DCVSMNet: Double Cost Volume Stereo Matching Network
We introduce Double Cost Volume Stereo Matching Network(DCVSMNet) which is a novel architecture characterised by by two small upper (group-wise) and lower (norm correlation) cost volumes. Each cost volume is processed separately, and a coupling module is proposed to fuse the geometry information extracted from the upper and lower cost volumes. DCVSMNet is a fast stereo matching network with a 67 ms inference time and strong generalization ability which can produce competitive results compared to state-of-the-art methods. The results on several bench mark datasets show that DCVSMNet achieves better accuracy than methods such as CGI-Stereo and BGNet at the cost of greater inference time.
Code (1)
Tasks
Stereo MatchingSimilar Papers 제목 키워드 기반
Iterative Volume Fusion for Asymmetric Stereo Matching
Stereo matching is vital in 3D computer vision, with most algorithms assuming symmetric visual properties between binocular visions. However, the rise of asymmetric multi-camera systems (e.g., tele-wide cameras) challeng…
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume
Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation. However, generating such cost volumes is computationally-intensive and memory-cons…
Disparity EstimationStereo MatchingDiffuVolume: Diffusion Model for Volume based Stereo Matching
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 i…
modelStereo MatchingZero-shot GeneralizationStereo Matching with Cost Volume based Sparse Disparity Propagation
Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we pr…
Stereo MatchingGhost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks
Depth estimation based on stereo matching is a classic but popular computer vision problem, which has a wide range of real-world applications. Current stereo matching methods generally adopt the deep Siamese neural netwo…
Depth EstimationStereo Matching