LiDAR-based 3D object detection has become an essential part of automated
driving due to its ability to localize and classify objects precisely in 3D.
However, object detectors face a critical challenge when dealing with unknown
foreground objects, particularly those that were not present in their original
training data. These out-of-distribution (OOD) objects can lead to
misclassifications, posing a significant risk to the safety and reliability of
automated vehicles. Currently, LiDAR-based OOD object detection has not been
well studied. We address this problem by generating synthetic training data for
OOD objects by perturbing known object categories. Our idea is that these
synthetic OOD objects produce different responses in the feature map of an
object detector compared to in-distribution (ID) objects. We then extract
features using a pre-trained and fixed object detector and train a simple
multilayer perceptron (MLP) to classify each detection as either ID or OOD. In
addition, we propose a new evaluation protocol that allows the use of existing
datasets without modifying the point cloud, ensuring a more authentic
evaluation of real-world scenarios. The effectiveness of our method is
validated through experiments on the newly proposed nuScenes OOD benchmark. The
source code is available at this https URL

利用合成的离群对象数据生成特征图以对未知物体进行检测并使用多层感知器分类，从而有效处理自动驾驶中的风险和可靠性问题。

重温基于 LiDAR 的 3D 物体检测中的超出分布检测

Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object  Detection

Semantic segmentation models, while effective for in-distribution categories,
face challenges in real-world deployment due to encountering
out-of-distribution (OoD) objects. Detecting these OoD objects is crucial for
safety-critical applications. Existing methods rely on anomaly scores, but
choosing a suitable threshold for generating masks presents difficulties and
can lead to fragmentation and inaccuracy. This paper introduces a method to
convert anomaly Score To segmentation Mask, called S2M, a simple and effective
framework for OoD detection in semantic segmentation. Unlike assigning anomaly
scores to pixels, S2M directly segments the entire OoD object. By transforming
anomaly scores into prompts for a promptable segmentation model, S2M eliminates
the need for threshold selection. Extensive experiments demonstrate that S2M
outperforms the state-of-the-art by approximately 10\% in IoU and 30\% in mean
F1 score, on average, across various benchmarks including Fishyscapes,
Segment-Me-If-You-Can, and RoadAnomaly datasets.

提出一种名为 S2M 的方法，将异常得分转换为分割掩码，用于语义分割中的 OoD 检测，通过将异常得分转化为提示来消除阈值选择的需求，实验证明 S2M 在各种基准测试中的 IoU 平均值上超过了现有技术约 10％，F1 平均值上超过了现有技术约 30％。

对每个非分布对象进行分割

Segment Every Out-of-Distribution Object

Classical close-set semantic segmentation networks have limited ability to
detect out-of-distribution (OOD) objects, which is important for
safety-critical applications such as autonomous driving. Incrementally learning
these OOD objects with few annotations is an ideal way to enlarge the knowledge
base of the deep learning models. In this paper, we propose an open world
semantic segmentation system that includes two modules: (1) an open-set
semantic segmentation module to detect both in-distribution and OOD objects.
(2) an incremental few-shot learning module to gradually incorporate those OOD
objects into its existing knowledge base. This open world semantic segmentation
system behaves like a human being, which is able to identify OOD objects and
gradually learn them with corresponding supervision. We adopt the Deep Metric
Learning Network (DMLNet) with contrastive clustering to implement open-set
semantic segmentation. Compared to other open-set semantic segmentation
methods, our DMLNet achieves state-of-the-art performance on three challenging
open-set semantic segmentation datasets without using additional data or
generative models. On this basis, two incremental few-shot learning methods are
further proposed to progressively improve the DMLNet with the annotations of
OOD objects.

本文提出了一种开放领域语义分割系统，包括两个模块：一个开放集语义分割模块来检测分布内及分布外的目标对象，和一个少样本学习模块来逐渐将这些边缘领域对象纳入已有知识库。我们采用深度度量学习网络 (DMLNet) 与对比聚类来实现开放集语义分割。相比于其他开放集语义分割方法，在三个具有挑战性的数据集上表现出最先进的性能。此外，本文还提出了两个增量少样本学习方法来逐步提高 DMLNet 的性能。