mlsanityData to 7 Oct 2026

PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

arXiv 2008.01179 ↗First posted 3 Aug 2020cs.CV cs.LG cs.ROcited by 16

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Abstract (arXiv)

In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV flow accurately but also improves tracking performance of both dynamic and static objects.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 1; 2023: 5; 2024: 5; 2025: 1; 2026: 1.

16 papers.

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  1. 2021Scalable Scene Flow from Point Clouds in the Real Worldcited by 49
  2. 2021Self-Supervised Pillar Motion Learning for Autonomous Drivingcited by 44
  3. 2023TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Drivingcited by 30
  4. 2024Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimationcited by 22
  5. 2023360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye Viewcited by 18
  6. 2024Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signalscited by 5
  7. 2021View Birdification in the Crowd: Ground-Plane Localization from Perceived Movementscited by 4
  8. 2022ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric viewcited by 3
  9. 2023ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Cloudscited by 3
  10. 2024Instantaneous Perception of Moving Objects in 3Dcited by 3
  11. 2023Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Priorcited by 2
  12. 2025Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Drivingcited by 2
4 more
  1. 20243D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Datacited by 1
  2. 2024TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learningcited by 1
  3. 2023Pedestrian Environment Model for Automated Drivingcited by 0
  4. 2026Synthetic-to-Real Translation for Class-Agnostic Motion Predictioncited by 0