mlsanityData to 7 Oct 2026

SynPick: A Dataset for Dynamic Bin Picking Scene Understanding

arXiv 2107.04852 ↗First posted 10 Jul 2021cs.RO cs.CVcited by 3

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Abstract (arXiv)

We present SynPick, a synthetic dataset for dynamic scene understanding in bin-picking scenarios. In contrast to existing datasets, our dataset is both situated in a realistic industrial application domain -- inspired by the well-known Amazon Robotics Challenge (ARC) -- and features dynamic scenes with authentic picking actions as chosen by our picking heuristic developed for the ARC 2017. The dataset is compatible with the popular BOP dataset format. We describe the dataset generation process in detail, including object arrangement generation and manipulation simulation using the NVIDIA PhysX physics engine. To cover a large action space, we perform untargeted and targeted picking actions, as well as random moving actions. To establish a baseline for object perception, a state-of-the-art pose estimation approach is evaluated on the dataset. We demonstrate the usefulness of tracking poses during manipulation instead of single-shot estimation even with a naive filtering approach. The generator source code and dataset are publicly available.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 26 references that are in the map. First posted on arXiv 10 Jul 2021.

Builds on

23 of 26 references.

References · 23

  1. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
  2. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  3. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  4. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  5. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  6. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  7. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  8. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  9. 2020BOP Challenge 2020 on 6D Object Localizationcited by 192
  10. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  11. 2019HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objectscited by 110
  12. 2019Self-supervised 6D Object Pose Estimation for Robot Manipulationcited by 100
11 more
  1. 2018Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimationcited by 92
  2. 2019PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Trackingcited by 91
  3. 2020se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domainscited by 87
  4. 20196-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypointscited by 78
  5. 2018Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimationcited by 74
  6. 2019Photorealistic Image Synthesis for Object Instance Detectioncited by 66
  7. 2015A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Placecited by 35
  8. 2021PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimationcited by 14
  9. 2018Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packingcited by 12
  10. 2020Stillleben: Realistic Scene Synthesis for Deep Learning in Roboticscited by 11
  11. 2019Refining 6D Object Pose Predictions using Abstract Render-and-Comparecited by 8

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2022MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesiscited by 13
  2. 2022MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networkscited by 12
  3. 2023Novel Synthetic Data Tool for Data-Driven Cardboard Box Localizationcited by 0