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We study the problem of reconstructing 3D feature curves of an object from aset of calibrated multi-view images. To do so, we learn a neural implicit fieldrepresenting the density distribution of 3D edges which we refer to as NeuralEdge Field (NEF). Inspired by NeRF, NEF is optimized with a view-basedrendering loss where a 2D edge map is rendered at a given view and is comparedto the ground-truth edge map extracted from the image of that view. Therendering-based differentiable optimization of NEF fully exploits 2D edgedetection, without needing a supervision of 3D edges, a 3D geometric operatoror cross-view edge correspondence. Several technical designs are devised toensure learning a range-limited and view-independent NEF for robust edgeextraction. The final parametric 3D curves are extracted from NEF with aniterative optimization method. On our benchmark with synthetic data, wedemonstrate that NEF outperforms existing state-of-the-art methods on allmetrics. Project page: https://yunfan1202.github.io/NEF/.

NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images