Abstract
We observe that Gaussians exhibit distinct roles and characteristics analogous to traditional artistic techniques -- like how artists first sketch outlines before filling in broader areas with color, some Gaussians capture high-frequency features such as edges and contours, while others represent broader, smoother regions analogous to brush strokes that add volume and depth. Based on this observation, we propose a hybrid representation that categorizes Gaussians into (i) Sketch Gaussians, which represent high-frequency, boundary-defining features, and (ii) Patch Gaussians, which cover low-frequency, smooth regions. This semantic separation naturally enables layered progressive streaming, where the compact Sketch Gaussians establish the structural skeleton before Patch Gaussians incrementally refine volumetric detail. In this work, we extend our previous method to arbitrary 3D scenes by proposing a novel hierarchical adaptive categorization framework that operates directly on the 3DGS representation. Our approach employs multi-criteria density-based clustering, combined with adaptive quality-driven refinement. This method eliminates dependency on external 3D line primitives while ensuring optimal parametric encoding effectiveness. Our comprehensive evaluation across diverse scenes, including both man-made and natural environments, demonstrates that our method achieves up to 1.74 dB improvement in PSNR, 6.7% in SSIM, and 41.4% in LPIPS at equivalent model sizes compared to uniform pruning baselines. For indoor scenes, our method can maintain visual quality with only 0.5\% of the original model size. This structure-aware representation enables efficient storage, adaptive streaming, and rendering of high-fidelity 3D content across bandwidth-constrained networks and resource-limited devices.
Abstract (translated)
我们观察到，高斯函数（Gaussians）表现出类似于传统艺术技法的角色和特征——就像艺术家在用色块填充之前会先画出轮廓一样，一些高斯函数捕捉到了高频特性如边缘和轮廓，而另一些则代表了更广泛、更平滑的区域，类似于为物体增加体积感与深度的笔触。基于这一观察，我们提出了一种混合表示方法，将高斯函数分为两类：(i) 轮廓高斯（Sketch Gaussians），用于表示高频特征和边界定义特性；(ii) 区块高斯（Patch Gaussians），则覆盖低频、平滑的区域。这种语义上的分离自然地实现了分层渐进式流媒体处理，即首先使用紧凑型轮廓高斯建立结构骨架，随后区块高斯逐步细化体积细节。 在此研究中，我们扩展了先前的方法以适应任意3D场景，并提出了一个新颖的层次自适应分类框架，该框架直接应用于三维高斯混合（3DGS）表示。我们的方法采用多标准密度聚类结合自适应质量驱动精化策略，这种方法消除了对外部3D线性原语的依赖并确保了最优参数编码效率。 我们对多样化的场景进行了全面评估，包括人造和自然环境，在相同模型大小的情况下，与均匀剪枝基线相比，我们的方法在PSNR（峰值信噪比）上提高了高达1.74 dB，在SSIM（结构相似性指数）上提高了6.7%，在LPIPS（感知损失指标）上降低了41.4%。对于室内场景，我们所提出的方法仅使用原始模型大小的0.5%就能保持视觉质量。 这种具备结构意识的表示方式能够高效地存储、适应流媒体处理以及在网络带宽有限和设备资源受限的情况下渲染高保真度的3D内容。
URL
https://arxiv.org/abs/2601.05394
