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

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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6d object pose estimation via viewpoint relation reasoning

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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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Learning-Based Point Cloud Registration for 6d object pose estimation in the Real World 1

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17th European Conference on Computer Vision (ECCV)

作者： dang, Zheng Wang, Lizhou Guo, Yu Salzmann, Mathieu Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Clearspace Renens Switzerland

ISBN: (数字)9783031197697

ISBN: (纸本)9783031197680;9783031197697

In this work, we tackle the task of estimating the 6d pose of an object from point cloud data. While recent learning-based approaches to addressing this task have shown great success on synthetic datasets, we have observed them to fail in the presence of real-world data. We thus analyze the causes of these failures, which we trace back to the difference between the feature distributions of the source and target point clouds, and the sensitivity of the widely-used SVd-based loss function to the range of rotation between the two point clouds. We address the first challenge by introducing a new normalization strategy, Match Normalization, and the second via the use of a loss function based on the negative log likelihood of point correspondences. Our two contributions are general and can be applied to many existing learning-based 3d object registration frameworks, which we illustrate by implementing them in two of them, dCP and IdAM. Our experiments on the real-scene TUd-L [26], LINEMOd [23] and Occluded-LINEMOd [T] datasets evidence the benefits of our strategies. They allow for the first time learning-based 3d object registration methods to achieve meaningful results on real-world data. We therefore expect them to be key to the future development of point cloud registration methods.

关键词： 6d object pose estimation Point cloud registration

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Sim-to-Real 6d object pose estimation via Iterative Self-training for Robotic Bin Picking 17th

Sim-to-Real 6D Object Pose Estimation via Iterative Self-tra...

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17th European Conference on Computer Vision (ECCV)

作者： Chen, Kai Cao, Rui James, Stephen Li, Yichuan Liu, Yun-Hui Abbeel, Pieter dou, Qi Chinese Univ Hong Kong Hong Kong Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9783031198410;9783031198427

6d object pose estimation is important for robotic binpicking, and serves as a prerequisite for many downstream industrial applications. However, it is burdensome to annotate a customized dataset associated with each specific bin-picking scenario for training pose estimation models. In this paper, we propose an iterative self-training framework for sim-to-real 6d object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photorealistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data. With these predictions, we further design a comprehensive adaptive selection scheme to distinguish reliable results, and leverage them as pseudo labels to update a student model for pose estimation on real data. To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and relabel real data using the refined teacher model. We evaluate our method on a public benchmark and our newly-released dataset, achieving an Add(-S) improvement of 11.49% and 22.62% respectively. Our method is also able to improve robotic bin-picking success by 19.54%, demonstrating the potential of iterative sim-to-real solutions for robotic applications. Project homepage: ***/similar to kaichen/sim2real ***.

关键词： Sim-to-real adaptation 6d object pose estimation Iterative self-training Robotic bin picking

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Perspective Flow Aggregation for data-Limited 6d object pose estimation 1

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17th European Conference on Computer Vision (ECCV)

作者： Hu, Yinlin Fua, Pascal Salzmann, Mathieu EPFL CVLab Lausanne Switzerland ClearSpace SA Renens Switzerland

ISBN: (数字)9783031200861

ISBN: (纸本)9783031200854;9783031200861

Most recent 6d object pose estimation methods, including unsupervised ones, require many real training images. Unfortunately, for some applications, such as those in space or deep under water, acquiring real images, even unannotated, is virtually impossible. In this paper, we propose a method that can be trained solely on synthetic images, or optionally using a few additional real ones. Given a rough pose estimate obtained from a first network, it uses a second network to predict a dense 2d correspondence field between the image rendered using the rough pose and the real image and infers the required pose correction. This approach is much less sensitive to the domain shift between synthetic and real images than state-of-the-art methods. It performs on par with methods that require annotated real images for training when not using any, and outperforms them considerably when using as few as twenty real images.

