Overview

We present Mosaic3D, a comprehensive solution for open-vocabulary 3D scene understanding that addresses three essential aspects: precise 3D region segmentation, comprehensive textual descriptions, and sufficient dataset scale. Our approach combines state-of-the-art open-vocabulary image segmentation models with region-aware vision-language models to create an automatic pipeline for generating high-quality 3D mask-text pairs.

Key Contributions

• Mosaic3D-5.6M Dataset: The largest 3D mask-text paired dataset to date, encompassing over 30K indoor scenes and approximately 1M RGB-D frames, yielding 5.6M region captions with 30M total text tokens
• Mosaic3D Model: A 3D visual foundation model (3D-VFM) combining a 3D encoder trained with contrastive learning and a lightweight mask decoder for open-vocabulary 3D semantic and instance segmentation
• State-of-the-art Performance: Achieves leading results on open-vocabulary 3D semantic and instance segmentation benchmarks including ScanNet200, Matterport3D, and ScanNet++

Dataset Advantages

Our Mosaic3D-5.6M dataset offers significant advantages over existing datasets:

• Scale: 5.6M mask-text pairs across 30K+ scenes (significantly larger than existing datasets)
• Precision: Leverages advanced open-vocabulary segmentation for precise region boundaries
• Rich Descriptions: Captures object attributes, spatial relationships, and scene context
• Quality: Combines robust region-aware VLMs for comprehensive textual annotations

Dataset

Mosaic3D-5.6M Download

The dataset can be found in Huggingface. Follow the instruction there to download and organize the data into required structure.

Environment Setup

Docker (Recommended)

# Build docker image
bash docker/docker_build.sh

# Run docker container with dataset path
bash docker/docker_run.sh /path/to/datasets

Conda Environment

# Create conda environment
conda env create -f environment.yaml

# Install requirements
pip install -r requirements.txt

# Install pre-commit hooks
pre-commit install

Model Architecture

Mosaic3D employs a two-stage training approach:

1. Per-point Language Alignment: Trains a 3D encoder using contrastive learning to align 3D point features with textual descriptions
2. Mask Decoder Training: Trains a lightweight mask decoder to predict instance segments from the aligned features

This design enables effective open-vocabulary 3D semantic and instance segmentation across diverse indoor scenes.

Training

Encoder Training

# Train Mosaic3D model with default configuration
python src/train.py experiment=train_spunet_multidata_ppt data=sc trainer.ddp trainer.devices=8 logger=wandb

Mask Decoder Training

# Download Segment3D checkpoint
python src/models/networks/opensegment3d/download_ckpt.py

# Train a lightweight mask decoder with default configuration
python src/train.py experiment=train_opensegment3d_scannet model.net.backbone_ckpt=/path/to/encoder.ckpt trainer.ddp trainer.devices=8 logger=wandb

Configuration Override

You can override any configuration parameter from the command line:

python src/train.py experiment=train_spunet_multidata_ppt data=sc+ar model=spunet34c trainer.max_epochs=100

Evaluation

The model achieves state-of-the-art results on multiple benchmarks:

• Annotation-free 3D semantic segmentation: ScanNet20 & ScanNet200, Matterport3D, ScanNet++
• Annotation-free 3D instance segmentation: ScanNet200

Annotation-free 3D semantic segmentation on ScanNet20 & ScanNet200.

Run the following commands to evaluate the pretrained models on ScanNet20 and ScanNet200 validation.

python src/eval.py experiment=train_spunet_multidata_ppt data=sc ckpt_path=[path/to/model/checkpoint]

Model Zoo

We provide pretrained models for both model_scale and data_scale experiments. All models are available on Hugging Face.

Available Models

Data-Scale Experiments

Models trained on different combinations of datasets:

Model	Training Data	Size	f-mIoU on ScanNet200
sc.ckpt	ScanNet	2.74 GB	13.0%
sc+ar.ckpt	ScanNet + ARKitScenes	2.74 GB	14.8%
sc+ar+sc++.ckpt	ScanNet + ARKitScenes + ScanNet++	2.74 GB	15.5%
sc+ar+sc+++ma.ckpt	SC + AR + SC++ + Matterport3D	2.74 GB	15.4%
sc+ar+sc+++ma+st.ckpt	SC + AR + SC++ + MA + Structured3D	2.74 GB	15.7%

Evaluate the impact of training data scale by testing models trained on different dataset combinations. Set the data configuration to match the training data of your checkpoint:

# Model trained on ScanNet only
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc \
  ckpt_path=ckpt_raw/converted/sc.ckpt

# Model trained on ScanNet + ARKitScenes
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar \
  ckpt_path=ckpt_raw/converted/sc+ar.ckpt

# Model trained on ScanNet + ARKitScenes + ScanNet++
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  ckpt_path=ckpt_raw/converted/sc+ar+sc++.ckpt

# Model trained on four datasets (+ Matterport3D)
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc+++ma \
  ckpt_path=ckpt_raw/converted/sc+ar+sc+++ma.ckpt

# Model trained on all five datasets (+ Structured3D)
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc+++ma+st \
  ckpt_path=ckpt_raw/converted/sc+ar+sc+++ma+st.ckpt

Model-Scale Experiments

Models with different backbone architectures (trained on ScanNet + ARKitScenes + ScanNet++):

Model	Architecture	Parameters	Size	f-mIoU on ScanNet200
spunet14a.ckpt	SparseUNet-14A	~14M	2.61 GB	13.2%
spunet18a.ckpt	SparseUNet-18A	~18M	2.64 GB	14.5%
spunet34c.ckpt	SparseUNet-34C	~34M	2.74 GB	15.5%
spunet50.ckpt	SparseUNet-50	~50M	2.97 GB	15.8%
spunet101.ckpt	SparseUNet-101	~101M	3.59 GB	16.0%

Evaluate different model architectures on the combined dataset (ScanNet + ARKitScenes + ScanNet++):

# SparseUNet-14A (smallest, fastest)
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  model=spunet14a+ppt \
  ckpt_path=ckpt_raw/converted/spunet14a.ckpt

# SparseUNet-18A
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  model=spunet18a+ppt \
  ckpt_path=ckpt_raw/converted/spunet18a.ckpt

# SparseUNet-34C (recommended for balance)
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  model=spunet34c+ppt \
  ckpt_path=ckpt_raw/converted/spunet34c.ckpt

# SparseUNet-50
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  model=spunet50+ppt \
  ckpt_path=ckpt_raw/converted/spunet50.ckpt

# SparseUNet-101 (largest, best performance)
python src/eval.py experiment=train_spunet_multidata_ppt \
  data=sc+ar+sc++ \
  model=spunet101+ppt \
  ckpt_path=ckpt_raw/converted/spunet101.ckpt

Citation

If you find this work useful, please consider citing:

@inproceedings{lee2025mosaic3d,
  title={Mosaic3d: Foundation dataset and model for open-vocabulary 3d segmentation},
  author={Lee, Junha and Park, Chunghyun and Choe, Jaesung and Wang, Yu-Chiang Frank and Kautz, Jan and Cho, Minsu and Choy, Chris},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={14089--14101},
  year={2025}
}

Acknowledgments

Our work builds upon several fantastic open-source projects. We'd like to express our gratitude to the authors of:

• Pointcept
• PLA & RegionPLC
• SPConv
• Segment3D
• OpenIns3D