32 citations · 157 across the 45 of their papers we have counts for
12 papers · 1 filter
NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
Dejia Xu, Yifan Jiang, Peihao Wang +3
Virtual reality and augmented reality (XR) bring increasing demand for 3D content. However, creating high-quality 3D content requires tedious work that a human expert must do. In t…
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
Ajay Jaiswal, Peihao Wang, Tianlong Chen +3
Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging probl…
NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes
Zhiwen Fan, Peihao Wang, Yifan Jiang +3
Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and ap…
Signal Processing for Implicit Neural Representations
Dejia Xu, Peihao Wang, Yifan Jiang +2
Implicit Neural Representations (INRs) encoding continuous multi-media data via multi-layer perceptrons has shown undebatable promise in various computer vision tasks. Despite many…
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Keyu Duan, Zirui Liu, Peihao Wang +5
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs). Due to the nature of evolving graph structures into the training process, vanilla…
Neural Implicit Dictionary via Mixture-of-Expert Training
Peihao Wang, Zhiwen Fan, Tianlong Chen +1
Representing visual signals by coordinate-based deep fully-connected networks has been shown advantageous in fitting complex details and solving inverse problems than discrete grid…
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations
Tianlong Chen, Peihao Wang, Zhiwen Fan +1
Neural Radiance Field (NeRF) regresses a neural parameterized scene by differentially rendering multi-view images with ground-truth supervision. However, when interpolating novel v…
Is Attention All That NeRF Needs?
Mukund Varma T, Peihao Wang, Xuxi Chen +3
We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to renders novel views on the fly from…
Equivariant Hypergraph Diffusion Neural Operators
Peihao Wang, Shenghao Yang, Yunyu Liu +2
Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction…
Unified Implicit Neural Stylization
Zhiwen Fan, Yifan Jiang, Peihao Wang +3
Representing visual signals by implicit representation (e.g., a coordinate based deep network) has prevailed among many vision tasks. This work explores a new intriguing direction:…
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image
Dejia Xu, Yifan Jiang, Peihao Wang +3
Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. While several recent works have attempted to…
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice
Peihao Wang, Wenqing Zheng, Tianlong Chen +1
Vision Transformer (ViT) has recently demonstrated promise in computer vision problems. However, unlike Convolutional Neural Networks (CNN), it is known that the performance of ViT…