We present a framework for learning 3D object shapes and dense cross-object
3D correspondences from just an unaligned category-specific image collection.
The 3D shapes are generated implicitly as deformations to a category-specific
signed distance field and are learned in an unsupervised manner solely from
unaligned image collections and their poses without any 3D supervision.
Generally, image collections on the internet contain several intra-category
geometric and topological variations, for example, different chairs can have
different topologies, which makes the task of joint shape and correspondence
estimation much more challenging. Because of this, prior works either focus on
learning each 3D object shape individually without modeling cross-instance
correspondences or perform joint shape and correspondence estimation on
categories with minimal intra-category topological variations. We overcome
these restrictions by learning a topologically-aware implicit deformation field
that maps a 3D point in the object space to a higher dimensional point in the
category-specific canonical space. At inference time, given a single image, we
reconstruct the underlying 3D shape by first implicitly deforming each 3D point
in the object space to the learned category-specific canonical space using the
topologically-aware deformation field and then reconstructing the 3D shape as a
canonical signed distance field. Both canonical shape and deformation field are
learned end-to-end in an inverse-graphics fashion using a learned recurrent ray
marcher (SRN) as a differentiable rendering module. Our approach, dubbed TARS,
achieves state-of-the-art reconstruction fidelity on several datasets:
ShapeNet, Pascal3D+, CUB, and Pix3D chairs. Result videos and code at
this https URL

无需对齐的图像集合，基于拓扑意识的隐式形变场的学习、随后进行形状重构，该方法名为 TARS，在多个数据集中均取得了最先进的重构精度 (ShapeNet, Pascal3D+, CUB, and Pix3D chairs)。

基于拓扑感知的变形场单视图三维重建

Topologically-Aware Deformation Fields for Single-View 3D Reconstruction

We propose an end-to-end variational generative model for scene layout
synthesis conditioned on scene graphs. Unlike unconditional scene layout
generation, we use scene graphs as an abstract but general representation to
guide the synthesis of diverse scene layouts that satisfy relationships
included in the scene graph. This gives rise to more flexible control over the
synthesis process, allowing various forms of inputs such as scene layouts
extracted from sentences or inferred from a single color image. Using our
conditional layout synthesizer, we can generate various layouts that share the
same structure of the input example. In addition to this conditional generation
design, we also integrate a differentiable rendering module that enables layout
refinement using only 2D projections of the scene. Given a depth and a
semantics map, the differentiable rendering module enables optimizing over the
synthesized layout to fit the given input in an analysis-by-synthesis fashion.
Experiments suggest that our model achieves higher accuracy and diversity in
conditional scene synthesis and allows exemplar-based scene generation from
various input forms.

提出一种基于变分生成模型和场景图条件的端到端场景布局合成方法，并使用可微分渲染模块优化生成的布局。实验表明，该模型可以更精确和多样地进行条件场景合成，并允许从各种输入形式生成样本化场景。