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

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 features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201

Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI

Background: Augmented Reality (AR) represents an innovative technology to improve data visualization and strengthen the human perception. Among Human–Machine Interaction (HMI), medicine can benefit most from the adoption of these digital technologies. In this perspective, the literature on orthopedic surgery techniques based on AR was evaluated, focusing on identifying the limitations and challenges of AR-based healthcare applications, to support the research and the development of further studies. Methods: Studies published from January 2018 to December 2021 were analyzed after a comprehensive search on PubMed, Google Scholar, Scopus, IEEE Xplore, Science Direct, and Wiley Online Library databases. In order to improve the review reporting, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used. Results: Authors selected sixty-two articles meeting the inclusion criteria, which were categorized according to the purpose of the study (intraoperative, training, rehabilitation) and according to the surgical procedure used. Conclusions: AR has the potential to improve orthopedic training and practice by providing an increasingly human-centered clinical approach. Further research can be addressed by this review to cover problems related to hardware limitations, lack of accurate registration and tracking systems, and absence of security protocols