ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via
  Gaussian Splatting
Authors: name: Ruijie Zhu, name: Mulin Yu, name: Linning Xu, name: Lihan Jiang, name: Yixuan Li, name: Tianzhu Zhang, name: Jiangmiao Pang, name: Bo Dai

3D Gaussian Splatting is renowned for its high-fidelity reconstructions and
real-time novel view synthesis, yet its lack of semantic understanding limits
object-level perception. In this work, we propose ObjectGS, an object-aware
framework that unifies 3D scene reconstruction with semantic understanding.
Instead of treating the scene as a unified whole, ObjectGS models individual
objects as local anchors that generate neural Gaussians and share object IDs,
enabling precise object-level reconstruction. During training, we dynamically
grow or prune these anchors and optimize their features, while a one-hot ID
encoding with a classification loss enforces clear semantic constraints. We
show through extensive experiments that ObjectGS not only outperforms
state-of-the-art methods on open-vocabulary and panoptic segmentation tasks,
but also integrates seamlessly with applications like mesh extraction and scene
editing. Project page: https://ruijiezhu94.github.io/ObjectGS_page