Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.
翻译:估计物体的6D构成是3D计算机愿景的主要领域之一。由于从实例一级得出的有希望的结果构成估计,研究趋势正走向类别一级,从而提出更实际的应用设想。然而,与公认的实例一级构成数据集不同,现有的类别一级数据集缺乏说明质量和提供数量。我们提议新的6D类别构成数据集HouseCat6D, 其特征为:(1) 极光度RGB+P和深度的多式RGB+P和深度;(2) 10个家庭目标类别的194个高度多样化的物体,包括2个光度具有挑战性的类别;(3) 高品质的外形说明,误差范围仅为1.35毫米至1.74毫米;(4) 41大比例图,覆盖广泛视野;(5) 整个场景中没有检查板的环境。我们还提供了最先进的类别一级构成估计网络的基准结果。