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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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ADAMANIP: ADAPTIVE ARTICULATED OBJECT MANIPULATION ENVIRONMENTS AND POLICY LEARNING

arXiv

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arXiv 2025年

作者： Wang, Yuanfei Zhang, Xiaojie Wu, Ruihai Li, Yu Shen, Yan Wu, Mingdong He, Zhaofeng Wang, Yizhou Dong, Hao Center on Frontiers of Computing Studies School of Computer Science Peking University China Beijing University of Posts and Telecommunications China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China

Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated objects are endowed with diverse functional mechanisms through complex relative motions. For example, a safe consists of a door, a handle, and a lock, where the door can only be opened when the latch is unlocked. The internal structure, such as the state of a lock or joint angle constraints, cannot be directly observed from visual observation. Consequently, successful manipulation of these objects requires adaptive adjustment based on trial and error rather than a one-time visual inference. However, previous datasets and simulation environments for articulated objects have primarily focused on simple manipulation mechanisms where the complete manipulation process can be inferred from the object’s appearance. To enhance the diversity and complexity of adaptive manipulation mechanisms, we build a novel articulated object manipulation environment and equip it with 9 categories of objects. Based on the environment and objects, we further propose an adaptive demonstration collection and 3D visual diffusion-based imitation learning pipeline that learns the adaptive manipulation policy. The effectiveness of our designs and proposed method is validated through both simulation and real-world experiments. Our project page is available at: https://*** Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Lesion-Aware Open Set Medical Image Recognition with Domain Shift

Lesion-Aware Open Set Medical Image Recognition with Domain ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yiqian Xu Rui-Wei Zhao Rui Feng School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China

Medical image diagnosis in real clinical scenario faces the challenging open set recognition problem with domain shift. Based on the observation that it is often merely the local lesion areas in the whole image decide or cause the true seen or potential unseen disease labels, we propose a unified deep learning framework to address it by establishing a lesion-aware mask generator to guide the model towards focusing on the lesion areas for both seen and unseen diseases, followed by a multi-task feature learning module to extract the lesion image features; and an open set recognizer with improved capability to reject unseen classes. Last but not least, we propose tailored training strategies with mixed up data and mask augmentation for improved OSR and multi-clue cross-domain feature alignment for domain generalization. Through comparative experiments and ablation studies on the representative skin lesion benchmarks, we successfully validate the effectiveness of our proposed framework.

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SVTR: Scene Text Recognition with a Single visual Model

arXiv

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arXiv 2022年

作者： Du, Yongkun Chen, Zhineng Jia, Caiyan Yin, Xiaoting Zheng, Tianlun Li, Chenxia Du, Yuning Jiang, Yu-Gang School of Computer and Information Technology Beijing Jiaotong University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University China Baidu Inc. China

Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://***/PaddlePaddle/PaddleOCR. © 2022, CC BY.

关键词： Mixing

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Learning Photometric Feature Transform for Free-form Object Scan

arXiv

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arXiv 2023年

作者： Feng, Xiang Kang, Kaizhang Pei, Fan Ding, Huakeng You, Jinjiang Tan, Ping Zhou, Kun Wu, Hongzhi The State Key Lab of CAD & CG Zhejiang University Hangzhou310058 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Visual Computing Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques. Copyright © 2023, The Authors. All rights reserved.

关键词： Stereo image processing

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Balanced Contrastive Learning for Long-Tailed visual Recognition

arXiv

引用

arXiv 2022年

作者： Zhu, Jianggang Wang, Zheng Chen, Jingjing Chen, Yi-Ping Phoebe Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Department of Computer Science and Information Technology La Trobe University Australia

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied, methods for representing imbalanced data are under-explored. In this paper, we focus on representation learning for imbalanced data. Recently, supervised contrastive learning has shown promising performance on balanced data recently. However, through our theoretical analysis, we find that for long-tailed data, it fails to form a regular simplex which is an ideal geometric configuration for representation learning. To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, we propose a novel loss for balanced contrastive learning (BCL). Compared with SCL, we have two improvements in BCL: class-averaging, which balances the gradient contribution of negative classes;class-complement, which allows all classes to appear in every mini-batch. The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. Equipped with BCL, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on long-tailed benchmark datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at this URL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Rethinking Super-Resolution as Text-Guided Details Generation

arXiv

引用

arXiv 2022年

作者： Ma, Chenxi Yan, Bo Lin, Qing Tan, Weimin Chen, Siming School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China School of Data Science Fudan University China

Deep neural networks have greatly promoted the performance of single image super-resolution (SISR). Conventional methods still resort to restoring the single high-resolution (HR) solution only based on the input of image modality. However, the image-level information is insufficient to predict adequate details and photo-realistic visual quality facing large upscaling factors (×8, ×16). In this paper, we propose a new perspective that regards the SISR as a semantic image detail enhancement problem to generate semantically reasonable HR image that are faithful to the ground truth. To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities. Different from existing methods, the proposed TGSR could generate HR image details that match the text descriptions through a coarse-to-fine process. Extensive experiments and ablation studies demonstrate the effect of the TGSR, which exploits the text reference to recover realistic images. © 2022, CC BY.

关键词： Deep neural networks

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∂H: Differentiable Holography

引用

Laser and Photonics Reviews 2023年第9期17卷

作者： Chen, Ni Wang, Congli Heidrich, Wolfgang Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Visual Computing Center King Abdullah University of Science and Technology Jeddah Thuwal23955 Saudi Arabia

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. Differentiable holography ((Formula presented.)), a novel framework for automatically self-calibrating experimental imperfections in inverse holographic imaging, is presented here. The technique is demonstrated on auto-focused complex field imaging from a single intensity-only inline hologram. © 2023 Wiley-VCH GmbH.

关键词： Inverse problems

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CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

arXiv

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arXiv 2024年

作者： Wen, Jie Zhang, Zheng Xu, Yong Zhang, Bob Fei, Lunke Xie, Guo-Sen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China Pengcheng Laboratory Shenzhen China Department of Computer and Information Science University of Macau Taipa China School of Computer Science and Technology Guangdong University of Technology Guangzhou China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods. © 2024, CC BY.

关键词： Statistics

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Temporal Feature Aggregation for Efficient 2D Video Grounding

Temporal Feature Aggregation for Efficient 2D Video Groundin...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Mohan Chen Yiren Zhang Jueqi Wei Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics School of Information Technology Deakin University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Video grounding aims to locate the target video moment in an untrimmed video based on a text query. Most existing methods employ 3D CNNs as the video feature extractor, incurring substantial computational costs. Only a few methods use 2D backbones for video feature extraction, and they suffer from diminished accuracy due to the inherent lack of temporal information within 2D features. To address this problem, we propose a novel 2D video grounding method called TFA that improves accuracy while minimizing computational costs. Our approach involves a query-guided temporal feature aggregation module designed to explicitly capture temporal information. We disentangle time intervals of input video frames and prediction spans to reduce computational overhead. Additionally, we introduce deformable attention into the multi-modal encoder for further enhancement. Extensive experiments on two public datasets demonstrate that our method outperforms previous 2D video grounding methods and achieves competitive results with most 3D methods at significantly reduced costs.

关键词： Three-dimensional displays Accuracy Costs Grounding Streaming media Feature extraction Computational efficiency

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