# Quantifying Data Augmentation for LiDAR based 3D Object Detection

Source: [Quantifying Data Augmentation for LiDAR based 3D Object Detection](<https://www.sota2.com/research/paper/68fa6d211e209131cea38023>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:26:36.450Z

Authors: Martin Hahner, Dengxin Dai, Alexander Liniger, Luc Van Gool

Published: 2020-04-03T16:09:14.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.01643>)

[PDF](<https://arxiv.org/pdf/2004.01643.pdf>)