GPU-Accelerated nearest neighbor search for 3d registration

Abstract. Nearest Neighbor Search (NNS) is employed by many computer vision algorithms. The computational complexity is large and constitutes a challenge for real-time capability. The basic problem is in rapidly processing a huge amount of data, which is often addressed by means of highly sophisticated search methods and parallelism. We show that NNS based vision algorithms like the Iterative Closest Points algorithm (ICP) can achieve real-time capability while preserving compact size and moderate energy consumption as it is needed in robotics and many other domains. The approach exploits the concept of general purpose computation on graphics processing units (GPGPU) and is compared to parallel processing on CPU. We apply this approach to the 3D scan registration problem, for which a speed-up factor of 88 compared to a sequential CPU implementation is reported

Explicit Cache Management for Volume Ray-Casting on Parallel Architectures

A major challenge when designing general purpose graphics hardware is to allow efficient access to texture data. Although different rendering paradigms vary with respect to their data access patterns, there is no flexibility when it comes to data caching provided by the graphics architecture. In this paper we focus on volume ray-casting, and show the benefits of algorithm-aware data caching. Our Marching Caches method exploits inter-ray coherence and thus utilizes the memory layout of the highly parallel processors by allowing them to share data through a cache which marches along with the ray front. By exploiting Marching Caches we can apply higher-order reconstruction and enhancement filters to generate more accurate and enriched renderings with an improved rendering performance. We have tested our Marching Caches with seven different filters, e. g., Catmul-Rom, B- spline, ambient occlusion projection, and could show that a speed up of four times can be achieved compared to using the caching implicitly provided by the graphics hardware, and that the memory bandwidth to global memory can be reduced by orders of magnitude. Throughout the paper, we will introduce the Marching Cache concept, provide implementation details and discuss the performance and memory bandwidth impact when using different filters

Visualisation interactive de grands bâtiments

Best paper awardNational audienceLes performances des algorithmes de lancer de rayons sont directement liées à la structure accélératrice utilisée. En ce qui concerne les environnements architecturaux, plusieurs travaux ont précédemment démontré que la structure accélératrice la plus efficace est la structure cellules-et-passages. Dans cet article, nous proposons une nouvelle structure accélératrice qui consiste en une extension des structures cellules-et-passages classiques par une description topologique complète de la scène. La structure de données est décrite par un graphe dont le parcours, utilisant l'ensemble des propriétés topologiques de notre modèle, est particulièrement simple et rapide. Nous montrons dans cet article que notre structure permet un rendu interactif même pour de grands bâtiments composés de plusieurs centaines de pièces meublées en prenant en compte l'éclairage direct de plusieurs milliers de sources lumineuses ponctuelles

Accelerating Radio Wave Propagation Algorithms by Implementation on Graphics Hardware

Radio wave propagation prediction is a fundamental prerequisite for planning, analysis and optimization of radio networks. For instance coverage analysis, interference estimation or channel and power allocation all rely on propagation predictions. In wireless communication networks optimal antenna sites are determined by either conducting a serie

Visualization of Industrial Structures with Implicit GPU Primitives

International audienceWe present a method to interactively visualize large industrial models by replacing most triangles with implicit GPU primitives: cylinders, cone and torus slices. After a reverse-engineering process that recovers these primitives from triangle meshes, we encode their implicit parameters in a texture that is sent to the GPU. In rendering time, the implicit primitives are visualized seamlessly with other triangles in the scene. The method was tested on two massive industrial models, achieving better performance and image quality while reducing memory use

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.This work was partially funded by the Spanish Government DPI2013-40534-R grant and Valencian Government GV/2013/005 grant