Parcel3D: Shape Reconstruction from Single RGB Images for Applications in Transportation Logistics

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.Comment: Accepted at CVPR workshop on Vision-based InduStrial InspectiON (VISION) 2023, see https://vision-based-industrial-inspection.github.io/cvpr-2023

Meshed Up: Learnt Error Correction in 3D Reconstructions

Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation (ICRA) 201