- unsupervised-depth-completion-visual-inertial-odometry VS instant-ngp
- unsupervised-depth-completion-visual-inertial-odometry VS dino
- unsupervised-depth-completion-visual-inertial-odometry VS calibrated-backprojection-network
- unsupervised-depth-completion-visual-inertial-odometry VS xivo
- unsupervised-depth-completion-visual-inertial-odometry VS simclr
- unsupervised-depth-completion-visual-inertial-odometry VS void-dataset
- unsupervised-depth-completion-visual-inertial-odometry VS bpycv
- unsupervised-depth-completion-visual-inertial-odometry VS learning-topology-synthetic-data
- unsupervised-depth-completion-visual-inertial-odometry VS PASS
- unsupervised-depth-completion-visual-inertial-odometry VS DiverseDepth
Unsupervised-depth-completion-visual-inertial-odometry Alternatives
Similar projects and alternatives to unsupervised-depth-completion-visual-inertial-odometry
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Scout Monitoring
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calibrated-backprojection-network
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
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bpycv
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learning-topology-synthetic-data
Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
unsupervised-depth-completion-visual-inertial-odometry reviews and mentions
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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
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, you can visit our github page for examples (gifs) of point clouds produced by our method.
Stats
alexklwong/unsupervised-depth-completion-visual-inertial-odometry is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of unsupervised-depth-completion-visual-inertial-odometry is Python.