GraspNet-1Billion provides large-scale training data and a standard evaluation platform for the task of general robotic grasping. The dataset contains 97,280 RGB-D image with over one billion grasp poses.
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Accompanying expert data and trained models for 2021 IROS paper on Multiview Manipulation.
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Introduction NBMOD is a dataset created for researching the task of specific object grasp detection by robots in noisy environments. The dataset comprises three subsets: Simple background Single-object Subset (SSS), Noisy background Single-object Subset (NSS), and Multi-Object grasp detection Subset (MOS). The SSS subset contains 13,500 images, the NSS subset contains 13,000 images, and the MOS subset contains 5,000 images.
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Includes several sets of synthetic stereo images labelled with grasp rectangles representing parallel-jaw grasps (Cornell-like format).
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current categorylevel datasets, however, fall short in annotation quality and pose variety. Addressing this, we introduce HouseCat6D, a new category-level 6D pose dataset. It features 1) multimodality with Polarimetric RGB and Depth (RGBD+P), 2) encompasses 194 diverse objects across 10 household categories, including two photometrically challenging ones, and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage, 5) a checkerboard-free environment, and 6) dense 6D parallel-jaw robotic grasp annotations. Additionally, we present benchmark results for leading category-level pose estimation networks.
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