论文标题
Nodeslam：多视图形状重建的神经对象描述符
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
论文作者
论文摘要
场景表示形式的选择在所需的形状推理算法及其启用的智能应用程序中至关重要。我们将提供高效且优化的多级学习对象描述符以及一种新颖的概率和差分渲染引擎，用于从一个或多个RGB-D图像中原理的完整对象形状推断。我们的框架允许精确，稳健的3D对象重建，可实现多个应用程序，包括机器人抓握和放置，增强现实以及能够与摄像机轨迹共同优化对象姿势和形状的第一个对象级别的大满贯系统。
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine, for principled full object shape inference from one or more RGB-D images. Our framework allows for accurate and robust 3D object reconstruction which enables multiple applications including robot grasping and placing, augmented reality, and the first object-level SLAM system capable of optimising object poses and shapes jointly with camera trajectory.
