Search Results for author:
Found 8 papers, 3 papers with code
Deep Surface Normal Estimation on the 2-Sphere with Confidence Guided Semantic Attention
We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with a low-quality depth channel.
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks
Specifically, based on a spectral analysis of multilayer GCN output, we derive a spectrum prior for the graph Laplacian matrix $\mathbf{L}$ to robustify the model expressiveness against over-smoothing.
Towards Geometry Guided Neural Relighting with Flash Photography
By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map.
A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications
Spoken language understanding (SLU) acts as a critical component in goal-oriented dialog systems.
Deep Surface Normal Estimation with Hierarchical RGB-D Fusion
The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding.
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising.
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.
