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Found 16 papers, 4 papers with code
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Our experiments show 4x the performance of RegNeRF and 8x that of FreeNeRF on average F1@2cm for ETH3D MVS benchmark, suggesting a fruitful research direction to improve the geometric accuracy of NeRF-based models, and sheds light on a potential future approach to enable NeRF-based optimization to eventually outperform traditional MVS.
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
There has been growing interest in models that learn the operator from the parameters of a partial differential equation (PDE) to the corresponding solutions.
Region-Based Representations Revisited
We investigate whether region-based representations are effective for recognition.
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
This study proposes HyperDeepONet, which uses the expressive power of the hypernetwork to enable the learning of a complex operator with a smaller set of parameters.
Finite Element Operator Network for Solving Parametric PDEs
Partial differential equations (PDEs) underlie our understanding and prediction of natural phenomena across numerous fields, including physics, engineering, and finance.
QFF: Quantized Fourier Features for Neural Field Representations
Instead, we propose to encode features in bins of Fourier features that are commonly used for positional encoding.
Sparse SPN: Depth Completion from Sparse Keypoints
Our long term goal is to use image-based depth completion to quickly create 3D models from sparse point clouds, e. g. from SfM or SLAM.
Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling
Recent work in multi-view stereo (MVS) combines learnable photometric scores and regularization with PatchMatch-based optimization to achieve robust pixelwise estimates of depth, normals, and visibility.
opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation
We propose a hybrid framework opPINN: physics-informed neural network (PINN) with operator learning for approximating the solution to the Fokker-Planck-Landau (FPL) equation.
Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
We combine the PDIO with the neural operator to develop a \textit{pseudo-differential neural operator} (PDNO) and learn the nonlinear solution operator of PDEs.
DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
DIVeR's representation is a voxel based field of features.
Solving PDE-constrained Control Problems Using Operator Learning
Once the surrogate model is trained in Phase 1, the optimal control can be inferred in Phase 2 without intensive computations.
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals.
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence
We address estimating dense correspondences between two images depicting different but semantically related scenes.
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach
The model reduction of a mesoscopic kinetic dynamics to a macroscopic continuum dynamics has been one of the fundamental questions in mathematical physics since Hilbert's time.
Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach
The issue of the relaxation to equilibrium has been at the core of the kinetic theory of rarefied gas dynamics.
