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We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e.translation and rotation, of texture-less rigid objects. The dataset featuresthirty industry-relevant objects with no significant texture and nodiscriminative color or reflectance properties. The objects exhibit symmetriesand mutual similarities in shape and/or size. Compared to other datasets, aunique property is that some of the objects are parts of others. The datasetincludes training and test images that were captured with three synchronizedsensors, specifically a structured-light and a time-of-flight RGB-D sensor anda high-resolution RGB camera. There are approximately 39K training and 10K testimages from each sensor. Additionally, two types of 3D models are provided foreach object, i.e. a manually created CAD model and a semi-automaticallyreconstructed one. Training images depict individual objects against a blackbackground. Test images originate from twenty test scenes having varyingcomplexity, which increases from simple scenes with several isolated objects tovery challenging ones with multiple instances of several objects and with ahigh amount of clutter and occlusion. The images were captured from asystematically sampled view sphere around the object/scene, and are annotatedwith accurate ground truth 6D poses of all modeled objects. Initial evaluationresults indicate that the state of the art in 6D object pose estimation hasample room for improvement, especially in difficult cases with significantocclusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.

T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects