Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes (CVPR 2021 Oral)

Related tags

Deep Learningnglod
Overview

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces

Official code release for NGLOD. For technical details, please refer to:

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces
Towaki Takikawa*, Joey Litalien*, Kangxue Xin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler
In Computer Vision and Pattern Recognition (CVPR), 2021 (Oral)
[Paper] [Bibtex] [Project Page]

If you find this code useful, please consider citing:

@article{takikawa2021nglod,
    title = {Neural Geometric Level of Detail: Real-time Rendering with Implicit {3D} Shapes}, 
    author = {Towaki Takikawa and
              Joey Litalien and 
              Kangxue Yin and 
              Karsten Kreis and 
              Charles Loop and 
              Derek Nowrouzezahrai and 
              Alec Jacobson and 
              Morgan McGuire and 
              Sanja Fidler},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2021},
}

New: Sparse training code with Kaolin now available in app/spc! Read more about it here

Directory Structure

sol-renderer contains our real-time rendering code.

sdf-net contains our training code.

Within sdf-net:

sdf-net/lib contains all of our core codebase.

sdf-net/app contains standalone applications that users can run.

Getting started

Python dependencies

The easiest way to get started is to create a virtual Python 3.8 environment:

conda create -n nglod python=3.8
conda activate nglod
pip install --upgrade pip
pip install -r ./infra/requirements.txt

The code also relies on OpenEXR, which requires a system library:

sudo apt install libopenexr-dev 
pip install pyexr

To see the full list of dependencies, see the requirements.

Building CUDA extensions

To build the corresponding CUDA kernels, run:

cd sdf-net/lib/extensions
chmod +x build_ext.sh && ./build_ext.sh

The above instructions were tested on Ubuntu 18.04/20.04 with CUDA 10.2/11.1.

Training & Rendering

Note. All following commands should be ran within the sdf-net directory.

Download sample data

To download a cool armadillo:

wget https://raw.githubusercontent.com/alecjacobson/common-3d-test-models/master/data/armadillo.obj -P data/

To download a cool matcap file:

wget https://raw.githubusercontent.com/nidorx/matcaps/master/1024/6E8C48_B8CDA7_344018_A8BC94.png -O data/matcap/green.png

Training from scratch

python app/main.py \
    --net OctreeSDF \
    --num-lods 5 \
    --dataset-path data/armadillo.obj \
    --epoch 250 \
    --exp-name armadillo

This will populate _results with TensorBoard logs.

Rendering the trained model

If you set custom network parameters in training, you need to also reflect them for the renderer.

For example, if you set --feature-dim 16 above, you need to set it here too.

python app/sdf_renderer.py \
    --net OctreeSDF \
    --num-lods 5 \
    --pretrained _results/models/armadillo.pth \
    --render-res 1280 720 \
    --shading-mode matcap \
    --lod 4

By default, this will populate _results with the rendered image.

If you want to export a .npz model which can be loaded into the C++ real-time renderer, add the argument --export path/file.npz. Note that the renderer only supports the base Neural LOD configuration (the default parameters with OctreeSDF).

Core Library Development Guide

To add new functionality, you will likely want to make edits to the files in lib.

We try our best to keep our code modular, such that key components such as trainer.py and renderer.py need not be modified very frequently to add new functionalities.

To add a new network architecture for an example, you can simply add a new Python file in lib/models that inherits from a base class of choice. You will probably only need to implement the sdf method which implements the forward pass, but you have the option to override other methods as needed if more custom operations are needed.

By default, the loss function used are defined in a CLI argument, which the code will automatically parse and iterate through each loss function. The network architecture class is similarly defined in the CLI argument; simply use the exact class name, and don't forget to add a line in __init__.py to resolve the namespace.

App Development Guide

To make apps that use the core library, add the sdf-net directory into the Python sys.path, so the modules can be loaded correctly. Then, you will likely want to inherit the same CLI parser defined in lib/options.py to save time. You can then add a new argument group app to the parser to add custom CLI arguments to be used in conjunction with the defaults. See app/sdf_renderer.py for an example.

