointly learning semantic segmentation and depth completion within a multi-task framework is highly effective.
  • The integration of boundary features enhances the model's ability to capture cross-task relationships.
  • This approach offers a promising direction for advancing scene understanding in complex environments.