# Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View

arXiv: https://arxiv.org/abs/2104.11021 · HTML: https://mlsanity.com/paper/2104.11021/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://mlsanity.com/paper/1809.08495/) (2018)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation](https://mlsanity.com/paper/1911.02744/) (2019)
- [ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2009.03456/) (2020)
- [BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud](https://mlsanity.com/paper/2006.11436/) (2020)
- [Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data](https://mlsanity.com/paper/1905.08955/) (2019)
- [Sem-GAN: Semantically-Consistent Image-to-Image Translation](https://mlsanity.com/paper/1807.04409/) (2018)
- [SynthCity: A large scale synthetic point cloud](https://mlsanity.com/paper/1907.04758/) (2019)
- [LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving](https://mlsanity.com/paper/1905.07290/) (2019)
- [BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View](https://mlsanity.com/paper/2003.04188/) (2020)
- [LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2003.01174/) (2020)

## Built on by (most cited first, 10 of 10)

- [SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation](https://mlsanity.com/paper/2304.03251/) (2023)
- [Learning to Simulate Realistic LiDARs](https://mlsanity.com/paper/2209.10986/) (2022)
- [SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling](https://mlsanity.com/paper/2401.04230/) (2024)
- [BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection](https://mlsanity.com/paper/2211.17126/) (2022)
- [DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception](https://mlsanity.com/paper/2401.08687/) (2024)
- [Improving Multimodal Distillation for 3D Semantic Segmentation under Domain Shift](https://mlsanity.com/paper/2511.17455/) (2025)
- [Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation](https://mlsanity.com/paper/2504.14231/) (2025)
- [BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2509.14151/) (2025)
- [A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems](https://mlsanity.com/paper/2312.15817/) (2023)
- [Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Renderers](https://mlsanity.com/paper/2402.10079/) (2024)

## Abstract (arXiv)

The performance of object detection methods based on LiDAR information is heavily impacted by the availability of training data, usually limited to certain laser devices. As a result, the use of synthetic data is becoming popular when training neural network models, as both sensor specifications and driving scenarios can be generated ad-hoc. However, bridging the gap between virtual and real environments is still an open challenge, as current simulators cannot completely mimic real LiDAR operation. To tackle this issue, domain adaptation strategies are usually applied, obtaining remarkable results on vehicle detection when applied to range view (RV) and bird's eye view (BEV) projections while failing for smaller road agents. In this paper, we present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark. The obtained results show the advantages of the proposed method over the existing alternatives.