Cross-scene generalizable NeRF models, which can directly synthesize nov...
An increasing number of researchers are finding use for nth-order gradie...
The accurate estimation of six degrees-of-freedom (6DoF) object poses is...
Computer vision researchers are embracing two promising paradigms: Visio...
Despite the significant progress in six degrees-of-freedom (6DoF) object...
Learning efficient and interpretable policies has been a challenging tas...
Recent advances in neural rendering imply a future of widespread visual ...
Virtual reality and augmented reality (XR) bring increasing demand for 3...
Multi-task learning (MTL) encapsulates multiple learned tasks in a singl...
Implicit Neural Representations (INRs) encoding continuous multi-media d...
In this paper, we propose a data-model-hardware tri-design framework for...
Neural volumetric representations have shown the potential that Multi-la...
Vision Transformers (ViTs) have proven to be effective, in solving 2D im...
Representing visual signals by coordinate-based deep fully-connected net...
Neural Radiance Field (NeRF) regresses a neural parameterized scene by
d...
Representing visual signals by implicit representation (e.g., a coordina...
Despite the rapid development of Neural Radiance Field (NeRF), the neces...
Access to large and diverse computer-aided design (CAD) drawings is crit...
Deep learning based 3D shape generation methods generally utilize latent...
The deep multi-view stereo (MVS) and stereo matching approaches generall...
The need for fast acquisition and automatic analysis of MRI data is grow...
Single image rain streaks removal is extremely important since rainy ima...
In multi-contrast magnetic resonance imaging (MRI), compressed sensing t...
Compressed sensing MRI is a classic inverse problem in the field of
comp...
Compressed sensing (CS) theory assures us that we can accurately reconst...
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits imag...