  Vision-centric occupancy networks, which represent the surrounding
environment with uniform voxels with semantics, have become a new trend for
safe driving of camera-only autonomous driving perception systems, as they are
able to detect obstacles regardless of their shape and occlusion. Modern
occupancy networks mainly focus on reconstructing visible voxels from object
surfaces with voxel-wise semantic prediction. Usually, they suffer from
inconsistent predictions of one object and mixed predictions for adjacent
objects. These confusions may harm the safety of downstream planning modules.
To this end, we investigate panoptic segmentation on 3D voxel scenarios and
propose an instance-aware occupancy network, PanoSSC. We predict foreground
objects and backgrounds separately and merge both in post-processing. For
foreground instance grouping, we propose a novel 3D instance mask decoder that
can efficiently extract individual objects. we unify geometric reconstruction,
3D semantic segmentation, and 3D instance segmentation into PanoSSC framework
and propose new metrics for evaluating panoptic voxels. Extensive experiments
show that our method achieves competitive results on SemanticKITTI semantic
scene completion benchmark.
