Leica Geosystems Ag

Cyra Technologies, Inc.

Leica Geosystems Hds, Inc.

Richard William Bukowski

Mark Damon Wheeler

Jonathan Apollo Kung

Laura Michele Downs

Jeffrey Minoru Adachi

Minghua Wu

Christopher Robin Thewalt

Laslo Vespremi

Barry Joel Wheeler

Barry Joel Schwarz

Daniel Chudak

Yuelin Chen

A high-density, distance-measuring laser system and an associated computer that processes the data collected by the laser system. The computer determines a data partition structure and stores that structure as a header file for the scan before data is collected. As the scan progresses, the computer collects data points until a predetermined threshold is met, at which point a block of data consisting of the data points up to the threshold is written to disk. The computer indexes each data block using all three coordinates of its constituent data points using, preferably, a flexible index, such as an R-tree. When a data block is completely filled, it is written to disk preferably with its index and, as a result, each data block is ready for access and manipulation virtually immediately after having been collected. Also, each data block can be independently manipulated and read from disk.

Rapid, spatial-data viewing and manipulating including data partition and indexing

A computer model of a physical structure (or object) can be generated using context-based hypothesis testing. For a set of point data, a user selects a context specifying a geometric category corresponding to the structure shape. The user specifies at least one seed point from the set that lies on a surface of the structure of interest. Using the context and point data, the system loads points in a region near the seed point(s), and determines the dimensions and orientation of an initial surface component in the context that corresponds to those points. If the selected component is supported by the points, that component can be added to a computer model of the surface. The system can repeatedly find points near a possible extension of the surface model, using the context and current surface component(s) to generate hypotheses for extending the surface model to these points. Well-supported components can be added to the surface model until the surface of the structure of interest has been modeled as far as is well-supported by the point data.

Identification of 3D surface points using context-based hypothesis testing

A representation of a physical object can be displayed even where the amount of geometric data is too large to be stored in resident memory. A primary viewing window displays point data for the object using a substantially even sampling of data at an appropriate point density for the system. At least one auxiliary viewing window displays a two-dimensional representation of the point data. A user can select a portion of the data in the auxiliary window(s), such as by selecting cells of an overlaid grid, to be displayed in the primary window using a rendering such as a 'visible' rendering. The remainder of the displayed data can be displayed using a rendering such as a 'hidden' or &#x201c;transparent&#x201d; rendering. The resolution of the selected region can be increased while maintaining a substantially even spacing among points for the region. The resolution of the unselected region can be decreased accordingly.

Browsers for large geometric data visualization

An improved interface and algorithm(s) can be used to simplify and improve the process for locating an edge from a series of points in a point cloud. An interface can allow the user to select a hint point thought to be near an edge of interest, which can be used to generate an initial edge profile. An interface can allow the user to adjust the fit of the initial profile in cross-section, then can use that profile to generate a profile of the entire edge. A moving fit window can use a moving average to extend the edge and determine proper end locations. An interface then can display the results of the fit to the user and allow the user to adjust the fit, such as by adjusting the end points of the calculated edge. Such a process can be used to fit linear or curvilinear edges, and can fit a number of irregular shapes as well as regular shaped such as 'v-shaped' edges.

Identification of edge regions from 3D point data

A laser scanner scans a scene to form a point cloud representing points on a surface within the scene. The point cloud is used to create a corresponding visual representation of the point cloud from a single eye point that is displayed as an image array, such as a cube map, on a computer screen. Each point of the point cloud is represented on the computer screen as a pixel having a value other than a background value. An offset is determined between a reference position of the pixel and an intersection point, which is the point at which a ray from the point to the scanning position intersects a plane coincident with plane of the computer screen including the pixel. The offset is stored in an offset grid, whereby each pixel of the image array has a corresponding offset value in the offset grid. That is, the invention provides for encoding actual point coordinates with respect to the pixels of the image array by storing depth and angular offsets at each pixel in the image array that has an associated point. If the point spacing in a certain region is wider than the image array pixels, the gaps can be filled with, for example, RGB values to make the displayed image continuous.

Image-mapped point cloud with ability to accurately represent point coordinates

An improved interface and algorithm(s) can be used to simplify and improve the process for locating an occluded edge from a series of points in a point cloud. An interface can allow the user to select a hint point thought to be near an edge of interest, which can be used to generate an initial edge profile. An interface can allow the user to adjust the fit of the initial profile in cross-section, then can use that profile to generate a profile of the entire edge. A moving fit window can use an imaginary plane to provide an additional constraint, and can utilize a moving average to extend the edge and determine proper end locations. An interface then can display the results of the fit to the user and allow the user to adjust the fit, such as by adjusting the end points of the calculated edge. Such a process can be used to fit linear or curvilinear occluded edges, and can fit a number of irregular shapes as well as regular shaped edges such as 'v-shaped' edges.

Identification of occluded edge regions from 3D point data

A method relating to a point cloud includes defining a line of sight of a point cloud on a display of a computer, estimating a normal vector for at least one point of the plurality of points, and determining the appearance on the display of at least one point of the plurality of points based on the step of estimating a normal vector. One can use the computer to manipulate the point cloud to display a selected view of the scene and calculate the angle between the normal vector of the at least one point and a line of sight. The step of determining the appearance can include determining the transparency, color or size of the point on the display according to the angle between the normal vector and the line of sight.

Determining appearance of points in point cloud based on normal vectors of points

Large data sets can be stored and processed in real time by combining and registering the large data sets into a single data set. The data can be stored in a data tree structure formed of layers of spatially organized blocks of data. Such storage allows portions of the data to be viewed efficiently, displaying actual point data at an acceptable resolution for the viewing mechanism. Density limited queries can be executed that allow sub-sampling to be done directly and evenly without geometric constraint, to provide a subset of points that is limited in size and includes a spatially-even decomposition of that set of points. This allows the system as a whole to support arbitrarily large point sets while allowing full partitioning functionality, which is efficient to use in both time and space.

System and method for efficient storage and manipulation of extremely large amounts of scan data

A system and method are described for concurrently modeling of any element of a geometric model. The geometric model is stored in a database as a number of model objects. The model objects are loaded into a computer, and are then used to generate a display. When a model is edited, a representation of the model object is changed. The change is sent to the database to update the corresponding model object stored in the database. Any computers displaying this model will be notified of the change and will reload and redisplay the updated model object.

Growing community of inventors

Orinda, CA, United States of America

Richard William Bukowski