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Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Depth-consistent 3D Gaussian Splatting via physical defocus modeling and multi-view geometric supervision.

Yu Deng¹, Baozhu Zhao¹, Junyan Su¹

¹Department of Future Technology, South China University of Technology, Guangzhou, 511400, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 20, 2025

Summary

This study introduces a new framework for 3D reconstruction, improving depth accuracy in challenging scenes with varying distances. The method enhances 3D Gaussian Splatting using depth-of-field and multi-view consistency for better scene representation.

Keywords:

3D scene reconstruction Depth-of-field Differentiable rendering Novel view synthesis

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Area of Science:

Computer Vision
3D Reconstruction
Computational Imaging

Background:

3D reconstruction faces challenges with extreme depth variations, leading to poor depth estimation in distant areas and structural issues in near-field regions.
Existing methods struggle to balance supervisory signals for both near and far scene elements.

Purpose of the Study:

To develop a novel computational framework for advancing 3D Gaussian Splatting.
To improve depth fidelity in 3D reconstructions across scenes with significant depth stratification.

Main Methods:

Integration of depth-of-field supervision using a scale-recovered monocular depth estimator and defocus convolution.
Implementation of multi-view consistency supervision with LoFTR-based feature matching and least squares optimization.
Novel depth-of-field loss to enforce geometric consistency and enhance depth accuracy.

Main Results:

Achieved superior depth fidelity compared to state-of-the-art methods.
Demonstrated a 0.8 dB PSNR improvement on the Waymo Open Dataset.
Successfully enhanced both far-field and near-field depth estimation.

Conclusions:

The proposed framework unifies physical imaging principles with learning-based depth regularization.
Offers a scalable solution for complex depth stratification in urban environments.
Bridges the gap between accurate depth estimation and structural integrity in 3D reconstruction.