Deep implicit functions (DIFs) have emerged as a powerful paradigm for many
computer vision tasks such as 3D shape reconstruction, generation,
registration, completion, editing, and understanding. However, given a set of
3D shapes with associated covariates there is at present no shape
representation method which allows to precisely represent the shapes while
capturing the individual dependencies on each covariate. Such a method would be
of high utility to researchers to discover knowledge hidden in a population of
shapes. We propose a 3D Neural Additive Model for Interpretable Shape
Representation (NAISR) which describes individual shapes by deforming a shape
atlas in accordance to the effect of disentangled covariates. Our approach
captures shape population trends and allows for patient-specific predictions
through shape transfer. NAISR is the first approach to combine the benefits of
deep implicit shape representations with an atlas deforming according to
specified covariates. Although our driving problem is the construction of an
airway atlas, NAISR is a general approach for modeling, representing, and
investigating shape populations. We evaluate NAISR with respect to shape
reconstruction, shape disentanglement, shape evolution, and shape transfer for
the pediatric upper airway. Our experiments demonstrate that NAISR achieves
competitive shape reconstruction performance while retaining interpretability.Comment: 20 page