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3d without friction (Torch, TF, Jax, Numpy)
The main features of google-research/visu3d are: Neural Radiance Field Implementations, Visualization Tools.
Projects with overlapping indexed features include: nerfies/nerfies.github.io — This project is a computer vision pipeline and volumetric rendering system used to transform photos and videos into… nvlabs/neuralangelo — Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and… nvidia/isaac-gr00t. nerfstudio-project/nerfstudio — Nerfstudio is a modular development framework for training, visualizing, and exporting three-dimensional scene… google-research/google-research — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum… devnev/refdir.
This project is a computer vision pipeline and volumetric rendering system used to transform photos and videos into high-fidelity 3D models. It implements a deformable neural radiance field framework that optimizes deformation fields to represent non-rigid moving subjects in three dimensions. The system utilizes volumetric deformation fields to map 3D coordinates from a static canonical space to a deformed state. This allows for the reconstruction of photorealistic scenes and the synthesis of high-fidelity images from camera perspectives not present in the original input data. The framework
Nerfstudio is a modular development framework for training, visualizing, and exporting three-dimensional scene representations derived from two-dimensional image datasets. It provides a neural scene reconstruction pipeline that converts raw images and camera data into high-fidelity 3D assets and cinematic video using a differentiable volumetric renderer. The system features an interactive web-based visualizer that allows users to monitor training progress and inspect neural scene geometry in real time. It decouples neural network architectures from the training loop through a standardized mod
Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste
