Core Concepts
Developing a learning-free LiDAR scene flow estimator incorporating rigid-motion assumption and achieving top performance.
Abstract
Introduction
Motion is crucial for visual perception in autonomous vehicles.
Scene flow estimation calculates point-wise motion from two LiDAR scans.
Method
Utilizes Iterative Closest Point (ICP) algorithm for aligning objects over time.
Incorporates rigid-motion assumption and histogram-based initialization to aid ICP.
Experiments
Outperforms state-of-the-art baselines on Waymo, Argoverse-v2, and nuScenes datasets.
Achieves real-time inference with high-quality pseudo labels for neural network training.
Limitations
Can fail in cases of over/under segmentation or multiple similar objects nearby.
Conclusion
Proposes a learning-free framework for scene flow estimation in autonomous driving, showing competitive results across datasets.
Stats
We propose ICP-Flow, a learning-free flow estimator that outperforms state-of-the-art baselines on the Waymo dataset and performs competitively on Argoverse-v2 and nuScenes.
Our model achieves top performance among all models capable of real-time inference.
Quotes
"We propose ICP-Flow, a learning-free flow estimator."
"Our model builds on top of the Iterative Closest Point (ICP) algorithm."