HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects

Roman Kaskman, Sergey Zakharov, Ivan Shugurov, Slobodan Ilic

This is the webpage for the dataset introduced in HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects (ICCV Workshops 2019, bib).

  • ✔    Reconstructed 3D models of high quality.
  • ✔    Validation & test sequences for 13 scenes.
  • ✔    Ground truth pose annotations for each object in the scene.
  • ✔    RGB and Depth images captured with PrimeSense Carmine and Kinect 2.
  • ✔    Scalability, light and texture change benchmarks.


We present a dataset for 6D pose estimation that covers the above-mentioned challenges, mainly targeting training from 3D models (both textured and textureless), scalability, occlusions, and changes in light conditions and object appearance. The dataset features 33 objects (17 toy, 8 household and 8 industry-relevant objects) over 13 scenes of various difficulty. We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter. We also set a baseline for the presented benchmarks using a state-of-the-art DPOD detector. Considering the difficulty of making such datasets, we plan to release the code allowing other researchers to extend this dataset or make their own datasets in the future.


The dataset feature 33 objects of different purpose (toys, household objects, industrial objects). The reconstructed 3D models are stored as .ply files with associated per-vertex colors. The models are stored in millimeters, with surface normal vectors already precomputed.

Validation and test sequences

The dataset features 13 scenes of varying complexity. Each scene was captured with two sensors: PrimeSense Carmine and Kinect 2. For each scene there are 340 validation RGBD frames captured on a rotating turntable and 1000 test RGBD frames captured in a handheld mode. The pose labels are provided for each of the objects in each frame.

This data set is released under Creative Comon Zero License.

The dataset is hosted on BOP challenge web server and is composed of two versions:

Download: Base archive, Object models, PBR-BlenderProc4BOP Synthetic Training Images, Validation images (Primesense), Validation Images (Kinect 2), BOP'19 Test images (Primesense)

Download: All Test Images (Primesense), All Test Images (Kinect2)


The source code used for preparing the dataset has been released on siemens github and it is open source. It supports data acquisition and has interfaces to Primesense, Kinect2, Kinect Azure and Intel Realsense cameras, as well as interface for loading images previously saved to the hard drive. Additionally it allows estimation and refinement of 6D poses of 3D objects that has to be available beforehand as CAD models or 3D reconstructed models. We used ARTEC Eva commercial scanner for creation of 3D models for this dataset. The initial 6D pose estimation is based on PPF method of B. Drost and S. Ilic, Model globally, match locally: Efficient and robust 3D object recognition, CVPR 2010, that is still one of the best performing methods for on BOP 6D pose estimation leader board. For that we used implementation from HALCON that is either commercially available or free for educational purposes (check licenses on github repo of our open source).

The creation of this dataset has been generously supported by
Siemens AG Corporate Technology, Research & Technology Center.

For questions, concerns and general feedback, please contact Slobodan Ilic.