Efficient and high-accuracy 3D occupancy prediction is crucial for ensuring the performance of autonomous driving (AD) systems. However, many existing methods involve trade-offs between accuracy and efficiency. Some achieve high precision but with slow inference speed, while others adopt purely bird's-eye-view (BEV)-based 2D representations to accelerate processing, inevitably sacrificing vertical cues and compromising geometric integrity. To overcome these limitations, we propose a pure 2D framework that achieves efficient 3D occupancy prediction while preserving geometric integrity. Unlike conventional Lift-Splat-Shoot (LSS) methods that rely solely on depth scores to lift 2D features into 3D space, our approach additionally introduces a height-score projection to encode vertical geometric structure. We further employ direction-aware convolution to extract geometric features along both vertical and horizontal orientations, effectively balancing accuracy and computational efficiency. On the Occ3D-nuScenes, the proposed method achieves an mIoU of 39.3\% and an inference speed of 27.7 FPS, effectively balancing accuracy and efficiency. In simulations on edge devices, the inference speed reaches 14.8 FPS, further demonstrating the method's applicability for real-time deployment in resource-constrained environments.

翻译：高效且高精度的三维占据预测对于保障自动驾驶系统性能至关重要。然而，现有许多方法在精度与效率之间存在权衡。一些方法实现了高精度但推理速度慢，而另一些方法采用纯鸟瞰图（BEV）的二维表示来加速处理，却不可避免地牺牲了垂直方向线索并损害了几何完整性。为克服这些局限，我们提出了一种纯二维框架，在保持几何完整性的同时实现高效的三维占据预测。不同于仅依赖深度分数将二维特征提升至三维空间的传统Lift-Splat-Shoot（LSS）方法，我们的方法额外引入了高度分数投影以编码垂直几何结构。我们进一步采用方向感知卷积来沿垂直和水平方向提取几何特征，有效平衡了精度与计算效率。在Occ3D-nuScenes数据集上，所提方法取得了39.3%的mIoU和27.7 FPS的推理速度，有效平衡了精度与效率。在边缘设备上的仿真中，推理速度达到14.8 FPS，进一步证明了该方法在资源受限环境中实时部署的适用性。