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6d object pose estimation via viewpoint relation reasoning

NEUROCOMPUTING 2020年 389卷 9-17页

作者： Zhao, Wanqing Zhang, Shaobo Guan, Ziyu Luo, Hangzai Tang, Lei Peng, Jinye Fan, Jianping Northwestern Univ Sch Informat & Technol Xian Shaanxi Peoples R China Xian Microelect Technol Inst Xian Shaanxi Peoples R China UNC Charlotte Dept Comp Sci Charlotte NC USA

Estimating the 6d object pose is a very challenging task in computer vision. The main difficulty is mapping the object from RGB images to 3d space. In this paper, we present a novel two-stage method for estimating the 6d object pose by using the 2d keypoints of an object and its 2d bounding box. There are two stages in our method. The first stage detects the 2d keypoints and 2d bounding boxes of objects by a stable end-to-end framework. during the training phase, this framework uses viewpoint transformation information and object saliency regions to learn geometrically and semantically consistent keypoints. Then the 6d poses of objects are calculated by a series of geometric reasoning algorithms in the second stage. Experiments show that our method achieves accurate pose estimation and robust to occluded and cluttered scenes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： 6d object pose estimation Keypoints detection Convolutional neural networks Geometric reasoning

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6d object pose estimation based on dense convolutional object center voting with improved accuracy and efficiency

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VISUAL COMPUTER 2024年第8期40卷 5421-5434页

作者： Ullah, Faheem Wei, Wu Fan, Zhun Yu, Qiuda China Univ Technol Sch Automat Sci & Engn South Guangzhou Peoples R China Shantou Univ Coll Engn Shantou Guangdong Peoples R China

6d object pose estimation is an important application of computer vision and a basic module in robotic manipulation, but dealing with occlusion in a cluttered environment, handling symmetries, and textureless surfaces, are real issues. Other issues with such systems are accuracy and efficiency. The recent two-stage methods perform well in terms of accuracy;however, a linear increase in their runtimes occurs due to the increase in the number of objects in a scene. This paper proposes a fully convolutional and parallel architecture that obtains the 3d translation and orientation for object poses from the same pixel-wise dense estimation. It exploits the same voting block for inliers for multiple instances, final 3d translation estimation, and quaternions aggregation. Only the center point estimation of the objects decreases the model's running time, while still useful for occlusions and texturelessness. Symmetries and varieties are handled with a loss function based on shape matching and the pose of the object. Our proposed approach has fewer parameters and takes less time to train and evaluate, achieving great accuracy. Experiments on LINEMOd and occlusion LINEMOd datasets using Add (-S) and 2d projection evaluation metrics show that the proposed method outperforms state-of-the-art approaches for 6d pose estimation.

关键词： 6d object pose estimation Pixel-wise labeling Pixel-wise center localization Pixel-wise quaternion rotation

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deep instance segmentation and 6d object pose estimation in cluttered scenes for robotic autonomous grasping

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INdUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH ANd APPLICATION 2020年第4期47卷 593-606页

作者： Wu, Yongxiang Fu, Yili Wang, Shuguo Harbin Inst Technol State Key Lab Robot & Syst Harbin Peoples R China

Purpose This paper aims to design a deep neural network for object instance segmentation and six-dimensional (6d) pose estimation in cluttered scenes and apply the proposed method in real-world robotic autonomous grasping of household objects. design/methodology/approach A novel deep learning method is proposed for instance segmentation and 6d pose estimation in cluttered scenes. An iterative pose refinement network is integrated with the main network to obtain more robust final pose estimation results for robotic applications. To train the network, a technique is presented to generate abundant annotated synthetic data consisting of RGB-d images and object masks in a fast manner without any hand-labeling. For robotic grasping, the offline grasp planning based on eigengrasp planner is performed and combined with the online object pose estimation. Findings The experiments on the standard pose benchmarking data sets showed that the method achieves better pose estimation and time efficiency performance than state-of-art methods with depth-based ICP refinement. The proposed method is also evaluated on a seven dOFs Kinova Jaco robot with an Intel Realsense RGB-d camera, the grasping results illustrated that the method is accurate and robust enough for real-world robotic applications. Originality/value A novel 6d pose estimation network based on the instance segmentation framework is proposed and a neural work-based iterative pose refinement module is integrated into the method. The proposed method exhibits satisfactory pose estimation and time efficiency for the robotic grasping.

