  In this paper we present mono-stixels, a compact environment representation
specially designed for dynamic street scenes. Mono-stixels are a novel approach
to estimate stixels from a monocular camera sequence instead of the
traditionally used stereo depth measurements. Our approach jointly infers the
depth, motion and semantic information of the dynamic scene as a 1D energy
minimization problem based on optical flow estimates, pixel-wise semantic
segmentation and camera motion. The optical flow of a stixel is described by a
homography. By applying the mono-stixel model the degrees of freedom of a
stixel-homography are reduced to only up to two degrees of freedom.
Furthermore, we exploit a scene model and semantic information to handle moving
objects. In our experiments we use the public available DeepFlow for optical
flow estimation and FCN8s for the semantic information as inputs and show on
the KITTI 2015 dataset that mono-stixels provide a compact and reliable depth
reconstruction of both the static and moving parts of the scene. Thereby,
mono-stixels overcome the limitation to static scenes of previous
structure-from-motion approaches.
