What Really Matters for Robust Multi-Sensor HD Map Construction?

• URL: http://arxiv.org/abs/2507.01484v1
• Date: Wed, 02 Jul 2025 08:46:27 GMT
• Title: What Really Matters for Robust Multi-Sensor HD Map Construction?
• Authors: Xiaoshuai Hao, Yuting Zhao, Yuheng Ji, Luanyuan Dai, Peng Hao, Dingzhe Li, Shuai Cheng, Rong Yin,
• Abstract summary: High-definition (HD) map construction methods are crucial for providing precise and comprehensive static environmental information.<n>Existing approaches primarily focus on improving model accuracy and often neglect the robustness of perception models.<n>We propose strategies to enhance the robustness of multi-modal fusion methods for HD map construction while maintaining high accuracy.
• Score: 9.108124985480046
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: High-definition (HD) map construction methods are crucial for providing precise and comprehensive static environmental information, which is essential for autonomous driving systems. While Camera-LiDAR fusion techniques have shown promising results by integrating data from both modalities, existing approaches primarily focus on improving model accuracy and often neglect the robustness of perception models, which is a critical aspect for real-world applications. In this paper, we explore strategies to enhance the robustness of multi-modal fusion methods for HD map construction while maintaining high accuracy. We propose three key components: data augmentation, a novel multi-modal fusion module, and a modality dropout training strategy. These components are evaluated on a challenging dataset containing 10 days of NuScenes data. Our experimental results demonstrate that our proposed methods significantly enhance the robustness of baseline methods. Furthermore, our approach achieves state-of-the-art performance on the clean validation set of the NuScenes dataset. Our findings provide valuable insights for developing more robust and reliable HD map construction models, advancing their applicability in real-world autonomous driving scenarios. Project website: https://robomap-123.github.io.

Related papers

• Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning [49.10890099624699]
  We introduce a dynamic dataset pruning framework that adaptively selects training samples based on task-driven difficulty and cross-modality semantic consistency.<n>Our work highlights the potential of integrating cross-modality alignment for robust sample selection, advancing data-centric learning toward more efficient and robust practices across application domains.
  arXiv  Detail & Related papers  (2025-07-17T03:08:26Z)
• TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset [90.97440987655084]
  Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources.<n>To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN.<n>This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m2$ and currently 767 GB of data.
  arXiv  Detail & Related papers  (2025-05-12T09:48:32Z)
• LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving [52.83707400688378]
  LargeAD is a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets.<n>Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds to generate high-quality contrastive samples.<n>Our approach delivers significant performance improvements over state-of-the-art methods in both linear probing and fine-tuning tasks for both LiDAR-based segmentation and object detection.
  arXiv  Detail & Related papers  (2025-01-07T18:59:59Z)
• UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised [12.440461420762265]
  Road segmentation is a critical task for autonomous driving systems. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets.
  arXiv  Detail & Related papers  (2024-09-10T03:57:30Z)
• Enhancing Edge Detection by Texture Handling Architecture and Noiseless Training Data [0.0]
  Image edge detection (ED) is a fundamental task in computer vision.<n>We propose a novel ED model incorporating Cascaded Skipping Density Blocks (CSDB) to enhance precision and robustness.<n>Second, we introduce a novel data augmentation strategy that enables the integration of noiseless annotations during training.
  arXiv  Detail & Related papers  (2024-07-29T13:24:55Z)
• Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development [67.55944651679864]
  We present a new sandbox suite tailored for integrated data-model co-development.<n>This sandbox provides a feedback-driven experimental platform, enabling cost-effective and guided refinement of both data and models.
  arXiv  Detail & Related papers  (2024-07-16T14:40:07Z)
• FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection [33.225938984092274]
  We propose a Foreground Self-Distillation (FSD) scheme that effectively avoids the issue of distribution discrepancies. We also design two Point Cloud Intensification ( PCI) strategies to compensate for the sparsity of point clouds. We develop a Multi-Scale Foreground Enhancement (MSFE) module to extract and fuse multi-scale foreground features.
  arXiv  Detail & Related papers  (2024-07-14T09:39:44Z)
• RU-AI: A Large Multimodal Dataset for Machine-Generated Content Detection [11.265512559447986]
  We introduce RU-AI, a new large-scale multimodal dataset for robust and effective detection of machine-generated content in text, image and voice.<n>Our dataset is constructed on the basis of three large publicly available datasets: Flickr8K, COCO and Places205.<n>The results reveal that existing models still struggle to achieve accurate and robust detection on our dataset.
  arXiv  Detail & Related papers  (2024-06-07T12:58:14Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles [9.748996198083425]
  This paper proposes a federated learning based dynamic map fusion framework to achieve high map quality. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform.
  arXiv  Detail & Related papers  (2021-03-05T16:28:46Z)
• Secrets of 3D Implicit Object Shape Reconstruction in the Wild [92.5554695397653]
  Reconstructing high-fidelity 3D objects from sparse, partial observation is crucial for various applications in computer vision, robotics, and graphics. Recent neural implicit modeling methods show promising results on synthetic or dense datasets. But, they perform poorly on real-world data that is sparse and noisy. This paper analyzes the root cause of such deficient performance of a popular neural implicit model.
  arXiv  Detail & Related papers  (2021-01-18T03:24:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.