Remorins (REMs) are proteins of unknown function specific to vascular plants. We have used imaging and biochemical approaches and in situ labeling to demonstrate that REM clusters at plasmodesmata and in ;70-nm membrane domains, similar to lipid rafts, in the cytosolic leaflet of the plasma membrane. From a manipulation of REM levels in transgenic tomato (Solanum lycopersicum) plants, we show that Potato virus X (PVX) movement is inversely related to REM accumulation. We show that REM can interact physically with the movement protein TRIPLE GENE BLOCK PROTEIN1 from PVX. Based on the localization of REM and its impact on virus macromolecular trafficking, we discuss the potential for lipid rafts to act as functional components in plasmodesmata and the plasma membrane.

Abstract-We introduce a benchmark for evaluating the performance of large scale sketch-based image retrieval systems. The necessary data is acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.

We develop a system for 3D object retrieval based on sketched feature lines as input. For objective evaluation, we collect a large number of query sketches from human users that are related to an existing data base of objects. The sketches turn out to be generally quite abstract with large local and global deviations from the original shape. Based on this observation, we decide to use a bag-of-features approach over computer generated line drawings of the objects. We develop a targeted feature transform based on Gabor filters for this system. We can show objectively that this transform is better suited than other approaches from the literature developed for similar tasks. Moreover, we demonstrate how to optimize the parameters of our, as well as other approaches, based on the gathered sketches. In the resulting comparison, our approach is significantly better than any other system described so far.

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Figure 1: Left to right: an actor performing in the capture setup; one of sixteen views from the camera array; reconstructed T-shirt geometry; the real T-shirt is replaced by a rendering of the captured geometry with different appearance characteristics. AbstractA lot of research has recently focused on the problem of capturing the geometry and motion of garments. Such work usually relies on special markers printed on the fabric to establish temporally coherent correspondences between points on the garment's surface at different times. Unfortunately, this approach is tedious and prevents the capture of off-the-shelf clothing made from interesting fabrics.In this paper, we describe a marker-free approach to capturing garment motion that avoids these downsides. We establish temporally coherent parameterizations between incomplete geometries that we extract at each timestep with a multiview stereo algorithm. We then fill holes in the geometry using a template. This approach, for the first time, allows us to capture the geometry and motion of unpatterned, off-the-shelf garments made from a range of different fabrics.

The amount of captured 3D data is continuously increasing, with the democratization of consumer depth cameras, the development of modern multi‐view stereo capture setups and the rise of single‐view 3D capture based on machine learning. The analysis and representation of this ever growing volume of 3D data, often corrupted with acquisition noise and reconstruction artefacts, is a serious challenge at the frontier between computer graphics and computer vision. To that end, segmentation and optimization are crucial analysis components of the shape abstraction process, which can themselves be greatly simplified when performed on lightened geometric formats. In this survey, we review the algorithms which extract simple geometric primitives from raw dense 3D data. After giving an introduction to these techniques, from the acquisition modality to the underlying theoretical concepts, we propose an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches. We conclude by giving hints for how to evaluate these methods and a set of research challenges to be explored.

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