Matei Stroila

Next generation map making: geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction

This paper presents a novel method to process large scale, ground level Light Detection and Ranging (LIDAR) data to automatically detect geo-referenced navigation attributes (traffic signs and lane markings) corresponding to a collection travel path. A mobile data collection device is introduced. Both the intensity of the LIDAR light return and 3-D information of the point clouds are used to find retroreflective, painted objects. Panoramic and high definition images are registered with 3-D point clouds so that the content of the sign and color can subsequently be extracted.

Clip Art Rendering of Smooth Isosurfaces

Clip art is a simplified illustration form consisting of layered filled polygons or closed curves used to convey 3D shape information in a 2D vector graphics format. This paper focuses on the problem of direct conversion of smooth surfaces, ranging from the free-form shapes of art and design to the mathematical structures of geometry and topology, into a clip art form suitable for illustration use in books, papers, and presentations. We show how to represent silhouette, shadow, gleam, and other surface feature curves as the intersection of implicit surfaces and derive equations for their efficient interrogation via particle chains. We further describe how to sort, orient, identify, and fill the closed regions that overlay to form clip art. We demonstrate the results with numerous renderings used to illustrate the paper itself.

Augmented transit maps

We introduce a new class of mobile augmented reality navigation applications that allow people to interact with transit maps in public transit stations and vehicles. Our system consists of a database of coded transit maps, a vision engine for recognizing and tracking planar objects, and a graphics engine to overlay relevant real-time navigation information, such as the users current location and the time to destination. We demonstrate this system with a prototype application built from open source components only. The application runs on a Nokia N900 mobile phone equipped with Maemo, a Debian Linux-based operating system. We use the OpenCV library and the new Frankencamera API for the vision engine. The application is written using the LGPL licensed Qt C++ Framework.

Accelerating arrays of linear classifiers using approximate range queries

Modern object detection methods apply binary linear classifiers on Euclidean feature vectors. This paper shows that projecting feature vectors onto a hypersphere allows an approximate range query to accelerate these detectors within acceptable levels of accuracy. The expense of constructing the k-d tree used by these range queries is justified when many detectors are used. We demonstrate our acceleration technique on several existing detection systems, including a state of the art logo detector, and show that approximate range queries can detect logos at least half as well at 11× the speed of the full fidelity method.

Route Visualization in Indoor Panoramic Imagery with Open Area Maps

Route visualization in outdoor panoramic imagery is a very useful feature, available in most map web applications. On the other hand, indoor maps and routing are not yet largely available, even less so related visualizations. In this paper, we present a framework for visualization of indoor points of interest (POIs) and routes. The framework is based on an existing indoor mapping platform for simple creation of navigable floor plans, and comprises tools to manually and semi-automatically align panoramic imagery with the floor plans, and algorithms to select the relevant images and camera orientations for the visualization of the POIs and routes.

Robust classification of curvilinear and surface-like structures in 3d point cloud data

The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.

Enabling large-scale outdoor Mixed Reality and Augmented Reality

While there is significant recent progress in technologies supporting augmented reality for small indoor environments, there is still much work to be done for large outdoor environments. This workshop focuses primarily on research that enables high-quality outdoor Mixed Reality (MR) and Augmented Reality (AR) applications. These research topics include, but are not restricted to: — 3D geo-referenced data (images, point clouds, and models) — Algorithms for object recognition from large databases of geo-referenced data — Algorithms for object tracking in outdoor environment — Multi-cue fusion to achieve improved performance of object detection and tracking — Novel representation schemes to facilitate large-scale content distribution — 3D reasoning to support intelligent augmentation — Novel and improved mobile capabilities for data capture (device sensors), processing, and display — Applications, experiences, and user interface techniques. The workshop will also showcase existing prototypes of applications enabled by these technologies: mirror worlds, high-fidelity virtual environments, applications of panoramic imagery, and user studies relating to these media types. This workshop aims to bring together academic and industrial researchers and to foster discussion amongst participants on the current state of the art and future directions for technologies that enable large-scale outdoor MR and AR applications. The workshop will start with a session in which position statements and overviews of the state of the art are presented. In the afternoon, we will follow up with discussion sessions and a short closing session.