Pub Date : 2026-01-15DOI: 10.1109/tpami.2026.3654201
Wangbo Zhao,Yizeng Han,Jiasheng Tang,Kai Wang,Hao Luo,Yibing Song,Gao Huang,Fan Wang,Yang You
Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior perfor mance but suffers from substantial computational costs. Our investigations reveal that these costs primarily stem from the static inference paradigm, which inevitably introduces redundant computation in certain diffusion timesteps and spatial regions. To overcome this inefficiency, we propose Dynamic Diffusion Transformer (DyDiT), an architecture that dynamically adjusts its computation along both timestep and spatial dimensions. Specifically, we introduce a Timestep-wise Dynamic Width (TDW) approach that adapts model width conditioned on the generation timesteps. In addition, we design a Spatial-wise Dynamic Token (SDT) strategy to avoid redundant computation at unnecessary spatial locations. TDW and SDT can be seamlessly integrated into DiT and significantly accelerate the generation process. Building on these designs, we present an extended version, DyDiT++, with improvements in three key aspects. First, it extends the generation mechanism of DyDiT beyond diffusion to flow matching, demon strating that our method can also accelerate flow-matching based generation, enhancing its versatility. Furthermore, we enhance DyDiT to tackle more complex visual generation tasks, including video generation and text-to-image generation, thereby broadening its real-world applications. Finally, to address the high cost of full fine-tuning and democratize technology access, we investigate the feasibility of training DyDiT in a parameter efficient manner and introduce timestep-based dynamic LoRA (TD-LoRA). Extensive experiments on diverse visual generation models, including DiT, SiT, Latte, and FLUX, demonstrate the effectiveness of DyDiT++. Remarkably, with <3% additional f ine-tuning iterations, our approach reduces the FLOPs of DiT XL by 51%, yielding 1.73× realistic speedup on hardware, and achieves a competitive FID score of 2.07 on ImageNet. The code is available at https://github.com/alibaba-damo-academy/DyDiT.
{"title":"DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation.","authors":"Wangbo Zhao,Yizeng Han,Jiasheng Tang,Kai Wang,Hao Luo,Yibing Song,Gao Huang,Fan Wang,Yang You","doi":"10.1109/tpami.2026.3654201","DOIUrl":"https://doi.org/10.1109/tpami.2026.3654201","url":null,"abstract":"Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior perfor mance but suffers from substantial computational costs. Our investigations reveal that these costs primarily stem from the static inference paradigm, which inevitably introduces redundant computation in certain diffusion timesteps and spatial regions. To overcome this inefficiency, we propose Dynamic Diffusion Transformer (DyDiT), an architecture that dynamically adjusts its computation along both timestep and spatial dimensions. Specifically, we introduce a Timestep-wise Dynamic Width (TDW) approach that adapts model width conditioned on the generation timesteps. In addition, we design a Spatial-wise Dynamic Token (SDT) strategy to avoid redundant computation at unnecessary spatial locations. TDW and SDT can be seamlessly integrated into DiT and significantly accelerate the generation process. Building on these designs, we present an extended version, DyDiT++, with improvements in three key aspects. First, it extends the generation mechanism of DyDiT beyond diffusion to flow matching, demon strating that our method can also accelerate flow-matching based generation, enhancing its versatility. Furthermore, we enhance DyDiT to tackle more complex visual generation tasks, including video generation and text-to-image generation, thereby broadening its real-world applications. Finally, to address the high cost of full fine-tuning and democratize technology access, we investigate the feasibility of training DyDiT in a parameter efficient manner and introduce timestep-based dynamic LoRA (TD-LoRA). Extensive experiments on diverse visual generation models, including DiT, SiT, Latte, and FLUX, demonstrate the effectiveness of DyDiT++. Remarkably, with <3% additional f ine-tuning iterations, our approach reduces the FLOPs of DiT XL by 51%, yielding 1.73× realistic speedup on hardware, and achieves a competitive FID score of 2.07 on ImageNet. The code is available at https://github.com/alibaba-damo-academy/DyDiT.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"47 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/tpami.2026.3654243
Hao Wang, Keyan Hu, Xin Guo, Haifeng Li, Chao Tao
{"title":"A Gift from the Integration of Discriminative and Diffusion-based Generative Learning: Boundary Refinement Remote Sensing Semantic Segmentation","authors":"Hao Wang, Keyan Hu, Xin Guo, Haifeng Li, Chao Tao","doi":"10.1109/tpami.2026.3654243","DOIUrl":"https://doi.org/10.1109/tpami.2026.3654243","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"5 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/tpami.2026.3653989
Renlang Huang,Li Chai,Yufan Tang,Zhoujian Li,Jiming Chen,Liang Li
