We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured models of these objects, suitable for generating synthetic training data, as well as RGBD images of the objects in challenging, cluttered scenes exhibiting partial occlusion, extreme lighting variations, multiple instances per image, and a large variety of poses. Using semi-automated RGBD-to-model texture correspondences, the images are annotated with ground truth poses accurate within a few millimeters. We also propose a new pose evaluation metric called ADD-H based on the Hungarian assignment algorithm that is robust to symmetries in object geometry without requiring their explicit enumeration. We share pre-trained pose estimators for all the toy grocery objects, along with their baseline performance on both validation and test sets. We offer this dataset to the community to help connect the efforts of computer vision researchers with the needs of roboticists.

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.

视觉感知任务通常需要大量的标记数据，包括3D姿势和图像空间分割掩码。创建此类培训数据集的过程可能很难或耗时，可以扩展到一般使用的功效。考虑对刚性对象的姿势估计的任务。在大型公共数据集中接受培训时，基于神经网络的深层方法表现出良好的性能。但是，将这些网络调整为其他新颖对象，或针对不同环境的现有模型进行微调，需要大量的时间投资才能产生新标记的实例。为此，我们提出了ProgressLabeller作为一种方法，以更有效地以可扩展的方式从彩色图像序列中生成大量的6D姿势训练数据。 ProgressLabeller还旨在支持透明或半透明的对象，以深度密集重建的先前方法将失败。我们通过快速创建一个超过1M样品的数据集来证明ProgressLabeller的有效性，我们将其微调一个最先进的姿势估计网络，以显着提高下游机器人的抓地力。 ProgressLabeller是https://github.com/huijiezh/progresslabeller的开放源代码。

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce Normalized Object Coordinate Space (NOCS)-a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new contextaware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.

We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses. Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene. Our approach explicitly handles object symmetries, does not require depth measurements, is robust to missing or incorrect object hypotheses, and automatically recovers the number of objects in the scene. Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views. This is achieved by solving an object-level bundle adjustment problem that refines the poses of cameras and objects to minimize the reprojection error in all views. We demonstrate that the proposed method, dubbed Cosy-Pose, outperforms current state-of-the-art results for single-view and multi-view 6D object pose estimation by a large margin on two challenging benchmarks: the YCB-Video and T-LESS datasets. Code and pre-trained models are available on the project webpage. 5

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/.

We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a region of interest displaying the object in the image and (ii) a CAD model of the observed object. The contributions of this work are threefold. First, we present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. The shape and coordinate system of the novel object are provided as inputs to the network by rendering multiple synthetic views of the object's CAD model. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner. Third, we introduce a large-scale synthetic dataset of photorealistic images of thousands of objects with diverse visual and shape properties and show that this diversity is crucial to obtain good generalization performance on novel objects. We train our approach on this large synthetic dataset and apply it without retraining to hundreds of novel objects in real images from several pose estimation benchmarks. Our approach achieves state-of-the-art performance on the ModelNet and YCB-Video datasets. An extensive evaluation on the 7 core datasets of the BOP challenge demonstrates that our approach achieves performance competitive with existing approaches that require access to the target objects during training. Code, dataset and trained models are available on the project page: https://megapose6d.github.io/.

我们介绍了日常桌面对象的998 3D型号的数据集及其847,000个现实世界RGB和深度图像。每个图像的相机姿势和对象姿势的准确注释都以半自动化方式执行，以促进将数据集用于多种3D应用程序，例如形状重建，对象姿势估计，形状检索等。3D重建由于缺乏适当的现实世界基准来完成该任务，并证明我们的数据集可以填补该空白。整个注释数据集以及注释工具和评估基线的源代码可在http://www.ocrtoc.org/3d-reconstruction.html上获得。

