GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting (2403.10242v2)

Abstract: 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.

• The paper introduces FDGaussian, a two-stage framework that integrates an orthogonal plane-based diffusion model with Gaussian splatting for enhanced 3D reconstruction.
• It employs an epipolar attention mechanism to boost multi-view consistency by effectively leveraging geometric correlations across generated views.
• Empirical results show superior performance in PSNR, SSIM, and LPIPS, reducing reconstruction time while improving visual fidelity compared to existing methods.

Overview of FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware Diffusion Model

The paper introduces FDGaussian, a two-stage framework dedicated to enhancing the process of 3D object reconstruction from single images. This work addresses two significant challenges that persist in current methods: the issue of multi-view inconsistency and the absence of geometric fidelity. The proposed approach leverages both geometric-aware techniques and advancements in diffusion models to synthesize consistent and high-fidelity multi-view images from single views, leading to the construction of high-quality 3D models.

The methodology consists of a generation stage that uses a diffusion model conditioned by an orthogonal plane decomposition mechanism, facilitating the extraction of 3D geometric features from 2D images. This process enhances the multi-view consistency during image synthesis. Subsequently, a reconstruction stage optimizes Gaussian Splatting through epipolar attention, integrating images from multiple viewpoints to yield visually coherent 3D reconstructions.

Methodological Innovations

• Geometric-aware Multi-view Generation: The authors extend upon the diffusion models by integrating an orthogonal plane decomposition mechanism. This mechanism disentangles 3D features efficiently and allows the model to predict novel views while preserving the geometric structure inherent in the input image.
• Epipolar Attention Mechanism: Introduced during the reconstruction stage, this mechanism harnesses the geometric correlation between different views, leading to enhanced multi-view consistency. By focusing on the epipolar constraints, the attention mechanism limits the search space for correspondences, thus facilitating more robust and efficient feature integration across views.
• Gaussian Divergent Significance (GDS): A noteworthy contribution of this paper is the introduction of GDS, a metric designed to optimize the split and clone operations in Gaussian Splatting. By considering the spatial correlation between Gaussian elements, unnecessary computational burdens are reduced, significantly speeding up the optimization process without compromising quality.

Empirical Results

The empirical assessment across datasets such as Objaverse and Google Scanned Objects showcases FDGaussian's ability to generate high-quality, multi-view consistent 3D reconstructions quantitatively outperforming existing methods such as Zero-1-to-3 and DreamGaussian. Metrics such as PSNR, SSIM, and LPIPS uniformly highlight the enhanced fidelity and consistency of reconstructions. Furthermore, the reduced reconstruction time offers practical advantages, demonstrating that the proposed innovations lead to both qualitative and computational improvements.

Implications and Future Research

The proposed FDGaussian framework illustrates a significant step forward in single-image-based 3D reconstruction, demonstrating potential applications in VR, AR, and robotics by improving visual realism and interaction quality. The innovations in multi-view consistency promise enhanced real-world applications where geometric accuracy and visual coherence are critical.

Future research might build upon this work by exploring adaptive view generation and extending the methodology to complex scene reconstructions beyond single objects. Additionally, fine-tuning the integration with text-to-image models suggests future work to streamline the text-to-3D pipeline, potentially augmenting applications in digital content creation and interactive design environments.

In conclusion, FDGaussian presents a compelling advancement in integrating diffusion models with geometric-aware techniques to address critical challenges in single-image 3D reconstruction. The incorporation of epipolar attention and optimization enhancements underscores the potential for more efficient, accurate, and widespread applications of this technology.