IEEE transactions on image processing : a publication of the IEEE Signal Processing Society最新文献_第2页

Revisiting Pixel Erasure: A Simple Method for Weakly Supervised Video Instance Segmentation. 重访像素擦除：弱监督视频实例分割的一种简单方法。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-09-28 DOI: 10.1109/TIP.2026.3731974

Guixun Luo, Huilin Ai, Zhongwei Ren, Xiaojie Jin, Yunchao Wei

{"title":"Revisiting Pixel Erasure: A Simple Method for Weakly Supervised Video Instance Segmentation.","authors":"Guixun Luo, Huilin Ai, Zhongwei Ren, Xiaojie Jin, Yunchao Wei","doi":"10.1109/TIP.2026.3731974","DOIUrl":"https://doi.org/10.1109/TIP.2026.3731974","url":null,"abstract":"We aim to develop a simple framework for weakly supervised video instance segmentation (VIS) without pixelwise video annotations that can match or outperform fully supervised counterparts. To this end, we revisit pixel erasure, which is a common data augmentation strategy, and reveal that it can be naturally used to resist label noise within pseudo masks. Thus, we propose PieVIS, which exploits abundant sources of image datasets and requires only box-level annotations for the video dataset. Specifically, PieVIS consists of two stages: pseudo mask generation and self-training with pixel erasure. In the first stage, PieVIS generates pseudo masks for the video data using a model trained with image datasets. Afterward, for self-training, we adopt a simple pixel erasure technique that randomly removes colour information for some regions while keeping the pseudo masks unchanged. For the first time, we show that VIS without video mask annotations can perform comparably to or even outperform state-of-the-art fully supervised methods on YouTube-VIS (YTVIS) 2019 and 2021. More importantly, we further demonstrate that the pixel erasure strategy can also advance other weakly supervised tasks that involve learning from pseudo masks.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"PP ","pages":""},"PeriodicalIF":13.7,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical Model-Driven Similarity Hashing for Unsupervised Multimedia Retrieval. 统计模型驱动的无监督多媒体检索相似性哈希。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-09-11 DOI: 10.1109/TIP.2026.3730836

Binghong Chen, Yinan Li, Mingjin Kuai, Longzhi Sun, Zhan Yang

{"title":"Statistical Model-Driven Similarity Hashing for Unsupervised Multimedia Retrieval.","authors":"Binghong Chen, Yinan Li, Mingjin Kuai, Longzhi Sun, Zhan Yang","doi":"10.1109/TIP.2026.3730836","DOIUrl":"https://doi.org/10.1109/TIP.2026.3730836","url":null,"abstract":"Unsupervised deep cross-modal hash retrieval aims to map multi-modal features into binary hash codes without labels, which is of interest due to the storage efficiency, query speed, and convenient applications. However, existing approaches suffer from two main limitations: (1) Insufficient consideration of text instance similarity, along with independent or redundant fusion to learn multi-modal similarity information. (2) Ignoring the noisy adjacent correlations between multi-modal instances leads to a lack of discriminative capacity in the generated hash codes. To address the challenges, we propose a novel approach called Statistical Model-driven Similarity Hashing. Specifically, Jaccard similarity is introduced to construct the text similarity matrix, which reduces the similarity error between text instances while better considering the asymmetry of elements in text features. After that, original similarity information between various modalities is integrated to construct a unified similarity matrix, where the modality gaps are effectively bridged while reducing the redundant information. In addition, we present a Statistical Model-driven Similarity Enhancement approach, which reduces the noise of similarity relations between multi-modal instances by utilizing Gaussian Mixture Models to keep instances with lower semantic similarity as far away from each other as possible. Moreover, we introduce an enhanced version, which integrates a structure-aware self-paced contrastive learning framework that progressively schedules reliable and fuzzy-positive pairs, jointly optimizing statistical reconstruction and discriminative alignment, and further extends to noisy environments. Extensive experiments on three benchmark datasets demonstrate the excellent performance of the proposed method.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"PP ","pages":""},"PeriodicalIF":13.7,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148946316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Retrieval Queries Fine-Tuning for Composed Image Retrieval. 组合图像检索的双检索查询微调。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-08-03 DOI: 10.1109/TIP.2025.3597043