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

本文介绍了一个数据集，用于培训和评估方法，以估算由标准RGB摄像机捕获的任务演示中手持工具的6D姿势。尽管6D姿势估计方法取得了重大进展，但它们的性能通常受到严重遮挡的对象的限制，这在模仿学习中是一个常见的情况，而操纵手通常会部分遮住对象。当前，缺乏数据集可以使这些条件的稳健6D姿势估计方法开发。为了克服这个问题，我们收集了一个新的数据集（IMITROB），该数据集针对模仿学习和其他人类持有工具并执行任务的其他应用中的6D姿势估计。该数据集包含三个不同工具和六个操纵任务的图像序列，这些任务具有两个相机观点，四个人类受试者和左/右手。每个图像都伴随着由HTC Vive运动跟踪设备获得的6D对象姿势的准确地面真相测量。通过训练和评估各种设置中的最新6D对象估计方法（DOPE）来证明数据集的使用。数据集和代码可在http://imitrob.ciirc.cvut.cz/imitrobdataset.php上公开获得。

基于视觉的机器人组装是一项至关重要但具有挑战性的任务，因为与多个对象的相互作用需要高水平的精度。在本文中，我们提出了一个集成的6D机器人系统，以感知，掌握，操纵和组装宽度，以紧密的公差。为了提供仅在现成的RGB解决方案的情况下，我们的系统建立在单眼6D对象姿势估计网络上，该估计网络仅使用合成图像训练，该图像利用了基于物理的渲染。随后，提出了姿势引导的6D转换以及无碰撞组装来构建具有任意初始姿势的任何设计结构。我们的新型3轴校准操作通过解开6D姿势估计和机器人组件进一步提高了精度和鲁棒性。定量和定性结果都证明了我们提出的6D机器人组装系统的有效性。

本文介绍了一种新型的多视图6 DOF对象姿势细化方法，重点是改进对合成数据训练的方法。它基于DPOD检测器，该检测器会在每个帧中产生密集的2D-3D对应关系。我们选择使用多个具有已知相机转换的帧，因为它允许通过可解释的ICP样损耗函数引入几何约束。损耗函数是通过可区分的渲染器实现的，并经过迭代进行了优化。我们还证明，仅根据合成数据训练的完整检测和完善管道可用于自动标记的真实数据。我们对linemod，caslusion，自制和YCB-V数据集执行定量评估，并与对合成和真实数据训练的最新方法相比，报告出色的性能。我们从经验上证明，我们的方法仅需要几个帧，并且可以在外部摄像机校准中关闭相机位置和噪音，从而使其实际用法更加容易且无处不在。

透明的物体在家庭环境中无处不在，并且对视觉传感和感知系统构成了不同的挑战。透明物体的光学特性使常规的3D传感器仅对物体深度和姿势估计不可靠。这些挑战是由重点关注现实世界中透明对象的大规模RGB深度数据集突出了这些挑战。在这项工作中，我们为名为ClearPose的大规模现实世界RGB深度透明对象数据集提供了一个用于分割，场景级深度完成和以对象为中心的姿势估计任务的基准数据集。 ClearPose数据集包含超过350K标记的现实世界RGB深度框架和5M实例注释，涵盖了63个家用对象。该数据集包括在各种照明和遮挡条件下在日常生活中常用的对象类别，以及具有挑战性的测试场景，例如不透明或半透明物体的遮挡病例，非平面取向，液体的存在等。 - 艺术深度完成和对象构成清晰度上的深神经网络。数据集和基准源代码可在https://github.com/opipari/clearpose上获得。

我们提出了一种称为DPODV2（密集姿势对象检测器）的三个阶段6 DOF对象检测方法，该方法依赖于致密的对应关系。我们将2D对象检测器与密集的对应关系网络和多视图姿势细化方法相结合，以估计完整的6 DOF姿势。与通常仅限于单眼RGB图像的其他深度学习方法不同，我们提出了一个统一的深度学习网络，允许使用不同的成像方式（RGB或DEPTH）。此外，我们提出了一种基于可区分渲染的新型姿势改进方法。主要概念是在多个视图中比较预测并渲染对应关系，以获得与所有视图中预测的对应关系一致的姿势。我们提出的方法对受控设置中的不同数据方式和培训数据类型进行了严格的评估。主要结论是，RGB在对应性估计中表现出色，而如果有良好的3D-3D对应关系，则深度有助于姿势精度。自然，他们的组合可以实现总体最佳性能。我们进行广泛的评估和消融研究，以分析和验证几个具有挑战性的数据集的结果。 DPODV2在所有这些方面都取得了出色的成果，同时仍然保持快速和可扩展性，独立于使用的数据模式和培训数据的类型

