  Training deep models for semantic scene completion (SSC) is challenging due
to the sparse and incomplete input, a large quantity of objects of diverse
scales as well as the inherent label noise for moving objects. To address the
above-mentioned problems, we propose the following three solutions: 1)
Redesigning the completion sub-network. We design a novel completion
sub-network, which consists of several Multi-Path Blocks (MPBs) to aggregate
multi-scale features and is free from the lossy downsampling operations. 2)
Distilling rich knowledge from the multi-frame model. We design a novel
knowledge distillation objective, dubbed Dense-to-Sparse Knowledge Distillation
(DSKD). It transfers the dense, relation-based semantic knowledge from the
multi-frame teacher to the single-frame student, significantly improving the
representation learning of the single-frame model. 3) Completion label
rectification. We propose a simple yet effective label rectification strategy,
which uses off-the-shelf panoptic segmentation labels to remove the traces of
dynamic objects in completion labels, greatly improving the performance of deep
models especially for those moving objects. Extensive experiments are conducted
in two public SSC benchmarks, i.e., SemanticKITTI and SemanticPOSS. Our SCPNet
ranks 1st on SemanticKITTI semantic scene completion challenge and surpasses
the competitive S3CNet by 7.2 mIoU. SCPNet also outperforms previous completion
algorithms on the SemanticPOSS dataset. Besides, our method also achieves
competitive results on SemanticKITTI semantic segmentation tasks, showing that
knowledge learned in the scene completion is beneficial to the segmentation
task.
