从RGB-D图像中对刚性对象的6D姿势估计对于机器人技术中的对象抓握和操纵至关重要。尽管RGB通道和深度（d）通道通常是互补的，分别提供了外观和几何信息，但如何完全从两个跨模式数据中完全受益仍然是非平凡的。从简单而新的观察结果来看，当对象旋转时，其语义标签是姿势不变的，而其关键点偏移方向是姿势的变体。为此，我们提出了So（3）pose，这是一个新的表示学习网络，可以探索SO（3）equivariant和So（3） - 从深度通道中进行姿势估计的特征。 SO（3） - 激素特征有助于学习更独特的表示，以分割来自RGB通道外观相似的对象。 SO（3） - 等级特征与RGB功能通信，以推导（缺失的）几何形状，以检测从深度通道的反射表面的对象的关键点。与大多数现有的姿势估计方法不同，我们的SO（3） - 不仅可以实现RGB和深度渠道之间的信息通信，而且自然会吸收SO（3） - 等级的几何学知识，从深度图像中，导致更好的外观和更好的外观和更好几何表示学习。综合实验表明，我们的方法在三个基准测试中实现了最先进的性能。

估计对象的6D姿势是必不可少的计算机视觉任务。但是，大多数常规方法从单个角度依赖相机数据，因此遭受遮挡。我们通过称为MV6D的新型多视图6D姿势估计方法克服了这个问题，该方法从多个角度根据RGB-D图像准确地预测了混乱场景中所有对象的6D姿势。我们将方法以PVN3D网络为基础，该网络使用单个RGB-D图像来预测目标对象的关键点。我们通过从多个视图中使用组合点云来扩展此方法，并将每个视图中的图像与密集层层融合。与当前的多视图检测网络（例如Cosypose）相反，我们的MV6D可以以端到端的方式学习多个观点的融合，并且不需要多个预测阶段或随后对预测的微调。此外，我们介绍了三个新颖的影像学数据集，这些数据集具有沉重的遮挡的混乱场景。所有这些都从多个角度包含RGB-D图像，例如语义分割和6D姿势估计。即使在摄像头不正确的情况下，MV6D也明显优于多视图6D姿势估计中最新的姿势估计。此外，我们表明我们的方法对动态相机设置具有强大的态度，并且其准确性随着越来越多的观点而逐渐增加。

A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose. Our code and video are available at https://sites.google.com/view/densefusion/.

This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise unit vectors pointing to the keypoints and use these vectors to vote for keypoint locations using RANSAC. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code will be avaliable at https://zju-3dv.github.io/pvnet/.

Estimating 6D poses of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using a disentangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over stateof-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at https://rse-lab.cs.washington.edu/projects/posecnn/.

6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning, many breakthroughs have been made; however, approaches continue to struggle when they encounter unseen instances, new categories, or real-world challenges such as cluttered backgrounds and occlusions. In this study, we will explore the available methods based on input modality, problem formulation, and whether it is a category-level or instance-level approach. As a part of our discussion, we will focus on how 6D object pose estimation can be used for understanding 3D scenes.

对象姿态估计有多个重要应用，例如机器人抓握和增强现实。我们提出了一种估计了提高当前提案的准确性的6D对象的6D姿势，仍然可以实时使用。我们的方法使用RGB-D数据作为段对象的输入并估计它们的姿势。它使用具有多个头部的神经网络，一个头估计对象分类并生成掩码，第二估计转换向量的值，最后一个头估计表示对象旋转的四元轴的值。这些头部利用特征提取和特征融合期间使用的金字塔架构。我们的方法可以实时使用，其低推理时间为0.12秒并具有高精度。通过这种快速推理和良好准确性的组合，可以在机器人挑选和放置任务和/或增强现实应用中使用我们的方法。

