---
license: other
task_categories:
- depth-estimation
- image-segmentation
- object-detection
language:
- en
tags:
- synthetic
- computer-vision
- depth
- optical-flow
- instance-segmentation
- camera-pose
- tracking
- rendered-scenes
size_categories:
- 1T<n<10T
pretty_name: MRQ Dataset
---

# MRQ Dataset

MRQ is a large-scale synthetic computer vision dataset with rendered multi-scene trajectories. It provides paired RGB frames, depth maps, optical-flow encodings, instance labels, camera poses, object/id mappings, and tracking annotations for depth, flow, segmentation, pose, and synthetic-to-real vision research.

<video controls src="https://huggingface.co/datasets/Yukki1011/PromptDepth/resolve/main/assets/195d399c3d6aa09243d813cd8d54d1ae.mp4" style="max-width: 100%; border-radius: 8px;"></video>

## Dataset Composition

The local source dataset is approximately 4.8 TB. The current Hub release is generated by the scene-packing upload pipeline in `pack_and_upload_scenes.py` and contains:

| Component | Count |
| --- | ---: |
| Top-level archive entries | 42 |
| Trajectory folders (`pathXX`) | 78 |
| Scene config files | 41 |
| RGB frames (`image/*.png`) | 197,877 |
| Depth maps (`depth/*.png`) | 197,577 |
| Optical-flow maps (`flow/*.png`) | 197,577 |
| Scaled-flow variants (`flow_scaled/*.png`) | 90,329 |
| Instance label arrays (`instance/*.npy`) | 197,577 |
| Camera pose files (`camera/*.json`) | 197,577 |
| Tracking annotation files (`track/*.txt`) | 407,325 |

Scene/archive entries include indoor, outdoor, urban, industrial, classroom, market, parking, forest, library, office, and stylized environments such as `CityStreets`, `DeepMarket`, `ModernCity`, `TropicalRainForest`, `ParkingGarage`, `Rome`, `Vol8`, and others.

## Hub Release Layout

To make the dataset practical to upload and download from Hugging Face, the release is stored as one compressed archive per top-level scene entry:

```text
archives/
├── AIUE_V02_002.tar.zst
├── BikeShop.tar.zst
├── CityStreets.tar.zst
├── DeepMarket.tar.zst
└── ...
archive_manifest.json
assets/
└── 195d399c3d6aa09243d813cd8d54d1ae.mp4
tools/
└── depth2pointcloud_downsampled.py
```

`archive_manifest.json` lists the archive entries included in the release. Each archive expands back to the original scene directory name:

```bash
tar --use-compress-program=zstd -xf archives/CityStreets.tar.zst
```

## Scene Structure

Most scenes contain one or more trajectory folders named `pathXX`:

```text
<scene>/
├── scene_config.json
└── pathXX/
    ├── camera/       # per-frame camera pose JSON files
    ├── depth/        # 16-bit grayscale PNG depth maps
    ├── flow/         # 16-bit RGB PNG optical-flow encodings
    ├── flow_scaled/  # optional generated flow variant
    ├── image/        # rendered RGB PNG frames
    ├── instance/     # NumPy instance label arrays
    ├── track/        # tracking correspondences/annotations
    ├── id2newid.json
    └── name2id.json
```

Some archives may contain auxiliary visualization or generated-output folders in addition to canonical `pathXX` trajectories.

## Upload Processing Notes

The scene-packing upload logic stages each scene before archiving:

- RGB/image PNG files are resized to fit within `1280 x 720` using Lanczos interpolation.
- Depth and optical-flow PNG encodings are resized to fit within `1280 x 720` using Triangle interpolation.
- Instance `.npy` label maps are resized with nearest-neighbor sampling to preserve ids.
- Camera JSON files are preserved as pose files. If downstream code uses intrinsics, scale `fx/cx` by `resized_width/original_width` and `fy/cy` by `resized_height/original_height`.
- Each staged scene includes `_resize_metadata.json` documenting resized files and modality-specific interpolation.
- Scene archives are processed in ascending source-directory size order, so smaller scenes are packed and uploaded before larger scenes.

This release format avoids millions of small Hub files while preserving the original per-scene/per-trajectory organization after extraction.

## Downsampled Visualization Tools

The repository includes `tools/depth2pointcloud_downsampled.py`, a point-cloud utility adapted from the verified local depth-to-pointcloud script. It first resizes RGB/depth to the Hub upload resolution, keeps camera extrinsics unchanged, recomputes FOV-based intrinsics from the downsampled image size, and then fuses colored point clouds.

For the current downsampled point-cloud visualization convention, the camera vertical axis is flipped relative to the original helper:

```python
z_cam = (v_grid - half_rows) * tan_fov_y * depths
```

Example:

```bash
python tools/depth2pointcloud_downsampled.py \
  --path-dir MRQ/DekoClass_night/path00 \
  --start 0 \
  --frames 80 \
  --subsample-step 6 \
  --camera-fov 90 \
  --output outputs/DekoClass_night_downsampled_pointcloud.ply
```

## Demo Videos

The following Rome demo was generated from `Rome/path00`, frames `000000` through `000299`. The original frames are `1920 x 1080` and are resized to `1280 x 720` before visualization.

<video controls src="https://huggingface.co/datasets/Yukki1011/PromptDepth/resolve/main/assets/Rome_path00_000000_000299_1920x1080_to_1280x720_rgb_semantic_disparity_demo_30fps.webm" style="max-width: 100%; border-radius: 8px;"></video>

<video controls src="https://huggingface.co/datasets/Yukki1011/PromptDepth/resolve/main/assets/Rome_path00_000000_000299_1920x1080_to_1280x720_flow_direction_demo_30fps.webm" style="max-width: 100%; border-radius: 8px;"></video>

## File Formats

- RGB images: PNG, 8-bit RGB.
- Depth maps: PNG, 16-bit grayscale.
- Optical flow: PNG, 16-bit RGB normalized encoding.
- Instance labels: NumPy `.npy` arrays.
- Camera poses: JSON files with `position` and `orientation`.
- Tracks: `.txt` files.
- Scene configuration and id maps: JSON files.

Example camera pose file:

```json
{
  "position": [-105.0, -93.2143783569336, 20.6917781829834],
  "orientation": [
    0.9553737342265323,
    0.03291562659926051,
    -0.1167528882766679,
    0.2693443011364547
  ]
}
```

## Loading Example

```python
from pathlib import Path
import json
import numpy as np
from PIL import Image

root = Path("MRQ")
scene = "CityStreets"
path = "path00"
frame = "000479"

rgb = Image.open(root / scene / path / "image" / f"{frame}.png")
depth = Image.open(root / scene / path / "depth" / f"{frame}.png")
flow = Image.open(root / scene / path / "flow" / f"{frame}.png")
instance = np.load(root / scene / path / "instance" / f"{frame}.npy")

with open(root / scene / path / "camera" / f"{frame}.json", "r") as f:
    camera = json.load(f)
```

## Intended Use

MRQ is intended for research and development in monocular and multi-frame depth estimation, optical flow, visual odometry/camera pose, instance segmentation, object tracking, and synthetic-to-real computer vision experiments.

## Notes

- The dataset is large. Prefer downloading only the scene archives needed for an experiment.
- Generated helper scripts, local editor folders, upload caches, and intermediate upload state are not part of the intended dataset content.
- Please verify licensing and redistribution terms for downstream public use.

## Citation

If you use this dataset, please cite the associated project or paper when available.