# StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection

arXiv: https://arxiv.org/abs/2407.08277 · HTML: https://mlsanity.com/paper/2407.08277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes](https://mlsanity.com/paper/1908.06316/) (2019)
- [Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation](https://mlsanity.com/paper/2012.07890/) (2020)
- [The Stixel world: A medium-level representation of traffic scenes](https://mlsanity.com/paper/1704.00280/) (2017)
- [Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks](https://mlsanity.com/paper/1604.02316/) (2016)
- [Improved Semantic Stixels via Multimodal Sensor Fusion](https://mlsanity.com/paper/1809.08993/) (2018)
- [Slanted Stixels: A way to represent steep streets](https://mlsanity.com/paper/1910.01466/) (2019)
- [GPU-accelerated real-time stixel computation](https://mlsanity.com/paper/1610.04124/) (2016)
- [Mono-Stixels: Monocular depth reconstruction of dynamic street scenes](https://mlsanity.com/paper/1908.02635/) (2019)

## Built on by (most cited first, 1 of 1)

- [StixelNExT++: Lightweight Monocular Scene Segmentation and Representation for Collective Perception](https://mlsanity.com/paper/2507.06687/) (2025)

## Abstract (arXiv)

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.