, they can be generalized to unseen objects without retraining. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K-D tree ICP algorithm. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates . In this paper we tackle the problem of estimating the pose of novel object categories in a zero-shot manner. Pose estimation is a task that involves identifying the location of specific points in an image, usually referred to as keypoints. In this paper, we aim to learn 6D poses for roboticassembly by natural language instructions. In this Dec 4, 2023 · Single-pose estimation is used to estimate the poses of a single object in a given scene, while multi-pose estimation is used when detecting poses for multiple objects. We first compute a pose-induced flow This tracking process is more efficient compared to pose estimation, which speeds exceeding 120 FPS on the Jetson Orin platform. Our first method, neural networks (CNNs) to provide real-time, accurate pose estimation of known objects in cluttered scenes. The problem of object pose estimation is an inherently 3D problem; it is the shape of the object which gives away its pose regardless of its appearance. Motivated by this, we introduce SAM-6D, a novel For pose estimation, we first initialize global poses uniformly around the object, which are then refined by the refinement network (Sec. We propose a new method named OnePose for object pose estimation. e. Nevertheless, they still need to obtain the object CAD model or annotate a few reference images of the object. In the following tables, 3D CAD model is noted as model and 2D pictured object is noted as object . Mar 2, 2022 · This work proposes a novel pose estimation model for object categories that can be effectively transferred to previously unseen environments. As recently as 2019, in the Benchmark for 6D Object Pose Estimation (a nearly annual competition), geometric pose estimation was still outperforming deep-learning based approaches Hodan20. Accordingly, the letter presents a new approach for object pose estimation from RGB-D images, utilizing the affordance-instance segmentation constraint Jun 19, 2022 · The 6D object pose estimation is a forward-looking technology in the field of computer vision, which has great application potential in metaverse, VRI AR, robot operation, intelligent driving and other fields. Instance-level pose estimation methods [20,21,31,50] assume a textured CAD model is given for the object. Recently, thanks to the advances in 3D registration techniques, the community has witnessed a growing interest in 3D registration-based 6D object pose estimation, which recovers the object’s 6D pose by estimating the rigid transformation between the object (source) and the model point clouds. Nov 19, 2019 · Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. 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. Compared to Aug 17, 2020 · This paper presents a comprehensive survey on vision-based robotic grasping. At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and Apr 5, 2024 · The objective of human pose estimation (HPE) derived from deep learning aims to accurately estimate and predict the human body posture in images or videos via the utilization of deep neural Jun 23, 2022 · Estimating the 6D pose for unseen objects is in great demand for many real-world applications. Several researchers have pointed out the We introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. isaac_ros_dope is used in a graph of nodes to estimate the pose of a known object with 3D bounding cuboid dimensions. However, the general optical flow methods typically do not consider the target's 3D shape information during matching, making them less effective in 6D object pose estimation. These limitations mainly come from the necessity of 3D models, closed-category detection, and a large number of densely annotated support views. The state-of-the-art models for pose estimation are convolutional neural network (CNN)-based. First, it does not provide the 3D model of the satellite, and while it can be reconstructed from the images, the final pose estimate will depend not only on the pose estimation algorithm but also on the quality of this reconstruction. [CVPR 2022] Pytorch implementation of "Templates for 3D Object Pose Estimation Revisited: Generalization to New objects and Robustness to Occlusions" paper. As a result, the multiscale information in 2D images cannot be fully exploited. Second, training the deep network relies on a surrogate loss that does not Jan 30, 2024 · 6-DoF object pose estimation from a monocular image is a challenging problem, where a post-refinement procedure is generally needed for high-precision estimation. Top-down Approach to Pose Estimation Jul 21, 2022 · Inside my school and program, I teach you my system to become an AI engineer or freelancer. Our Nov 12, 2023 · Pose Estimation. segmentation-driven 6D pose estimation network in which each visible object patch contributes a pose estimate for the object it belongs to in the form of the predicted 2D projec-tions of predefined 3D keypoints. This eliminates the significant increase in runtime when dealing with Nov 1, 2017 · Estimating the 6D pose of known objects is important for robots to interact with the real world. Our approach leverages the temporal information from a video sequence for pose refinement, along with being computationally efficient and robust. In this work This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image. ods for object pose estimation return a single point estimate of the object’s pose. •We develop