Training and Evaluation Code for Neural Volumes

Overview

Neural Volumes

This repository contains training and evaluation code for the paper Neural Volumes. The method learns a 3D volumetric representation of objects & scenes that can be rendered and animated from only calibrated multi-view video.

Neural Volumes

Citing Neural Volumes

If you use Neural Volumes in your research, please cite the paper:

@article{Lombardi:2019,
 author = {Stephen Lombardi and Tomas Simon and Jason Saragih and Gabriel Schwartz and Andreas Lehrmann and Yaser Sheikh},
 title = {Neural Volumes: Learning Dynamic Renderable Volumes from Images},
 journal = {ACM Trans. Graph.},
 issue_date = {July 2019},
 volume = {38},
 number = {4},
 month = jul,
 year = {2019},
 issn = {0730-0301},
 pages = {65:1--65:14},
 articleno = {65},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/3306346.3323020},
 doi = {10.1145/3306346.3323020},
 acmid = {3323020},
 publisher = {ACM},
 address = {New York, NY, USA},
}

File Organization

The root directory contains several subdirectories and files:

data/ --- custom PyTorch Dataset classes for loading included data
eval/ --- utilities for evaluation
experiments/ --- location of input data and training and evaluation output
models/ --- PyTorch modules for Neural Volumes
render.py --- main evaluation script
train.py --- main training script

Requirements

  • Python (3.6+)
    • PyTorch (1.2+)
    • NumPy
    • Pillow
    • Matplotlib
  • ffmpeg (in PATH, needed to render videos)

How to Use

There are two main scripts in the root directory: train.py and render.py. The scripts take a configuration file for the experiment that defines the dataset used and the options for the model (e.g., the type of decoder that is used).

A sample set of input data is provided in the v0.1 release and can be downloaded here and extracted into the root directory of the repository. experiments/dryice1/data contains the input images and camera calibration data, and experiments/dryice1/experiment1 contains an example experiment configuration file (experiments/dryice1/experiment1/config.py).

To train the model:

python train.py experiments/dryice1/experiment1/config.py

To render a video of a trained model:

python render.py experiments/dryice1/experiment1/config.py Render

License

See the LICENSE file for details.

Comments
  • Training with our own data

    Training with our own data

    Hi,
    I have a few questions on how the data should be formatted and the data format of the provided dryice1.

    • The model expects world space coordinate in meters? i.e if my extrinsics are already in meters do I still need the world_scale=1/256. in config.py file?
    • The extrinsics are in world2cam and the rotation convention is like opencv? i.e, y-down,z-forward and x-right, assuming identity for pose.txt file?
    • how long do I need to train for about 200 frames? And in the config.py file it seems you are skipping some frames? This is ok to do for my own sequence as well?
    • in the KRT file, I see that there's 5 parameters above the RT matrix. This is the distortion correction in opencv format? But it is not used yes?
    • I did not visualize your cameras, so I am not sure how they are distributed. Is it gonna be a problem if I use 50 cameras equally distributed in a half-hemisphere and the subject is already at world origin and 3.5 meters from every cameras? My question is do I need to filter the training cameras so that the back side of subject that is not seen by input 3 cameras is excluded?
    • How do I choose the input cameras? I have a visualization of the cameras . Which camera config should I use? Is this more a question of which testing camera poses I intend to have, i.e narrower the testing cameras' range of view, the closer input training cameras can be? Config_0 is more orthogonal and Config_1 sees less of the backside.
    opened by zawlin 32
  • Some questions about coordination transformation

    Some questions about coordination transformation

    Hello, Thanks for releasing your code. I am impressed by your work. Now I hope to run your code with my our dataset. I have two questions.

    Firstly, I see the pose.txt is used in the code to put the objects in the center. If I use my own data, will the file still work?

    Secondly, I see the code set the raypos is among -1 and 1. Is it the matrix in this pose file that narrows the range to -1 to 1? My own dataset' range is different.

    Thirdly, does the code limit the scope of the template? Does it have to be between 0-255?

    Thanks a lot in advance!

    opened by maobenz 3
  • Location of the volume

    Location of the volume

    Hi there,

    I wonder whether the origin of the volume is (0,0,0)?

    I'm testing the method on a public dataset (http://people.csail.mit.edu/drdaniel/mesh_animation), and I know exactly where (0,0,0) is in the images. But the volume seems to float around the scene. This is the first preview for training process: prog_000001

    Each camera is pointing to the opposite side of the scene, so I expect the same for the volume location in images. But for some reason, they are on the same side in the images. Can you help?

    Thank you.

    opened by lochuynh1989 3
  • Any plan to release all data that presented in the paper?

    Any plan to release all data that presented in the paper?

    Hi @stephenlombardi ,

    Thanks for sharing this great work. I was wondering do you have any plan to release all the data that you used in the paper (apart from the dryice)?

    Best, Zirui

    opened by ziruiw-dev 2
  • Block-wise initialization scheme

    Block-wise initialization scheme

    Hi, is there any paper describing the used block-wise weight initialization scheme?

    https://github.com/facebookresearch/neuralvolumes/blob/8c5fad49b2b05b4b2e79917ee87299e7c1676d59/models/utils.py#L73

    opened by denkorzh 2
  • Is there a way to render a 3D file from this?

