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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yaqi Shen Le Hui Jin Xie Jian Yang PCA Lab Nanjing University of Science and Technology Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

3D scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3D scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering methods, which cannot characterize local regions with similar motions for complex 3D scenes well, leading to inaccurate scene flow estimation. To this end, we propose an iterative end-to-end super point based scene flow estimation framework, where the superpoints can be dynamically updated to guide the point-level flow prediction. Specifically, our framework consists of a flow guided superpoint generation module and a superpoint guided flow refinement module. In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds. With the generated superpoints, we first reconstruct the flow for each point by adaptively aggregating the superpoint-level flow, and then encode the consistency between the reconstructed flow of pairwise point clouds. Finally, we feed the consistency encoding along with the reconstructed flow into GRU to refine point-level flow. Extensive experiments on several different datasets show that our method can achieve promising performance. Code is available at https://***/supersyq/SPFlowNet.

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A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization 37

A Single-Loop Accelerated Extra-Gradient Difference Algorith...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Yuanyuan Shang, Fanhua An, Weixin Liu, Junhao Liu, Hongying Lin, Zhouchen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China School of Computer Science and Technology College of Intelligence and Computing Tianjin University China Medical College Tianjin University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

ISBN: (纸本)9781713899921

In this paper, we propose a novel extra-gradient difference acceleration algorithm for solving constrained nonconvex-nonconcave (NC-NC) minimax problems. In particular, we design a new extra-gradient difference step to obtain an important quasi-cocoercivity property, which plays a key role to significantly improve the convergence rate in the constrained NC-NC setting without additional structural assumption. Then momentum acceleration is also introduced into our dual accelerating update step. Moreover, we prove that, to find an Ε-stationary point of the function f, the proposed algorithm attains the complexity O(Ε-2) in the constrained NC-NC setting, while the best-known complexity bound is Oe(Ε-4), where Oe(·) hides logarithmic factors compared to O(·). As the special cases of the constrained NC-NC setting, the proposed algorithm can also obtain the same complexity O(Ε-2) for both the nonconvex-concave (NC-C) and convex-nonconcave (C-NC) cases, while the best-known complexity bounds are Oe(Ε-2.5) for the NC-C case and Oe(Ε-4) for the C-NC case. For fair comparison with existing algorithms, we also analyze the complexity bound to find Ε-stationary point of the primal function φ for the constrained NC-C problem, which shows that our algorithm can improve the complexity bound from Oe(Ε-3) to O(Ε-2). To the best of our knowledge, this is the first time that the proposed algorithm improves the best-known complexity bounds from Oe(Ε-4) and Oe(Ε-3) to O(Ε-2) in both the NC-NC and NC-C settings. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Constrained optimization

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Along-Track Displacement Retrieval Using Sentinel-1 images

Along-Track Displacement Retrieval Using Sentinel-1 Images

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Mohamed Amine Latrouci Faiza Hocine Saoussen Belhadj Aissa Ishak Daoud Geophysics Laboratory University of Science and Technology Houari Boumediene Algiers Algeria Image Processing and Radiation Lab University of Science and Technology Houari Boumediene Algiers Algeria Jet Propulsion Laboratory California Institute of Technology California USA

Both classic and Differential Interferometric Synthetic Aperture Radar (InSAR & DInSAR) are used to detect and measure surface topography and deformations, in sensor's line of sight (LOS) direction. However, diverse phenomena such as earthquakes, volcanic eruptions, land subsidence, etc., can result in deformations in different directions including the sensor's movement direction (azimuth). This type of deformation can be detected using the phase of the backscattered signal and the Doppler spectra of the interferometric pairs. In this work, we focus on the application of Multiple Aperture Interferometry (MAI) for detecting surface and ground deformations in the azimuth direction. The effectiveness of this technique is demonstrated using a Sentinel-1 SLC image pair that covers the Thessaly region in Greece, hit by several earthquakes on March 2021, the largest of which was of 6.3 Magnitude. The results show that MAI can provide complementary information on deformations occurring in the azimuth direction, complementing the capabilities of DInSAR.

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Center-Based Decoupled Point Cloud Registration for 6D Object Pose Estimation

Center-Based Decoupled Point Cloud Registration for 6D Objec...

