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: this https URL

研究重建物体的三维特征曲线的问题，引入神经边缘场来学习三维边缘的密度分布，在没有三维几何操作或交叉视图边缘对应的监督下，借助基于视图渲染的可微优化处理，从视图提取地面真值边缘图与渲染边缘图进行比较以进行优化，最后通过迭代优化法从神经边缘场中提取参数三维曲线，实验结果表明，在基准测试中，该模型在所有指标上均优于现有的最先进方法。