通过原始初始化快速学习非合作航天器3D模型
Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
AI总结：
本文提出基于CNN的3DGS原始初始化方法，通过单目图像快速学习航天器3D模型，降低训练成本并适应噪声姿态估计。
摘要：
新型视图合成技术如NeRF和3D高斯点划法（3DGS）的出现，使仅从姿态单目图像中学习精确的3D模型成为可能。尽管这些方法具有吸引力，但它们有两个主要限制，阻碍了它们在空间应用中的使用：它们需要训练期间的姿态，且在训练和推理过程中计算成本高。为了解决这些限制，本文做出了以下贡献：（1）基于卷积神经网络（CNN）的3DGS原始初始化器，使用单目图像；（2）能够使用噪声或隐式姿态估计进行训练的流程；（3）分析初始化变体，以减少精确3D模型的训练成本。CNN接收单张图像作为输入，并输出一个由原始元素组成的粗略3D模型，以及目标相对于相机的姿态。这些原始元素随后用于初始化3DGS，显著减少了训练迭代次数和所需输入图像的数量——至少减少一个数量级。为了增加灵活性，CNN组件有不同的变体，采用不同的姿态估计技术。本文对这些变体进行了比较，评估了它们在噪声或隐式姿态估计下的下游3DGS训练效果。结果表明，即使在姿态监督不完美的情况下，流程也能学习到高保真的3D表示，为在空间应用中使用新型视图合成技术打开了大门。
英文摘要：
The advent of novel view synthesis techniques such as NeRF and 3D Gaussian Splatting (3DGS) has enabled learning precise 3D models only from posed monocular images. Although these methods are attractive, they hold two major limitations that prevent their use in space applications: they require poses during training, and have high computational cost at training and inference. To address these limitations, this work contributes: (1) a Convolutional Neural Network (CNN) based primitive initializer for 3DGS using monocular images; (2) a pipeline capable of training with noisy or implicit pose estimates; and (3) and analysis of initialization variants that reduce the training cost of precise 3D models. A CNN takes a single image as input and outputs a coarse 3D model represented as an assembly of primitives, along with the target's pose relative to the camera. This assembly of primitives is then used to initialize 3DGS, significantly reducing the number of training iterations and input images needed -- by at least an order of magnitude. For additional flexibility, the CNN component has multiple variants with different pose estimation techniques. This work performs a comparison between these variants, evaluating their effectiveness for downstream 3DGS training under noisy or implicit pose estimates. The results demonstrate that even with imperfect pose supervision, the pipeline is able to learn high-fidelity 3D representations, opening the door for the use of novel view synthesis in space applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
