# Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training

Source: [Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training](<https://www.sota2.com/research/paper/68fa70381e209131cea49a7f>)

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

Data updated: 2025-10-28T13:57:00.297Z

Authors: Mrigank Rochan, Xingxin Chen, Alaap Grandhi, Eduardo R. Corral-Soto, Bingbing Liu

Published: 2022-10-18T22:03:37.000Z

## Abstract

We consider the problem of domain adaptation in LiDAR-based 3D object detection. Towards this, we propose a simple yet effective training strategy called Gradual Batch Alternation that can adapt from a large labeled source domain to an insufficiently labeled target domain. The idea is to initiate the training with the batch of samples from the source and target domain data in an alternate fashion, but then gradually reduce the amount of the source domain data over time as the training progresses. This way the model slowly shifts towards the target domain and eventually better adapt to it. The domain adaptation experiments for 3D object detection on four benchmark autonomous driving datasets, namely ONCE, PandaSet, Waymo, and nuScenes, demonstrate significant performance gains over prior arts and strong baselines.

## 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/2210.10180>)

[PDF](<https://arxiv.org/pdf/2210.10180.pdf>)