A Multi-parameter Approach to Automated Building Grouping and Generalization

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,' ‘average' or ‘weak.' The ‘weak' groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in block

Variable-resolution Compression of Vector Data

The compression of spatial data is a promising solution to reduce the space of data storage and to decrease the transmission time of spatial data over the Internet. This paper proposes a new method for variable-resolution compression of vector data. Three key steps are encompassed in the proposed method, namely, the simplification of vector data via the elimination of vertices, the compression of removed vertices, and the decoding of the compressed vector data. The proposed compression method was implemented and applied to compress vector data to investigate its performance in terms of the compression ratio, distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data, is able to achieve good compression ratios and maintains the main shape characteristics of the spatial objects within the compressed vector dat

Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the globally optimal correspondences through optimizing the energy function by the Kuhn-Munkres algorithm and then calculate the transformation based on the correspondences. Finally,we iteratively refine the transformation between two point clouds by conducting optimal correspondences matching and transformation calculation in a mutually reinforcing manner, to achieve the coarse-to-fine registration under an unified framework.The proposed method is evaluated and compared to several state-of-the-art methods on selected challenging datasets with repetitive, symmetric and incomplete structures.Comprehensive experiments demonstrate that the proposed IGSP algorithm obtains good performance and outperforms the state-of-the-art methods in terms of both rotation and translation errors.Comment: Accepted to International Conference on 3DVision (3DV) 2018 [8 pages, 6 figures and 3 tables

MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching

Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes during training. To build the correspondence between two data modalities, previous methods usually apply adversarial training to match the global shape features extracted by the encoder. However, this ignores the correspondence between multi-scaled geometric information embedded in the pyramidal hierarchy of the decoder, which makes previous methods struggle to generate high-quality complete shapes. To address this problem, we propose a novel unpaired shape completion network, named MFM-Net, using multi-stage feature matching, which decomposes the learning of geometric correspondence into multi-stages throughout the hierarchical generation process in the point cloud decoder. Specifically, MFM-Net adopts a dual path architecture to establish multiple feature matching channels in different layers of the decoder, which is then combined with the adversarial learning to merge the distribution of features from complete and incomplete modalities. In addition, a refinement is applied to enhance the details. As a result, MFM-Net makes use of a more comprehensive understanding to establish the geometric correspondence between complete and incomplete shapes in a local-to-global perspective, which enables more detailed geometric inference for generating high-quality complete shapes. We conduct comprehensive experiments on several datasets, and the results show that our method outperforms previous methods of unpaired point cloud completion with a large margin

CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration

Image-to-point cloud (I2P) registration is a fundamental task in the fields of robot navigation and mobile mapping. Existing I2P registration works estimate correspondences at the point-to-pixel level, neglecting the global alignment. However, I2P matching without high-level guidance from global constraints may converge to the local optimum easily. To solve the problem, this paper proposes CoFiI2P, a novel I2P registration network that extracts correspondences in a coarse-to-fine manner for the global optimal solution. First, the image and point cloud are fed into a Siamese encoder-decoder network for hierarchical feature extraction. Then, a coarse-to-fine matching module is designed to exploit features and establish resilient feature correspondences. Specifically, in the coarse matching block, a novel I2P transformer module is employed to capture the homogeneous and heterogeneous global information from image and point cloud. With the discriminate descriptors, coarse super-point-to-super-pixel matching pairs are estimated. In the fine matching module, point-to-pixel pairs are established with the super-point-to-super-pixel correspondence supervision. Finally, based on matching pairs, the transform matrix is estimated with the EPnP-RANSAC algorithm. Extensive experiments conducted on the KITTI dataset have demonstrated that CoFiI2P achieves a relative rotation error (RRE) of 2.25 degrees and a relative translation error (RTE) of 0.61 meters. These results represent a significant improvement of 14% in RRE and 52% in RTE compared to the current state-of-the-art (SOTA) method. The demo video for the experiments is available at https://youtu.be/TG2GBrJTuW4. The source code will be public at https://github.com/kang-1-2-3/CoFiI2P.Comment: demo video: https://youtu.be/TG2GBrJTuW4 source code: https://github.com/kang-1-2-3/CoFiI2

SparseDC: Depth Completion from sparse and non-uniform inputs

We propose SparseDC, a model for Depth Completion of Sparse and non-uniform depth inputs. Unlike previous methods focusing on completing fixed distributions on benchmark datasets (e.g., NYU with 500 points, KITTI with 64 lines), SparseDC is specifically designed to handle depth maps with poor quality in real usage. The key contributions of SparseDC are two-fold. First, we design a simple strategy, called SFFM, to improve the robustness under sparse input by explicitly filling the unstable depth features with stable image features. Second, we propose a two-branch feature embedder to predict both the precise local geometry of regions with available depth values and accurate structures in regions with no depth. The key of the embedder is an uncertainty-based fusion module called UFFM to balance the local and long-term information extracted by CNNs and ViTs. Extensive indoor and outdoor experiments demonstrate the robustness of our framework when facing sparse and non-uniform input depths. The pre-trained model and code are available at https://github.com/WHU-USI3DV/SparseDC

Infection of inbred BALB/c and C57BL/6 and outbred Institute of Cancer Research mice with the emerging H7N9 avian influenza virus

A new avian-origin influenza virus A (H7N9) recently crossed the species barrier and infected humans; therefore, there is an urgent need to establish mammalian animal models for studying the pathogenic mechanism of this strain and the immunological response. In this study, we attempted to develop mouse models of H7N9 infection because mice are traditionally the most convenient models for studying influenza viruses. We showed that the novel A (H7N9) virus isolated from a patient could infect inbred BALB/c and C57BL/6 mice as well as outbred Institute of Cancer Research (ICR) mice. The amount of bodyweight lost showed differences at 7 days post infection (d.p.i.) (BALB/c mice 30%, C57BL/6 and ICR mice approximately 20%), and the lung indexes were increased both at 3 d.p.i. and at 7 d.p.i.. Immunohistochemistry demonstrated the existence of the H7N9 viruses in the lungs of the infected mice, and these findings were verified by quantitative real-time polymerase chain reaction (RT-PCR) and 50% tissue culture infectious dose (TCID50) detection at 3 d.p.i. and 7 d.p.i.. Histopathological changes occurred in the infected lungs, including pulmonary interstitial inflammatory lesions, pulmonary oedema and haemorrhages. Furthermore, because the most clinically severe cases were in elderly patients, we analysed the H7N9 infections in both young and old ICR mice. The old ICR mice showed more severe infections with more bodyweight lost and a higher lung index than the young ICR mice. Compared with the young ICR mice, the old mice showed a delayed clearance of the H7N9 virus and higher inflammation in the lungs. Thus, old ICR mice could partially mimic the more severe illness in elderly patients. </p

Variable-resolution Compression of Vector Data

The compression of spatial data is a promising solution to reduce the space of data storage and to decrease the transmission time of spatial data over the Internet. This paper proposes a new method for variable-resolution compression of vector data. Three key steps are encompassed in the proposed method, namely, the simplification of vector data via the elimination of vertices, the compression of removed vertices, and the decoding of the compressed vector data. The proposed compression method was implemented and applied to compress vector data to investigate its performance in terms of the compression ratio, distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data, is able to achieve good compression ratios and maintains the main shape characteristics of the spatial objects within the compressed vector data