GBR: Generative Bundle Refinement for High-fidelity Gaussian Splatting and Meshing (2412.05908v1)

Abstract: Gaussian splatting has gained attention for its efficient representation and rendering of 3D scenes using continuous Gaussian primitives. However, it struggles with sparse-view inputs due to limited geometric and photometric information, causing ambiguities in depth, shape, and texture. we propose GBR: Generative Bundle Refinement, a method for high-fidelity Gaussian splatting and meshing using only 4-6 input views. GBR integrates a neural bundle adjustment module to enhance geometry accuracy and a generative depth refinement module to improve geometry fidelity. More specifically, the neural bundle adjustment module integrates a foundation network to produce initial 3D point maps and point matches from unposed images, followed by bundle adjustment optimization to improve multiview consistency and point cloud accuracy. The generative depth refinement module employs a diffusion-based strategy to enhance geometric details and fidelity while preserving the scale. Finally, for Gaussian splatting optimization, we propose a multimodal loss function incorporating depth and normal consistency, geometric regularization, and pseudo-view supervision, providing robust guidance under sparse-view conditions. Experiments on widely used datasets show that GBR significantly outperforms existing methods under sparse-view inputs. Additionally, GBR demonstrates the ability to reconstruct and render large-scale real-world scenes, such as the Pavilion of Prince Teng and the Great Wall, with remarkable details using only 6 views.

• The paper introduces GBR, a framework that achieves high-fidelity 3D reconstruction and meshing from sparse input views by refining Gaussian splatting.
• GBR integrates techniques like Neural Bundle Adjustment and a Generative Depth Refinement module to improve geometry accuracy and detail.
• Experimental results show GBR outperforms prior methods in geometric accuracy and novel view synthesis quality, particularly with limited input data.

Generative Bundle Refinement for High-fidelity Gaussian Splatting and Meshing

The paper "Generative Bundle Refinement for High-fidelity Gaussian Splatting and Meshing" presents GBR, an advanced methodological framework aimed at enhancing the quality of 3D scene reconstruction using sparse-view inputs. The research primarily focuses on overcoming the limitations associated with Gaussian splatting methods, which traditionally require dense-view inputs to achieve satisfactory geometric accuracy and mesh fidelity.

3D Gaussian splatting has gained popularity due to its ability to facilitate high-quality 3D reconstructions with reduced computational and memory overheads compared to traditional voxel or mesh-based approaches. However, these methods typically necessitate a substantial number of input views to maintain geometric and photometric consistency. The GBR framework innovatively combines various state-of-the-art techniques to tackle challenges such as geometry accuracy, mesh fidelity, and limited supervision in sparse-view scenarios without the necessity of pre-calibrated camera poses.

Key Contributions:

• Neural Bundle Adjustment (Neural-BA): By integrating neural network-based geometry estimation with traditional bundle adjustment techniques, the GBR approach significantly enhances the accuracy of camera parameter estimations and 3D point cloud generation. Specifically, it uses the DUSt3R network to produce dense initializations from unposed image pairs and employs a dual filtering approach to refine point matches, yielding a robust starting point for subsequent Gaussian splatting tasks.
• Generative Depth Refinement Module: This module employs a diffusion model to iteratively refine depth maps, enhancing depth scale consistency and incorporating high-resolution RGB information to boost geometric details. The integration of scale-consistent depth algorithms ensures that the depth refinement is both accurate and efficient, ultimately providing reliable input for the Gaussian splatting optimization process.
• Multimodal Loss Function: The novel loss function designed in this work incorporates multiple supervision components, such as depth consistency, normal consistency, synthesized pseudo-view supervision, and photometric losses. This comprehensive supervision schema addresses the sparse-view limitation, enabling more accurate and robust optimization of Gaussian primitives.

Experimental validation across the DTU, TNT, and MIP datasets demonstrates that the GBR framework outperforms existing methods in both geometric accuracy and novel view synthesis quality, even with extremely sparse view inputs. The proposed approach is particularly noteworthy for its capability to reconstruct large-scale real-world scenes, such as the Pavilion of Prince Teng and the Great Wall, based on only a handful of images.

Implications and Future Directions:

The implications of this research are significant both theoretically and practically. From a theoretical standpoint, the integration of neural networks with traditional geometric methods opens up new avenues for enhancing sparse-view reconstruction accuracy. Practically, the ability to produce high-fidelity reconstructions from sparse input data could greatly benefit fields such as virtual reality, autonomous navigation, and robotics, where complete image datasets are often unattainable.

Future research could explore the integration of advanced foundation models and polarized imaging data to handle challenging contexts like specular reflections and transparent surfaces, which currently pose limitations. Furthermore, extending the GBR framework to accommodate 4D reconstruction might involve developing motion-aware modules, thereby expanding its applicability to dynamic scenes.

Overall, GBR: Generative Bundle Refinement substantiates a promising advancement in the domain of 3D scene reconstruction, establishing a foundation for further research into high-fidelity 3D rendering and meshing with minimal input data requirements.