Papers › LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
Tak-Wai Hui, Xiaoou Tang, Chen Change Loy
FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper we present an alternative network that outperforms FlowNet2 on the challenging Sintel final pass and KITTI benchmarks, while being 30 times smaller in the model size and 1.36 times faster in the running speed. This is made possible by drilling down to architectural details that might have been missed in the current frameworks: (1) We present a more effective flow inference approach at each pyramid level through a lightweight cascaded network. It not only improves flow estimation accuracy through early correction, but also permits seamless incorporation of descriptor matching in our network. (2) We present a novel flow regularization layer to ameliorate the issue of outliers and vague flow boundaries by using a feature-driven local convolution. (3) Our network owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2. Our code and trained models are available at https://github.com/twhui/LiteFlowNet .
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Optical Flow Estimation | KITTI 2012 | LiteFlowNet-ft | Average End-Point Error | 1.6 | #11 of 12 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 | LiteFlowNet-ft | Fl-all | 9.38 | #15 of 18 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-clean | LiteFlowNet-ft | Average End-Point Error | 4.54 | #26 of 29 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel-final | LiteFlowNet-ft | Average End-Point Error | 5.38 | #25 of 28 | 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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