Search Results for author:
Found 10 papers, 4 papers with code
DA-STC: Domain Adaptive Video Semantic Segmentation via Spatio-Temporal Consistency
To solve the challenge, we propose a novel DA-STC method for domain adaptive video semantic segmentation, which incorporates a bidirectional multi-level spatio-temporal fusion module and a category-aware spatio-temporal feature alignment module to facilitate consistent learning for domain-invariant features.
Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering
In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering.
Disentangling Light Fields for Super-Resolution and Disparity Estimation
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation
By combining the local transformer with the multiscale structure, the network is able to capture long-short range correspondences efficiently and accurately.
Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network
In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques.
Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications
The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling.
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems.
Spatial-Angular Attention Network for Light Field Reconstruction
In this paper, we propose a spatial-angular attention network to perceive correspondences in the light field non-locally, and reconstruction high angular resolution light field in an end-to-end manner.
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction
We then propose a novel network architecture for the LapEPI structure, termed as LapEPI-net.
Light Field Reconstruction Using Deep Convolutional Network on EPI
In this paper, we take advantage of the clear texture structure of the epipolar plane image (EPI) in the light field data and model the problem of light field reconstruction from a sparse set of views as a CNN-based angular detail restoration on EPI.
