# DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model

arXiv: https://arxiv.org/abs/2405.02008 · HTML: https://mlsanity.com/paper/2405.02008/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 28 of its 33 references that are in the map.
- First posted on arXiv 3 May 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [MagicDrive: Street View Generation with Diverse 3D Geometry Control](https://mlsanity.com/paper/2310.02601/) (2023)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Neural Map Prior for Autonomous Driving](https://mlsanity.com/paper/2304.08481/) (2023)
- [Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps](https://mlsanity.com/paper/2311.04079/) (2023)
- [SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation](https://mlsanity.com/paper/2211.15656/) (2022)
- [MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models](https://mlsanity.com/paper/2308.12963/) (2023)
- [PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors](https://mlsanity.com/paper/2403.09079/) (2024)
- [MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction](https://mlsanity.com/paper/2306.10301/) (2023)
- [Denoising Diffusion Semantic Segmentation with Mask Prior Modeling](https://mlsanity.com/paper/2306.01721/) (2023)

## Built on by (most cited first, 13 of 13)

- [MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving](https://mlsanity.com/paper/2507.21423/) (2025)
- [What Really Matters for Robust Multi-Sensor HD Map Construction?](https://mlsanity.com/paper/2507.01484/) (2025)
- [LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation](https://mlsanity.com/paper/2511.06272/) (2025)
- [Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors](https://mlsanity.com/paper/2411.17161/) (2024)
- [How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic](https://mlsanity.com/paper/2412.04020/) (2024)
- [PriorFusion: Unified Integration of Priors for Robust Road Perception in Autonomous Driving](https://mlsanity.com/paper/2507.23309/) (2025)
- [From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving](https://mlsanity.com/paper/2509.23641/) (2025)
- [NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2512.03317/) (2025)
- [UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving](https://mlsanity.com/paper/2606.25736/) (2026)
- [L2G-Map: Local-to-Global Mapping via Hierarchical Diffusion Refinement and Elliptical Bayesian Fusion](https://mlsanity.com/paper/2503.02578/) (2025)
- [LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation](https://mlsanity.com/paper/2605.01478/) (2026)
- [Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior](https://mlsanity.com/paper/2606.31814/) (2026)
- [HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors](https://mlsanity.com/paper/2607.21281/) (2026)

## Abstract (arXiv)

Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements in Bird's-Eye View (BEV) perception. However, existing models still encounter challenges in producing realistic and consistent semantic map layouts. One prominent issue is the limited utilization of structured priors inherent in map segmentation masks. In light of this, we propose DiffMap, a novel approach specifically designed to model the structured priors of map segmentation masks using latent diffusion model. By incorporating this technique, the performance of existing semantic segmentation methods can be significantly enhanced and certain structural errors present in the segmentation outputs can be effectively rectified. Notably, the proposed module can be seamlessly integrated into any map segmentation model, thereby augmenting its capability to accurately delineate semantic information. Furthermore, through extensive visualization analysis, our model demonstrates superior proficiency in generating results that more accurately reflect real-world map layouts, further validating its efficacy in improving the quality of the generated maps.