Wenyue Tang, Jianze Wei, Xingyu Gao

{"title":"Dual Retrieval Queries Fine-Tuning for Composed Image Retrieval.","authors":"Wenyue Tang, Jianze Wei, Xingyu Gao","doi":"10.1109/TIP.2025.3597043","DOIUrl":"10.1109/TIP.2025.3597043","url":null,"abstract":"Composed Image Retrieval (CIR) is a popular multi-modal retrieval task that aims to retrieve a target image based on a query composed of a reference image and modification text. The challenge lies in how to effectively retrieve a target image that preserves the visual content of the reference image while incorporating the changes described by the modification text. Existing CIR methods primarily employ a fusion-based strategy or a textual-inversion strategy during training. Although these methods have achieved promising results, they are limited in fully leveraging multi-modal information. This results in modality redundancy, where the retrieval process is dominated by one modality while ignoring the other. To address this issue, we propose an asymmetric fusion mechanism to generate dual retrieval queries of different granularity, enabling the model to fully use multi-modal information. Specifically, we propose a novel method termed Dual Retrieval Queries Fine-Tuning for Composed Image Retrieval (DRQ-CIR), which consists of two key components: 1) a Bilateral Multi-Modal Fusion (BMMF) module based on pre-trained VLMs, which combines the reference image and modification text to generate an enriched retrieval query; and 2) a Dual Retrieval Queries Fine-Tuning (DRQ-FT) module, which employs latent prompts to generate an enhanced retrieval query. Dual retrieval queries are used for contrastive learning with the target image to fine-tune the model and improve retrieval performance. Additionally, we introduce a bi-directional training paradigm to ensure retrieval consistency and further exploit the triplets. Extensive experiments validate the effectiveness of our proposed method on four established benchmark datasets. (Code will be available at: https://github.com/Crystal-twy998/DRQ-CIR).","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"PP ","pages":""},"PeriodicalIF":13.7,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Tailed and Inter-Class Homogeneity Matters in Multi-Class Weakly Supervised Tissue Segmentation of Histopathology Images 组织病理学图像的多类弱监督组织分割中的长尾和类间同质性问题

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-12 DOI: 10.1109/TIP.2026.3671622

Siyang Feng;Xipeng Pan;Huadeng Wang;Zhenbing Liu;Weidong Zhang;Rushi Lan

{"title":"Long-Tailed and Inter-Class Homogeneity Matters in Multi-Class Weakly Supervised Tissue Segmentation of Histopathology Images","authors":"Siyang Feng;Xipeng Pan;Huadeng Wang;Zhenbing Liu;Weidong Zhang;Rushi Lan","doi":"10.1109/TIP.2026.3671622","DOIUrl":"10.1109/TIP.2026.3671622","url":null,"abstract":"Using image-level weakly supervised semantic segmentation (WSSS) techniques to segment tissue regions in giga-pixel histopathological whole slide images (WSI) has garnered widespread attention, as it can reduce many annotation workloads for pathologists. Most recent studies are based on class activation mapping (CAM) to generate pseudo masks, which are then used to train segmentation model in a fully supervised manner. However, it is still a challenge to accurately segment non-predominant tissue categories due to the existence of long-tailed and inter-class homogeneity matters. For these matters, we propose three designs to solve them: 1) Diffusion-based Data Generation to synthesis new images of tail class to expand data distribution; 2) Feature Recalibration to reassign the logits in CAM to narrow the feature-level prediction gap between predominant and non-predominant classes; 3) Grade-skip Learning to correct the under-fitting tendency of hard samples during the segmentation phase. Moreover, we also design a powerful pipeline LoHo for histopathology tissue segmentation. Extensive experiments demonstrate that our method not only achieves new state-of-the-art performances but also significantly improves segmentation of tail classes. In addition, our methods are plug-and-play, making it easily integrable into many mainstream WSSS frameworks.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2513-2528"},"PeriodicalIF":13.7,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DiffLLFace: Learning Alternate Illumination-Diffusion Adaptation for Low-Light Face Super-Resolution and Beyond DiffLLFace：学习交替照明扩散适应低光面超分辨率和超越

