We dream a magic button for 3D point cloud processing
 Joon
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We dream a magic button for 3D point cloud processing
Free technology preview of Point Cloud Feature Extraction for Autodesk Revit Now Available (January 24, 2012)
New free way to do auto feature extraction in Revit (February 1, 2012)
The history of 3D point cloud processing is dating back to 1960’s.
The most fatal misconception of 3D point cloud processing may be the edge detection in point cloud. Edge detection in 2D image is worth, but not in 3D point cloud. Some people have been applying edge detection, a 2D image processing technique, to 3D point cloud data. Radially and laterally from the viewpoint of measuring device, the edges of 3D point cloud are generally unstable and unreliable.
Taking an image is generating a 2D map of 3D space. Edge detection in 2D image is finding the boundary points of 2D regions. The 2D boundaries may be represented by 2D curves fitted to the 2D edgels (edge elements) extracted. Worth!
3D measurement is generating a 3D point cloud, the boundary points of 3D volumetric regions. The 3D boundaries may be represented by 3D surfaces fitted to the 3D points measured. Worth!
3D space is more complex than 2D space. 2D space is a constrained subset of 3D space. Data processing techniques applied to 2D images have practically nothing to do with 3D point cloud and should not be applied to.
For 3D point cloud processing, first, we must identify/understand the physics and the statistics & probability theory of 3D measurement. Second, we have to more understand than known the analytic geometry of surfaces.
We must process the point cloud directly without meshing. First, meshing requires much time. Second, the meshes generated from point cloud are arbitrary artifacts that do not really exist. Finally, if the measurement noise of point cloud is large in comparison with the distances between neighboring points, meshing will be resulting in spiky meshes.
By signal processing theory, the variance of the normal vectors of mesh triangles will be amplified by the measurement noise, because the normal vectors of mesh triangles are inherently differentials of points, i.e. slopes between points.
We must suppress the noises of 3D point cloud to get the geometry of object surface. The accurate & reliable shapes, sizes, positions & orientations of 3D object surfaces are what we want to know in real time. All the necessary mathematics and knowledge for the 3D point cloud processing mentioned above are applied to the videos below:
Real Time 3D Object Recognition
youtu.be/6tn4rMeIBaw
FindSurface SDK  Revit Plugin Demo
youtu.be/ThpXAFJEBJ4
What must be done is the combination of 2D image processing and 3D point cloud processing. The information of 2D image and 3D point cloud are inherently mutually orthogonal, delivering complementary information. 3D surfaces can be determined by 3D point cloud processing, of which boundaries can be determined by 2D image processing.
Joon
New free way to do auto feature extraction in Revit (February 1, 2012)
The history of 3D point cloud processing is dating back to 1960’s.
The most fatal misconception of 3D point cloud processing may be the edge detection in point cloud. Edge detection in 2D image is worth, but not in 3D point cloud. Some people have been applying edge detection, a 2D image processing technique, to 3D point cloud data. Radially and laterally from the viewpoint of measuring device, the edges of 3D point cloud are generally unstable and unreliable.
Taking an image is generating a 2D map of 3D space. Edge detection in 2D image is finding the boundary points of 2D regions. The 2D boundaries may be represented by 2D curves fitted to the 2D edgels (edge elements) extracted. Worth!
3D measurement is generating a 3D point cloud, the boundary points of 3D volumetric regions. The 3D boundaries may be represented by 3D surfaces fitted to the 3D points measured. Worth!
3D space is more complex than 2D space. 2D space is a constrained subset of 3D space. Data processing techniques applied to 2D images have practically nothing to do with 3D point cloud and should not be applied to.
For 3D point cloud processing, first, we must identify/understand the physics and the statistics & probability theory of 3D measurement. Second, we have to more understand than known the analytic geometry of surfaces.
We must process the point cloud directly without meshing. First, meshing requires much time. Second, the meshes generated from point cloud are arbitrary artifacts that do not really exist. Finally, if the measurement noise of point cloud is large in comparison with the distances between neighboring points, meshing will be resulting in spiky meshes.
By signal processing theory, the variance of the normal vectors of mesh triangles will be amplified by the measurement noise, because the normal vectors of mesh triangles are inherently differentials of points, i.e. slopes between points.
