Reconstructing Hand-Held Objects in 3D from Images and Videos (2404.06507v3)

Abstract: Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is small relative to all possible objects. With these insights in mind, we present a scalable paradigm for hand-held object reconstruction that builds on recent breakthroughs in large language/vision models and 3D object datasets. Given a monocular RGB video, we aim to reconstruct hand-held object geometry in 3D, over time. In order to obtain the best performing single frame model, we first present MCC-Hand-Object (MCC-HO), which jointly reconstructs hand and object geometry given a single RGB image and inferred 3D hand as inputs. Subsequently, we prompt a text-to-3D generative model using GPT-4(V) to retrieve a 3D object model that matches the object in the image(s); we call this alignment Retrieval-Augmented Reconstruction (RAR). RAR provides unified object geometry across all frames, and the result is rigidly aligned with both the input images and 3D MCC-HO observations in a temporally consistent manner. Experiments demonstrate that our approach achieves state-of-the-art performance on lab and Internet image/video datasets. We make our code and models available on the project website: https://janehwu.github.io/mcc-ho

• The paper presents MCC-HO, a joint reconstruction method that infers both hand and object geometry from a single RGB image using transformer architectures.
• It introduces a novel Retrieval-Augmented Reconstruction (RAR) leveraging GPT-4(V) for semantic matching and alignment of 3D models via ICP.
• Experimental results show state-of-the-art performance with improved Chamfer distances and F-scores, demonstrating scalability for computer vision and robotics applications.

Reconstructing Hand-Held Objects in 3D: A Scalable Paradigm

The paper, "Reconstructing Hand-Held Objects in 3D," presents a novel approach addressing the challenging problem of 3D reconstruction of hand-held objects from monocular RGB images. The difficulty in this domain arises from the occlusions introduced by the manipulating hand and the limited number of image pixels dedicated to the object in hand. Despite these limitations, the authors exploit two significant anchors in this setup: the availability of estimated 3D hand data, which can provide cues regarding the object’s location and scale, and the restricted set of manipulanda common in interactions, providing a focused domain for modeling.

Key Contributions and Methodology

The authors propose a method termed the MCC-Hand-Object (MCC-HO), which builds on recent advancements in large language and vision models and comprehensive 3D object datasets. This model performs a joint reconstruction of the hand and object geometry from a single RGB image coupled with an inferred 3D hand model. The approach leans on the capabilities of transformer architectures for encoding and decoding the input data into a neural implicit function representing occupancy, color, and segmentation labels.

A crucial innovation presented is the Retrieval-Augmented Reconstruction (RAR) technique. Here, the authors leverage GPT-4(V) for semantic understanding and recognition of the object in the image to match it with a 3D object database, using a text-to-3D generative approach, specifically Genie. This 3D model is then aligned with the network-inferred geometry using rigid alignment techniques like Iterative Closest Point (ICP).

Results and Performance

Experimental evaluations of MCC-HO demonstrate state-of-the-art performance on various datasets, including lab and internet datasets, achieving superior results in terms of Chamfer distance and F-scores as compared to existing approaches. The authors highlight significant improvements over baseline models, like traditional MCC when adapted for hand-object interactions, thereby underlining the importance of conditioning on hand geometry for such tasks.

Furthermore, through the use of RAR, the authors showcase a method for autonomously obtaining 3D labels for in-the-wild images, proving the scalability and practicality of their approach. This facet of the paper has strong implications for creating extensive, annotated datasets for widespread applications in computer vision and robotics.

Practical and Theoretical Implications

Practically, this work has far-reaching implications for the fields of augmentation reality, virtual reality, and human-computer interaction where understanding and modeling of human-object interactions are imperative. Theoretically, it opens avenues for enhancing the efficiency and capabilities of transformer-based models in vision tasks and showcases the integration of LLMs in visual perception pipelines.

Speculations on Future Developments

Future directions might involve further augmentation of the model to handle a broader range of objects beyond common household items or enhancing its robustness under varying image capture conditions, such as lighting or occlusions. Additionally, tighter integration and optimization of retrieval methods leveraging larger LLMs and improved generative models could enhance precision in 3D object reconstruction.

In conclusion, this paper marks a significant step forward in automated hand-object interaction recognition, leveraging synergies between advanced LLMs, vision transformers, and 3D dataset availability, demonstrating strong potential for further advancements in automated 3D modeling and interaction understanding.