CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians with Dual Feature Fusion

• URL: http://arxiv.org/abs/2401.05925v3
• Date: Tue, 30 Jan 2024 12:46:04 GMT
• Title: CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians with Dual Feature Fusion
• Authors: Bin Dou, Tianyu Zhang, Yongjia Ma, Zhaohui Wang, Zejian Yuan
• Abstract summary: We propose a method for compact 3D-consistent scene segmentation at fast rendering speed with only RGB images input. Our model outperforms baselines on both semantic and panoptic zero-shot segmentation task.
• Score: 17.778755539808547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Compact and Swift Segmenting 3D Gaussians(CoSSegGaussians), a method for compact 3D-consistent scene segmentation at fast rendering speed with only RGB images input. Previous NeRF-based segmentation methods have relied on time-consuming neural scene optimization. While recent 3D Gaussian Splatting has notably improved speed, existing Gaussian-based segmentation methods struggle to produce compact masks, especially in zero-shot segmentation. This issue probably stems from their straightforward assignment of learnable parameters to each Gaussian, resulting in a lack of robustness against cross-view inconsistent 2D machine-generated labels. Our method aims to address this problem by employing Dual Feature Fusion Network as Gaussians' segmentation field. Specifically, we first optimize 3D Gaussians under RGB supervision. After Gaussian Locating, DINO features extracted from images are applied through explicit unprojection, which are further incorporated with spatial features from the efficient point cloud processing network. Feature aggregation is utilized to fuse them in a global-to-local strategy for compact segmentation features. Experimental results show that our model outperforms baselines on both semantic and panoptic zero-shot segmentation task, meanwhile consumes less than 10% inference time compared to NeRF-based methods. Code and more results will be available at https://David-Dou.github.io/CoSSegGaussians

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