241100
Improved Scene Reconstruction from Range Images
The modeling of real scenes is a complex and challenging task for which the use of laser rangefinders is one of the most promising approaches. Unfortunately, in many situations, it is not possible or practical to guarantee appropriate sampling of all surfaces in the scene. For example, occlusions and accessibility limitations to certain regions of the scene may cause some areas not to be visible by the scanner, resulting in incomplete or incorrectly reconstructed models. This paper describes a pipeline and a system implementation for improving model reconstruction from incomplete information available from range images.
8736539
A hole-filling strategy for reconstruction of smooth surfaces in range images
Creating models of real scenes is a complex task for which the use of traditional modelling techniques is inappropriate. For this task, laser rangefinders are frequently used to sample the scene from several viewpoints, with the resulting range images integrated into a final model. In practice, due to surface reflectance properties, occlusions and accessibility limitations, certain areas of the scenes are usually not sampled, leading to holes and introducing undesirable artifacts in the resulting models. We present an algorithm for filling holes on surfaces reconstructed from point clouds. The algorithm is based on moving least squares and can recover both geometry and shading information, providing a good alternative when the properties to be reconstructed are locally smooth. The reconstruction process is mostly automatic and the sampling rate in the reconstructed areas follows the given samples. We demonstrate the use of the algorithm on both real and synthetic data sets to obtain complete geometry and reasonable shading.
19002258
Reconstructing manifold and non-manifold surfaces from point clouds
This paper presents a novel approach for surface reconstruction from point clouds. The proposed technique is general in the sense that it naturally handles both manifold and non-manifold surfaces, providing a consistent way for reconstructing closed surfaces as well as surfaces with boundaries. It is also robust in the presence of noise, irregular sampling and surface gaps. Furthermore, it is fast, parallelizable and easy to implement because it is based on simple local operations. In this approach, surface reconstruction consists of three major steps: first, the space containing the point cloud is subdivided, creating a voxel representation. Then, a voxel surface is computed using gap filling and topological thinning operations. Finally, the resulting voxel surface is converted into a polygonal mesh. We demonstrate the effectiveness of our approach by reconstructing polygonal models from range scans of real objects as well as from synthetic data.
15090515
Filling holes on locally smooth surfaces reconstructed from point clouds
Abstract Creating models of real scenes is a complex task for which the use of traditional modeling techniques is inappropriate. For this task, laser rangefinders are frequently used to sample the scene from several viewpoints, with the resulting range images integrated into a final model. In practice, due to surface reflectance properties, occlusions and accessibility limitations, certain areas of the scenes are usually not sampled, leading to holes and introducing undesirable artifacts in the resulting models. We present an algorithm for filling holes on surfaces reconstructed from point clouds. The algorithm is based on moving least squares and can interpolate both geometry and shading information. The reconstruction process is mostly automatic and the sampling rate of the given samples is preserved in the reconstructed areas. We demonstrate the use of the algorithm on both real and synthetic datasets to obtain complete geometry and plausible shading.
31774377
Factors in graphs with odd-cycle property
Abstract We present some conditions for the existence of a ( g , f )-factor or a ( g , f )-parity factor in a graph G with the odd-cycle property that any two odd cycles of G either have a vertex in common or are joined by an edge.
28830107
Introducing software architecture specification and analysis in SAM through an example
Abstract Software architecture study has become one of the most active research areas in software engineering in the recent years. Although there have been many published results on specification and analysis method of software architectures, information on sound systematic methodology for modeling and analyzing software architectures is lacking. In this article, we present a formal systematic software architecture specification and analysis methodology called SAM and show how to apply SAM to specify a command control (C2) system and to analyze its real-time constraints.
12859800
Building an information retrieval test collection for spontaneous conversational speech
Test collections model use cases in ways that facilitate evaluation of information retrieval systems. This paper describes the use of search-guided relevance assessment to create a test collection for retrieval of spontaneous conversational speech. Approximately 10,000 thematically coherent segments were manually identified in 625 hours of oral history interviews with 246 individuals. Automatic speech recognition results, manually prepared summaries, controlled vocabulary indexing, and name authority control are available for every segment. Those features were leveraged by a team of four relevance assessors to identify topically relevant segments for 28 topics developed from actual user requests. Search-guided assessment yielded sufficient inter-annotator agreement to support formative evaluation during system development. Baseline results for ranked retrieval are presented to illustrate use of the collection.
21909317
Generating Concept Hierarchies from Text for Intelligence Analysis
It is important to automatically extract key information from sensitive text documents for intelligence analysis. Text documents are usually unstructured and information extraction from texts usually focuses on named entity extraction that is insufficient to reflect the concepts contained within documents. Other research on texts focuses on classifying the whole text into categories. However, texts might be diverse in subjects and suffer from classification accuracy. In this paper, our goal is to present a feasible approach to extract relevant information from texts and organize into concept hierarchies with the help of Web corpora. The approach is designed considering the need of information intelligence and the adaptation to different application domains.
18621072
Primal and dual neural networks for shortest-path routing
Presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the dual routing network has even simpler architecture. While being simple in architecture, the primal and dual routing networks are capable of shortest-path routing like their predecessor.