3D sketches are widely used for visually representing the 3D shape and
structure of objects or scenes. However, the creation of 3D sketch often
requires users to possess professional artistic skills. Existing research
efforts primarily focus on enhancing the ability of interactive sketch
generation in 3D virtual systems. In this work, we propose Diff3DS, a novel
differentiable rendering framework for generating view-consistent 3D sketch by
optimizing 3D parametric curves under various supervisions. Specifically, we
perform perspective projection to render the 3D rational B\'ezier curves into
2D curves, which are subsequently converted to a 2D raster image via our
customized differentiable rasterizer. Our framework bridges the domains of 3D
sketch and raster image, achieving end-toend optimization of 3D sketch through
gradients computed in the 2D image domain. Our Diff3DS can enable a series of
novel 3D sketch generation tasks, including textto-3D sketch and image-to-3D
sketch, supported by the popular distillation-based supervision, such as Score
Distillation Sampling (SDS). Extensive experiments have yielded promising
results and demonstrated the potential of our framework.

借助 Diff3DS 框架，通过在 2D 图像域中计算的梯度，实现了 3D 草图的端到端优化。该框架可以用于文本到 3D 草图和图像到 3D 草图的生成任务，支持基于蒸馏的监督学习，如 Score Distillation Sampling (SDS)。大量实验证明了该框架的潜力和有望的结果。