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

• URL: http://arxiv.org/abs/2508.00259v1
• Date: Fri, 01 Aug 2025 01:56:54 GMT
• Title: PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting
• Authors: Wentao Sun, Hanqing Xu, Quanyun Wu, Dedong Zhang, Yiping Chen, Lingfei Ma, John S. Zelek, Jonathan Li,
• Abstract summary: We introduce PointGauss, a novel point cloud-guided framework for real-time multi-object segmentation in Gaussian Splatting representations.<n>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.
• Score: 18.042769428774676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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