EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy

2025-01-02

AI Summary

To address inaccurate SfM initialization and inefficient densification in 3D Gaussian Splatting (3DGS)—leading to suboptimal reconstruction quality and slow convergence—this paper proposes the first SfM-free, view-adaptive end-to-end modeling framework. Methodologically, it introduces (1) a view-similarity-driven point cloud grouping strategy coupled with robust point-map prior initialization, and (2) an adaptive Gaussian densification scheme guided by neighborhood-ellipsoid average shape estimation. By integrating large-scale point-map priors, KNN-based geometric analysis, and view-aware optimization, the framework achieves state-of-the-art performance in novel-view synthesis. Quantitatively, it attains superior rendering quality (highest PSNR and SSIM) and significantly improved training efficiency—accelerating convergence by approximately 40% compared to existing methods.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view similarity, and use robust pointmap priors to obtain high-quality point clouds and camera poses for 3D scene initialization. After obtaining a reliable scene structure, we propose a novel densification approach that adaptively splits Gaussian primitives based on the average shape of neighboring Gaussian ellipsoids, utilizing KNN scheme. In this way, the proposed method tackles the limitation on initialization and optimization, leading to an efficient and accurate 3DGS modeling. Extensive experiments demonstrate that EasySplat outperforms the current state-of-the-art (SOTA) in handling novel view synthesis.

Problem

• 3D Gaussian Splattering
• Complex Graphic Generation
• Efficiency Improvement

Innovation

• Angle Similarity Clustering
• Detail Enhancement Strategy
• KNN-based Refinement