We propose a new and efficient method for 3d object localization and fine-grained3d pose estimation from a single 2d image. Our approach follows the classical paradigm of matching a 3dmodel to the 2d observations. O...
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ISBN:
(纸本)9781479930227
We propose a new and efficient method for 3d object localization and fine-grained3d pose estimation from a single 2d image. Our approach follows the classical paradigm of matching a 3dmodel to the 2d observations. Our first contribution is a 3d object model composed of a set of 3d edge primitives learned from 2d object blueprints, which can be viewed as a 3d generalization of HOG features. This model is used to define a matching cost obtained by applying a rigid-body transformation to the 3d object model, projecting it onto the image plane, and matching the projectedmodel to HOG features extracted from the input image. Our second contribution is a very efficient branch-and-bound algorithm for finding the 3d pose that maximizes the matching score. For this, 3d integral images of quantized HOGs are employed to evaluate in constant time the maximum attainable matching scores of individual model primitives. We applied our method to three different datasets of cars and achieved promising results with testing times as low as less than half a second.
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