Jean-Baptiste Weibel

• PhD Student at TU Wien

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estima...

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estima...

Transparent objects and surfaces are pervasive in man-made environments and need to be considered in any vision system. Accurate depth data is a key factor for the reliability of such systems, requiring methods tailored for transparency to overcome the sensing shortcomings. However, the current state-of-the-art methods to predict the depth of such...

Abstract — Safety is essential in mission-critical operations with respect to human life and financial costs. However, despite the progress achieved in robot hardware and software, safety concerns remain a major issue hindering the effective integration of these robots. Verification and audit in mission-critical processes are essential not only to...

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well suited for robotics applications. We observe that previous surveys on establish th...

Transparent objects and surfaces are pervasive in man-made environments and need to be considered in any vision system. Accurate depth data is a key factor for such systems reliability, requiring transparency to be inferred, due to the sensing challenges. However, the current state-of-the-art methods to predict the depth of such objects are not rel...

Zusammenfassung Während matte Objekte visuell gut erkannt und mit Robotern gegriffen werden können, stellen transparente Objekte neue Herausforderungen dar. So liefern moderne Farb- und Tiefenbildkameras (RGB-D) keine korrekten Tiefendaten, sondern verzerrte Abbildungen des Hintergrunds. Wir zeigen in diesem Beitrag, welche Methoden geeignet sind,...

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 providing estimates for novel objects, that is, objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected...

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 sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surv...

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 providing estimates for novel objects, that is objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected t...

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 pose estimation gained considerable momentum with the rise of high-performing deep learning-based solutions and is particularly interesting for the community since sensors...

Structured light-based depth sensors provide accurate depth information independently of the scene appearance by extracting pattern positions from the captured pixel intensities. Spatial neighborhood encoding, in particular, is a popular structured light approach for off-the-shelf hardware. However, it suffers from the distortion and fragmentation...

While object semantic understanding is essential for service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of the annotation necessary to approach this problem, but today’s methods still struggle with the differences between artificial and real 3D data. We conjecture that on...

Perceiving the environment geometry is necessary for a robot to perform safe motions and actions. To decide upon meaningful actions, however, semantic understanding is also required. At the object level, this semantic classification task can directly be performed using the extracted object 3D data. While continuously improving, the performance of m...

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 the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial and real 3D data. We conjecture...

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 robotics and computer graphics. While the advent of deep learning has progressed the field of 3D object classification, most work using this data type are solely evaluated on...

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 robotics and computer graphics. While the advent of deep learning has progressed the field of 3D object classification, most work using this data type are solely evaluated on...

Object classification is an important capability for robots as it provides vital semantic information that underpin most practical high-level tasks. Classic handcrafted features, such as point pair features, have demonstrated their robustness for this task. Combining these features with modern deep learning methods provide discriminative features t...

Handcrafted geometric features for object classification are heavily relied on in robot vision because of their demonstrated robustness. While modern deep learning approaches typically outperform classical methods, transferring this success to 3D data in a robust manner is still an open question because of the challenges introduced by the additiona...