learning-topology-synthetic-data VS unsupervised-depth-completion-visual-inertial-odometry

Compare learning-topology-synthetic-data vs unsupervised-depth-completion-visual-inertial-odometry and see what are their differences.

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learning-topology-synthetic-data unsupervised-depth-completion-visual-inertial-odometry
5 2
37 180
- -
4.3 5.0
8 months ago 8 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

Posts with mentions or reviews of learning-topology-synthetic-data. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-24.

unsupervised-depth-completion-visual-inertial-odometry

Posts with mentions or reviews of unsupervised-depth-completion-visual-inertial-odometry. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-30.
  • Unsupervised Depth Completion from Visual Inertial Odometry
    3 projects | news.ycombinator.com | 30 Aug 2021
    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.

  • [N][R] ICRA 2020 extended talk for Unsupervised Depth Completion from Visual Inertial Odometry
    4 projects | /r/MachineLearning | 30 Aug 2021
    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?

When comparing learning-topology-synthetic-data and unsupervised-depth-completion-visual-inertial-odometry you can also consider the following projects:

instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)

calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

void-dataset - Visual Odometry with Inertial and Depth (VOID) dataset

student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings

xivo - X Inertial-aided Visual Odometry

simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

DECA - DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

PASS - The PASS dataset: pretrained models and how to get the data