Pose Hashing with Microlens Arrays
We design and demonstrate a passive physical object whose appearance changes to give a discrete encoding of its pose. This object is created with a microlens array that is placed on top of a black and white pattern; when viewed from a particular viewpoint
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Abstract. We design and demonstrate a passive physical object whose appearance changes to give a discrete encoding of its pose. This object is created with a microlens array that is placed on top of a black and white pattern; when viewed from a particular viewpoint, the lenses appear black or white depending on the part of the pattern that each microlens projects towards that viewpoint. We analyze different design considerations that impact the information gained from the appearance of microlens array. In addition, we introduce the process through which the discrete microlens pattern can be turned into a viewpoint and a pose estimate. We empirically evaluate factors that impact viewpoint and pose estimation accuracy. Finally, we compare the pose estimation accuracy of the microlens array to other related fiducial markers.
Keywords: Microlens array
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· Fiducial marker · Pose estimation
Introduction
Fiducial markers are critical to many visual, augmented reality and robotic tasks. Most often, fiducial markers serve to create geometric constraints that relate the known position of the marker on the object to the observed position of the marker in the image. Multiple fiducial markers, or larger patterns with several observable points can be combined to constraint the relative pose of an object. These approaches that relate the fiducial marker to the image suffer from two shortcomings. First, the geometric constraints from a set of points on a fronto-parallel plane are known to be sensitive to noise [1–3]. Second, for any viewpoint, patterns or sets of markers that are collinear or nearly collinear lead to poorly conditioned estimators for the pose estimation problem [4,5]. Some recent work attacks these limitations by designing fiducial markers whose appearance changes depending on the direction from which they are viewed. Images of these fiducial markers directly encode constraints on the object rotation and therefore require fewer markers and eliminate the need for noncollinear markers for pose estimation. Marker designs include lenticular arrays and microlens arrays that have a moire-pattern to encode the viewing angle and go by the name “Lentimark” [6,7] and “Arraymark” [8,9]. Other work uses lenticular arrays that change color depending on their viewing angle [10,11]. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 600–614, 2016. DOI: 10.1007/978-3-319-46487-9 37
Pose Hashing with Microlens Arrays
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This paper expands this line of work. We propose the integration of a microlens array with a random black and white pattern adhered to the back. Because these microlenses focus parallel rays onto an approximately single point of their back-plane pattern, they will appear either black or white. Using different patterns behind different microlens dots means that a given viewing direction will cause some dots to be black, some to be white, and some to be in between because the viewpoint is on the boundary of black and white regions. The major contributions of this
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