LS-VOS：利用潜在空间虚拟离群点合成识别3D目标检测中的离群值
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
AI总结：
针对自动驾驶中基于LiDAR的3D目标检测器易产生误检的问题，提出LS-VOS框架，通过自编码器潜在空间生成可调节相似度的虚拟离群点优化决策边界，在保持检测性能的同时提升离群点识别能力。
摘要：
基于激光雷达（LiDAR）的3D目标检测器已在自动驾驶应用中实现了前所未有的速度与精度。然而，与其他神经网络类似，这类检测器往往偏向于输出高置信度预测，或是在不存在真实目标的位置返回检测结果。此类检测会降低环境感知的可靠性，严重影响自动驾驶车辆的功能与安全性。针对这一问题，我们提出了LS-VOS——一种用于识别3D目标检测中离群值的框架。我们的方法基于虚拟离群点合成（Virtual Outlier Synthesis, VOS）的思路，在训练过程中引入离群点知识，使模型能够学习到更紧凑的决策边界。具体而言，我们提出了一种新的合成方法，该方法依托自编码器网络的潜在空间，生成与分布内特征相似度可参数化调节的离群点特征。在大量实验中，我们证明该方法在保持高水平3D目标检测性能的同时，提升了当前最优目标检测器的离群点检测能力。
英文摘要：
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications. However, similar to other neural networks, they are often biased toward high-confidence predictions or return detections where no real object is present. These types of detections can lead to a less reliable environment perception, severely affecting the functionality and safety of autonomous vehicles. We address this problem by proposing LS-VOS, a framework for identifying outliers in 3D object detections. Our approach builds on the idea of Virtual Outlier Synthesis (VOS), which incorporates outlier knowledge during training, enabling the model to learn more compact decision boundaries. In particular, we propose a new synthesis approach that relies on the latent space of an auto-encoder network to generate outlier features with a parametrizable degree of similarity to in-distribution features. In extensive experiments, we show that our approach improves the outlier detection capabilities of a state-of-the-art object detector while maintaining high 3D object detection performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
