OccupancyDETR：将DETR用于混合稠密-稀疏3D占据预测
OccupancyDETR: Using DETR for Mixed Dense-sparse 3D Occupancy Prediction
AI总结：
针对3D语义占据感知计算资源消耗高的问题，提出OccupancyDETR方法，采用类DETR检测与混合稠密-稀疏解码器区分前后景并分别处理，在SemanticKITTI上实现14的mIoU与10FPS速度，兼顾效率与精度。
摘要：
基于视觉的3D语义占据感知是机器人技术（包括自动驾驶汽车）的一项关键技术，可通过3D形式增强对环境的理解。但这类方法通常比BEV或2D方法需要更多计算资源。我们提出了一种新型3D语义占据感知方法OccupancyDETR，该方法采用类DETR目标检测机制以及混合稠密-稀疏3D占据解码器。我们的方法可区分场景中的前景与背景：首先，通过类DETR目标检测识别前景物体；随后，将前景与背景物体的查询输入混合稠密-稀疏3D占据解码器，分别采用稠密和稀疏方式进行上采样；最后，利用MaskFormer推断背景体素的语义。我们的方法在效率与精度之间取得了平衡，具备更快的推理速度、更低的资源消耗，且小目标检测性能有所提升。我们在SemanticKITTI数据集上验证了所提方法的有效性，其mIoU达14，处理速度为10 FPS，为实时3D语义占据感知提供了一种颇具前景的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
