README.md

metadata

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:

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:

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:

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 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.