LERF: Language Embedded Radiance Fields

This is the official implementation for LERF.

NOTE: LERF is fully usable, however complete integration with Nerfstudio is not fully complete, as it requires a temporary separate branch lerf-merge in the Nerfstudio repo. In the coming few weeks this will be removed when code is refactored to more seamlessly support LERF. Remaining TODOs:

• Refactor Nerfstudio viewer to allow adding javascript components from within model files (this will remove the need for hard-coding a textbox into the viewer)
• Integrate into ns-render commands to render videos from the command line with custom prompts

Installation

LERF follows the integration guidelines described here for custom methods within Nerfstudio.

1. Install Nerfstudio From Source

Follow instructions at this link to install Nerfstudio from source. Checkout the Nerfstudio branch lerf-merge.

2. Build the viewer

NOTE: When full integration is complete, this will be unnecessary, however in the meantime we need to build the viewer ourselves to enable text prompts.

Follow the instructions at this link to build the viewer locally. Make sure you are on the branch lerf-merge, since this has extra code for a textbox in the viewer.

3. Install the lerf package

Navigate to this folder and run python -m pip install -e . This installs entrypoints for Nerfstudio to use

4. Run ns-install-cli

This will update the Nerfstudio ns-train command to register the LERF method.

Checking the install

Run ns-train -h: you should see a list of "subcommands" with lerf, lerf-big, and lerf-lite included among them.

Using LERF

Now that LERF is installed you can play with it!

• Launch training with ns-train lerf --data <data_folder>. This specifies a data folder to use. For more details, see Nerfstudio documentation.
• Launch the viewer by navigating to nerfstudio/nerfstudio/viewer/app and executing yarn start. This will provide a port number <server_port> (typically 4000).
• Connect to the viewer by forwarding the viewer port (we use VSCode to do this), and connect to http://localhost:<server_port>/?websocket_url=ws://localhost:<viewer_port> in your browser. The viewer port is provided in the ns-train output in a green box at the bottom.
• Within the viewer, you can type text into the textbox, then select the relevancy_0 output type to visualize relevancy maps.

Relevancy Map Normalization

By default, the viewer shows raw relevancy scaled with the turbo colormap. As values lower than 0.5 correspond to irrelevant regions, we recommend setting the range parameter to (-1.0, 1.0). To match the visualization from the paper, check the Normalize tick-box, which stretches the values to use the full colormap.

The images below show the rgb, raw, centered, and normalized output views for the query "Lily".

Resolution

The Nerfstudio viewer dynamically changes resolution to achieve a desired training throughput.

To increase resolution, pause training. Rendering at high resolution (512 or above) can take a second or two, so we recommend rendering at 256px

lerf-big and lerf-lite

If your GPU is struggling on memory, we provide a lerf-lite implementation that reduces the LERF network capacity and number of samples along rays. If you find you still need to reduce memory footprint, the most impactful parameters for memory are num_lerf_samples, hashgrid levels, and hashgrid size.

lerf-big provides a larger model that uses ViT-L/14 instead of ViT-B/16 for those with large memory GPUs.

Extending LERF

Be mindful that code for visualization will change as more features are integrated into Nerfstudio, so if you fork this repo and build off of it, check back regularly for extra changes.

Issues

Please open Github issues for any installation/usage problems you run into. We've tried to support as broad a range of GPUs as possible with lerf-lite, but it might be necessary to provide even more low-footprint versions. Thank you!

Using custom image encoders

We've designed the code to modularly accept any image encoder that implements the interface in BaseImageEncoder (image_encoder.py). An example of different encoder implementations can be seen in clip_encoder.py vs openclip_encoder.py, which implement OpenAI's CLIP and OpenCLIP respectively.

Code structure

(TODO expand this section) The main file to look at for editing and building off LERF is lerf.py, which extends the Nerfacto model from Nerfstudio, adds an additional language field, losses, and visualization. The CLIP and DINO pre-processing are carried out by pyramid_interpolator.py and dino_dataloader.py.