点云实例分割在深度学习的出现方面取得了巨大进展。然而，这些方法通常是具有昂贵且耗时的密度云注释的数据饥饿。为了减轻注释成本，在任务中仍申请未标记或弱标记的数据。在本文中，我们使用标记和未标记的边界框作为监控，介绍第一个半监控点云实例分段框架（SPIB）。具体而言，我们的SPIB架构涉及两级学习程序。对于阶段，在具有扰动一致性正则化（SPCR）的半监控设置下培训边界框提案生成网络。正规化通过强制执行对应用于输入点云的不同扰动的边界框预测的不变性，为网络学习提供自我监督。对于阶段，使用SPCR的边界框提案被分组为某些子集，并且使用新颖的语义传播模块和属性一致性图模块中的每个子集中挖掘实例掩码。此外，我们介绍了一种新型占用比导改进模块，以优化实例掩码。对挑战队的攻击v2数据集进行了广泛的实验，证明了我们的方法可以实现与最近的完全监督方法相比的竞争性能。

从非结构化的3D点云学习密集点语义，虽然是一个逼真的问题，但在文献中探讨了逼真的问题。虽然现有的弱监督方法可以仅具有小数点的点级注释来有效地学习语义，但我们发现香草边界箱级注释也是大规模3D点云的语义分割信息。在本文中，我们介绍了一个神经结构，称为Box2Seg，以了解3D点云的点级语义，具有边界盒级监控。我们方法的关键是通过探索每个边界框内和外部的几何和拓扑结构来生成准确的伪标签。具体地，利用基于注意的自我训练（AST）技术和点类激活映射（PCAM）来估计伪标签。通过伪标签进行进一步培训并精制网络。在两个大型基准测试中的实验，包括S3DIS和Scannet，证明了该方法的竞争性能。特别是，所提出的网络可以培训，甚至是均匀的空缺边界箱级注释和子环级标签。

当前的3D分割方法很大程度上依赖于大规模的点状数据集，众所周知，这些数据集众所周知。很少有尝试规避需要每点注释的需求。在这项工作中，我们研究了弱监督的3D语义实例分割。关键的想法是利用3D边界框标签，更容易，更快地注释。确实，我们表明只有仅使用边界框标签训练密集的分割模型。在我们方法的核心上，\ name {}是一个深层模型，灵感来自经典的霍夫投票，直接投票赞成边界框参数，并且是专门针对边界盒票的专门定制的群集方法。这超出了常用的中心票，这不会完全利用边界框注释。在扫描仪测试中，我们弱监督的模型在其他弱监督的方法中获得了领先的性能（+18 MAP@50）。值得注意的是，它还达到了当前完全监督模型的50分数的地图的97％。为了进一步说明我们的工作的实用性，我们在最近发布的Arkitscenes数据集中训练Box2mask，该数据集仅使用3D边界框注释，并首次显示引人注目的3D实例细分掩码。

Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limiting the scalability of 3D recognition tasks. Thus, we study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D Recognition (SL3D) framework. SL3D simultaneously solves two coupled objectives, i.e., clustering and learning feature representation to generate pseudo-labeled data for unsupervised 3D recognition. SL3D is a generic framework and can be applied to solve different 3D recognition tasks, including classification, object detection, and semantic segmentation. Extensive experiments demonstrate its effectiveness. Code is available at https://github.com/fcendra/sl3d.

深度学习的快速发展在分割方面取得了长足的进步，这是计算机视觉的基本任务之一。但是，当前的细分算法主要取决于像素级注释的可用性，这些注释通常昂贵，乏味且费力。为了减轻这一负担，过去几年见证了越来越多的关注，以建立标签高效，深度学习的细分算法。本文对标签有效的细分方法进行了全面的审查。为此，我们首先根据不同类型的弱标签提供的监督（包括没有监督，粗略监督，不完整的监督和嘈杂的监督和嘈杂的监督），首先开发出一种分类法来组织这些方法，并通过细分类型（包括语义细分）补充，实例分割和全景分割）。接下来，我们从统一的角度总结了现有的标签有效的细分方法，该方法讨论了一个重要的问题：如何弥合弱监督和密集预测之间的差距 - 当前的方法主要基于启发式先导，例如交叉像素相似性，跨标签约束，跨视图一致性，跨图像关系等。最后，我们分享了对标签有效深层细分的未来研究方向的看法。

