2020 was the year in which *neural volume rendering* exploded onto the scene, triggered by the impressive NeRF paper by Mildenhall et al. I wrote a post as a way of getting up to speed in a fascinating and very young field and share my journey with you: dellaert.github.io/NeRF/
The precursors to NeRF are approaches that use an *implicit* surface representation. At CVPR 2019, 3 papers introduced the use of neural nets as *scalar function approximators* to define occupancy and/or signed distance functions.
Occupancy networks (avg.is.tuebingen.mpg.de/publications/o…) introduce implicit, coordinate-based learning of occupancy. A network consisting of 5 ResNet blocks take a feature vector and a 3D point and predict binary occupancy.