A planar region in an image is low-rank if it is possible to recover a canonical view which rectifies the region. This typically holds for repetitive pattern (corners, edges, etc.) on planar surfaces. Such a region enables obtaining the camera orientation/position with respect to a 3D plane using the TILT (Transform Invariant Low-rank Textures) algorithm. This is even possible without using any prior knowledge of the visual information (features) on that plane. However, there is no method which automatically detects such regions and a brute force solution applied on all image patches is not feasible because of the heavy computation workload (iterative convex optimization) needed.
To overcome this limitation, we introduce a self-supervised region proposal network to directly extract planar regions containing a low-rank texture from an image. First, we use TILT to compute a novel low-rank likelihood map using a sliding window approach at multiple scales and at given steps. Second, we use these maps to train our network to generate such maps directly from new images. We evaluate or method on real-world datasets and show the performance by comparing it with an implementation of the current state-of-the-art, the hand-crafted method TILT. The results show that our method reliably detects low-rank regions which can successfully be used to compute accurate camera orientations/positions.