由于其广泛的应用，尤其是在现场理解领域，因此在3D点云上进行的实例细分一直在吸引越来越多的关注。但是，大多数现有方法都需要完全注释培训数据。在点级的手动准备地面真相标签非常繁琐且劳动密集型。为了解决这个问题，我们提出了一种新颖的弱监督方法RWSEG，该方法仅需要用一个点标记一个对象。有了这些稀疏的标签，我们使用自我注意事项和随机步行引入了一个带有两个分支的统一框架，分别将语义和实例信息分别传播到未知区域。此外，我们提出了一个跨画竞争的随机步行（CGCRW）算法，该算法鼓励不同实例图之间的竞争以解决紧密放置对象中的歧义并改善实例分配的性能。 RWSEG可以生成定性实例级伪标签。 Scannet-V2和S3DIS数据集的实验结果表明，我们的方法通过完全监督的方法实现了可比的性能，并且通过大幅度优于先前的弱监督方法。这是弥合该地区弱和全面监督之间差距的第一项工作。

Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to leverage unlabeled point clouds. However, these methods adopt global augmentation with scene-level transformations and hence are sub-optimal for instance-level object detection. In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds, making the augmented data more useful for object detector training. Extensive experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods under various experimental settings. The source code will be available at https://github.com/nomiaro/OPA.

Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations for training. In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i.e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models. Our LWSIS not only exploits the complementary information in multimodal data during training, but also significantly reduces the annotation cost of the dense 2D masks. In detail, LWSIS consists of two crucial modules, Point Label Assignment (PLA) and Graph-based Consistency Regularization (GCR). The former module aims to automatically assign the 3D point cloud as 2D point-wise labels, while the latter further refines the predictions by enforcing geometry and appearance consistency of the multimodal data. Moreover, we conduct a secondary instance segmentation annotation on the nuScenes, named nuInsSeg, to encourage further research on multimodal perception tasks. Extensive experiments on the nuInsSeg, as well as the large-scale Waymo, show that LWSIS can substantially improve existing weakly supervised segmentation models by only involving 3D data during training. Additionally, LWSIS can also be incorporated into 3D object detectors like PointPainting to boost the 3D detection performance for free. The code and dataset are available at https://github.com/Serenos/LWSIS.

大多数现有的点云实例和语义分割方法在很大程度上依赖于强大的监督信号，这需要场景中每个点的点级标签。但是，这种强大的监督遭受了巨大的注释成本，引起了研究有效注释的需求。在本文中，我们发现实例的位置对实例和语义3D场景细分都很重要。通过充分利用位置，我们设计了一种弱监督的点云分割算法，该算法仅需要单击每个实例以指示其注释的位置。通过进行预处理过度分割，我们将这些位置注释扩展到seg级标签中。我们通过将未标记的片段分组分组到相关的附近标签段中，进一步设计一个段分组网络（SEGGROUP），以在SEG级标签下生成点级伪标签，以便现有的点级监督的分段模型可以直接消耗这些PSEUDO标签为了训练。实验结果表明，我们的SEG级监督方法（SEGGROUP）通过完全注释的点级监督方法获得了可比的结果。此外，在固定注释预算的情况下，它的表现优于最近弱监督的方法。

弱监督的点云分段，即，由于在整个3D场景中只有几个标记点的点云，因此由于为模型训练收集丰富的密集注释的沉重负担，因此是非常可取的。但是，由于有限的注释数据可能导致标签传播的指导不足，因此现有方法仍然具有挑战性，无法准确细分3D点云。考虑到基于平滑度的方法已经取得了令人鼓舞的进步，在本文中，我们主张在各种扰动下应用一致性约束，以有效地正规化未标记的3D点。具体而言，我们提出了一个新颖的dat（\ textbf {d} ual \ textbf {a} dappive \ textbf {t} ransformations模型，用于弱监督点云进行分割，其中双重自适应转换是通过两种对敌方策略在点 - 级别和区域级别，旨在在3D点云上执行局部和结构平滑度约束。我们在大规模S3DIS和SCANNET-V2数据集上使用两个流行的骨干评估了我们提出的DAT模型。广泛的实验表明，我们的模型可以有效地利用未标记的3D点并在两个数据集上实现显着的性能增长，从而为弱监督的点云进行了新的最新性能。

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance.

点云的语义分割通常依赖于累累且昂贵的致密注释，因此它吸引了广泛的关注，以研究弱监督方案的解决方案，仅稀疏点注释。现有作品从给定的标签开始，并将其传播到高度相关但无标记的点，例如数据的指导，例如内部关系。但是，它遭受了（i）对数据信息的效率低下的利用，并且（ii）在给出更少的注释时，很容易抑制对标签的强烈依赖。因此，我们提出了一个新颖的框架，即DimpMatch，它通过将一致性正则化应用于数据本身的足够探测信息，并同时利用弱标签作为帮助，该框架具有数据和标签。通过这样做，可以从数据和标签中学习有意义的信息，以获得更好的表示，这也使模型可以在标签稀疏度的范围内更强大。简单而有效的是，提议的尖头竞赛在Scannet-V2和S3DIS数据集上都在各种弱监督的方案下实现了最先进的性能，尤其是在具有极为稀疏标签的设置上，例如。在0.01％和0.1％的扫描仪V2设置上，SQN超过21.2％和17.2％。

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.

