danielepanozzo/gp

gp's Issues

Assignment 4 Part 1

Good evening, I have a question about the hard constraint.

I wanted to approach the problem in a lazy fashion such that I did not need to remove the constrained x values or the "known" variables from my LHS. My approach was that whenever we encounter a face that is constrained, we do not add its e_f or e_g to the Q matrix. Instead, we bring e_f multiplied by its constrained vector to the RHS.

Therefore, after completing the Q matrix, I added extra rows for my Q matrix such that each "known" variable has its data essentially as 1 and its RHS as its vector value in complex form.

However, by doing so, our solved x values when converted back to vector form deviates from the values provided from the constraints because I think converting the constraint values into complex form and converting it back induces numerical rounding errors. Is this an okay approach or should I reorder the x values such that free variables to be solved are in the upper column whereas the known variables are just completely excluded from the column vector?

Assignment 4 Part III

I try mp.subplot(.., uv=UV) in instructions, it looks like that:

The requirement is:

What is the color map here? How could we overlay the gradient map over uv checkerboard/grid?

Thank you.

igl.per_corner_normals not found.

It seems that the function igl.per_corner_normals has not been defined in the python bindings. All I can see in the documentation are the following functions: per_edge_normals, per_face_normals, and per_vertex_normals.

Since I am running the code in both C++ and python, I did not find this issue in C++.

How to install libigl on mac m1?

Hi Professor,

I have a problem installing the igl library on my mac m1. The conda install does not work and I tried to compile the bindings from scratch but there are still errors. I found similar issue in https://github.com/libigl/libigl/issues/1686 and someone metioned using header only mode but I am still confused about how to do it.

Is there any way to solve the problem in detail?

Install Problem about Assignment1

Hi Professor,

When I tried to install the requirements in general instruction,
conda install -c conda-forge igl shows "Solving environment: failed with initial frozen solve. Retrying with flexible solve."
Then I tried to use conda env create -f environment.yml directly, it shows
"Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:

• zlib==1.2.11=h7795811_1010
• nodejs==14.7.0=hdde0ff8_0
• jedi==0.18.0=py38h50d1736_2
  ..."

I'm not sure how to solve this porblem, the solutions I tried:
1.conda config --set channel_priority flexible
2. conda config --set channel_priority false
3. recreate an env and activate it.
But they all not work. So could I use Colab instead of Jupyter Notebook? Or how to solve this problem...

Also, when I used Colab to run the sample code
meshplot.plot(bunny_v, bunny_f, shading={"wireframe": True})
It throws “TraitError: The 'shadow' trait of a DirectionalLight instance expected an Uninitialized or a LightShadow, not the str 'IPY_MODEL_[object Object]'.”
Could you please tell me how to solve it? Thanks a lot.

Assignment1 shading

Hi Professor,
I have a question about how to do the per_vertex_shading. I compute the exploded V and F, call "igl.per_vertex_normals" to get the per_vertex_normals, which is n, and plot the image using "meshplot.plot(exploded_v, exploded_f, n=n)". However, I got the exact same picture as per_face_normals. Is there anything wrong with my steps?

Assignment 2 MLS

Good morning, I just want to confirm that we are solving MLS by picking points from our "3n" points set that are within a certain radius "h" from the current grid point "xi" and calculating the coefficients of our polynomial based on points that are within "h" distance from "xi". Then, we are using the polynomial to evaluate "xi"?

How do we pick the Wendlend radius or do we incorporate the W(x) matrix when approximating the LS? What are sensible radius to pick or we have to experiment to see ourselves?

How can we be certain that when a specific grid point has less than twice the number of polynomial coefficients of points near it, it is a positive outside point instead of an inside point?

Thank you.