Add dataset card

README.md

@@ -0,0 +1,83 @@
+---
+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** of the full set: **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.
+
+## 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.
+- **Depth:** projective **z-depth in metres** (distance along the camera z-axis,
+  not Euclidean ray length). Decoded from the source 16-bit mm depth (`÷1000`);
+  invalid pixels (source value `0`) are zeroed — use `valid_mask` to ignore them.
+- **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).
+
+## 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)
+K     = np.load("structured3d/scene_00000/intrinsics.npy")   # (N, 3, 3)
+w2c   = np.load("structured3d/scene_00000/extrinsics.npy")    # (N, 4, 4)
+```
+
+## License
+
+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. Please cite the original Structured3D paper if you use this data.