An intra-oral optical scanning method for intra-oral optical scanning including projecting a pattern, the pattern including at least a first area illuminated by a first color of light and a second area illuminated by a second color of light and at least one non-illuminated area onto an intra-oral feature, making a first image of the first area, the second area and the non-illuminated area differentiating between the first color of light and the second color of light in the first image of the projected pattern, and determining from the image of the non-illuminated area at least one of an ambient light level, a level of scattered light, a level of light absorption and a level of light reflected from at least one of the first area and the second area. Related apparatus and methods are also described.

A method for generating a three-dimensional (3D) lane model, the method including calculating a free space indicating a driving-allowed area based on a driving image captured from a vehicle camera, generating a dominant plane indicating plane information of a road based on either or both of depth information of the free space and a depth map corresponding to a front of the vehicle, and generating a 3D short-distance road model based on the dominant plane.

A method for generating a three-dimensional (3D) lane model, the method including calculating a free space indicating a driving-allowed area based on a driving image captured from a vehicle camera, generating a dominant plane indicating plane information of a road based on either or both of depth information of the free space and a depth map corresponding to a front of the vehicle, and generating a 3D short-distance road model based on the dominant plane.

Systems and methods for foreground/background separation and for studio production of a FVV. A method includes projecting, onto objects in a filming area within a studio, a predefined pattern including a large set of features; generating, based on signals reflected off of the objects and captured by a plurality of depth cameras deployed in proximity to the filming area, a local point cloud for each depth camera; separating, based on the local point clouds, between a background and a foreground of the filming area; creating, based on the local point clouds, a unified point cloud; meshing points in the unified point cloud to generate a 3D model of the objects; texturing the 3D model based on the separation and images captured by the depth cameras; and rendering the textured 3D model as a FVV including a series of video frames with respect to at least one viewpoint.

Systems and methods for foreground/background separation and for studio production of a FVV. A method includes projecting, onto objects in a filming area within a studio, a predefined pattern including a large set of features; generating, based on signals reflected off of the objects and captured by a plurality of depth cameras deployed in proximity to the filming area, a local point cloud for each depth camera; separating, based on the local point clouds, between a background and a foreground of the filming area; creating, based on the local point clouds, a unified point cloud; meshing points in the unified point cloud to generate a 3D model of the objects; texturing the 3D model based on the separation and images captured by the depth cameras; and rendering the textured 3D model as a FVV including a series of video frames with respect to at least one viewpoint.

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.

Methods and systems for generating free viewpoint videos (FVVs) based on images captured in a sports arena. A method includes projecting, onto objects within a filming area within the sports arena, a predefined pattern including a large set of features; generating, based on signals captured by each of a plurality of depth cameras, a point cloud for each depth camera, wherein the plurality of depth cameras is deployed in proximity to the filming area, wherein the captured signals are reflected off of the objects within the filming area; creating, based on the plurality of point clouds, a unified point cloud; meshing points in the unified point cloud to generate a three-dimensional (3D) model of the objects; texturing the 3D model based on images captured by the plurality of depth cameras; and rendering the textured 3D model as a FVV including a series of video frames with respect to a viewpoint.

Methods and systems for generating free viewpoint videos (FVVs) based on images captured in a sports arena. A method includes projecting, onto objects within a filming area within the sports arena, a predefined pattern including a large set of features; generating, based on signals captured by each of a plurality of depth cameras, a point cloud for each depth camera, wherein the plurality of depth cameras is deployed in proximity to the filming area, wherein the captured signals are reflected off of the objects within the filming area; creating, based on the plurality of point clouds, a unified point cloud; meshing points in the unified point cloud to generate a three-dimensional (3D) model of the objects; texturing the 3D model based on images captured by the plurality of depth cameras; and rendering the textured 3D model as a FVV including a series of video frames with respect to a viewpoint.

Disclosed are improved methods, systems and devices for color night vision that reduce the number of intensifiers and/or decrease noise. In some embodiments, color night vision is provided in system in which multiple spectral bands are maintained, filtered separately, and then recombined in a unique three-lens-filtering setup. An illustrative four-camera night vision system is unique in that its first three cameras separately filter different bands using a subtractive Cyan, Magenta and Yellow (CMY) color filtering-process, while its fourth camera is used to sense either additional IR illuminators or a luminance channel to increase brightness. In some embodiments, the color night vision is implemented to distinguish details of an image in low light. The unique application of the three-lens subtractive CMY filtering allows for better photon scavenging and preservation of important color information.

An endoscope system includes an endoscope that captures a living tissue in a body cavity, and an image processing unit. The endoscope includes an objective lens provided on a front side of a light receiving surface of an image sensor and configured to simultaneously form images of the living tissue, obtained through a plurality of windows, on the light receiving surface as the captured image. The image processing unit includes a three-dimensional expansion processor configured to calculate different directions of a feature part visible through the plurality of windows based on position information in each of images of the feature part, which is distinguishably identified from other parts and included in common in the plurality of images obtained through the plurality of windows in the captured image captured by the endoscope, and to expand two-dimensional information of the images of the feature part to three-dimensional information.