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
a playground for making 3D art with lisp and math
Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.
Create, ray trace & export programatically defined Signed Distance Function CSG geometries with an API suited for generative art - in your browser! 🎉
Marching cubes with and without color interpolation, and edge subsampling.
Pytorch code for ECCV'22 paper. ShAPO: Implicit Representations for Multi-Object Shape, Appearance and Pose Optimization
Fast and light-weight Marching Cubes library in C++ without any dependencies.
A Flexible Framework for Robot visualization and programming in Python
[CVPR2023 Highlight] Marching-Primitives: Shape Abstraction from Signed Distance Function
Volumetric structures such as voxels and SDFs implemented in pytorch
Sphere tracing signed distance functions.
Signed is a 3D modeling and construction language based on Lua and SDFs. Signed will be available for macOS and iOS and is heavily optimized for Metal.
A Go library for signed distance function shape generation. Read as 3D printing shape design.
Signed Distance Function from triangle mesh.
A fast and cross-platform Signed Distance Function (SDF) viewer, easily integrated with your SDF library.
Implementation of Differentiable Sign-Distance Function Rendering - in Pytorch
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