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"abstract": "Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching. However, most of the prior works focus only on location estimation, ignoring orientation, which cannot meet the requirements in outdoor AR applications. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance. Furthermore, we present an approach to extend the single image query-based geo-localization approach by utilizing temporal information from a navigation pipeline for robust continuous geo-localization. Experimentation on several large-scale real-world video sequences demonstrates that our approach enables high-precision and stable AR insertion.",
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"abstract": "Visual neuroscience data support monocular depth cues such as texture gradients. Although the binocular disparity energy model has been developed, it concentrates on binocular depth perception. We present a monocular stereoscopic model based on an ordered wave particle swarm to explain monocular cues to depth. The ordering of the particle swarm is triggered by the stereoscopic potential embedded in textures to create monocular depth perception. First, each receptive field (RF) as a wave particle was expressed as a wave equation, and a stereoscopic potential field was extracted from monocular depth cues as the initial conditions. Second, the solutions of wave equations showed that the wave particles affected by the potential field were ordered by frequency. Finally, the consistency of the monocular model with the Gabor filter was discussed.",
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"abstract": "Optical see-through head-mounted displays (HMDs) are less commonly used because they are difficult to accurately calibrate. In this article, we report a user study to compare the accuracy of 4 variants of the SPAAM calibration method. Among the 4 variants, Stylus-marker calibration, where the user aligns a crosshair projected in the HMD with a tracked stylus tip, achieved the most accurate result. A decomposition and analysis of the calibration matrices from the trials is performed and the characteristics of the computed calibration matrices are examined. A physiological engineering point of view is also discussed to explain why calibrating optical see-through HMD is so difficult for users.",
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"abstract": "Effectively evaluating optical see-through (OST) head-mounted display (HMD) calibration is problematic and largely relies on feedback from the user. Studies evaluating OST HMD calibration, such as those by McGarrity, Tang, and Navab et al. [2, 3, 1], utilize user interaction methods, such as touch pads, to facilitate on-line evaluation and correction of calibration results. In all of these studies, however, only the users themselves receive any visual feedback related to the calibration quality or the corrective actions taken to improve it. In this video, we present the use of standard frustum visualization to provide calibration quality information to the researcher in real time. We use a standard Single Point Active Alignment Method (SPAAM) calibration, [4], after which both the eye location estimate and resulting intrinsic values are displayed superimposed onto the user. Presenting the eye position relative to the user's head benefits studies on system error sources, and rendering on-screen visuals also allows outside observers to identify calibration issues and offer corrective suggestions. We believe that techniques, such as frustum visualization, will expand the amount of information available for evaluating calibration results, and will greatly aid those investigating new and improved calibration procedures.",
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"content": "Effectively evaluating optical see-through (OST) head-mounted display (HMD) calibration is problematic and largely relies on feedback from the user. Studies evaluating OST HMD calibration, such as those by McGarrity, Tang, and Navab et al. [2, 3, 1], utilize user interaction methods, such as touch pads, to facilitate on-line evaluation and correction of calibration results. In all of these studies, however, only the users themselves receive any visual feedback related to the calibration quality or the corrective actions taken to improve it. In this video, we present the use of standard frustum visualization to provide calibration quality information to the researcher in real time. We use a standard Single Point Active Alignment Method (SPAAM) calibration, [4], after which both the eye location estimate and resulting intrinsic values are displayed superimposed onto the user. Presenting the eye position relative to the user's head benefits studies on system error sources, and rendering on-screen visuals also allows outside observers to identify calibration issues and offer corrective suggestions. We believe that techniques, such as frustum visualization, will expand the amount of information available for evaluating calibration results, and will greatly aid those investigating new and improved calibration procedures.",
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"abstract": "We propose a spatial calibration method for Optical See-Through Head-Mounted Displays (OST-HMDs) having complex optical distortion such as wide field-of-view (FoV) designs. Viewpoint-dependent non-linear optical distortion makes existing spatial calibration methods either impossible to handle or difficult to compensate without intensive computation. To overcome this issue, we propose OSTNet, a non-parametric data-driven calibration method that creates a generative 2D distortion model for a given six-degree-of-freedom viewpoint pose.",
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"abstract": "Global illumination effects such as inter-reflections, diffusion and sub-surface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors caused by global illumination in structured light-based shape recovery. Based on this analysis, we design structured light patterns that are resilient to individual global illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows us to detect any residual errors that are corrected by acquiring a few additional images. Our techniques do not require explicit separation of the direct and global components of scene radiance and hence work even in scenarios where the separation fails or the direct component is too low. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results on a variety of scenes with complex shape and material properties and challenging global illumination effects.",
