1 citations · 2 across the 5 of their papers we have counts for
5 papers
Invertible Neural Warp for NeRF
Shin-Fang Chng, Ravi Garg, Hemanth Saratchandran +1
This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for ca…
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Recently, neural networks utilizing periodic activation functions have been proven to demonstrate superior performance in vision tasks compared to traditional ReLU-activated networ…
Architectural Strategies for the optimization of Physics-Informed Neural Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep lear…
Preconditioners for the Stochastic Training of Neural Fields
Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey
Neural fields encode continuous multidimensional signals as neural networks, enabling diverse applications in computer vision, robotics, and geometry. While Adam is effective for s…
Multi-Body Neural Scene Flow
Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li +1
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art pe…