RoSI：从少量关节图像中恢复三维形状内部结构
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
AI总结：
本文提出RoSI框架，从少量多视角关节图像中恢复三维模型的内部平面结构，并预测部件关节运动，在未知类别和真实图像上验证了泛化能力。
摘要：
在游戏、VR/AR 以及用于训练几何深度学习算法的三维模型中，绝大多数是不完整的，因为它们被建模为表面网格，缺少内部结构。我们提出一个学习框架 RoSI，仅利用现有多视角和多关节图像中的外观信息来恢复三维模型的形状内部结构。给定一组在不同关节姿态下捕捉目标三维物体的 RGB 图像（可能仅来自少数视角），我们的方法推断输入图像中可观察到的内部平面。我们的神经架构以类别无关的方式进行训练，包括一个运动感知的多视角分析阶段，该阶段包含姿态、深度和运动估计，随后在图像和三维空间中进行内部平面检测，最后进行多视角平面融合。此外，我们的方法还能预测部件关节运动，并能够在目标三维物体上实现甚至外推所捕捉的运动。我们通过定量和定性对比基线方法和替代方案来评估我们的方法，并在未经训练的物体类别和真实图像输入上进行测试，以评估其泛化能力。
英文摘要：
The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures. We present a learning framework to recover the shape interiors (RoSI) of existing 3D models with only their exteriors from multi-view and multi-articulation images. Given a set of RGB images that capture a target 3D object in different articulated poses, possibly from only few views, our method infers the interior planes that are observable in the input images. Our neural architecture is trained in a category-agnostic manner and it consists of a motion-aware multi-view analysis phase including pose, depth, and motion estimations, followed by interior plane detection in images and 3D space, and finally multi-view plane fusion. In addition, our method also predicts part articulations and is able to realize and even extrapolate the captured motions on the target 3D object. We evaluate our method by quantitative and qualitative comparisons to baselines and alternative solutions, as well as testing on untrained object categories and real image inputs to assess its generalization capabilities.
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。
