OpenSpatial is an open-source 3D spatial understanding data engine engineered for high quality, extensive scalability, broad task diversity, and optimized efficiency.

By bridging the gap between massive 2D web data and complex 3D spatial reasoning, OpenSpatial provides a comprehensive suite for the next generation of Embodied AI and World Models.

OpenSpatial Pipeline: From 2D Web Data to 3D Spatial Understanding

• [2026.09.02] 🚀 We have released the 3D Lifting Module, which reconstructs metric scene geometry and object-level 3D bounding boxes from ordered images or video. See the 3D Lifting documentation.
• [2026.09.01] 🤗 We have released the model weights for four OpenSpatial spatial reasoning models, covering InternVL2.5, InternVL3, Qwen2.5-VL, and Qwen3-VL backbones. Explore the Hugging Face Collection or see the Model Zoo.
• [2026.06.20] 🎉 OpenSpatial has been accepted to ECCV 2026!
• [2026.04.15] 🎉 We have released the open-source subset of the OpenSpatial-3M dataset! Check it out on Hugging Face.
• [2026.04.08] 🎉 The OpenSpatial 3D data engine is now officially open-sourced.

• Web Data 3D Lifting: Advanced pipelines to transform large-scale 2D web imagery into geometrically consistent 3D representations.
• Diverse Data Generation: Automated engine for creating rich spatial understanding datasets, covering various environments and object-level details.
• Multi-Task Integration: Support for a wide range of tasks including 3D grounding, spatial reasoning, and scene captioning.
• Comprehensive Evaluation: Built-in benchmarking suite to evaluate spatial understanding capabilities across different model architectures.
• High Efficiency: Optimized for large-scale data processing with scalable distributed computing support.

The OpenSpatial-3M dataset is now available on Hugging Face. It contains 3 million high-fidelity samples designed to enhance 3D spatial reasoning in large multi-modal models.

• Repository: jdopensource/JoyAI-Image-OpenSpatial

We release four OpenSpatial checkpoints fine-tuned for spatial understanding and reasoning. The repositories contain full, inference-ready weights in safetensors format and are grouped in the OpenSpatial Models Collection.

Download a checkpoint with the Hugging Face CLI:

hf download VINHYU/OpenSpatial-Qwen3-VL-8B \
  --local-dir OpenSpatial-Qwen3-VL-8B

See each model card for architecture-specific loading instructions and base-model requirements.

The lifting module converts an ordered image sequence or video of one scene into metric depth, estimated cameras, a colored scene point cloud, and object-level 3D oriented bounding boxes. It preserves the released pipeline's Depth Anything 3 reconstruction, Gemini detection prompt, SAM 3 box-prompt segmentation, multi-view point aggregation, and floor-based Z-up alignment.

Use a separate Python 3.12 environment. Install a PyTorch build suitable for your CUDA driver, the regular OpenSpatial requirements, and the optional lifting dependencies:

conda create -n openspatial-lifting python=3.12 -y
conda activate openspatial-lifting
pip install torch==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt
pip install -r data_preprocessing/lifting/requirements.txt

Video input additionally requires ffmpeg on PATH. Object detection uses the public Gemini API and reads the credential from GEMINI_API_KEY; no API credential is included in this repository.

The original implementation uses Depth Anything 3 Nested Giant-Large and SAM 3. DA3's weights are CC BY-NC 4.0. SAM 3 uses Meta's SAM License and requires accepting its Hugging Face access request before download.

hf auth login
hf download depth-anything/DA3NESTED-GIANT-LARGE \
  --local-dir models/DA3NESTED-GIANT-LARGE
hf download facebook/sam3 sam3.pt --local-dir models/sam3

The semantic label matcher downloads sentence-transformers/all-MiniLM-L6-v2 automatically. A local path may instead be supplied with --semantic-model.

Pass one image, a directory containing one scene's ordered .jpg, .jpeg, .png, .bmp, or .webp images, or a video. Directory images are processed in lexical filename order. Video frames are extracted with ffmpeg; use --fps or --max-frames to limit them.

export GEMINI_API_KEY="<your-api-key>"
python -m data_preprocessing.lifting \
  --input /path/to/scene/images \
  --output outputs/lifting \
  --da3-model models/DA3NESTED-GIANT-LARGE \
  --sam3-checkpoint models/sam3/sam3.pt \
  --device cuda

The defaults preserve the original 504-pixel upper-bound resize, at most 100 reconstruction frames, and at most 80 detection views. These may be changed explicitly with --process-resolution, --target-frames, and --detection-views.

Each run creates <output>/<scene>_<timestamp>/ containing:

• openspatial.parquet: one multiview row in OpenSpatial's dataset format;
• pointclouds/scene_stitched.ply and per-object .ply files in aligned world coordinates;
• pointclouds/bounding_boxes.json: object labels, descriptions, centers, sizes, and Z rotations;
• processed_images/, depths/, masks/, and view_pointclouds/: aligned per-view assets referenced by the Parquet file;
• cameras/ and camera_extrinsics.json: camera-to-world text matrices and world-to-camera JSON matrices;
• detections/, scene_metadata.json, and summary.json: detector and run metadata.

Camera coordinates use OpenCV convention: X right, Y down, Z forward. Saved world geometry is metric and Z-up after floor alignment; the detected floor is shifted to Z=0. If no floor is detected, DA3 orientation is retained and the lowest reconstructed object point is shifted to Z=0. In openspatial.parquet, boxes use OpenSpatial's [cx, cy, cz, xl, yl, zl, z_angle, 0, 0] representation with zxy Euler order. Set an OpenSpatial config's dataset.data_dir to the generated Parquet file to use it in downstream multiview tasks.

For a small smoke run over four ordered frames:

python -m data_preprocessing.lifting \
  --input examples/room_frames \
  --output outputs/lifting \
  --da3-model models/DA3NESTED-GIANT-LARGE \
  --sam3-checkpoint models/sam3/sam3.pt \
  --device cuda --max-frames 4 --target-frames 4 --detection-views 4

• 3D Data Engine: Open-source the core 3D spatial understanding data engine.
• OpenSpatial-3M Dataset Release: Publicly release the large-scale 3M spatial understanding dataset. [HF Link]
• Model Weights Release: Release four trained spatial understanding models with InternVL and Qwen-VL backbones. [Model Zoo] [HF Collection]
• 3D Lifting Module: Release the core engine for lifting image sequences and video to metric 3D representations. [Documentation]

If you find OpenSpatial useful for your research, please consider citing our paper:

@inproceedings{openspatial2026,
  title={OpenSpatial: An Open-Source 3D Spatial Understanding Data Engine},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}