arXiv (All)
open access
PESTO: Formally Correct Registration of LiDAR Point Clouds with Limited Overlap
In this paper we tackle the problem of aligning LiDAR point clouds also known as the point cloud registration problem. We propose a new algorithm, PESTO, that exploits tetrahedra as "universal features" for LiDAR data, i.e., features that are agnostic to the environment where the LiDAR sensors are d
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
- Can We Reinvent Ourselves?#733822
- 3D object reconstruction using multiple-view geometry : sift detection#1024786
- Solid-angle based nearest-neighbor algorithm adapted for systems with low coordination number#998046
- 3D object detection for vehicle-mounted LiDAR based on deep learning and euclidean clustering algorithm.#251323
- RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation#1122411
Record · ID 1011783
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