0000000000369272

AUTHOR

Clara Fernandez-labrador

showing 3 related works from this author

Unsupervised learning of category-specific symmetric 3D keypoints from point sets

2020

Lecture Notes in Computer Science, 12370

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]FOS: Computer and information sciencesComputer sciencePlane symmetryComputer Vision and Pattern Recognition (cs.CV)Point cloudComputer Science - Computer Vision and Pattern Recognition02 engineering and technology010501 environmental sciences01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Linear basis0202 electrical engineering electronic engineering information engineeringComputingMilieux_COMPUTERSANDEDUCATIONPoint (geometry)0105 earth and related environmental sciencesbusiness.industryCategory specific[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognition16. Peace & justiceBenchmark (computing)Unsupervised learning020201 artificial intelligence & image processingArtificial intelligenceSymmetry (geometry)business
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Indoor Scene Understanding using Non-Conventional Cameras

2020

Humans understand environments effortlessly, under a wide variety of conditions, by the virtue of visual perception. Computer vision for similar visual understanding is highly desirable, so that machines can perform complex tasks by interacting with the real world, to assist or entertain humans. In this regard, we are particularly interested in indoor environments, where humans spend nearly all their lifetime.This thesis specifically addresses the problems that arise during the quest of the hierarchical visual understanding of indoor scenes.On the side of sensing the wide 3D world, we propose to use non-conventional cameras, namely 360º imaging and 3D sensors. On the side of understanding, …

Intelligence artificielle - Robotique mobile - Vision par ordinateur[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Artificial IntelligenceVision par ordinateur[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Robotique mobile[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionIntelligence artificielleMobile roboticsMobile robotics - Artificial Intelligence - computer vision[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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PanoRoom: From the Sphere to the 3D Layout

2018

We propose a novel FCN able to work with omnidirectional images that outputs accurate probability maps representing the main structure of indoor scenes, which is able to generalize on different data. Our approach handles occlusions and recovers complex shaped rooms more faithful to the actual shape of the real scenes. We outperform the state of the art not only in accuracy of the 3D models but also in speed.

FOS: Computer and information sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputingMethodologies_COMPUTERGRAPHICS
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