NeurIPS 2025

∆Flow: An Efficient Multi-frame Scene Flow
Estimation Method
Qingwen Zhang1,B
Xiaomeng Zhu1,3
Yushan Zhang2,B
Yixi Cai1
Olov Andersson1
Patric Jensfelt1
1KTH Royal Institute of Technology
2Linköping University
3Scania CV AB
qingwen@kth.se, yushan.zhang@liu.se
Abstract
Previous dominant methods for scene flow estimation focus mainly on input from
two consecutive frames, neglecting valuable information in the temporal domain.
While recent trends shift towards multi-frame reasoning, they suffer from rapidly
escalating computational costs as the number of frames grows. To leverage tem-
poral information more efficiently, we propose DeltaFlow (∆Flow), a lightweight
3D framework that captures motion cues via a ∆scheme, extracting temporal
features with minimal computational cost, regardless of the number of frames.
Additionally, scene flow estimation faces challenges such as imbalanced object
class distributions and motion inconsistency. To tackle these issues, we introduce a
Category-Balanced Loss to enhance learning across underrepresented classes and
an Instance Consistency Loss to enforce coherent object motion, improving flow
accuracy. Extensive evaluations on the Argoverse 2, Waymo and nuScenes datasets
show that ∆Flow achieves state-of-the-art performance with up to 22% lower error
and 2× faster inference compared to the next-best multi-frame supervised method,
while also demonstrating a strong cross-domain generalization ability. The code
is open-sourced at https://github.com/Kin-Zhang/DeltaFlow along
with trained model weights.
1
Introduction
Scene flow estimation determines the 3D motion of each point between consecutive point clouds,
making it an important task in computer vision and essential for autonomous driving [31, 3, 43, 41]
and motion compensation [8, 46]. While early approaches focused on per-point feature learning [49,
47, 25, 26, 48, 40] and achieve high accuracy on small-scale datasets, they become computationally
expensive when processing large-sca...