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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: Oct 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Multi-Scale Spatial Attention-Guided Monocular Depth Estimation With Semantic Enhancement.

Xianfa Xu, Zhe Chen, Fuliang Yin

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

|October 21, 2021

Summary

This summary is machine-generated.

This study introduces a novel unsupervised monocular depth estimation method using multi-scale spatial attention and semantic enhancement. The approach improves depth map accuracy, particularly for small objects and object edges.

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Monocular depth estimation is crucial for 3D vision but challenging.
Existing unsupervised methods struggle with small objects and blurred edges.

Purpose of the Study:

To enhance unsupervised monocular depth estimation.
To address limitations of missing small objects and object edge blurring.

Main Methods:

A multi-scale spatial attention-guided block using atrous spatial pyramid pooling and spatial attention.
Mutual information for exploring left-right view correlation.
A double-path prediction network for simultaneous depth and semantic map generation.

Main Results:

Improved focus on objects, especially small ones.
Sharper object edges in predicted depth maps due to semantic information.
Outperformed existing self-supervised methods on KITTI and Make3D datasets.

Conclusions:

The proposed method effectively improves unsupervised monocular depth estimation.
Semantic enhancement and multi-scale attention are key to better performance.