Weakly supervised point cloud semantic segmentation is an increasingly active topic, because fully supervised learning acquires well-labeled point clouds and entails high costs. The existing weakly supervised methods either need meticulously designed data augmentation for self-supervised learning or ignore the negative effects of learning on pseudolabel noises. In this article, by designing different granularity of cross-cloud structures, we propose a cross-cloud consistency method for weakly supervised point cloud semantic segmentation which forms the expectation-maximum (EM) framework. Benefiting from the cross-cloud constraints, our method allows effective learning alternatively between refining pseudolabels and updating network parameters. Specifically, in E-step, we propose a pseudolabel selecting (PLS) strategy based on cross subcloud consistency, improving the credibility of selected pseudolabels explicitly. In M-step, a cross-scene contrastive regularization enforces cross-scene prototypes with the same label in different scenes to be more similar, while keeping prototypes with different labels to be a clear margin, reducing the noise fitting. Finally, we give some insight into the optimization of our method in the EM theoretical way. The proposed method is evaluated on three challenging datasets, where experimental results demonstrate that our method significantly outperforms state-of-the-art weakly supervised competitors. Our code is available online: https://github.com/Yachao-Zhang/Cross-Cloud-Consistency.

As manual point-wise label is time and labor-intensive for fully supervised large-scale point cloud semantic segmentation, weakly supervised method is increasingly active. However, existing methods fail to generate high-quality pseudo labels effectively, leading to unsatisfactory results. In this paper, we propose a weakly supervised point cloud semantic segmentation framework via receptive-driven pseudo label consistency and structural consistency to mine potential knowledge. Specifically, we propose three consistency contrains: pseudo label consistency among different scales, semantic structure consistency between intra-class features and class-level relation structure consistency between pair-wise categories. Three consistency constraints are jointly used to effectively prepares and utilizes pseudo labels simultaneously for stable training. Finally, extensive experimental results on three challenging datasets demonstrate that our method significantly outperforms state-of-the-art weakly supervised methods and even achieves comparable performance to the fully supervised methods.

Spiking Neural Networks (SNNs) have attracted enormous research interest due to their low-power and biologically plausible nature. Existing ANN-SNN conversion methods can achieve lossless conversion by converting a well-trained Artificial Neural Network (ANN) into an SNN. However, converted SNN requires a large amount of time steps to achieve competitive performance with the well-trained ANN, which means a large latency. In this paper, we propose an efficient unified ANN-SNN conversion method for point cloud classification and image classification to significantly reduce the time step to meet the fast and lossless ANN-SNN transformation. Specifically, we first adaptively adjust the threshold according to the activation state of spiking neurons, ensuring a certain proportion of spiking neurons are activated at each time step to reduce the time for accumulation of membrane potential. Next, we use an adaptive firing mechanism to enlarge the range of spiking output, getting more discrimination features in short time steps. Extensive experimental results on challenging point cloud and image datasets demonstrate that the suggested approach significantly outmatches state-of-the-art ANN-SNN conversion based methods.

In this paper, we model the cognitive radio network with channel reservation for primary users using two-dimensional continuous Markov process. Based on this model, we propose an algorithm to maximize the achievable arrival rate of secondary users (SUs) under specific grade of service (GoS) constraints. Moreover, to reduce the computational complexity of the algorithm, the closed-form expressions of the steady-state probabilities are derived using an approximate approach. Moreover the formulas for the blocking probability and the dropping probability of SUs are obtained. Finally, simulations are conducted to verify the accuracy of our approximation and the correctness of the proposed algorithm.