# SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations

arXiv: https://arxiv.org/abs/2405.00250 · HTML: https://mlsanity.com/paper/2405.00250/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [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)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [Hierarchical Multi-Scale Attention for Semantic Segmentation](https://mlsanity.com/paper/2005.10821/) (2020)
- [OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping](https://mlsanity.com/paper/2304.10440/) (2023)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning](https://mlsanity.com/paper/2310.06753/) (2023)
- [Probabilistic Semantic Mapping for Urban Autonomous Driving Applications](https://mlsanity.com/paper/2006.04894/) (2020)
- [Automatic Construction of Lane-level HD Maps for Urban Scenes](https://mlsanity.com/paper/2107.10972/) (2021)
- [OpenStreetMap-based LiDAR Global Localization in Urban Environment without a Prior LiDAR Map](https://mlsanity.com/paper/2202.07516/) (2022)
- [Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping](https://mlsanity.com/paper/2010.13150/) (2020)
- [Exploring Navigation Maps for Learning-Based Motion Prediction](https://mlsanity.com/paper/2302.06195/) (2023)
- [OSM vs HD Maps: Map Representations for Trajectory Prediction](https://mlsanity.com/paper/2311.02305/) (2023)
- [SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving](https://mlsanity.com/paper/2306.14941/) (2023)

## Built on by (most cited first, 12 of 12)

- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction](https://mlsanity.com/paper/2409.08688/) (2024)
- [MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis](https://mlsanity.com/paper/2501.06660/) (2025)
- [SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs](https://mlsanity.com/paper/2502.02773/) (2025)
- [HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors](https://mlsanity.com/paper/2503.06821/) (2025)
- [A Concise Survey on Lane Topology Reasoning for HD Mapping](https://mlsanity.com/paper/2504.01989/) (2025)
- [MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training](https://mlsanity.com/paper/2511.19527/) (2025)
- [Neural Rendering for Sensor Adaptation in 3D Object Detection](https://mlsanity.com/paper/2508.12695/) (2025)
- [Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments](https://mlsanity.com/paper/2509.25542/) (2025)
- [FIN: Fast Inference Network for Map Segmentation](https://mlsanity.com/paper/2510.00651/) (2025)
- [LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation](https://mlsanity.com/paper/2605.01478/) (2026)

## Abstract (arXiv)

Vector maps are essential in autonomous driving for tasks like localization and planning, yet their creation and maintenance are notably costly. While recent advances in online vector map generation for autonomous vehicles are promising, current models lack adaptability to different sensor configurations. They tend to overfit to specific sensor poses, leading to decreased performance and higher retraining costs. This limitation hampers their practical use in real-world applications. In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations. The pipeline leverages probabilistic semantic mapping to generate a bird's-eye-view (BEV) semantic map as an intermediate representation. This intermediate representation is then converted to a vector map using the MapTRv2 decoder. By adopting a BEV semantic map robust to different sensor configurations, our proposed approach significantly improves the generalization performance. We evaluate the model on datasets with sensor configurations not used during training. Our evaluation sets includes larger public datasets, and smaller scale private data collected on our platform. Our model generalizes significantly better than the state-of-the-art methods.