README.md

---
language:
- en
license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- novel view synthesis
- dynamic scene novel view segmentation
- 3d segmentation
- neural radiance fields
- gaussian splatting
datasets:
- hypernerf
- nerf-ds
- neural-3d-video
- google-immersive
- technicolor-light-field
configs:
- config_name: HyperNeRF-Mask
  data_files:
  - split: test
    path:
    - Mask-Benchmark/HyperNeRF-Mask/*/gt_masks/*.png
- config_name: NeRF-DS-Mask
  data_files:
  - split: test
    path:
    - Mask-Benchmark/NeRF-DS-Mask/*/gt_masks/*.png
- config_name: Neu3D-Mask
  data_files:
  - split: test
    path:
    - Mask-Benchmark/Neu3D-Mask/*/gt_masks/*.png
- config_name: Immersive-Mask
  data_files:
  - split: test
    path:
    - Mask-Benchmark/Immersive-Mask/*/gt_masks/*.png
- config_name: Technicolor-Mask
  data_files:
  - split: test
    path:
    - Mask-Benchmark/Technicolor-Mask/*/gt_masks/*.png
- config_name: default
  data_files:
  - split: test
    path:
    - Mask-Benchmark/*/*/gt_masks/*.png
---

# Mask-Benchmark Dataset

[**Project Page**](https://yunjinli.github.io/project-sadg/) | [**Paper**](https://huggingface.co/papers/2411.19290) | [**Code**](https://github.com/yunjinli/SADG-SegmentAnyDynamicGaussian)

This repository contains the dynamic scene novel-view segmentation benchmarks used in the paper "**TRASE: Tracking-free 4D Segmentation and Editing**" (also referred to as "**SADG: Segment Any Dynamic Gaussian Without Object Trackers**"). The benchmarks are designed for evaluating segmentation performance in dynamic novel view synthesis across various datasets.

## Overview

The Mask-Benchmark dataset provides ground truth segmentation masks for multiple dynamic scene datasets, including:
- **HyperNeRF** (A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields, ACM Transactions on Graphics (TOG))
- **NeRF-DS** (NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects, CVPR 2023)
- **Neu3D** (Neural 3D Video Synthesis from Multi-view Video, CVPR 2022)
- **Google Immersive** (Immersive Light Field Video with a Layered Mesh Representation, SIGGRAPH 2020 Technical Paper)
- **Technicolor Light Field** (Dataset and Pipeline for Multi-View Light-Field Video, CVPRW 2017)

These benchmarks allow for quantitative evaluation of segmentation accuracy (mIoU and mAcc) in novel view synthesis for dynamic scenes, which was previously lacking in the field.

# License Information for Mask-Benchmark Dataset

This Mask-Benchmark dataset is primarily licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC 4.0).

You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material

Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
- NonCommercial — You may not use the material for commercial purposes

For the full license text, please visit: https://creativecommons.org/licenses/by-nc/4.0/legalcode

## Component Datasets and Their License Terms

The Mask-Benchmark incorporates data derived from multiple source datasets, each with their own license terms that must be respected:

### 1. Neural 3D Video Dataset (Neu3D)
Licensed under CC-BY-NC 4.0.

### 2. HyperNeRF Dataset
Licensed under Apache License 2.0.

### 3. NeRF-DS Dataset
Licensed under Apache License 2.0.

### 4. Google Immersive Dataset
Refer to the original license terms provided by the Google Immersive project.

### 5. InterDigital Light-Field Dataset (Technicolor)
**INTERDIGITAL LIGHT-FIELD DATASET RELEASE AGREEMENT**

The goal of the InterDigital Light-Field dataset is to contribute to the development and assessment of new techniques, technology, and algorithms for Light-Field video processing. InterDigital has copyright and all rights of authorship on the dataset and is the principal distributor of the Light-Field dataset.

**CONSENT**
The researcher(s) agrees to restrictions including:
1. **Redistribution**: Shall not be further distributed without prior written approval.
2. **Modification and Non Commercial Use**: May not be modified or used for commercial purposes.
3. **Publication Requirements**: Permits publication for scientific purposes only.
4. **Citation/Reference**: All documents must acknowledge use by citing:
   *Dataset and Pipeline for Multi-View Light-Field Video*. N. Sabater, et al. CVPR Workshops, 2017.

## Using the Mask-Benchmark Dataset

By using the Mask-Benchmark dataset, you agree to:
1. Comply with the CC-BY-NC 4.0 license governing the overall dataset.
2. Adhere to all component dataset license terms listed above.
3. Properly cite both the Mask-Benchmark and the original source datasets.
4. Use the dataset for scientific and research purposes only.
   
# BibTex
```bibtex
@article{li2024sadg,
    title={SADG: Segment Any Dynamic Gaussian Without Object Trackers},
    author={Li, Yun-Jin and Gladkova, Mariia and Xia, Yan and Cremers, Daniel},
    journal={arXiv preprint arXiv:2411.19290},
    year={2024}
}
```