# Quantifying Data Augmentation for LiDAR based 3D Object Detection

arXiv: https://arxiv.org/abs/2004.01643 · HTML: https://mlsanity.com/paper/2004.01643/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 18 of 18)

- [Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation](https://mlsanity.com/paper/2103.14962/) (2021)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://mlsanity.com/paper/2305.19812/) (2023)
- [Pattern-Aware Data Augmentation for LiDAR 3D Object Detection](https://mlsanity.com/paper/2112.00050/) (2021)
- [3D Data Augmentation for Driving Scenes on Camera](https://mlsanity.com/paper/2303.10340/) (2023)
- [Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator](https://mlsanity.com/paper/2312.12772/) (2023)
- [Context-Aware Data Augmentation for LIDAR 3D Object Detection](https://mlsanity.com/paper/2211.10850/) (2022)
- [Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation](https://mlsanity.com/paper/2206.07634/) (2022)
- [SAILOR: Scaling Anchors via Insights into Latent Object Representation](https://mlsanity.com/paper/2210.07811/) (2022)
- [JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention](https://mlsanity.com/paper/2407.04926/) (2024)
- [UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes](https://mlsanity.com/paper/2211.11950/) (2022)
- [DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion](https://mlsanity.com/paper/2303.12743/) (2023)
- [LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels](https://mlsanity.com/paper/2308.01424/) (2023)
- [GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2503.08639/) (2025)
- [Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization](https://mlsanity.com/paper/2306.05663/) (2023)
- [False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy](https://mlsanity.com/paper/2403.02639/) (2024)
- [D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes](https://mlsanity.com/paper/2404.11127/) (2024)
- [Easy-Poly: An Easy Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2502.17822/) (2025)

## Abstract (arXiv)

In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. For the bulk of our experiments, we utilize the well known PointPillars pipeline and the well established KITTI dataset. We investigate a variety of global and local augmentation techniques, where global augmentation techniques are applied to the entire point cloud of a scene and local augmentation techniques are only applied to points belonging to individual objects in the scene. Our findings show that both types of data augmentation can lead to performance increases, but it also turns out, that some augmentation techniques, such as individual object translation, for example, can be counterproductive and can hurt the overall performance. We show that these findings transfer and generalize well to other state of the art 3D Object Detection methods and the challenging STF dataset. On the KITTI dataset we can gain up to 1.5% and on the STF dataset up to 1.7% in 3D mAP on the moderate car class.