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.

翻译：基于视觉的3D语义占用感知（亦称3D语义场景补全）是一种面向自动驾驶等机器人应用的新型感知范式。与鸟瞰视角感知相比，它扩展了垂直维度，显著增强了机器人对周围环境的理解能力。然而正因如此，当前3D语义占用感知方法的计算需求通常高于鸟瞰感知方法和2D感知方法。我们提出了一种新颖的3D语义占用感知方法OccupancyDETR，它由一个类似DETR的目标检测模块和一个3D占用解码器模块组成。目标检测的集成在结构上简化了我们的方法——它并非预测每个体素的语义，而是识别场景中的目标及其各自的3D占用网格。这提升了方法速度，减少了所需资源，并充分利用目标检测算法，使我们的方法在小目标上展现出显著性能。我们在SemanticKITTI数据集上验证了所提方法的有效性，展示了23的平均交并比和每秒6帧的处理速度，从而为实时3D语义场景补全提供了一种有前景的解决方案。