Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Last update: Dec 05, 2022

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Deep Learning gbp-poplar

Overview

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU

Implementation of CVPR 2020 paper: Bundle Adjustment on a Graph Processor

Find the Poplar SDK documentation here. Code has been tested on Poplar SDK 1.2.

Find the corresponding python implementation here.

Running Bundle Adjustment

cd ba
make ba
./ba --bal_file ../sequences/fr1xyz.txt

For more options

./ba --help

Running SLAM

cd ba
make slam
./slam --bal_file ../sequences/fr2robot2.txt

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{OrtizCVPR2020,
author = {Ortiz, Joseph and Pupilli, Mark and Leutenegger, Stefan and Davison, Andrew J.},
title = {Bundle Adjustment on a Graph Processor},
booktitle = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
year = {2020}
}

Owner

Joe Ortiz

Computer Vision PhD Student at Imperial College London | MPhys from University of Oxford | https://joeaortiz.github.io/

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Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Related tags

Overview

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU

Running Bundle Adjustment

Running SLAM

Citation

Owner

Joe Ortiz

atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

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End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model

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Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Related tags

Overview

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU

Running Bundle Adjustment

Running SLAM

Citation

Owner

Joe Ortiz

atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

POCO: Point Convolution for Surface Reconstruction

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].

Transfer Learning for Pose Estimation of Illustrated Characters

Face Transformer for Recognition

End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model

Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

Distance correlation and related E-statistics in Python

Anagram Generator in Python

Emotional conditioned music generation using transformer-based model.

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

People movement type classifier with YOLOv4 detection and SORT tracking.

Multiview 3D object detection on MultiviewC dataset through moft3d.

Single/multi view image(s) to voxel reconstruction using a recurrent neural network

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

Open source Python module for computer vision

Code for One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)

68 keypoint annotations for COFW test data

Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)

Optimizing Deeper Transformers on Small Datasets

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, and Trevor Darrell. CVPR 2015 and PAMI 2016.