HiCo-GS：面向八叉树高斯溅射的分层上下文聚合与几何一致性
HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting
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中文总结 AI 辅助
HiCo-GS是结合跨层级上下文聚合与深度-法向量几何一致性正则化的高保真重建框架，在多城市数据集上实现了最优渲染质量与清晰几何。
中文摘要 AI 辅助
基于八叉树的锚点高斯溅射已成为城市尺度新视图合成的可扩展表示，其中多级锚点自适应地捕捉从粗粒度建筑结构到细粒度建筑细节的场景内容。然而，我们发现现有方法存在一个根本局限：跨层级特征隔离，即每一层的锚点特征独立优化，无层级间通信，导致建筑立面颜色漂移和纹理区域过度平滑。我们提出HiCo-GS，一个高保真重建框架，包含两个互补模块。跨层级上下文聚合（CLCA）利用八叉树的空间包含结构，将每一层的上下文向量聚合成父-自身-子三元组，通过带残差连接的轻量级多层感知机（MLP）融合，实现双向分层先验注入：粗粒度结构先验向下传递以指导细粒度锚点，细粒度细节统计反馈以防止过度平滑，计算开销可忽略。深度-法向量几何一致性（DNGC）正则化通过alpha加权一致性损失强制渲染法向量与深度导出法向量一致，辅以带渐进预热的边缘感知平滑损失，利用城市几何中普遍存在的强平面先验抑制浮伪影。我们进一步推出China-Pagoda数据集，包含8座中国古代佛塔，每座超过1200张图像，具有密集装饰性雕刻、弯曲多层屋檐和重复细粒度纹理。在Mill19、UrbanScene3D、MatrixCity和China-Pagoda上的大量实验表明，HiCo-GS在真实世界和合成城市场景中实现了最先进的渲染质量和显著更清晰的几何结构。
英文摘要
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.
发表机构
- Northwestern Polytechnical University（西北工业大学）
机构由 AI 辅助整理，请以论文原文为准。
