12 accepted papers
Estimating the geometric and volumetric properties of transparent deformable liquids is challenging due to optical complexities and dynamic surface deformations induced by container movements. Autonomous robots performing precise liquid manipulation tasks—such as dispensing, aspiration, and mixing—m
To automate processes like polishing or cleaning at scale, robots must be able to adapt learned skills to new object instances without manual reprogramming. Applications requiring tool-surface interactions face a significant challenge in transferring manipulation strategies to novel objects due to s
Transparent objects are ubiquitous in laboratory automation settings, as liquids need to be visually controlled regularly. Automating laboratory processes would make the creation of small-batch medication feasible, thus making more personalized and better-targeted treatments more accessible. However
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 <italic xmln
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 estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pos…
Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore…
"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…
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
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
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…
RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the fe…