论文标题
3D房间布局估算来自全景图像通过深曼哈顿霍夫变换的库
3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform
论文作者
论文摘要
在单个全景图像对3D房间布局的估计中，全局线框可以通过全局线框进行紧密描述。基于此观察，我们提出了一种替代方法，通过对可学习的霍夫变换块中的远程几何模式进行建模，以估算3D空间中的壁。我们将图像特征从库emap瓷砖转换为曼哈顿世界的霍夫空间，并将该功能直接映射到几何输出。卷积层不仅学习了局部梯度式的线特征，而且还利用全局信息成功预测具有简单网络结构的遮挡墙。与大多数以前的工作不同，预测是在每个Cubemap瓷砖上单独执行的，然后组装以获取布局估计。实验结果表明，我们在预测准确性和性能方面获得了可比的结果。代码可在https://github.com/starrah/dmh-net上找到。
Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at https://github.com/Starrah/DMH-Net.
