Depth Map Super-Resolution

5 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 )

Most implemented papers

Deformable Kernel Networks for Joint Image Filtering

jun0kim/dkn 17 Oct 2019

Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result.

Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

Sangluisme/MultiviewLightEnhancedDepthSR 13 Dec 2019

A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented.

Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

kingcong/FDSR CVPR 2021

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks.

Discrete Cosine Transform Network for Guided Depth Map Super-Resolution

zhaozixiang1228/gdsr-dctnet CVPR 2022

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 of HR RGB images of the same scene.

Deep Attentional Guided Image Filtering

zhwzhong/dagf 13 Dec 2021

Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.