Tianke Youke

A sanctuary for secreting and rushing at night.


I am currently burying myself into the sea of NeRF. I plan to archive my learning notes here. I am still a beginner, so the notes absolutely contain errors, and are not finished yet.


Learning NeRF: Reading list, learning references, and plans

Notes of CVPR22' Tutorial:

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This is part of my journey of learning NeRF.

2.4. Prior-based reconstruction of neural fields

Sounds like a one-shot task: instead of fitting and optimizing a neural field each for one scene; let's learn a prior distribution of neural field. Then, given a specific scene, it adjusts the neural field in just one forward.

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Reference: https://stackoverflow.com/questions/48302810/whats-the-difference-between-hidden-and-output-in-pytorch-lstm

I was so confused when doing a homework on implementing the Luong Attention, because it tells that the decoder is a RNN, which takes \(y_{t-1}\) and \(s_{t-1}\) as input, and outputs \(s_t\), i.e., \(s_t = RNN(y_{t-1}, s_{t-1})\).

But the pytorch implementation of RNN is: \(outputs, hidden\_last = RNN(inputs, hidden\_init)\), which takes in a sequence of elements, computes in serials, and outputs a sequence also.

I was confused about what is the \(s_t\). Is it the \(outputs\), or the \(hidden\_states\)?

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