HANDAL：一個包含姿勢標註、可利用性和重建的真實世界可操作物體類別數據集。

摘要

我們提出了 HANDAL 資料集，用於類別級別的物體姿勢估計和可負擔性預測。與以往的資料集不同，我們的資料集專注於適合機器人操縱器官功能性抓握的機器人就緒可操作物體，如鉗子、餐具和螺絲刀。我們的標註流程經過了精簡，僅需要一台現成相機和半自動處理，使我們能夠製作高質量的三維標註，而無需眾包。該資料集包含了來自212個現實世界物體的17個類別中2.2k個影片的308k個標註圖像幀。我們專注於硬件和廚房工具物體，以促進在機器人操縱器需要與環境進行互動的實際情境中的研究，超越簡單的推動或不加選擇的抓握。我們概述了我們的資料集對於類別級別姿勢+尺度估計和相關任務的用途。我們還提供了所有物體的三維重建網格，並概述了需要解決的一些瓶頸，以實現像這樣的資料集的民主化收集。

English

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.

HANDAL：一個包含姿勢標註、可利用性和重建的真實世界可操作物體類別數據集。

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

摘要

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