mlsanityData to 7 Oct 2026

Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case

arXiv 2503.03548 ↗First posted 5 Mar 2025cs.CV cs.LG cs.SYcited by 1

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Abstract (arXiv)

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modelling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Learning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 5 Mar 2025.

Builds on

18 of 53 references.

References · 18

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  7. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  8. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  9. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  10. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  11. 2019MVX-Net: Multimodal VoxelNet for 3D Object Detectioncited by 162
  12. 2022PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDARcited by 25
6 more
  1. 2022PCGen: Point Cloud Generator for LiDAR Simulationcited by 20
  2. 2023InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representationcited by 11
  3. 2023Deep Learning Safety Concerns in Automated Driving Perceptioncited by 10
  4. 2022PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarioscited by 8
  5. 2022PSA-Det3D: Pillar Set Abstraction for 3D object Detectioncited by 1
  6. 20233D Object Detection in LiDAR Point Clouds using Graph Neural Networkscited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025DriveSOTIF: Advancing Perception SOTIF Through Multimodal Large Language Modelscited by 4