implicit-representions
There are 16 repositories under implicit-representions topic.
autonomousvision/convolutional_occupancy_networks
[ECCV'20] Convolutional Occupancy Networks
autonomousvision/differentiable_volumetric_rendering
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
POSTECH-CVLab/SCNeRF
[ICCV21] Self-Calibrating Neural Radiance Fields
subeeshvasu/Awsome_Deep_Geometry_Learning
A list of resources about deep learning solutions on 3D shape processing
zubair-irshad/shapo
Pytorch code for ECCV'22 paper. ShAPO: Implicit Representations for Multi-Object Shape, Appearance and Pose Optimization
pablopalafox/npms
Official implementation of NPMs: Neural Parametric Models for 3D Deformable Shapes - ICCV 2021
ShivamDuggal4/TARS3D
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction (CVPR 2022)
basilevh/occlusions-4d
Revealing Occlusions with 4D Neural Fields (CVPR 2022 Oral) - Official Implementation
Wuziyi616/IF-Defense
This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
JuliaGeometry/Descartes.jl
Software Defined Solid Modeling
jloveric/high-order-layers-torch
High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected and convolutional layers included.
jloveric/high-order-implicit-representation
Implicit representation of various things using PyTorch and high order layers
benjaminocampo/ISHate
This repository contains the dataset and implementation details of the paper "An In-depth Analysis of Implicit and Subtle Hate Speech Messages" accepted at EACL 2023.
omkarchittar/Implicit_Neural_Representation
With INR, we parameterize some signal (in our case images) with a neural network (in this assignment, we will use a basic feed-forward network).
Yannick-Kees/Shape-space-learning
Implementation of two phase field approaches for the surface reconstruction problem and shape space learning. One based of the Modica-Mortola theorem and the other based on Ambrosio-Tortorelli
ychen921/Implicit-Neural-Representation
Simple implementation and improvement of INRs