Master Practical Training Project
This project aims to address the challenges associated with the expensive and time-consuming annotation of 3D data by exploring a self-supervised approach for the extraction of 4D spatio-temporal features from dynamic point cloud data. Specifically, the project investigates the prediction of the temporal order of shuffled point cloud clips as an auxiliary task, inspired by , but with a special focus on working with datasets from real-world scenarios. Different models for 4D point cloud processing will be applied to a large dataset to evaluate the suitability of these methods to improve the performance of action recognition methods on smaller 3D datasets (MSRA and own curated dataset).
- Literature review on the state of the art of self-supervised methods for dynamic point clouds
- Implement different models for 4D point cloud feature learning
- Implement self-supervised method
- Evaluation on MSRA dataset and own curated dataset
- Preprocess and annotate real-world data