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| license: mit |
| viewer: false |
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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> |
| <p align="center"> |
| <a href='http://yuqunw.github.io/SceneDiff' style='padding-left: 0.5rem;'> |
| <img src='https://img.shields.io/badge/Project-Page-blue?style=flat&logo=Google%20chrome&logoColor=blue' alt='Project Page'></a> |
| <a href='https://arxiv.org/abs/2512.16908'><img src='https://img.shields.io/badge/arXiv-2512.16908-b31b1b.svg' alt='Arxiv'></a> |
| <a href='https://github.com/yuqunw/scene_diff' style='padding-left: 0.5rem;'> |
| <img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white' alt='Code'></a> |
| <a href='https://github.com/yuqunw/scenediff_annotator' style='padding-left: 0.5rem;'> |
| <img src='https://img.shields.io/badge/GitHub-Data%20Annotator-black?style=flat&logo=github&logoColor=white' alt='Data Annotator'></a> |
| </p> |
| </p> |
| |
| 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 |
| - **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 |
| ```bash |
| wget https://huggingface.co/datasets/yuqun/SceneDiff/resolve/main/scenediff_bechmark.zip |
| unzip scenediff_bechmark.zip |
| ``` |
|
|
| ### Dataset Structure |
|
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| ``` |
| 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/ |
| ``` |
|
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| ### Segments.pkl Structure: |
| ```python |
| segments = { |
| 'scenetype': str, # Type of scene change |
| 'video1_objects': { |
| 'object_id': { |
| 'frame_id': RLE_Mask # Run-length encoded mask |
| } |
| }, |
| 'video2_objects': { |
| 'object_id': { |
| 'frame_id': RLE_Mask # Run-length encoded mask |
| } |
| }, |
| 'objects': { |
| 'object_1': { |
| 'label': str, # Object label/name |
| 'in_video1': bool, # Present in video 1 |
| 'in_video2': bool, # Present in video 2 |
| 'deformability': str # 'rigid' or 'deformable' |
| } |
| } |
| } |
| ``` |
|
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| ### Loading Masks |
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| To convert RLE masks back to tensors: |
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| ```python |
| import torch |
| from pycocotools import mask as mask_utils |
| |
| # Load and decode RLE mask |
| tensor_mask = torch.tensor(mask_utils.decode(rle_mask)) |
| ``` |
|
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| ### Visualization |
| Run the command |
| ```bash |
| cd vis && pip install -r requirements.txt |
| python vis/visualizer.py |
| ``` |
| Open the link `http://localhost:5002` for visualized videos. |
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| ### Evaluation |
| Please refer to the [code repo](https://github.com/yuqunw/scene_diff?tab=readme-ov-file#evaluation) for evaluation. |
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