SA-GS: Semantic-Aware Gaussian Splatting for Large Scene Reconstruction with Geometry Constrain

• URL: http://arxiv.org/abs/2405.16923v2
• Date: Tue, 28 May 2024 09:57:56 GMT
• Title: SA-GS: Semantic-Aware Gaussian Splatting for Large Scene Reconstruction with Geometry Constrain
• Authors: Butian Xiong, Xiaoyu Ye, Tze Ho Elden Tse, Kai Han, Shuguang Cui, Zhen Li,
• Abstract summary: We propose a novel method, named SA-GS, for fine-grained 3D geometry reconstruction using semantic-aware 3D Gaussian Splats. We leverage prior information stored in large vision models such as SAM and DINO to generate semantic masks. We extract the point cloud using a novel probability density-based extraction method, transforming Gaussian Splats into a point cloud crucial for downstream tasks.
• Score: 43.80789481557894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the emergence of Gaussian Splats, recent efforts have focused on large-scale scene geometric reconstruction. However, most of these efforts either concentrate on memory reduction or spatial space division, neglecting information in the semantic space. In this paper, we propose a novel method, named SA-GS, for fine-grained 3D geometry reconstruction using semantic-aware 3D Gaussian Splats. Specifically, we leverage prior information stored in large vision models such as SAM and DINO to generate semantic masks. We then introduce a geometric complexity measurement function to serve as soft regularization, guiding the shape of each Gaussian Splat within specific semantic areas. Additionally, we present a method that estimates the expected number of Gaussian Splats in different semantic areas, effectively providing a lower bound for Gaussian Splats in these areas. Subsequently, we extract the point cloud using a novel probability density-based extraction method, transforming Gaussian Splats into a point cloud crucial for downstream tasks. Our method also offers the potential for detailed semantic inquiries while maintaining high image-based reconstruction results. We provide extensive experiments on publicly available large-scale scene reconstruction datasets with highly accurate point clouds as ground truth and our novel dataset. Our results demonstrate the superiority of our method over current state-of-the-art Gaussian Splats reconstruction methods by a significant margin in terms of geometric-based measurement metrics. Code and additional results will soon be available on our project page.

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