点云的语义分割，旨在为每个点分配语义类别，对3D场景的理解至关重要。尽管近年来取得了重大进展，但大多数现有方法仍然遭受对象级别的错误分类或边界级别的歧义。在本文中，我们通过深入探索被称为Geosegnet的点云的几何形状来提出一个强大的语义分割网络。我们的Geosegnet由一个基于多几何的编码器和边界引导的解码器组成。在编码器中，我们从多几何的角度开发了一个新的残差几何模块，以提取对象级特征。在解码器中，我们引入了一个对比边界学习模块，以增强边界点的几何表示。从几何编码器模型中受益，我们的GEOSEGNET可以在使两个或多个对象的相交（边界）清晰地确定对象的分割。从总体分割精度和对象边界清除方面，实验显示了我们方法对竞争对手的明显改善。代码可在https://github.com/chen-yuiyui/geosegnet上找到。

Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior works either address this issue by post-processing or jointly learn object boundaries to implicitly improve feature encoding of the networks. These approaches often require additional modules which are difficult to integrate into the original architecture. To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism. This mechanism is achieved by exploiting a multi-task learning framework that aims to explicitly guide the boundaries to their original locations. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. The predicted boundaries and directions are fused to propagate the learned features to refine the segmentation. We conduct extensive experiments on the S3DIS and SensatUrban datasets against various baseline methods, demonstrating that our proposed approach yields consistent improvements by reducing boundary errors. Our code is available at https://github.com/shenglandu/PushBoundary.

点云的语义分割通过密集预测每个点的类别来产生对场景的全面理解。由于接收场的一致性，点云的语义分割对于多受感受性场特征的表达仍然具有挑战性，这会导致对具有相似空间结构的实例的错误分类。在本文中，我们提出了一个植根于扩张图特征聚集（DGFA）的图形卷积网络DGFA-NET，该图由通过金字塔解码器计算出的多基质聚集损失（Maloss）引导。为了配置多受感受性字段特征，将建议的扩张图卷积（DGCONV）作为其基本构建块，旨在通过捕获带有各种接收区域的扩张图来汇总多尺度特征表示。通过同时考虑用不同分辨率的点集作为计算碱基的点集惩罚接收场信息，我们引入了由Maloss驱动的金字塔解码器，以了解接受田间的多样性。结合这两个方面，DGFA-NET显着提高了具有相似空间结构的实例的分割性能。 S3DIS，ShapenetPart和Toronto-3D的实验表明，DGFA-NET优于基线方法，实现了新的最新细分性能。

Downsampling and feature extraction are essential procedures for 3D point cloud understanding. Existing methods are limited by the inconsistent point densities of different parts in the point cloud. In this work, we analyze the limitation of the downsampling stage and propose the pre-abstraction group-wise window-normalization module. In particular, the window-normalization method is leveraged to unify the point densities in different parts. Furthermore, the group-wise strategy is proposed to obtain multi-type features, including texture and spatial information. We also propose the pre-abstraction module to balance local and global features. Extensive experiments show that our module performs better on several tasks. In segmentation tasks on S3DIS (Area 5), the proposed module performs better on small object recognition, and the results have more precise boundaries than others. The recognition of the sofa and the column is improved from 69.2% to 84.4% and from 42.7% to 48.7%, respectively. The benchmarks are improved from 71.7%/77.6%/91.9% (mIoU/mAcc/OA) to 72.2%/78.2%/91.4%. The accuracies of 6-fold cross-validation on S3DIS are 77.6%/85.8%/91.7%. It outperforms the best model PointNeXt-XL (74.9%/83.0%/90.3%) by 2.7% on mIoU and achieves state-of-the-art performance. The code and models are available at https://github.com/DBDXSS/Window-Normalization.git.

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。

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.

