3 citations · 3 across the 5 of their papers we have counts for
5 papers
ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs
Yordanka Velikova, Mahdi Saleh, Liming Kuang +1
Modeling continuous object deformation is important for many computer vision and robotics tasks, such as manipulation and simulation. Existing approaches rely on learning-based met…
Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs
Hiran Sarkar, Liming Kuang, Yordanka Velikova +1
Predicting scene dynamics from visual observations is challenging. Existing methods capture dynamics only within observed boundaries failing to extrapolate far beyond the training…
ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
Liming Kuang, Yordanka Velikova, Mahdi Saleh +3
Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurrently, large-scale vision langua…
Topology-Aware and Highly Generalizable Deep Reinforcement Learning for Efficient Retrieval in Multi-Deep Storage Systems
Funing Li, Yuan Tian, Ruben Noortwyck +3
In modern industrial and logistics environments, the rapid expansion of fast delivery services has heightened the demand for storage systems that combine high efficiency with incre…
CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers
Alex Ranne, Liming Kuang, Yordanka Velikova +2
In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's…