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Depth Map Super-Resolution
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Depth map super-resolution is the task of upsampling depth images.
<span style="color:grey; opacity: 0.6">( Image credit: A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Scene Prior Filtering for Depth Super-Resolution
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
DSR-Diff: Depth Map Super-Resolution with Diffusion Model
Cutting-Edge Techniques for Depth Map Super-Resolution
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Guided Depth Map Super-resolution: A Survey
Learning Continuous Depth Representation via Geometric Spatial Aggregator
Deep Attentional Guided Image Filtering
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
Unpaired Depth Super-Resolution in the Wild
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach
PAG-Net: Progressive Attention Guided Depth Super-resolution Network
Deformable Kernel Networks for Joint Image Filtering
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Joint convolutional neural pyramid for depth map super-resolution
ATGV-Net: Accurate Depth Super-Resolution
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.