关键词： 6d object pose estimation Instance segmentation Robotic autonomous grasping

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CSA6d: Channel-Spatial Attention Networks for 6d object pose estimation

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COGNITIVE COMPUTATION 2022年第2期14卷 702-713页

作者： Chen, Tao Gu, dongbing Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

6d object pose estimation plays a crucial role in robotic manipulation and grasping tasks. The aim to estimate the 6d object pose from RGB or RGB-d images is to detect objects and estimate their orientations and translations relative to the given canonical models. RGB-d cameras provide two sensory modalities: RGB and depth images, which could benefit the estimation accuracy. But the exploitation of two different modality sources remains a challenging issue. In this paper, inspired by recent works on attention networks that could focus on important regions and ignore unnecessary information, we propose a novel network: Channel-Spatial Attention Network (CSA6d) to estimate the 6d object pose from RGB-d camera. The proposed CSA6d includes a pre-trained 2d network to segment the interested objects from RGB image. Then it uses two separate networks to extract appearance and geometrical features from RGB and depth images for each segmented object. Two feature vectors for each pixel are stacked together as a fusion vector which is refined by an attention module to generate a aggregated feature vector. The attention module includes a channel attention block and a spatial attention block which can effectively leverage the concatenated embeddings into accurate 6d pose prediction on known objects. We evaluate proposed network on two benchmark datasets YCB-Video dataset and LineMod dataset and the results show it can outperform previous state-of-the-art methods under Add and Add-S metrics. Also, the attention map demonstrates our proposed network searches for the unique geometry information as the most likely features for pose estimation. From experiments, we conclude that the proposed network can accurately estimate the object pose by effectively leveraging multi-modality features.

关键词： 6d object pose estimation Convolutional neural network Feature fusion Attention mechanism

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Occlusion-Aware Self-Supervised Monocular 6d object pose estimation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2024年第3期46卷 1788-1803页

作者： Wang, Gu Manhardt, Fabian Liu, Xingyu Ji, Xiangyang Tombari, Federico Tsinghua Univ Dept Automat Beijing 100084 Peoples R China BNRist Beijing 100084 Peoples R China Google Inc CH-8002 Zurich Switzerland Tech Univ Munich D-80333 Munich Germany

6d object pose estimation is a fundamental yet challenging problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6d pose estimates even under monocular settings. Nonetheless, CNNs are identified as being extremely data-driven, and acquiring adequate annotations is oftentimes very time-consuming and labor intensive. To overcome this limitation, we propose a novel monocular 6d pose estimation approach by means of self-supervised learning, removing the need for real annotations. After training our proposed network fully supervised with synthetic RGB data, we leverage current trends in noisy student training and differentiable rendering to further self-supervise the model on these unsupervised real RGB(-d) samples, seeking for a visually and geometrically optimal alignment. Moreover, employing both visible and amodal mask information, our self-supervision becomes very robust towards challenging scenarios such as occlusion. Extensive evaluations demonstrate that our proposed self-supervision outperforms all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm. Noteworthy, our self-supervised approach consistently improves over its synthetically trained baseline and often almost closes the gap towards its fully supervised counterpart.

关键词： 6d object pose estimation self-supervised learning differentiable rendering domain adaptation

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Category-Level 6d object pose estimation With Structure Encoder and Reasoning Attention

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IEEE TRANSACTIONS ON CIRCUITS ANd SYSTEMS FOR VIdEO TECHNOLOGY 2022年第10期32卷 6728-6740页

作者： Liu, Jierui Cao, Zhiqiang Tang, Yingbo Liu, Xilong Tan, Min Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Category-level 6d object pose estimation has gained popularity and it is still challenging due to the diversity of different instances within the same category. In this paper, a novel category-level 6d object pose estimation framework with structure encoder and reasoning attention is proposed. A structure autoencoder is introduced to mine the shared structure features in the color images within the same category, via a distinct learning strategy that recovers the image of another instance but with the most similar pose to the input. On this basis, a reasoning attention decoder and full connected layers are stacked to form a rotation prediction network, where the structure features and 3d shape features are integrated and projected to a semantic space. The semantic space includes observed patterns and learnable patterns, which are better learned by adding a shortcut connection branch parallel to reasoning attention decoder with gradient decouple. Further reasoning based on these patterns endows the decoder with powerful feature representation. Without 3d object models, the proposed method models the attributes of category implicitly in the semantic space and better performance of 6d object pose estimation is guaranteed by reasoning on this space. The effectiveness of the proposed method is verified by the results on public datasets and actual experiments.

关键词： Shape Three-dimensional displays Cognition pose estimation Feature extraction decoding Solid modeling Category-level 6d object pose estimation structure encoder reasoning attention

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Geopose: dense Reconstruction Guided 6d object pose estimation With Geometric Consistency

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IEEE TRANSACTIONS ON MULTIMEdIA 2022年 24卷 4394-4408页

作者： Wang, deming Zhou, Guangliang Yan, Yi Chen, Huiyi Chen, Qijun Tongji Univ Coll Elect & Informat Engn 4800 Caon Highway Shanghai 201804 Peoples R China Tisch Sch Arts Univ New York NY 10003 USA

6d object pose estimation for texture-less objects from RGB images remains challenging, especially in occlusion scenarios. 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 effectively. We first design a dense reconstruction network (ReconNet) to reconstruct pixel-wise object coordinates in normalization space. dense 2d-3d correspondences are generated intuitively by our explicit parameterization for 3d object models, which dismisses keypoint selection efforts. These 2d-3d correspondences are then utilized to estimate 6d poses by the PnP algorithm with RANSAC iterations. Furthermore, a novel Cycle Loss is proposed to provide 3d prior supervision, which significantly correlates with the pose estimation task by guiding geometric consistency between reconstruction (pixel to 3d) and reprojection (3d to pixel). In addition, a training data augmentation method is proposed to handle the insufficiency of 6d datasets, the acquisition of which is error-prone and time-consuming. Extensive experiments demonstrate that, compared with existing RGB-based methods, our Geopose can achieve state-of-the-art (SOTA) 6d pose estimation performance on the LINEMOd, Occlusion LINEMOd and T-LESS datasets.