a robust multi-view center predictor, us-ing BEV and front-view source-feature projections to jointly vote for the object center. However, these approaches usually require a large amount of training data containing objects of interest anno- 2. Using confidence values also predicted by our network, we then combine the most reliable 2D projections for each 3D keypoint, which 6D object pose estimation for texture-less objects from RGB images remains challenging, especially in occlusion scenarios. MIT license 703 stars 86 forks Branches Tags Activity. Notably, the source This is the official repository for NVIDIA's Deep Object Pose Estimation, which performs detection and 6-DoF pose estimation of known objects from an RGB camera. This task provides the regions of Nov 28, 2022 · The 6D pose estimation of an object from an image is a central problem in many domains of Computer Vision (CV) and researchers have struggled with this issue for several years. , 2023a). Most recent studies have formulated methods for predicting the projected 2-D locations of 3-D keypoints through a deep neural network and then used a PnP algorithm to compute the 6-DOF poses. Nov 17, 2023 · Object pose estimation is a crucial task for semantic robot manipulation involving the detection of suitable manipulation regions. The input sizes include 256x192 and 384x288. The object pose is often solved by direct regression [37,73], or con-structing 2D-3D correspondences followed by PnP [50,61], A Zhihu column that provides insights and discussions on various topics. This provides flexibility to select the right model for different speed and Jan 16, 2024 · Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Pose estimation is usually approached by seeking the single best estimate of an object's pose, but this approach is ill-suited for tasks involving visual ambiguity. 2UQ for Object Pose Estimation Despite the importance of reliable pose estimates, there are few works that focus on UQ in the context of 6D object pose estim-ation (Thalhammer et al. In this work, we propose two learned methods for estimating a distribution over an object’s orientation. The 3D rotation of the object is estimated by regressing to a quaternion representation. Here the problem is tackled using mathematical optimization, which is another traditional way to approach the problem of object pose estimation. Download the pose estimation model we have trained. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. 1. The most elemental problem in augmented reality is the estimation of the camera pose respect of an object in the case of computer vision area to do later some 3D rendering or in the case of robotics obtain an object pose in order to grasp it and do some manipulation. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting lolrudy/gpv_pose • • CVPR 2022 While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the first stage predicts 2D landmarks using a deep network and the second stage solves for 6DOF pose from 2D-3D correspondences. For this purpose, Language-Instructed 6D Pose Regression Network (LanPose) is proposed to jointly predict the 6D poses of the observed object and the corresponding assembly position. This dataset, however, has several limitations. It introduces GenPose++, an enhanced version of the SOTA category-level pose estimation framework, and provides a comprehensive benchmarking analysis. Robust 6D Object Pose Estimation. Dec 24, 2023 · 6D object pose estimation is an important task in computer vision, and the task of estimating 6D object pose from a single RGB image is even more challenging. Pose estimation of rigid objects. Evaluation of 6D object pose estimates is not straightforward. While most existing Aug 24, 2018 · We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. Bottom-up vs. This letter proposes an approach that estimates the grasp state by combining finger measurements, i. Category-level 6D object pose estimation. B. 3. Equipped with the multi-head self Mar 15, 2024 · This paper introduces GS-Pose, an end-to-end framework for locating and estimating the 6D pose of objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. Pose estimation is critical in many robotics applications, particularly to enable autonomous vehicles to perceive other vehicles around them. We present a flexible approach that can deal with generic objects, both textured and texture-less. Nevertheless, several challenges persist in contemporary methods, including their enhance the generalization of object pose estimation, i. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. Apr 6, 2022 · The workflow of the proposed method for micro/nano-object pose estimation is illustrated as follows. Star The study of reconstruction of hands and objects from color monocular images has garnered considerable attention in recent years. The proposed method focuses on the 6D pose estimation of objects by taking advantage of the high accuracy and high efficiency of existing object detection algorithms [31] Therefore, same as [13], [16], [18], [32], we decouple the training of the proposed model and the object detector. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Input Release RTMW3D, a real-time model for 3D wholebody pose estimation. 5 days ago · Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields. Nov 23, 2023 · We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images. Go inside the ROS/src/ur3_moveit folder and create a folder CAD Model-based Object Pose Estimation. , detecting a single class object (like a person or an animal) and estimating the pose of the object Jun 6, 2024 · Omni6DPose is a large-scale dataset and a framework for 6D object pose estimation and tracking, covering 149 categories and 1. While Vision-Language Models (VLMs) enable using natural language descriptions to support 6D pose estimation of unseen objects, these solutions underperform compared to model-based methods. The state-of-the-art performance demonstrates the efficiency of the proposed system. 3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. Release RTMO, a state-of-the-art real-time method for multi-person pose estimation. These markers would become the core of the popular software framework ARToolkit [], and were essential to robustly estimate the locations and orientations of objects in the 3D space, a geometric information required for the proper integration of the virtual objects with the real book, and their manipulation by the shovel. Second For a robot to perform complex manipulation tasks, such as an in-hand manipulation, knowledge about the state of the grasp is required at all times. In this work we present Horyon, an open-vocabulary VLM-based architecture that addresses relative pose estimation Jan 1, 2024 · For a more detailed discussion of instance-level object pose estimation, we refer the readers to the review paper [21]. Object recognition and pose estimation are critical components in autonomous robot manipulation systems, playing a crucial role in enabling robots to interact effectively with the environment. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. Inside that folder you should have a src folder and inside that one 5 folders: moveit_msgs, robotiq, ros_tcp_endpoint, universal_robot and ur3_moveit. It is mainly to get the translation and rotation of rigid object in three-dimensional rectangular coordinate system under x, y and z axes. Life-time access, personal help by me and I will show you exactly May 25, 2023 · Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied agents and downstream 3D vision tasks. In this paper, we inject two fundamental changes, namely conformal keypoint detection and In your root Robotics-Object-Pose-Estimation folder, you should have a ROS folder. At the same time, the lack of feature Oct 24, 2022 · We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos. Many works use Bingham distributions to model the orienta- Sep 16, 2021 · Local optimal estimation problem: Given the shell parts, the 3D vision sensor can only capture the point cloud of its visible side. Human pose estimation on the popular MS COCO Dataset can detect 17 different keypoints (classes). To address this limitation, we propose an approach that takes a single image of a new object as input and predicts the relative pose of this object in new images without prior knowledge of the object's 3D model and without Nov 27, 2023 · Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability. Compared to instance-level object pose estimation, existing studies on category-level object pose estimation are still rare in the literature. Finally, we forward the refined poses to the pose selection module which predicts their scores. We collect a dataset with both real and Nov 11, 2022 · Given (a) reference images of an object with known poses and (b) query images containing the same object with unknown poses, our pose estimator is able to accurately estimate (c) their object poses in the query images, where green color means ground-truth and blue color means estimation. In this paper, we propose a framework, dubbed RNNPose, based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. The accurate estimation of object pose and size is cru-cial for a variety of real-world applications, including au-tonomous driving, augmented reality [1], scene understand-ing [2][3], and robotic manipulation [4]. 3). CNN-based pose estimation techniques enable significant improvements in the accuracy of object detection and pose estimation. In this study, we propose a novel solution by reframing Dec 8, 2023 · Real-time pose estimation leverages machine learning algorithms and computer vision to identify the position and orientation of an object in real time. Often, UQ for object pose estimation is referred to as the estimation of a pose distribution. PoseCNN estimates the 3D translation of an object by Oct 20, 2023 · Comprehending natural language instructions is a critical skill for robots to cooperate effectively with humans. Given the diversity of object shapes and scene complexities, object pose estimation remains an immense challenge. Pose estimation takes a step further by foretelling the precise location of the object’s key points. The head for YOLO-NAS Pose is designed for its multi-task objective, i. This two-stage process, however, is suboptimal: First, it is not end-to-end trainable. A lot of research pour in this field. More Dec 3, 2023 · 3D pose estimation works to transform an object in a 2D image into a 3D object by adding a z-dimension to the prediction. In this paper, we propose a novel rotation estimation network, termed as VI-Net, to make the task easier by decoupling the rotation as the combination of a viewpoint rotation and an in-plane rotation. Lately, Transformers, an architecture originally proposed for natural language processing, is achieving state-of-the-art results in many computer vision tasks as well. Release RTMW models in various sizes ranging from RTMW-m to RTMW-x. 3D pose estimation allows us to predict the actual spatial positioning of a depicted person or object. 