提出两阶段方法，提升激光雷达中未知物体的检测鲁棒性。
HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data
- 采用两阶段框架，先定位后分类，增强对未知物体的识别能力。
- 在标准评估下，顶5自标注策略优于简单缩放法，提升未知物检测率。
- 揭示超参数对评估结果的关键影响，适合自动驾驶感知研究者参考。
自动驾驶系统依赖于激光雷达数据中的精确3D目标检测，但现有检测器通常仅针对预定义类别，易受分布外（OOD）物体影响。本文提出HD-OOD3D，一种新型两阶段方法，用于检测未知物体。实验表明，相比单阶段方法，两阶段方案在未知物体检测上更具鲁棒性，并有效应对评估协议中的关键挑战。我们深入分析了标准OOD检测评估协议，发现超参数选择具有决定性影响。为解决未知物体学习的可扩展性问题，探索了无监督训练策略以生成伪标签。在多种方法中，顶5自动标注表现更优，优于简单的图像缩放技术。
原文摘要 · Abstract (English)
Autonomous systems rely on accurate 3D object detection from LiDAR data, yet most detectors are limited to a predefined set of known classes, making them vulnerable to unexpected out-of-distribution (OOD) objects. In this work, we present HD-OOD3D, a novel two-stage method for detecting unknown objects. We demonstrate the superiority of two-stage approaches over single-stage methods, achieving more robust detection of unknown objects while addressing key challenges in the evaluation protocol. Furthermore, we conduct an in-depth analysis of the standard evaluation protocol for OOD detection, revealing the critical impact of hyperparameter choices. To address the challenge of scaling the learning of unknown objects, we explore unsupervised training strategies to generate pseudo-labels for unknowns. Among the different approaches evaluated, our experiments show that top-5 auto-labelling offers more promising performance compared to simple resizing techniques.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。