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-12 DOI: 10.1109/TIP.2026.3671638

Runmin Cong;Kaisheng Pang;Feng Li;Hua Li;Huihui Bai;Sam Kwong;Wei Zhang

{"title":"DiffLLFace: Learning Alternate Illumination-Diffusion Adaptation for Low-Light Face Super-Resolution and Beyond","authors":"Runmin Cong;Kaisheng Pang;Feng Li;Hua Li;Huihui Bai;Sam Kwong;Wei Zhang","doi":"10.1109/TIP.2026.3671638","DOIUrl":"10.1109/TIP.2026.3671638","url":null,"abstract":"Facial image acquisition under constrained illumination and with limited-resolution imaging devices often results in coupled photometric and geometric degradations, manifesting as low-light and low-resolution (LLR) conditions. Prevailing research predominantly follows fragmented optimization paradigms that address low-light image enhancement (LLIE) and face super-resolution (FSR) as isolated tasks. This approach overlooks the compound nature of the degradations, thereby significantly limiting their applicability in practical scenarios. To bridge this gap, we present DiffLLFace, a unified framework that harnesses diffusive generative capabilities with illumination-aware trajectories to achieve robust FSR from LLR observations. The core of our method lies in its alternate illumination-diffusion adaptation, which operates throughout the generation process. This mechanism not only captures degradation patterns in both brightness and structure to harmonize latent representations but also dynamically calibrates the illumination prior with the generative knowledge inherent to diffusion models. As such, DiffLLFace attains precise control over conditional adaptation and illumination rectification. We further devise a simple yet effective non-parametric Fourier enhancement strategy, which provides structural appearance clues that work in concert with the alternate adaptation to ensure texture and color consistency. Extensive experiments demonstrate the superiority of DiffLLFace over existing methods and remarkable generalizability on complex natural scenes. Code is available at <uri>https://github.com/KaishengPang/DiffLLFace</uri>","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2499-2512"},"PeriodicalIF":13.7,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement JDPNet：基于联合退化处理的水下图像增强网络

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-12 DOI: 10.1109/TIP.2025.3641833

Tao Ye;Hongbin Ren;Chongbing Zhang;Haoran Chen;Xiaosong Li

{"title":"JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement","authors":"Tao Ye;Hongbin Ren;Chongbing Zhang;Haoran Chen;Xiaosong Li","doi":"10.1109/TIP.2025.3641833","DOIUrl":"10.1109/TIP.2025.3641833","url":null,"abstract":"Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods focus on designing specific branches, modules, or strategies for specific degradations, with little attention paid to the potential information embedded in their coupling. Consequently, they struggle to effectively capture and process the nonlinear interactions of multiple degradations from a bottom-up perspective. To address this issue, we propose JDPNet, a joint degradation processing network, that mines and unifies the potential information inherent in coupled degradations within a unified framework. Specifically, we introduce a joint feature-mining module, along with a probabilistic bootstrap distribution strategy, to facilitate effective mining and unified adjustment of coupled degradation features. Furthermore, to balance color, clarity, and contrast, we design a novel AquaBalanceLoss to guide the network in learning from multiple coupled degradation losses. Experiments on six publicly available underwater datasets, as well as two new datasets constructed in this study, show that JDPNet exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2423-2437"},"PeriodicalIF":13.7,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear Transformed Low-Rank Quaternion Tensor Total Variation for Multidimensional Color Image Completion 非线性变换低秩四元数张量全变分的多维彩色图像补全。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-05 DOI: 10.1109/TIP.2026.3666728

