IEEE Trans Image Process. 2018 Jul;27(7):3164-3177. doi: 10.1109/TIP.2018.2818931.
Helping robots understand indoor scenes has considerable value in computer vision. However, due to the diversity of indoor scenes, understanding them remains a big challenge. There are many spatial right-angles in indoor scenes. These spatial right-angles are projected into diverse 2D projections. These projections can be considered a composition of a pair of lines (line-pairs). Given the vanishing points (VPs), line segments can be assigned to 1 of 3 main orthogonal directions. The line-pairs (intersection of 2 lines), such that each of them converges to a different VP, are likely to be the projection of a spatial right-angle onto the image plane. These projections may enable us to estimate their original orientation and position in 3D scenes. In this paper, we presented a method to efficiently understand indoor scenes from a single image, without training or any knowledge of the camera's internal calibration. Through geometric inference of line-pairs, it is possible to find these spatial right-angle projections. Then, these projections can be assigned to different clusters, and the line that lies in the neighbor-cluster helps us estimate the layout of the indoor scene. The proposed approach required no prior training. We compared the room layout estimated by our algorithm against the room box ground truth, measuring the percentage of pixels that were correctly classified. These experiments showed that our method estimated not only room layout, but also details of the indoor scene.
帮助机器人理解室内场景在计算机视觉中具有重要价值。然而，由于室内场景的多样性，理解它们仍然是一个巨大的挑战。室内场景中有许多空间直角。这些空间直角被投影到不同的 2D 投影中。这些投影可以被认为是一对线（线对）的组合。给定消失点 (VP)，线段可以被分配到 3 个主要正交方向之一。线对（两条线的交点），每条线都收敛到不同的 VP，很可能是空间直角在图像平面上的投影。这些投影可以帮助我们估计它们在 3D 场景中的原始方向和位置。在本文中，我们提出了一种从单张图像中高效理解室内场景的方法，无需训练或任何关于相机内部校准的知识。通过对线对的几何推断，可以找到这些空间直角投影。然后，这些投影可以被分配到不同的聚类中，位于邻聚类中的线有助于我们估计室内场景的布局。所提出的方法不需要预先训练。我们将我们的算法估计的房间布局与房间盒子的真实布局进行了比较，测量了正确分类的像素百分比。这些实验表明，我们的方法不仅可以估计房间布局，还可以估计室内场景的细节。
