外观
论文arXiv 2610.1933014 天前
NavAble：用于盲人导航的大规模数据集与合成数据生成流水线
原文链接：NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation（arXiv 2610.19330 · 发布日期：2026-10-14）
NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation
NavAble面向BLV导航中无障碍关键物体识别数据稀缺问题，结合真实数据与合成流水线，并在分割模型和助行机器人自视角数据上验证。
核心要点
- 现有大规模视觉数据集缺少无障碍关键物体，相关数据集也存在类别覆盖有限、标注不一致和多样性不足。
- NavAble覆盖11类无障碍关键物体，结合41K开源图像与8K新采集、密集标注的真实图像。
- 合成流水线利用3D资产在37个环境中渲染452K帧，并结合网页抓取、VLM过滤和SAM 3D资产生成降低人工成本。
- 实验显示微调分割模型在留出测试集上超过80% mIoU，合成增强较仅真实数据最高提升3.5 mIoU；数据集和流水线已公开。
验证范围与局限：验证范围包括留出测试集，以及来自两台移动辅助机器人的自视角真实导航数据。
英文摘要 (Abstract)
Reliable recognition of accessibility-critical objects (e.g., audible pedestrian signals, door-activation buttons, and handrails) is essential for assistive navigation technologies that support safe, independent mobility for blind and low-vision (BLV) users. Yet these categories are underrepresented in existing large-scale vision datasets, and the few datasets that include them suffer from limited class coverage, inconsistent annotations, and poor diversity. We introduce NavAble, a large-scale dataset spanning 11 accessibility-critical object classes, combining 41K curated open-source images with 8K newly collected, densely annotated real-world images. To extend scale and diversity, we develop a synthetic data generation pipeline that renders high-fidelity 3D assets across 37 environments under diverse styles and viewpoints, yielding 452K frames with rich ground-truth annotations from 565 assets. The pipeline minimizes manual effort by combining automated web image crawling, VLM-based filtering, SAM 3D-based asset generation, and convenient camera trajectory configuration, enabling large-scale data generation at a diversity unattainable through manual collection. Experiments show that fine-tuning existing segmentation models on the NavAble Dataset achieves reliable accessibility-object segmentation (>80% mIoU on our held-out test set). We also demonstrate the effectiveness of synthetic data augmentation, which yields up to +3.5 mIoU over real-world-only fine-tuning. Beyond benchmark evaluation, validation on egocentric data from two mobility-assistive robots demonstrates improved perception reliability in real-world navigation scenarios. Together, NavAble establishes a scalable foundation for BLV navigation perception, directly addressing the data scarcity and embodiment gaps. The dataset and pipeline are publicly available at https://huggingface.co/datasets/NavAble/NeurIPS_2026_BLV.
作者：Hochul Hwang, Jahir Sadik Monon, Soowan Yang, Shiven Umeshbhai Patel, Anh Nhat Hong Nguyen, Kien Nguyen, Keshav Garg, Dylan Gage, Duretti Hordofaa, Anshu Anjna
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