[Submitted on 23 Oct 2026]
Title:TinyFlow: Breaking the Warping Bottleneck for Edge Optical Flow
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Abstract:High-performance optical flow networks are difficult to deploy on resource-constrained edge accelerators because their iterative refinement warps features by the current flow, which relies on flow-dependent resampling and data-dependent memory access. We present TinyFlow, a 1.26M-parameter warp-free optical flow network that preserves iterative coarse-to-fine refinement by replacing the warp with a fixed-window correlation over constant offsets, so the whole network compiles as a single static INT8 graph. Quantization-aware fine-tuning, warp-teacher distillation, and model souping reduce the Float32-to-INT8 penalty to below 0.2 end-point error (EPE) on MPI-Sintel. TinyFlow outperforms EdgeFlowNet and NanoFlowNet on Sintel Clean, Sintel Final, and FlyingChairs in both Float32 and INT8, while using 54% fewer parameters than EdgeFlowNet, and our INT8 model achieves 6.29/7.13 EPE on Sintel Clean/Final. In flow-only monocular visual odometry on TartanAir and UZH-FPV, the same INT8 model yields the lowest trajectory error on seven of eleven sequences.
Comments: 9 Pages, 5 Figures, 4 Tables
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.33338 [cs.RO]
(or arXiv:2610.33338v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.33338
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chahat Singh [view email] [v1] Fri, 23 Oct 2026 22:21:29 UTC (20,640 KB)
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