This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
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Updated
Jul 6, 2021 - Python
This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
A list of resources about deep learning solutions on 3D shape processing
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction (CVPR 2022)
[ICCV21] Self-Calibrating Neural Radiance Fields
Revealing Occlusions with 4D Neural Fields (CVPR 2022 Oral) - Official Implementation
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
Official implementation of NPMs: Neural Parametric Models for 3D Deformable Shapes - ICCV 2021
[ECCV'20] Convolutional Occupancy Networks
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).
Simple implementation and improvement of INRs
Pytorch code for ECCV'22 paper. ShAPO: Implicit Representations for Multi-Object Shape, Appearance and Pose Optimization
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.
Software Defined Solid Modeling
Implicit representation of various things using PyTorch and high order layers
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 a…
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