Towards Generalising Neural Implicit Representations
This work addresses the need for more versatile neural representations in 3D data processing, though it is incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of neural implicit representations being limited to storage and reconstruction by showing that multi-task training with reconstruction, classification, and segmentation can produce more general encodings. The result is equal quality reconstructions and improved performance on conventional tasks compared to single-task encodings.
Neural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats. However, the focus of existing work has mainly been on storage and subsequent reconstruction. In this work, we show that training neural representations for reconstruction tasks alongside conventional tasks can produce more general encodings that admit equal quality reconstructions to single task training, whilst improving results on conventional tasks when compared to single task encodings. We reformulate the semantic segmentation task, creating a more representative task for implicit representation contexts, and through multi-task experiments on reconstruction, classification, and segmentation, show our approach learns feature rich encodings that admit equal performance for each task.