nerf-pytorch
colmap
nerf-pytorch | colmap | |
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3 | 28 | |
5,052 | 6,794 | |
- | 2.7% | |
0.0 | 9.2 | |
4 months ago | 2 days ago | |
Python | C++ | |
MIT License | GNU General Public License v3.0 or later |
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nerf-pytorch
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[D] Something basic I don't understand about Nerfs
Found relevant code at https://github.com/yenchenlin/nerf-pytorch + all code implementations here
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helpful pointers to state-of-the-art material for depth estimation from multi-view videos captured from cameras with arbitrary poses.
I have been curious about NeRF and have tried out the software. I really like the idea, but I have found generating the NeRF representation to be too slow and does not do seem to do too well if the number of cameras in the rig are a bit sparse. I have seen that there have been some evolution and variations of the NeRF concept and I am looking into some of them currently.
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[P] Minimal PyTorch implementation of NeRF. Full model implementation and training code in 320 lines.
While there are other PyTorch implementations out there (e.g., this one and this one), I personally found them somewhat difficult to follow, so I decided to do a complete rewrite of NeRF myself. I tried to stay as close to the authors' text as possible, and I added comments in the code referring back to the relevant sections/equations in the paper. The final result is a tight 374 lines of heavily commented code (320 sloc—"source lines of code"—on GitHub) all contained in a single file. For comparison, this PyTorch implementation has approximately 970 sloc spread across several files, while this PyTorch implementation has approximately 905 sloc. A Colab notebook for the full model can be found here.
colmap
- Magic123: One Image to High-Quality 3D Object Generation
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Drone mapping is pretty dang cool
Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap
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Import many photogrammetry software's scenes into Blender
Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)
- Best options for monocular reconstruction?
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improving camera pose estimation using multiple aruco markers
See colmap for example https://colmap.github.io/
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2D images to 3D Object reconstruction
You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.
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Framework generate 3d meshes from camera images
COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/
- Nerfstudio: A collaboration friendly studio for NeRFs
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Neural Radiance Fields and input shape
I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here
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3D reconstruction of an object from videos/few images
Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.
What are some alternatives?
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Meshroom - 3D Reconstruction Software
pytorch-nerf - Minimal PyTorch implementations of NeRF and pixelNeRF.
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
nerf-pytorch - A PyTorch re-implementation of Neural Radiance Fields
Hierarchical-Localization - Visual localization made easy with hloc
nerf - Code release for NeRF (Neural Radiance Fields)
depthai - DepthAI Python API utilities, examples, and tutorials.
openMVS - open Multi-View Stereo reconstruction library
BundleFusion - [Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration