We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part-aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, subsequently feeding them into a transformer decoder that converts them to shape embeddings suitable for editing 3D neural implicit shapes. SENS provides intuitive sketch-based generation and editing, and also succeeds in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract and imprecise sketches. Additionally, SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal. It also offers part-based modeling capabilities, enabling the combination of features from multiple sketches to create more complex and customized 3D shapes. We demonstrate the effectiveness of our model compared to the state-of-the-art using objective metric evaluation criteria and a user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase our method's intuitive sketch-based shape editing capabilities, and validate it through a usability study.

翻译：我们提出SENS，一种从手绘草图（包括抽象草图）生成和编辑3D模型的新方法。该方法允许用户快速轻松地勾勒形状，随后将草图映射至部分感知神经隐式形状架构的潜在空间。SENS分析草图并将其各部分编码为ViT补丁编码，随后将其输入转换器解码器，该解码器将编码转换为适用于编辑3D神经隐式形状的形状嵌入。SENS提供直观的基于草图的生成与编辑功能，并能成功捕捉用户草图的意图，生成多样化的新颖且富有表现力的3D形状，即使是抽象和不精确的草图也能实现。此外，SENS通过部分重建支持细化调整，允许进行细微修正和去除伪影。它还提供基于部分的建模能力，能够整合多个草图的特征以创建更复杂且定制化的3D形状。我们通过客观指标评估标准和用户研究证明了模型相比现有技术的有效性，两者均表明模型在处理中等抽象程度草图时表现优异。此外，我们展示了该方法直观的基于草图的形状编辑能力，并通过可用性研究进行了验证。