Systems and methods for estimating a parameter for a 3d model

Abstract

The present invention estimates parameters for 3D models. Parameters may include, without limitation, surface topology, edge geometry, luminous or reflective characteristics, visual properties, characterization of noise in the signal, or other. A metric is estimated by quantifying a relationship between a received signal and a reference signal. The metric is then utilized to determine a parameter for a 3D model. The metric may include a measurement such as the cross-correlation of the received signal and the reference signal, or standard deviation of the difference of the received signal and the reference signal, for example. The parameter obtained may then be used to create a reference signal for determination of another parameter.

1 - 34 . (canceled) 
     
     
         35 . A method for refining a surface of a 3D object, the method comprising;
 receiving data representing the surface of a 3D object into memory of a processing unit, said data comprising data from a data acquisition device indicative of a surface property of an object,   estimating the level of noise associated with the data with a processing unit;   identifying a breakline or a plurality of breaklines with a processing unit;   refining the surface or surfaces within a breakline with a processing unit; and   refining the breakline or plurality of breaklines with the processing unit.   
     
     
         36 . The method of  claim 35  wherein said received data comprises;
 3D spatial data that have been previously filtered. 
 
     
     
         37 . The method of  claim 35  wherein said received data comprises;
 3D spatial data that have been previously segmented using an edge detection algorithm applied to a material property of the data. 
 
     
     
         38 . The method of  claim 37  wherein said edge detection algorithm comprises;
 a Canny edge detection method, Sobel edge detection method, Hough Transform feature detection method, or a binary mask based on an aspect of at least of said surface's material properties. 
 
     
     
         39 . The method of  claim 35  wherein said estimating the level of noise comprises;
 utilizing a clustering algorithm to segment said received data by distance, planarity, or orientation of acquisition angle; 
 calculating a statistical metric associated with the cluster; 
 apply said statistical metric to the data within the cluster. 
 
     
     
         40 . The method of  claim 35  wherein estimating the level of noise comprises;
 applying a filter to said data; estimating the deviation of the filtered data from said received data; calculating the characteristics of the said deviation. 
 
     
     
         41 . The method of  claim 35  wherein estimating the level of noise further comprises;
 applying a filter to said data; estimating the deviation of the filtered data from said received data; calculating the noise characteristics of the said deviation. 
 
     
     
         42 . The method of  claim 35  wherein estimating the level of noise comprises;
 receiving into memory of a processing unit a previously computed estimate of the level of noise or a plurality of previously computed estimates of the level of noise. 
 
     
     
         43 . The method of  claim 35  wherein:
 said breakline is a boundary comprising at least one breakline datum that is adjacent to, but unconnected to at least one other breakline datum. 
 
     
     
         44 . The method of  claim 35  wherein identifying a breakline or plurality of breaklines comprises;
 filtering said received raw data; and identifying filtered data that do not exhibit expected characteristics relative to the raw data when compared to the estimated level of noise present. 
 
     
     
         45 . The method of  claim 35  wherein identifying a breakline or plurality of breaklines comprises;
 calculating a histogram of normal vectors; 
 identifying the peak of said histogram; 
 computing the dot product of all normal vectors of surface data and the normal vector at the peak of the histogram; 
 segmenting said histogram according to the deviation from the computed dot product. 
 
     
     
         46 . The method of  claim 35  wherein refining a breakline or plurality of breaklines, or surface or plurality of surfaces comprises;
 filtering said received data; and adjusting the filtered data to correct the surface so that a higher statistical likelihood of accuracy is attained, based on a characteristic of the estimated noise present. 
 
     
     
         47 . The method of  claim 35  wherein refining a breakline or plurality of breaklines, or surface or plurality of surfaces comprises;
 computing parameters for a directional filter; and 
 applying said directional filter to said received data; and adjusting the directionally filtered data to correct the surface so that a higher statistical likelihood of accuracy is attained, based on a characteristic of the estimated noise present. 
 
     
     
         48 . The method of  claim 47  wherein computing said parameters for a directional filter comprises;
 computing a metric based on a material or geometric property of the received data; 
 assigning parameters for a directional filter based on said metric. 
 
     
     
         49 . The method of  claim 48  wherein computing said parameters for a directional filter comprises updating said directional parameters from one iteration of data refinement to the next. 
     
     
         50 . The method of  claim 35  wherein refining a plurality of breaklines and surfaces enclosed within breaklines comprises;
 receiving data corresponding to a plurality of breaklines and surfaces enclosed within breaklines into memory of a plurality of parallel processing units; 
 refining said breaklines and surfaces independently with said plurality of parallel processing units; 
 receiving the plurality of refined breaklines into memory of a central processing unit; 
 
     
     
         51 . The method of  claim 35  wherein refining said surface further comprises;
 eliminating undesired polygons. 
 
     
     
         52 . The method of  claim 51  wherein eliminating undesired polygons further comprises;
 comparing refined polygons to a user defined tolerance; 
 replacing a plurality of relatively coplanar polygons with a single polygon that is within a distance of the user specified tolerance. 
 
     
     
         53 . The method of  claim 52  wherein the deviation between said single polygon and the refined polygons is received into memory as an array of deviations to be utilized as a depth map. 
     
     
         54 . The method of  claim 44  wherein filtering said received data comprises;
 receiving a basis segment of data; 
 receiving a reference signal; 
 estimating a correlation between the basis segment of data and the reference signal; 
 determining a metric or a plurality of metrics associated with said correlation; 
 determining the likelihood of the basis segment of data resembling the reference signal based on the estimated noise.