Joint 3-D Shape Estimation and Landmark Localization From Monocular Cameras of Intelligent Vehicles

Abstract

3-D reconstruction is at the core for many driving applications of Internet of Intelligent Vehicles. Previous works on reconstruction of a 3-D point shape commonly use a two-step framework. Precisely localizing a series of feature points in an image is performed on the first step. Then the second procedure attempts to fit the 3-D data to the observations to get the real 3-D shape. Such an approach has high time consumption, and easily gets stuck into local minimum. To address this problem, we propose a method to jointly estimate the global 3-D geometric structure of car and localize 2-D landmarks from a single viewpoint image. First, we represent the 3-D shape with a set of predefined shape bases, while parametrizing it by the coefficients of the linear combination of them. Second, we adopt a cascaded regression framework to regress the global shape encoded by the prior bases, by jointly minimizing the appearance and shape fitting differences. The position fitting item can help cope with the description ambiguity of local appearance, and provide more information for 3-D reconstruction. We apply the proposed approach on a multiview car dataset. Experimental results demonstrate favorable improvements on pose estimation and shape prediction, compared with some previous methods.