  We study the problem of reconstructing 3D feature curves of an object from a
set of calibrated multi-view images. To do so, we learn a neural implicit field
representing the density distribution of 3D edges which we refer to as Neural
Edge Field (NEF). Inspired by NeRF, NEF is optimized with a view-based
rendering loss where a 2D edge map is rendered at a given view and is compared
to the ground-truth edge map extracted from the image of that view. The
rendering-based differentiable optimization of NEF fully exploits 2D edge
detection, without needing a supervision of 3D edges, a 3D geometric operator
or cross-view edge correspondence. Several technical designs are devised to
ensure learning a range-limited and view-independent NEF for robust edge
extraction. The final parametric 3D curves are extracted from NEF with an
iterative optimization method. On our benchmark with synthetic data, we
demonstrate that NEF outperforms existing state-of-the-art methods on all
metrics. Project page: https://yunfan1202.github.io/NEF/.
