simplerecon
unsupervised-depth-completion-visual-inertial-odometry
| simplerecon | unsupervised-depth-completion-visual-inertial-odometry | |
|---|---|---|
| 4 | 2 | |
| 1,205 | 183 | |
| 1.2% | - | |
| 3.3 | 5.0 | |
| 11 months ago | 10 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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simplerecon
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Real time 3D reconstruction with SimpleRecon
An app would soon be there I think for iPad as well. Right now they have made the code public here: https://github.com/nianticlabs/simplerecon
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SimpleRecon: A Computer Vision Framework that Produces 3D Reconstructions Without the Use of 3D Convolutions
Continue reading | Check out the paper and github link
- SIMPLERECON — 3D Reconstruction without 3D Convolutions — 73ms per frame !
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.
What are some alternatives?
dream-creator - Quickly and easily create / train a custom DeepDream model
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
IGEV - [CVPR 2023] Iterative Geometry Encoding Volume for Stereo Matching and Multi-View Stereo
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
Monocular-Depth-Estimation-Toolbox - Monocular Depth Estimation Toolbox based on MMSegmentation.
xivo - X Inertial-aided Visual Odometry
FusionConverter - Design files for the open-hardware NeoGeo MVS to AES converter
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
deep-video-mvs - Code for "DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion" (CVPR 2021)
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset