Learning Two-View Correspondences and Geometry Using Order-Aware Network
Intel Labs China（英特尔中国研究院）；Shenzhen Zhuke Innovation Technology (Altizure)（深圳珠科创新科技（Altizure））；Hong Kong University of Science and Technology（香港科技大学）；Everest Innovation Technology (Altizure)（顶峰创新科技（Altizure））；Tsinghua University（清华大学）
Jiahui Zhang, Dawei Sun, Zixin Luo, Anbang Yao, Lei Zhou, Tianwei Shen, Yurong Chen, Long Quan, Hongen Liao
备注：
Accepted to ICCV 2019, and Winner solution to both tracks of CVPR IMW 2019 Challenge. Code will be available soon at https://github.com/zjhthu/OANet.git
Establishing correspondences between two images requires both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential matrix. Specifically, this proposed network is built hierarchically and comprises three novel operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in a canonical order and invariant to input permutations. Next, the clusters are spatially correlated to form the global context of correspondences. After that, the context-encoded clusters are recovered back to the original size through a proposed upsampling operator. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts. Code will be available at https://github.com/zjhthu/OANet.git.
Correspondences estimation or feature matching is a key step in the image-based 3D reconstruction problem. In this paper, we propose two algebraic properties for correspondences. The first is a rank deficient matrix construct from the correspondences of at least nine key-points on two images (two-view correspondences) and the second is also another rank deficient matrix built from the other correspondences of six key-points on at least five images (multi-view correspondences). To our knowledge, there are no theoretical results for multi-view correspondences prior to this paper. To obtain accur…
The performance of existing robust homography estimation algorithms is highly dependent on the inlier rate of feature point correspondences. In this paper, we present a novel procedure for homography estimation that is particularly well suited for inlier-poor domains. By utilizing the scale and rotation byproducts created by affine aware feature detectors such as SIFT and SURF, we obtain an initial homography estimate from a single correspondence pair. This estimate allows us to filter the correspondences to an inlier-rich subset for use with a robust estimator. Especially at low inlier rates,…
Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is imp…
A dense depth-map of a scene at an arbitrary view orientation can be estimated from dense view correspondences among multiple lower-dimensional views of the scene. These low-dimensional view correspondences are dependent on the geometrical relationship among the views and the scene. Determining dense view correspondences is difficult in part due to presence of homogeneous regions in the scene and due to presence of occluded regions and illumination differences among the views. We present a new multi-resolution factor graph-based stereo matching algorithm (MR-FGS) that utilizes both intra- and …
Beijing University of Posts and Telecommunications（北京邮电大学）；Zhejiang University（浙江大学）；Baidu Research（百度研究院）；Beijing Institute of Technology（北京理工大学）；ETH Zurich（苏黎世联邦理工学院）；University of Technology Sydney（悉尼科技大学）
Our target is to learn visual correspondence from unlabeled videos. We develop LIIR, a locality-aware inter-and intra-video reconstruction framework that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, instead of most existing efforts focusing on intra-video self-supervision only, we exploit cross video affinities as extra negative samples within a unified, inter-and intra-video reconstruction scheme. This enables instance discriminative representation learning by contrasting de…
Structure-Aware Correspondence Learning for Relative Pose Estimation
University of Science and Technology of China（中国科学技术大学）；National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory（国家空间科学探测重点实验室，深空探测实验室）；Sangfor Technologies
City University of Hong Kong（香港城市大学）；Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；University of Surrey（萨里大学）
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle…
Weizmann Institute of Science（魏茨曼科学研究所）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
In general it requires at least 7 point correspondences to compute the fundamental matrix between views. We use the cross ratio invariance between corresponding epipolar lines, stemming from epipolar line homography, to derive a simple formulation for the relationship between epipoles and corresponding points. We show how it can be used to reduce the number of required points for the epipolar geometry when some information about the epipoles is available and demonstrate this with a buddy search app.
