上海科技大学知识管理系统

Described herein are methods and non-transitory computer-readable media of a computing system configured to obtain a plurality of images of an object from a plurality of orientations at a plurality of times. A machine learning model is encoded to represent a continuous density field of the object that maps a spatial coordinate to a density value. The machine learning model comprises a deformation module configured to deform the spatial coordinate in accordance with a timestamp and a trained deformation weight. The machine learning model further comprises a neural radiance module configured to derive the density value in accordance with the deformed spatial coordinate, the timestamp, a direction, and a trained radiance weight. The machine learning model is trained using the plurality of images. A three-dimensional structure of the object is constructed based on the trained machine learning model.

本文描述了被配置为在多个时间从多个取向获得对象的多个图像的计算系统的方法和非暂时性计算机可读介质。 机器学习模型被编码以表示对象的连续密度场，该连续密度场将空间坐标映射到密度值。 所述机器学习模型包括变形模块，所述变形模块被配置为根据时间戳和训练的变形权重对所述空间坐标进行变形。 所述机器学习模型还包括神经辐射模块，所述神经辐射模块被配置为根据所述变形的空间坐标、所述时间戳、方向和经训练的辐射权重导出所述密度值。 使用所述多个图像来训练所述机器学习模型。 基于所述训练好的机器学习模型构建所述物体的三维结构。