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[williamlai3a <>]

Hi Simon,

Thanks for your reply.

In my case, the massive point cloud is a laser scan of an indoor
environment.
At a later procedure, I will have to call Advanced Front Face Reconstruction 
(I guess in my case, it is a better choice than Poisson, because I want to
fit planes and cylinders)
But the next step I am going to do is shape detection and structuring.
I am still thinking if I should do simplification and edge aware upsampling.

>> And yes, if I set k=6, it seems MST can only manage to orientate one
>> point, so the code block erase all other points except one single point.
>> But setting a bigger K, say 12, already eat up all of my main memory and
>> swap space.

Would you have any advise for my case, if I could just step normal
orientation?

Thanks a lot.

William


Simon Giraudot-2 wrote
> Hello,
> 
>> [Update]
>> I have tried k=6, where it also used 43660 Mb main memory.
> If I understand correctly, the code stores a graph that would k*N in 
> size (with N your number of points). I've tried on a 6 million points 
> and k=19 and the RAM usage goes to 7GB, so I'm not really surprised by 
> your results on such a large point cloud. I'll have a look into the code 
> to see if something can be improved…
> 
> For the record, what do you need oriented normals for? Many algorithms 
> only require unoriented normals (I can mostly think of Poisson 
> reconstruction in CGAL which does require orientation).
> 
>> But the resulting point cloud only being erased to one single point by
>> the
>> code block:
>>
>>    // Optional: delete points with an unoriented normal
>>    // if you plan to call a reconstruction algorithm that expects
>> oriented
>> normals.
>>    points.erase(unoriented_points_begin, points.end());
> I'm not sure I understand what's your problem here: does this erase ALL 
> of your points except for one? Or something else?
> 
> I don't know if this is your case, but just a note: algorithms that are 
> applied to K nearest neighbors can give incorrect and unexpected results 
> if the point cloud is highly non-uniform (for example, if you have very 
> dense scanlines with a high space between each scanline).
> 
> Best,
> 
> -- 
> Simon Giraudot, PhD
> R&D Engineer
> GeometryFactory - http://geometryfactory.com/
> 
> 
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