469 research outputs found

    Fast sparse coding for range data denoising with sparse ridges constraint

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    Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency

    Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies

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    Field robots encounter dynamic unstructured environments containing a vast array of unique objects. In order to make sense of the world in which they are placed, they collect large quantities of unlabelled data with a variety of sensors. Producing robust and reliable applications depends entirely on the ability of the robot to understand the unlabelled data it obtains. Deep Learning techniques have had a high level of success in learning powerful unsupervised representations for a variety of discriminative and generative models. Applying these techniques to problems encountered in field robotics remains a challenging endeavour. Modern Deep Learning methods are typically trained with a substantial labelled dataset, while datasets produced in a field robotics context contain limited labelled training data. The primary motivation for this thesis stems from the problem of applying large scale Deep Learning models to field robotics datasets that are label poor. While the lack of labelled ground truth data drives the desire for unsupervised methods, the need for improving the model scaling is driven by two factors, performance and computational requirements. When utilising unsupervised layer outputs as representations for classification, the classification performance increases with layer size. Scaling up models with multiple large layers of features is problematic, as the sizes of subsequent hidden layers scales with the size of the previous layer. This quadratic scaling, and the associated time required to train such networks has prevented adoption of large Deep Learning models beyond cluster computing. The contributions in this thesis are developed from the observation that parameters or filter el- ements learnt in Deep Learning systems are typically highly structured, and contain related ele- ments. Firstly, the structure of unsupervised filters is utilised to construct a mapping from the high dimensional filter space to a low dimensional manifold. This creates a significantly smaller repre- sentation for subsequent feature learning. This mapping, and its effect on the resulting encodings, highlights the need for the ability to learn highly overcomplete sets of convolutional features. Driven by this need, the unsupervised pretraining of Deep Convolutional Networks is developed to include a number of modern training and regularisation methods. These pretrained models are then used to provide initialisations for supervised convolutional models trained on low quantities of labelled data. By utilising pretraining, a significant increase in classification performance on a number of publicly available datasets is achieved. In order to apply these techniques to outdoor 3D Laser Illuminated Detection And Ranging data, we develop a set of resampling techniques to provide uniform input to Deep Learning models. The features learnt in these systems outperform the high effort hand engineered features developed specifically for 3D data. The representation of a given signal is then reinterpreted as a combination of modes that exist on the learnt low dimensional filter manifold. From this, we develop an encoding technique that allows the high dimensional layer output to be represented as a combination of low dimensional components. This allows the growth of subsequent layers to only be dependent on the intrinsic dimensionality of the filter manifold and not the number of elements contained in the previous layer. Finally, the resulting unsupervised convolutional model, the encoding frameworks and the em- bedding methodology are used to produce a new unsupervised learning stratergy that is able to encode images in terms of overcomplete filter spaces, without producing an explosion in the size of the intermediate parameter spaces. This model produces classification results on par with state of the art models, yet requires significantly less computational resources and is suitable for use in the constrained computation environment of a field robot

    Depth Superresolution using Motion Adaptive Regularization

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    Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras. Recent work has explored the idea of improving the resolution of depth using higher resolution intensity as a side information. In this paper, we demonstrate that further incorporating temporal information in videos can significantly improve the results. In particular, we propose a novel approach that improves depth resolution, exploiting the space-time redundancy in the depth and intensity using motion-adaptive low-rank regularization. Experiments confirm that the proposed approach substantially improves the quality of the estimated high-resolution depth. Our approach can be a first component in systems using vision techniques that rely on high resolution depth information

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

    GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute

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    In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color omponents, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on the recent G-PCC coding standard test model, 0.43 dB, 0.25 dB, and 0.36 dB Bjontegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3%, and 14.5% BD-rate savings can be achieved on dense point clouds for the Y, Cb, and Cr components, respectively.Comment: 13 pages, 11 figures, submitted to IEEE TI
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