According to one embodiment, an optical filter associated with a lens unit having a first end includes a filter part, a filter frame, and an attachment part. The filter part includes a first filter area that transmits light rays of a first wavelength band and a second filter area that transmits light rays of a second wavelength band. The first wavelength band and the second wavelength band include a common wavelength band. The filter frame holds the filter part. The attachment part is coupled with the filter frame and is detachably attached to the first end of the lens unit. When the attachment part is attached to the first end of the lens unit, a distance between the filter part and an aperture within the lens unit is longer than a distance between the first end and the aperture.

A mobile device is provided comprising a housing, at least two cameras supported by the housing and arranged to capture image data and a multi-view display. The multi-view display may be a lightfield display and may comprise a diffractive lightfield backlighting system. The multi-view display may be configured to display multi-view video derived from image data captured by the at least two cameras and optionally operate in at least one of a multi-view mode or a multi-dimensional display mode.

A mobile device is provided comprising a housing, at least two cameras supported by the housing and arranged to capture image data and a multi-view display. The multi-view display may be a lightfield display and may comprise a diffractive lightfield backlighting system. The multi-view display may be configured to display multi-view video derived from image data captured by the at least two cameras and optionally operate in at least one of a multi-view mode or a multi-dimensional display mode.

A camera, including: two imaging systems each comprising a different optical path corresponding to a different viewing angle of an object; one or more illumination sources; a mask disposed with multiple pairs of apertures, wherein each aperture of each aperture pair corresponds to a different one of the imaging systems; at least one detector configured to acquire multiple image pairs of the object from the two imaging systems via the multiple pairs of apertures; and a processor configured to produce from the multiple acquired image pairs a multi-layer three dimensional reconstruction of the object.

According to one embodiment, an optical filter associated with a lens unit having a first end includes a filter part, a filter frame, and an attachment part. The filter part includes a first filter area that transmits light rays of a first wavelength band and a second filter area that transmits light rays of a second wavelength band. The first wavelength band and the second wavelength band include a common wavelength band. The filter frame holds the filter part. The attachment part is coupled with the filter frame and is detachably attached to the first end of the lens unit. When the attachment part is attached to the first end of the lens unit, a distance between the filter part and an aperture within the lens unit is longer than a distance between the first end and the aperture.

An apparatus and method, the apparatus including a light guide element including a plurality of input diffraction gratings configured to couple a plurality of incident beams of light into the light guide element and at least one output diffraction grating configured to couple the plurality of beams of light out of the light guide element to at least one image sensor to enable a plurality of images to be captured.

An apparatus and method, the apparatus including a light guide element including a plurality of input diffraction gratings configured to couple a plurality of incident beams of light into the light guide element and at least one output diffraction grating configured to couple the plurality of beams of light out of the light guide element to at least one image sensor to enable a plurality of images to be captured.

An example method for training a machine learning model is provided. The method includes receiving training data collected by a three-dimensional (3D) imager, the training data comprising a plurality of training sets. The method further includes generating, using the training data, a machine learning model from which a disparity map can be inferred from a pair of images that capture a scene where a light pattern is projected onto an object.

An example method for training a machine learning model is provided. The method includes receiving training data collected by a three-dimensional (3D) imager, the training data comprising a plurality of training sets. The method further includes generating, using the training data, a machine learning model from which a disparity map can be inferred from a pair of images that capture a scene where a light pattern is projected onto an object.

A fiberscope for stereoscopic imaging has at least one wavefront manipulator which, for creating a sample beam, is configured to pre-shape a wavefront of the light from a light source such that the pre-shaped light is focusable substantially on an object point in an object region and raster-deflectable to a multiplicity of object points. The fiberscope also includes an illumination fiber for supplying the pre-shaped sample beam to the object region, and a detector fiber for supplying scattered light reflected and/or scattered at the respective object point to a detector which captures the scattered light and is connected to a computer unit. The computer unit is configured to compose the stereoscopic image from the captured scattered light. A method is for acquiring stereoscopic image data from a fiberscope.