Agisoft PhotoScan 1.0.0 pre-release

[airmap3d]

Can anyone tell me what the default settings for 'classify ground points' are (max distance, max angle, cell size)?  I have adjusted mine and forgotten what the defaults were.  Every time I close and re-open PS it maintains my last set parameters and does not return to the default.

Many thanks.

max distance: 15
max angle: 1
cell size: 50

in build 1754

Thanks!

[andyroo]

Hi Alexey, thank you for clarifying.

I am having some unexpected behavior - and I wonder if it is from some behavior in the "custom" dialog for batch processing the mesh creation.

My steps - Initially processed pointcloud on medium Pointcloud settings, and medium mesh settings. Then after setting GCPs reprocessed on high and custom 50 000 000 ... (or I thought I did), but after processing I only had 5 000 000 faces. I reset processing to 50 000 000 and ran the batch and again I only have 5 000 000 faces. I went in to look at my batch parameters and it looks like they either were not updated, or were re-truncated to 5 000 000 again?!

I know that the dense pointcloud was reprocessed on high settings. I have 104 125 440 points, and there are hundreds of points per face on the big points.

Either way, my mesh once again is 5 000 000 faces. Two questions:

(1) If I specify face count "high" in mesh creation, is that the highest face count I can get for a give dense point cloud?

(2) If I specify a custom face count that is higher than the end result at "high", is it possible that the software "adjusts" my custom face count number (by removing a zero) during/before processing?

otherwise I mistakenly entered 5 000 000 instead of 50 000 000 twice, and since I always count my zeros when I enter them in the millions or more, I am finding that hard to believe.

Reprocessing on "high" now.

[EDIT] I just noticed that if I go into the batch window and <edit> the batch parameters, then cancel out of the batch processing after editing that it doesn't save the edits. Maybe I canceled to check something like a point count before going back in and didn't check before starting mesh generation. So this might be user error.

[Alexey Pasumansky]

Hello Andy,

You can always check reconstruction parameters used in the Chunk Info from the Workspace pane.

As for the High, Medium and Low presets in Build Mesh dialog they are based on the number of points in the Dense cloud. But if you use Custom option you should get either this value or the maximal possible for the current quality of dense cloud.

[airmap3d]

I am definitely struggling with the 'Classify Ground Points'.

Initial run on default settings seemed to work ok.
I then ran it again with very different settings and very little seemed to happen.
I have since tried numerous other settings and again, very little (if anything), is happening.

I am leaving the parameter 'any class' selected in the 'from' box and have also tried a few of the others.

Is this process not yet fully functional or am I not doing something right??  I am using build 1745

It seems like it will  be a very impressive to function to have and I'd love to be able to figure it out!!  It would also be good to be able to export a point file (las etc) of individual class types.

[andyroo]

I am struggling with the mesh quality still. I generated point clouds on "high" setting and have a detailed surface with ~100 000 000 points, but when I generate a mesh on "high" setting I only get 18 000 000 faces, (would prefer more like 50 000 000 to keep the detail) and the surface appears triangulated and much more artificial than the pointcloud surface.

If I generate the pointcloud with less smoothing, will that result in a mesh that has more faces?

[Edit] - I also noticed that in the "info" option for the mesh, there is a depth filtering parameter, but no ability to change it in the batch - is that just bookkeeping that's keeping track of the pointcloud generation parameters, or is there a "moderate" depth filter being applied twice?

[Alexey Pasumansky]

Hello airmap3d,

That's because Classification to Ground assigns Ground class to points matching criteria in the cloud but does not take off already assigned classes if the point doesn't match the classification criterion.
So if you wish to perform ground classification with different parameters you need to assign any different class to ground class points: select all the points in the dense cloud, select Assign Class from Tools menu, select From Ground to any other (unclassified, for example). Then just start classification.


Hello Andy,

Depth filtering parameter in the chunks info refers to the depth maps/dense cloud generation it could be duplicated twice if both model and dense cloud are present in the chunk.

Polygon count presets (High, Medium and Low) in Build Mesh dialog are based on the number of points in the dense cloud, if you require more faces than highest preset than you should use Custom input. The filtering parameter doesn't matter for the numbers automatically calculated in the polygon count field.

[airmap3d]

Got it.  Thanks Alexey.

[captured]

I was having some issues with my scripts, even after adapting for the new api parameters. I think I finally traced it to a problem with the buildModel function when I assign an arbitrary number of faces rather than a 'low', 'medium', or 'high' setting.

i.e.:

Code: [Select]

import PhotoScan
doc = PhotoScan.app.document
chunk = doc.activeChunk
chunk.buildModel(surface='arbitrary', source='sparse', interpolation='enabled', faces=100000)

The same problem seems to apply across projects and regardless of 'sparse' vs 'dense'.

Here's the error I'm getting:

TypeError: bad argument type for built-in operation

I won't give the whole traceback, since I think it is not working properly. I was scratching my head chasing several lines of code it was referring to in tracebacks. After investigation, all the lines I was tracing were just fine. I think it was just referencing them incorrectly.

