A Probabilistic Framework for Geometry Reconstruction using Prior Information

Abstract

In this paper, we propose a probabilistic framework for reconstructing scene geometry utilizing prior knowledge of a class of scenes, for example, scenes captured by a camera mounted on a vehicle driving through city streets. In this framework, we assume the video camera is calibrated, i.e., the intrinsic and extrinsic parameters are known all the time. While we assume a single camera moving during capturing, the framework can be generalized to multiple cameras as well. Traditional approaches try to match the points, lines or patches in multiple images to reconstruct scene geometry. The proposed framework also takes advantage of each patch's appearance and location to infer its orientation using prior information based on statistical learning from training data. The prior hence enhances the geometry reconstruction performance. We show that prior-based 3D reconstruction outperforms traditional 3D reconstruction with both synthetic data and real data, especially in the textureless areas.