# Range Adaptation for 3D Object Detection in LiDAR

arXiv: https://arxiv.org/abs/1909.12249 · HTML: https://mlsanity.com/paper/1909.12249/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

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

- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection](https://mlsanity.com/paper/2103.14198/) (2021)
- [AGO-Net: Association-Guided 3D Point Cloud Object Detection Network](https://mlsanity.com/paper/2208.11658/) (2022)
- [Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning](https://mlsanity.com/paper/2308.16484/) (2023)
- [STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning](https://mlsanity.com/paper/2406.19362/) (2024)
- [nuScenes Revisited: Progress and Challenges in Autonomous Driving](https://mlsanity.com/paper/2512.02448/) (2025)
- [DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/2102.03747/) (2021)
- [Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection](https://mlsanity.com/paper/2512.24922/) (2025)

## Abstract (arXiv)

LiDAR-based 3D object detection plays a crucial role in modern autonomous driving systems. LiDAR data often exhibit severe changes in properties across different observation ranges. In this paper, we explore cross-range adaptation for 3D object detection using LiDAR, i.e., far-range observations are adapted to near-range. This way, far-range detection is optimized for similar performance to near-range one. We adopt a bird-eyes view (BEV) detection framework to perform the proposed model adaptation. Our model adaptation consists of an adversarial global adaptation, and a fine-grained local adaptation. The proposed cross range adaptation framework is validated on three state-of-the-art LiDAR based object detection networks, and we consistently observe performance improvement on the far-range objects, without adding any auxiliary parameters to the model. To the best of our knowledge, this paper is the first attempt to study cross-range LiDAR adaptation for object detection in point clouds. To demonstrate the generality of the proposed adaptation framework, experiments on more challenging cross-device adaptation are further conducted, and a new LiDAR dataset with high-quality annotated point clouds is released to promote future research.