ContactPose: A Dataset of Grasps with Object Contact and Hand Pose (2007.09545v1)

Abstract: Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and the object. Understanding and modeling this contact can potentially improve hand models, AR/VR experiences, and robotic grasping. Yet, we currently lack datasets of hand-object contact paired with other data modalities, which is crucial for developing and evaluating contact modeling techniques. We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images. ContactPose has 2306 unique grasps of 25 household objects grasped with 2 functional intents by 50 participants, and more than 2.9 M RGB-D grasp images. Analysis of ContactPose data reveals interesting relationships between hand pose and contact. We use this data to rigorously evaluate various data representations, heuristics from the literature, and learning methods for contact modeling. Data, code, and trained models are available at https://contactpose.cc.gatech.edu.

• The paper presents a novel dataset of 2306 grasps that pairs detailed hand-object contact data with hand pose and multimodal RGB-D images.
• The paper reveals that grasp intent affects hand pose variability, offering insights to improve anthropomorphic robotic hand design.
• The paper shows that learning-based models using rich hand shape features outperform heuristic approaches in predicting contact accurately.

ContactPose: A Dataset of Grasps with Object Contact and Hand Pose

The paper "ContactPose: A Dataset of Grasps with Object Contact and Hand Pose" introduces a novel dataset aimed at addressing the complex problem of hand-object contact modeling. The dataset stands out by pairing hand-object contact information with hand pose, object pose, and multimodal RGB-D images. This comprehensive approach facilitates the development of more accurate contact modeling techniques, which have widespread applications in fields such as computer vision, computer graphics, and robotics. Given the multifaceted nature of grasping, which involves intricate hand configurations and interactions between soft tissue and objects, understanding and accurately modeling these contacts can significantly enhance human-computer interactions and robotic manipulations.

Dataset Overview

ContactPose comprises 2306 unique grasps of 25 household objects captured with two different functional intents by 50 participants. The dataset includes over 2.9 million RGB-D grasp images, object contact maps, and 3D hand joint data, providing a rich source of information for research into realistic contact modeling. The high resolution of the contact and pose data allows for detailed analysis and the improvement of existing hand models. Importantly, the paper emphasizes the benefits of using thermal cameras to capture ground-truth contact data, avoiding the pitfalls associated with other capture methods, such as the influence of data gloves on natural grip behavior.

Key Contributions

The paper's key contributions lie in data provision, analysis, and algorithmic exploration:

1. Data Provision: ContactPose fills a significant gap by providing a dataset that captures hand-object interactions with contact annotations. This offers an exceptional opportunity to paper the intricate dynamics of functional grasps, which are crucial for developing advanced Virtual Reality (VR) simulations and robotic grasping technologies.
2. Data Analysis: The authors perform a detailed analysis of the dataset, revealing correlations between hand pose diversity and grasp intent. Notably, use grasps have less variation in hand pose compared to hand-off grasps. Furthermore, the dataset also enables exploration into the association of contact points with particular hand parts, which could guide anthropomorphic robotic hand design.
3. Algorithmic Exploration: The paper rigorously evaluates various data representations and learning methods for contact modeling. It shows that learning-based techniques, particularly when leveraging rich hand shape features (e.g., MANO hand meshes), can outpace heuristic approaches in predicting contact accurately. Techniques using pointcloud representations, for instance, benefit from richer hand and object geometry encoding, capturing nuanced contact patterns that heuristic methods miss.

Implications and Future Directions

This dataset has immense practical and theoretical implications. Practically, the findings can improve the dynamics simulation in robotics, leading to better manipulation strategies for anthropomorphic robotic hands. Theoretically, the robust dataset enables the evaluation and refinement of hand-object interaction models, pushing the boundaries of contact prediction accuracy.

Future research might delve into developing dynamic contact models that account for changes during manipulation, leveraging the static grasp data as a foundation. Moreover, enhancing deformable hand mesh models to better represent the interaction between soft tissue and object surfaces is a promising direction. This adaptation could dramatically improve the accuracy of hand pose and contact estimations in variable contexts, such as in VR or teleoperation scenarios, thus broadening the scope of applications for realistic hand-object interaction modeling.

ContactPose represents an essential step forward in capturing and modeling the complex interplays within hand-object interactions, fostering advancements in both theoretical understanding and practical applications.