    Is there a way to render a 3D file from this?

    Hello, I was wondering if there is a way to export an .obj/,fbx file along with corresponding materials from this? If not, do you have any suggestions as to how to go about that if I were to try extend the code to incorporate that functionality?

    opened by arlorostirolla 1
  • How Can I train and render a Person Image

    How Can I train and render a Person Image

    Hi my name is Luan I am trying to render a Person Image but I am not being able to run can you create and for me a folder with the Setting setup to use a person image? Thank you.

    opened by LuanDalOrto 1
  • code for hybrid rendering (section 6.2) doesn't exist?

    code for hybrid rendering (section 6.2) doesn't exist?

    Hello,

    First of all, thank you for releasing the code for your seminal work. I really think neural volumes is one of the works that popularized differentiable rendering and inspired future works such as neural radiance fields.

    My question is whether this codebase includes the code for the hybrid rendering method outlined in section 6.2 of the paper. I'm trying to fit Neural Volumes to multi-view video of a full-body human being, similar to the 5th subfigure in Fig. 1 of the main paper, but after reading it more carefully it seems as though I would need to use hybrid rendering to be able to render the fine details of the human being.

    Could you

    1. confirm the existence of hybrid rendering in this codebase AND
    2. whether or not hybrid rendering was used to render the full-bodied human being in Fig. 1 of the main paper.

    Thank you in advance.

    opened by andrewsonga 1
  • Misaligned views in rendering

    Misaligned views in rendering

    Hi,

    I am working on MIT dataset to test the network. When I specify a camera to render, it looks fine throughout timeline. However, while rendering the rotating video, the cameras are misaligned as shown in attached screenshot. All cameras look like clustered at the center and views are spread around within the range cameras cover. Is it possible to be any error in KRT or configuration?

    Any suggestion is welcome. issue_MIT_5_cams

    opened by CorneliusHsiao 1
Releases(v0.1)
Owner
Meta Research
Meta Research
HNECV: Heterogeneous Network Embedding via Cloud model and Variational inference

HNECV This repository provides a reference implementation of HNECV as described in the paper: HNECV: Heterogeneous Network Embedding via Cloud model a

4 Jun 28, 2022
[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

GNeRF This repository contains official code for the ICCV 2021 paper: GNeRF: GAN-based Neural Radiance Field without Posed Camera. This implementation

Quan Meng 191 Dec 26, 2022
Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

gts3.org (<a href=[email protected])"> 129 Dec 15, 2022
SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.

SweiNet SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging. SweiNet takes as in

Felix Jin 3 Mar 31, 2022
"3D Human Texture Estimation from a Single Image with Transformers", ICCV 2021

Texformer: 3D Human Texture Estimation from a Single Image with Transformers This is the official implementation of "3D Human Texture Estimation from

XiangyuXu 193 Dec 05, 2022
Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"

Intelligent Robotics and Machine Vision Lab 4 Jul 19, 2022
Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022
PyTorch implementation of the ExORL: Exploratory Data for Offline Reinforcement Learning

ExORL: Exploratory Data for Offline Reinforcement Learning This is an original PyTorch implementation of the ExORL framework from Don't Change the Alg

Denis Yarats 52 Jan 01, 2023
for taichi voxel-challange event

Taichi Voxel Challenge Figure: result of python3 example6.py. Please replace the image above (demo.jpg) with yours, so that other people can immediate

Liming Xu 20 Nov 26, 2022
PROJECT - Az Residential Real Estate Analysis

AZ RESIDENTIAL REAL ESTATE ANALYSIS -Decided on libraries to import. Includes pa

2 Jul 05, 2022
ThunderGBM: Fast GBDTs and Random Forests on GPUs

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Xtra Computing Group 647 Jan 04, 2023
Dataset used in "PlantDoc: A Dataset for Visual Plant Disease Detection" accepted in CODS-COMAD 2020

PlantDoc: A Dataset for Visual Plant Disease Detection This repository contains the Cropped-PlantDoc dataset used for benchmarking classification mode

Pratik Kayal 109 Dec 29, 2022
Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.

Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.

Ibai Gorordo 99 Dec 31, 2022
ObjectDetNet is an easy, flexible, open-source object detection framework

Getting started with the ObjectDetNet ObjectDetNet is an easy, flexible, open-source object detection framework which allows you to easily train, resu

5 Aug 25, 2020
Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Mohammad Asif Zaman 34 Dec 23, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
MT3: Multi-Task Multitrack Music Transcription

MT3: Multi-Task Multitrack Music Transcription MT3 is a multi-instrument automatic music transcription model that uses the T5X framework. This is not

Magenta 867 Dec 29, 2022
a pytorch implementation of auto-punctuation learned character by character

Learning Auto-Punctuation by Reading Engadget Articles Link to Other of my work 🌟 Deep Learning Notes: A collection of my notes going from basic mult

Ge Yang 137 Nov 09, 2022