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Photorealistic Learned Landscapes for Augmented Reality

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SMDGS: Scale-Aligned Monocular Depth-Guided 3D Gaussian Splatting for Rendering and Surface Reconstruction.

Xiaosong Wei, Pengwei Zhou, Annan Zhou

IEEE Transactions on Visualization and Computer Graphics

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces scale-aligned monocular depth-guided 3D Gaussian Splatting (3DGS) for improved 3D surface reconstruction and novel view synthesis (NVS). The method enhances geometric accuracy and rendering quality using depth priors and consistency supervision.

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Area of Science:

Computer Vision
3D Reconstruction
Computer Graphics

Background:

3D Gaussian Splatting (3DGS) shows potential for surface reconstruction.
Existing 3DGS methods struggle with accuracy and novel view synthesis (NVS) due to unstructured point clouds.
Monocular depth estimation offers geometric cues but suffers from scale ambiguity.

Purpose of the Study:

To develop a novel framework for high-quality 3D surface reconstruction and NVS.
To address the limitations of unstructured point clouds in 3DGS.
To leverage monocular depth estimation for improved geometric accuracy.

Main Methods:

Proposed a scale-aligned monocular depth-guided 3DGS framework.
Implemented a K-Nearest Neighbor (KNN)-based depth alignment using Structure from Motion (SfM) point clouds for regularization.
Introduced a pseudo-mesh-based multi-view consistency module for surface refinement.
Utilized a pixel-level isotropic gradient aware method to guide Gaussian growth.

Main Results:

Achieved accurate surface reconstruction across diverse datasets (indoor, outdoor, object-centered).
Demonstrated excellent Novel View Synthesis (NVS) performance.
Significantly improved rendering quality and geometric representation compared to existing methods.

Conclusions:

The proposed scale-aligned monocular depth-guided 3DGS framework effectively enhances surface reconstruction and NVS quality.
Combining geometric prior regularization and consistency supervision is key to overcoming 3DGS limitations.
The method shows broad applicability for various scene types.