Abstract: The invention relates to the technical field of battery charging and swap, and in particular to an off-line battery swap method, a battery charging and swap station, a vehicle with a battery to be swapped, and a readable storage medium. The invention is intended to solve the problem that battery swap cannot be performed at a battery charging and swap station when a vehicle is not connected to a network or a cloud server has a fault.

Abstract: An under-screen camera is provided. A camera is positioned behind a see-through display screen and positioned to capture scene image data of objects in front of the display screen. The camera captures scene image data of a real-world scene including a user. The scene image data is processed to remove artifacts in the scene image data created by capturing the scene image data through the see-through display screen such as blur, noise, backscatter, wiring effect, and the like.

Abstract: Images of a scene are received. The images represent viewpoints corresponding to the scene. A pixel map of the scene is computed based on the plurality of images. Multi-plane image (MPI) layers from the pixel map are extracted in real-time. The MPI layers are aggregated. The scene is rendered from a novel viewpoint based on the aggregated MPI layers.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

Abstract: An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Abstract: A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

Abstract: An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Abstract: An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Abstract: A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.

Abstract: A device that measures a size of a user's face, referred to as face scaling, using a monocular camera. Depth is calculated from sparse feature points. A face mesh is used to improve the estimation accuracy. A processing pipeline detects face features by applying a face landmark detection algorithm to find the important face feature points such as the eyes, nose, and mouth. The processing pipeline estimates feature points depth using depth obtained through image defocus. The processing pipeline further scales the face using an estimated depth of the face features.

Abstract: A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.

Abstract: Images of a scene are received. The images represent viewpoints corresponding to the scene. A pixel map of the scene is computed based on the plurality of images. Multi-plane image (MPI) layers from the pixel map are extracted in real-time. The MPI layers are aggregated. The scene is rendered from a novel viewpoint based on the aggregated MPI layers.

Abstract: Disclosed are a mobile Internet-based integrated vehicle energy replenishment system and method, and a storage medium.

Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

Abstract: The invention relates to the technical field of battery charging and swap, and in particular to an off-line battery swap method, a battery charging and swap station, a vehicle with a battery to be swapped, and a readable storage medium. The invention is intended to solve the problem that battery swap cannot be performed at a battery charging and swap station when a vehicle is not connected to a network or a cloud server has a fault.

Abstract: A system upgrade assessment method based on system parameter correlation coefficients is provided. The problem that the existing system upgrade assessment method cannot accurately assess an upgraded system is solved. For such a purpose, the system upgrade assessment method comprises the following steps: acquiring first data for a plurality of parameters before system upgrade (S110); acquiring second data for the plurality of parameters after the system upgrade (S120); calculating first correlation coefficients of the first data and second correlation coefficients of the second data (S130); calculating third correlation coefficients between the first data and the corresponding second data (S140); and determining, based on the magnitudes of the first correlation coefficients, the second correlation coefficients and the third correlation coefficients, whether the system upgrade succeeds (S150).

Abstract: A system for a wavefront imaging sensor with high resolution (WISH) comprises a spatial light modulator (SLM), a plurality of image sensors and a processor. The system further includes the SLM and a computational post-processing algorithm for recovering an incident wavefront with a high spatial resolution and a fine phase estimation. In addition, the image sensors work both in a visible electromagnetic (EM) spectrum and outside the visible EM spectrum.

Abstract: A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.

Abstract: A method for imaging objects includes illuminating an object with a light source of an imaging device, and receiving an illumination field reflected by the object. An aperture field that intercepts a pupil of the imaging device is an optical propagation of the illumination field at an aperture plane. The method includes receiving a portion of the aperture field onto a camera sensor, and receiving a sensor field of optical intensity. The method also includes iteratively centering the camera focus along the Fourier plane at different locations to produce a series of sensor fields and stitching together the sensor fields in the Fourier domain to generate an image. The method also includes determining a plurality of phase information for each sensor field in the series of sensor fields, applying the plurality of phase information to the image, receiving a plurality of illumination fields reflected by the object, and denoising the intensity of plurality of illumination fields using Fourier ptychography.