xivo
r3live
| xivo | r3live | |
|---|---|---|
| 2 | 1 | |
| 828 | 1,866 | |
| 0.0% | 2.7% | |
| 0.0 | 0.0 | |
| about 1 year ago | 10 months ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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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.
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[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.
r3live
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LiDAR-Camera fusion
Not really my area, but some light google work turned up this review paper from 2020 (conclusion: it's still a pretty new area of study) and this github repo for something released in 2022 that seems fairly functional.
What are some alternatives?
open_vins - An open source platform for visual-inertial navigation research.
openMVS - open Multi-View Stereo reconstruction library
rtabmap - RTAB-Map library and standalone application
AliceVision - Photogrammetric Computer Vision Framework
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)
LIO-SAM - LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
INSTINCT - INS Toolkit for Integrated Navigation Concepts and Training
Open3D - Open3D: A Modern Library for 3D Data Processing
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
SuperPoint_SLAM - SuperPoint + ORB_SLAM2
meshlab - The open source mesh processing system