基于2D图像的3D对象的推理由于从不同方向查看对象引起的外观差异很大，因此具有挑战性。理想情况下，我们的模型将是对物体姿势变化的不变或等效的。不幸的是，对于2D图像输入，这通常是不可能的，因为我们没有一个先验模型，即在平面外对象旋转下如何改变图像。唯一的$ \ mathrm {so}（3）$ - 当前存在的模型需要点云输入而不是2D图像。在本文中，我们提出了一种基于Icosahedral群卷积的新型模型体系结构，即通过将输入图像投影到iCosahedron上，以$ \ mathrm {so（3）} $中的理由。由于此投影，该模型大致与$ \ mathrm {so}（3）$中的旋转大致相当。我们将此模型应用于对象构成估计任务，并发现它的表现优于合理的基准。

当前基于RGB的6D对象姿势估计方法在数据集和现实世界应用程序上取得了明显的性能。但是，从单个2D图像特征中预测6D姿势容易受到环境和纹理或相似物体表面的变化的干扰。因此，基于RGB的方法通常比基于RGBD的方法获得的竞争结果较低，后者既部署图像特征和3D结构特征。为了缩小这一性能差距，本文提出了一个6D对象姿势估计的框架，该框架从2个RGB图像中学习隐式3D信息。结合学习的3D信息和2D图像功能，我们在场景和对象模型之间建立了更稳定的对应关系。为了寻求从RGB输入中使用3D信息的最佳方法，我们对三种不同的方法进行了调查，包括早期融合，中融合和晚融合。我们确定中融合方法是恢复最精确的3D关键点的最佳方法，可用于对象姿势估计。该实验表明，我们的方法优于最先进的RGB方法，并通过基于RGBD的方法获得了可比的结果。

本文介绍了一个有效的对称性和无对应框架，称为SC6D，对于单个单眼RGB图像的6D对象姿势估计。SC6D既不需要对象的3D CAD模型，也不需要对称对称的任何先验知识。姿势估计分解为三个子任务：a）对象3D旋转表示学习和匹配；b）估计对象中心的2D位置；和c）通过分类的比例不变距离估计（沿Z轴的翻译）。SC6D在三个基准数据集（T-less，YCB-V和ITODD）上进行了评估，并在T-less数据集中获得最先进的性能。此外，SC6D在计算上比以前的最新方法Surfemb更有效。实施和预培训模型可在https://github.com/dingdingcai/sc6d-pose上公开获得。

In RGB-D based 6D pose estimation, direct regression approaches can directly predict the 3D rotation and translation from RGB-D data, allowing for quick deployment and efficient inference. However, directly regressing the absolute translation of the pose suffers from diverse object translation distribution between the training and testing datasets, which is usually caused by the diversity of pose distribution of objects in 3D physical space. To this end, we generalize the pin-hole camera projection model to a residual-based projection model and propose the projective residual regression (Res6D) mechanism. Given a reference point for each object in an RGB-D image, Res6D not only reduces the distribution gap and shrinks the regression target to a small range by regressing the residual between the target and the reference point, but also aligns its output residual and its input to follow the projection equation between the 2D plane and 3D space. By plugging Res6D into the latest direct regression methods, we achieve state-of-the-art overall results on datasets including Occlusion LineMOD (ADD(S): 79.7%), LineMOD (ADD(S): 99.5%), and YCB-Video datasets (AUC of ADD(S): 95.4%).

6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects are placed in a cluttered scene and the level of occlusion is high. Prior works have tried to overcome this problem but could not achieve accuracy that can be considered reliable in real-world applications. In this paper, we present an architecture that, unlike prior work, is context-aware. It utilizes the context information available to us about the objects. Our proposed architecture treats the objects separately according to their types i.e; symmetric and non-symmetric. A deeper estimator and refiner network pair is used for non-symmetric objects as compared to symmetric due to their intrinsic differences. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset, which is considered a benchmark for pose estimation in the occluded and cluttered scenes, against the prior state-of-the-art DenseFusion. Our results also show that the inference time we got is sufficient for real-time usage.

