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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting
TL;DR
HiCo-GS introduces a novel framework for octree Gaussian splatting that enhances city-scale view synthesis by addressing cross-level feature isolation. It achieves superior rendering quality and cleaner geometry through hierarchical context aggregation and geometric consistency techniques.
✦ Why It Matters
Implement HiCo-GS to enhance your urban scene rendering projects with improved visual fidelity.
Key Takeaways
- Utilize HiCo-GS for high-fidelity urban scene rendering.
- Incorporate Cross-Level Context Aggregation to improve feature integration.
- Apply Depth-Normal Geometric Consistency to reduce rendering artifacts.
- Explore the China-Pagoda dataset for testing complex architectural details.
How It Works
HiCo-GS employs Cross-Level Context Aggregation (CLCA) to facilitate communication between different levels of the octree structure. This allows coarse-level features to inform fine-level details, preventing over-smoothing while enhancing overall scene fidelity.
Additionally, Depth-Normal Geometric Consistency (DNGC) is used to align rendered normals with those derived from depth data, ensuring a more accurate representation of urban geometry.