We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. Alignment of non-rigid structures is one of the most challenging computer vision tasks due to the high number of unknowns needed to model the correspondence. We have seen a leap forward using DNN models in template alignment and functional maps, but those methods fail for inter-class alignment where nonisometric deformations exist. Here we propose to rethink this task and use unrolling concepts on a dual graph structure - one for a forward map and one for a backward map, where the features are pulled back matching probabilities from the target into the source. We report state of the art results on stretchable domains alignment in a rapid and stable solution for meshes and cloud of points.
Nanyang Technological University（南洋理工大学）；Applied Research Center, Tencent PCG（腾讯PCG应用研究中心）；The Chinese University of Hong Kong（香港中文大学）；SIAT – SenseTime Joint Lab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院——商汤科技联合实验室）
Deformable convolution, originally proposed for the adaptation to geometric variations of objects, has recently shown compelling performance in aligning multiple frames and is increasingly adopted for video super-resolution. Despite its remarkable performance, its underlying mechanism for alignment remains unclear. In this study, we carefully investigate the relation between deformable alignment and the classic flow-based alignment. We show that deformable convolution can be decomposed into a combination of spatial warping and convolution. This decomposition reveals the commonality of deformab…
Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所模式识别国家重点实验室智能感知与计算研究中心）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）
Most Video Super-Resolution (VSR) methods enhance a video reference frame by aligning its neighboring frames and mining information on these frames. Recently, deformable alignment has drawn extensive attention in VSR community for its remarkable performance, which can adaptively align neighboring frames with the reference one. However, we experimentally find that deformable alignment methods still suffer from fast motion due to locally loss-driven offset prediction and lack explicit motion constraints. Hence, we propose a Matching-based Flow Estimation (MFE) module to conduct global semantic f…
Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人学会）
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts when dealing with real-world super-resolution problems due to the difficulty in learning misaligned optical zoom. In this paper, we introduce a Squared Deformable Alignment Network (SDAN) to address this issue. Our network learns squared per-point offsets for convolutional kernels, and then aligns features in corrected convolutional windows based on the offsets. So the misalignment will be minimized by the extracted a…
Megvii Technology（旷视科技）；University of Electronic Science and Technology of China（电子科技大学）
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-resolution RAW bursts. To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction. To achieve this goal, we propose a Pyramid Flow-Guided Deformable Convolution Network (Pyramid FG-DCN) and incorporate Swin Transformer Blocks and Groups as our main backbone. More specifically, we…
Shantou University（汕头大学）；Sun Yat-sen University（中山大学）；First Affiliated Hospital of Shantou University（汕头大学第一附属医院）
Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengt…
Evaluation of Deformable Image Registration under Alignment-Regularity Trade-off
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital, Munich, Germany（慕尼黑技术大学医院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）
University of Rochester（罗切斯特大学）；Northeastern University（东北大学）
Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple neighboring frames (supporting frames). Due to varying motion of cameras or objects, the reference frame and each support frame are not aligned. Therefore, temporal alignment is a challenging yet important problem for VSR. Previous VSR methods usually utilize optical flow between the reference frame and each supporting frame to wrap the supporting frame for temporal alignment. Therefore, the performance of these ima…
South China University of Technology（华南理工大学）；Birkbeck College（伦敦大学伯贝克学院）；The University of Sydney（悉尼大学）
Part-level representations are essential for robust person re-identification. However, common errors that arise during pedestrian detection frequently result in severe misalignment problems for body parts, which degrade the quality of part representations. Accordingly, to deal with this problem, we propose a novel model named Convolutional Deformable Part Models (CDPM). CDPM works by decoupling the complex part alignment procedure into two easier steps: first, a vertical alignment step detects each body part in the vertical direction, with the help of a multi-task learning model; second, a hor…
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of …
DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
Min Sun School of Materials Science and Engineering Hainan University（孙敏 海南大学材料科学与工程学院）；Fenghui Guo School of Materials Science and Engineering Hainan University（郭峰 海南大学材料科学与工程学院）
GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment
National Taiwan University（台湾大学）；National Taiwan Normal University（台湾师范大学）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究院）；Microsoft Taiwan Corporation（微软台湾公司）；VinUniversity；National Taiwan University of Science and Technology（台湾科技大学）
College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学与技术学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging to incorporate angular information due to disparities among LF images. In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Specifically, we design an angular deformable alignment module (ADAM) for feature-level alignment. Based on ADAM, we further propose a collect-and-distribute approach to perform bidirectional alignment between the center-vi…
Progressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection
School of Electrical and Information Engineering, Jiangsu University（江苏大学电气与信息工程学院）；School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）
Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss
University of Oxford（牛津大学）；Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院 NHS 基金会信托基金）；Oxford Radiology Research Unit（牛津放射学研究单元）
The de facto algorithm for facial landmark estimation involves running a face detector with a subsequent deformable model fitting on the bounding box. This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialisation for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (\eg, one for profile and one for frontal faces). In this work, we propose the first, to th…
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
iPEAR: Iterative Pyramid Estimation with Attention and Residuals for Deformable Medical Image Registration
College of Software, Jilin University（吉林大学软件学院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University（老年活跃生活卓越研究中⼼（NTU-UBC）, 新加坡国立大学）；College of Computer Science（计算机科学学院）；Technology, Zhejiang University（技术, 浙江大学）；Key Laboratory of Symbolic Computation（符号计算重点实验室）；Knowledge Engineering of Ministry of Education, College of Computer Science（教育部知识工程重点实验室, 计算机科学学院）；Technology, Jilin University（技术, 吉林大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by using its corresponding low-resolution (LR) frame and multiple neighboring frames. At present, many deep learning-based VSR methods rely on optical flow to perform frame alignment. The final recovery results will be greatly affected by the accuracy of optical flow. However, optical flow estimation cannot be completely accurate, and there are always some errors. In this paper, we propose a novel deformable non-local network (DNLN) which is a non-optical-flow-based method. Specifically, we apply the defor…
Video deblurring remains a challenging task due to various causes of blurring. Traditional methods have considered how to utilize neighboring frames by the single-scale alignment for restoration. However, they typically suffer from misalignment caused by severe blur. In this work, we aim to better utilize neighboring frames with high efficient feature alignment. We propose a Pyramid Feature Alignment Network (PFAN) for video deblurring. First, the multi-scale feature of blurry frames is extracted with the strategy of Structure-to-Detail Downsampling (SDD) before alignment. This downsampling st…
Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, C…
West Virginia University（西弗吉尼亚大学）；Huazhong University of Science and Technology（华中科技大学）
Video super-resolution, which attempts to reconstruct high-resolution video frames from their corresponding low-resolution versions, has received increasingly more attention in recent years. Most existing approaches opt to use deformable convolution to temporally align neighboring frames and apply traditional spatial attention mechanism (convolution based) to enhance reconstructed features. However, such spatial-only strategies cannot fully utilize temporal dependency among video frames. In this paper, we propose a novel deep learning based VSR algorithm, named Deformable Kernel Spatial Attent…
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion
School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；ToolSensing Technologies Co., Ltd（拓深科技有限公司）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院多维信息处理上海重点实验室）
We present the first utterly self-supervised network for dense correspondence mapping between non-isometric shapes. The task of alignment in non-Euclidean domains is one of the most fundamental and crucial problems in computer vision. As 3D scanners can generate highly complex and dense models, the mission of finding dense mappings between those models is vital. The novelty of our solution is based on a cyclic mapping between metric spaces, where the distance between a pair of points should remain invariant after the full cycle. As the same learnable rules that generate the point-wise descript…
Megvii Technology（旷视科技）；Sichuan University（四川大学）；University of Electronic Science and Technology of China（电子科技大学）
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two intractable challenges of how to handle saturation and noise properly and how to tackle misalignments caused by object motion or camera jittering. To address the former, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range (LDR) images for fusion. For the latter one, we propose to align the gamma-corrected images in the feature-level with a Pyrami…
Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well a…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Division of Image Processing, Leiden University Medical Center（莱顿大学医学中心图像处理部）
In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration …
University of North Carolina（北卡罗来纳大学）；Shanghai Jiao Tong University（上海交通大学）
Deformable image registration is fundamental to longitudinal and population analysis. Geometric alignment of the infant brain MR images is challenging, owing to rapid changes in image appearance in association with brain development. In this paper, we propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields to obtain highly accurate correspondences. Instead of training multiple registration networks, our method estimates the deformation fields by invoking a single network multiple times for iterative deformation refine…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features. Current methods use 2D features extracted from early-fused RGB-D images for 2D segmentation to improve 3D scene completion. We argue that this sequential scheme does not ensure these two tasks fully benefit each other, and present an Iterative Mutual Enhancement Network (IMENet) to solve them jointly, which interactively refines the two tasks at the late…
