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arXiv · 2312.03790

Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume

Gangwei Xu·Shujun Chen·Hao Jia·Miaojie Feng·Xin Yang
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Abstract

The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their memory consumption increases quadratically with input resolution, rendering them impractical for high-resolution images. In this paper, we present MeFlow, a novel memory-efficient method for high-resolution optical flow estimation. The key of MeFlow is a recurrent local orthogonal cost volume representation, which decomposes the 2D search space dynamically into two 1D orthogonal spaces, enabling our method to scale effectively to very high-resolution inputs. To preserve essential information in the orthogonal space, we utilize self attention to propagate feature information from the 2D space to the orthogonal space. We further propose a radius-distribution multi-scale lookup strategy to model the correspondences of large displacements at a negligible cost. We verify the efficiency and effectiveness of our method on the challenging Sintel and KITTI benchmarks, and real-world 4K ($2160\!\times\!3840$) images. Our method achieves competitive performance on both Sintel and KITTI benchmarks, while maintaining the highest memory efficiency on high-resolution inputs.

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Gangwei Xu, Shujun Chen, Hao Jia, Miaojie Feng, Xin Yang. 2025-03-04. Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume. https://arxiv.org/abs/2312.03790

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