  Visual-based 3D semantic occupancy perception is a key technology for
robotics, including autonomous vehicles, offering an enhanced understanding of
the environment by 3D. This approach, however, typically requires more
computational resources than BEV or 2D methods. We propose a novel 3D semantic
occupancy perception method, OccupancyDETR, which utilizes a DETR-like object
detection, a mixed dense-sparse 3D occupancy decoder. Our approach
distinguishes between foreground and background within a scene. Initially,
foreground objects are detected using the DETR-like object detection.
Subsequently, queries for both foreground and background objects are fed into
the mixed dense-sparse 3D occupancy decoder, performing upsampling in dense and
sparse methods, respectively. Finally, a MaskFormer is utilized to infer the
semantics of the background voxels. Our approach strikes a balance between
efficiency and accuracy, achieving faster inference times, lower resource
consumption, and improved performance for small object detection. We
demonstrate the effectiveness of our proposed method on the SemanticKITTI
dataset, showcasing an mIoU of 14 and a processing speed of 10 FPS, thereby
presenting a promising solution for real-time 3D semantic occupancy perception.
