Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images (2501.14231v1)

Abstract: 3D reconstruction from unconstrained image collections presents substantial challenges due to varying appearances and transient occlusions. In this paper, we introduce Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel approach designed to enhance 3D reconstruction by disentangling scene representations into global, refined, and intrinsic components. The proposed method features two key innovations: Micro-macro Projection, which allows Gaussian points to capture details from feature maps across multiple scales with enhanced diversity; and Wavelet-based Sampling, which leverages frequency domain information to refine feature representations and significantly improve the modeling of scene appearances. Additionally, we incorporate a Hierarchical Residual Fusion Network to seamlessly integrate these features. Extensive experiments demonstrate that MW-GS delivers state-of-the-art rendering performance, surpassing existing methods.

• The paper introduces Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel method for 3D reconstruction from unconstrained images that integrates global, refined, and intrinsic scene components.
• Key technical innovations include Micro-macro Projection for adaptive multi-scale detail capture and Wavelet-based Sampling using DWT for improved texture modeling and artifact reduction.
• Experimental results show MW-GS achieves state-of-the-art performance on key metrics like PSNR, SSIM, and LPIPS, enhancing detail and texture fidelity for applications like VR/AR and autonomous driving.

Overview of "Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images"

The paper presents Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel methodology designed to address the complexities involved in 3D reconstruction from unconstrained image sets. The approach seeks to disentangle and optimize scene representation by integrating global, refined, and intrinsic components. The authors identify significant limitations in existing methodologies, especially under dynamic and real-world conditions where traditional approaches often encounter artifacts and performance degradation.

Key Innovations

The MW-GS approach is characterized by two primary innovations:

1. Micro-macro Projection (MP): This technique enhances Gaussian point sampling to accommodate detail capture across diverse scales. It employs an adaptive jitter projection within narrow conical frustums, allowing Gaussian points to optimally sample fine-grained textures.
2. Wavelet-based Sampling (WS): This leverages frequency domain information through the Discrete Wavelet Transform (DWT) to improve modeling of scene textures. The method allows multi-resolution sampling that maintains intricate features while ensuring diversity, thus addressing issues related to blurriness and artifacts commonly encountered in traditional 3DGS and NeRF-based approaches.

Technical Contributions

The MW-GS framework supports robust modeling of dynamically varying scenes by incorporating a Hierarchical Residual Fusion Network (HRFN), which integrates global, refined, and intrinsic features. This structured feature decomposition and integration enhance scene fidelity and mitigate the impact of transient occlusions.

1. Structured Feature Decomposition: It decomposes Gaussian features into global, refined, and intrinsic categories, capturing scene-wide characteristics and local textures effectively.
2. Hierarchical Residual Fusion Network (HRFN): Facilitates cohesive integration of these decomposed features by employing a multi-layer approach, thereby improving fidelity and coherence in 3D reconstructions.
3. Handling Transient Objects: The framework includes visibility map regularization, which helps manage the influence of temporally inconsistent elements across image captures.

Experimental Insights

The MW-GS framework has demonstrated state-of-the-art performance across multiple datasets, significantly outperforming previous methods. Quantitative improvements were realized particularly in PSNR, SSIM, and LPIPS metrics. Notable among these is the superior performance in capturing detailed textures and overall appearance fidelity. Furthermore, the integration of MP and WS enhances the resolution of structural details and texture representation in 3D scenes.

Implications and Future Prospects

The improvements presented by MW-GS offer potential advancements in applications ranging from virtual and augmented reality to automated driving and 3D content creation. While the method shows exceptional potential, future work could explore advanced transient masking techniques to further enhance performance, and diffuse model integration to address areas with sparse texture details.

Overall, MW-GS provides a significant step forward in rendering quality and adaptability, showcasing advancements in capturing multi-scale and high-frequency details essential for realistic 3D reconstruction. This could prompt new research directions focused on further integrating adaptive sampling techniques and frequency domain analysis into 3DGS methods.