NG-GS: 基于NeRF引导的3D高斯点云分割
NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation
AI总结：
本文提出NG-GS框架，通过掩码方差分析识别边界模糊的高斯点，利用RBF插值和多分辨率哈希编码构建连续特征场，结合NeRF模块实现高质量3D高斯点云分割。
摘要：
近年来，3D高斯点云（3DGS）在高效且逼真地生成新视角方面取得了进展。然而，由于高斯表示的离散性，3DGS中物体的准确分割仍具挑战性，这导致物体边界处出现混叠和伪影。本文提出NG-GS，一种新的框架，用于在3DGS中实现高质量的物体分割，明确解决边界离散化问题。我们的方法首先通过掩码方差分析自动识别物体边界处的模糊高斯点。然后应用径向基函数（RBF）插值构建空间连续的特征场，通过多分辨率哈希编码进行高效多尺度表示。联合优化策略通过对齐和空间连续性损失将3DGS与轻量级NeRF模块对齐，确保分割边界平滑且一致。在NVOS、LERF-OVS和ScanNet基准测试中，我们的方法在边界mIoU上取得了显著提升。代码可在https://github.com/BJTU-KD3D/NG-GS获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
