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:
this https URL

我们提出了一种 HASSC 方法，通过对困难体素进行设计，训练语义场景完成模型。全局难度和局部难度用于认知体素区域，同时引入自我蒸馏策略使得训练过程更加稳定和一致。广泛的实验表明，我们的 HASSC 方案可以有效提高基线模型的准确性，而不会增加额外的推断成本。