Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page. Relevant computer vision tasks: bounding box detection classification instance segmentation keypoint estimation 3D bounding box estimation 3D voxel reconstruction 3D reconstruction The dataset is for academic research use only, since it uses resources with restrictive licenses. For a detailed description of how the resources are used, we refer to our paper and project page. Licenses of the resources in detail: Google Scanned Objects: CC BY 4.0 (for details on which files are used, see the respective meta folder) Cardboard Dataset: CC BY 4.0 Shipping Label Dataset: CC BY-NC 4.0 Other Labels: See file misc/source_urls.json LDR Dataset: License for Non-Commercial Use Large Logo Dataset (LLD): Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately. You can use our textureless models (i.e. the obj files) of damaged parcels under CC BY 4.0 (note that this does not apply to the textures). If you use this resource for scientific research, please consider citing @inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }