thomasmdbrooke.com/2015/10/21/1...
Also, the Earthsea book series

Are fictional maps okay? If yes, the inheritance cycle by Christopher paolini, also the Throne of Glass series by Sarah J Maas

Happy to get feedback + questions! For more experiments and technical details, check out our paper! 😄

We also show improved performance in downstream applications like rendering, collision simulation, and PDE solving.
(n/n)

We show the effectiveness of our method in computing accurate normals and curvatures over a variety of challenging neural SDFs learned over the FamousShape dataset. Our approach shows a 4x improvement in gradients and mean curvature over the baselines.
(6/n)

Second, to enable smoother gradients directly with autodiff over the network, we propose a fine-tuning approach that can use any smooth gradient operator to smooth out the artifacts in the gradients.
(5/n)

To mitigate this noise, we propose a two-pronged solution. First, we leverage the classical technique of polynomial-fitting to fit low-order polynomials through the learned signal and take autodiff over the fitted polynomial.
(4/n)

What causes these artifacts? We note that signals learned by hybrid neural fields exhibit high-frequency noise (see FFT of a 1D slice of a 2D SDF), which gets amplified when we take derivatives using standard tools like autodiff.
(3/n)

Hybrid neural fields like Instant NGP have made training neural fields extremely efficient. However, we find that they fall short of being "faithful" representations, exhibiting noisy artifacts when we compute their spatial derivatives with autodiff.
(2/n)