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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: Jul 2, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

A Novel Method for Monocular Depth Estimation Using an Hourglass Neck Module.

Seung-Jin Oh¹, Seung-Ho Lee¹

¹Department of Electronic Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This study introduces a new monocular depth estimation method using an hourglass neck module and Swin Transformer V2. The novel approach achieves superior accuracy, outperforming existing techniques for generating depth images.

Keywords:

deformable attention hourglass neck module masked image modeling monocular depth estimation swin transformer V2

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Area of Science:

Computer Vision
Deep Learning

Background:

Monocular depth estimation is crucial for scene understanding.
Existing methods face challenges in accurately capturing local and global features.

Purpose of the Study:

To propose a novel monocular depth estimation method.
To improve the accuracy and efficiency of depth prediction using advanced deep learning architectures.

Main Methods:

Utilizing Swin Transformer V2 with masked image modeling (MIM) for feature extraction.
Employing an hourglass neck module to preserve feature map properties.
Incorporating deformable attention to reduce computational cost and enhance locality.
A decoder with deconvolution and upsampling layers generates the final depth map.

Main Results:

The proposed method achieved a Root Mean Square Error (RMSE) of 0.274 on the NYU Depth V2 dataset.
This RMSE value is lower than those reported by previously published methods.
Demonstrated superior performance in monocular depth estimation accuracy.

Conclusions:

The novel method effectively estimates depth from single images.
The integration of Swin Transformer V2 and the hourglass neck module proves advantageous.
The approach offers a more efficient and accurate solution for monocular depth estimation.