In this paper, we describes a correspondence identification method between two-views of regular RGB camera that can be run in real-time. The basic idea is first applying normalized cross correlation to retrieve a sparse set of matching pairs from image pair. Then loopy belief propagation scheme is applied to the the set of possible candidates to densely identify correspondences from different views. The experiment results demonstrate superb accuracy and precision that outperform the state-of-the-art in the computer vision field. Meanwhile, the implementation is simple enough that can be optimi…
Sun Yat-Sen University（中山大学）；ByteDance（字节跳动）；UiT The Arctic University of Norway（挪威北极大学）；Peng Cheng Laboratory（鹏城实验室）
In this paper, we target image-based person-to-person virtual try-on in the presence of diverse poses and large viewpoint variations. Existing methods are restricted in this setting as they estimate garment warping flows mainly based on 2D poses and appearance, which omits the geometric prior of the 3D human body shape. Moreover, current garment warping methods are confined to localized regions, which makes them ineffective in capturing long-range dependencies and results in inferior flows with artifacts. To tackle these issues, we present 3D-aware global correspondences, which are reliable fl…
MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography
Technical University of Munich（慕尼黑工业大学）；German Heart Center Munich（慕尼黑德国心脏中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；German Centre for Cardiovascular Research, Munich Heart Alliance（德国心血管研究中心，慕尼黑心脏联盟）；HELIOS Hospital West of Munich（慕尼黑西部黑利奥斯医院）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI - 康拉德·楚泽可靠人工智能卓越学院）
University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；DJI Technology（大疆创新科技有限公司）；Huawei（华为技术有限公司）；University of Adelaide（阿德莱德大学）
Multi-view depth estimation plays a critical role in reconstructing and understanding the 3D world. Recent learning-based methods have made significant progress in it. However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence. Thus, they suffer unsatisfactory precision and generalization capability…
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on point clouds. Temporally consistent 3D deformations are estimated without the need for dense correspondences at training time, by exploiting cycle consistency. Besides its ability to learn dense correspondences, GNPMs also enable latent-space manipulations such as interpolation and shape/pose transfer. We evaluate GNPMs on various datasets of clothed humans,…
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the r…
University of Southern Denmark（南丹麦大学）；Maersk Mc-Kinney Moller Institute（马士基·麦金尼·穆勒研究所）
In many automation tasks involving manipulation of rigid objects, the poses of the objects must be acquired. Vision-based pose estimation using a single RGB or RGB-D sensor is especially popular due to its broad applicability. However, single-view pose estimation is inherently limited by depth ambiguity and ambiguities imposed by various phenomena like occlusion, self-occlusion, reflections, etc. Aggregation of information from multiple views can potentially resolve these ambiguities, but the current state-of-the-art multi-view pose estimation method only uses multiple views to aggregate singl…
Xi’an Jiaotong University（西安交通大学）；Jiangnan University（江南大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
This paper proposes a global approach for the multi-view registration of unordered range scans. As the basis of multi-view registration, pair-wise registration is very pivotal. Therefore, we first select a good descriptor and accelerate its correspondence propagation for the pair-wise registration. Then, we design an effective rule to judge the reliability of pair-wise registration results. Subsequently, we propose a model augmentation method, which can utilize reliable results of pair-wise registration to augment the model shape. Finally, multi-view registration can be accomplished by operati…
Massachusetts Institute of Technology（麻省理工学院）；Waabi；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondence…
Technical University of Munich（慕尼黑工业大学）；University of Toulouse（图卢兹大学）；Mikros Image
We introduce a variational method for multi-view shape-from-shading under natural illumination. The key idea is to couple PDE-based solutions for single-image based shape-from-shading problems across multiple images and multiple color channels by means of a variational formulation. Rather than alternatingly solving the individual SFS problems and optimizing the consistency across images and channels which is known to lead to suboptimal results, we propose an efficient solution of the coupled problem by means of an ADMM algorithm. In numerous experiments on both simulated and real imagery, we d…
Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法研究北京市重点实验室）；Department of Information Engineering, the Chinese University of Hong Kong（香港中文大学信息工程系）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localization without category annotations, i.e., localizing the sounding object and recognizing its category. To address this problem, we propose a two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision. First, we propose to determine the sounding area via coarse-graine…
Understanding the geometry and pose of objects in 2D images is a fundamental necessity for a wide range of real world applications. Driven by deep neural networks, recent methods have brought significant improvements to object pose estimation. However, they suffer due to scarcity of keypoint/pose-annotated real images and hence can not exploit the object's 3D structural information effectively. In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation. First, we learn pose-invariant local descriptors of object parts from …
A minimal solution using two affine correspondences is presented to estimate the common focal length and the fundamental matrix between two semi-calibrated cameras - known intrinsic parameters except a common focal length. To the best of our knowledge, this problem is unsolved. The proposed approach extends point correspondence-based techniques with linear constraints derived from local affine transformations. The obtained multivariate polynomial system is efficiently solved by the hidden-variable technique. Observing the geometry of local affinities, we introduce novel conditions eliminating …
Standard breast cancer screening involves the acquisition of two mammography X-ray projections for each breast. Typically, a comparison of both views supports the challenging task of tumor detection and localization. We introduce a deep learning, patch-based Siamese network for lesion matching in dual-view mammography. Our locally-fitted approach generates a joint patch pair representation and comparison with a shared configuration between the two views. We performed a comprehensive set of experiments with the network on standard datasets, among them the large Digital Database for Screening Ma…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment results and lead to face shape distortion. To solve this problem, this paper proposes to use 3D Morphable Model (3DMM) for explicit facial semantic decomposition and identity disentanglement. Instead of using 3D coefficients alone for re-enactment control, we take the advantage of the generative ability of 3DMM to render textured face proxies. These pr…
City University of New York（纽约城市大学）；Northwestern Polytechnical University（西北工业大学）
The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically…
Learning Cross-View Semantic Priors for Single-Reference Unseen Object Pose Estimation
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）
