GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting
(2402.10259)Abstract
Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, leading to two significant challenges: 1) Difficulty in building multi-view consistency as images for matching are too few; 2) Partially omitted or highly compressed object information as view coverage is insufficient. To tackle these challenges, we propose GaussianObject, a framework to represent and render the 3D object with Gaussian splatting, that achieves high rendering quality with only 4 input images. We first introduce techniques of visual hull and floater elimination which explicitly inject structure priors into the initial optimization process for helping build multi-view consistency, yielding a coarse 3D Gaussian representation. Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined. We design a self-generating strategy to obtain image pairs for training the repair model. Our GaussianObject is evaluated on several challenging datasets, including MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction results from only 4 views and significantly outperforming previous state-of-the-art methods.
Overview
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The GaussianObject framework tackles the challenges of 3D reconstruction from sparse views by utilizing Gaussian splatting, achieving high-quality 3D objects with only four input images.
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It introduces a Gaussian repair model to refine initial coarse 3D reconstructions by fixing omitted or distorted object details, showcasing an innovative approach to sparse data.
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The framework contributes to the fields of differentiable point-based rendering and neural rendering, providing a solution for high-quality 3D reconstructions with minimal inputs and setting a new standard in rendering quality and efficiency.
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GaussianObject simplifies the process of capturing 3D datasets, impacting AR/VR, game development, and other fields by making high-quality 3D reconstruction more accessible and efficient.
GaussianObject: Achieving High-Quality 3D Reconstruction with Minimal Views
Introduction to GaussianObject Framework
3D reconstruction from sparse views presents significant challenges due to limited 3D information and difficulties in achieving multi-view consistency. The newly introduced GaussianObject framework innovatively addresses these challenges. The framework utilizes Gaussian splatting to reconstruct and render 3D objects from merely four input images. This approach, grounded in visual hull techniques for Gaussian initialization and the employment of a Gaussian repair model, significantly advances the state of sparse-view 3D object reconstruction.
Advancements in Sparse-View 3D Reconstruction
Foundational Techniques
GaussianObject leverages 3D Gaussian Splatting (3DGS) for its base representation, which excels in fast and explicit scene depiction. To counter the sparse data challenge, the framework embeds structure priors through visual hull and floater elimination techniques, enhancing the preliminary 3D representation. This initialization is crucial for dealing with the inherent information scarcity in sparse viewpoints.
Gaussian Repair Model
A novel aspect of GaussianObject is its Gaussian repair model, developed to refine the coarse 3D reconstruction by addressing omitted or distorted object details. The approach involves a diffusive repair mechanism, utilizing large diffusion models adapted for 2D to 3D context translations. The employment of self-generating strategies for training the model with adequate image pairs is particularly notable, underscoring the framework's innovative handling of sparse data.
Theoretical and Practical Implications
Relevance to Current Research
GaussianObject aligns with and contributes to ongoing research in differentiable point-based rendering and neural rendering for sparse view reconstruction. By offering a robust framework capable of dealing with extremely sparse setups, this work addresses a significant gap — achieving high-quality 3D reconstructions with minimal image inputs. The comparison with related methods such as DVGO, 3DGS, and various NeRF adaptations, positions GaussianObject as a leading approach in terms of rendering quality and efficiency.
Implications for 3D Vision Applications
From a practical standpoint, GaussianObject's ability to operate with limited inputs vastly simplifies the process of capturing 3D datasets, making high-quality 3D reconstruction more accessible and efficient. This has far-reaching implications for fields relying on 3D content, including AR/VR, game development, and beyond, potentially lowering the barriers to entry for creators and enhancing the user experience with more immersive content.
Future Directions and Conclusion
While GaussianObject presents a significant step forward, areas such as reliance on precise camera parameters, handling of extreme views, and color accuracy in reconstructions are identified for further improvement. Future developments might explore optimizing camera parameters in tandem with 3D reconstructions or advanced anti-aliasing techniques for enhanced visual outputs.
In summary, GaussianObject introduces a compelling framework for sparse-view 3D object reconstruction, combining structure-prior optimization with an innovative repair model to produce high-fidelity 3D representations from a minimal set of images. The framework not only advances the technical capabilities in the field of 3D vision but also opens new avenues for practical applications, promising to make high-quality 3D reconstruction more accessible and applicable across a variety of domains.