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Dataset of Industrial Metal Objects

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arxiv 2208.04052 v1 pith:2ROSHIUG submitted 2022-08-08 cs.CV

Peter De Roovere , Steven Moonen , Nick Michiels , Francis Wyffels This is my paper · ORCID
classification cs.CV
keywords datasetimagesreal-worldsyntheticconditionsdataobjectschallenging
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 11 citations worldwide. Full citation record

  1. XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity

    cs.CV 2025-05 conditional novelty 6.0 of 10

    XYZ-IBD is a new industrial bin-picking benchmark with 273k pose annotations on 15 reflective, symmetric metal objects, and it demonstrates large performance drops for state-of-the-art pose estimators.

  2. Harlequin: Color-driven Generation of Synthetic Data for Referring Expression Comprehension

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Pre-training referring expression comprehension models on a fully synthetic, color-varied dataset generated with GLIGEN improves their fine-tuned accuracy on real benchmarks.

  3. SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation

    cs.CV 2025-10 conditional novelty 5.0 of 10

    A regression-based composite of existing image-quality and trainability metrics, called SDQM, correlates with YOLOv11 mAP (r=0.87 on one split; 0.78±0.09 k-fold) on three synthetic-to-real detection benchmarks.

  4. Development of Hybrid Artificial Intelligence Training on Real and Synthetic Data: Benchmark on Two Mixed Training Strategies

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Fine-tuning on real data after synthetic pretraining beats mixing both data types in most settings, but simple mixing wins for CNNs on large-gap sketch data.

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