R2D2: Reliable and Repeatable Detector and Descriptor

By Jérôme Revaud, César Roberto De Souza, Martin Humenberger, Philippe Weinzaepfel. Neural Information Processing Systems, 2019

477 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/36208a48f89c45ea4e51af47eaee93f88430cedf

How researchers see this paper

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

1. Ming-Hsuan Yang
2. Varun Jampani
3. Junhwa Hur
4. Deqing Sun
5. Charles Herrmann
6. Junyi Zhang
7. Luisa Polania Cabrera
8. Martin Danelljan

Citations in the corpus

• A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence (2023, background): “Deep models [15, 40, 45, 52, 66, 71] learn effective features for dense correspondence that can be robust to photometric and geometric changes such as rotation, scaling, or perspective transformation.”
• DUSt3R: Geometric 3D Vision Made Easy (2023, unknown): “…for 3D reconstruction consists in assembling the fruits of decades of advances in various sub-fields such as keypoint detection [26, 28, 63, 97] and matching [10, 60, 100, 120], robust estimation [3, 10, 181], Structure-from-Motion (SfM) and Bundle Adjustment (BA) [20, 59, 106], dense…”
• RoMa: Robust Dense Feature Matching (2023, unknown): “Feature matching has traditionally been approached by key-point detection and description followed by matching the descriptions [4, 14, 33, 39, 41, 53].”