Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends

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Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid

SHOT: Unique signatures of histograms for surface and texture description

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.

3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey

AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.

AprilTag 2: Efficient and robust fiducial detection

Augmented reality (AR) allows to seamlessly insert virtual objects in an image sequence. In order to accomplish this goal, it is important that synthetic elements are rendered and aligned in the scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation or, equivalently, a camera localization process. This paper aims at presenting a brief but almost self-contented introduction to the most important approaches dedicated to vision-based camera localization along with a survey of several extension proposed in the recent years. For most of the presented approaches, we also provide links to code of short examples. This should allow readers to easily bridge the gap between theoretical aspects and practical implementations.

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Pose Estimation for Augmented Reality: A Hands-On Survey

Over the last decades fiducial markers have provided widely adopted tools to add reliable model-based features into an otherwise general scene. Given their central role in many computer vision tasks, countless different solutions have been proposed in the literature. Some designs are focused on the accuracy of the recovered camera pose with respect to the tag; some other concentrate on reaching high detection speed or on recognizing a large number of distinct markers in the scene. In such a crowded area both the researcher and the practitioner are licensed to wonder if there is any need to introduce yet another approach. Nevertheless, with this paper, we would like to present a general purpose fiducial marker system that can be deemed to add some valuable features to the pack. Specifically, by exploiting the projective properties of a circular set of sizeable dots, we propose a detection algorithm that is highly accurate. Further, applying a dot pattern scheme derived from error-correcting codes, allows for robustness with respect to very large occlusions. In addition, the design of the marker itself is flexible enough to accommodate different requirements in terms of pose accuracy and number of patterns. The overall performance of the marker system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented.

https://www.dsi.unive.it/~atorsell/papers/Conferences/cvpr2011-tag.pdf

RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience

During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiments and comparisons with recent techniques at the state of the art.

/pdf/a-scale-independent-selection-process-for-3d-object-58ustungn5.pdf

A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

We consider the problem of minimum distortion intrinsic correspondence between deformable shapes, many useful formulations of which give rise to the NP-hard quadratic assignment problem (QAP). Previous attempts to use the spectral relaxation have had limited success due to the lack of sparsity of the obtained “fuzzy” solution. In this paper, we adopt the recently introduced alternative L1 relaxation of the QAP based on the principles of game theory. We relate it to the Gromov and Lipschitz metrics between metric spaces and demonstrate on state-of-the-art benchmarks that the proposed approach is capable of finding very accurate sparse correspondences between deformable shapes.

/pdf/a-game-theoretic-approach-to-deformable-shape-matching-3wuwlso4z2.pdf

A game-theoretic approach to deformable shape matching

In this paper, an observational space–time ensemble of sea surface elevations is investigated in search of the highest waves of the sea state. Wave data were gathered by means of a stereo camera system, which was installed on top of a fixed oceanographic platform located in the Adriatic Sea (Italy). Waves were measured during a mature sea state with an average wind speed of 11 m s−1. By examining the space–time ensemble, the 3D wave groups have been isolated while evolving in the 2D space and grabbed “when and where” they have been close to the apex of their development, thus exhibiting large surface displacements. The authors have selected the groups displaying maximal crest height exceeding the threshold adopted to define rogue waves in a time record, that is, 1.25 times the significant wave height (Hs). The records at the spatial positions where such large crests occurred have been analyzed to derive the empirical distributions of crest and wave heights, which have been compared against standar...

/pdf/observation-of-extreme-sea-waves-in-a-space-time-ensemble-3vvbft5w77.pdf

Observation of Extreme Sea Waves in a Space–Time Ensemble

Visual marker systems have become an ubiquitous tool to supply a reference frame onto otherwise uncontrolled scenes. Throughout the last decades, a wide range of different approaches have emerged, each with dif- ferent strengths and limitations. Some tags are optimized to reach a high accuracy in the recovered camera pose, others are based on designs that aim to maximizing the detection speed or minimizing the effect of occlusion on the detec- tion process. Most of them, however, employ a two-step pro- cedure where an initial homography estimation is used to translate the marker from the image plane to an orthonormal world, where it is validated and recognized. In this paper, we present a general purpose fiducial marker system that performs both steps directly in image-space. Specifically, by exploiting projective invariants such as collinearity and cross-ratios, we introduce a detection and recognition algo- rithm that is fast, accurate and moderately robust to occlu- sion. The overall performance of the system is evaluated in an extensive experimental section, where a comparison with a well-known baseline technique is presented. Additionally, several real-world applications are proposed, ranging from camera calibration to projector-based augmented reality.

/pdf/pi-tag-a-fast-image-space-marker-design-based-on-projective-47tx8fjyzr.pdf

Filippo Bergamasco

Papers

RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience

A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

A game-theoretic approach to deformable shape matching

Observation of Extreme Sea Waves in a Space–Time Ensemble

Pi-Tag: a fast image-space marker design based on projective invariants