How does Mesh Regularization improve quality? The after photo on the right looks like there was a heavy loss of detail, particularly in the eyes and nose.
emilyzhong
I think visually it looks like there's a loss of quality, but that's because with the different sizes on the left it's easier for the human eye to see where the eyes/nose are. The increased number of points should improve the quality — with this visual, I think the finer mesh lines obfuscates the increased quality, making it look less detailed.
zehric
I think mesh regularization might be analogous to many of the methods we talked about for antialiasing before. There might be a loss in sharpness, but it looks smoother.
williampeng20
Agreed, while the sharp indentation in the eyes is harder to notice after mesh regularization, if you look closely it's still there, just harder to notice among all the mesh lines and smoothed out from normalization.
Zyy7390
This is similar to level sampling that reconciles different requirements for sampling frequency in different parts of the picture generated.
AnastasiaMegabit
It seems strange to me that we would want to make it smoother over the loss of detail. It almost feels like when we overblur the edges of an object and it loses some of its shape. The image on the right has lost key pieces of the animals shape and we are left with more of an outline. I mean, its almost impossible to tell where its eyes were and the nostils are all but gone.
yzyz
I'd say that mesh regularization does lose a bit of detail, but it has other advantages. It removes long skinny triangles, which are difficult to work with in many contexts. As we saw in the first assignment, long skinny triangles had very noticeable aliasing compared to more regular. Long skinny triangles also aren't "local," so I would imagine that they would also present problems when trying to deform a mesh, like for animation purposes.
glee-
This makes me wonder if mesh regulation can lead to performance benefits as well. Since the triangles that need to be rendered are much more uniform in size, the task seems to be more readily parallelized compared to the nonregularized version.
fywu85
I agree. I think evenly sized triangles generally require similar amount of time to process, which leads to nice behaviors in terms of load balancing, which can be a plus for HPC applications.
kavimehta
In the extreme, wouldn't this blur down images to the point of all being fuzzy blobs? Most modern rendering pipelines must have a limit on how much interpolation exist to ensure that we select jagged and distinct figures rather than blurry blobs.
amandaawan
The above comments state that the picture on the right leads to a loss in quality due to the uniform sizes. However, we see the meshes without color. Perhaps if we placed color on both the meshes the continuity and the smoothness of the shapes make it look like that of higher quality.
AronisGod
The very point of regularization is to prevent detail. In the context of optimizing a function to a set of data points, it prevents from finding an exact, high order solution in exchange for a lower order, smooth function that describes the trend of the data.
Therefore in this context, instead of rendering each and every data point we have on the cow, we put less importance on outlier points, thus finding a mesh that more smoothly describes the general shape of the cow.
How does Mesh Regularization improve quality? The after photo on the right looks like there was a heavy loss of detail, particularly in the eyes and nose.
I think visually it looks like there's a loss of quality, but that's because with the different sizes on the left it's easier for the human eye to see where the eyes/nose are. The increased number of points should improve the quality — with this visual, I think the finer mesh lines obfuscates the increased quality, making it look less detailed.
I think mesh regularization might be analogous to many of the methods we talked about for antialiasing before. There might be a loss in sharpness, but it looks smoother.
Agreed, while the sharp indentation in the eyes is harder to notice after mesh regularization, if you look closely it's still there, just harder to notice among all the mesh lines and smoothed out from normalization.
This is similar to level sampling that reconciles different requirements for sampling frequency in different parts of the picture generated.
It seems strange to me that we would want to make it smoother over the loss of detail. It almost feels like when we overblur the edges of an object and it loses some of its shape. The image on the right has lost key pieces of the animals shape and we are left with more of an outline. I mean, its almost impossible to tell where its eyes were and the nostils are all but gone.
I'd say that mesh regularization does lose a bit of detail, but it has other advantages. It removes long skinny triangles, which are difficult to work with in many contexts. As we saw in the first assignment, long skinny triangles had very noticeable aliasing compared to more regular. Long skinny triangles also aren't "local," so I would imagine that they would also present problems when trying to deform a mesh, like for animation purposes.
This makes me wonder if mesh regulation can lead to performance benefits as well. Since the triangles that need to be rendered are much more uniform in size, the task seems to be more readily parallelized compared to the nonregularized version.
I agree. I think evenly sized triangles generally require similar amount of time to process, which leads to nice behaviors in terms of load balancing, which can be a plus for HPC applications.
In the extreme, wouldn't this blur down images to the point of all being fuzzy blobs? Most modern rendering pipelines must have a limit on how much interpolation exist to ensure that we select jagged and distinct figures rather than blurry blobs.
The above comments state that the picture on the right leads to a loss in quality due to the uniform sizes. However, we see the meshes without color. Perhaps if we placed color on both the meshes the continuity and the smoothness of the shapes make it look like that of higher quality.
The very point of regularization is to prevent detail. In the context of optimizing a function to a set of data points, it prevents from finding an exact, high order solution in exchange for a lower order, smooth function that describes the trend of the data. Therefore in this context, instead of rendering each and every data point we have on the cow, we put less importance on outlier points, thus finding a mesh that more smoothly describes the general shape of the cow.