arXiv (All)
open access
Hybrid-PCGC: Hybrid Point Cloud Geometry Compression Bridging Generalization-based Model and Implicit Neural Representation
Efficient point cloud geometry compression (PCGC) is essential for 3D sensing applications such as autonomous driving and digital twins. Hybrid methods combine the generalization of learned models with the adaptability of Implicit Neural Representations (INRs), but existing schemes often use complex
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Displacement Geometry Captures Platonic Shared Reality Across Models and Modalities#1050392
- P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing#926277
- Direct Topology Tracking in Continuous Implicit Models#1000123
- Collision-Aware and Observation-Aligned Object-Centric Scene Reconstruction from Point Cloud#1141304
- Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling#1186724
Record · ID 1422816
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive