Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Nuechter, Andreas"'
We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that supports conveniently a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RG
Externí odkaz:
http://arxiv.org/abs/2405.07847
Lunar caves are promising features for long-term and permanent human presence on the moon. However, given their inaccessibility to imaging from survey satellites, the concrete environment within the underground cavities is not well known. Thus, to fu
Externí odkaz:
http://arxiv.org/abs/2404.09230
Autor:
Yuan, Yijun, Nuechter, Andreas
We present Uni-Fusion, a universal continuous mapping framework for surfaces, surface properties (color, infrared, etc.) and more (latent features in CLIP embedding space, etc.). We propose the first universal implicit encoding model that supports en
Externí odkaz:
http://arxiv.org/abs/2303.12678
Autor:
Bredenbeck, Anton, Vyas, Shubham, Zwick, Martin, Borrmann, Dorit, Olivares-Mendez, Miguel, Nüchter, Andreas
Space robotics applications, such as Active Space Debris Removal (ASDR), require representative testing before launch. A commonly used approach to emulate the microgravity environment in space is air-bearing based platforms on flat-floors, such as th
Externí odkaz:
http://arxiv.org/abs/2207.10693
Autor:
Yuan, Yijun, Nuechter, Andreas
Implicit representations are widely used for object reconstruction due to their efficiency and flexibility. In 2021, a novel structure named neural implicit map has been invented for incremental reconstruction. A neural implicit map alleviates the pr
Externí odkaz:
http://arxiv.org/abs/2206.08712
Autor:
Bredenbeck, Anton, Vyas, Shubham, Suter, Willem, Zwick, Martin, Borrmann, Dorit, Olivares-Mendez, Miguel, Nüchter, Andreas
The recent increase in yearly spacecraft launches and the high number of planned launches have raised questions about maintaining accessibility to space for all interested parties. A key to sustaining the future of space-flight is the ability to serv
Externí odkaz:
http://arxiv.org/abs/2206.03993
Autor:
Yuan, Yijun, Nuechter, Andreas
In recent years, implicit functions have drawn attention in the field of 3D reconstruction and have successfully been applied with Deep Learning. However, for incremental reconstruction, implicit function-based registrations have been rarely explored
Externí odkaz:
http://arxiv.org/abs/2205.15954
Autor:
Arzberger, Fabian, Schubert, Tim, Wiecha, Fabian, Zevering, Jasper, Rothe, Julian, Borrmann, Dorit, Montenegro, Sergio, Nüchter, Andreas
Publikováno v:
In Robotics and Autonomous Systems February 2025 184
In this work, we propose to learn local descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. On top of our previous work, that directly solves the t
Externí odkaz:
http://arxiv.org/abs/2003.05199
In this work, we propose to directly find the one-step solution for the point set registration problem without correspondences. Inspired by the Kernel Correlation method, we consider the fully connected objective function between two point sets, thus
Externí odkaz:
http://arxiv.org/abs/2003.00457