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Related Concept Videos

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 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Published on: December 3, 2013

E²LNet: An Efficient and Effective Lightweight Network for Panoramic Depth Estimation.

Jiayue Xu¹, Jianping Zhao¹, Hua Li¹

¹School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

This study introduces a new framework for monocular panoramic depth estimation, addressing distortion and global context. The method achieves competitive performance with fewer parameters, offering a scalable solution for mobile augmented reality.

Keywords:

dilated convolution global average pooling panoramic depth estimation pixel-wise attention

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Monocular panoramic depth estimation is crucial for robotics and autonomous driving, enabling a full field of view.
Existing methods struggle with global context capturing and panoramic distortion.
Accurate depth perception across the entire panorama is essential for real-world applications.

Purpose of the Study:

To develop a novel framework for monocular panoramic depth estimation.
To simultaneously address panoramic distortion and extract global context information.
To improve the performance and scalability of panoramic depth estimation.

Main Methods:

Proposed an adaptive attention dilated convolution module to perceive distortion by adaptively adjusting receptive fields.
Designed a global scene understanding module to integrate global context into feature maps.
Trained and evaluated the model on virtual and real-world RGB-D panorama datasets.

Main Results:

The proposed method achieves competitive quantitative and qualitative performance compared to existing techniques.
Demonstrated effective perception of distortion and integration of global context.
The model shows fewer parameters and greater flexibility than prior methods.

Conclusions:

The developed framework offers a robust solution for monocular panoramic depth estimation.
The method is a scalable and flexible option for applications like mobile augmented reality.
Addresses key challenges in panoramic depth estimation, enhancing its practical utility.