• Target
    • Is it better to explain to someone like me who has a rough understanding of Neural Networks before starting the investigation?
  • Structure
    • Show it at the beginning and explain how it works.
    • Break down the elements gradually.
    • Since I have been researching and summarizing on Scrapbox, it seems good to incorporate that into the presentation.
    • Flow: HyperNeRF -> NeRF -> Volume rendering
  • HyperNeRF
    • SIGGRAPH Asia’s 09 neural rendering.
    • After listening to various technical papers in different fields at SIGGRAPH Asia, I became most interested in this research and delved deeper into it.
    • Since I didn’t have much prior knowledge in this field, I will try to explain it in a way that can be understood by people without that background.
  • Before explaining HyperNeRF, let me explain the non-hyper NeRF, simply called NeRF.
    • What can it do?
    • https://www.youtube.com/watch?time_continue=92&v=JuH79E8rdKc&feature=emb_logo
    • In the previous approach, it was a neural network that outputs images.
    • However, when trying to do something like NeRF, there was a problem of inconsistent images.
    • When generating images of an object from the top and from the left, they each looked plausible but lacked consistency.
    • NeRF solves this problem by changing the fundamental problem.
    • It aims to train a neural network that can answer what color and opacity a certain point in space has when viewed from a certain angle.
    • For example, when asked about the color and opacity of a specific point on a tea bottle when viewed from the side, the network should answer brown and semi-transparent.
    • For the cap, it should answer green and completely opaque.
    • For coordinates in empty space, it should answer colorless and transparent.
    • In other words, it trains a neural network that can represent a specific three-dimensional scene with a set of colors and densities.
    • This set of colors and densities is called a Radiance Field.
    • Neural Radiance Field -> NeRF
    • What can we do with this?
    • Show a video.
    • By discretizing it for easier calculations, it can be represented by a function that is differentiable.
    • Then various things can be createdimage
    • image
  • Not good at discontinuous changes.
  • HyperNeRF solves this problem.
  • Evaluation
    • For quantitative evaluation, they used these three evaluation criteria.
  • In summary,
    • This research is not the first of its kind, but as shown in the video, it can render shape changes like opening and closing the mouth without any problems.
    • Furthermore, the remarkable point of this research is that it can render shape changes with topological changes without any problems.
  • Impressions
    • I should think about it in case I am asked.
    • Impressions of HyperNeRF:
      • When I read it to a level where I can explain it to others, I felt that there is a kind of genealogy for each topic, as I traced the background of HyperNeRF.
      • Also, in 2020, many derivative studies including HyperNeRF have already been published.
    • Impressions of SIGGRAPH:
      • I feel like I got to see the overall picture of the field.