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

摘要: In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM-based algorithm dense depth and occlusion maps from wide-baseline pairs using descriptor. This yields much better results in situations than the pixel correlation-based algorithms that are commonly used narrow-baseline stereo. Also, descriptor makes our robust against many photometric geometric transformations. Our inspired earlier ones such as SIFT GLOH but can be computed faster for purposes. Unlike SURF, efficiently at every pixel, it does not artifacts degrade matching performance when It important note approach first attempts estimate pairs, show good one with experiments estimation accuracy, detection, comparing other descriptors on laser-scanned ground truth scenes. tested variety of indoor outdoor scenes different transformations support claim being these.