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A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

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

Cheng Zhao¹, Li Sun², Pulak Purkait³

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

Sensors (Basel, Switzerland)

|September 19, 2018

Summary

This summary is machine-generated.

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 SLAM semantic mapping visual 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.