google-research/multinerfArchived
Multinerf
MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel views from sets of 2D images. It provides a system for generating new perspectives of a scene by optimizing a neural network based on images and camera poses.
The toolkit includes research implementations such as Mip-NeRF 360 and Ref-NeRF for high-fidelity volumetric rendering. It features a structure-from-motion pipeline to calculate camera positions and orientations from image datasets to prepare data for training.
The project covers a full workflow for volumetric rendering, including data preprocessing with custom dataset loaders, the training of implicit neural representations, and the photorealistic rendering of images and video sequences. It also includes tools for evaluating model accuracy by computing error metrics across multiple scenes.
Features
- Novel View Synthesis Engines - Implements a framework for generating high-quality novel viewpoints from sparse image data using volumetric calculations.
- Coordinate-Based Neural Representations - Uses neural networks to map spatial coordinates directly to scene properties like color and density.
- Neural Scene Reconstructions - Creates high-fidelity 3D environments from multi-view image datasets using neural networks for visualization.
- Neural Radiance Field Synthesizers - Offers a toolkit for optimizing neural representations of spatial volumes to synthesize novel views.
- Radiance Field Training Pipelines - Optimizes neural networks to create high-fidelity 3D scene representations from 2D images.
- Volumetric Integration - Computes pixel colors by numerically integrating density and radiance along rays to enable gradient-based optimization.
- Camera Pose Estimators - Provides tools to calculate camera orientations and positions from images to facilitate 3D reconstruction.
- Implicit Volume Representations - Represents 3D shapes using neural density fields and feature dimensions instead of explicit polygons.
- Structure from Motion Pipelines - Implements a pipeline for recovering 3D structures and camera poses from collections of images.
- Spatial Coordinate Encodings - Transforms low-dimensional 3D coordinates into high-dimensional frequency space to capture geometric details.
- Cone-Based Renderers - Integrates radiance along viewing rays using conical frustums to eliminate aliasing artifacts in high-resolution scenes.
- High-Quality Scene Rendering - Generates high-quality images and video sequences from 3D neural scene data.
- Scene Video Renderers - Renders a sequence of 3D scene views into video files along a defined camera path.
- Reconstruction Accuracy Evaluators - Provides tools for quantifying 3D reconstruction precision by comparing rendered views against ground truth data.
- Neural Scene Representation - Unbounded anti-aliased neural radiance fields.
Star history
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