Abstract

Graphics models are getting increasingly bulkier with de-tailed geometry, textures, normal maps, etc. There is a lotof interest to model and navigate through detailed models oflarge monuments. Many monuments of interest have bothrich detail and large spatial extent. Rendering them for navi-gation on a single workstation is practically impossible, evengiven the power of today’s CPUs and GPUs. Many modelsmay not fit the GPU memory, the CPU memory, or eventhe secondary storage of the CPU. Distributed renderingusing a cluster of workstations is the only way to navigatethrough such models. In this paper, we present a design ofa distributed rendering system intended for massive mod-els. Our design has a server that holds the skeleton of thewhole model, namely, its scenegraph with actual geometryreplaced by bounding boxes at all levels. The server dividesthe screen space among a number of clients and sends thema list of objects they need to render using a frustum cullingstep. The clients use 2 GPUs with one devoted to visibilityculling and the other to rendering. Frustum culling at theserver, visibility culling on one GPU, and rendering on thesecond GPU form the stages of our distributed renderingpipeline. We describe the design and implementation of oursystem and demonstrate the results of rendering relativelylarge models using different clusters of clients in this paper

Abstract

Large scale reconstructions of camera matrices and pointclouds have been created using structure from motion from communityphoto collections. Such a dataset is rich in information; it representsa sampling of the geometry and appearance of the underlying space.In this paper, we encode the visibility information between and amongpoints and cameras asvisibility probabilities. The conditional visibilityprobability of a set of points on a point (or a set of cameras on a camera)can rank points (or cameras) based on their mutual dependence. Wecombine the conditional probability with a distance measure to prioritizepoints for fast guided search for the image localization problem. Wedefine dual problem of feature triangulation as finding the 3D coordinatesof a given image feature point. We use conditional visibility probabilityto quickly identify a subset of cameras in which a feature is visible

Abstract

Matching patches of a source image with patches of itself ora target image is a first step for many operations. Finding the optimumnearest-neighbors of each patch using a global search of the image isexpensive. Optimality is often sacrificed for speed as a result. We presentthe Mixed-Resolution Patch-Matching (MRPM) algorithm that uses apyramid representation to perform fast global search. We compare mixed-resolution patches at coarser pyramid levels to alleviate the effects ofsmoothing. We store more matches at coarser resolutions to ensure widersearch ranges and better accuracy at finer levels. Our method achievesnear optimality in terms of average error compared to exhaustive search.Our approach is simple compared to complex trees or hash tables used byothers. This enables fast parallel implementations on the GPU, yieldingupto 70×speedup compared to other iterative approaches. Our approachis best suited when multiple, global matches are needed

Abstract

Displays remain flat and passive amidst the many changes in their fundamental technologies. One natural step ahead is tocreate displays that merge seamlessly in shape and appearance with one’s natural surroundings. In this paper, we present a system todesign, render to, and build view-dependentmultiplanar displaysof arbitrary piecewise-planar shapes, built using polygonal facets.Our system provides high quality, interactive rendering of 3D environments to a head-tracked viewer on arbitrary multiplanar displays.We develop a novel rendering scheme that produces exact image and depth map at each facet, producing artifact-free images on andacross facet boundaries. The system scales to a large number of display facets by rendering all facets in a single pass of rasterization.This is achieved using a parallel, perframe, view-dependent binning and prewarping of scene triangles. The display is driven using oneor more targetquilt imagesinto which facet pixels are packed. Our method places no constraints on the scene or the display and allowsfor fully dynamic scenes to be rendered interactively at high resolutions. The steps of our system are implemented efficiently oncommodity GPUs. We present a few prototype displays to establish the scalability of our system on different display shapes, formfactors, and complexity: from a cube made out of LCD panels to spherical/cylindrical projected setups to arbitrary complex shapes insimulation. Performance of our system is demonstrated for both rendering quality and speed, for increasing scene and display facetsizes. A subjective user study is also presented to evaluate the user experience using a walk-around display compared to a flat panelfor a game-like setting.

