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A Deep Primal-Dual Network for Guided Depth Super-Resolution

https://doi.org/10.5244/c.30.7

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Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution

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Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network

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Uncertainty Guided Deep Lucas-Kanade Homography for Multimodal Image Alignment

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Learning Steering Kernels for Guided Depth Completion

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Toward Unaligned Guided Thermal Super-Resolution

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Frequency-Dependent Depth Map Enhancement via Iterative Depth-Guided Affine Transformation and Intensity-Guided Refinement

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CrossHomo: Cross-Modality and Cross-Resolution Homography Estimation

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cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey

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Primal-dual residual networks

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Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution

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