RGB color model
Related papers: 20
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The RGB color model is a foundational framework for representing color digitally, combining red, green, and blue light channels to produce a full spectrum of visible colors. Each pixel in an RGB image carries intensity values for these three channels, enabling rich visual information capture through standard cameras. In robotics and AI, RGB data is ubiquitous: it powers object detection, semantic segmentation, visual SLAM, pose estimation, and scene understanding pipelines. RGB imaging is frequently paired with depth sensing to form RGB-D systems, where color and geometric information are fused to give robots a more complete understanding of their environment — enabling applications such as dense 3D mapping, robotic grasp detection, human activity recognition, and autonomous navigation. Deep learning models trained on RGB imagery have demonstrated strong performance across these tasks, and large-scale RGB and RGB-D datasets have accelerated benchmark-driven progress. The model matters because cameras are low-cost, widely available sensors, making RGB-based perception a practical and scalable foundation for intelligent robotic systems operating in real-world environments.
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A benchmark for the evaluation of RGB-D SLAM systems
Jrgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, Daniel Cremers
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SECOND: Sparsely Embedded Convolutional Detection
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel
Citations: 2736 • 2017
Deep learning for detecting robotic grasps
Ian Lenz, Honglak Lee, Ashutosh Saxena
Citations: 1646 • 2015
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, Jitendra Malik
Citations: 1531 • 2014
A large-scale hierarchical multi-view RGB-D object dataset
Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox
Citations: 1322 • 2011
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, Dieter Fox
Citations: 1170 • 2012
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese
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DeepFruits: A Fruit Detection System Using Deep Neural Networks
Inkyu Sa, Zongyuan Ge, Feras Dayoub, Ben Upcroft, Tristán Pérez, Chris McCool
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DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
Chao Yu, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei
Citations: 1052 • 2018
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Berta Bescos, José M. Fácil, Javier Civera, José Neira
Citations: 924 • 2018
3-D Mapping With an RGB-D Camera
Felix Endres, Jürgen Hess, Jürgen Sturm, Daniel Cremers, Wolfram Burgard
Citations: 822 • 2013
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, Dieter Fox
Citations: 811 • 2013
Visual SLAM algorithms: a survey from 2010 to 2016
Takafumi Taketomi, Hideaki Uchiyama, Sei Ikeda
Citations: 700 • 2017
Learning human activities and object affordances from RGB-D videos
Hema Swetha Koppula, Rudhir Gupta, Ashutosh Saxena
Citations: 699 • 2013
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
John McCormac, Ankur Handa, Andrew J. Davison, Stefan Leutenegger
Citations: 657 • 2017
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
Albert S. Huang, Abraham Bachrach, Peter Henry, Michael Krainin, Daniel Maturana, Dieter Fox, Nicholas Roy
Citations: 610 • 2016
Robust odometry estimation for RGB-D cameras
Christian Kerl, Jürgen Sturm, Daniel Cremers
Citations: 553 • 2013
Robotic grasp detection using deep convolutional neural networks
Sulabh Kumra, Christopher Kanan
Citations: 543 • 2017
Disease detection on the leaves of the tomato plants by using deep learning
Halil Durmuş, Ece Olcay Güneş, Mürvet Kırcı
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