Active shimming for a 25 T NMR superconducting magnet by spectrum convergence method.

In the design of ultrahigh field nuclear magnetic resonance (NMR) superconducting magnets, it typically requires a high homogeneous magnetic field in the diameter of spherical volume (DSV) to obtain high spectrum resolution. However, shimming technique presents challenges due to the magnet bore space limitations, as accurate measurement of magnetic field distribution is very difficult, especially for customized micro-bore magnets. In this study, we introduced an active shimming method that utilized iterative adjustment of shim coil currents to improve the magnetic field homogeneity based on the full width at half maximum (FWHM) of the spectrum. The proposed method can determine the optimal set of currents for shim coils, effectively enhancing spatial field homogeneity by converging the FWHM. Experimental validation on a 25 T NMR superconducting magnet demonstrated the efficacy of the proposed method. Specifically, the active shimming method improved the field homogeneity of a 10 mm DSV from 7.09 ppm to 2.27 ppm with only four shim coils, providing a superior magnetic field environment for solid NMR and further magnetic resonance imaging (MRI) experiment. Furthermore, the proposed method can be promoted to more customized micro-bore magnets that require high magnetic field homogeneity.

Adaptive Surface Normal Constraint for Geometric Estimation From Monocular Images.

We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints. By dynamically determining reliable local geometry from randomly sampled candidates, we establish a surface normal constraint, where the validity of these candidates is evaluated using the geometric context. Furthermore, our normal estimation leverages the geometric context to prioritize regions that exhibit significant geometric variations, which makes the predicted normals accurately capture intricate and detailed geometric information. Through the integration of geometric context, our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images. We validate the superiority of our approach over state-of-the-art methods through extensive evaluations and comparisons on diverse indoor and outdoor datasets, showcasing its efficiency and robustness. Code and data are available at https://github.com/xxlong0/ASNDepth.

Distance-Aware Occlusion Detection with Focused Attention.

For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant progress studying semantic relationship detection, such as human-object interaction detection and visual relationship detection. We take the study of visual relationships a step further from semantic to geometric. In specific, we predict relative occlusion and relative distance relationships. However, detecting these relationships from a single image is challenging. Enforcing focused attention to task-specific regions plays a critical role in successfully detecting these relationships. In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection. Specifically, our model increases the distance F1-score from 33.8% to 38.6% and boosts the occlusion F1-score from 34.4% to 41.2%. Our code and data will be publicly available.

FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras.

Aiming at automatic, convenient and non-instrusive motion capture, this paper presents a new generation markerless motion capture technique, the FlyCap system, to capture surface motions of moving characters using multiple autonomous flying cameras (autonomous unmanned aerial vehicles(UAVs) each integrated with an RGBD video camera). During data capture, three cooperative flying cameras automatically track and follow the moving target who performs large-scale motions in a wide space. We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera. We leverage the using of visual-odometry information provided by the UAV platform, and formulate the surface tracking problem in a non-linear objective function that can be linearized and effectively minimized through a Gaussian-Newton method. Quantitative and qualitative experimental results demonstrate the plausible surface and motion reconstruction results.