Harbin Institute of Technology（哈尔滨工业大学）；Tencent AI Lab（腾讯人工智能实验室）
The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames. Therefore, mainstream methods align the adjacent frames based on the estimated optical flows and fuse the alignment frames for restoration. However, these methods sometimes generate unsatisfactory results because they rarely consider the blur levels of pixels, which may introduce blurry pixels from video frames. Actually, not all the pixels in the video frames are sharp and beneficial for deblurring. To address this problem, we propo…
Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（集成空天地海大数据应用技术国家工程实验室，计算机科学与工程学院，西北工业大学）
University of Basel（巴塞尔大学）；University of Basel Hospital（巴塞尔大学医院）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；University of Bern（伯尔尼大学）
Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain, because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. We reformulate the pairwise registration problem as a re…
The University of Sydney（悉尼大学）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. How…
University of Edinburgh（爱丁堡大学）；Google Research（谷歌研究院）
Given unstructured videos of deformable objects, we automatically recover spatiotemporal correspondences to map one object to another (such as animals in the wild). While traditional methods based on appearance fail in such challenging conditions, we exploit consistency in object motion between instances. Our approach discovers pairs of short video intervals where the object moves in a consistent manner and uses these candidates as seeds for spatial alignment. We model the spatial correspondence between the point trajectories on the object in one interval to those in the other using a time-var…
Texture reconstruction techniques generally suffer from the errors in keyframe poses. We present a non-iterative method for seamless texture reconstruction of a given 3D scene. Our method finds the best texture alignment in a single shot using a global optimisation framework. First, we automatically select the best keyframe to texture each face of the mesh. This leads to a decomposition of the mesh into small groups of connected faces associated to a same keyframe. We call such groups fragments. Then, we propose a geometry-aware matching technique between the 3D keypoints extracted around the …
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Department of Electrical and Computer Engineering, Duke University（电子工程系，杜克大学）；Department of Biostatistics & Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结果与生物医学信息学系，医学院，佛罗里达大学）
Nanjing University of Information Science and Technology（南京信息工程大学）；JD Finance America Corporation（京东金融美国公司）
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop an effective spatiotemporal feature alignment network tailored to VSP, mainly including two key sub-networks: a multi-scale deformable convolutional alignment network (MDAN) and a bidirectional convolutional Long Short-Term Memory (Bi-ConvLSTM) network. The MDAN learns to align the features of the neighboring frames to the reference one in a coarse-to-fine manner, which can well handle v…
This paper presents a iterative optimization method, explicit shape regression, for face pose detection and localization. The regression function is learnt to find out the entire facial shape and minimize the alignment errors. A cascaded learning framework is employed to enhance shape constraint during detection. A combination of a two-level boosted regression, shape indexed features and a correlation-based feature selection method is used to improve the performance. In this paper, we have explain the advantage of ESR for deformable object like face pose estimation and reveal its generic appli…
Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neuroimaging data. Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image. Conventional methods for multi-stage registration can often blur the source image as the pixel/voxel values are repeatedly interpola…
Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-…
Huazhong University of Science and Technology（华中科技大学）；Huawei Cloud（华为云）
A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process. However, such single-step attention is insufficient in practice, since complicated relations between inter- and intra-modality are usually obtained through multi-step reasoning. In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter- and in…
Smart retail stores are becoming the fact of our lives. Several computer vision and sensor based systems are working together to achieve such a complex and automated operation. Besides, the retail sector already has several open and challenging problems which can be solved with the help of pattern recognition and computer vision methods. One important problem to be tackled is the planogram compliance control. In this study, we propose a novel method to solve it. The proposed method is based on object detection, planogram compliance control, and focused and iterative search steps. The object de…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Technical University of Munich（慕尼黑工业大学）；New York University（纽约大学）
Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios. Unfortunately, CAD model retrieval is limited by the availability of models in standard 3D shape collections (e.g., ShapeNet). In this work, we address this shortcoming by introducing CAD-Deform, a method which obtains more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models. Our key contribution is a new non-rigid deformation model incorporating smooth transformations and preservat…
