differentiable_volumetric_rendering
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision" (by autonomousvision)
nvdiffrec
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images". (by NVlabs)
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| differentiable_volumetric_rendering | nvdiffrec | |
|---|---|---|
| 1 | 13 | |
| 775 | 2,048 | |
| 0.0% | 1.5% | |
| 1.8 | 4.8 | |
| over 2 years ago | 4 months ago | |
| Python | Python | |
| MIT License | 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.
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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.
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.
differentiable_volumetric_rendering
Posts with mentions or reviews of differentiable_volumetric_rendering.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-18.
nvdiffrec
Posts with mentions or reviews of nvdiffrec.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-18.
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[D] Found top conference papers using test data for validation.
It depends on which CV research you’re in. In NeRF view synthesis, it’s pretty common to use test sets as validation sets. This has been done in several papers, including oral papers.
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3D NeRF of a footstool
I think there came a paper recently nerf2mesh, which I still have to evaluate (but haven't found time yet). There's also https://github.com/NVlabs/nvdiffrec/. And there's cool easy-to-use research software like nerfstudio (at least if you compare it to a lot of the raw code releases from research papers).
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Fitting the texture from an image to the corresponding 3D model
For your use case, why is your model devoid of texture? You can try 3D scanning your desired object so that it comes with texture. Either that or use Nvidia's MoMA here to get your object from images.
- WHAT IS THE PROBLEM ???? HELP ME PLZ!!
- Blender animation augmented with AI
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[R] BUNGEENeRF: progressive neural radiance field for extreme multi-scale scene rendering
Have you seen this project: https://github.com/NVlabs/nvdiffrec (I haven't tried it). Also videos tend to have compression. If you can get images you'll get higher quality results with most photogrammetry software. Projects like meshroom are probably better for this if you have high quality pictures. There's a few articles that cover high quality scans that can help also.
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is NeRF photogrammetry? please don't call me old, but this technology, in my mind, does not fit the strict concept.
You can generate an accurate mesh from a NeRF: https://github.com/NVlabs/nvdiffrec, and measure from that.
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NeRF export options and pgrammetry application question
NERF specifically generates a radiance field, but there are research codes for turning that into a mesh (https://github.com/NVlabs/nvdiffrec) (not easy to use yet)
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[D] nvdiffrec setup
Hi, I'm not sure if this is the right place, but I was looking into seeing what the latest photo to model reconstruction looks like from here with NVIDIA (ArXiV paper is included there). There's a couple of neat examples, and after one dumb mistake setup was pretty easy. However, the meshes are not converging except very loosely when using the examples from the paper.
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nvdiffrec tutorial?
Hi everyone! I'm not sure this is the right place to ask, but I've been drooling over these cool ml and deep learning techniques showcased in videos. I was wondering if anyone could help me out in getting something like nvdiffrec to work with my own sample. https://github.com/NVlabs/nvdiffrec
What are some alternatives?
When comparing differentiable_volumetric_rendering and nvdiffrec you can also consider the following projects:
curated-list-of-awesome-3D-Morphable-Model-software-and-data - The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.
nvdiffrast - Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering
VIBE - Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
differentiable_volumetric_rendering vs curated-list-of-awesome-3D-Morphable-Model-software-and-data
nvdiffrec vs nvdiffrast
differentiable_volumetric_rendering vs VIBE
nvdiffrec vs transformers
nvdiffrec vs Real-Time-Voice-Cloning
nvdiffrec vs yolov5
nvdiffrec vs curated-list-of-awesome-3D-Morphable-Model-software-and-data