Computer graphics processing and selective visual display system – Computer graphics processing – Three-dimension
Reexamination Certificate
1998-10-23
2002-10-29
Nguyen, Phu K. (Department: 2772)
Computer graphics processing and selective visual display system
Computer graphics processing
Three-dimension
Reexamination Certificate
active
06473079
ABSTRACT:
TECHNICAL FIELD
The present invention relates generally to systems that document the geometry and other attributes of objects in three dimensions and, specifically, to a system that employs a scanning lidar (range finding laser) to quickly and accurately sense the position in three-dimensional space of selected points on the surface of an object to generate a point cloud which represents the sensed positions of the selected points; that recognizes geometric shapes represented by groups of points in the point cloud, and that generates a model that represents these geometric shapes. The model may be transformed into a further model usable by computer-aided design (CAD) tools, including conventional CAD tools.
BACKGROUND
Mapping the geometry (shape, dimensions and location) and other attributes (e.g., color, texture and reflectance intensity) of complex real objects (whether small components such as small mechanical parts or large objects such as buildings and sites) has conventionally been a tedious and time consuming process. That is, such measurement have traditionally been performed manually. In addition, transforming these measurements into drawings or computer models required manual drafting or input into a CAD system for the production of the drawing or computer models.
Recently innovations have endeavored to simplify this process, but all have fallen short of achieving full integration, automation, precision, speed and range. For example, in the building industry, mapping a structure conventionally requires three basic steps:
1. Field data gathering
2. Data reduction and preparation
3. Drafting and CAD
The field data gathering step is performed by a team of surveyors who manually measure and record dimensions of pertinent components of the structure such as walls, ceilings, beams, columns, doors, windows, fixtures, pipes, conduits and equipment. The surveyors attempt to determine the geometry of the components as well as the relative location of the components in the structure. The surveyors recorded the data in a field notebook. The field-collected data is then organized and reduced to tables and organized sketches, and a CAD operator or drafter utilizes these tables to generate final drawings or models.
This process is labor intensive, time consuming, and error prone. In addition, using traditional surveying methods, the number of points which can actually be measured is very limited, due to the high cost of acquiring each point in terms of time and effort. Furthermore, if it is desired to acquire color, texture and other attribute information, additional field notes must be taken (e.g., still photographs and video).
Recently, the field step has been somewhat automated by using a laser ranging device built into or mounted on an electronic theodolite. Precision reflection targets (retro reflectors) are placed at the locations of the object for which measurements are desired. Then, the laser ranging device obtains a precise measurement of the distance between the instrument and the target, which the theodolite provides an accurate indication of the horizontal and vertical angle offsets to the point relative to a given coordinate system. The distance and angle data are either recorded automatically on a magnetic device connected to the instrument or are reduced within the instrument to Cartesian coordinates relative to the instrument axes. This procedure is then repeated as many times as necessary to map a desired number of points of the object. The collected coordinates data can then be plotted directly on a CAD system.
Unfortunately, the plot is of little practical use since it does not indicate the object geometry. Moreover, because of the requirement for retro reflectors which must be manually placed, and because of the relatively long time per reading required by the laser range finder, the gathering of sufficient points to describe most objects is very labor intensive, time consuming and error prone.
Another known field gathering data process employs stereo photography and aerial photogrammetry. That is, stereoscopic images are taken of the objects and the resulting stereo photographs are registered either manually or using computerized techniques to reproduce the relative location of the camera picture plane location at the time each photograph was taken. The data reduction and preparation step is performed manually by a specially trained operator. Specifically, with the aid of specially mounted stereoscopic viewing lenses, the operator digitizes the coordinates of a sufficient number of points to allow the definition of the objects using the stereo photographs. Again, the digitized data is input into a CAD system or is manually drawn on paper.
SUMMARY
The present invention is an integrated system for generating a model of a three-dimensional object. A scanning laser device scans the three-dimensional object and generates a point cloud. The points of the point cloud each indicate a location of a corresponding point on a surface of the object. A first model is generated, responsive to the point cloud, representing constituent geometric shapes of the object. A data file is generated, responsive to the first model, that can be inputted to a computer-aided design system.
The subject invention further includes a method of controlling the timing of output pulses from a laser for use in a device which requires scanning of the laser output, wherein each output pulse is generated in response to a pump pulse comprising the steps of: monitoring the time delay between the initiation of the pump pulses and the subsequent generation of the associated output pulses; predicting the time delay between the initiation of next pump pulse and the associated output pulse based on the monitored time delays and; initiating the next pump pulse at a time selected to insure the output pulse is generated at a time to permit proper positioning of the laser output during the scan of the beam.
The present invention further includes a method of manually separating from a plurality of clouds of points, representing three-dimensional features in a scene, a subset of the points that represents a desired feature in the scene, the method comprising: selecting all the point clouds that include at least some data points representing the desired feature; and changing a view of the clouds and drawing a polygonal lasso to refine a selected subset of points to be included in a point sub-cloud and repeating the refining as many times as required to obtain the desired sub-cloud.
The present invention further includes a method for automatically segmenting a scan field of a scene into subsets of points that represent different surfaces in the scene, comprising the steps of: separating the scan field into a depth grid that includes depth information for scanned points of surfaces in the scene and a normal grid that includes an estimate of a normal to scanned points of the surfaces; convolving the depth information of the depth grid to generate a depth rating image whose values represent a gradient of depth change from one scanned point to another scanned point in the scene; convolving the components of the normal grid to generate a scalar value for each component for each point of the normal grid; for each point of the normal grid, determining from the scalar values for the components of that particular point a gradient of the normal at that point, wherein the gradients determined for the points of the normal grid collectively constitute a normal rating image; converting the depth rating image to a binary depth image using a recursive thresholding technique; converting the normal rating image to a binary normal image using a recursive thresholding technique; combining the binary depth image and the binary normal image to determine a single edge image; and grouping subsets of non-edge points as belonging to corresponding surfaces of the scene.
The method can further include the steps of determining the type of geometric primitive that would best first each group of points; fitting the geometric primitive to the d
Brunkhart Mark
Dimsdale Jerry
Kacyra Ben K.
Kung Jonathan Apollo
Thewalt Christopher Robin
Cyra Technologies, Inc.
Nguyen Phu K.
Stallman & Pollock LLP
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