---
license: other
language:
- en
pretty_name: Event-Guided Video Depth Estimation Workshop Dataset
tags:
- computer-vision
- depth-estimation
- event-camera
- video
- low-light
task_categories:
- depth-estimation
source_datasets:
- DVD sde_in_release
- DVD sde_out_release
---

# Event-Guided Video Depth Estimation Workshop Dataset

This dataset is a mirrored and aligned workshop-ready version of the DVD event-guided video depth estimation data.

It packages each scene into a canonical folder tree that aligns:

- low-light RGB frames
- per-frame event slices
- a scene-level `lowlight_event.npz`
- the matched depth ground truth copied from `inference_results/*/normal/depth.npz`

The dataset is designed for direct upload to Hugging Face as a dataset repository.

The official competition split is scene-level and uses a 6:2:2 train/val/test ratio by video count. The split is encoded directly in the top-level `train/`, `val/`, and `test/` folders.

## Dataset Goals

This dataset is intended for event-guided video depth estimation in low-light conditions.

The expected input is:

- low-light event data
- low-light RGB images

The expected output is:

- aligned depth supervision for the corresponding scene

## Directory Layout

The canonical structure is:

```text
workshop_data/
  train/
    <scene_name>/
      low/
      normal/
  val/
    <scene_name>/
      low/
      normal/
  test/
    <scene_name>/
      low/
      normal/
```

Each scene directory contains a `manifest.json` with the release and split metadata.

Each scene contains:

```text
scene_name/
  low/
    <timestamp>.png
    <timestamp>.npz
    lowlight_event.npz
  normal/
    <timestamp>.png
    depth.npz
    manifest.json
```

## File Semantics

### Low-light RGB frames

The PNG files under `low/` are the low-light image sequence used as model input.

### Event slices

Each per-frame `.npz` file in `low/` stores a slice of the event stream. The slices are timestamp-aligned and may overlap with neighboring slices in the raw source data. During materialization, the overlap is trimmed at timestamp boundaries so that adjacent slices do not double-count the same events.

`lowlight_event.npz` stores the concatenated trimmed event stream for the scene.

### Depth ground truth

`normal/depth.npz` is copied from the corresponding inference result directory:

`inference_results/<release>_png_depth/<scene>/normal/depth.npz`

This file is used as the aligned depth target for evaluation and training.

## Data Preparation

The repository includes a preprocessing script:

```bash
python utils/process_workshop_data.py
```

By default it reads from:

- `data/sde_in_release`
- `data/sde_out_release`
- `inference_results`

and materializes the final dataset under:

- `../workshop_data`

## Intended Use

This dataset is meant for research on event-guided video depth estimation under low-light conditions.

Recommended usage:

- feed low-light RGB sequences together with event information as input
- use the copied `normal/depth.npz` as supervision or evaluation ground truth
- keep the scene-level temporal alignment intact when training or evaluating temporal models

## Notes

- The dataset tree is already aligned for scene-level consumption.
- The raw source event slices contain overlap; use the trimmed workshop copy instead of the raw source tree.
- The competition split is by scene/video count, not by frame count.
- The folder name itself is the split assignment; no separate top-level split manifest is required.

## Competition Draft

See [COMPETITION.md](COMPETITION.md) for a Codabench-style challenge description draft that follows the same page-tab structure as the reference competition.

## Citation

If you use this dataset, please cite the original DVD project and mention that you are using the workshop-aligned export.