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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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Improving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model.

José E Valdez-Rodríguez¹, Hiram Calvo¹, Edgardo Felipe-Riverón¹

¹Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Ciudad de México 07738, Mexico.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This study improves single image depth estimation by integrating semantic segmentation. Combining 2D and 3D Convolutional Neural Networks (CNNs) enhances accuracy in determining object shapes and positions for better depth perception.

Keywords:

3D CNN depth estimation hybrid convolutional neural networks semantic segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Single image depth estimation is challenging due to foreground and background confusion.
Existing methods struggle to accurately delineate object boundaries.

Purpose of the Study:

To enhance single image depth estimation by incorporating semantic segmentation information.
To develop a hybrid Convolutional Neural Network (CNN) architecture for simultaneous depth and segmentation tasks.

Main Methods:

A novel hybrid 2D-3D CNN architecture was proposed.
Semantic segmentation was encoded as one-hot planes representing object categories.
The model was trained and tested on the SYNTHIA-AL dataset.

Main Results:

The proposed method achieved a σ3 score of 0.95 with manual segmentation, outperforming the state-of-the-art (0.81) by 0.14.
With automatic semantic segmentation, a σ3 score of 0.89 was obtained.
Integrating semantic information significantly improved depth estimation accuracy.

Conclusions:

Semantic segmentation provides crucial information for accurate depth estimation.
The hybrid 2D-3D CNN architecture effectively performs joint depth and segmentation tasks.
Knowledge of object shape and position demonstrably improves depth estimation performance.