论文标题
从单视图像中无监督的严重变形网格重建（DMR）
Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image
论文作者
论文摘要
从多视图图像或视频中对刚性对象进行3D重建的监督学习，已经取得了很大进展。但是，以无监督的方式从单视RGB图像重建严重变形的对象更具挑战性。尽管已证明基于培训的方法，例如特定类别级培训，可以成功地重建刚性物体和略微变形的物体，例如来自单视图图像中的鸟类，但它们不能有效地处理严重变形的物体，并且由于无法重创的crivectials tecructial cripcials tecret tection tection the defection teception tecrips tecrips tecription tecription teception n dection teception n dection tection n decte te tauge te tauge tex temples te tauge te tauge te的态度。在这项工作中，我们引入了一种基于模板的方法，从单视图像推断3D形状，并将重建的网格应用于下游任务，即绝对长度测量。在不使用3D地面真相的情况下，我们的方法忠实地重建了3D网格并在严重变形的鱼类数据集上的长度测量任务中实现了最先进的精度。
Much progress has been made in the supervised learning of 3D reconstruction of rigid objects from multi-view images or a video. However, it is more challenging to reconstruct severely deformed objects from a single-view RGB image in an unsupervised manner. Although training-based methods, such as specific category-level training, have been shown to successfully reconstruct rigid objects and slightly deformed objects like birds from a single-view image, they cannot effectively handle severely deformed objects and neither can be applied to some downstream tasks in the real world due to the inconsistent semantic meaning of vertices, which are crucial in defining the adopted 3D templates of objects to be reconstructed. In this work, we introduce a template-based method to infer 3D shapes from a single-view image and apply the reconstructed mesh to a downstream task, i.e., absolute length measurement. Without using 3D ground truth, our method faithfully reconstructs 3D meshes and achieves state-of-the-art accuracy in a length measurement task on a severely deformed fish dataset.
