The KITTI Vision Benchmark Suite

Cross-view matching is fundamentally achieved
through cross-attention mechanisms. However,
matching of high-resolution images remains
challenging due to the quadratic complexity and
lack of explicit matching constraints in the
existing cross-attention. This paper proposes an
attention mechanism, MatchAttention, that
dynamically matches relative positions. The
relative position determines the attention
sampling center of the key-value pairs given a
query. Continuous and differentiable sliding-
window attention sampling is achieved by the
proposed BilinearSoftmax. The relative positions
are iteratively updated through residual
connections across layers by embedding them into
the feature channels. Since the relative position
is exactly the learning target for cross-view
matching, an efficient hierarchical cross-view
decoder, MatchDecoder, is designed with
MatchAttention as its core component. To handle
cross-view occlusions, gated cross-MatchAttention
and a consistency-constrained loss are

MatchStereo-B, 76M parameters

@article{yan2025matchattention,
title={MatchAttention: Matching the Relative
Positions for High-Resolution Cross-View Matching},
author={Tingman Yan and Tao Liu and Xilian Yang
and Qunfei Zhao and Zeyang Xia},
journal={arXiv preprint arXiv:2510.14260},
year={2025}
}