近年来，3D对象检测已疯狂地研究，特别是对于机器人感知系统。但是，现有的3D对象检测位于闭合状态条件下，这意味着网络只能输出培训的类盒。不幸的是，这种封闭式条件不够强大，因为实际使用，因为它将识别错误的未知对象。因此，在本文中，我们提出了一个开放式3D对象检测器，其旨在（1）识别已知对象，如闭合设置检测，并且（2）识别未知对象并提供其准确的边界框。具体而言，我们将开放式3D对象检测问题分为两个步骤：（1）找出包含具有高概率的未知对象的区域和（2）用适当的边界框封闭这些区域的点。第一步是通过该发现，未知对象通常被归类为具有低置信度的已知对象，并且我们表明基于度量学习的欧几里德距离总和是比天真的Softmax概率与来自已知对象区分区别的更好的置信度。 。在此基础上，使用无人监督的群集来改进未知对象的边界框。所提出的方法组合度量学习和无监督群集称为MLUC网络。我们的实验表明，我们的MLUC网络实现了最先进的性能，可以根据预期识别已知和未知的物体。

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN.

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.

近年来，自主驾驶LIDAR数据的3D对象检测一直在迈出卓越的进展。在最先进的方法中，已经证明了将点云进行编码为鸟瞰图（BEV）是有效且有效的。与透视图不同，BEV在物体之间保留丰富的空间和距离信息;虽然在BEV中相同类型的更远物体不会较小，但它们包含稀疏点云特征。这一事实使用共享卷积神经网络削弱了BEV特征提取。为了解决这一挑战，我们提出了范围感知注意网络（RAANET），提取更强大的BEV功能并产生卓越的3D对象检测。范围感知的注意力（RAA）卷曲显着改善了近距离的特征提取。此外，我们提出了一种新的辅助损耗，用于密度估计，以进一步增强覆盖物体的Raanet的检测精度。值得注意的是，我们提出的RAA卷积轻量级，并兼容，以集成到用于BEV检测的任何CNN架构中。 Nuscenes DataSet上的广泛实验表明，我们的提出方法优于基于LIDAR的3D对象检测的最先进的方法，具有16 Hz的实时推断速度，为LITE版本为22 Hz。该代码在匿名GitHub存储库HTTPS://github.com/Anonymous0522 / ange上公开提供。

In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects. Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability. * Majority of the work done as an intern at Nuro, Inc. depth to point cloud 2D region (from CNN) to 3D frustum 3D box (from PointNet)

基于LIDAR的传感驱动器电流自主车辆。尽管进展迅速，但目前的激光雷达传感器在分辨率和成本方面仍然落后于传统彩色相机背后的二十年。对于自主驾驶，这意味着靠近传感器的大物体很容易可见，但远方或小物体仅包括一个测量或两个。这是一个问题，尤其是当这些对象结果驾驶危险时。另一方面，在车载RGB传感器中清晰可见这些相同的对象。在这项工作中，我们提出了一种将RGB传感器无缝熔化成基于LIDAR的3D识别方法。我们的方法采用一组2D检测来生成密集的3D虚拟点，以增加否则稀疏的3D点云。这些虚拟点自然地集成到任何基于标准的LIDAR的3D探测器以及常规激光雷达测量。由此产生的多模态检测器简单且有效。大规模NUSCENES数据集的实验结果表明，我们的框架通过显着的6.6地图改善了强大的中心点基线，并且优于竞争融合方法。代码和更多可视化可在https://tianweiy.github.io/mvp/上获得

尽管收集了越来越多的数据集用于培训3D对象检测模型，但在LiDar扫描上注释3D盒仍然需要大量的人类努力。为了自动化注释并促进了各种自定义数据集的生产，我们提出了一个端到端的多模式变压器（MTRANS）自动标签器，该标签既利用LIDAR扫描和图像，以生成来自弱2D边界盒的精确的3D盒子注释。为了减轻阻碍现有自动标签者的普遍稀疏性问题，MTRAN通过基于2D图像信息生成新的3D点来致密稀疏点云。凭借多任务设计，MTRANS段段前景/背景片段，使LIDAR POINT CLUENS云密布，并同时回归3D框。实验结果验证了MTRAN对提高生成标签质量的有效性。通过丰富稀疏点云，我们的方法分别在Kitti中度和硬样品上获得了4.48 \％和4.03 \％更好的3D AP，而不是最先进的自动标签器。也可以扩展Mtrans以提高3D对象检测的准确性，从而在Kitti硬样品上产生了显着的89.45 \％AP。代码位于\ url {https://github.com/cliu2/mtrans}。

