Splat in Place: Point-Pinned Gaussians for 3D Point Cloud Segmentation

3D Gaussian Splatting (3DGS) provides rich geometric and appearance cues for point cloud segmentation. However, existing Gaussian-based methods rely on rendering-oriented primitives and establish point-Gaussian correspondence only after reconstruction, causing misaligned semantic evidence and limiting the use of Gaussian anisotropic support for neighborhood reasoning. To address these limitations, we introduce Splat In Place (SIP), a segmentation-oriented reconstruction framework that establishes point-Gaussian correspondence during reconstruction. SIP proposes Point Pinned Reconstruction, which initializes one Gaussian per point and preserves its identity, enabling direct access to point-aligned Gaussian attributes. Based on this representation, Joint Support Aggregation (JSA) exploits Gaussian support as a structural metric for adaptive neighbor aggregation, while Addressable Gaussian Recurrent Field (AGRF) further refines point features with Gaussian-aware relations. Experiments on ScanNet, S3DIS, and KITTI-360 demonstrate that SIP improves segmentation performance over existing methods, especially under sparse input conditions, validating the effectiveness of point-indexed Gaussian representations for 3D semantic understanding.