The KITTI Vision Benchmark Suite

As a fundamental vision task, stereo matching has
made remarkable progress. While recent iterative
optimization-based methods have achieved promising
performance, their feature extraction capabilities
still have room for improvement. Inspired by the
ability of vision foundation models (VFMs) to
extract general representations, in this work,
we propose AIO-Stereo which can flexibly select
and transfer knowledge from multiple heterogeneous
VFMs to a single stereo matching model. To better
reconcile features between heterogeneous VFMs and
the stereo matching model and fully exploit prior
knowledge from VFMs, we proposed a dual-level
feature utilization mechanism that aligns
heterogeneous features and transfers multi-level
knowledge. Based on the mechanism, a dual-level
selective knowledge transfer module is designed to
selectively transfer knowledge and integrate the
advantages of multiple VFMs. Experimental results
show that AIO-Stereo achieves start-of-the-art
performance.

lr=1e-4

@article{zhou2024all,
title={All-in-One: Transferring Vision
Foundation Models into Stereo Matching},
author={Zhou, Jingyi and Zhang, Haoyu and Yuan,
Jiakang and Ye, Peng and Chen, Tao and Jiang, Hao
and Chen, Meiya and Zhang, Yangyang},
journal={arXiv preprint arXiv:2412.09912},
year={2024}
}