Overview of nvdiffrec
The nvdiffrec project is an innovative approach developed by NVIDIA for converting multi-view image observations into high-quality 3D models. This project focuses on optimizing the geometry, materials, and lighting of 3D models to accurately represent real-world objects. The approach is detailed in the paper titled "Extracting Triangular 3D Models, Materials, and Lighting From Images".
Key Features
Differentiable Marching Tetrahedrons
nvdiffrec employs differentiable marching tetrahedrons for 3D model extraction. This technique is a crucial element adapted from NVIDIA's Kaolin library, designed to expedite 3D deep learning research.
Latest Updates
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Renderutils Library with Slangpy: As of October 20, 2023, the project introduced a version of the renderutils library developed with slangpy, a tool that uses automatic differentiation. This update simplifies the coding process without compromising runtime performance, and is available in the slang branch of the project repository.
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FlexiCubes Isosurfacing Support: On September 15, 2023, nvdiffrec added support for the FlexiCubes isosurfacing technique. This provides enhanced capabilities for creating smooth and adaptable surface models.
Getting Started
Requirements
To work with nvdiffrec, you need Python 3.6+, Visual Studio 2019+, Cuda 11.3+, and PyTorch 1.10+. It has been tested with Python 3.9 and PyTorch 1.10 in an Anaconda3 environment.
Installation
To set up nvdiffrec, you must have the Cuda toolkit installed. Here's a quick setup guide for Windows:
conda create -n dmodel python=3.9
activate dmodel
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
pip install ninja imageio PyOpenGL glfw xatlas gdown
pip install git+https://github.com/NVlabs/nvdiffrast/
pip install --global-option="--no-networks" git+https://github.com/NVlabs/tiny-cuda-nn#subdirectory=bindings/torch
imageio_download_bin freeimage
Start by activating the conda environment:
activate dmodel
Example Usage
nvdiffrec is optimized for high-end NVIDIA GPUs. If using a mid-range GPU, you can reduce the batch size in the configuration files.
To execute a simple genus 1 reconstruction example:
python train.py --config configs/bob.json
To visualize the training progress on Windows:
python train.py --config configs/bob.json --display-interval 20
Datasets
The project includes configurations for reproducing the main results using various datasets:
- NeRF Synthetic Dataset: Utilized for view interpolation results. Available for download via a Google Drive link provided in the documentation.
- NeRD Dataset: Contains real-world photogrammetric data with imperfect segmentation masks. It is accessible by cloning from GitHub and resizing the images.
Automated Dataset Setup
You can automatically download and preprocess all datasets by running the following script:
activate dmodel
cd data
python download_datasets.py
Server Deployment
nvdiffrec can also be deployed using Docker. To set up:
- Build the Docker image:
cd docker
./make_image.sh nvdiffrec:v1
- Start an interactive Docker container:
docker run --gpus device=0 -it --rm -v /raid:/raid -it nvdiffrec:v1 bash
For detached Docker runs, specify your preferred device and command configurations.
Conclusion
nvdiffrec is an advanced project aimed at precise 3D model extraction from images. With continuous updates and the support of various datasets and implementation options, it is a versatile tool for researchers and practitioners in 3D modeling and computer vision.