Robust camera pose recovery using stochastic geometry
نویسنده
چکیده
The Problem: The objective of 3-D machine vision is to infer geometric properties (e.g. shape and size) and photometric attributes (e.g. color, texture, reflectance) from a set of 2-D images. Every such vision task relies on accurate camera calibration, that is, knowledge of the camera’s intrinsic parameters (focal length, lens distortion, etc.) and extrinsic parameters—orientation, position, and scale relative to a fixed frame of reference. This research concerns the automatic recovery of precise extrinsic pose among a large set of images, assuming that accurate intrinsic parameters and rough estimates of extrinsic parameters are available. This work also investigates models of geometric uncertainty and the application of projective inference techniques.
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