Cross-scene generalizable NeRF models, which can directly synthesize nov...
The accurate estimation of six degrees-of-freedom (6DoF) object poses is...
Despite the significant progress in six degrees-of-freedom (6DoF) object...
Diffusion models are powerful, but they require a lot of time and data t...
Scaling transformers has led to significant breakthroughs in many domain...
Virtual reality and augmented reality (XR) bring increasing demand for 3...
Implicit Neural Representations (INRs) encoding continuous multi-media d...
Despite the enormous success of Graph Convolutional Networks (GCNs) in
m...
Large-scale graph training is a notoriously challenging problem for grap...
Neural volumetric representations have shown the potential that Multi-la...
We present Generalizable NeRF Transformer (GNT), a pure, unified
transfo...
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...
Vision Transformer (ViT) has recently demonstrated promise in computer v...
Multi-agent control is a central theme in the Cyber-Physical Systems (CP...
Graph Convolutional Networks (GCN) with multi-hop aggregation is more
ex...
Training deep graph neural networks (GNNs) is notoriously hard. Besides ...
We present a learning-based scheme for robustly and accurately estimatin...