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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.

Perceptual Constancy

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...

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Related Experiment Video

Updated: Nov 15, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Joint Depth and Defocus Estimation From a Single Image Using Physical Consistency.

Anmei Zhang, Jian Sun

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 2, 2021

Summary

This summary is machine-generated.

This study introduces a dual network for joint depth and defocus estimation from single images. By enforcing physical consistency during training, the method significantly improves performance on real-world images.

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Last Updated: Nov 15, 2025

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Area of Science:

Computer Vision
Deep Learning

Background:

Estimating depth and defocus maps are crucial computer vision tasks, often tackled separately using deep learning.
Current methods struggle with real images due to the scarcity of dense ground truth data, relying heavily on synthetic datasets.

Purpose of the Study:

To develop a method for jointly estimating depth and defocus from a single image.
To improve the performance of depth and defocus estimation on real-world images.

Main Methods:

A dual deep learning network with two subnets for depth and defocus estimation was designed.
The network was jointly trained on synthetic data with a physical consistency constraint.
A novel labeling method for real image datasets and two new accuracy metrics were introduced.

Main Results:

Joint training with physical constraints allowed subnets to mutually guide each other.
The proposed method demonstrated improved depth and defocus estimation performance on real defocused images.

Conclusions:

Joint estimation of depth and defocus using physical consistency is effective.
The approach enhances the robustness and accuracy of computer vision models on real-world data.