Deep learning-based feature matching has showcased great superiority for point cloud registration. While coarse-to-fine matching architectures are prevalent, they typically perform sparse and geometrically inconsistent coarse matching. This forces the subsequent fine matching to rely on computationally expensive optimal transport and hypothesis-and-selection procedures to resolve inconsistencies, leading to inefficiency and poor scalability for large-scale real-time applications. In this paper, we design a consistency-aware spot-guided Transformer (CAST) to enhance the coarse matching by explicitly utilizing geometric consistency via two key sparse attention mechanisms. First, our consistency-aware self-attention selectively computes intra-point-cloud attention to a sparse subset of points with globally consistent correspondences, enabling other points to derive discriminative features through their relationships with these anchors while propagating global consistency for robust correspondence reasoning. Second, our spot-guided cross-attention restricts cross-point-cloud attention to dynamically defined "spots"-the union of correspondence neighborhoods of a query's neighbors in the other point cloud, which are most likely to cover the true correspondence of the query ensured by local consistency, eliminating interference from similar but irrelevant regions. Furthermore, we design a lightweight local attention-based fine matching module to precisely predict dense correspondences and estimate the transformation. Extensive experiments on both outdoor LiDAR datasets and indoor RGB-D camera datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness. Besides, our method showcases superior generalization ability on our newly constructed challenging relocalization and loop closing benchmarks in unseen domains. Our code and models are available at https://github.com/RenlangHuang/CASTv2.
{"title":"Consistency-Aware Spot-Guided Transformer for Accurate and Versatile Point Cloud Registration.","authors":"Renlang Huang,Li Chai,Yufan Tang,Zhoujian Li,Jiming Chen,Liang Li","doi":"10.1109/tpami.2026.3653989","DOIUrl":"https://doi.org/10.1109/tpami.2026.3653989","url":null,"abstract":"Deep learning-based feature matching has showcased great superiority for point cloud registration. While coarse-to-fine matching architectures are prevalent, they typically perform sparse and geometrically inconsistent coarse matching. This forces the subsequent fine matching to rely on computationally expensive optimal transport and hypothesis-and-selection procedures to resolve inconsistencies, leading to inefficiency and poor scalability for large-scale real-time applications. In this paper, we design a consistency-aware spot-guided Transformer (CAST) to enhance the coarse matching by explicitly utilizing geometric consistency via two key sparse attention mechanisms. First, our consistency-aware self-attention selectively computes intra-point-cloud attention to a sparse subset of points with globally consistent correspondences, enabling other points to derive discriminative features through their relationships with these anchors while propagating global consistency for robust correspondence reasoning. Second, our spot-guided cross-attention restricts cross-point-cloud attention to dynamically defined \"spots\"-the union of correspondence neighborhoods of a query's neighbors in the other point cloud, which are most likely to cover the true correspondence of the query ensured by local consistency, eliminating interference from similar but irrelevant regions. Furthermore, we design a lightweight local attention-based fine matching module to precisely predict dense correspondences and estimate the transformation. Extensive experiments on both outdoor LiDAR datasets and indoor RGB-D camera datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness. Besides, our method showcases superior generalization ability on our newly constructed challenging relocalization and loop closing benchmarks in unseen domains. Our code and models are available at https://github.com/RenlangHuang/CASTv2.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"50 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/tpami.2026.3654092
Xiaoyang Xu,Wenzhe Yi,Juan Wang,Hongxin Hu,Mengda Yang,Ziang Li,Yong Zhuang,Yaxin Liu,Mang Ye
Split Learning (SL) is a distributed learning framework that has gained popularity for its privacy-preserving nature and low computational demands. However, recent studies have the potential that a server adversary to carry out inference attacks, compromising the privacy of victim clients. Nevertheless, upon re-evaluating prior studies, we found that existing methods rely on overly strong assumptions to enhance their performance, resulting in a significant decline in effectiveness under more realistic scenarios. In this work, we provide new insights into the inherent vulnerabilities of SL. Specifically, we discover that both the smashed data and the server model contain the client's representation preference, which the server adversary can exploit to build a substitute client that approximates the target client's unique feature extraction behavior. With a well-trained substitute client, the server can perfectly steal the target client's functionality, training data, and labels. Building on this observation, we introduce Split Leakage (SLeak), a new threat that targets multiple privacy stealing objectives against SL. Notably, SLeak does not depend on strong privacy priors and only requires partial same-domain auxiliary public data to conduct the attacks. Experimental results on diverse datasets and target models show that SLeak surpasses the state-of-the-art method across multiple metrics. Moreover, ablation studies further confirm its robustness and applicability under various scenarios and assumptions.
