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Related Experiment Video

Updated: May 9, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Occupancy mapping and surface reconstruction using local Gaussian processes with Kinect sensors.

Soohwan Kim, Jonghyuk Kim

    IEEE Transactions on Cybernetics
    |July 30, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an efficient Gaussian process method for creating continuous occupancy maps and reconstructing surfaces from RGB-D sensor data. The approach significantly reduces computation time for large-scale mapping, enabling real-time navigation and visualization.

    Related Experiment Videos

    Last Updated: May 9, 2026

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    Area of Science:

    • Robotics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • RGB-D sensors are widely used for visual SLAM and surface reconstruction, primarily for visualization purposes.
    • Existing methods often struggle with the high computational complexity of large-scale mapping, especially with high-resolution data.
    • There is a need for integrated frameworks that support both navigation and visualization using continuous occupancy maps and surface reconstruction.

    Purpose of the Study:

    • To propose a novel, unified framework for building continuous occupancy maps and reconstructing surfaces using Gaussian processes.
    • To address the computational complexity limitations of Gaussian process classification in large-scale mapping scenarios.
    • To enable efficient and accurate 3D mapping for both navigation and visualization applications.

    Main Methods:

    • Employing Gaussian process classification, a Bayesian nonparametric approach, for occupancy mapping.
    • Implementing a coarse-to-fine clustering method to partition training and test data, applying Gaussian processes to local clusters.
    • Utilizing Gaussian processes as implicit functions and extracting iso-surfaces via the marching cubes algorithm for surface reconstruction.

    Main Results:

    • The proposed approximated method achieves accuracy comparable to previous work in 2-D simulated environments.
    • A dramatic reduction in computational time was observed, making large-scale mapping feasible.
    • Demonstrated the method's effectiveness and feasibility with 3-D real-world data in large-scale environments.

    Conclusions:

    • The developed framework successfully integrates continuous occupancy mapping and surface reconstruction within a single Gaussian process-based system.
    • The coarse-to-fine clustering approach effectively mitigates the computational burden of Gaussian processes for large datasets.
    • The method shows significant promise for real-time, large-scale 3D mapping applications in robotics and computer vision.