Methods, systems, and computer readable media for generating a three-dimensional reconstruction of an object with reduced distortion are described. In some aspects, a system includes at least two image sensors, at least two projectors, and a processor. Each image sensor is configured to capture one or more images of an object. Each projector is configured to illuminate the object with an associated optical pattern and from a different perspective. The processor is configured to perform the acts of receiving, from each image sensor, for each projector, images of the object illuminated with the associated optical pattern and generating, from the received images, a three-dimensional reconstruction of the object. The three-dimensional reconstruction has reduced distortion due to the received images of the object being generated when each projector illuminates the object with an associated optical pattern from the different perspective.

Systems and methods for macro stereo time-lapse photography, producing a stereographic time-lapse digital video, and macro stereographic time-lapse digital videos. A method of producing a sequence of time-lapse stereographic images of a subject, by positioning a camera with a macro lens at a first position relative to the subject; using the camera to obtain a first stack of images of the subject from the first position; positioning the camera at a second position relative to the subject; using the camera to obtain a second stack of images of the subject from the second position; and storing the first stack of images and the second stack of images as a stack pair; and then selectively repeating.

A device determines positions of objects in a scene. The device obtains object detection data (ODD) which identifies the objects and locations of reference points of the objects in 2D images of the scene. The device processes the ODD to generate candidate association data (CAD) which associates pairs of objects between the images, computes estimated 3D positions in the scene for associated pairs of objects in the CAD, and performs clustering of the estimated positions. The device further generates, based on estimated 3D positions in one or more clusters, final association data (FAD) which associates one or more objects between the images, and computes one or more final 3D positions in the scene for one or more reference points of the one or more objects in the FAD. The final 3D position(s) represent the 3D position or the 3D pose of the respective object in the scene.

Systems and methods for estimating depth from projected texture using camera arrays are described. A camera array includes a conventional camera and at least one two-dimensional array of cameras, where the conventional camera has a higher resolution than the cameras in the at least one two-dimensional array of cameras, an illumination system configured to illuminate a scene with a projected texture, where an image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture, capture a set of images of the scene illuminated with the projected texture, and determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images.

A user-guidance system that utilizes augmented-reality (AR) components and human-posture-detection techniques is presented. The user-guidance system can help users to use smart devices to conduct 3D body scans more efficiently and accurately. AR components are computer generated for the on-screen guidance to guide a camera operator to position the camera in a particular location in relation to a target object with a particular tilt orientation in relation to the target object to capture an image that includes a region of the target object for 3D reconstruction of the target object. Human-posture-detection techniques are used to detect a human user's real-time posture and provide real-time on-screen guidance feedback and instructions to the human user to adopt an intended best posture for 3D reconstruction of the human user.

An information processing apparatus includes: a plurality of stereo cameras arranged so that directions of baseline lengths of the stereo cameras intersect each other; a depth estimation unit that estimates, from captured images captured by the plurality of stereo cameras, a depth of an object included in the captured images; and an object detection unit that detects the object based on the depth estimated by the depth estimation unit and reliability of the depth, the reliability being determined in accordance with an angle of a direction of an edge line of the object with respect to the directions of the baseline lengths of the plurality of stereo cameras.

Method for selecting stereo pairs of satellite or aerial images to generate elevation data for an area of interest, the method being computer-implemented and including a phase of selecting eligible stereo pairs from an initial set of images representing the area of interest, followed by a phase of ranking the selected stereo pairs according to their quality. The method further includes a phase of defining N image clusters, where N is an integer greater than or equal to 2, each grouping images from the initial set according to a similarity criterion, and a phase of selecting the best stereo pairs per cluster on the basis of the ranking established during the ranking phase and on the fact that a pair belongs to a cluster if the two images of the pair belong to the cluster.

Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.

A depth estimation device includes a difference map generating network and a depth transformation circuit. The difference map generating network generates, from a monocular input image and using a plurality of neural networks, a plurality of difference maps corresponding to a plurality of baselines. The plurality of difference maps includes a first difference map corresponding to a first baseline and a second difference map corresponding to a second baseline. The depth transformation circuit generates a depth map using one of the plurality of difference maps.

Techniques for estimating a perimeter of a room environment at least partially enclosed by a set of adjoining walls using posed images are disclosed. A set of images and a set of poses are obtained. A depth map is generated based on the set of images and the set of poses. A set of wall segmentation maps are generated based on the set of images, each of the set of wall segmentation maps indicating a target region of a corresponding image that contains the set of adjoining walls. A point cloud is generated based on the depth map and the set of wall segmentation maps, the point cloud including a plurality of points that are sampled along portions of the depth map that align with the target region. The perimeter of the environment along the set of adjoining walls is estimated based on the point cloud.