Voxels | based implementation of Dual Contouring | 3D Animation library

I am reading the example: https://matplotlib.org/stable/gallery/mplot3d/voxels_rgb.html#sphx-glr-gallery-mplot3d-voxels-rgb-py about creating a 3d sphere. But I don't understand how the indexing works in the example. Can any one help me to understand. Thanks

I have a setup with multiple cameras that all point towards the same scene. All cameras are calibrated to the same world coordinate system (i.e.: I know the location of all the cameras with respect to the origin of the world coordinate system). In each image from the cameras, I will detect objects in the scene (segmentation). My goal is to count all objects in the scene and I do not want to count an object twice as it will appear in multiple images. This means that if I detect an object in image A and I detect an object in image B, then I should be able to confirm that this is the same object or not. It should be possible to do this using the 3D info I have due to my calibrated cameras. I was thinking of the following:

• Voxel carving. I create silhouettes out of all images with the detected objects. I apply voxel carving and then count the unique number of clustered voxels I have. this will be the number of unique objects in the scene?

• I also thought about for example taking the center of the object and then casting a ray from it into the 3D world, this for each camera and then detecting if the lines cross each other (from different cameras). But this would be very error-prone as the objects might have a slightly different size/shape in each image and the center might be off. Also, the locations of the cameras are not 100% exact, which will result in the ray being off.

What would be a good approach to tackle this issue?

Say you have a point in 3D space with coordinate (2,2,2). How can you vectorize the operation with either numpy (I was thinking of using meshgrid, I just have not been able to get it to work) or scipy to find the 26 nearest neighbors in 3D space? There are 26 neighbors because I am considering the point as a cube, and thus the neighbors would be the 6 neighbors along the cube faces + 8 neighbors along the cube corners +12 neighbors connected to cube edges.

So for point (2,2,2), how can I get the following coordinates:

(1, 1, 1), (1, 1, 2), (1, 1, 3), (1, 2, 1), (1, 2, 2), (1, 2, 3), (1, 3, 1), (1, 3, 2), (1, 3, 3), (2, 1, 1), (2, 1, 2), (2, 1, 3), (2, 2, 1), (2, 2, 3), (2, 3, 1), (2, 3, 2), (2, 3, 3), (3, 1, 1), (3, 1, 2), (3, 1, 3), (3, 2, 1), (3, 2, 2), (3, 2, 3), (3, 3, 1), (3, 3, 2), (3, 3, 3)

I have already implemented this with a triple for loop, which works. However, speed is critical for my system and thus I need to vectorize this operation in order for my system not to fail. The triple for loop is as follows:

I was looking for a way to build cubes of the same size, then draw a line through this space and output the result in the form of coordinates of cubes that this line intersects and paint these cubes with a different color. The line can be either straight or curved.

I used matplotlib to plot cubes and lines. From these sources:

https://www.geeksforgeeks.org/how-to-draw-3d-cube-using-matplotlib-in-python/

Representing voxels with matplotlib

Example code:

What it spits out

My raycast spits out a position way off from what it's supposed to be. I'm trying to place objects procedurally on a procedural mesh. I've been scratching my head at this for a while. Please help. Sorry for the long script.

The start of the code is just some declares and stuff. GenObjects is run once in FixedUpdate after Start has finished. I'm using a marching cubes library by Scrawk and a noise library by Auburn

I am using CSV dataset with 1 feature column (string) and 97 label columns (multi-label classification) with 1 or 0 for every row. Data:

I am trying to use an algorithm for the problem "Max area of island" in a 3D problem, so it would be more like max volume of island. I was using total volumes of 200x200x200 voxels as input, but I am having trouble because it does not work when there are very big 'islands' in the volume I input ('core dumped' in the Ubunut terminal). Here is the code with the modifications I did to apply it to my 3D problem:

I would like to get the data of PET and CT interpolated so underlying arrays will have the same dimensions - in normal case it is simple yet I have data set where ranges of the PET and CT scans differ - hence I Would need to first trim the bigger study.

The problem is that may appear is choosing the subset of slices will lead to small yet observable part of the bigger image to overhang from the smaller ones (because voxels are of different sizes) and if I understand it correctly it may spoil the interpolation .

I suppose it is common problem so how to achieve interpolation where not only voxel dimension is different but also the range ?

Code below do not work as I would like to in case of different ranges of images

I ran into an issue in my code with std::vector, giving me: vector too long. I'm using a vector of char in this case. The code is treating 3D (tomography) images, so I have a lot of voxels. I have exactly the same issue on windows using the VS compiler as on Mac using CLANG, not tested gcc yet.

To inspect the issue, I added the following lines: