User:Bralani/GSoC2024/Log
Contents
- 1 Development Logs
- 2 Community Bonding Period
- 2.1 Monday, July 1st, 2024
- 2.2 Tuesday, July 2st, 2024
- 2.3 Wednesday, July 3st, 2024
- 2.4 Thursday, July 4st, 2024
- 2.5 Friday, July 5st, 2024
- 2.6 Saturday, July 6st, 2024
- 2.7 Sunday, July 7st, 2024
- 2.8 Monday, July 8st, 2024
- 2.9 Tuesday, July 9st, 2024
- 2.10 Wednesday, July 10st, 2024
- 2.11 Friday, July 12st, 2024
- 2.12 Sunday, July 14st, 2024
- 2.13 Monday, July 15st, 2024
Development Logs
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 still of the order of 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%.
