Novel view synthesis, so based on some images of a scene, rendering views from positions and angles that were not originally recorded. 3D Gaussian Splatting, besides beating the state-of-the-art in terms of visual quality at its time of release, also has some nice physical properties like having the splats associated with actual points in 3D (obtained through feature extraction).
I think these days we can quite reliably train robots to do single tasks from a few demonstrations. The problem is that "quite reliably" simply is not good enough for the real world. Also a "low-cost" system as presented in this work is still 32k (ignoring hardware and software integration costs).
I guess generative AI using copilot or ChatGPT might overtake model-driven engineering as a time-saving technique, which seems worrisome for the "software factory" industry. At the same time, I doubt it will replace MDE as a formal method (i.e. if you need to prove that your software does what it is supposed to do).
It's weird how mixed quality their books are, and how many overlapping books they have. From Packt I like Sebastian Raschka's books on Python Machine Learning. Other than that from time to time they do have some good "recipes", but hardly any of their books seems worth reading cover to cover.
I think that's a case where a good author has chosen the publisher for some reason. They are good enough to be published by someone else but went with Packt possibly because it was an easy onboarding.
Weights are just numbers, essentially by using a neural network you are telling the system to "find the best way to represent the scene with a budget of X numbers/parameters". Modern NeRFs like instant-ngp also use some grid representations. I guess Gaussian Splatting is slightly more geometrically appealing because you get points around the surfaces that you are trying to model. These points are however not guaranteed to be exactly on the surface, which additional surface losses try to solve (e.g. NeuSG).
AIST | Research Assistant or Technical Assistant | Onsite in Tokyo, Japan | Python, C++
We are a Japanese semi-governmental research and development institute, our center does research on industrial cyber-physical systems and our team is focused on robotics and automation.
We are looking for candidates with good programming skills who are interested in robotics and have some familiarity with Neural Radiance Fields or are eager to learn.
This is a good tip but I'm referring to the inadequacy of markdown tables + Obsidian plugins that deal with that. I'm yet to see a solution that works as well as tables in wiki. I think Obsidian should focus on improving that as part of the core software.
+1 for Obsidian, though until now I haven't felt the need to write actual papers in. Zotero + latexmk + vim + Skim/SumatraPDF mostly scratches that itch for me.
