Make-It-3D
unsupervised-depth-completion-visual-inertial-odometry
| Make-It-3D | unsupervised-depth-completion-visual-inertial-odometry | |
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| 1 | 2 | |
| 1,693 | 185 | |
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| 6.9 | 5.0 | |
| 7 months ago | 10 months ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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Make-It-3D
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Meet Make-it-3D: An Artificial Intelligence (AI) Framework For High-Fidelity 3D Object Generation From A Single Image
Github: https://github.com/junshutang/Make-It-3D
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?
DreamCraft3D - [ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
NeuralRecon - Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
SegmentAnythingin3D - Segment Anything in 3D with NeRFs (NeurIPS 2023)
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
xivo - X Inertial-aided Visual Odometry
AvatarPoser - Official Code for ECCV 2022 paper "AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing"
simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
AvatarCLIP - [SIGGRAPH 2022 Journal Track] AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset