Difference between revisions of "User:Bralani/GSoC2024/Log"
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==Tuesday, July 2st, 2024== | ==Tuesday, July 2st, 2024== | ||
| − | Implemented a different version of the neural network (NIF) to make it work with hit/miss task. Got a 0. | + | 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== | ==Wednesday, July 3st, 2024== | ||
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==Friday, July 5st, 2024== | ==Friday, July 5st, 2024== | ||
| − | Solved issues with rendering, got | + | 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. |
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| + | == 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. | ||
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| + | == Sunday, July 7st, 2024== | ||
| + | Implemented a grid encoding approach => results are still of the order of 98.8%. | ||
Latest revision as of 17:12, 7 July 2024
Contents
Development Logs[edit]
Community Bonding Period
- Familiarizing with previous work,especially [Neural Intersection Functions](https://arxiv.org/abs/2306.07191)
Monday, July 1st, 2024[edit]
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[edit]
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[edit]
Compared a normal rendering with the neural network (NIF) one: results are still far from being acceptable.
Thursday, July 4st, 2024[edit]
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[edit]
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[edit]
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[edit]
Implemented a grid encoding approach => results are still of the order of 98.8%.
