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High-speed, high-resolution stereoscopic (H2-Stereo) video allows us toperceive dynamic 3D content at fine granularity. The acquisition of H2-Stereovideo, however, remains challenging with commodity cameras. Existing spatialsuper-resolution or temporal frame interpolation methods provide compromisedsolutions that lack temporal or spatial details, respectively. To alleviatethis problem, we propose a dual camera system, in which one camera captureshigh-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatialdetails, and the other captures low-spatial-resolution high-frame-rate(LSR-HFR) videos with smooth temporal details. We then devise a LearnedInformation Fusion network (LIFnet) that exploits the cross-camera redundanciesto enhance both camera views to high spatiotemporal resolution (HSTR) forreconstructing the H2-Stereo video effectively. We utilize a disparity networkto transfer spatiotemporal information across views even in large disparityscenes, based on which, we propose disparity-guided flow-based warping forLSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusionmethod in feature domain is proposed to minimize occlusion-induced warpingghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end mannerusing our collected high-quality Stereo Video dataset from YouTube. Extensiveexperiments demonstrate that our model outperforms existing state-of-the-artmethods for both views on synthetic data and camera-captured real data withlarge disparity. Ablation studies explore various aspects, includingspatiotemporal resolution, camera baseline, camera desynchronization,long/short exposures and applications, of our system to fully understand itscapability for potential applications.

H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System