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

📄 arXiv: 2510.09997v1 📥 PDF

作者: Zhigang Cheng, Mingchao Sun, Yu Liu, Zengye Ge, Luyang Tang, Mu Xu, Yangyan Li, Peng Pan

分类: cs.GR, cs.CV

发布日期: 2025-10-11

💡 一句话要点

提出CLoD-GS以解决离散细节层次的存储与视觉问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)

关键词: 细节层次 3D高斯点云 实时渲染 计算机图形学 视觉效果

📋 核心要点

  1. 现有的离散细节层次方法需要大量存储并导致视觉切换不流畅，影响用户体验。
  2. CLoD-GS框架通过3D高斯点云表示实现连续细节层次，允许在单一模型中平滑调整细节。
  3. 实验结果显示，CLoD-GS在多个性能目标下实现了高保真度渲染，减少了原语数量和内存占用。

📝 摘要（中文）

细节层次（LoD）是实时计算机图形学中管理复杂场景渲染成本的基本技术。传统的离散细节层次（DLoD）方法存在存储需求高和视觉切换不流畅等缺陷。本文提出的CLoD-GS框架通过引入连续细节层次（CLoD）机制，利用3D高斯点云表示，允许在单一模型中实现平滑的质量缩放。该方法通过可学习的距离依赖衰减参数动态调整高斯原语的不透明度，从而有效创建细节的连续谱。实验表明，CLoD-GS在多个性能目标下实现了高保真度的平滑渲染。

🔬 方法详解

问题定义：本文旨在解决传统离散细节层次（DLoD）方法在存储和视觉切换方面的不足，DLoD需要多个模型副本并导致视觉“跳变”现象。

核心思路：通过引入连续细节层次（CLoD）机制，利用3D高斯点云表示，允许在单一模型中实现细节的平滑调整，避免了DLoD的核心问题。

技术框架：CLoD-GS框架集成了连续细节层次机制，采用可学习的距离依赖衰减参数，动态调整高斯原语的不透明度。整体流程包括模型训练、虚拟距离缩放机制和粗到细的训练策略。

关键创新：最重要的创新在于引入了可学习的衰减参数，使得每个高斯原语的透明度根据视点距离动态调整，从而实现细节的连续谱。与DLoD的离散切换相比，CLoD-GS提供了更为流畅的视觉体验。

关键设计：模型训练中采用了渲染点数正则化的策略，确保模型在不同距离下的鲁棒性，同时设计了虚拟距离缩放机制以增强训练效果。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，CLoD-GS在多个性能目标下实现了高保真度的平滑渲染，相较于传统DLoD方法，显著减少了原语数量和内存占用，提升了渲染效率和视觉质量。

🎯 应用场景

CLoD-GS的研究成果在实时渲染、游戏开发和虚拟现实等领域具有广泛的应用潜力。通过提供高效的细节管理，该方法能够提升用户体验，减少资源消耗，推动高保真度图形渲染技术的发展。

📄 摘要（原文）

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.