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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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A Survey on Self-Supervised Monocular Depth Estimation Based on Deep Neural Networks.

Qiulei Dong, Zhengming Zhou, Xiaolan Qiu

IEEE Transactions on Neural Networks and Learning Systems

|April 1, 2025

Summary

This summary is machine-generated.

This survey reviews self-supervised monocular depth estimation methods, which predict depth from single images without ground truth data. It categorizes 89 works and analyzes current techniques for applications like autonomous driving.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Monocular depth estimation predicts scene depth from single images, crucial for robotics, autonomous driving, and AR.
Self-supervised methods are gaining traction due to their ability to train using only images, eliminating the need for ground truth depth maps.

Purpose of the Study:

To provide a comprehensive survey of recent advancements in self-supervised monocular depth estimation.
To systematically review and categorize existing literature in the field.

Main Methods:

A thorough literature review and categorization of 89 existing works on self-supervised monocular depth estimation.
Analysis of public datasets and evaluation metrics commonly used in the field.
Comparative performance analysis of state-of-the-art methods.

Main Results:

Categorization and review of 89 self-supervised monocular depth estimation methods.
Introduction to standard datasets and evaluation metrics.
Performance comparison and analysis of leading techniques.

Conclusions:

The survey addresses the lack of a comprehensive review in self-supervised monocular depth estimation.
Identifies open challenges and outlines potential future research directions in the field.