| --- |
| task_categories: |
| - object-detection |
| - depth-estimation |
| tags: |
| - 3d-object-detection |
| - 3d-bounding-box |
| - point-cloud |
| - monocular-3d |
| pretty_name: WildDet3D Visualization Data |
| --- |
| |
| # WildDet3D Visualization Data |
|
|
| This repository hosts the visualization data for the **WildDet3D-Bench** benchmark — a human-annotated evaluation set for monocular 3D object detection in the wild. |
|
|
| ## Dataset Overview |
|
|
| WildDet3D-Bench is a validation set of **2,470 images** drawn from three source datasets, with **9,256 human-verified 3D bounding box annotations** across 2,196 images. |
|
|
| | Source | Images | Description | |
| |--------|-------:|-------------| |
| | COCO Val | 424 | MS-COCO 2017 validation | |
| | LVIS Train | 1,113 | LVIS v1.0 (COCO train images) | |
| | Objects365 Val | 933 | Objects365 v2 validation | |
| | **Total** | **2,470** | | |
|
|
| Each annotation has exactly **one human-selected 3D bounding box**, chosen from candidates generated by multiple 3D estimation algorithms (LA3D, SAM3D, Algorithm, DetAny3D, 3D-MooD) and validated through a multi-stage pipeline of crowdsourced annotation, quality control, human rejection review, and geometric filtering. |
|
|
| ## Repository Structure |
|
|
| ``` |
| . |
| ├── data/ # WildDet3D-Bench ground truth (for benchmark visualization) |
| │ ├── index.json # Master index with image metadata and scene hierarchy |
| │ ├── boxes/ # Per-image JSON: 2D/3D boxes, categories, quality flags |
| │ ├── images/ # Super-resolution images (4× upscaled) |
| │ ├── images_annotated/ # Thumbnails with pre-rendered 3D box overlays |
| │ ├── camera/ # Camera intrinsic parameters |
| │ └── pointclouds/ # PLY point clouds (~250k points each) |
| │ |
| └── model/ # Model predictions on WildDet3D-Bench (for model comparison visualization) |
| ├── images/ # Images with model prediction overlays |
| ├── box/ # Per-image model prediction boxes |
| └── text/ # Per-image model prediction metadata |
| ``` |
|
|
| ### `data/` — Benchmark Ground Truth |
|
|
| Contains the full WildDet3D-Bench validation set with human-annotated 3D bounding boxes: |
|
|
| - **2,196 images** with at least one valid 3D annotation (274 images filtered out) |
| - **Per-image box data** includes: 2D boxes (in 4× SR coordinates), 3D boxes (10D: center + dimensions + quaternion), category names, `ignore3D` flags, human quality ratings |
| - **Point clouds** reconstructed from monocular depth estimation |
| - **Annotated thumbnails** with 3D boxes projected onto images, colored by object category |
|
|
| ### `model/` — Model Predictions |
|
|
| Contains predictions from different 3D detection models evaluated on the benchmark, used by a separate model comparison visualization server. |
|
|
| ## 3D Box Format |
|
|
| Each 3D bounding box is represented as a 10-element array: |
|
|
| ``` |
| [cx, cy, cz, w, h, l, qw, qx, qy, qz] |
| ``` |
|
|
| | Field | Description | |
| |-------|-------------| |
| | `cx, cy, cz` | Box center in camera coordinates (meters) | |
| | `w, h, l` | Box dimensions (meters) | |
| | `qw, qx, qy, qz` | Rotation as unit quaternion | |
|
|
| **Coordinate system**: OpenCV camera convention (X-right, Y-down, Z-forward). |
|
|
| ## Annotation Pipeline |
|
|
| 1. **Monocular depth estimation** — per-pixel depth maps |
| 2. **4× super-resolution** — higher quality point clouds |
| 3. **Multi-algorithm 3D box generation** — candidate boxes per 2D detection |
| 4. **VLM scoring** — automated quality scoring (6 criteria, 0–12 total) |
| 5. **Human annotation** (Prolific) — workers select best candidate and rate quality |
| 6. **Human rejection review** — second-pass review of selected boxes |
| 7. **Geometric filtering** — GPT-estimated size validation and depth ratio checks |
| 8. **Composite image removal** — filter collage/grid images |
|
|
