Motion-aware Event Suppression for Event Cameras

Roberto Pellerito, Nico Messikommer, Giovanni Cioffi, Marco Cannici, Davide Scaramuzza

Robotics and Perception Group, University of Zürich

Robotics: Science and Systems (RSS) 2026

This is the official PyTorch implementation of the RSS 2026 paper Motion-aware Event Suppression for Event Cameras.

Citation

If you use any part of this code or datasets accompanying the paper please consider citing the following:

@inproceedings{Pellerito2026Suppression,
  title={Motion-aware Event Suppression for Event Cameras},
  author={Pellerito, Roberto and Messikommer, Nico and Cioffi, Giovanni and Cannici, Marco and Scaramuzza, Davide},
  booktitle={Robotics: Science and Systems 2026},
  year={2026}
}

Info

This repository contains the essential training and validation code for dynamic object mask prediction from event-camera data. The public release focuses on:

• training on DSEC;
• training on EVIMO v1;
• validation on EVIMO v1 at the current instant t0 and future instant t1;
• validation entry point for EED at t0 and t1.

Data loading is delegated to the external repository checked out at ev-loader/. The current ev-loader copy contains DSEC and EVIMO loaders. It does not currently expose an EED loader, so EED validation raises an explicit error until an evloader.EED_dataloader.EEDSequence implementation is added.

ev-loader Checkout

This repository expects ev-loader/ at the repository root. It is tracked as a Git submodule from senecobis/ev-loader and is pinned to commit b0d86a00bf35883b5ead089e3ca01bb7442e4379.

When cloning this repository, fetch the pinned loader checkout with:

git clone --recurse-submodules <event_suppression_repo_url>
cd event_suppression

If the repository was already cloned without submodules, run:

git submodule update --init --recursive

To recreate the same ev-loader/ checkout manually:

git clone https://github.com/senecobis/ev-loader.git ev-loader
git -C ev-loader checkout b0d86a00bf35883b5ead089e3ca01bb7442e4379

Installation

Create a minimal conda environment and install the Python packages with pip:

conda create -n evsup python=3.10 -y
conda activate evsup
export PYTHONNOUSERSITE=1

Install PyTorch. NVIDIA drivers are backward-compatible with older CUDA runtimes, so a machine reporting CUDA 13.x through nvidia-smi can run the CUDA 12.1 PyTorch wheels. For CUDA-capable machines:

python -m pip install --no-cache-dir \
  torch==2.5.1 torchvision==0.20.1 \
  --index-url https://download.pytorch.org/whl/cu121 \
  --extra-index-url https://pypi.org/simple

For CPU-only machines:

python -m pip install --no-cache-dir \
  torch==2.5.1 torchvision==0.20.1 \
  --index-url https://download.pytorch.org/whl/cpu \
  --extra-index-url https://pypi.org/simple

Then install Event Suppressor:

python -m pip install -r requirements.txt
python -m pip install -e .
python -m pip install pytest

PyTorch is intentionally not listed in requirements.txt because the correct wheel depends on your CUDA/CPU setup.

If importing PyTorch fails with ImportError: libcudnn.so.9, user-site packages are likely leaking into the conda environment. Keep PYTHONNOUSERSITE=1 set and repair the PyTorch stack with:

python -m pip install --force-reinstall --no-cache-dir \
  torch==2.5.1 torchvision==0.20.1 \
  --index-url https://download.pytorch.org/whl/cu121 \
  --extra-index-url https://pypi.org/simple

python -m pip show torch nvidia-cudnn-cu12 | grep -E 'Name|Version|Location'

The Location lines should point inside $CONDA_PREFIX/lib/python3.10/site-packages, not ~/.local/lib/python3.10/site-packages.

Do not install ev-loader with pip install -e ./ev-loader unless you also want all of its optional loader and visualization dependencies. This repository imports ev-loader directly from the checked-out ./ev-loader folder.

After installation, run:

python -m pytest -q
python train.py --help
python validate.py --help

Repository Layout

evsup/
  configs/
    train_dsec.json        # DSEC training config
    train_evimo.json       # EVIMO training config
    validate_evimo.json    # EVIMO t0/t1 validation config
    validate_eed.json      # EED t0/t1 validation config
  models/                  # Event Suppressor / Hydra recurrent U-Net
  loss/                    # Mask and event-warping losses
  data.py                  # Dataset builders backed by ev-loader
  training.py              # Training loop
  validation.py            # Validation loop
ev-loader/                 # External event-data loader repository
train.py                   # CLI wrapper
validate.py                # CLI wrapper
tests/                     # Public smoke/unit tests

Dataset Structure

Set data.path in the JSON configs to the dataset root.

DSEC:

DSEC/
  train/
    zurich_city_00_a/
    ...
  test/ or validation/
    ...

EVIMO v1 after conversion to HDF5:

EVIMO1/
  train/
    box/
      seq_00.h5
      ...
  test/
    box/
      seq_00.h5
      ...

EED expected structure:

EED/
  test/
    <sequence directories or files expected by the EED loader>

The EED structure depends on the missing ev-loader EED loader. Add that loader to ev-loader/evloader/EED_dataloader and keep the public validation command unchanged.

Training

Edit the dataset path in the config first:

"data": {
  "dataset": "evimo",
  "path": "/path/to/EVIMO1"
}

Train on EVIMO:

python train.py --config evsup/configs/train_evimo.json

Train on DSEC:

python train.py --config evsup/configs/train_dsec.json

Resume or fine-tune from a checkpoint:

Download the pretrained checkpoints from event_suppression_checkpoints.zip.

python train.py \
  --config evsup/configs/train_evimo.json \
  --checkpoint checkpoints/EventSuppressor_EVIMO_<timestamp>/model_epoch_10.pth

Checkpoints are written under loader.checkpoints_path.

Validation

Validate EVIMO at current and future instants:

python validate.py \
  --config evsup/configs/validate_evimo.json \
  --checkpoint checkpoints/EventSuppressor_EVIMO_<timestamp>/model_epoch_49.pth \
  --output results/evimo_model_epoch_49

Validate EED after adding the EED loader to ev-loader:

python validate.py \
  --config evsup/configs/validate_eed.json \
  --checkpoint checkpoints/EventSuppressor_EVIMO_<timestamp>/model_epoch_49.pth \
  --output results/eed_model_epoch_49

Validation writes results.json with per-sequence and aggregate metrics:

• IoU/t0, mIoU/t0, pIoU/t0, [email protected]/t0;
• IoU/t1, mIoU/t1, pIoU/t1, [email protected]/t1.

For short smoke runs, configs may include:

• loader.max_batches: stop training after this many batches per epoch;
• eval.max_sequences: validate only the first N sequences;
• eval.max_samples: validate only the first N pairs per sequence.

Tests

python -m pytest -q

The tests cover public config loading, metric computation, public module imports, and the explicit EED-loader error.