Visual-based 3D semantic occupancy perception (also known as 3D semantic
scene completion) is a new perception paradigm for robotic applications like
autonomous driving. Compared with Bird's Eye View (BEV) perception, it extends
the vertical dimension, significantly enhancing the ability of robots to
understand their surroundings. However, due to this very reason, the
computational demand for current 3D semantic occupancy perception methods
generally surpasses that of BEV perception methods and 2D perception methods.
We propose a novel 3D semantic occupancy perception method, OccupancyDETR,
which consists of a DETR-like object detection module and a 3D occupancy
decoder module. The integration of object detection simplifies our method
structurally - instead of predicting the semantics of each voxels, it
identifies objects in the scene and their respective 3D occupancy grids. This
speeds up our method, reduces required resources, and leverages object
detection algorithm, giving our approach notable performance on small objects.
We demonstrate the effectiveness of our proposed method on the SemanticKITTI
dataset, showcasing an mIoU of 23 and a processing speed of 6 frames per
second, thereby presenting a promising solution for real-time 3D semantic scene
completion.

我们提出了一种新的 3D 语义占有率感知方法 OccupancyDETR，它包括一个类 DETR 的目标检测模块和一个 3D 占有率解码器模块，通过集成目标检测简化了我们的方法结构，提高了速度和性能，在 SemanticKITTI 数据集上展示了在实时 3D 语义场景完成方面的有效性。