Explore >> Select a destination
|
You are here 
|
c0de517e.com | ||
| | | | |
thenumb.at
|
|
| | | | | [AI summary] The text provides an in-depth overview of various neural field techniques, focusing on their applications in representing images, geometry, and light fields. It discusses methods such as positional encoding, hash encoding, and neural SDFs, highlighting their advantages in terms of model size, training efficiency, and quality of representation. The text also touches on the broader implications of these techniques in fields like computer vision and real-time rendering, emphasizing their potential to revolutionize how we model and interact with digital content. | |
| | | | |
simoncoenen.com
|
|
| | | | | A graphics study of Doom Eternal | |
| | | | |
solid-angle.blogspot.com
|
|
| | | | | Programmers don't generally have reels, but we do have blogs. I've been explaining the rendering work I did on BioShock Infinite quite a b... | |
| | | | |
peterbloem.nl
|
|
| | | [AI summary] The pseudo-inverse is a powerful tool for solving matrix equations, especially when the inverse does not exist. It provides exact solutions when they exist and least squares solutions otherwise. If multiple solutions exist, it selects the one with the smallest norm. The pseudo-inverse can be computed using the singular value decomposition (SVD), which is numerically stable and handles cases where the matrix does not have full column rank. The SVD approach involves computing the SVD of the matrix, inverting the non-zero singular values, and then reconstructing the pseudo-inverse using the modified SVD components. This method is preferred due to its stability and ability to handle noisy data effectively. | ||
