<div dir="ltr"><br><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Jun 22, 2015 at 6:00 PM, Stephen Mather <span dir="ltr"><<a href="mailto:stephen@smathermather.com" target="_blank">stephen@smathermather.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><div>Ah, ugly. Well, pretty really, but ugly from a data perspective. So systematic bias in structure from motion is an issue with all systems, and difficult to overcome with drone imagery in particular, if the drone is flown in a single Z plane, particularly over relatively flat terrain. This is an indictment of the data, just stating a known difficulty. This is why balloon and kite flights, aside from areal coverage, are so pleasant to process -- plenty of x, y, and z movement, so less chance for systematic bias. I've been wondering if once a dataset like Open Terrain (a global highest-available) is available, if it might be helpful for compensating for bias in datasets, and also help with data classification pipelines that can have separate meshing mechanisms for ground, vegetation, and human structures.<br></div></div></div></div></div></div></blockquote><div><br></div><div>I was thinking about compensating for the bias based on the lidar data I have, but I haven't tried it yet. </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><div><br></div>That said, let's get a little more context on the dataset and processing -- did you use ground control and if so, how was it distributed? It would be interesting to know if you had ground control in the center of the scene, if ODM is ignoring this extra info. Also, what kind of camera is it? It is possible that the three parameter corrections that ODM is performing are inadequate, and a 6-parameter model would be better.<br></div></div></div></div></div></blockquote><div><br></div><div>There isn't really any gcp in the middle of it: </div><div><a href="https://drive.google.com/file/d/0B7CQoT4YE2mMNzJSbEZsaGtrZ3hnRUdPQ3VDbF9DS0o2SS1j/view?usp=sharing" target="_blank">https://drive.google.com/file/d/0B7CQoT4YE2mMNzJSbEZsaGtrZ3hnRUdPQ3VDbF9DS0o2SS1j/view?usp=sharing</a></div><div><br></div><div>but how could the GCPs help? Georeferencing won't really change the shape, isn't it just rotation and translation?</div><div><br></div><div>All I know about the camera is in a log file I got (I kept just the relevant part):</div><div><br></div><div><a href="https://drive.google.com/file/d/0B7CQoT4YE2mMOHREZ0VMaWliRE0/view?usp=sharing">https://drive.google.com/file/d/0B7CQoT4YE2mMOHREZ0VMaWliRE0/view?usp=sharing</a><br></div><div>There was not much information in the exif.</div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><br></div>Finally, if you have the time, try processing through the OpenSfM pipeline <a href="https://github.com/OpenDroneMap/OpenDroneMap/pull/50" target="_blank">https://github.com/OpenDroneMap/OpenDroneMap/pull/50</a> . It uses a 6-parameter model, and some alternate assumptions for SfM in general.<br><br></div></div></div></div></blockquote><div><br></div><div>I know this alternative exists, but has anyone here used it successfully? I mean, there is probably some reason why it's not merged yet?</div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div></div>Cheers,<br></div>Best,<br></div>Steve<br><div><div><div><div><br></div></div></div></div></div></blockquote><div><br></div><div>Thanks!</div><div><br></div><div>Anna </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div><div><div><div><br><br></div></div></div></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div>On Mon, Jun 22, 2015 at 10:26 AM, Anna Petrášová <span dir="ltr"><<a href="mailto:kratochanna@gmail.com" target="_blank">kratochanna@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div><div dir="ltr"><div><span style="font-size:12.8000001907349px">Hi,</span><div style="font-size:12.8000001907349px"><br></div><div style="font-size:12.8000001907349px">I was comparing a georeferenced DSM generated from ODM mesh with a DEM derived from lidar data. The results look great except for this big distortion - the picture shows the difference (lidar - odm). I know this happens also with DSMs created by Agisoft. Does anybody know where does this come from? Is there there any hope this could be fixed in the future?</div><div style="font-size:12.8000001907349px"><br></div><div style="font-size:12.8000001907349px"><span style="font-size:small"><a href="https://drive.google.com/file/d/0B7CQoT4YE2mMcU9PRDYzUFRMVlU/view?usp=sharing" target="_blank">https://drive.google.com/file/d/0B7CQoT4YE2mMcU9PRDYzUFRMVlU/view?usp=sharing</a></span><br></div><div style="font-size:12.8000001907349px"><br></div><div style="font-size:12.8000001907349px">Thanks!</div></div><span><font color="#888888"><div style="font-size:12.8000001907349px"><br></div><div style="font-size:12.8000001907349px">Anna</div></font></span></div>
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