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Found 9 papers, 2 papers with code
Preconditioners for the Stochastic Training of Implicit Neural Representations
Implicit neural representations have emerged as a powerful technique for encoding complex continuous multidimensional signals as neural networks, enabling a wide range of applications in computer vision, robotics, and geometry.
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks
In this paper, we aim to address this gap by providing a theoretical understanding of periodically activated networks through an analysis of their Neural Tangent Kernel (NTK).
Architectural Strategies for the optimization of Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep learning with fundamental physics principles.
Multi-Body Neural Scene Flow
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 performance.
Curvature-Aware Training for Coordinate Networks
Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities.
On Quantizing Implicit Neural Representations
The role of quantization within implicit/coordinate neural networks is still not fully understood.
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses.
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation.
Quantum Robust Fitting
Many computer vision applications need to recover structure from imperfect measurements of the real world.
