Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.

Autonomous driving safety based on LiDAR perception is increasingly becoming a hot spot. Specifically, 3D adversarial examples always make the prediction results of deep neural network models unpredictable, which poses a major security risk to autonomous driving systems. However, the vulnerability of 3D neural network models to adversarial examples is less explored. At present, existing adversarial attack methods often obtain 3D adversarial examples by perturbing the entire point cloud, which requires a large number of perturbed points, i.e. requires a large perturbation budget. In this paper, we propose a Salient Region Adversarial attack method (SR-Adv) to generate adversarial point clouds, by perturbing fewer regions and fewer points. To our knowledge, we are the first to propose region-based attacks for 3D point clouds. First, the proposed SR-Adv employs game theory to extract salient regions of point clouds. This mechanism assigns a value to each region to measure its importance to the 3D neural network model prediction results and realizes the vulnerability analysis of the 3D model. Second, we propose a novel optimization-based gradient attack algorithm to achieve adversarial attacks on salient regions. We evaluate the proposed SR-Adv attack method on the synthetic datasets ModelNet40 and ShapeNetPart as well as the real-world dataset KITTI and NuScenes. Experimental results show that the proposed SR-Adv achieves a state-of-the-art attack success rate and better imperceptibility by perturbing fewer points on 3D point clouds.

Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. However, they require objects to be captured with absolutely clean backgrounds and fixed viewpoints, which highly limits their application in the real environment. To guide 3D point cloud generation, we propose a novel network, RealPoint3D, to integrate prior 3D shape knowledge into the network. Taking additional 3D information, RealPoint3D can handle 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, provided a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image, together with the retrieved shape model, is fed into RealPoint3D to generate a fine-grained 3D point cloud. We evaluated the proposed RealPoint3D on the ShapeNet dataset and ObjectNet3D dataset for the 3D point cloud generation. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance. Furthermore, our proposed framework works well for real images in complex backgrounds (the image has the remaining objects in addition to the reconstructed object, and the reconstructed object may be occluded or truncated) with various viewing angles.

The contours, one of the most significant human perceptual features, have a significant impact on point cloud processing. In urban scenes, contour extraction is quite challenging due to the enormous number of unstructured and irregular points (typically greater than 10 ⁷ points). In this paper, we propose a Large-scale 3D point cloud Contour Extraction Network (LCE-NET) to generate contours consistent with human perception of outdoor scenes. To our knowledge, it is the first time that an end-to-end learning-based framework has been proposed for contour extraction on point cloud at this scale. The proposed LCE-Net is essentially a two-phase system. In the first phase, potential vertexes are detected from the input point cloud by the vertex detection module, then in the second phase, a designed overcomplete line proposal set is generated, and invalid line segments are further suppressed by the line proposal discrimination module. The two phases are jointly trained by a uniform loss function to promote the information interchange, thus leading to extraction results with satisfied accurate and false alarm ratings. Since there is hardly any available dataset with labeled contours for the large-scale outdoor scene, we open sourced SemanticLine, the first dataset for large-scale point clouds with labeled contour information, based on re-annotation of previous mapping level point cloud dataset semantic3D. Experimental results demonstrate that LCE-NET can effectively extract parametric contour lines from large-scale point clouds of urban scenes. Additionally, it outperforms the state-of-the-art approaches. The code will be open source on GitHub soon.