most citedAttention-based Point Cloud Edge Sampling

3 citations · 5 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2023

Security Fence Inspection at Airports Using Object Detection

Nils Friederich, Andreas Specker, Jürgen Beyerer

To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this purpose, security fences are commonly used, but they require regular i…

cs.CV2023

Attention-based Part Assembly for 3D Volumetric Shape Modeling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape repres…

cs.CV20233 cited

Attention-based Point Cloud Edge Sampling

Chengzhi Wu, Junwei Zheng, Julius Pfrommer +1

Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point…

cs.CV20231 cited

Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions

Tobias Kalb, Jürgen Beyerer

Deep neural networks for scene perception in automated vehicles achieve excellent results for the domains they were trained on. However, in real-world conditions, the domain of ope…

cs.CV2023

Effects of Architectures on Continual Semantic Segmentation

Tobias Kalb, Niket Ahuja, Jingxing Zhou +1

Research in the field of Continual Semantic Segmentation is mainly investigating novel learning algorithms to overcome catastrophic forgetting of neural networks. Most recent publi…

cs.CV2023

Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly

Chengzhi Wu, Xuelei Bi, Julius Pfrommer +3

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossib…