  We focus on enabling damage and tampering detection in logistics and tackle
the problem of 3D shape reconstruction of potentially damaged parcels. As input
we utilize single RGB images, which corresponds to use-cases where only simple
handheld devices are available, e.g. for postmen during delivery or clients on
delivery. We present a novel synthetic dataset, named Parcel3D, that is based
on the Google Scanned Objects (GSO) dataset and consists of more than 13,000
images of parcels with full 3D annotations. The dataset contains intact, i.e.
cuboid-shaped, parcels and damaged parcels, which were generated in
simulations. We work towards detecting mishandling of parcels by presenting a
novel architecture called CubeRefine R-CNN, which combines estimating a 3D
bounding box with an iterative mesh refinement. We benchmark our approach on
Parcel3D and an existing dataset of cuboid-shaped parcels in real-world
scenarios. Our results show, that while training on Parcel3D enables transfer
to the real world, enabling reliable deployment in real-world scenarios is
still challenging. CubeRefine R-CNN yields competitive performance in terms of
Mesh AP and is the only model that directly enables deformation assessment by
3D mesh comparison and tampering detection by comparing viewpoint invariant
parcel side surface representations. Dataset and code are available at
https://a-nau.github.io/parcel3d.