自主驾驶应用中的对象检测意味着语义对象的检测和跟踪通常是城市驾驶环境的原产，作为行人和车辆。最先进的基于深度学习的物体检测中的主要挑战之一是假阳性，其出现过于自信得分。由于安全问题，这在自动驾驶和其他关键机器人感知域中是非常不可取的。本文提出了一种通过将新的概率层引入测试中的深度对象检测网络来缓解过度自信预测问题的方法。建议的方法避免了传统的乙状结肠或Softmax预测层，其通常产生过度自信预测。证明所提出的技术在不降低真实阳性上的性能的情况下降低了误报的过度频率。通过yolov4和第二（基于LiDar的探测器）对2D-Kitti异点检测验证了该方法。该方法使得能够实现可解释的概率预测，而无需重新培训网络，因此非常实用。

使用3D激光点云数据的对象检测和语义分割需要昂贵的注释。我们提出了一种数据增强方法，该方法多次利用已经注释的数据。我们提出了一个重用真实数据的增强框架，自动在场景中找到合适的位置要增加，并明确地处理遮挡。由于使用真实数据，新插入的物体在增强中的扫描点维持了激光雷达的物理特征，例如强度和射线表。该管道证明在训练3D对象检测和语义分割的最佳模型中具有竞争力。新的增强为稀有和基本类别提供了显着的性能增长，尤其是在Kitti对象检测中“硬”行人级的平均精度增益为6.65％，或者2.14表示在Semantickitti细分挑战中获得的iOU在艺术状态下的增益。

自主驾驶的当代深度学习对象检测方法通常会假定前缀类别的共同交通参与者，例如行人和汽车。大多数现有的探测器无法检测到罕见的物体和拐角案例（例如，越过街道的狗），这可能会导致某些情况下发生严重的事故，从而使真实世界应用可靠的自动驾驶不确定。阻碍了真正可靠的自动驾驶系统发展的主要原因是缺乏评估对象探测器在角案例上的性能的公共数据集。因此，我们介绍了一个名为CODA的具有挑战性的数据集，该数据集揭示了基于视力的检测器的关键问题。该数据集由1500个精心选择的现实世界驾驶场景组成，每个场景平均包含四个对象级角案例（平均），涵盖30多个对象类别。在CODA上，在大型自动驾驶数据集中训练的标准对象探测器的性能显着下降到3月的12.8％。此外，我们试验了最新的开放世界对象检测器，发现它也无法可靠地识别尾声中的新对象，这表明对自主驾驶的强大感知系统可能远离触及。我们希望我们的CODA数据集有助于对现实世界自动驾驶的可靠检测进行进一步的研究。我们的数据集将在https://coda-dataset.github.io上发布。

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.

由遮挡，信号丢失或手动注释错误引起的3D边界框的地面真相注释的固有歧义可能会使训练过程中的深3D对象检测器混淆，从而使检测准确性恶化。但是，现有方法在某种程度上忽略了此类问题，并将标签视为确定性。在本文中，我们提出了GLENET，这是一个从条件变异自动编码器改编的生成标签不确定性估计框架，以建模典型的3D对象与其潜在的潜在基边界框之间具有潜在变量的一对一关系。 Glenet产生的标签不确定性是一个插件模块，可以方便地集成到现有的深3D检测器中，以构建概率检测器并监督本地化不确定性的学习。此外，我们提出了概率探测器中的不确定性质量估计量架构，以指导对IOU分支的培训，并预测了本地化不确定性。我们将提出的方法纳入各种流行的3D检测器中，并观察到它们的性能显着提高到Waymo Open DataSet和Kitti数据集中的当前最新技术。

由于缺乏深度信息，单眼3D对象检测在自主驾驶中非常具有挑战性。本文提出了一种基于多尺度深度分层的单眼单目眼3D对象检测算法，它使用锚定方法检测每像素预测中的3D对象。在所提出的MDS-Net中，开发了一种新的基于深度的分层结构，以通过在对象的深度和图像尺寸之间建立数学模型来改善网络的深度预测能力。然后开发出新的角度损耗功能，以进一步提高角度预测的精度并提高训练的收敛速度。最终在后处理阶段最终应用优化的软，以调整候选盒的置信度。基蒂基准测试的实验表明，MDS-Net在3D检测中优于现有的单目3D检测方法，并在满足实时要求时进行3D检测和BEV检测任务。