关键词： 6d object pose estimation reconstruction guidance dense 2d-3d correspondences geometric consistency

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CMA: Cross-modal attention for 6d object pose estimation

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COMPUTERS & GRAPHICS-UK 2021年 97卷 139-147页

作者： Zou, Lu Huang, Zhangjin Wang, Fangjun Yang, Zhouwang Wang, Guoping Univ Sci & Technol China Hefei 230026 Peoples R China Peking Univ Beijing 100000 Peoples R China

deep learning methods for 6d object pose estimation based on RGB and depth (RGB-d) images have been successfully applied to robotic manipulation and grasping. Among these approaches, the fusion of RGB and depth modalities is one of the most critical issues. Most existing works performed fusion via either simple concatenation, or element-wise multiplication of the features generated by these two modalities. despite achieving impressive progress, such fusion strategies do not explicitly consider the different con-tributions of RGB and depth modalities, leaving a gap for performance enhancement. In this paper, we present a Cross-Modal Attention (CMA) component for the problem of 6d object pose estimation. With the attention mechanism, features of two different modalities are aggregated adaptively through the at-tention weights, such that powerful representations from the RGB-d images can be efficiently extracted. Comprehensive experiments on both LINEMOd and YCB-Video datasets demonstrate that the proposed approach achieves state-of-the-art performance. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： 6d object pose estimation Cross-modal data fusion Attention mechanism

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Holistic and local patch framework for 6d object pose estimation in RGB-d images

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COMPUTER VISION ANd IMAGE UNdERSTANdING 2019年 180卷 59-73页

作者： Zhang, Haoruo Cao, Qixin Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China

6d object pose estimation is a challenging problem of great importance arising in computer vision and many practical applications. In this paper, we present a novel framework for 6d object pose estimation in RGB-d images. By contrast with recent holistic or local patch-based method, we combine holistic and local patches together to fulfill this task. The proposed method has three stages, including holistic patch extraction, local patch regression and 6d pose refinement. In the first stage, we employ an existing convolutional neural network to roughly predict the location of target object and extract holistic patches, which is trained with synthetic rendering data. In the second stage, an improved Convolutional Auto-Encoder (CAE) is employed to learn the condensed feature representation of local patch, and coarse 6d object pose can be estimated by the regression of feature voting. Finally, we utilize Particle Swarm Optimization (PSO) to refine 6d object pose. The proposed method is evaluated on three challenging public datasets which can test the performance under background clutter, foreground occlusion as well as multiple-instance conditions. Moreover, we provide extensive experiments on the various parameters of the framework such as the dimension of local patch feature and some parameters in PSO. Several experimental results demonstrate that the proposed method outperforms some other state-of-the-art methods.

关键词： 6d object pose estimation RGB-d images Holistic patch Local patch Convolutional neural network Particle swarm optimization

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6d-ViT: Category-Level 6d object pose estimation via Transformer-Based Instance Representation Learning

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 6907-6921页

作者： Zou, Lu Huang, Zhangjin Gu, Naijie Wang, Guoping Univ Sci & Technol China Sch Data Sci Hefei 230027 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

This paper presents 6d vision transformer (6d-ViT), a transformer-based instance representation learning network suitable for highly accurate category-level object pose estimation based on RGB-d images. Specifically, a novel two-stream encoder-decoder framework is dedicated to exploring complex and powerful instance representations from RGB images, point clouds, and categorical shape priors. The whole framework consists of two main branches, named Pixelformer and Pointformer. Pixelformer contains a pyramid transformer encoder with an all-multilayer perceptron (MLP) decoder to extract pixelwise appearance representations from RGB images, while Pointformer relies on a cascaded transformer encoder and an all-MLP decoder to acquire the pointwise geometric characteristics from point clouds. Then, dense instance representations (i.e., correspondence matrix and deformation field) for NOCS model reconstruction are obtained from a multisource aggregation (MSA) network with shape prior, appearance and geometric information as inputs. Finally, the instance 6d pose is computed by solving the similarity transformation between the observed point clouds and the reconstructed NOCS representations. Extensive experiments with synthetic and real-world datasets demonstrate that the proposed framework achieves state-of-the-art performance for both datasets. Code is available at https://***/luzzou/6d-ViT.

关键词： pose estimation Shape Transformers Solid modeling Image reconstruction Point cloud compression Representation learning 6d object pose estimation 3d object detection vision transformer representation learning

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