标题： CLoD-GS：通过3D高斯点云的连续细节层次 显示英文标题

标题： CLoD-GS: Continuous Level-of-Detail via 3D Gaussian Splatting

摘要： 细节层次（LoD）是实时计算机图形学中一种基本技术，用于在保持视觉保真度的同时管理复杂场景的渲染成本。传统上，LoD是通过离散层次（DLoD）实现的，其中在不同距离下切换模型的多个不同版本。然而，这种长期存在的范式存在两个主要缺点：它需要为多个模型副本占用大量存储空间，并在转换过程中导致令人不安的视觉“弹跳”伪影，从而降低用户体验。我们认为，新兴的3D高斯点云（3DGS）技术的显式、基于原始对象的特性，使得一种更理想的范式成为可能：连续LoD（CLoD）。CLoD方法能够在单一统一模型内实现平滑、无缝的质量缩放，从而避免DLOD的核心问题。为此，我们引入了CLoD-GS，这是一个将连续LoD机制直接集成到3DGS表示中的框架。我们的方法为每个高斯原始对象引入了一个可学习的距离相关衰减参数，该参数根据视角接近程度动态调整其不透明度。这使得可以逐步且平滑地过滤不重要的原始对象，从而在一个模型内有效地创建一个连续的细节范围。为了使该模型在所有距离下都具有鲁棒性，我们引入了一种虚拟距离缩放机制和一种新颖的从粗到精的训练策略，并结合了渲染点数正则化。我们的方法不仅消除了离散方法的存储开销和视觉伪影，还减少了最终模型的原始对象数量和内存占用。大量实验表明，CLoD-GS能够从单一模型中实现平滑、质量可扩展的渲染，在广泛的性能目标下都能提供高保真结果。 显示英文摘要

摘要： Level of Detail (LoD) is a fundamental technique in real-time computer graphics for managing the rendering costs of complex scenes while preserving visual fidelity. Traditionally, LoD is implemented using discrete levels (DLoD), where multiple, distinct versions of a model are swapped out at different distances. This long-standing paradigm, however, suffers from two major drawbacks: it requires significant storage for multiple model copies and causes jarring visual ``popping" artifacts during transitions, degrading the user experience. We argue that the explicit, primitive-based nature of the emerging 3D Gaussian Splatting (3DGS) technique enables a more ideal paradigm: Continuous LoD (CLoD). A CLoD approach facilitates smooth, seamless quality scaling within a single, unified model, thereby circumventing the core problems of DLOD. To this end, we introduce CLoD-GS, a framework that integrates a continuous LoD mechanism directly into a 3DGS representation. Our method introduces a learnable, distance-dependent decay parameter for each Gaussian primitive, which dynamically adjusts its opacity based on viewpoint proximity. This allows for the progressive and smooth filtering of less significant primitives, effectively creating a continuous spectrum of detail within one model. To train this model to be robust across all distances, we introduce a virtual distance scaling mechanism and a novel coarse-to-fine training strategy with rendered point count regularization. Our approach not only eliminates the storage overhead and visual artifacts of discrete methods but also reduces the primitive count and memory footprint of the final model. Extensive experiments demonstrate that CLoD-GS achieves smooth, quality-scalable rendering from a single model, delivering high-fidelity results across a wide range of performance targets.