用伪细节增强3D重建，解决缩放时的模糊扭曲问题。
Beyond Existance: Fulfill 3D Reconstructed Scenes with Pseudo Details
- 结合扩散模型与多尺度训练生成伪真实数据
- 显著减少缩放时的高斯点畸变和模糊现象
- 适合需要精细3D场景重建的研究者
3D高斯溅射（3D-GS）在多种场景下实现了高质量且快速的3D重建。现有工作多聚焦于模型结构优化以压缩数据或减少缩放时的伪影，却常忽略训练采样不足这一根本问题。在放大视角下，由于高斯原型的膨胀限制及特定尺度训练样本不足，其表现常显混乱与失真。为此，本文提出一种新训练方法，利用扩散模型与多尺度训练生成伪真实数据，不仅有效缓解了膨胀与放大伪影，还使重建场景在原有基础上获得精确细节。该方法在多个基准测试中达到当前最优性能，拓展了3D重建对训练数据集外场景的泛化能力。
原文摘要 · Abstract (English)
The emergence of 3D Gaussian Splatting (3D-GS) has significantly advanced 3D reconstruction by providing high fidelity and fast training speeds across various scenarios. While recent efforts have mainly focused on improving model structures to compress data volume or reduce artifacts during zoom-in and zoom-out operations, they often overlook an underlying issue: training sampling deficiency. In zoomed-in views, Gaussian primitives can appear unregulated and distorted due to their dilation limitations and the insufficient availability of scale-specific training samples. Consequently, incorporating pseudo-details that ensure the completeness and alignment of the scene becomes essential. In this paper, we introduce a new training method that integrates diffusion models and multi-scale training using pseudo-ground-truth data. This approach not only notably mitigates the dilation and zoomed-in artifacts but also enriches reconstructed scenes with precise details out of existing scenarios. Our method achieves state-of-the-art performance across various benchmarks and extends the capabilities of 3D reconstruction beyond training datasets.
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