IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations (2412.12083v3)

Abstract: Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs, while still struggling with inherent ambiguities between lighting and material. On the other hand, learning-based approaches leverage rich material priors from existing 3D object datasets but face challenges with maintaining multi-view consistency. In this paper, we introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations. Our method achieves accurate and multi-view consistent estimation on surface normals and material properties. This is made possible through a novel cross-view, cross-domain attention module and an illumination-augmented, view-adaptive training strategy. Additionally, we introduce ARB-Objaverse, a new dataset that provides large-scale multi-view intrinsic data and renderings under diverse lighting conditions, supporting robust training. Extensive experiments demonstrate that IDArb outperforms state-of-the-art methods both qualitatively and quantitatively. Moreover, our approach facilitates a range of downstream tasks, including single-image relighting, photometric stereo, and 3D reconstruction, highlighting its broad applications in realistic 3D content creation.

• The paper introduces IDArb, a diffusion model for intrinsic decomposition that separates image components across arbitrary input views and illumination.
• IDArb uses novel cross-view and cross-domain attention mechanisms to improve consistency in decompositions across different perspectives.
• The model demonstrates superior performance over state-of-the-art methods and improves downstream applications like material editing and image relighting.

An Expert Analysis of "IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations"

The paper presents a sophisticated model, IDArb, which addresses the long-standing problem of intrinsic decomposition in the field of computer vision. Intrinsic decomposition involves the separation of an image into its underlying components, such as albedo, surface normals, metallic, and roughness properties, which are independent of environmental lighting and viewing angles. This task is critical for numerous applications, including photorealistic rendering, relighting, and 3D reconstruction, all of which benefit from decoupled material and geometric information.

Key Innovations

1. Diffusion-Based Model: IDArb leverages a diffusion-based approach to achieve intrinsic decomposition. Unlike traditional optimization methods that require extensive computation time and large datasets of multi-view images, the diffusion model can efficiently handle arbitrary numbers of input views and varying lighting conditions, enabling rapid inference.
2. Cross-View and Cross-Domain Attention: The model employs a novel attention mechanism that operates across different views and intrinsic components, enhancing the consistency of decompositions across various perspectives. This innovation significantly mitigates the common issue of inconsistencies found in other learning-based methods when applied to multi-view settings.
3. Custom Dataset Creation: A noteworthy contribution is the introduction of ARB-Objaverse, a large-scale dataset that offers diverse multi-view and multi-illumination scenarios. This dataset enhances the robustness of IDArb, providing a rich set of training data to better understand and generalize complex scene variations.
4. Adapted Training Strategies: The paper discusses an illumination-augmented training strategy that improves the model's robustness to complex lighting conditions. Additionally, the use of a view-adapted training scheme allows for effective handling of both single and multi-view inputs, ensuring the model's flexibility and adaptability to different scenarios.

Experimental Observations

IDArb exhibits superior performance compared to state-of-the-art methods in both qualitative and quantitative evaluations. The authors report improvements in albedo, normal, metallic, and roughness predictions with impressive precision, validated by comprehensive experiments on synthetic and real-world datasets. Notably, the model's ability to generalize from synthetic training data to real-world examples is a substantial advancement, indicating strong potential for practical applications.

Implications and Future Directions

The implications of this research extend beyond the immediate improvements in intrinsic decomposition. By providing a more accurate and efficient method, IDArb facilitates enhanced downstream applications, including material editing, image relighting, and photometric stereo. Furthermore, the model's capacity to serve as a regularizing prior for optimization-based inverse rendering approaches suggests its potential in improving the results of geometry and material estimation in challenging situations.

Future developments could explore the integration of real-world data through unsupervised techniques to further enhance the generalization capabilities of the model. Additionally, optimizing the cross-view attention mechanism to efficiently manage high-resolution inputs and dense view scenarios may unlock further opportunities for practical applications in dynamic and complex environments.

Conclusion

In summary, the IDArb model presents a significant advancement in the domain of intrinsic decomposition, offering stability, efficiency, and consistency across varying illumination and perspectives. This work not only pushes the boundaries of current methodologies but also lays a foundation for future innovations in realistic 3D content creation and manipulation, fostering a deeper understanding of the physical world through improved computer vision techniques.