Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis.The key to RS-CNN is learning from relation, i.e., the geometric topology constraint among points. Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others. In this way, an inductive local representation with explicit reasoning about the spatial layout of points can be obtained, which leads to much shape awareness and robustness. With this convolution as a basic operator, RS-CNN, a hierarchical architecture can be developed to achieve contextual shape-aware learning for point cloud analysis. Extensive experiments on challenging benchmarks across three tasks verify RS-CNN achieves the state of the arts.

注意机制在点云分析中发挥了越来越重要的作用，并且渠道注意是热点之一。通过这么多的频道信息，神经网络难以筛选有用的信道信息。因此，提出了一种自适应信道编码机制以在本文中捕获信道关系。它通过明确地编码其特征信道之间的相互依赖来提高网络生成的表示的质量。具体地，提出了一种通道 - 明智的卷积（通道-Chim）以自适应地学习坐标和特征之间的关系，以便编码信道。与流行的重量方案不同，本文提出的通道CONN实现了卷积操作的适应性，而不是简单地为频道分配不同的权重。对现有基准的广泛实验验证了我们的方法实现了艺术的状态。

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.

Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional networks. We treat convolution kernels as nonlinear functions of the local coordinates of 3D points comprised of weight and density functions. With respect to a given point, the weight functions are learned with multi-layer perceptron networks and density functions through kernel density estimation. The most important contribution of this work is a novel reformulation proposed for efficiently computing the weight functions, which allowed us to dramatically scale up the network and significantly improve its performance. The learned convolution kernel can be used to compute translation-invariant and permutation-invariant convolution on any point set in the 3D space. Besides, PointConv can also be used as deconvolution operators to propagate features from a subsampled point cloud back to its original resolution. Experiments on ModelNet40, ShapeNet, and ScanNet show that deep convolutional neural networks built on PointConv are able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds. Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.

通过当地地区的点特征聚合来捕获的细粒度几何是对象识别和场景理解在点云中的关键。然而，现有的卓越点云骨架通常包含最大/平均池用于局部特征聚集，这在很大程度上忽略了点的位置分布，导致细粒结构组装不足。为了缓解这一瓶颈，我们提出了一个有效的替代品，可以使用新颖的图形表示明确地模拟了本地点之间的空间关系，并以位置自适应方式聚合特征，从而实现位置敏感的表示聚合特征。具体而言，Papooling分别由两个关键步骤，图形结构和特征聚合组成，分别负责构造与将中心点连接的边缘与本地区域中的每个相邻点连接的曲线图组成，以将它们的相对位置信息映射到通道 - 明智的细心权重，以及基于通过图形卷积网络（GCN）的生成权重自适应地聚合局部点特征。 Papooling简单而且有效，并且足够灵活，可以随时为PointNet ++和DGCNN等不同的流行律源，作为即插即说运算符。关于各种任务的广泛实验，从3D形状分类，部分分段对场景分割良好的表明，伪装可以显着提高预测准确性，而具有最小的额外计算开销。代码将被释放。

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

学习地区内部背景和区域间关系是加强点云分析的特征表示的两项有效策略。但是，在现有方法中没有完全强调的统一点云表示的两种策略。为此，我们提出了一种名为点关系感知网络（PRA-NET）的小说框架，其由区域内结构学习（ISL）模块和区域间关系学习（IRL）模块组成。ISL模块可以通过可差的区域分区方案和基于代表的基于点的策略自适应和有效地将本地结构信息动态地集成到点特征中，而IRL模块可自适应和有效地捕获区域间关系。在涵盖形状分类，关键点估计和部分分割的几个3D基准测试中的广泛实验已经验证了PRA-Net的有效性和泛化能力。代码将在https://github.com/xiwuchen/pra-net上获得。

