Detection Of Texture-less Objects Research Articles

An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm’s template depth sensitivity problem.

A Fast Approach to Texture-Less Object Detection Based on Orientation Compressing Map and Discriminative Regional Weight

This paper presents a fast algorithm for texture-less object recognition, which is designed to be robust to cluttered backgrounds and small transformations. At its core, the proposed method demonstrates a two-stage template-based procedure using an orientation compressing map and discriminative regional weight (OCM-DRW) to effectively detect texture-less objects. In the first stage, the proposed method quantizes and compresses all the orientations in a neighborhood to obtain the orientation compressing map which then is used to generate a set of possible object locations. To recognize the object in these possible object locations, the second stage computes the similarity of each possible object location with the learned template by using discriminative regional weight, which can effectively distinguish different categories of objects with similar parts. Experiments on publiclyavailable, texture-less object datasets indicate that apart from yielding efficient computational performance, the proposed method also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors in the presence of high-clutter, occlusion and scale-rotation changes. It improves the accuracy and speed by 8% and 370% respectively, relative to the previous best result on D-Textureless dataset.

Texture-less object detection and 6D pose estimation in RGB-D images

We present a cascade-like texture-less object detection and 6D pose estimation method exploiting both depth and color information from the RGB-D sensor. This is accomplished through both an offline and online phase. During the offline phase, a set of rendered templates is created from uniformly distributed sampling viewpoints obtained by employing the model of electrostatic charge distribution. During the online phase, our method converts sliding windows to scale-invariant RGB-D patches and employs a hash voting-based hypothesis generation scheme to compute a rough 6D pose hypothesis. Particle swarm optimization is employed to refine the 6D pose of the target object. We evaluate the algorithm against three datasets with the result that this method achieves high precision and good performance under various conditions. It was also applied to augmented reality and intelligent robotic manipulation yielding robust detection results.

Feasibility Study of Textureless Object Detection and Pose Estimation Based on a Model with 3D Edgels and Surfaces

Abstract This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions.

Combine color and shape in real-time detection of texture-less objects

Object instance detection is a fundamental problem in computer vision and has many applications. Compared with the problem of detecting a texture-rich object, the detection of a texture-less object is more involved because it is usually based on matching the shape of the object with the shape primitives extracted from an image, which is not as discriminative as matching appearance-based local features, such as the SIFT features. The Dominant Orientation Templates (DOT) method proposed by Hinterstoisser et al. is a state-of-the-art method for the detection of texture-less objects and can work in real time. However, it may well generate false detections in a cluttered background. In this paper, we propose a new method which has three contributions. Firstly, it augments the DOT method with a type of illumination insensitive color information. Since color is complementary to shape, the proposed method significantly outperforms the original DOT method in the detection of texture-less object in cluttered scenes. Secondly, we come up with a systematic way based on logistic regression to combine the color and shape matching scores in the proposed method. Finally, we propose a speed-up strategy to work with the proposed method so that it runs even faster than the original DOT method. Extensive experimental results are presented in this paper to compare the proposed method directly with the original DOT method and the LINE-2D method, and indirectly with another two state-of-the-art methods.