Recently, people on YouTube and TikTok have actually been taking advantage of the stitching challenges of these cameras to create videos with very goofy views :) I'd recommend looking at 360 YouTube shorts to check out how the video looks somewhat warped and not particularly seamless
ttalati
I remember the professor pointed out that currently all of the imaging methods have this "blind spot" where we cannot get the point based ideal sampling even though there are papers and studies that have proposed designs that would allow us to get the ideal ray sampling. Why are those designs not implemented? Are there some physical limitations that leave those designs as theoretical?
brianqch
This makes me think about why when we take panoramic pictures on our phones, a lot of the time our images may be partially distorted (where straight lines may appear curved). And I think it has to do with how much information we are missing or the angles might be off as we pan our camera across the scene we are trying to capture.
Alina6618
Recent developments have focused on improving the efficiency and accuracy of ray sampling in the context of volume rendering and neural radiance fields (NeRF). There's a shift from classical methods, which rely on a uniform distribution of ray sampling that doesn't necessarily reflect the real-world surfaces and can miss capturing high-frequency details such as sharp edges in images. The new strategies aim to rectify this by optimizing the distribution of sampled rays.
The latest methods involve pixel and depth-guided ray sampling strategies. Pixel-guided strategies focus on the color variation between pixels, using this information to guide non-uniform ray sampling and emphasize areas with greater detail by detecting higher standard deviations in the color of pixel neighborhoods. This strategy ensures that more sampling is done in areas of an image that are rich in detail, thus more critical to the visual outcome.
Depth-guided strategies, on the other hand, address the variations in depth within a scene, which are particularly challenging in regions with rapid changes. These techniques aim to sample more densely in areas with more significant depth variations to avoid issues like blurring of three-dimensional objects at their edges
Recently, people on YouTube and TikTok have actually been taking advantage of the stitching challenges of these cameras to create videos with very goofy views :) I'd recommend looking at 360 YouTube shorts to check out how the video looks somewhat warped and not particularly seamless
I remember the professor pointed out that currently all of the imaging methods have this "blind spot" where we cannot get the point based ideal sampling even though there are papers and studies that have proposed designs that would allow us to get the ideal ray sampling. Why are those designs not implemented? Are there some physical limitations that leave those designs as theoretical?
This makes me think about why when we take panoramic pictures on our phones, a lot of the time our images may be partially distorted (where straight lines may appear curved). And I think it has to do with how much information we are missing or the angles might be off as we pan our camera across the scene we are trying to capture.
Recent developments have focused on improving the efficiency and accuracy of ray sampling in the context of volume rendering and neural radiance fields (NeRF). There's a shift from classical methods, which rely on a uniform distribution of ray sampling that doesn't necessarily reflect the real-world surfaces and can miss capturing high-frequency details such as sharp edges in images. The new strategies aim to rectify this by optimizing the distribution of sampled rays.
The latest methods involve pixel and depth-guided ray sampling strategies. Pixel-guided strategies focus on the color variation between pixels, using this information to guide non-uniform ray sampling and emphasize areas with greater detail by detecting higher standard deviations in the color of pixel neighborhoods. This strategy ensures that more sampling is done in areas of an image that are rich in detail, thus more critical to the visual outcome.
Depth-guided strategies, on the other hand, address the variations in depth within a scene, which are particularly challenging in regions with rapid changes. These techniques aim to sample more densely in areas with more significant depth variations to avoid issues like blurring of three-dimensional objects at their edges