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Photorealistic Learned Landscapes for Augmented Reality

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DiGS: Depth-Initialized Gaussian Splatting for Single-Object Reconstruction.

Jacopo Meglioraldi¹, Pasquale Cascarano¹, Gustavo Marfia¹

¹Department of the Arts, University of Bologna, Via Barberia 4, 40123 Bologna, Italy.

Journal of Imaging

|May 26, 2026

Summary

This summary is machine-generated.

Accurate initial point clouds significantly improve 3D object reconstruction quality in Gaussian Splatting. The Depth-initialized Gaussian Splatting (DiGS) method leverages depth data for enhanced detail recovery and faster optimization.

Keywords:

3D reconstruction RGB computer vision depth map gaussian splatting image processing

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Area of Science:

Computer Vision
3D Graphics
Geometric Modeling

Background:

Gaussian Splatting is a leading method for high-fidelity 3D scene reconstruction.
Initialization strategies critically impact the convergence and quality of 3D reconstruction.
Existing methods may struggle with fine details and initial optimization phases.

Purpose of the Study:

To investigate the impact of initialization strategies on Gaussian Splatting.
To introduce and evaluate a novel depth-initialized approach for improved 3D reconstruction.
To enhance the recovery of fine-grained details and accelerate early optimization.

Main Methods:

Developed Depth-initialized Gaussian Splatting (DiGS) pipeline.
Utilized depth data from calibrated stereo cameras for initialization.
Performed evaluations using synthetic and real-world datasets.
Conducted objective metric analysis and a 20-participant user study.

Main Results:

Depth-based initialization significantly enhances early optimization stages.
DiGS demonstrates superior performance in reconstructing isolated single objects.
Improved recovery of fine-grained details compared to standard methods.
User study confirmed enhanced perceptual quality.

Conclusions:

Initialization strategy is crucial for effective Gaussian Splatting.
DiGS offers a robust and efficient method for high-quality 3D reconstruction.
The approach shows promise for applications requiring detailed object modeling.