OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation
(2404.15891)Abstract
Recent advancements in 3D reconstruction technologies have paved the way for high-quality and real-time rendering of complex 3D scenes. Despite these achievements, a notable challenge persists: it is difficult to precisely reconstruct specific objects from large scenes. Current scene reconstruction techniques frequently result in the loss of object detail textures and are unable to reconstruct object portions that are occluded or unseen in views. To address this challenge, we delve into the meticulous 3D reconstruction of specific objects within large scenes and propose a framework termed OMEGAS: Object Mesh Extraction from Large Scenes Guided by GAussian Segmentation. OMEGAS employs a multi-step approach, grounded in several excellent off-the-shelf methodologies. Specifically, initially, we utilize the Segment Anything Model (SAM) to guide the segmentation of 3D Gaussian Splatting (3DGS), thereby creating a basic 3DGS model of the target object. Then, we leverage large-scale diffusion priors to further refine the details of the 3DGS model, especially aimed at addressing invisible or occluded object portions from the original scene views. Subsequently, by re-rendering the 3DGS model onto the scene views, we achieve accurate object segmentation and effectively remove the background. Finally, these target-only images are used to improve the 3DGS model further and extract the definitive 3D object mesh by the SuGaR model. In various scenarios, our experiments demonstrate that OMEGAS significantly surpasses existing scene reconstruction methods. Our project page is at: https://github.com/CrystalWlz/OMEGAS
Overview
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OMEGAS is a new framework developed by researchers from Beijing University of Posts and Telecommunications for reconstructing detailed 3D meshes of specific objects within large scenes, integrating advanced segmentation and mesh extraction technologies.
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The methodology of OMEGAS includes initial segmentation using SAM and 3D Gaussian Splatting, enhancement of model details through diffusion models, and precise mesh extraction using refined models and the SuGaR model.
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Tests on various datasets have shown that OMEGAS outperforms traditional scene reconstruction methods, offering better texture detail and robustness against occlusions, with potential applications in virtual reality, gaming, and augmented reality.
OMEGAS: Enhanced Object Mesh Extraction Guided by Gaussian Segmentation
Overview of Research
OMEGAS, the proposed framework by Wang, Zhou, and Yin from Beijing University of Posts and Telecommunications, addresses critical challenges in reconstructing detailed 3D meshes of specific objects within large scenes. With the growing complexity of scene reconstruction required in fields such as virtual reality and robotics, traditional methods often struggle with preserving high-fidelity textures and reconstructing obscured or invisible parts of objects. OMEGAS integrates multiple advanced methodologies, including Segment Anything Model (SAM), 3D Gaussian Splatting (3DGS), large-scale diffusion models, and SuGaR model, to segment scenes, refine object details, and extract precise object meshes.
Methodology
Segmentation and Initial Model Construction
The initial phase involves the use of SAM integrated with 3D Gaussian Splatting for preliminary segmentation. This segmentation includes a novel approach to consistent mask production across different views using identity vectors. These vectors are then utilized to classify segments and enforce consistency through additional layers and loss functions. This initial setup forms a rudimentary 3D model of the target object.
Detail Refinement via Diffusion Models
After establishing a base model, OMEGAS employs large-scale diffusion priors to enhance texture details and reconstruct partially visible or invisible object segments. Here, random camera renderings curated via Stable Diffusion techniques optimize the model based on actual image data, focusing on photographic fidelity and detail completeness.
Mesh Extraction
In the final step, the refined 3DGS model is re-rendered and segmented to derive precise target masks and clear background data. These re-renderings, coupled with original scene views, support the SuGaR model in executing the final mesh extraction, ensuring high-quality and detailed 3D object mesh outputs.
Experiments and Results
The framework has been tested across various datasets and scenes, demonstrating significant advancements over existing methodologies. In scenarios like Tanks and Temples dataset, OMEGAS has shown superior mesh texture detail and occlusion robustness in object reconstruction compared to methods like SuGaR alone or in combination with NeRF-based models.
Implications and Future Work
The introduction of OMEGAS provides a promising solution to the longstanding challenge of high-fidelity object-specific reconstruction in complex 3D scenes. Its capability to integrate segmentation, detail refinement, and mesh extraction in one framework seamlessly contributes practically to fields like augmented reality, gaming, and large-scale 3D data generation. Future developments could explore the efficiency of the model under different scene complexities and further integration with real-time processing systems for dynamic applications.
Conclusion
The study successfully demonstrates a methodological and practical advancement in the niche of 3D reconstruction, specifically in extracting detailed and accurate meshes of specific objects within large scenes. By innovatively combining existing tools and introducing new segmentation and optimization techniques, OMEGAS sets a new standard for mesh reconstruction that could significantly impact various technology sectors.
GitHub - CrystalWlz/OMEGAS (37 stars)