| --- |
| license: other |
| license_name: structured3d-research-only |
| task_categories: |
| - depth-estimation |
| - image-to-3d |
| tags: |
| - 3d |
| - depth |
| - posed-rgbd |
| - indoor-scenes |
| - synthetic |
| - structured3d |
| pretty_name: Structured3D Subset (3DVLM) |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Structured3D Subset (3DVLM) |
|
|
| A small, fast-to-download slice of the Structured3D synthetic indoor dataset, |
| converted to a uniform posed-RGB-D format for quick model test-runs. This is a |
| **subset**: **100 scenes** (randomly sampled, seed 0) from collection `00`, using |
| the pre-rendered **`full`** (furnished) perspective views. Across the 100 scenes |
| there are **2,198 frames** (3–49 per scene). |
|
|
| These are photorealistic synthetic renders with **perfect dense ground-truth |
| depth** and exact camera poses — no reconstruction or pseudo-labelling involved. |
|
|
| This subset is part of a family of uniformly-formatted posed-RGB-D test-run |
| datasets: see also `3dvlm-replica_subset`, `3dvlm-hm3d_subset`, and |
| `3dvlm-taskonomy_subset` (same on-disk layout and conventions). |
|
|
| ## Contents |
|
|
| 100 scenes, one `.tar` each under `structured3d/`. Each tar extracts to a scene |
| directory: |
|
|
| ``` |
| scene_00000/ |
| ├── images/ # frame_000000.jpg … (N RGB frames, 720×1280) |
| ├── depth.npy # (N, 720, 1280) float32 |
| ├── valid_mask.npy # (N, 720, 1280) bool — True where depth is valid |
| ├── extrinsics.npy # (N, 4, 4) float32 — world→camera (w2c) |
| ├── intrinsics.npy # (N, 3, 3) float32 — pinhole K (per-frame) |
| └── meta.json # scene_id, frame_ids, image_size, is_single_image |
| ``` |
|
|
| `N` varies per scene (3–49 frames). The first axis of every array is the frame, |
| in the same order as `meta.json`'s `frame_ids` and the sorted `images/` files. |
|
|
| > **Note — independent captures.** Each frame is a separate perspective view at a |
| > distinct room/camera position, **not** a video trajectory. `meta.json` sets |
| > `is_single_image: true`; do **not** assume cross-frame overlap or temporal |
| > continuity within a scene. |
|
|
| ## Conventions |
|
|
| - **Coordinate frame:** OpenCV (x-right, y-down, z-forward). `extrinsics` is the |
| **world→camera (w2c)** matrix; invert it for camera→world. The translation is in |
| **metres** (the source millimetre world is rescaled on conversion). |
| - **Depth:** projective **z-depth in metres** (distance along the camera z-axis, |
| not Euclidean ray length). Decoded from the source 16-bit millimetre depth |
| (`÷1000`); the source is already planar z-buffer depth, so **no Euclidean→z |
| cosine correction is applied**. Invalid pixels (source value `0`) are zeroed — |
| use `valid_mask` to ignore them. Typical valid coverage is **≈99.5%**, with |
| depths in roughly the **0.05–7 m** range. |
| - **Intrinsics:** **per-frame** pinhole `K`, reconstructed from each frame's |
| horizontal/vertical field of view (separate `fx`/`fy`, principal point centred). |
| Image size is 720×1280 (H×W). The reconstructed `(K, w2c)` are verified to |
| round-trip through the project's ray-map (DA3) convention. |
|
|
| ## Source & provenance |
|
|
| Built from the official **Structured3D perspective `full`** renders, collection |
| `00` (`Structured3D_perspective_full_00.zip`). Only three files per frame are used: |
| `rgb_rawlight.png` (the RGB modality in the full-perspective zip is |
| `rgb_rawlight.png`, **not** `rgb.png`), `depth.png`, and `camera_pose.txt`. Camera |
| poses come straight from `camera_pose.txt` (eye / view-dir / up / half-FOVs), built |
| into a right-handed look-at and converted to OpenCV `w2c`. No depth model or |
| pseudo-labelling is involved — depth and poses are the renderer's exact values. |
|
|
| There is no separate "full" mirror of this conversion; this 100-scene slice of |
| collection `00` is the published extent. |
|
|
| ## Quick start |
|
|
| ```python |
| import tarfile, json, numpy as np |
| from huggingface_hub import hf_hub_download |
| |
| p = hf_hub_download("helioom/3dvlm-structured3d_subset", "structured3d/scene_00000.tar", repo_type="dataset") |
| tarfile.open(p).extractall("structured3d/") |
| |
| meta = json.load(open("structured3d/scene_00000/meta.json")) |
| depth = np.load("structured3d/scene_00000/depth.npy") # (N, 720, 1280) |
| mask = np.load("structured3d/scene_00000/valid_mask.npy") # (N, 720, 1280) |
| K = np.load("structured3d/scene_00000/intrinsics.npy") # (N, 3, 3) |
| w2c = np.load("structured3d/scene_00000/extrinsics.npy") # (N, 4, 4) |
| |
| # Back-project frame 0 to a camera-frame point cloud (metres): |
| H, W = meta["image_size"] |
| fx, fy, cx, cy = K[0,0,0], K[0,1,1], K[0,0,2], K[0,1,2] |
| ys, xs = np.mgrid[0:H, 0:W] |
| z = depth[0] |
| X = (xs - cx) / fx * z |
| Y = (ys - cy) / fy * z |
| pts = np.stack([X, Y, z], -1)[mask[0]] # (M, 3) valid points |
| ``` |
|
|
| ## License & citation |
|
|
| Built on [Structured3D](https://github.com/bertjiazheng/Structured3D), released for |
| **research use only** under its original data agreement; the same terms apply to |
| this derived subset. If you use this data, please cite the original paper: |
|
|
| ```bibtex |
| @inproceedings{Structured3D, |
| title = {Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling}, |
| author = {Zheng, Jia and Zhang, Junfei and Li, Jing and Tang, Rui and Gao, Shenghua and Zhou, Zihan}, |
| booktitle = {Proceedings of The European Conference on Computer Vision (ECCV)}, |
| year = {2020} |
| } |
| ``` |
|
|
