Randomness and Statistical Inference of Shapes via the Smooth Euler Characteristic Transform
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Updated
Jun 7, 2024 - R
Randomness and Statistical Inference of Shapes via the Smooth Euler Characteristic Transform
code for our ICCV 2021 paper "DeepCAD: A Deep Generative Network for Computer-Aided Design Models"
Topological data analytic approach for discovering biophysical signatures in protein dynamics
single 3D shape diffusion model
R package for generating synthetic shapes and images using the α-shape sampler
Creating an animating & attractive website design which have massive library of free 3d shape using HTML & CSS.
[ICCV23] SATR: Zero-Shot Semantic Segmentation of 3D Shapes
code for our CVPR 2020 paper "PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes"
A statistical framework for feature selection and association mapping with 3D shapes
The model can load in a dataset with point clouds of different objects. The PointNet architecture is created from the bottom, trained it on the dataset, and then predicted on new point clouds that the neural network has not seen before.
Used to demonstrate how VBA can be integrated with 3D shapes to deliver smooth and simple animations within Excel.
code for paper "Learning to Generate 3D Shapes from a Single Example", SIGGRAPH Asia 2022
Random triangles form a cube. Made with three.js.
Reproducibility repo for the simulation and real data results in the SINATRA manuscript set to appear in AoAS.
This is a Python library which is used to find area and perimeter of 2D figures like square, rectangle, triangle, circle, ellipse, rhombus, parallelogram, trapezium, pentagon, hexagon, heptagon, octagon, nonagon and decagon You can also find volume, Total surface area and Curved surface are of 3D Figures like cube, cuboid, cylinder, cone, sphere…
Testing the pre-trained model provided by Differential Volumetric Renderer repository.
Photometric optimization code for creating the FLAME texture space and other applications
MeshNet: Mesh Neural Network for 3D Shape Representation (AAAI 2019)
A list of resources about deep learning solutions on 3D shape processing
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