estimation of the ground plane in pinhole camera model
I am trying to understand the pinhole camera model and the geometry behind some computer vision and camera calibration stuff that I am looking at.
So, if I understand correctly, the pinhole camera model maps the pixel coordinates to 3D real world coordinates. So, the model looks as:
y = K [R|T]x
Here y is pixel coordinates in homogeneous coordinates, R|T represent the extrinsic transformation matrix and x is the 3D world coordinates also in homogeneous coordinates.
Now, I am looking at a presentation which says
project the center of the focus region onto the ground plane using [R|T]
Now the center of the focus region is just taken to be the center of the image. I am not sure how I can estimate the ground plane? Assuming, the point to be projected is in input space, the projection should that be computed by inverting the [R|T] matrix and multiplying that point by the inverted matrix?
EDIT
Source here on page 29: http://romilbhardwaj.github.io/static/BuildSys_v1.pdf
computer-vision geometry projection camera-calibration perspectivecamera
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
add a comment |
I am trying to understand the pinhole camera model and the geometry behind some computer vision and camera calibration stuff that I am looking at.
So, if I understand correctly, the pinhole camera model maps the pixel coordinates to 3D real world coordinates. So, the model looks as:
y = K [R|T]x
Here y is pixel coordinates in homogeneous coordinates, R|T represent the extrinsic transformation matrix and x is the 3D world coordinates also in homogeneous coordinates.
Now, I am looking at a presentation which says
project the center of the focus region onto the ground plane using [R|T]
Now the center of the focus region is just taken to be the center of the image. I am not sure how I can estimate the ground plane? Assuming, the point to be projected is in input space, the projection should that be computed by inverting the [R|T] matrix and multiplying that point by the inverted matrix?
EDIT
Source here on page 29: http://romilbhardwaj.github.io/static/BuildSys_v1.pdf
computer-vision geometry projection camera-calibration perspectivecamera
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
1
Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18
add a comment |
I am trying to understand the pinhole camera model and the geometry behind some computer vision and camera calibration stuff that I am looking at.
So, if I understand correctly, the pinhole camera model maps the pixel coordinates to 3D real world coordinates. So, the model looks as:
y = K [R|T]x
Here y is pixel coordinates in homogeneous coordinates, R|T represent the extrinsic transformation matrix and x is the 3D world coordinates also in homogeneous coordinates.
Now, I am looking at a presentation which says
project the center of the focus region onto the ground plane using [R|T]
Now the center of the focus region is just taken to be the center of the image. I am not sure how I can estimate the ground plane? Assuming, the point to be projected is in input space, the projection should that be computed by inverting the [R|T] matrix and multiplying that point by the inverted matrix?
EDIT
Source here on page 29: http://romilbhardwaj.github.io/static/BuildSys_v1.pdf
computer-vision geometry projection camera-calibration perspectivecamera
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
I am trying to understand the pinhole camera model and the geometry behind some computer vision and camera calibration stuff that I am looking at.
So, if I understand correctly, the pinhole camera model maps the pixel coordinates to 3D real world coordinates. So, the model looks as:
y = K [R|T]x
Here y is pixel coordinates in homogeneous coordinates, R|T represent the extrinsic transformation matrix and x is the 3D world coordinates also in homogeneous coordinates.
Now, I am looking at a presentation which says
project the center of the focus region onto the ground plane using [R|T]
Now the center of the focus region is just taken to be the center of the image. I am not sure how I can estimate the ground plane? Assuming, the point to be projected is in input space, the projection should that be computed by inverting the [R|T] matrix and multiplying that point by the inverted matrix?
EDIT
Source here on page 29: http://romilbhardwaj.github.io/static/BuildSys_v1.pdf
computer-vision geometry projection camera-calibration perspectivecamera
computer-vision geometry projection camera-calibration perspectivecamera
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
edited Nov 14 '18 at 15:16
Luca
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
asked Nov 13 '18 at 23:24
LucaLuca
3,25052782
3,25052782
1
Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18
add a comment |
1
Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18
1
1
Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18
add a comment |
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Signal Processing might be the place for computer vision questions of general and mathematical nature, dsp.stackexchange.com S.O. is fine if it's coding details, API technicalities, compiling, etc.
– DarenW
Nov 14 '18 at 1:39
@Luca Can you share the source of this statement? Some context might help here.
– Nico Schertler
Nov 14 '18 at 15:09
@NicoSchertler Yes, I have added a link to the presentation which I was using. It is basically, after I get the calibration matrix, to project some fixed point onto the ground plane.
– Luca
Nov 14 '18 at 15:17
That approach assumes that you know where the ground plane is or at least that it can be inferred from the image. Nothing that a bare pinhole camera model gives you.
– Nico Schertler
Nov 14 '18 at 18:18