我们提出了一种对类别级别的6D对象姿势和大小估计的新方法。为了解决类内的形状变化，我们学习规范形状空间（CASS），统一表示，用于某个对象类别的各种情况。特别地，CASS被建模为具有标准化姿势的规范3D形状深度生成模型的潜在空间。我们训练变形式自动编码器（VAE），用于从RGBD图像中的规范空间中生成3D点云。 VAE培训以跨类方式培训，利用公开的大型3D形状存储库。由于3D点云在归一化姿势（具有实际尺寸）中生成，因此VAE的编码器学习视图分解RGBD嵌入。它将RGBD图像映射到任意视图中以独立于姿势的3D形状表示。然后通过将对象姿势与用单独的深神经网络提取的输入RGBD的姿势相关的特征进行对比姿势估计。我们将CASS和姿势和大小估计的学习集成到最终的培训网络中，实现了最先进的性能。

In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.

本文提出了一种可对应的点云旋转登记的方法。我们学习为每个点云嵌入保留所以（3）-equivariance属性的特征空间中的嵌入，通过最近的Quifariant神经网络的开发启用。所提出的形状登记方法通过用隐含形状模型结合等分性的特征学习来实现三个主要优点。首先，由于网络架构中类似于PointNet的网络体系结构中的置换不变性，因此删除了数据关联的必要性。其次，由于SO（3）的性能，可以使用喇叭的方法以闭合形式来解决特征空间中的注册。第三，由于注册和隐含形状重建的联合培训，注册对点云中的噪声强大。实验结果显示出优异的性能与现有的无对应的深层登记方法相比。

很少有6D姿势估计方法使用骨干网络从RGB和深度图像中提取功能，而Uni6D是这样做的先驱。我们发现UNI6D中性能限制的主要原因是实例外部和实例 - 内噪声。 uni6d不可避免地会由于其固有的直接管道设计而从接收场中的背景像素引入实例外部噪声，并忽略了输入深度数据中的实例 - 内侧噪声。在这项工作中，我们提出了一种两步的denoising方法，以处理UNI6D中上述噪声。在第一步中，实例分割网络用于裁剪和掩盖实例，以消除非实施区域的噪声。在第二步中，提出了一个轻巧的深度剥夺模块，以校准深度特征，然后再将其输入姿势回归网络。广泛的实验表明，我们称为uni6dv2的方法能够有效，稳健地消除噪声，在不牺牲过多的推理效率的情况下超过UNI6D。它还减少了对需要昂贵标签的注释真实数据的需求。

我们介绍了一种简单而有效的算法，它使用卷积神经网络直接从视频中估计对象。我们的方法利用了视频序列的时间信息，并计算了支持机器人和AR域的计算上高效且鲁棒。我们所提出的网络采用预先训练的2D对象检测器作为输入，并通过经常性神经网络聚合视觉特征以在每个帧处进行预测。YCB-Video数据集的实验评估表明，我们的方法与最先进的算法相提并论。此外，通过30 FPS的速度，它也比现有技术更有效，因此适用于需要实时对象姿态估计的各种应用。

估计看不见对象的6D姿势对许多现实世界应用非常有需求。但是，当前的最新姿势估计方法只能处理以前训练的对象。在本文中，我们提出了一项新任务，以使算法能够估计测试过程中新颖对象的6D姿势估计。我们收集一个具有真实图像和合成图像的数据集，并且在测试集中最多可见48个看不见的对象。同时，我们提出了一个名为infimum Add（IADD）的新指标，这是对具有不同类型姿势歧义的对象的不变测量。还提供了针对此任务的两个阶段基线解决方案。通过训练端到端的3D对应网络，我们的方法可以准确有效地找到看不见的对象和部分视图RGBD图像之间的相应点。然后，它使用算法鲁棒到对象对称性从对应关系中计算6D姿势。广泛的实验表明，我们的方法的表现优于几个直观基线，从而验证其有效性。所有数据，代码和模型都将公开可用。项目页面：www.graspnet.net/unseen6d

Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. Specifically, we design an effective fusion strategy to take advantage of multi-modal input data, including images and point cloud, and make full use of temporal information to guide the network to learn natural and coherent human motions. Without relying on any 3D pose annotations, our method exploits the inherent geometry constraints of point cloud for self-supervision and utilizes 2D keypoints on images for weak supervision. Extensive experiments on public datasets and our newly collected dataset demonstrate the superiority and generalization capability of our proposed method.