learning-topology-synthetic-data
unsupervised-depth-completion-visual-inertial-odometry
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learning-topology-synthetic-data | unsupervised-depth-completion-visual-inertial-odometry | |
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37 | 182 | |
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9 months ago | 9 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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learning-topology-synthetic-data
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Want to use synthetic data, but don't want to deal with domain gap?
For those interested, here are our source code with pretrained mdoels (it is light-weight so it runs on your local machine!) and arxiv version of our paper.
paper: https://arxiv.org/pdf/2106.02994.pdf
Here are some of the reconstructions produced by our method:
https://github.com/alexklwong/learning-topology-synthetic-da...
https://github.com/alexklwong/learning-topology-synthetic-da...
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[N][R] Want to leverage synthetic data for 3d reconstruction, but don't want to deal with the photometric domain gap? (ICRA 2021 talk)
Code for https://arxiv.org/abs/2106.02994 found: https://github.com/alexklwong/learning-topology-synthetic-data
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?
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bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings
xivo - X Inertial-aided Visual Odometry
DECA - DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
Make-It-3D - [ICCV 2023] Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset
STEPS - This is the official repository for ICRA-2023 paper "STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation"
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners