3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial information for robot navigation and autonomous driving systems. However, due to their large overhead from dense network structure designs, existing networks face challenges balancing accuracy and latency. In this paper, we introduce OccRWKV, an efficient semantic occupancy network inspired by Receptance Weighted Key Value (RWKV). OccRWKV separates semantics, occupancy prediction, and feature fusion into distinct branches, each incorporating Sem-RWKV and Geo-RWKV blocks. These blocks are designed to capture long-range dependencies, enabling the network to learn domain-specific representation (i.e., semantics and geometry), which enhances prediction accuracy. Leveraging the sparse nature of real-world 3D occupancy, we reduce computational overhead by projecting features into the bird's-eye view (BEV) space and propose a BEV-RWKV block for efficient feature enhancement and fusion. This enables real-time inference at 22.2 FPS without compromising performance. Experiments demonstrate that OccRWKV outperforms the state-of-the-art methods on the SemanticKITTI dataset, achieving a mIoU of 25.1 while being 20 times faster than the best baseline, Co-Occ, making it suitable for real-time deployment on robots to enhance autonomous navigation efficiency. Code and video are available on our project page: https://jmwang0117.github.io/OccRWKV/.

本研究解决了现有3D语义占据预测网络在准确性和延迟之间难以平衡的问题。研究者提出了OccRWKV，这是一种高效的语义占据网络，通过将语义、占据预测和特征融合分成不同的分支，并引入了Sem-RWKV和Geo-RWKV模块，有效捕获长距离依赖性。实验表明，OccRWKV在SemanticKITTI数据集上的表现优于现有最优方法，达到25.1的mIoU，同时比最佳基线更快20倍，适合机器人实时自主导航部署。

OccRWKV：重新思考高效的3D语义占据预测与线性复杂度