Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

An Iterative 3D Registration Algorithm using Random Pair of Plane Patches

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이범희.본 논문은 3차원 공간에서 평면 패치를 기반으로 이미지 정합을 하는 새로운 기법을 제시한다. 정합에 사용되는 대부분의 알고리즘은 특정점 간의 유사도를 이용하여 대응관계를 구하고 이를 기반으로 두 좌표계 사이의 강체변환을 구한다. 그러나 대응관계를 구하는 문제는 특징점에 대한 정보가 부족하거나 이상점이 발생하는 경우 부정확한 결과를 초래할 수 있고 이는 곧 정합의 실패에 영향을 미칠 수 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 평면 패치들의 집합으로 이루어진 두 3차원 좌표계에서 각각 임의의 평면을 추출한 후 거리 제곱 평균 함수의 값을 계산하여 두 좌표계 간의 유사도를 측정한다. 이 과정을 반복적으로 수행하여 정합하고자 하는 프레임을 가장 유사하게 만드는 강체변환을 결정한다. 그 다음 고정된 강체변환에 대하여 거리 제곱 평균 함수의 값을 최소화 시키는 방향으로 평행이동 벡터를 보정하여 정합을 완료한다. 본 논문의 기법은 대응관계를 찾는 데 걸리는 시간을 줄일 수 있고 이상점에 강인하다는 데 의의가 있으며, 이를 시뮬레이션 및 실제 환경에서의 실험을 통해 검증하였다.목차 초록 i 제 1 장 Introduction 1 1.1 Backgrounds and Motivations . . . . . . . . . . . . . . . . . . . . 1 1.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Point-based Registration . . . . . . . . . . . . . . . . . . . 3 1.2.2 Line-based Registration . . . . . . . . . . . . . . . . . . . 3 1.2.3 Plane-based Registration . . . . . . . . . . . . . . . . . . 4 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 제 2 장 Preliminaries 10 2.1 Plane Patch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Notation for Plane Patch . . . . . . . . . . . . . . . . . . 10 2.1.2 Problem Formulation using Plane Patch . . . . . . . . . . 14 2.2 Quaternion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1 Basis of quaternion . . . . . . . . . . . . . . . . . . . . . . 16 2.3 RANSAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 제 3 장 Proposed Method 21 3.1 Selection of plane patch pair . . . . . . . . . . . . . . . . . . . . . 22 3.2 Evaluation of Rigid Transformation . . . . . . . . . . . . . . . . . 25 3.2.1 Evaluation of Rotation Matrix based on Quaternion . . . 25 3.2.2 Evaluation of Translation Vector based on Moore-Penrose Pseudo Inverse Matrix . . . . . . . . . . . . . . . . . . . . 27 3.3 Transformation of Plane Patches . . . . . . . . . . . . . . . . . . 27 3.4 Mean Square Distance Function . . . . . . . . . . . . . . . . . . . 32 3.5 Selection of Rigid Transformation . . . . . . . . . . . . . . . . . . 37 3.6 Optimization of Translation Vector . . . . . . . . . . . . . . . . . 38 제 4 장 Simulations 40 4.1 Preconditions for the Simulation . . . . . . . . . . . . . . . . . . 40 4.2 Validity of Random Iteration . . . . . . . . . . . . . . . . . . . . 41 4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 46 제 5 장 Real Experiments 49 5.1 Environments for the Experiments . . . . . . . . . . . . . . . . . 49 5.2 Extraction of plane patches from point cloud . . . . . . . . . . . 50 5.3 Results of Real Experiments . . . . . . . . . . . . . . . . . . . . . 51 제 6 장 Conclusion 52 참고문헌 53 Abstract 59 감사의 글 61Maste

Super edge 4-points congruent sets-based point cloud global registration

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.</jats:p