Building discriminative representations for 3D data has been an important
task in computer graphics and computer vision research. Convolutional Neural
Networks (CNNs) have shown to operate on 2D images with great success for a
variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a
plausible and promising next step. Unfortunately, the computational complexity
of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since
most 3D geometry representations are boundary based, occupied regions do not
increase proportionately with the size of the discretization, resulting in
wasted computation. In this work, we represent 3D spaces as volumetric fields,
and propose a novel design that employs field probing filters to efficiently
extract features from them. Each field probing filter is a set of probing
points --- sensors that perceive the space. Our learning algorithm optimizes
not only the weights associated with the probing points, but also their
locations, which deforms the shape of the probing filters and adaptively
distributes them in 3D space. The optimized probing points sense the 3D space
"intelligently", rather than operating blindly over the entire domain. We show
that field probing is significantly more efficient than 3DCNNs, while providing
state-of-the-art performance, on classification tasks for 3D object recognition
benchmark datasets.

通过使用体积场表示三维空间，使用场探测滤波器来高效地提取特征更为有效，与 3D CNNs 相比，在三维物体识别基准数据集的分类任务上展现出一流的性能。

FPNN：用于 3D 数据的场探测神经网络

FPNN: Field Probing Neural Networks for 3D Data

Recent work has shown good recognition results in 3D object recognition using
3D convolutional networks. In this paper, we show that the object orientation
plays an important role in 3D recognition. More specifically, we argue that
objects induce different features in the network under rotation. Thus, we
approach the category-level classification task as a multi-task problem, in
which the network is trained to predict the pose of the object in addition to
the class label as a parallel task. We show that this yields significant
improvements in the classification results. We test our suggested architecture
on several datasets representing various 3D data sources: LiDAR data, CAD
models, and RGB-D images. We report state-of-the-art results on classification
as well as significant improvements in precision and speed over the baseline on
3D detection.

该论文研究了 3D 物体识别中物体方向的重要性，提出将物体姿态预测作为一项并行任务以实现多任务分类，通过多个数据集的测试获得了最佳性能。

面向方向提升的体素网络用于三维物体识别

Orientation-boosted Voxel Nets for 3D Object Recognition

We have implemented a convolutional neural network designed for processing
sparse three-dimensional input data. The world we live in is three dimensional
so there are a large number of potential applications including 3D object
recognition and analysis of space-time objects. In the quest for efficiency, we
experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.

研究了用于处理稀疏三维输入数据的卷积神经网络，并以 2D 三角晶格和 3D 四面体晶格的 CNN 效率为研究重点，适合于 3D 物体识别和时空对象分析等应用。

稀疏 3D 卷积神经网络

Sparse 3D convolutional neural networks

Recognizing 3D objects in the presence of noise, varying mesh resolution,
occlusion and clutter is a very challenging task. This paper presents a novel
method named Rotational Projection Statistics (RoPS). It has three major
modules: Local Reference Frame (LRF) definition, RoPS feature description and
3D object recognition. We propose a novel technique to define the LRF by
calculating the scatter matrix of all points lying on the local surface. RoPS
feature descriptors are obtained by rotationally projecting the neighboring
points of a feature point onto 2D planes and calculating a set of statistics
(including low-order central moments and entropy) of the distribution of these
projected points. Using the proposed LRF and RoPS descriptor, we present a
hierarchical 3D object recognition algorithm. The performance of the proposed
LRF, RoPS descriptor and object recognition algorithm was rigorously tested on
a number of popular and publicly available datasets. Our proposed techniques
exhibited superior performance compared to existing techniques. We also showed
that our method is robust with respect to noise and varying mesh resolution.
Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and
96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari
Venezia Datasets.

本研究提出了一种基于 RoPS 和 LRF 的 3D 物体识别算法，通过计算散布矩阵和投影统计学方法对邻域进行特征描述，从而实现对噪声和网格分辨率的鲁棒性，取得了比现有技术更好的表现。