PePESeg3D: Perception Prior Enhances Multi-Scale Segmentation for 3D Gaussian Splatting

While 3D Gaussian Splatting (3DGS) has established new standards for high-fidelity 3D scene modeling, interpreting massive, unstructured Gaussian primitives into meaningful geospatial entities remains a critical challenge for remote sensing and urban applications. Existing 2D-to-3D distillation methods suffer from projection ambiguities, geometric inconsistencies, and computational bottlenecks when applied to complex, large-scale topographies. Furthermore, current benchmarks lack natively aligned 2D-3D ground truth tailored for splatting-based representations. To address these limitations, we propose \textit{PointGauss}, a unified 3D-native framework for robust semantic parsing and instance segmentation. By treating Gaussian primitives as unstructured point sets, PointGauss leverages Point Transformer V3 (PTv3) to extract scale-invariant geometric features directly from Gaussian attributes, effectively resolving projection ambiguities. To ensure scalability in building-scale scenes, we introduce an adaptive region-of-interest cropping strategy and an instance-aware distance-constrained rasterization pipeline for pixel-level, view-consistent projection. Additionally, we present \textbf{SplatSeg-360}, the first rigorous cross-scale benchmark tailored for 3DGS, comprising 32 complex scenes with full 360$^\circ$ coverage and over 6,300 natively aligned 2D-3D masks. Extensive experiments demonstrate that PointGauss operates in real-time and achieves state-of-the-art performance. Notably, it attains approximately 90% 3D-mIoU in large-scale building scenarios and roughly 80% 2D-mIoU in view-consistent 2D instance segmentation, outperforming baseline methods by 16%. (\href{https://github.com/hbycswt/pointgauss#}{Code})

Reliable instance-level scene understanding is a fundamental prerequisite for object-level interactions and high-fidelity 3D representations. While current methods often leverage 2D foundation segmentation models to obtain these priors, their 2D-centric design typically yields fragmented masks and inconsistent predictions across different views. To address these issues, we propose a novel framework that produces consistent 2D instance masks to guide the optimization of 3D Gaussian Splatting (3DGS) feature fields. Our framework consists of three main stages. (1) Multi-Cue Extraction that generates synergistic semantic, geometric, and structural priors from input images. (2) Multi-Cue-Guided Mask Merging process that consolidates fragmented masks using a composite merge score derived from semantic, depth, and edge cues. (3) Cross-View Mask Matching that establishes globally consistent identity assignments across all viewpoints. By transforming viewpoint-specific segments into coherent 3D primitives, our approach enables stable 3D instance segmentation and effective downstream editing tasks. Experiments demonstrate that our method significantly improves cross-view consistency and segmentation stability over existing baselines while maintaining high-fidelity photometric reconstruction.

Recent advances in 3D Gaussian Splatting (3DGS) have enabled highly efficient and photorealistic novel view synthesis. However, segmenting objects accurately in 3DGS remains challenging due to the discrete nature of Gaussian representations, which often leads to aliasing and artifacts at object boundaries. In this paper, we introduce NG-GS, a novel framework for high-quality object segmentation in 3DGS that explicitly addresses boundary discretization. Our approach begins by automatically identifying ambiguous Gaussians at object boundaries using mask variance analysis. We then apply radial basis function (RBF) interpolation to construct a spatially continuous feature field, enhanced by multi-resolution hash encoding for efficient multi-scale representation. A joint optimization strategy aligns 3DGS with a lightweight NeRF module through alignment and spatial continuity losses, ensuring smooth and consistent segmentation boundaries. Extensive experiments on NVOS, LERF-OVS, and ScanNet benchmarks demonstrate that our method achieves state-of-the-art performance, with significant gains in boundary mIoU. Code is available at https://github.com/BJTU-KD3D/NG-GS.