# Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data

arXiv: https://arxiv.org/abs/2311.07247 · HTML: https://mlsanity.com/paper/2311.07247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 13 Nov 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception](https://mlsanity.com/paper/2304.06547/) (2023)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [Using Machine Learning to Detect Ghost Images in Automotive Radar](https://mlsanity.com/paper/2007.05280/) (2020)
- [Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data](https://mlsanity.com/paper/2212.03690/) (2022)
- [Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++](https://mlsanity.com/paper/2303.09530/) (2023)
- [Fast Rule-Based Clutter Detection in Automotive Radar Data](https://mlsanity.com/paper/2108.12224/) (2021)
- [Anomaly Detection in Radar Data Using PointNets](https://mlsanity.com/paper/2109.09401/) (2021)

## Abstract (arXiv)

The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter, i.e. erroneous points that do not correspond to real objects. Another common objective is the semantic segmentation of moving road users. These two problems are handled strictly separate from each other in literature. The employed neural networks are always focused entirely on only one of the tasks. In contrast to this, we examine ways to solve both tasks at the same time with a single jointly used model. In addition to a new augmented multi-head architecture, we also devise a method to represent a network's predictions for the two tasks with only one output value. This novel approach allows us to solve the tasks simultaneously with the same inference time as a conventional task-specific model. In an extensive evaluation, we show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset.