Extrusion-Based Primitive Fitting: RGB-D Occluded Object Dataset

This dataset contains 200 single-view RGB-D scenes of real household objects recorded using the onboard RGB-D camera of a TIAGo robot. The robot observes a tabletop workspace from an inclined top–down viewpoint, and each scene includes four real objects selected from a set of 12 instances. The objects are deliberately arranged to create natural partial occlusions between them, providing challenging conditions for single-view 3D perception. The dataset supports research on shape and pose estimation, occlusion handling and primitive-based modeling. For each scene, we provide the corresponding ROS bag file (including point cloud, RGB, depth, and TF data), standalone color and depth images, 2D bounding boxes, object labels, and the estimated superellipsoid parameters obtained using our extrusion-based primitive fitting method. All data are captured in real-world conditions using a physical TIAGo robot and real objects, with natural sensor noise, depth discontinuities, and occlusions. The fitted superellipsoid parameters included in the dataset are algorithmic estimates produced by the extrusion-based reconstruction and optimization pipeline, not ground-truth object geometry. This dataset accompanies the methods presented in:Menendez, E.; Martínez, S.; Díaz-de-María, F.; Balaguer, C. (2024). *Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios*. Biomimetics, 9(2), 100. https://doi.org/10.3390/biomimetics9020100本数据集包含200组单视角RGB-D场景，由TIAGo机器人的机载RGB-D相机采集，场景中的物体均为真实家居物品。该机器人以倾斜俯视视角观测桌面工作空间，每个场景包含从12个实物样本中选取的4个真实物体。物体经刻意排布以产生自然的局部遮挡，为单视角三维感知任务构建了具有挑战性的测试环境。 本数据集可支撑形状与位姿估计、遮挡处理以及基于图元建模方向的研究工作。针对每个场景，我们提供了对应的ROS包文件（包含点云、RGB图像、深度图以及TF数据）、独立的彩色与深度图像、二维边界框、物体标签，以及通过我们提出的基于拉伸的图元拟合方法得到的超椭球估计参数。 所有数据均在真实物理环境下采集，使用实体TIAGo机器人与真实物体，包含自然的传感器噪声、深度不连续现象以及遮挡情况。本数据集所包含的拟合超椭球参数，是通过基于拉伸的重建与优化流程得到的算法估计结果，并非物体的真实几何真值。 本数据集配套的研究方法发表于如下文献：Menendez, E.; Martínez, S.; Díaz-de-María, F.; Balaguer, C. (2024). 《部分遮挡场景下结合自我中心视觉与机器人视觉实现基于孪生网络与超二次曲面估计的物体识别》，Biomimetics, 9(2), 100. https://doi.org/10.3390/biomimetics9020100