I loaded this in yesterday but assumed the data must have been wrong
because it wasn't kidney-shaped. Anyways this was what I did if you're
curious:
using JLD, Plots
> data = load("/home/tom/windowsshared/testpts.jld")
> x, y, z = map(i->vec(data["pts"][:,i]), (1,2,3))
> scatter3d(x,y,z,m=(3,0.05,stroke(0.1)))
On Thu, Apr 21, 2016 at 11:12 AM, Chris <[email protected]> wrote:
> Thanks. Yes, this is interpreted correctly - the scale will change quite a
> bit, but this is a typical data set. While one dimension is much more
> pronounced than the others, the 3D structure is important.
>
> I'll look into the Ball Pivoting algorithm for sure.
>
>
> On Thursday, April 21, 2016 at 10:49:32 AM UTC-4, Florian Rhiem wrote:
>>
>> I rendered your data points as spheres using GR and GR3, to get an idea
>> of what you are dealing with. They seem to show a curve, rather than a
>> surface, though. Is the 'kidney bean' meant to be this elongated or am I
>> interpreting your data in a wrong way?
>>
>> In general, you might try using a Ball Pivoting algorithm for surface
>> reconstruction. The ball radius parameter allows you to pick how much
>> concavity you allow, with an infinite radius yielding the convex hull.
>>
>> Here's the script for rendering the spheres and I'll attach a screenshot
>> from it.
>>
>> using JLD
>> using GR
>> using GR.GR3
>>
>> points = load("testpts.jld")["pts"]
>> center = mean(points, 1)
>> scaling_factor = 1 / maximum([maximum(points[:, i])-minimum(points[:, i])
>> for i in 1:3])
>>
>> for i in 1:3
>> points[:, i] = (points[:, i]-center[i]) * scaling_factor
>> end
>>
>> positions = vec(points')
>> colors = ones(positions)
>> radii = 0.01*ones(size(points, 1))
>>
>> GR3.drawspheremesh(size(points, 1), positions, colors, radii)
>> GR.setviewport(0, 1, 0, 1)
>> for i in 1:400
>> GR.clearws()
>> GR3.cameralookat(2*sin(2π*i/200), 0, 2*cos(2π*i/200), 0, 0, 0, 0, 1, 0)
>> GR3.drawimage(0, 1, 0, 1, 500, 500, GR3.DRAWABLE_GKS)
>> GR.updatews()
>> end
>>
>>
>>
>> Am Mittwoch, 20. April 2016 22:52:24 UTC+2 schrieb Chris:
>>>
>>> I've attached a sample dataset. It's a set of 500 x,y,z points. I still
>>> haven't been able to make much headway on this, so if someone could take
>>> the time to show me what's possible, I'd be very appreciative.
>>>
>>> On Monday, February 22, 2016 at 3:55:14 PM UTC-5, Chris wrote:
>>>>
>>>> I will work on creating a small sample dataset, but the shape is
>>>> essentially a "kidney bean" in 3D space. In fact, the actual "point cloud"
>>>> is (right now) actually samples from a 3D probability density function,
>>>> i.e. it's a "blob", and I want the 3D bounding surface of that blob. I
>>>> imagine this makes things more difficult (at least computationally), and so
>>>> I'm thinking of ways to go from "blob" to the "convex hull" of this point
>>>> cloud -- even though convex hull isn't exactly what I want, since there is
>>>> some concavity.
>>>>
>>>> On Saturday, February 20, 2016 at 8:05:44 PM UTC-5, Steve Kelly wrote:
>>>>>
>>>>> Do you have a sample dataset? The algorithms for triangulating a
>>>>> signed distance field can be found in Meshing.jl. I implemented Marching
>>>>> Cubes recently and started Marching Squares today, but have yet to tag a
>>>>> release because I need to settle on an API.
>>>>>
>>>>> I currently am working on solid modeling via implicit functions. More
>>>>> generally I work in digital fabrication (Fab Lab) and would love to have
>>>>> 3D
>>>>> scanning in the Julia workflow. If you can share more about the dataset
>>>>> you
>>>>> have, I'll see if we can make it work with the tools we have available
>>>>> now.
>>>>> On Feb 19, 2016 2:36 AM, "Igor" <[email protected]> wrote:
>>>>>
>>>>>> Chris , I'm interested in this area too. Please post here if you
>>>>>> come up with some solution you would like.
>>>>>>
>>>>>> Best regards, Igor
>>>>>
>>>>>
