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

arXiv: https://arxiv.org/abs/2008.01179 · HTML: https://mlsanity.com/paper/2008.01179/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Scalable Scene Flow from Point Clouds in the Real World](https://mlsanity.com/paper/2103.01306/) (2021)
- [Self-Supervised Pillar Motion Learning for Autonomous Driving](https://mlsanity.com/paper/2104.08683/) (2021)
- [TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving](https://mlsanity.com/paper/2303.09998/) (2023)
- [Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation](https://mlsanity.com/paper/2407.07995/) (2024)
- [360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View](https://mlsanity.com/paper/2303.11910/) (2023)
- [Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals](https://mlsanity.com/paper/2401.11499/) (2024)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view](https://mlsanity.com/paper/2210.06332/) (2022)
- [ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds](https://mlsanity.com/paper/2304.12589/) (2023)
- [Instantaneous Perception of Moving Objects in 3D](https://mlsanity.com/paper/2405.02781/) (2024)
- [Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior](https://mlsanity.com/paper/2310.11284/) (2023)
- [Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving](https://mlsanity.com/paper/2509.13116/) (2025)
- [3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data](https://mlsanity.com/paper/2401.06946/) (2024)
- [TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning](https://mlsanity.com/paper/2412.16229/) (2024)
- [Pedestrian Environment Model for Automated Driving](https://mlsanity.com/paper/2308.09080/) (2023)
- [Synthetic-to-Real Translation for Class-Agnostic Motion Prediction](https://mlsanity.com/paper/2607.06319/) (2026)

## 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.