Papers › Fast Neural Scene Flow
Fast Neural Scene Flow
Xueqian Li, Jianqiao Zheng, Francesco Ferroni, Jhony Kaesemodel Pontes, Simon Lucey
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points. The approach utilizes a coordinate neural network to estimate scene flow at runtime, without any training. However, it is up to 100 times slower than current state-of-the-art learning methods. In other applications such as image, video, and radiance function reconstruction innovations in speeding up the runtime performance of coordinate networks have centered upon architectural changes. In this paper, we demonstrate that scene flow is different -- with the dominant computational bottleneck stemming from the loss function itself (i.e., Chamfer distance). Further, we rediscover the distance transform (DT) as an efficient, correspondence-free loss function that dramatically speeds up the runtime optimization. Our fast neural scene flow (FNSF) approach reports for the first time real-time performance comparable to learning methods, without any training or OOD bias on two of the largest open autonomous driving (AV) lidar datasets Waymo Open and Argoverse.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Flow Estimation | Argoverse 2 | FastNSF | EPE 3-Way | 0.111820 | #7 of 7 | Archive leaderboard | report |
| Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Background Static | 0.090712 | #7 of 7 | Archive leaderboard | report |
| Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Foreground Dynamic | 0.163388 | #7 of 7 | Archive leaderboard | report |
| Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Foreground Static | 0.081360 | #7 of 7 | Archive leaderboard | report |
| Self-supervised Scene Flow Estimation | Argoverse 2 | FastNSF | EPE 3-Way | 0.111820 | #6 of 6 | Archive leaderboard | report |
| Self-supervised Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Background Static | 0.090712 | #6 of 6 | Archive leaderboard | report |
| Self-supervised Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Foreground Dynamic | 0.115796 | #6 of 6 | Archive leaderboard | report |
| Self-supervised Scene Flow Estimation | Argoverse 2 | FastNSF | EPE Foreground Static | 0.031576 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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