  3D shape modeling is labor-intensive, time-consuming, and requires years of
expertise. To facilitate 3D shape modeling, we propose a 3D shape generation
network that takes a 3D VR sketch as a condition. We assume that sketches are
created by novices without art training and aim to reconstruct geometrically
realistic 3D shapes of a given category. To handle potential sketch ambiguity,
our method creates multiple 3D shapes that align with the original sketch's
structure. We carefully design our method, training the model step-by-step and
leveraging multi-modal 3D shape representation to support training with limited
training data. To guarantee the realism of generated 3D shapes we leverage the
normalizing flow that models the distribution of the latent space of 3D shapes.
To encourage the fidelity of the generated 3D shapes to an input sketch, we
propose a dedicated loss that we deploy at different stages of the training
process. The code is available at https://github.com/Rowl1ng/3Dsketch2shape.

Computer Vision and Pattern Recognition