Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching
Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching. However, these methods struggle to simultaneously capture high-frequency information in edges and low-frequency information in smooth regions due to the fixed receptive field. As a result, they tend to lose details, blur edges, and produce false matches in textureless areas. In this paper, we propose Selective Recurrent Unit (SRU), a novel iterative update operator for stereo matching. The SRU module can adaptively fuse hidden disparity information at multiple frequencies for edge and smooth regions. To perform adaptive fusion, we introduce a new Contextual Spatial Attention (CSA) module to generate attention maps as fusion weights. The SRU empowers the network to aggregate hidden disparity information across multiple frequencies, mitigating the risk of vital hidden disparity information loss during iterative processes. To verify SRU's universality, we apply it to representative iterative stereo matching methods, collectively referred to as Selective-Stereo. Our Selective-Stereo ranks $1^{st}$ on KITTI 2012, KITTI 2015, ETH3D, and Middlebury leaderboards among all published methods. Code is available at https://github.com/Windsrain/Selective-Stereo.
Code (1)
Tasks
Stereo MatchingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning Accurate and Enriched Features for Stereo Image Super-Resolution
Stereo image super-resolution (stereoSR) aims to enhance the quality of super-resolution results by incorporating complementary information from an alternative view. Although current methods have shown significant advanc…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionMAFNet:Multi-frequency Adaptive Fusion Network for Real-time Stereo Matching
Existing stereo matching networks typically rely on either cost-volume construction based on 3D convolutions or deformation methods based on iterative optimization. The former incurs significant computational overhead du…
Disparity EstimationWireless Communications in Doubly Selective Channels with Domain Adaptivity
Wireless communications are significantly impacted by the propagation environment, particularly in doubly selective channels with variations in both time and frequency domains. Orthogonal Time Frequency Space (OTFS) modu…
StereoGen: High-quality Stereo Image Generation from a Single Image
State-of-the-art supervised stereo matching methods have achieved amazing results on various benchmarks. However, these data-driven methods suffer from generalization to real-world scenarios due to the lack of real-world…
Depth EstimationImage GenerationMonocular Depth EstimationStereo Matching+1Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due to domain shifts and imbalanced disparity…
Inductive BiasMixture-of-ExpertsStereo Matching