论文标题
建筑外墙解析R-CNN
Building Facade Parsing R-CNN
论文作者
论文摘要
预测用于建筑外墙的像素级标签的建筑外墙解析已在计算机视觉感知中应用自动驾驶汽车（AV）驾驶。但是，AV的板载摄像头没有额定视图，它捕获了由于相机的透视图，AV在道路两侧的建筑物外墙的变形视图。我们提出了外墙R-CNN，其中包括一个TransConv模块，广义边界框检测和凸正则化，以执行变形的立面视图。实验表明，立面R-CNN比当前最新的立面解析模型取得更好的性能，该模型主要用于额叶视图。我们还发布了一个新的建筑物立面解析数据集，该数据集衍生自牛津机器人数据集，我们称之为牛津机器人的立面数据集。该数据集包含来自牛津机器人数据集的500张街景图像，并具有对建筑物立面对象的准确注释。已发布的数据集可从https://github.com/sijieaaa/oxford-robotcar-facade获得
Building facade parsing, which predicts pixel-level labels for building facades, has applications in computer vision perception for autonomous vehicle (AV) driving. However, instead of a frontal view, an on-board camera of an AV captures a deformed view of the facade of the buildings on both sides of the road the AV is travelling on, due to the camera perspective. We propose Facade R-CNN, which includes a transconv module, generalized bounding box detection, and convex regularization, to perform parsing of deformed facade views. Experiments demonstrate that Facade R-CNN achieves better performance than the current state-of-the-art facade parsing models, which are primarily developed for frontal views. We also publish a new building facade parsing dataset derived from the Oxford RobotCar dataset, which we call the Oxford RobotCar Facade dataset. This dataset contains 500 street-view images from the Oxford RobotCar dataset augmented with accurate annotations of building facade objects. The published dataset is available at https://github.com/sijieaaa/Oxford-RobotCar-Facade
