论文标题
魔鬼在姿势中：通过姿势吸引卷积的无歧义3D旋转不变的学习
The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution
论文作者
论文摘要
旋转不变（RI）3D深度学习方法会遭受性能降低，因为它们通常将RI表示形式设计为输入，而与3D坐标相比，将失去关键的全局信息。大多数最先进的方法是通过以沉重而无效的方式招致其他块或复杂的全球表示来解决它。在本文中，我们揭示了全球信息丢失源于未开发的姿势信息损失问题，因为我们只需要在每一层中恢复更轻巧的本地姿势，因此可以更有效地解决该问题，并且可以在深层网络中层次聚集，而无需额外的努力即可在深层网络中进行层次聚合。为了解决这个问题，我们开发了一种姿势感知的旋转不变卷卷积（即Pari-Conv），该卷积会根据相对姿势动态适应其内核。为了实现它，我们提出了一个增强点对功能（APPF），以完全编码RI相对姿势信息，并为姿势感知的内核生成一个分解的动态内核，可以通过将内核分解为共享基础矩阵和姿势应对的姿势 - 应对姿势 - 应对姿势的基础矩阵，从而进一步减少计算成本和内存负担。关于形状分类和部分分割任务的广泛实验表明，我们的Pari-CONV超过了最新的RI方法，同时更加紧凑，效率更高。
Rotation-invariant (RI) 3D deep learning methods suffer performance degradation as they typically design RI representations as input that lose critical global information comparing to 3D coordinates. Most state-of-the-arts address it by incurring additional blocks or complex global representations in a heavy and ineffective manner. In this paper, we reveal that the global information loss stems from an unexplored pose information loss problem, which can be solved more efficiently and effectively as we only need to restore more lightweight local pose in each layer, and the global information can be hierarchically aggregated in the deep networks without extra efforts. To address this problem, we develop a Pose-aware Rotation Invariant Convolution (i.e., PaRI-Conv), which dynamically adapts its kernels based on the relative poses. To implement it, we propose an Augmented Point Pair Feature (APPF) to fully encode the RI relative pose information, and a factorized dynamic kernel for pose-aware kernel generation, which can further reduce the computational cost and memory burden by decomposing the kernel into a shared basis matrix and a pose-aware diagonal matrix. Extensive experiments on shape classification and part segmentation tasks show that our PaRI-Conv surpasses the state-of-the-art RI methods while being more compact and efficient.