We must suppress the noises of 3D point cloud to get the geometry of object surface. The accurate & reliable shapes, sizes, positions & orientations of 3D object surfaces are what we want to know in real time. All the necessary mathematics and knowledge for the 3D point cloud processing mentioned above are applied to the videos below:
Real Time 3D Object Recognition
youtu.be/6tn4rMeIBaw
FindSurface SDK  Revit Plugin Demo
youtu.be/ThpXAFJEBJ4
What must be done is the combination of 2D image processing and 3D point cloud processing. The information of 2D image and 3D point cloud are inherently mutually orthogonal, delivering complementary information. 3D surfaces can be determined by 3D point cloud processing, of which boundaries can be determined by 2D image processing.
Joon
Last edited by Joon on Tue Oct 09, 2018 3:39 am, edited 3 times in total.
 Joon
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 Location: Seongnamsi, Korea
 Contact:
Re: We dream a magic button for 3D point cloud processing
Appended to the original post:
We must process the point cloud directly without meshing. First, meshing requires much time. Second, the meshes generated from point cloud are arbitrary artifacts that do not really exist. Finally, if the measurement noise of point cloud is large in comparison with the distances between neighboring points, meshing will be resulting in spiky meshes.
By signal processing theory, the variance of the normal vectors of mesh triangles will be amplified by the measurement noise, because the normal vectors of mesh triangles are inherently differentials of points, i.e. slopes between points.
Experiment on ARKit with iPad Pro
youtu.be/2TlJ_8Tq6js
We must process the point cloud directly without meshing. First, meshing requires much time. Second, the meshes generated from point cloud are arbitrary artifacts that do not really exist. Finally, if the measurement noise of point cloud is large in comparison with the distances between neighboring points, meshing will be resulting in spiky meshes.
By signal processing theory, the variance of the normal vectors of mesh triangles will be amplified by the measurement noise, because the normal vectors of mesh triangles are inherently differentials of points, i.e. slopes between points.
Experiment on ARKit with iPad Pro
youtu.be/2TlJ_8Tq6js
 Joon
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 Posts: 185
 Joined: Wed Aug 21, 2013 8:01 pm
 Full Name: Sung Joon Ahn
 Company Details: CurvSurf
 Company Position Title: CEO_CTO
 Country: Republic of Korea
 Linkedin Profile: Yes
 Location: Seongnamsi, Korea
 Contact:
Re: We dream a magic button for 3D point cloud processing
Optical 3D measurement techniques, e.g., photogrammetry, structuredlight, timeofflight, are subject to lateral and radial measurement uncertainties. The lateral uncertainties are mainly concerning about the resolution of encoder or sensor cells. On the other hand, the radial uncertainties are concerns about the S/N ratio.
If 3D measurement data are accurate and free from the problems of concentricity/collinearity/coplanarity, 3 points are enough for clearly determining the parameter values of an object plane measured (1 point for point, 2 points for line, 3 points for plane, 4 points for sphere, 5 points for cylinder, 6 points for cone, and 7 points for torus, etc.). In general, 3D measurement data are containing millions of points.
By the 3D point cloud processing with PCA (principal component analysis) for line/plane, or with ODF (orthogonal distance fitting) for curve/surface, there will be no more practical advantages in parameter accuracy by processing more than about 1,000 inliers. Thus, to accelerate the speed of data processing, we can randomly sample the input point cloud data to limit the number of inliers to about 1,000. As a result, the time for recognizing a 3D object geometry is quasiconstant independently on the size of input point cloud, because the data random sampling is much cheaper than the data processing with PCA or ODF.
The number 1,000 of data sampling is optimal from the viewpoint of statistics and economy (cost).
The number 1,000 of data sampling is the magic number compromising between accuracy and cost.
The number 1,000 is the large number to which we can count in real life from 1.
When we recognize a 3D object geometry, we first gaze a 3D object point and analyze the neighboring points around the gazing point. Gazing only a single 3D point is crucial for accelerating the 3D object recognition. We need not care the all environment around us in order to recognize a geometric object. The size of the neighboring region around the gazing point is determined by the scale of the 3D object geometry, i.e., the curvature radii of the object surface to be recognized.
