prolificdreamer
colmap
prolificdreamer | colmap | |
---|---|---|
5 | 28 | |
1,387 | 6,863 | |
3.0% | 3.7% | |
4.5 | 9.2 | |
6 months ago | 7 days ago | |
Python | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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prolificdreamer
- ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation
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Magic123: One Image to High-Quality 3D Object Generation
Interesting work! But given the time and complexity of the generation, I think VSD-loss based implementations have higher fidelity. See https://github.com/thu-ml/prolificdreamer or https://github.com/threestudio-project/threestudio/tree/main...
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[D] NeRF, LeRF, Prolific Dreamer, Neuralangelo, and a lot of other cool NeRF research
[Code] https://github.com/thu-ml/prolificdreamer
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Is it possible for me to approximate a depth map from a generated image and make a 3D model?
Personally, I'm waiting for code to drop for this one: https://github.com/thu-ml/prolificdreamer
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?
hyperreel - Code release for HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
Meshroom - 3D Reconstruction Software
stable-diffusion-howto - Run Stable Diffusion on your M1 Mac’s GPU (Intel and non-Apple PCs are also supported)
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
instruct-pix2pix
Hierarchical-Localization - Visual localization made easy with hloc
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
nerf - Code release for NeRF (Neural Radiance Fields)
threestudio - A unified framework for 3D content generation.
openMVS - open Multi-View Stereo reconstruction library
BungeeNeRF - [ECCV22] BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering