122 research outputs found
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
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