Novel View Synthesis Using NeRF
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Updated
Jan 11, 2024 - Jupyter Notebook
Novel View Synthesis Using NeRF
[ECCV 2024] RoDUS: Robust Decomposition of Static and Dynamic Elements in Urban Scenes
Trying to make a NeRF run in a browser.
A simple 2D toy example to play around with NeRFs, implemented in pytorch-lightning. Repository can be used as a template to speed up further research on nerfs.
Using Fourier Feature Embedding within Neural Radiance Fields on Procedural Noise Functions tasks with focus on Perlin Noise.
Perform Differentiable Volume Rendering: Ray sampling from cameras, Point sampling along rays. Optimizing a basic implicit volume, Optimizing a Neural Radiance Field.
The primary goal of the project is to develop a system that allows for the editing of NeRF scenes based on natural language instructions. This system builds on the strengths of diffusion models and advanced segmentation techniques to achieve localized and precise modifications in 3D scenes.
An implementation of Neural Radiance Fields for Novel Views (https://arxiv.org/pdf/2003.08934.pdf)
Implementation of NeRF paper
This repository contains the code to my bachelors project. An analysis of NeRF literature with a focus on training speed, followed by experimentation to make quick convergence models more robust.
Custom integration of Neural Radiance Fields (NeRF) and Visual Simultaneous Localization and Mapping (VSLAM) based in NerfStudio.
Utility for converting Nvidia Instant-NGP snapshots to JSON format.
A curated list of awesome Neural Radiance Fields (NeRF) papers
Trying Various Algorithms related to 3d-reconstruction
Code implementation for NeRF and DietNeRF papers. NeRF presents a method for synthesizing novel views of complex 3D scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.
Playing Around with NeRF Models and 2D Images
A concise PyTorch re-implementation of NeRF paper
Reconstruction of a 3d scene from a set of images with different view points (camera in motion)
✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an IMAGINATION (A1) PROXIA when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).
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