Papers Depth Map Super-Resolution
“Depth Map Super-Resolution” 태그가 달린 논문 28편 · 필터 해제
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is gene…
BenchmarkingDepth Map Super-ResolutionSuper-ResolutionInferring 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. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects f…
Depth Map Super-ResolutionSuper-ResolutionPAG-Net: Progressive Attention Guided Depth Super-resolution Network
In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet. It is based on residual dense networks and involves the attention mechanism to suppress the textur…
Depth Map Super-ResolutionSuper-ResolutionDeformable Kernel Networks for Joint Image Filtering
Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing noise. Previous methods based on convol…
Depth Map Super-ResolutionImage RestorationSemantic SegmentationDeeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN based de…
BenchmarkingBlockingDepth Map Super-ResolutionNovel View Synthesis+1Joint convolutional neural pyramid for depth map super-resolution
High-resolution depth map can be inferred from a low-resolution one with the guidance of an additional high-resolution texture map of the same scene. Recently, deep neural networks with large receptive fields are shown t…
Depth Map Super-ResolutionSuper-ResolutionATGV-Net: Accurate Depth Super-Resolution
In this work we present a novel approach for single depth map super-resolution. Modern consumer depth sensors, especially Time-of-Flight sensors, produce dense depth measurements, but are affected by noise and have a low…
Depth Map Super-ResolutionImage Super-ResolutionRolling Shutter CorrectionSuper-ResolutionA Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which i…
Depth Map Super-ResolutionSuper-Resolution