Deep Fusion of Camera and LIDAR

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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Main Author: Schneider, Nick
Format: Online
Language:English
Published: KIT Scientific Publishing 2026
Subjects:
Online Access:1613-4214 (Online)
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author Schneider, Nick
author_browse Schneider, Nick
author_facet Schneider, Nick
author_sort Schneider, Nick
collection Directory of Open Access Books
description 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.
format Online
id doab-20.500.12854ir-173704
institution Directory of Open Access Books
language eng
publishDate 2026
publishDateRange 2026
publishDateSort 2026
publisher KIT Scientific Publishing
publisherStr KIT Scientific Publishing
record_format ojs
spelling doab-20.500.12854ir-1737042026-03-19T13:50:16Z Deep Fusion of Camera and LIDAR Schneider, Nick 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 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. 2026-03-19T13:50:15Z 2026-03-19T13:50:15Z 2026-03-11T15:50:02Z 2026 book 1613-4214 (Online) https://library.oapen.org/handle/20.500.12657/111439 9783731513612 9783731513261 https://directory.doabooks.org/handle/20.500.12854/173704 eng Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie open access KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000169933 10.5445/KSP/1000169933 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513612 9783731513261 KIT Scientific Publishing 140 Karlsruhe, Germany open access
spellingShingle 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
Schneider, Nick
Deep Fusion of Camera and LIDAR
title Deep Fusion of Camera and LIDAR
title_full Deep Fusion of Camera and LIDAR
title_fullStr Deep Fusion of Camera and LIDAR
title_full_unstemmed Deep Fusion of Camera and LIDAR
title_short Deep Fusion of Camera and LIDAR
title_sort deep fusion of camera and lidar
topic 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
topic_facet 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
url 1613-4214 (Online)
work_keys_str_mv AT schneidernick deepfusionofcameraandlidar