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。
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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.
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Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.
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您将如何修复大量错过的物理物体?您可能首先恢复其全球且粗糙的形状,并逐步增加其本地细节。我们有动力模仿上述物理维修程序,以解决点云完成任务。我们为各种3D模型提出了一个新颖的逐步点云完成网络(SPCNET)。 SPCNET具有层次的底部网络体系结构。它以迭代方式实现形状完成,1)首先扩展了粗糙结果的全局特征; 2)然后在全球功能的帮助下注入本地功能; 3)最终借助局部特征和粗糙的结果来渗透详细的结果。除了模拟物理修复的智慧之外,我们还新设计了基于周期损失%的训练策略,以增强SPCNET的概括和鲁棒性。广泛的实验清楚地表明了我们的SPCNET优于3D点云上最先进的方法,但错过了很大。
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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。
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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。
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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.
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在3D点云的一代任务中,点云完成越来越流行,因为从其部分观察结果中恢复了3D对象的完整形状是一个具有挑战性但必不可少的问题。在本文中,我们提出了一种新型的种子形式,以提高点云完成中细节保存和恢复的能力。与以前的基于全局特征向量的方法不同,我们引入了一种新的形状表示形式,即补丁种子,不仅可以从部分输入中捕获一般结构,而且还保留了本地模式的区域信息。然后,通过将种子特征集成到生成过程中,我们可以以粗到精细的方式恢复忠实的细节,以获取完整的点云。此外,我们通过将变压器结构扩展到点发生器的基本操作来设计上样本变压器,该结构有效地结合了相邻点之间的空间和语义关系。定性和定量评估表明,我们的方法在多个基准数据集上优于最先进的完成网络。我们的代码可从https://github.com/hrzhou2/seedformer获得。
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3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.
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We present Kernel Point Convolution 1 (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of kernel points gives KP-Conv more flexibility than fixed grid convolutions. Furthermore, these locations are continuous in space and can be learned by the network. Therefore, KPConv can be extended to deformable convolutions that learn to adapt kernel points to local geometry. Thanks to a regular subsampling strategy, KPConv is also efficient and robust to varying densities. Whether they use deformable KPConv for complex tasks, or rigid KPconv for simpler tasks, our networks outperform state-of-the-art classification and segmentation approaches on several datasets. We also offer ablation studies and visualizations to provide understanding of what has been learned by KPConv and to validate the descriptive power of deformable KPConv.
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变压器一直是自然语言处理(NLP)和计算机视觉(CV)革命的核心。 NLP和CV的显着成功启发了探索变压器在点云处理中的使用。但是,变压器如何应对点云的不规则性和无序性质?变压器对于不同的3D表示(例如,基于点或体素)的合适性如何?各种3D处理任务的变压器有多大的能力?截至目前,仍然没有对这些问题的研究进行系统的调查。我们第一次为3D点云分析提供了越来越受欢迎的变压器的全面概述。我们首先介绍变压器体系结构的理论,并在2D/3D字段中审查其应用程序。然后,我们提出三种不同的分类法(即实现 - 数据表示和基于任务),它们可以从多个角度对当前的基于变压器的方法进行分类。此外,我们介绍了研究3D中自我注意机制的变异和改进的结果。为了证明变压器在点云分析中的优势,我们提供了基于各种变压器的分类,分割和对象检测方法的全面比较。最后,我们建议三个潜在的研究方向,为3D变压器的开发提供福利参考。
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您将如何通过一些错过来修复物理物体?您可能会想象它的原始形状从先前捕获的图像中,首先恢复其整体(全局)但粗大的形状,然后完善其本地细节。我们有动力模仿物理维修程序以解决点云完成。为此,我们提出了一个跨模式的形状转移双转化网络(称为CSDN),这是一种带有全循环参与图像的粗到精细范式,以完成优质的点云完成。 CSDN主要由“ Shape Fusion”和“ Dual-Refinect”模块组成,以应对跨模式挑战。第一个模块将固有的形状特性从单个图像传输,以指导点云缺失区域的几何形状生成,在其中,我们建议iPadain嵌入图像的全局特征和部分点云的完成。第二个模块通过调整生成点的位置来完善粗糙输出,其中本地改进单元通过图卷积利用了小说和输入点之间的几何关系,而全局约束单元则利用输入图像来微调生成的偏移。与大多数现有方法不同,CSDN不仅探讨了图像中的互补信息,而且还可以在整个粗到精细的完成过程中有效利用跨模式数据。实验结果表明,CSDN对十个跨模式基准的竞争对手表现出色。
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Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object. Specifically, by assigning proper attentional weights to different neighboring points, GAC is designed to selectively focus on the most relevant part of them according to their dynamically learned features. The shape of the convolution kernel is then determined by the learned distribution of the attentional weights. Though simple, GAC can capture the structured features of point clouds for finegrained segmentation and avoid feature contamination between objects. Theoretically, we provided a thorough analysis on the expressive capabilities of GAC to show how it can learn about the features of point clouds. Empirically, we evaluated the proposed GAC on challenging indoor and outdoor datasets and achieved the state-of-the-art results in both scenarios.
