Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness.

Ensuring trust, security, and privacy among all participating parties in the process of sharing supervision data is crucial for engineering quality and safety. However, the current centralized architecture platforms that are commonly used for engineering supervision data have problems such as low data sharing and high centralization. A blockchain-based framework for the secure sharing of engineering supervision data is proposed by utilizing the tamper-proof, decentralized, and traceable characteristics of blockchain. The secure storage of supervision data is achieved by combining it with the IPFS (InterPlanetary File System), reducing the storage pressure of on-chain data. Additionally, a fast data retrieval framework is designed based on the storage characteristics of supervision data. Then, CP-ABE (Ciphertext Policy Attribute Based Encryption) is combined with a data storage framework to ensure the privacy, security, and reliability of supervisory data during the sharing process. Finally, smart contracts are designed under the designed framework to ensure the automatic and trustworthy execution of access control processes. The analysis and evaluation results of the security, encryption and decryption, and cost performance of the proposed blockchain framework show that the encryption and decryption time is completed within 0.1 s, the Gas cost is within the normal consumption range, and the time cost of smart contract invocation does not exceed 5 s, demonstrating good availability and reusability of the method proposed in this article.

Six degrees-of-freedom (6D) object pose estimation plays an important role in pattern recognition of fields such as robotics and augmented reality. However, there are issues with low accuracy and real-time performance of 6D object pose estimation in complex scenes. To address these challenges, in this article, RFF-PoseNet (a 6D object pose estimation network based on robust feature fusion) is proposed for complex scenes. Firstly, a more lightweight Ghost module is used to replace the convolutional blocks in the feature extraction network. Then, a pyramid pooling module is added to the semantic label branch of PoseCNN to fuse the features of different pooling layers and enhance the network’s ability to capture information about objects in complex scenes and the correlations between contextual information. Finally, a pose regression and optimization module is utilized to further improve object pose estimation in complex scenes. Simulation experiments conducted on the YCB-Video and Occlusion LineMOD datasets show that the RFF-PoseNet algorithm can strengthen the correlation of features between different levels and the recognition ability of unclear targets, thereby achieving excellent accuracy and real-time performance, as well as strong robustness.

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm.

Gesture interaction allows users to interact with objects through natural hand movements without direct physical contact. However, in gesture interaction, the hand moves frequently with large variations in direction, and hand joints are prone to self-occlusion. The accuracy of existing 3D point cloud gesture estimation methods is insufficient to meet the requirements of natural gesture interaction. This article proposes a 3D point cloud gesture estimation method based on EdgeConv to reconstruct joint features. The method first converts depth maps into point cloud data to reduce the impact of viewpoint variations on depth map representation of the same gesture, thereby mitigating the effect on estimation accuracy. Then, the global features are reconstructed using EdgeConv in the initial joint estimation module so that the reconstructed features contain structural information between hand joints. Finally, local joint refinement is performed by constructing local features centered around the refined joint twice using EdgeConv. These features include reference information from within group points to group center points and structural information between joint points. Using EdgeConv to enrich hand joint feature information can effectively improve the performance of hand joint estimation. Comparative experimental analysis was conducted on the 3D point cloud datasets ICVL, NYU, and MSRA, and complex scene experimental analysis was conducted on the InterHand2.6M dataset. Furthermore, the development of virtual–real interaction applications was carried out. The experimental results show that the 3D point cloud gesture estimation method proposed in this paper has high accuracy, strong generalization and robustness, providing solid support for natural gesture interaction applications.