Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+<p align="center">
+
+  <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/2409.18964'><img src='https://img.shields.io/badge/arXiv-2409.18964-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.
+
+### Overview
+
+The SceneDiff Benchmark contains **350 video sequence pairs** and **1,009 annotated objects** across two subsets:
+
+- **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
+
+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:
+
+![Dataset Statistics](media/dataset_stat.jpg)
+
+### Dataset Download
+```bash
+wget https://huggingface.co/datasets/yuqun/SceneDiff/resolve/main/scenediff_bechmark.zip
+unzip scenediff_bechmark.zip
+```
+
+### Dataset Structure
+
+```
+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/
+```
+
+### 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'
+        }
+    }
+}
+```
+
+### Loading Masks
+
+To convert RLE masks back to tensors:
+
+```python
+import torch
+from pycocotools import mask as mask_utils
+
+# Load and decode RLE mask
+tensor_mask = torch.tensor(mask_utils.decode(rle_mask))
+```
+
+### 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.
+
+### Evaluation
+Please refer to the [code repo](https://github.com/yuqunw/scene_diff?tab=readme-ov-file#evaluation) for evaluation.