Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using a FLIR A65 thermal infrared (TIR) camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. TRansPose dataset can be accessed from the following link: https://sites.google.com/view/transpose-dataset

翻译：透明物体在我们的日常生活中随处可见，但由于其独特的材料特性，传统视觉传感器（如RGB或深度相机）难以有效感知它们。为克服这一局限，热红外相机应运而生，可提供透明物体的增强可见性与形状信息。本文提出TRansPose——首个结合立体RGB-D、热红外（TIR）图像与物体位姿的大规模多光谱数据集，旨在推动透明物体研究。该数据集包含99个透明物体，涵盖43种家居用品、27种可回收垃圾、29种化学实验室器具以及12种非透明物体。数据集包含海量采集的333,819张图像与4,000,056个标注，提供实例级分割掩码、真实位姿与补全深度信息。数据采集设备包括FLIR A65热红外（TIR）相机、两台Intel RealSense L515 RGB-D相机以及Franka Emika Panda机器人操作臂。TRansPose涵盖87个序列，涉及各类具有挑战性的真实场景，包括盛水物体、多样化光照条件、高度杂乱场景、非透明或半透明容器、塑料袋内物体以及多层堆叠物体。TRansPose数据集可通过以下链接访问：https://sites.google.com/view/transpose-dataset