This paper proposes a novel method to simultaneously perform relative camera pose estimation and planar reconstruction of a scene from two RGB images. We start by extracting and matching superpixel information from both images and rely on a novel multi-model RANSAC approach to estimate multiple homographies from superpixels and identify matching planes. Ambiguity issues when performing homogra-phy decomposition are handled by proposing a voting system to more reliably estimate relative camera pose and plane parameters. A non-linear optimization process is also proposed to perform bundle adjustment that exploits a joint representation of homographies and works both for image pairs and whole sequences of image (vSLAM). As a result, the approach provides a mean to perform a dense 3D plane reconstruction from two RGB images only without relying on RGB-D inputs or strong priors such as Manhattan assumptions, and can be extented to handle sequences of images. Our results compete with keypoint-based techniques such as ORB-SLAM while providing a dense representation and are more precise than direct and semi-direct pose estimation techniques used in LSD-SLAM or DPPTAM.