DiverseDepth
unsupervised-depth-completion-visual-inertial-odometry
| DiverseDepth | unsupervised-depth-completion-visual-inertial-odometry | |
|---|---|---|
| - | 2 | |
| 216 | 185 | |
| - | - | |
| 0.0 | 5.0 | |
| almost 2 years 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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DiverseDepth
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Tracking mentions began in Dec 2020.
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?
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
AdaBins - Official implementation of Adabins: Depth Estimation using adaptive bins
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)
xivo - X Inertial-aided Visual Odometry
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)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
PASS - The PASS dataset: pretrained models and how to get the data
second.pytorch - SECOND for KITTI/NuScenes object detection