99 citations · 194 across the 36 of their papers we have counts for
7 papers · 2 filters
LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation
Hongli Xu, Zhaowei Lu, Junwen Huang +5
Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted together with a canonical obje…
Track2Art: Articulated Object Model Recovery with Visual-Geometric Track Representations
Xiaotong Li, Yixiong Jing, Junsheng Ding +4
Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic relations. Existing approaches…
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…
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…