变压器在各种计算机视觉地区发挥着越来越重要的作用，并且在点云分析中也取得了显着的成就。由于它们主要专注于点亮变压器，因此本文提出了一种自适应通道编码变压器。具体地，被设计为对频道的通道卷积旨在对信道进行编码。它可以通过捕获坐标和特征之间的潜在关系来编码特征通道。与简单地为每个通道分配注意重量相比，我们的方法旨在自适应地对信道进行编码。此外，我们的网络采用了邻域搜索方法的低级和高级双语义接收领域，以提高性能。广泛的实验表明，我们的方法优于三个基准数据集的最先进的点云分类和分段方法。

3D点云的卷积经过广泛研究，但在几何深度学习中却远非完美。卷积的传统智慧在3D点之间表现出特征对应关系，这是对差的独特特征学习的内在限制。在本文中，我们提出了自适应图卷积（AGCONV），以供点云分析的广泛应用。 AGCONV根据其动态学习的功能生成自适应核。与使用固定/各向同性核的解决方案相比，AGCONV提高了点云卷积的灵活性，有效，精确地捕获了不同语义部位的点之间的不同关系。与流行的注意力体重方案不同，AGCONV实现了卷积操作内部的适应性，而不是简单地将不同的权重分配给相邻点。广泛的评估清楚地表明，我们的方法优于各种基准数据集中的点云分类和分割的最新方法。同时，AGCONV可以灵活地采用更多的点云分析方法来提高其性能。为了验证其灵活性和有效性，我们探索了基于AGCONV的完成，DeNoing，Upsmpling，注册和圆圈提取的范式，它们与竞争对手相当甚至优越。我们的代码可在https://github.com/hrzhou2/adaptconv-master上找到。

Few prior works study deep learning on point sets. PointNet [20] is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

标准空间卷积假设具有常规邻域结构的输入数据。现有方法通常通过修复常规“视图”来概括对不规则点云域的卷积。固定的邻域大小，卷积内核大小对于每个点保持不变。然而，由于点云不是像图像的结构，所以固定邻权给出了不幸的感应偏压。我们提出了一个名为digress图卷积（diffconv）的新图表卷积，不依赖常规视图。DiffConv在空间 - 变化和密度扩张的邻域上操作，其进一步由学习屏蔽的注意机制进行了进一步调整。我们在ModelNet40点云分类基准测试中验证了我们的模型，获得最先进的性能和更稳健的噪声，以及更快的推广速度。

借助深度学习范式，许多点云网络已经发明了用于视觉分析。然而，由于点云数据的给定信息尚未完全利用，因此对这些网络的发展存在很大的潜力。为了提高现有网络在分析点云数据中的有效性，我们提出了一个即插即用模块，PNP-3D，旨在通过涉及更多来自显式3D空间的本地背景和全球双线性响应来改进基本点云特征表示隐含的功能空间。为了彻底评估我们的方法，我们对三个标准点云分析任务进行实验，包括分类，语义分割和对象检测，在那里我们从每个任务中选择三个最先进的网络进行评估。作为即插即用模块，PNP-3D可以显着提高已建立的网络的性能。除了在四个广泛使用的点云基准测试中实现最先进的结果，我们还提供了全面的消融研究和可视化，以展示我们的方法的优势。代码将在https://github.com/shiqiu0419/pnp-3d上获得。

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel endto-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-theart robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise. Our code is released through https: //github.com/yanx27/PointASNL.

点云识别是工业机器人和自主驾驶中的重要任务。最近，几个点云处理模型已经实现了最先进的表演。然而，这些方法缺乏旋转稳健性，并且它们的性能严重降低了随机旋转，未能扩展到具有不同方向的现实情景。为此，我们提出了一种名为基于自行轮廓的转换（SCT）的方法，该方法可以灵活地集成到针对任意旋转的各种现有点云识别模型中。 SCT通过引入轮廓感知的转换（CAT）提供有效的旋转和翻译不变性，该转换（CAT）线性地将点数的笛卡尔坐标转换为翻译和旋转 - 不变表示。我们证明猫是一种基于理论分析的旋转和翻译不变的转换。此外，提出了帧对准模块来增强通过捕获轮廓并将基于自平台的帧转换为帧内帧来增强鉴别特征提取。广泛的实验结果表明，SCT在合成和现实世界基准的有效性和效率的任意旋转下表现出最先进的方法。此外，稳健性和一般性评估表明SCT是稳健的，适用于各种点云处理模型，它突出了工业应用中SCT的优势。

