# Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes

arXiv: https://arxiv.org/abs/2006.08547 · HTML: https://mlsanity.com/paper/2006.08547/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [ODAM: Gradient-based instance-specific visual explanations for object detection](https://mlsanity.com/paper/2304.06354/) (2023)
- [Improving Crowded Object Detection via Copy-Paste](https://mlsanity.com/paper/2211.12110/) (2022)
- [Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR](https://mlsanity.com/paper/2202.13505/) (2022)
- [PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery](https://mlsanity.com/paper/2105.12990/) (2021)
- [From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks](https://mlsanity.com/paper/2405.06038/) (2024)
- [Density-based Object Detection in Crowded Scenes](https://mlsanity.com/paper/2504.09819/) (2025)
- [DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection](https://mlsanity.com/paper/2301.05565/) (2023)
- [Learned Non-Maximum Suppression for 3D Object Detection](https://mlsanity.com/paper/2606.03568/) (2026)

## Abstract (arXiv)

Object detection is an important task in environment perception for autonomous driving. Modern 2D object detection frameworks such as Yolo, SSD or Faster R-CNN predict multiple bounding boxes per object that are refined using Non-Maximum-Suppression (NMS) to suppress all but one bounding box. While object detection itself is fully end-to-end learnable and does not require any manual parameter selection, standard NMS is parametrized by an overlap threshold that has to be chosen by hand. In practice, this often leads to an inability of standard NMS strategies to distinguish different objects in crowded scenes in the presence of high mutual occlusion, e.g. for parked cars or crowds of pedestrians. Our novel Visibility Guided NMS (vg-NMS) leverages both pixel-based as well as amodal object detection paradigms and improves the detection performance especially for highly occluded objects with little computational overhead. We evaluate vg-NMS using KITTI, VIPER as well as the Synscapes dataset and show that it outperforms current state-of-the-art NMS.