806 citations · 810 across the 9 of their papers we have counts for
10 papers
Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methods
Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer
We study Bayesian optimization (BO) through the lens of information geometry. Pulling back the Fisher information metric through the surrogate posterior map yields a local sensitiv…
6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly
Chengzhi Wu, Hao Fu, Jan-Philipp Kaiser +5
The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, i…
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning
Chengzhi Wu, Qianliang Huang, Kun Jin +2
Contrastive learning is an essential method in self-supervised learning. It primarily employs a multi-branch strategy to compare latent representations obtained from different bran…
SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity
Chengzhi Wu, Yuxin Wan, Hao Fu +5
Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer…
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer
To reduce the curse of dimensionality for Gaussian processes (GP), they can be decomposed into a Gaussian Process Network (GPN) of coupled subprocesses with lower dimensionality. I…
Rethinking Attention Module Design for Point Cloud Analysis
Chengzhi Wu, Kaige Wang, Zeyun Zhong +5
In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis. However, attention module variants featured in various research…
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…
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…
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…
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh +11
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge…