void-dataset VS instant-ngp

Compare void-dataset vs instant-ngp and see what are their differences.

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void-dataset instant-ngp
3 147
103 15,388
- 1.3%
0.0 6.7
almost 2 years ago 22 days ago
Shell Cuda
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

void-dataset

Posts with mentions or reviews of void-dataset. 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
    Code for https://arxiv.org/abs/1905.08616 found: https://github.com/alexklwong/void-dataset

instant-ngp

Posts with mentions or reviews of instant-ngp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-04.

What are some alternatives?

When comparing void-dataset and instant-ngp you can also consider the following projects:

unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)

awesome-NeRF - A curated list of awesome neural radiance fields papers

xivo - X Inertial-aided Visual Odometry

tiny-cuda-nn - Lightning fast C++/CUDA neural network framework

DAD-3DHeads - Official repo for DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image (CVPR 2022).

nerf-pytorch - A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)

TensoRF - [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields

colmap - COLMAP - Structure-from-Motion and Multi-View Stereo

instant-meshes - Interactive field-aligned mesh generator

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

nerf - Code release for NeRF (Neural Radiance Fields)