User:Bralani/GSoC2024/Log

Community Bonding Period

• Familiarizing with previous work,especially [Neural Intersection Functions](https://arxiv.org/abs/2306.07191)

Monday, July 1st, 2024

Downloaded the code from the repo of fall rainy in order to have a common base source code. Then, installed different libraries (like pytorch) to make it work.

Tuesday, July 2st, 2024

Implemented a different version of the neural network (NIF) to make it work with hit/miss task. Got a 0.99 accuracy with bounding sphere approach in the training set.

Wednesday, July 3st, 2024

Compared a normal rendering with the neural network (NIF) one: results are still far from being acceptable.

Thursday, July 4st, 2024

Got an average accuracy of 50% with NIF in predicting the true rendering. Tried with a simple KNN with a billion rays stored, got always 50% of accuracy. -> This means that there are some errors in the pipeline (maybe in generating the rays or conversions in spherical coordinates) because the KNN should be better than the random classifier with a billion rays of training set.

Friday, July 5st, 2024

Solved issues with rendering, got a maximum accuracy of 98.6% with the true rendering (bounding sphere sampling). Pay attention that this result has been achieved only with a single render.

Saturday, July 6st, 2024

Implemented a benchmark function that generate different angles of the object (test set). Implemented a different version of loss function (symmetric). Got a maximum accuracy of 98.8% and this result is unbiased -> high confidence.

Sunday, July 7st, 2024

Implemented a grid encoding approach => results are lower than 98.8%.

Monday, July 8st, 2024

Tried with different optimizers, results improved to 99.1% with Nesterov Adam that converges faster and it is very stable between epochs.

Tuesday, July 9st, 2024

Started organizing source code to better understand the workflow.

Wednesday, July 10st, 2024

Continuing organizing source code.

Friday, July 12st, 2024

Finished organizing source code.

Sunday, July 14st, 2024

Added acceleration and support for Metal (Mac OS).

Monday, July 15st, 2024

Implemented a positional encoding like in NeRF's work -> results improved to 99.3% (accuracy).

Tuesday, July 16st, 2024

Implemented an importance sampling approach so as to gather more samples in uncertain areas -> results improved to 99.4% (accuracy) and 99.1% (F1).

Wednesday, July 17st, 2024

The main drawback of current NN is that around boundaries it is not so precise. Time to study more complex encodings: I mention useful papers like DeepSDF, Neural Pull, 2D/3D Gaussian Splatting.