Going off of the slides, it appears that most of the meshes use uniform sampling, where the sample rate is consistent across the entire model. In this case, is uniform sampling preferred to adaptive sampling? I would imagine that it is, since the point of steps like isotropic re-meshing is to make the triangles uniform in shape and size. However, I feel like an adaptively sampled mesh with a higher sampling rate in more detailed areas could also makes sense to use. Do adaptively sampled meshes have drawbacks like being computationally taxing, or being more difficult to wrap textures around for example?
muuncakez
@weinatalie (if anyone can provide a more solid followup, that would be great, I was also wondering the same question and just taking a jab at the question) I would assume adaptive is more computationally taxing because its not only error estimating its also using multiple matrices to compute said error estimate. While uniform sampling generates one, refined mesh (even if it has a bunch of extra nodes).
But then I was also wondering would adaptive be better than uniform in situations that one would think requires adaption? Like with an object in motion, more nodes being placed at the moving parts with less focus on the non-moving parts would maybe save time using prediction compared to uniformly placing nodes and likely having more redundant nodes now because you're meshing an area you don't super need to mesh because its not in motion. With more static things I would imagine the focus would be edges and contour (e.i. the outline of the cows body/head, outline of the eyes, nose aka the parts where the uniform sampling are denser I'd imagine) to create a crisp finish but then what would happen with parts with less sampling (e.i. the middle of the cows face)? Would they be lower quality? And if we learned about using blur on the edges when rendering in previous lectures, why would I want more nodes on the edges? And is uniform sampling 100% better than adaptive when handling static objects?
muuncakez
Would having extra nodes (which one would think is wasted computation) from uniform sampling ever end up being more "expensive" than the computational tax of adaptive sampling? And at what point would a higher sampling rate for adaptive sampling defeat the point of adaptive sampling?
carolyn-wang
I'm curious what kinds of challenges are associated with mesh upsampling? For example, how do we maintain geometric fidelity to the original model or prevent visual artifacts from being generated?
S-Muddana
Mesh upsampling involves increasing the vertex density in a mesh, but it poses challenges such as maintaining the mesh's topology, preserving surface smoothness and geometric features, and avoiding the introduction of visual artifacts. To address these challenges, algorithms must carefully balance factors like surface smoothness, feature preservation, and computational efficiency, often requiring iterative tuning of parameters. Techniques such as edge collapsing, edge splitting, and smoothing are employed to mitigate artifacts, while efficient algorithms and data structures help manage computational complexity. Ultimately, successful mesh upsampling ensures that the resulting mesh maintains geometric fidelity to the original model while meeting the requirements of specific applications.
JunoLee128
This is an interesting subject, and it's intriguing what kinds of math might be going into the optimization, and what the constants are chosen as in smoothness equations. Also on the subjectivity of model "niceness" versus performance
Going off of the slides, it appears that most of the meshes use uniform sampling, where the sample rate is consistent across the entire model. In this case, is uniform sampling preferred to adaptive sampling? I would imagine that it is, since the point of steps like isotropic re-meshing is to make the triangles uniform in shape and size. However, I feel like an adaptively sampled mesh with a higher sampling rate in more detailed areas could also makes sense to use. Do adaptively sampled meshes have drawbacks like being computationally taxing, or being more difficult to wrap textures around for example?
@weinatalie (if anyone can provide a more solid followup, that would be great, I was also wondering the same question and just taking a jab at the question) I would assume adaptive is more computationally taxing because its not only error estimating its also using multiple matrices to compute said error estimate. While uniform sampling generates one, refined mesh (even if it has a bunch of extra nodes).
But then I was also wondering would adaptive be better than uniform in situations that one would think requires adaption? Like with an object in motion, more nodes being placed at the moving parts with less focus on the non-moving parts would maybe save time using prediction compared to uniformly placing nodes and likely having more redundant nodes now because you're meshing an area you don't super need to mesh because its not in motion. With more static things I would imagine the focus would be edges and contour (e.i. the outline of the cows body/head, outline of the eyes, nose aka the parts where the uniform sampling are denser I'd imagine) to create a crisp finish but then what would happen with parts with less sampling (e.i. the middle of the cows face)? Would they be lower quality? And if we learned about using blur on the edges when rendering in previous lectures, why would I want more nodes on the edges? And is uniform sampling 100% better than adaptive when handling static objects?
Would having extra nodes (which one would think is wasted computation) from uniform sampling ever end up being more "expensive" than the computational tax of adaptive sampling? And at what point would a higher sampling rate for adaptive sampling defeat the point of adaptive sampling?
I'm curious what kinds of challenges are associated with mesh upsampling? For example, how do we maintain geometric fidelity to the original model or prevent visual artifacts from being generated?
Mesh upsampling involves increasing the vertex density in a mesh, but it poses challenges such as maintaining the mesh's topology, preserving surface smoothness and geometric features, and avoiding the introduction of visual artifacts. To address these challenges, algorithms must carefully balance factors like surface smoothness, feature preservation, and computational efficiency, often requiring iterative tuning of parameters. Techniques such as edge collapsing, edge splitting, and smoothing are employed to mitigate artifacts, while efficient algorithms and data structures help manage computational complexity. Ultimately, successful mesh upsampling ensures that the resulting mesh maintains geometric fidelity to the original model while meeting the requirements of specific applications.
This is an interesting subject, and it's intriguing what kinds of math might be going into the optimization, and what the constants are chosen as in smoothness equations. Also on the subjectivity of model "niceness" versus performance