Through the curvature analysis of the neighboring points (i.e., initial inlier points) around the gazing point, the shape type of object surface and the initial values of the shape parameters can be determined. ODF to the current inlier points updates the shape parameter values. And, region growing based on the current object shape updates the inlier points. By alternating ODF (model fitting) and region growing (segmentation), the shape parameter values and the inlier points can be determined simultaneously.
Fully automatic feature extraction from point cloud
youtu.be/42ZKbkjpBbY
CT: Fully automatic feature extraction from point cloud
youtu.be/pEFpHJ9HcC8
On the other hand, RANSAC (random sample consensus) selects randomly three points from the input point cloud to determine the parameter values of object plane. But, the probability of encountering 3 points of an object plane out of a large point cloud is extremely low. In order to find an object plane, we must try many times selecting 3 points. RANSAC is a singlemodal method, finding a line (2 points), a plane (3 points), a sphere (4 points), a cylinder (5 points), a cone (6 points), or a torus (7 points), one by one. Furthermore, the computing time increases exponentially as the number of the points to be selected is increasing. The Hough transform suffers from the similar shortcomings of RANSAC.
In summary, the gazing (selecting) a single point per object and the direct processing of point cloud without meshing are crucial for automatic realtime object recognition.
One shot one get.
Joon
If 3D measurement data are accurate and free from the problems of concentricity/collinearity/coplanarity, 3 points are enough for clearly determining the parameter values of an object plane measured (1 point for point, 2 points for line, 3 points for plane, 4 points for sphere, 5 points for cylinder, 6 points for cone, and 7 points for torus, etc.). In general, 3D measurement data are containing millions of points.
By the 3D point cloud processing with PCA (principal component analysis) for line/plane, or with ODF (orthogonal distance fitting) for curve/surface, there will be no more practical advantages in parameter accuracy by processing more than about 1,000 inliers. Thus, to accelerate the speed of data processing, we can randomly sample the input point cloud data to limit the number of inliers to about 1,000. As a result, the time for recognizing a 3D object geometry is quasiconstant independently on the size of input point cloud, because the data random sampling is much cheaper than the data processing with PCA or ODF.
The number 1,000 of data sampling is optimal from the viewpoint of statistics and economy (cost).
The number 1,000 of data sampling is the magic number compromising between accuracy and cost.
The number 1,000 is the large number to which we can count in real life from 1.
When we recognize a 3D object geometry, we first gaze a 3D object point and analyze the neighboring points around the gazing point. Gazing only a single 3D point is crucial for accelerating the 3D object recognition. We need not care the all environment around us in order to recognize a geometric object. The size of the neighboring region around the gazing point is determined by the scale of the 3D object geometry, i.e., the curvature radii of the object surface to be recognized.
Through the curvature analysis of the neighboring points (i.e., initial inlier points) around the gazing point, the shape type of object surface and the initial values of the shape parameters can be determined. ODF to the current inlier points updates the shape parameter values. And, region growing based on the current object shape updates the inlier points. By alternating ODF (model fitting) and region growing (segmentation), the shape parameter values and the inlier points can be determined simultaneously.
Fully automatic feature extraction from point cloud
youtu.be/42ZKbkjpBbY
CT: Fully automatic feature extraction from point cloud
youtu.be/pEFpHJ9HcC8
On the other hand, RANSAC (random sample consensus) selects randomly three points from the input point cloud to determine the parameter values of object plane. But, the probability of encountering 3 points of an object plane out of a large point cloud is extremely low. In order to find an object plane, we must try many times selecting 3 points. RANSAC is a singlemodal method, finding a line (2 points), a plane (3 points), a sphere (4 points), a cylinder (5 points), a cone (6 points), or a torus (7 points), one by one. Furthermore, the computing time increases exponentially as the number of the points to be selected is increasing. The Hough transform suffers from the similar shortcomings of RANSAC.
In summary, the gazing (selecting) a single point per object and the direct processing of point cloud without meshing are crucial for automatic realtime object recognition.
One shot one get.
Joon
Last edited by Joon on Sat Nov 03, 2018 10:46 pm, edited 7 times in total.
 Joon
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Re: We dream a magic button for 3D point cloud processing
Another crucial requirement of an efficient and accurate 3D point cloud processing is the separation between the internal and the external parameters of object surfaces in space.
Object surfaces in space have shapes, sizes, positions, and rotations. The shape and size of an object surface will not be changed by the movement of the object surface. The shape and size are internal parameters of object surface in space, and, the position and rotation are external parameters.
