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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: Nov 10, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Online supervised attention-based recurrent depth estimation from monocular video.

Dmitrii Maslov¹, Ilya Makarov^1,2

¹School of Data Analysis and Artificial Intelligence, HSE University, Moscow, Russia.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

This study enhances monocular depth estimation by integrating recurrent neural networks (RNNs) like convolutional gated recurrent units (convGRU) and convolutional long short-term memory (convLSTM) to leverage temporal information from video sequences for safer autonomous driving.

Keywords:

Augmented Reality Autonomous Vehicles Computer Science Methods Computer Vision Deep Convolutional Neural Networks Depth Reconstruction Recurrent Neural Networks

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Depth information is crucial for autonomous driving safety.
Current single-frame depth estimation methods face quality limitations due to deep neural network constraints.
Utilizing temporal information from video sequences offers a path to improve depth reconstruction quality.

Purpose of the Study:

To investigate intelligent integration of recurrent blocks into supervised depth estimation pipelines.
To propose and compare novel methods using convolutional gated recurrent units (convGRU) and convolutional long short-term memory (convLSTM).
To develop new deep neural network architectures for monocular depth estimation from video using past frames and attention mechanisms.

Main Methods:

Integration of convGRU and convLSTM blocks within supervised depth estimation frameworks.
Comparative analysis of convGRU and convLSTM for real-time depth estimation.
Development of novel deep neural network architectures incorporating attention mechanisms.
Training strategy optimization for recurrent depth estimation models.

Main Results:

Demonstrated the effectiveness of temporal information integration for monocular depth reconstruction.
Identified the optimal recurrent block (convGRU or convLSTM) for real-time applications.
Achieved improved depth estimation quality compared to existing single-frame and video-based methods.

Conclusions:

Recurrent neural networks significantly enhance monocular depth estimation by utilizing temporal context.
The proposed methods offer a promising direction for improving depth perception in autonomous driving systems.
Leveraging past frames with attention mechanisms provides a robust approach for accurate real-time depth reconstruction.