In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our method trains a convolutional neural network to simultaneously detect salient junctions and straight lines, as well as predict their 3D depth and vanishing points. Compared with the state-of-the-art learning-based wireframe detection methods, our network is much simpler and more unified, leading to better 2D wireframe detection. With global structural priors such as Manhattan assumption, our method further reconstructs a full 3D wireframe model, a compact vector representation suitable for a variety of high-level vision tasks such as AR and CAD. We conduct extensive evaluations on a large synthetic dataset of urban scenes as well as real images. Our code and datasets will be released.

A compelling use case of o ine reinforcement learning (RL) is to obtain a policy initialization from existing datasets, which allows e cient ne-tuning with limited amounts of active online interaction. However, several existing o ine RL methods tend to exhibit poor online ne-tuning performance. On the other hand, online RL methods can learn e ectively through online interaction, but struggle to incorporate o ine data, which can make them very slow in settings where exploration is challenging or pre-training is necessary. In this paper, we devise an approach for learning an e ective initialization from o ine data that also enables fast online ne-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL) accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from o ine data, while also being calibrated, in the sense that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and de ne it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that o ine RL algorithms that learn such calibrated value functions lead to e ective online ne-tuning, enabling us to take the bene ts of o ine initializations in online ne-tuning. In practice, Cal-QL can be implemented on top of existing conservative methods for o ine RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 10/11 ne-tuning benchmark tasks that we study in this paper.

Normalization techniques have become a basic component in modern convolutional neural networks (ConvNets). In particular, many recent works demonstrate that promoting the orthogonality of the weights helps train deep models and improve robustness. For ConvNets, most existing methods are based on penalizing or normalizing weight matrices derived from concatenating or flattening the convolutional kernels. These methods often destroy or ignore the benign convolutional structure of the kernels; therefore, they are often expensive or impractical for deep ConvNets. In contrast, we introduce a simple and efficient "convolutional normalization" method that can fully exploit the convolutional structure in the Fourier domain and serve as a simple plug-and-play module to be conveniently incorporated into any ConvNets. Our method is inspired by recent work on preconditioning methods for convolutional sparse coding and can effectively promote each layer's channel-wise isometry. Furthermore, we show that convolutional normalization can reduce the layerwise spectral norm of the weight matrices and hence improve the Lipschitzness of the network, leading to easier training and improved robustness for deep ConvNets. Applied to classification under noise corruptions and generative adversarial network (GAN), we show that convolutional normalization improves the robustness of common ConvNets such as ResNet and the performance of GAN. We verify our findings via extensive numerical experiments on CIFAR-10, CIFAR-100, and ImageNet.