Search Results for author:
Found 6 papers, 4 papers with code
Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e. g., bicubic downsampling).
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges.
Scene Prior Filtering for Depth 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.
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning.
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network
Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance.
