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.

翻译：细节层次（LoD）是实时计算机图形学中的一项基础技术，用于在保持视觉保真度的同时管理复杂场景的渲染开销。传统上，LoD采用离散层次（DLoD）实现，通过在不同距离切换多个不同版本的模型来工作。然而，这一长期存在的范式存在两大缺陷：需要大量存储空间保存多个模型副本，且切换时会产生刺眼的视觉"突变"伪影，从而降低用户体验。我们认为，新兴的三维高斯泼溅（3DGS）技术所具备的显式基元特性能够实现更理想的范式——连续细节层次（CLoD）。CLoD方法可在单一统一模型内实现平滑无缝的质量缩放，从而规避DLoD的核心问题。为此，我们提出CLoD-GS框架，该框架直接将连续LoD机制融入3DGS表征。本方法为每个高斯基元引入一个可学习的距离相关衰减参数，该参数根据视点距离动态调整其不透明度。这能渐进式平滑过滤次要基元，在单一模型中有效构建连续细节光谱。为使模型在所有距离下均具鲁棒性，我们引入了虚拟距离缩放机制与新颖的从粗到精训练策略，并辅以渲染点计数正则化。本方法不仅消除了离散方法的存储开销与视觉伪影，还降低了最终模型的基元数量与内存占用。大量实验表明，CLoD-GS能够通过单一模型实现平滑可伸缩的渲染，在广泛性能目标范围内均能呈现高保真结果。