Examples of things that are considered apps include, but are not limited to:

  • visualizers
  • training code
  • downstream applications

Third-Party Libraries

This code includes code derived from 3 third-party libraries, all distributed under the MIT License:

https://github.com/zekunhao1995/DualSDF

https://github.com/rogersce/cnpy

https://github.com/krrish94/nerf-pytorch

Acknowledgements

We would like to thank Jean-Francois Lafleche, Peter Shirley, Kevin Xie, Jonathan Granskog, Alex Evans, and Alex Bie at NVIDIA for interesting discussions throughout the project. We also thank Peter Shirley, Alexander Majercik, Jacob Munkberg, David Luebke, Jonah Philion and Jun Gao for their help with paper editing.

We also thank Clement Fuji Tsang for his help with the code release.

The structure of this repo was inspired by PIFu: https://github.com/shunsukesaito/PIFu

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au

14 Nov 28, 2022
Repo for "Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks"

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

zhangxian 54 Jan 03, 2023
Pytorch implementation for M^3L

Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification (CVPR 2021) Introduction This is the Py

Yuyang Zhao 45 Dec 26, 2022
Research code for CVPR 2021 paper "End-to-End Human Pose and Mesh Reconstruction with Transformers"

MeshTransformer ✨ This is our research code of End-to-End Human Pose and Mesh Reconstruction with Transformers. MEsh TRansfOrmer is a simple yet effec

Microsoft 473 Dec 31, 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

BEAS Blockchain Enabled Asynchronous and Secure Federated Machine Learning Default Network Configuration: The default application uses the HyperLedger

Harpreet Virk 11 Nov 20, 2022
Losslandscapetaxonomy - Taxonomizing local versus global structure in neural network loss landscapes

Taxonomizing local versus global structure in neural network loss landscapes Int

Yaoqing Yang 8 Dec 30, 2022
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

Active Vision Laboratory 411 Dec 26, 2022
A simple log parser and summariser for IIS web server logs

IISLogFileParser A basic parser tool for IIS Logs which summarises findings from the log file. Inspired by the Gist https://gist.github.com/wh13371/e7

2 Mar 26, 2022
🍷 Gracefully claim weekly free games and monthly content from Epic Store.

EPIC 免费人 🚀 优雅地领取 Epic 免费游戏 Introduction 👋 Epic AwesomeGamer 帮助玩家优雅地领取 Epic 免费游戏。 使用 「Epic免费人」可以实现如下需求: get:搬空游戏商店,获取所有常驻免费游戏与免费附加内容; claim:领取周免游戏及其免

571 Dec 28, 2022
Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Isaac ROS DNN Inference Overview This repository provides two NVIDIA GPU-accelerated ROS2 nodes that perform deep learning inference using custom mode

NVIDIA Isaac ROS 62 Dec 14, 2022
Auditing Black-Box Prediction Models for Data Minimization Compliance

Data-Minimization-Auditor An auditing tool for model-instability based data minimization that is introduced in "Auditing Black-Box Prediction Models f

Bashir Rastegarpanah 2 Mar 24, 2022
A PyTorch re-implementation of Neural Radiance Fields

nerf-pytorch A PyTorch re-implementation Project | Video | Paper NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall

Krishna Murthy 709 Jan 09, 2023
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022
Efficient Two-Step Networks for Temporal Action Segmentation (Neurocomputing 2021)

Efficient Two-Step Networks for Temporal Action Segmentation This repository provides a PyTorch implementation of the paper Efficient Two-Step Network

8 Apr 16, 2022
Compute descriptors for 3D point cloud registration using a multi scale sparse voxel architecture

MS-SVConv : 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning Compute features for 3D point cloud registration

42 Jul 25, 2022
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

openpifpaf Continuously tested on Linux, MacOS and Windows: New 2021 paper: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Te

VITA lab at EPFL 50 Dec 29, 2022
A simple tutoral for error correction task, based on Pytorch

gramcorrector A simple tutoral for error correction task, based on Pytorch Grammatical Error Detection (sentence-level) a binary sequence-based classi

peiyuan_gong 8 Dec 03, 2022
(ICONIP 2020) MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image

MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image This repo contains the source code for MobileHand, real-time estimation of 3D

90 Dec 12, 2022
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Hybrid solving process for combinatorial optimization problems Combinatorial optimization has found applications in numerous fields, from aerospace to

117 Dec 13, 2022
FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction

FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction. It uses a customized encoder decoder architecture with spatio-temporal convolutions and channel ga

Tarun K 280 Dec 23, 2022