void-dataset
xivo
| void-dataset | xivo | |
|---|---|---|
| 3 | 2 | |
| 103 | 828 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 year ago | |
| Shell | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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void-dataset
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Unsupervised Depth Completion from Visual Inertial Odometry
Hey there, interested in camera and range sensor fusion for point cloud (depth) completion?
Here is an extended version of our [talk](https://www.youtube.com/watch?v=oBCKO4TH5y0) at ICRA 2020 where we do a step by step walkthrough of our paper Unsupervised Depth Completion from Visual Inertial Odometry (joint work with Fei Xiaohan, Stephanie Tsuei, and Stefano Soatto).
In this talk, we present an unsupervised method (no need for human supervision/annotations) for learning to recover dense point clouds from images, captured by cameras, and sparse point clouds, produced by lidar or tracked by visual inertial odometry (VIO) systems. To illustrate what I mean, here is an [example](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry/blob/master/figures/void_teaser.gif?raw=true) of the point clouds produced by our method.
Our method is light-weight (so you can run it on your computer!) and is built on top of [XIVO] (https://github.com/ucla-vision/xivo) our VIO system.
For those interested here are links to the [paper](https://arxiv.org/pdf/1905.08616.pdf), [code](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry) and the [dataset](https://github.com/alexklwong/void-dataset) we collected.
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[N][R] ICRA 2020 extended talk for Unsupervised Depth Completion from Visual Inertial Odometry
Code for https://arxiv.org/abs/1905.08616 found: https://github.com/alexklwong/void-dataset
xivo
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Unsupervised Depth Completion from Visual Inertial Odometry
Hey there, interested in camera and range sensor fusion for point cloud (depth) completion?
Here is an extended version of our [talk](https://www.youtube.com/watch?v=oBCKO4TH5y0) at ICRA 2020 where we do a step by step walkthrough of our paper Unsupervised Depth Completion from Visual Inertial Odometry (joint work with Fei Xiaohan, Stephanie Tsuei, and Stefano Soatto).
In this talk, we present an unsupervised method (no need for human supervision/annotations) for learning to recover dense point clouds from images, captured by cameras, and sparse point clouds, produced by lidar or tracked by visual inertial odometry (VIO) systems. To illustrate what I mean, here is an [example](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry/blob/master/figures/void_teaser.gif?raw=true) of the point clouds produced by our method.
Our method is light-weight (so you can run it on your computer!) and is built on top of [XIVO] (https://github.com/ucla-vision/xivo) our VIO system.
For those interested here are links to the [paper](https://arxiv.org/pdf/1905.08616.pdf), [code](https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry) and the [dataset](https://github.com/alexklwong/void-dataset) we collected.
-
[N][R] ICRA 2020 extended talk for Unsupervised Depth Completion from Visual Inertial Odometry
Our method is light-weight (so you can run it on your computer!) and is built on top of XIVO our VIO system.
What are some alternatives?
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
open_vins - An open source platform for visual-inertial navigation research.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
rtabmap - RTAB-Map library and standalone application
DAD-3DHeads - Official repo for DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image (CVPR 2022).
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
r3live - A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
Open3D - Open3D: A Modern Library for 3D Data Processing
SuperPoint_SLAM - SuperPoint + ORB_SLAM2
VINS-Mono - A Robust and Versatile Monocular Visual-Inertial State Estimator
vortex-auv - Software for guidance, navigation and control for the Vortex AUVs. Purpose built for competing in AUV/ROV competitions.