ETScenes: Event-RGB Dataset for Fast Path Tracing-based Event Stream Rendering

ETScenes is a synthetic event-RGB dataset released with our paper:

EventTracer: Fast Path Tracing-based Event Stream Rendering
Zhenyang Li*, Xiaoyang Bai*, Jinfan Lu, Pengfei Shen, Edmund Y. Lam, Yifan Peng
arXiv 2025

ETScenes is generated by EventTracer, a path tracing-based event rendering pipeline designed to produce high-fidelity event streams from complex 3D scenes. Unlike conventional video-to-event simulators that rely on low-frame-rate RGB videos, EventTracer directly renders temporally dense event data from 3D scenes using low-sample-per-pixel path tracing and a lightweight physics-aware spiking network.

This dataset is intended to support research in:

• Event-based vision
• Synthetic event data generation
• Event-to-video reconstruction
• Event-based depth estimation
• Real2Sim / Sim2Real evaluation
• Neuromorphic vision under complex lighting and high-frequency motion
• Physically grounded rendering for event cameras

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Why ETScenes?

Event cameras asynchronously capture per-pixel brightness changes with extremely high temporal resolution and high dynamic range. However, collecting large-scale paired event-RGB data with dense annotations is expensive, hardware-dependent, and difficult to scale.

ETScenes addresses this challenge by providing synthetic event-RGB data rendered from 3D scenes, where the rendering pipeline naturally supports controlled camera trajectories, complex illumination, and extensible annotations.

Compared with conventional video-to-event pipelines, ETScenes is designed to better preserve:

• Fine structural details
• High-frequency visual patterns
• Fast temporal changes
• Complex lighting effects
• Event distributions closer to real-world event data

In our paper, ETScenes is used to evaluate the fidelity of simulated event streams through downstream Real2Sim tests on event-to-video reconstruction and event-based depth estimation.

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Dataset Highlights

• Path-traced synthetic scenes with realistic global illumination.
• Paired RGB / EXR / event data for event-based vision research.
• Multiple event simulation variants, including EventTracer outputs and baseline simulator outputs.
• EvSNet training data for studying event stream generation from low-SPP path-traced videos.
• Scene diversity, including indoor, outdoor, day, night, fast-motion, kitchen, classroom, and staircase scenes.
• Large-scale release, approximately 105GB in total.

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Repository Structure

The dataset is organized by scene folders. A typical scene folder contains rendered images, HDR EXR images, and event streams generated by different simulation methods.

ETScenes/
├── Bistro-Exterior-Day/
│   ├── images/
│   ├── images_exr/
│   ├── events-ours.npz
│   ├── events-v2e.npz
│   ├── events-v2ce.npz
│   ├── events-optix.npz
│   ├── events-nrd.npz
│   └── events-dvm.npz
│
├── Bistro-Exterior-Dark/
├── Bistro-Exterior-Fast/
├── Bistro-Exterior-Night/
├── Bistro-Interior/
├── classroom/
├── kitchen/
├── staircase/
│
└── EvSNet-training/
    ├── Bistro-Exterior-Day_2048SPP_events/
    ├── Bistro-Exterior-Day_64SPP_images/
    ├── Bistro-Exterior-Night_2048SPP_events/
    ├── Bistro-Exterior-Night_64SPP_images/
    ├── Bistro-Interior_2048SPP_events/
    ├── Bistro-Interior_64SPP_images/
    ├── classroom_2048SPP_events/
    ├── classroom_64SPP_images/
    ├── kitchen_2048SPP_events/
    ├── kitchen_64SPP_images/
    ├── staircase_2048SPP_events/
    └── staircase_64SPP_images/

File naming

File / Folder	Description
images/	Standard rendered RGB frames.
images_exr/	High-dynamic-range EXR rendered frames.
events-ours.npz	Event streams generated by EventTracer.
events-v2e.npz	Event streams generated by V2E baseline.
events-v2ce.npz	Event streams generated by V2CE baseline.
events-optix.npz, events-nrd.npz, events-dvm.npz	Additional rendering / simulation variants used for comparison or ablation.
EvSNet-training/	Training data for the lightweight event spiking network used in EventTracer.

