用语言模型提升激光雷达对未知物体的识别能力，更安全。
ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection
- 将激光雷达物体特征与视觉语言模型对齐，实现零样本检测。
- 在nuScenes OOD基准上表现优异，优于现有方法。
- 适合自动驾驶中应对未见物体的场景，提升系统安全性。
基于激光雷达的3D目标检测在保障自动驾驶系统可靠性和安全性方面至关重要。然而，现有检测器对训练数据中未包含的分布外（OOD）物体常产生过度自信的预测，带来显著安全风险。这是由于这些未见过的物体导致错误判断。为此，我们提出ALOOD（对齐激光雷达表征用于分布外检测），一种新方法，利用视觉语言模型（VLM）的语言表示。通过将目标检测器中的物体特征对齐至VLM的特征空间，可将分布外物体检测视为零样本分类任务。我们在nuScenes OOD基准上展示了具有竞争力的性能，为基于语言表示的激光雷达分布外检测提供了新范式。代码已公开于https://github.com/uulm-mrm/mmood3d。
原文摘要 · Abstract (English)
LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for objects not belonging to known categories, posing significant safety risks. This is caused by so-called out-of-distribution (OOD) objects, which were not part of the training data, resulting in incorrect predictions. To address this challenge, we propose ALOOD (Aligned LiDAR representations for Out-Of-Distribution Detection), a novel approach that incorporates language representations from a vision-language model (VLM). By aligning the object features from the object detector to the feature space of the VLM, we can treat the detection of OOD objects as a zero-shot classification task. We demonstrate competitive performance on the nuScenes OOD benchmark, establishing a novel approach to OOD object detection in LiDAR using language representations. The source code is available at https://github.com/uulm-mrm/mmood3d.
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