Yuqun Wu · Chih-hao Lin · Henry Che · Aditi Tiwari · Chuhang Zou · Shenlong Wang · Derek Hoiem

This repository contains the code for the paper SceneDiff: A Benchmark and Method for Multiview Object Change Detection. We investigate the problem of identifying objects that have been changed between a pair of captures of the same scene at different times, introducing the first object-level multiview change detection benchmark and a new training-free method.

[Mar 2026] We update the evaluation protocol and default model parameters:

• Evaluation criteria. The previous evaluation used center-point matching for detection–GT correspondence. The evaluation now uses mask based IoU matching and reports three metrics:
  • px/im IoU – pixel-level IoU computed on merged masks across all views and frames.
  • obj/im AP – per-frame Average Precision with 0.5 IoU threshold (VOC-style).
  • obj/sc AP – per-scene Average Precision at the object level with 0.5 IoU threshold across all frames, reported in two variants:
    • without change-type requirement – a detection matches any GT object regardless of change type across both sequences.
    • with change-type requirement – a detection must also match the GT change type (moved vs. added/removed).
• Default evaluation parameters updated. --duplicate_match_threshold and --per_frame_duplicate_match_threshold now default to 1.

Download the SceneDiff benchmark dataset from 🤗 Hugging Face.

mkdir data && cd data
wget https://huggingface.co/datasets/yuqun/SceneDiff/resolve/main/scenediff_benchmark.zip
unzip scenediff_bechmark.zip

scenediff_benchmark/
├── data/                          # 350 sequence pairs
│   ├── sequence_pair_1/
│   │   ├── original_video1.mp4    # Raw video before change
│   │   ├── original_video2.mp4    # Raw video after change
│   │   ├── video1.mp4             # Video with annotation mask (before)
│   │   ├── video2.mp4             # Video with annotation mask (after)
│   │   ├── segments.pkl           # Dense segmentation masks for evaluation
│   │   └── metadata.json          # Sequence metadata
│   ├── sequence_pair_2/
│   │   └── ...
│   └── ...
├── splits/                        # Val/Test splits
│   ├── val_split.json
│   └── test_split.json
└── vis/                           # Visualization tools
    ├── visualizer.py              # Flask-based web viewer
    ├── requirements.txt
    └── templates/

About segments.pkl: See the detailed description here.

Visualization: For better visualization, run the command:

cd data/scenediff_benchmark/vis && pip install -r requirements.txt
python visualizer.py

We expect the method predictions have following structures:

output_dir/
├── sequence_pair_1/
│   └── object_masks.pkl           # Dense segmentations of changed objects (for evaluation)
├── sequence_pair_2/
└── ...

with object_masks.pkl following this structure:

object_masks = {
    'H': int,                           # Image height
    'W': int,                           # Image width
    'video_1': {                        # Objects existing in video_1
        'object_id_1': {                # Integer ID for each detected object
            'frame_id_1': {             # Integer frame number
                'mask': RLE_Mask,       # Run-length encoded mask
                'cost': float           # Confidence score of the prediction
            },
            ...
        },
        ...
    },
    'video_2': {                        # Objects existing in video_2
        'object_id_1': {                # Integer ID for each detected object
            'frame_id_1': {             # Integer frame number
                'mask': RLE_Mask,       # Run-length encoded mask
                'cost': float           # Confidence score of the prediction
            },
            ...
        },
        ...
    }
}

Then the evaluation script can be run with:

python scripts/evaluate_multiview.py \
    --pred_dir ${OUTPUT_DIR} \
    --splits val \
    --sets varied \
    --output_path ${OUTPUT_FILE_PATH} \
    --visualize False

Arguments:

• --duplicate_match_threshold: Max number of times a GT object can be matched at the object level (default: 1)
• --per_frame_duplicate_match_threshold: Max number of times a GT region can be matched per frame/view (default: 1)
• --splits: Choose from val, test, or all
• --sets: Choose from varied, kitchen, or All
• --visualize: Set to True to save visualization outputs

Output: The evaluation results will be saved to ${OUTPUT_FILE_PATH}

1. Clone this repository with submodules:

  git clone --recursive https://github.com/yuqunw/scene_diff.git
  cd scene_diff

2. Create conda environment and install dependencies:

  conda create -n scene_diff python=3.10 -y
  conda activate scene_diff
  pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121 # Install the pytorch fitting your nvcc version 
  pip install -r requirements.txt
  pip install torch-scatter -f https://data.pyg.org/whl/torch-2.5.1+cu121.html # install torch_scatter

3. Install submodules:

  # Install segment-anything submodule
  cd submodules/segment-anything-langsplat-modified
  pip install -e .
  cd ../..

1. Download the Segment-Anything checkpoint:

bash checkpoints/download_sam_checkpoint.sh

2. Configure DINOv3 checkpoint:

The DINOv3 checkpoint will be automatically downloaded on first use after filling in the checkpoint url. To set it up:

1. Visit the DINOv3 downloads page to apply for the checkpoint access
2. Right-click on dinov3_vith16plus_pretrain_lvd1689m-7c1da9a5.pth and copy the download link
3. Update the URL in configs/scenediff_config.yml:

 models:
   dinov3:
     weight_url: "<paste_your_copied_url_here>"

Run change detection on any two videos:

python scripts/demo.py \
    --config configs/scenediff_config.yml \
    --video1 path/to/video1.mp4 \
    --video2 path/to/video2.mp4 \
    --output output/demo

Output: The script generates point cloud visualizations including score maps and object segmentations for both videos in the specified output directory.

Parameters: You can modify parameters in configs/scenediff_config.yml. If the automatic threshold for change detection doesn't work well (score maps look correct but too many or few detections), you can manually set detection.object_threshold in the config file.

Run inference on all sequences in the benchmark:

python scripts/predict_multiview.py \
    --config configs/scenediff_config.yml \
    --splits val \
    --sets varied \
    --output_dir output/scenediff_benchmark

Arguments:

• --splits: Choose from val, test, or all
• --sets: Choose from varied, kitchen, or All
• --output_dir: Directory to save predictions
• Modify more arguments in the config file

We thank the great work from these repositories:

• Segment-Anything and LangSplat for region segmentation
• Pi3 for geometry estimation
• DINOv3 for appearance feature extraction

This project is released under the MIT License. See LICENSE for details.