The KITTI Vision Benchmark Suite

In light of the advancements in transformer
technology, existing research posits the
construction of stereo transformers as a potential
solution to the binocular stereo matching
challenge. However, constrained by the low-rank
bottleneck and quadratic complexity of attention
mechanisms, stereo transformers still fail to
demonstrate sufficient nonlinear expressiveness
within a reasonable inference time. The lack of
focus on key homonymous points renders the
representations of such methods vulnerable to
challenging conditions, including reflections and
weak textures. Furthermore, a slow computing speed
is not conducive to the application. To overcome
these difficulties, we present the Hadamard
Attention Recurrent Stereo Transformer (HART).

use mixed precision & True
batch size used during training & 8
crop size & 320 $\times$ 720
max learning rate & 0.0002
length of training schedule & 200000
recurrent-number during training & 22
Weight decay in optimizer & 0.00001

@article{chen2025hart,
title={Hadamard Attention Recurrent Transformer:
A Strong Baseline for Stereo Matching Transformer},
author={Chen, Ziyang and Zhang, Yongjun and Li,
Wenting and Wang, Bingshu and Wu, Yabo and Zhao,
Yong and Chen, CL},
journal={arXiv preprint arXiv:2501.01023},
year={2025}
}