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README.md

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+# 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" [1]. 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.