Recently, 3D Gaussian Splatting (3DGS) has shown encouraging performance for open vocabulary scene understanding tasks. However, previous methods cannot distinguish 3D instance-level information, which usually predicts a heatmap between the scene feature and text query. In this paper, we propose PanoGS, a novel and effective 3D panoptic open vocabulary scene understanding approach. Technically, to learn accurate 3D language features that can scale to large indoor scenarios, we adopt the pyramid tri-plane to model the latent continuous parametric feature space and use a 3D feature decoder to regress the multi-view fused 2D feature cloud. Besides, we propose language-guided graph cuts that synergistically leverage reconstructed geometry and learned language cues to group 3D Gaussian primitives into a set of super-primitives. To obtain 3D consistent instance, we perform graph clustering based segmentation with SAM-guided edge affinity computation between different super-primitives. Extensive experiments on widely used datasets show better or more competitive performance on 3D panoptic open vocabulary scene understanding. Project page: \href{this https URL}{this https URL}.
View on arXiv@article{zhai2025_2503.18107,
title={ PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding },
author={ Hongjia Zhai and Hai Li and Zhenzhe Li and Xiaokun Pan and Yijia He and Guofeng Zhang },
journal={arXiv preprint arXiv:2503.18107},
year={ 2025 }
}