Semantic scene completion, also known as semantic occupancy prediction, can provide dense geometric and semantic information for autonomous vehicles, which attracts the increasing attention of both academia and industry. Unfortunately, existing methods usually formulate this task as a voxel-wise classification problem and treat each voxel equally in 3D space during training. As the hard voxels have not been paid enough attention, the performance in some challenging regions is limited. The 3D dense space typically contains a large number of empty voxels, which are easy to learn but require amounts of computation due to handling all the voxels uniformly for the existing models. Furthermore, the voxels in the boundary region are more challenging to differentiate than those in the interior. In this paper, we propose HASSC approach to train the semantic scene completion model with hardness-aware design. The global hardness from the network optimization process is defined for dynamical hard voxel selection. Then, the local hardness with geometric anisotropy is adopted for voxel-wise refinement. Besides, self-distillation strategy is introduced to make training process stable and consistent. Extensive experiments show that our HASSC scheme can effectively promote the accuracy of the baseline model without incurring the extra inference cost. Source code is available at: https://github.com/songw-zju/HASSC.

翻译：语义场景补全，亦称语义占据预测，可为自动驾驶车辆提供密集的几何与语义信息，正受到学术界和工业界的日益关注。然而，现有方法通常将其视为体素级分类任务，并在训练过程中对三维空间中的每个体素平等对待。由于困难体素未能得到充分关注，在某些挑战性区域中的性能受到限制。三维稠密空间通常包含大量空体素，这些体素易于学习，但现有模型因需统一处理所有体素而耗费大量计算。此外，边界区域的体素比内部体素更难区分。本文提出HASSC方法，通过基于难度的设计训练语义场景补全模型。首先，从网络优化过程中定义全局难度，用于动态筛选困难体素；其次，采用具有几何各向异性的局部难度进行体素级精细化调整；同时引入自蒸馏策略，确保训练过程的稳定性与一致性。大量实验表明，所提出的HASSC方案可在不增加额外推理成本的情况下有效提升基线模型的精度。源代码已开源：https://github.com/songw-zju/HASSC。