8 papers
SOS! : A Streamlined Object-Conditional Transformer for Model-free Segmentation
Jiaqi Hu, Junwen Huang, Hongli Xu +4
Foundation segmentation models excel at generating high-quality, class-agnostic masks, but they struggle to associate these proposals with specific target objects. This semantic ga…
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…
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…
Object Pose Transformer: Unifying Unseen Object Pose Estimation
Weihang Li, Lorenzo Garattoni, Fabien Despinoy +2
Learning model-free object pose estimation for unseen instances remains a fundamental challenge in 3D vision. Existing methods typically fall into two disjoint paradigms: category-…
MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects
Shiyu Li, Hannah Schieber, Kristoffer Waldow +3
Multi-camera dynamic Augmented Reality (AR) applications require a camera pose estimation to leverage individual information from each camera in one common system. This can be achi…
Generative 6D Pose Estimation via Conditional Flow Matching
Amir Hamza, Davide Boscaini, Weihang Li +2
Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in or estimate it indirectly via loc…