Kernel_size ( int or tuple) – the size of the sliding blocksĭilation ( int or tuple, optional) – a parameter that controls the It is harder to describe, but this link has a nice visualization of what dilation does. Sides for padding number of points for each dimension beforeĭilation controls the spacing between the kernel points also known as the à trous algorithm. Padding controls the amount of implicit zero-paddings on both Stride controls the stride for the sliding blocks. The padding, stride and dilation arguments specify Therefore, indexing output at the last dimension (column dimension) Of input ( ∗ * ∗ above), and d d d is over all spatial L = ∏ d ⌊ spatial_size + 2 × padding − dilation × ( kernel_size − 1 ) − 1 stride + 1 ⌋, L = \prod_d \left\lfloor\frac spatial_size is formed by the spatial dimensions Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.(The example was made in MeshInspector application based on some open-source geometric library for operations like this one. red - vertices of subdivided mesh converted in point cloud.More scientifically, it can be said that the spectrum of such random points is white, while blue noise is good for dithering as having small low-frequency energy.Īs to unfolding the mesh back to a two dimensional representation, it is not possible in every case especially with closed surfaces having genus more than 0 (not sphere-like).Īnd the approach with the subdivision of long mesh edges does work good on practice, but only if after each subdivision, the mesh quality is improved locally following the ideas of Delaunay triangulation, which maximize the minimal angle in the triangles. And as in dithering some trivial Monte-Carlo approaches do no behave good, because completely random independent points tend to cluster together creating undesirable visual patterns. This task resembles image dithering - the approximation of some constant grey-level image with a random set of black dots. For very large data sets this can be prohibitively time consuming.
You'll need to do some extra checks that the seams of your mesh in the unfolding retain an appropriate level of sampling to ensure that your requirements of the minimal ball are met, when it is refolded.įrom past experience the caveat of this approach is that if your mesh isn't manifold (ie/ it has cracks, self intersections or t-junctions), you'll almost certainly have to clean these up before you start. The uniform distribution of points on the resultant unfolded mesh is then easy - you can use a low discrepancy sampling sequence (such as the Halton or Hammersly sequence) to produce an almost uniform distribution of points over your space, and rejection sampling to remove any points that don't fall within the unfolded mesh.
Mesh unfolder sample trial#
I believe there are a number of algorithms and techniques for approaching such problems, but selecting the best may be a case of trial and error depending on how degenerate your triangles are. Tools like blender and meshlab include tools to do this because the problem is related to generation of UV texture co-ordinates in 3D graphics. The unfolding process is most likely going to be the most challenging step if you need to implement everything yourself. 1) unfold the mesh back to a two dimensional representation, and 2) use a sampling technique that results in approximately uniformly distributed points on the surface. The simplest method that I know of for doing this is a two stage process.
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