(also, with the bundle adjustment of photogrammetry, we separate the camera parameters into the two groups. The internal parameters will not be changed substantially when the camera is being moved, except some correlations between parameters. The external parameters represent the movement of camera).
The separation of the object surface parameters into the two groups is of great advantage to 3D point cloud processing. First, there is not the problem of large coordinate values. Second, the parameter correlations are minimized. Third, object surface can be recognized (identified) based on the shape and size, independently on the position and rotation. Fourth, the parameter separation is beneficial to applications. Finally, the 3D point cloud processing is speedy and accurate.
Joon
Object surfaces in space have shapes, sizes, positions, and rotations. The shape and size of an object surface will not be changed by the movement of the object surface. The shape and size are internal parameters of object surface in space, and, the position and rotation are external parameters.
(also, with the bundle adjustment of photogrammetry, we separate the camera parameters into the two groups. The internal parameters will not be changed substantially when the camera is being moved, except some correlations between parameters. The external parameters represent the movement of camera).
The separation of the object surface parameters into the two groups is of great advantage to 3D point cloud processing. First, there is not the problem of large coordinate values. Second, the parameter correlations are minimized. Third, object surface can be recognized (identified) based on the shape and size, independently on the position and rotation. Fourth, the parameter separation is beneficial to applications. Finally, the 3D point cloud processing is speedy and accurate.
Joon
Last edited by Joon on Mon Oct 22, 2018 12:15 am, edited 1 time in total.

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Re: We dream a magic button for 3D point cloud processing
I am always interested reading your posts.
Thanks
Thanks
 Joon
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Re: We dream a magic button for 3D point cloud processing
Dear AJ Swanepoel,
Thank you for your interest.
I will be appending more logic of FindSurface SDK to this post, except sensitive original knowhow's of CurvSurf.
Joon
 Joon
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Re: We dream a magic button for 3D point cloud processing
Edgels (edge elements) are ‘boundary points’ of 2D regions in 2D image.
Surfels (surface elements) are ‘boundary points’ of 3D volumetric regions in 3D space.
2D cameras generate 2D images. Edge detection in 2D images extracts the ‘boundary points’ of 2D regions.
3D cameras generate 3D point clouds, the ‘boundary points’ of 3D volumetric regions.
What about the ‘boundary points’ of ‘boundary points’?
They are unstable and unreliable, dependent on the lateral resolution (point density) of sensor, environmental conditions, software algorithm used, etc. Useless!
Some Thoughts on 2D/3D Information Processing
youtu.be/9kDixwHUWSY
Surfels (surface elements) are ‘boundary points’ of 3D volumetric regions in 3D space.
2D cameras generate 2D images. Edge detection in 2D images extracts the ‘boundary points’ of 2D regions.
3D cameras generate 3D point clouds, the ‘boundary points’ of 3D volumetric regions.
What about the ‘boundary points’ of ‘boundary points’?
They are unstable and unreliable, dependent on the lateral resolution (point density) of sensor, environmental conditions, software algorithm used, etc. Useless!
Some Thoughts on 2D/3D Information Processing
youtu.be/9kDixwHUWSY
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Last edited by Joon on Wed Oct 31, 2018 10:28 am, edited 2 times in total.
 Joon
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Re: We dream a magic button for 3D point cloud processing
Dear Anybody,
Please find your ID & PW for FindSurface for Web below,
ID: YourCompanyName
PW: **********
(Please email to [email protected] for your ID/PW using your company/university email address).
Please do not let your ID & PW be known to others.
FindSurface for Web is a prototype web service for demonstrating the functionalities of FindSurface, without installing the FindSurface SDK on your computer.
FindSurface for Web is a prototype web service without optimization for web traffic.
Overview outlines the FindSurface for Web.
Interface explains the UI applied.
StepbyStep shows the howtouse to be followed.
The input parameters for FindSurface:
1. Accuracy:
The measurement accuracy of the input point cloud data. We recommend 1/2 ~ 2/1 of what you believe. FindSurface works based on your number input. FindSurface could analyze the input point cloud and estimate the number but would not.
2. Mean Distance:
The mean distance between neighboring points. We recommend 3~10 times larger value than the apparent value because of a possible large hole among object surfaces. In other words, a large hole inside object surface area decreases the mean density of inlier points (i.e., increases the mean distance of inlier points).
3. Touch Radius:
Concerns for object scales/curvatures. Large value for large object, small value for small object. We recommend 1/4 ~ 2/1 of the object radius, dependently on the measurement accuracy. Large value for inaccurate data, small value for accurate data.
4. Radial Expansion:
The level of radial region expansion, controlling the thickness of the ROI volume. Large value for accurate data, small value for inaccurate data. A small value suppresses noises.
5. Lateral Extension:
The level of lateral elongation, controlling the length/width of the ROI volume. Large value for long/wide objects, small value for short/narrow objects. A small value shortens object length/width.
6. Gazing point:
You have to select a seed point among point cloud. If you could automate the selection of seed points, the whole process could be automated. The seed points corresponding to the object surfaces in a scene might be prepared by 2D image segmentation. 3D CAD data could also provide seed points. It’s up to yours, the application developers!
We will be distributing an App together with its source codes for probing the principles of FindSurface.
You can probe the functional behavior of FindSurface by changing the program parameters.
Joon
Please find your ID & PW for FindSurface for Web below,
ID: YourCompanyName
PW: **********
(Please email to [email protected] for your ID/PW using your company/university email address).
Please do not let your ID & PW be known to others.
FindSurface for Web is a prototype web service for demonstrating the functionalities of FindSurface, without installing the FindSurface SDK on your computer.
FindSurface for Web is a prototype web service without optimization for web traffic.
Overview outlines the FindSurface for Web.
Interface explains the UI applied.
StepbyStep shows the howtouse to be followed.
The input parameters for FindSurface:
1. Accuracy:
The measurement accuracy of the input point cloud data. We recommend 1/2 ~ 2/1 of what you believe. FindSurface works based on your number input. FindSurface could analyze the input point cloud and estimate the number but would not.
2. Mean Distance:
The mean distance between neighboring points. We recommend 3~10 times larger value than the apparent value because of a possible large hole among object surfaces. In other words, a large hole inside object surface area decreases the mean density of inlier points (i.e., increases the mean distance of inlier points).
3. Touch Radius:
Concerns for object scales/curvatures. Large value for large object, small value for small object. We recommend 1/4 ~ 2/1 of the object radius, dependently on the measurement accuracy. Large value for inaccurate data, small value for accurate data.
4. Radial Expansion:
The level of radial region expansion, controlling the thickness of the ROI volume. Large value for accurate data, small value for inaccurate data. A small value suppresses noises.
5. Lateral Extension:
The level of lateral elongation, controlling the length/width of the ROI volume. Large value for long/wide objects, small value for short/narrow objects. A small value shortens object length/width.
6. Gazing point:
You have to select a seed point among point cloud. If you could automate the selection of seed points, the whole process could be automated. The seed points corresponding to the object surfaces in a scene might be prepared by 2D image segmentation. 3D CAD data could also provide seed points. It’s up to yours, the application developers!
We will be distributing an App together with its source codes for probing the principles of FindSurface.
You can probe the functional behavior of FindSurface by changing the program parameters.
Joon
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Last edited by Joon on Thu Nov 01, 2018 6:04 am, edited 8 times in total.
 Joon
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 Posts: 185
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 Location: Seongnamsi, Korea
 Contact:
Re: We dream a magic button for 3D point cloud processing
Now, we know that object curve/surface parameters can be divided into the two groups of internal and external parameters. The internal parameters represent the shape and size, and the external parameters represent the position and rotation of curve/surface in space.
We examine the position and rotation of curve/surface in space.
First, we look the formulas of 2D line.
1. Y = a X + b.
The most familiar formula of 2D line. The parameter a is the slope, and b is the yintersection of 2D line. But, this formula cannot represent a vertical 2D line. Furthermore, when the measurement data are remote from the origin, the value b is unstable and unreliable. The parameter b cannot represent the position of an object 2D line remote from the origin (the problem of large coordinate values).
2.  X sin(k) + Y cos(k)  d = 0.
( sin(k), cos(k) ) is the normal, and d the distance from the origin, and k the slope angle of a line. This formula is used by Hough transform of 2D lines. It works well! But, only for 2D lines in camera images with the bounded coordinate values up to few thousands. When the measurement data are remote from the origin, the value d is unstable and unreliable. The parameter d cannot represent the position of an object 2D line remote from the origin (the problem of large coordinate values).
