# Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds

arXiv: https://arxiv.org/abs/2207.04908 · HTML: https://mlsanity.com/paper/2207.04908/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [CNN-based Lidar Point Cloud De-Noising in Adverse Weather](https://mlsanity.com/paper/1912.03874/) (2019)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)
- [Weather Influence and Classification with Automotive Lidar Sensors](https://mlsanity.com/paper/1906.07675/) (2019)
- [Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion](https://mlsanity.com/paper/1905.10117/) (2019)
- [A Benchmark for Spray from Nearby Cutting Vehicles](https://mlsanity.com/paper/2108.10800/) (2021)

## Built on by (most cited first, 4 of 4)

- [LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis](https://mlsanity.com/paper/2310.00952/) (2023)
- [Towards Robust 3D Object Detection In Rainy Conditions](https://mlsanity.com/paper/2310.00944/) (2023)
- [SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions](https://mlsanity.com/paper/2406.09945/) (2024)
- [Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions](https://mlsanity.com/paper/2406.09906/) (2024)

## Abstract (arXiv)

LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas exhaust in cold weather. This everyday phenomenon can severely impact the quality of LiDAR measurements, resulting in a less accurate environment perception by creating artifacts like ghost object detections. In the literature, the semantic segmentation of adverse weather effects like rain and fog is achieved using learning-based approaches. However, such methods require large sets of labeled data, which can be extremely expensive and laborious to get. We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust. First, we identify for each vehicle in a scene its emission area and detect gas exhaust if present. Then, isolated clouds are detected by modeling through time the regions of space where gas exhaust is likely to be present. We test our method on real urban data, showing that our approach can reliably detect gas exhaust in different scenarios, making it appealing for offline pre-labeling and online applications such as ghost object detection.