大多数最先进的定位算法都依赖于稳健的相对姿势估计和几何验证来获得移动的对象不可知的摄像机在复杂的室内环境中姿势。但是，如果场景包含重复的结构，例如书桌，桌子，盒子或移动的人，则这种方法容易犯错。我们表明，可移动对象包含了不可忽略的本地化误差，并提出了一种新的直接方法，以预测六度自由（6DOF）更加坚固。我们为定位管道INLOC配备了实例分割网络yolact ++。动态对象的口罩用于相对姿势估计步骤和摄像头姿势建议的最终分类中。首先，我们过滤出放置在动态对象的掩模上的匹配。其次，我们跳过了与移动对象相关的区域上查询和合成图像的比较。此过程导致更强大的本地化。最后，我们描述并改善了由合成图像和查询图像之间的基于梯度的比较引起的错误，并发布了新的管道，以模拟MatterPort扫描中具有可移动对象的环境。所有代码均可在github.com/dubenma/d-inlocpp上获得。
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AR/VR应用程序和机器人需要知道场景何时更改。一个示例是从场景中移动，添加或删除对象时。我们提出了仅基于场景更改的3D对象发现方法。我们的方法不需要编码有关对象的任何假设，而是通过利用其连贯的动作来发现对象。最初将变化视为深度图的差异，并在对象进行刚性运动时被分割为对象。图切割优化将不断变化的标签传播到几何一致的区域。实验表明，我们的方法在针对竞争基线的3RSCAN数据集上实现了最先进的性能。我们方法的源代码可以在https://github.com/katadam/objectscanmove上找到。
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我们提出了一种解决RANSAC框架中的硬几何优化问题的方法。难度最小的问题是从放宽原始几何优化问题中的许多虚假解决方案的最小问题。我们的方法避免了计算大量杂散解决方案。我们设计一种学习策略，用于选择一个启动问题解决方案对，可以在数值上继续存在问题和感兴趣的解决方案。我们通过开发RANSAC求解器来展示我们的方法，以通过在每个视图中使用四个点计算三个校准相机的相对姿势的问题。平均而言，我们可以在70美元\ mu s下解决一个问题。$我们还通过两个校准相机的相对姿势计算和研究我们的工程选择，通过两个校准相机的相对姿势。意见。
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We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of i) three points and one line and the novel case of ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Groebner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver framework MINUS, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We characterize their number of solutions and show with simulated experiments that our solvers are numerically robust and stable under image noise, a key contribution given the borderline intractable degree of nonlinearity of trinocular constraints. We show in real experiments that i) SIFT feature location and orientation provide good enough point-and-line correspondences for three-view reconstruction and ii) that we can solve difficult cases with too few or too noisy tentative matches, where the state of the art structure from motion initialization fails.
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We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end-to-end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current stateof-the-art compact image representations on standard image retrieval benchmarks.
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Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
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In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".
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Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filtering, especially when side information over users or items is available. Bayesian formulations of FMs have been proposed to provide confidence intervals over the predictions made by the model, however they usually involve Markov-chain Monte Carlo methods that require many samples to provide accurate predictions, resulting in slow training in the context of large-scale data. In this paper, we propose a variational formulation of factorization machines that allows us to derive a simple objective that can be easily optimized using standard mini-batch stochastic gradient descent, making it amenable to large-scale data. Our algorithm learns an approximate posterior distribution over the user and item parameters, which leads to confidence intervals over the predictions. We show, using several datasets, that it has comparable or better performance than existing methods in terms of prediction accuracy, and provide some applications in active learning strategies, e.g., preference elicitation techniques.
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By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io
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This case study investigates the extent to which a language model (GPT-2) is able to capture native speakers' intuitions about implicit causality in a sentence completion task. We first reproduce earlier results (showing lower surprisal values for pronouns that are congruent with either the subject or object, depending on which one corresponds to the implicit causality bias of the verb), and then examine the effects of gender and verb frequency on model performance. Our second study examines the reasoning ability of GPT-2: is the model able to produce more sensible motivations for why the subject VERBed the object if the verbs have stronger causality biases? We also developed a methodology to avoid human raters being biased by obscenities and disfluencies generated by the model.
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