mlsanityData to 14 Oct 2026

Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception

arXiv 2309.05380 ↗First posted 11 Sep 2023cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Comprehensive perception of the environment is crucial for the safe operation of autonomous vehicles. However, the perception capabilities of autonomous vehicles are limited due to occlusions, limited sensor ranges, or environmental influences. Collective Perception (CP) aims to mitigate these problems by enabling the exchange of information between vehicles. A major challenge in CP is the fusion of the exchanged information. Due to the enormous bandwidth requirement of early fusion approaches and the interchangeability issues of intermediate fusion approaches, only the late fusion of shared detections is practical. Current late fusion approaches neglect valuable information for local detection, this is why we propose a novel fusion method to fuse the detections of cooperative vehicles within the local LiDAR-based detection pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends the PV-RCNN++ framework to fuse collective detections. Code is available at https://github.com/ekut-es

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 11 Sep 2023.

Builds on

14 of 32 references.

References · 14

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  7. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  8. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  9. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  10. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  11. 2019Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscited by 110
  12. 2021Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Drivingcited by 45
2 more
  1. 2022PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDARcited by 25
  2. 2022Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devicescited by 8

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024End-to-End Autonomous Driving through V2X Cooperationcited by 42
  2. 2023Vehicle-to-Everything Cooperative Perception for Autonomous Drivingcited by 15
  3. 2024SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditionscited by 8
  4. 2025Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspectivecited by 5
  5. 2024MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perceptioncited by 2
  6. 2025DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoisingcited by 0