Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images
This addresses challenges in 3D reconstruction for applications like computer vision and graphics, though it appears incremental as it builds on existing Gaussian splatting methods.
The paper tackles 3D reconstruction from unconstrained image collections by introducing Micro-macro Wavelet-based Gaussian Splatting (MW-GS), which disentangles scene representations into global, refined, and intrinsic components, resulting in state-of-the-art rendering performance.
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.