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Dense RGB-D Semantic Mapping with Pixel-Voxel Neural Network.

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  • 1Extreme Robotics Lab, University of Birmingham, Birmingham B15 2TT, UK. IRobotCheng@gmail.com.

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Summary

This study introduces a novel Pixel-Voxel network for 3D semantic mapping, enhancing accuracy by fusing 2D image and 3D point cloud data. The system achieves near real-time performance for detailed 3D scene understanding.

Keywords:
RGB-D SLAMsemantic mappingvisual mapping

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Dense 3D semantic mapping is crucial for scene understanding.
  • Existing methods struggle with fusing multi-modal data effectively for accurate semantic labeling.

Purpose of the Study:

  • To propose a novel Pixel-Voxel network for dense 3D semantic mapping.
  • To improve semantic labeling accuracy by adaptively fusing information from 2D images and 3D point clouds.

Main Methods:

  • A novel Pixel-Voxel network architecture combining PixelNet (for 2D contextual information) and VoxelNet (for 3D geometric shapes).
  • A softmax weighted fusion stack to adaptively combine score maps based on confidence levels from different modalities.
  • Evaluation on SUN RGB-D and NYU V2 benchmarks.

Main Results:

  • Achieved competitive results on benchmark datasets for dense 3D semantic mapping.
  • Demonstrated near real-time performance at approximately 13 Hz.
  • Successfully enabled simultaneous 3D mapping and semantic category labeling.

Conclusions:

  • The proposed Pixel-Voxel network effectively leverages multi-modal data for accurate dense 3D semantic mapping.
  • The adaptive fusion strategy outperforms equal-weight fusion.
  • The system offers efficient near real-time performance for practical applications.