{"title":"SLeak: Multi-Target Privacy Stealing Attack against Split Learning.","authors":"Xiaoyang Xu,Wenzhe Yi,Juan Wang,Hongxin Hu,Mengda Yang,Ziang Li,Yong Zhuang,Yaxin Liu,Mang Ye","doi":"10.1109/tpami.2026.3654092","DOIUrl":"https://doi.org/10.1109/tpami.2026.3654092","url":null,"abstract":"Split Learning (SL) is a distributed learning framework that has gained popularity for its privacy-preserving nature and low computational demands. However, recent studies have the potential that a server adversary to carry out inference attacks, compromising the privacy of victim clients. Nevertheless, upon re-evaluating prior studies, we found that existing methods rely on overly strong assumptions to enhance their performance, resulting in a significant decline in effectiveness under more realistic scenarios. In this work, we provide new insights into the inherent vulnerabilities of SL. Specifically, we discover that both the smashed data and the server model contain the client's representation preference, which the server adversary can exploit to build a substitute client that approximates the target client's unique feature extraction behavior. With a well-trained substitute client, the server can perfectly steal the target client's functionality, training data, and labels. Building on this observation, we introduce Split Leakage (SLeak), a new threat that targets multiple privacy stealing objectives against SL. Notably, SLeak does not depend on strong privacy priors and only requires partial same-domain auxiliary public data to conduct the attacks. Experimental results on diverse datasets and target models show that SLeak surpasses the state-of-the-art method across multiple metrics. Moreover, ablation studies further confirm its robustness and applicability under various scenarios and assumptions.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"20 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/tpami.2026.3653901
Wenyuan Zhang,Chunsheng Wang,Kanle Shi,Yu-Shen Liu,Zhizhong Han
Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors with the UDF to the multi-view ground truth. However, these differentiable renderers are mainly handcrafted, which makes them either biased on ray-surface intersections, or sensitive to unsigned distance outliers, or not scalable to large scenes. To resolve these issues, we present a novel differentiable renderer to infer UDFs more accurately. Instead of using handcrafted equations, our differentiable renderer is a neural network which is pre-trained in a data-driven manner. It learns how to render unsigned distances into depth images, leading to a prior knowledge, dubbed volume rendering priors. To infer a UDF for an unseen scene from multiple RGB images, we generalize the learned volume rendering priors to map inferred unsigned distances in alpha blending for RGB image rendering. To reduce the bias of sampling in UDF inference, we utilize an auxiliary point sampling prior as an indicator of ray-surface intersection, and propose novel schemes towards more accurate and uniform sampling near the zero-level sets. We also propose a new strategy that leverages our pretrained volume rendering prior to serve as a general surface refiner, which can be integrated with various Gaussian reconstruction methods to optimize the Gaussian distributions and refine geometric details. Our results show that the learned volume rendering prior is unbiased, robust, scalable, 3D aware, and more importantly, easy to learn. Further experiments show that the volume rendering prior is also a general strategy to enhance other neural implicit representations such as signed distance function and occupancy. We evaluate our method on both widely used benchmarks and real scenes, and report superior performance over the state-of-the-art methods.
