Towards Real-World Category-level Articulation Pose Estimation

Abstract

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. Considering these limitations, we reform this problem setting for real-world environments and suggest a CAPE-Real (CAPER) task setting. This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world. To support this task, we build an articulated model repository ReArt-48 and present an efficient dataset generation pipeline, which contains Fast Articulated Object Modeling (FAOM) and Semi-Authentic MixEd Reality Technique (SAMERT). Accompanying the pipeline, we build a large-scale mixed reality dataset ReArtMix and a real world dataset ReArtVal. We also propose an effective framework ReArtNOCS that exploits RGB-D input to estimate part-level pose for multiple instances in a single forward pass. Extensive experiments demonstrate that the proposed ReArtNOCS can achieve good performance on both CAPER and CAPE settings. We believe it could serve as a strong baseline for future research on the CAPER task.

References in corpus (6)

• ShapeNet: An Information-Rich 3D Model Repository
• Deep Part Induction from Articulated Object Pairs
• Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
• Applying Domain Randomization to Synthetic Data for Object Category Detection
• CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
• Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation

Cited by in corpus (2)

• Challenges for Monocular 6D Object Pose Estimation in Robotics
• A Survey of Embodied Learning for Object-Centric Robotic Manipulation