Abstract

This article describes a pipeline that receives as input a se- quence of images acquired by a calibrated stereo rig and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. It firstly detects the 3D planes viewed by each stereo pair from semi-dense depth estimation. This is followed by estimating the pose be- tween consecutive views using a new closed-form minimal algorithm that relies in point correspondences only when plane correspondences are in- sufficient to fully constrain the motion. Finally, the camera motion and the PPR are jointly refined, alternating between discrete optimization for generating plane hypotheses and continuous bundle adjustment. The approach differs from previous works in PPR by determining the poses from plane-primitives, by jointly estimating motion and piecewise-planar structure, and by operating sequentially, being suitable for applications of SLAM and visual odometry. Experiments are carried in challenging wide-baseline datasets where conventional point-based SfM usually fails.