Difference between revisions of "User:Bralani/GSoC2024/Log"
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== Saturday, July 6st, 2024== | == 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%. | + | 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. |
Revision as of 03:23, 7 July 2024
Contents
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.
