Mask-Benchmark Dataset

This repository contains the dynamic scene novel-view segmentation benchmarks used in the paper "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

[Original license terms for Google Immersive Dataset]

5. InterDigital Light-Field Dataset

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.

RELEASE OF THE DATASET

To advance the state-of-the-art in Light-Field video processing and editing, the InterDigital Light-Field dataset is made available to the researcher community for scientific research only. All other uses of the InterDigital Light-Field dataset will be considered on a case-by-case basis. To receive a copy of the Light-Field dataset, the requestor must agree to observe all of these Terms of use.

CONSENT

The researcher(s) agrees to the following restrictions on the Light-Field dataset:

1. Redistribution: Without prior written approval from InterDigital, the InterDigital Light-Field dataset, in whole or in part, shall not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. For the avoidance of any doubt, this prohibition includes further distributing, copying or disseminating to a different facility or organizational unit in the requesting university, organization, or company.

2. Modification and Non Commercial Use: Without prior written approval from InterDigital, the InterDigital Light-Field dataset, in whole or in part, may not be modified or used for commercial purposes.

3. Publication Requirements: In no case should the still frames or videos be used in any way that could directly or indirectly harm InterDigital. InterDigital permits publication (paper or web-based) of the data for scientific purposes only. Any other publication without scientific and academic value is strictly prohibited.

4. Citation/Reference: All documents and papers that report on research that uses the InterDigital Light-Field dataset must acknowledge the use of the dataset by including an appropriate citation to the followings:

   Dataset and Pipeline for Multi-View Light-Field Video. N. Sabater, G. Boisson, B. Vandame, P. Kerbiriou, F. Babon, M. Hog, T. Langlois, R. Gendrot, O. Bureller, A. Schubert, and V. Allie. CVPR Workshops, 2017.

5. No Warranty: THE PROVIDER OF THE DATA MAKES NO REPRESENTATIONS AND EXTENDS NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED. THERE ARE NO EXPRESS OR IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE MATERIAL WILL NOT INFRINGE ANY PATENT, COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS.

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

@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}
}