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README.md

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-license: cc-by-sa-4.0
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+license: cc-by-sa-4.0
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 # SSCBench: Monocular 3D Semantic Scene Completion Benchmark in Street Views
 
 [Yiming Li*](https://roboticsyimingli.github.io/), 
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 # Abstract
 Semantic scene completion (SSC) is crucial for holistic 3D scene understanding by jointly estimating semantics and geometry from sparse observations. However, progress in SSC, particularly in autonomous driving scenarios, is hindered by the scarcity of  high-quality datasets. To overcome this challenge, we introduce SSCBench, a comprehensive benchmark that integrates scenes from widely-used automotive datasets (e.g., KITTI-360, nuScenes, and Waymo). SSCBench follows an established setup and format in the community, facilitating the easy exploration of the camera- and LiDAR-based SSC across various real-world scenarios. We present quantitative and qualitative evaluations of state-of-the-art algorithms on SSCBench and commit to continuously incorporating novel automotive datasets and SSC algorithms to drive further advancements in this field.
 
-# SSCBench Dataset
-SSCBench consists of three carefully designed datasets, all based on existing data sources. For more details, please refer to the [dataset](./dataset) folder.
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-# Model Checkpoints
-We provide the model checkpoints of the experiments reported in the paper. The checkpoints can be accessed on [google drive](https://drive.google.com/drive/folders/1583Xy0nh46vNXg_StWvIp2B8IXij92Bm?usp=sharing). 
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-Note that the provided checkpoints are trained with the unified class labels. Details of class mappings can be found in the [configs](./dataset/configs) folder.
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 # Related SSC Projects
 - [Semantic Scene Completion from a Single Depth Image](https://github.com/shurans/sscnet), CVPR 2017
 - [LMSCNet: Lightweight Multiscale 3D Semantic Completion](https://github.com/astra-vision/LMSCNet), 3DV 2020
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 - SSCBench-nuScenes: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 - SSCBench-Waymo: [Waymo Dataset License Agreement for Non-Commercial Use (August 2019)](https://waymo.com/open/terms/)
 
-For more details, please refer to the [dataset](./dataset) folder file.
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 # Bibtex
 If this work is helpful for your research, please cite the following BibTeX entry.