Learning continuous neural fields
Abstract
We present a compact representation for high-fidelity scene reconstruction. Our method learns a continuous field directly from sparse observations.
1 Introduction
Neural fields provide a flexible foundation for representing complex signals. In this paper, we introduce a stable training objective and evaluate its convergence.
Figure 1. Reconstruction quality over training epochs.












