Meshroom
CLIP
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Meshroom | CLIP | |
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126 | 103 | |
10,571 | 22,051 | |
2.3% | 5.6% | |
9.4 | 1.2 | |
1 day ago | 13 days ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
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Meshroom
- AI bots saying I made a fake post: NOT FAKE POST
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Gaussian Splatting is pretty cool
Take a look at Meshroom from https://alicevision.org/
It's a pretty decent tool, although not sure if videos work well.
I once tried to build a mesh from an aerial video by extracting certain frames and the result was ok-ish
- Magic123: One Image to High-Quality 3D Object Generation
- How would you tackle modeling something like this? Mesh modeling or sculpting?
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Sorry if this question is not allowed but I was directed to this page. I volunteer for an animal sanctuary with many disabled dogs and I was informed that 3d printers can be used to customise wheelchairs? Does anyone here do this or can point me in the right direction? Thank you
You'll need to get some practice with photogrammetry or other 3D scanning to get the appropriate shapes for each animal, this should be more or less something the right size with adequate straps and padding - https://alicevision.org is a good place to learn about photogrammetry.
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Can anyone scan this if I mail it to them?
Have you tried Alice Vision? https://alicevision.org
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Is there any free software/tools that will give me a percentage of overlap?
Huh. https://github.com/alicevision/meshroom/issues/1650 suggests you can get the camera positions into blender, and then putting pyramids views in, and then get the interested area.
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Best gazing ball and spray paint recipe for 18% gray ball?
Another alternative for ptgui is https://alicevision.org and it is free
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A comparison of Meshroom 2021.1 and 2023.1
A new version of Meshroom released last month, so I decided to compare the performance and results of the two with the same dataset and mostly the same settings.
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An AI-generated 3D scene created from photos
Photogrametrie https://alicevision.org/
CLIP
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How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
What are some alternatives?
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
open_clip - An open source implementation of CLIP.
ODM - A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Open3D - Open3D: A Modern Library for 3D Data Processing
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
disco-diffusion
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
meshroomcl - MeshroomCL: An OpenCL implementation of photogrammetry with the Meshroom interface
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation