Generation and Optimization of Local Shape Descriptors for Point Matching in 3D Surfaces
 
Summary
The goal of object recognition is to identify and localize objects of interest in an image. We formulate Local Shape Descriptor selection for modelbased object recognition in range data as an optimization problem and offer a platform that facilitates a solution. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, dependa on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through preprocessing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images.  
General Overview
 
Modelbased object recognition in range data involves the detection and localization of
3D models in range images. Given a set of 3D models and a range image, detection is
defined as identifying the visible models, and localization is defined as finding the 3D rigid
transformations that align the visible models with the image. A 3D rigid transformation has three
positional and three rotational degrees of freedom
and exhaustive search through this 6D pose space is infeasible. A large class of techniques
aim at efficiently solving this problem without requiring exhaustive search by following a
threephase scheme consisting of point matching, hypothesis generation, and pose
refinement. In the first phase, tentative matches are established between several points on the image and their corresponding points on models by comparing the local shapes of various regions of the two data sets. Since the output of the first phase often contains some incorrect matches (i.e. outliers), a statistically robust algorithm such as RANSAC [32] or the Generalized Hough Transform [9], is then utilized in the second phase to generate and verify rigid transformations that align visible models with the image. Finally, in the third phase, the accuracy of the recovered alignments are improved using a refinement algorithm. Figure 1.2 illustrates the block diagram of these three phases.  
Results
 
Data Set
If you use this data set in your research, please cite the following:
For more Models please see the Queen's Range Image Data Base. Publications
