COTS Cluster-based Sort-last Rendering: Performance Evaluation and Pipelined Implementation

Sort-last parallel rendering is an efficient technique to visualize huge datasets on COTS clusters. The dataset is subdivided and distributed across the cluster nodes. For every frame, each node renders a full resolution image of its data using its local GPU, and the images are composited together using a parallel image compositing algorithm. In this paper, we present a performance evaluation of standard sort-last parallel rendering methods and of the different improvements proposed in the literature. This evaluation is based on a detailed analysis of the different hardware and software components. We present a new implementation of sort-last rendering that fully overlaps CPU(s), GPU and network usage all along the algorithm. We present experiments on a 3 years old 32-node PC cluster and on a 1.5 years old 5-node PC cluster, both with Gigabit interconnect, showing volume rendering at respectively 13 and 31 frames per second and polygon rendering at respectively 8 and 17 frames per second on a 1024×768 render area, and we show that our implementation outperforms or equals many other implementations and specialized visualization clusters

Geogram: a library for geometric algorithms

We present a set of geometric algorithms for grid generation. Tehe implementation of the algorithms is available in the public GEOGRAM [1] library (BSD Open-Source License). The sey of algorithm comprises: ·Robust geometric predicates, using adaptative precision floating point arithmetics simulation of simplicity to handle the corner cases, and arithmetic filters to carly detect and quicly handle the easy cases; ·Geometric search data structures, including Kd-Tress (for ncarest neighbors queries) and Axis-Aligned BBox Trees (for mesh intersection); ·Delaunay triangulation and power diagrams in 3d; ·Restricted Vorono Diagram in arbitrary dimension. The design of the library follows a minimalistic principle, combined with the algorithmic state of the art, inspired by. Compared with CGAL, our impementation is significantly more memory efficient, sometimes faster, much casier to understand (no generic programming), at the cost of being less generic/dynamic. All the algorithms are parallized using an abstract model taht suports pthreads, OpenMP and Windows theads. In addition, the library has no dependancy and is easy to compile on any platform that has IEEE754 floating point arithmetics (Linux, Mac, Windows, Android, ...). The library was used to implement a solver for Optimal Transport

From Step Edge to Line Edge: Combining Geometric and Photometric Information

International audienceAs a gradient detector produces two extrema when applied to a line, we propose an original method which is based on such a detector but which appropriately responds to both step and line edges. The main ideas are first to identify line contours using geometric and photometric properties and second to substitute a line for a single contour using a simple geometric algorithm

COOPERATION OF 3D SEGMENTS A N D 3D FACETS INFORMATION FOR OBJECT RECONSTRUCTION

International audienceGiven a serie of trinocular images of an object, we have developed a method for building 3D facets and 3D segments model of the object. Frorn each triplet, a partial description of the object, called 3D View, is extracted. From the set of all extracted 3D Facets, a strategy for guiding the object reconstruction, based on a statistical method is developed. A 3D Matching Builder computes matchings between 3D Primitives of consecutive 3D views. Guided by the Strategy, a Superstructure gathered all the matching informations given by the 3D Matching Builder in a set of equivalence classes. For each equivalence class, of Superstructure, a representative is derived. The 3D Primitives model is finally computed merging informations of 3D Facets representatives and 3D Segment representatives. This method implemented in Smalltalk80 has been applied on a series of stereoscopic real images triplets; some results are provided at the end of the paper

COOPERATION OF 3D SEGMENTS A N D 3D FACETS INFORMATION FOR OBJECT RECONSTRUCTION

International audienceGiven a serie of trinocular images of an object, we have developed a method for building 3D facets and 3D segments model of the object. Frorn each triplet, a partial description of the object, called 3D View, is extracted. From the set of all extracted 3D Facets, a strategy for guiding the object reconstruction, based on a statistical method is developed. A 3D Matching Builder computes matchings between 3D Primitives of consecutive 3D views. Guided by the Strategy, a Superstructure gathered all the matching informations given by the 3D Matching Builder in a set of equivalence classes. For each equivalence class, of Superstructure, a representative is derived. The 3D Primitives model is finally computed merging informations of 3D Facets representatives and 3D Segment representatives. This method implemented in Smalltalk80 has been applied on a series of stereoscopic real images triplets; some results are provided at the end of the paper

Construction of 3D Views from Strereoscopic Triplet of Images

International audienceWe present an approach to construct; 3D facets using trinocular stereovision and characterize them by a set of reliable attributes. A facet of a polyedric object is supposed to be projected on the image plane as a homogeneous region surrounded by a chain of 2D segments. These ordered chains, called 2D facets, are extracted from each image of a stereoscopic triplet using the cooperation between single contour map and a series of region maps. These maps are automatically generated according to a coarse-to-fine approach, in order to make the most contrasted regions appear first. The 3D facets are then reconstructed by a technique based on the cooperation of 2D information (2D facets and 2D segments) and 3D information (3D segments provided by a stereovision algorithm). The difficult point is the ordering of the 3D segments constituting the sides of a 3D facet, and is solved by an original algorithm which uses the ordering of three matched 2D facets. Each 3D facet is characterized by a set of intrinsic and extrinsic parameters in order to be efficiently used during the 3D reconstruction of the object to be modelled. As an example, we explain the estimation of the surface of a facet. The original contributions of this work are the cooperation between several heterogeneous data and the use of low quality images, which require robust algorithms

Reliable 3d Primitives For Polyhedral Object Reconstruction

This paper describes the first part of the modelling module and, more precisely, how

Cots cluster-based sort-last rendering: Performance evaluation and pipelined implementation

Figure 1: Views of the head section (512x512x209) of the visible female CT data with 16 nodes (a space has been left between the subvolumes to highlight their boundaries). Using a 3 years old 32-node COTS cluster, a volume dataset can be rendered at constant 13 frames per second on a 1024 × 768 rendering area using 5 nodes. On a 1.5 years old, fully optimized, 5-node COTS cluster, the frame rate obtained for the same rendering area reaches constant 31 frames per second. We truly expect our future work, including further algorithm optimizations and hardware tuning on a modern PC cluster, to provide higher frame rates for bigger datasets (using more nodes) on larger rendering areas. Sort-last parallel rendering is an efficient technique to visualize huge datasets on COTS clusters. The dataset is subdivided and distributed across the cluster nodes. For every frame, each node renders a full resolution image of its data using its local GPU, and the images are composited together using a parallel image compositing algorithm. In this paper, we present a performance evaluation of standard sort-last parallel rendering methods and of the different improvements proposed in the literature. This evaluation is based on a detailed analysis of the different hardware and software components