Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction

• URL: http://arxiv.org/abs/2412.06234v2
• Date: Thu, 12 Dec 2024 06:17:36 GMT
• Title: Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
• Authors: Seungtae Nam, Xiangyu Sun, Gyeongjin Kang, Younggeun Lee, Seungjun Oh, Eunbyung Park,
• Abstract summary: We propose Generative Densification, an efficient and generalizable method to densify Gaussians generated by feed-forward models. We show that our method outperforms state-of-the-art approaches with comparable or smaller model sizes.
• Score: 6.273357335397336
• License:
• Abstract: 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.

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