Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation

• URL: http://arxiv.org/abs/2401.05925v4
• Date: Sun, 28 Jul 2024 02:40:29 GMT
• Title: Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation
• Authors: Bin Dou, Tianyu Zhang, Zhaohui Wang, Yongjia Ma, Zejian Yuan,
• Abstract summary: Zero-shot neural scene segmentation serves as an effective way for scene understanding. Existing models, especially the efficient 3D Gaussian-based methods, struggle to produce compact segmentation results. Our work proposes the Feature Unprojection and Fusion module as the segmentation field. We show that our model surpasses baselines on zero-shot semantic segmentation task, improving by 10% mIoU over the best baseline.
• Score: 16.57158278095853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot neural scene segmentation, which reconstructs 3D neural segmentation field without manual annotations, serves as an effective way for scene understanding. However, existing models, especially the efficient 3D Gaussian-based methods, struggle to produce compact segmentation results. This issue stems primarily from their redundant learnable attributes assigned on individual Gaussians, leading to a lack of robustness against the 3D-inconsistencies in zero-shot generated raw labels. To address this problem, our work, named Compact Segmented 3D Gaussians (CoSegGaussians), proposes the Feature Unprojection and Fusion module as the segmentation field, which utilizes a shallow decoder generalizable for all Gaussians based on high-level features. Specifically, leveraging the learned Gaussian geometric parameters, semantic-aware image-based features are introduced into the scene via our unprojection technique. The lifted features, together with spatial information, are fed into the multi-scale aggregation decoder to generate segmentation identities for all Gaussians. Furthermore, we design CoSeg Loss to boost model robustness against 3D-inconsistent noises. Experimental results show that our model surpasses baselines on zero-shot semantic segmentation task, improving by ~10% mIoU over the best baseline. Code and more results will be available at https://David-Dou.github.io/CoSegGaussians.

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