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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR 2023 Highlight] Neural Kernel Surface Reconstruction
| Date | Stars |
|---|---|
| 2026-07-24 | 980 |
| 2026-07-25 | 981 |
| 2026-07-28 | 981 |
| 2026-07-30 | 981 |
| 2026-08-06 | 981 |
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# Neural Kernel Surface Reconstruction  **Neural Kernel Surface Reconstruction**<br> [Jiahui Huang](https://huangjh-pub.github.io/), [Zan Gojcic](https://zgojcic.github.io/), [Matan Atzmon](https://matanatz.github.io/), [Or Litany](https://orlitany.github.io/), [Sanja Fidler](https://www.cs.toronto.edu/~fidler/), [Francis Williams](https://www.fwilliams.info/) <br> **[Paper](https://arxiv.org/abs/2305.19590), [Project Page](https://research.nvidia.com/labs/toronto-ai/NKSR/)** Abstract: *We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced [Neural Kernel Fields (NKF)](https://nv-tlabs.github.io/nkf/) representation. It enjoys similar generalization capabilities to NKF, while simultaneously addressing its main limitations: (a) We can scale to large scenes through compactly supported kernel functions, which enable the use of memory-efficient sparse linear solvers. (b) We are robust to noise, through a gradient fitting solve. (c) We minimize training requirements, enabling us to learn from any dataset of dense oriented points, and even mix training data consisting of objects and scenes at different scales. Our method is capable of reconstructing millions of points in a few seconds, and handling very large scenes in an out-of-core fashion. We achieve state-of-the-art results on reconstruction benchmarks consisting of single objects, indoor scenes, and outdoor scenes.* For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/) ## News - 2025-09-08: Due to the expiration of the pre-built wheels, we uploaded the full CUDA code so that users can compile on their own. The new wheel is compatible with PyTorch 2.7.0 and CUDA 12.8. - 2023-06-24: Want to build a mesh from an iPhone scan? Our friend [Phong Nguyen Ha](https://github.com/phongnhhn92) kindly shared his tutorial for doing this! Check it out [here](https://drive.google.com/drive/folders/1RESJ6w6DND4mh8V-YY0a3rvSfSR0dQdD?usp=sharing). - 2023-06-08: Code released! ## Environment setup We recommend using the latest Python and PyTorch to run our algorithm. To install all dependencies using [conda](https://www.anaconda.com/): ```bash # Clone the repository git clone [email protected]:nv-tlabs/nksr.git cd nksr # Create conda environment conda env create -f environment.yml conda activate nksr pip install -r requirements.txt # Build NKSR pip install --no-build-isolation package/ ``` ## Testing NKSR on your own data We have tested our algorithm on multiple different spatial scales. It can reconstruct scenes spanning kilometers with millions of points+ on an RTX 3090 GPU. To use our `kitchen-sink` model (released under CC-BY-SA 4.0 license), the following code snippet suffices: ```python import torch import nksr bunny_geom = load_bunny_example() input_xyz = torch.from_numpy(np.asarray(bunny_geom.points)).float().to(device) input_normal = torch.from_numpy(np.asarray(bunny_geom.normals)).float().to(device) reconstructor = nksr.Reconstructor(device) field = reconstructor.reconstruct(input_xyz, input_normal, detail_level=1.0) mesh = field.extract_dual_mesh(mise_iter=1) ``` We have prepared detailed instructions about data preparation and different example usages at [NKSR Documentation Page](NKSR-USAGE.md). ## Reproducing results from the paper Our training and inference system is based on the [Zeus Deep Learning](ZEUS_DL.md) infrastructure, supporting both tensorboard and wandb (recommended) as loggers. To config Zeus, copy the default yaml file and modify the related paths: ```bash cp configs/default/zeus.yaml zeus_config.yaml ``` Modify the contents of `zeus_config.yaml` as needed to include your `wandb` account name and checkpoint/test results save directory. ### Training Data download links: - ShapeNet: Data is available [here](https://
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Jiahui Huang · @NVIDIA
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:afb9c6ad205d38e6, topic:computer-vision, topic:3d-reconstruction