Abstract. We here describe a novel approach for locally obtaining pose estimation of match feature points, observed using RGB-D cameras, in order to apply to locally planar object pose estimation with RANSAC method for augmented reality systems. Conventionally, object pose estimation based on RGB-D cameras are achieved by the correlation between observed 3D points captured by feature point matching and known 3D points of the object. However, in such methods, features are simplified as single 3D points, losing information of the feature and its neighborhood surface. This approach based on local 3D pose estimation of locally planar feature points, brings richer information for 3D pose estimation of planar, 3D rigid or deformable objects. This information enables more stable pose estimation across RANSAC settings than conventional threepoints RANSAC methods.
A 208-item scale was developed to measure self-reported anthropomorphic tendencies during interactions with various non-human entities. The potential targets of anthropomorphism included technology-laden machines such as computers, other objects such as backpacks, living things such as houseplants, and abstract entities such as a god or higher power. Scale items assessed the degree to which participants agreed with statements regarding the perceived attributes of the entities, speech directed toward the entities and the treatment of the entities. A factor analysis suggested that the scale measures four independent types of anthropomorphism: “extreme” anthropomorphic tendencies, anthropomorphism of a god or higher power, anthropomorphism of pets, and “negative” anthropomorphism. Further analyses indicated that anthropomorphic tendencies were self-reported when pertaining to pets and a god or higher power. However, participants tended not to self-report inappropriate “negativeâ” anthropomorphism toward computers, cars, microwaves, etc. These disparate findings appear to be due to social desirability of anthropomorphism.
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