点云分析没有姿势前导者在真实应用中非常具有挑战性，因为点云的方向往往是未知的。在本文中，我们提出了一个全新的点集学习框架prin，即点亮旋转不变网络，专注于点云分析中的旋转不变特征提取。我们通过密度意识的自适应采样构建球形信号，以处理球形空间中的扭曲点分布。提出了球形Voxel卷积和点重新采样以提取每个点的旋转不变特征。此外，我们将Prin扩展到称为Sprin的稀疏版本，直接在稀疏点云上运行。 Prin和Sprin都可以应用于从对象分类，部分分割到3D特征匹配和标签对齐的任务。结果表明，在随机旋转点云的数据集上，Sprin比无任何数据增强的最先进方法表现出更好的性能。我们还为我们的方法提供了彻底的理论证明和分析，以实现我们的方法实现的点明智的旋转不变性。我们的代码可在https://github.com/qq456cvb/sprin上找到。

学习3D点云的新表示形式是3D视觉中的一个活跃研究领域，因为订单不变的点云结构仍然对神经网络体系结构的设计构成挑战。最近的作品探索了学习全球或本地功能或两者兼而有之，但是均未通过分析点的局部方向分布来捕获上下文形状信息的早期方法。在本文中，我们利用点附近的点方向分布，以获取点云的表现力局部邻里表示。我们通过将给定点的球形邻域分为预定义的锥体来实现这一目标，并将每个体积内部的统计数据用作点特征。这样，本地贴片不仅可以由所选点的最近邻居表示，还可以考虑沿该点周围多个方向定义的点密度分布。然后，我们能够构建涉及依赖MLP（多层感知器）层的Odfblock的方向分布函数（ODF）神经网络。新的ODFNET模型可实现ModelNet40和ScanObjectNN数据集的对象分类的最新精度，并在Shapenet S3DIS数据集上进行分割。

This paper presents PointWeb, a new approach to extract contextual features from local neighborhood in a point cloud. Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region. A novel module, namely Adaptive Feature Adjustment (AFA) module, is presented to find the interaction between points. For each local region, an impact map carrying element-wise impact between point pairs is applied to the feature difference map. Each feature is then pulled or pushed by other features in the same region according to the adaptively learned impact indicators. The adjusted features are well encoded with region information, and thus benefit the point cloud recognition tasks, such as point cloud segmentation and classification. Experimental results show that our model outperforms the state-of-the-arts on both semantic segmentation and shape classification datasets.

最近，深度神经网络在3D点云分类方面取得了显着成就。然而，现有的分类方法主要在理想化点云上实施，并在非理想情况下遭受重大降解的每种性能。为了处理该Prob-LEM，提出了一个名为双邻居深度融合网络（DNDFN）的特征表示学习方法，以用作非理想点云分类任务的改进点云编码器。 DNDFN利用称为TN学习的培训邻域学习方法来捕获全局关键邻域。然后，全球邻居与当地邻居融合，以帮助网络实现更强大的推理能力。此外，提出了一个信息传输卷积（IT-CONV）为DNDFN学习点对对之间的边缘信息，并使特征传输过程受益。 IT-CONV中的信息传输类似于图中的信息的传播，其使DNDF​​N更靠近人工理工模式。关于现有基准的广泛实验尤其是非理想的数据集验证了DNDFN和DNDFN实现了最先进的效果。

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed Spi-derCNN, to efficiently extract geometric features from point clouds. Spi-derCNN is comprised of units called SpiderConv, which extend convolutional operations from regular grids to irregular point sets that can be embedded in R n , by parametrizing a family of convolutional filters. We design the filter as a product of a simple step function that captures local geodesic information and a Taylor polynomial that ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical architecture from classical CNNs, which allows it to extract semantic deep features. Experiments on ModelNet40[4] demonstrate that SpiderCNN achieves state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.