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Scene segmentation mainly consists of semantic segmentation and instance segmentation. The latest research points out that combining the two segmentation methods to achieve panoramic segmentation can understand the current scene better. The point cloud contains rich spatial information, but panoramic segmentation research in this field is rarely discussed. How to use the unified model framework to obtain the results of instance segmentation and semantic segmentation is the key to realize the task of point cloud panoramic segmentation. In this paper, we propose a panoramic segmentation network for point cloud. In feature encoding stage, we introduce the potential correlation information among points to improve the performance of feature extraction. Then, an output module is presented to combine the results of the two decoders which uses objective distance to enhance the semantic and instance segmentation. Experiments show that our model has good performance on the panoramic segmentation task of point cloud.

The inherent disorder and irregularity of 3D point clouds pose great challenges to classification and segmentation tasks. To tackle these problems, we propose a geometric relation based point clouds classification and segmentation network. Specifically, we design two novel modules named geometric relation based convolution (GRC) and relational attention interpolation (RAI) to infer the local relations of point clouds. In GRC module, the convolutional weights and local features are both reasoned from predefined local geometric relations between the central point of each local point clouds and its neighboring points. The global shape awareness is obtained by stacking convolutional layers of the GRC. In RAI, a relational attention interpolation approach is proposed for the segmentation task. The attentional weights of different neighboring points are inferred from local relations of geometry and features, which is capable of guiding RAI to pay more attention to the relevant points and be sensitive to the boundaries of segments. Experimental results show that the proposed method makes full use of the geometric relations between local points, and presents good performance on both classification and segmentation tasks.

Cloud service providers, including Google, Amazon, and Alibaba, have now launched machine-learning-as-a-service (MLaaS) platforms, allowing clients to access sophisticated cloud-based machine learning models via APIs. Unfortunately, however, the commercial value of these models makes them alluring targets for theft, and their strategic position as part of the IT infrastructure of many companies makes them an enticing springboard for conducting further adversarial attacks. In this paper, we put forth a novel and effective attack strategy, dubbed InverseNet, that steals the functionality of black-box cloud-based models with only a small number of queries. The crux of the innovation is that, unlike existing model extraction attacks that rely on public datasets or adversarial samples, InverseNet constructs inversed training samples to increase the similarity between the extracted substitute model and the victim model. Further, only a small number of data samples with high confidence scores (rather than an entire dataset) are used to reconstruct the inversed dataset, which substantially reduces the attack cost. Extensive experiments conducted on three simulated victim models and Alibaba Cloud's commercially-available API demonstrate that InverseNet yields a model with significantly greater functional similarity to the victim model than the current state-of-the-art attacks at a substantially lower query budget.