The Proposed Datasets

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DREDS Dataset (simulated)

Making use of domain randomization and depth simulation, we construct the large-scale simulated dataset, DREDS. In total, DREDS dataset consists of two subsets: 1) DREDS-CatKnown: 100,200 training and 19,380 testing RGBD images made of 1,801 objects spanning 7 categories from ShapeNetCore, with randomized specular, transparent, and diffuse materials, 2) DREDS-CatNovel: 11,520 images of 60 category-novel objects, which is transformed from GraspNet-1Billion that contains CAD models and annotates poses, by changing their object materials to specular or transparent, to verify the ability of our method to generalize to new object categories.

STD Dataset (real)

To further examine the proposed method in real scenes, we curate a real-world dataset, STD, composing of Specular, Transparent, and Diffuse objects. Similar to DREDS dataset, STD dataset contains: 1) STD-CatKnown: 27000 RGBD images of 42 category-level objects spanning 7 categories, captured from 25 different scenes with various backgrounds and illumination. 2) STD-CatNovel: 11000 data of 8 category-novel objects from 5 scenes, for evaluating the generalization ability of the proposed method.