随着激光雷达传感器和3D视觉摄像头的扩散，3D点云分析近年来引起了重大关注。经过先驱工作点的成功后，基于深度学习的方法越来越多地应用于各种任务，包括3D点云分段和3D对象分类。在本文中，我们提出了一种新颖的3D点云学习网络，通过选择性地执行具有动态池的邻域特征聚合和注意机制来提出作为动态点特征聚合网络（DPFA-NET）。 DPFA-Net有两个可用于三维云的语义分割和分类的变体。作为DPFA-NET的核心模块，我们提出了一个特征聚合层，其中每个点的动态邻域的特征通过自我注意机制聚合。与其他分割模型相比，来自固定邻域的聚合特征，我们的方法可以在不同层中聚合来自不同邻居的特征，在不同层中为查询点提供更具选择性和更广泛的视图，并更多地关注本地邻域中的相关特征。此外，为了进一步提高所提出的语义分割模型的性能，我们提出了两种新方法，即两级BF-Net和BF-Rengralization来利用背景前台信息。实验结果表明，所提出的DPFA-Net在S3DIS数据集上实现了最先进的整体精度分数，在S3DIS数据集上进行了语义分割，并在不同的语义分割，部分分割和3D对象分类中提供始终如一的令人满意的性能。与其他方法相比，它也在计算上更有效。

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.

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。

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。

在本文中，我们提出了一个全面的点云语义分割网络，该网络汇总了本地和全球多尺度信息。首先，我们提出一个角度相关点卷积（ACPCONV）模块，以有效地了解点的局部形状。其次，基于ACPCONV，我们引入了局部多规模拆分（MSS）块，该块从一个单个块中连接到一个单个块中的特征，并逐渐扩大了接受场，这对利用本地上下文是有益的。第三，受HRNET的启发，在2D图像视觉任务上具有出色的性能，我们构建了一个针对Point Cloud的HRNET，以学习全局多尺度上下文。最后，我们介绍了一种融合多分辨率预测并进一步改善点云语义分割性能的点上的注意融合方法。我们在几个基准数据集上的实验结果和消融表明，与现有方法相比，我们提出的方法有效，能够实现最先进的性能。

我们在本文中重新审视语义场景（SSC），是预测3D场景的语义和占用表示的有用任务。此任务的许多方法始终基于用于保存本地场景结构的体蛋白化场景表示。然而，由于存在可见空体素，当网络更深时，这些方法总是遭受重型计算冗余，从而限制完成质量。为了解决这种困境，我们提出了我们为此任务的新型点体素聚集网络。首先，我们通过去除这些可见的空体素来将Voxized场景传输到点云，并采用深点流，以有效地从场景中捕获语义信息。同时，仅包含两个3D卷积层的轻重体素流保留了体蛋白化场景的局部结构。此外，我们设计一个各向异性体素聚合运算符，将结构细节从体素流融合到点流中，并通过语义标签来增强点流中的上采样过程的语义感知传播模块。我们展示了我们的模型在两个基准上超越了最先进的余量，只有深度图像作为输入。

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.

捕获不规则点云的局部和全局特征对于3D对象检测（3OD）至关重要。但是，主流3D探测器，例如，投票机及其变体，要么放弃池操作过程中的大量本地功能，要么忽略整个场景中的许多全球功能。本文探讨了新的模块，以同时学习积极服务3OD的场景点云的局部全球特征。为此，我们通过同时局部全球特征学习（称为3DLG-detector）提出了一个有效的3OD网络。 3DLG检测器有两个关键贡献。首先，它会开发一个动态点交互（DPI）模块，该模块可在合并过程中保留有效的本地特征。此外，DPI是可拆卸的，可以将其合并到现有的3OD网络中以提高其性能。其次，它开发了一个全局上下文聚合模块，以汇总编码器不同层的多尺度特征，以实现场景上下文意识。我们的方法在SUN RGB-D和扫描仪数据集的检测准确性和鲁棒性方面显示了13个竞争对手的进步。源代码将在出版物时提供。

Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance in terms of point cloud registration and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape descriptors constructed with respect to reference frames defined over different scales, e.g., local patches and global topology, to generate rotation-invariant latent shape codes. Within the integration stage, we propose Aligned Integration Transformer to produce a discriminative feature representation by integrating point-wise self- and cross-relations established within the shape codes. Meanwhile, we adopt rigid transformations between reference frames to align the shape codes for feature consistency across different scales. Finally, the deep integrated feature is registered to both rotation-invariant shape codes to maximize feature similarities, such that rotation invariance of the integrated feature is preserved and shared semantic information is implicitly extracted from shape codes. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work. Our project page is released at: https://rotation3d.github.io/.

Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference, and the performance saturates over the past few years. In this paper, we present a novel perspective on this task. We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively. Equipped with a proposed lightweight geometric affine module, PointMLP delivers the new state-of-the-art on multiple datasets. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. We emphasize that PointMLP achieves this strong performance without any sophisticated operations, hence leading to a superior inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster, tests 7x faster, and is more accurate on ModelNet40 benchmark. We hope our PointMLP may help the community towards a better understanding of point cloud analysis. The code is available at https://github.com/ma-xu/pointMLP-pytorch.

与卷积神经网络相比，最近开发的纯变压器架构已经实现了对点云学习基准的有希望的准确性。然而，现有点云变压器是计算昂贵的，因为它们在构建不规则数据时浪费了大量时间。要解决此缺点，我们呈现稀疏窗口注意（SWA）模块，以收集非空体素的粗粒颗粒特征，不仅绕过昂贵的不规则数据结构和无效的空体素计算，还可以获得线性计算复杂性到体素分辨率。同时，要收集关于全球形状的细粒度特征，我们介绍了相对的注意（RA）模块，更强大的自我关注变体，用于对象的刚性变换。我们配备了SWA和RA，我们构建了我们的神经结构，称为PVT，将两个模块集成到Point云学习的联合框架中。与以前的变压器和关注的模型相比，我们的方法平均达到了分类基准和10x推理加速的最高精度为94.0％。广泛的实验还有效地验证了PVT在部分和语义分割基准上的有效性（分别为86.6％和69.2％Miou）。

大规模发光点云的快速有效语义分割是自主驾驶中的一个基本问题。为了实现这一目标，现有的基于点的方法主要选择采用随机抽样策略来处理大规模点云。但是，我们的数量和定性研究发现，随机抽样可能不适合自主驾驶场景，因为LiDAR点遵循整个空间的不均匀甚至长尾巴分布，这阻止了模型从从中捕获足够的信息，从而从中捕获了足够的信息不同的距离范围并降低了模型的学习能力。为了减轻这个问题，我们提出了一种新的极性缸平衡的随机抽样方法，该方法使下采样的点云能够保持更平衡的分布并改善不同空间分布下的分割性能。此外，引入了采样一致性损失，以进一步提高分割性能并降低模型在不同采样方法下的方差。广泛的实验证实，我们的方法在Semantickitti和Semanticposs基准测试中都产生了出色的性能，分别提高了2.8％和4.0％。

Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full potential of multi-modal sensor suites, radar sensors are essential for safety critical tasks and are already installed in most new vehicles today. In this paper, we address the problem of semantic segmentation of moving objects in radar point clouds to enhance the perception of the environment with another sensor modality. Instead of aggregating multiple scans to densify the point clouds, we propose a novel approach based on the self-attention mechanism to accurately perform sparse, single-scan segmentation. Our approach, called Gaussian Radar Transformer, includes the newly introduced Gaussian transformer layer, which replaces the softmax normalization by a Gaussian function to decouple the contribution of individual points. To tackle the challenge of the transformer to capture long-range dependencies, we propose our attentive up- and downsampling modules to enlarge the receptive field and capture strong spatial relations. We compare our approach to other state-of-the-art methods on the RadarScenes data set and show superior segmentation quality in diverse environments, even without exploiting temporal information.