We present a large-scale stereo RGB image object pose estimation dataset named the $\textbf{StereOBJ-1M}$ dataset. The dataset is designed to address challenging cases such as object transparency, translucency, and specular reflection, in addition to the common challenges of occlusion, symmetry, and variations in illumination and environments. In order to collect data of sufficient scale for modern deep learning models, we propose a novel method for efficiently annotating pose data in a multi-view fashion that allows data capturing in complex and flexible environments. Fully annotated with 6D object poses, our dataset contains over 396K frames and over 1.5M annotations of 18 objects recorded in 183 scenes constructed in 11 different environments. The 18 objects include 8 symmetric objects, 7 transparent objects, and 8 reflective objects. We benchmark two state-of-the-art pose estimation frameworks on StereOBJ-1M as baselines for future work. We also propose a novel object-level pose optimization method for computing 6D pose from keypoint predictions in multiple images.
翻译:我们提出了一个名为 $\ textbf{ StereOBJ-1M} 的大型立体 RGB 图像天体构成估算数据集。该数据集旨在处理具有挑战性的案例,如物体透明性、透明性和镜像反射,此外还包括隔离、对称、以及照明和环境变化等共同挑战。为了收集足够规模的现代深层学习模型数据,我们提出了一个以多视角方式有效说明显示数据的新方法,以便能够在复杂和灵活的环境中收集数据。在加上6D 对象外形,我们的数据集包含超过396K 框架和超过1.5M 的183 屏幕所记录的183 个物体在11个不同环境中记录的说明。这18个对象包括8个对称对象、7个透明对象和8个反射对象。我们为未来工作的基线对StereOBJ-1M 设定了两个最先进的估计框架。我们还提出一个新的对象级显示优化方法,用于从多个图像中的关键点预测中计算6D 姿势。