CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer
The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud. The proposed CAPT methods accurately estimate joint parameters and states for various articulated objects with high precision and robustness. The paper also introduces a motion loss approach, which improves articulation estimation performance by emphasizing the dynamic features of articulated objects. Additionally, the paper presents a double voting strategy to provide the framework with coarse-to-fine parameter estimation. Experimental results on several category datasets demonstrate that our methods outperform existing alternatives for articulation estimation. Our research provides a promising solution for applying Transformer-based architectures in articulated object analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
parameter estimationState EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Category-Level Articulated Object Pose Estimation
This project addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accommodates object instances previously unse…
Objectparameter estimationPose EstimationSCAPO: Self-Supervised Category-Level Articulated Pose Estimation from a Single 3D Observation
Existing methods for category-level object articulation from a single 3D observation often rely on dense supervision, multi-frame inputs, or CAD templates, and still struggle to disentangle geometry from articulation or …
Pose EstimationTowards Real-World Category-level Articulation Pose Estimation
Human life is populated with articulated objects. Current Category-level Articulation Pose Estimation (CAPE) methods are studied under the single-instance setting with a fixed kinematic structure for each category. Consi…
Dataset GenerationMixed RealityPose EstimationScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to-day tasks. Existing methods either requ…
BenchmarkingSplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision o…
3DGS