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GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
Created by
Haebom
Author
Ken Deng, Yunhan Yang, Jingxiang Sun, Xihui Liu, Yebin Liu, Ding Liang, Yan-Pei Cao
Outline
DetailGen3D is a generative model designed to address the problem of latent space lacking fine geometric details due to the computational overhead of existing 3D generative models. It directly models the transformation from coarse to fine shapes through data-dependent flows in the latent space, thereby avoiding the computational overhead of large-scale 3D generative models. A token-matching strategy maintains accurate spatial correspondences during the refinement process, preserving global structure while synthesizing local details. By designing training data tailored to the characteristics of coarse shapes generated by various 3D generation and reconstruction techniques (from single-view to sparse multi-view inputs), it effectively enhances shapes. Extensive experiments demonstrate that DetailGen3D achieves high-fidelity geometric detail synthesis while maintaining efficient training.
Takeaways, Limitations
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Takeaways:
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We present a novel method to address the computational cost of existing 3D generative models and effectively add fine geometric details by utilizing data-dependent flows within latent space.
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Token matching strategy enables synthesis of local details while maintaining global structure.
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Ensure generality applicable to outputs from various 3D creation and reconstruction methods.
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Generating high-fidelity results through efficient training.
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Limitations:
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The paper lacks specific references to Limitations or future research directions.
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It may be optimized only for certain types of 3D data (needs verification through further experiments).
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Performance may vary depending on the characteristics of the training data.
