Systems, methods, and computer-readable media are described for training a neural network to perform keypoint detection and view-invariant keypoint representation generation. A locally learned database of three-dimensional (3D) keypoint landmarks extracted from a sample set of training depth images can be populated with view-invariant keypoint representations of the keypoint landmarks stored in association with corresponding 3D locations of the keypoint landmarks. The populated 3D keypoint landmark database can be used to find 3D keypoints that match 2D keypoints extracted from a test depth image having an unknown pose. A parameter estimation algorithm can be executed on the 3D locations of the matching keypoint landmarks to determine a pose corresponding to the test depth image.
PART IDENTIFICATION USING A LOCALLY LEARNED THREEDIMENSIONAL (3D) LANDMARK DATABASE