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"abstract": "Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We have developed an automatic scheme that first computes a simple pointalistic Euclidean model of the object and then enriches this model using hyper-patches. These hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3D object motion. We show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.",
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"content": "Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We have developed an automatic scheme that first computes a simple pointalistic Euclidean model of the object and then enriches this model using hyper-patches. These hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3D object motion. We show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.",
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"abstract": "Splatting is a popular direct volume rendering algorithm. However, the algorithm does not correctly render cases where the volume sampling rate is higher than the image sampling rate (e.g. more than one voxel maps into a pixel). This situation arises with orthographic projections of high-resolution volumes, as well as with perspective projections of volumes of any resolution. The result is potentially severe spatial and temporal aliasing artifacts. Some volume ray-casting algorithms avoid these artifacts by employing reconstruction kernels which vary in width as the rays diverge. Unlike ray-casting algorithms, existing splatting algorithms do not have an equivalent mechanism for avoiding these artifacts. The authors propose such a mechanism, which delivers high-quality splatted images and has the potential for a very efficient hardware implementation.",
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"content": "Splatting is a popular direct volume rendering algorithm. However, the algorithm does not correctly render cases where the volume sampling rate is higher than the image sampling rate (e.g. more than one voxel maps into a pixel). This situation arises with orthographic projections of high-resolution volumes, as well as with perspective projections of volumes of any resolution. The result is potentially severe spatial and temporal aliasing artifacts. Some volume ray-casting algorithms avoid these artifacts by employing reconstruction kernels which vary in width as the rays diverge. Unlike ray-casting algorithms, existing splatting algorithms do not have an equivalent mechanism for avoiding these artifacts. The authors propose such a mechanism, which delivers high-quality splatted images and has the potential for a very efficient hardware implementation.",
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"abstract": "Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at http://opensurfaces.cs.cornell.edu/saw.",
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"abstract": "Point-based geometries evolved into a valuable alternative to surface representations based on polygonal meshes, because of their conceptual simplicity and superior flexibility. Elliptical surface splats were shown to allow for high-quality anti-aliased rendering by sophisticated EWA filtering. Since the publication of the original software-based EWA splatting, several authors tried to map this technique to the GPU in order to exploit hardware acceleration. Due to the lacking support for splat primitives, these methods always have to find a trade-off between rendering quality and rendering performance. In this paper, we discuss the capabilities of today's GPUs for hardware-accelerated surface splatting. We present an approach that achieves a quality comparable to the original EWA splatting at a rate of more than 20M elliptical splats per second. In contrast to previous GPU renderers, our method provides per-pixel Phong shading even for dynamically changing geometries and high-quality anti-aliasing by employing a screen-space pre-filter in addition to the object-space reconstruction filter. The use of deferred shading techniques effectively avoids unnecessary shader computations and additionally provides a clear separation between the rasterization and the shading of elliptical splats, which considerably simplifies the development of custom shaders. We demonstrate quality, efficiency, and flexibility of our approach by showing several shaders on a range of models.",
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"abstract": "Direct volume rendering of large volumetric data sets on programmable graphics hardware is often limited by the amount of available graphics memory and the bandwidth from main memory to graphics memory. Therefore, several approaches to volume rendering from compact representations of volumetric data have been published that avoid most of the data transfer between main memory and the graphics programming unit (GPU) at the cost of additional data decompression by the GPU. To reduce this performance cost, adaptive sampling techniques were proposed; which are, however, usually restricted to the sampling in view direction. In this work, we present a GPU-based volume rendering algorithm with adaptive sampling in all three spatial directions; i.e., not only in view direction but also in the two perpendicular directions of the image plane. This approach allows us to reduce the number of samples dramatically without compromising image quality; thus, it is particularly well suited for many compressed representations of volumetric data that require a computational expensive GPU-based sampling of data",
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"abstract": "The Elliptic Curve Digital Signature Algorithm (ECDSA) is an essential building block of various cryptographic protocols. In particular, most blockchain systems adopt it to ensure transaction integrity. However, due to its high computational intensity, ECDSA is often the performance bottleneck in blockchain transaction processing. Recent work has accelerated ECDSA algorithms on the CPU; in contrast, success has been limited on the GPU, which has great potential for parallelization but is challenging for implementing elliptic curve functions. In this paper, we propose RapidEC, a GPU-based ECDSA implementation for SM2, a popular elliptic curve. Specifically, we design architecture-aware parallel primitives for elliptic curve point operations, and parallelize the processing of a single SM2 request as well as batches of requests. Consequently, our GPU-based RapidEC outperformed the state-of-the-art CPU-based algorithm by orders of magnitude. Additionally, our GPU-based modular arithmetic functions as well as point operation primitives can be applied to other computation tasks.",
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"affiliation": "University of Utah,SCI Institute",
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