Depth Map Super-Resolution
5 papers with code • 0 benchmarks • 2 datasets
Depth map super-resolution is the task of upsampling depth images.
( Image credit: A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution )
Benchmarks
These leaderboards are used to track progress in Depth Map Super-Resolution
Middlebury 2005
RGB-D-D
Most implemented papers
Deformable Kernel Networks for Joint Image Filtering
jun0kim/dkn
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Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result.
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach
A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented.
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks.
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
zhaozixiang1228/gdsr-dctnet
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CVPR 2022
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene.
Deep Attentional Guided Image Filtering
zhwzhong/dagf
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Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.
