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-datasetComment: Under revie