PlenOctree Extraction algorithm

Related tags

Deep Learning octree render volume-rendering spherical-harmonics
Overview

PlenOctrees_NeRF-SH

This is an implementation of the Paper PlenOctrees for Real-time Rendering of Neural Radiance Fields. Not only the code provides the implementation of the NeRF-SH，but also provides the conversion code from NeRF-SH to PlenOctree. You can use the code to generate the .npz file so as to run the C++ renderer by the PlenOctrees for Real-time Rendering of Neural Radiance Fields. And the conversion code is in the tools/PlenOctrees.ipynb. But before using the code, you must train the NeRF-SH model. If you don't want to train the model, please concat the mail:[email protected].

Quick Start

The implementation of dataloader is from the Multi-view Neural Human Rendering (NHR). So the datasets format should be the same as theNHR.
To train the code:

    
cd tools && python train_net.py <gpu id>

And you can run the tools/PlenOctrees.ipynb to generate the .npz file which can run the C++ renderer by the PlenOctrees for Real-time Rendering of Neural Radiance Fields.

Requirements

  • yacs (Yet Another Configuration System)

  • PyTorch (An open source deep learning platform)

  • ignite (High-level library to help with training neural networks in PyTorch)

  • If you have any questions, you can contact [email protected].

Citation

@inproceedings{yu2021plenoctrees,
      title={PlenOctrees for Real-time Rendering of Neural Radiance Fields},
      author={Alex Yu and Ruilong Li and Matthew Tancik and Hao Li and Ren Ng and Angjoo Kanazawa},
      year={2021},
      booktitle={arXiv},
}
You might also like...
Cross-media Structured Common Space for Multimedia Event Extraction (ACL2020)
Cross-media Structured Common Space for Multimedia Event Extraction (ACL2020)

Cross-media Structured Common Space for Multimedia Event Extraction Table of Contents Overview Requirements Data Quickstart Citation Overview The code

Manling Li 49 Nov 21, 2022
Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

Wenxuan Zhou 146 Nov 29, 2022
Training data extraction on GPT-2

Training data extraction from GPT-2 This repository contains code for extracting training data from GPT-2, following the approach outlined in the foll

Florian Tramer 62 Dec 7, 2022
Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction
Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction

Welcome to Barlow Barlow is a tool for identifying the failure modes for a given neural network. To achieve this, Barlow first creates a group of imag

Sahil Singla 33 Dec 5, 2022
This repository contains the code for our fast polygonal building extraction from overhead images pipeline.
This repository contains the code for our fast polygonal building extraction from overhead images pipeline.

Polygonal Building Segmentation by Frame Field Learning We add a frame field output to an image segmentation neural network to improve segmentation qu

Nicolas Girard 186 Jan 4, 2023
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).

null 3.4k Jan 4, 2023
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
[ACL 20] Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction

REval Table of Contents Introduction Overview Requirements Installation Probing Usage Citation License 🎓 Introduction REval is a simple framework for

null 13 Jan 6, 2023
Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021.

UniRE Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021. Requirements python: 3.7.6 pytorch: 1.8.1 transformers:

Wang Yijun 109 Nov 29, 2022
Comments
  • How to generate the .npz file for my own datasets?

    How to generate the .npz file for my own datasets?

    I try to run the tools/PlenOctrees.ipynb, but I don't know what's the correct datasets format, could anybody set it correctly?

    微信图片_20211206200916

    aaa.py is exported from the tools/PlenOctrees.ipynb

    opened by lmTEDug 0
  • Problem with mask and background

    Problem with mask and background

    Hi,

    I tried to train with sport_1 dataset, with below config, but it seems "USE_MASK" option doesn't work and I still see the background. Any idea what could be the problem?

    SOLVER: OPTIMIZER_NAME: "Adam" BASE_LR: 0.0005 WEIGHT_DECAY: 0.0000000 IMS_PER_BATCH: 2 START_ITERS: 3000 END_ITERS: 60000 LR_SCALE: 0.1 WARMUP_ITERS: 0

    MAX_EPOCHS: 100 CHECKPOINT_PERIOD: 3000 LOG_PERIOD: 30 BUNCH: 1000 COARSE_STAGE: 0

    INPUT: SIZE_TRAIN: [256,192] SIZE_TEST: [256,192]

    DATASETS: TRAIN: "/home/Downloads/sport_1" NUM_FRAME: 1 SHIFT: 0.0 MAXRATION: 0.0 ROTATION: 0.0 USE_MASK: True

    DATALOADER: NUM_WORKERS: 8

    MODEL: COARSE_RAY_SAMPLING: 64 FINE_RAY_SAMPLING: 128 SAMPLE_METHOD: "NEAR_FAR" # "NEAR_FAR" "BBOX" BOARDER_WEIGHT: 1e10 SAME_SPACENET: True TKERNEL_INC_RAW: True USE_SH: True DEVICE_IDS: [0]

    TEST: IMS_PER_BATCH: 1

    OUTPUT_DIR: "/media/Data_DL_HD/projects/PlenOctrees_NeRF-SH/output/sport_1"

    Screenshot-20210416220214-1149x880

    opened by vahidEtt 1
Owner
null
GitHub
An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

Karbo 45 Dec 21, 2022
RL algorithm PPO and IRL algorithm AIRL written with Tensorflow.

RL algorithm PPO and IRL algorithm AIRL written with Tensorflow. They have a parallel sampling feature in order to increase computation speed (especially in high-performance computing (HPC)).

Fangjian Li 3 Dec 28, 2021
Feature extraction made simple with torchextractor

torchextractor: PyTorch Intermediate Feature Extraction Introduction Too many times some model definitions get remorselessly copy-pasted just because

Antoine Broyelle 89 Oct 31, 2022
PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

Princeton Natural Language Processing 657 Jan 9, 2023
git《Joint Entity and Relation Extraction with Set Prediction Networks》(2020) GitHub:

Joint Entity and Relation Extraction with Set Prediction Networks Source code for Joint Entity and Relation Extraction with Set Prediction Networks. W

null 130 Dec 13, 2022
Code for the paper "Relation of the Relations: A New Formalization of the Relation Extraction Problem"

This repo contains the code for the EMNLP 2020 paper "Relation of the Relations: A New Paradigm of the Relation Extraction Problem" (Jin et al., 2020)

YYY 27 Oct 26, 2022
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

MilaGraph 36 Nov 22, 2022
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 1, 2022
Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

Argument Extraction by Generation Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21' Dependencies pytorch=1.6 tr

Zoey Li 87 Dec 26, 2022
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

null 139 Jan 1, 2023