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GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting

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arxiv 2403.10242 v2 pith:W6ARZAEQ submitted 2024-03-15 cs.CV

Qijun Feng , Zhen Xing , Zuxuan Wu , Yu-Gang Jiang This is my paper
classification cs.CV
keywords imagesgaussiangeogs3dacrossdiffusionduringgeometricnovel
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We introduce GeoGS3D, a novel two-stage framework for reconstructing detailed 3D objects from single-view images. Inspired by the success of pre-trained 2D diffusion models, our method incorporates an orthogonal plane decomposition mechanism to extract 3D geometric features from the 2D input, facilitating the generation of multi-view consistent images. During the following Gaussian Splatting, these images are fused with epipolar attention, fully utilizing the geometric correlations across views. Moreover, we propose a novel metric, Gaussian Divergence Significance (GDS), to prune unnecessary operations during optimization, significantly accelerating the reconstruction process. Extensive experiments demonstrate that GeoGS3D generates images with high consistency across views and reconstructs high-quality 3D objects, both qualitatively and quantitatively.

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Cited by 4 Pith papers

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    F-RNG generates relightable 3D Gaussian splatting assets from sparse views via feed-forward distillation of IDM priors into an LRM without retraining the base models.

  3. A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation

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    A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.

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