While 3D Gaussian Splatting enables high-quality real-time rendering, existing Gaussian-based frameworks for 3D semantic segmentation still face significant challenges in boundary recognition accuracy. To address this, we propose a novel 3DGS-based framework named GradiSeg, incorporating Identity Encoding to construct a deeper semantic understanding of scenes. Our approach introduces two key modules: Identity Gradient Guided Densification (IGD) and Local Adaptive K-Nearest Neighbors (LA-KNN). The IGD module supervises gradients of Identity Encoding to refine Gaussian distributions along object boundaries, aligning them closely with boundary contours. Meanwhile, the LA-KNN module employs position gradients to adaptively establish locality-aware propagation of Identity Encodings, preventing irregular Gaussian spreads near boundaries. We validate the effectiveness of our method through comprehensive experiments. Results show that GradiSeg effectively addresses boundary-related issues, significantly improving segmentation accuracy without compromising scene reconstruction quality. Furthermore, our method's robust segmentation capability and decoupled Identity Encoding representation make it highly suitable for various downstream scene editing tasks, including 3D object removal, swapping and so on.
虽然3D高斯散射（3D Gaussian Splatting）能够实现高质量的实时渲染，但基于高斯的3D语义分割框架在边界识别准确性方面仍面临显著挑战。为此，我们提出了一种新颖的基于3DGS的框架，名为GradiSeg，通过引入身份编码（Identity Encoding）来构建对场景的更深层次语义理解。 我们的方法包含两个关键模块：身份梯度引导密化模块（Identity Gradient Guided Densification, IGD）和局部自适应K近邻模块（Local Adaptive K-Nearest Neighbors, LA-KNN）。IGD模块利用身份编码的梯度信息对高斯分布进行监督，使其在物体边界处更加精确，与边界轮廓对齐。与此同时，LA-KNN模块通过位置梯度自适应地建立身份编码的局部传播，避免边界附近出现不规则的高斯扩散。 我们通过全面实验验证了方法的有效性。结果表明，GradiSeg在解决边界相关问题方面表现卓越，大幅提升了分割准确性，同时不影响场景重建质量。此外，我们方法的强大分割能力及其解耦的身份编码表示，使其在各种下游场景编辑任务中具有很高的适用性，包括3D物体移除、交换等操作。