Phase Filters for 3D Localization of Point Light Sources

Algorithm for 3d Localization Microscopy Using Realistic Modeling of the Point Spread Function by Phase Retrieval

A Practical Method for Estimation of Point Light-Sources

We introduce a general model for point light-sources and show how the parameters of a source at finite distance can be estimated by shading on an object with known geometry and Lambertian reflectance. The parameters we estimate include not only the direction but also the location of the source. Furthermore, we argue that the system introduced here can be used in arbitrarily complex background i...

Photometric Linearization under Near Point Light Sources

We present a method for generating linearized images that consist of only diffuse reflection from images taken under near point light sources. Previous photometric linearization method cannot work under near point light sources, since it assumes parallel light. For satisfying this assumption, our method utilizes a photometric linearization method by dividing images into small regions. It also s...

Random phase mask in a filamentation regime: application to the localization of point sources.

We present a optical system with an extended point-spread function (PSF) for the localization of point sources in the visible and IR spectral ranges with a subpixel precision. This compact system involves a random phase mask (RPM) as its unique component. It exhibits original properties, because this RPM is used in a particular regime, called the "filamentation regime," before the speckle regio...

LocNet: Global localization in 3D point clouds for mobile robots

Global localization in 3D point clouds is a challenging problem of estimating the pose of robots without priori knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global priori map. Specifically, we present a semi-handcrafted representation learning method for LIDAR point cloud using siamese LocNets, which states th...