3D Object Reconstruction from Hand-Object Interactions
원문 초록
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.
함께 살펴볼 논문
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DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
HuSc3D: Human Sculpture dataset for 3D object reconstruction
Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
AutoPartGen: Autogressive 3D Part Generation and Discovery
PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling
토론
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