WACV 2022 – Main Conference: StickyLocalization: Robust End-To-End Relocalization on Point Clouds using Graph Neural Networks
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Kai Fischer, Martin Simon, Stefan Milz, Patrick Mader Abstract: Relocalization inside pre-built maps provides a big benefit in the course of today’s autonomous driving tasks where the map can be […]
ITSC 2021 – Main Conference: BEVDetNet: Bird’s Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving
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Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig, Stefan Milz, Patrick Mader Abstract: 3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range […]
CVPR 2021 – Spleenlab coorganizes Safety Workshop
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Together with well known partners in the Automotiv Industry Spleenlab coorganizes Safety Workshop. Motivation Autonomously driving vehicles will undoubtedly change the world. The realization of these changes is a huge […]
CVPR 2021: Spleenlab coorganizes the 2nd WS for Omnidirectional Computer Vision
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Motivation From immersive experiences to autonomous driving to medical imaging and more, we are increasingly seeing that maximizing a camera’s field of view can solve real-world problems. In the past […]
CVPR 2021 – WS on Safe Artificial Intelligence for Automated Driving: Development Methodologies for Safety Critical Machine Learning Applications in the Automotive Domain: A Survey
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Martin Rabe, Stefan Milz, Patrick Mäder Abstract: Enabled by recent advances in the field of machine learning, the automotive industry pushes towards automated driving. The development of traditional safety-critical automotive […]
CVPR 2021 – Main Conference: StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
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Kai Fischer, Martin Simon, Florian Olsner, Stefan Milz, Horst-Michael Groß, Patrick Mäder Abstract Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional […]
CVPR 2020: Spleenlab coorganizes the first WS for Omnidirectional Computer Vision
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Our objective is to provide a venue for novel research in omnidirectional computer vision with an eye toward actualizing these ideas for commercial or societal benefit. As omnidirectional cameras become […]
Proceedings of the NeurIPS 2019 Competition and Demonstration Track: Catch Me, If You Can! A Mediated Perception Approach Towards Fully Autonomous Drone Racing
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Florian Ölsner, Stefan Milz Abstract: Automated flight, e.g. first person view drone racing is a challenging task involving many sub-problems like monocular object detection, 3D pose estimation, mapping, optimal path […]
IROS 2020 – Main Conference: UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
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Varun Ravi Kumar, Senthil Yogamani, Markus Bach, Christian Witt, Stefan Milz, Patrick Mader Abstract: In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes […]
NeuRIPS 2018 – WS on Machine Learning for Intelligent Transportation Systems: AI-Survey for Self-Flying Vehicles: Exploring the Challenges of Deep Learning
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Stefan M., Sebastian Süss, Tobias Rüdiger, Florian Ölsner, Friedrich Möller, Mateusz Olichwer, Michal Uricar Abstract: Everyone is talking about intuitive and automated transportation. An important and very challenging part of […]