unsupervised-depth-completion-visual-inertial-odometry
PASS
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| unsupervised-depth-completion-visual-inertial-odometry | PASS | |
|---|---|---|
| 2 | 4 | |
| 183 | 257 | |
| - | - | |
| 5.0 | 0.0 | |
| 10 months ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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unsupervised-depth-completion-visual-inertial-odometry
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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.
PASS
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[D] Does anyone know a large varied image dataset that do NOT contain humans?
Here is what you are looking for https://github.com/yukimasano/PASS
- PASS
- Pass: A large-scale image dataset that doesn't include any humans
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University of Oxford Researchers Release ‘PASS’ Dataset With 1.4M+ Images (Free From Humans) For Self-Supervised Machine Learning
3 Min Read | Paper | Project | Code
What are some alternatives?
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
auth-source-pass - Integrate Emacs' auth-source with password-store
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
self-supervised - Whitening for Self-Supervised Representation Learning | Official repository
xivo - X Inertial-aided Visual Odometry
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
lightly - A python library for self-supervised learning on images.