# Mono-Stixels: Monocular depth reconstruction of dynamic street scenes

arXiv: https://arxiv.org/abs/1908.02635 · HTML: https://mlsanity.com/paper/1908.02635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection](https://mlsanity.com/paper/2407.08277/) (2024)
- [StixelNExT++: Lightweight Monocular Scene Segmentation and Representation for Collective Perception](https://mlsanity.com/paper/2507.06687/) (2025)

## Abstract (arXiv)

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.