Instant neural graphics primitives: lightning fast NeRF and more
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Updated
Apr 18, 2024 - Cuda
Instant neural graphics primitives: lightning fast NeRF and more
Simple SDF mesh generation in Python
Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/
A simple CAD package using signed distance functions
Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.
Fast and light-weight Marching Cubes library in C++ without any dependencies.
A Flexible Framework for Robot visualization and programming in Python
Marching cubes with and without color interpolation, and edge subsampling.
Signed Distance Function from triangle mesh.
Pytorch code for ECCV'22 paper. ShAPO: Implicit Representations for Multi-Object Shape, Appearance and Pose Optimization
Create, ray trace & export programatically defined Signed Distance Function CSG geometries with an API suited for generative art - in your browser! 🎉
A Go library for signed distance function shape generation. Read as 3D printing shape design.
A public domain/MIT header-only marching cube implementation in C++ without anything fancy.
a playground for making 3D art with lisp and math
Source code for the paper: Modeling Rocky Scenery using Implicit Blocks, published in The Visual Computer and presented at Computer Graphics International 2020.
Sphere tracing signed distance functions.
[CVPR2023 Highlight] Marching-Primitives: Shape Abstraction from Signed Distance Function
Volumetric Modelling components for Rhino Grasshopper.
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