Generative Densification: Learning to Densify Gaussians for
  High-Fidelity Generalizable 3D Reconstruction
Authors: name: Seungtae Nam, name: Xiangyu Sun, name: Gyeongjin Kang, name: Younggeun Lee, name: Seungjun Oh, name: Eunbyung Park

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.