LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.Comment: 28 pages, 14 figure

Point Pair Feature based Object Detection for Random Bin Picking

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach

3D keypoint detectors and descriptors for 3D objects recognition with TOF camera

International audienceThe goal of this work is to evaluate 3D keypoints detectors and descriptors, which could be used for quasi real time 3D object recognition. The work presented has three main objectives: extracting descriptors from real depth images, obtaining an accurate degree of invariance and robustness to scale and viewpoints, and maintaining the computation time as low as possible. Using a 3D time-of-flight (ToF) depth camera, we record a sequence for several objects at 3 different distances and from 5 viewpoints. 3D salient points are then extracted using 2 different curvatures-based detectors. For each point, two local surface descriptors are computed by combining the shape index histogram and the normalized histogram of angles between the normal of reference feature point and the normals of its neighbours. A comparison of the two detectors and descriptors was conducted on 4 different objects. Experimentations show that both detectors and descriptors are rather invariant to variations of scale and viewpoint. We also find that the new 3D keypoints detector proposed by us is more stable than a previously proposed Shape Index based detector

Détecteurs de points d'intérêt 3D basés sur la courbure

National audienceDans cet article, nous proposons un nouveau détecteur de points d'intérêt 3D (keypoint). Notre sélection des points saillants se base sur l'expression de la variation locale de la surface à travers les courbures principales calculées sur un nuage de points ordonnés, associé à une seule vue (deux dimensions et demie). Nous avons comparé sept méthodes qui combinent ces courbures et extraient des keypoints en se basant sur: 1) un seuillage des valeurs d'un facteur de qualité: Quality Factor (FQ), 2) un seuillage sur une mesure de l'indice de forme: Shape Index (SI), 3) les composantes connexes d'une carte de classification basée sur SI, 4) les composantes connexes d'une carte de classification basée sur SI et l'intensité de courbure : Curvedness (C), 5) les composantes connexes d'une carte de classification basée sur la courbure gaussienne (H) et la courbure moyenne (K), 6) une combinaison des deux derniers critères 4 et 5 (SC_HK) avec un tri final selon C et 7) une combinaison des trois critères 1, 4 et 5 (SC_HK_FQ). Une évaluation de la performance de ces détecteurs en termes de stabilité et répétabilité, montre la supériorité des deux nouveaux détecteurs SC_HK et SC_HK_FQ

The 3D object recognition with environmental adaptability based on VFH descriptor and region growing segmentation

3D object recognition is a basic research in the machine vision field. Microsoft KINECT V2 is utilized to collect external environmental information. The point cloud file is obtained after processing the collected information. In order to filter the point cloud and obtain point cloud model of a single object in the environment after region growing segmentation, the point cloud is applied to point cloud library. Then, the VFH descriptor of the point cloud model is calculated. After inputting point cloud model of the trained target, the point cloud model with the minimum CHI square distance between the VFH descriptor of the target and VFH descriptor of the point cloud model can be found. The 3D object corresponding to the found model is the identified object. For the 3D object recognition in an unfamiliar environment, the algorithm of 3D object recognition with environmental adaptability is proposed. After the 3D object recognition system built, the physical verification is conducted about the proposed algorithm. Giving the target model, the system successfully identifies the 3D object in the unfamiliar environment, that demonstrates the correctness of the algorithm