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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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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Related Experiment Video

Updated: Aug 31, 2025

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

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Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo.

Qingshan Xu, Weihang Kong, Wenbing Tao

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 19, 2022

Summary

This study introduces efficient multi-view stereo methods for accurate depth map estimation, particularly improving results in challenging low-textured areas. The novel approaches enhance detail recovery and overall performance in 3D reconstruction.

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Last Updated: Aug 31, 2025

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Published on: April 16, 2017

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Area of Science:

Computer Vision
3D Reconstruction
Photogrammetry

Background:

Accurate depth map estimation is crucial for 3D reconstruction.
Handling ambiguous regions, especially low-textured areas, remains a significant challenge in multi-view stereo (MVS).
Existing methods often struggle with detail preservation and completeness in challenging scenes.

Purpose of the Study:

To propose efficient multi-view stereo methods for accurate and complete depth map estimation.
To address the challenge of depth estimation in ambiguous and low-textured regions.
To achieve state-of-the-art performance in 3D scene reconstruction.

Main Methods:

Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH & ACMH+) form the basic methods.
Two frameworks were developed: multi-scale information fusion (ACMM) and planar geometric clue assistance (ACMP).
A combined framework (ACMMP) integrates multi-scale geometric consistency and planar priors for enhanced performance.

Main Results:

The proposed methods demonstrate state-of-the-art performance on extensive datasets.
Depth estimation is significantly improved, especially in low-textured areas.
The methods successfully recover fine details and ensure completeness in depth maps.

Conclusions:

The developed multi-view stereo methods provide accurate and complete depth maps.
The novel frameworks effectively handle ambiguous regions, advancing the field of 3D reconstruction.
The ACMMP framework offers a robust solution for detailed and reliable depth sensing.