Image-Based Localization Using the Plenoptic Function

In this report we study the ways to exploit the vast amount of information inherent in the plenoptic space and constraints of the plenoptic function to improve the efficiency of image retrieval, recognition and matching techniques. The specific application we are concerned with is image-based location recognition on mobile devices. The plenoptic space is formed by extending the notion of traditional two-dimensional by adding more dimensions for viewing direction, time and wavelength. Using current mobile devices' built-in cameras, one can easily capture a large sequence of pictures from a single static scene by moving the camera in one direction, which form a three dimensional plenoptic function

An evaluation of volumetric interest points

Abstract—This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of depth sensors and the diminishing returns to be had from appearance alone have seen a surge in shape-based methods. In this work we investigate the performance of several detectors of interest points in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application. Keywords-Volumetric interest points; performance evaluation; I

An evaluation of volumetric interest points

This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of depth sensors and the diminishing returns to be had from appearance alone have seen a surge in shape-based methods. In this work we investigate the performance of several detectors of interest points in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application. © 2011 IEEE