  Single-view 3D shape retrieval is a challenging task that is increasingly
important with the growth of available 3D data. Prior work that has studied
this task has not focused on evaluating how realistic occlusions impact
performance, and how shape retrieval methods generalize to scenarios where
either the target 3D shape database contains unseen shapes, or the input image
contains unseen objects. In this paper, we systematically evaluate single-view
3D shape retrieval along three different axes: the presence of object
occlusions and truncations, generalization to unseen 3D shape data, and
generalization to unseen objects in the input images. We standardize two
existing datasets of real images and propose a dataset generation pipeline to
produce a synthetic dataset of scenes with multiple objects exhibiting
realistic occlusions. Our experiments show that training on occlusion-free data
as was commonly done in prior work leads to significant performance degradation
for inputs with occlusion. We find that that by first pretraining on our
synthetic dataset with occlusions and then finetuning on real data, we can
significantly outperform models from prior work and demonstrate robustness to
both unseen 3D shapes and unseen objects.
