# Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++

arXiv: https://arxiv.org/abs/2303.09530 · HTML: https://mlsanity.com/paper/2303.09530/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (2019)
- [Using Machine Learning to Detect Ghost Images in Automotive Radar](https://mlsanity.com/paper/2007.05280/) (2020)
- [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)

## Built on by (most cited first, 7 of 7)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [GPT Self-Supervision for a Better Data Annotator](https://mlsanity.com/paper/2306.04349/) (2023)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data](https://mlsanity.com/paper/2311.07247/) (2023)
- [4D-CAAL: 4D Radar-Camera Calibration and Auto-Labeling for Autonomous Driving](https://mlsanity.com/paper/2601.21454/) (2026)

## Abstract (arXiv)

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input data, network architectures and training configuration are adjusted specifically for this task. Special attention is paid to the downsampling of point clouds composed of multiple sensor scans. In an extensive evaluation, the new setups display substantially better performance than existing approaches. Because there is no suitable public data set in which clutter is annotated, we design a method to automatically generate the respective labels. By applying it to existing data with object annotations and releasing its code, we effectively create the first freely available radar clutter data set representing real-world driving scenarios. Code and instructions are accessible at www.github.com/kopp-j/clutter-ds.