Monocular Road Planar Parallax Estimation

Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using expensive 3D sensors such as LiDAR or directly predicting the depth of points via deep learning. Instead of following existing methodologies, we propose Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences based on planar parallax, which takes full advantage of the commonly seen road plane geometry in driving scenes. RPANet takes a pair of images aligned by the homography of the road plane as input and outputs a $\gamma$ map for 3D reconstruction. Beyond estimating the depth or height, the $\gamma$ map has a potential to construct a two-dimensional transformation between two consecutive frames while can be easily derived to depth or height. By warping the consecutive frames using the road plane as a reference, the 3D structure can be estimated from the planar parallax and the residual image displacements. Furthermore, to make the network better perceive the displacements caused by planar parallax, we introduce a novel cross-attention module. We sample data from the Waymo Open Dataset and construct data related to planar parallax. Comprehensive experiments are conducted on the sampled dataset to demonstrate the 3D reconstruction accuracy of our approach in challenging scenarios.