Panoptic occupancy poses a novel challenge by aiming to integrate instance
occupancy and semantic occupancy within a unified framework. However, there is
still a lack of efficient solutions for panoptic occupancy. In this paper, we
propose Panoptic-FlashOcc, a straightforward yet robust 2D feature framework
that enables realtime panoptic occupancy. Building upon the lightweight design
of FlashOcc, our approach simultaneously learns semantic occupancy and
class-aware instance clustering in a single network, these outputs are jointly
incorporated through panoptic occupancy procession for panoptic occupancy. This
approach effectively addresses the drawbacks of high memory and computation
requirements associated with three-dimensional voxel-level representations.
With its straightforward and efficient design that facilitates easy deployment,
Panoptic-FlashOcc demonstrates remarkable achievements in panoptic occupancy
prediction. On the Occ3D-nuScenes benchmark, it achieves exceptional
performance, with 38.5 RayIoU and 29.1 mIoU for semantic occupancy, operating
at a rapid speed of 43.9 FPS. Furthermore, it attains a notable score of 16.0
RayPQ for panoptic occupancy, accompanied by a fast inference speed of 30.2
FPS. These results surpass the performance of existing methodologies in terms
of both speed and accuracy. The source code and trained models can be found at
the following github repository: this https URL

Panoptic-FlashOcc 是一个简单而强大的 2D 特征框架，通过在单个网络中学习语义占用和类感知实例聚类，以实时实现全景占用，提高速度和准确性。