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Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area.

A total of 67 teeth in 64 patients were treated with apicectomy and retrograde fillings. They were randomized to receive fillings of amalgam or glass ionomer cement in a comparative clinical study. Healing was evaluated clinically and radiographically after 1 and 5 years. Evaluation showed no difference in healing capacity between the two materials. Overall success rates in both groups were registered as 90% at 1 year and 85% at 5 years. Contamination with blood or saliva during insertion of the filling material did not affect healing adversely. The study shows that the 5-year follow-up result can be predicted in more than 95% of the cases at the 1-year follow-up. It can be concluded that glass ionomer cement is a valid alternative to amalgam as an apical sealant after apicectomy with equally good long-term clinical results.