Implementing mesh neural network for 3D shape representation.
The project will be an implementation of the paper MeshNet: Mesh Neural Network for 3D Shape Representation (AAAI 2019). Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. A lot of research is going on to improve the accuracy of the 3D model classification. This paper has achieved 91.9% accuracy. You are expected to implement this research paper. The additional work may include modifying the NN Architecture and train the model on additional datasets.
The following points must be included in the proposal for the project:
- Your motivation and understanding of the project
- Background in ML/DL (include your previous projects)
- Experience with Python and libraries (Numpy, Pandas, TensorFlow, PyTorch, etc)
|Week 1||Brush-up Python, ML & Various Deep Learning Architecture|
|Week 2||Understand the main paper and study input data structure of the 3D model|
|Week 3||Pre-processing of the data|
|Week 4||Test the input data on simple DNN model|
|Week 5||Implement the MeshNet Model|
|Week 6||Hyperparameter Tuning of the Model|
|Week 7||Improve the architecture for better performance|
|Week 8||Generate additional dataset and testing the robustness of the model|