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fix WISP_HEADLESS doc error & quick links missing link #98

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Jan 10, 2023
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fix WISP_HEADLESS doc error & quick links missing link #98

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4 changes: 2 additions & 2 deletions INSTALL.md
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Expand Up @@ -124,13 +124,13 @@ docker run --rm -it --gpus=all --net=host --ipc=host -e DISPLAY=$DISPLAY wisp
2. Extracted the data somewhere
3. You can train a NeRF using [NGLOD](https://nv-tlabs.github.io/nglod/) with:
```
WISP_HEADLESS=0 python3 app/main_nerf.py -config /app/nerf/configs/nerf_hash.yaml --dataset-path /path/to/lego --dataset-num-workers 4
WISP_HEADLESS=1 python3 app/main_nerf.py -config /app/nerf/configs/nerf_hash.yaml --dataset-path /path/to/lego --dataset-num-workers 4

```

4. To run training with the interactive renderering engine, run:
```
WISP_HEADLESS=1 python3 app/main_nerf.py -config /app/nerf/configs/nerf_hash.yaml --dataset-path /path/to/lego --dataset-num-workers 4
python3 app/main_nerf.py -config /app/nerf/configs/nerf_hash.yaml --dataset-path /path/to/lego --dataset-num-workers 4
```

See `README.md` for an elaborate explanation about running wisp.
4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -59,8 +59,8 @@ The following links contain additional information about various aspects of the
The Wisp repository includes:
* [app](app) - Complete implementations of papers & projects using Wisp components.
* [app/nerf](app/nerf) is the Wisp's upgraded NeRF app, with support for various grids types.
* [app/nerf](app/nglod) is an implementation of the paper _Neural Geometric Level of Detail ([Takikawa et al. 2021](https://nv-tlabs.github.io/nglod/)).
* `examples/` - Smaller demos demonstrating specific features & use cases of Wisp.
* [app/nglod](app/nglod) is an implementation of the paper _Neural Geometric Level of Detail ([Takikawa et al. 2021](https://nv-tlabs.github.io/nglod/)).
* [examples](examples) - Smaller demos demonstrating specific features & use cases of Wisp.
* [examples/latent_nerf](examples/latent_nerf) - demonstrates how to add a new NeuralField module, which exposess the latent dimensions as output & visualizes it.
* [examples/spc_browser](examples/spc_browser) - an app for converting meshes to [Structured Point Clouds](https://kaolin.readthedocs.io/en/latest/notes/spc_summary.html), and visualizing them.

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