arXiv (All)
open access
Reducing the Gap Between Spatial and Space-Filling Curve Neighbors in 3D Point Cloud Segmentation
As a lightweight and highly accurate method for nearest-neighbor search in 3D point clouds, approaches based on space-filling curves (SFC) have been proposed. However, due to the inherent properties of SFC, a gap arises between proximity relationships along the SFC and those in the original Euclidea
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
- Deep learning-based auto-contouring for organs at risk in three-dimensional image-guided brachytherapy for cervical cancer and endometrial cancer.#3759
- Sequence-Informed Geometric Evaluation of RNA 3D Structures#997643
- Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos#819236
- Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos#809244
- Applications of computational homology to 3D morphology transitions (High Performance Algorithms for Computational Science and Their Applications)#1346171
Record · ID 1345353
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