Fast Feature Field (F³): A Predictive Representation of Events

Official repository for the paper Fast Feature Field (F³): A Predictive Representation of Events.

Richeek Das, Kostas Daniilidis, Pratik Chaudhari

GRASP Laboratory, University of Pennsylvania

[📜 Paper] • [🎬 Video] • [🌐 Website] • [📖 BibTeX]

Overview

F³ is a predictive representation of events. It is a statistic of past events, sufficient to predict future events. We prove that such a representation retains information about the structure and motion in the scene. F³ architecture is designed specifically for high-performance processing of events. F³ achieves low-latency computation by exploiting the sparsity of event data using a multi-resolution hash encoder and permutation-invariant architecture. Our implementation can compute F³ at 120 Hz and 440 Hz at HD and VGA resolutions, respectively, and can predict different downstream tasks at 25-75 Hz at HD resolution. These HD inference rates are roughly 2-5 times faster than the current state-of-the-art event-based methods. Please refer to the paper for more details.

An overview of the neural architecture for Fast Feature Field (F³) and its downstream variants.

Quickstart

Installation

See "Using F³ with torch.hub" below for a quick way to load F³ models for inference without cloning the repository. If you want to train F³ models or use the codebase for your own tasks, please install F³ locally by following the instructions below.

conda create -n f3 python=3.11
conda activate f3

Install F³ locally:

git clone git@github.com:grasp-lyrl/fast-feature-fields.git
cd fast-feature-fields
pip install -e .

Inference using pretrained F³ and its downstream variants [minimal.ipynb]

To get you up and running quickly, we can download a small sequence from M3ED and run some inference tasks on it with pretrained weights. Head over to [minimal.ipynb] to explore the inference pipeline for F³ and its downstream variants. This is the recommended way to get started. You can also load pretrained F³ models using torch.hub as shown below.

Using F³ with torch.hub

You can directly load pretrained F³ models using PyTorch Hub without cloning the repository:

import torch
model = torch.hub.load('grasp-lyrl/fast-feature-fields', 'f3',
                      name='1280x720x20_patchff_ds1_small', 
                      pretrained=True, return_feat=True, return_logits=False)

The name parameter can be replaced with any of the configuration names available under confs/ff/modeloptions/ (without the .yml extension).

Training an F³

Please refer to data/README.md for detailed instructions on setting up the datasets. This is important if you want to train F³ models on the M3ED, DSEC or MVSEC datasets. As an example, we show how to train an F³ model on the car urban daytime driving sequences of M3ED below. You can run the following command after setting up the car urban sequences of M3ED as per the instructions in data/README.md:

accelerate launch --config_file confs/accelerate_confs/2GPU.yml main.py\
                  --conf confs/ff/trainoptions/patchff_fullcardaym3ed_small_20ms.yml\
                  --compile

Training downstream tasks using F³

• Coming soon!

Using F³ as a pretrained backbone for your task

• Coming soon!

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Citation

If you find this code useful in your research, please consider citing:

@misc{das2025fastfeaturefield,
  title={Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events}, 
  author={Richeek Das and Kostas Daniilidis and Pratik Chaudhari},
  year={2025},
  eprint={2509.25146},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2509.25146},
}

Issues

If you encounter any issues, please open an issue on the GitHub Issues page or contact sudoRicheek