ObjectGS：基于高斯溅射的对象感知场景重建与场景理解

摘要

3D高斯泼溅技术以其高保真重建和实时新视角合成而著称，但其缺乏语义理解能力，限制了物体层面的感知。在本研究中，我们提出了ObjectGS，一个具备物体感知能力的框架，它将3D场景重建与语义理解相统一。不同于将场景视为整体，ObjectGS将各个物体建模为局部锚点，这些锚点生成神经高斯分布并共享物体ID，从而实现精确的物体级重建。训练过程中，我们动态地扩展或修剪这些锚点，并优化其特征，同时采用独热编码与分类损失相结合的方式，强化明确的语义约束。通过大量实验，我们证明ObjectGS不仅在开放词汇和全景分割任务上超越了现有最先进方法，还能无缝集成于网格提取和场景编辑等应用之中。项目页面：https://ruijiezhu94.github.io/ObjectGS_page

English

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

ObjectGS：基于高斯溅射的对象感知场景重建与场景理解

ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting

摘要

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