Search Results for author:
Found 12 papers, 2 papers with code
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
Digital twin is a problem of augmenting real objects with their digital counterparts.
Mutual Scene Synthesis for Mixed Reality Telepresence
Remote telepresence via next-generation mixed reality platforms can provide higher levels of immersion for computer-mediated communications, allowing participants to engage in a wide spectrum of activities, previously not possible in 2D screen-based communication methods.
Contextual Scene Augmentation and Synthesis via GSACNet
Indoor scene augmentation has become an emerging topic in the field of computer vision and graphics with applications in augmented and virtual reality.
GenScan: A Generative Method for Populating Parametric 3D Scan Datasets
To address this challenge, we introduce GenScan, a generative system that populates synthetic 3D scan datasets in a parametric fashion.
SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors
Spatial computing experiences are constrained by the real-world surroundings of the user.
Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, retrieval, and completion.
Optimization and Manipulation of Contextual Mutual Spaces for Multi-User Virtual and Augmented Reality Interaction
This limitation is elevated in remote multi-user interaction scenarios, where finding a common virtual ground physically accessible for all participants becomes challenging.
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class.
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
Compressive Shift Retrieval
The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