{"title":"VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priors.","authors":"Wenyuan Zhang,Chunsheng Wang,Kanle Shi,Yu-Shen Liu,Zhizhong Han","doi":"10.1109/tpami.2026.3653901","DOIUrl":"https://doi.org/10.1109/tpami.2026.3653901","url":null,"abstract":"Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors with the UDF to the multi-view ground truth. However, these differentiable renderers are mainly handcrafted, which makes them either biased on ray-surface intersections, or sensitive to unsigned distance outliers, or not scalable to large scenes. To resolve these issues, we present a novel differentiable renderer to infer UDFs more accurately. Instead of using handcrafted equations, our differentiable renderer is a neural network which is pre-trained in a data-driven manner. It learns how to render unsigned distances into depth images, leading to a prior knowledge, dubbed volume rendering priors. To infer a UDF for an unseen scene from multiple RGB images, we generalize the learned volume rendering priors to map inferred unsigned distances in alpha blending for RGB image rendering. To reduce the bias of sampling in UDF inference, we utilize an auxiliary point sampling prior as an indicator of ray-surface intersection, and propose novel schemes towards more accurate and uniform sampling near the zero-level sets. We also propose a new strategy that leverages our pretrained volume rendering prior to serve as a general surface refiner, which can be integrated with various Gaussian reconstruction methods to optimize the Gaussian distributions and refine geometric details. Our results show that the learned volume rendering prior is unbiased, robust, scalable, 3D aware, and more importantly, easy to learn. Further experiments show that the volume rendering prior is also a general strategy to enhance other neural implicit representations such as signed distance function and occupancy. We evaluate our method on both widely used benchmarks and real scenes, and report superior performance over the state-of-the-art methods.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"60 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/tpami.2026.3654260
Gengyun Jia,Xin Ma,Bing-Kun Bao
Ordinal regression aims to predict ordered classes. Existing methods mainly focus on label distribution shapes and feature distance relationships, while the directional characteristics in the representation space remain underexplored. In this paper, we propose deep orientational representation learning (ORL), aiming to ensure the trajectory of features sequentially connected by ordinal categories approximates a geodesic. We treat the output layer weights as ordinal prototypes and introduce two constraints, the co-directional constraint and the counter-directional constraint. They operate by constraining the angles between pairs of vectors. The former minimizes the angle between vectors with matching start and end categories, while the latter maximizes the angle between vectors whose start categories are the same but whose end categories are on opposite sides. The two constraints optimize the representation from different ordinal directions. ORL is extended to a multi-prototype setting (MORL) to mitigate misalignment between features and oriented prototypes caused by large intra-class variations. Theoretical analysis links ORL to distribution unimodality and distance orderliness, highlighting its advantages. The effectiveness of ORL (MORL) is demonstrated on various tasks including facial age estimation, historical image dating, and aesthetic quality assessment.
{"title":"Deep Orientational Representation Learning for Ordinal Regression.","authors":"Gengyun Jia,Xin Ma,Bing-Kun Bao","doi":"10.1109/tpami.2026.3654260","DOIUrl":"https://doi.org/10.1109/tpami.2026.3654260","url":null,"abstract":"Ordinal regression aims to predict ordered classes. Existing methods mainly focus on label distribution shapes and feature distance relationships, while the directional characteristics in the representation space remain underexplored. In this paper, we propose deep orientational representation learning (ORL), aiming to ensure the trajectory of features sequentially connected by ordinal categories approximates a geodesic. We treat the output layer weights as ordinal prototypes and introduce two constraints, the co-directional constraint and the counter-directional constraint. They operate by constraining the angles between pairs of vectors. The former minimizes the angle between vectors with matching start and end categories, while the latter maximizes the angle between vectors whose start categories are the same but whose end categories are on opposite sides. The two constraints optimize the representation from different ordinal directions. ORL is extended to a multi-prototype setting (MORL) to mitigate misalignment between features and oriented prototypes caused by large intra-class variations. Theoretical analysis links ORL to distribution unimodality and distance orderliness, highlighting its advantages. The effectiveness of ORL (MORL) is demonstrated on various tasks including facial age estimation, historical image dating, and aesthetic quality assessment.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"56 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145971744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of "slow thinking" into multimodal large language models (MLLMs). Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity. We propose a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic steps. Unlike existing methods that rely on structured templates or free-form paradigms, our method not only generates flexible CoT structures for various complex tasks but also mitigates the phenomenon of overthinking for easier tasks. To introduce structured reasoning into visual cognition, we design a novel AtomThink framework with four key modules: (i) a data engine to generate high-quality multimodal reasoning paths; (ii) a supervised fine-tuning (SFT) process with serialized inference data; (iii) a policy-guided multi-turn inference method; and (iv) an atomic capability metric to evaluate the single-step utilization rate. Extensive experiments demonstrate that the proposed AtomThink significantly improves the performance of baseline MLLMs, achieving more than 10% average accuracy gains on MathVista and MathVerse. Compared to state-of-the-art structured CoT approaches, our method not only achieves higher accuracy but also improves data utilization by 5 × and boosts inference efficiency by 85.3%. Our code is publicly available at https://github.com/Kun-Xiang/AtomThink.