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International Conference on Computer Vision (ICCV)

作者： Haobo Jiang Zheng Dang Shuo Gu Jin Xie Mathieu Salzmann Jian Yang PCA Lab Nanjing University of Science and Technology China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China CVLab EPFL Switzerland

In this paper, we propose a novel center-based decoupled point cloud registration framework for robust 6D object pose estimation in real-world scenarios. Our method decouples the translation from the entire transformation by predicting the object center and estimating the rotation in a center-aware manner. This center offset-based translation estimation is correspondence-free, freeing us from the difficulty of constructing correspondences in challenging scenarios, thus improving robustness. To obtain reliable center predictions, we use a multi-view (bird’s eye view and front view) object shape description of the source-point features, with both views jointly voting for the object center. Additionally, we propose an effective shape embedding module to augment the source features, largely completing the missing shape information due to partial scanning, thus facilitating the center prediction. With the center-aligned source and model point clouds, the rotation predictor utilizes feature similarity to establish putative correspondences for SVD-based rotation estimation. In particular, we introduce a center-aware hybrid feature descriptor with a normal correction technique to extract discriminative, part-aware features for high-quality correspondence construction. Our experiments show that our method outperforms the state-of-the-art methods by a large margin on real-world datasets such as TUD-L, LINEMOD, and Occluded-LINEMOD. Code is available at https://***/JiangHB/CenterReg.

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Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design

arXiv

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arXiv 2024年

作者： Zhou, Jiayi Li, Renzhong Tang, Junxiu Tang, Tan Li, Haotian Cui, Weiwei Wu, Yingcai State Key Lab of CAD & CG Lab of Art and Archaeology Image Zhejiang University Zhejiang Hangzhou China State Key Lab of CAD & CG Zhejiang University Zhejiang Hangzhou China Lab of Art and Archaeology Image Zhejiang University Zhejiang Hangzhou China The Hong Kong University of Science and Technology Hong Kong Microsoft Research Asia Beijing China

Creative design is a nonlinear process where designers generate diverse ideas in the pursuit of an open-ended goal and converge towards consensus through iterative remixing. In contrast, AI-powered design tools often employ a linear sequence of incremental and precise instructions to approximate design objectives. Such operations violate customary creative design practices and thus hinder AI agents' ability to complete creative design tasks. To explore better human-AI co-design tools, we first summarize human designers' practices through a formative study with 12 design experts. Taking graphic design as a representative scenario, we formulate a nonlinear human-AI co-design framework and develop a proof-of-concept prototype, OptiMuse. We evaluate OptiMuse and validate the nonlinear framework through a comparative study. We notice a subconscious change in people's attitudes towards AI agents, shifting from perceiving them as mere executors to regarding them as opinionated colleagues. This shift effectively fostered the exploration and reflection processes of individual designers. Copyright © 2024, The Authors. All rights reserved.

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Simulated Data for SAR Tomographic Reconstruction in Urban Areas

Simulated Data for SAR Tomographic Reconstruction in Urban A...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Ishak Daoud Saoussen Belhadj-Aissa Faiza Hocine Assia Kourgli Department of Telecommunication Image Processing and radiation Lab University of Science and Technology Houari Boumediene Algiers Algeria Jet Propulsion Laboratory California Institute of Technology Pasadena California USA

The evaluation of SAR tomographic reconstruction models requires an examination of their robustness with respect to various parameters. Typically, a simple simulation of reflectivity profiles with different scatters at different elevations and SNRs is used to evaluate the robustness of any reconstruction model. However, these simulation setups do not account for most of the geometric acquisition variables present in the reconstruction of real data. Most of the data used for SAR tomographic reconstruction in urban areas are acquired using high-resolution X-band sensors because of their weak penetration, which aids in recovering the geometry of the target. In this work, we propose simulating an urban area acquisition scene that considers the primary acquisition parameters of high-resolution X-band sensors. By doing so, one can simulate various measurements for different building shapes and elevations, providing a more comprehensive evaluation of the reconstruction methods. In this simulation, the geometry of SAR tomography is respected by considering different geometry distortions such as layover, foreshortening, and shadowing. The simulation is evaluated by implementing different reconstruction methods to the simulated data. Results obtained show that sparse reconstruction methods such as 11 and 12 norms and SL1MMER are more suitable to evaluate the reconstruction due to the high-resolution acquisitions. On the other hand, we used other conventional reconstruction models such as SVD-Weiner and MUSIC for assessment purposes.

