A type of potential-based recurrent neural networks implemented with PyTorch
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Updated
Feb 25, 2023 - Python
A type of potential-based recurrent neural networks implemented with PyTorch
Fork of the most popular PyTorch implementation of NeRF (Neural Radiance Fields), extended to support additional datasets such as DTU.
Official Code for "Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields"
Neural Fields for Sea Surface Height Interpolation.
Learning neural fields that produce Gaussian-smoothed versions of the original signal. (SIGGRAPH 2024)
Rendering code for Megascans from "EpiGRAF: Rethinking training of 3D GANs" [NeurIPS 2022]
Official source code for "Latent Field Discovery in Interacting Dynamical Systems with Neural Fields". In NeurIPS 2023.
[CVPR'24] MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video
Implementation of "Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel"
Code for "Accurate Differential Operators for Hybrid Neural Fields"
[CVPR'24] NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes (CoRL 2022)
Implementation Tutorial for Neural Radiosity [Hadadan et al. 2021] in Mitsuba 3
Github Page for the 3D Deep Learning Reading Group
Official implementation of the ICASSP 2023 paper "HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields"
[NeurIPS'23] Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors
Polynomial Neural Fields for Subband Decomposition and Manipulation
Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning
Experiments of coordinate MLPs
The official implementation for NeurIPS 2022 Spotlight Neural Shape Deformation Priors
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