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README.md
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# Abstract
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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.
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# Related SSC Projects
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- [Semantic Scene Completion from a Single Depth Image](https://github.com/shurans/sscnet), CVPR 2017
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- [LMSCNet: Lightweight Multiscale 3D Semantic Completion](https://github.com/astra-vision/LMSCNet), 3DV 2020
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# Abstract
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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.
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## Data Usage
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Use the following command to merge the split parts of the datasets.
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```
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cat split_parts_* > combined.sqfs
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```
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# Related SSC Projects
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- [Semantic Scene Completion from a Single Depth Image](https://github.com/shurans/sscnet), CVPR 2017
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- [LMSCNet: Lightweight Multiscale 3D Semantic Completion](https://github.com/astra-vision/LMSCNet), 3DV 2020
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