Object Pose Estimation by Locally Linearly Embedded Regression

In this paper we propose a new local learning algorithm for appearance-based object pose estimation, called Locally Linearly Embedded Regression (LLER). LLER uses a constrained version of Locally Linear Embedding (LLE) to simultaneously embed into an intermediate low-dimensional space the training images, the query image and a grid of pose parameters. A linear map is learned between the points in the local neighborhood of the query representation in this low-dimensional intermediate space and their corresponding pose parameters, which is used to directly recover the pose of the query image. The proposed method has been evaluated in a pose estimation task on a database of 16 different objects, consistently outperforming several representative global and local appearance-based pose estimation methods.