3D occupancy, an advanced perception technology for driving scenarios, represents the entire scene without distinguishing between foreground and background by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, we propose our learning-first view attention mechanism for effective multi-view feature aggregation. Moreover, we showcase the scalability of our view attention across diverse multi-view 3D tasks, such as map construction and 3D object detection. Leveraging the proposed view attention as well as an additional multi-frame streaming temporal attention, we introduce ViewFormer, a vision-centric transformer-based framework for spatiotemporal feature aggregation. To further explore occupancy-level flow representation, we present FlowOcc3D, a benchmark built on top of existing high-quality datasets. Qualitative and quantitative analyses on this benchmark reveal the potential to represent fine-grained dynamic scenes. Extensive experiments show that our approach significantly outperforms prior state-of-the-art methods. The codes and benchmark will be released soon.

翻译：三维占据感知作为面向驾驶场景的先进感知技术，通过将物理空间量化为网格地图来表征完整场景而无需区分前景与背景。广泛采用的投影优先可变形注意力机制虽能高效地将图像特征转化为三维表征，但受限于传感器部署约束，在聚合多视角特征时面临挑战。为解决该问题，我们提出学习优先的视图注意力机制，以实现有效的多视角特征聚合。此外，我们展示了该视图注意力在多种多视角三维任务（如地图构建和三维目标检测）中的可扩展性。基于所提出的视图注意力与额外的多帧流式时序注意力，我们构建了以视觉为中心的Transformer框架ViewFormer，用于时空特征聚合。为深入探索占据级别的流表征，我们基于现有高质量数据集构建了基准FlowOcc3D。在该基准上的定性与定量分析揭示了细粒度动态场景表征的潜力。大量实验表明，本方法显著优于现有最佳方法。相关代码与基准数据集即将发布。