40 citations · 51 across the 10 of their papers we have counts for
11 papers
OSCAR: Open-Set CAD Retrieval from a Language Prompt and a Single Image
Tessa Pulli, Jean-Baptiste Weibel, Peter Hönig +3
6D object pose estimation plays a crucial role in scene understanding for applications such as robotics and augmented reality. To support the needs of ever-changing object sets in…
Phys-Liquid: A Physics-Informed Dataset for Estimating 3D Geometry and Volume of Transparent Deformable Liquids
Ke Ma, Yizhou Fang, Jean-Baptiste Weibel +5
Estimating the geometric and volumetric properties of transparent deformable liquids is challenging due to optical complexities and dynamic surface deformations induced by containe…
SCOPE: Semantic Conditioning for Sim2Real Category-Level Object Pose Estimation in Robotics
Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel +2
Object manipulation requires accurate object pose estimation. In open environments, robots encounter unknown objects, which requires semantic understanding in order to generalize b…
ReFlow6D: Refraction-Guided Transparent Object 6D Pose Estimation via Intermediate Representation Learning
Hrishikesh Gupta, Stefan Thalhammer, Jean-Baptiste Weibel +2
Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refract…
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation
Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel +2
Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due…
ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers
Philipp Ausserlechner, David Haberger, Stefan Thalhammer +2
As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estima…
Challenges for Monocular 6D Object Pose Estimation in Robotics
Stefan Thalhammer, Dominik Bauer, Peter Hönig +3
Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB senso…
Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects
Stefan Thalhammer, Jean-Baptiste Weibel, Markus Vincze +1
Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on pr…
Open Challenges for Monocular Single-shot 6D Object Pose Estimation
Stefan Thalhammer, Peter Hönig, Jean-Baptiste Weibel +1
Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases. Monocular object p…
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme
Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates th…
Addressing the Sim2Real Gap in Robotic 3D Object Classification
Jean-Baptiste Weibel, Timothy Patten, Markus Vincze
Object classification with 3D data is an essential component of any scene understanding method. It has gained significant interest in a variety of communities, most notably in robo…