Energy-efficient epipolar imaging is applied to the ToF domain to significantly expand the versatility of ToF sensors. The described system exhibits 15+ m range outdoors in bright sunlight; robustness to global transport effects such as specular and diffuse inter-reflections; interference-free 3D imaging in the presence of many ToF sensors, even when they are all operating at the same optical wavelength and modulation frequency; and blur- and distortion-free 3D video in the presence of severe camera shake. The described embodiments are broadly applicable in consumer and robotics domains.

A stereoscopic visualization system using shape from shading algorithm is an image conversion device connected between a monoscopic endoscope and a 3D monitor. The system applies the algorithm which generates a depth map for a 2D image of video frames. The algorithm first calculates a direction of a light source for the 2D image. Based upon the information of light distribution and shading for the 2D image, the depth map is generated. The depth map is used to calculate another view of the original 2D image by depth image based rendering algorithm in generation of stereoscopic images. After the new view is rendered, the stereoscopic visualization system also needs to convert the display format of the stereoscopic images for different kinds of 3D displays. Based on this method, it can replace the whole monoscopic endoscope with a stereo-endoscope system and no modification is required for the monoscopic endoscope.

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

A depth thermal imaging module, including a thermal imager array, which includes a plurality of at least two thermal imagers that capture thermal radiation of a wavelength of a scene from different viewpoints. Each thermal imager includes a thermal imager chip, a lens stack, and a focal plane with focal length f. The thermal imagers are separated by a baseline distance of 2h and depth measurement Z is performed on an object of interest based on Z=2hf/Δ where Δ is the difference in location of the object of interest between its location in the thermal image captured by a first thermal imager and the location of the object of interest in the thermal image captured by a second thermal imager and represents as an offset of the point on the focal plane of the first thermal imager and the second thermal imagers relative to their optical axis.

This application relates to a depth image generation method, a reference image generation method, and an electronic device. The depth image generation method includes: emitting structured light to a reference plane, and imaging the reference plane onto a plurality of first effective pixels and a plurality of second effective pixels of an image sensor to obtain a reference image. The method includes emitting the structured light to a target object, and imaging the target object to the plurality of first effective pixels to obtain a target image. The method includes generating a depth image of the target object based on the target image and the reference image.

A method for intelligent light field depth classification based on optoelectronic computing includes capturing and identifying binocular images of a scene within a depth range through a pair of binocular cameras; mapping each depth value in the depth range to a disparity value between the binocular images, to obtain a disparity range of the scene within the depth range; labeling training data based on the disparity range to obtain a pre-trained diffraction neural network model; loading a respective weight for each layer of a network obtained after training into a corresponding optical element based on the pre-trained diffraction neural network model; and after the respective weight for each layer of the network is loaded, performing forward propagation inference on new input data of the scene, and outputting a depth classification result corresponding to each pixel in the binocular images of the scene.

A method for intelligent light field depth classification based on optoelectronic computing includes capturing and identifying binocular images of a scene within a depth range through a pair of binocular cameras; mapping each depth value in the depth range to a disparity value between the binocular images, to obtain a disparity range of the scene within the depth range; labeling training data based on the disparity range to obtain a pre-trained diffraction neural network model; loading a respective weight for each layer of a network obtained after training into a corresponding optical element based on the pre-trained diffraction neural network model; and after the respective weight for each layer of the network is loaded, performing forward propagation inference on new input data of the scene, and outputting a depth classification result corresponding to each pixel in the binocular images of the scene.

A device for determining the surface topology and associated color of a structure, such as a teeth segment, includes a scanner for providing depth data for points along a two-dimensional array substantially orthogonal to the depth direction, and an image acquisition means for providing color data for each of the points of the array, while the spatial disposition of the device with respect to the structure is maintained substantially unchanged. A processor combines the color data and depth data for each point in the array, thereby providing a three-dimensional color virtual model of the surface of the structure. A corresponding method for determining the surface topology and associate color of a structure is also provided.

A device for determining the surface topology and associated color of a structure, such as a teeth segment, includes a scanner for providing depth data for points along a two-dimensional array substantially orthogonal to the depth direction, and an image acquisition means for providing color data for each of the points of the array, while the spatial disposition of the device with respect to the structure is maintained substantially unchanged. A processor combines the color data and depth data for each point in the array, thereby providing a three-dimensional color virtual model of the surface of the structure. A corresponding method for determining the surface topology and associate color of a structure is also provided.

Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.