Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point cloud registration. The main objective is to evaluate the feasibility of applying deep learning-based point cloud registration methods for image-to-patient registration in augmented reality-guided surgery. We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2. We evaluate three well-established deep learning models in registering these data pairs. While we find that some deep learning methods show promise, we show that a conventional registration pipeline still outperforms them on our challenging dataset.

rollama is an R package that wraps the Ollama API, which allows you to run different Generative Large Language Models (GLLM) locally. The package and learning material focus on making it easy to use Ollama for annotating textual or imagine data with open-source models as well as use these models for document embedding. But users can use or extend rollama to do essentially anything else that is possible through OpenAI's API, yet more private, reproducible and for free.

The advancement of Large Language Models (LLMs) has opened up new avenues for tackling annotation tasks in the field of social sciences. These models, especially the newer iterations like Chat-GPT or GPT-4, are now being used to annotate textual data (Gilardi, Alizadeh, & Kubli, 2023; Heseltine & Hohenberg, 2023; Møller, Dalsgaard, Pera, & Aiello, 2023; Ziems et al., 2023), which can be helpful for analyzing various social and political phenomena (Törnberg, 2023; Ziems et al., 2023). However, a significant challenge arises when there is a necessity to share research data with proprietary and closed models that are provided by companies due to the utilization of APIs (Ollion, Shen, Macanovic, & Chatelain, 2023; Spirling, 2023). This is particularly concerning in scenarios where data sharing is not preferable due to data privacy. In light of this, open models which can be operated on independent devices like university servers, present a viable alternative (Alizadeh et al., 2023). They allow researchers to harness the potential of generative large language models without compromising data security. This paper endeavors to promote the adoption of open models by providing two examples and guidelines for leveraging them instead of proprietary models for annotation tasks within the social sciences.