LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis

• URL: http://arxiv.org/abs/2310.00952v1
• Date: Mon, 2 Oct 2023 07:44:26 GMT
• Title: LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
• Authors: Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Meissner, Klaus Dietmayer
• Abstract summary: LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications. They are often biased toward high-confidence predictions or return detections where no real object is present. We propose LS-VOS, a framework for identifying outliers in 3D object detections.
• Score: 10.920640666237833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Open-Set 3D object detection in LiDAR data as an Out-of-Distribution problem [6.131026007721572]
  3D Object Detection from LiDAR data has achieved industry-ready performance in controlled environments. Our work redefines the open-set 3D Object Detection problem in LiDAR data as an Out-Of-Distribution (OOD) problem to detect outlier objects.
  arXiv  Detail & Related papers  (2024-10-31T09:29:55Z)
• Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection [12.633311483061647]
  Out-of-distribution (OOD) objects can lead to misclassifications, posing a significant risk to the safety and reliability of automated vehicles. We propose a new evaluation protocol that allows the use of existing datasets without modifying the point cloud. The effectiveness of our method is validated through experiments on the newly proposed nuScenes OOD benchmark.
  arXiv  Detail & Related papers  (2024-04-24T13:48:38Z)
• Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns [12.384452095533396]
  Integrity monitoring of automated driving systems (ADS) is paramount for ensuring safety. Recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors remains a significant concern.
  arXiv  Detail & Related papers  (2024-04-11T12:24:47Z)
• Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments [67.83787474506073]
  We tackle the limitations of current LiDAR-based 3D object detection systems. We introduce a universal textscFind n' Propagate approach for 3D OV tasks. We achieve up to a 3.97-fold increase in Average Precision (AP) for novel object classes.
  arXiv  Detail & Related papers  (2024-03-20T12:51:30Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [70.75100533512021]
  In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. We propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors.
  arXiv  Detail & Related papers  (2022-07-06T06:26:17Z)
• siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection [65.03384167873564]
  A siamese network is integrated into the pipeline of a well-known 3D object detector approach. associations are exploited to enhance the 3D box regression of the object. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.
  arXiv  Detail & Related papers  (2020-02-19T15:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.