论文标题
PID：3D点云的关节点相互作用搜索
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
论文作者
论文摘要
点的相互作用和尺寸是设计点运算符以提供分层3D模型的两个重要轴。然而，这两个轴是异质的，充分探索的挑战。现有的工艺工艺点运算符在单轴下，并在3D型号的所有部分重复使用制作的操作员。这忽略了通过利用3D点云的变化几何形状/密度来更好地结合点相互作用和维度的机会。在这项工作中，我们建立了PID，这是一种新型的范式，可共同探索点相互作用和点维，以在点云数据上提供语义分割。我们建立了一个较大的搜索空间，以共同考虑多功能点的相互作用和点维。这支持了具有各种几何/密度考虑的点运算符。具有异质搜索组件的扩大搜索空间要求更好地排名候选模型。为了实现这一目标，我们通过利用基于预测变量的神经体系结构搜索（NAS）来改善搜索空间探索，并通过根据其先验将独特的编码分配给异构搜索组件来提高预测质量。我们彻底评估了PID在两个语义分割基准上制定的网络，显示了Semantickitti和S3DIS对最先进的3D模型的改进约1％。
The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.
