标题： PointGauss：点云引导的高斯泼溅多目标分割 显示英文标题

标题： PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

摘要： 我们引入了PointGauss，一种新颖的点云引导框架，用于高斯点云表示中的实时多目标分割。 与现有方法相比，我们的方法在初始化时间较长和多视角一致性有限方面有所改进，通过点云分割驱动的流程直接解析高斯原始数据，实现了高效的3D分割。 关键创新在于两个方面：(1) 基于点云的高斯原始数据解码器，可在1分钟内生成3D实例掩码，(2) GPU加速的2D掩码渲染系统，确保多视角一致性。 大量实验表明，与之前最先进的方法相比有显著提升，在多视角mIoU上性能提高了1.89至31.78%，同时保持卓越的计算效率。 为了解决当前基准的局限性（单一目标焦点、不一致的3D评估、小规模和部分覆盖），我们提出了DesktopObjects-360，一个用于辐射场中3D分割的新颖全面数据集，具有以下特点：(1) 复杂的多目标场景，(2) 全局一致的2D注释，(3) 大规模训练数据（超过27千个2D掩码），(4) 完全的360{\deg }覆盖，以及(5) 3D评估掩码。 显示英文摘要

摘要： We introduce PointGauss, a novel point cloud-guided framework for real-time multi-object segmentation in Gaussian Splatting representations. Unlike existing methods that suffer from prolonged initialization and limited multi-view consistency, our approach achieves efficient 3D segmentation by directly parsing Gaussian primitives through a point cloud segmentation-driven pipeline. The key innovation lies in two aspects: (1) a point cloud-based Gaussian primitive decoder that generates 3D instance masks within 1 minute, and (2) a GPU-accelerated 2D mask rendering system that ensures multi-view consistency. Extensive experiments demonstrate significant improvements over previous state-of-the-art methods, achieving performance gains of 1.89 to 31.78% in multi-view mIoU, while maintaining superior computational efficiency. To address the limitations of current benchmarks (single-object focus, inconsistent 3D evaluation, small scale, and partial coverage), we present DesktopObjects-360, a novel comprehensive dataset for 3D segmentation in radiance fields, featuring: (1) complex multi-object scenes, (2) globally consistent 2D annotations, (3) large-scale training data (over 27 thousand 2D masks), (4) full 360{\deg} coverage, and (5) 3D evaluation masks.