3. (X, Y) = (Xo, Yo) + u ( cos(k), sin(k) ).
( cos(k), sin(k) ) is the direction, and (Xo, Yo) is the position of line. (Xo, Yo) is the mass center of the measurement data. ( cos(k), sin(k) ) is the axis direction (eigenvector) of the minimum rotational inertia (eigenvalue) centered at (Xo, Yo). Once measurement data are given, k and (Xo, Yo) are the most stable and reliable representation of an object 2D line.
Next, we will look the position/rotation of cylinder/cone in 3D space.
Joon
We examine the position and rotation of curve/surface in space.
First, we look the formulas of 2D line.
1. Y = a X + b.
The most familiar formula of 2D line. The parameter a is the slope, and b is the yintersection of 2D line. But, this formula cannot represent a vertical 2D line. Furthermore, when the measurement data are remote from the origin, the value b is unstable and unreliable. The parameter b cannot represent the position of an object 2D line remote from the origin (the problem of large coordinate values).
2.  X sin(k) + Y cos(k)  d = 0.
( sin(k), cos(k) ) is the normal, and d the distance from the origin, and k the slope angle of a line. This formula is used by Hough transform of 2D lines. It works well! But, only for 2D lines in camera images with the bounded coordinate values up to few thousands. When the measurement data are remote from the origin, the value d is unstable and unreliable. The parameter d cannot represent the position of an object 2D line remote from the origin (the problem of large coordinate values).
3. (X, Y) = (Xo, Yo) + u ( cos(k), sin(k) ).
( cos(k), sin(k) ) is the direction, and (Xo, Yo) is the position of line. (Xo, Yo) is the mass center of the measurement data. ( cos(k), sin(k) ) is the axis direction (eigenvector) of the minimum rotational inertia (eigenvalue) centered at (Xo, Yo). Once measurement data are given, k and (Xo, Yo) are the most stable and reliable representation of an object 2D line.
Next, we will look the position/rotation of cylinder/cone in 3D space.
Joon
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Last edited by Joon on Mon Nov 19, 2018 10:47 pm, edited 2 times in total.
 Joon
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 Contact:
Re: We dream a magic button for 3D point cloud processing
We examine the positions of cylinder/cone in space.
The rotation/direction of cylinder/cone is represented by the axis. The position of cylinder/cone is a point on axis. But, there are infinite number of axis points. We must define an axis point as the cylinder/cone position.
Many people have been defining the position of cylinder/cone on the XYplane, or, at the nearest point from the origin, or, at the vertex of cone.
They are unstable and unreliable, if the measurement points are remote from them. In other words, they have nothing to do with the position of object cylinder/cone. A work on coordinate metrology and applications, based on such definitions of object cylinder/cone, is destined to be unsuccessful.
The position of object cylinder/cone should be defined inside the object cylinder/cone, then it will be stable and reliable: the nearest point on axis from the mass center of measurement points.
PS: Cylinder is a special instance of cone with zero vertex angle. It can be verified by making a paper cylinder/cone. The analogue of cylinder/cone, sphere/cylinder/torus will be posted at the end of this posting.
Joon
The rotation/direction of cylinder/cone is represented by the axis. The position of cylinder/cone is a point on axis. But, there are infinite number of axis points. We must define an axis point as the cylinder/cone position.
Many people have been defining the position of cylinder/cone on the XYplane, or, at the nearest point from the origin, or, at the vertex of cone.
They are unstable and unreliable, if the measurement points are remote from them. In other words, they have nothing to do with the position of object cylinder/cone. A work on coordinate metrology and applications, based on such definitions of object cylinder/cone, is destined to be unsuccessful.
The position of object cylinder/cone should be defined inside the object cylinder/cone, then it will be stable and reliable: the nearest point on axis from the mass center of measurement points.
PS: Cylinder is a special instance of cone with zero vertex angle. It can be verified by making a paper cylinder/cone. The analogue of cylinder/cone, sphere/cylinder/torus will be posted at the end of this posting.
Joon
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Last edited by Joon on Sat Dec 08, 2018 1:30 am, edited 3 times in total.