3D object recognition using scale-invariant features

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이건우.As 3D scanning technology has developed, it has become easier to acquire various 3D surface datathus, there is a growing need for 3D data registration and recognition technology. In particular, techniques for finding the exact positions of 3D objects in a cluttered scene in which many parts of an object are occluded and multiple objects may be present is an important technology required by various fields such as industrial inspections, medical imaging, and games. Many existing studies have used local descriptors with local surface patches, and most of these use a fixed support radius so they cannot cope perfectly when the model and scene are at different scales. In this paper, we propose a new object recognition algorithm that exceeds the performance of existing studies. The process of 3D object recognition in a cluttered scene is largely composed of three steps: feature selection, feature description, and matching. In this study, we propose a perfectly scale-invariant feature selection algorithm by extending the 2D SIFT algorithm to a 3D mesh. The feature selection method proposed in this study can obtain highly repeatable feature points and support radii regardless of the scale. The selected features can effectively describe local information using the new shape descriptor proposed in this study. Unlike existing shape descriptors, it is possible to perform scale-invariant 3D object recognition and achieve a high recognition rate when combined with the feature point selection algorithm proposed in this study using the gradients of the scalar functions as defined on the 3D surface. We also reduced the searching space and lowered the false positive rate by suggesting a new RANSAC-based transformation hypothesis generation algorithm. Our 3D object recognition algorithm achieves recognition rates of 99.5% and 97.8% when tested on U3OR and CFVD datasets, respectively, which exceeds the results of previous studies.CHAPTER 1. INTRODUCTION 1 1.1 Background 1 CHAPTER 2. RELATED WORKS 5 2.1 Feature selection 5 2.1.1 Fixed-scale methods 5 2.1.2 Adaptive-scale methods 6 2.2 Feature description 8 2.2.1 Signature-based methods 8 2.2.2 Histogram-based method 9 2.3 Surface matching 12 CHAPTER 3. Datasets 14 3.1 U3OR dataset 14 3.2 CFVD dataset 16 CHAPTER 4. FEATURE SELECTION 18 4.1 Concepts 18 4.2 Gaussian and DoG pyramid 21 4.3 Local Extrema Detection 24 CHAPTER 5. Feature description 28 5.1 LRF construction 28 5.2 Feature orientation assignment 32 5.3 Feature vector generation 35 CHAPTER 6. 3D object recognition 38 6.1 Offline processing 38 6.2 Matching 39 6.3 Transformation hypotheses generation 41 6.4 Verification and segmentation 44 CHAPTER 7. Experiments 51 7.1 Results on the U3OR dataset 51 7.2 Results on the CFVD dataset 64 CHAPTER 8. Conclusion 70 REFERENCES 72 ABSTRACT (Korean) 80Docto

Performance evaluation of single and cross-dimensional feature detection and description

Three-dimensional (3D) local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However, cross-dimensional (mixed 2D and 3D) feature detection and description are yet to be investigated. Here, the authors evaluated the performance of both single and cross-dimensional feature detection and description methods on several 3D data sets and demonstrated the superiority of cross-dimensional over single-dimensional schemes