Re: [julia-users] Options for constructing a 3D surface from a point cloud
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 <7hunderstr...@gmail.com> 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" 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
>
>
Re: [julia-users] Options for constructing a 3D surface from a point cloud
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" 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
Re: [julia-users] Options for constructing a 3D surface from a point cloud
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" 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
>>>
>>>
Re: [julia-users] Options for constructing a 3D surface from a point cloud
I am hoping for a solution I can use from Julia itself, but this is a good idea, thanks. On Wednesday, April 20, 2016 at 5:38:20 PM UTC-4, Kristoffer Carlsson wrote: > > You could use WriteVTK.jl and render the point cloud in Paraview.
Re: [julia-users] Options for constructing a 3D surface from a point cloud
You could use WriteVTK.jl and render the point cloud in Paraview.
Re: [julia-users] Options for constructing a 3D surface from a point cloud
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" 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 >> >> testpts.jld Description: Binary data
Re: [julia-users] Options for constructing a 3D surface from a point cloud
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" > 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 > >
Re: [julia-users] Options for constructing a 3D surface from a point cloud
There is a marching squares algorithm implemented in [Contour.jl](https://github.com/tlycken/Contour.jl) too; I have put some thoughts on API design in writing in [this issue](https://github.com/tlycken/Contour.jl/issues/29). Meshing.jl might be a better place for these algorithm anyway (and the name "Contour.jl" is poorly chosen anyway - a plural form would have been better) so the question of moving the existing code into a different repo has already been raised independently). Maybe it's time to combine efforts? :) I also saw discussions somewhere recently about a "marching triangles" algorithm for calculating contour curves on non-gridded data, which should probably live comfortably in the same space too. // T On Sunday, February 21, 2016 at 2:05:44 AM UTC+1, 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" > 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 > >
Re: [julia-users] Options for constructing a 3D surface from a point cloud
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" 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
Re: [julia-users] Options for constructing a 3D surface from a point cloud
Chris , I'm interested in this area too. Please post here if you come up with some solution you would like. Best regards, Igor
Re: [julia-users] Options for constructing a 3D surface from a point cloud
You're right, of course -- I'm sort of learning about the published methods while at the same time searching for implementations. For instance, the reason I mentioned Meshes is that I saw it had an implementation of the marching cubes algorithm, and I was hoping it might be easy enough to quickly try a test case. On Wednesday, February 17, 2016 at 10:05:44 AM UTC-8, Tamas Papp wrote: > > This is not a Julia-specific problem: you need to decide what algorithm > to use, which depends on what you know about your surface. > > Eg if it is a function f(x,y), and reasonably smooth and differentiable > over some domain, you can use a family of basis functions to interpolate > -- see ApproxFun.jl. If it is some more general surface, you would need > other algorithms or transformations; there are no general ones (this is > the nature of the problem) and you will need to make some assumptions. > > Best, > > Tamas > > On Wed, Feb 17 2016, Chris wrote: > > > If I have a set of 3D points, randomly sampled from some arbitrary > surface, > > what are my options (in terms of Julia packages) for reconstructing the > > surface for plotting? I've done a little research and found the > Meshes.jl > > package, but I can't find any good examples or documentation for it. In > > case it's not already obvious, I'm completely new to this topic, so any > > direction would be appreciated. > > > > Thanks, > > Chris > >
Re: [julia-users] Options for constructing a 3D surface from a point cloud
This is not a Julia-specific problem: you need to decide what algorithm to use, which depends on what you know about your surface. Eg if it is a function f(x,y), and reasonably smooth and differentiable over some domain, you can use a family of basis functions to interpolate -- see ApproxFun.jl. If it is some more general surface, you would need other algorithms or transformations; there are no general ones (this is the nature of the problem) and you will need to make some assumptions. Best, Tamas On Wed, Feb 17 2016, Chris wrote: > If I have a set of 3D points, randomly sampled from some arbitrary surface, > what are my options (in terms of Julia packages) for reconstructing the > surface for plotting? I've done a little research and found the Meshes.jl > package, but I can't find any good examples or documentation for it. In > case it's not already obvious, I'm completely new to this topic, so any > direction would be appreciated. > > Thanks, > Chris
[julia-users] Options for constructing a 3D surface from a point cloud
If I have a set of 3D points, randomly sampled from some arbitrary surface, what are my options (in terms of Julia packages) for reconstructing the surface for plotting? I've done a little research and found the Meshes.jl package, but I can't find any good examples or documentation for it. In case it's not already obvious, I'm completely new to this topic, so any direction would be appreciated. Thanks, Chris
