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

Scene understanding based on LiDAR point cloud is an essential task for
autonomous cars to drive safely, which often employs spherical projection to
map 3D point cloud into multi-channel 2D images for semantic segmentation. Most
existing methods simply stack different point attributes/modalities (e.g.
coordinates, intensity, depth, etc.) as image channels to increase information
capacity, but ignore distinct characteristics of point attributes in different
image channels. We design FPS-Net, a convolutional fusion network that exploits
the uniqueness and discrepancy among the projected image channels for optimal
point cloud segmentation. FPS-Net adopts an encoder-decoder structure. Instead
of simply stacking multiple channel images as a single input, we group them
into different modalities to first learn modality-specific features separately
and then map the learned features into a common high-dimensional feature space
for pixel-level fusion and learning. Specifically, we design a residual dense
block with multiple receptive fields as a building block in the encoder which
preserves detailed information in each modality and learns hierarchical
modality-specific and fused features effectively. In the FPS-Net decoder, we
use a recurrent convolution block likewise to hierarchically decode fused
features into output space for pixel-level classification. Extensive
experiments conducted on two widely adopted point cloud datasets show that
FPS-Net achieves superior semantic segmentation as compared with
state-of-the-art projection-based methods. In addition, the proposed modality
fusion idea is compatible with typical projection-based methods and can be
incorporated into them with consistent performance improvements.

该研究设计了 FPS-Net 网络，利用点云投影图像的不同特征进行多模态特征融合，旨在优化点云的分段。该网络的有效性在广泛实验中得到证明，可以实现优于现有方法的语义分割，并且引入的多模态融合思想也兼容于典型的投影方法。

FPS-Net：大规模 LiDAR 点云分割的卷积融合网络

FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point  Cloud Segmentation

In this paper, we introduce a new method for classifying 3D objects. Our main
idea is to project a 3D object onto a spherical domain centered around its
barycenter and develop neural network to classify the spherical projection. We
introduce two complementary projections. The first captures depth variations of
a 3D object, and the second captures contour-information viewed from different
angles. Spherical projections combine key advantages of two main-stream 3D
classification methods: image-based and 3D-based. Specifically, spherical
projections are locally planar, allowing us to use massive image datasets (e.g,
ImageNet) for pre-training. Also spherical projections are similar to
voxel-based methods, as they encode complete information of a 3D object in a
single neural network capturing dependencies across different views. Our novel
network design can fully utilize these advantages. Experimental results on
ModelNet40 and ShapeNetCore show that our method is superior to prior methods.

本文提出了一种基于球面投影和神经网络的新的三维物体分类方法，该方法结合了基于图像和基于三维模型的两种分类方法的关键优势，并通过实验证明其优于先前方法。