HashSDF: Accurate Implicit Surfaces with Fast Local Features on Permutohedral Lattices

Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with state-of-the-art results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface representation. We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster in three and higher dimensions. We additionally propose a regularization scheme which is crucial for recovering high-frequency geometric detail. We evaluate our method on multiple datasets and show that we can recover geometric detail at the level of pores and wrinkles while using only RGB images for supervision. Furthermore, using sphere tracing we can render novel views at 30 fps on an RTX 3090

Cardiac rehabilitation after catheter ablation of atrial fibrilation

Atrial fibrillation is the most common arrhythmia worldwide. Besides antiarrhythmic drugs and electrical cardioversion, atrial fibrillation can be treated with a newer technique called catheter ablation. Patients suffering a catheter ablation can benefit from an integrated rehabilitation programme like all other patients suffering a cardiac surgery. Physical training and psycho-educative consultations are specific after catheter ablation and integrated rehabilitation can improve mental health, physical capacity and permits return to sports activities

NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis

Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge of novel deep learning methods, learned MVS has surpassed the accuracy of classical approaches, but still relies on building a memory intensive dense cost volume. Novel View Synthesis (NVS) is a parallel line of research and has recently seen an increase in popularity with Neural Radiance Field (NeRF) models, which optimize a per scene radiance field. However, NeRF methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function, together with high-resolution color images. Our method uses only a sparse set of images as input and can generalize well to novel scenes. Additionally, we propose a coarse-to-fine sphere tracing approach in order to significantly increase speed. We show on various datasets that our method reaches comparable accuracy to per-scene optimized methods while being able to generalize and running significantly faster. We provide the source code at https://github.com/AIS-Bonn/neural_mvsComment: Accepted for International Joint Conference on Neural Networks (IJCNN) 2022. Code available at https://github.com/AIS-Bonn/neural_mv

Accuracy of Three-Dimensional Printed Dental Models Based on Ethylene Di-Methacrylate-Stereolithography (SLA) vs. Digital Light Processing (DLP)

Additive manufacturing is a technology that has many uses across a variety of fields. Its usage spans many fields, including the fields of art, design, architecture, engineering and medicine, including dentistry. The study aims to evaluate and compare the accuracy of three-dimensional printed dental models based on ethylene di-methacrylate using the SLA and DLP techniques. For evaluation, a reference model containing 16 maxillary permanent molars was chosen. An ATOS Capsule 3D scanner was used to scan the reference model. Using a photo-cured liquid resin, eight three-dimensional printed models were obtained using the reference model as benchmark. Four of the models (A1–A4) were obtained using SLA printing technology and four models (B1–B4) were manufactured using DLP printing technology. A standard best fit method was used to pre-align the reference and the printed model surfaces. The height of the teeth, and the mesial–distal and buccal–lingual distances were analyzed. The assessment of the two manufacturing methods was achieved by using non-parametric tests to compare the mean ranks for the assessed features. The results show that models obtained through DLP had a higher precision but also a higher bias. Both methods still are within the required accuracy range for dental models