The recognition of three-dimensional (3D) lidar (light detection and ranging) point clouds remains a significant issue in point cloud processing. Traditional point cloud recognition employs the 3D point clouds from the whole object. Nevertheless, the lidar data is a collection of two-and-a-half-dimensional (2.5D) point clouds (each 2.5D point cloud comes from a single view) obtained by scanning the object within a certain field angle by lidar. To deal with this problem, we initially propose a novel representation which expresses 3D point clouds using 2.5D point clouds from multiple views and then we generate multi-view 2.5D point cloud data based on the Point Cloud Library (PCL). Subsequently, we design an effective recognition model based on a multi-view convolutional neural network. The model directly acts on the raw 2.5D point clouds from all views and learns to get a global feature descriptor by fusing the features from all views by the view fusion network. It has been proved that our approach can achieve an excellent recognition performance without any requirement for three-dimensional reconstruction and the preprocessing of point clouds. In conclusion, this paper can effectively solve the recognition problem of lidar point clouds and provide vital practical value.

Farmers have been very precious for societies for ages. Their active experiments, valuable knowledge about their surroundings, environment, and crops’ requirements have been a vital part of society. However, the psychological perspectives have been a hole in the loop of farming. Hence, this study has investigated the antecedents of entrepreneurial behaviors of farmers with the mediating risk of their entrepreneurial self-efficacy (ESE). The population chosen for this study was the farming community of suburbs of China, and a sample size of 300 was selected for the data collection. This is a survey study, where a structured questionnaire was adapted on a five-point Likert scale. The data were collected from the farming community to know their psychological and behavioral preferences about their profession. This study has produced interesting results that education, training, and intrinsic motivation play a vital role in farmers’ ESE, affecting their entrepreneurial behaviors. This study will add to the body of knowledge and provide an eminent path for emerging entrepreneurs to find more mentorship opportunities to overcome the limitations in upcoming endeavors influencing education and training.

In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems. However, the Transformer architecture's high computational demands limit its scalability and deployment on resource-constrained platforms, hindering its practical applications in on-device multimedia processing. To address this challenge, we propose an efficient point cloud analysis architecture, Point MLP-Transformer (PointMT). This study tackles the quadratic complexity of the self-attention mechanism by introducing a linear complexity local attention mechanism for effective feature aggregation. Additionally, to counter the Transformer's focus on token differences while neglecting channel differences, we introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel, enhancing the precision of feature aggregation. To improve the Transformer's slow convergence speed due to the limited scale of point cloud datasets, we propose an MLP-Transformer hybrid module, which significantly enhances the model's convergence speed. Furthermore, to boost the feature representation capability of point tokens, we refine the classification head, enabling point tokens to directly participate in prediction. Experimental results on multiple evaluation benchmarks demonstrate that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy. This research provides an innovative solution for efficient point cloud analysis, offering significant potential for multimedia applications and other domains.

Recognition of three-dimensional (3D) shape is a remarkable subject in computer vision systems, because of the lack of excellent shape representations. With the development of 2.5D depth sensors, shape recognition is becoming more important in practical applications. Many methods have been proposed to preprocess 3D shapes, in order to get available input data. A common approach employs convolutional neural networks (CNNs), which have become a powerful tool to solve many problems in the field of computer vision. DeepPano, a variant of CNN, converts each 3D shape into a panoramic view and shows excellent performance. It is worth paying attention to the fact that both serious information loss and redundancy exist in the processing of DeepPano, which limits further improvement of its performance. In this work, we propose a more effective method to preprocess 3D shapes also based on a panoramic view, similar to DeepPano. We introduce a novel method to expand the training set and optimize the architecture of the network. The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation.

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior on relative pose gives a severe challenge to distinguish the inliers and outliers, and once the wrong inter-robot loop closures are used to optimize the pose graph, which can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, while it does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of inter-robot measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. Firstly, a clustering method integrating topology correctness of inter-robot loop closures is proposed to split the entire measurement set into multiple clusters. Then, our method decomposes the traditional high-dimensional consistency matrix into the sub-matrix blocks corresponding to the overlapping trajectory regions. Finally, we define the weight function to find the topologically correct and consistent subset with the maximum cardinality, then convert the weight function to the maximum clique problem from graph theory and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.