Recent advances in deep generative models have led to immense progress in 3D
shape synthesis. While existing models are able to synthesize shapes
represented as voxels, point-clouds, or implicit functions, these methods only
indirectly enforce the plausibility of the final 3D shape surface. Here we
present a 3D shape synthesis framework (SurfGen) that directly applies
adversarial training to the object surface. Our approach uses a differentiable
spherical projection layer to capture and represent the explicit zero
isosurface of an implicit 3D generator as functions defined on the unit sphere.
By processing the spherical representation of 3D object surfaces with a
spherical CNN in an adversarial setting, our generator can better learn the
statistics of natural shape surfaces. We evaluate our model on large-scale
shape datasets, and demonstrate that the end-to-end trained model is capable of
generating high fidelity 3D shapes with diverse topology.

通过直接应用对抗训练于对象表面的 3D 形状合成框架（SurfGen），使用可微分的球形投影层和球形 CNN 来更好地学习自然形状表面的统计数据，并在大规模形状数据集上评估和证明其能够生成具有多种拓扑结构的高保真 3D 形状。

SurfGen: 显式表面判别器的对抗性三维形状合成

SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface  Discriminators

Learning powerful deep generative models for 3D shape synthesis is largely
hindered by the difficulty in ensuring plausibility encompassing correct
topology and reasonable geometry. Indeed, learning the distribution of
plausible 3D shapes seems a daunting task for the holistic approaches, given
the significant topological variations of 3D objects even within the same
category. Enlightened by the fact that 3D shape structure is characterized as
part composition and placement, we propose to model 3D shape variations with a
part-aware deep generative network, coined as PAGENet. The network is composed
of an array of per-part VAE-GANs, generating semantic parts composing a
complete shape, followed by a part assembly module that estimates a
transformation for each part to correlate and assemble them into a plausible
structure. Through delegating the learning of part composition and part
placement into separate networks, the difficulty of modeling structural
variations of 3D shapes is greatly reduced. We demonstrate through both
qualitative and quantitative evaluations that PAGENet generates 3D shapes with
plausible, diverse and detailed structure, and show two applications, i.e.,
semantic shape segmentation and part-based shape editing.

通过使用部件感知深度生成网络 (PAGENet) 建模三维形状的变化，利用每个部件的 VAE-GANs 生成具有语义感知的部件，并使用部件组装模块将它们组装在一起，从而减少了建模三维形状变化的难度。该模型在语义形状分割和基于部件的形状编辑等应用中取得了可信度高、多样性和细节丰富的三维形状的生成效果。

面向结构感知形状合成的零件生成与组装学习

Learning Part Generation and Assembly for Structure-aware Shape  Synthesis

We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data.

本篇论文提出一种数据驱动的方法，结合使用体积深度神经网络和 3D 形状合成来完成部分扫描的 3D 形状。该方法使用 3D 卷积层来对填充缺失数据进行预测，旨在在未知区域精确地预测全局结构，并使用来自形状数据库的 3D 几何图形进行中间结果的相关处理。最后，通过基于补丁的 3D 形状合成方法，根据 3D-EPN 获取的全局网格结构作为约束，生成高分辨率的输出，重建精细的细节。