DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

—In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM-based algorithm to compute dense depth and occlusion maps from wide-baseline image pairs using this descriptor. This yields much better results in wide-baseline situations than the pixel and correlation-based algorithms that are commonly used in narrowbaseline stereo. Also, using a descriptor makes our algorithm robust against many photometric and geometric transformations. Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF, which can also be computed efficiently at every pixel, it does not introduce artifacts that degrade the matching performance when used densely. It is important to note that our approach is the first algorithm that attempts to estimate dense depth maps from wide-baseline image pairs, and we show that it is a good one at that with many experiments for depth estim...

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: Engin Tola, Vincent Lepetit, Pascal Fua
• Keywords: Algorithm, Dense Depth, Geometric Transformations, PAMI 2010