Liqiao Yang;Yexun Hu;Tai-Xiang Jiang;Yimin Wei;Guisong Liu;Michael K. Ng

{"title":"Nonlinear Transformed Low-Rank Quaternion Tensor Total Variation for Multidimensional Color Image Completion","authors":"Liqiao Yang;Yexun Hu;Tai-Xiang Jiang;Yimin Wei;Guisong Liu;Michael K. Ng","doi":"10.1109/TIP.2026.3666728","DOIUrl":"10.1109/TIP.2026.3666728","url":null,"abstract":"Completing multidimensional color images is a fundamental challenge in image processing and computer vision. However, some tensor-based methods often treat RGB channels as independent modes, thereby neglecting their intrinsic correlations. To address this limitation, we represent RGB values as pure quaternions and organize them into a quaternion tensor for holistic modeling that preserves chromatic relationships. To better capture the nonlinear characteristics inherent in visual data and to improve the compactness of low-rank representations, we propose a nonlinear transformation within the quaternion domain. This design enables more expressive modeling compared to conventional linear approaches. In addition, we introduce two novel regularization terms that jointly encode global low-rankness and local smoothness, with the nonlinear transformation further enhancing the exploitation of structural priors. The overall model is optimized via a nonlinear alternating direction method of multipliers (ADMM), with theoretical guarantees of convergence. Extensive experiments on several datasets demonstrate that the proposed method significantly outperforms state-of-the-art low-rank tensor and quaternion tensor recovery techniques in multidimensional color image completion tasks.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2470-2483"},"PeriodicalIF":13.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborated With Hallucination: Enhancing Egocentric Grounded Question Answering via Error Demonstrations 与幻觉合作：通过错误演示加强以自我为中心的扎根问题回答。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-04 DOI: 10.1109/TIP.2026.3666732

Shenshen Li;Xing Xu;Fumin Shen;Zhe Sun;Andrzej Cichocki;Heng Tao Shen

{"title":"Collaborated With Hallucination: Enhancing Egocentric Grounded Question Answering via Error Demonstrations","authors":"Shenshen Li;Xing Xu;Fumin Shen;Zhe Sun;Andrzej Cichocki;Heng Tao Shen","doi":"10.1109/TIP.2026.3666732","DOIUrl":"10.1109/TIP.2026.3666732","url":null,"abstract":"The grounded question answering in egocentric videos (Ego-GQA) aims to identify the relevant temporal window and generate corresponding responses in natural language given a textual question. Compared with third-person videos, egocentric video understanding requires more advanced human-centric thinking capability. However, existing Ego-GQA approaches often fail to distinguish the inherent limitations of dynamic egocentric context understanding, treating both first-person and third-person perspectives equally. This oversight leads to hallucinations and a lack of proper egocentric reasoning in first-person video understanding. To address this issue, we propose a novel Collaborated with Hallucination (CoHa) framework for the Ego-GQA, which quantifies the hallucinations generated by an Ego-GQA model and further leverages them as error demonstrations to constrain the model’s reasoning process, encouraging it to ground predictions in egocentric visual cues instead of relying on biased pretraining priors. Specifically, we first employ Subjective Logic to quantify the degree of uncertainty in unreliable answers. We then generate diffusion-based noisy visual inputs to amplify the hallucinations as error demonstrations, which are used to append appropriate constraints to the model according to the uncertainty. These constraints effectively steer predictions away from the unreliable semantics induced by inherent drawbacks in egocentric thinking. Additionally, we incorporate an interactive refinement module to facilitate the model to explore more fine-grained cues observed from the first-person view. Extensive experiments on two widely used benchmarks demonstrate that our CoHa method outperforms recent state-of-the-art methods. Our code is available at <uri>https://github.com/Mrshenshen/CoHa</uri>","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2438-2453"},"PeriodicalIF":13.7,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Unified Framework for Backdoor Trigger Segmentation 后门触发分割的统一框架。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-03-02 DOI: 10.1109/TIP.2026.3666796

