论文标题
暂时点云完成，姿势干扰
Temporal Point Cloud Completion with Pose Disturbance
论文作者
论文摘要
由现实世界传感器收集的点云始终是未对齐和稀疏的，这使得很难从单个数据框架中重建对象的完整形状。在这项工作中，我们设法通过有限的翻译和旋转来提供稀疏输入的完整点云。我们还使用时间信息来增强完成模型，并使用一系列输入来完善输出。借助封闭式恢复单元（GRU）和注意机制作为时间单元，我们提出了一个点云完成框架，该框架接受了一系列未对齐和稀疏输入的序列，输出一致且对准点云。我们的网络以在线方式执行，并为每个帧提供了精制的点云，从而可以将其集成到任何SLAM或重建管道中。据我们所知，我们的框架是第一个利用时间信息并确保有限转化的时间一致性。通过Shapenet和Kitti的实验，我们证明我们的框架在合成和现实世界中都有效。
Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this work, we manage to provide complete point clouds from sparse input with pose disturbance by limited translation and rotation. We also use temporal information to enhance the completion model, refining the output with a sequence of inputs. With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds. Our network performs in an online manner and presents a refined point cloud for each frame, which enables it to be integrated into any SLAM or reconstruction pipeline. As far as we know, our framework is the first to utilize temporal information and ensure temporal consistency with limited transformation. Through experiments in ShapeNet and KITTI, we prove that our framework is effective in both synthetic and real-world datasets.
