OccRWKV: 以线性复杂度重新思考高效3D语义占据预测
OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity
AI总结：
针对3D语义占据预测网络精度与延迟平衡的挑战，本文提出受RWKV启发的OccRWKV网络，通过分支设计与BEV模块实现高效推理，在SemanticKITTI数据集上mIoU达25.1且比Co-Occ快20倍，适用于机器人实时部署。
摘要：
3D语义占据预测网络在重建3D场景的几何与语义结构方面展现出卓越能力，为机器人导航和自动驾驶系统提供关键信息。然而，由于密集网络结构设计带来的大量开销，现有网络在平衡精度和延迟方面面临挑战。本文介绍OccRWKV，一种受Receptance Weighted Key Value（RWKV）启发的高效语义占据网络。OccRWKV将语义、占据预测和特征融合分为不同分支，每个分支都融入Sem-RWKV和Geo-RWKV模块。这些模块旨在捕捉长程依赖，使网络能够学习领域特定表示（即语义和几何），从而提升预测精度。利用真实世界3D占据的稀疏性，我们通过将特征投影到鸟瞰图（BEV）空间来减少计算开销，并提出BEV-RWKV模块以实现高效特征增强和融合。这使得网络能在不损失性能的情况下实现22.2 FPS的实时推理。实验表明，OccRWKV在SemanticKITTI数据集上优于现有最优方法，达到25.1的mIoU，同时比最佳基线Co-Occ快20倍，使其适合在机器人上实时部署以提升自主导航效率。代码和视频可在我们的项目页面获取：https://jmwang0117.github.io/OccRWKV/。
英文摘要：
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/.
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。
