Depth Map Super-Resolution
10 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
Latest papers with no code
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages.
Scene Prior Filtering for Depth Map Super-Resolution
Specifically, we design an All-in-one Prior Propagation that computes the similarity between multi-modal scene priors, i. e., RGB, normal, semantic, and depth, to reduce the texture interference.
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
Currently, joint image filtering-inspired deep learning-based methods represent the state-of-the-art for GIR tasks.
DSR-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 reality, and augmented reality.
Cutting-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.
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features.
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM).
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
The other is the content guidance bridge (CGBdg) designed for the depth map reconstruction process, which provides the content guidance learned from DSR task for MDE task.
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
Next, we propose a step-wise fusion strategy to restore the HR depth map.
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss.
