Development of Continuum Shape Constraint Analysis (CSCA) for Computer Vision Applications Using Range Data

This paper further presents continuum shape constraint analysis (CSCA) of surfaces. CSCA is a generalization of discrete-point based constraint analysis which can be used to predict performance of registration algorithms. A surface-based self-registration cost function to which constraint analysis can be applied is introduced. This cost function takes into account a direction the object is viewed at. A sample study is provided to illustrate this approach applied to the problem of pose estimation using range-data taken from a scanning instrument such as LIDAR. Specifically, CSCA is used to assess an object feature for suitability for local LIDAR scanning and subsequent application of the ICP (iterative closest-point) algorithm to determine pose. In this study, the constraint analysis uses noise amplification index (NAI) as an output measure. The continuum nature of the CSCA approach renders the registration cost matrix and the derived NAI as pure shape properties of the feature with a dependence on viewpoint.