Cross-source point cloud matching by exploring structure property

University of Technology Sydney. Faculty of Engineering and Information Technology.Cross-source point cloud are 3D data coming from heterogeneous sensors. The matching of cross-source point cloud is extremely difficult because they contain mixture of different variations, such as missing data, noise and outliers, different viewpoint, density and spatial transformation. In this thesis, cross-source point cloud matching is solved from three aspects, utilizing of structure information, statistical model and learning representation. Chapter 1 introduces the value of cross-source point cloud registration and summarizes the key challenges of cross-source point cloud registration problem. Chapter 2 reviews the existing registration methods and analyse their limitation in solving the cross-source point cloud registration problem. Chapter 3 proposes two algorithms to discuss how to utilize structure information to solve the cross-source point cloud registration problem. In the first part of this chapter, macro and micro structures are extracted based on 3D point cloud segmentation. Then, these macro and micro structure components are integrated into a graph. With novel descriptors generated, the registration problem is successfully converted into graph matching problem. In the second part, weak region affinity and pixel-wise refinement are proposed to solve the cross-source point cloud. These two components are unified represented into a tensor space and the registration problem is converted into tensor optimization problem. In this method, the tensor space is updated when the transformation matrix is updated to get feedback from the recent transformation estimation step. Chapter 4 discusses how to utilize the statistical distribution of cross-source point cloud to solve matching problem. The goal is to find the potential matching region and estimate the accurate registration relationship. In this chapter, ensemble of shape functions (ESF) is utilized to select potential regions and a novel registration is proposed to solve the matching problem. For the registration, Gaussian mixture models (GMM) is selected as our mathematical tool. However, different to previous GMM-based registration methods, which assume a GMM for each point cloud, the proposed algorithm assumes a virtual GMM and the cross-source point clouds are samples from the virtual GMM. Then, the transformation is optimized to project the samples into a same virtual GMM. When the optimization is convergence, both the parameters of GMM and the transformation matrices are estimated. In Chapter 5, a deep learning method is proposed to represent the local structure information. Because of arbitrary rotation in cross-source point clouds, a rotation-invariant 3D representation method is proposed to robust represent the 3D point cloud although there are arbitrary rotation and translation. Also, there is no robust keypoints in these cross-source point cloud because of they come from heterogenous sensors, train the network is very difficult. A region-based method is proposed to generate regions for each point cloud and synthetic labelled dataset is constructed for training the network. All these algorithms are aimed to solve the cross-source point cloud registration problem. The performance of these algorithms is tested on many datasets, which shows the effective and correctness. These algorithms also provide insightful knowledge for 3D computer vision workers to process 3D point cloud

Fate of localization in coupled free chain and disordered chain

It has been widely believed that almost all states in one-dimensional (1d) disordered systems with short-range hopping and uncorrelated random potential are localized. Here, we consider the fate of these localized states by coupling between a disordered chain (with localized states) and a free chain (with extended states), showing that states in the overlapped and un-overlapped regimes exhibit totally different localization behaviors, which is not a phase transition process. In particular, while states in the overlapped regime are localized by resonant coupling, in the un-overlapped regime of the free chain, significant suppression of the localization with a prefactor of $\xi^{-1} \propto t_v^4/\Delta^4$ appeared, where $t_v$ is the inter-chain coupling strength and $\Delta$ is the energy shift between them. This system may exhibit localization lengths that are comparable with the system size even when the potential in the disordered chain is strong. We confirm these results using the transfer matrix method and sparse matrix method for systems $L \sim 10^6 - 10^9$. These findings extend our understanding of localization in low-dimensional disordered systems and provide a concrete example, which may call for much more advanced numerical methods in high-dimensional models.Comment: 7 pages, 6 figure

Cross-source Point Cloud Registration: Challenges, Progress and Prospects

The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies. Different from the conventional same-source point clouds that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration generalizes the requirement of data acquisition from same-source to different sources, which leads to generalized applications and combines the advantages of multiple sensors. In this paper, we provide a systematic review on CSPC registration. We first present the characteristics of CSPC, and then summarize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area and explain the role in several application fields.Comment: Accepted by Neurocomputing 202

