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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
|