Scalable Reconstruction of Hand-Held Objects from Monocular RGB Images

Conceitos essenciais

A scalable paradigm for reconstructing hand-held objects from monocular RGB images by jointly inferring hand and object geometry, and leveraging large language/vision models for automated 3D object retrieval and alignment.

Resumo

The paper presents a novel approach for reconstructing hand-held objects from monocular RGB images. The key insights are:

1. Hands provide strong anchors for 3D location and scale of the object, enabling reconstruction from single images.
2. The space of manipulanda (hand-held objects) is much smaller than all possible objects, enabling the use of large language/vision models for automated object recognition and retrieval.

The method consists of three stages:

1. MCC-HO: A transformer-based model that jointly infers 3D hand and object geometry from a single RGB image and estimated 3D hand.
2. Retrieval-Augmented Reconstruction (RAR): Automatically retrieves a 3D object model using GPT-4(V) and Genie to recognize the hand-held object.
3. Rigid alignment: The retrieved 3D object model is rigidly aligned with the network-inferred object geometry using ICP.

Experiments show that MCC-HO achieves state-of-the-art performance on hand-held object reconstruction benchmarks like DexYCB, MOW, and HOI4D. The combination of MCC-HO and RAR also enables scalable 3D annotation of in-the-wild hand-object interaction images from the 100DOH dataset.

Estatísticas

The paper uses the following datasets for training and evaluation:

DexYCB: 1,000 lab videos of hands interacting with 21 YCB objects
MOW: 512 images from 100 Days of Hands dataset labeled with 3D hands and objects
HOI4D: 4,000 lab videos of hands interacting with 6 common object categories
100DOH: 131 days of footage from Internet videos showing hand-object interactions

Citações

"Man is the measure of all things."
"In manipulation, one may philosophize that the hand is the measure of all objects."

Principais Insights Extraídos De

Reconstructing Hand-Held Objects in 3D

by Jane Wu,Geor... às arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06507.pdf

Perguntas Mais Profundas

How can the proposed approach be extended to handle a wider variety of objects beyond common household items?

The proposed approach can be extended to handle a wider variety of objects by incorporating a more diverse and extensive dataset that includes a broader range of object categories. By training the model on a dataset that encompasses a larger variety of objects, the model can learn to generalize better to unseen objects. Additionally, the retrieval process can be enhanced by leveraging more sophisticated language/vision models that have been trained on a wider array of objects. This would enable the model to recognize and retrieve a more extensive set of 3D object models based on textual descriptions provided by the language model.

How can the potential limitations of using large language/vision models for automated object recognition and retrieval be addressed in the context of hand-object reconstruction?

While large language/vision models offer significant capabilities for automated object recognition and retrieval, they come with potential limitations. One limitation is the reliance on the quality and diversity of the training data used to train these models. To address this limitation, it is essential to continuously update and diversify the training data to ensure that the models are exposed to a wide range of objects and scenarios. Additionally, incorporating mechanisms for fine-tuning the language/vision models specifically for the task of hand-object reconstruction can help improve their performance in this context. Moreover, integrating feedback loops that allow for human validation and correction of the retrieved 3D object models can help mitigate potential errors or inaccuracies in the automated retrieval process.

How can the insights from this work be applied to enable more natural and intuitive human-robot interaction scenarios involving dexterous manipulation of objects?

The insights from this work can be applied to enhance human-robot interaction scenarios involving dexterous manipulation of objects by improving the robot's ability to understand and interact with objects in a more human-like manner. By leveraging the learned hand-object interactions and 3D reconstruction capabilities, robots can better understand how humans manipulate objects and adapt their actions accordingly. This can lead to more intuitive and natural interactions between humans and robots, enabling robots to assist with tasks that require delicate object manipulation. Additionally, the automated object recognition and retrieval techniques developed in this work can be integrated into robotic systems to enable robots to autonomously identify and interact with a wide range of objects in various environments.