Multi-data Scene Flow Challenge

Leaderboard

Scene flow on Argoverse 2, Waymo, TruckScenes, nuScenes and Aeva, ranked by mean Dynamic (lower is better). Challenge details

Track: supervised (clear filter)
# Method mean Dynamic [0-70] mean Dynamic [0-35] mean Dynamic [35-70] AV2 [0-70]Waymo [0-70]TruckScenes [0-70]NuScenes [0-70]Aeva [0-70] Date
1 SynFlow-4k w real-data 0.2200 0.1602 0.2799 0.1151 0.1258 0.2775 0.3125 0.2692 2026-04-16
2 UniFlow-deltaflow 0.2421 0.1740 0.3103 0.1242 0.1397 0.2997 0.3613 0.2856 2026-04-06
3 UniFlow-flow4d 0.2644 0.1955 0.3333 0.1496 0.1608 0.3138 0.3618 0.3358 2026-04-06
4 DeltaFlow-av2 0.2688 0.1886 0.3491 0.1232 0.1444 0.3504 0.4184 0.3076 2026-04-06
5 UniFlow-ssf 0.2968 0.2177 0.3760 0.1754 0.1867 0.3186 0.3881 0.4153 2026-04-06
6 SSF-long-av2 0.4691 0.4133 0.5248 0.2032 0.2518 0.6205 0.6519 0.6179 2026-04-06
Not evaluated at 35–70 m · 1 entry. The team chose to evaluate only 0–35 m (e.g. the model crops the point cloud at 51.2 m), so it is not compared here. See the 0–35 m ranking.
– UncertaintyFlow — 2.4297 not evaluated not evaluated at 35–70 m (team's choice) 2026-06-01

Click a label such as supervised, unsupervised or multi-dataset to filter by it. Bold green marks the best value in each column. Dynamic metrics are speed-normalized EPE (unitless); Static and Threeway metrics are EPE in meters. Every [0-70] value is the mean of the 0–35 m and 35–70 m values (per dataset in the dataset columns); full tables are under “+ details”.