A unique method and a device to generate free space “pop-out” & “sink-in” holograms is disclosed herein. The hologram disclosed herein does not use any special medium, mirrors, reflective screens or wearables such as headgear & special glasses. The hologram disclosed herein can be created in free space, outer space or in air, without any other optical components except for the special display screen of the hologram device. This device demonstrates a free space hologram and the hologram Augmented Reality & hologram Virtual Reality. A camera capable of hologram quality images equipped with a smart lens which mimics the human eye by changing it's lens aperture according to the light intensity as the pupil of the human eye and focus & capture “pop-out” & “sink-in” hologram images is disclosed herein. The audio which is incorporated with the device provide multi dimensional multi directional audio effects.

A method for generating a three-dimensional model through data matching according to an embodiment of the present disclosure comprises: a maxillary scanning step of acquiring maxillary oral data including maxillary teeth by scanning the maxilla of a patient by using an oral scanner, a mandibular scanning step of acquiring mandibular oral data including mandibular teeth by scanning the mandible of the patient by using the oral scanner, an occlusion step of acquiring occlusal oral data in a state in which the maxillary teeth and the mandibular teeth are occluded, by using the oral scanner; a face scanning step of acquiring first facial data by scanning a face of the patient by using the oral scanner; and a matching step of matching the first facial data with at least one of the maxillary oral data, the mandibular oral data, and the occlusal oral data.

A method for generating a three-dimensional model through data matching according to an embodiment of the present disclosure comprises: a maxillary scanning step of acquiring maxillary oral data including maxillary teeth by scanning the maxilla of a patient by using an oral scanner, a mandibular scanning step of acquiring mandibular oral data including mandibular teeth by scanning the mandible of the patient by using the oral scanner, an occlusion step of acquiring occlusal oral data in a state in which the maxillary teeth and the mandibular teeth are occluded, by using the oral scanner; a face scanning step of acquiring first facial data by scanning a face of the patient by using the oral scanner; and a matching step of matching the first facial data with at least one of the maxillary oral data, the mandibular oral data, and the occlusal oral data.

An electronic apparatus includes a processor, and a memory storing a program which, when executed by the processor, causes the electronic apparatus to perform control to display an image in a display unit based on a third image including a first image captured through a first optical system, and a second image having a parallax with respect to the first image, captured through a second optical system, receive an enlargement instruction for enlarging a part of the image displayed in the display unit, and perform control, upon reception of the enlargement instruction while the third image is displayed, to display in the display unit an enlarged image including an enlarged portion of either one of the first image and the second image.

An interchangeable lens attachable to and detachable from an image pickup apparatus that includes a single image sensor includes a plurality of lens units that include a first lens unit and a second lens unit, and a communication unit configured to transmit to the image pickup apparatus information for setting an imaging area acquired by the first lens unit that is used in monaurally displaying image to a photometric area.

An interchangeable lens attachable to and detachable from an image pickup apparatus that includes a single image sensor includes a plurality of lens units that include a first lens unit and a second lens unit, and a communication unit configured to transmit to the image pickup apparatus information for setting an imaging area acquired by the first lens unit that is used in monaurally displaying image to a photometric area.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.

System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.