  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.
