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Related Experiment Video

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Photorealistic Learned Landscapes for Augmented Reality

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Panoramic Image Driven Point Cloud Initialization for 3D Reconstruction.

Haoyu Qian^1,2, Lidong Yang^1,2, Jing Wang³

¹School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

This study introduces a novel text-driven 3D scene generation pipeline using panoramic images and 3D Gaussian Splatting. It overcomes initialization challenges for improved virtual reality and digital twin applications.

Keywords:

3D reconstruction panoramic image point cloud initialization

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Area of Science:

Computer Vision
Computer Graphics
Artificial Intelligence

Background:

Differentiable rendering has advanced static 3D scene reconstruction.
Existing methods often rely on dense multi-view imagery, which is difficult to acquire.
Insufficient initialization remains a significant challenge in 3D scene reconstruction.

Purpose of the Study:

To propose a text-driven 3D scene generation pipeline that addresses initialization issues.
To enhance the quality and efficiency of 3D scene reconstruction.
To reduce the dependency on dense multi-view imagery.

Main Methods:

Utilizing panoramic images as an intermediate representation.
Integrating 3D Gaussian Splatting for improved reconstruction.
Implementing Fibonacci lattice sampling for point cloud initialization.
Employing a dense perspective pseudo label strategy for teacher-student distillation.

Main Results:

Achieved more accurate scene geometry and robust feature learning.
Demonstrated superior performance compared to state-of-the-art methods.
Validated effectiveness across standard reconstruction metrics without explicit multi-view ground truth.

Conclusions:

The proposed pipeline effectively addresses initialization challenges in 3D scene reconstruction.
The method offers a promising approach for text-driven 3D scene generation.
This work advances virtual reality and digital twin technologies by improving reconstruction capabilities.