{"title":"AtomThink: Multimodal Slow Thinking With Atomic Step Reasoning.","authors":"Kun Xiang,Zhili Liu,Terry Jingchen Zhang,Yinya Huang,Yunshuang Nie,Kaixin Cai,Yiyang Yin,Runhui Huang,Hanhui Li,Yihan Zeng,Yu-Jie Yuan,Jianhua Han,Lanqing Hong,Hang Xu,Xiaodan Liang","doi":"10.1109/tpami.2026.3653573","DOIUrl":"https://doi.org/10.1109/tpami.2026.3653573","url":null,"abstract":"In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of \"slow thinking\" into multimodal large language models (MLLMs). Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity. We propose a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic steps. Unlike existing methods that rely on structured templates or free-form paradigms, our method not only generates flexible CoT structures for various complex tasks but also mitigates the phenomenon of overthinking for easier tasks. To introduce structured reasoning into visual cognition, we design a novel AtomThink framework with four key modules: (i) a data engine to generate high-quality multimodal reasoning paths; (ii) a supervised fine-tuning (SFT) process with serialized inference data; (iii) a policy-guided multi-turn inference method; and (iv) an atomic capability metric to evaluate the single-step utilization rate. Extensive experiments demonstrate that the proposed AtomThink significantly improves the performance of baseline MLLMs, achieving more than 10% average accuracy gains on MathVista and MathVerse. Compared to state-of-the-art structured CoT approaches, our method not only achieves higher accuracy but also improves data utilization by 5 × and boosts inference efficiency by 85.3%. Our code is publicly available at https://github.com/Kun-Xiang/AtomThink.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"259 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1109/tpami.2026.3653765
Yeongyu Choi,Fabien Moutarde,Ju H Park,Ho-Youl Jung
We propose a novel post-processing approach for the local optimization of Locally Optimized RANdom SAmple Consensus (LO-RANSAC), called the Multi-Estimation-based Parameter Centroid (MEPC) decision. It is observed that the optimal thresholds for hypothesis generation and evaluation differ in local optimization with the inner RANSAC. Instead of binary labeling for inliers and outliers, a new ternary labeling for inliers, midliers, and outliers is introduced, using two thresholds. Our experimental results show that the highest-scoring model measured by the ternary method is closer to the real model than that measured by the existing binary method. However, it should be noted that the highest score still does not correspond to the best model due to inaccurate evaluation by data noise. We introduce a new linear model centroid decision method to compensate for the highest-scoring model distorted by noise. In this process, an efficient method for measuring the similarity between two hypotheses is introduced, and candidates close to the real model are found by comparing their similarity with the highest-scoring model. Our approach determines a representative model of the multiple candidate hypotheses, which is defined as the geometric centroid of hyperplanes. We test on various datasets for homography, fundamental, and essential matrices, demonstrating that applying MEPC to existing RANSAC algorithms achieves more accurate and stable model estimation. Moreover, additional experiments on vanishing point detection show the potential of our approach for various model estimation applications.
{"title":"An Efficient Multi-Estimation-Based Parameter Centroid Decision Via Linear Regression Approach.","authors":"Yeongyu Choi,Fabien Moutarde,Ju H Park,Ho-Youl Jung","doi":"10.1109/tpami.2026.3653765","DOIUrl":"https://doi.org/10.1109/tpami.2026.3653765","url":null,"abstract":"We propose a novel post-processing approach for the local optimization of Locally Optimized RANdom SAmple Consensus (LO-RANSAC), called the Multi-Estimation-based Parameter Centroid (MEPC) decision. It is observed that the optimal thresholds for hypothesis generation and evaluation differ in local optimization with the inner RANSAC. Instead of binary labeling for inliers and outliers, a new ternary labeling for inliers, midliers, and outliers is introduced, using two thresholds. Our experimental results show that the highest-scoring model measured by the ternary method is closer to the real model than that measured by the existing binary method. However, it should be noted that the highest score still does not correspond to the best model due to inaccurate evaluation by data noise. We introduce a new linear model centroid decision method to compensate for the highest-scoring model distorted by noise. In this process, an efficient method for measuring the similarity between two hypotheses is introduced, and candidates close to the real model are found by comparing their similarity with the highest-scoring model. Our approach determines a representative model of the multiple candidate hypotheses, which is defined as the geometric centroid of hyperplanes. We test on various datasets for homography, fundamental, and essential matrices, demonstrating that applying MEPC to existing RANSAC algorithms achieves more accurate and stable model estimation. Moreover, additional experiments on vanishing point detection show the potential of our approach for various model estimation applications.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"52 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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