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变压器在自然语言处理中的成功最近引起了计算机视觉领域的关注。由于能够学习长期依赖性,变压器已被用作广泛使用的卷积运算符的替代品。事实证明,这种替代者在许多任务中都取得了成功,其中几种最先进的方法依靠变压器来更好地学习。在计算机视觉中,3D字段还见证了使用变压器来增加3D卷积神经网络和多层感知器网络的增加。尽管许多调查都集中在视力中的变压器上,但由于与2D视觉相比,由于数据表示和处理的差异,3D视觉需要特别注意。在这项工作中,我们介绍了针对不同3D视觉任务的100多种变压器方法的系统和彻底审查,包括分类,细分,检测,完成,姿势估计等。我们在3D Vision中讨论了变形金刚的设计,该设计使其可以使用各种3D表示形式处理数据。对于每个应用程序,我们强调了基于变压器的方法的关键属性和贡献。为了评估这些方法的竞争力,我们将它们的性能与12个3D基准测试的常见非转化方法进行了比较。我们通过讨论3D视觉中变压器的不同开放方向和挑战来结束调查。除了提出的论文外,我们的目标是频繁更新最新的相关论文及其相应的实现:https://github.com/lahoud/3d-vision-transformers。
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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。
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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。
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本文解决了从给定稀疏点云生成密集点云的问题,以模拟物体/场景的底层几何结构。为了解决这一具有挑战性的问题,我们提出了一种新的基于端到端学习的框架。具体地,通过利用线性近似定理,我们首先明确地制定问题,这逐到确定内插权和高阶近似误差。然后,我们设计轻量级神经网络,通过分析输入点云的局部几何体,自适应地学习统一和分类的插值权重以及高阶改进。所提出的方法可以通过显式制定来解释,因此比现有的更高的内存效率。与仅用于预定义和固定的上采样因子的现有方法的鲜明对比,所提出的框架仅需要一个单一的神经网络,一次性训练可以在典型范围内处理各种上采样因子,这是真实的-world应用程序。此外,我们提出了一种简单但有效的培训策略来推动这种灵活的能力。此外,我们的方法可以很好地处理非均匀分布和嘈杂的数据。合成和现实世界数据的广泛实验证明了所提出的方法在定量和定性的最先进方法上的优越性。
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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.
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变压器在图像处理领域取得了显着的成就。受到这一巨大成功的启发,变形金刚在3D点云处理中的应用引起了越来越多的关注。本文提出了一个新颖的点云表示学习网络,具有双重自我注意的3D点云变压器(3DPCT)和一个编码器解码器结构。具体而言,3DPCT具有一个层次编码器,该编码器包含两个用于分类任务的局部全球双重注意模块(分段任务的三个模块),每个模块都包含一个局部特征聚合(LFA)块和全局特征学习( GFL)块。 GFL块是双重的自我注意事项,既有在点上的自我注意力,又可以提高特征提取。此外,在LFA中,为更好地利用了提取的本地信息,设计了一种新颖的点自我发明模型,称为点斑点自我注意力(PPSA)。在分类和分割数据集上都评估了性能,其中包含合成数据和现实世界数据。广泛的实验表明,所提出的方法在分类和分割任务上都达到了最新的结果。
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