Papers Depth Map Super-Resolution
“Depth Map Super-Resolution” 태그가 달린 논문 28편 · 필터 해제
Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
Recovering high-quality depth maps from compressed sources has gained significant attention due to the limitations of consumer-grade depth cameras and the bandwidth restrictions during data transmission. However, current…
Depth Map Super-ResolutionSuper-ResolutionCompressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
The increasing demand for augmented reality (AR) and virtual reality (VR) applications highlights the need for efficient depth information processing. Depth maps, essential for rendering realistic scenes and supporting a…
Depth Map Super-ResolutionSuper-ResolutionCoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution
Consumer-grade depth sensors provide low-resolution depth maps; however, a high-resolution RGB camera is usually mounted on the same device and acquires a high-resolution image of the same scene. While deep learning and …
Contrastive LearningDecoderDepth Map Super-ResolutionGenerative Adversarial Network+1Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have…
Depth Map Super-ResolutionSuper-ResolutionScene Prior Filtering for Depth Super-Resolution
Multi-modal fusion is vital to the success of super-resolution of depth maps. However, commonly used fusion strategies, such as addition and concatenation, fall short of effectively bridging the modal gap. As a result, g…
Depth Map Super-ResolutionSuper-ResolutionGuided Image Restoration via Simultaneous Feature and Image Guided Fusion
Guided image restoration (GIR), such as guided depth map super-resolution and pan-sharpening, aims to enhance a target image using guidance information from another image of the same scene. Currently, joint image filteri…
Depth Map Super-ResolutionImage RestorationSuper-ResolutionSGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain …
Depth Map Super-ResolutionSuper-ResolutionDSR-Diff: Depth Map Super-Resolution with Diffusion Model
Color-guided depth map super-resolution (CDSR) improve the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual r…
3D ReconstructionDepth Map Super-ResolutionSuper-ResolutionCutting-Edge Techniques for Depth Map Super-Resolution
To overcome hardware limitations in commercially available depth sensors which result in low-resolution depth maps, depth map super-resolution (DMSR) is a practical and valuable computer vision task. DMSR requires upscal…
Depth Map Super-ResolutionImage RestorationSuper-ResolutionSpherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the s…
Contrastive LearningDepth Map Super-ResolutionSuper-ResolutionGuided Depth Map Super-resolution: A Survey
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental prob…
Depth Image UpsamplingDepth Map Super-ResolutionImage Quality AssessmentSuper-Resolution+1Learning Continuous Depth Representation via Geometric Spatial Aggregator
Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inef…
Depth Map Super-ResolutionSuper-ResolutionDeep Attentional Guided Image Filtering
Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods construct filter kernels from the guida…
Collaborative FilteringDepth Image UpsamplingDepth Map Super-ResolutionContent-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
Convolutional neural networks (CNNs) have been not only widespread but also achieved noticeable results on numerous applications including image classification, restoration, and generation. Although the weight-sharing pr…
Depth Map Super-Resolutionimage-classificationImage ClassificationSemantic Segmentation+1BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an extra branch to extract high-frequency de…
Depth EstimationDepth Map Super-ResolutionMonocular Depth EstimationMulti-Task Learning+1Unpaired Depth Super-Resolution in the Wild
Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely …
Depth Map Super-ResolutionImage-to-Image TranslationSuper-ResolutionTranslationDiscrete Cosine Transform Network for Guided Depth Map Super-Resolution
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 o…
Depth EstimationDepth Map Super-ResolutionSuper-ResolutionTowards 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. Thus, depth map super-resolution (SR) is a practical and valuable task, which…
Depth Map Super-ResolutionSuper-ResolutionHigh-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grade RGB-D cameras suffers from low spatial…
Depth Map Super-ResolutionSuper-ResolutionMulti-Scale Progressive Fusion Learning for Depth Map Super-Resolution
Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR)…
Depth Map Super-ResolutionImage Super-ResolutionSingle Image DerainingSuper-Resolution