Reconstructing 3D object from a single image (RGB or depth) is a fundamental
problem in visual scene understanding and yet remains challenging due to its
ill-posed nature and complexity in real-world scenes. To address those
challenges, we adopt a primitive-based representation for 3D object, and
propose a two-stage graph network for primitive-based 3D object estimation,
which consists of a sequential proposal module and a graph reasoning module.
Given a 2D image, our proposal module first generates a sequence of 3D
primitives from input image with local feature attention. Then the graph
reasoning module performs joint reasoning on a primitive graph to capture the
global shape context for each primitive. Such a framework is capable of taking
into account rich geometry and semantic constraints during 3D structure
recovery, producing 3D objects with more coherent structure even under
challenging viewing conditions. We train the entire graph neural network in a
stage-wise strategy and evaluate it on three benchmarks: Pix3D, ModelNet and
NYU Depth V2. Extensive experiments show that our approach outperforms the
previous state of the arts with a considerable margin.

采用基于原始体的表示法，提出了一个两阶段图网络进行基于原始体的 3D 物体估计的方法，并使用丰富的几何和语义约束来提高了 3D 结构的恢复质量。

基于原始图网络的单图像 3D 物体估计

Single Image 3D Object Estimation with Primitive Graph Networks

3D Shape representation has substantial effects on 3D shape reconstruction.
Primitive-based representations approximate a 3D shape mainly by a set of
simple implicit primitives, but the low geometrical complexity of the
primitives limits the shape resolution. Moreover, setting a sufficient number
of primitives for an arbitrary shape is challenging. To overcome these issues,
we propose a constrained implicit algebraic surface as the primitive with few
learnable coefficients and higher geometrical complexities and a deep neural
network to produce these primitives. Our experiments demonstrate the
superiorities of our method in terms of representation power compared to the
state-of-the-art methods in single RGB image 3D shape reconstruction.
Furthermore, we show that our method can semantically learn segments of 3D
shapes in an unsupervised manner. The code is publicly available from
this https URL .

本论文中，我们提出了基于约束隐式代数曲面和深度神经网络的方法，以实现对高复杂度 3D 形状的更准确重建和分割，相较于现有单 RGB 图像 3D 形状重建技术，该方法表现出更强的表现力和性能，可无监督地进行学习。