Riccardo Spezialetti

Aerospace Engineering · 2017

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Aerospace Engineering — 19 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Geology — 6 citations into it

Whom Riccardo Spezialetti trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David G. Lowe
2. Samuele Salti
3. Yu Zhong

Papers from Riccardo Spezialetti's perspective

Ordered by how strongly the researchers Riccardo Spezialetti trusts endorse each paper. Same corpus, a different order for every researcher.

1. Distinctive Image Features from Scale-Invariant Keypoints
2. Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
3. Random Forests
4. ORB: An efficient alternative to SIFT or SURF
5. Good features to track
6. BRISK: Binary Robust invariant scalable keypoints (Vision)
7. Fast Point Feature Histograms (FPFH) for 3D registration
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes
10. Speeded-Up Robust Features (SURF)

Their papers

• Learning to Detect Good 3D Keypoints (2017) — 49 citations