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Color Vision

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

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Related Experiment Video

Updated: Jun 21, 2025

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

Neural Colour Correction for Indoor 3D Reconstruction Using RGB-D Data.

Tiago Madeira^1,2, Miguel Oliveira^1,3, Paulo Dias^1,2

¹Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Intelligent System Associate Laboratory (LASI), University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

This study introduces a novel neural network for color correction in 3D reconstruction. The method harmonizes colors in sparse indoor captures, significantly improving photo-realistic model generation.

Keywords:

3D reconstruction colour correction neural network

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Last Updated: Jun 21, 2025

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Area of Science:

Computer Vision
Computer Graphics
Machine Learning

Background:

Generating photo-realistic 3D models is crucial for human-centered applications.
Multiview 3D reconstruction of indoor scenes suffers from color inconsistencies due to varying acquisition conditions.
These inconsistencies lead to visual artifacts in the final 3D models.

Purpose of the Study:

To propose a novel neural-based approach for color correction in indoor 3D reconstruction.
To harmonize colors from sparse captures in complex indoor environments.
To address the challenge of generating photo-realistic 3D models.

Main Methods:

A lightweight and efficient neural network approach is developed.
A fully connected deep neural network learns an implicit representation of color in 3D space.
Camera-dependent effects are captured, and transformations are estimated to regenerate pixels.

Main Results:

The proposed method effectively harmonizes color from sparse captures.
It outperforms existing state-of-the-art approaches on the MP3D dataset.
The approach generates visually appealing and artifact-free 3D models.

Conclusions:

The neural-based color correction method is effective for indoor 3D reconstruction.
It offers a significant improvement over current methods for generating photo-realistic models.
The approach is lightweight, efficient, and suitable for complex indoor scenes.