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. Our resources are released on
this https URL

通过引入 SSCBench，这是一个综合基准数据集，其中包括广泛使用的汽车数据集，本文提出了一种方法来解决自动驾驶场景中 SSC 发展受限的问题。我们对目前的算法进行了定量和定性的评估，并承诺不断地将新颖的汽车数据集和 SSC 算法纳入我们的资源中，以推动此领域的进一步发展。