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Thank you for the wonderful dataset! I have noticed significant aliasing in the images in the dataset (especially the low-resolution data, but also the high-resolution data). Here are some examples:
(Aliasing is evident in the rooftops, as well as in the wires crossing the water.)
Perhaps there is some aliasing from the rendering process (if anti-aliasing is not used when rendering high-res textures, and perhaps there is some aliasing resulting from downsampling high-resolution images to low-resolution ones.
I'm thinking this aliasing might hinder learning from this dataset. Is it possible to look into this issue and potentially rerender the dataset?
The text was updated successfully, but these errors were encountered:
Thanks for your suggestion! We use a simple renderer to render the high-res images, and generate the low-res ones via image downsampling. I think both steps might cause the aliasing problem.
I am not sure whether I have time to rerender the whole dataset in the next few months but I will definitely try to fix this problem in the future. If it is urgent for you, you could consider generating low-res blended images directly from texture meshes and low-res input images
Thank you for the wonderful dataset! I have noticed significant aliasing in the images in the dataset (especially the low-resolution data, but also the high-resolution data). Here are some examples:
High-resolution image:
https://github.com/kwea123/BlendedMVS_scenes/blob/master/large/5afacb69ab00705d0cefdd5b.jpg
(Aliasing is noticable in various places, but maybe most noticeable in the race track in the stadium in the background (top middle) of image.)
Low-resolution image (5bf26cbbd43923194854b270\blended_images\00000003.jpg):
(Aliasing is evident in the rooftops, as well as in the wires crossing the water.)
Perhaps there is some aliasing from the rendering process (if anti-aliasing is not used when rendering high-res textures, and perhaps there is some aliasing resulting from downsampling high-resolution images to low-resolution ones.
I'm thinking this aliasing might hinder learning from this dataset. Is it possible to look into this issue and potentially rerender the dataset?
The text was updated successfully, but these errors were encountered: