Exif data + Sparse reconstruction about bundler_sfm HOT 3 CLOSED

Hi,

Bundler doesn't need RPY metadata -- in fact it doesn't use it even if it
is there. However, it does expect focal length information in the Exif data
and so if you are stripping that out it will likely produce bad results.

The point cloud in the second screenshot looks possibly okay -- hard to
tell from a still image though.

The camera positions will be in the same coordinate system as the points,
but the whole reconstruction will be in an arbitrary coordinate system --
definitely not a geocentric one, for instance.

Hope this helps,

Noah

On Aug 23, 2016 1:11 PM, "Venkataraman Ganesh" [email protected]
wrote:

Hi,
I'm trying to use Bundler to create a sparse map of the following
structure:

[image: dji_0104]
https://cloud.githubusercontent.com/assets/7323040/17905977/33471c7a-6943-11e6-83e1-99c2cdf7caa0.jpg

I have about 40 images (resized to 3600 x 2400) representing different
views of the structure.
The exif data for the image does not contain the pitch, roll and yaw
(RPY) by default. I was curious to see how Bundler handled it- here's the
output:

[image: full_images]
https://cloud.githubusercontent.com/assets/7323040/17906361/f758a524-6944-11e6-9e55-b62d25940cbb.png

Next, I ran Bundler on images with RPY as part of the exif data and
here's the output:

[image: rpy_images]
https://cloud.githubusercontent.com/assets/7323040/17907845/b1e6c38e-694b-11e6-8d86-8dbef42b8d71.png

I'd appreciate any insight to help me understand the following:

• I'm unsure if this is a correct sparse point cloud.
• How critical is it for Bundler to be provided with the RPY data.
• Are the camera poses in the bundler.out file represented in the
  world coordinate frame?

Thanks!

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Hi Noah,

Thanks for your answer! I'm a little confused about this statement:

The camera positions will be in the same coordinate system as the points,
but the whole reconstruction will be in an arbitrary coordinate system --
definitely not a geocentric one, for instance.

I was under the impression that the converse was true: the points would be in the same coordinate frame as the camera pose (which I had thought would be in the world frame based on the GPS coordinates in the EXIF metadata).

I'd like to georeference this point cloud (sparse/dense) to compare it with similarly georeferenced objects. How can i determine a transformation from this arbitrary reference frame to a geocentric one?

Thanks!

from bundler_sfm.

You might think that the model would be georegistered in your case, but the
truth is that Bundler ignores GPS coordinates in the Exif metadata.

In order to georeference the model, you will need to compute the
transformation the maps the camera positions that Bundler computes to the
geotags (unfortunately, Bundler doesn't have this feature). The easiest way
to do this would be to convert your GPS positions to a coordinate system
like ECEF or local ENU, and then use absolute orientations to compute the
transformation from Bundler coordinates to ECEF/ENU. (There is absolute
orientations code for instance in Matlab
https://www.mathworks.com/matlabcentral/fileexchange/26186-absolute-orientation-horn-s-method?requestedDomain=www.mathworks.com
.)

Hope this helps,
Noah

On Wed, Aug 31, 2016 at 12:14 PM Venkataraman Ganesh <
[email protected]> wrote:

Hi Noah,

Thanks for your answer! I'm a little confused about this statement:

The camera positions will be in the same coordinate system as the points,

but the whole reconstruction will be in an arbitrary coordinate system

definitely not a geocentric one, for instance.

I was under the impression that the converse was true: the points would be
in the same coordinate frame as the camera pose (which I had thought would
be in the world frame based on the GPS coordinates in the EXIF metadata).

I'd like to georeference this point cloud (sparse/dense) to compare it
with similarly georeferenced object. How can i determine a transformation
from this arbitrary reference frame to a geocentric one?

Thanks!

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