18802 results about "Point cloud" patented technology

A lidar-based 3-D point cloud measuring system and method. An example system includes a base, a housing, a plurality of photon transmitters and photon detectors contained within the housing, a rotary motor that rotates the housing about the base, and a communication component that allows transmission of signals generated by the photon detectors to external components. The rotary component includes a rotary power coupling configured to provide power from an external source to the rotary motor, the photon transmitters, and the photon detectors. In another embodiment, the photon transmitters and detectors of each pair are held in a fixed relationship with each other. In yet another embodiment, a single detector is “shared” among several lasers by focusing several detection regions onto a single detector, or by using a single, large detector.

A system for creating an environment and for sharing an experience based on the environment includes a plurality of mobile devices having a camera employed near a point of interest to capture random, crowdsourced images and associated metadata near said point of interest, wherein the metadata for each image includes location of the mobile device and the orientation of the camera. Preferably, the images include depth camera information. A wireless network communicates with the mobile devices to accept the images and metadata and to build and store a point cloud or 3D model of the region. Users connect to this experience platform to view the 3D model from a user selected location and orientation and to participate in experiences with, for example, a social network.

A lidar-based system and method are used for the solid state beamforming and steering of laser beams using optical phased array (OPA) photonic integrated circuits (PICs) and the detection of laser beams using photodetectors. Transmitter and receiver electronics, power management electronics, control electronics, data conversion electronics and processing electronics are also included in the system and used in the method.Laser pulses beamformed by the OPA PIC reflect from objects in the field of view (FOV) of said OPA, and are detected by a detector or a set of detectors.A lidar system includes at least one lidar, and any subset and any number of complementary sensors, data processing / communication / storage modules, and a balance of system for supplying power, protecting, connecting, and mounting the components of said system.Direct correlation between the 3D point cloud generated by the lidar and the color images captured by an RGB (Red, Green, Blue) video camera can be achieved by using an optical beam splitter that sends optical signals simultaneously to both sensors.A lidar system may contain a plurality of lidar sensors, a lidar sensor may contain a plurality of optical transmitters, and an optical transmitter may contain a plurality of OPA PICs.

Systems and methods for 2D image and spatial data capture for 3D stereo imaging are disclosed. The system utilizes a cinematography camera and at least one reference or “witness” camera spaced apart from the cinematography camera at a distance much greater that the interocular separation to capture 2D images over an overlapping volume associated with a scene having one or more objects. The captured image date is post-processed to create a depth map, and a point cloud is created form the depth map. The robustness of the depth map and the point cloud allows for dual virtual cameras to be placed substantially arbitrarily in the resulting virtual 3D space, which greatly simplifies the addition of computer-generated graphics, animation and other special effects in cinemagraphic post-processing.

Systems, apparatus, and methods are provided for producing an improved panoramic image. A three-dimensional point cloud image is generated from an optical distancing system. Additionally, at least one two-dimensional image is generated from at least one camera. The three-dimensional point cloud image is colorized with the at least one two-dimensional image, thereby forming a colorized three-dimensional point cloud image. An equal area projection panoramic image is synthesized from the colorized three-dimensional point cloud, wherein the panoramic image comprises a plurality of pixels, and each pixel in the plurality of pixels represents a same amount of area.

A method and apparatus for automatically generating a three-dimensional computer model from a “point cloud” of a scene produced by a laser radar (LIDAR) system. Given a point cloud of an indoor or outdoor scene, the method extracts certain structures from the imaged scene, i.e., ceiling, floor, furniture, rooftops, ground, and the like, and models these structures with planes and / or prismatic structures to achieve a three-dimensional computer model of the scene. The method may then add photographic and / or synthetic texturing to the model to achieve a realistic model.

Camera motion is determined in a three-dimensional image capture system using a combination of two-dimensional image data and three-dimensional point cloud data available from a stereoscopic, multi-aperture, or similar camera system. More specifically, a rigid transformation of point cloud data between two three-dimensional point clouds may be more efficiently parameterized using point correspondence established between two-dimensional pixels in source images for the three-dimensional point clouds.

This invention provides a system and method for determining the three-dimensional alignment of a modeledobject or scene. After calibration, a 3D (stereo) sensor system views the object to derive a runtime 3D representation of the scene containing the object. Rectified images from each stereo head are preprocessed to enhance their edge features. A stereo matching process is then performed on at least two (a pair) of the rectified preprocessed images at a time by locating a predetermined feature on a first image and then locating the same feature in the other image. 3D points are computed for each pair of cameras to derive a 3D point cloud. The 3D point cloud is generated by transforming the 3D points of each camera pair into the world 3D space from the world calibration. The amount of 3D data from the point cloud is reduced by extracting higher-level geometric shapes (HLGS), such as line segments. Found HLGS from runtime are corresponded to HLGS on the model to produce candidate 3D poses. A coarse scoring process prunes the number of poses. The remaining candidate poses are then subjected to a further more-refined scoring process. These surviving candidate poses are then verified by, for example, fitting found 3D or 2D points of the candidate poses to a larger set of corresponding three-dimensional or two-dimensional model points, whereby the closest match is the best refined three-dimensional pose.

A method extracts planes from three-dimensional (3D) points by first partitioning the 3D points into disjoint regions. A graph of nodes and edges is then constructed, wherein the nodes represent the regions and the edges represent neighborhood relationships of the regions. Finally, agglomerative hierarchical clustering is applied to the graph to merge regions belonging to the same plane.

The invention discloses a Kinect-based robot self-positioning method. The method includes the following steps that: the RGB image and depth image of an environment are acquired through the Kinect, and the relative motion of a robot is estimated through the information of visual fusion and a physical speedometer, and pose tracking can be realized according to the pose of the robot at a last time point; depth information is converted into three-dimensional point cloud, and a ground surface is extracted from the point cloud, and the height and pitch angle of the Kinect relative to the ground surface are automatically calibrated according to the ground surface, so that the three-dimensional point cloud can be projected to the ground surface, and therefore, two-dimensional point cloud similar to laser data can be obtained, and the two-dimensional point cloud is matched with pre-constructed environment raster map, and thus, accumulated errors in a robot tracking process can be corrected, and the pose of the robot can be estimated accurately. According to the Kinect-based robot self-positioning method of the invention, the Kinect is adopted to replace laser to perform positioning, and therefore, cost is low; image and depth information is fused, so that the method can have high precision; and the method is compatible with a laser map, and the mounting height and pose of the Kinect are not required to be calibrated in advance, and therefore, the method is convenient to use, and requirements for autonomous positioning and navigation of the robot can be satisfied.

The present invention relates to methods for determining meniscal size and shape for use in designing therapies for the treatment of various joint diseases. The invention uses an image of a joint that is processed for analysis. Analysis can include, for example, generating a thickness map, a cartilage curve, or a point cloud. This information is used to determine the extent of the cartilage defect or damage and to design an appropriate therapy, including, for example, an implant. Adjustments to the designed therapy are made to account for the materials used.

There are provided a step of acquiring a main point cloud data on a predetermined measurement range by a laser scanner, a step of detecting a range with no data acquired, a step of acquiring a supplementary image data of the range with no data acquired by an auxiliary image pickup device, a step of preparing a stereoscopic image based on the supplementary image data obtained by the auxiliary image pickup device, a step of acquiring supplementary point cloud data from the stereoscopic image, and a step of supplementing the range with no data acquired of the main point cloud data by matching of the main point cloud data and the supplementary point cloud data.