  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.