We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw point cloud as input to generate accurate proposals by seeding each point with a new spherical anchor. It achieves a high recall with less computation compared with prior works. Then, PointsPool is applied for generating proposal features by transforming their interior point features from sparse expression to compact representation, which saves even more computation time. In box prediction, which is the second stage, we implement a parallel intersection-over-union (IoU) branch to increase awareness of localization accuracy, resulting in further improved performance. We conduct experiments on KITTI dataset, and evaluate our method in terms of 3D object and Bird's Eye View (BEV) detection. Our method outperforms other stateof-the-arts by a large margin, especially on the hard set, with inference speed more than 10 FPS.

Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap between LiDAR-based and monocular-based methods. In this paper, we find that the ill-posed nature of monocular imagery can lead to depth ambiguity. Specifically, objects with different depths can appear with the same bounding boxes and similar visual features in the 2D image. Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training. To facilitate depth learning, we propose a simple yet effective plug-and-play module, One Bounding Box Multiple Objects (OBMO). Concretely, we add a set of suitable pseudo labels by shifting the 3D bounding box along the viewing frustum. To constrain the pseudo-3D labels to be reasonable, we carefully design two label scoring strategies to represent their quality. In contrast to the original hard depth labels, such soft pseudo labels with quality scores allow the network to learn a reasonable depth range, boosting training stability and thus improving final performance. Extensive experiments on KITTI and Waymo benchmarks show that our method significantly improves state-of-the-art monocular 3D detectors by a significant margin (The improvements under the moderate setting on KITTI validation set are $\mathbf{1.82\sim 10.91\%}$ mAP in BEV and $\mathbf{1.18\sim 9.36\%}$ mAP in 3D}. Codes have been released at https://github.com/mrsempress/OBMO.

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.

激光器传感器的进步提供了支持3D场景了解的丰富的3D数据。然而，由于遮挡和信号未命中，LIDAR点云实际上是2.5D，因为它们仅覆盖部分底层形状，这对3D感知构成了根本挑战。为了解决挑战，我们提出了一种基于新的LIDAR的3D对象检测模型，被称为窗帘检测器（BTCDET）后面，该模型学习物体形状前沿并估计在点云中部分封闭（窗帘）的完整物体形状。 BTCDET首先识别受遮挡和信号未命中的影响的区域。在这些区域中，我们的模型预测了占用的概率，指示区域是否包含对象形状。与此概率图集成，BTCDET可以产生高质量的3D提案。最后，占用概率也集成到提案细化模块中以生成最终边界框。关于基蒂数据集的广泛实验和Waymo Open DataSet展示了BTCDET的有效性。特别是，对于Kitti基准测试的汽车和骑自行车者的3D检测，BTCDET通过显着的边缘超越所有公布的最先进的方法。代码已发布（https://github.com/xharlie/btcdet}（https：//github.com/xharlie/btcdet）。

来自LIDAR或相机传感器的3D对象检测任务对于自动驾驶至关重要。先锋尝试多模式融合的尝试补充了稀疏的激光雷达点云，其中包括图像的丰富语义纹理信息，以额外的网络设计和开销为代价。在这项工作中，我们提出了一个名为SPNET的新型语义传递框架，以通过丰富的上下文绘画的指导来提高现有基于激光雷达的3D检测模型的性能，在推理过程中没有额外的计算成本。我们的关键设计是首先通过训练语义绘制的教师模型来利用地面真实标签中潜在的指导性语义知识，然后引导纯LIDAR网络通过不同的粒度传播模块来学习语义绘制的表示：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类别：类：类别：类别：类别：类别：类别：类别：类别： - 通过，像素的传递和实例传递。实验结果表明，所提出的SPNET可以与大多数现有的3D检测框架无缝合作，其中AP增益为1〜5％，甚至在KITTI测试基准上实现了新的最新3D检测性能。代码可在以下网址获得：https：//github.com/jb892/sp​​net。

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark [1] while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avod

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-theart performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, Center-Point outperforms all previous single model methods by a large margin and ranks first among all Lidar-only submissions. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint.