Towards Generalising Neural Implicit Representations

TL;DR

This paper demonstrates that multi-task training of neural implicit representations enhances their generality and performance across various 3D data tasks, including reconstruction, classification, and segmentation.

Contribution

It introduces a multi-task training framework that produces more versatile neural implicit representations capable of handling multiple 3D tasks simultaneously.

Findings

Multi-task training improves generalization of neural implicit representations.

Encodings trained on multiple tasks perform equally well across those tasks.

The approach enhances traditional 3D reconstruction and segmentation results.

Abstract

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.