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

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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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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HFT6d: Multimodal 6d object pose estimation based on hierarchical feature transformer

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MEASUREMENT 2024年 224卷

作者： An, Yunnan Yang, dedong Song, Mengyuan Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China

Visual information is usually multimodal, including texture, color (2d information), and space (3d information). However, there are two problems in establishing multimodal 6d object pose estimation: (1) substantial differences between RGB images and depth data;(2) systematic noise in the depth images and lack contextual information in the association process. To solve the above problems, this paper proposes an end -to -end hierarchical feature transformer (HFT6d) containing four independent stages of crossmodal transformer. The novel hierarchical feature architecture suppresses the effect of noise by modeling the spatial correspondence between two different modalities. The core module of HFT6d is the bi-directional crossmodal attention, which aligns the appearance and geometric representation by recalibrating RGB-d data. In addition, our proposed HFT6d is real-time and achieves robustness against occluded scenes. Comprehensive experiments on two benchmark datasets show that HFT6d achieves state-of-the-art performance in terms of accuracy and speed.

关键词： 6d object pose estimation Hierarchical feature transformer Bi-directional crossmodal attention

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Generalizable and Accurate 6d object pose estimation Network 6th

Generalizable and Accurate 6D Object Pose Estimation Network

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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Fu, Shouxu Li, Xiaoning Yu, Xiangdong Cao, Lu Li, Xingxing Sichuan Nomal Univ Coll Comp Sci Chengdu 610101 Peoples R China

ISBN: (纸本)9789819984343;9789819984350

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. Many methods use deep learning to acquire 2d feature points from images to establish 2d-3d correspondences, and further predict 6d object pose with Perspective-n-Points (PnP) algorithm. However, most of these methods have problems with inaccurate acquisition of feature points, poor generality of the network and difficulty in end-to-end training of the network. In this paper, we design an end-to-end differentiable network for 6d object pose estimation. We propose Random Offset distraction (ROd) and Full Convolution Asymmetric Feature Extractor (FCAFE) with the Probabilistic Perspective-n-Points (ProPnP) algorithm to improve the accuracy and robustness of 6d object pose estimation. Experiments show that our method achieves a new state-of-the-art result on the LineMOd dataset, with an accuracy of 97.42% in the Add(-S) metric. Our approach is also very competitive on the Occlusion LineMOd dataset.

关键词： 6d object pose estimation Coordinates-based Method RGB data

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Research on Model-Free 6d object pose estimation Based on Vision 3d Matching 2

Research on Model-Free 6D Object Pose Estimation Based on Vi...

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2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition (CVIPPR)

作者： Chen, Yan Yi, Pengfei Guo, Yujie Liu, Rui dong, Jing Zhou, dongsheng Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Liao Ning Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian Liao Ning Peoples R China

ISBN: (纸本)9798400716607

6d object pose estimation is a crucial and fundamental task in the field of human-robot interaction. Existing visual target localization methods either need to rely on an additional depth camera to obtain spatial information of the target object or need to resort to a specific CAd model, which leads to high cost and poor adaptability of pose estimation. In this paper, we propose a 6d pose estimation method for objects based on RGB images and without CAd models, starting from the 3d reconstruction of objects. First, this paper constructs a sparse model of the target object by simple RGB scanning, then, 2d key points in the image are matched with 3d points in the object model using a feature-matching network, and finally, to alleviate the poor matching of such methods on low-resolution images, we introduce a 2d-3d-3d key points matching method, which achieves efficient and robust object 6d pose estimation results. Experimental results on the Onepose dataset demonstrate the accuracy and robustness of the method.

关键词： 6d object pose estimation Key Points Matching RGB Modeling

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Uncertainty Quantification with deep Ensembles for 6d object pose estimation

Uncertainty Quantification with Deep Ensembles for 6D Object...

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ISPRS TC II Mid-term Symposium on Role of Photogrammetry for a Sustainable World

作者： Wursthorn, Kira Hillemann, Markus Ulrich, Markus Karlsruhe Inst Technol KIT Inst Photogrammetry & Remote Sensing IPF Karlsruhe Germany

The estimation of 6d object poses is a fundamental task in many computer vision applications. Particularly, in high risk scenarios such as human-robot interaction, industrial inspection, and automation, reliable pose estimates are crucial. In the last years, increasingly accurate and robust deep-learning-based approaches for 6d object pose estimation have been proposed. Many top-performing methods are not end-to-end trainable but consist of multiple stages. In the context of deep uncertainty quantification, deep ensembles are considered as state of the art since they have been proven to produce well-calibrated and robust uncertainty estimates. However, deep ensembles can only be applied to methods that can be trained end-to-end. In this work, we propose a method to quantify the uncertainty of multi-stage 6d object pose estimation approaches with deep ensembles. For the implementation, we choose SurfEmb as representative, since it is one of the top-performing 6d object pose estimation approaches in the BOP Challenge 2022. We apply established metrics and concepts for deep uncertainty quantification to evaluate the results. Furthermore, we propose a novel uncertainty calibration score for regression tasks to quantify the quality of the estimated uncertainty.

关键词： deep Learning Uncertainty Quantification deep Ensembles 6d object pose estimation Reliability

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SAM-6d: Segment Anything Model Meets Zero-Shot 6d object pose estimation

SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pos...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lin, Jiehong Liu, Lihua Lu, dekun Jia, Kui DexForce Co Ltd Shenzhen Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9798350353006

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. Fortunately, the recent Segment Anything Model (SAM) has showcased remarkable zero-shot transfer performance, which provides a promising solution to tackle this task. Motivated by this, we introduce SAM-6d, a novel framework designed to realize the task through two steps, including instance segmentation and pose estimation. Given the target objects, SAM-6d employs two dedicated sub-networks, namely Instance Segmentation Model (ISM) and pose estimation Model (PEM), to perform these steps on cluttered RGB-d images. ISM takes SAM as an advanced starting point to generate all possible object proposals and selectively preserves valid ones through meticulously crafted object matching scores in terms of semantics, appearance and geometry. By treating pose estimation as a partial-to-partial point matching problem, PEM performs a two-stage point matching process featuring a novel design of background tokens to construct dense 3d-3d correspondence, ultimately yielding the pose estimates. Without bells and whistles, SAM-6d outperforms the existing methods on the seven core datasets of the BOP Benchmark for both instance segmentation and pose estimation of novel objects.

关键词： 6d object pose estimation Segment Anything Model Zero-shot Learning

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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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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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