Abstract

Global optimization of MRF energy using graph cuts iswidely used in computer vision. As the images are gettinglarger, faster graph cuts are needed without sacrificing op-timality. Initializing or reparameterizing a graph using re-sults of a similar one has provided efficiency in the past. Inthis paper, we present a method to speedup graph cuts us-ing shrink-expand reparameterization. Our scheme mergesthe nodes of a given graph to shrink it. The resulting graphand its mincut are expanded and used to reparameterize theoriginal graph for faster convergence. Graph shrinking canbe done in different ways. We use a block-wise shrinkingsimilar to multiresolution processing of images in our Mul-tiresolution Cuts algorithm. We also develop a hybrid ap-proach that can mix nodes from different levels without af-fecting optimality. Our algorithm is particularly suited forprocessing large images. The processing time on the fulldetail graph reduces nearly by a factor of 4. The overallapplication time including all book-keeping is faster by afactor of 2 on various types of images

Abstract

General purpose programming on the graphics processing units(GPGPU) has received a lot of attention in the parallel computing community as it promises to offer a large computational power at a very low price. GPGPU is best suited for regular data parallel algorithms. They are not directly amenable for algorithms which have irregular data access patterns such as convex hull, list ranking etc. In this paper, we present a GPU-optimized implementation for finding the convex hull of a two dimensional point set. Our implementation tries to minimize the impact of irregular data access patterns. Our implementation can find the convex hull of 10 million random points in less than 0.2 seconds and achieves a speedup of up to 14 over the standard sequential CPU implementation. We also discuss some of the practical issues relating to the implementation of convex hull algorithms on massively multi-threaded architectures like that of the GPU.

Abstract

K-Means is a popular clustering algorithm with wideapplications in Computer Vision, Data mining, Data Visu-alization, etc. Clustering is an important step for indexingand searching of documents, images, video, etc. Clusteringlarge numbers of high-dimensional vectors is very compu-tation intensive. In this paper, we present the design andimplementation of theK-Means clustering algorithm on themodern GPU. All steps are performed entirely on the GPUefficiently in our approach. We also present a load balancedmulti-node, multi-GPU implementation which can handleup to 6 million, 128-dimensional vectors. We use efficientmemory layout for all steps to get high performance. TheGPU accelerators are now present on high-end workstationsand low-end laptops. Scalability in the number and dimen-sionality of the vectors, the number of clusters, as well as inthe number of cores available for processing are importantfor usability to different users. Our implementation scaleslinearly or near-linearly with different problem parameters.We achieve up to 2 times increase in speed compared to thebest GPU implementation forK-Means on a single GPU.We obtain a speed up of over 170 on a single Nvidia FermiGPU compared to a standard sequential implementation.We are able to execute one iteration ofK-Means in 136seconds on off-the-shelf GPUs to cluster 6 million vectorsof 128 dimensions into 4K clusters and in 2.5 seconds tocluster 125K vectors of 128 dimensions into 2K clusters.

Abstract

Many image filtering operations provide ample par-allelism, but progressive non-linear processing of im-ages is among the hardest to parallelize due to long,sequential, and non-linear data dependency. A typicalexample of such an operation is error diffusion dither-ing, exemplified by the Floyd-Steinberg algorithm. Inthis paper, we present its parallelization on multicoreCPUs using a block-based approach and on the GPUusing a pixel based approach. We also present a hybridapproach in which the CPU and the GPU operatein parallel during the computation. High PerformanceComputing has traditionally been associated with highend CPUs and GPUs. Our focus is on everyday com-puters such as laptops and desktops, where significantcompute power is available on the GPU as on theCPU. Our implementation can dither an8K8Kimage on an off-the-shelf laptop with an Nvidia 8600MGPU in about 400 milliseconds when the sequentialimplementation on its CPU took about 4 seconds.

Abstract

Visual surveillance is increasingly prevalent todaybut the privacy issues of individuals involved in surveillancevideos have not been dealt with adequately so far. In mostcases, if not all, the chief purpose of placing a camera can beserved without knowing the identity of the individuals involvedunless the activity is of some predetermined kind. One needsto transform these videos to protect identity of individualsinvolved possibly at the source camera itself. In this paperwe present the De-Identification Camera, which is a scalable,low cost and real-time solution to the privacy protection issues.Our main contribution lies in proposing a privacy protectionarchitecture which transforms the video at the camera level itself,We also present and implement a de-identification pipeline whichis suitable for real-time implementation. We implemented thesystem on a Texas Instrument OMAP4 based embedded platformand was able to de-identify videos in real time, transformingthe video on the camera itself ensures protection from variousattacks. We also address issues like data utility of surveillancevideos by making this solution customizable. We propose thatsuch systems can replace the traditional surveillance cameras inthe future by providing all the surveillance and privacy protectionsolutions on hardware, probably with few performance upgrades.

Abstract

s the complexity of 3D models used in computergraphics applications grows, there arises a need to visualize theoverall distribution of detail on them. Detail is a function ofthe amount of information present on a surface. In this paper,we present a method to quantify detail using a combination oflocal measures of curvature and density. We show that detailcan be used for applications like ordering for mesh decimation,visualizing abnormalities in a mesh and so on