Fast GPU-based locality sensitive hashing for k-nearest neighbor computation

By Jia Pan, Dinesh Manocha. 2011

122 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, ML Systems & Hardware, Algorithms & Complexity, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Minyi Guo
2. Michael Zollhöfer
3. Matthias Nießner
4. Shahram Izadi
5. Xing Xie

In Computer Vision and Pattern Recognition

1. Matthias Nießner
2. Michael Zollhöfer
3. Marc Stamminger
4. Christian Theobalt
5. Shahram Izadi
6. Luc Van Gool
7. Jitendra Malik
8. Angela Dai

In Signal Processing

1. Marc Stamminger
2. Michael Zollhöfer
3. Matthias Nießner

Citations in the corpus

• Billion-Scale Similarity Search with GPUs (2017, neutral): “There are many implementations of similarity search on GPUs, but mostly with binary codes [36], small datasets [44], or exhaustive search [14, 40, 41].”
• Real-time 3D reconstruction at scale using voxel hashing (2013, background): “However, if this happens, we append a linked list entry, filling up other free spots in the next available buckets.”
• Efficient Selection Algorithm for Fast k-NN Search on GPUs (2015, background): “Agarwal et al. [2] estimate that pairwise matching in a particular 3D reconstruction task, which uses k-NN extensively, would take 11.5 days with 500 processor cores working on a dataset of 100,000 images.”