Pittman Casey (soycamera5)
The total energy is optimized in a block coordinate descent fashion, updating one term at a time while keeping others constant. CW069 Experiments on three publicly available datasets show that the method performs significantly better than several state-of-the-art algorithms in registering pairwise point cloud data.3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. However, the generalization of semantic segmentation in the wild remains challenging. In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation. Motivated by the fact that source and target domain have invariant semantic structures, we propose to exploit such invariance across domains by leveraging co-occurring patterns between pairwise pixels in the output of structured semantic segmentation. This is different from most existing approaches that attempt to adapt domains based on individual pixel-wise information in image, feature, or output level. Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain. To this end, we develop two affinity space adaptation strategies affinity space cleaning and adversarial affinity space alignment. Extensive experiments demonstrate that the proposed method achieves superior performance against some state-of-the-art methods on several challenging benchmarks for semantic segmentation across domains. The code is available at https//github.com/idealwei/ASANet.The 3-D ultrasound imaging is essential for a wide range of clinical applications in diagnostic and interventional cardiology, radiology, and obstetrics for prenatal imaging. 3-D ultrasound imaging is also pivotal for advancing technical developments of emerging imaging technologies, such as elastography, blood flow imaging, functional ultrasound (fUS), and super-resolution microvessel imaging. At present, however, existing 3-D ultrasound imaging methods suffer from low imaging volume rate, suboptimal imaging quality, and high costs associated with 2-D ultrasound transducers. Here, we report a novel 3-D ultrasound imaging technique, fast acoustic steering via tilting electromechanical reflectors (FASTER), which provides both high imaging quality and fast imaging speed while at low cost. Capitalizing upon unique water immersible and fast-tilting microfabricated mirror to scan ultrafast plane waves in the elevational direction, FASTER is capable of high volume rate, large field-of-view (FOV) 3-D imaging with conventional 1-D transducers. In this arti
