flor1s

I mean the PDF reader that is contained in Zotero. Am I using the wrong terminology?

Some PDF readers have a Back button (like the built-in Zotero PDF reader).

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.

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).

+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.

I'm not sure if it's truly a timeless paper, but "Attention is all you need" by Vaswani et al. has been super influential in recent years. Also "Deep Unsupervised Learning using Nonequilibrium Thermodynamics" by Sohl-Dickstein et al. (about Diffusion Models) and "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" by Mildenhall et al. were hugely influential to me.

tmux + tmuxinator seems like a solution?

Though my workflow right now is mostly SSH + tmux + jupyterlab

Neural Radiance Fields are a technique from the neural rendering research field, while photogrammetry is a research field on its own. However these are just turf wars and in practice there is a lot of overlap between both fields.

For example, most NeRF implementations recommend the use of COLMAP (traditionally a photogrammetry tool) to obtain camera positions/rotations that are used alongside their images. So this multi-view stereo step is shared between both NeRF (except a few research works that also optimize for camera positions/rotations through a neural network) and photogrammetry.

After the multi-view stereo step in NeRF you train a neural renderer, while in photogrammetry you would run a multi-view geometry step/package that uses more traditional optimization algorithms.

The expected output of both techniques is slightly different. NeRF produces renderings and can optionally export a mesh (using the marching cubes algorithm). Photogrammetry produces meshes and in the process might render the scene for editting purposes.