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：基于视觉的三维语义占用感知（亦称三维语义场景补全）是用于自动驾驶等机器人应用的新型感知范式。与鸟瞰视图（BEV）感知相比，该方法在垂直维度上进行了拓展，显著增强了机器人理解周围环境的能力。然而，正因如此，当前三维语义占用感知方法的计算需求普遍高于BEV感知方法和二维感知方法。我们提出了一种新颖的三维语义占用感知方法——OccupancyDETR，其由类DETR目标检测模块与三维占用解码器模块组成。目标检测的引入从结构上简化了我们的方法：无需预测每个体素的语义信息，而是识别场景中的物体及其各自的三维占用网格。这加速了方法的运行效率、降低了资源需求，并通过利用目标检测算法，使我们的方法在小目标上展现出显著的性能优势。我们在SemanticKITTI数据集上验证了所提方法的有效性，实现了23的平均交并比（mIoU）与每秒6帧的处理速度，从而为实时三维语义场景补全提供了一种有前景的解决方案。