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--- |
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license: mit |
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viewer: false |
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--- |
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<p align="center"> |
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<h1 align="center">SceneDiff: A Benchmark and Method for Multiview Object Change Detection</h1> |
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<p align="center"> |
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<a href='http://yuqunw.github.io/SceneDiff' style='padding-left: 0.5rem;'> |
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<img src='https://img.shields.io/badge/Project-Page-blue?style=flat&logo=Google%20chrome&logoColor=blue' alt='Project Page'></a> |
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<a href='https://arxiv.org/abs/2512.16908'><img src='https://img.shields.io/badge/arXiv-2512.16908-b31b1b.svg' alt='Arxiv'></a> |
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<a href='https://github.com/yuqunw/scene_diff' style='padding-left: 0.5rem;'> |
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<img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white' alt='Code'></a> |
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<a href='https://github.com/yuqunw/scenediff_annotator' style='padding-left: 0.5rem;'> |
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<img src='https://img.shields.io/badge/GitHub-Data%20Annotator-black?style=flat&logo=github&logoColor=white' alt='Data Annotator'></a> |
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</p> |
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</p> |
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This repository contains the data for the paper [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](http://yuqunw.github.io/SceneDiff). 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. |
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### Overview |
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The SceneDiff Benchmark contains **350 video sequence pairs** and **1,009 annotated objects** across two subsets: |
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- **Varied subset (SD-V)**: 200 sequence pairs collected in a wide variety of daily indoor and outdoor scenes |
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- **Kitchen subset (SD-K)**: 150 sequence pairs from the [HD-Epic dataset](https://hd-epic.github.io/) with changes that naturally occur during cooking activities |
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For each video pair, we record all changed objects' attributes, including object names and deformability, and annotate their full segmentation masks in all visible frames. Each object is categorized with a change status: *Added*, *Removed*, or *Moved*. Statistics for each subset: |
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### Dataset Download |
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```bash |
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wget https://huggingface.co/datasets/yuqun/SceneDiff/resolve/main/scenediff_bechmark.zip |
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unzip scenediff_bechmark.zip |
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``` |
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### Dataset Structure |
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``` |
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scenediff_benchmark/ |
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├── data/ # 350 sequence pairs |
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│ ├── sequence_pair_1/ |
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│ │ ├── original_video1.mp4 # Raw video before change |
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│ │ ├── original_video2.mp4 # Raw video after change |
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│ │ ├── video1.mp4 # Video with annotation mask (before) |
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│ │ ├── video2.mp4 # Video with annotation mask (after) |
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│ │ ├── segments.pkl # Dense segmentation masks for evaluation |
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│ │ └── metadata.json # Sequence metadata |
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│ ├── sequence_pair_2/ |
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│ │ └── ... |
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│ └── ... |
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├── splits/ # Val/Test splits |
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│ ├── val_split.json |
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│ └── test_split.json |
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└── vis/ # Visualization tools |
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├── visualizer.py # Flask-based web viewer |
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├── requirements.txt |
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└── templates/ |
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``` |
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### Segments.pkl Structure: |
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```python |
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segments = { |
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'scenetype': str, # Type of scene change |
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'video1_objects': { |
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'object_id': { |
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'frame_id': RLE_Mask # Run-length encoded mask |
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} |
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}, |
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'video2_objects': { |
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'object_id': { |
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'frame_id': RLE_Mask # Run-length encoded mask |
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} |
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}, |
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'objects': { |
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'object_1': { |
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'label': str, # Object label/name |
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'in_video1': bool, # Present in video 1 |
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'in_video2': bool, # Present in video 2 |
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'deformability': str # 'rigid' or 'deformable' |
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} |
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} |
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} |
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``` |
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### Loading Masks |
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To convert RLE masks back to tensors: |
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```python |
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import torch |
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from pycocotools import mask as mask_utils |
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# Load and decode RLE mask |
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tensor_mask = torch.tensor(mask_utils.decode(rle_mask)) |
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``` |
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### Visualization |
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Run the command |
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```bash |
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cd vis && pip install -r requirements.txt |
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python vis/visualizer.py |
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``` |
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Open the link `http://localhost:5002` for visualized videos. |
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### Evaluation |
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Please refer to the [code repo](https://github.com/yuqunw/scene_diff?tab=readme-ov-file#evaluation) for evaluation. |
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