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Download

Install dependencies

pip install -U huggingface_hub hf_xet

Download the full dataset

hf download andrewbxy/ETScenes \
  --repo-type dataset \
  --local-dir ETScenes

Download a single scene

For example, to download only Bistro-Exterior-Day:

hf download andrewbxy/ETScenes \
  --repo-type dataset \
  --include "Bistro-Exterior-Day/*" \
  --local-dir ETScenes

To download the EvSNet training subset:

hf download andrewbxy/ETScenes \
  --repo-type dataset \
  --include "EvSNet-training/*" \
  --local-dir ETScenes

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Quick Start

The event files are stored as .npz files. You can inspect the available keys as follows:

import numpy as np

path = "ETScenes/Bistro-Exterior-Day/events-ours.npz"

data = np.load(path, allow_pickle=True)
print(data.files)

for key in data.files:
    arr = data[key]
    print(key, type(arr), getattr(arr, "shape", None), getattr(arr, "dtype", None))

Since different downstream pipelines may expect different event representations, we recommend first inspecting the .npz keys and then adapting the loading script to your model or benchmark format.

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Recommended Use Cases

ETScenes can be used for:

1. Benchmarking event simulators
   Compare EventTracer against V2E, V2CE, and other video-to-event or scene-to-event simulation methods.

2. Event-to-video reconstruction
   Evaluate whether pretrained event-to-video models can reconstruct high-quality intensity frames from synthetic event streams.

3. Event-based depth estimation
   Study how realistic synthetic events transfer to geometric perception tasks.

4. Real2Sim fidelity evaluation
   Test how closely synthetic event data matches real-world event distributions by evaluating real-data-pretrained models on simulated data.

5. Training and validating event generation models
   Use the EvSNet training subset to study event stream generation from noisy low-SPP path-traced images.

6. Rendering-aware neuromorphic vision
   Explore how global illumination, HDR lighting, and high-frequency textures affect event camera simulation.

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Relation to EventTracer

ETScenes is released as part of the EventTracer project. EventTracer is a fast path tracing-based event stream rendering pipeline. It combines:

• Low-SPP Monte Carlo path tracing for efficient temporally dense rendering.
• A lightweight event spiking network, EvSNet, for converting noisy path-traced pixel illuminance into event streams.
• A bipolar leaky integrate-and-fire unit, BiLIF, to better model event sensor behavior.
• A bidirectional earth mover’s distance loss to train event sequence generation.

The goal is to generate synthetic event streams that are both efficient to render and more faithful to real event camera outputs.

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Dataset Scope and Limitations

ETScenes is primarily designed for research on synthetic event generation and event-based vision benchmarking. It is not a replacement for all real-world event camera datasets.

Please note:

• The dataset is synthetic and rendered from 3D scenes.
• The current release focuses on scene-level event-RGB data and simulator comparisons.
• Large-scale Sim2Real training may require additional scene diversity, more dynamic objects, and broader real-world validation.
• Users should carefully evaluate domain gaps before deploying models trained only on ETScenes to real sensors.

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License

This dataset is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

You may use this dataset for academic and non-commercial research purposes under the terms of the license. Please check the license carefully before redistribution or commercial use.

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Citation

If you find ETScenes or EventTracer useful for your research, please cite our paper:

@article{li2025eventtracer,
  title={EventTracer: Fast Path Tracing-based Event Stream Rendering},
  author={Li, Zhenyang and Bai, Xiaoyang and Lu, Jinfan and Shen, Pengfei and Lam, Edmund Y. and Peng, Yifan},
  journal={arXiv preprint arXiv:2508.18071},
  year={2025}
}

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Contact

For questions, issues, or discussions, please open a discussion on this Hugging Face dataset page.

Project page / code release will be updated here when available.