STEPS
unsupervised-depth-completion-visual-inertial-odometry
| STEPS | unsupervised-depth-completion-visual-inertial-odometry | |
|---|---|---|
| 1 | 2 | |
| 170 | 185 | |
| - | - | |
| 10.0 | 5.0 | |
| over 1 year ago | 10 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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STEPS
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Meet STEPS: A New Computer Vision Method That Jointly Learns A Nighttime Image Enhancer And A Depth Estimator Without Using Ground Truth
Quick Read: https://www.marktechpost.com/2023/02/07/meet-steps-a-new-computer-vision-method-that-jointly-learns-a-nighttime-image-enhancer-and-a-depth-estimator-without-using-ground-truth/ Paper: https://arxiv.org/pdf/2302.01334v1.pdf Github: https://github.com/ucaszyp/STEPS
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?
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
manydepth - [CVPR 2021] Self-supervised depth estimation from short sequences
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
deep-video-mvs - Code for "DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion" (CVPR 2021)
xivo - X Inertial-aided Visual Odometry
simplerecon - [ECCV 2022] SimpleRecon: 3D Reconstruction Without 3D Convolutions
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)