我们提出了一种方法，用于估计具有单个RGB图像的可用3D模型的刚性对象的6DOF姿势。与基于经典对应的方法不同，该方法可以预测输入图像的像素的3D对象坐标，该建议的方法可以预测3D对象坐标在相机frustum中采样的3D查询点。从像素到3D点的移动，这是受到3D重建方法的最新PIFU式方法的启发，可以对整个对象（包括（自我）遮挡部分）进行推理。对于与与像素对齐的图像功能相关的3D查询点，我们训练完全连接的神经网络来预测：（i）相应的3D对象坐标，以及（ii）签名到对象表面的签名距离，首先定义仅适用于地表附近的查询点。我们将该网络实现的映射称为神经通信字段。然后，通过Kabsch-Ransac算法从预测的3D-3D对应关系中稳健地估计对象姿势。所提出的方法在三个BOP数据集上实现了最先进的结果，并且在咬合挑战性案例中表现出了优越。项目网站在：linhuang17.github.io/ncf。

在许多机器人应用中，要执行已知，刚体对象及其随后的抓握的6多-DOF姿势估计的环境设置几乎保持不变，甚至可能是机器人事先知道的。在本文中，我们将此问题称为特定实例的姿势估计：只有在有限的一组熟悉的情况下，该机器人将以高度准确性估算姿势。场景中的微小变化，包括照明条件和背景外观的变化，是可以接受的，但没有预期的改变。为此，我们提出了一种方法，可以快速训练和部署管道，以估算单个RGB图像的对象的连续6-DOF姿势。关键的想法是利用已知的相机姿势和刚性的身体几何形状部分自动化大型标记数据集的生成。然后，数据集以及足够的域随机化来监督深度神经网络的培训，以预测语义关键。在实验上，我们证明了我们提出的方法的便利性和有效性，以准确估计物体姿势，仅需要少量的手动注释才能进行训练。

我们介绍了Amazon Berkeley对象（ABO），这是一个新的大型数据集，旨在帮助弥合真实和虚拟3D世界之间的差距。ABO包含产品目录图像，元数据和艺术家创建的3D模型，具有复杂的几何形状和与真实的家用物体相对应的物理基础材料。我们得出了具有挑战性的基准，这些基准利用ABO的独特属性，并测量最先进的对象在三个开放问题上的最新限制，以了解实际3D对象：单视3D 3D重建，材料估计和跨域多视图对象检索。

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.

商业深度传感器通常会产生嘈杂和缺失的深度，尤其是在镜面和透明的对象上，这对下游深度或基于点云的任务构成了关键问题。为了减轻此问题，我们提出了一个强大的RGBD融合网络Swindrnet，以进行深度修复。我们进一步提出了域随机增强深度模拟（DREDS）方法，以使用基于物理的渲染模拟主动的立体声深度系统，并生成一个大规模合成数据集，该数据集包含130k Photorealistic RGB图像以及其模拟深度带有现实主义的传感器。为了评估深度恢复方法，我们还策划了一个现实世界中的数据集，即STD，该数据集捕获了30个混乱的场景，这些场景由50个对象组成，具有不同的材料，从透明，透明，弥漫性。实验表明，提议的DREDS数据集桥接了SIM到实地域间隙，因此，经过训练，我们的Swindrnet可以无缝地概括到其他真实的深度数据集，例如。 ClearGrasp，并以实时速度优于深度恢复的竞争方法。我们进一步表明，我们的深度恢复有效地提高了下游任务的性能，包括类别级别的姿势估计和掌握任务。我们的数据和代码可从https://github.com/pku-epic/dreds获得

Traditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segmentation, single-frame rotation prediction and keypoint detection, from automatically generated real-world data. We use a transformation trick to get EE pose estimates from rotation predictions and a matching algorithm to get EE pose estimates from keypoint predictions. We further utilize the iterative closest point algorithm, multiple-frames, filtering and outlier detection to increase calibration robustness. Our evaluations with training data from multiple camera poses and test data from previously unseen poses give sub-centimeter and sub-deciradian average calibration and pose estimation errors. We also show that a carefully selected single training pose gives comparable results.