A High-Performance Distributed Library for Large-Scale Bundle Adjustment

Overview

MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment

This repo contains an official implementation of MegBA.

MegBA is a fast and distributed library for large-scale Bundle Adjustment (BA). MegBA has a novel end-to-end vectorised BA algorithm which can fully exploit the massive parallel cores on GPUs, thus speeding up the entire BA computation. It also has a novel distributed BA algorithm that can automatically partition BA problems, and solve BA sub-problems using distributed GPUs. The GPUs synchronise intermediate solving state using network-efficient collective communication, and the synchronisation is designed to minimise communication cost. MegBA has a memory-efficient GPU runtime and it exposes g2o-compatible APIs. Experiments show that MegBA can out-perform state-of-the-art BA libraries (i.e., Ceres and DeepLM) by ~50x and ~5x respectively, in public large-scale BA benchmarks.

Version

  • 2021/12/06 Beta version released! It corresponds to this paper.
  • General version code release (Expected Dec 31 2021)
  • memory-efficient version with implicit Hessian (TBD)
  • analytical differential module, IMU factor, prior factor (TBD)

Paper:

Quickstart

Dependencies:

You can also easily install all dependencies with script: script

Demo with BAL dataset:

  • Download any pre.txt.bz2 file from BAL Dataset: https://grail.cs.washington.edu/projects/bal/ and uncompressed.

  • Compile

    mkdir build
    cd build
    cmake ..
    make -j4 BAL_Double
  • Run the demo (Venice-1778)

    cd examples
    ./BAL_Double --name=Venice --world_size=2 --iter=100 --solver_tol=1e-1 --solver_refuse_ratio=1 --solver_max_iter=100 --tau=1e4 --epsilon1=1 --epsilon2=1e-10
    • world_size: number of GPUs available
    • iter: the maximal number of LM iteration
    • epsilon: threshold in LM
    • solver_tol: tolerance of solver (distributed PCG solver)
    • solver_refuse_ratio: early stop for the solver
    • solver_max_iter: the maximal iteration of solver
    • tau: the initial region

Notes for the practitioners

  • Currently, MegBA implements automatic differentation only for generalizability. Please consider implementing your own analytical differentiation module.
  • If you use devices without modern inter-device communication (i.e., NVLinks..), you might find the data transfer is the bottleneck.
  • Empirically, we found it is necessary to customize the LM trust-region strategies and tune its hyper-parameters to further boost the performance.

Documentation

Under doc/ (Coming soon...)

Collaborate with Us

Please check here for MegBA's future plan.

If you are intereted in MegBA and want to collaborate, you can:

  • Apply for an Internship at Megvii Research 3D, please send your resume to [email protected], with your expected starting date. (subject: 3D组CUDA实习生-Name) Unfortunately, now we are only able to host interns with work authorization in China.
  • As an external collaborator (coding), just fork this repo and send PRs. We will review your PR carefully (and merge it into MegBA).
  • As an algorithm/novelty contributor, please send an email to [email protected].
  • Any new feature request, you can send an email to [email protected] as well. Note that it is not guaranteed the requested feature will be added or added soon

Contact Information:

BibTeX Citation

If you find MegBA useful for your project, please consider citing:

@misc{2021megba,
  title={MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment}, 
  author={Jie Ren and Wenteng Liang and Ran Yan and Luo Mai and Shiwen Liu and Xiao Liu},
  year={2021},
  eprint={2112.01349},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

License

MegBA is licensed under the Apache License, Version 2.0.

Owner
旷视研究院 3D 组
旷视科技(Face++)研究院 3D 组(原 SLAM 组)
旷视研究院 3D 组
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023
the official implementation of the paper "Isometric Multi-Shape Matching" (CVPR 2021)

Isometric Multi-Shape Matching (IsoMuSh) Paper-CVF | Paper-arXiv | Video | Code Citation If you find our work useful in your research, please consider

Maolin Gao 9 Jul 17, 2022
An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates neural fields, predictive coding, top-down-bottom-up, and attention (consensus between columns)

GLOM - Pytorch (wip) An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates neural fields, predictive coding,

Phil Wang 173 Dec 14, 2022
Neural HMMs are all you need (for high-quality attention-free TTS)

Neural HMMs are all you need (for high-quality attention-free TTS) Shivam Mehta, Éva Székely, Jonas Beskow, and Gustav Eje Henter This is the official

Shivam Mehta 0 Oct 28, 2022
Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models.

Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models.

2.7k Jan 05, 2023
A note taker for NVDA. Allows the user to create, edit, view, manage and export notes to different formats.

Quick Notetaker add-on for NVDA The Quick Notetaker add-on is a wonderful tool which allows writing notes quickly and easily anytime and from any app

5 Dec 06, 2022
CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax ⚠️ Latest: Current repo is a complete version. But we delet

FishYuLi 341 Dec 23, 2022
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

69 Dec 10, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
This is my research project for the Irving Center for Cancer Dynamics/Azizi Lab, Columbia University.

bayesian_uncertainty This is my research project for the Irving Center for Cancer Dynamics/Azizi Lab, Columbia University. In this project I build a s

Max David Gupta 1 Feb 13, 2022
Neural Network Libraries

Neural Network Libraries Neural Network Libraries is a deep learning framework that is intended to be used for research, development and production. W

Sony 2.6k Dec 30, 2022
a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version

pytorch-liteflownet This is a personal reimplementation of LiteFlowNet [1] using PyTorch. Should you be making use of this work, please cite the paper

Simon Niklaus 365 Dec 31, 2022
Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

Thomas Roddick 219 Dec 20, 2022
This repository accompanies the ACM TOIS paper "What can I cook with these ingredients?" - Understanding cooking-related information needs in conversational search

In this repository you find data that has been gathered when conducting in-situ experiments in a conversational cooking setting. These data include tr

6 Sep 22, 2022
Official codebase for Pretrained Transformers as Universal Computation Engines.

universal-computation Overview Official codebase for Pretrained Transformers as Universal Computation Engines. Contains demo notebook and scripts to r

Kevin Lu 210 Dec 28, 2022
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

DOC | Quick Start | 中文 Breaking News !! 🔥 🔥 🔥 OGB-LSC KDD CUP 2021 winners announced!! (2021.06.17) Super excited to announce our PGL team won TWO

1.5k Jan 06, 2023
Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)

MUC Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018) Performance Details for Accuracy: | Dataset

Yijun Su 3 Oct 09, 2022
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks Requirements python 0.10+ rdkit 2020.03.3.0 biopython 1.78 openbabel 2.4

Neeraj Kumar 3 Nov 23, 2022
Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Daniele Grattarola 37 Dec 13, 2022
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021) PyTorch implementation of SnapMix | paper Method Overview Cite

DavidHuang 126 Dec 30, 2022