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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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Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models.

Jaka Kravanja¹, Mario Žganec², Jerneja Žganec-Gros³

¹Alpineon d.o.o., Ulica Iga Grudna 15, Ljubljana SI-1000, Slovenia. jaka.kravanja@alpineon.si.

Sensors (Basel, Switzerland)

|October 25, 2016

Summary

This study introduces a new structured light sensor for robust outdoor depth image acquisition. The novel sensor effectively counteracts sunlight interference, enabling accurate 3D scene reconstruction in challenging environments.

Keywords:

3D reconstruction depth imaging modulated acquisition probabilistic graphical models structured light triangulation

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Area of Science:

Computer Vision
Robotics
Sensor Technology

Background:

Structured light depth imaging is difficult outdoors due to sunlight.
Existing sensors struggle with ambient light interference.

Purpose of the Study:

To develop a novel structured light sensor for robust outdoor depth image acquisition.
To overcome limitations of current sensors in uncontrolled lighting conditions.

Main Methods:

Utilized a modulated sequence of structured light.
Estimated a spatial distortion map to counteract environmental factors.
Employed a probabilistic graphical model for pattern-distortion correspondence.
Reconstructed depth images using reference frames from calibration.

Main Results:

Demonstrated robust depth image acquisition in outdoor environments.
Achieved accurate spatial distortion mapping despite ambient sunlight.
Experimental validation in both indoor and outdoor settings.

Conclusions:

The proposed sensor offers a reliable solution for outdoor structured light depth sensing.
Outperforms existing methods and commercial sensors in challenging conditions.
Enables new applications for 3D reconstruction in outdoor robotics and surveying.