mlsanityData to 7 Oct 2026

Dataset of Industrial Metal Objects

arXiv 2208.04052 ↗First posted 8 Aug 2022cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our dataset contains both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. This results in over 30,000 images, accurately labelled using a new public tool. Synthetic data is obtained by carefully simulating real-world conditions and varying them in a controlled and realistic way. This leads to over 500,000 synthetic images. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-to-real research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. The dataset and accompanying resources are made available on the project website at https://pderoovere.github.io/dimo.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

12 of 16 references.

References · 12

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  2. 2019Objects as Pointscited by 1,172
  3. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
  4. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  5. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  6. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  7. 2017T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objectscited by 210
  8. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  9. 2018The Limits and Potentials of Deep Learning for Roboticscited by 104
  10. 2019BlenderProccited by 76
  11. 2019Photorealistic Image Synthesis for Object Instance Detectioncited by 66
  12. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2022Analysis of Training Object Detection Models with Synthetic Datacited by 9
  2. 2024Towards Sim-to-Real Industrial Parts Classification with Synthetic Datasetcited by 7
  3. 2025XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexitycited by 7
  4. 2024Harlequin: Color-driven Generation of Synthetic Data for Referring Expression Comprehensioncited by 3
  5. 2022CenDerNet: Center and Curvature Representations for Render-and-Compare 6D Pose Estimationcited by 2
  6. 2024SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspectioncited by 2
  7. 2025SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluationcited by 2
  8. 2026MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methodscited by 0