METHOD OF GENERATING A SMOOTH IMAGE FROM POINT CLOUD DATA

• US 20140313527A1
• Filed: 01/28/2014
• Published: 10/23/2014
• Est. Priority Date: 06/25/2012
• Status: Active Grant

• Current Assignee: Yoldas Askan
• Original Assignee: Yoldas Askan
• Inventors: Askan, Yoldas
• Granted Patent: US 9,300,841 B2
• Time in Patent Office: Days
• US Class Current: 358/1.9
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G06T 1/60 Memory management
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 2200/08 involving all processing steps from image acquisition to 3D model generation
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: H04N 1/38 Circuits or arrangements for blanking or otherwise eliminating unwanted parts of pictures H04N1/387 takes precedence

Abstract

A method for processing an array of pixels in a point cloud, comprises calculating local error limits for each distance value for each pixel in the processed point cloud data set. One may then determine the error bar. One begins a distance value adjusting loop by for each pixel in the processed point cloud data set by calculating the difference between the distance value in the pixel of the point cloud data set being processed and each of the neighboring pixels or the most suitable neighboring pixel distance value is determined whether the difference is within the range defined by the error bar. It the difference is not within the error bar, the distance value is changed for the pixel being processed by a small fraction while keeping the new distance value within the range defined by the original distance value for the pixel being processed plus or minus the error bar. If the difference is within the error bar the distance value in the pixel being processed is replaced by a weighted average value. The number of neighboring pixels with their distance values within the error bar for the pixel being processed is counted and if the count is greater than a predetermined threshold, average the counted distance values and substitute the average for the pixel distance value, but if the count is below the threshold leave the pixel distance value unchanged. It is determined whether loop exit criteria have been met and if loop exit criteria have not been met beginning the loop again, and if loop exit criteria have been met, terminating the loop.

Claims

1. A method for processing an array of pixels in a point cloud, comprising:

(a) calculating local error limits for each distance value for each pixel in the processed point cloud data set;

(b) determining the error bar;

(c) beginning a distance value adjusting loop by for each pixel in the processed point cloud data set by calculating the difference between the distance value in the pixel of the point cloud data set being processed and each of the neighboring pixels or the most suitable neighboring pixel distance value;

(d) determining whether the difference is within the range defined by the error bar;

(e) if the difference is not within the error bar, changing the distance value for the pixel being processed by a small fraction while keeping the new distance value within the range defined by the original distance value for the pixel being processed plus or minus the error bar;

(f) if the difference is within the error bar the distance value in the pixel being processed is replaced by a weighted average value;

(g) count the number of neighboring pixels with their distance values within the error bar for the pixel being processed and if the count is greater than a predetermined threshold, average the counted distance values and substitute the average for the pixel distance value, but if the count is below the threshold leave the pixel distance value unchanged; and

(h) determining whether loop exit criteria have been met and if loop exit criteria have not been met beginning the loop again, and if loop exit criteria have been met, terminating the loop.

2. A method as in claim 1, further comprising calculating resolution.

3. A method as in claim 1, wherein said calculating local error limits for each distance value for each pixel in the processed point cloud data set is done by observing the maximum and minimum values observed in the eight intermediate neigh pixels.

4. A method as in claim 1, wherein said calculating local error limits for each distance value for each pixel in the processed point cloud data set is done by observing the maximum and minimum values observed in the eight intermediate neighboring pixels and the 16 second-order neighboring pixels, such calculation of local error limits being calculated where the requisite number of intermediate neighboring pixels exist.

5. A method as in claim 1, wherein distance values are processed only where the requisite number of intermediate neighboring pixels exist.

6. A method as in claim 1, wherein the weighted average value is calculated using the formula:

new distance value=((Current_distance_value*loop_count)+Changed_distance_value)/(loop_count+1).

7. A method as in claim 1, wherein the processed point cloud data set is generated by a method comprising:

(i) performing a first scanning of a surface of an object or a portion thereof with a light source;

(j) collecting first light reflected by the surface in response to the first scanning and converting the first light reflected by the surface into a first scan file of matrices of data points representing point position measurements and saving as a first scan file;

(k) performing additional surface scanning and light collection to generate a plurality of additional matrices of additional data points representing additional point position measurements, and saving said matrices as additional three dimensional scan files, said first three dimensional scan file and said additional three dimensional scan files, comprising a generated set of scan files;

(l) optionally selecting some of all of said generated set of scan files to form a selected set of scan files;

(m) determining a template for receiving the matrices of data points corresponding to said selected set of scan files;

where such pixel labeling convention is row multiplied by column

(n) loading said first three dimensional scan file into the template to form a first data array;

(o) computing an error bar for each of the pixels in the first data array;

(p) comparing each additional data point to the corresponding point in said first data array to generate a corresponding difference value;

(q) replacing each additional data point, associated with a difference value within its respective error bar, with the average of all data points within the respective error bar, and discarding other data points to generate a processed data array.

8. A method as in claim 1, wherein the processed point cloud data set is generated by a method comprising:

(i) performing a first scanning of a surface of an object or a portion thereof with a light source;

laser;

(j) collecting first light reflected by the surface in response to the first scanning and converting the first light reflected by the surface into a first scan file of matrices of data points representing point position measurements and saving as a first three dimensional scan file;

(k) computing an error bar for each of the pixels is the processed data array;

(l) for each pixel in the processed data array, counting the number of surrounding pixels having values within the error bar;

(m) for each pixel in the processed data array, retaining those pixels having a minimum number of surrounding pixels having values within the error bar and discarding pixels not having a minimum number of surrounding pixels having values within the error bar to generate a twice processed data array

(n) calculating the error bar for the intermediate processed data set;

(o) for each pixel in the intermediate processed data set, calculating the difference between the pixel value and either a corresponding pixel in another data set or an adjacent pixel in the three times processed data set; and

(p) if the difference is within the error bar for the intermediate processed data set, replacing pixel distance.

9. A method as in claim 1, wherein the processed point cloud data set is generated by a method comprising:

(i) performing a first scanning of a surface of an object or a portion thereof with a light source;

laser;

(j) collecting first light reflected by the surface in response to the first scanning and converting the first light reflected by the surface into a first scan file of matrices of data points representing point position measurements and saving as a first three dimensional scan file;

(k) for a plurality of pixels in the processed data array, determine the maximum and minimum values in the surrounding set of neighboring pixels;

(l) for each of said plurality of pixels in the processed data array, compute an error bar;

(m) for each of said plurality of pixels in the processed data array determine the difference between the maximum and minimum values of surrounding neighboring pixels; and

(n) if the difference between the maximum and minimum values of surrounding neighboring pixels is smaller than the difference between the maximum and minimum values of surrounding neighboring pixels, store the average value of the maximum and minimum values, otherwise retain the original value of the pixel, to generate an intermediate processed data set.

10. A method as in claim one, wherein the loop exit criteria is determined by a fixed number of loops.

11. A method as in claim one, wherein the loop exit criteria is deviation outside of the range defined between upper and lower distance value limits.