Monocular 3D structure estimation for urban scenes

We propose a 3D structure estimation framework that adopts the slanted-planes representation in order to provide a dense estimation. The proposed approach fuses sparse 3D reconstructed point cloud obtained using several feature matching methods and noisy dense optical flow in order to perform accurate structure fitting and visually appealing results. We formulate the problem as a weighted total least square model that takes into account the occlusion boundaries between neighboring planes. We also propose an extended flow-based superpixel segmentation which is adaptive to the sparse feature points density for more balanced reconstruction. To validate our approach, we present 3D models obtained using the KITTI dataset [1] compared with other methods.