2 Related Work There is a vast literature in the area of pose estimation and object detection, including instance and category recognition, rigid and articulated objects, and We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. The object intra-class shape variation [21], [22] is the main challenge of applying the network to novel objects for accurate pose estimation. Moreover, even simple pick-and-place tasks may fail because unexpected motions of the object during the grasp are not accounted for. The final version is optimized with Intel OpenVINO and implemented together with the pose estimation in C++. In the 3D object detection process, classifications are centered on the object’s size, position, and direction. Owing to the poorly assigned initial poses and the similarity of inner and outer surfaces of shell parts, improper corresponding relationships of point pairs will be identified by ICP, which results in local optimal pose estimation Nov 1, 2017 · Estimating the 6D pose of known objects is important for robots to interact with the real world. 4. It was originally achieves accurate object pose estimation with stereo images. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. Yet, learning the entire correspondences from scratch is highly 2 days ago · Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields. Related work2. Estimating the 6D pose of a rigid object with a known 3D model from a single image is one of the oldest computer vision problems [77]. In existing methods, parametric models are constructed at single scale, and the interaction between hands and objects has not fully be explored. GigaPose first leverages discriminative "templates", rendered images of the CAD models, to recover the out-of-plane rotation and then uses patch correspondences to estimate the four remaining parameters. During the recurrent iterations, object pose pose estimation performance. During actual execution, the robot must recognize the object in the current scene, estimate its pose, and then select a feasible grasp pose from the pre-defined grasp configurations. Fortunately, the recent Segment Anything Model (SAM) has showcased remarkable zero-shot transfer performance, which provides a promising solution to tackle this task. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In such cases it is desirable to estimate the uncertainty as a pose distribution to allow downstream tasks to make informed Apr 7, 2022 · Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally. Instead of localizing sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion, we introduce GeoPose, a novel reconstruction guided pose estimation pipeline that predicts dense correspondences and leverages geometric consistency Oct 22, 2021 · Practical object pose estimation demands robustness against occlusions to the target object. Contributing the answer was a resounding "I'd give up depth; don't take away my color!" That's a big change from just a few years ago. The key new concept is a learned, intermediate representation Mar 22, 2023 · The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Nov 9, 2020 · In this paper we introduce EfficientPose, a new approach for 6D object pose estimation. 6D Object pose estimation Nov 24, 2016 · A pose of a rigid object has 6 degrees of freedom and its full knowledge is required in many robotic and scene understanding applications. , joint The object detection algorithm is the YOLOX-S model from the YOLOX repository, which is transfer learned on the LOCO dataset. Moreover, it can detect the 2D bounding box of multiple objects and instances as well as estimate their full 6D poses in a single shot. Thus, existing methods often rely on intermediate 3D shape representations to increase performance. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Human hands are highly articulated and versatile at handling objects. Many methods use deep learning to acquire 2D feature points from images to establish 2D-3D correspondences, sity as part of a satellite pose estimation challenge. Jul 21, 2023 · 6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. Nov 1, 2022 · To our knowledge, this is new and meaningful for 6D object pose estimation. Albeit widely adopted, such two-stage approaches could suffer from novel occlusions when Jan 1, 2023 · This early system relied on visual markers on the real book and the shovel. It arises in computer vision or robotics where the pose or transformation of an object can be used for alignment of a computer-aided design models, identification, grasping , or manipulation of the object. May 13, 2024 · Abstract: Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Category-Level Object Pose Estimation Category-level object pose estimation aims to predict the pose of novel objects. This technique involves tracking the orientation or the specific posture of an object, generally a human, by determining specific key points such as the head, shoulders, elbows, hands, knees Nov 7, 2023 · Both the Object Detection models and the Pose Estimation models have the same backbone and neck design but differ in the head. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. For full details, see our CoRL 2018 paper and video. Traditional pose estimation methods (1) leveraged on geometrical approaches, exploiting manually annotated local features, or (2) relied on 2D object representations from different points of view and their comparisons Oct 27, 2023 · The pose of rigid objects can be represented as a combination of a 3D rotation matrix and a 3D translation vector; in particular, 6D object pose estimation is an important step in many computer vision applications [], such as autonomous driving [2,3,4], augmented reality [5,6,7], and simultaneous localization and mapping (SLAM) [8,9,10]. This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. But by the 2020 version of the Mar 9, 2023 · Object pose estimation is a core computer vision problem and often an essential component in robotics. Successful application of these strategies A repo to summarize resources used in object pose estimation as well as viewpoint estimation. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance- or category-specific network training. This Apr 15, 2024 · Hand-object configuration recovery is an important task in computer vision. 4). This extends much of the existing literature by removing the need for pose-labelled datasets or category-specific CAD models for training or inference This paper builds on over 60 years of research in object pose estimation, and on the recent large-scale vision foundation models. GS-Pose begins with a set of posed RGB images of a previously unseen object and builds three distinct representations stored in a database. The estimation of pose and shape for both hands and objects during interactive scenarios has various applications, particularly in augmented reality, virtual reality, or imitation-based robot learning. We design algorithms that allow 6D pose estimation in challenging scenarios, including heavy occlusion, while under computational constraints. 2. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multihypothesis issue. tion framework for robust real-world 6D object pose estimation, which first identifies the object center to decouple translation and rotation prediction. Depending on the application needs, the object pose is estimated up to varying degrees of freedom (DoF) such as 3DoF that only includes 3D rotation, 6DoF that additionally includes 3D translation, or Jun 24, 2021 · The aim of this paper is to estimate the six-degree-of-freedom (6-DOF) poses of objects from a single RGB image in which the target objects are partially occluded. • Target on the rotation ambiguous and occlusion challenges, we propose to solve from the parameter space and data structure. Our method is highly accurate, efficient and scalable over a wide range of computational resources. To mitigate this issue, we propose Apr 17, 2023 · With the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. The problem is particularly challenging when the hand is interacting with objects in the environment, as this setting images of 20 objects captured each under three di erent lighting conditions and labelled with accurate 6D pose, which will be made publicly available. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive features. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation Aug 19, 2023 · Rotation estimation of high precision from an RGB-D object observation is a huge challenge in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO(3). Although significant progress has been made in the area of object pose estimation, several challenges persist in cur- Jun 24, 2024 · The generalisation to unseen objects in the 6D pose estimation task is very challenging. In this work, we propose a shape-constraint recurrent matching framework for 6D object pose estimation. Jun 23, 2023 · Most recent 6D object pose methods use 2D optical flow to refine their results. 5M annotations. During training, we use zoomed-in dynamically to process the Mar 31, 2022 · We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. arXiv, Project Mar 18, 2024 · However, this localization is usually coarse-grained and consists of a bounding box that contains the object. Our methods take into account both the inaccuracies in the pose estimation as well as the object symmetries. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. To produce the estimate, a DOPE (Deep Object Pose Estimation) pre-trained model is required. May 13, 2024 · Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Our approach samples templates in only a two-degrees-of-freedom space instead Jun 18, 2023 · Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Through the inherent handling of multiple objects and instances and the fused single shot 2D object detection as well as 6D pose estimation, our approach runs even with multiple objects (eight) end-to-end at over 26 FPS, making it highly attractive to many real world scenarios. Object pose may be ambiguous due to object symmetries and Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. You’ll gain experience integrating ROS with Unity, importing URDF models, collecting labeled training data, and training and deploying a deep learning model. Instead of Awesome work on object 6 DoF pose estimation License. 2. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. Training and testing is performed on the exact same instance. Given correspondences between the object in 'rest pose' (pastel lemon color) and its rotated and translated counter part (pastel honeydew color), the problem can be formulated as a minimization problem. While most cur-rent object pose estimation networks focus on instance-level object pose estimation [5][6][7][8], which requires Mar 23, 2023 · The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. The pose with the best score is selected as output (Sec. (1) Step 1: to reduce the domain gap between the simulated data and the experiment data, a Object pose estimation is a crucial technology for augmented reality[1, 2, 3], robotic manipulation[4, 5], hand-object interaction[6, 7], etc.
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