# HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions

arXiv: https://arxiv.org/abs/2308.01477 · HTML: https://mlsanity.com/paper/2308.01477/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 44 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 30 of 34)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [The Perfect Match: 3D Point Cloud Matching with Smoothed Densities](https://mlsanity.com/paper/1811.06879/) (2018)
- [Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations](https://mlsanity.com/paper/2012.09988/) (2020)
- [BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects](https://mlsanity.com/paper/2303.14158/) (2023)
- [FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism](https://mlsanity.com/paper/2103.07054/) (2021)
- [DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency](https://mlsanity.com/paper/2103.06526/) (2021)
- [se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains](https://mlsanity.com/paper/2007.13866/) (2020)
- [BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models](https://mlsanity.com/paper/2108.00516/) (2021)
- [6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark](https://mlsanity.com/paper/2203.05701/) (2022)
- [You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration](https://mlsanity.com/paper/2201.12716/) (2022)
- [ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization](https://mlsanity.com/paper/2207.13691/) (2022)
- [CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://mlsanity.com/paper/2203.01929/) (2022)
- [Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset](https://mlsanity.com/paper/2206.15436/) (2022)
- [CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://mlsanity.com/paper/2109.09163/) (2021)
- [PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects](https://mlsanity.com/paper/2205.08811/) (2022)
- [Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image](https://mlsanity.com/paper/2109.06161/) (2021)
- [SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation](https://mlsanity.com/paper/2208.06661/) (2022)
- [TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation](https://mlsanity.com/paper/2303.16730/) (2023)
- [Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands](https://mlsanity.com/paper/2003.03518/) (2020)
- [iCaps: Iterative Category-level Object Pose and Shape Estimation](https://mlsanity.com/paper/2201.00059/) (2021)
- [Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation](https://mlsanity.com/paper/2205.11047/) (2022)
- [MobilePose: Real-Time Pose Estimation for Unseen Objects with Weak Shape Supervision](https://mlsanity.com/paper/2003.03522/) (2020)

## Built on by (most cited first, 30 of 48)

- [HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos](https://mlsanity.com/paper/2411.19167/) (2024)
- [Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking](https://mlsanity.com/paper/2406.09598/) (2024)
- [HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction](https://mlsanity.com/paper/2406.06843/) (2024)
- [ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives](https://mlsanity.com/paper/2411.19083/) (2024)
- [RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies](https://mlsanity.com/paper/2604.09860/) (2026)
- [BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation](https://mlsanity.com/paper/2504.02812/) (2025)
- [GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation](https://mlsanity.com/paper/2505.11865/) (2025)
- [V2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence](https://mlsanity.com/paper/2511.20886/) (2025)
- [PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments](https://mlsanity.com/paper/2312.15130/) (2023)
- [Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision](https://mlsanity.com/paper/2506.03605/) (2025)
- [RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping](https://mlsanity.com/paper/2507.23734/) (2025)
- [2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos](https://mlsanity.com/paper/2503.09320/) (2025)
- [Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors](https://mlsanity.com/paper/2505.24103/) (2025)
- [UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models](https://mlsanity.com/paper/2409.20551/) (2024)
- [AffordanceSAM: Segment Anything Once More in Affordance Grounding](https://mlsanity.com/paper/2504.15650/) (2025)
- [Segmenting Object Affordances: Reproducibility and Sensitivity to Scale](https://mlsanity.com/paper/2409.01814/) (2024)
- [GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024](https://mlsanity.com/paper/2412.01552/) (2024)
- [MUSE: Model-based Uncertainty-aware Similarity Estimation for zero-shot 2D Object Detection and Segmentation](https://mlsanity.com/paper/2510.17866/) (2025)
- [NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields](https://mlsanity.com/paper/2309.11966/) (2023)
- [Weakly-Supervised Learning of Dense Functional Correspondences](https://mlsanity.com/paper/2509.03893/) (2025)
- [Beyond 'Templates': Category-Agnostic Object Pose, Size, and Shape Estimation from a Single View](https://mlsanity.com/paper/2510.11687/) (2025)
- [IndustryShapes: An RGB-D Benchmark dataset for 6D object pose estimation of industrial assembly components and tools](https://mlsanity.com/paper/2602.05555/) (2026)
- [Event6D: Event-based Novel Object 6D Pose Tracking](https://mlsanity.com/paper/2603.28045/) (2026)
- [SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them](https://mlsanity.com/paper/2607.27703/) (2026)
- [ManipGPT: Is Affordance Segmentation by Large Vision Models Enough for Articulated Object Manipulation?](https://mlsanity.com/paper/2412.10050/) (2024)
- [Vision6D: 3D-to-2D Interactive Visualization and Annotation Tool for 6D Pose Estimation](https://mlsanity.com/paper/2504.15329/) (2025)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [MR6D: Benchmarking 6D Pose Estimation for Mobile Robots](https://mlsanity.com/paper/2508.13775/) (2025)
- [OPFormer: Object Pose Estimation leveraging foundation model with geometric encoding](https://mlsanity.com/paper/2511.12614/) (2025)
- [AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping](https://mlsanity.com/paper/2602.03547/) (2026)

## Abstract (arXiv)

We present the HANDAL dataset for category-level object pose estimation and affordance prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects that are of the proper size and shape for functional grasping by robot manipulators, such as pliers, utensils, and screwdrivers. Our annotation process is streamlined, requiring only a single off-the-shelf camera and semi-automated processing, allowing us to produce high-quality 3D annotations without crowd-sourcing. The dataset consists of 308k annotated image frames from 2.2k videos of 212 real-world objects in 17 categories. We focus on hardware and kitchen tool objects to facilitate research in practical scenarios in which a robot manipulator needs to interact with the environment beyond simple pushing or indiscriminate grasping. We outline the usefulness of our dataset for 6-DoF category-level pose+scale estimation and related tasks. We also provide 3D reconstructed meshes of all objects, and we outline some of the bottlenecks to be addressed for democratizing the collection of datasets like this one.