A point cloud capture system is provided to detect and correct data density during point cloud generation. The system obtains data points that are distributed within a space and that collectively represent one or more surfaces of an object, scene, or environment. The system computes the different densities with which the data points are distributed in different regions of the space, and presents an interface with a first representation for a first region of the space in which a first subset of the data points are distributed with a first density, and a second representation for a second region of the space in which a second subset of the data points are distributed with a second density.

A method for ascertaining a depth information image for an input image. The input image is processed using a convolutional neural network, which includes multiple layers that sequentially process the input image, and each converts an input feature map into an output feature map. At least one of the layers is a depth map layer, the depth information image being ascertained as a function of a depth map layer. In the depth map layer, an input feature map of the depth map layer is convoluted with multiple scaling filters to obtain respective scaling maps, the multiple scaling maps are compared pixel by pixel to generate a respective output feature map in which each pixel corresponds to a corresponding pixel from a selected one of the scaling maps.

A method for ascertaining a depth information image for an input image. The input image is processed using a convolutional neural network, which includes multiple layers that sequentially process the input image, and each converts an input feature map into an output feature map. At least one of the layers is a depth map layer, the depth information image being ascertained as a function of a depth map layer. In the depth map layer, an input feature map of the depth map layer is convoluted with multiple scaling filters to obtain respective scaling maps, the multiple scaling maps are compared pixel by pixel to generate a respective output feature map in which each pixel corresponds to a corresponding pixel from a selected one of the scaling maps.

Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

A device, method and system for estimating elevation in images from camera devices is provided. The device detects humans at respective positions in images from a camera device, the camera device having a fixed orientation and fixed focal length. The device estimates, for the humans in the images, respective elevations of the humans, relative to the camera device, at the respective positions based at least on camera device parameters defining the fixed orientation and the fixed focal length. The device associates the respective elevations with the respective positions in the images. The device determines, using the respective elevations associated with the respective positions, a function that estimates elevation in an image from the camera device, using a respective image position coordinate as an input. The device provides the function to a video analytics engine to determine relative real-world positions in subsequent images from the camera device.

A device, method and system for estimating elevation in images from camera devices is provided. The device detects humans at respective positions in images from a camera device, the camera device having a fixed orientation and fixed focal length. The device estimates, for the humans in the images, respective elevations of the humans, relative to the camera device, at the respective positions based at least on camera device parameters defining the fixed orientation and the fixed focal length. The device associates the respective elevations with the respective positions in the images. The device determines, using the respective elevations associated with the respective positions, a function that estimates elevation in an image from the camera device, using a respective image position coordinate as an input. The device provides the function to a video analytics engine to determine relative real-world positions in subsequent images from the camera device.

A method to culling parts of a 3D reconstruction volume is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data with low usage of computational resources and storage spaces. The method includes culling parts of the 3D reconstruction volume against a depth image. The depth image has a plurality of pixels, each of which represents a distance to a surface in a scene. In some embodiments, the method includes culling parts of the 3D reconstruction volume against a frustum. The frustum is derived from a field of view of an image sensor, from which image data to create the 3D reconstruction is obtained.

A method to culling parts of a 3D reconstruction volume is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data with low usage of computational resources and storage spaces. The method includes culling parts of the 3D reconstruction volume against a depth image. The depth image has a plurality of pixels, each of which represents a distance to a surface in a scene. In some embodiments, the method includes culling parts of the 3D reconstruction volume against a frustum. The frustum is derived from a field of view of an image sensor, from which image data to create the 3D reconstruction is obtained.

A method and processor system are provided which analyze a depth map, which may be obtained from a range sensor capturing depth information of a scene, to identify where an object is located in the scene. Accordingly, a region of interest may be identified in the scene which includes the object, and image data may be selectively obtained of the region of interest, rather than of the entire scene containing the object. This image data may be acquired by an image sensor configured for capturing visible light information of the scene. By only selectively obtaining the image data within the region of interest, rather than all of the image data, improvements may be realized in the computational complexity of a possible further processing of the image data, the storage of the image data and/or the transmission of the image data.