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SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

arXiv

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arXiv 2023年

作者： Jiang, Haobo Salzmann, Mathieu Dang, Zheng Xie, Jin Yang, Jian PCA Lab Nanjing University of Science and Technology China CVLab EPFL Switzerland ClearSpace Switzerland PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud to obtain a precise alignment with the model point cloud. Training our framework involves two operations: An SE(3) diffusion process and an SE(3) reverse process. The SE(3) diffusion process gradually perturbs the optimal rigid transformation of a pair of point clouds by continuously injecting noise (perturbation transformation). By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation. Unlike standard diffusion models used in linear Euclidean spaces, our diffusion model operates on the SE(3) manifold. This requires exploiting the linear Lie algebra se(3) associated with SE(3) to constrain the transformation transitions during the diffusion and reverse processes. Additionally, to effectively train our denoising network, we derive a registration-specific variational lower bound as the optimization objective for model learning. Furthermore, we show that our denoising network can be constructed with a surrogate registration model, making our approach applicable to different deep registration networks. Extensive experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets. Code is available at https://***/JiangHB/DiffusionReg. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

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14th International Conference on Digital image Processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware

arXiv

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arXiv 2024年

作者： Liu, Chaoqun Li, Xuanpeng Gong, Chen Li, Guangyu PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China Southeast University Nanjing210094 China

Traffic prediction is an indispensable component of urban planning and traffic management. Achieving accurate traffic prediction hinges on the ability to capture the potential spatio-temporal relationships among road sensors. However, the majority of existing works focus on local short-term spatiotemporal correlations, failing to fully consider the interactions of different sensors in the long-term state. In addition, these works do not analyze the influences of anomalous factors, or have insufficient ability to extract personalized features of anomalous factors, which make them ineffectively capture their spatiotemporal influences on traffic prediction. To address the aforementioned issues, We propose a global spatio-temporal fusion-based traffic prediction algorithm that incorporates anomaly awareness. Initially, based on the designed anomaly detection network, we construct an efficient anomalous factors impacting module (AFIM), to evaluate the spatio-temporal impact of unexpected external events on traffic prediction. Furthermore, we propose a multi-scale spatio-temporal feature fusion module (MTSFFL) based on the transformer architecture, to obtain all possible both long and short term correlations among different sensors in a wide-area traffic environment for accurate prediction of traffic flow. Finally, experiments are implemented based on real-scenario public transportation datasets (PEMS04 and PEMS08) to demonstrate that our approach can achieve state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Mass transportation

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Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration

arXiv

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arXiv 2023年

作者： Wu, Qianliang Jiang, Haobo Ding, Yaqing Luo, Lei Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Efficiently finding optimal correspondences between point clouds is crucial for solving both rigid and nonrigid point cloud registration problems. Existing methods often rely on geometric or semantic feature embedding to establish correspondences and estimate transformations or flow fields. Recently, state-of-the-art methods have employed RAFT-like iterative updates to refine the solution. However, these methods have certain limitations. Firstly, their iterative refinement design lacks transparency, and their iterative updates follow a fixed path during the refinement process, which can lead to suboptimal results. Secondly, these methods overlook the importance of refining or optimizing correspondences (or matching matrices) as a precursor to solving transformations or flow fields. They typically compute candidate correspondences based on distances in the point feature space. However, they only project the candidate matching matrix into some matrix space once with Sinkhorn or dual softmax operations to obtain final correspondences. This one-shot projected matching matrix may be far from the globally optimal one, and these approaches do not consider the distribution of the target matching matrix. In this paper, we propose a novel approach that exploits the Denoising Diffusion Model to predict a searching gradient for the optimal matching matrix within the Doubly Stochastic Matrix Space. Our method incorporates the diffusion model to learn a denoising gradient direction. During the reverse denoising process, our method iteratively searches for better solutions along this denoising gradient, which points towards the maximum likelihood direction of the target matching matrix. To improve efficiency, we employ a lightweight design for our denoising module and utilize the sampling strategy in the Denoising Diffusion Implicit Model (DDIM [27]). Experimental evaluations on the 3DMatch/3DLoMatch and 4DMatch/4DLoMatch datasets demonstrate the effectiveness of our newly designe

关键词： Flow fields

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