  We present ANISE, a method that reconstructs a 3D~shape from partial
observations (images or sparse point clouds) using a part-aware neural implicit
shape representation. The shape is formulated as an assembly of neural implicit
functions, each representing a different part instance. In contrast to previous
approaches, the prediction of this representation proceeds in a coarse-to-fine
manner. Our model first reconstructs a structural arrangement of the shape in
the form of geometric transformations of its part instances. Conditioned on
them, the model predicts part latent codes encoding their surface geometry.
Reconstructions can be obtained in two ways: (i) by directly decoding the part
latent codes to part implicit functions, then combining them into the final
shape; or (ii) by using part latents to retrieve similar part instances in a
part database and assembling them in a single shape. We demonstrate that, when
performing reconstruction by decoding part representations into implicit
functions, our method achieves state-of-the-art part-aware reconstruction
results from both images and sparse point clouds.When reconstructing shapes by
assembling parts retrieved from a dataset, our approach significantly
outperforms traditional shape retrieval methods even when significantly
restricting the database size. We present our results in well-known sparse
point cloud reconstruction and single-view reconstruction benchmarks.
