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Deep Fusion of Camera and LIDAR

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Fusing camera and LIDAR data in autonomous driving poses challenges such as accurate calibration, differing data representations, and extensive training data requirements. This dissertation addresses these by three contributions: a deep neural network for LIDAR-to-camera calibration, two depth compl...

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Guardat en:
Autor principal: Schneider, Nick
Format: Online
Idioma:anglès
Publicat: KIT Scientific Publishing 2026
Matèries:
Bildverstehen
Computer Vision
Machine Learning
Neural Networks
Neuronale Netze
Sensor Fusion
Sensorfusion
Maschinelles Lemen
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering
Accés en línia:1613-4214 (Online)
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Sumari:Fusing camera and LIDAR data in autonomous driving poses challenges such as accurate calibration, differing data representations, and extensive training data requirements. This dissertation addresses these by three contributions: a deep neural network for LIDAR-to-camera calibration, two depth completion approaches for processing sparse depth measurements in the image space, and a large-scale dataset of 93k RGB and depth images for training and evaluating deep networks.

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