论文标题
长期4D点云视频理解的点原始变压器
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding
论文作者
论文摘要
本文提出了一个4D主干，以了解长期的云视频理解。捕获空间上下文的一种典型方法是使用无层次结构的4DCONV或变压器。但是，由于相机运动，场景变化，采样模式和4D数据的复杂性，这些方法既没有有效也没有高效的效率。为了解决这些问题，我们利用原始平面作为中层表示，以捕获4D点云视频中的长期时空上下文，并提出了一个名为Point Primitive Transformer（PPTR）的新型层次骨架，该骨架主要由基因内点变压器和原始变压器组成。广泛的实验表明，PPTR在不同任务上优于先前的艺术状态。
This paper proposes a 4D backbone for long-term point cloud video understanding. A typical way to capture spatial-temporal context is using 4Dconv or transformer without hierarchy. However, those methods are neither effective nor efficient enough due to camera motion, scene changes, sampling patterns, and the complexity of 4D data. To address those issues, we leverage the primitive plane as a mid-level representation to capture the long-term spatial-temporal context in 4D point cloud videos and propose a novel hierarchical backbone named Point Primitive Transformer(PPTr), which is mainly composed of intra-primitive point transformers and primitive transformers. Extensive experiments show that PPTr outperforms the previous state of the arts on different tasks.
