3D Object Reconstruction from Hand-Object Interactions

Abstract

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.

International Conference on Computer Vision (ICCV) 2015, http://files.is.tue.mpg.de/dtzionas/In-Hand-Scanning

References in corpus (1)

• Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

Cited by in corpus (7)

• Embodied Hands: Modeling and Capturing Hands and Bodies Together
• Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
• Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion
• Depth Adaptive Deep Neural Network for Semantic Segmentation
• Random Forest with Learned Representations for Semantic Segmentation
• EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild
• Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models