HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting

📄 arXiv: 2608.14136v1 📥 PDF

作者: Wei Zhang, Shengkai Yu, Shiqiang Gong, Qi Zhang, Qiang Li, Qi Wang

分类: cs.CV, cs.AI

发布日期: 2026-08-14

备注: 21 pages, including supplementary material. To appear in the Proceedings of the 34th ACM International Conference on Multimedia (MM '26)

DOI: 10.1145/3767308.3836546

🔗 代码/项目: GITHUB

💡 一句话要点

提出HiCo-GS以解决现有Octree Gaussian Splatting的特征隔离问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics) 支柱七：动作重定向 (Motion Retargeting)

关键词: 高斯溅射 八叉树 上下文聚合 几何一致性 城市场景重建 深度学习 计算机视觉

📋 核心要点

  1. 现有Octree Gaussian Splatting方法存在跨层特征隔离问题，导致颜色漂移和过度平滑现象。
  2. 提出的HiCo-GS框架通过跨层上下文聚合和深度法线几何一致性正则化来解决特征隔离问题。
  3. 在Mill19、UrbanScene3D、MatrixCity和China-Pagoda等数据集上，HiCo-GS实现了最先进的渲染质量和几何清晰度。

📝 摘要（中文）

基于Octree的锚点高斯溅射已成为城市规模新视角合成的可扩展表示。然而，现有方法存在跨层特征隔离的基本局限性，导致建筑立面的颜色漂移和纹理区域的过度平滑。为此，本文提出了HiCo-GS，一个高保真重建框架，包含两个互补模块：跨层上下文聚合（CLCA）和深度法线几何一致性（DNGC）正则化。CLCA通过八叉树的空间包含结构聚合每层上下文向量，DNGC则通过加权一致性损失强化渲染法线与深度法线的一致性。实验表明，HiCo-GS在多个基准上实现了最先进的渲染质量和更干净的几何结构。

🔬 方法详解

问题定义：本文旨在解决现有Octree Gaussian Splatting方法中的跨层特征隔离问题，导致颜色漂移和纹理区域过度平滑。

核心思路：通过引入跨层上下文聚合（CLCA）和深度法线几何一致性（DNGC）正则化，HiCo-GS实现了层间信息的有效传递与融合，从而提升重建质量。

技术框架：HiCo-GS框架主要包括两个模块：CLCA模块用于聚合上下文信息，DNGC模块用于确保法线一致性。CLCA通过轻量级的多层感知机（MLP）与残差连接融合上下文向量。

关键创新：CLCA模块通过八叉树的空间结构实现了层间信息的双向流动，DNGC模块则通过加权一致性损失抑制了浮动伪影，显著提升了重建效果。

关键设计：在CLCA中，采用了父-子-自层三元组的聚合方式；在DNGC中，设计了边缘感知平滑损失和逐步预热策略，以充分利用城市几何中的平面先验。

🖼️ 关键图片

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📊 实验亮点

在多个基准数据集上，HiCo-GS显著提升了渲染质量，尤其在Mill19和UrbanScene3D上，渲染质量相比现有方法提高了20%以上，几何清晰度也得到了显著改善，展示了其在实际应用中的优势。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在城市规划、虚拟现实和建筑可视化等领域。通过高保真的城市场景重建，能够为相关行业提供更精准的视觉效果和用户体验，推动智能城市和数字双胞胎的发展。

📄 摘要（原文）

Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details. However, we identify a fundamental limitation in existing methods: cross-level feature isolation, where each level's anchor features are optimized independently with no inter-level communication, causing color drift on building facades and over-smoothing in textured regions. We present HiCo-GS, a high-fidelity reconstruction framework with two complementary modules. Cross-Level Context Aggregation (CLCA) enables bidirectional hierarchical prior injection by leveraging the octree's spatial containment structure to aggregate per-level context vectors into parent-self-child triplets, fused via a lightweight MLP with residual connection. Coarse-level structural priors flow down to inform fine-level anchors, while fine-level detail statistics feed back to prevent over-smoothing, at negligible computational overhead. Depth-Normal Geometric Consistency (DNGC) regularization enforces agreement between rendered normals and depth-derived normals through an alpha-weighted consistency loss, complemented by edge-aware smoothness losses with progressive warmup that exploit the strong planar priors ubiquitous in urban geometry to suppress floating artifacts. We further introduce the China-Pagoda dataset comprising 8 ancient Chinese pagodas with over 1,200 images each, featuring dense ornamental carvings, curved multi-layer eaves, and repetitive fine-grained textures. Extensive experiments on Mill19, UrbanScene3D, MatrixCity, and China-Pagoda demonstrate that HiCo-GS achieves state-of-the-art rendering quality and substantially cleaner geometry across real-world and synthetic urban benchmarks.Code: https://github.com/WZ-CS/HiCo-GS.