From single-particle to many-body mobility edges and the fate of overlapped spectra in coupled disorder models

Mobility edge (ME) has played an essential role in disordered models. However, while this concept has been well established in disordered single-particle models, its existence in disordered many-body models is still under controversy. Here, a general approach based on coupling between extended and localized states in their overlapped spectra for ME is presented. We show that in the one-dimensional (1d) disordered single-particle models, all states are localized by direct coupling between them. However, in $d \ge 2$ disordered single-particle and 1d disordered many-body models, the resonant hybridization between these states in their overlapped spectra makes all states be extended, while these in the un-overlapped spectra are unchanged, leading to tunable MEs. We propose several models, including two disordered many-body spin models, to verify this mechanism. Our results establish a unified mechanism for MEs and demonstrate its universality in single-particle and many-body models, which opens an intriguing avenue for the realization and verification of MEs in many-body localization.Comment: 7+11 page

Theory of mobility edge and non-ergodic extended phase in coupled random matrices

The mobility edge, as a central concept in disordered models for localization-delocalization transitions, has rarely been discussed in the context of random matrix theory (RMT). Here we report a new class of random matrix model by direct coupling between two random matrices, showing that their overlapped spectra and un-overlapped spectra exhibit totally different scaling behaviors, which can be used to construct tunable mobility edges. This model is a direct generalization of the Rosenzweig-Porter model, which hosts ergodic, localized, and non-ergodic extended (NEE) phases. A generic theory for these phase transitions is presented, which applies equally well to dense, sparse, and even corrected random matrices in different ensembles. We show that the phase diagram is fully characterized by two scaling exponents, and they are mapped out in various conditions. Our model provides a general framework to realize the mobility edges and non-ergodic phases in a controllable way in RMT, which pave avenue for many intriguing applications both from the pure mathematics of RMT and the possible implementations of ME in many-body models, chiral symmetry breaking in QCD and the stability of the large ecosystems.Comment: 7+10 pages, 5+7 figure

General approach to tunable critical phases with two coupled chains

Critical phase (CP) with multifractal wave functions has attracted much attention in the past decades. However, the underlying mechanism for this phase is still ambiguous. Here we propose that the coupling between the localized and the extended states in their overlapped spectra can provide a general recipe for this phase with tunable structures. We demonstrate this picture using two models. In the first model, we show that the CP can be realized in the overlapped spectra with quasiperiodic potential, in which the CP regime can be tailored by the offset between the two chains, yielding tunable CP. This phase is insensitive to the forms of inter-chain couplings and quasiperiodic potentials. In the second model, we consider the CP by a disordered flat bands coupling with an extended band. We show that the localized states in the flat bands turn into critical too. Finally, we account for the emergence of this phase as a result of unbounded potential, which yields singular continuous spectra and excludes the extended states. Our approach opens a remarkable avenue for various CPs with tailored structures, which have wide applications in higher-dimensional single-particle CPs and many-body CPs.Comment: 7+11 pages, 5+13 figure

Multimodal Learning for Non-small Cell Lung Cancer Prognosis

This paper focuses on the task of survival time analysis for lung cancer. Although much progress has been made in this problem in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning-based survival time analyses for lung cancer are mostly based on textual clinical information such as staging, age, histology, etc. Unlike existing methods that predicting on the single modality, we observe that a human clinician usually takes multimodal data such as text clinical data and visual scans to estimate survival time. Motivated by this, in this work, we contribute a smart cross-modality network for survival analysis network named Lite-ProSENet that simulates a human's manner of decision making. Extensive experiments were conducted using data from 422 NSCLC patients from The Cancer Imaging Archive (TCIA). The results show that our Lite-ProSENet outperforms favorably again all comparison methods and achieves the new state of the art with the 89.3% on concordance. The code will be made publicly available.Comment: 11 pages, 6 figures, Multimodal learning, NSCLC, Survival analysis, Transforme