---
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