Dizhan Xue;Shengsheng Qian;Changsheng Xu

{"title":"A Unified Framework for Backdoor Trigger Segmentation","authors":"Dizhan Xue;Shengsheng Qian;Changsheng Xu","doi":"10.1109/TIP.2026.3666796","DOIUrl":"10.1109/TIP.2026.3666796","url":null,"abstract":"Recently, backdoor attacks on Deep Neural Networks (DNNs) have raised urgent security threats, which can manipulate the behavior of an attacked model by embedding the backdoor trigger into the input. Since triggers can be designed to be stealthy and hard to recognize by the naked eye, segmenting these triggers in backdoor samples becomes a significant challenge. However, finding triggers embedded by the attacker can be crucial for analyzing the attacks and formulating a defense strategy. Therefore, in this paper, we propose the Backdoor Trigger Segmentation (BTS) task with a comprehensive benchmark consisting of 8 attack methods, 8 unique triggers, and 179 attack settings for image or text data. Moreover, we construct a mathematical system for BTS, abstracting various backdoor triggers into a unified theoretical framework. Based on the theoretical guarantees, we propose a unified Trigger Locator (TriLoc) algorithm to segment various triggers in backdoor samples of both image and text modalities, without prior knowledge of triggers. Extensive experimental results on our benchmark demonstrate the superior performance of our algorithm compared to state-of-the-art methods. Our benchmark and code are available at <uri>https://github.com/LivXue/Backdoor-Trigger-Segmentation</uri>","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2364-2379"},"PeriodicalIF":13.7,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147329218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-Pose-Prior NeRF: Recursive Radiance Field Reconstruction From Unposed and Unordered Images 零姿态先验NeRF：递归辐射场重建从未定位和无序图像。

IF 13.7

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Pub Date : 2026-02-27 DOI: 10.1109/TIP.2026.3666734

Xinxin Liu;Qi Zhang;Xue Wang;Guoqing Zhou;Qing Wang

{"title":"Zero-Pose-Prior NeRF: Recursive Radiance Field Reconstruction From Unposed and Unordered Images","authors":"Xinxin Liu;Qi Zhang;Xue Wang;Guoqing Zhou;Qing Wang","doi":"10.1109/TIP.2026.3666734","DOIUrl":"10.1109/TIP.2026.3666734","url":null,"abstract":"The dependence of neural radiance fields (NeRF) on accurate camera poses has emerged as a critical obstacle to their widespread real-world applications. While recent advances have demonstrated the potential for simultaneously addressing camera registration and scene reconstruction, these methods inherently rely on reasonable initialization derived from pose or scene priors and struggle with complex scenes involving large camera motions, particularly in unordered 360-degree scenes. In this work, we propose Zero-Pose-Prior NeRF to recover radiance fields from unposed and unordered image collections without any prior knowledge. Our key insight is to decompose this complex problem into smaller sub-problems, wherein the sub-problems’ camera poses are initially estimated to provide self-bootstrapping priors for the global pose estimation, followed by a recursive registration and reconstruction. To achieve this, we first perform scene partitioning to establish a hierarchical structure that describes registration order from local to global. Thereafter, we devise a conditionally-decoupled positional encoding for NeRFs, which serves as the basic model for camera pose estimation and scene representation. Following this, we develop a recursive registration to recursively estimate the poses of local scenes and register them into a unified global pose space, ultimately enabling the reconstruction of the entire scene. Experiments on real-world scenes show that our approach outperforms the state-of-the-art pose-free methods in terms of accurate camera poses and robust radiance field reconstruction, resulting in high-fidelity view synthesis.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"35 ","pages":"2320-2334"},"PeriodicalIF":13.7,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0