We present SKD, a novel keypoint detector that uses saliency to determine the best candidates from point cloud for tasks such as registration and reconstruction. The approach can be applied any differentiable deep learning descriptor by using gradients of with respect 3D position input points measure their saliency. is combined original context information in neural network, which trained learn...

Abstract We are interested in estimating the location of what we call “smooth change-point” from n independent observations an inhomogeneous Poisson process. The smooth change-point is a transition intensity function process one level to another which happens smoothly, but over such small interval, that its length $$\delta _n$$ <mml:msub...

Quantification of uncertainty in point cloud matching is critical many tasks such as pose estimation, sensor fusion, and grasping. Iterative closest (ICP) a commonly used estimation algorithm which provides estimate the transformation between two clouds. There are sources this process that may arise due to noise, ambiguous environment, initial condition, occlusion. However, for safety problems ...

Use of risk adjusted control charts for monitoring patients’ surgical outcomes is now popular.These charts are developed based on considering the patient’s pre-operation risks. Change point detection is a crucial problem in statistical process control (SPC).It helpsthe managers toanalyzeroot causes of out-of-control conditions more effectively. Since the control chart signals do not necessarily...