simclr
unsupervised-depth-completion-visual-inertial-odometry
Our great sponsors
| simclr | unsupervised-depth-completion-visual-inertial-odometry | |
|---|---|---|
| 13 | 2 | |
| 3,927 | 183 | |
| 1.8% | - | |
| 2.9 | 5.0 | |
| 11 months ago | 10 months ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simclr
-
Positive and Negative Sampling Strategies for Representation Learning in Semantic Search
For visual representations, you could look into SimCLR and MoCo. https://github.com/google-research/simclr https://github.com/facebookresearch/moco
-
[D] Why is random cropping necessary in SimCLR?
Yeah I think so, it's not hard to check https://github.com/google-research/simclr/blob/2fc637bdd6a723130db91b377ac15151e01e4fc2/data_util.py
-
[R] Deep Learning with a Small Training Batch (or Lack Thereof). Part 1
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
-
[D] Current trends in computer vision related to unsupervised learning
SimCLR v2.0 - https://arxiv.org/abs/2006.10029
-
Self-Supervised Contrastive Learning model for video dataset?
My data consists of binary labels (normal & anomalous) where the videos are already broken up into frames in the directory, I'm looking for a model where I can feed a normal-labeled video alongside an anomalous-labeled video like visualized in this example from the SimCLR Repo, a dog will represent the normal video and the chair the anomalous video.
-
[P] Choosing a self-supervised learning framework that's easy to use
No, go to the "tf2" folder in the repo root. https://github.com/google-research/simclr/tree/master/tf2
-
[D] What is meant by width in the SimCLRv2 paper?
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
-
[D] Funding PhD in Europe
[1] https://github.com/google-research/simclr [2] https://www.tensorflow.org/tfrc?hl=en&authuser=2
unsupervised-depth-completion-visual-inertial-odometry
-
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
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.
What are some alternatives?
swav - PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
SimCLR - PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
torchlars - A LARS implementation in PyTorch
xivo - X Inertial-aided Visual Odometry
Supervised-Constrastive-Learning-in-TensorFlow-2 - Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al. [Moved to: https://github.com/sayakpaul/Supervised-Contrastive-Learning-in-TensorFlow-2]
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)