The KITTI Vision Benchmark Suite

While recent methods leveraging 4D cost volume and
iterative refinement (represented by RAFT) have
achieved state-of-the-art performance, two
critical limitations persist: (i) simplistic
construction method for 4D cost volume fails to
preserve semantic information in images, and (ii)
zero-initialized flow estimation necessitates
excessive iterations (typically 32 iterations). To
address these challenges, we propose AMFlow, a
novel framework that integrates an attention-based
cost volume and matching initialization.
Specifically, AMFlow improves accuracy and
efficiency by refining the initial cost volume and
leveraging high-quality initial flow fields,
achieving competitive or superior results with
only 8 iterations compared to 32 iterations in
most RAFT-style models. Additionally, extensive
experimental results show our method maintains a
balance between accuracy and computational
efficiency regarding both inference time and GPU
memory usage.

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