Wanderland Dataset

Dataset Description

Wanderland is a large-scale urban dataset designed for geometrically grounded simulation and open-world embodied AI research. The dataset contains diverse urban scenes captured with dual fisheye cameras, providing high-quality data for 3D reconstruction, novel view synthesis, and navigation tasks.

Key Features

• Urban Scenes: Diverse outdoor environments with varying complexity
• Multi-Modal Data: RGB images, depth, 3D point clouds, 3D Gaussian Splatting models
• Camera Data: Fisheye images + undistorted pinhole images (800×800, 90° FOV)
• 3D Reconstructions: COLMAP sparse models + dense point clouds + 3DGS models
• Navigation Data: Isaac Sim compatible scene files (USDZ) + episode configurations
• Official Splits: 235 training scenes + 200 evaluation scenes (as used in the paper)

Supported Tasks

• 3D Reconstruction: Multi-view stereo, structure-from-motion, depth estimation
• Novel View Synthesis: NeRF, 3D Gaussian Splatting, view interpolation
• Embodied AI Navigation: Visual navigation, path planning, sim-to-real transfer
• Scene Understanding: 3D scene parsing, object detection, spatial reasoning

Dataset Statistics (V1)

Metric	Value
Total Scenes	435
Training Scenes	235
Evaluation Scenes	200
Images per Scene	200-1000 (varies)
Total Images	~420,000
Image Resolution (Undistorted)	800×800
Image Resolution (Fisheye)	2K
Camera Model	Dual fisheye → Pinhole projection
Point Cloud Size	1-10M points per scene
Total Dataset Size	~1.24TB

Dataset Structure

Each scene in the dataset contains the following files and directories:

data/
└── <scene_name>/
    ├── fisheye.tar.gz           # Original fisheye images (JPG, 1920×1080)
    ├── fisheye_mask.tar.gz      # Validity masks for fisheye images
    ├── images.tar.gz            # Undistorted images (PNG, 800×800, 90° FOV)
    ├── images_mask.tar.gz       # Validity masks for undistorted images
    ├── raw_pcd.ply              # Dense 3D point cloud (PLY format)
    ├── 3dgs.ply                 # Pre-trained 3D Gaussian Splatting model
    ├── transforms.json          # Camera parameters (intrinsics + extrinsics)
    ├── scene.usdz               # Isaac Sim compatible scene file
    ├── episodes.json            # Navigation episode configurations
    ├── sparse/                  # COLMAP sparse reconstruction
    │   └── 0/
    │       ├── cameras.bin      # Camera intrinsics (PINHOLE model)
    │       ├── images.bin       # Camera poses (quaternion + translation)
    │       └── points3D.bin     # Sparse 3D points
    └── nvs_split/               # Train/val splits for novel view synthesis
        ├── train.txt            # Training images (per-scene split)
        └── val.txt              # Validation images (per-scene split)

File Descriptions

Image Data:

• images/: Undistorted pinhole images (800×800, 90° FOV, PNG format)
• images_mask/: Validity masks indicating valid pixel regions
• fisheye/: Original fisheye images (JPG format)
• fisheye_mask/: Validity masks for fisheye images

3D Data:

• raw_pcd.ply: Dense point cloud with RGB colors (PLY format)
• 3dgs.ply: Pre-trained 3D Gaussian Splatting model
• sparse/0/: COLMAP sparse reconstruction (cameras, poses, sparse points)

Camera Parameters:

• transforms.json: Complete camera parameters (intrinsics, extrinsics, distortion)
• Coordinate system: COLMAP convention (camera-to-world)

Navigation Data:

• scene.usdz: USD scene file for NVIDIA Isaac Sim
• episodes.json: Navigation episode configurations

Data Splits:

• nvs_split/: Per-scene image splits for novel view synthesis
• train_scenes_v1.txt: Scene-level training split (235 scenes)
• eval_scenes_v1.txt: Scene-level evaluation split (200 scenes)

Camera Models

Fisheye Camera (Original):

• Distortion: 4-parameter fisheye model (k1, k2, k3, k4)
• Dual camera setup (left + right)

Undistorted Camera (Processed):

• Model: PINHOLE (rectilinear projection)
• Intrinsics: fx=fy=400.0, cx=cy=400.0
• Resolution: 800×800 pixels
• Field of view: 90 degrees

Coordinate System:

• Camera poses follow COLMAP convention
• Right-handed coordinate system
• Units: Meters

Download Instructions

For complete download instructions, options, and examples, see the download README.

License

This dataset is released under the Apache 2.0 License. See the LICENSE file for details.

Citation

If you use the Wanderland dataset in your research, please cite:

@article{liu2025wanderland,
  title={Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI},
  author={Liu, Xinhao and Li, Jiaqi and Deng, Youming and Chen, Ruxin and Zhang, Yingjia and Ma, Yifei and Guo, Li and Li, Yiming and Zhang, Jing and Feng, Chen},
  journal={arXiv preprint arXiv:2511.20620},
  year={2025}
}

Links

• Paper: arXiv:2511.20620
• Project Page: ai4ce.github.io/wanderland
• GitHub Repository: github.com/ai4ce/wanderland
• Download Tool: Download README