As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

/pdf/image-matching-from-handcrafted-to-deep-features-a-survey-27kn6w026w.pdf

Image Matching from Handcrafted to Deep Features: A Survey

This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching. The synergy of these two well-developed areas (ML and CO) can potentially give transformative change to artificial intelligence, whose foundation relates to these two building blocks. For its representativeness and wide-applicability, this paper is more focused on the problem of weighted graph matching, especially from the learning perspective. For graph matching, we show that many learning techniques e.g. convolutional neural networks, graph neural networks, reinforcement learning can be effectively incorporated in the paradigm for extracting the node features, graph structure features, and even the matching engine. We further present outlook for the new settings for learning graph matching, and direction towards more integrated combinatorial optimization solvers with prediction models, and also the mutual embrace of traditional solver and machine learning components.

/pdf/learning-for-graph-matching-and-related-combinatorial-3r36n4xlfz.pdf

Learning for Graph Matching and Related Combinatorial Optimization Problems

Next generation smart and augmented reality systems demand a computational understanding of monocular footage that captures humans in physical spaces to reveal plausible object arrangements and human-object interactions. Despite recent advances, both in scene layout and human motion analysis, the above setting remains challenging to analyze due to regular occlusions that occur between objects and human motions. We observe that the interaction between object arrangements and human actions is often strongly correlated, and hence can be used to help recover from these occlusions. We present iMapper, a data-driven method to identify such human-object interactions and utilize them to infer layouts of occluded objects. Starting from a monocular video with detected 2D human joint positions that are potentially noisy and occluded, we first introduce the notion of interaction-saliency as space-time snapshots where informative human-object interactions happen. Then, we propose a global optimization to retrieve and fit interactions from a database to the detected salient interactions in order to best explain the input video. We extensively evaluate the approach, both quantitatively against manually annotated ground truth and through a user study, and demonstrate that iMapper produces plausible scene layouts for scenes with medium to heavy occlusion. Code and data are available on the project page.

iMapper: interaction-guided scene mapping from monocular videos

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts; and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds. Code at https://github.com/huangjh-pub/multibody-sync.

/pdf/multibodysync-multi-body-segmentation-and-motion-estimation-26t29oloom.pdf

MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the binary nature of the problem, ensuring that the result is a set of permutations requires extra care. Hence, we: (i) show how to insert permutation constraints into a QUBO problem and (ii) solve the constrained QUBO problem on the current generation of the adiabatic quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global optimality with high probability while sampling the energy landscape to yield confidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave computer demonstrates that quantum machines offer a promising way to solve the prevalent yet difficult synchronization problems.

/pdf/quantum-permutation-synchronization-444sjiwfd0.pdf

Quantum Permutation Synchronization

In this paper we propose an optimization-based framework to multiple object matching. The framework takes maps computed between pairs of objects as input, and outputs maps that are consistent among all pairs of objects. The central idea of our approach is to divide the input object collection into overlapping sub-collections and enforce map consistency among each sub-collection. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competitive against state-of-the-art multi-object matching techniques.

/pdf/distributable-consistent-multi-object-matching-2g683az43p.pdf

Distributable Consistent Multi-object Matching

Distributable Consistent Multi-Object Matching.

In this paper we propose an optimization-based framework to multiple graph matching. The framework takes as input maps computed between pairs of graphs, and outputs maps that 1) are consistent among all pairs of graphs, and 2) preserve edge connectivity between pairs of graphs. The central idea of our approach is to divide the input graph into overlapping sub-graphs and enforce consistency among sub-graphs. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competent against state-of-the-art global optimization-based techniques.

Distributable Consistent Multi-Graph Matching

Many real-world tasks for autonomous agents benefit from understanding dynamic inter-object interactions. Detecting, analyzing and differentiating between the various ways that an object can be interacted with provides implicit information about its function. This can help train autonomous agents to handle objects and understand unknown scenes. We describe a general mathematical framework to analyze and classify interactions, defined as dynamic motions performed by an active object onto a passive one. We factorize interactions via motion features computed in the spatio-temporal domain, and encoded into a global, object-centric signature. Equipped with a similarity measure to compare such signatures, we showcase classification of interactions with a single object. We also propose a novel acquisition setup combining RGBD sensing with a virtual reality (VR) display, to capture interactions with purely virtual objects.

Shape-aware spatio-temporal descriptors for interaction classification

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Distributable Consistent Multi-object Matching

Distributable Consistent Multi-Object Matching.

Distributable Consistent Multi-Graph Matching

Shape-aware spatio-temporal descriptors for interaction classification