关键词： 6d object pose estimation 6d object pose refinement Image synthesis dense 2d correspondence domain adaptation

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Learning geometric consistency and discrepancy for category-level 6d object pose estimation from point clouds

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PATTERN RECOGNITION 2024年 145卷

作者： Zou, Lu Huang, Zhangjin Gu, Naijie Wang, Guoping Univ Sci & Technol China Hefei 230027 Peoples R China Anhui Prov Key Lab Software Comp & Commun Hefei 230027 Peoples R China USTC Deqing Alpha Innovat Res Inst Huzhou 313299 Peoples R China Peking Univ Beijing 100871 Peoples R China

Category-level 6d object pose estimation aims to predict the position and orientation of unseen object instances, which is a fundamental problem in robotic applications. Previous works mainly focused on exploiting visual cues from RGB images, while depth images received less attention. However, depth images contain rich geometric attributes about the object's shape, which are crucial for inferring the object's pose. This work achieves category-level 6d object pose estimation by performing sufficient geometric learning from depth images represented by point clouds. Specifically, we present a novel geometric consistency and geometric discrepancy learning framework called Cd-pose to resolve the intra-category variation, inter-category similarity, and objects with complex structures. Our network consists of a pose-Consistent Module and a pose-discrepant Module. First, a simple MLP-based pose-Consistent Module is utilized to extract geometrically consistent pose features of objects from the pre-computed object shape priors for each category. Then, the pose discrepant Module, designed as a multi-scale region-guided transformer network, is dedicated to exploring each instance's geometrically discrepant features. Next, the NOCS model of the object is reconstructed according to the integration of consistent and discrepant geometric representations. Finally, 6d object poses are obtained by solving the similarity transformation between the reconstruction and the observed point cloud. Experiments on the benchmark datasets show that our Cd-pose produces superior results to state-of-the-art competitors.

关键词： 6d object pose estimation 3d object detection Point cloud processing Shape recovery

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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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deep Learning-based Methods for 6d object pose estimation: A Comprehensive Review

Deep Learning-based Methods for 6D Object Pose Estimation: A...

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第35届中国过程控制会议

作者： Nanxin Huang Chi Xu Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

With the rapid development of computer vision and robotics, 6-dOF pose estimation of objects has become a key technology in the field of computer vision, finding extensive applications in emerging fields such as robot grasping, autonomous driving, virtual reality, and augmented reality. The primary objective of this task is to accurately estimate the 3-dOF rotation and 3-dOF translation of an object relative to the camera. In recent years, significant research efforts have been devoted to 6-dOF pose estimation. This paper provides a comprehensive review of the current research status in this domain while introducing relevant datasets, evaluation metrics, and specific research methodologies. In addition to briefly introducing traditional methods, it provides detailed elaboration on deep learning approaches containing hand-object pose estimation. Finally, we discuss future directions for development in this field.

关键词： 6d object pose estimation Computer vision deep learning Hand-object interaction PnP

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VP-KLNet: efficient 6d object pose estimation with an enhanced vector-field prediction network and a keypoint localization network

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 053026-053026页

作者： Zhang, Yaoyin Wan, Lili Zhu, Yazhi Xu, Wanru Wang, Shenghui Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China

6d object pose estimation is essential for many applications with high demands in accuracy and speed. Compared with end-to-end approaches, pixel-wise voting network (PVNet), a vector-field based two-stage approach, has shown the superiority in accuracy but the inferiority in speed because of the time-consuming RANSAC-based voting strategy. To resolve this problem, we propose an efficient deep architecture that consists of an enhanced vector-field prediction network (VPNet) and a keypoint localization network (KLNet) and call it VP-KLNet. Specifically, the KLNet replaces PVNet's time-consuming voting scheme by directly regressing 2d keypoints from the vector fields, which significantly improves the inference speed. Furthermore, to capture multiscale contextual information, we embed the pyramid pooling module between the encoder and decoder in VPNet to obtain more accurate object segmentation and vector-field prediction, with negligible speed loss. Experiments demonstrate that our method has more than 50% improved in the running speed to the baseline method PVNet and achieves comparable accuracy with the state-of-the-art methods on the LINEMOd and occlusion LINEMOd datasets. (c) 2022 SPIE and IS&T

关键词： 6d object pose estimation vector fields keypoint localization semantic segmentation pyramid pooling module

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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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