我们介绍了一种方法，例如针对3D点云的提案生成。现有技术通常直接在单个进料前进的步骤中回归建议，从而导致估计不准确。我们表明，这是一个关键的瓶颈，并提出了一种基于迭代双边滤波的方法。遵循双边滤波的精神，我们考虑了每个点的深度嵌入以及它们在3D空间中的位置。我们通过合成实验表明，在为给定的兴趣点生成实例建议时，我们的方法会带来巨大的改进。我们进一步验证了我们在挑战性扫描基准测试中的方法，从而在自上而下的方法的子类别中实现了最佳实例分割性能。

Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection in 2D images to propose 3D boxes. Few works have attempted to directly detect objects in point clouds. In this work, we return to first principles to construct a 3D detection pipeline for point cloud data and as generic as possible. However, due to the sparse nature of the data -samples from 2D manifolds in 3D space -we face a major challenge when directly predicting bounding box parameters from scene points: a 3D object centroid can be far from any surface point thus hard to regress accurately in one step. To address the challenge, we propose VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting. Our model achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency. Remarkably, VoteNet outperforms previous methods by using purely geometric information without relying on color images.

本文在3D Point Cloud中介绍了一个新问题：很少示例实例分割。给定一些带注释的点云举例说明了目标类，我们的目标是在查询点云中细分该目标类的所有实例。这个问题具有广泛的实用应用，在重点实例分段注释非常昂贵的收集中。为了解决此问题，我们提出了测量形式 - 第一个用于3D点云实例分割的地球引导变压器。关键的想法是利用大地距离来应对LIDAR 3D点云的密度不平衡。 LIDAR 3D点云在物体表面附近茂密，在其他地方稀疏或空，使欧几里得距离较差以区分不同的物体。另一方面，大地测量距离更合适，因为它编码了场景的几何形状，该几何形状可以用作变压器解码器中注意机制的指导信号，以生成代表实例的不同特征的内核。然后将这些内核用于动态卷积以获得最终实例掩模。为了评估新任务上的测量形式，我们提出了两个常见的3D点云实例分割数据集的新拆分：ScannETV2和S3DIS。地球形式始终优于根据最新的3D点云实例分割方法的强大基线，并具有明显的余量。代码可从https://github.com/vinairesearch/geoformer获得。

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.

我们提出了一种基于动态卷积的3D点云的实例分割方法。这使其能够在推断时适应变化的功能和对象尺度。这样做避免了一些自下而上的方法的陷阱，包括对超参数调整和启发式后处理管道的依赖，以弥补物体大小的不可避免的可变性，即使在单个场景中也是如此。通过收集具有相同语义类别并为几何质心进行仔细投票的均匀点，网络的表示能力大大提高了。然后通过几个简单的卷积层解码实例，其中参数是在输入上生成的。所提出的方法是无建议的，而是利用适应每个实例的空间和语义特征的卷积过程。建立在瓶颈层上的轻重量变压器使模型可以捕获远程依赖性，并具有有限的计算开销。结果是一种简单，高效且健壮的方法，可以在各种数据集上产生强大的性能：ScannETV2，S3DIS和Partnet。基于体素和点的体系结构的一致改进意味着提出的方法的有效性。代码可在以下网址找到：https：//git.io/dyco3d

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part-A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part-A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data. Code is available at https://github.com/sshaoshuai/PointCloudDet3D.

半弱监督和监督的学习最近在对象检测文献中引起了很大的关注，因为它们可以减轻成功训练深度学习模型所需的注释成本。半监督学习的最先进方法依赖于使用多阶段过程训练的学生老师模型，并大量数据增强。为弱监督的设置开发了自定义网络，因此很难适应不同的检测器。在本文中，引入了一种弱半监督的训练方法，以减少这些训练挑战，但通过仅利用一小部分全标记的图像，并在弱标记图像中提供信息来实现最先进的性能。特别是，我们基于通用抽样的学习策略以在线方式产生伪基真实（GT）边界框注释，消除了对多阶段培训的需求和学生教师网络配置。这些伪GT框是根据通过得分传播过程累积的对象建议的分类得分从弱标记的图像中采样的。 PASCAL VOC数据集的经验结果表明，使用VOC 2007作为完全标记的拟议方法可提高性能5.0％，而VOC 2012作为弱标记数据。同样，有了5-10％的完全注释的图像，我们观察到MAP中的10％以上的改善，表明对图像级注释的适度投资可以大大改善检测性能。