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Depth Map Super-Resolution

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 )

Papers

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Showing 1–28 of 28 papers

TitleStatusHype
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-ResolutionCode1
Guided Depth Map Super-resolution: A SurveyCode1
Learning Continuous Depth Representation via Geometric Spatial AggregatorCode1
Deep Attentional Guided Image FilteringCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal FusionCode1
Deformable Kernel Networks for Joint Image FilteringCode1
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-ResolutionCode0
Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
Learning Hierarchical Color Guidance for Depth Map Super-Resolution—0
Scene Prior Filtering for Depth Super-Resolution—0
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion—0
DSR-Diff: Depth Map Super-Resolution with Diffusion ModelCode0
Cutting-Edge Techniques for Depth Map Super-Resolution—0
Spherical Space Feature Decomposition for Guided Depth Map Super-ResolutionCode0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
Unpaired Depth Super-Resolution in the WildCode0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution—0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Joint convolutional neural pyramid for depth map super-resolution—0
ATGV-Net: Accurate Depth Super-Resolution—0
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution—0
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