This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing works that introduce explicit regularizations into objective functions, we look into a different space for implicit regularization -- the structure of latent space. Specifically, we restrict the structure of latent space to capture a topological causal ordering of latent factors (i.e., representing causal dependency as a directed acyclic graph). We first show that different causal orderings matter for 3D reconstruction, and then explore several approaches to find a task-dependent causal factor ordering. Our experiments demonstrate that the latent space structure indeed serves as an implicit regularization and introduces an inductive bias beneficial for reconstruction.

翻译：本文考虑了由全景图像进行不受监督的三维天体重建的问题。 由于模糊性和内在的不正确性,这个问题在本质上难以解决,因此需要强有力的规范化,以解析不同的潜在因素。与将明确的规范化引入客观功能的现有工程不同,我们探索了隐含规范化的不同空间 -- -- 潜伏空间的结构。具体地说,我们限制潜伏空间的结构,以捕捉潜在因素的地形因果序列(即作为定向循环图代表因果依赖性 ) 。我们首先表明,不同的因果顺序对于三维天体重建很重要,然后探索几种办法,找到一个取决于任务的因果因素秩序。我们的实验表明,潜伏空间结构的确是一种隐含的规范化,并引入了一种有利于重建的感性偏差。