Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [11] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster -50 fps on a Titan X (Pascal) GPU -and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [28,29] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm.For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent 26] when they are all used without postprocessing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.

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/.

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。

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.

We present a method for 3D object detection and pose estimation from a single image. In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box. The first network output estimates the 3D object orientation using a novel hybrid discrete-continuous loss, which significantly outperforms the L2 loss. The second output regresses the 3D object dimensions, which have relatively little variance compared to alternatives and can often be predicted for many object types. These estimates, combined with the geometric constraints on translation imposed by the 2D bounding box, enable us to recover a stable and accurate 3D object pose. We evaluate our method on the challenging KITTI object detection benchmark [2] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes. Although conceptually simple, our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation, instance level segmentation and flat ground priors [4] and sub-category detection [23][24]. Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26].

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

We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a "holistic" approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the corners of their 3D bounding boxes. This, however, is not sufficient for handling objects from the recent T-LESS dataset: These objects exhibit an axis of rotational symmetry, and the similarity of two images of such an object under two different poses makes training the CNN challenging. We solve this problem by restricting the range of poses used for training, and by introducing a classifier to identify the range of a pose at run-time before estimating it. We also use an optional additional step that refines the predicted poses. We improve the state-of-the-art on the LINEMOD dataset from 73.7% [2] to 89.3% of correctly registered RGB frames. We are also the first to report results on the Occlusion dataset [1] using color images only. We obtain 54% of frames passing the Pose 6D criterion on average on several sequences of the T-LESS dataset, compared to the 67% of the state-of-the-art [10] on the same sequences which uses both color and depth. The full approach is also scalable, as a single network can be trained for multiple objects simultaneously.

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.

预测对象的姿势是核心计算机视觉任务。基于深度学习的姿势估计方法需要CAD数据来使用3D中间表示或项目2D外观。但是，当感兴趣对象的CAD数据不可用时，不能使用这些方法。此外，现有方法并未精确地反映了学习过程的透视变形。此外，由于自闭锁而尚未得到很好的信息损失。在这方面，我们提出了一种新的姿势估计系统，该系统由空间雕刻模块组成，该空间雕刻模块重构参考3D特征来替换CAD数据。此外，我们的新型转换模块，动态投射空间变压器（DPROST），转换参考3D功能，以在考虑透视失真的同时反映姿势。此外，我们通过新的双向Z缓冲（Biz缓冲区）方法克服了自闭锁问题，其提取了对象的前视图和自闭合的背视图。最后，我们建议一个透视电网距离损失（PGDL），从而能够稳定地学习没有CAD数据的姿势估计。实验结果表明，我们的方法在LineMod DataSet上占据了最先进的方法，以及LineMod-occlusion数据集上的可比性，甚至与在网络训练中需要CAD数据的方法相比。

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

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.

促使是令人印象深刻的3D对象检测模型。它已被证明是快速，可扩展和准确的，特别是在考虑它仅使用RGB输入时。在本文中，我们尝试通过使其推断对象尺寸的能力来提高培养，并通过简化数据收集和损耗计算。我们使用LineMod DataSet进行了评估的epose和它的新子集称为“闭塞1-class”。我们还概述了我们目前的进展和关于使用NUSCENES和2017年Kitti 3D对象检测数据集的进步和思考。源代码可在https://github.com/tbd-clip/effile上获得。

光有许多可以通过视觉传感器被动测量的特性。色带分离波长和强度可以说是单眼6D对象姿态估计的最常用的波长。本文探讨了互补偏振信息的互补信息，即光波振荡的方向，可以影响姿态预测的准确性。一种混合模型，利用数据驱动的学习策略共同利用物理代理，并在具有不同量的光度复杂度的物体上进行设计和仔细测试。我们的设计不仅显着提高了与光度 - 最先进的方法相关的姿态精度，而且还使对象姿势估计用于高反射性和透明的物体。

实时机器人掌握，支持随后的精确反对操作任务，是高级高级自治系统的优先目标。然而，尚未找到这样一种可以用时间效率进行充分准确的掌握的算法。本文提出了一种新的方法，其具有2阶段方法，它使用深神经网络结合快速的2D对象识别，以及基于点对特征框架的随后的精确和快速的6D姿态估计来形成实时3D对象识别和抓握解决方案能够多对象类场景。所提出的解决方案有可能在实时应用上稳健地进行，需要效率和准确性。为了验证我们的方法，我们进行了广泛且彻底的实验，涉及我们自己的数据集的费力准备。实验结果表明，该方法在5CM5DEG度量标准中的精度97.37％，平均距离度量分数99.37％。实验结果显示了通过使用该方法的总体62％的相对改善（5cm5deg度量）和52.48％（平均距离度量）。此外，姿势估计执行也显示出运行时间的平均改善47.6％。最后，为了说明系统在实时操作中的整体效率，进行了一个拾取和放置的机器人实验，并显示了90％的准确度的令人信服的成功率。此实验视频可在https://sites.google.com/view/dl-ppf6dpose/上获得。

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。

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.

在Robocup小型联盟（SSL）中，鼓励团队提出解决方案，以便仅使用嵌入式感应信息在SSL领域执行基本足球任务。因此，这项工作提出了一种嵌入式的单眼视觉方法，用于检测物体和估计足球场内的相对位置。通过假设对象放在地面上，并且板载摄像头的位置固定在机器人上，可以利用来自环境的先验知识。我们在NVIDIA Jetson Nano上实施了建议的方法，并使用SSD Mobilenet V2用于2D对象检测，并具有张力优化，检测球，机器人和目标，距离高达3.5米。球定位评估表明，所提出的解决方案克服了当前使用的SSL视觉系统，该系统的位置超过1米，距离板载摄像头14.37毫米。此外，所提出的方法以每秒30帧的平均处理速度实现实时性能。

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/.

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。