Pose Estimation using Local Structure-Specific Shape and Appearance Context
This addresses pose estimation for computer vision applications, but appears incremental as it builds on existing descriptor methods.
The paper tackles the problem of estimating alignment pose between models by developing structure-specific local descriptors that combine 2D image and 3D shape data, showing high discriminative power compared to state-of-the-art approaches in experiments.
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations which describe the neighborhood of a descriptor. By quantitative evaluations, we show that our descriptors provide high discriminative power compared to state of the art approaches. In addition, we show how to utilize this for the estimation of the alignment pose between two point sets. We present experiments both in controlled and real-life scenarios to validate our approach.