Multi-view dual attention network for 3D object recognition

The existing view-based 3D object classification and recognition methods ignore the inherent hierarchical correlation and distinguishability of views, making it difficult to further improve the classification accuracy. In order to solve this problem, this paper proposes an end-to-end multi-view dual attention network framework for high-precision recognition of 3D objects. On one hand, we obtain three feature layers of query, key, and value through the convolution layer. The spatial attention matrix is generated by the key-value pairs of query and key, and each feature in the value of the original feature space branch is assigned different importance, which clearly captures the prominent detail features in the view, generates the view space shape descriptor, and focuses on the detail part of the view with the feature of category discrimination. On the other hand, a channel attention vector is obtained by compressing the channel information in different views, and the attention weight of each view feature is scaled to find the correlation between the target views and focus on the view with important features in all views. Integrating the two feature descriptors together to generate global shape descriptors of the 3D model, which has a stronger response to the distinguishing features of the object model and can be used for high-precision 3D object recognition. The proposed method achieves an overall accuracy of 96.6% and an average accuracy of 95.5% on the open-source ModelNet40 dataset, compiled by Princeton University when using Resnet50 as the basic CNN model. Compared with the existing deep learning methods, the experimental results demonstrate that the proposed method achieves state-of-the-art performance in the 3D object classification accuracy.

Ontology-Based Framework for Cooperative Learning of 3D Object Recognition

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots.

Efficient 3D Object Recognition from Cluttered Point Cloud

Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10–30×) the efficiency of the original SAC-IA method without sacrificing accuracy.