31 citations · 36 across the 8 of their papers we have counts for
8 papers
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…
Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images
Zeyu Lu, Di Huang, Lei Bai +4
Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing…
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…
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…
MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors
Chengzhi Wu, Kanran Zhou, Jan-Philipp Kaiser +7
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to…
SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning
Chengzhi Wu, Linxi Qiu, Kanran Zhou +2
In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a r…
Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding
Chengzhi Wu, Julius Pfrommer, Jürgen Beyerer +2
We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed meth…
Self-Supervised Generative-Contrastive Learning of Multi-Modal Euclidean Input for 3D Shape Latent Representations: A Dynamic Switching Approach
Chengzhi Wu, Julius Pfrommer, Mingyuan Zhou +1
We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes. The architecture uses two encoder branches for vox…