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 https://github.com/ai4ce/SSCBench.

翻译：语义场景补全（SSC）通过从稀疏观测中联合估计语义与几何信息，对于实现整体3D场景理解至关重要。然而，SSC领域（尤其在自动驾驶场景中）的发展受限于高质量数据集的匮乏。为应对这一挑战，我们提出SSCBench——一个整合了广泛使用的汽车数据集（如KITTI-360、nuScenes和Waymo）中场景的综合基准。SSCBench遵循学界已建立的设定与格式，便于在不同真实世界场景中轻松探索基于相机和激光雷达的SSC方法。我们对SSCBench上的现有最优算法进行了定性与定量评估，并承诺持续纳入新型汽车数据集与SSC算法，以推动该领域的进一步发展。相关资源已发布在https://github.com/ai4ce/SSCBench。