Of course, there's a chance I'm just doing something wrong...

[andyroo]

I have an odd floating divider in my workspace pane. see attached images. v 1.0 build 1748. The first image shows the pane with the divider in the middle (left) and bottom (right) of the workspace pane. When it's on the bottom docked next to my console pane, there's an extra gray space when comparing the two (second image).

Andy

[James]

Andy, if you select a photo from the chunk in the workspace pane it will be displayed in the pane below under that divider. If the divider isn't visible but you have the extra gray space then you can drag that up to make the photo preview/info pane visible.

[andyroo]

Thanks James. Never noticed that before. Guess I don't need it... 

[Alexey Pasumansky]

Hello captured,

Thank you for reporting. The problem will be fixed in the next update.

[andyroo]

I don't know if this is me or PS 1.0, but in 1.0 when placing GCP, I have to re-apply the filter-by-marker option more times, and unplaced markers are further away from the actual targets on the ground before I place a few (after establishing initial ground control plane). I use the quick load method to load GCPs in from previous surveys of the same area, then I place them.

In 0.9.x, after the first three points were entered to define a plane, I could just update as I placed imported GCPs and when I filtered by GCP I would typically get all but one or two images when working my way up the flight path and regularly updating ground control. Then I could go back through the project when I'd entered GCPs and look for any unplaced markers.

Now when I am entering GCPs, I have to re-apply the "filter-by-marker" when I am finished and I will get 2-3x as many images as initially, which I then have to place GCP on.

One complicating factor that could make it my fault is that in previous surveys I loaded the imagery in ~200 image ~5 km chunks, and I am doing this one as a single 30 km project to eliminate duplicate entry of GCPs (1628 cameras, 198 GCPs). But I don't know the details of how the marker filter feature works, so I am not sure if it should be expected to affect image selection behavior like it does, given a longer ribbon type aerial survey.

[andyroo]

I also just noticed that when editing the sparse point cloud if I have part of my model disappeared in the foreground and I select points, the disappeared points within range of my selection get selected too even though they are not visible.

[andyroo]

Tried to regenerate dense point cloud and mesh after entering GCPs and got interesting results. I generated the initial dense point cloud and mesh on "low" setting, and tried this one on "ultra".

I imagine I'll have to go back to my 256GB RAM machine or try it on a lower setting, but I was surprised my CPU didn't do any work according to the log. I am posting more because of the lack of CPU numbers than the "bad allocation" error. Guessing that means I should have tried a lower resolution reconstruction.

Project is 1 chunk, 1628 12MP images, 119 markers.

relevant part of log follows:

Code: [Select]

Using device: GeForce GTX 670, 7 compute units, 2048 MB global memory
  max work group size 1024
  max work item sizes [1024, 1024, 64]
  max mem alloc size 512 MB
initializing...
skipping pair: camera visibility test
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selected 1501 cameras from 1519 in 490.592 sec
Loading photos...
Reconstructing depth...
[GPU] estimating 1063x3768x320 disparity using 532x942x8u tiles, offset 0
timings: rectify: 18.408 disparity: 77.901 borders: 0.881 filter: 3.188 fill: 0
[GPU] estimating 1736x3920x384 disparity using 868x784x8u tiles, offset 0
timings: rectify: 0.356 disparity: 11.373 borders: 0.097 filter: 1.955 fill: 0
[GPU] estimating 2524x4042x256 disparity using 842x809x8u tiles, offset 0
[CPU] estimating 4398x3994x224 disparity using 1100x999x8u tiles, offset 0
Error: bad allocation
timings: rectify: 0.306 disparity: 13.552 borders: 0.138 filter: 8.249 fill: 0
[GPU] estimating 2432x3989x256 disparity using 811x798x8u tiles, offset 0
timings: rectify: 0.37 disparity: 7.254 borders: 0.143 filter: 2.24 fill: 0
[GPU] estimating 1803x3967x416 disparity using 902x794x8u tiles, offset 0
timings: rectify: 0.24 disparity: 6.969 borders: 0.101 filter: 1.133 fill: 0
[GPU] estimating 3332x4806x352 disparity using 833x801x8u tiles, offset 0
timings: rectify: 1.056 disparity: 14.842 borders: 0.101 filter: 1.207 fill: 0
[GPU] estimating 4579x4532x288 disparity using 764x907x8u tiles, offset 0
ocl_engine.cpp line 298: clEnqueueNDRangeKernel failed, CL_MEM_OBJECT_ALLOCATION_FAILURE
GPU processing failed, switching to CPU mode
[CPU] estimating 4579x4532x288 disparity using 1145x1133x8u tiles, offset 0
Error: bad allocation
finished depth reconstruction in 202.769 seconds
Device 1 performance: 0 million samples/sec (CPU)
Device 2 performance: 197.047 million samples/sec (GeForce GTX 670)
Total performance: 197.047 million samples/sec
Error: bad allocation
Finished processing in 1813.53 sec (exit code -1)