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Related Experiment Video

Updated: Sep 15, 2025

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Photorealistic Learned Landscapes for Augmented Reality.

David Martínez-Miranzo¹, Sergio Suescun-Ferrandiz¹, Miguel Cazorla²

¹University Institute for Computer Research, University of Alicante.

Journal of Visualized Experiments : Jove

|July 14, 2025

Summary

This summary is machine-generated.

This study introduces a novel system for creating realistic 3D models from images using Gaussian Splatting and virtual reality (VR). The technology enhances immersive experiences for applications like healthcare therapy and industrial design.

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Area of Science:

Computer Vision
Virtual Reality
3D Reconstruction

Background:

Photorealistic 3D reconstruction from images is crucial for diverse applications.
Existing methods face challenges in detail and interactivity.

Purpose of the Study:

To present a novel system integrating Structure from Motion, incremental scene registration, and Gaussian Splatting for detailed 3D reconstruction.
To enable seamless integration of 3D models into virtual reality environments for immersive interaction.

Main Methods:

Capture 360-degree images with a matrix-based pattern.
Process images using COLMAP for camera pose estimation and sparse point cloud generation.
Refine reconstruction with Gaussian Splatting for high fidelity and render in Unity for VR interaction.

Main Results:

Achieved highly realistic 3D environments with detailed models.
Enabled immersive user experiences through VR integration.
Demonstrated a healthcare use case for autism spectrum disorder therapy with interactive avatars.

Conclusions:

The system offers high visual fidelity, easy image acquisition, and immersive user experiences.
Challenges include computational cost and reliance on accurate camera pose estimation.
Potential to transform fields like therapy, industrial design, and cultural preservation.