Experiments in neural and inverse rendering

When I learned about traditional computer graphics and photogrammetry I missed the big picture about how all the pieces connect: with hardware, physics and machine learning aspects. It made it harder to understand recent research and its meaning for the field. Rendering 3D models from 2D images remains a challenging problem but incredible progress has been made since I first became interested in the topic 20 years ago (see below)

Catching up with newer research in image based rendering: A TLDR on how traditional computer graphics fits with computer vision, machine learning and capture hardware.

https://github.com/3a1b2c3/seeingSpace

PDF] InverseRenderNet: Learning Single Image Inverse Rendering | Semantic  Scholar

Source: https://dl.acm.org/doi/fullHtml/10.1145/3359998.3369399