Abstract
Scene and object reconstruction is an important problem in robotics, in particular in planning collision-free trajectories or in object manipulation. This paper compares two strategies for the reconstruction of nonvisible parts of the object surface from a single RGB-D camera view. The first method, named DeepSDF predicts the Signed Distance Transform to the object surface for a given point in 3D space. The second method, named MirrorNet reconstructs the occluded objects' parts by generating images from the other side of the observed object. Experiments performed with objects from the ShapeNet dataset, show that the view-dependent MirrorNet is faster and has smaller reconstruction errors in most categories.
Abstract (translated)
场景和物体重建是机器人技术中的一个重要问题，尤其是在规划无碰撞轨迹或操作对象时。本文比较了从单个RGB-D相机视角重构未见部分的对象表面的两种策略。第一种方法名为DeepSDF，它预测给定3D空间中点到物体表面的带符号距离变换（Signed Distance Transform）。第二种方法名为MirrorNet，通过生成观察对象另一侧的图像来重建被遮挡的部分。使用ShapeNet数据集中的对象进行的实验表明，在大多数类别中，视图依赖性的MirrorNet比DeepSDF更快且重构误差更小。
URL
https://arxiv.org/abs/2501.16101
