Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://github.com/wzzheng/TPVFormer.

翻译：现代基于视觉的自动驾驶感知方法广泛采用鸟瞰图（BEV）表示来描述三维场景。尽管BEV相比体素表示具有更高的效率，但难以通过单一平面描述场景的精细三维结构。为此，我们提出三视角表示（Tri-Perspective View, TPV），在BEV基础上增加两个垂直平面。通过将三维空间中每个点在三个平面上的投影特征求和，实现对其建模。为将图像特征提升至三维TPV空间，我们进一步提出基于Transformer的TPV编码器（TPVFormer）以高效获取TPV特征。我们采用注意力机制聚合每个TPV平面中每个查询对应的图像特征。实验表明，仅使用稀疏监督训练的模型即可有效预测所有体素的语义占用。首次证明仅使用摄像头输入即可在nuScenes的LiDAR分割任务中取得与基于LiDAR方法相当的性能。代码：https://github.com/wzzheng/TPVFormer。