Why did you use neural networks? There are faster techniques in analytical geometry that can extract surface contours from color gradients from images, and they do this faster and directly.
1) My bread and butter for the last 10 years has been machine learning. When all you have is a hammer...
2) We don't extract surface contours, we learn a volumetric radiance field! To oversimplify, we learn a (smooth) function that, given a position in space, produces the differential opacity and color at that space. To render an image from a camera viewpoint, we approximately integrate along rays emitted from each pixel of the camera.
Check out NeRF and our paper to learn more about this representation!
Neural networks are better compared to classical methods.
One of the best non-classical methods is this one (https://grail.cs.washington.edu/projects/sq_rome_g1/), and our method is significantly improves upon it. We do not compare directly with it, but Neural Rerendering in the Wild does, and we improve upon it.
2) We don't extract surface contours, we learn a volumetric radiance field! To oversimplify, we learn a (smooth) function that, given a position in space, produces the differential opacity and color at that space. To render an image from a camera viewpoint, we approximately integrate along rays emitted from each pixel of the camera.
Check out NeRF and our paper to learn more about this representation!