用生成式方法高效补全3D高斯点，提升细节还原能力
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
- 通过上采样特征生成细粒度高斯点，单次前向传播完成
- 在物体与场景重建任务中显著提升细节表现力
- 适合需要高质量通用3D重建的工业与科研应用
通用前馈高斯模型在稀疏视角3D重建中已取得显著进展，但受限于高斯点数量，难以表征高频细节。尽管可借鉴单场景3D高斯溅射（3D-GS）的稠密化策略，但该方法不适用于泛化场景。本文提出生成式稠密化（Generative Densification），一种高效且可泛化的高斯点稠密化方法。不同于3D-GS逐次分裂原始高斯参数的方式，本方法从前馈模型中上采样特征表示，并在单次前向传播中生成对应细粒度高斯点，利用嵌入先验知识增强泛化性。在物体级和场景级重建任务上的实验表明，该方法以相当或更小的模型规模超越现有最优方法，在细节呈现上取得显著提升。
原文摘要 · Abstract (English)
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details due to the limited number of Gaussians. While the densification strategy used in per-scene 3D Gaussian splatting (3D-GS) optimization can be adapted to the feed-forward models, it may not be ideally suited for generalized scenarios. In this paper, we propose Generative Densification, an efficient and generalizable method to densify Gaussians generated by feed-forward models. Unlike the 3D-GS densification strategy, which iteratively splits and clones raw Gaussian parameters, our method up-samples feature representations from the feed-forward models and generates their corresponding fine Gaussians in a single forward pass, leveraging the embedded prior knowledge for enhanced generalization. Experimental results on both object-level and scene-level reconstruction tasks demonstrate that our method outperforms state-of-the-art approaches with comparable or smaller model sizes, achieving notable improvements in representing fine details.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。