# Toward Robust LiDAR based 3D Object Detection via Density-Aware Adaptive Thresholding

arXiv: https://arxiv.org/abs/2404.13852 · HTML: https://mlsanity.com/paper/2404.13852/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment](https://mlsanity.com/paper/2607.01983/) (2026)
- [Image Quality Dependent Degradation for AI Systems](https://mlsanity.com/paper/2607.25736/) (2026)

## Abstract (arXiv)

Robust 3D object detection is a core challenge for autonomous mobile systems in field robotics. To tackle this issue, many researchers have demonstrated improvements in 3D object detection performance in datasets. However, real-world urban scenarios with unstructured and dynamic situations can still lead to numerous false positives, posing a challenge for robust 3D object detection models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. 3D object detection models usually perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. While conventional perception algorithms typically employ a single threshold in post-processing, the proposed algorithm addresses this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in urban scenarios. The results show performance enhancements in 3D object detection models across a range of scenarios, not only in dynamic urban road conditions but also in scenarios involving adverse weather conditions.