Stereo matching is a significant part in many computer vision tasks and
driving-based applications. Recently cost volume-based methods have achieved
great success benefiting from the rich geometry information in paired images.
However, the redundancy of cost volume also interferes with the model training
and limits the performance. To construct a more precise cost volume, we
pioneeringly apply the diffusion model to stereo matching. Our method, termed
DiffuVolume, considers the diffusion model as a cost volume filter, which will
recurrently remove the redundant information from the cost volume. Two main
designs make our method not trivial. Firstly, to make the diffusion model more
adaptive to stereo matching, we eschew the traditional manner of directly
adding noise into the image but embed the diffusion model into a task-specific
module. In this way, we outperform the traditional diffusion stereo matching
method by 22% EPE improvement and 240 times inference acceleration. Secondly,
DiffuVolume can be easily embedded into any volume-based stereo matching
network with boost performance but slight parameters rise (only 2%). By adding
the DiffuVolume into well-performed methods, we outperform all the published
methods on Scene Flow, KITTI2012, KITTI2015 benchmarks and zero-shot
generalization setting. It is worth mentioning that the proposed model ranks
1st on KITTI 2012 leader board, 2nd on KITTI 2015 leader board since 15, July
2023.

通过将扩散模型应用于立体匹配，本研究提出了一种名为 DiffuVolume 的方法，通过过滤成本体积以提高性能。