VoroNet: A scalable object network based on Voronoi tessellations
By Olivier Beaumont, Anne-Marie Kermarrec, Loris Marchal, Etienne Riviere. 2007
72 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Graph Learning, 3D Vision & Pose
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Yury Malkov
- Dmitry A. Yashunin
- Olivier Beaumont
- Etienne Riviere
- Anne-Marie Kermarrec
- Jimmy J. Lin
- Sebastian Riedel
- Sheng-Chieh Lin
In Graph Learning
In 3D Vision & Pose
Citations in the corpus
- Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, methodology): “This idea has inspired the development of many K-NNS and K-ANNS algorithms based on the navigation effect [37], [38], [39], [40].”
- Peer to Peer Multidimensional Overlays: Approximating Complex Structures (2007, methodology): “For example, maintaining a Voronoï tessellation as in [4] involves a high overhead when the dimension is larger than 2 [6], and is prone to high levels of calculation degeneracy.”
- A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search (2021, methodology): “Recently, machine learning (ML)-based methods are proposed to improve the speedup vs recall trade-off of the algorithms [14, 59, 78].”
- Scalable Distributed Algorithm for Approximate Nearest Neighbor Search Problem in High Dimensional General Metric Spaces (2012, methodology): “MAAN [24], SCRAP[25] , Mercury [26] support multi-dimensional range queries and Voronet [27] is p2p network oriented to search nearest neighbor in E2 based on Voronoi tessellation [8].”
- Approximate Nearest Neighbor Search Small World Approach (2011, background): “Originally Voronet was envisioned as a p2p network, where every node has coordinates in E2.”